Note - I use the terms supply side aggregator, demand side aggregator and vertically integrated capital ecosystem quite frequently in this piece. For ease:

1. A supply side aggregator is one that pulls in differentiated market participants to offer a commoditised product (eg., Uber).

2. A demand side aggregator is one that expands offerings to a collective that looks similar from the outside (eg., Amazon).

3. A vertically integrated capital aggregator is a financial entity that brings together multiple parts of an ecosystem to offer users several products within the same venue.

TL;DR - Blockchains are money rails. The value of a protocol lies in its economic output. Composability and real-time verifiability allow blockchain native businesses to integrate vertically. Tokens allow participants across the stack to be incentivised with a shared medium. Teams that are intentional about how each part of the stack accrues value, have moats. Vertical integrations aid in accelerating capital velocity within ecosystems. Done well, they become a source for revenue.

The short, twitter version of the article is linked here

This is a precursor to a series exploring the slow, but gradual enterprise-ification of crypto.

It is a beautiful Saturday morning in Bangalore. As is often a habit, I spend most of it in Cubbon Park. Built over a century ago, the trees there are often older than the country they find themselves in. Near it are old buildings that were once used to design fighter jets. “2607,” I say for my OTP as the driver plugs into the streams of traffic that choke the city. I rush for Spotify to drown out the noise. J. Cole’s latest album to the rescue.

Boredom strikes a few moments later, and I seek refuge in Substack. Maybe I could order snacks from Swiggy - the equivalent of Uber Eats in town on my way home.

In a matter of minutes, I’d have used four aggregators, each of differing kinds.

Uber aggregates riders across town and positions itself as a demand aggregator. So does Swiggy. You may not find much difference in the average rider or hungry person. They can be commoditised. So as a platform, Uber gets to take 30% of each commodity (I mean human users) they have aggregated to scale. The restaurants hate this. So do the drivers. Both try to circumvent by asking the user if they can pay directly in cash and skip the platform.

The rider (or me, in this case) understands that the platform’s ability to enforce a reputation system is what makes it valuable. No cash transactions today.

You see a version of this with Jupiter on Solana. Its influence in its early days was a byproduct of its ability to route orders across exchanges and offer the best prices to users. Instead of restaurants and drivers, Jupiter aggregates venues for my WIF buy orders to flow towards.

Substack and Spotify, however, operate on different parallels. Spotify pays up to 70% of its revenue to labels. As of last year, it paid roughly $13.75 billion to rights holders out of $20.22 billion in revenue. $0.04 of each dollar it makes reaches the artist. Substack gets to keep just 10% of what I may have paid the author of the newsletter I was reading. It has neither commodified the reader nor the writer. It simply positions itself as a tool. Perhaps, intentionally because if it were to command pricing power, the writers on the platform may simply abandon it.

Spotify is an instance of a loosely knit supply side aggregator by virtue of the number of record labels it has collaborated with. Substack on the other hand positions itself as a demand side aggregator with no pricing power by virtue of the readers on it.

Each of these apps aggregates a different side of the market. But their ability to accumulate capital (or pricing power) hinges on how well knit they are within their ecosystem. We will soon see a different version of this.

Web2 native aggregators tapped into an abundance of users that came online through two forces.

1. First was Moore’s law working its magic on the cost of a smartphone. India alone has 800 million smartphone users. Roughly 5.5 billion individuals are online today. (Cheap phones, yay!)

2. Reduction in internet costs and the rapid decline in cost of bandwidth. (Cheap internet too! yay!?!)

Crypto as an economy - in contrast has a much smaller TAM. By best estimates, ~560 million users have interacted with crypto. Last month, DeFi saw ~10 million active wallets. These are distinctly different economies, with very different rulesets.

One runs on attention. The other, powers through what flows through your wallet on-chain. We often convolute behaviour from the attention economy into what may happen in the transaction economy. People on prediction markets for instance do not behave the same way they do on Instagram.

Revisiting Aggregation

Three years back, when I first wrote of aggregators, I argued that blockchains reduce the cost of verification and trust. The original promise of the web was access. You could sit in your pajamas in New York and buy Temu goods from Shenzhen. I argued that blockchains make it possible to verify and settle a transaction with the vendor in real time at minimal cost. Or so I prophesied the industry would evolve.

Writing in 2022, with my first sponsor for this newsletter (thanks Nansen) I wrote:

We believe that blockchains will enable an entirely new category of markets that are able to verify on-chain events instantaneously. This will collapse the cost of validating intellectual property at a massive scale and thereby create new business models.

In the years since, the new markets did come on-chain. NFTs did ~$100 billion in volume since. Perps became an economy that cleared ~$14.6 trillion since. Decentralised exchanges cleared ~$10.8 trillion. The understanding that the technology will be used to clear transactions with anonymous counterparties at a global scale was true.

But at the same time, it ought to be noted that OpenSea’s volume went from ~$5 billion back then to ~$70 million this month.

Markets emerged, evolved and died. As with many things in life. In the process, they left us fuel to meander and ponder upon. Sid believes that the speculation in crypto is a feature not a bug. All new markets are undefined in the early days with participants not understanding what they are buying or why something may hold value. The novelty is baked into the price. As sanity returns, valuations become efficient and the bubble becomes memories of times past. NFTs and DeFi have had their own cycles of such mania.

These bubbles were crucial to stress-testing and validating the monetary rails that eventually set the stage for markets like the one on Hyperliquid.

Which leads me to the past few weeks of operating in crypto. Between Drift and Kelp, close to ~$578 million was hacked in the past three weeks. In the trailing twelve months, roughly ~$1.7 billion was stolen across DeFi and crypto protocols tracked by DefiLlama. Excluding stablecoin issuers - all of DeFi made roughly $3.42 billion in protocol revenue over the same window.

In other words, this past year, close to $0.50 was lost to hacks for every $1 DeFi made in revenue.

At the same time we are seeing an increase in the number of apps being released. Software itself has become a commodity. The App Store saw ~84% more apps being submitted this quarter compared to the same quarter last year. We are simultaneously seeing two forces at play. More software demanding the attention of fewer users. And fewer platforms dominating the bulk of the revenue created within crypto.

A moment back, we were with the trees in Cubbon Park. We are now realizing we lose $0.50 on every dollar earned in the industry. Sharp curve I know - but stay with me.

If we break down the $3.5 billion earned across decentralised avenues, you will see a quick pattern. Roughly 40% of it comes from derivatives platforms. Hyperliquid alone contributed some ~$902 million towards it. The second largest category is decentralised exchanges, within which the leader Uniswap made some ~$927 million in fees. Lending platforms like MakerDAO follow at third, with close to ~$500 million in combined revenue. What is common amongst all of them is that they are capital dense businesses.

Unlike products that can be built with lines of code on a Saturday afternoon, they require coordination of patient capital that is willing to accept the risks of these platforms. I explore these themes in great depth in the two stories linked below.

This is where you realise the primary difference between web2 aggregators and web3 native ones. Because blockchains are primarily toolsets to move money and verify that the nature of the transaction follows rule-sets created by developers - they become valuable only when they are able to engage in capital dense operations. Perpetual exchanges are able to put large sums of money to productive use repeatedly on the same day. Lending platforms skim a small share of the large amount of money generated as yield.

Aave for instance earned ~$123 million off the ~$920 million it made in yield over the past year. But such aggregators dominate only when they are able to own three key things.

1. The supply side (of liquidity),

2. The demand side (of users) and

3. Distribution.

Hyperliquid is a unique beast in this regard. It has paid out close to $100 million in builder code fees but the vast majority of revenue comes from its own native front end. It is simultaneously able to retain its best users while expanding the surface area for new ones to interact with the protocol.

But what is the logic behind that? One theory is that distribution is a toll-fee paid in Web3. Large protocols tend to own and retain their best users. You realise this when you notice what amount in revenue has been created by decentralised exchanges, compared to on-chain aggregators who route orders.

On Ethereum, aggregators account for 36% of all DEX trades. On Solana, that number can be as low as ~7%, depending on the month. Kyber, 1inch, CoW and ParaSwap have combined made about ~$112 million in fees since launch. Compare that with Uniswap’s ~$5.5 billion in lifetime fees as the standalone exchange that sees the bulk of the volume. You can see a similar phenomenon in Hyperliquid too.

Combined, builder codes account only for ~6% of the cumulative ~$1.1 billion Hyperliquid has made in revenue. MetaMask’s tight-knit integration on Ethereum has earned it $184 million in fees last year through swaps. Phantom generated close to $180 million but consider that this is a small chunk of a huge economy of scale ecosystem. These products work when they are built atop a single protocol that has both the liquidity and the economic activity that is required to drive value towards them.

These products are able to attract and retain users because of the depth of liquidity available on them. Look through these lenses - capital in crypto is no longer a commoditised product. It is the most necessary ingredient. Vertical integration of capital gives participants more reasons to stay within the ecosystem. In such systems, capital begets liquidity because it can be put to productive use.

Capital is not the moat. It is a consequence of vertical integration you built. Vertical integration is the moat, and capital is the byproduct

The caveat is that it works only when parking capital is not incentivised. Don’t believe me? Take any pre-launch protocol that has an airdrop programme for deposits and you will see the case in point. Or the countless L2s that have struggled to generate value.

Any business that does capital aggregation is by extension a hack target. Drift was a target because it had ~$570 million in TVL. KelpDAO was a target because it held close to ~$1.6 billion in restaked ETH. Hyperliquid’s bridge holds close to ~$2 billion in user deposits, making it one of the most valuable standing targets in the space. You could see this play out with Ronin (~$625 million) and Nomad (~$190 million) too.

Because blockchain native businesses need high amounts of capital to be valuable, we have a dynamic where to win requires being vulnerable at least until the mechanisms to secure capital and freeze capital movements are in place.

And even when you do have capital, having a large TVL does not warrant success on its own. Capital in an economy that is parked idly, or put to little use can be a liability in the event of a hack. This is why you see protocols trying to differentiate themselves on the basis of what economic output they are able to generate, from niche usecases.

1. CHIP, the firm behind USDAI, has been able to place close to ~$100 million in loans this quarter, with a $1.5 billion pipeline in motion that will generate close to ~16% APY on its riskier tranches over the course of the year.

2. The highest risk pools on Maple generate ~15–20% APY which is comparable to or higher than the ~12.6% APY on USDC pools on Aave today. It has aggregated borrowers that can use the liquidity on the protocol to create that economic output.

3. Naturally, Hyperliquid is the best instance of a supply side aggregator that has been able to put capital to meaningful use. The last year saw Hyperliquid do ~$942 million in revenue with an average of roughly ~$3.5 billion in TVL. By some very crude math, each dollar parked on the protocol has rotated close to ~285 times annually, yielding ~$0.30 in fees per $1 of TVL. Compare that to Aave’s ~$0.05 in fees generated per $1 of TVL across its lending markets.
   

I can almost hear my finance professor screaming at me for comparing the yield on TVL between Hyperliquid (an exchange protocol) and Aave (a lending avenue). But in a market where consumer preferences are not predetermined, and investor loyalty is not deep, capital will flow where it produces the best outcome. When accounting for hacks, investors will demand a premium for the risks involved. As it stands, perpetual exchanges are the one place that are able to take idle capital and repeatedly use it on-chain to produce fees.

I initially thought Hyperliquid is simply a supply side aggregator. It supplies capital for users willing to trade on-chain. This has been my thesis. And that is indeed true. But it breaks when you consider how it uses tokens to incentivise vertically integrating into it. But before we go there, let me explain how a vertically integrated ecosystem works.

Ticketmaster is responsible for 70% of all major live events in the United States. It is able to take 30% off tickets you buy to perhaps see Justin Bieber play his old tracks on YouTube at Coachella because it owns the venue, promotes the tour, ensures you have merch at the concert and coordinates with sponsors. The 30% you pay, is 15 times what Stripe would charge to process a ticket sold online. But you pay that premium because Ticketmaster is vertically integrated across the value chain.

You have the illusion of a marketplace: artists, venues and fans are all parties but nobody gets to argue with Ticketmaster on its cut. The same applies to Apple’s App Store. Apple is responsible for curating the store, collecting payments, ensuring the device works and bringing in the millions of users that are now attuned to the ding Apple Pay makes when you subscribe to yet another app you will never use.

Vertical Integrations in Crypto

Protocols have slowly begun carrying out the same thesis.

In Web3, a capital provider can be treated as commodity if vertical integrations do not make it easier to work with. Users do not have loyalty until the ecosystem is integrated such that the cumulative experience is non-existent anywhere else.

For Maple, such an integration would involve years of experience working with hedge funds and market makers. For Centrifuge, the integration involves receiving close to ~$1 billion from Grove for their JAAA bond offerings from Janus Henderson. Instead of capturing loose, abstract parts of an economy, they integrate vertically to offer the end-user a better product. In doing so, they create moats that are hard to replicate overnight.

Maple’s years of underwriting experience, or Centrifuge’s moat in being a trusted capital coordinator are moats in a world where capital and relationships have become the only things that are hard to replicate.

Firms doing vertical aggregation may routinely give up parts of the stack to a third party. Part of the reason for doing it is the lack of economic benefit from doing so. Maple owning custody or Metamask issuing its own cards may not be massively profitable when compared to the capital they generate in swaps and credit underwriting.

However, when a business scales exponentially, owning the whole stack is where the competitive edge is built. This is partly the reasoning behind M&As in the industry.

When a business has a vertical integration in play, you are not competing with a single product. The battle is against the combined experience a user has within it. On Hyperliquid, once HIP-4 is on, a user would be able to be on-ramped for free (through Native Markets), take a prediction market position, and use that position as collateral to trade on its perpetual product. Its risk engine allows this experience. And for what it’s worth, this is not possible even in TradFi today without a merchant banker.

Hyperliquid owns the user, on-ramp, risk engine, trade interface, liquidity and issues the token. For a new business to compete against it, would mean battling on six different fronts, all at the same time.

For new apps that are launching, tapping into a small share of this is meaningfully a better outcome than building on a new protocol like Monad, which has done about ~$2.6 billion in cumulative derivatives volume across all five of its perps protocols.

Integrated ecosystems like the one on Hyperliquid attract developers, more integrations, headlines and happy token holders. (Or in my case - a writer that spends his time obsessing about how these things work)

Exchanges see this shift. Coinbase acquired Deribit, has a custodial business, co-issues the currency (USDC) with Circle and captures revenue on reserves, a massive wallet infrastructure and on-boarding rails in 100+ countries. It also launched its own chain in pursuit of vertically integrating the experience. Admittedly, Coinbase may have been too early in pursuing retail users that clearly did not want to “coin” content on a blockchain or use Farcaster.

The integrations for Coinbase exists in loose terms but sits hidden behind layers of bureaucracy, regulatory hurdles and internal priorities. This is perhaps the primary difference between an open-access integrated system and a walled one. Coinbase by virtue of being an exchange with ~$60 billion in market cap has little incentive to pursue the marginal developer.

Hyperliquid in contrast benefits from developing its core avenue to the best place to trade and simultaneously creating an ecosystem whilst driving value to the underlying token.

The token is part of the integration in this context as it is the shared substrate that keeps these integrations alive and valuable. This is where you see the industry confusing itself between a tokenised protocol, and a tokenised business. A tokenised protocol is predicated on the ease with which a third-party developer can build on it. It incentivises people to drive value (downwards) to the token - often in the form of buying it back from the market.

Firms like Robinhood and Coinbase are powerful economic participants, but they cannot replicate Hyperliquid’s core network of owner-operators.

The protocol’s airdrop ensured the people that own it, were individuals that contribute economically towards it. They own enough tokens to warrant driving value towards it. Hyperliquid commits to that cause, by giving up 99% of its revenue to buy the tokens back from the market. Imagine a listed firm using 100% of its revenue to buy back ESOPs from its employees. We may see people become more accepting of capitalism perhaps.

And this is where you see how the industry is evolving whether we like it culturally or not. Solana focuses on immutability. Ethereum has its focus on censorship resistance and open source. But observe carefully where traction resides and you will see the industry adjusting ideology to commercial reality.

Hyperliquid, while a beautiful garden, is a walled one. The source code is not open-source as far as I understand. How its risk engine works is not verifiable either as far as I understand. Nobody knows what parameters go into Maple’s risk underwriting. (Or at least, I don’t). As a lender, I may not even know who underwrote the loans on USDAI. But the question is, are these parameters being openly accessible a requirement for me? I don’t quite know. Probably not.

Negotiating With Chaos

An economy cannot be built if half a dollar on every dollar created in revenue is lost to hacks in a single quarter. Founders will flock to AI, if they are expected to be responsible for hundreds of millions of dollars parked as TVL on their product. Each time there is a hack we rush to hope stablecoins are frozen. Which in turn, are often centralised.

A vertically integrated stack will eventually require giving up complete decentralisation for the sake of economic progress.

And this is not a new story on the web. In the late 1990s, visions of an open internet that allowed free speech without consequences were dreamt of heavily. Nazi memorabilia was once auctioned on Yahoo until French courts intervened in 2000. Tim Wu explores this theme in great depth in Who Controls the Internet? The story of the web, or perhaps all human commercial networks - is that of complete decentralisation paving way for a toned down version that gives up controls for the sake of economic interaction.

We settle for a diluted version of the original so that the commerce can scale, because in the absence of dilution, chaos will ensue.

This massive expansion in energy is caught in how we describe those ages. The “wild” West. The dot-com “boom”. Perhaps, even crypto is undergoing a similar expansion and toning down of energy. I explored these themes in great depth last year in the stories below.

What does this mean for founders? Observe Metamask and Phantom’s numbers. Those businesses make more money than most L2s, because they are downstream in an ecosystem with tremendous economic output. Building bridges to nowhere and exchanges for nobody is no longer a business model. Especially when the pain associated with it involves hacks. You build where the liquidity and users are today.

Mimicking a vertically integrated product overnight may not be possible. But you can build atop it.

Operating systems saw a similar pattern. When Blackberry declined and iOS became a dominant player - developers had to choose where they built. We are seeing a version of that in crypto too. Except this time, capital incentives may keep developers blinded longer.

Remember how I mentioned Cubbon Park? I often think of why the park exists clean and well kept for the decade or so I have spent time in Bangalore. Part of the reason is that there are rules. You are not allowed to spend time in it past 6 pm for instance. Or hosting an event in the middle of it may get you screamed at. Sometimes, they are harsh. But the rules keep the place functional.

Platforms and protocols on the internet are a lot similar. We may not like rules around it but they keep things functional.

In the age of vertical integrators, we may find ourselves increasingly agreeing to rules so that our dollars stay with us and the economies we spend so much of our time on, continue to scale. The trends point towards this. Stablecoins, RWAs, perpetual exchanges with closed-access risk engines, lending desks with risk underwriters that are unknown and off-chain RFQ products like Derive all point to the same trend.

That of vertically integrated capital aggregators that are willing to give up dreams of complete decentralisation for the sake of progress.

Reading loads of Neil Postman
Joel John

TL;DR: Decentralised.co is increasing its exposure to perpetual exchanges through a position in Drift Protocol. Solana is yet to benefit from positive tailwinds in the growth of perpetual exchanges. Drift is best positioned to benefit from RWA assets and internet capital market instruments issued on Solana. At a price-to-earnings multiple of five based on last year’s earnings, it is deeply discounted relative to its position in perpetual exchanges.

This brief note should help explain why we are going long on the long-standing perpetual exchange in Solana. Venture@decentralised.co is the best place to reach out if you are building in the theme, and would like to chat.

Over the past month, we built a position in Drift. The protocol generated $19.8 million in fees over the last year. It currently trades at an FDV of $95 million. At the same time, it sits at a price-to-book of 3.5x on its operational balance sheet alone. We believe Solana will continue to be the chain on which fintech applications are built and scaled. Drift is the longest-standing perpetual exchange within it. These two forces blend to make it an interesting asset to own. Our primary reasoning for involvement with Drift boils down to three core factors.

1. Drift’s team has worked relentlessly towards scaling perpetuals on Solana. We are comfortable going long on the team itself.

2. Drift’s architecture for providing liquidity will become valuable in a world where centralised sequencers are seen with suspicion. DLOBs, JIT, and vAMM collectively offer a better experience to end users. Open, verifiable markets will be a necessity as on-chain markets evolve.

3. The DeFi landscape on Solana has significant runway for growth in equities, real estate assets, and internet-native capital instruments. A perpetual exchange will be needed for sophisticated actors to interact with these instruments. Drift can be the preferred avenue for price discovery and hedging.

To keep terminologies tight, we use the following throughout this note.

• Earnings - What is left in the protocol’s balance sheet after cost of incentivising economic activity

• Revenue - Total dollar value made by protocol and users

As a general heuristic, we use price-to-earnings throughout much of the document, except in the initial section.

Before getting into the why for Drift, it helps to understand how its fundamentals stack up.

Drift Protocol is the longest-standing perpetual exchange on Solana. It currently holds a quarter of all TVL allocated to perpetual exchanges on Solana. Its yearly revenue grew by 193% in 2025, from $14 million to $41 million. Annualised, in 2026, the protocol is on pace to generate $30.75 million in revenue, extrapolating from what the protocol made in January and February. (Revenue here combines fees paid by users alongside staking rewards from Drift-staked-Sol). Drift holds $27 million in its fee treasury accumulated entirely from protocol earnings over four years, excluding the $52.3 million raised from investors.

Perpetual exchanges are booming as a category, but they are not entirely open, verifiable, or decentralised. Part of our interest in Drift comes from its open-source nature and direct settlement on Solana. But before we go into how it operates, it helps to have a perspective on why open, verifiable financial networks matter.

Open Markets Need Verifiable Infrastructure

Human institutions work well during periods without stress. A dictatorship can be more efficient than a democracy until the citizenry questions those in power. People think about the mechanism that elects someone to power only when the power is abused. The same applies to markets. We don’t often think about how a market functions until something breaks. Markets, like democracies, are bundles of rules that must function in sequence. When the rules are not followed or broken, trust in the market declines. And a byproduct of that is citizens (or traders) simply moving elsewhere.

This is why trustlessness is a fundamental need for markets to evolve. Bitcoin serves this function for spot assets. AMM model markets on Ethereum and Solana solve this for spot markets. The fact that we know how much money is lost to MEV is a feature that happens because the rulesets and infrastructure through which a spot order is settled on-chain are verifiable. Perpetual exchanges currently do not have that verifiability or decentralisation. We have accepted centralised sequencers as a default for perpetual exchanges as a trade off for speed.

Open source, verifiable markets built on public infrastructure will be tremendously valuable as the world’s assets settle on-chain. To understand why, it helps to have a perspective on what happens when markets are walled off and centralised.

1. The London Interbank Offered Rate Scandal
   LIBOR was a benchmark rate used to determine the cost at which banks paid to borrow from one another. It was determined through collecting lending rates from individual banks who in turn submitted data without verification. Since there was an active derivatives market to trade these rates, traders at banks were incentivised to provide faulty data or collude to manipulate the benchmark and profit off the derivatives. In 2012, Barclays paid $453 million to settle the allegations. Part of the reason the scandal occurred was that centralised parties (the traders) had incentives large enough to abuse the system through a liquid derivatives market.
   
   The equivalent of a LIBOR cannot happen on Aave because the lending rates are open and verifiable. The lending rates are not set by individuals, but rather by supply and demand. Anyone can audit the numbers at any time, since there is no central trader providing these rates. The blockchain provides records that cannot be falsified.
   

2. The Nickel Squeeze of 2022
   In 2022, as the war between Ukraine and Russia raged, the nickel markets began acting strangely. Nickel futures jumped 250% between March 4 and 8 that year - going from $25k to $100k per ton on the London Metal Exchange. A large entity (named Tsingshan) had short exposure that would have resulted in it owing $15 billion to multiple large banks. The LME (London Metal Exchange) had visibility into only about 20% of the entity’s nickel short exposure, as much of it was in OTC transactions.
   
   To avoid losses from Tsingshan taking down the entire market, the LME suspended trading on March 8th, wiping out $3.9 billion in transactions. The market lost its integrity and shattered the trust people placed in it. 145 years of reputation, lost on a March morning.

Libor was about rate-setting collusion. LME’s situation was a byproduct of a lack of visibility into where the exposure lay. Both instances were fuelled by the lack of transparency in financial markets. Perpetual exchanges face a similar vulnerability to centralised sequencers, which create opacity around order fulfillment.

Verifiable infrastructure is a market necessity, not an option.

Today, regulators can audit and verify these markets. But it requires human intervention, and even when the regulator is involved, there can be slip-ups. Bernie Madoff’s scheme was known to the SEC as early as 1999. The point of introducing technology in these markets would be to avoid requiring individual regulators to verify, and instead have a steady stream of audits every block.

When market infrastructure (like the NYSE) can reverse transactions, it gives undue power to a handful of people who can hurt retail investors and hedge funds that are on the right side of trade. These exchanges are themselves at risk, as they often lack visibility on the complex OTC transactions market participants engage in. Blockchains provide mechanisms to solve all three problems.

1. They bring radical transparency through making it considerably easier to verify and vet where exposure stands.

2. The immutable nature of a blockchain ensures transaction finality. Settlement finalities are a guarantee unless the network forks.

3. They allow participants to verify and vet how orders were sequenced to ensure fairness in the system

Perpetual exchanges in crypto are not entirely open and verifiable. Many of the largest names have centralised sequencers whose order sequencing logic and history cannot be verified. As our markets turn increasingly reliant on blockchains for settlement, there will be primitives that aid in making markets open, verifiable and trustless. Part of our interest in Drift emanates from this view. Unlike many of its peers, the way it sequences orders, its codebase, and its liquidity sourcing can all be verified. Besides, within the mix of assets one can pick within Solana, Drift is mispriced for the traction it holds

Understanding Solana’s Perpetual Ecosystem

Solana accounts for less than 5% of all perpetual exchange volume. On an average day, 60% of its current activity is on Pacifica. Solana’s early foray into perpetuals did not go quite well. Mango Markets was exploited early on. The FTX crash left a nascent DeFi ecosystem bleeding. Much of the activity on Solana in the quarters that followed was in the spot market. This is where players like Jupiter, Meteora, and PUMP earned their mettle.

Hyperliquid, in contrast, focused on the perpetual meta aggressively when it launched. It listed meme asset perpetuals faster than centralised avenues did, thereby attracting a core subset of retail users. Combined with marketmakers that benefited from the exchange’s faster settlement times, this created a nascent but healthy ecosystem. It set the stage for what we know as the decentralised perpetual ecosystem. As of writing, Hyperliquid accounts for ~30% of all volume on decentralised perpetual exchanges, compared to ~5% on Solana.

Jupiter is a leading contender for perpetual products on Solana. It is hard to compete against the network effects the protocol has from its multitude of products. It has also issued JLP - an index instrument that represents BTC, SOL, and a mix of stablecoins. Jupiter’s higher TVL and take-rate make it interesting, but a bet on Jupiter is a bet on a generalist that has too much to do. We had earlier explored Jupiter in this article.

Jup has generated over $1.2 billion in fees from its perpetual product alone. The challenge is that much of the fees go back to liquidity providers on the exchange. Jup’s token model passes on fees in the form of USDC in yield back to token holders. 66.7% of fees generated on Jupiter go back to liquidity providers on JLP. Think of this as the cost of capital for TVL on Jupiter’s exchange. The product has capital density but pays a heavy premium for it. 22.2% of the fees go toward the protocol, and the remaining 11.1% is distributed to users as staking yield. So even though the perpetuals product generates a lot of fees, much of it is not captured or reflected in the token.

A different way to see it is through the lens of take rates. As seen in the chart above, Jupiter’s take rate on its perpetual exchange is 16 times what Drift charges. This is both a feature and a bug. Jupiter’s fee rate steadily climbs due to its linear pricing mechanism for its perpetual exchanges. Large orders that move markets tend to pay more. It is a good strategy as the exchange has made verifiably more than Drift in the past year. For large institutional traders & high frequency traders, the model will simply be ineffective as pricing cannot be predicted.

The parallel here is that of Google and Yahoo in the early 2000s. Yahoo branched out to multiple product lines considerably before Google did. The former focused on its core business (advertising) before expanding into email (Gmail), streaming (YouTube), and operating systems (Android). Yahoo expanded early through costly acquisitions like Geocities and Broadcast.com, but struggled to integrate them into its core business. Google focused on search and kept the product simple, as that is what the users wanted. Today, close to 80% of Google’s revenue is its core search and advertising business. Another 11% is from its cloud business. Only 9% of its revenue comes from the expanded product line.

Pacifica is the new upstart on Solana that has seen more trading volume than both Jupiter and Drift. Despite being in closed beta, the product generated over $6.7B in volume, compared to Jupiter’s $3.3B and Drift’s $ 1.4B. It accounts for nearly 60% of the total volume in Solana’s perpetual exchange ecosystem. However, there are two key concerns. Firstly, it has a centralised sequencer that is neither open nor verifiable. Secondly, the liquidity model used is that of a CLOB. If we were pursuing a native CLOB exchange, our bet on Hyperliquid would already cover that side of the market. Between these two points of friction and the fact that Pacifica does not have a liquid token, we had to explore further.

For us, the composability, verifiability, and openness of Drift is worth a premium.

Jup, Pacifica, and Drift are three entirely different products when it comes to perpetual exchanges. For JUP, the perpetual exchange is an extension of their product suite, and the fee model is optimised towards yield. For Pacifica, the focus is on being a CLOB product that is at parity with centralised exchanges. For Drift, the focus is on composability, verifiability, and openness. We go deeper into Drift because of how its liquidity model could scale, as the mix of asset types on Solana evolves from where the ecosystem is today.

Where Drift Differs

Drift is a perpetual exchange focused solely on improving infrastructure for margin trades and can iterate faster than an aggregator. The market tends to discount Jupiter for such reasons. Drift is also home to the second-largest TVL among perpetual exchanges. It holds close to 15% of all TVL committed to perpetual exchanges across all chains.

Drift has not seen meaningful competition from the emergence of other perpetual exchanges on Solana, either, until Pacifica came. Jup and Drift collectively own 95% of the TVL in perpetual exchanges. Drift’s share has stayed steady over the course of the year.

Drift trades at a $95 million FDV, compared to Jupiter’s $1.05 Billion. Core to our thesis on Drift is how its liquidity model works. Conventional exchanges (like Hyperliquid and Pacifica) follow a centrally sequenced CLOB model (CLOB stands for central limit order book). The exchange holds the power to determine how individual transactions are ordered.

In traditional markets, having the power to outrun the way orders are routed to an exchange is a multi-billion-dollar endeavour. Flash Boys by Michael Lewis famously exposed how a handful of firms had an undue edge through latency arbitrage. While CLOB-based models are necessary to compete with CEX-like features, the cost is a lack of verification and real-time settlement.

This is where Drift’s liquidity model becomes interesting.

When a user sends an order to Drift, the order routes through three forms of liquidity.

A Just-in-Time Auction

A Just-in-Time Auction Orders on Drift has a 5-second phase during which multiple market makers can competitively bid to fill an order. This is similar to the RFQ system we had written about bebop here. RFQ systems benefit consumers by enabling large orders to be competitively bid among multiple parties. It helps a market-maker, as they have the option to avoid toxic flow and reuse idle assets in external venues where they could be more productive.

In a CLOB model, consumers see the price and spread they could expect. A JIT model can absorb more in both volume and size at tighter spreads, even when the order book does not appear highly liquid. The downside of such a model is the time it takes for orders to settle. Both market-makers and traders are ultimately settling on Solana, so there will be an infrastructure bottleneck.

The other assumption required for this to work is that a multitude of market-makers want the order flow from such an auction. If competing platforms offer points or rebates alongside high amounts of volume, market-makers may have little reason to bid on Drift’s JIT auctions.

A DLOB maintained by keepers

If no market-maker bids on the order, a network of keeper bots financially incentivised by Drift can place bids. Rebates from Drift’s trading fees are passed on to keeper bots. Individual keepers are similar to brokers on the NYSE. Except that each of them can verify and maintain their own version of the orderbook permissionlessly. Everyone keeps a copy of the orders, open for anyone to verify.

A decentralised limit order book orders transactions by price, age, and size. When trigger conditions are met, the bot fills the order, depending on how it is configured. Drift - the protocol itself does not dictate how orders are sequenced. Everyone has a copy of the ledger because orders are settled on Solana.

Individual keeper bots have the ability to arbitrage against third party venues or trade among themselves to give the trader a bid. Their primary role is to keep a record of all orders. The DLOB keeper bot model is open-source. Anyone can spin up their own bot, run it, and thereby verify any of Drift’s claims. Verifiable.

A Virtual AMM

In edge scenarios, market makers can simply choose not to bid (in JIT), and keeper bots can avoid providing liquidity. This is what usually happens on days when liquidity cascades take markets down. Given that, in CLOB models, liquidity is a byproduct of humans (or APIs) providing it, traders can be left without a guaranteed bid. Drift’s approach to the problem is to provide a virtual AMM pricing model (similar to Uniswap’s) that uses user collateral and protocol balance sheet assets (liquidity provider deposits and the insurance fund).

It ensures that there is always a bid for traders, although spreads may widen and slippage increase. This has proven to be effective in scenarios where auto-deleverage kicks in and liquidates users on a CLOB-based market.

This aspect of Drift is currently underpriced by the market. Most exchanges that operate on CLOBs offer instantaneous settlement on the underlying network, but the order sequencing itself is neither verifiable nor composable. The use of perpetual exchanges cannot occur without infrastructure that is scalable and verifiable. Drift’s core architecture can deliver on these promises while incurring some sacrifices in settlement time.

As real-world assets like gold, equities, and commodities come on-chain, markets will prefer avenues that offer these benefits over recreations of the centralised structures that already exist. The ones that dominate CLOB models will be avenues that compete on liquidity, speed, and depth. All of those metrics are currently dominated by Hyperliquid. Drift sits on the other end of the spectrum, where a relative lag in an order going through is fine if the transaction sequencing itself is verifiable.

This positioning of Drift is tremendously valuable due to a few core factors that collide

1. New instruments on Solana are yet to scale. Internet capital markets in their current format are at best a meme. Startups and founders will raise capital on the network for longer-standing assets. MetaDAO is a sign of this shift. These instruments will need a perpetual exchange to enable a rich capital market. Lending needs hedging. Margin allows risk-on exposure. Spot trading allows a singular user persona
   

2. Real-world assets - like equities, foreign currencies, and tokenised real estate will increasingly come to Solana given its heavy retail-oriented user base and strong developer base. These markets will need a decentralised avenue to hedge. Composable workflows, such as taking a loan in USD and hedging against currency risk on a perpetual exchange, do not exist yet. Drift will be able to capture a reasonable share of this market.
   
   

3. Commodities - like gold, silver, and platinum are yet to find a market (of scale) on Solana. Decentralised.co’s own portfolio company (Orogold) is working on the problem. If commodities are to scale on-chain, market makers will need an avenue to hedge exposure with minimal risk on the same network. This is where Drift can play a role.

Ultimately, Solana benefits from a healthy ecosystem of traders, developers, and capital allocators that seems to be struggling to find a shared language for discussing risk. Perpetual exchanges are that language. Drift is where the language is spoken.

One way to see Drift’s potential for growth is by comparing Solana’s active user base with Hyperliquid’s, as shown in the chart below.

Assuming even 2% of Solana’s existing user base turns to Drift Protocol as users, the number of active traders on Drift will be in parity with what Hyperliquid has. When new primitives launch, they generally focus on a single application. Hyperliquid focused on trading for over a year before enabling lending. But the core user base of traders does not need lending at scale. The broader ecosystem within Hyperliquid struggles to capture the core users who came there to trade.

Drift, in contrast, benefits from Solana’s varied user base.

Solana users have the choice among NFT collections, gaming, or payments, and a meaningful share of them will find themselves on Drift. The exchange benefits from the organic, cross-category demand that a single-purpose chain may not see. This assumption, however, requires the asset types on Solana to evolve beyond where they are today, from meme assets to primitives that enable capital formation at scale.

We already see primitive versions of these trends in our portfolio companies. Asgard.fi mimics a traditional prime brokerage through routing lending positions across perpetual exchanges to generate yield. Close to 45% of their overall volume and 95% of their volume on JLP go through Drift today. Asgard could offer this infrastructure to a traditional fintech player focused on on-ramping, creating a neo-banking experience where users can earn yield on their dollars through such composable layers.

Given the network effects of existing Solana-focused service providers, we believe the surface area for such opportunities remains underexplored.

While these statements about product direction are forward-looking, it is important to note that Drift’s team is one of the few that have consistently built a product with meaningful revenue, across cycles. Q4 2025, in fact, marked an ATH for multiple exchange metrics. Compared to Q2 2024, earnings increased from a paltry $970k to $6M on the exchange. Its average daily volume doubled - from $126M to $268M, and the total volume traded on the exchange scaled from $11B to $25B.

The Revenue, Valuation Divergence

Drift remains undervalued when compared to its historical trends, too. Drift is trading at the lowest multiple (on fees) it has ever had. At its peak, Drift’s price-to-earnings ratio was 80. It generated $5.7 million in earnings in Q4 2024 and traded at an FDV of $1.85 billion. ( We annualise the quarterly figure to $23.1M for earnings) Last year, Drift generated a total of $19.8M in earnings. It trades at an annualised protocol earnings of ±4.8x. If it were to simply return to its all-time high multiple of eighty, it would trade at an FDV of $1.58B. A more conservative and realistic outcome would be a return to a multiple of twenty (based on current earnings of $19.8M), which would have the token trading at $398M, or four times its current level.

Another way to understand this divergence is by observing the difference between current volume metrics and past highs. When the market is in favour of an ecosystem, it can offer an extreme premium (such as a 40x multiple) relative to Drift’s current valuation. We model two scenarios. One where Solana’s perpetual exchange volumes surpass the $40B it sits at today. And one where Drift is able to return to capturing 60% market share within Solana’s ecosystem. In both scenarios, we use fees to measure multiples as the protocol’s retained earnings may change in the coming years.

There are three catalysts that will drive valuation up.

1. Traders who left for Pacifica to receive airdrops will return to Drift. Users routinely switch between products in anticipation of token rewards. Over time, users go where a pre-existing relationship and better liquidity exist.

2. Secondly, the nature of RWA assets available on Drift expansion will contribute to its overall valuation.

3. Lastly, Drift’s presumed 2.2 bps in our modeling is extremely conservative. As shown further above in the document, that metric is usually twice as much and closer to 4 bps on average.
   

Solana is steadily positioning itself as the layer on which all fintech primitives will emerge. There needs to be an avenue for hedging assets, for price discovery to occur, and for traders to have easy access to leverage. Drift sits at the intersection of all three needs. It will see positive tailwinds as Solana’s focus shifts from being mostly on crypto-native spot assets to a mix of commodities, indices, and equities in the coming quarter. We see the world coming on-chain, through Solana. The data below explains our reasoning for it.

Solana is undergoing its own infrastructure evolution. If Alpenglow, from Anza, comes to market, transaction finality will happen in roughly a third of the time (150ms versus 400ms currently) directly reducing the latency cost that JIT auctions currently carry. A frequently debated point of view is that Solana’s transaction throughput is the bottleneck for perpetual exchanges. Alpenglow changes that equation on finality.

Firedancer, an independent validator client built by Jump Crypto addresses throughput through parallelisation. Once live, Solana’s ecosystem will have two independent validator clients built by entirely separate teams. This matters specifically for Drift because its verifiability argument only holds if the chain it settles on is itself resilient. In the meantime, Drift’s gasless trading features have already pushed the product closer to parity with its peers on speed of execution

The World will Trade Onchain

Solana’s RWA base has grown from a little over $10 million in January of 2024 to over $1.6 billion in the span of two years. It is far smaller than Ethereum’s $14.7 billion, which accounts for ~59% of all RWA assets represented on-chain.

According to RWA.xyz,

1. Solana has the second-highest holder base of RWA assets on-chain at 283k, compared to 160k on Ethereum

2. It was home to 60k active addresses engaging with RWAs on-chain in January of 2026 alone.

Solana has been where retail participants already are. It is where they come to hold meme assets, send dollars cheaply, and stay up-to-date on culture. The fact that these users currently generate $120k each day on Phantom through builder codes is a sign of how big the market for retail participants trading perpetuals could be. There is a segment of the world that needs access to commodities, equities, and foreign currencies without going through local brokers. HIP-3 serves the crypto-native crowd quite well today.

But when you consider tooling such as wallets, off-ramps, and private key management solutions, along with the mix of spot assets, Solana is positioned as a better ecosystem for developers to build on. We are already witnessing an uptick of RWA assets on Solana.

Currently, xStocks alone does $600 million in on-chain volume in spot markets each month. HIP-3 native markets are expected to reach $100 billion in the coming months. Presume half of that would trend to Solana and be worth $50 billion each month. Given Drift’s market positioning, it could command half of that volume - and capture $25 billion. Assuming a fee rate of 2.2 bps on that volume, we are looking at a fee uptick of $5.5 million per month. The number is 5.34 times what Drift reported in earnings in January, at $1.03M. Annualised, that amount will be $66 million. Considering a 5x PF, we are looking at a $ 330 M valuation.

Drift can capture half of the RWA segment volume as it has demonstrated the ability to capture 60% of the market on Solana in the past, and its architecture is meaningfully better for institutions that care about order sequencing, settlement times, and verifiability.

In 2025, Drift was early to collaborate with Securitize to bring a tokenised version of Apollo’s Credit fund on-chain. They have proven their ability to structure, on-board, and create MVPs with the largest asset issuers to extend asset representations on-chain. As the demand for RWAs increases, Drift benefits from its storied past and strategic positioning within Solana.

Assets on Solana usually trade at deep discounts to their fees due to the seasonality of revenue sources or being relatively early. Drift, by comparison, commands a premium due to its steady, consistent governance structure and the fact that the team has silently built a cash-flow machine that sustains itself through market cycles across all market environments. Everyone needs a perpetual exchange regardless of whether tokens go up or down. Drift has consistently been the best avenue for traders on Solana. It has the opportunity to simultaneously expand its volume base while commanding a premium in the Solana ecosystem.

If it is to establish itself as the home of all DeFi on Solana, we expect this premium to grow substantially. Solana has yet to respond to Hyperliquid.

What Could Break

For Decentralised.co - the bet is not about going against Hyperliquid. It is a long on Solana’s ecosystem through a player that is deeply discounted for the value it generates and the premium its team should command. There are several assumptions that can break here.

1. As it stands, Drift’s revenue is being used to sustain the team. This is considerably better than selling tokens to fund operations, but if other perpetual exchanges continue to command volume and Drift struggles to catch up, it may find itself having next to no capital to reinvest in the community or product through incentives.
   

2. We also presume that price-to-earnings ratios are good indicators of a network’s value, but over the past four years, owning the token has not meant much in terms of how earnings flow back to users. The assumption here is that investing earnings in the team’s operation is currently the best use of them, but that assumption can falter.

3. Hyperliquid trades at a 29 PE because much of its earnings flow back into the token through buybacks. Currently, Drift’s model does not have a buyback program. The markets may continue to punish this through valuation discounts if there is no confidence that the product is better off leaving the fees unused.
   

4. Lastly, Jupiter, given its aggregator model, can compete with Drift on several fronts. Ultimately, the choice between Jupiter and Drift boils down to a specialist platform vs an aggregator. Our assumption is that a specialist will beat an aggregator during this phase of the market, where a sectoral leader is not yet established in Solana. Jup’s new chain and ability to acquire players within Solana ecosystem are meaningful threats to Drift.

One challenge we see is with tying the token’s revenue to its valuation. This will require working with the team on governance and on the long-term relationship between token holders and the business’s core fundamentals. As it stands, the token could benefit from more proactive governance and engagement from its holders.

For investors looking to build exposure to perpetual exchanges, Hyperliquid could be a better choice, given that the assistance fund on Hyperliquid routinely buys back HYPE tokens from the market. One of the things we would like to work on in the coming months is to propose a framework for Drift’s token to accrue more value from the traction the protocol generates.

Drift’s team has shown exceptional commitment towards building open, verifiable markets. They have been at the cutting edge of delivering a better user experience (via solutions like gasless trading) without compromising decentralization. Although markets do not currently talk about the need for verifiability or trustlessness, we believe it is only a matter of time before these elements become important.

In the past four years, Drift has shown commitment towards that mission. Our investment is a bet that as Solana’s capital markets mature, venues that prioritise verifiability, composability, and openness will command a premium.

We are ultimately long open, verifiable, composable markets. Drift is a position that expresses that conviction.

Verifying markets,
Joel John

Today’s piece might scream existential crisis, but hope is what it delivers. With VCs leaving the industry and founders wondering whether AI is the play to make, now is a good time to explore why we are even here. We broke down the numbers and traced where revenue flows. Today is part one of a series of articles that speak to the crux of why crypto matters, where it stands, and how it evolves from here.

Decentralised.co is actively investing in the liquid markets. The research that shapes pieces like today’s informs how we allocate. If you are an investor or a founder looking to explore how fundamentals tie back to crypto assets, join us at Telegram. Or drop us an email at venture@decentralised.co.

Let’s dig in..

The fear and greed index for crypto is at the lowest it’s ever been. At the same time, the industry has never been more profitable. Since 2018, DeFiLlama has tracked $74.8B in fees generated by crypto-native protocols. Nearly half - $31.4B was generated in the eighteen months between January 2024 and June 2025.

How is it that an industry can have its best series of quarters in eight years and still find itself in fear?

Entropy Protocol. Milkyway Protocol. Nifty Gateway. Rodeo. Forgotten Runiverse. Slingshot. Polynomial. Zerolend. Grix Finance. Parsec Finance. Angle Protocol. Step Finance. These are twelve names that shut down over the past two months. Products that have sustained for years and were built by passionate founders we respect OKX, Mantra, Polygon Labs, Gemini and Binance have each had layoffs, too.

Conferences have fewer people visiting. VCs are pivoting to AI. Developers are flocking to AI. The doom and gloom are real. If you are in crypto, pivot to AI - has become the prevailing sentiment.

But should you?

We have been thinking about this question for the past few weeks. When a technology emerges, markets initially assign it a premium due to its novelty and the scale of its ambition. Close to 6% of the UK’s GDP was invested in rail stocks in the 1800s. Hyperscaler capital expenditures will represent 2% of US GDP in 2026. But as reality hits, the technology trends towards more reasonable valuations. What matters is whether an industry can prove itself useful in the return to sanity.

In today’s issue, I break down how crypto revenue has historically evolved, the stickiness of the money generated, and the nature of moats in our industry.

Studying The Ledger

Crypto-native businesses have generated revenue since the industry’s inception. Exchanges like Bitmex, Binance and Coinbase were massively profitable endeavours. They were centralised, owned by a few individuals, and their revenue was not public. DeFi native primitives like exchanges (Uniswap) and lending platforms (Aave) changed the mood. You could verify how much the protocol made each day.

Tokens were expected to trade at valuations that reflected the economic activity these primitives enabled.

As late as 2022, decentralised exchanges accounted for 28.4% of revenue generated. Together, they generated $2.27B that year. Lending told a similar story and was highly concentrated. Aave and Compound accounted for 82% of all lending fees. There were category leaders, but there was also hope in the long-tail of protocols evolving to capture market share.

The technology itself was novel enough to command a high valuation.

Crypto’s consumer expansion closely followed. NFTs represented a hopeful vision of culture being priced on-chain. Celebrities that the average person knew were changing display pictures on Twitter. People presumed this would translate to massive adoption. OpenSea generated $1.55B or 71.7% of all NFT market revenue. In hindsight, the marketplace’s $13 billion valuation looks less absurd. They could have evolved to be a sustained monopoly.

Fate and markets had other plans. In 2025, NFTs accounted for less than 1% of revenue. We had our Beanie Baby moment, but it left us with no physical memorabilia. Decentralised exchanges, in contrast, grew but struggled to capture any valuation gains. Last year, decentralised exchanges generated $5.03B in fees. Lending platforms generated $1.65B in fees. Combined, these two segments account for 22.9% of overall fees, down from 33.1% in 2022.

Their economic activity represents a smaller share of a larger pie, but their valuations have dropped drastically.

What grew then?
How did crypto’s native business models evolve in the years since 2022?

The chart below offers clues.

In January 2026, stablecoin issuers Tether and Circle accounted for 34.3% of all fees generated. Put differently, $0.34 of every dollar earned in the industry goes to two firms. Their revenue has doubled from $4.95B in January 2023 to $9.89B in 2025, driven almost entirely by T-bills. These are startup metrics for bank-scale financial products. Tether generates almost three times as much revenue as Circle.

Their rise came down to two forces.

1. The first was demand. The Global South always needed instruments to hedge against local inflation and to move money freely. Dollars, even digital ones, fill that gap in a way local currencies cannot. Capital flight is a necessity. Not a feature.
   

2. The second was the cost structure. Blockchains absorb the operational aspects of running a stablecoin business. Unlike traditional banks or fintechs, Tether and Circle do not need to hire in proportion to the amount of stablecoins issued on-chain. The marginal cost of issuing the next $1 billion on-chain and moving the next $100 billion between addresses is next to zero.

The two forces tangoed. One was pulled from the demand side as citizens voted with their money. The other flattened the cost curve. Together, they made stablecoin issuance one of the most capital-efficient businesses in financial history.

Stablecoin businesses require moats in liquidity, compliance, and the Lindy effect. The number of issuers that have survived multiple cycles is incredibly small. Between Tether and Circle, close to 99% of all stablecoin issuance revenue is captured. Why is that the case? Both assets benefited from being early. The network effects of multiple exchanges plugging in legitimised them in a way technology could not.

Tether had initially launched on Omni as a side-chain. It was slow and clunky, but it was also accessible in the touchpoints OTC desks and exchanges interacted with. That was a distribution moat, not a technology one. A moat that crypto-native founders often struggle to replicate with code alone.

Stablecoins benefited from the Lindy effect.
A new category would soon benefit from distribution moats.

We teased the idea that crypto is a transaction economy in two of our pieces. One was Money Moves, and the other was written last year in Everything Is a Market. What we did not account for at the time was the pace at which transactional products built around Telegram trading bots and trading interfaces were growing.

Combined, in Jan of 2025, these two sectors alone accounted for $575M in fees. It makes sense when you consider what consumers want. Meme trading and perpetual exchanges allow users to make quick profits. They pay hefty fees in pursuit of quick returns. The category rose from 1% of overall revenue to just over 15% between 2022 and 2025.

Products like TryFomo and Moonshot generated millions of dollars in revenue by focusing on the end user. The product was not technically sophisticated. On the contrary, it was the ability to aggregate crypto-native primitives and bundle them to create better user experiences. Developers no longer had to incentivise liquidity or bother about wallet management thanks to tooling like Privy coming of age.

The primitives that we were all excited by in 2022 were now mature. Applications (such as BullX and Photon) were built on top of it. The sector alone created some $1.93B in fees between January 2024 and February 2026.

Meme assets had one fatal flaw. They were thin applications with great amounts of seasonality attached to them. Feels like Déjà vu? It is because NFTs and Web3 gaming had similar spurts of growth and eventual collapse. This seasonality is both a bug and a feature in our industry. We will revisit this theme shortly. But for now, let’s try to map out where revenue went instead.

Perpetual exchanges (and later prediction markets) represented new avenues with longevity. PumpFun democratised asset issuance through meme coins, but the game was not fair.

Eventually, markets wised up to the fact that meme coins die. Dreams of being a millionaire through buying a token named ShibaInuYouShouldShareThisNewsletter coin died with them. People did not want to manage a portfolio of random tokens. They wanted risk. Perpetual exchanges offered that.

Share

You could trade Bitcoin, Solana or Ethereum with extreme leverage. Market-makers and traders that needed an alternative to centralised avenues flocked in. The core product in this category was liquidity. Hyperliquid was the dominant player because its orderbook depth was comparable to that of a centralised exchange. Without that parity, users had no reason to move over. Hyperliquid and Jupiter accounted for the bulk of the fees in the category over the past three years.

Perpetual exchanges and trading platforms ripped the band-aid off crypto. They made it apparent that capturing small sums of fees off high-velocity transactions was where the money had to be made. Meme trading platforms and perpetual exchanges were dopamine machines that packaged and sold risk.

One of them would mature to be a core financial primitive. One that the world would use to trade commodities, equities and digital assets even on a weekend. Blockchain-native apps replicated what Robinhood and Binance had long offered: avenues for risk.

Starving a Fat Protocol

Notice that I haven’t mentioned protocols so far? The base layer where all the magical internet money movements were recorded? That is because they have an entirely different (but pertinent) story to say. They are casualties of the novelty premium, which is trending towards a discount. Let me explain

In January 2023, Optimism was trading at PF (price-to-fee) multiple of 465. Solana was at 706. Arbitrum and BNB were around 206. Today, Solana is at 138, Arbitrum is at 62, and OP is at 37. Polygon trades closer to a fintech company at 20. Tron sustains the stablecoin ecosystem and sits at a multiple of 10.2. Taken individually, Optimism, Solana, Arbitrum, and Polygon have enabled more sophisticated products in the years since. Each of them has more users, better liquidity and a more complex suite of financial applications built on top of them.

The discount in their price-to-fee multiples tells us about how the market perceives them.

Historically, L1s and L2s have traded at extreme premiums compared to standalone infrastructure or ventures. That premium, when invested well, could have created new economies. It could have funded developers to build real applications that mattered to real people outside our industry. Instead, the open-source nature of the products and the ease with which things could be tokenised meant we had fifty replicas of the same thing across thirty networks, with broken composability.

That was okay, because we had bridges. And cross-chain messaging. And countless other mechanisms to move money. All of which were now being valued at increasingly lower levels.

Consider the fate of DeFi primitives. Investor optionality and the lack of novelty butchered valuations, even when these primitives drove more economic activity. These are fragmented markets where investors have a multitude of choices to bet on. The novelty of being “decentralised” or on a blockchain has long since worn off. Kamino, Euler, Fluid, Meteora and PumpSwap have entered the chat - all at lower price-to-fee ratios than what protocols commanded in 2022. As the chart below from TokenTerminal shows, price-to-fee multiples of decentralised exchanges collapsed between 2023 and 2025. Some now trade at a multiple of 1.

In other words, the market values them below the fees they will generate over the coming year. A strange paradox is on display. Although the underlying protocols, be they DeFi primitives or the L1 itself, have trended lower in valuation, the apps built on them have generated more revenue over shorter periods.

The number of teams doing over a million in revenue every quarter has steadily risen since the early 2020s. The figure is over one hundred. Protocols that took twenty-four months to reach $10 million in annual revenue in 2020 were considered fast. By 2024, protocols were reaching that milestone in approximately six months. Pump.Fun, launched in early 2024, reached $10 million in revenue in roughly two months, the fastest recorded trajectory.

This acceleration reflects both the maturation of the underlying infrastructure (faster chains, cheaper transactions) and the expanding pool of on-chain capital looking for yield and entertainment. If you’re a developer or a founder, consider the facts:

• There are nearly 900 protocols generating revenue in crypto today.

• Each of them competes for a smaller share of median revenue, but the broad arc shows more teams generating more revenue. For context, revenue-generating protocols grew almost eight times, from 116 to 889.

• Median revenue has declined to $13k every month

Blockchain native businesses have three forms of moat. Each of which is evident when we study how revenue is made.

1. First Mover Advantages: The network effects that Tether and Circle gain from being early will be hard to replicate. They have been around through multiple cycles and are established duopolies, despite the emergence of new players. As it stands, these businesses are non-tokenised and hyperfinancial. Tether is a centralised entity whose revenue mostly comes from T-Bills.
   

2. Liquidity Moats: Aave has sustained liquidity depth across cycles in an industry where capital has historically been mercenary. Hyperliquid has replicated it, but it is too early to say. These protocols have an incentive to return capital to liquidity providers and tweak tokens towards governance.

3. Distribution Moats: Seasonal applications, such as meme-coin trading platforms, rely on capital velocity and consumer demand. Web3 games and NFTs stick here. AI-fuelled productivity would mean small, lean teams can now ship consumer-oriented products faster. Where does edge come from? It would boil down to onboarding and retaining the most users when markets are hot.

Products built on distribution moats can be tremendously valuable, but they are outliers and not the norm. A startup was traditionally valuable because its learnings could be replicated. Y Combinator works partly because of the lindy effects of the ideas that prevailed. Crypto evolves at a pace that is too fast for such Lindy effect learnings to be instilled. This is partly why we do not see founders replicate their success in consumer products as a category. The seasonality that helped scale a venture in the first place may not replicate itself.

That is not to suggest founders should not pursue the opportunity. Segments such as prediction markets or data providers for agentic-economy products may generate a great deal of cash flow in the short run. It is just pertinent to understand that they are high-velocity, short-term games that may not last. The trap for such product categories is in needlessly raising venture capital or being stuck with a token issued long after the meta that enabled the product in the first place dies.

What makes tokenised businesses valuable then? Are their valuations even justified?
The data offers some clues.

Questioning Governance

In 1999, many tech companies traded at a price-to-sales (P/S) ratio of 10x-20x. Akamai, a content delivery network, traded at 7,434 times sales. By 2004, Akamai had fallen to 8x. Many companies went from P/S of 30x-50x to less than 10x. The dot-com bubble burst evaporated trillions of dollars in speculative value. Yet many of the companies survived because the underlying businesses were real. Amazon fell 94% from its dot-com peak and went on to become one of the most valuable companies in history.

Crypto is going through the same compression, faster. In 2020, when DeFi was still an experiment, crypto generated just about $21 million in total annual revenue. The aggregate P/S across tracked protocols sat at 40,400x. The market was all about the future: “What could crypto be?” By 2021, as DeFi summer turned protocol revenue into real numbers, P/S collapsed to 338x. Today, with $18 billion in annualised revenue, it sits at ~170x. The compression from 40400x to 170x took five years.

However, there is a catch. When Visa trades at 18x, shareholders receive dividends and buybacks. They have a legal claim on the company’s earnings and a seat at the governance table enforced by securities law. When Aave trades at 4x, token holders have governance rights but, until recently, no direct economic claim on revenue. Hyperliquid uses its assistance fund for buybacks, making HYPE holders the closest thing to equity holders in DeFi. Aave approved a $50 million annual buyback programme in 2025.

These are meaningful moves, but they are exceptions. Across the broader market, most protocols lack a mechanism to return value to token holders. The multiples look cheap, but the rights attached to those multiples are thinner than anything in traditional markets. What makes these multiples possible is an industry that generates revenue at a scale and efficiency unlike anything in traditional business.

The protocols compressing crypto’s P/S ratio are not large organisations with thousands of employees. They are small teams running global financial infrastructure with near-zero marginal costs and no physical footprint. How thin can those costs get? And how much will holders trust these teams with doing the right thing with protocol revenue?

Segregate the market by sector to get a much better picture. Aave, the largest lending protocol in DeFi, trades at a P/S ratio of roughly 4x. Hyperliquid, which controls ~80% of the decentralised perpetual futures market, trades at roughly 7x. These are not bubble multiples. Arguably, they are lower than their closest traditional comparables. Coinbase, the only major publicly listed crypto exchange, trades at roughly 9x revenue. CME Group, the world’s largest derivatives exchange, trades at ~16x. Visa, the gold standard of payments infrastructure, trades at ~15x.

Will Clemente mentioned on our podcast that crypto is the purest form of capitalism. In no other industry do successful businesses achieve profit-per-employee numbers that approach Tether’s estimated $100 million. To put this in perspective, Nvidia generates $5.2 million in revenue per employee, Apple generates $2.4 million, and Google, $2 million. Tether, with 125 employees and roughly $12.5 billion in annual revenue, operates at a scale that suggests the highest profit per employee in corporate history.

Despite the crazy 170x P/S number, the market is not irrational about the protocols that actually generate revenue. It prices them at or below traditional financial infrastructure.

Which leads me to the next question - what even is the use of tokens? In many categories, tokens are powerful instruments for coordinating capital towards a shared vision. Crypto is in this phase where entrenched duopolies are the norm. Traditionally, a founder had to take debt (against equity) or raise money to infuse capital for financial products. Hyperliquid, Uniswap, Jupiter, and Blur are proof that, with token incentives, individuals will allocate capital to new products. The same individuals could contribute substantially when tokens have governance rights attached. In this regard, tokens may evolve to serve two functions.

1. Firstly, to coordinate capital and resources from the right kind of individuals

2. Secondly, to empower them to govern a protocol.

Tokens on their own are no longer valuable. Even equities are tokenised now. These instruments will need to have claims to economic activity and the ability to guide governance. Many Layer 1 and Layer 2 tokens struggle to deliver on either. Teams and VCs often hold the bulk of the token, leaving holders in disarray. It leaves no reason for the marginal person to care about newly listed digital assets.

Today, these experiments are split. MetaDAO allows holders to receive a full refund of their investments if teams make false representations. There are no large protocols on it yet. Crypto’s core reckoning is that tokens have traditionally given holders few rights. Protocols are now trying to answer the age-old question of why anyone should hold these instruments in the first place. In a future issue, we will explore the correlation between holder rights and valuations.

The Fork In The Road

Capital markets have become more intertwined over the past two decades. Much of it can be attributed to technological improvements. We can trade commodities, foreign indices, digital assets and even compute (GPUs) in the near future. Blockchains have made it possible to trade these markets globally, at all times. The fact that Nasdaq and NYSE are now trending towards 24/7 markets is an instance of a technology changing the zeitgeist.

We live in a hyperfinancial world, where ironically, news of war has us scrambling to figure out which prediction markets are the best to bet on.

For founders, that means rethinking what they build and how they build it. If the data here makes anything apparent, it is that all blockchain products eventually monetise through two core maxims.

1. Through taking a small cut of high-frequency transactions or

2. Taking a large cut of transactions where verifiability and trust assumptions matter

The edge is either in velocity or verifiable transparency.

Profit motives are the purest incentives for participants in the capital market. There is a core belief that eventually, markets trend towards extreme efficiency. We see versions of this with categorical leaders. That chart where we saw 70% of multiple segments being owned by two key players? That is a brutal aspect of life and markets we all grapple with. What this means for founders is that the capital that once went towards their token is now redirected to assets with higher volatility or higher returns on capital.

Long-term capital exists, and may even pay a premium, but only if it values the underlying business. Investors in Google and Amazon did not have to scramble for the exits because the underlying businesses were valuable.

In an age where the value of software itself is under question, blockchain native apps will have to find new ways to be valuable. We can restructure tokens. Perhaps, we could even make start-up equity tradable on-chain. But this is not just a token problem. It is a business model problem, too. The vast majority of long-tail blockchain applications - like Web3 social, identity and gaming products have struggled to see scale or meaningfully differentiate from their traditional counterparts. It is not that those experiments were not valuable. It is that we struggled to monetise them effectively.

The infrastructure age of crypto is behind us. What lies ahead is a period when it blurs with the Internet. Nobody talks about an “online” business anymore; you simply exist on the Internet. Nobody is a “mobile app developer” anymore; you are a developer.

Long live the age of the blockchain enthusiast. We are simply ledger maximalists wondering what the best use of these ledgers is.

Craving a good shawarma,
Joel John

P.S - I wrote this piece, part monitoring drone hits on the town I call home - Dubai. There is a silent resilience in continuing, even when it may seem as if the world is coming to an end. A rebellion in its own right. Many founders in crypto are displaying the same resilience. If you are still here and building, we’d love to chat and see how our data and learnings could be put to use in building scalable protocols.

Research here is guided by data from DefiLlama, TokenTerminal, Codex, Santiment and a mix of traditional data vendors.

Base just announced it's moving away from Optimism's OP Stack to build its own architecture. OP is down almost 25% in a day after the largest source of revenue for Optimism chose not to pay them anymore.

We didn’t know about this when we started writing the article, but we made the case for further fragmentation in crypto. Fragmentation at the level of chains, token standards, and liquidity. Over the past few weeks, we have been working with the LI.FI team to make the case that fragmentation in crypto is not a temporary phase. Base walking away just proves the point.

If you are building across chains, trying to bring all of crypto together, write to us at venture@decentralised.co.

TLDR:

Complex systems have a tendency to fragment before they consolidate. Telephones got automatic switches. Banks got clearinghouses. The internet got TCP/IP, then search engines. Crypto followed a similar path. The industry expanded into a landscape of L1s, L2s, and app chains; bridges and DEXs now connect them. But the problem is that delivering an outcome still depends on multiple independent services behaving correctly in sequence.

LI.FI is an orchestration infrastructure for this problem. It distils cross-chain complexity into composable building blocks that developers can combine to build the flows their products require.

Three shifts make this timely.

1. Fragmentation is compounding

2. Regulation is driving more fragmentation.

3. Execution is becoming autonomous.

In a world of increasing asset and liquidity fragmentation, the winners will be systems that can scale with the complexity of orchestrating the steps needed to fulfil users’ needs.

Almon Brown Strowger was a mortician in Kansas City, Missouri, USA. His legend goes like this. Strowger was losing business to his competitor. He was convinced that his competitor was getting all his orders thanks to his wife, who worked in a telephone switching company. In 1891, Strowger built a device that would route calls without human intervention.

Whether the conspiracy was real or not, Strowger’s response changed telecommunications. A caller would express their intent (the number they wanted to reach), and the switch would figure out the physical path through the network.

Before automatic switching, every call was a negotiation with the phone operator. You waited to talk to the other person because the operator physically connected the wires. As networks grew, operators became bottlenecks. The complexity of possible connections outpaced human coordination capacity.

The switch absorbed routing into the network itself. Instead of expressing an intent to a human intermediary, the caller dialled, and the system connected. This provided a much better user experience.

Lasting complex systems tend to follow this path. When they grow beyond what individuals can coordinate on their own, coordination shifts to systems designed to manage it at scale.

We have seen this pattern repeated across communication, banking, and modern food delivery apps. The Internet itself was not immune to the problem of scaling. We are also seeing this play out with blockchains. Blockchains are about making distributed computers independently agree on a version of the truth. The fewer things they have to agree on, the better. This is why blockchains are closed worlds. Something is needed to coordinate how value and information move across these closed systems. Bridges were the first step towards this coordination.

This article examines how increasing fragmentation creates orchestration bottlenecks, how LI.FI is improving the user experience across several blockchains and is shaping the interoperability (interop) space as more and more global finance shifts onto blockchain rails.

Multiple Networks to One Internet

The Internet grew out of many small, purpose-built networks run by universities, research labs, and government programmes, each solving a local communication problem rather than trying to coordinate globally.

Until the early 1980s, computers were connected through shared institutional systems and research networks where access was limited, connectivity was intermittent, and communication depended on telephone infrastructure. These networks worked, but they were expensive and not designed to interoperate by default. As the number of networks grew, coordinating between them became more difficult.

CompuServe, AOL, and Prodigy each operated its own network. Users subscribed to one service, logged into that service’s servers, and interacted only with other users and content inside that network. Communication across services was not supported.

From the user’s perspective, this meant that “being online” did not mean accessing a global network. It meant choosing which system to subscribe to. Content did not flow freely between services. Messages sent on one platform were invisible to users on another. There was no shared discovery layer, no shared messaging standard, and no shared economic rail.

Crucially, this problem could not be solved by any single platform growing larger. Even as AOL scaled rapidly, it remained incompatible with other services. Growth improved coordination within a network, but did nothing to reduce coordination costs between networks.

The breakthrough came by standardising how data moved between machines rather than trying to standardise user experience or consolidate platforms. This led to the development and adoption of TCP/IP, a set of transport standards that defined how information could be addressed, transmitted, and reassembled across heterogeneous networks.

It allowed networks to retain their internal structure while conforming to a shared set of transport standards. Systems no longer need custom, bilateral integrations to exchange data. As long as they followed the same protocol rules, packets of data could move reliably between otherwise incompatible networks.

When communication between networks was standardised, the system could finally scale beyond bespoke integrations. Standardised transport created a shared substrate on which independent services could be built and reached reliably, even though those services were not coordinated with each other.

What was the impact of this? If there are three separate networks, you need at least three custom integrations to enable them to communicate with each other. For four networks, you need six custom integrations. Ten integrations for five networks, and so on. Statistics nerds know that it’s ^NC₂ custom integrations for N networks or systems. So if 100 different businesses wanted to exchange data, they would have needed 9,900 custom integrations. TCP/IP eliminated that need with one shared standard.

However, this did not make the Internet easy to use by default. As more organisations began publishing information and building services on top of shared protocols, the number of reachable destinations grew faster than any individual could reasonably keep track of.

The problem was no longer whether systems could talk to each other, but how users decided where to go and what to interact with. Information was technically accessible, but discovery became expensive in human terms. Knowing which site mattered, which service was trustworthy, or where value actually lived required effort that did not scale with the network’s size.

Search engines, app stores, marketplaces, and media platforms emerged to solve this by making accessing the web easier. They indexed a rapidly growing universe of independent services, applied ranking and filtering rules, and routed users towards a small set of relevant options at any given moment. So, instead of picking from millions of unrelated options, users could choose from ten highly probable options.

Search engines did nothing to unify the underlying system. Websites and services remained separate, owned by different operators. What changed was how users discovered them. Instead of users having to reason about destinations, protocols, or addresses, these platforms absorbed that complexity. They handled discovery and access at the point of interaction, allowing users to focus on what they wanted to do, from finding information to consuming content, or completing a task, without having to think about how the underlying system worked.

Crypto has gone through a similar transition. What started as a single shared execution environment has spread across many independent chains, each with its own liquidity, rules, and constraints. The orchestration problems that shaped the early internet have returned, at the level of making actions actually complete across different execution environments.

How did Crypto Get Here?

From 2015 to 2020, most on-chain activity occurred on Ethereum. Smart contracts, liquidity, and users shared a single execution environment. Assets were native, and transactions entered the same mempool and were ultimately ordered by the same set of validators. Because everything lived on the same chain, composability was the default.

During the DeFi Summer of 2020, routine actions such as token approvals, swaps, and liquidity provisioning frequently cost hundreds of dollars.

Any burst of activity delays unrelated transactions across the network.

Alternative Layer 1s and rollups both emerged as responses to Ethereum’s execution constraints. L1s reduced fees by adopting different design trade-offs, while rollups improved throughput by batching transactions and settling back to Ethereum. New chains offered aggressive subsidies to bootstrap activity.

Users were no longer tied to one chain, so applications had no reason to stay on one either. Uniswap launched on Polygon, Arbitrum, and Optimism with largely identical core logic. Aave deployed across Ethereum, Polygon, Avalanche, and later other networks. The same applications now existed in parallel across different execution environments.

As assets moved across chains, the same token began to exist in multiple places at once. Stablecoins, ETH, and governance tokens were bridged into separate liquidity pools on each network. Liquidity that had once aggregated naturally on a single chain now sat in parallel pools with different depths and conditions.

This made prices less uniform. A token swap on Ethereum might clear at a different price than the same swap on Polygon or Avalanche, simply because liquidity was thinner or arbitrage was slower. During periods of volatility, users could see materially different outcomes for the same trade depending on where it was executed.

This fragmentation also extended beyond liquidity into the state. User balances, approvals, LP positions, and borrowing relationships were now distributed across chains. Actions that were previously atomic, such as moving collateral or rebalancing positions, became multi-step workflows involving bridges, with long wait times and repeated approvals.

Bridges became the critical coordination layer. Unlike single-chain execution, bridges operate across separate security domains, relying on validator sets, multisigs, or other off-chain actors to authorise transfers. Compromising even part of this verification layer is sufficient to break the bridge, even if both chains remain secure.

This is the point at which crypto’s interoperability stack began to stratify. What had been a single problem of “how do I move assets?” split into distinct layers.

1. transport (how messages and proofs move between chains),

2. standards (how intents and cross-chain calls are expressed),

3. execution (how orders are fulfilled), and a

4. applications (how users interact with all of it).

Each layer would evolve at different speeds and face different competitive dynamics.

At a high level, bridges enable assets and actions to be used across otherwise independent execution environments. Over time, two dominant approaches have emerged, reflecting different answers to the same question: how much of the cross-chain complexity should be pushed onto users, and how much should be absorbed by the system.

On the one hand, interoperability has converged around standardised token representations and mint-and-burn models, in which assets move across chains via shared token standards and messaging layers. These systems aim to make assets broadly portable and composable across many environments, but they still require applications and users to manage execution details across chains.

On the other hand, intent-based approaches have grown in prominence, where users specify outcomes and the system orchestrates how those outcomes are achieved across chains. Instead of exposing each intermediate step, these systems shift more responsibility to the infrastructure layer.

The aim here is not to delve into bridge taxonomy, but to understand their role in the orchestration layer. Their design choices shape how failures propagate, how liquidity is accessed, and how much complexity applications must absorb.

As execution spread across Layer 1s, rollups, and Ethereum itself, fragmentation began to compound. Each environment had introduced its own sequencer, block cadence, fee market, and operational risks. Gas mechanics added another layer of friction, as users frequently arrived on destination chains without the correct gas token to complete execution. Partial solutions such as gas sponsorships existed, but they were very inconsistent and application-specific.

Teams took on this burden because blockchains were no longer just execution environments. They had become distinct markets. The chart below shows trading volume in different ecosystems. If you exclude Ethereum, there’s no clear winner. Applications could not afford to ignore other chains. Even if it were for a brief period, if your application were on a chain that attracted activity for any reason, you would stand to gain users.

Applications had a clear economic incentive to expand. Remaining on one chain meant leaving users, volume, and liquidity on the table as activity migrated elsewhere. Expanding to additional chains allowed applications to meet demand where it already existed, rather than trying to pull users back into a single environment. Put simply, if you stayed on just one chain, you left money on the table. Going multi-chain had become the table stakes.

That expansion came with significant operational overhead, but it was often the only way to maintain growth, relevance, and competitiveness in a system where usage was no longer concentrated in one place.

For developers, multi-chain execution dramatically increased operational complexity. Applications could no longer be deployed once and operated globally. Every network required its own contract instance, RPC providers, and indexers, and each introduced its own failure modes.

Moreover, this was not a one-time cost. Engineering teams repeatedly described multi-chain expansion as running the same application under different operational assumptions, requiring them to handle upgrades, monitoring, and incident response independently for each chain.

Before 1773, bank clerks physically walked across London to settle debts with every other bank. Every morning, dozens of clerks trudged through mud and cobblestones, carrying paper and coin, visiting every counterparty individually. As the number of banks grew, bilateral settlement became untenable because its complexity scaled quadratically.

The solution emerged in a coffeehouse on Lombard Street. Clerks started meeting at the Five Bells tavern at day’s end. Instead of settling bilaterally, they netted their balances. Dozens of physical journeys collapsed into one meeting. The clearing house separated what banks owed from how they settled.

Crypto today is in a similar spot, but the shape of the problem has changed. New blockchains no longer connect through simple, pair-wise native bridges. Instead, they launch already wired into the ecosystem via third-party liquidity bridges, interop token standards, and shared execution infrastructure spanning many chains. Connectivity is no longer the bottleneck.

The difficulty lies in composing actions across what is already connected. Capital can move, assets can be represented across chains, and liquidity can be reached from many venues, but every user action still depends on multiple independent systems working together. The complexity adds up in the same way it did on the streets of 1770s London.

Luckily, history offers a direction towards a solution. What is needed is not more bridges, but an orchestration layer that converges routes. Something akin to a Lombard Street for on-chain settlement, a layer that separates what users want from how it is executed. This is the gap that systems like LI.FI fill by providing a single integration surface where multiple execution providers (like liquidity pools, bridges, DEXs, fast paths, and intent-based systems) come together to fulfil actions across any chain.

The Aggregation Trap

When execution fragmented across chains, the most immediate problem was navigation. Knowing where liquidity lived, which bridge supported a given asset, or which DEX offered the best price required manually checking multiple interfaces.

Aggregators addressed this by building backend infrastructure that indexed routes across bridges and DEXs and exposed them through a single integration surface. Instead of every application needing to maintain its own view of the ecosystem, they could rely on a shared routing layer to discover viable paths.

The pain point shifted from discoverability to coordination.

At that stage, aggregation seemed the best approach for orchestration and abstraction. It aligned well with how the system had evolved. Liquidity was already fragmented. Execution environments were already independent. Aggregators only needed to observe them and present options. The most obvious friction was informational, and aggregators solved that cleanly.

However, aggregation did not improve the user experience to the desired extent. Presenting information in a single interface did not mean that all user actions could be handled with a single click. In addition to worrying about execution costs, users also had to manage gas tokens across multiple chains. They had to undergo the ordeal of acquiring gas tokens on the destination chain to complete any cross-chain action.

Let’s say you had ETH and wanted to deploy USDC in some lending pool on Avalanche. Aggregators would swap your ETH to USDC, bridge to Avalanche, but could not deposit into the lending pool because you didn’t have AVAX (the gas token) on the destination chain.

Apps like Jumper solved the gas problem. Many routes now include refuelling or gasless execution. But this only fixes one visible dependency. Cross-chain journeys still lack end-to-end atomicity. Even with gas handled, the final protocol action can revert if the destination state changes while funds are in transit. The bridging is complete, but the intended outcome is not. Users end up holding the asset on the destination chain rather than completing the intended action.

This limitation becomes clearer when compared to aggregation in Web2. According to Ben Thompson’s Aggregation Theory, aggregation works because the aggregator controls fulfilment. Marketplaces and platforms not only surface options, but also standardise how transactions are fulfilled. Even when supply is fragmented, execution remains predictable because the aggregator enforces it.

Crypto aggregation operates differently. Aggregators can suggest paths, but execution is carried out by independent protocols deployed across different chains. Increased usage doesn’t give the aggregator more control or improve reliability. It simply increases the number of times users are exposed to inconsistent execution outcomes.

This is why crypto aggregators do not produce strong demand-side lock-in. Users continue to switch based on incentives, price, and liquidity because aggregation alone does not guarantee the outcome users want.

Aggregation without a bias for action works up to a point. Once information is available everywhere, what users need is not more options but an infrastructure that can complete their desired actions.

From Transactions to Intents

Information is no longer the bottleneck. What matters now is how users’ choices become on-chain instructions.

In practice, this means users submit a blockchain transaction.

A transaction specifies exactly which contracts to call, in what order, with what parameters and fee conditions. The network executes it as written or rejects it if any condition fails.

This is important because execution conditions on a blockchain are not static.

Liquidity changes from block to block as trades settle, pools rebalance, and gas prices move continuously based on network demand. Cross-chain transfers introduce additional delays that are beyond the control of any single chain. The transaction ordering can change at any time, depending on how sequencers or validators include transactions in a block.

In a multi-chain system, these factors vary not only over time but also across environments. If conditions change between submission and execution, as they often do, the transaction cannot adapt. It executes under the assumptions encoded at submission time, or it fails. The result is that either users face failed transactions or worse pricing. Neither is tolerable when they happen often.

The constraint is not that commitment exists, but when it happens. In a standard transaction, the user has to commit very early to a specific path. That path is fixed even before the system has access to the most current state.

Avoiding premature commitment requires a different kind of input. Instead of submitting an instruction that describes how execution should happen, the user submits a statement describing the outcome they want to achieve, along with the conditions under which that outcome is acceptable. The system is not told which contracts to call or which route to follow. It is told what must be true when execution completes.

For example, a user can instruct “acquire X amount of an asset within a given price range before a deadline” without specifying where liquidity should come from or which chain to use.

These instructions are called “intents”.

Pre-commitment still exists. A specific path must still be chosen before the transaction settles. The difference is when that choice is made. With intents, the path is determined closer to execution, using live information about liquidity, fees, and network conditions.

This separation between what the user wants and how execution occurs allows specialist actors, called solvers, to take over. When a user states an intent, they enter a shared execution environment where solvers observe it, simulate possible outcomes against live state, and compete to fulfil it under the specified constraints. The solver that delivers the best valid outcome earns a fee for the service.

What changes in an intent-based model is where most of the path-selection risk sits. Instead of users committing to a fixed set of steps upfront, solvers determine whether and how an intent can be completed under current conditions. If liquidity shifts or prices move, the solver must either adapt the path in real time or decline to fill the intent.

This reduces the user’s exposure to stale execution assumptions, but does not eliminate risk. Users still depend on competitive solvers and healthy market conditions. If there is no demand or liquidity for the desired asset, the intent may not be fulfilled.

Solvers compete only in markets where they can make a profit. When a user wants to participate in a market that’s not profitable for solvers, they may still be unable to sell the asset at the desired price.

Intents do not solve the coordination problem on their own. Solvers still need infrastructure that can reach liquidity across fragmented environments and adapt in real time. The open question is who builds the infrastructure that makes fulfilment dependable at scale.

Interoperable Markets

Early DeFi was built around the idea of “money legos”. Protocols were designed to be composable, meaning you could take an asset from one protocol, use it in another, and build increasingly complex financial products by stacking these primitives together. However, this only worked as long as everything lived on the same chain.

When execution spread across chains, composability broke. The building blocks still existed, but no longer snapped together. Every interaction now required assumptions about which chain, bridge, and pool would hold up under stress.

This kind of fragmentation is not unique to crypto. It’s similar to what Plaid solved for applications that wanted to read financial data across multiple banks.

Plaid created a common interface that allowed applications to read financial data regardless of where it resided. Developers could assume that balances and transaction history are accessible and focus on designing experiences rather than rebuilding plumbing.

Before LI.FI, liquidity technically existed across chains, but applications could not assume that access to it would be consistent. Every bridge, DEX, and chain exposes liquidity through its own interface, with its own limits. If an application wanted to move value across chains or compose multiple actions, it had to manage those differences. Building anything cross-chain meant stitching together bridges, swaps, gas handling, and fallback logic, and then hoping execution held up under real-world conditions.

LI.FI applies the same pattern to liquidity. It standardises how liquidity is accessed and executed across them. Applications no longer need to care where liquidity lives, which bridge supports which asset, or how execution should be sequenced. They can treat liquidity as something that can be reached programmatically and reliably, regardless of the underlying path.

The complexity now moves into the infrastructure layer, where it can be managed once rather than being rebuilt repeatedly. Instead of determining where liquidity lives, what matters now is who decides how it gets used.

At that point, the scarce resource is not liquidity itself, but rather order flow. In traditional finance, the two are largely interchangeable, but the subtle differences matter in crypto. Liquidity is passive. It sits in pools, vaults, and contracts. Order flow is active. It represents users and applications trying to swap, move, rebalance, or deploy capital. A pool with $10 million in USDC on a chain where nothing is happening has liquidity but no order flow.

What changes the equation is consistent, programmable access. When an orchestration layer reliably routes activity to the venues where execution is best, two things happen. First, market makers, solvers, and liquidity providers allocate to surfaces where order flow already concentrates and outcomes are reliable. Second, the system that controls the routing gains leverage. It can set expectations for execution quality, enforce latency and reliability requirements, and, over time, shape where liquidity is deployed.

This is the flywheel LI.FI is building. More order flow attracts more competitive participants. More competition improves execution. Better execution attracts more integrations, which generates more order flow.

Interoperability concentrates order flow, and concentrated order flow becomes a marketplace.

When an interoperability layer aggregates meaningful order flow, it starts behaving like a marketplace by default. The job stops being “move tokens between chains” and becomes “take a user’s intent and reliably fulfil it under live conditions”. The user expectation has gone from “move tokens between chains” to “perform this action with these constraints”. In response, the interoperability layer must evolve as well. As a result, interoperability has order flow transitioning towards a marketplace.

Meta aggregation is a marketplace where intents serve as demand and solvers as supply. Multiple executors compete to fulfil each intent. The system picks the best option given constraints such as price, speed, reliability, and compliance.

Solvers that fail, misquote, or deliver poor outcomes lose flow, margin, and eventually relevance.

For solvers and liquidity providers, the value is predictable flow. Predictable flow lets them quote tighter, rebalance more efficiently, and run thinner margins. Unpredictable flow forces them to hold idle inventory, price wider, and avoid long-tail venues.

That supply and demand loop is also why marketplaces are hard to bootstrap. Nobody wants to supply early when demand is uncertain, and demand does not show up until outcomes are reliable. The typical solution is to proxy one side until the other has a reason to participate.

How LI.FI Became a Marketplace

LI.FI took a path that evolved into a marketplace. It started as distribution and execution aggregation.

The first move was embedding into production applications. Over 800 businesses now route through LI.FI. The demand side of the marketplace was seeded before the supply side existed.

The second move was going beyond comparing prices to handling the actual execution. LI.FI deployed smart contracts across chains that could combine multiple steps into a single transaction for the user. Say you want to swap a token on Arbitrum, bridge it to Base, and swap it again into a different token there. Without LI.FI, that is three separate transactions you sign and monitor.

With LI.FI’s contracts in the middle, the user signs once. This entire process is non-custodial. LIFI doesn’t get to hold or direct users’ funds at any point. Funds move directly between smart contracts while remaining under user control at all times. The contract handles the swap, sends it through the bridge, picks it up on the other side, completes the final swap, and delivers the result.

The system that makes this possible uses a modular architecture based on the Diamond Proxy pattern, also known as ERC-2535. Applications interact with a single entry point to the contract, which delegates actions to specialised modules based on what needs to happen. Each module handles one capability, such as a specific bridge, a DEX, an approval, or a protocol deposit. These capabilities can be added, replaced, or updated without redeploying the rest of the system.

As the cross-chain landscape is constantly evolving, this makes sense. New bridges launch, existing ones deprecate routes, DEX aggregators update interfaces, and protocols change their contract logic. A monolithic contract architecture accumulates technical debt with every change. Each new bridge integration or DEX update touches shared code paths, which means every modification carries the risk of breaking something that was already working.

Over time, engineering teams spend more effort maintaining existing integrations than building new ones. The Diamond Proxy pattern avoids this by keeping each integration isolated. When a bridge updates its interface, LI.FI updates one module. When a new chain launches, LI.FI deploys a new module to the entry point. The rest of the system is untouched.

For simpler integrations, LI.FI forwards generic contract calls with encoded data. For most major protocols, however, it maintains dedicated execution modules that interact directly with protocol interfaces. As of early 2026, LI.FI supports more than 80 such modules covering bridges, DEXs, and DeFi protocols. Each module is a well-defined execution primitive that integrators can combine in different ways.

This architecture also enabled LI.FI to offer redundancy at the infrastructure level. Some integrations intentionally overlap to ensure that if one path fails, another is available. Enterprise clients like Robinhood and Binance integrate LI.FI because they need consistent execution across every route, on every chain, with fallback mechanisms built in. The Diamond Proxy is what makes that possible without the contract system becoming unmanageable.

The practical result was that LI.FI could observe the full lifecycle of every transaction passing through its contracts. Where did failures happen? Which bridges were slow on which routes? Which DEX aggregators won on which chains? This data telemetry became a compounding advantage, one that competitors could only replicate by processing comparable volume through comparable infrastructure.

The third move was building a consumer surface. Jumper is LI.FI’s retail interface, where UX primitives ship fast and inform the B2B product. The clearest example is destination gas. Jumper’s Refuel feature lets users acquire native gas on the destination chain during the journey, eliminating the “funds arrived but I cannot do anything” failure mode. Once you stop failing at infrastructure, the question becomes whether the user achieved their intended outcome. Did the swap execute at a fair price? That is the problem worth optimising for.

The fourth move was making journeys programmable. Pure aggregation hits a scaling wall once users start expressing multi-step outcomes instead of simple “bridge and swap” requests. With a few chains, bespoke integrations are manageable. However, every new chain introduces new edge cases, every protocol has different smart contract interfaces, and composing actions across them manually does not scale. LI.FI spent two years building Composer to solve this.

Composer is a transaction orchestration primitive. It lets developers define multi-step, multi-chain DeFi workflows and execute them as a single transaction. The user signs once on their source chain. Everything else happens behind the scenes.

Under the hood, Composer compiles user journeys into executable blockchain instructions using a domain-specific language in TypeScript. A developer can write logic such as check the wallet balance, if it exceeds $1,000, swap into ETH, deposit the ETH as collateral into Aave, take out a 75% loan, and swap the proceeds into a stablecoin: multi-chain, multi-protocol, one transaction. The system uses dynamic call data injection to pass outputs from each step into the next, so there is no manual parameter handling between legs of the journey.

Critically, Composer includes its own transaction simulator. Before anything touches the blockchain, the full execution path is simulated to verify it will work. This is more than a nice-to-have feature. Failed transactions on complex multi-step journeys cost users gas and erode trust. Pre-execution simulation turns “hope it works” into “verified it will work”.

The practical result is visible in products like Jumper Earn, where users make one-click cross-chain vault deposits. Behind that single click, Composer is orchestrating a swap, a bridge, and a protocol deposit across different chains. It is also what LI.FI is rolling out with enterprise partners like Alipay, where the end user cares only about the outcome (yield) and not the six on-chain steps required to get there.

Composer matters because it expands what applications can express and execute with a single integration.

Before Composer, an intent was limited to “swap this” or “bridge that”. After Composer, an intent can describe an entire strategy. When you can express an outcome programmatically and simulate it cheaply, you can auction the right to fulfil it.

The fifth move was opening execution into an auction. LI.FI’s intent system is a modular auction layer where solvers compete to fulfil user intents. As mentioned earlier, the system is non-custodial. User funds remain in their wallets and move only via transactions they explicitly sign.

Every intent system faces a bootstrap problem: solvers won’t appear without order flow, and order flow needs solvers. In LI.FI’s case, years of aggregation had already built the demand side. Billions in monthly volume flowing through MetaMask, Phantom, Robinhood, and hundreds of partners meant solvers had an economic reason to participate from day one. The cold start problem was already solved before the product launched by proxying demand.

That is why acquiring Catalyst made strategic sense. The logic was to buy the best execution technology and pair it with the order flow moat that no intent-native competitor could replicate. Catalyst brought a flexible architecture supporting multiple transaction flows, settlement layers, and oracle options. LI.FI brought the distribution that makes solver economics work.

The intent system also changes what developers can build. A lending protocol cares about collateral thresholds and liquidation timing. A wallet cares about speed and simplicity. A payments app cares about predictable fees and settlement finality. Each has a different definition of “good execution”, and a generic intent format would be a terrible design choice to serve all of them.

LI.FI’s architecture lets each integrator define application-specific intents with their own constraints, routing preferences, and execution criteria, but all of this is resolved through the same solver marketplace. Developers get the expressiveness to build complex cross-chain flows through a single integration, without having to reason about the underlying execution paths themselves.

The result is an intent system that did not have to compromise on either side. LI.FI still aggregates other intent systems and does not lock applications into a single execution path. However, it now also operates its own solver marketplace, where the same order flow concentration that made aggregation defensible makes intent execution viable.

Google’s ad exchange followed a similar journey. Before programmatic advertising, advertisers negotiated with individual publishers. Google built an orchestration layer that aggregated both sides and introduced real-time bidding. The exchange became the marketplace for price discovery, its leverage stemming from concentrated order flow across fragmented supply.

The economics were similar, too. Advertisers did not want to integrate with thousands of publishers individually, nor did publishers want to integrate with advertisers. Both sides were okay with paying the exchange fees because the alternative was worse. The exchange captured value by reducing friction and allocating attention efficiently.

LI.FI sits in the same position. Applications do not want to integrate with dozens of bridges and DEXs individually. Execution providers do not want to build relationships with thousands of applications. Both sides accept routing through LI.FI because the alternative is worse.

Why apps keep outsourcing to LI.FI

A lending market, perp DEX, or wallet competes on liquidity, spreads, risk, and distribution. Interop reliability sits in the critical path, but it is still a cost centre. Owning the entire execution surface across chains, bridges, and protocols means running an integration business.

Many teams integrate with LI.FI because it lets them buy a single execution surface that already sits on top of many venues, learns from aggregate telemetry, and is moving towards outcome-based fulfilment via solver competition. As more apps route deposits and complex actions through the same layer, order flow concentrates further. That concentration is the moat. It improves solver economics, enhances routing quality, and deepens the marketplace.

The game-theoretical outcome is that one player concentrates order flow, and solvers have to compete for access to it. If they try to extract too much margin, someone else undercuts them. If they fail to execute, they lose allocation. The dominant order flow aggregator becomes the default price-setting venue.

Why Now?

When everything is growing, even mediocre infrastructure looks like it’s working. That’s what happened in the early days of crypto interop. But it’s changing now. The bar has moved from “does it connect chains” to “does the transaction actually go through every time”. Teams that aren’t specialised enough to win on performance or integrated enough to win on distribution are getting squeezed out. This consolidation is happening alongside three shifts that make orchestration infrastructure more necessary than ever.

1. Fragmentation is compounding.

The early fragmentation problem was about chains. That problem still exists, but it is now joined by asset-level and rail-level fragmentation. Stablecoins illustrate this clearly because they are the single most used asset class in crypto, and the fragmentation runs through every layer. Stablecoins are fragmented at three levels – issuer, chain, and token standard.

Starting with the number of instruments, the stablecoin market now exceeds $300 billion in total supply, up from roughly $205 billion at the start of 2025. Although Tether and Circle control ~85% of the market share, there are more than 50 issuers with over $100 million in supply. These stablecoins are scattered throughout applications on multiple chains. The chart below shows the distribution of stablecoins across applications.

USDC is natively issued on 30 networks. USDT is supported on roughly 20 chains, and with USDT0 (its omnichain variant on LayerZero’s OFT standard), it reaches 18 more. DefiLlama tracks stablecoin activity across more than 100 chains.

Finally, there is the question of how the same stablecoin is represented. USDT exists as an ERC-20 token on Ethereum, a TRC-20 token on Tron, a BEP-20 token on BNB Chain, an SPL token on Solana, and so on. These are not interchangeable. You cannot send TRC-20 USDT to an ERC-20 wallet. Beyond chain-native standards, there is now a competing layer of cross-chain token standards designed to unify exactly this fragmentation.

LayerZero’s OFT (Omnichain Fungible Token) standard powers USDT0, USDe, PYUSD, and others. Wormhole offers NTT (Native Token Transfer). Chainlink has CCT (Cross-Chain Token). Axelar has ITS (Inter-chain Token Service). A newer standard, ERC-7802, allows tokens to expose structured metadata about their origin chain and verification endpoints. Each standard has its own set of stablecoin integrations, security assumptions, and developer ecosystem. The infrastructure designed to solve fragmentation is itself fragmenting.

The result is that a single dollar-pegged asset can exist as native USDT on Ethereum, native USDT on Tron, USDT0 via LayerZero’s OFT, wrapped USDT through various bridges, and bridged representations through A single dollar-pegged asset can simultaneously exist as native USDT on Ethereum, USDT0 via LayerZero, wrapped USDT through various bridges, and bridged representations through Wormhole or Chainlink. The same underlying dollar had different contracts, pools, and trust models. The coordination problem is now stablecoins × chains × token standards, and every combination creates a distinct execution surface.

As more real-world assets get tokenised, the same pattern compounds. A US Treasury bond might exist as an OFT on one chain, a native mint on another, and a CCT on a third. These differences determine how an asset is traded, bridged, and composed.

The execution surface is also splitting. Compliant rails with KYC’d liquidity pools are emerging for institutions. Privacy rails are emerging for those who need to keep the data confidential. Every new rail type is an additional execution surface that needs to be aggregated.

Companies are building their own chains to retain control over compliance, fee structures, and value accrual. Robinhood has its own chain. Stripe acquired Bridge and is building Tempo. Circle launched Arc. These are distribution-motivated chains built by companies with hundreds of millions of existing users, and every one of them creates new liquidity venues, new token representations, and new routing complexity.

If tokenisation follows the trajectory its proponents expect, every value chain that moves on-chain will introduce its own standards, its own chains, and its own fragmentation. The coordination problem will compound before it converges.

2. Regulation is accelerating asset issuance.

The GENIUS Act, now law, established the first comprehensive federal stablecoin framework in the United States. Although the regulatory clarity is good for adoption, the second-order effect is that it legitimised stablecoins as a product class and opened the door for a wave of new entrants.

This is not just a US phenomenon. MiCA is in force in Europe. Hong Kong passed its Stablecoin Ordinance in 2025. Each jurisdiction has its own compliance requirements, approved issuers, and preferred rails.

The GENIUS Act also prohibits stablecoin issuers from paying interest to holders. It does two things.

1. It drives the growth of crypto native stablecoins like USDe and USDH that are willing to pass on the yield to users.

2. It pushes yield-seeking innovation into adjacent instruments: tokenised treasuries and structured products that sit one layer above the stablecoin itself.

Each of these instruments needs its own token representation, its own chain deployments, and its own liquidity. Regulatory legitimacy accelerates issuance. Issuance accelerates fragmentation, and fragmentation accelerates the need for a coordinated infrastructure that can map, route, and execute across it all.

3. More and more executions are becoming autonomous.

As more AI-driven and agentic systems come online, execution increasingly occurs without a human in the loop. Agents rebalance portfolios, deploy capital, and move funds in accordance with predefined rules. There is no user selecting chains, bridges, or routes. There is only an intent and a requirement that it executes correctly under changing conditions.

According to Flashbots, autonomous execution is increasingly a first-order driver of how blockspace is consumed and how order flow is routed. Spam bots across rollups consume more than 50% of the blockspace while paying less than 10% fees. Meanwhile, private order flow accounted for over half of Ethereum gas usage. Bots on Ethereum accounted for more than 73% of DEX activity in August 2025, reinforcing the idea that execution quality and inclusion guarantees are increasingly determined upstream of the chain itself.

Agents do not encode chain-specific logic, bridge choices, or sequencing decisions. They express outcomes. They need something downstream to resolve their intent into an executable path, using live information about liquidity, fees, and network conditions. As the agentic economy grows, systems that can reliably execute without human intervention will matter more than interfaces.

The coordination problem is permanent. There will be no single chain; there will be hundreds, each with economic and technical reasons to exist. The infrastructure that maps standards, routes value, and coordinates execution across all of them becomes increasingly essential with every chain that launches.

What Could Undo This?

LI.FI’s thesis depends on fragmentation being a lasting feature of crypto, not a temporary phase.

The first is chain convergence. If activity consolidates onto one or two chains to the point where cross-chain movement becomes irrelevant, there is not much left to orchestrate. If everything that matters happens on Hyperliquid, you do not need a meta-aggregator. The counterargument is that if this were to happen, it would have by now. There have been stretches when one ecosystem dominated activity, but none in which the others disappeared. Cardano still exists. Every cycle produces new chains with real usage, real liquidity, and real reasons to exist.

The second scenario is standard convergence. Currently, the same asset can be represented across multiple cross-chain token standards, such as OFT, CCTP, NTT, and others. If one of these standards wins so completely that everything runs on the same rails, the mapping and routing complexity that LI.FI navigates gets much simpler. Today, at least three standards are prominent and growing independently. If a single standard captures enough adoption, the value of sitting between them shrinks.

Neither outcome looks likely in the near term, but they represent the structural conditions under which the LI.FI thesis would need to be revisited.

Where Interoperability Goes From Here

If the Internet pattern holds, raw transport is on the path to becoming a utility. Pure bridges are already headed in that direction. For a $1,000 cross-chain swap, most bridges and aggregators will land you at a similar execution price. The meaningful difference is whether the solution can navigate the maze of DEXs and bridges when some stop working, route around failures without the user noticing, and do so consistently across dozens of chains. Applications like wallets integrating cross-chain functionality natively are not necessarily optimising for the cheapest option, but for the one that does not break.

As this happens, bridges or DEXs will lose their identity. They fade into the background, as physical network infrastructure did once the Internet standardised its transport layer. Users and applications will stop thinking about which bridge moves their assets. They will care about whether assets arrive, and at what cost. In this world, the value migrates up the stack, away from the pipes and towards whoever coordinates the journey between what a user wants and where liquidity actually sits.

The solver networks that attract the most order flow will attract the best solvers. The best solvers will deliver better execution. Better execution will attract more order flow. The flywheel is already observable. Only consistent demand makes this durable, though. Orchestration solutions are in a unique position as they sit close to where the demand originates.

Bridges and liquidity venues hold supply; orchestration layers control demand routing. As order flow concentrates through these layers, suppliers compete for access to the demand surface rather than the other way around. Greater competition means their margins compress. Their negotiating leverage diminishes. The value accrues to whoever sits between user intent and liquidity deployment.

Then the cycle restarts at a higher layer of abstraction.

When execution infrastructure is reliable enough that applications stop worrying about whether cross-chain transactions land, they start worrying about how those transactions land.

A payments company needs predictable fees and settlement times. A regulated exchange needs KYB-verified execution partners. An AI agent rebalancing a portfolio needs reliability guarantees that differ from those required by a human clicking “swap.” Alipay does not care about the composable transaction steps underneath. They care about one-click yield. A company tokenising US Treasuries needs solvers with permissioned access to mint and burn those specific tokens, not a generic solver pool.

This is the fragmentation that comes after connectivity is solved. First, bridges enabled the movement of assets across chains. Then, token standards from the likes of LayerZero, Chainlink, and Circle made interoperability more structured. At the application layer, users’ needs diverge faster than any generalised system can accommodate. Infrastructure that forces uniformity loses to infrastructure that enables specificity. The teams that win will be the ones letting applications define their own constraints, solver selection, oracle preferences, compliance filters, and execution timing, without having to rebuild the stack from scratch.

There is no single end state here. We will be tokenising different assets using different standards across different chains for the foreseeable future. The coordination problem will compound before it converges.

LI.FI’s bet is that the on-chain world is headed towards a landscape of millions of tokens, hundreds of standards, hundreds of chains that matter, and application-specific intents that a one-size-fits-all execution cannot serve. In that world, the durable advantage belongs to whoever has the deepest integration footprint into the surfaces where intent originates, the solver depth to execute across the widest range of conditions, and the flexibility to let each application define what “good execution” means for its users.

Strowger did not build a better telephone operator. He made the operator unnecessary by letting the caller’s intent route itself. The bridges, DEXs, and liquidity venues are the operators. LI.FI is building the switch.

Streaming the T20 World Cup,
Saurabh Deshpande

Disclosure: This piece was part of a sponsored collaboration. Authors or DCo may own assets mentioned in the article.

In the previous episode, we explored how LI.FI is tackling fragmentation in a multichain world. This time, we zoom out and ask what happens when crypto’s favourite derivative meets the $100 trillion traditional futures market?

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Robert Shiller published a paper proposing perpetual futures as a way to create derivative markets for illiquid assets like housing back in 1993. The idea sat in academic journals for nearly two decades. Traditional exchanges never picked it up. About two decades later, BitMEX and a group of crypto exchanges turned the perpetuals into an actual product that people could trade.

It caught on because perpetuals solved real problems. People did not have to bother with monthly rollovers or the logistical overhead of physical deliveries. Funding rates kept prices anchored to spot. Within a few years, perpetuals accounted for over 90% of all crypto derivatives volume. The concept that traditional finance invented became crypto’s signature instrument.

Brett Harrison, founder of Architect and its exchange AX, is running the playbook in reverse. AX is a regulated centralised exchange offering perpetual futures on traditional asset classes: gold, silver, currencies like the Indian rupee and Mexican peso, single stocks like Apple and Tesla, and GPU compute power. The settlement rails are crypto native. Customers can post USDC as collateral and move money in seconds rather than waiting days for a wire to clear.

You might wonder why incumbents like CME never did this themselves. The answer lies in regulation, infrastructure and incentives.

US regulation does not clearly classify perpetuals as futures. They might be swaps, which would restrict retail access entirely. CME’s clearing architecture runs on fixed daily settlement cycles incompatible with continuous funding rate mechanisms. And CME earns substantial revenue from customers rolling expiring futures every month and every quarter. Each roll creates two transactions, and both are charged. Launching a superior product that cannibalises that revenue is a textbook innovator’s dilemma.

Traditional futures still win in specific contexts: regulatory clarity under CFTC oversight, central clearing that eliminates counterparty risk, and predictable costs without ongoing funding payments. $19 billion was lost collectively during the 10/10 liquidation event across crypto markets. A lot of it could have been avoided if the exchanges had safeguards like price bands, circuit breakers, and margin calls rather than automatic liquidations. Traditional finance built these failsafes over decades, and AX incorporates them all.

The bet Harrison is making is that for a very large class of participants, from CFD brokers hedging exposure to crypto native firms branching into equities, perpetuals on crypto rails are simply better. CME alone supports more than $8T in average notional daily volume compared to all of crypto at $250B. The overall futures market is estimated to turn over at least $15T per day, and AX wants a piece of that.

It is clear to me that the whole of finance will meaningfully change in a few years, and crypto rails will play a major role. Traditional assets coming on-chain and traditional finance using crypto rails to cut costs and time will likely be a secular trend going forward.

Thinking about new markets,
Saurabh Deshpande

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Imagine a future where JP Morgan settles trillions of dollars on-chain and none of the DeFi behemoths we know today benefit from it. Adoption of the industry does not directly translate to existing ventures benefiting from it. This is why LI.FI is betting on markets, rather than chains. With billions in volume already facilitated, they know a thing or two about how space evolves.

Founder and CEO at LI.FI, Philipp Zentner, joined me for a conversation to unpack where crypto, tokenisation, interoperability, and distribution are headed.

Two major trends are underway in crypto right now.

1. On the one hand, crypto-native players like Trade.xyz, Markets.xyz, and many others are bringing traditional assets to crypto.

2. On the other hand, institutions are using private (customisable) blockchain rails like Canton to tokenise traditional assets.

Amidst these, what is clear is that assets of interest on-chain will increase. Obviously, these will be across different chains and standards, making sure that the fragmentation of assets only increases for the foreseeable future. Meanwhile, it is also true that users don’t want to bother with these standards. They want to go from asset A to asset B in a click, regardless of which chain either lives on, which token standard wraps them, or which compliance framework governs them. The gap between how assets are issued and how users expect to interact with them is growing. Whoever bridges this gap will likely capture the flow of assets.

This is where distribution becomes crucial. Tokenising an asset is only the beginning of the journey. Finding demand for it, getting it into wallets, making it tradeable with tight spreads and reliable execution across venues, that is the hard part. And it is the part most institutions have not yet figured out. LI.FI, with integrations across 800+ applications, from MetaMask to Phantom to Robinhood, LI.FI is positioning itself as the solution to a question institutions are only beginning to ask.

Among so many interop solutions, how is LI.FI building its moat? The answer lies in data telemetry and orderflow. Given LI.FI’s distribution, more and more apps are integrating it. This leads to more orderflow going through LI.FI’s system. All of this data going through LI.FI allows it to understand where the pain points are and make usre that it is the first to address those. This leads to more orderflow, and so on.

What’s interesting to me is how LI.FI, as an infrastructure layer, may have a better understanding of capital flow than the individual apps through which this capital passes. Philipp and his team think of these transactions as a database engineer thinks of queries. Every failed transaction is a data point that makes the next transaction smarter. The conversation delves into why the on-chain world will remain fragmented, and how LI.FI is building to thrive in that fragmented future.

See you in the next one!
Saurabh Deshpande

We closed 2025 with Mike from Talus Network on why blockchains need brains and hands to participate in an agentic economy. We kick off this year asking how a joke has survived twelve years in an industry that forgets everything in twelve days.

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Belief is the true asset. What people believe in, they hold - and it tends to rally in price due to limited supply. Gold is what people are betting on these days. It is up 68% against the dollar in the past year, so anyone holding it is having a good time. Meme assets represent the other end of the belief-monetary value spectrum. They have nothing backing them but a high degree of holders believing in them. In turn, over time, they become money.

Don’t worry - we are not about to shill a cult to you.

We are, however, about to talk about Dogecoin. Crypto’s original meme asset. It was launched as a joke in late 2013 by Billy Markus and Jackson Palmer.

Timothy Stebbing, CTO of House of Doge and Director at the Dogecoin Foundation, joined me for the first episode of the year. In his spare time, he tells people to stay away from crypto as an investment. Instead of investing in Dogecoin, he wants you to spend it. Five billion new coins get minted every year. This mechanism was created as a joke to mock bitcoin maximalists. Turns out roughly 2% inflation is actually pretty good for something you want people to use as currency rather than hoard as digital gold.

But is that what holders are doing? It depends on who you ask. Dogecoin has historically been a retail participant’s preferred “entry-drug” to crypto. It used to be much cheaper. So, tossing less than $1 to get a large number of tokens would appeal to people. It has been exceedingly volatile when compared to its larger cousins - Bitcoin, Ethereum and Solana. Most importantly, it is the only meme asset that has lived through multiple cycles and set a new market-cap high in each cycle. Bitcoin’s distant, amusing cousin somehow teaches retail about crypto better than most VC-backed tokens.

But Dogecoin is a meme that is building something productive. Companies with treasury strategies are buying in. The community now spans memecoin traders, open source developers, and institutional investors. When Metallica performs with a symphony orchestra, two kinds of crowds are in the audience. One full of fans in ripped jeans and Ride the Lightning shirts. The other in suits, the usual symphony crowd. But they were all going to see the same show. That is the Dogecoin community now. People who look different, speak different languages, come from different worlds. But something made them all show up.

Doge worked and perhaps outperformed many of its peers, probably because it set out without intending to. It is an idea that took its time and played out over a decade. Most crypto-native assets cannot make that claim. There is no legacy of sponsoring NASCAR drivers in the trenches of PumpFun today. Perhaps that is what will be needed for those assets to catch up with Doge. We don’t quite know. But here’s what’s evident: Doge has survived a decade, and it may survive another one. Tune in to our conversation with Timothy to understand the reasons why.

Trying to do only good every day,
Saurabh Deshpande

RFQ, aka Request for Quote, offers a simple premise. What if you could simply ask for a price before you bought? What if that price was guaranteed, signed, and settled without anyone else getting a peek? Bebop has been dominating the category for the past two years. These primitives are key to scaling new markets and have been used in TradFi for decades. Bebop has been recreating it on-chain to aid developers looking to empower users to access liquidity across asset types. They do $500 million a week in volume, according to DefiLama.

Today’s article, written in collaboration with Katia Banina, looks at how an ancient practice is being rebuilt on-chain, and why Bebop thinks it has found a way to filter out the lemons.

If you are exploring new markets, providing liquidity, or are a market-maker firm, consider reaching out to us at venture@decentralised.co. We are scouting for partners to provide liquidity to several of our portfolio companies.

Information about the product you purchase is critical to establish its price. The more you know before making a purchase, the easier the price discovery process is. Often, you verify the quality of a product only after you have purchased it. If you can verify it cheaply and have some recourse in the form of return or refund, it is all good. During the summer, I purchase mangoes directly from the farm and pay a premium over the market price. The premium is worth it due to the superior quality of these mangoes.

The farmer knows they are high-quality mangoes; I can verify it against regular mangoes. And agree to pay the premium. The information asymmetry between the seller and buyer disappears when verification is inexpensive.

What happens when the ability to verify is no longer available?

In 1970, George Akerlof wrote a paper called The Market for ‘Lemons’, for which he would win the Nobel prize in economics in 2001. The paper refers to the idea that information asymmetry destroys markets. He uses an example of used cars. Only the sellers know whether their cars run smoothly. The buyers do not, until they’ve bought and used these cars. Buyers anticipate this information asymmetry, and they tend to pay average prices. Owners of good cars (peaches) cannot afford to sell at average prices, and they eventually exit the market. So, all that is left are the low-quality cars and the lemons.

When three market forces align i) the ability to profit from an event, ii) access to technology to exploit it, and iii) access to material information, sophisticated market participants will move to maximise their profits at the expense of average participants. Call it the “three-body problem” of markets: when these three forces interact, they create conditions where extraction becomes inevitable.

You do not want that. Regulators and watchdogs are supposed to monitor markets to prevent these three forces from aligning in ways that harm regular participants.

We aimed to address these issues arising from information asymmetry by utilising the Web3 infrastructure. Blockchains are public. Smart contracts are auditable. Every transaction is visible. The code is law, and the law is transparent. But it introduced different issues.

Traditional finance exchanges are based on order books. They are populated with buyers’ bids and sellers’ asks. When you market buy something, you fill the lowest ask first and then the second lowest, and so on. And the other way around when you market sell. However, given Ethereum’s slow blocks and gas limit constraints, placing and changing limit orders was difficult, making on-chain order book-based designs infeasible. AMMs came to the rescue. It also meant that nobody had to keep placing limit orders. You had a passive pool of capital that people could trade against.

AMMs allowed market participants to buy and sell assets based on a formula that prevented prices from fluctuating excessively. Importantly, that could operate independently from any single party that is opinionated on the fair asset value, i.e., a market maker. This is what you want from a market. Buyers and sellers should be able to trade in reasonably high quantities without significantly affecting the price.

However, the issue arises because of the way transactions are ordered from the mempool. When you want to buy or sell an asset and broadcast the order, everyone can see it. If there’s a possibility of ordering transactions in a certain way to profit, someone will do it. And it is a zero-sum game, when someone profits, someone is losing, which is most likely a retail participant. There’s the issue of how sophisticated players can exploit participants who lack information or access to technology. Sumanth has written extensively about it in his discussion of the inevitability of MEV.

When no professional market makers would touch crypto, automated pools allowed anyone to provide liquidity and earn fees. They made DeFi possible. But as markets mature, different tools emerge for different needs.

RFQ, also known as a Request for Quote, is an ancient concept. Humans have done it since the earliest days of commerce. It is about asking around for the best price before making a large purchase commitment. It then became a practice in industrial procurement. When governments award construction contracts to companies or an automaker like Tesla or General Motors wants to procure 5,000 tonnes of steel, they send specific requests to the relevant parties to obtain the best offer.

RFQ found its way to finance in foreign exchange, fixed income, and wider financial markets that are traded over the counter (OTC). Instead of posting orders to the public orderbook, you ask for quotes from market makers, who provide guaranteed prices for short periods of time.

The crux here is that there is competition to fill an order, allowing the buyer to get a better price. Consistent better pricing gives buyers the confidence, and thus, demand remains robust. Competition leads to better price discovery.

Financial markets adopted RFQ for the same reasons. It offers better UX. For example, Robinhood uses quotes from market makers for asset prices instead of routing to exchanges. In cases when orders are large, customised, or in markets with thin public order books or markets like foreign exchange with no order books, you can get a significantly worse price that eats into your margins.

A company that needs to exchange $100 million would not want to move the market by trading it in public markets. They would request quotes from several banks. Banks would have their internal risk assessment frameworks to provide quotes that stand for a stipulated time.

RFQ brings several inherent advantages that matter increasingly as trading becomes more sophisticated:

Superior price discovery. Market makers have a holistic view of the entire market. They see prices across centralised exchanges, multiple DEXs, derivatives markets, and order flow patterns. A Uniswap pool knows only what’s happened within that specific pool. When you request a quote, the market maker synthesises information from every venue they monitor, giving you pricing that reflects the genuine market rather than the isolated state of one pool.

CEX-quality execution on-chain. The best market makers in crypto are the same firms providing liquidity on Binance, Coinbase, and OKX. RFQ allows them to bring that same quality of pricing and execution to on-chain trading. You get centralised exchange depth (or better, because capital can sit on the same chain and be utilised across venues) without the custody risk.

Dramatic capital efficiency. In an AMM, liquidity must sit idle in pools, waiting for trades. RFQ liquidity lives in the market maker’s inventory system, where it can be actively managed, hedged across venues, and deployed only when needed. This means market makers can offer much larger fills at competitive prices. Where a pool might require $10 million of TVL to fill a $100,000 trade without excessive slippage, an RFQ market maker can fill that same trade using exactly $100,000 because they hedge dynamically across multiple venues.

Guaranteed execution and fixed prices. When a market maker signs a quote, they commit cryptographically. You know exactly what price you will receive. There is no slippage between quote and execution, no price that moves against you while your transaction waits in the mempool. The certainty matters enormously for larger trades or during volatile periods.

Modern intent protocols and sophisticated solvers are also addressing many of the MEV challenges that plagued early AMMs. The infrastructure is getting better across the board. But RFQ offers something fundamentally different: professional market-making rather than formula-based pricing. It’s not that AMMs are broken, it’s that RFQ enables use cases that passive pools cannot serve effectively.

Where AMMs excel at providing baseline liquidity for routine swaps, RFQ shines for larger sizes, exotic pairs, volatile conditions, and any situation where execution certainty matters more than permissionless simplicity. The future likely involves both, with aggregation layers intelligently routing each trade to wherever it can be executed most efficiently.

Both AMMs and RFQs have different privacy characteristics, each with its own tradeoffs.

When you request a quote through RFQ, you reveal your trading intent to the market makers before committing to anything. They see that you want to sell 100 ETH. They know current prices across every venue they monitor, and they understand which direction the market is moving. You have less complete information. This creates an asymmetry.

However, this asymmetry can be managed through good system design. Protocols implement various mechanisms to limit what information flows to makers, when they see it, and how they can respond. The challenge is not inherent to RFQ as a concept—it’s a design problem with known solutions.

Interestingly, AMMs reveal nothing about your intent during the quoting phase because there is no quoting phase. You see the current pool state and decide whether to trade. Your intent only becomes visible when you submit your transaction. At that moment, though, it becomes completely public. Your pending transaction sits in the mempool, where anyone can see it, analyse it, and act on it before you execute.

This is where RFQ provides a crucial advantage. Once you accept a quote, the market maker has cryptographically committed to that price. Your transaction settles atomically with no opportunity for someone else to insert themselves in between. No frontrunning, no sandwiching, no MEV extraction from reordering. The maker sees your intent earlier in the process, but no one can act on it adversarially.

The question becomes: what happens between the moment you request a trade and the moment it executes? In AMM pools broadcasting to the public mempool, sophisticated actors can sandwich your transaction or frontrun it, extracting value from the gap between your expected and actual execution price.

In RFQ systems, the signed quote eliminates this attack surface. The price is fixed at the moment of signature. So, there’s no slippage to exploit or opportunity to insert transactions before or after yours. For small routine swaps, the difference may be negligible. For anything substantial, guaranteed execution at a committed price offers meaningful protection against the extraction strategies that are prevalent in public transaction pools.

We should also note that both models continue to evolve. Intent protocols are adding privacy-preserving mechanisms to AMM execution. RFQ platforms are implementing techniques to minimise what information flows to makers and when. The privacy landscape is not static, and good design can address vulnerabilities in both architectures.

History of on-chain RFQ

Airswap kept RFQ peer-to-peer early in DeFi. Traders found each other, agreed on prices off-chain, and settled on-chain through the Airswap contract. This was suitable for sporadic trades but not for high-frequency trading.

In a traditional RFQ, once a maker quotes you, they are stuck with that price. Airswap implemented LastLook, which flips it. You sign the order, then the maker gets “last look” before executing. This makes trades gasless for users. The maker pays gas and takes execution risk.

0x approached the market from the other side and created an open protocol that anyone could use. Their RFQ-T (RFQ for Traders) system, launched around 2019, allowed professional market makers to provide quotes through a standardised API.

The separation of the infrastructure layer from the application layer allowed market makers to plug into 0x’s system, and any front-end could request quotes from these makers. This worked well for professional trading firms and aggregators, such as Matcha (0x’s own interface), but it remained primarily an infrastructure - most retail users never directly interacted with 0x’s RFQ system.

Every trading venue has a chicken-and-egg problem of liquidity and users. Without liquidity, there will not be users, and without users, there is no liquidity. Hashflow, launched in 2021, addressed the liquidity problem by throwing money at it. But capital alone will not solve the problem if using the product comes with technical overheads. Hashflow developed a pool-based architecture to address the issue. Traditional market makers were figuring out blockchains; you could not just throw smart contracts at them and expect them to solve technical complexities in-house. They could park capital in pools, and Hashflow used it for RFQ.

0x demonstrated that RFQ could work on-chain, but it did not solve the go-to-market challenge. How do you make RFQ accessible and understandable to regular DeFi users who were accustomed to simply swapping on Uniswap?

Hashflow, launched in 2021, took an aggressive growth approach. They raised significant capital and used token incentives to onboard market makers. For a nascent market where order flow was uncertain, these incentives proved effective in bootstrapping liquidity. Hashflow positioned itself as a full-stack RFQ platform with its own interface, market maker network, and user base.

When the incentives were on, Hashflow volumes declined. In 2023, the overall 1inch volume increased around the USDC depeg.

One can think of RFQ as a quote produced from an order book that’s frozen in time. Market maker is essentially pausing time and finding the best quote from across venues. It usually yields better results than trading on a single venue. This can be verified with low slippage and high depth offered by Bebop on many pairs. But RFQ does have its issues with informed toxic flow.

Imagine you are a liquidity provider in a DeFi RFQ system, offering guaranteed price quotes that live for 30 to 60 seconds. Every trade request that comes through could fall anywhere on a spectrum, ranging from toxic to soft on one axis and from uninformed to informed on the other.

In the bottom-right quadrant sits spam. These are high-frequency, uninformed trades that arrive without any real market knowledge. They are soft because they follow predictable patterns and do not adversely select against you.

Move up to the top-right quadrant and you find good traders. These are informed participants who understand markets deeply but are making genuine directional trades. A fund manager rebalancing positions, a treasury executing a planned allocation. Still profitable to serve. But now the trouble begins.

The extractors and MEV searchers lurk in the top-left quadrant. These are highly informed toxic traders who understand precisely what they are doing. They might request two quotes from you 15 seconds apart, then execute both in the same atomic transaction, profiting from any price movement while you absorb the loss. Or they will sit on your quote, simultaneously watching prices across other venues, only executing when they can arbitrage against you.

They might even slice a large order into ten smaller pieces to exploit your tighter spreads on small sizes, then bundle everything into one transaction. They get the price of ten small trades with the economics of one large trade.

You cannot tell these quadrants apart. Every request looks identical on-chain. Just as used car buyers could not distinguish lemons from peaches, you cannot distinguish MEV searchers from good traders or spam until you have been arbitraged. So you widen your spreads to protect yourself. You shorten quote validity windows. You become more cautious. But this defensive posture punishes everyone, including the good traders and even the spam flow that would have been profitable to serve. Other liquidity providers observe the same pattern and follow suit.

Eventually, the best makers exit entirely because they cannot sustainably serve a market where they cannot accurately price risk. The toxic flow has degraded the entire market structure, driving out the quality liquidity providers and leaving behind only those willing to extract value or charge prohibitive spreads. Remember how bad sellers drove buyers out of the market, as seen in the case of used cars? In this case, instead of bad sellers driving out good sellers, bad buyers drive out good sellers.

RFQs protect trades from leakage. Liquidity is created on demand. The taker asks privately, the maker quotes privately, both sides decide privately, and only the final execution is recorded.

In the example below, Joel submits an RFQ for 1 ETH. The market maker scans venues, including AMMs, CEXs, and other dealers, to reduce price risk. He then quotes the price of $3000 to Joel. If Joel accepts, the market maker hedges his position by buying ETH at a lower price, say $2,999 (not guaranteed).

The anticipated spread is $1. The quote is live for 30 seconds. If Joel accepts, the transaction is sent on-chain and settled atomically. Note that the market maker can only hedge once Joel signs the intent.

When the order flow consists of legitimate buyers, these takers receive competitive execution. Makers earn a small but fair spread. Makers price tightly because they’re confident they won’t be picked off and can keep the quote live for longer.

How Bebop Differs

Bebop tackles toxic flow through several defensive layers. The first is the last look, borrowed from traditional finance. When a user requests a quote, they provide a signature committing to the trade. The market maker then provides their own signature to complete it. But crucially, the maker retains the option to reject the trade if the market has moved adversely between quote and execution.

This disproportionately affects arbitrageurs who only submit trades when prices have shifted in their favour, while having minimal impact on regular users executing near the quoted price.

This dual approach matters because different types of trades benefit from different liquidity sources. A large swap into a major token might get better pricing from market makers who can internalise the order and manage their inventory efficiently. By maintaining both channels, Bebop captures the advantages of each.

So, where will you trade in the future?

The trajectory seems clear. Pure AMMs, as they are, face an existential version of the lemons problem. As markets mature with more sophisticated participants, the toxic flow will continue to drive good liquidity away from passive pools. We have already seen this play out. The most significant trades, the most professional users, and institutions tiptoeing into DeFi are not using raw AMM pools. They are routing through aggregators that tap into RFQ liquidity alongside automated market makers.

This is where Ben Thompson’s aggregation theory begins to show itself. It says that when the supply is cheap to spin up, value flows to the entity that gathers this supply and maintains a relationship with the user. RFQ venues aggregate AMMs, market makers, centralised exchanges, and DEXs with order books. They see the most traffic, understand which makers are best suited for each type of order, and they serve as the default entry point for users. Once that loop tightens, it becomes difficult for a standalone pool to compete for serious size on its own.

Sure, for simple swaps, AMMs are fine. However, for anything significant, exotic, or during volatile periods when prices fluctuate significantly before blocks are finalised, we’ll want an RFQ. The future looks like aggregation layers that understand this distinction, routing each trade to the location where it can be executed most efficiently. Obviously, Uniswap will still exist, but it will increasingly serve as one liquidity source among many, with RFQ market makers filling the gaps that formula-based pricing cannot handle.

I see no reason why an RFQ should stop at spot markets. Consider options for a moment. Sophisticated derivative instruments are used tremendously in traditional finance. Barely has PMF in DeFi. Options pricing requires sophisticated models that account for volatility surfaces, time decay, Greeks, correlation structures across strikes and expiries. You cannot encode this into a simple bonding curve. Market makers who understand these dynamics can quote options via RFQ, bringing genuine two-sided markets to on-chain derivatives.

The same logic applies to structured products, anything combining multiple instruments into a single payoff. These products are too complex for AMMs, but they are perfect for RFQs, where a market maker can price the entire structure and offer a single quote.

Tokenised assets or RWAs are a consensus bet in DeFi. They will become practical only with an RFQ. Traditional finance hates locking up liquidity. Every dollar that is locked could be earning yield or doing something productive. So, it is doubtful that only AMMs will house RWA liquidity. RFQ lets you bring the pricing models from traditional markets on-chain, with market makers who understand both worlds providing the bridge.

What is emerging is a more mature market structure. DeFi’s early phase needed AMMs because it was in a stage where nobody believed in us. There were no institutions, no market makers, no professional LPs. But now, they are here. As markets mature, the infrastructure needs to get more sophisticated. RFQ does not replace AMMs so much as it completes the picture, filling in everything that passive formulas cannot.

We are creating a financial system that combines the transparency and composability of DeFi with the efficiency and sophistication of traditional markets. You could trade anything, of any size, with confidence in execution. Market makers compete on price rather than on who can extract the most MEV. Users receive genuine best execution, rather than hoping their transaction does not get sandwiched. And new markets become possible that serve needs we have not even articulated yet.

This is how DeFi evolves without sacrificing what made it interesting in the first place. The infrastructure becomes more sophisticated, but the fundamental openness remains. Anyone can still participate. The protocols stay neutral. The difference is that now there are real professionals providing liquidity, not just passive pools hoping they do not get picked off. The lemons get filtered out, and what remains is a market where quality can survive.

Learning how payments scaled on the internet,
Saurabh Deshpande

Hello,

In the three years that we have been writing this newsletter, we have never encountered a moment where so many disruptive factors converged simultaneously.

On one hand, everything is being financialised. Pokemon cards? There’s a market for it. There’s one for betting on where Bitcoin’s price will be 15 minutes from now. There’s even one to bet on whether Jesus Christ will return this year. On the other, we are seeing a rapid decline in the accrual of value to tokens. The marginal bids from retail participants on exchanges are now with equities, ETFs, and meme assets. This shift changes how we as an industry think of business models and value capture.

We are also witnessing the rise of AI-induced slop taking over feeds at a pace like never before. When the cost of expression is low and the medium for doing so is easily game-able, everything said becomes a (growth) hack. Open your X feeds, and you’ll see countless views on how anything should be done. The means to produce noise have evolved, but the ability to filter signals has been on the decline. Our brains were never equipped to consume so much and understand so little.

Human attention has never been under attack more than it is now.

How does this translate to our work? Internally, we have identified three seismic shifts:

1. We will be tinkering with briefer articles with dense insights where the message is better served in a short form. Not everything has to be a 40-minute long read. Early on, it made sense to invest in long forms as it helped differentiate. We now have a critical mass of audience that allows us to tinker with briefer, quick snippets from founders.
   

2. We will be more explicit with our commercial investments. We have been committed to being the best partners to our portfolio of companies as narrative builders and strategic partners. Now that we are moving towards capital allocation, you will see more of our investments being covered on the newsletter. That involves communicating with our audience about where we deploy our capital and why. It is a natural evolution.
   

3. There will be a renewed focus on community. Our Telegram community is now close to ±5000 members strong. Our venture network is close to 100 firms. In this age of AI slop, communities that capture the attention of real humans, with reputations tied to them, will continue to stand out. Our in person events in 2025 were an effective way for us to meet and learn from many of you. Expect more of that. More real humans. Real. And human.

With that out of the way, I wanted to share what the focus areas for us internally look like over the coming year. In 2025, we mostly focused on the emergence of revenue in crypto and its economic ripple effects within the industry. In 2026, that equation inverts.

We believe that the crypto industry has reached a point of maturity where it is able to produce tangible impact on the broader economy. Firms that are able to integrate crypto into its operational flows will scale faster or see operational efficiencies that improvise unit economics. We see this playing out in three forms

The Stable-coinisation of Everything

Blockchains are structurally money movement technologies. Stablecoins are the most preferred form of moving money with this technology. With an entrenched duopoly of Tether and Circle, the sector is likely to start seeping into conventional business models requiring capital flow. The lowest hanging fruit here is micro-incentivising individuals with stablecoins. EarnOS rewards users in stablecoins for engaging with brands. The stables can then be used directly through Apple Pay.

Similarly, Architect Exchange allows traders to build exposure to currency markets (like USDEUR) and settle in stablecoins. Such models of value transfer cut down on conventional pay-out periods for traders and consumers alike. We anticipate more web2-esque businesses that benefit from faster money movement through stablecoin integration to emerge this year. In emerging markets, an inbound wire can take up to 60 days to clear. A stablecoin transfer takes two minutes on Ethereum and even lesser on other EVM and non-EVM chains. That is tangible progress. That is time the money could be reinvested into more productive use.

Enterprises Using Blockchains

Much like the internet, blockchains will go through phases of bundling and unbundling. In the last two years, everything had to be bundled onto singular networks as that is where (trader) liquidity lay. Capital formation through meme-like assets on Solana or Base determined where users went. In the next few years, we will see a period of unbundling where enterprise blockchains see more inbound liquidity. It is not so much about enterprise blockchains, but more about enterprises using blockchains to expedite functions that took forever.

Zk Sync’s Prividium allows enterprises to have customisable networks deployed with privacy. The Depository Trust Company is now moving towards tokenising assets on-chain. Nasdaq joined Canton recently as a validator. Each of these are moves that indicate that more traditional, institutional allocators will use blockchains albeit with trade offs. For founders, the choice is between the permissionless of a public blockchain or building the future of fintech with an extremely regulated partner that sees blockchains merely as infrastructure. Intercontinental Exchange (ICE) backing Polymarket is an extension of this phenomenon

The focus on revenue in 2025 was a precursor to where crypto is headed. DATs were a way for large capital allocators (like venture funds) to access public market liquidity. For founders, the mechanism of doing the same would be to partner with enterprise blockchains, and avoiding tokenisation altogether. Instead, their margins will come from the take rates on individual products that are sold through institutional outlets.

Will this be the antithesis of open money? Yes but it is also a direction towards scale. Ultimately, many talented founders may lean towards enterprise blockchain ecosystems in the coming years as scale is more valuable for them than idealism.

New Market Primitives

Up until last year what could be done on-chain was throttled by limits of scale. Transactions were limited and fees would escalate if the number of users on a product surged past a point. This is not taking into account the lack of on-ramps and clarity around stablecoins in multiple jurisdictions. But with the Genius Act passed we might see more unique instruments coming on-chain.

This will play out in two forms. First is niche-specific financialisation. USDAI enables the issuance of loans against GPUs. Glow uses game theory to incentivise solar farms. We will see founders with deep knowledge of individual sectors leveraging blockchains as a mechanism to raise capital and deploy them in productive ways that were previously not done.

The second is geo-specific financialisation. Aspora - powers 4% of global remittance volume with a focus on 700k Non-Residential Indians. Understanding cultural nuances and regulatory structures will be key for such businesses to scale. They could expand into being a neo-bank that plugs into DeFi markets for yield and offers commodities, currencies, and equities by using perpetuals (perps) as a settlement market.

This also explains why we have been looking at perpetual exchanges. One of the means through which non-conventional financial products like energy markets could find avenues to hedge, trade or settle on would be perpetual exchanges. Currently, players like Hyperliquid and Lighter are focused on commodities and currencies. The extension of asset types to new ones that are not readily accessible in traditional markets is what will help them scale.

The next phase for crypto will involve more boring things that scale exponentially while building moats.

In many ways, this part of crypto rhymes a lot with the internet. But unlike what most VCs suggest ,we don’t think it’s closer to 1998. We think it is 2004. Firms have bundled and begun tapping into public market liquidity through IPOs. More institutional allocators are looking at the industry whilst demanding clear growth metrics. Early entrants have either booked profits and left, or lost it at the casino. New ones want more tangible measures of value generated.

Every industry has a phase where it goes from pursuing idealism solely to economic value creation because capitalism demands that sooner or later - we generate value that is worth paying for.

Crypto is currently undergoing that transition. From a cluster driven by idealism to one earning its keep amongst technologies that have matured enough to matter.

As for us, what it means is we have begun filtering ideas and our pursuits a bit more harshly. The tolerance for hypothetical business models have now been replaced with curiosity for what makes systems scale. The real and tangible. As storytellers, you can live in the hypotheticals. As capital allocators, you cannot. There is a thin line between conviction and blindness. If the market is an objective measure of truth, you want the stories you tell to be as close to the truth as possible to be able to weave both storytelling and capital allocation properly.

In some sense, that sums up where our focus lies for the year.
On finding and sharing more truths about systems that scale efficiently.

We will be back to regular programming on Monday with a breakdown of how RFQ systems work..

Sliding into the weekend,
Siddharth Jain

Hey there,

This time of the year is like Thursday every week. Everybody knows the weekend is just around the corner. Some have dates planned. Some look forward to meeting family. And many simply wonder what to do with the existential dread that comes with holidays. Writing over the next few weeks will be choosing to battle the conflicting demands of attention from family and brands putting out reports.

So we will be taking a break until early January to plug off. There are birds outside the office we need to catch up with—the mountains beckon. Breaks and plugging off are crucial to the creative process.

As has been the tradition for the past two years, here is a PDF of everything we wrote in 2025 for those who want to export it to a reader, print it, or feed it into an LLM. If you are new here, you can grab the 2024 issue here and the 2023 version here. You can get the epub version and PDF version on Google Drive here in case you want to sync it to an external reader.

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This year, we published fewer things, but with higher accuracy. For instance, we wrote about interface fees and embedded markets in April, months before prediction markets took off. Saurabh wrote about mergers and acquisitions in May, a quarter before Coinbase decided it was the season to buy out startups. In August, we wrote about the emergence of neo-banks. We wrote about the divergence of protocols generating revenue and ones without in July, almost two quarters before it was trending on X.

Admittedly, we may have been too late to catch the DAT party.

The Wall Street-ification of crypto is a trend we are watching from a distance and building a better understanding of.

Everything we wrote boils down to three themes that represent the disruptive cycles crypto is undergoing. These themes guide how we think about startups and our involvement with them:

Distribution and Scale Are King
When a technology first emerges, simply using it makes it novel. Mobile apps and the internet were once novel. So were crypto apps. But those days are behind us. Firms and founders are now judged by the scale and moats of their distribution. They are also heavily punished if they do not have it. The reason why many tokens are in down-only paradigms post-listing is that they confuse exchange listings with distribution.

Margins Matter
The most common business model in crypto today is interface fees on trading products. Farcaster had to pivot from a social network protocol to a trading-oriented wallet. Hyperliquid is currently in a shaky position with Lighter competing on a zero-fee model. All apps will undergo a period of creative destruction as they compete on lower margins to acquire and retain users.

For founders, the most complex problem will not be scaling in markets with multiple peers. It will be creating margins that justify their valuations. We will see the emergence of two kinds of crypto in pursuit of margin. One would be cypherpunk money, which commands a premium due to the extent of decentralisation and privacy it offers. The other is what we call - enterprise cash. These would be real-world assets and fintechs being built with a blockchain in the back-end capturing small slices of high transaction volumes.

The Niche Leverage
Where does margin come from then? It will often be due to vertical integration. USD.AI focuses on equipment financing, with an emphasis on GPUs. Qiro underwrites lending in private credit. Semiliquid is building infrastructure for lending between institutions in crypto. As the infrastructure layer matures, founders will focus on niche pockets for higher margins and reduced competition. As Thiel puts it - competition is for losers.

So, what’s in plan for 2026?

There are a few broad strokes that affect storytelling in crypto today. For the first time, our niche industry is eating into fintech. And with it, the audience base we can write for is expanding. The way stories are communicated is no longer just for the person buying tokens on Binance. We are writing for everyone who takes an interest in how the way money moves affects systems. The market has expanded faster than our minds could grasp that shift.

Crypto has always been a global phenomenon. We are writing for the person in Mumbai as much as for the person in Manhattan. We want to continue to make the newsletter a breeding ground for great ideas, good people, and the leverage of capital to come together. We want to be the hub, where writing is still human, bleeding with curiosity - and probably riddled with typos in the age of LLM-generated perfection. We want to speak to a global culture, as much as we are constantly defined by it.

With that in mind, here are two core priorities for 2026:

A Galapagos for Antimemetics

Do you want to be famous, or be understood? What matters more if you are a startup? In the age of LLM-generated content and declining attention spans, long, well-researched, humane stories will continue to have their place. As our feeds become hostile ground, the written medium will serve as a wedge for uncomfortable conversations, sharing unpopular ideas, and fostering collaboration—a corner for nerds in the age of glitz and glam.

Nadia Aspraouhova defines these forms of ideas that are often not viral as antimemetics. Memes and antimemes are the yin and yang of the attention economy. We want to continue making the newsletter the best place for long-form research and discussion of unpopular, often uncomfortable ideas. As crypto goes mainstream, the publication will remain a curated, safe corner for people to enter and explore the industry. We may lean into the memetic side of distribution on X going forward because who doesn’t like attention? Distribution, after all, is influence.

Liquid Markets Investing

One of the ideas we teased in Crypto, capital and culture, is that a culture is preserved or represented best when it has capital backing it. As writers, our interests are best protected when there are healthy margins in what we spend time on. This is where our interests come into conflict. Or what the big kids call - a conflict of interest.

If we believe in what we publish and the accuracy of our research, we should be putting capital behind it. And that is what we are doing. As we pen these words, we are in the last leg of signing one of the largest term-sheets we have invested in from our entity.

In 2026, you will see us speaking more of the bets we have made and the ways we get involved with our portfolio of companies. More growth stage, less pre-seed - in line with crypto’s current expansionary arc. Capital and effective storytelling are two levers that help shape reality. As DCo grows, we want the two to intertwine.

Our priorities don’t exactly change year to year, as much of the work we set out to do remains to be done.

Most people don’t realise the extent to which crypto has grown in the past three years. In May 2022, we saw $40 billion in value wiped out because of a stablecoin that rhymed with a Ponzi scheme. In November of that year, the largest exchange disappeared, taking user deposits with it. As late as December 2023, the most remarkable thing on Solana was a dog with a hat.

In contrast, under the current administration in the United States, fintech firms have embraced stablecoins, Robinhood is integrating prediction markets, heck - ICE invested in Polymarket, Arbitrum is bringing thousands of stocks on-chain, and the DTCC is working towards settling treasuries on Canton. We are witnessing what can be considered cultural inertia. That feeling of not wanting to give up a way of being, during the early innings of a sector coming of age.

Crossing the chasm is as much a psychological feat as it is a technological and socio-economic one.

I don’t mean to imply that in 2026, we will have to swap our NFT display pictures on Twitter for suits. But we do need to realise that the ways of building a business, capturing value, and scaling it have shifted. For us at DCo, the priority is to stay slightly ahead of the curve as it evolves, so we can share what we see with our readers and apply it to our portfolio of companies that has now grown to over 25 in size. Perhaps that is the cost of winning.

In a year marked by change, we hope you found the fortitude to evolve. If these changes have disrupted your life, we hope you find the strength and calm needed to regain stability. In 2026, we hope to continue to be inspired by beauty and guided by the curiosity that has marked much of our writing. We will see you in January - with long form articles, more depth, more quadrants, and perhaps messier art accompanied by a bundle of typos.

Signing off for now,
Joel, Sid - and the DCo Team

Before we begin, a big shout-out to Oliver Jaros from CMT Digital, who’s our guest author for this edition.

A few weeks ago, we wrote about how protocols like x402 are enabling machines to pay each other. Trust is a requirement for any payment network to scale. Visa allows refunds if fraud occurs. There is no “contact the manager” button on Metamask when a hack occurs on Ethereum. How do we build trust in a system dominated by agents paying one another? Between the previous piece and this one, there’s a common theme. We believe that stablecoin adoption will skyrocket in the agentic economy.

For this article, Oliver and I set out to explore challenges for stablecoin payments in the age of agents. We lay out a mental model for the wedges startups can use to crack scale in the agentic economy.

At DCo, we are seeing early consumer-oriented experiments in this theme. Drop us a DM on X - if you are building at the intersection of payments, crypto, and AI, and want to chat.

Let’s dive in!

In Pricing the Internet, we argued that machines will pay when metering is frictionless. Humans did not fully embrace micropayments due to the hassle and mental capacity required to pay attention to metering. But machines are different. All they see is 1s and 0s. Mental capacity or context switching doesn’t affect their ability to execute tasks. If going to sub-cent granularity makes their processes more efficient, they will do it, unlike humans.

We ended the previous article with a question: What happens when the agent screws up? It doesn’t matter whether the agent’s intention was right. At the end of the day, we are not supervising agents every step of the way.

We are left in a gap where new tech misses out on the good part of the old infrastructure: the ability to undo payments when things go wrong. This is what this article is about. We discuss what is needed for agents to be autonomous, who is building the infrastructure for it, and why new startups will emerge at the intersection of blockchain rails and autonomous agents.

Emerging Standards

Any form of commerce involves three parties – buyer, seller, and the middlemen who facilitate their interaction. Middlemen can be platforms or marketplaces like Amazon, or card networks like Visa that facilitate payments.

Buyer Side

Consumer applications tend to handle the money or transactions and get a cut in the process. But what happens when the consumer is AI acting on our behalf? There are a few emerging standards that are searching for the answer.

ChatGPT has 700 million active users trying to get something out of AI. While we don’t buy or sell things through an agent interface yet, but use it for discovery. Whether it’s buying running shoes or looking for hotels in El Calafate, I’ve been using AI to compare things. Having the option to buy things from the same interface makes it way more convenient. And that’s what OpenAI is doing with the Agentic Commerce Protocol (ACP) in partnership with Stripe.

This is the most straightforward way agents are handling money today. The user remains in control every step of the way. When someone places an order, ChatGPT sends the necessary details to the merchant’s backend using ACP. The merchant then accepts or declines the order, processes the payment via their existing provider, and handles fulfilment and customer support just as they do now.

Think of commerce through ACP as allowing your intern to spend fixed dollars, with you authorising product or service selection, the merchant, and the final payment.

While OpenAI and Stripe have ACP, Google offers Agents to Payments (AP2). Before we delve into that, let’s zoom out a bit. What Google wants to address is the interoperability problem. Currently, AI agents operate in silos. Gemini doesn’t talk to Claud. ChatGPT has no clue what’s going on in Perplexity. You get the point.

Ideally, we want these agents to communicate in a common language when tasks become complex, and we need them to work together. Google developed A2A (Agent2Agent), a protocol that allows different agents to communicate and coordinate.

But it’s not enough for agents to be able to talk to each other. They need to be able to use tools, access APIs, and services. Model Context Protocol (MCP) allows agents to use tools like Google Calendar, Notion, and Figma.

MCP defines a common language. Agents can use any tool without any custom code as long as they all speak MCP. Anthropic built it, but the specification is open, and companies are adopting it quickly. An MCP server is essentially a translation layer that sits in front of a company’s existing API. It exposes the company’s services in a standardised format that any MCP-compatible agent can understand.

Okay, let’s get back to AP2. A simpler way to think about what it enables is that MCP gave agents the means to access data, files, and tools. A2A gave them a voice. They could communicate with other agents. AP2 gives them a wallet. They can now spend money securely.

All these protocols keep the user in charge, with agents having limited spending power. This solves distribution and flow. But neither one has figured out what happens when the agent gets it wrong.

Seller side

Buyer’s side is only half the story. Different standards are emerging on the seller side. These focus on how machines pay for access to APIs, data, and content.

The most talked about right now is the x402 standard, an open protocol that Coinbase developed. It revives HTTP status code 402, which was defined way back in 1997 as “Payment Required”. But, it was never actually used. x402 brings it to life by pairing it with stablecoin payments that can actually settle micropayments economically.

x402 turns an HTTP request into a paid request. Whenever payment is required, the server asks for it. Since the agent has a pre-allocated budget, it pays the server in the same flow and receives the data. This makes pay-per-request or pay-per-call practical for machine commerce.

With x402, an agent can pay for exactly what it needs in that moment. For example, a single article behind a paywall for two cents. One API call for a fraction of a penny. The transaction settles in seconds on-chain with no ongoing relationship required.

Cloudflare took this concept and built something more specific with their Pay-per-Crawl system. It uses HTTP 402 under the hood, but it matters because of Cloudflare’s dominant position in the market. 20% of all web traffic goes through Cloudflare, which gives them enormous leverage.

Pay-per-Crawl uses Cloudflare’s edge to require payment from AI crawlers before serving the content. This turns access into enforced metering. Publishers have been watching their traffic crater as people stop clicking through from search engines and just read AI-generated summaries instead. With Pay-per-Crawl, they can charge the AI labs directly every time a crawler visits.

Card networks are also trying to extend their existing rails to handle agent transactions. Visa announced an MCP Server plus an Acceptance Agent Toolkit. Mastercard has a program called Agent Pay. Both are extremely early, still in pilot stages, but they matter because Visa and Mastercard already have global distribution, issuer relationships, and merchant acceptance everywhere. The basic idea is to register agents, add spending controls, and enable agent-initiated transactions on the same card rails that handle human purchases today.

Urgent Need to Fill the Trust Gap

All of these standards assume that the payment will go through smoothly and that the outcome will meet expectations. ACP and AP2 keep humans in the loop during checkout, providing some safety. x402 variants handle machine-to-machine data access, where the stakes are typically lower. Card networks extend their familiar protections, but they come with slower settlement times and higher fees.

Speed is the primary goal when enabling micropayments at scale. Card rails take days to settle and cost merchants a few percentage points per transaction. Crypto rails settle in seconds and cost fractions of a cent. But this efficiency comes with irreversibility. Once a crypto payment is completed, there’s no going back.

Commerce has an entire infrastructure built around the possibility of mistakes. When you buy something with a credit card and it goes wrong, there’s a process. You call your bank. The bank disputes the charge. The card network investigates, holds the funds in the meantime, and either refunds you or sides with the merchant. 261 million transactions were disputed with a total value of $34 billion in 2025.

Agents operating on stablecoin rails have none of this.

This becomes more complicated when agents start coordinating with other agents. When hundreds or thousands of multi-agent workflows are involved, untangling liability can become a nightmare.

Card networks won’t take on that risk, at least not at their current economics. Visa and Mastercard’s agent programs still carry the standard interchange fees and multi-day settlement times. They could pivot to instant stablecoin settlement, but doing so would mean giving up the dispute infrastructure that justifies their fees.

The traditional financial system didn’t come with built-in dispute resolution mechanisms. Diner’s Club, the first credit card, was issued circa 1950. But it took another 24 years before consumers could dispute transactions. The modern infrastructure we take for granted today emerged gradually as problems surfaced and institutions responded.

Agentic commerce doesn’t have that luxury of time. API requests already account for 60% of the dynamic HTTP traffic that Cloudflare processes. Bots and automation make up nearly half of all web traffic. ChatGPT’s 700 million users can now check out directly through ACP with Etsy, and Shopify integration is coming. The volume is already here. There is latent behaviour to use agents for tasks. It won’t be too long before agents are used for commerce.

So we can let the legacy financial infrastructure continue with longer settlement times or deliberately build trust infrastructure that complements quick blockchain settlements. The former will keep agents from realising their potential. The latter is an opportunity and a logical extension of how agentic commerce will progress.

So what does that actually look like?

Not surprisingly, there are two parts to this. Before and after the transaction.

Before the transaction

Whether an agent should even be allowed to transact depends on three factors: identifying the counterparty, employing fraud detection, and using reputation scores to make informed choices about pricing and access.

Plaid connects to one in two US bank accounts and processes a million account connections every day. When you want to prove to Venmo who you are, it uses Plaid to verify your identity.

Currently, any agent that interacts with an API or attempts to scrape the web or make a payment has no equivalent. All the server sees is some vague ID, like a wallet address or an API key. It has no idea who is making the call. There’s no consistent identity that could build up a good reputation across different services, so every new interaction starts from scratch with zero trust.

American adults have lost approximately $47 billion to identity fraud in 2024.

A Know Your Agent (KYA) layer would work similarly to how Plaid became identity infrastructure for fintech. It would issue persistent, revocable credentials that link an agent to a human or organisation.

Card networks have spent decades building systems that can spot suspicious patterns across millions of transactions. They know what normal human spending looks like and can flag anomalies in real time. If an agent gets compromised and starts making unauthorised purchases across multiple merchants, there’s no shared fraud graph to catch it.

Visa says it blocked $40 billion in attempted fraud after investing $11 billion in securing its systems over 2019-24. Stripe processes over $1.4 trillion in payments annually and trains its Radar fraud detection system on that entire volume. During Black Friday and Cyber Monday 2024, Radar blocked 20.9 million fraudulent transactions worth $917 million.

The fraud detection layer doesn’t exist for agents. Whenever an agent is making an x402 payment, no shared fraud graph flags suspicious behaviour, such as higher-than-normal spend or higher spend frequency.

Without a persistent identity and reputation, every agent interaction starts from zero. Reputation is deeply embedded in human commerce. You get ads based on the reels you scroll. Your Uber star rating is a factor when drivers accept your request. The credit score you generate by borrowing money and paying off loans follows you to every financial institution. It shouldn’t be any different for agents.

After the transaction

Chargebacks are how card rails handle disputes by reversing funds from the merchant after a customer disputes a credit/debit card transaction with their bank. They get abused, too. Chargebacks cost merchants an estimated $117.47 billion in 2023. For every $1 lost to chargebacks, businesses typically incur at least $3.75–$4.61 in total costs, when you factor in fees, lost merchandise, and administrative overhead.

Merchants win only 8.1% of the disputes they contest. 84% of customers find filing chargebacks simpler than requesting refunds from merchants directly.

Stablecoin transactions initiated by agents will settle in seconds and there is no recourse right now. Cloudflare has proposed deferred settlement extensions to x402, which would allow for hold periods before funds move irreversibly.

Developers are already building these infrastructure primitives. At ETHGlobal Buenos Aires, a team built Private-Escrow x402. The escrow scheme has buyers deposit funds into a smart contract upfront and sign an intent off-chain at the time of payment. A facilitator batches hundreds of these signed intents into a single settlement transaction, cutting gas costs by a factor of 28.

But that’s just an infrastructure primitive. Someone needs to productise it.

Who builds all of this?

I can’t help but think of a time when carriers dominated the telecom industry. They had billing relationships with every phone owner, and yet they missed generating value from smartphones. They captured distribution and mobile advertising. Together, they generate hundreds of billions in revenue that telecom carriers could have captured.

Card networks face a version of this problem now. Visa and Mastercard have spent decades building exactly the trust infrastructure that agentic commerce lacks. But their entire model depends on interchange fees that only exist because they control the payment rail. They spend billions maintaining and improving this infrastructure, which can only be funded by a few per cent transaction fees. Building consumer protection for stablecoin transactions would mean subsidising a competing rail with none of the revenue attached.

If the card networks are not going to do it, the next incumbents are AI labs like OpenAI, Google, and Anthropic. All these labs want their agents to be used everywhere, with every merchant and across every protocol. Running a centralised identity registry would mean they become liable when those agents misbehave, and they do not want to become the court of record for your booking mistake.

They would much rather someone else build identity and recourse as infrastructure they can plug into, the same way they plug into payments or search today.

Cloudflare occupies a strange position. They already see a massive share of web traffic. They already run bot detection. Their AI Audit tool lets publishers track which crawlers access their content and how often. Extending from “identify the bot” to “verify the agent’s credentials and reputation” is not a huge technical leap.

But Cloudflare has always positioned itself as neutral infrastructure. The moment they start issuing trust scores or adjudicating disputes, they become something more like a regulator. That’s a different business with different liabilities.

Startups exploiting the three wedges

You won’t beat OpenAI with model quality or Cloudflare with more traffic. You will have to find something in the stack that their business model doesn’t allow them to touch (at least for now) and still has value. I think those wedges are – identity, recourse, and attribution.

Agent identity is the most straightforward. The registry model is proven. Although Plaid is a cliché example, it fits well. They did it for bank accounts. A startup could do it for agents. Issue credentials, let them accumulate reputation, and give merchants a score to check before accepting payment. The defensibility comes from network effects. Once enough merchants check credentials against your registry, agents will have no choice but to maintain good standing.

Recourse is harder because it requires taking on risk. Think of it like insurance. You collect a small fee on every transaction and absorb losses when something goes wrong. Volume is the name of the game. Card interchange runs 1.5% to 3% and covers dispute resolution. Stablecoin rails cost a fraction of that, so a recourse layer could offer comparable protection at 50 basis points and still have margin.

Attribution is the most speculative of all, but it will eventually be built. When agents start influencing purchases, brands will pay to shape what gets recommended. The auction mechanics are designable. But this one has a cold start problem, the other two don’t. You need brands, agents, and merchants all participating before the market functions.

These wedges matter differently depending on where the agent economy sits. Identity is critical when agents act without human approval for every transaction. Recourse matters whenever agents start moving real money. Attribution only kicks in when agent-to-agent commerce reaches enough volume to support an advertising market. This brings us to what the trajectory actually looks like.

Startups will build parts of the agentic economy infra

The journey of agents can be mapped in three phases.

1. They first act as interfaces.

2. Then they execute under human supervision.

3. They transact with each other autonomously.

We are in the first phase. ChatGPT’s Etsy checkout integration is a good example. We browse products through conversation in a chat interface (though not everyone is doing that yet), the agent surfaces options, but we humans pull the final trigger. Trust is borrowed entirely from existing infrastructure.

This phase belongs to incumbents because it’s a distribution game. The value accrues to whoever owns the interface where purchase decisions happen.

The second phase is marked by agents getting more autonomy. Instead of only suggesting the itinerary, agents book flights, car rentals, and hotels. We provide intents or constraints, the agents execute, and we verify the final results.

This is where the trust layer becomes essential. Users will not delegate authority to agents without recourse. Merchants will not accept agent-initiated payments without some way to verify the agent’s identity and authorisation.

This is also where startups have a real opportunity. Incumbents perhaps don’t have the clear incentives to direct their resources towards building trust infrastructure for stablecoin rails because tremendous growth lies ahead of them within the existing phase, where they dominate.

OpenAI generated $13 billion in revenue this year. For context, Tether is on track to make more than that in profit, with $10 billion in profits until October 2025. The identity, recourse, and attribution layers will emerge from new companies solving specific problems at the boundary between what agents can do and what users will permit.

The third phase is autonomous agent commerce. Your agent doesn’t ask permission for routine decisions. It negotiates with other agents, bids for compute and bandwidth, participates in ad auctions, and settles thousands of small transactions continuously. Stablecoins fit the bill for the default settlement layer because nothing else handles the volume, speed, and granularity that machine-to-machine commerce requires.

In this phase, the differentiator is no longer who has the best model or the fastest chain. What matters is who built the most trusted infrastructure. The passports that agents carry. The courts that adjudicate disputes. The credit systems allow agents to transact beyond their immediate balance. These institutions for software will determine which agents can participate in the economy and on what terms.

We’ve built the plumbing for agents to spend money before building any way to verify they should be allowed to. HTTP 402 sat dormant for thirty years, waiting for micropayments to make sense. Now it works. But the trust infrastructure that enables human commerce, including identity verification, fraud detection, and dispute resolution, lacks an agent equivalent. We solved the easy problem first. It will take some time before agents are ready to do business with each other.

Looking forward to hiking during the holidays,
Saurabh Deshpande

The previous episode with Eric Saraniecki, co-founder of Canton Network, was about what institutions need to start using blockchain rails. For the latest, we talk about how blockchains need the ability to reason and act autonomously to be embedded in an agentic economy.

Spotify

iTunes

I recently sat down with Mike Hanono, co-founder and CEO of Talus Network, to talk about how crypto can help AI move forward.

The $342 billion invested in AI by five major tech companies would rank as the 45th-largest GDP in the world. However, almost none of that investment has been spent on making AI accountable or verifiable. Most companies have internal governance frameworks for verifiability, but there’s nothing visible to the consumer. You are flying blind when you use most models. I’ve written about AI markets and verifiability here in detail.

Half the traffic on the web is bots, and computers have already started driving us around. It won’t be too long before the agentic economy is real, too. By that, I mean agents that hold assets, make decisions, and coordinate with other agents. These agents will need to live inside rule-based systems, interacting programmatically and economically, where every action leaves an audit trail. And this is where blockchains enter the picture.

Talus is building the infrastructure to make this happen. Blockchains of today are strict. Input A gives output B. They lack a brain, the ability to reason and adapt. And they lack hands, the ability to act autonomously. Talus’s bet is that incorporating both reasoning and autonomy into blockchains is essential to advancing AI commerce.

As a market participant, I’m curious about where the value accrues in our experiments with AI. Remember the fat protocol thesis? The idea that in crypto, protocols capture most of the value while applications remain thin. But we’ve seen that change.

Applications that users actually want to use are starting to capture value, too. Platforms like Aave, Base, Hyperliquid, and Jupiter are living proofs that it’s not just about the protocols. I believe something similar happens with AI. Value concentrates at both ends. Chip manufacturers like Nvidia, on the one hand, and applications that build relationships with users, on the other.

See you in the next one,
Suarabh Deshpande

There are two distinct schools of thought in crypto. As a publication, we get to have a front-row view of both. One argues that everything is a market and pricing things is how we get clarity. The other believes crypto is better fintech rails. Our publishing calendar swings between both views because, as with all markets, there is no single truth. We are simply curating all possible models.

In today’s issue, we have Sumanth breaking down how a new payment standard is evolving on the web. Put simply, it asks the question: what would happen if you could pay per article? To find the answer, we go back to the early 1990s and see what happened when AOL tried pricing access to the internet on a per-minute basis. We walk down the alley of Microsoft pricing its SaaS subscriptions. And eventually, we sit at a cafe where Claude has been pricing conversations on a per-text basis.

In the process, we lay down an explanation of what x402 is, who the key players are, and what it means for platforms like Substack. The agentic web is a theme we have been increasingly exploring internally. If you are building, researching, or investing along these themes, reach out to us at venture@decentralised.co.

Joel

The Internet’s business model is out of touch with how we use it. In 2009, the average American visited over one hundred sites per month. Today, the average user opens fewer than thirty apps per month, yet spends far more time inside them. About half an hour a day then, closer to five hours now.

The winners - Amazon, Spotify, Netflix, Google, and Meta, became aggregators who pooled consumer demand and converted wandering into habits. They priced those habits as subscriptions.

This works because human attention follows patterns. We watch Netflix most evenings. We order from Amazon weekly. Prime bundles include shipping, returns, and streaming for $139/year. The subscription removes much of the constant pain. Amazon is now pushing ads to subscribers to juice margins, forcing users to sit through the ads or pay more. When aggregators can’t justify subscriptions, they fall back on advertising like Google, which monetises attention rather than intent.

Look at what moves through the pipes now:

• Bots and automation now make up nearly half of all web traffic. This was primarily driven by the rapid adoption of AI and large language models (LLMs), which have made bot creation more accessible and scalable.

• API requests account for 60% of the dynamic HTTP that Cloudflare processes. In other words, machine-to-machine chatter already accounts for most of the traffic.

We designed today’s pricing models for a human-only internet. Now the traffic is machine-to-machine and bursty. Subscriptions assume habits. Spotify on the way to work, Slack during work hours. Netflix in the evening. Ads assume eyeballs - someone scrolling, clicking, considering. But machines have neither habits nor eyeballs. They have triggers and tasks.

Pricing content is not just a function of market constraints but also of the underlying distribution rails. Music spent decades as albums because physical media demanded bundling. Burning one song or twelve songs on the same CD costs nearly the same. Retailers needed high margins, and shelf space was finite. In 2003, iTunes changed the unit of account to the song when the distribution medium shifted to the internet. Buy any song for $0.99 from iTunes on your PC and sync it with your iPod.

Unbundling increased discovery, but it also eroded revenues. Most fans bought the top hits, not the ten fillers, compressing per-listener revenue for many artists.

Then the rails changed again when the iPhone came out. Cheap cloud storage, 4G and global CDNs made accessing any song instant and smooth. Phones were always online, instantly tapping into an effectively infinite number of songs. Streaming rebundled everything at the access layer: $9.99/month for all music ever recorded.

Music subscriptions now make up more than 85% of music revenues. Something Taylor Swift is unhappy about: she was forced to return to Spotify.

Enterprise software tracks the same logic. Because the product is digital, vendors can charge for the exact resources used. B2B SaaS vendors offer predictable access to a service on a monthly or annual basis, typically “per seat,” with tiers that restrict features, such as $50/user/month plus $0.001 per API call.

The subscription covers predictable human usage, and metering handles machine bursts.

When AWS Lambda runs your functions, you pay for exactly what you consume. B2B deals often involve bulk orders or high-value purchases, resulting in larger transaction sizes and significant recurring revenue from a smaller, concentrated base. Last year, B2B SaaS revenue reached $500 billion, which is twenty times that of the music streaming industry.

If most consumption is now machine-driven and bursty, why are we still pricing like it’s 2013? Because we designed today’s rails for humans to make occasional choices. Subscriptions became the default because one monthly decision beats a thousand micropayments.

It’s not that crypto created the underlying rails that can now support micropayments. There’s that too, but the internet itself has become such a gargantuan beast that it needs new ways to price usage.

Why Micropayments Failed

The dream of paying cents for content is as old as the web itself. Digital Equipment’s Millicent protocol promised sub-cent transactions in the 1990s. Chaum’s DigiCash ran bank pilots. Rivest’s PayWord had solved the cryptography. Every few years, someone rediscovered the same elegant idea: what if you could pay $0.002 per article, $0.01 per song, exactly what things cost?

They all died the same death: humans hate metering their own enjoyment.
AOL learned this quite expensively in 1995.

They charged dial-up usage by the hour. For most users, this was objectively cheaper than a flat subscription. Yet customers hated it because of the mental overload. Every minute online felt like a meter running, and every click carried a tiny cost. People can’t help tallying each micro-cost as a “loss,” even if the amounts are small. Every click became a micro-decision: is this link worth $0.03?

When AOL switched to unlimited in 1996, usage tripled overnight.

People paid more to think less. “Pay exactly for what you use” sounds efficient, but for humans, it often feels like anxiety with a price tag.

Odlyzko sums this up in his 2003 paper - The Case Against Micropayments: people pay more for flat-rate plans not because they’re rational, but because they crave predictability over efficiency. We’d rather overpay $30/month for Netflix than optimise each $0.99 rental. Later experiments, such as Blendle and Google One Pass, attempted to charge $0.25 to $0.99 per article but ultimately failed. The unit economics don’t work unless a significant percentage of the reader base converts, and the UX adds a cognitive toll.

Subscription Hell

If we crave the simplicity of subscriptions, why are we complaining about subscription hell today? A simple way to reason about pricing is to ask how often you feel the pain the product removes.

Entertainment has infinite demand. The black line in the graph represents this constant pain point, the dream for both users and companies, a flat, predictable, constant pain curve. This is why Netflix went from a quirky DVD-by-mail service to membership of the elite FAANG club. It provided an endless amount of content and eliminated billing fatigue.

The simplicity of subscriptions reshaped the entire entertainment industry. As Hollywood studios watched Netflix’s stock price soar, they began yanking their catalogues back to build their own subscription empires: Disney+, HBO Max, Paramount+, Peacock, Apple TV+, Lionsgate and more.

The fragmented libraries of content forced users to buy more subscriptions. If you want to watch Anime, you need to subscribe to Crunchyroll. For Pixar movies, you need a Disney subscription. Watching content became a portfolio construction problem for users.

Pricing depended on two things: how precisely the underlying rails can meter and settle usage, and who must make the decision each time value is consumed.

One-time payments work beautifully for rare, spiky events. Buying a book. Renting a movie. Paying for a one-off consultation. The pain hits hard once, then disappears. This model works when the task is infrequent and the value is obvious. In some cases, the pain is even desirable. We romanticise trips to the theatre or a bookstore.

Measure usage precisely, and price snaps to the unit of that work. That’s why you don’t pay for half a movie. Value is ambiguous there. Figma can’t skim a fixed slice of your monthly output; creation value is hard to meter.

It’s much easier to charge a monthly fee even when it’s not the most profitable.

Compute is different: the cloud can observe every millisecond. Once AWS could meter execution at that granularity, renting whole servers stopped making sense. A server spins up only when it is needed, and you pay only when it runs. Twilio did the same for telecom: one API call, one SMS segment, one charge.

The irony is that even where we can meter perfectly, we still bill as if it’s cable TV. The usage meter runs in milliseconds, but the money flows through a monthly credit-card subscription, an invoice PDF, or a prepaid “credits” bucket. To make that work, every vendor drags you through the same gauntlet - create an account, set up OAuth/SSO for identity, issue API keys for authorisation, stash a card, set a monthly cap, and pray that you don’t get overcharged.

Some tools make you pre-load credits. Others, like Claude, throttle you to the lower model when a quota is reached.

Most SaaS lives in the green “predictable pain” band. Too frequent for one-off purchases, and too steady to justify accurate per-event metering. The playbook is bucketed tiers. You pick a plan sized to your typical month and step up when usage exceeds the limit.

Microsoft’s 1TB-per-user fence is an example of this threshold that separates light from heavy users without metering every file operation. CFO’s limit the number of users who need to access higher tiers by assigning permissions.

The Messy Middle

A neat way to sort pricing models is a two-axis map with usage frequency on the x-axis and usage variance on the y-axis. Variance here means burstiness - how jagged the pattern is for a single user over time. Two hours of Netflix most evenings is low variance; an AI agent that slams 800 API calls in ten seconds, then goes quiet, is high variance.

In the bottom-left corner, one-time purchases sit. When the job is rare and predictable, simple buy-and-go pricing works because you feel the cost once and move on.

The top-left is the chaotic casual web with irregular news binges, link-hopping, and low willingness to pay. Subscriptions are overkill, while per-click micropayments collapse under decision and transaction friction. Advertising became the financing layer, aggregating millions of tiny, inconsistent views. Global ad revenues crossed the $1T mark, with digital capturing 70% of spend, indicating how much of the web lives in this low-commitment corner.

The bottom right is where subscriptions make a lot of sense. Slack, Netflix, and Spotify match human routines. Most SaaS lives here, with tiers that fence off heavy users from light users. Most products offer a freemium tier to encourage users to start using their product, and then gradually shift their usage from top-left to bottom-right with daily, steady habits.

Subscriptions account for around $500B in global annual revenue.

The top-right is the modern Internet’s centre of gravity with LLM queries, agent actions, serverless bursts, API calls, cross-chain transactions, batch jobs, and IoT device chatter. Usage is both constant and jagged. Flat seat-based fees misprice this reality but lower the psychological barrier to starting to pay. Light users overpay while heavy users are subsidised, and revenue drifts away from actual consumption.

That’s why seat-led products have been creeping toward meters. Keep the base plan for collaboration and support, but charge for heavy loads. Dune, for example, offers limited credits per month. Small, simple queries are cheap, while larger queries that take longer to run cost more credits.

Cloud normalised per-millisecond billing for compute, data, and API platforms’ sell credits that scale with actual work. It is moving toward tying revenue to the smallest unit the network can observe. In 2018, less than 30% of software was usage-based. Today, usage-based pricing accounts for close to 50% by eating into commission-based pricing, while subscriptions remain dominant at 40%.

If spending is drifting toward consumption, the market is telling us that pricing wants to sit on the same clock as the work. Machines are rapidly becoming the largest consumers of the internet, with half of all consumers using AI-powered search. And machines are now creating content more than humans, too.

The problem is that our rails still run on yearly accounts. Once you sign up with a software provider, you get access to their dashboard with API keys, prepaid credits, and end-of-month invoices. They’re fine for humans with habits; they’re clumsy for software that bursts. In theory, you could set up a monthly recurring bill using ACH, UPI, or Venmo. These, however, would need to be batched for use as their fee structure breaks down at sub-cent, high-frequency traffic.

This is where crypto matters for internet economics. Stablecoins give you programmable, global, and granular to fractions of a cent. They settle in seconds, run 24/7, and can be held directly by agents instead of being trapped behind a bank UI. If usage is becoming event-shaped, settlement should be too, and crypto is the first rail that can realistically keep up.

What X402 Actually Is

x402 is a payment standard that works with HTTP, using the decades-old 402 status code, which was reserved for micropayments.

x402 is just a way for sellers to verify that a transaction has gone through. Sellers who want to accept on-chain gasless payments through x402 must plug into facilitators like Coinbase and Thirdweb.

Imagine Substack charging $0.50 for a premium post. When you hit the ‘Pay to Read’ button, Substack returns a 402 code that includes the price, accepted asset (e.g., USDC), network (e.g., Base or Solana), and policy. Something that looks like this:

Your Metamask wallet authorises $0.50 through a signed message and passes it to the facilitator. The facilitator sends the text on-chain and informs Substack to open the article.

Stablecoins make accounting easy. They settle at network speed, in tiny denominations, without opening an account with every vendor. With x402, you don’t pre-fund five credit buckets, you don’t rotate API keys across environments, and you don’t discover at 4:00 a.m. that a quota tripped and your job died. Human billing can stay where it works best on a credit card, while all the spiky, machine-to-machine paths become automatic and cheap behind the scenes.

You can feel the difference in an agentic checkout. Let’s say you are experimenting with new fashion styles on Daydream, the AI fashion chatbot. Today, a shopping flow would redirect you to Amazon so that you can pay with your saved card details. In an x402 world, the agent understands context, fetches the merchant’s address and pays from your Metamask wallet without leaving the thread.

The interesting part about x402 is that, currently, it isn’t a single entity; it’s made up of the layers you’d expect to find in a real rail. Anyone building AI agents through Cloudflare’s Agent Kit can create bots that are priced per action. Payment giants like Visa and PayPal are also adding x402 as a supported rail.

QuickNode has a hands-on guide for adding an x402 paywall to any endpoint. The direction of travel is clear: unify “agentic checkout” at the SDK layer and let x402 be the way agents pay for APIs, tools, and, eventually, retail purchases.

Integrating x402

Once the web supports native payments, the obvious question is where it will take off first. The answer lies in the high-frequency usage zone, with transaction values under $1. That’s the terrain where subscriptions overcharge light users. This monthly commitment forces light users to pay the minimum sub amount to get going. x402 can settle every request at machine speed, with as low granularity as $0.01, as long as the blockchain fees remain feasible.

Two forces make this shift feel urgent. On the supply side, the “tokenisation” of work is exploding - LLM tokens, API calls, vector searches, IoT pings. Every meaningful action on the modern web already has a tiny, machine-readable unit attached to it. On the demand side, SaaS pricing leads to absurd wastage. Roughly four in ten licenses sit idle because the finance team prefers to pay per seat, as it’s easy to monitor and forecast. We’re metering the work at the technical layer but billing the humans at the seat layer.

Event-native billing with caps is a way to line those worlds up without freaking buyers out. We could have soft caps that reconcile to the best price. A news site or developer API charges per request throughout the day, then automatically refunds down to a posted daily ceiling.

If The Economist posts “$0.02/article, cap $2/day,” a curious reader can bounce through 180 links without doing mental maths.

At midnight, the protocol settles everything down to $2. This same pattern applies to developer surfaces. News agencies can charge for each LLM crawl to sustain future AI browser revenue. A search API like Algolia can charge $0.0008 per query, summing up daily usage to $3.

You can already see consumer AI edging in this direction. When you hit Claude’s message limit, it doesn’t just say “Limit hit, come back next week .” It offers two paths on the same screen: upgrade to a higher subscription, or pay per message to finish what you’re doing.

What’s missing is a programmable rail that lets agents make that second choice automatically, per request, without UI pop-ups, cards, or manual upgrades.

For most B2B tools, the practical end-state looks like a “subscription floor + x402 bursts. Teams keep a base plan tied to headcount for collaboration, support, and background usage. The occasional heavy compute (build minutes, vector searches, image generations) bills flow through x402 instead of forcing an upgrade to the next tier.

Better networks can plug in as well. Double Zero wants to sell a faster, cleaner internet over dedicated fibre. Route agent traffic through them, and you can price per gigabyte over x402 with explicit SLAs and caps. An agent that needs low latency for a trade, a render, or a model hop can temporarily step into the fast lane, pay for that specific burst, and step back out.

SaaS will accelerate towards usage-based pricing, but with guardrails:

1. Acquisition and activation get cheaper. You earn on the first call. Drive-by developers who never complete an OAuth or card form can still pay $0.03. Agents favour vendors who are instantly payable.

2. Revenue scales with real usage instead of seat inflation. That’s the cure for the 30–50% seat waste in most orgs. The heavy lifting moves to bursts with caps.

3. Pricing becomes a product surface. “Fast lane for $0.002 extra per request,” “batch mode at half price,” are knobs that startups can experiment with to increase revenue.

4. Lock-in weakens. Switching costs decrease with the ability to try vendors without integration effort and time.

A World Without Ads

Micropayments won’t wipe out advertising; they’ll shrink the terrain where ads are the only workable model. Ads will still excel in casual intent spaces. x402 prices the surfaces ads can’t reach and the occasional human might choose to pay for one great article without taking a monthly subscription.

X402 reduces the friction of paying; at a certain scale, it might tilt the future.

Substack has 50 million users, with a 10% conversion rate, resulting in 5 million subscribers paying around $7 per month. When the paying subscriber base doubles to 10 million, that’s probably when Substack will start making more from micropayments. Because of lower friction, more casual readers can shift towards paying for each article, accelerating the revenue curve.

The same logic applies to any seller with high-variance, low-frequency sales: when people use a product occasionally rather than habitually, paying per use feels more natural than committing to a long-term plan.

It’s a bit like my visits to the local badminton courts. I play two or three times a week, usually at different places with different friends. Most of these courts offer monthly memberships, but I’d rather not tie myself to one. I like the freedom to decide the court we go to, how often I go, and to skip a session when I’m tired.

Don’t get me wrong, I know this varies from person to person. Some prefer sticking to their nearest court, some like having a subscription that nudges them into a routine, and others might want to share one with friends.

I can’t speak for offline payments, but with x402, this kind of individuality can be reflected digitally. Users can set their own payment preferences through policies, and companies can respond with flexible pricing models that adapt to each person’s habits and choices.

Where x402 really shines is in agentic workflows. If the last decade was about turning humans into logged-in users, the next one is about turning agents into paying customers.

We’re already halfway there. AI routers like Huggingface let you choose among multiple LLMs. OpenAI’s Atlas is an AI browser that uses LLMs to run tasks for you. x402 slots into that world as the missing payment rail. It’s a way for software to settle tiny tabs with other software at the exact moment work gets done.

However, a rail alone doesn’t make a market. Web2 built an entire scaffolding around the card networks. KYC at the bank, PCI for merchants, PayPal disputes, block card for fraud, and chargebacks when things go wrong. Agentic commerce doesn’t have any of that yet. Stablecoins plus HTTP 402 give agents a way to pay, but they also rip out the built-in recourse people are used to.

When your shopping agent buys the wrong flight or your research bot blows through a data budget, how do you recover your money?

That’s what we’ll be digging into next: how developers can use x402 without worrying about future breakages.

Watching ads on Prime,
Sumanth

Spotify

iTunes

Eric Saraniecki joined me from Canton Network for our latest podcast. We have been tracking the blurring of lines between TradFi and crypto-native business models over the past few quarters here at DCo. Canton sits at the heart of this transition. It is a network that seeks to build what traditional institutions need to come fully on-chain.

Eric is a cofounder and head of network strategy at Digital Asset and is building Canton Network. They have raised a strategic round of $135 million led by DRW Venture Capital and Tradeweb. DRW is also in talks to raise an additional $500 million for Canton’s token treasury. Eric is also a co-founder of Cumberland, DRW’s trading arm and one of the largest liquid trading desks in crypto. In other words, he has both the experience and the capital needed to bring institutions on-chain.

Four things specifically stood out to me from the conversation.

1. Privacy is a spectrum, not black or white. Crypto is about more efficient infrastructure rails. However, that does not mean real-world finance should have to trade privacy for quick settlements. One can debate the complex trade-offs of decentralisation, speed, and privacy to no end. Still, the market is telling us that it has started valuing privacy as it remains the only sector with double-digit returns over the past few weeks. Debates aside, it makes sense to pay attention to those signals.

2. Stablecoins have not really found PMF. BCG reported recently that only 8% of the total stablecoin settlements in 2024 were for real-world use cases like RWAs and payments. However, the integration with the world outside crypto has been underway with the likes of Stripe and Circle tweaking the plumbing to fit those use cases. I think that this number will only increase in the not so distant future.

3. Ethereum’s incentive model is extractive. Ethereum inherited Bitcoin’s approach of rewarding only infrastructure providers but found product-market fit in the application layer. Aave creates tremendous value but earns no protocol rewards for it. Canton, like Sonic, aims to change that by allocating 50% of rewards to applications, rather than validators.

4. Darkpools are the next iteration of on-chain DeFi. For power users considering where Canton fits, Eric’s answer is managing inventory, collateral, and risk while preserving privacy. Knowing everyone’s positions in perp DEXs is dangerous. The fix isn’t better transparency, it’s proper opacity.

Canton is an attempt to give large institutions a set of rails that they feel familiar with. Institutions were never keen on using blockchains, as they were apprehensive of the benefits of integrating new infrastructure. Can that change? Sure, but only if the underlying infrastructure has evolved. Large traditional institutions will conduct business on-chain if it makes them more money. In theory, it’s possible, and it will take time before we see this in practice.

Signing off,
Saurabh Deshpande

Today’s piece, written in collaboration with Azuro and Trendle, is a breakdown of how liquidity flows across markets through their protocol in prediction markets. Last week, I wrote about how curated social feeds on Web3 social graphs could be a necessary primitive to replace surveillance economies. The week before, I broke down how embedded markets will change the unit economics of attention.

Azuro and its flagship product, Trendle, look like what would happen when these themes merge. Sumanth breaks down how the product came to be, why it matters, where it fits, and how it compares to traditional equity in today’s piece. It is a brilliant breakdown of what prediction markets would need to scale a hundred times from here.

If you are building plumbing for these new markets or conceptualizing them, we’d love to speak to you. Venture@decentralised.co is the best place to reach out. We are steadily building up a portfolio of companies around the theme.

Joel

On election night 2024, Polymarket showed clear conviction while television pundits had it wrong. The prediction market had Donald Trump at 58% to win hours before major networks called the race. By the time the results were official, Polymarket had processed $3.2 billion in trading volume, proving what many had long suspected: that markets aggregate information more effectively than experts.

This wasn’t Polymarket’s first validation, but its most visible. Throughout 2024, the platform had become the default source for real-time probability on everything from Fed rate decisions to Supreme Court rulings. Political analysts cited its odds in op-eds. Traders used it to hedge positions. Casual observers checked it like weather forecasts. After years of regulatory limbo and false starts, prediction markets had finally arrived.

Within months, the legal fog that shadowed prediction markets began to clear. A DC Circuit ruling allowed Kalshi to list congressional control contracts under the CFTC’s supervision.

For years, prediction markets faced a patchwork of restrictions - banned in most U.S. states, tolerated in others, operating offshore to serve American users while technically violating terms. The ruling paved the way for a clear regulatory path for event markets.

CFTC-supervised exchanges can now list event contracts nationwide, superseding state-by-state regulations for sports betting. Polymarket acquired a CFTC-licensed venue in early 2025, securing its route back into U.S. markets after operating offshore.

Now the money and the licenses are catching up. Intercontinental Exchange (ICE), the owner of the NYSE, is investing $2 billion in Polymarket, valuing it near $10 billion post-money and positioning ICE to distribute event-driven data to Wall Street clients. Kalshi has also raised $300 million at a $5 billion valuation from investors like Andreessen Horowitz (a16z), Sequoia Capital, and Paradigm. It signals that “odds as a data feed” is moving from crypto novelty to institutional plumbing.

The scale of what’s being built extends far beyond politics. Global sports betting is a $140 billion annual market. Add financial event contracts - Fed decisions, GDP prints, corporate earnings, and you’re past $200 billion. Layer in cultural prediction markets for entertainment, social trends, and product launches, and the addressable market approaches half a trillion dollars. Anything with an uncertain outcome that people care about can become a tradable market.

As prediction markets gain legitimacy, they risk becoming what they were meant to disrupt. Polymarket is permissionless in theory but centralised in practice. The company controls the order book, determines which markets are listed, and can freeze accounts or block regions at its discretion. It’s crypto-enabled, allowing users to deposit assets and participate, but the markets are not on-chain. Kalshi is a centralised Prediction Market that’s currently building out its crypto team to accelerate moving on-chain.

This is where infrastructure protocols that bring liquidity on-chain matter. Azuro’s protocol combines traditional odds-making with AMMs and settles everything on-chain. Rather than controlling the experience, Azuro enables it, providing a single concentrated liquidity pool that any front-end can tap, connecting professional data providers to create markets, and allowing permissionless access for builders. This architecture addresses several longstanding issues in prediction markets, including high fees, geographic restrictions, and opaque odds-making processes.

In today’s sponsored collaboration, we examine how Azuro’s approach—featuring a single concentrated liquidity pool, professional data providers, and permissionless front-ends — creates a more efficient and accessible betting market. We’ll explore the core mechanics, analyse the growing ecosystem, and assess whether Azuro can fulfil its ambitious vision of creating a new market for attention.

How Prediction Markets Work

Prediction markets are coordination engines. Their power lies in how they match people with different convictions. Someone who follows polling data closely believes Trump’s odds are undervalued at 60% and will buy until the price reflects their view; someone who thinks the market is too optimistic will sell.

The ones who get it right win it all. It is a zero-sum game. But how do you ensure there’s always someone on the other side of the trade? There are two architectures with different tradeoffs - Order books and AMMs.

Order books are the oldest and most familiar. They work like exchanges: users post bids and asks, such as “I’ll buy Yes at $0.58” or “I’ll sell Yes at $0.62”. Trades execute when they meet. Prices emerge organically from the tension between buyers and sellers. When there’s enough liquidity, spreads tighten to less than 1 cent. That’s how political markets look during election weeks with deep books and tight spreads. Market makers also step in to supply liquidity and earn a commission for facilitating trades. It’s the same machinery that keeps Hyperliquid operating on tight spreads.

The problem is liquidity fragmentation. Each market needs its own set of active buyers and sellers, maintaining two-way pricing. A market on “Fed Decision on October?” might have 5000 active traders providing liquidity with $70M in open interest. However, a market on “Richest person on December 30” might have only 20 traders with $2 million in open interest. Thin markets have wide spreads, making them effectively untradeable. Market makers also step away when markets are shaky, as we saw in the recent $19B wipeout.

Automated Market Maker (AMM) models solve this through pooled liquidity. Instead of peer-to-peer trading, users trade against a pool of liquidity governed by a mathematical curve. The more Yes tokens you buy, the more expensive they get. The best-known version is the Constant Product Market Maker (CPMM), used by Uniswap, which ensures liquidity is always available.

AMMs are elastic and open, capable of spawning new markets instantly without waiting for counterparties to establish them. But that flexibility costs precision. To achieve the same depth as an order book, an AMM needs far more locked capital. The result is slippage; a $10,000 trade can move a market from 60¢ to 65¢ as it consumes liquidity along the curve. Order books could execute the same size at 61¢.

The Sports Betting Problem

The limits of current prediction markets show up clearly when you zoom in on sports. Elections or policy events evolve slowly, and probabilities shift as new information becomes available. Sports are chaotic, compressed into minutes. A goal, a red card, or a split-second VAR reversal can swing the odds by 50%. The infrastructure that works for macro-scale prediction breaks under that tempo.

During the 2022 World Cup final, France’s win probability jumped from 5% to 60% in under two minutes after Mbappé’s second goal. Any market maker maintaining an order book would face catastrophic losses as their stale orders get instantly sniped.

Traditional sports bookmakers employ sophisticated trading desks precisely to handle such rapid swings, adjusting odds in microseconds to protect their positions. You can’t expect retail users to monitor every match, ready to cancel and repost orders within seconds of a goal being scored. After a few experiences getting sniped, rational liquidity providers stop posting orders on sports markets.

The scale problem compounds it. A typical Polymarket election might host thirty or forty active contracts at once. A single sportsbook covers thousands of events. Every match, goal, corner, and card is priced in real time. Each of those markets needs active liquidity to stay tradable. The math doesn’t work. Fragmented liquidity makes it impossible to run deep, responsive odds without massive capital overhead.

Despite these problems, Prediction Markets are focusing on the $140 billion sports betting industry because of the advantages they bring. Traditional sportsbooks are black boxes. They maintain 5-8% margins on typical bets, refuse payouts at their discretion, freeze accounts without explanation, and arbitrarily ban winning players. Users have no recourse beyond contacting customer service or hiring lawyers.

But in their current form, prediction markets cannot scale to fill that gap. Kalshi and Polymarket today handle around $1 billion a month. Global sportsbooks handle more than $20 billion in the same time. All of Polymarket’s election-year frenzy equals a single busy weekend for DraftKings, Bet365, and FanDuel combined.

In their current form, Prediction markets are dependent on liquidity providers to step in. Estimates suggest that liquidity disparities have led to price discrepancies of around 15% during peak trading hours, eating into user gains. Better liquidity management solutions are needed to improve the user experience of on-chain prediction markets.

Enter Azuro.

Azuro

On a chilly morning in Yerevan, Armenia, in 2012, Paruyr Shahbazyan launched a modest website using a $50 template. His mission was ambitious yet straightforward - to bring transparency to the murky world of sports betting. Having spent years as a professional bettor himself, he had an intimate understanding of the industry’s problems: bookmakers refusing payouts, mysterious account freezes, and odds that seemed to shift unfairly against winning players.

Through his platform Bookmaker Ratings, he helped recover over $12 million for wronged bettors, building deep trust within the community. But as cryptocurrency payments began flowing into betting sites and blockchain technology matured, Shahbazyan saw an opportunity to solve these problems at their root. With Azuro, he set out to create an on-chain betting protocol that would make intermediaries obsolete.

Azuro’s core idea is to rebuild the sportsbook’s machinery as public infrastructure. Instead of every market maintaining its own liquidity pool, Azuro runs a single shared pool. One reservoir of capital that dynamically allocates funds across thousands of outcomes. The protocol acts as a financial grid: when liquidity demand spikes around the World Cup, the same capital that was idling in eSports or regional matches can instantly flow there. When Europe sleeps, that liquidity rebalances to American games.

Azuro’s ecosystem comprises three primary interconnected actors:

1. Data Providers sit at the heart of the system, playing a role similar to traditional odds providers, such as Sportradar. They create betting markets, stream live odds, and resolve outcomes.

2. Front-end Applications (or simply Apps) are the consumer-facing layer that punters actually interact with. Rather than building its own betting interface, Azuro enables anyone to create applications that leverage its liquidity and markets.

3. Liquidity Providers are the financial engine. By depositing capital into Azuro’s single pool, they gain exposure to every betting market on the platform. Think of them as the house - but rather than running complex trading operations, they provide capital and earn returns.

When a Data Provider creates a new market, they specify something called the “Reinforcement” amount. This reinforcement represents the maximum potential loss the pool could face from this specific market. However, and this is crucial, the pool doesn’t actually lock up this full amount. Instead, it uses a system of Virtual Funds to track real-time liquidity needs.

The Data Provider distributes this reinforcement across two possible outcomes based on their analysis:

• Argentina Win: $60,000 → 1.67 odds (60% probability)

• France Win: $40,000 → 2.50 odds (40% probability)

The protocol doesn’t actually offer these “true” odds to punters. Instead, it adds a 7.5% margin by increasing each probability by 7.5%, resulting in slightly reduced odds:

• Argentina Win: 1.55 (instead of 1.67)

• France Win: 2.32 (instead of 2.50)

This margin is how Azuro ensures long-term profitability—similar to how traditional bookmakers add their vig. The difference lies in transparency. This margin is fixed and publicly visible, unlike the opaque and variable margins often buried in traditional betting odds.

As pre-match betting heats up, the bets start flowing. Saurabh, confident in Messi’s destiny, places a $2,000 bet on Argentina at odds of 1.55. Joel, backing the defending champions, puts down $1,500 on France at 2.32.

The virtual fund for a market is calculated as the reinforcement minus the sum of all bets minus the maximum potential payout. In other words, it tracks the maximum liability of the protocol in the worst-case scenario. For our example, after our duo makes their bets, the Virtual Fund changes to:

• Initial reinforcement: $100,000

• Sum of placed bets: $3,500 ($2,000 + $1,500)

• Maximum potential payout: $3,100 (if Argentina wins)

• Profit for Pool with Slippage at 7.5%: $200 from Joel + $200 from Saurabh

Virtual Fund = $100,000 - $3,500 - $3,100 = $93,400

When Messi scores in the 23rd minute, the data Provider temporarily pauses the markets while updating the odds. These new odds trigger an automatic recalculation of the Virtual Funds. If they remain positive, betting continues. If any Virtual Fund approaches zero, that particular outcome stops accepting bets until either existing positions are settled or more liquidity becomes available.

If any disputes arise over resolutions, AzuroDAO acts as the final arbiter through on-chain voting, with voting power corresponding to the staked AZUR tokens and the duration of staking.

This “pool” architecture is paired with the Liquidity Tree, a mechanism that tracks every active bet and redistributes virtual funds to maintain solvency across all outcomes. Liquidity providers are exposed to the aggregate flow of the entire protocol. It’s like being the house in every sportsbook simultaneously, but with algorithmic risk management and transparent accounting.

This architecture proves particularly powerful for what the industry calls “long-tail events”—matches or tournaments that attract fewer bets. Traditional bookmakers often avoid these markets entirely because maintaining separate liquidity pools isn’t economically viable. However, Azuro’s singleton pool can support a Division 2 football match in Bulgaria with the same infrastructure that backs World Cup finals. The protocol allocates smaller reinforcement amounts to these markets while maintaining the same efficient capital utilisation across the entire system.

Banking on the House

For as long as organised betting has existed, being “the house” has been an enviable position. William Hill began as a single bookmaker in 1934 and has since grown into a multi-billion-pound operation. Bet365 started from a portable building in Stoke-on-Trent and is now worth over £7 billion. In betting, the house rarely loses.

Azuro democratises this position through its liquidity provision mechanism. Rather than William Hill’s family or Bet365’s Coates clan controlling all the upside, anyone can become a partial owner of the house by depositing capital into the protocol’s singleton pool.

The mechanics are straightforward: deposit assets into the pool and start earning a share of betting activity across every market the protocol supports. Think of it like buying shares in a global sportsbook, but with near-instant liquidity and without the corporate overhead. In our World Cup Final example, when the protocol generated $400 in profit from Saurabh and Joel’s bets, LPs collectively earned $80—their 20% share of the revenue. Scale this across thousands of daily bets, and those small percentages compound into historical returns averaging 15-20% APY.

The accounting system operates similarly to other DeFi vault models, such as Hyperliquid’s LP vault or GMX’s GLP pool. Your deposit gives you a proportional claim on the pool’s net asset value. As bets settle, profits and losses flow through in real-time, adjusting everyone’s position simultaneously.

This isn’t entirely risk-free money. When liquidity providers (LPs) first deposit funds, they are subject to a mandatory 7-day lock period. This prevents sophisticated traders from gaming the system by quickly entering and exiting positions around major sporting events. During this initial week, and sometimes extending into the first month, positions typically show negative returns.

This is by design; the protocol’s accounting system requires time to attribute profits and losses across the LP base accurately.

The protection mechanisms are equally sophisticated. Remember how Data Providers can pause markets during sudden events, like when Mbappé scored those two quick goals? This also protects LP capital from extreme swings. The protocol even implements automatic “stop-loss” thresholds that pause markets if potential losses approach dangerous levels.

The Nature of Choice

Azuro has processed $361 million in betting volume across 44 front-end applications. Activity grew steadily to over 23 million monthly transactions at its peak. The protocol has attracted over 34,000 unique users who’ve shown meaningful engagement, placing an average bet volume of $5,000.

Single bets make up the bulk of activity with $170 million in volume, while combo bets have attracted nearly 250,000 wagers worth $13 million. Azuro’s growth has been particularly notable in regions where traditional betting faces significant challenges. The protocol has seen substantial adoption in Latin America and Turkey, regions with passionate sports cultures but complex relationships with betting.

Take Turkey, where sports betting is technically legal but heavily restricted to a state-owned monopoly. Combine this with strict capital controls and a volatile currency, and you have millions of sports fans effectively locked out of global betting markets. Meanwhile, across Latin America, a patchwork of regulations and limited access to international payment systems creates similar barriers.

Azuro taking care of all the backend work creates powerful incentives. Consider a Vietnamese developer who identifies an opportunity to cater to local football fans struggling with English-language betting sites. They can build a culturally tailored interface with local language support, regional payment methods, and Vietnam-specific marketplaces. Another team might focus on creating the smoothest mobile experience for African users, while a third could integrate betting widgets directly into football statistics websites. The protocol’s liquidity pool instantly plugs into any event.

For users in these regions, decentralised betting solves multiple problems at once. They can access global betting markets without worrying about currency restrictions or complex KYC requirements. More importantly, they can be confident that successful bets will actually be paid out, which is a serious concern when using other alternatives.

From Sports to Attention

Prediction markets are splintering into every niche, like sports, politics, culture, and finance. But the thing they all circle around has never been directly tradable: attention. The internet figured out how to price clicks decades ago. It never figured out how to price the scarce focus that decides what trends, who sells out a drop, or which idea crosses the chasm.

With roughly five billion people on social platforms, feeds work like a 24/7 global exchange for human attention. Brands and creators already pay for it through ads, sponsorships, and collabs. Meta’s ad business generated $160 billion, which is approximately 98% of its total revenue. That’s roughly more than twice what global newspapers bring in.

The tools we have now measure attention, but don’t let you trade it. Google Trends shows which topics are spiking in search interest. “Ozempic” searches increased 300% in 2023, while “Strava” searches declined 40%. But you can’t bet on that shift six months before Novo Nordisk’s stock reflects it.

Social media analytics platforms, such as Brandwatch and Sprinklr, track brand mentions and “share of voice” across various platforms, providing Fortune 500 companies with data on how they are dominating the conversation compared to their competitors. News organisations use tools like CrowdTangle to spot which stories are going viral in real-time. Nielsen’s Social Content Ratings measure what Americans are watching on TV.

Brands currently make decisions using these descriptive tools. They check Google Trends to see if a topic is hot. They use platforms like HypeAuditor or Social Blade to catalogue top creators by follower count and engagement rates. They look at metrics - followers, likes, comments, shares, and partner with whoever has the highest numbers. A brand launching an energy drink might want to partner with fitness influencers. The ROI for these deals is calculated by the number of followers and the average engagement rate per follower.

This system is backwards. It measures what already happened (followers accumulated over years, past engagement) and assumes it predicts what will happen (future campaign performance). But attention doesn’t work like that.

A creator with 5 million followers might be experiencing declining relevance, while a creator with 500K might be on a rocket trajectory. Follower count is a lagging indicator. Attention momentum is what matters.

Santiment does a version of this for crypto. The platform tracks social volume, sentiment scores, and development activity across crypto assets, pulling data from thousands of Telegram channels, Reddit threads, Twitter accounts, and GitHub repos. Traders use it to spot narrative shifts before they hit price. When “Bitcoin ETF” social mentions surged in December, users saw the narrative building weeks before the actual approval.

Kaito made it much easier to track mindshare among hot narratives and identify which creators are driving market movements. You can see “Stablecoins” heating up, while the “AI agents” trend is slowing down. However, this is constrained to crypto. You can’t use it to compare whether “Barbie” is getting more buzz than “Oppenheimer,” or whether people care more about the new iPhone than whatever OpenAI’s new video model is.

The natural evolution is trading narrative momentum across verticals, treating it like a sector index instead of picking individual stocks. Take an example, if you believe AI is going to eat up search, you can long “AI search engines”. You’re betting on the conversation shift, not trying to pick whether Perplexity’s Comet or ChatGPT wins.

What’s missing is a clean, tradeable index, something you can go long when a narrative is building or short when it’s turning stale, without spinning up a token or running a media buy.

That’s where Trendle enters the frame. It sits on Azuro’s rails, using the same liquidity logic that made sports betting scalable, but applies it to the internet’s most universal commodity: mindshare.

Trendle: Trading the conversation

If prediction markets price answers to questions, attention contracts price the conversation itself. You pick a topic the world is buzzing about, bet ‘Up’ if you think attention rises or ‘Down’ if you feel it fades, and if conviction is high, use Boost for up to 5× leverage.

Under the hood, the architecture is more precise than “just counting mentions.” Azuro’s protocol aggregates signals from multiple platforms, including Twitter, Reddit, and YouTube. Data for every topic is continuously pulled from each of these platforms, and each raw signal is adjusted to a common baseline because these platforms differ in scale.

What this means is that Reddit’s thousands of daily posts versus YouTube’s billions of views are normalised, and the system sums activity into a single value, aligning every platform’s data onto the same time grid.

These engagement signals are transformed through a time-decay function, so more recent activity carries extra weight. The result is a composite attention index that reflects momentum, not raw volume. The last step is to measure the delta change in the index over a seven-day memory. This allows Trendle to capture spikes (such as a viral tweet) and persistence (when a narrative sustains itself over days).

With these indexes, it’s not about perfect counts but signal momentum: did the attention index go up or down over the defined period? It’s a bet on momentum, not on absolute volume.

The system is also designed to resist gaming: it cross-checks activity across platforms, flags anomalies in engagement ratios, and limits the weight any single entity or channel can contribute. In the future, Trendle can add more data sources, such as Discord, Spotify, and Google searches, to increase coverage.

Prediction markets often have hard deadlines; trends do not. Topics can explode and crash in hours. Instead of fixing a precise outcome, Trendle uses directional resolution: the question is not “what’s the exact score?” but “did attention rise or fall over the window?

Sports betting taught Azuro how to handle fragmented, spiky markets. Attention behaves the same way. It fragments into thousands of micro-topics that emerge in different time zones and fade just as quickly. Most of these markets are too thin for order books. Spinning up a new pool for each market is just capital that sits there.

This is where perps fit better. Instead of liquidity flowing across different markets, the Azuro team built a product called Trendle that backs perpetual markets across these cultural indexes. Funding rate ensures that a balance exists between longs and shorts.

Prediction markets resolve on specific deadlines, but trends don’t. Attention whipsaws on the internet. Sports settle on game time: a 90-minute match, divided into four quarters, and the final whistle. On the internet, topics can peak and crash in hours. Perpetual markets can handle this. The question isn’t “what’s the exact score?”. It’s “did attention rise or fall over the window?”

The obvious cases are settled fast. Disputes are escalated only when data sources conflict or the methodology is flawed. Keep governance out of the normal trading flow but make it available when needed.

Because the rails are permissionless, front ends can specialise for different users. A creator tool can wrap the same markets into release calendars and fan analytics. A brand planner can oversee the pool with pre-test dashboards and campaign windows. A crypto-native skin can express relative bets between narratives. Different skins have the same shared liquidity and resolution.

The distribution layer experiments; the market plumbing stays constant. In effect, Trendle transforms the internet’s feed into a perpetual price discovery machine.

Why Attention Beats Equity

Markets price stories before they price cash flows. Traders know this. Tetlock showed in 2007 that negative media tone predicted next-day return reversals. Later research extended that logic to Google Search Volume. When searches for a stock spiked, so did short-term volatility and trading volume. Retail investors overwhelmingly chase “attention-grabbing” stocks, the ones making headlines or trending on CNBC.

Today, if you think a narrative will catch fire, you can only express it indirectly by buying the underlying or fiddling with options. An attention contract lets you trade the driver instead of the symptom: go long if you expect a news cycle to build, short if you think it’s a one-day pop, without touching the stock.

Take this chart, which overlays two normalised series: headline intensity about the “Tesla Robotaxi” and TSLA’s intraday price volatility over the same window. The spikes in TSLA’s share prices rhyme with the increase in the number of headlines mentioning Tesla Robotaxi.

If you think the feed is about to light up with mentions of a particular company, trading the stock is a blunt instrument. You are buying the implicit value of the stock, which combines fundamentals, macro, and positioning. What you actually have conviction on is attention over the next few days.

It is a different way of expressing an opinion. One had to consider the second-order effects of a ‘trend’ and determine the instrument that best captures their opinion on ‘what will happen to the trend’. Every conjecture you have to make to reach that instrument adds a layer of uncertainty. You don’t have to deal with it when you are trading attention itself.

New financial primitives usually make markets better in two significant ways: 1) they offer a new way of expressing your opinion, and 2) they allow you to express your opinion more effectively. Instruments like VIX futures (launched in 2004) or credit default swaps (CDS) are an example of 1. Bitcoin ETFs are an example of 2.

Before CDS, if you thought that a company’s quality was deteriorating, you would short its bonds. However, that meant you needed to understand other concepts, such as interest rates, liquidity, duration, and credit risk. You were right about the company’s creditworthiness, but you still lost out due to a different factor. CDS let you isolate and trade pure default risk. Michael Burry wanted to bet against subprime mortgages, but shorting mortgage bonds proved to be messy and expensive. CDS gave him a clean instrument to express his view that “These mortgages will default”.

Attention contracts isolate that layer and pay you with an option-like payoff structure. Say an “Up” contract priced at 0.42 for the week. If attention rises, you get $1. If it falls, you get $0.

You buy 100 contracts for $42. If you’re right about the Robotaxi buzz building, you get $100 back, a $58 profit on your $42 investment (138% return). If you’re wrong, you lose the $42.

Compare this to trading TSLA stock. To achieve the same return, you’d need TSLA to increase by 138% in a week, which is almost impossible. You need to buy short-dated options where one negative headline about Elon wipes out your position, even if Robotaxi’s attention is actually building. Instead of using Tesla as a proxy for your view that ‘more people will talk about Robotaxi’ and taking exposure to things like Elon’s tweet, macro conditions, and Tesla’s production numbers, attention markets directly let you bet on Robotaxi conversations.

This is why the contract shape matters. Weekly attention is inherently volatile. Binary, directional resolution turns narrative heat into a clean P&L line: you’re right about the conversation, you get paid; you’re wrong, you don’t. You’re not hostage to whether the stock digests the cycle in a single print, whether a macro headline offsets it, or whether market hours clip the move. And because the tickets on prediction markets are naturally small - $10, $25, $50, you can spread that thesis across a basket of narratives the way a quant spreads factor bets, letting the law of large numbers compound a modest edge instead of swinging for home runs on one ticker.

How It Scales

Front-ends can specialise for different user segments and create new attention markets. A music analytics startup could wrap perps around album rollouts. A K-pop label could use it to compare comeback concepts. A brand planner might integrate it into campaign dashboards.

Music dashboards can track attention around album rollouts, just as labels already track streaming data. When Taylor Swift announces a new album, attention doesn’t arrive all at once. It builds through Easter eggs in lyrics, cryptic social media posts, surprise singles, and then explodes at the time of release. Fans could go long on attention two weeks out, when the marketing push starts, and then short it three days post-release, as the initial wave crests. Studios can compare two potential comeback concepts for a K-pop group before committing to one of them.

This beats current methods because the feedback is real-time and financially backed. Focus groups tell you what people claim they care about. Attention markets reveal what people are willing to bet actual money on. Take the Kendrick vs Drake beef. Attention whipsawed between both artists as each diss track dropped. Labels get actual market signals on what’s breaking through.

Labels can test unconventional concepts in attention markets before burning seven figures on production. Run attention markets on ten wild ideas for a total of $ 100k, then funnel the $1 million production budget to the top two ideas.

Brands can do the same. Digital advertising is a $700 billion machine that still relies on A/B testing, surveys, and focus groups. Focus groups said New Coke would dominate. Market research dismissed the iPhone as too expensive. Attention markets flip that model. Instead of asking what people say they’ll care about, you measure what they’re willing to stake on.

Let’s say Nike wants to test two creative concepts. Create attention markets for both, seed with modest budgets, and watch which one the crowd thinks dominates the conversation. It’s faster and cheaper than focus groups. Coca-Cola can test its slogans “Taste the Feeling” against “Open Happiness”. Let the market decide on the allocation of the $100M media budget.

Each front-end speaks a different language: dashboards for agencies, mobile interfaces for fans, candlestick charts for degens. But the underlying structure stays identical. Azuro abstracts the hard part, allowing developers to focus on the product experience.

Where This Goes

For centuries, price has been our best approximation of truth. What a crowd believes something is worth at a point in time. Prediction markets extend that truth-seeking machinery to the future. Attention markets extend it to culture itself.

In the 1990s, online brokerages unlocked retail access to equities. In the late 2010s, DeFi did the same for financial primitives. Now, Azuro’s infrastructure and Trendle’s model are doing it for information. Every feed refresh, every viral post, every creator drop becomes a data point feeding an always-on oracle of collective intent.

Azuro faces several challenges that it’ll need to work on. One compromised Data Provider can threaten the protocol. Azuro will need to ensure that it takes inputs from multiple sources and evaluates whether any of these sources have been compromised. Traditional bookmakers have insurance and balance sheets to absorb shocks. Azuro’s pool is only as deep as LP deposits. A black swan could trigger exit runs and liquidity death spirals.

Attention markets add new attack surfaces. Coordinated campaigns across Twitter, Reddit, and YouTube could manipulate outcomes during low-liquidity windows, especially for niche topics where verification is thin. The protocol weights multiple sources to prevent single-platform manipulation, but a dedicated fan base could theoretically spike attention across enough platforms to influence the resolution.

Attention markets sit upstream of all three: equities, derivatives, and token markets. They capture the variable that moves them when it matters. Will the world talk about a thing more this week than last? Trendle makes that bet expressible. Azuro makes it scalable, neutral, and always on.

If you’re building consumer apps, Azuro’s permissionless model means you can launch a betting or attention-trading interface in weeks, not years. There’s no need to bootstrap liquidity or negotiate with odds providers. Build the best user experience for your market and integrate it with existing infrastructure.

If you’re a trader, attention markets add a new edge that traditional finance can’t offer. You already track narrative momentum informally; this lets you express those views with leverage and defined risk.

If you’re an LP, you’re betting on whether people will continue to wager on the future. That’s been true for 2,500 years. It’ll be true for the next 2,500.

If culture starts in the feed, price discovery should start there too.

Streaming Pluribus,
Sumanth

Disclosure: This piece was part of a sponsored collaboration.

In 2023, we published an article exploring a vision for a new internet. In the years since, AI-generated spam has taken over feeds, and social networks have become insufferable due the excessive amount of performative theatrics. The human mind seeks connection and depth in what it consumes.

Curation is the pathway to fixing the internet. In the age of abundance, taste and curation are what help pick and choose what should command our limited attention. Blockchains, as payrails and open social graphs, can help with this transition from algorithmic, randomised catering to our emotions to curated, user-owned ones. This is the basis for my article today.

In my previous story, I explored the role embedded markets will play in defining the web. Today, we will explore how curation & community will contribute to it.

Sahil from Karma3 Labs was kind enough to offer notes, observations and a great amount of product insights. His team has been building OpenRank, an open ranking and reputation protocol. They power social feeds on Farcaster, provide token recommendations for wallets like Rainbow, and have recently launched Cura, a Reddit-like app for crypto communities. Why are they focusing on ranking? Because it is the foundational layer for all kinds of curation.

Cura is building the most cypherpunk vision of what a social network could look like. It allows users to customise, confirm, and verify where content originates from on social feeds like those on Farcaster. It also gives tooling for communities to incentivise the right members. Underlying Cura is OpenRank - a verifiable reputation system that combines web2 and web3 native forms of reputation.

Bitcoin lets you own your money. We are seeing the dawn of protocols and tools that let you own and incentivise your attention. Not using black box algorithms, but open, verifiable, and customisable algorithms.

OpenRank is one of our portfolio companies. The piece below is a summation of months of iterating on Web3 social networks and the primitives they will need to scale. If you are building along these lines, drop us a note at venture@decentralised.co. We’d love to chat.

Special thanks to Joe Gerber from IDEO CoLab for helping me think through the arguments here.

It’s February 2022. The weather in Dubai was all too pleasant, but my body couldn’t make use of it. My brain was on the verge of becoming pickled - or having a diabetic coma, given the amount of sugar in my body at the time. Normal sugar ranges tend to be around 100mg/dl; mine was averaging close to 400. If it weren’t for the timely recommendation to use a glucose monitor, my life would have turned out different.

Amidst the rush to move to a new country, build a life, maintain a career, find friends, set up a home and everything else that 20-somethings do, I didn’t realise I’d have to deal with being pre-diabetic. I was fortunate enough to get the best medical care, and my sugars are under control now.

Part of what helped was finding a community. Deep in the alleys of Reddit, I read about lovers wondering what to do for their partners who were diabetic. I read stories of parents wondering how a life will pan out if their child is diabetic. I also kept track of stories of sugar ranges trending to normal.

Reddit was human, curated and contextual.

I did not have to deal with the judgment of being diabetic at 27. No worried parents. No stern doctors. Just humans sharing stories with other humans. Most of them - anonymous. Faces I never knew, somehow delivered boxes of hope. Years later, I realise how valuable that was for me.

In fact, the markets recognise this too. The Dow Jones Internex Index is up 42% since Reddit’s IPO in March 2024. In contrast, Reddit is up close to 500%. Part of the reason is the fact that Reddit is the sixth most googled word. Consumers routinely add “+reddit” to their search queries. Millennials and Gen-Z alike flock to Reddit for reasons ranging from wondering if they were a jerk to seeking fashion advice. There’s even one on retiring early.

Why would a social network with mostly anonymous handles, no creator monetisation, broken sub-Reddits and overly opinionated individuals be the last bastion of hope for the web? Our understanding is that it does a better job at curation. This year, it sued Perplexity for using its data and generated close to $35M from data licensing deals alone. How does this extend to the web?

The web evolved onto what it is today due to algorithmic optimisations that enabled the development of social networks and the consumer Internet. If platforms could not surface content that was relevant, entertaining and useful for users, many of us would have stayed offline reading books. However, that has now gone to the extreme.

Curation offers an alternative. Why do we need it at all? To understand, we should know how the web monetises itself today and how the plumbing behind it came together.

Bidding Context

Why did Google become a verb, instead of AOL, MSNBC, Yahoo or AskJeeves? We clearly don’t use Bing for anything. Part of the reason is the algorithm used to decide page ranking on Google search. You can read the original paper here (from 1998) or refer to these slides. Put simply, Google began ranking pages that received a high amount of human traffic instead of maintaining a directory. It also started prioritising pages that had links to other prominent pages. It was practical, accurate and useful, much like ChatGPT or Claude today.

It was a whole new way of interacting with the world’s catalogue of information, but it needed a monetisation model. The web was racing to figure out how to do ads better than its traditional counterparts.

In 2007, Google acquired the ad-tech company DoubleClick for $3.1 billion. This was in addition to the acquisition of YouTube in 2006 for $1.65billion. One was a lovely creation that captures cultural icons, such as this video of how Charlie bit a finger. The other is a sophisticated exchange. What were the guys at Google doing? Keep in mind, this was also the crew that shipped Gmail, acquired Android, developed Google Maps and owned roughly 70% of US search traffic.

They were building context. To understand Google, you need to know how the financial plumbing under the behemoth works. Think of DoubleClick as the equivalent of Nasdaq for human attention. As of 2017, it was the largest advertising exchange.

When a user visits a website (like WSJ), it needs to quickly figure out three things quickly:

1. The best ad to serve.

2. The highest price it can derive for said ad.

3. The inventory for both advertisers and attention.

Contrary to popular belief, the auction for running an ad often happens on Google itself. There are multiple participants in the auction. Ninety per cent of all auctions are cleared through Google. The reason? They possess context most that others lack. It is knowing the exact stop losses or taking profits of an idle order in an exchange. This conflict, between gathering information on users and being the auction house where ads are cleared, is at the heart of what makes Google a monopoly.

Google has that context because it mines data from users. It knows about the videos you watch, the places you visit, the search queries you run, the emails you send and even the battery levels of your phone. It knows the meetings you skipped, the crush you’ve been hoping to take out on a date and even the fact that you searched for flower shops last week. Imagine a stockbroker targeting you with specific stock suggestions based on your purchase history and travel decisions. Google does that, with your time and attention.

In fact, the average person’s data is bid on 747 times each day. Google transmits location and browsing data of Europeans and Americans about 178 trillion times each year. By Google’s own admission, there are close to 4,700 companies that access your personal data via them.

Now imagine the said stockbroker also had life insurance, loans, and credit cards to sell to you. Google routinely prioritises its own products over its peers when serving ads. Google’s context trifecta has three components:

1. Being the central hub from where you peer at the Internet (search),

2. Being the exchange house that decides the ads you see (AdX), and

3. Being a behemoth with a multitude of products that build context on you (Nest, Android, YouTube).

Talk about a toxic relationship.

This paper by Dina Srinivasan provides a thorough breakdown of Google’s positioning in the advertising market. Google had a good run up until 2020 using these models. The digital ad economy market is approaching $1trillion in spending today. However, Google is capturing a decreasing share of it. In 2018, it was responsible for 60% of digital ad spends; as of 2025, that figure is below 50%.

In fact, in the US, Google is no longer the most visited website in the US. That title goes to TikTok, which commands only about 4% of digital ad spend.

In other words, websites that command much of the attention on the Internet do not make much money. The ones that do hold the financial plumbing are seeing a decline in attention.

What even is going on?

There are three broad factors:

1. Targeting became significantly more difficult with Apple’s update which began requiring consent from users to be tracked.

2. Human-generated content is declining. There is more AI-generated content on the web than there are human-generated bits.

3. Search itself has undergone a period of disruption owing to consumer preferences now being formed in LLMs (which in turn are trained on Reddit data). This is partly why, for both Alphabet and Meta, a huge chunk of their OpEx for 2025 is their investments in AI.

This is why Reddit matters. It gives individual users the ability to curate through upvote buttons. The platform itself is facing challenges from bots plaguing the platform. However, within its niche, with its strange hierarchy of anonymous handles, upvotes, and a lack of algorithmic newsfeed (to the extent that other social networks have), it has created a parallel web. One that runs on contribution and curation.

In 2006, the Nielsen group published an observation that for most communities, 90% of users are simply lurkers. 9% of users contribute from time to time, and 1% do the bulk of the contribution. The same blog post has other intriguing figures.

Back when the Internet had 1 billion users, only about 1.6 million blog posts were created. Or roughly 0.1% of users were posters. Wikipedia’s top 0.003% of users were responsible for 2/3rds of the site’s edits. It also notes that close to 167k reviews on Amazon were made by just the top one hundred reviewers. A single individual contributed 200k photos to Google Maps and gathered 11 billion views in the process.

When you look at these two patterns, you quickly realise something.

1. The web is undergoing a period of unbundling —one where large data-mines struggle to be as accurate as they could be. Reddit is the largest experiment in creating niche communities and having humans decide which contributors’ content is worth paying attention to.
   

2. The emergence of curated, niche communities is a real thing. We do not know what the business model for them could look like. Web3 native primitives may power them. There have been a few attempts to do this in the past.

I can almost hear some readers snickering and saying, ‘This is the part where he says curators should be rewarded in tokens and that blockchains fix everything!’ Perhaps the argument is that a prediction market can help determine who is the best curator, ultimately leading to better communities.

Or wait, I think every click should be tokenised and traded on Solana, instead of Google’s adX. We could call it internet click markets?

But calm down. I think it helps to understand where we are with Web3 native models of curation and how social networks within it are evolving.

Context Machines for Digital Assets

Every time a new is token released, teams scramble to identify the right people to give those tokens to. Remember how I said the web was built on identifying users and products that need one another? The mechanism for finding the right users has not yet fully matured within Web3. Part of the reason is that, unlike Google, the context required to find and serve the right pieces of content has not yet evolved.

Token generation events are wealth formation events. There is the proverbial “big boom” where a large amount of capital chases a few tokens at the launch of a new network. Due to the same reason, there is heavy demand and competition for it. Most tokens see their ATH in two weeks since launch. Who you give tokens to or allow purchases of tokens to be made from determines who contributes and aids in the evolution of a token economy.

Take MegaETH’s token sale, for example. It was ~28 times oversubscribed. How do you decide who gets the allocation? MegaETH asked users to link their social profiles and wallets with on-chain history to curate a list of token holders who they believe are best aligned with the project.

Every serious project that wants to sell limited tokens and an upper cap on valuation would want to do that. This is similar to every project maintaining its own data indexer instead of using Dune. Open curation infrastructure will play a massive role in crypto’s capital formation.

It is practically king-making in the digital asset era.

This economy may not seem large from the outside, but consider this report shared by Dragonfly with the SEC. It is a fascinating read that explores how much the US citizenry has lost due to geoblocked airdrops. The figure that caught my attention is that approximately $26 billion in airdrops was distributed between 2021 and 2023. Conservatively, roughly $5 billion of value is distributed annually to users from protocols.

If one accounts for NFTs, token-purchase allocations and grants, the number is likely closer to $10 billion per year or approximately 1% of the digital ad economy online today. Can it grow 100 times from here? At which point will it be as big as the digital advertisement space today? I think so.

As assets go on-chain, stablecoin economies expand, and changing regulations around digital assets draw more founders to build within the ecosystem, we will likely see more ad spending within the ecosystem. However, we still don’t have the tooling needed to find users and content that are net beneficial. Before you argue that I’m suggesting moon math, consider that both stablecoin supply and prediction market volumes have evolved a hundred times in the last five years. So, we have seen it. These things are possible.

A handful of startups are currently targeting this problem. They are effectively the forerunners of the curation business. They dance on three verticals: context, transaction, and content. They shed light on what happens in our digital economies in real-time without human interaction, today.

Kaito scrapes through the entirety of X feed for Twitter-related content and ranks individuals on it. They weigh individuals based on reach but also consider the number of other high-value accounts that follow them. They call this metric a “smart follower”. This metric follows a logic similar to that of PageRank.

Large protocols need help finding creators who speak and raise awareness about them. Kaito maps these creators and gives them in a pre-curated fashion. In this regard, Kaito functions essentially like Google’s adX. It maps out creators (publishers), ties protocols (advertisers) and gives them verifiable metrics of reach. Instead of having its own distribution engine (like Google’s search), the avenue it uses is X.

More recently, they tied transaction revenue to its own avenue by having launchpads. Eligible users on Kaito get to invest more at lower valuations. Kaito built an economy around the nature of the content we consume on crypto-twitter. In curating content, they also enabled a parallel economy.

Nansen does a similar job with their AI product, which was launched in 2021 and focuses on labeling wallets for activity. In 2025, they pivoted heavily to an AI-centric focus, with the bulk of it being delivered through a mobile app. You can currently open their app and request tokens within a specific market-cap that has experienced a high degree of smart money flows.

This switch comes with a pricing collapse. From $1k for their premium product to $49. This retailification of their product partly occurred because AI can generate context from transaction data and provide it to users in real-time. Their pricing is probably not where the bulk of their revenue will be generated. Nansen has nearly $2 billion staked with it from close to 400k users.

Soon, users will be able to ask a query and purchase tokens directly from Nansen. The labels and AI integrations will still be integral to the business, but its revenue will come from the volume of assets traded. Users come for the AI, pay for the transaction. Again, Nansen is mimicking Google because its core value proposition for attracting users is the labels it holds on millions of wallets. Wallet addresses in this regard are like Google Maps. Nansen also receives a cut of the transaction that occurs when a customer walks into a location.

While Nansen focuses on transactional flows, AskSurf focuses on the qualitative side of crypto. Think of it as deep research for crypto. You could ask niche questions, such as APYs on a DeFi pool, the timing of an airdrop, or the projects with the highest revenue, and AskSurf would have the answers for you. It gathers highly niche, contextual information from the crypto industry and makes it readily available to the marginal user.

As crypto expands, it effectively becomes the context layer for individuals to understand what is going on. Why does this matter? A large exchange could integrate it with their product and have users ask about a token before buying it. It makes the process of doing due diligence on a token incredibly easier.

We have the necessary tooling to find creators, label wallets, and create context. However, they each exist in isolation. Humans don’t really live lives that are entirely detached from others. Part of what made Facebook powerful in the early 2010s was that you could overlay your personal, professional, and niche interests in a single product. These graphs, existing in isolation, restrict what crypto can be.

This matters when you consider that niche assets, such as concert tickets, product rewards, foreign currencies, and pieces of art, are all being tokenised. Motif, for instance, allows users to create custom, niche portfolios of investment products that are non-speculative using blockchain rails and AI.

Finance is coming on-chain. It is also safe to imply that much of where our attention goes — be it politics, sports, pop culture, or technology —has an underlying financial market around it.

Content is the substrate that brings these two worlds together.

Curation is what will make it safer for individuals to interact with these micro-markets, as everything steadily becomes a market.

Curation is the glue that ties together context, transaction data and content from creators in the digital realm. This happened in Web2 already. As buying gadgets transitioned from a store-first experience to a digital one, creators like Linus Tech Tips, Marques Brownlee, and Dave2D made it easier for buyers to know what they were getting into. Creators like Packy (NotBoring) and Ben Thomson (Stratechery) curate what is happening in technology and redirect attention there.

In Web2, attention markets (such as X feeds or Facebook) and transaction engines (like adX) remained isolated. Creators rarely received a significant portion of the transactions they powered. In Web3, capital markets (like Hyperliquid) directly share transaction revenue with users, but they struggle to have the scale of attention a traditional social network has.

Web3 social networks, like Farcaster, can bring together these three elements: a feed for context, transaction revenue, and a multitude of digital assets. Curation by individuals with both taste and knowledge is what will power that transition.

Curators Are Our Last Resort

The counterbalance for algorithmic content on the Internet has been individuals sharing good content. Perhaps, there’s something innately human with the act. We send reels to friends. Create playlists for lovers. Sharing reading material with colleagues. In the digital age, this is how we maintain bonds. We find ways to say, ‘This made me think of you.’

Tools like Sublime allow individuals to create lists that can be shared. It also surfaces similar content. Think of it as Pocket, but for 2025 (RIP). Communities that concentrate around chat groups have become hubs for curation. They combine reputation, context and content - without the tools to monetise any of it.

WhatsApp has steadily increased the size of a group from 32 to 256 over the years, as it see this transition. For such groups to scale, we will need three things.

1. A shared unit of value. This can be tied to social reputation as is often the case, or an actual on-chain asset that can be traded.

2. A social graph that is user-owned.

3. The ability to surface related content based on (1) and (2).

You see primitive versions of platforms trying to incentivise contributors across the web. Reddit has badges for long-term users who contribute directly, but it does not translate to actual capital. Twitter currently does allow creators to benefit commercially from their contributions.

On average, a user receives between $1 and $2 per million impressions. Last year, it distributed $45 million to its creators. It has close to 15k users on it. In comparison, YouTube paid close to $70 billion over the last three years to approximately 3 million channels. Surely, social networks have found a way of giving value back to users, but the fields creators sow - continue to be owned by third parties.

One of the projects doing interesting work in this regard is REP. They empower Telegram users to tie their platform reputation to on-chain activity. Therefore, a user who is active in a prominent chat (such as LobsterDAO) and has a history of contributing early to DeFi protocols could be identified and incentivised by them. They map out social graphs of users by having them invite friends.

The image below, shared by the team, illustrates how individual user profiles are linked to one another on Telegram.

Much like Google uses PageRank, REP utilizes proximity to other users and their combined activity to assess the legitimacy of an individual. They utilise Telegram Gifts, each of which requires manual purchase via the app store to verify if an account is genuine. Such systems of verifiability, identification, or value transfer do not exist in conventional Web 2.0 social networks today.

For products or protocols that are newly launching, REP offers a powerful mechanism for identifying which users are real.

How does this feed into reputation? Communities need verifiable mechanisms to confirm a user to scale. In the age of bots spamming feeds, reputation systems like REP’s enable the identification of potential members and the granting of access. On the web, you want systems to be open access. The possibility of anyone, from any corner in the world, interacting with a community is what makes it interesting in the first place.

However, without effective mechanisms to weed out bad actors or onboard the right ones, much of our communities will remain booted. This is where tools like the one enabled by REP step in. They combine reputation systems from both Web 2.0 and Web 3.0.

The fact is that, despite all the talk about community in Web3, the tools to verify, confirm, and incentivise community members have not matured. We barely know what algorithms are used to create Kaito dashboards or how Nansen ranks its wallets. The data is publicly available and accessible to anyone who runs an indexer. But the algorithms are black boxes.

Part of what the ecosystem will need to mature is verifiable credentialing systems that work in the context of social networks.

Tying Together Context, Content & Transactions

If you open Farcaster’s landing page, you’d see a transition that has been in the works for a while. Their copy makes their mission explicit: to discover new people, projects, and ideas in the crypto space. They are now focused on combining a wallet and a social network into a single interface.

Underlying this transition is the idea that crypto-natives love the financialisaton of everything. We enjoy content, but we enjoy trading even more.

Farcaster is blending these two elements into a single interface. Earlier this month, Farcaster acquired Clanker—a tool for launching new tokens and managing liquidity. Tokens launched with Clanker pass on 40% of the token’s trading pool transaction fees to its creators.

What we are seeing on Farcaster is the slow, steady financialisaton of our social feeds. Unlike X or Meta, the product is not the primary focus of the user’s attention. The product is the user’s wallet. It’s the transactions that make the social network functional. However, as we saw last year, simply launching a token does not automatically create a sustainable community.

To understand communities, it may be helpful to explore how religion or sports bring people together. Both the catholic church and a football league (or soccer as some call it) are similar in that they offer members three core parts.

1. A sense of belonging through proximity to one another in shared spaces.

2. A shared language fuelled by culture.

3. Curation - of hierarchy, events and shared spaces.

Communities struggle to sustain in crypto as profit incentives fuel the culture. These incentives often last too short to create any sense of belonging. Since the formation and death of these communities usually occur in short spurts, there is neither shared space, lore, nor content that emerges from them. What we have is a frenzy to make as much money as possible, with numbers fluctuating up and down.

This is a good juncture in crypto’s evolution for us to pause and reflect. In culture, crypto and capital, I argued that crypto is now crossing the chasm and becoming relevant for the marginal person on the web. We are witnessing this transition in real-time as prediction markets encroach on both sports and politics.

How do we retain, sustain and empower these communities to stay? Web3 has cracked the financial rails well and is at the cusp of breaking into the mainstream. As it does, we can empower individuals to confirm and verify how content is served. Why should we do it? Because in its absence, we will have the conventional loop of capital incentives spiking attention, struggling to retain and leading to churn.

People tend not to return to places where they consistently lose money. There’s a reason why casinos are a once-a-year visit, while your local cafe is frequented much more often. Crypto’s obsession with hyper-speculation puts off people who could use these tools.

What is a fix?

There are three components.

1. Firstly, all communities run on hierarchies of influence. You need to find whose spheres of influence have the most impact. Said users also need to be incentivised to keep coming back and contribute. Leaderboards are an effective way of doing it. Token-based communities could offer tokens as compensation for sharing information and educating individuals.

   
   A big part of fixing hierarchy is having openly verifiable ranking systems. All human communities eventually tend towards a hierarchy. Tribes have elders, kingdoms have kings. Corporations have CEOs. The reasoning behind the choice of the individuals at the helm is usually known. For social networks, platforms decide the hierarchy without a mechanism for understanding how and why an individual holds the weight it does. This is partly why open-ranking matters. It brings a sense of fairness to the great online game.
   

2. Secondly, you need the ability to curate. This can happen from the ground up. Individuals can bring in content from any corner of the web and share it with others. On X, until recently, sharing a link was punished. It is a strange form of platform censorship. On Web3 native primitives, such behaviour can be incentivised instead. Individuals who discover, curate and share the best link can be given micro-tips.
   
   Ranking allows individuals with higher ranks to be given more weight in their views. An individual who proactively contributes to and maintains a community is often more valuable than someone who constantly posts spam.
   

3. Thirdly, users who rank high on hierarchy (direct posters) and curation (third-party content) should be routinely incentivised. Instead of Zora’s philosophy of coining all content, communities should have a single token that can be released over a period of time to their highest contributors.

In fact, Reddit explored this in 2020 but eventually shelved it as it pursued an IPO. Limiting the emission of a community token, while expanding the amount of attention a community gets, is the only way a healthy token-linked community can form.

Why would people even come to such communities? What does crypto have to offer other than tokens? Part of what gives me hope is the ability to verify source, distribution and validation of content. It also allows you to bring together users, traders and contributors with incentives that can be distributed based on verifiable reputation systems.

Web2 social networks routinely fall victim to fake news. Sadly enough, the long-term effects of that are the death of democracy and genocide. In the age of AI misinformation, blockchain rails enable mechanisms to identify the source of news, the individuals who distributed it, and the reason it appeared in your feed. This is not a hypothetical, distant vision. It exists here today on Farcaster.

On a community feed powered by Cura, you could not just confirm why a creator is valued higher. You could see the algorithms used to determine it and tweak how content surfaces on your feed. Instead of having an ad network determine what surfaces to the end user, Cura allows users to tweak what surfaces in their feed. Instead of “hoping” Google or Meta targets the right user, Cura uses on-chain activity and contributions to communities as a measure of who is incentivised. This is a rewiring of how the web itself monetises today.

On X, the owner (Elon Musk) holds the right to boost his own posts. These are black-box algorithms with no visibility that define how social networks run. Primitives like those being built by Cura allow for an alternative where the data that goes into ranking and the algorithms are verifiable. Additionally, users can customise how content appears on their feed.

A community of Football fans may want content that is specific to the game in which their own team is playing. Or a hyper-local community may want news that is only specific to that region. Surely these changes are done by algorithms on their own today. What happens to knowing why content emerged? Or verifying why it is there in the first place.

Why does that matter? We are seeing an evolution of how the web will monetise itself in the years to come. Instead of paying Google to mine consumer data and target them through black boxes, brands will have the mechanism to incentivise power users directly. Imagine you could visit Dubai’s sub-Reddit, find the users who talk about coffee the most, and give them free coupons to grab a cup of coffee.

Or if the top contributors to a fashion sub-Reddit were identified, such that labels gave them a discount to try the product.

Currently, platforms own that data. As social graphs and transaction data emerge on-chain, primitives used to find, distribute value, and incentivise users will become the new Google AdX.

Will this change in incentives change how social networks work today?

Quite possibly. I think it will enable smaller, niche communities to form, coordinate and govern themselves. Present-day communities begin to matter to brands only when they reach a specific size. The tooling to create niche communities (like cafe enthusiasts in Dubai) does not exist, because how will they be monetised? Combining on-chain micropayments (through behaviours like tipping) with verifiable social graphs is an alternative to the status quo.

Communities will need to aspire to be the hub for brands to drop goodies and communicate with individuals. Currently, that happens through creators who engage in hyperbole or platforms that own the data. Retaining a community’s attention for more extended periods is how this transition could occur. Curation is the wedge that makes that transition possible.

The influencer economy is a precursor to the curator economy. We already trust individuals and their taste. Curation allows influence, to be a multi-player game that is focused on specific niches. It turns cults of personality into collaborative effort.

A 1000 True Curators

In 2008, Kevin Kelly wrote an essay on finding 1,000 true fans, setting the stage for what we now consider the creator economy today. The dream was to find a thousand fans who cared enough to pay a few dollars, so that individual creators could sustain a livelihood. And yet, almost two decades later, we could not be farther from the truth.

Most creators never reach a thousand people paying them on the web. Scroll through Substack’s feeds and you’d find hundreds of writers lamenting about how the power laws on the internet work against them. Blockchain payrails make it easier to distribute payments, and with innovations like x402, we will probably see more creators getting paid. However, perhaps the solution is not better payment models. The fix is better communities.

True privilege as a creator on the web today is the ability to take a break. Most creators understand that producing high-quality content at a high frequency is a one-way street to burnout. This is why writers like Packy and Ben Thomson now have multiple associated brands. The core distribution is used to gather eyeballs, while other creators align themselves behind these established names. Effectively, these brands are also micro-communities where the creators are taste-makers who operate in a top-down manner.

They pick and choose what goes on a mailing list.

What if a community decided that in an open space, like a social network? You’d see individual creators having the rights to take breaks and plug off, while still having the ability to return to a curated, niche group of people who care. Instead of engaging in hyperbole, the creators would have the time to think and share good bits of art and thought that push humanity forward, rather than engaging in attention games.

In such a world, the creator goes from being the person who toils on the fields of algorithmic feeds to a landed owner. One where their work can be amplified by the community and also remixed by high-level contributors. A community’s GDP would ideally be proportional to the level of activity among its members. This may seem like a distant concept, but $11AM is already tinkering with such an approach to blending content and capital markets.

Substack is the only platform where writers can do this today, as their relationship with the end consumer is owned by the creator through mailing lists. We will see a version of this with on-chain primitives. The exciting part is that taste makers and curators will be able to incentivise and reward users directly for participation.

This is not a distant vision. LobsterDAO holders routinely get given airdrops. Most recently, they were rewarded by Monad. Users who contributed early on continue to be rewarded, as early participation is seen as a credible signal. Social spaces are increasingly closer to what we consider museums.

Early contributors are the tastemakers who set the tone, culture and nature of content that is shared within it. This model of balancing curation, creation and community is how the web would rewire itself from being a machine for outrage.

In 2023, I proposed a vision for the new internet. Part of my thinking was that blockchain rails would upend how value flows in social networks. I may have been a bit too early - but here’s what we have today.

1. You can verify, confirm and independently audit why a piece of content came on your feed.

2. You can examine a rank board and understand why individual creators are valued more highly than others.

3. You can tweak algorithms to surface different types of content.

Is this vision overly optimistic? It probably is. Cura is one of many social experiments being built on Farcaster. The user base there is smaller than that of a sub-Reddit. Many of these promises were also made in 2022, during the NFT boom.

The difference is that the underlying networks have since evolved, so low-cost payments are a possibility. The culture has also evolved, so instead of selling monkey pictures for $10k, we are now talking about paying $1 for curation. The fatigue caused by social media has risen and that has people flocking to social networks like Substack. Blend these elements, and you will realise that we are in just the right spot for a new social network to emerge.

Much like the meals served back home, perhaps the secret sauce to a better information diet is a bit of curation and people who make sharing it worth it.

Going to meet the Dalai Lama!
Joel John

Art by A. Lozano and Soham

The previous episode with Andrew Hill was about how Recall is building a PageRank for AI models to sort the slop. In this episode, I delve into the evolution of gaming business models and explore how Web3 fits into that narrative with Justin Waldron, founder of Zynga.

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Every industry around creativity shares an underlying truth. When you change the economics and the incentives, you change the output. A new way of distributing music gave rise to different genres. YouTube gave us a different form of content altogether. When Justin Waldron helped pioneer the freemium model at Zynga, he revealed that nearly everyone is a gamer.

Three billion people play games each year. Games became a distribution rails on the internet. Business models shifted to attract more people. We transitioned from console games that required upfront purchases to freemium titles, which allow anyone to start playing, with only a small percentage of users paying. At Zynga, Justin rode the first wave of social games that treated Facebook as the actual platform. Later, with Playco, he developed games for instant formats, where the link itself served as the store.

Under the hood, these shifts rewired who controls discovery, who pays for attention and who captures the upside. Today, developers spend tens of billions of dollars annually on user acquisition, with most of this expenditure going to large ad platforms. The next question is whether that same budget can be routed directly to players and communities instead.

After making games free to play and creating instant game formats with the Open Game Protocol, Justin wants to pay players to show up, stay, and return. It is doing so by trying to turn tokens and wallets into a new distribution surface, without turning every game into a financial product. OG Protocol is avoiding the pitfalls of the Axie Infinity era by building better experiences and not extensively relying on creating perfect token sinks.

I understand why the next business model change in gaming might come from paying players rather than platforms. But the market has clearly told us that play-to-earn doesn’t work. Perhaps it was because we were obsessed with creating closed economies within these games. Who knows, slightly more open economies where one game is not tied to one token may end up working. That’s what OG Protocol is betting on.

Learning to make markets in DeFi,
Saurabh Deshpande

It’s been a while since we released an episode. We were busy revamping the aesthetics of the podcast. In the previous episode with Luca, we explored how Luca Netz turned Pudgy Penguins from a dying NFT collection into a $500M consumer brand by moving beyond digital assets into physical products and licensing. If you are building at this intersection, we would love to help. Please write to us at venture@decentralised.co.

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I spent weeks writing an 8,000-word article on AI model discovery and how Recall is solving the challenge. By the time I hit publish, there were more agents, the x402 protocol, and Google’s new agent payments protocol. I was already behind. The feeling of being unable to keep up with the industry haunts me from time to time. Of late, it was at its peak when so-called devs were vibecoding agents with promises as big as curing cancer.

Andrew Hill, CEO of Recall, saw this coming about eighteen months ago. He had spent years building data infrastructure products, first at Textile and then through a merger with 3Box. He was already ahead of the curve. With backing from Union Square Ventures, Multicoin, and Coinbase, Recall is solving the problem of plenty for AI models and agents. It will rank models based on their performance in different skills. For example, if you want a trading agent or one good at compliance, Recall will provide agents ranked in their respective categories. Pretty much in the same spirit as Google solved the problem for webpages way back when I was a toddler in the late 1990s.

The premise is simple. Make agents compete in the real world, doing the actual tasks they claim to be good at. A Stanford paper found that just like us humans, LLMs game benchmarks. So, designing competitions in a way that makes gaming benchmarks almost impossible is critical for the quality of rankings. The nature of the competition will vary depending on the skill they are competing for. Currently, Recall Rank shows the top agents in categories like perp trading, JavaScript coding, ethics, and communication.

Complex tasks will need multiple agents that excel at different skills. Recall is building a coordination layer that will fetch the best combination of agents to finish your task. The bet is that AI agents will explode in numbers and most will be useless. Someone needs to build the infrastructure to separate signal from noise before we drown in empty promises.

AI slop is already unbearable. Wait till there are thousands more.

Tune it to understand how we are building filters.

Signing off,
Saurabh Deshpande

I hope life is treating you well. And if it is not, I hope you find the strength to endure until the tides turn, because the laws of nature dictate that, with time, tides do turn.

Long-time readers of this publication may know that I am addicted to two things: the attention economy and markets. I’ve flirted with these themes in abundance here in the past. In Volatility as a Service, I argued that products offering volatility will hyperscale.
In Money Moves, I explained that crypto’s core promise is in moving money faster online.
In Culture, Capital and Crypto, I suggested that culture is a lever for taking crypto mainstream.

Today’s piece brings together these three ideas to lay a case for prediction markets. My argument is that the web is becoming boring. Creators serve algorithms and, in the process, destroy culture and capital formation. Prediction markets, as a primitive, offer an alternative. They are a mechanism to separate the art from the commercialisation of it. Stablecoins and agentic economies will fuel this shift.

Some housekeeping before we begin

1. If you enjoy reading this piece, please tag our brand handle here and let us know what you think. Our X account has been getting loads of love lately, and I’d like to keep that streak going. Share the article with your friends if you reading it. Be a menace. Cause chaos.
   
   Also join our Telegram community here.
   

2. If you have something mean to say, tag me instead. As always, if you are building on these primitives or thinking about how the web is evolving, drop us a note at venture@decentralised.co
   

3. Speaking of venture .. Sid will be in New York next week. He is due to meet several of our portfolio companies and meet some of our partners. We are scoping out hedge funds, exchanges and foundations we’d collaborate with going into 2026. If you are any of them, drop me a note as a response to this email and I’ll set you up.

Alright, let’s go.
Joel

Something is dying in my living room, and it stinks.
Its stench powers through the rug we try to hide it beneath, probably because the blood seeps through.

We call it the Internet.

The web used to work because the pool of attention within it has been ever-expanding. Between 2010 and 2025, nearly 2 billion people came online. However, that figure is now stagnating.

Founders who have built, scaled, and sold companies in Web2 were tapping into an ever-expanding resource. That of human attention. With each marginal person coming online and spending a third of their day looking at their bright screens, we had more of the same resources to mine for data. Gather enough data, and you can sell to individuals. The Internet is equal parts a machine for generating desire and for enabling commerce. Its cogwheels enable dissent, as much as the network powers collaboration.

Capture small slices of large enough transactions, and you have a trillion-dollar economy that runs on attention. This is the basis for the surveillance economy that exists today.

Where does the crisis emerge from? What is killing it?
There are three core parallel forces at play.

1. The constant consumption of content has created a marketplace with an abundant supply of attention but a lack of demand. Not all attention is worth the same. Is anyone famous at all when everyone has fifteen minutes of fame? What if those fifteen minutes were compressed into ten seconds? What use is attention if it does not drive intention? What if everyone is an anxious mess?
   

2. The expansion of AI-generated content means the human experience that once fuelled the creation of bits of work becomes redundant. Artists once tapped into pain, torment, hope, and desire. When you compress that into a few prompts, you create junk for the human mind. We may be trending towards peak obesity. The writing is on the wall. Meta released a social network primarily focused on AI-generated content, and OpenAI is also working on one.
   
   The generation that came after the rise of Meta has lower literacy rates and higher rates of depression. If you think millennials have it better, consider that they live in an age with the highest number of single 40-year-olds. (Maybe, that’s a feature, not a bug - but I’ll leave that discussion for another time).
   

3. A younger generation of net-izens is logging out of the web entirely. As the New York Times put it in this June essay, thinking is the new luxury good. Being constantly on the web is the new smoking. Logging in is not the same thing as being locked in.
   

In other words, between an abundance of supply (of content), decline (of attention), and a mental health epidemic, the web is undergoing a shift. Everybody is an influencer today. There needs to be filters, a content calendar, and an aesthetic that appeals to the audience. No performance is worth watching if everyone is performative. That means we will see a new web emerge. One that slowly unplugs from the zeitgeist and recreates a new form of expression.

These shifts in the attention economy are not new. Print media took power from the scribes and priests of ages past and gave it to the writer. That medium, in turn, was consumed as the Internet empowered individuals to build an audience from home—a privilege once held by newspapers. Niche, isolated blogs were eaten alive by large social networks that condensed networks of people. Remember WordPress? How about Tumblr?

Now, as we trend towards peak social media, we may be seeing a resurgence of long-form written content as people grow tired of constant entertainment.

The hedonic treadmill of human attention keeps us moving—and it appears we have walked our way to new shores.

Today’s piece explores how it may arrive.
The basis for my thesis is three core points.

1. Advertisements powered the web because we did not have mechanisms to pay for when the Internet came along. Most individuals did not have debit cards or bank accounts in the early 2000s. Businesses relied on capturing eyeballs, as wallets were not yet online.

2. Much of the “value” on the internet emerges from transactions. As our transaction systems evolve—owing to stablecoins and primitives like single-sign-on wallets (e.g., Privy or Para) —we will see more transaction-enabled business models emerge in consumer-facing segments like social networks.

3. There will be a new crop of businesses that are built on this shift from the attention economy to the transaction economy.

Knowingly or unknowingly, those working in Web3 social networks and payment systems have built the Lego blocks that could breathe new life back into the internet. This piece is my attempt at putting them together.

Weapons of Mass Distraction

Apple’s scan-and-pay and Amazon’s one-click checkout buttons tap into the same psyche—that of human impulse. When making a payment becomes frictionless, we think little of the effort needed to earn that money.
Humans tend to spend more when we do not pay in physical cash. Did the switch from physical, handwritten notes to text messaging have a similar impact? Tapping away an emoji simply does not compare to finding the words needed to capture an emotion.

Our monkey brains have had less than four centuries of physical cash to interact with. In agrarian societies, value transfer often involved labour. You do not go around offering milk to everyone in the village if you must protect the cow and ensure it is well fed. You do it for friendly neighbours as an act of love.

We live in a world that is separate from the labour required to generate value. It is a beautiful thing because I do not want to go back up the hills my grandparents once toiled in. It is also a psychological blind spot that one should be aware of. The easier a transaction is, the less we understand the risks & variables involved. This is the reason why so many in crypto lose their money to leverage.

The lack of friction in taking absurd amounts of risk is equal parts a bug and a feature.

In the early 2000s, platforms had very few mechanisms for tracing attention to the transaction it produced. If I saw an advertisement for an iPod on the web in the 2000s, and asked my dad to buy it for me on his way back from UK, the platform showing me the advertisement would have no way of tracking that transaction. (India had strict restrictions on gadget imports, and this is how I got my first iPod nano like many teenagers at the time)

This division between content consumption and transaction traceability was a key issue as the web evolved. Ad-attributions helped fix that by the early 2010s. This podcast episode, which we did with Antonio Martinez, explains how it came to be. I recommend reading his book Chaos Monkeys for context on attribution systems.

Cookies have enabled tracking of transactions across platforms since at least the early 2010s. You could see a post on Facebook, have an ad from Google, and make a purchase on Amazon - and attribution systems that track how that purchase happened have evolved sufficiently to track a user’s journey. Advertisers knew that the trick to making a purchase happen is to repeatedly show a user similar content to generate desire, and that is why the web reinforces our views.

We tend to see the same things repeatedly once the algorithm detects preferences.

Platforms like Twitch took this one step further by allowing users to buy sticker packs and tip directly. In the process, we created a creator economy where a creator’s ability to hold attention became the product.

The creator economy, in its current form, is socialism without the benefits.
The audience enjoys the content, the platform owns the audience, and the creator must keep producing to stay relevant. The serfs serve the algorithms in the modern day.

As a generation finds itself stuck in bullshit jobs, more people are turning to digital platforms to find gigs and sustain themselves, and that is a beautiful thing. More people should be able to tap into their own creativity to find meaning, community, and a livelihood. However, the underlying infrastructure may not be able to sustain it. Creators are incentivised to tap into the algorithm for reach. One risks replacing the torment of living in a cubicle with the pain of serving an algorithm.

It’s like we try to take food fresh out the frying pan into the fire.

The web needs a better way to monetise itself. What we see with experiments in creator economies, prediction markets, and whatever the kind folks at Zora are trying—are early primitives that aim to address these issues. Can an individual make a living with a small subset of audience without catering to the incentives of a platform or competing with other creators?

We saw an early instance of what this could look like with crypto debit cards and wallets. Over the past month, we saw Phantom, Rainbow, Kast, and Based integrate Hyperliquid’s perpetuals into their products. Unbeknownst to the world, a slight shift had occurred. Instead of powering products through ads, these wallets were now monetising themselves through transaction fees on a hyper-financialised product.

Both Kast and Based are debit card issuers—why would they integrate perpetuals? They did it because that is how you increase a user’s lifetime value (LTV). Consider the numbers below. Both Based and Phantom make six figures in revenue from less than thousand active users on their Hyperliquid integration

The integration of perpetuals into these wallets is a sign of what is to come. It is the emergence of markets in our feeds. We are witnessing the real-time compression of the time it takes for attention to become a transaction. Don’t believe me?

Consider oracle by $noice. It allows users to buy an asset with a single click. In fact, the button does not even need to be on a third-party platform. You simply like a tweet with a cash tag, and the agent automatically buys it for you. The transaction happens with a single click, and the cost is less than $0.01. It is a primitive version of what the future of social networks could look like. There is much to be said about the product, but here’s what stands out.

It allows a person to transfer value to a third party through an interaction we are very accustomed to: a Twitter-like one. No signing transactions, no wallet refreshes, no bridging. Just the click of a button. Why is this unique?

Farcaster Frames are an incredible primitive, but they require a separate social graph, which is still in its infancy. Platforms like Twitter already have the users. Enabling transactions via an embedded API lets you tap into the Twitter social graph without requiring users to leave Twitter.

Twitter understands the shift that is coming its way quite well. In June, there was a move to embed prediction markets within it. It has been months since, and X has not enabled it, but prediction markets have taken off on their own. Consider the two charts below. As late as April 2024, weekly volume on prediction markets was around $20 million. As of October 2025, that figure is close to $2 billion—each week! These markets see over 6 million transactions each week, so the average bet size is around $300.

Of some $950 million in bets placed on Kalshi, $877 million is for sports. One-third of all betting volume on Polymarket comes from the same category. Compared to the 1,030 users Polymarket had in April of 2025, the product commanded close to 150k users last week. In other words, we are seeing a rise in on-chain embedded markets that draw on culture.

Markets are pricing this transition efficiently. Facebook had close to 360 million users when it raised $200 million at a $10 billion valuation. Inflation-adjusted, that figure would be $15 billion today. Contrast that with Polymarket being valued $9 billion in a recent round led by the owners of the New York Stock Exchange.

This contrast captures how the web is evolving from one of attention to that of transactions.

Gaming economies have already seen this shift. They serve the same purpose as social networks do. Firstly, they offer a meaningful distraction. Secondly, they create a sense of community. Thirdly, they sell digital goods. Gaming economies like the one in Roblox are large-scale transaction products packaged as entertainment goods. Roblox sees approximately 111 million DAUs. Collectively, they spend ±27 billion human hours in those economies.

Why? Because, unlike meme coins, these markets have a sense of fairness to them, and the rate of value destruction is not as rapid. A meme coin has a community only as long as the numbers trend upwards. Gaming economies, in contrast, have lindy effects and sustain far longer.

Prediction markets are a new primitive with both elements. Large whales, or insiders, may not necessarily have an edge without undermining the platform’s integrity. Users usually know the range of possible outcomes. So, we are at this juncture where products (with relative distribution) try to embed these primitives to increase the lifetime value of individual users while reducing their own acquisition costs.

Crypto’s current rush to integrate perpetuals, prediction markets, on-chain stocks & cultural goods (like sports markets or Pokemon cards) is the industry’s expansion to the mainstream. How does this affect our brethren toiling in the digital farms? The serfs I addressed as creators a while back?

One way creator economies will evolve is by empowering creators to have their own markets. Do you think President Trump will visit China next month? A creator could embed a polymarket into the piece of content and earn from individuals trading on their analysis. The challenge is that when the medium of expression requires a transaction to be relevant, we will shift the nature of the conversations we have. There will be “influencers” whose value will be determined by their ability to create a market. What happens to art? Who speaks for stories?

What is the value of a wall of text that keeps someone from engaging in self-harm?
Do we really need a prediction market to value it?

I don’t know the answer to that yet, but it helps to have a view of how the market landscape itself is evolving.

How Much for Truth?

Human existence is an ongoing endeavour to find the truth. Philosophy asks what the point of life is. Biology seeks to understand how life happens. Maths asks how long this show lasts. Poetry and the arts keep us entertained while life happens. The truth is subjective, and, for all intents and purposes, markets are among the most potent tools (apart from science) to help us reach agreement on what the truth is. Blockchain rails, being money rails, mean we can now price what the truth is at a global scale.

Do you think Donald Trump will tweet covfefe again? You can bet on it. In fact, primitives like Liquid allow you to spin up a chat and bet with friends on anything. You could bet on whether a person would lose weight, find the love of their life, or finish a pending draft, so long as you have an oracle to verify the truth. The exact mechanics of how it works are not of interest (for today), but what we have are primitives for two things.

1. The ability to spin up a market for anything

2. For the value to be settled and distributed across the world.

Collectively, they allow opinions to be priced, traded, and settled globally. This is like when blogs allowed thought to be transmuted across the globe. Except, instead of isolated opinions, we have markets.

How then would the market evolve? This is akin to asking what the broad spectrum of blogs or individual pages will be. If money becomes a medium of expression, then the nature of markets will cover the breadth and depth of human interests. Consider the market map above for context. Broadly, markets can be divided by the frequency with which an event is resolved and the clarity of the outcome.

Most markets have clear outcomes. We no longer argue about Donald Trump being the President of the United States. Some markets, however, can be subjective. For instance, a market on whether J Cole is better than Kendick Lamar may be resolved based on popularity. Maybe Kendrick’s fans are vocal about his lyricism and spam aggressively in any way possible.

My point is that on-chain prediction markets will evolve to either reach consensus or seek objective truth, and we need both. Most products we see in pop culture and “sentiment” gathering are fuzzy about how the outcomes will be. Markets that orient towards sports, politics, and markets will have clear outcomes.

Democracies, for instance, are consensus-seeking machines. People voting for the losing party may see nothing.

Many markets that look at fuzzy outcomes have what can be called binary markets. You either win a dollar or lose the entirety of the bet you placed. The game theory here is interesting. Market participants are not just voting on what they think is right. They are betting on what everyone else thinks is right. Effectively, it forces individuals to “buy” into a position because they have capital incentives for doing so.

My thesis is that when these incentives scale, as they often do, the nature of our conversations and interactions on the Internet will shift from a focus on virality to one that enables transactions.

Platforms like Manifold and Zeitgeist use user social activity, such as posts under a market, to determine whether it should be trending. Popularity contests, on their own, are not interesting to us. However, they can find the truth in ways most other primitives have not been able to. Prediction markets like Polymarket have consistently beaten polling as a mechanism to surface the odds of a possibility like Donald Trump winning a swing state. In fact, while researching for this piece, I came across a paper by the United States Military exploring the use of prediction markets to assess security threats.

This is where this transition gets interesting. We tend to think crypto is all about speculation. However, that is akin to believing the Internet is all about pornography. By some estimates, it was the case. In the early 1990s, much of the Internet’s bandwidth and revenue came from adult content. However, market forces and capital incentives structured the attention economy that powers Meta, Alphabet, and a myriad of other internet monopolies in the decade that followed.

Crypto struggles to remain relevant to the average internet user. People do not wake up thinking about the TPS of an L2 or how the validators of a network went down with AWS. They do care about sports, music, and politics. Embedded prediction markets blur the line between social attention and on-chain transactions for the average individual. This is why prediction markets matter as a category for the industry.

All Content. No Contentment.

Embedded markets are one of the business models the Internet may embrace as a primitive to power content of all forms. In a hypothetical future, all exchanges (like Coinbase or Nasdaq) will be content platforms, and all content platforms will, in turn, be exchanges. Retail speculation in meme assets and derivatives is a symbol of late-stage internet. People gather on platforms like Reddit, X, and Telegram partly because of the capital incentives at play.

If that is indeed the world we are trending towards, then the lines will blur between financial platforms and social networks. The lines between our wallets and minds will blur. Coinbase’s recent acquisition of UpOnly and Echo is a predecessor to this switch. The owners of the NYSE probably understand that the alternative to Google’s ad marketplace is a prediction market.

Creating as a human in the age of AI slop will require incentives.
Embedded markets are one way of doing it.

As of today, prediction markets are trying to be both the product and the distribution. They build the venue, then spend the rest of their energy dragging attention back to it—screenshotted odds on X, quote-tweets from power users, Discord bots piping prices into chats. It works in bursts because a live market is inherently shareable. Discovery happens off-platform, intent dies in the tab hop, and the product starts optimising for virality (“markets that screenshot well”) instead of truth (tight rules, fast settlement).

Embedding decouples those roles. The market stays the product; existing media becomes the distribution. Prediction markets improve the two things media struggles with most: credibility and usefulness. They turn a claim into an object—a live probability with rules, sources, and a resolution clock. Instead of hedged paragraphs and selective quotes, a story carries a number that updates as facts land and settles when reality arrives. That shift makes coverage falsifiable in public. Readers aren’t just consuming takes; they watch beliefs priced and resolved with receipts.

Embedding is how this upgrade reaches people where attention already lives. Odds under a headline remove the tab-hop that kills intent. A policy piece with a belief sparkline attached turns “he said, she said” into “here’s where the price moved and why.”

Newsrooms get a credibility layer and a new line item: even modest conversion outperforms another ad slot because value is anchored to action, not impressions. If two out of every hundred readers place a single $15 trade, a 2% fee yields roughly $6 per thousand views, beating CPMs and far more aligned with the story. Creators trade outrage for calibration; a visible track record becomes a balance sheet they can carry across posts, and rev-share on embedded flow beats chasing the next viral rant.

This is why Polymarket is working on Embedding toolkits - making it easy to reference odds on any social media platform. Substack and X both support embeds, allowing writers to augment their reporting with odds. Writers like Matt Levine reference odds in their stories, browsers like Perplexity display odds as search results, and news portals like Axios use Polymarket charts to support their stories.

Reporting Live … From an LLM

So far, we have explored how humans will rally around events to trade them. Markets sustain only when they trend towards efficiency. When buyers or sellers engage in a transaction, they are either predicting the future probability of a price rise or a price dip. How will embedded markets become more efficient if nobody is market-making them? Sophisticated hedge funds do not have enough liquidity in these markets today to bother with them.

Currently, agents do a good job at aggregating information. Internally, we use Kaito and AskSurf to find relevant bits of media for our own consumption. Large news outlets have simultaneously integrated AI into their products to make content easier to consume or to aggregate information more easily. For instance, a good way to review our own writing is to use ChatGPT and ask it a query like “show me all Decentralised.co’s writing on revenue”. What if you could use agents to trade?

Our thinking is that agentic systems will play a key role in providing liquidity and conducting trades as prediction markets come of age. Currently, bots scan YouTube transcriptions of key events, such as Jerome Powell’s speeches, to take positions. In the future, we will see a mix of real-time media, prediction markets, and agents collaborating to surface more accurate information.

In fact, there are many ways agents can interact with prediction markets.

1. Human judgment - Political analysts and sports bettors read through magazines like Bloomberg and watch matches to get an intuition of what’s going to happen in the future. The key here is to stay highly informed, watch insightful commentators to sharpen your own knowledge, and apply it in PMs.

2. Hybrid Judgment - Analysts using AI to gather information, double-check their probabilities, and come here. Users can just talk to their preferred LLM. Some teams are developing specialised co-pilots that can help users make the right calls. On Billybets, you can select how much bankroll you have and what type of strategy you prefer. Billybets will make the bets for you depending on your choice.

3. Autonomous agents - Self-optimising AI systems that compete to generate the best signals. Networks like Bittensor and Olas have birthed subnets—Score, Zeus, Mantis —each specialising in a specific domain: sports, weather, macro. They work like evolutionary quant desks: thousands of models predicting outcomes, reweighted every time reality proves them right or wrong. Webuildscore uses sports video and statistics to train models that predict outcomes faster than bookmakers.

4. Aggregation Layer - Use AI to spot mispriced opportunities across different prediction markets. Agents can instantly send liquidity to mispriced markets. If a sports bet on Kalshi is priced at ManUnited 60% while every other prediction market is at 40%, the AI agent places a bet against ManU on Kalshi.

5. Agentic Prediction Markets -These are markets about markets, where autonomous agents themselves become the subject of prediction. In Fraction AI, agents trade with $100k virtual bankrolls, and humans bet on which agent will outperform. The market becomes a scoreboard for intelligence.

In fact, there is a live market where agents bet on one another’s performance, and you can see it here. It is an interesting look at how crypto-native primitives interact with AI agents and the market. What if an individual wanted to customise an agent that traded on their behalf? What if someone believed in chaos and wanted to short every market related to a particular politician? Or wanted to go optimistically long on a musician of their preference? You need tooling that consumes information and takes a position on your behalf, often with limited capital.

Talus is working on this last section of Agentic Prediction Markets. Their Nexus platform enables anyone to quickly spin up AI agents on-chain. According to them, AI agents will be used across prediction markets because they combine two traits that agents thrive on - information density and price sensitivity. It’s a high-frequency information environment where edges appear and vanish in minutes. Human forecasters simply can’t monitor thousands of events across politics, sports, and macro data simultaneously.

AI agents, by contrast, can parse hundreds of feeds, extract latent correlations, and react the moment a signal shifts. You can read about how it works in the Nexus documentation here.

Agents can provide both context and liquidity when pricing an event. This turns the bickering and fighting on social media platforms into measurable odds. It also reduces the amount of data needed to form an opinion to a single number. Whether this is the best way to make a decision remains to be seen, but if the agentic web does take off, prediction markets will play a key role.

The Social Paradox

I’ve been reading the Social Paradox by William Hippel over the past few weeks. It is an interesting read on how humans need to balance autonomy with reliance on one another. Leave a person alone too long, and they may (quite literally) wither away and die. Force them into crowds, and they may lose their sense of self. Most of balancing healthy relationships is respecting this need for connection and autonomy.

A version of this extends to our information appetite, too. We’d love to know what our friends are up to or share the random bits of art we created. That tendency is what got us on the web in the first place. Being bombarded with information we did not ask for leads to mental exhaustion. Modern workplaces require us to be plugged in and constantly consume information. So, most of us are probably never going to fully log off. In fact, some books argue that we are at the end of the human experience. All of us are mere zombies plugged into networks of information running an economy we did not sign up for.

Are prediction markets the fix? Not really. Prediction markets, and specifically embedded ones, are a gradual evolution of the web’s economy. Between agents, stablecoins, and composable markets, we are trending toward a world where all bits of information can be priced in for reliance. This should mean we trend towards a society where fake news declines and hyperbole does not rise. Creators will have to create fewer bits of content, and individuals will be able to have the weight of capital markets behind the views they consume. It is a step forward.

At the same time, most things worth consuming may never be monetised. Commerce has the beautiful ability to amplify something and make it worse at the same time. Most works of human art worth consuming were not created with mass-market distribution in mind. They were created to escape or to express. Sometimes, both. Would Shakespeare’s work be better off with a prediction market for what happens to Romeo and Juliet? I don’t know. Maybe Romeo would have a view on it.

As exciting as prediction markets are, there will be pockets of our attention that resist commercialisation. We don’t run ads in handwritten notes written for a loved one for instance.

In the words of a philosopher that I’ve come to like lately - freedom is often found in the space between stimulus and response. Not all bits of media will inspire a transaction. Not all forms of art require pricing. You can already see this diversion on the Internet. On Twitter, people optimise for viral hooks. On Substack, people speak their mind because they already have a relationship with the end user through mailing lists. Close-knit communities are the resistance to the rapid, relentless march of the modern-day attention-economy behemoths.

How will this dynamic evolve as prediction markets embed in media?
I don’t know, but for now, here’s what’s evident. The primitives for the next step in the attention economy are here. We are witnessing the birth of the transaction economy.

Embedding (in) Markets,
Joel

P.S. - Huge thanks to Joe from Anagram for pulling a lot of strings to ensure this publication had all it needed to create this story. He was kind enough to put us in touch with the CEOs of the four largest DATs for research. You should tune into his views on markets here. His brain is one I steal a lot of ideas from for our articles.

Thank you to James Zhang (Strategic Advisor to Sharps Technology) and Brian Rudick (Chief Strategy Advisor at Upexi) for sharing their views.

A business is an engine that takes in money and produces income that beats what simply keeping it at the bank does. Hopefully, the business makes a multiple of 4%, which justifies the risk of investing capital into it. In the early 2000s, some internet stocks traded at a 600 price-to-earnings ratio. It would take six centuries for the earnings to reflect what the price implied, assuming all variables stayed the same. That is the “optimism” premium.

Such premiums can be architected. DATs are one way of doing it. Here’s how it works:

Assume that you want to create a Solana treasury company. You have a company worth $100, and you own 100% of it. You take that $100 and buy Solana. Now you own $100 worth of Solana DAT company.

So far, so good. Now the interesting part. Investors notice that similar Solana treasury companies in the market are trading at a 2x multiple to their net asset value, or the amount of assets they hold. That multiple is the “optimism premium”. So now, your company is worth $200, even though you still only have $100 worth of Solana. You still own 100 shares, but each share is now worth $2. You own 100% of a $200 company.

Now comes the accretive issuance. You want to raise another $100 to buy more Solana. Because you’re trading at 2x NAV, you only need to issue 50 new shares at $2 each to raise $100. You now own 100 shares out of 150 total, which means you’ve been diluted to two-thirds ownership. With that new $100, you buy more Solana, and that $100 worth of Solana also takes on the 2x valuation, adding another $200 to the company’s market value.

The company is now worth $400. Your two-thirds equals $267 in value. Your investment went from $100 to $267, a 167% increase, purely from one accretive equity issuance. Your ownership was diluted from 100% to 67%, but the value of your stake increased because the new capital came in at a premium multiple.

This value creation comes from intelligent capital issuance at premium multiples. The company issues shares above book value, uses the proceeds to buy more of the underlying asset, and as long as the premium multiple holds, every issuance increases the digital asset per share for existing holders.

In October 2024, Strategy (fka MicroStrategy) set a three-year goal to raise $42 billion to buy more bitcoin. $21 billion from equity and $21 billion from fixed-income securities. Before I get into how DATs raise capital, let’s understand their role in financial markets.

What purpose do DATs serve?

The most important function of DAT companies is to unlock different ways of accessing crypto markets. It comes in two primary forms.

• Access to terms/deals that individual investors can’t put together. Upexi can negotiate a 15% discount on locked SOL. Saylor can raise money at a valuation higher than the current value of BTC the company holds, and increase the value per BTC.

• Access to crypto markets in brokerage accounts of traditional finance investors through different versions, like convertible notes that allow bond market participants to get crypto exposure. They hold crypto assets on their balance sheets and then act as wrappers for digital assets for equity market investors. These fundraising mechanisms and wrappers have created a new category of investment vehicle that enables investors to gain exposure to crypto assets in a different way.

Although buying crypto assets has never been easier, participants, such as pension funds, have restrictions on where they can deploy their assets. This is particularly important because, by the end of 2024, pension funds in the US alone were expected to control more than $43 trillion. With ETFs and DATs, digital assets are now viable investments for this massive pool of capital that was otherwise stranded. With funding mechanisms like convertible notes, DATs offer strategic optionality along with diversification.

Some DATs actively participate in blockchain networks by staking tokens as network validators or delegating their stake to existing validators. This improves the network’s security. Forward Industries recently announced the launch of its institutional-grade validator on Solana, powered by DoubleZero.

So far, we have seen five different capital-raising methods adopted by DATs. Four of them are used by Strategy.

An at-the-market (ATM) equity offering is the most flexible capital-raising mechanism for DATs. It allows incremental share sales into the market over extended periods, while avoiding large block offerings that might depress stock prices. Companies file shelf registration statements with governing bodies like the SEC and sell shares continuously as market conditions permit, adjusting timing and volume based on the premium to NAV calculations.

Strategy has a rulebook based on a ratio of market capitalisation of the stock to market value of bitcoin holdings, called mNAV (market-to-net-asset-value).

The second major financing vehicle is the convertible notes. They offer debt securities convertible into equity at predetermined prices and dates. Remarkably, many Bitcoin treasury companies issue near-zero-interest convertible notes despite benchmark rates near 5%, enabled by extreme stock price volatility, which creates profitable arbitrage opportunities for sophisticated investors.

What made Strategy convertible bonds work well so far

MicroStrategy has issued $8.2 billion in outstanding convertible notes with an average coupon of just 0.421%, despite the company holding a B minus credit rating from Standard & Poor’s. This rating was withdrawn in December 2024. Had Strategy borrowed pure debt, it would have had to pay 6 to 8% interest due to its B-minus credit rating. When MicroStrategy issued non-convertible bonds in June 2021, they paid interest of about 6.125%. The convertible structure enables the company to pay a significantly lower interest rate, making borrowing more feasible.

Strategy benefits from this, given that it has to pay much lower interest on the convertible debt. But there’s obviously the counterparty to this debt. Who is subscribing to these notes and why? Let’s break down the mechanics of these bond sales.

In November 2024, Strategy issued a note. Here’s how the note was structured.

• The price of the issue was $433.8. The coupon was zero.

• The conversion price was at a 55% premium at $672.40.

• The notes mature in 2029. Strategy aimed for $1.75 billion. Investor demand took it to $2.6 billion with a $0.4 billion option for initial purchasers.

• The company can redeem the notes and pay investors cash after 4 December 2026 if the share price has been at least 130% of the conversion price for a set period. That is roughly $874.

• On 1 June 2028, holders can require the company to repurchase their notes for cash at par ($433.8) on that date. Think of this as an early exit. It shortens the actual duration for investors and creates a date the company must plan for. If markets are open and the stock is healthy, the company can refinance or call around this date.

The note is a call option with the conversion price serving as the strike price and a five-year expiry. The feature ensures that if the underlying doesn’t go above the strike, you get your money back.

During this time, my question was who is buying this worth $3 billion and why. My naive assumption was that bond traders get a nice call option here. It’s like a call option for them with a guaranteed premium back. So they either make more than 55% in five years or get a full refund. So, it’s a good deal. This is only the tip of the iceberg.

The main reason why hedge funds were piling on to this was the volatility of MSTR. As of October 2, MSTR has an implied volatility of 57% versus bitcoin’s 41%. Higher volatility implies that options are more valuable. So, they are not waiting five years for the note to mature. Instead, they are harvesting the volatility opportunistically.

Let’s take a little detour before I share how this works. There are two things we must know about options. Delta and gamma. Delta measures how much the option’s value moves when the share price moves by one dollar. Let’s say the delta for a call option priced at $1 is 0.5. When the underlying increases by $1, the options price will increase by 50% of this move, i.e., by $0.5. The new price will be $1.5.

Gamma measures how fast delta itself changes as the price moves. A high gamma means delta jumps quickly when the share ticks up or down. That jump is the engine that creates the hedge trades. For those who remember their physics class, if the underlying is distance, delta is velocity, and gamma is acceleration.

Now, let’s understand how the trade works. When you hold the note, you are long the embedded call. The share starts at $100, and the option delta is about 0.5, so you short 50 shares. The share rises to $110 and delta lifts toward 0.6, so you short ten more shares at $110. The share falls back to $100, and delta drops toward 0.5 again, so you buy those ten back at $100.

You have sold high and bought back lower. That round trip is a realised cash gain. The bond embedded with the call option gained value on the way up and gave it back on the way down. This is fine because the profits you book came from the short side or the hedge you employed. You want as many such round trips as possible to keep adding profits.

This is why you want high volatility. Volatility is the product in disguise here. Since MSTR trades at a volatility even higher than bitcoin, this trade works. You see every convertible note getting subscribed. If the volatility of the stock drops, there may not be takers for these notes. However, since bitcoin is leveraged, as long as it is volatile, MSTR will likely remain volatile too.

The third way of raising capital is preferred stock offerings. It emerged in 2025 as companies diversified their capital structures to areas beyond common equity and convertible notes. Strategy launched multiple preferred share classes. An example of this is the March 2025 “Perpetual Strike Preferred Stock“ with a $21 billion programme size, offering 8% fixed perpetual dividends convertible into Class A common stock. The idea here is to minimise common stock dilution while maintaining the ability to raise capital. It also provides yield-seeking investors with alternative instruments.

The fourth way is by issuing senior secured debt. Companies tend to pay higher interest rates as per their credit ratings, since this debt doesn’t convert to equity. This debt often gets refinanced when cheaper sources of capital are available. Strategy did this with notes. Strategy redeemed $500 million of 6.125% senior secured notes, due in 2028, during September 2024, using zero-coupon convertible proceeds to eliminate high-interest costs.

Lastly, during 2024-25, companies raised capital from direct equity raises and PIPE (Private Investment in Public Equity) transactions. These transactions typically involve strategic investors acquiring large stakes through private placements, often concurrent with SPAC (Special Purpose Acquisition Company) mergers or strategic pivots.

Forward Industries is an example of this type of capital raise. It was the largest Solana treasury PIPE transaction in September 2025, raising $1.65 billion specifically for SOL accumulation. This single financing created the largest corporate Solana holder with 6.822 million SOL, valued at approximately $1.6 billion. It validates that institutions are embracing the public wrapper for the digital assets thesis. Galaxy Digital, Jump Crypto, and Multicoin Capital led the investment. All SOL controlled by the entity are staked. The proceeds from the yield will be used to compound treasury positions over time. The company benefits from backers’ operational expertise in validator operations and staking optimisation, beyond just providing financial capital.

These funding mechanisms are only a part of the story. It is also important to understand where a wrapper solves a local problem. Rules and taxes can make a listed balance sheet the best way to own a token in a given market. Japan and Hong Kong are the clearest examples of that.

Japan is a tax wedge turned into a product. Listed equity gains for individuals are taxed at roughly 20%. Since 2024, the new NISA also makes equity gains and dividends tax exempt inside the wrapper up to ¥3.6 million a year, with a ¥18 million lifetime allowance that recycles when you sell. Personal crypto gains are treated as miscellaneous income and can reach about 55% at the top combined rate. A listed company that holds bitcoin turns that gap into a simple share that sits in the equity tax lane. Metaplanet leaned into this.

Hong Kong is about plumbing. In 2021, there were no spot crypto funds. Meitu bought crypto directly and disclosed the numbers. Through March and April 2021, it acquired about 31,000 ether and about 941 bitcoin, funded from cash. That turned crypto exposure into a listed share that local investors could buy and settle like any other equity. Hong Kong opened the fund rail in 2024 and allowed spot bitcoin and ether ETFs, including an in-kind model for creations and redemptions. Meitu unwound the treasury in November and December 2024, selling roughly 31,000 ether and 940 bitcoin for about $180 million, booking a profit of approximately $80 million.

DAT Landscape

Estimates of the total assets held by all the DATs vary greatly because tracking hundreds of companies and their asset purchases is difficult. The Block quotes the value of assets held by DATs at $155 billion versus Blockwork’s $113 billion. Bitcoin is by far the most dominant asset, comprising more than 85% of the aggregated NAV.

The combined liabilities of all DATs are ~$73 billion, which is about half of their combined NAV, according to The Block. Tom Lee, who led Bitmine Immersion Tech, is the second largest DAT, having raised more than $10 billion via ATM issuances in the last three months.

SharpLink holds over 800,000 ETH worth over $3.3 billion, making it the second-largest institutional ETH treasury. It has received SEC approval to sell up to $6 billion in new shares through ATM sales and initially raised $425 million through a private placement led by ConsenSys in June 2025.

The performance of stocks after they announced DATs clearly suggests that there was a significant first-mover advantage. As the number of DATs increased, incremental new announcements didn’t move the needle for stocks.

Not all DATs are created equal. The asset they hold matters far beyond just the potential for price appreciation. Three distinct value accrual mechanisms separate sophisticated DATs from simple wrappers.

The first mechanism is capital issuance. This is what we covered earlier with the example of how you created increased original investors’ value from $100 to $267. When you issue equity above book value, you create value for existing shareholders.

The second mechanism is staking. Bitcoin DATs hold an inert asset. It sits there. Ethereum DATs can stake their holdings and earn roughly 2.5% to 3% annually. This turns the treasury into a productive asset. Solana DATs can stake and earn 7% to 9%. That difference compounds. A treasury earning 7% on its underlying asset is fundamentally more valuable than one earning zero. Of course, the volatility of the asset matters.

The third mechanism applies only to pre-mined assets like Solana. As mentioned earlier, these DATs can buy locked tokens at a discount to spot prices. When those tokens unlock over time and move to par, that discount translates into gains for shareholders. Put this locked token strategy together with staking, and you roughly double the effective yield. A Solana DAT earning 7% staking yield plus capturing 15% discounts on locked purchases is generating returns that a Bitcoin DAT simply cannot match.

What can go wrong

Leverage is a critical factor that will determine who survives the 60 to 70% drawdown. Strategy’s leverage is at 12% to NAV (this is the Debt/BTC NAV number). They have committed to never exceeding 20%. This is for Bitcoin, which is less volatile than other digital assets. If you believe Strategy has calibrated their leverage correctly for Bitcoin’s volatility profile, then a Solana DAT should run even lower leverage. Ethereum sits somewhere in between.

The type of debt matters as much as the amount. In-kind convertible notes eliminate basis risk. When Upexi issued convertible notes, the consideration was Solana. That SOL is held as collateral in a qualified custodian under a triparty agreement. If the notes do not convert to equity, Upexi simply returns the SOL. They are not on the hook for a specific USD amount.

Compare this to a USD-denominated convertible. If a company raises $80 million in a USD convertible and Solana crashes 70%, the company still owes $80 million. If they cannot refinance and the notes come due, they have to sell Solana into a depressed market. This is how forced selling cascades begin.

When Saylor started raising capital to scoop more BTC and offered an exciting trade to funds, they were quick to latch onto these raises. As I discussed earlier, Strategy provides them with two things: 1) volatility and 2) the ability to trade in size. Trader interest is what keeps MSTR trading above NAV and more volatile than bitcoin, both of which are critical for the continued success of the Strategy model. As more companies start offering similar raises, capital has more options to explore.

The newer DATs catering to assets that are further down the risk curve will have to stick to ATM equity issuances, as they may not find subscribers for convertible notes due to size and volatility constraints. Remember, traders need size and stock that is more volatile than the asset the company is trying to acquire. Their market cap can remain above NAVs only when traders are interested in them. mNAVs of most DATs have been trending lower, with many trading below 1. MSTR used to trade at an mNAV of more than 4, not too long ago. It now trades at 1.2.

What happens when they trade below NAV? It means that the market thinks the company is worth less than the assets it holds. It hampers the company’s ability to raise funds to buy more assets. Companies can take corrective measures.

First, you can convert to an open-ended fund, such as an ETF. This allows investors to redeem underlying assets at NAV, naturally eliminating the discount. It means that investors can acquire assets at a price lower than their prevailing market value. This is the route Greyscale went after the spot ETF for bitcoin was approved in 2024.

Second, the company can sell assets to repurchase its shares, ensuring they trade at parity with the assets. It also means that the company’s asset pool continues to decrease. This reduces outstanding shares and helps reduce the gap.

Third, we may see some activist shareholders push for mergers and acquisitions. This will help increase the asset pool, thereby raising the trading volume. Higher trading volume often comes with increased interest for the merged entity. We are already seeing this play out. Vivek Ramaswamy’s Strive bought Semler for $1.3 billion. Semler was trading at a discount to its 5048 bitcoin stash.

This is how the deal terms looked —

• The transaction is valued at $1.3 billion in an all-stock deal

• Each Semler share converts into 21.05 Strive Class A common shares, valuing each Semler share at $90.52, representing a 210% premium for Semler as per the closing price on September 19.

• Strive gains 5048 BTC that Semler currently holds.

But 5048 BTC at ~$115,000 is approx $580 million. Then why is Strive paying $1.3 billion? Because it is issuing new shares to acquire a DAT.

There’s also a lot of misinformation or hidden information about DATs. For example, companies sometimes announce massive allocations to assets, but there’s no information on whether they are asset purchases, in-kind allocations from investors, or teams already holding assets. In September 2025, Flora Growth, a medicinal cannabis company, announced that it had raised $401 million to build a $0G token treasury with approximately $23 million in investment from DeFi Development Corp. The catch was that only $13.7 million was new capital. A staggering 96.5% was raised from existing token holders in kind. No new buy pressure. The $0G token’s price was set at $3 even before the token was traded. As of October 16, the token is trading at $2.

The days when the playbook like Flora worked are probably behind us. With enough options to invest in, smart money won’t bother chasing the long tail of DATs in the absence of ingenuity. There are costs to running a public company. Announcing a DAT made sense a couple of months ago when the market would immediately send the stock soaring, but not anymore. So, you’d venture into a DAT only if the benefit of announcing a DAT outweighs the cost of remaining public.

Most DATs will fail

The number one determinant of any DAT’s performance is the performance of the underlying asset. Capital structure and value accrual mechanisms matter, but they are secondary. If the asset goes to zero, the DAT goes to zero regardless of how clever the financing was.

Most altcoins do not survive five years. Go back and look at the top 50 cryptocurrencies from 2018. How many are still relevant today? How many are down 80% or 90% from their peak? Most alts get left behind as capital rotates to newer narratives. A treasury company built on a dying asset is just a slow liquidation.

This is why only a handful of assets can support long-term treasury strategies. Bitcoin obviously qualifies. It has survived multiple cycles and has the deepest liquidity. Ethereum qualifies despite questions about its roadmap. It is the settlement layer for most on-chain activity. Solana qualifies because of its speed, cost structure, and growing ecosystem. A few others may make the cut.

Even when you have the right asset, it’s not going to be easy with so much competition. Only a handful of DATs can access the complete toolkit of capital raising mechanisms. This creates a structural advantage that compounds over time. This can also be seen in the stock performance.

Strategy can issue convertible notes at near-zero interest rates because of three factors working together. First, the stock trades with higher volatility than Bitcoin itself. Second, they have liquid options markets that allow convertible arbitrage funds to delta hedge their positions. Third, they have sufficient trading volume to execute large hedges without moving the market. This combination lets them sell embedded call options to sophisticated buyers who profit from harvesting volatility.

Smaller DATs cannot replicate this. When a Solana DAT with a $500 million market cap tries to issue converts, the options market is either non-existent or too illiquid to hedge. Without the ability to hedge, convertible buyers cannot capture the value of the embedded option before maturity. The company either pays a much higher coupon or cannot access convertible financing at all. They are stuck with equity-only issuance.

This creates tiers in the DAT market. Top-tier companies like Strategy issue converts at 0% and drip equity through ATMs at premium multiples. Mid-tier DATs issue converts at 2% to 3% and rely more heavily on equity. Bottom-tier DATs can only issue equity, often at compressed multiples, because they lack the scale to attract institutional interest.

The flywheel creates a power law where the top tier keeps getting more favourable terms. Cheap capital from converts allows more accretive issuances. More issuances drive the stock price higher. Higher prices create more trading activity. More activity improves liquidity in both the stock and options. Better liquidity attracts the next round of convertible buyers, and the advantage compounds.

This scale advantage is not a permanent moat. Another DAT could reach critical mass and access the same mechanisms. Tom Lee’s Bitmine is a good example of that. Getting there, however, requires hitting thresholds for market cap, trading volume, and options liquidity. Most DATs will never reach these thresholds. They will remain trapped in equity-only financing while watching the top tier compound advantages through cheaper, more flexible capital structures.

The implication for investors is clear. The gap between top-tier and bottom-tier DATs will continue to widen. A premium asset is necessary, but not sufficient. You also need the scale to access all the avenues of capital markets. Companies that cannot issue converts at attractive rates, maintain enough trading volume, or support a liquid options market will struggle to compete with those that can. The winners will be companies that reach escape velocity early and use that advantage to pull further ahead.

Wishing everyone a happy Diwali,
Saurabh Deshpande