Author: Spencer Applebaum, General Partner and Co-Head of Venture Capital at Multicoin Capital

Translation: Gu Yu, ChainCatcher

Five years ago, building a DEX or lending protocol meant raising $3 million, hiring 5 engineers, and spending 6 months creating an MVP. Now, founders can build most of it over a weekend with Claude Code. By using a cutting-edge AI model to create a perpetual contract exchange, you can get a matching engine, risk engine, trading interface, API, and most of what you need, ready to go.

AI is making competition easier in almost every aspect of the software industry, but financial services will be one of the hardest businesses to commoditize. This is because the most valuable parts of financial businesses have little to do with the software itself. Building a loyal customer base, accumulating a large balance sheet, and developing proprietary data and underwriting capabilities all take years to build relationships, establish track records, and earn trust. AI can significantly reduce the cost of building software, but it won’t make these things particularly easy to replicate.

In other words, a model can write a lending protocol in an afternoon. What it cannot do is conjure $10 billion in stablecoin deposits into the contract, or sign up 28 million clients for a new neobrokerage.

We believe this is very positive for DeFi. Over the next decade, trillions of dollars in financial activity will move on-chain, and AI will significantly lower the cost of building products to service these activities. However, the things that give financial businesses value and defensibility have not become easier to replicate.

New Pillars of Our DeFi Value Capture Framework

We have spent years thinking about value capture in DeFi, and as the market has grown, our views have evolved. In the early days, we examined how application tokens capture value through models like work tokens and balance minting and burning (BME). Later, we focused on the defensiveness and non-forkability of DeFi. The idea is that while anyone can fork the open-source code, they cannot replicate liquidity or network effects. More recently, we argued that DeFi protocols capture the most value when there is some risk that needs to be managed. These arguments have, to some extent, been validated, and this paper is an iteration of our thinking.

Before we delve into the rest of the paper, let's clarify a fact: we believe DeFi will continue to consume CeFi and eventually absorb all traditional finance. The market share of DEXs relative to CEXs is at a historic high and continues to rise:

Source: The Block, DefiLlama

We now also see real adoption of tokenized assets, whether government bonds, private credit, or stocks. Importantly, it's not just crypto-native users driving the growth. Existing institutions, fintechs, institutional asset managers, and traditional brokerages are building products on-chain and bringing their customers along.

Source: rwa.xyz

The problem is that today's blue-chip DeFi protocols won't necessarily be the ones capturing this growth. As software and data analysis become cheaper, value will flow to those who control inputs that software cannot create scarcity around.

We see three main moats in DeFi: 1) distribution, 2) capital, and 3) proprietary data. There's a fourth, risk bearing capacity, which functions differently and is essentially a wedge, which we will discuss at the end of the document.

Moat 1 - Distribution

One of the most groundbreaking arguments in the early crypto space was " Fat Protocols," written by Joel Monegro in 2016. The idea is that while the internet created huge value at the application layer (Google, Facebook, Amazon, etc.), blockchains would invert this relationship, attributing most of the value to the underlying protocols.

Evidence points in the opposite direction, and this trend is accelerating. As the startup costs of protocols decrease, value will flow to those who have customer relationships. This business can route users and balances to the highest bidder. Sometimes that’s an application, and sometimes, like Hyperliquid, it is a chain that is also an application.

Our definition of distribution is simple: it concerns the control of the flow of order or the destination of balances. It’s important to note that not all distributions have the same stickiness. Accounts that have difficulty transferring will create real barriers, while those with just interfaces will be more vulnerable to competitive incursions. We will discuss further below.

Exchanges and Other Stablecoin Distributors

Distribution is most clearly exhibited in the stablecoin value chain. Issuance has historically been a great business, contingent on reaching scale and interest rates not being close to zero. Tether and Circle hold dollar reserves off which they earn interest, while in most cases not paying anything to the holders. Just Tether alone has earned over $20 billion in interest income at margins near 100% since 2021:

Source: DefiLlama

But take Hyperliquid for example, which has over $6 billion USDC as collateral for derivatives on its platform. These balances generate substantial reserve income for Circle and its distribution partners. At some point, Hyperliquid realized that the reason for these balances' existence was itself, and thus it held leverage: if Circle was unwilling to share economic proceeds, Hyperliquid could replace USDC with stablecoin that would share or could theoretically create its own stablecoin.

Earlier this year, Hyperliquid used its Aligned Quote Asset (AQA) framework to secure a deal with Coinbase. Under this agreement, approximately 90% of USDC reserve yields on Hyperliquid flow to the protocol’s Assistance Fund, which uses them to buy HYPE instead of going to Circle or Coinbase. The treasury holds $5.5 billion of USDC (that 90% flowing to HYPE), creating a revenue line of about $185 million for Hyperliquid today.

Hyperliquid does not maintain any of the pipelines here, whether minting/redemption, treasury management, or regulatory licensing. It has distribution, which is enough to demand more economic proceeds.

Wallets and Frontends

Wallets and frontends are the other side of this equation. In the early days, non-custodial wallets struggled for monetization. They eventually realized that the most profitable business in the crypto space was trading, and about a few years ago made it a first-class product. On trading, MetaMask charges a fee of 0.875%, while Uniswap's average trading fee over the past 12 months was 0.13%. Most of that value flows to liquidity providers (LP) rather than the Uniswap protocol itself.

In short, wallets earn far more than the venues executing trades. On the flip side, exchanges must attract LPs, maintain complex infrastructure, and compete on execution; MetaMask must win and retain customer relationships. If a DEX does not offer a good price, MetaMask can route trades elsewhere. Over time, if they capture enough order flow, they can internalize a large portion of it and retain more economic proceeds.

We also see this dynamic emerging on the perpetual contract side. Phantom allows users to trade perpetual contracts on Hyperliquid and charges an additional 5 basis points on top of Hyperliquid's 4.5 basis points fee. Hyperliquid runs risk engines, matching, and liquidity, but Phantom earns more simply by having the interface and the users.

However, there is a limit here. Among the Hyperliquid builders, a new red ocean market exists that has already started price wars, with teams reducing fees to improve retention. In other words, even the frontend layer is not immune to commoditization.

More broadly, crypto wallets are somewhat fragile because switching costs are very low. Users can easily connect their private keys or seed phrases to another wallet and access the same funds through a new interface in a matter of minutes. Switching banks or fintechs is much more complicated. Users need to open new accounts, complete KYC, transfer funds through traditional rails, reconnect payrolls, and possibly update other connected services.

Fintech and Neobank

To illustrate the fintech example, take Robinhood, which has 28 million funded accounts. Currently, Uniswap is the main DEX on Robinhood Chain, which Robinhood does not mind. But now that AI is lowering the cost of building financial software, there is nothing preventing Robinhood from developing and launching its own DEX protocol and directing users there. With their reach and cutting-edge coding tools, they could nearly build one of the largest Layer 2 DEXs in DeFi overnight. By the way, we are not saying Robinhood will definitely do this, as having liquidity has historically not been a primary goal for Robinhood (although they have begun routing a significant amount of traffic to their own prediction market Rothera instead of Kalshi). We simply point out that building a protocol is no longer the hard part. Attracting users, capital, and proprietary data is what matters.

You can see a waterfall value chain forming in DeFi. The frontend (Phantom) is capturing more economic proceeds from trading venues, while trading venues (Hyperliquid) are capturing more from stablecoin issuers (Circle). In all cases, businesses that have distribution have pricing power over their underlying infrastructure.

But how much each layer can retain depends on the ease of users leaving. Hyperliquid captures about 90% of the reserve yield, while frontends on Hyperliquid have pushed fee competition down to low single digits.

Moat 2 - Capital

The natural conclusion from the previous section is that headless protocols and backend infrastructures are at a disadvantage. Yes, this is indeed the case in some instances. But beyond distribution, there are other moats, one of the most important of which is capital.

Capital is the lifeblood of financial markets. Traders need it to leverage, market makers need it to maintain quotes, and lending protocols require deposits to issue loans. AI can help write code and automate workflows, but it cannot conjure money from thin air, which is what these systems depend on. When something goes wrong (e.g., the Aave KelpDAO incident or MakerDAO's Black Thursday), someone has to bear the losses.

Before discussing further, let’s talk about liquidity. There is a log(n) network effect: traders go where liquidity is, and liquidity follows them. Hyperliquid's open interest is a real advantage, but that capital belongs to users who can move it elsewhere. We have seen liquidity in DeFi move from one protocol to another, often driven by token incentives and vampire attacks. And when these incentives dry up, liquidity often leaves.

The following two forms of capital can truly create defensible long-term value.

DeFi Prime Brokers

Prime brokers (PBs) are key market participants in traditional financial markets. They provide leverage, finance trades, and allow hedge funds to utilize a single pool of capital across different markets and strategies instead of posting collateral at each venue separately.

This is an absolutely massive business. Total industry PB revenues have grown from about $15 billion in 2020 to about $27 billion in 2024, fueled by growth in the hedge fund industry and a plethora of asset managers relying on financing.

We believe the opportunity in DeFi could be even larger. Traditional PBs primarily serve large institutions, as dealing with smaller clients is operationally expensive. Smart contracts can automate most of this work and provide it to anyone, from someone trading $500 to a pod managing hundreds of millions in Millennium.

Today, traders typically have to post collateral separately on Hyperliquid, Aave, Lighter, Morpho, Variational, and Kamino. This is extremely capital inefficient, and as tokenized stocks, government bonds, and other RWAs move on-chain, the problem will only worsen. A fund going long on tokenized stocks at one venue and shorting perpetual contracts at another should not be required to provide full collateral for both positions if part of the risk offsets.

Building software to manage all of this is becoming increasingly easier, while the difficult part lies in having a balance sheet to finance these positions, lower capital costs, and risk management expertise to avoid liquidation. Over time, prime brokers with cheaper capital can offer better financing terms, attract more clients, and leverage this scale for cheaper financing. In summary, this business should have good scale returns.

Providing Backing for Risks of DeFi Protocols

The second way capital serves as a moat is by providing backing for risks. This ties directly to our previous article on DeFi DAOs managing risk, in which we argued that tokens associated with protocols that manage risk are more easily forked out than those that do not.

For example, look at Hyperliquid’s HLP vault, which provides liquidity through market making and taking over liquidated positions. Depositors share in the profits but also absorb losses. In March 2025, HLP lost about $4 million while closing a large ETH position, followed by the JELLYJELLY incident, further exposing it to risks. Both of these situations highlight why having an insurance fund for derivatives DEX is crucial. Aave's Umbrella serves a similar purpose, allowing users to earn by backing bad debts.

Anyone can fork the HLP or Umbrella contracts, but they cannot fork the capital behind them. The issue is that this is third-party capital; if depositors find better yields, they might leave. The counterargument is that some capital is sticky because there may be overlap between token holders and depositors. For instance, HYPE holders might retain some US dollars in HLP to make the exchange safer and protect their token investments.

The most enduring version of this strategy is a protocol’s own balance sheet, built from retained earnings. This capital won’t leave to chase newer, shinier yields or points programs.

Binance recently illustrated why this approach is important. After losing $6.3 billion in an October 29 hack, it used its SAFU reserves and company funds (mostly the latter) to cover the losses, reopened withdrawals within days, and saw only a net 3% of assets leave. It is early days yet, but so far, the interpretation is that its perceived balance sheet helped prevent a bank run.

As DeFi moves into private credit, tokenized stocks, and other RWAs, the demand for insurance and risk capital will continue to grow. The net result is that protocols that truly provide and manage capital will capture much more value than those that merely deliver software.

Moat 3 - Proprietary Data

The third moat is information, but with a caveat. DeFi is built on the idea that financial data should be public, which works well for scenarios like crypto asset collateralized lending and spot trading. However, as more RWAs go on-chain, we will see more businesses built around non-public information. This moat combines proprietary data with the ability to underwrite risks using those data.

Credit Curators

Lending is the most obvious area where this is first revealed. Curators on platforms like Morpho, Kamino, and Euler evaluate risks for depositors’ funds, currently reaching a scale of $9.5 billion:

These curators are granted limited control within treasury smart contracts to adjust capital allocation to various lending markets, set risk parameters, and monitor collateral positions. Despite collectively managing $9.5 billion, the revenue generated is quite low, especially compared to traditional asset management firms that have larger discretionary power and responsibility. In fact, many curators charge nothing at all. Steakhouse Financial is the largest DeFi risk curator by AUM, with a take rate of 22 basis points, while the average take rate for curators is 19 basis points.

Partly, this is because DeFi's risk-adjusted yields currently lack appeal (likely partly due to idle capital sitting in Aave). Partly, it’s because Morpho and Kamino have made the treasury layer homogeneous and are positioned between curators and depositors, which goes back to our argument about distribution.

But the bigger issue is that curators mostly work based on the same data. If you’re borrowing against ETH or BTC, prices, liquidation rules, and collateral are all public. AI has made replicating risk modeling easy, allowing curators to only compete on judgment (e.g., taking risks, much of which is pre-defined by algorithms) and track record. Over sufficiently long time frames, this will inevitably put pressure on fees.

Compare this with a protocol like OnRe. It’s an on-chain reinsurance company that obtains funds from investors to underwrite real-world insurance risks. To do this, it needs historical claims data, loss probabilities, and the insurance contracts themselves. Not to mention, it needs people who understand that data. None of this exists on blockchain explorers, and a cutting-edge model that cannot access the reinsurance ledger, no matter how smart, cannot underwrite it.

This is why OnRe can charge a 75 basis point management fee plus 20% of the returns. Based on current returns, this is about 3% annually, which is 16 times higher than the average DeFi risk curator.

These are different businesses with different risks, and some fee premiums reflect the non-liquidity and the possibility of loss. But that’s part of the point: businesses with proprietary data, underwriting expertise, and relationships can charge much higher fees. If you have the same data and tools as everyone else, you will likely become a homogenized commodity.

What Does All This Mean for Value Capture?

L1 and L2

One cynical interpretation is that infrastructure tokens like L1 and L2 are in a tough spot. The reason is that blockchains have, in the past, provided two of the three moats for new DeFi applications: distribution and capital.

Historically, blockchains had a distribution advantage, as DeFi users would put assets on the chain they’re most active in. If you build on Ethereum, you can reach Ethereum users. If you build on Solana, you can reach Solana users. This all matters if you're starting from scratch with Uniswap, Compound, Aave, Kamino, or MakerDAO: you have no existing customers, no assets, and no financial relationships, so launching in the ecosystem where users are already present makes it much easier.

But now, improvements in interoperability are undermining this. Today, a fomo user can complete onboarding through their app and trade any asset on any chain without even thinking about it. Moreover, an increasing number of new users will enter DeFi through fintechs like Revolut, Nubank, and Robinhood, which already have tens of millions of customers and do not require funded wallets to access.

L1 also offers capital through ecosystem wealth effects. As SOL and ETH prices rise, users have more funds to deploy to applications on these chains. After this, capital tends to stay, because using cross-chain bridges is painful, and users often exhibit tribal behaviors. However, it has become easier to use cross-chain bridges, and capital is entering through stablecoins/tokenized assets/institutions, thus reducing reliance of new protocols on any single token wealth effect. RWA issuers also want to launch on every chain, which we have already seen from Securitize, Superstate, Ondo, etc.

On the data front, there has never been much of a moat. Almost all on-chain financial information is public, so chains and applications have never owned much proprietary data to defend.

AI is also eroding another historical advantage of blockchains: their developer ecosystems. One of Ethereum's biggest advantages relative to newer chains like Solana or Aptos is its mature tools, audited libraries, and integrations. But as AI makes it easier to port applications between different VMs, the significance of this advantage is diminishing.

More broadly, general-purpose chains are losing their advantages in distribution and capital, and they never had much of a data moat to begin with. This means they primarily compete on execution and settlement, while block space is abundant and fees continue to decline.

However, this does not mean chains cannot have value. As Tushar articulated in a summit talk years ago, L1 tokens are assets of capital, commodity assets, and stores of value. If Solana eventually processes a billion payments a day, charging a penny per transaction, that’s $3.65 billion a year in fees. As shown in the chart below, transaction volumes continue to grow:

Source: Blockworks Research

The conclusion is that the reasons they have value may differ significantly from what investors originally expected.

Applications and Protocols

For applications, the businesses capturing the most value will be those that possess at least one of the three moats: achieving distribution through sticky direct customer relationships (e.g., Robinhood or Hyperliquid); achieving capital through balance sheets that can absorb losses; or achieving information through the ability to underwrite risks that others cannot. Regardless of how sophisticated the model becomes, these three are much harder to replicate than software.

We do not believe it matters whether value flows to equity or tokens; our view is that value will flow to both. Recent SEC guidance and rule proposals have outlined compliance paths for tokens to capture value, and we fully intend to invest in those opportunities. What truly matters is whether a business (token or equity) controls some form of scarcity, thus having pricing power, and whether it can convert that into returns for investors, regardless of the form of the instrument.

As more financial activities move on-chain, the gap between protocols with moats and those that only have software will widen. As one of the largest investors in this space, we spend a lot of time figuring out which projects in the tech stack have those moats and can hold them.

Doesn't This All Favor the Incumbents?

A reasonable conclusion drawn from all of the above is that incumbents will win. Robinhood has customers, Goldman Sachs likely has more capital than any DeFi PB may ever possess, and Moody's has the data and licenses. If software is becoming free, and the moats are the expensive part, then companies that already have these moats should have an advantage.

But startups still have an edge: they are willing to take risks that incumbents are unwilling to take. Part of this is because AI plays a bi-directional role here. The same set of tools that enables Robinhood to launch a DEX over a weekend also allows a three-person startup to do the same. As the cost of building credible products declines, the real opportunity for startups lies in pursuing markets and customers that incumbents are unwilling to pursue.

There are indeed several compelling case studies already emerging.

Ethena built a synthetic dollar product around delta-neutral hedging, a strategy that traditional companies clearly understand (basis trading), but they could not or didn’t want to bring it to crypto exchanges. They have now scaled this to $5.4 billion in AUM.

Pump.fun and fomo emerged, creating a whole new trading experience in a novel asset class (memecoins) and built significant distribution around this wedge. They are now two of the biggest success stories of this crypto cycle.

Hyperliquid entered a derivatives market dominated by Binance, Bybit, and OKX and quickly captured market share. It is also onboarding perpetual contracts for pre-IPO companies like OpenAI and Anthropic, which traditional exchanges would not do. These markets attract new traders and liquidity, as you can see in the wallet created on the day of the SpaceX IPO:

Source: HyperTracker

In terms of capital, Project 0 provides financing for DeFi collateral that traditional PBs are unwilling to accept as lending collateral. And in proprietary data, USD.ai can underwrite GPU-backed loans, a type of asset that banks have almost no experience with.

The commonality is that these businesses pursue markets that incumbents do not serve, whether due to regulation, operational complexity, reputational risks, or simply because opportunities seem too small. For startups, there will always be an opening to compete asymmetrically, as we have seen throughout crypto history and expect to continue to see.

This is why the combination of AI and DeFi is so compelling. AI is significantly lowering the cost of building financial products, while DeFi enables startups to reach customers and access capital without having to rebuild the entire financial stack.

Of course, risk bearing capacity itself is not a lasting moat. Regulation will catch up, and once a market is proven, incumbents will follow (see the BTC spot market). But startups do not need to have a moat on day one. They only need an opening that an incumbent is unwilling to pursue and enough time to turn that into one (or more) of the three moats, and then they can start to compound growth from there.

Conclusion

We believe that over the next decade, trillions of dollars of financial activity will move on-chain, and AI will make building products to service these activities easier than ever.

But although software is becoming commoditized, what makes financial businesses valuable is not. Customers, capital, and proprietary data remain difficult to acquire, and firms that control them will have pricing power.

This is why we are so bullish on the next generation of DeFi companies. With the democratization of AI developer tools, startups can now compete with incumbents faster and cheaper than ever before. Once a new company establishes one of these moats, it can create an extremely durable business.

Ultimately, you can fork code and generate it using the latest and most powerful AI models. But you cannot simply generate any of these moats, and that is where our opportunity as crypto investors lies.

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