Stop for a moment and ask yourself: how many truly generational onchain projects exist in crypto today? Not promising projects or popular protocols, but durable structures with compounding advantages that are likely to endure for decades. 

The honest answer is: very few. 

This is not unusual. Dynamism is the hallmark of any early technological shift. The early internet looked similar — rapid experimentation, constant churn, and many companies that achieved scale without durability. As economic activity moved from the physical world to the digital one, it took time to understand where durable advantage actually formed. We are currently going through the same growing pains in crypto.

One of the clearest frameworks for understanding how moats form is Hamilton Helmer’s 7 Powers, which defines “Power” as a durable structural advantage that allows a business to sustain excess returns, even in the face of capable competitors. When Helmer introduced 7 Powers in 2016, it was grounded in traditional software and legacy industries. Crypto was not mentioned once. 

We are long overdue for a systematic examination of how Power applies to the new medium of crypto. Against that backdrop, we can walk through the 7 Powers and see which survive, which mutate, and which collapse in a fully onchain world. Taken together, this analysis reveals a deeper pattern about how Power forms in crypto: durable advantage accrues around the mechanisms that make ownership credible and scarce in an open, online environment.

Power #1: Scale Economies

Scale economies occur when the per-unit cost declines as production volume increases. Scale economies often present themselves through the amortization of a fixed-cost resource that is expensive to procure over an increasingly large number of units sold. 

As a canonical example, Helmer cites Netflix’s shift from licensing shows and movies to making original content (famously House of Cards, which reportedly cost $100m to produce), where the upfront production costs could be spread across a large subscriber base. Each additional subscriber lowered the effective cost per viewer, enabling more aggressive pricing. It became prohibitively expensive for new entrants to replicate Netflix’s content library without first achieving comparable scale.

The key idea in this version of scale economies is that there is an expensive fixed cost that is amortized across a growing customer base. But software (aside from foundation models) is generally not prohibitively expensive to build. And in crypto, it’s even cheaper to build because projects can benefit from existing software — most code and all APIs are fully public. This means that the cost for a competitor to reproduce (or use) fully onchain software is effectively zero, erasing the moat.

However, there is a much subtler scale economy that does not rely on the amortization of fixed costs: economies of Lindyness. Early depositors who face smart contract risk, shallow liquidity, and a limited track record demand extremely high yields. Over time, as a protocol proves reliable and its AUM grows, perceived risk falls and hurdle rates decline. Lower hurdle rates lead to a lower cost of capital, which in turn makes additional liquidity cheaper to attract — a genuine scale economy. 

A great example of this is Aave. On overcollateralized lending protocols, the cost of capital is as explicit as it comes — the APY required to onboard lending supply. In their go-to-markets, lending protocols often spend millions of dollars in subsidies to meet lenders’ hurdle rates since they are a new (not-Lindy) protocol. On the other hand, Aave, which has earned economies of Lindyness for surviving multiple cycles without a major security incident causing significant loss of user funds, can maintain ~5b in USDT supply at 2.7% APY and ~4b of USDC supply at 3.34% APY on Ethereum mainnet. This means that Aave can attract deposits more cheaply than even the US government! Meanwhile, Hyperlend, a fork of Aave v3 on Hyperliquid, can only attract ~20m of USDT at 3.02% APY and ~35m of USDC at 5.16% APY. The ability of Aave to attract >100x the lending supply significantly lower APY is an economy of Lindyness. Aave has earned moat that creates room to either charge higher margins or keep margins low to push their competitors out of the market.

Power #2: Network Effects

Network effects occur when the value of a product to a customer increases with each marginal user. 

Network effects are particularly powerful in platform businesses (e.g., Amazon, Uber), where the presence of one side of the platform attracts the other side (cross-side network effects), and for social graph businesses (e.g., Facebook, LinkedIn), where one side of the market attracts others from the same side (same-side network effects). Businesses that rely on network effects establish a moat through a tipping point: a competitor’s network is not useful to most users until it acquires enough participants, typically from the incumbent. The cost of acquiring enough users to reach that tipping point is usually insurmountable.

Yet again, building onchain challenges the way Power usually functions. This is because proprietary state often underpins the utility of networks — like who your friends are on Facebook or the ability to interact with a driver on Uber. By controlling access to this proprietary state, the business establishes Power; the platform operator acts as a gatekeeper you must go through to tap into the other participants that make the platform useful. But because information is open on public blockchains, onchain platforms cannot exercise this control. 

Yet, building onchain can establish other forms of network effects that do not rely on closedness. 

The first type is liquidity network effects. In many onchain protocols, each additional unit of liquidity increases the value of the system for all participants — through tighter pricing, deeper markets, and a higher probability of successful coordination. The pricing benefit of deeper liquidity is familiar to most in crypto and represents the primary moat for many protocols (e.g., Hyperliquid), so we will not be discussing it further here. The coordination benefit is less understood, and is best illustrated by Pump’s moat. 

Pump relies on a coordination liquidity network effect in primary issuance. Investors want to commit capital to launches where other investors are already participating and where quality issuers choose to launch. Issuers, in turn, want access to that concentrated pool of capital. These launches are incredibly binary: either sufficient liquidity materializes and the launch succeeds, or it fails outright. Pump’s bonding curve explicitly mechanizes the tipping point that is characteristic of a network-effect-based moat. Tokens that fail to attract enough liquidity never “graduate” (or, “tip over”) to the AMM and are effectively treated as failed launches. Each additional unit of capital locked into the bonding curve therefore increases the likelihood of success for future launches, making Pump more useful to all users and reinforcing its liquidity-driven moat.

The second is a subtler form of network effect: decentralization. In the case of Bitcoin and other tokens whose goals are to create some form of non-sovereign money, things like fault tolerance, the immutability of state, and the immutability of the protocol matter a lot. If something can be altered or halted on a whim, it won’t be great non-sovereign money. In such cases, every additional aligned participant (miners, investors, developers) using the protocol tends to strengthen its non-sovereign claim by improving its robustness. This isn’t the case for all protocols, though. For an app chain like Hyperliquid, where the core business is the application itself rather than the neutral management of state, additional decentralization contributes little to the user experience. As a result, it does not exhibit decentralization network effects.

Power #3: Counter-Positioning 

Counter-positioning is the least intuitive of the Powers. It occurs when a new entrant adopts a business model that is economically superior (e.g., by enabling higher margins) but the incumbent refuses to copy it because doing so would undermine its core business and cause too much collateral damage (in terms of lost profits). It’s in this collateral damage that the moat for a new entrant forms.

As an example, Helmer cites Vanguard. When it entered the market, asset management was dominated by active managers like Fidelity. Vanguard instead tracked the market, cutting out active management and financial advisors to offer dramatically lower fees at comparable aggregate performance. Active manager incumbents could have copied the model, but doing so would have undermined their high-margin active businesses and fee structures, making imitation economically self-destructive. That collateral damage gave Vanguard the space to scale into a category-defining business.

The unique nature of crypto yet again changes how counter-positioning is executed. In traditional businesses, incumbents like Fidelity can justify avoiding direct competition with new entrants by pointing to execution risk, technical complexity, or uncertainty about whether the new model will work at scale. For onchain incumbents, those justifications largely disappear because the new entrant’s entire business is public — from cashflows to business logic. 

However, in another sense, counter-positioning is more available for onchain challengers. Because hacks are irreversible, the consequences of errors are significant, meaning incumbents are generally less capable of turning on a dime than traditional businesses. Thus, counter-positioning is even more available onchain because it increases the collateral damage associated with a pivot to the new entrant’s business model. So it’s difficult to say precisely how counter-positioning in crypto stacks up against other industries. 

A concrete example of counter-positioning in crypto is Morpho’s focus on isolated lending markets, in contrast to Aave’s monolithic, governance-driven design. Aave pools all collateral into a single market, which is attractive because it allows the protocol to leverage existing liquidity to bootstrap new listings, with risk management and asset selection mediated by governance. Morpho counter-positioned by offering isolated markets that give sophisticated lenders direct control over risk, sacrificing pooled liquidity. Although Aave could clearly observe Morpho’s traction openly onchain, copying the model would have entailed significant collateral damage by undermining Aave’s pooled-liquidity design and governance economics, a challenge made even harder by the smart contract risk associated with building a new protocol. That collateral damage created the window Morpho needed to sprout and then blossom.

Power #4: Switching Costs 

Switching costs occur when a customer incurs significant costs moving to an alternative supplier or application. 

Apple’s ecosystem is a perfect example: by controlling the hardware and operating system, Apple creates a walled garden in which leaving the platform entails real friction: lost data, incompatible devices, etc. Most people avoid these high costs and choose to remain Apple users, allowing Apple to charge higher prices for add-on products/services than competitors.

As we can see from the Apple example, a key component of switching costs is the entanglement that comes from building on top of a closed, proprietary platform. When the underlying platform is fully open and publicly accessible, competitors can replicate the same base layer, sharply reducing the cost of switching and eroding the moat. This dynamic makes switching costs especially difficult to sustain onchain. 

As a result, switching costs are largely eroded as a source of Power in fully onchain businesses. In a permissionless environment, users face little to no explicit lock-in: a wallet is natively interoperable across protocols. 

That said, a weak form of switching-cost Power persists in the form of operational security and smart contract risk. While capital and users can move freely, each new protocol requires diligence. Over time, proven reliability compounds. In this way, Lindyness, alongside economies of scale, creates a modest but real switching cost rooted not in technical lock-in, but in risk and trust.

Power #5: Branding

Brand Power arises when a seller can charge a premium for an objectively identical offering due to historical information about the seller rather than functional superiority. The phrase “objectively identical” is doing the work here: if a seller actually builds better products, that is not brand Power — it is simply product differentiation. Brand Power exists when customers ascribe value to who the seller is, even when the underlying product is interchangeable with competitors’.

Helmer gives the example of Tiffany’s. Tiffany’s diamonds are virtually indistinguishable from those sold by other jewelers, yet they command significantly higher prices. In this case, the product is the brand. Buyers want to say they purchased an engagement ring from Tiffany’s because it signals status and taste. Another example is Advil versus generic ibuprofen — we all know they’re identical, but many are willing to pay extra for Advil because the generic manufacturer is obscure and Advil’s brand signals trust. In each example, brand Power allows the supplier to charge a premium for what is, in substance, the same product.

As these examples illustrate, branding is a particularly important source of Power in commodity businesses (especially those that cannot rely on scale economies). Many projects onchain fall squarely into this category. Core protocol software is open and easily replicated, making it effectively a commodity. In that environment, brand becomes one of the few durable ways to capture value. We see brand Power emerge most clearly in forms of exclusivity and social signaling (e.g., CryptoPunks, BAYC), as well as in trust and safety (e.g., Uniswap vs. its forks). 

The provenance crypto affords can harden brand Power in ways that are not available in other industries. Brands must constantly fight counterfeits because if users can’t distinguish between what’s authentic and what’s an imitator, the brand is diluted. But crypto has provenance baked in, so there are no questions of authenticity. Even if any party can freely copy code, that does not copy the social signaling or trust that accumulated with the brand because, unlike handbags, it’s trivial to look up which is the authentic version onchain. As a specific example, all the copy-and-pasting in the world couldn’t convince someone with a block explorer that they have a real CryptoPunk, which permits the NFT collection to charge higher prices for a functionally identical offering.

Power #6: Cornered Resource

Cornered resources arise when a firm has preferential access to a coveted asset that independently enhances value. With exclusive control over such a resource, competitors cannot replicate the offering, allowing the firm to charge higher prices or sustain superior margins.

The simplest examples of this Power involve exclusive control over physical resources (e.g., a mine with a scarce mineral) or intellectual property (e.g., a patent or proprietary data). 

Onchain, you cannot rely on proprietary information to create cornered resources because code and data are open and generally freely copyable. However, because crypto enables asset scarcity, it can function as a form of cornered resource (akin to physical resources). 

An example is native issuance. The blockchain that natively issues an asset — such as SOL on Solana or ETH on Ethereum — has effectively exclusive access to the asset, given the difficulty of making it trustlessly available for use elsewhere. As a consequence, Ethereum and Solana have essentially cornered the DeFi market for ETH, SOL, and other natively issued assets.

Power #7: Process Power

Process Power is the rarest of the Powers. It relies on deeply embedded organizational processes that improve over time and cannot be easily copied, even when competitors understand what is being done. 

Helmer gives the example of Toyota, whose Toyota Production System embedded decades of tacit manufacturing know-how into routines that competitors could observe but not replicate. Toyota even allowed GM to tour its factories, yet after years of effort and millions of dollars in investment, GM still failed to reproduce the Toyota Production System.

Process Power erodes when the output is copyable; even if GM never fully understood the Toyota Production System, that system would not accrue process Power if it could simply copy the car itself. This presents a problem onchain, because the final product — the protocol — is instantly replicable. When the output itself can be copied, the advantage of a superior underlying process collapses — Toyota’s process Power would be meaningless if GM could magically copy and paste the output, the car. As a result, openness largely neutralizes process Power as a source of defensibility.

Internet Bearer Assets

From our review of the 7 Powers, we can see that openness eliminates many of the mechanisms through which traditional businesses accumulated Power:

• Scale economies: When software and APIs are public and easily replicable, fixed-cost advantages tied to proprietary code or infrastructure largely disappear.
• Classic network effects: When the graph is open, competitors can replicate it, allowing them to bypass the bootstrapping problem that traditionally underpins network-effect moats.
• Counter-positioning: When business logic and cash flows are fully visible onchain, incumbents cannot rely on opacity or execution uncertainty to justify inaction.
• Switching costs: When users are no longer entangled in proprietary platforms, the frictions that historically locked users in largely dissolve.
• Cornered resource: When information is open and copyable, proprietary data and trade secrets are no longer scarce.
• Process power: When the output is instantly replicable, the importance of the underlying process fades away.

This dynamic of openness helps explain why so few generational onchain businesses exist today. Early internet businesses faced the same dilemma but ultimately resolved it by gatekeeping information and redefining property rights. Blockchains, however, preserve openness by design. The central question, then, is how Power forms in this new medium.

The answer begins with a defining feature of crypto: its ability to enable bearer assets on the internet for the first time. A bearer asset, like cash or a physical stock certificate, is owned by whoever holds it — possession itself confers property rights and multiple parties cannot simultaneously possess the same asset. 

On the internet, bearer assets were historically impossible: making information open meant it could be copied at near-zero cost, destroying its scarcity. Blockchains resolve this by combining cryptography, distributed systems, and economic incentives to make information function as a bearer asset, with ownership determined by cryptographic possession rather than custodial gatekeeping. This allows for scarcity even as the system stays open.

As a result, owners can freely move digital assets, and builders can openly program arbitrary logic to make them more useful — creating an environment analogous to the freedoms people have over physical assets in the “real world.” When value is held directly by end users in internet bearer assets, no intermediary can revoke, reassign, or expropriate that value, making ecosystems far more likely to form as builders can freely integrate on top of another protocol. Incumbents built on gatekeeping information cannot adopt this model without relinquishing the discretionary choke points that enable rent extraction, which would cannibalize their core business — the hallmark of a powerful counter-position. 

While this counter-positioning creates sources of Power against traditional internet businesses, internet bearer assets also help onchain protocols accrue Power relative to one another. Two mechanisms seem to matter most. 

First, internet bearer assets are inherently scarce because they are defined by exclusive ownership and cannot be duplicated or reused. That scarcity gives economic weight to where value is held. We saw this with the Power of liquidity network effects: capital committed to one protocol cannot simultaneously support another, which allows advantages to compound around a single coordination point. We also saw this with branding Power. Asset scarcity is a necessary condition of provenance; without scarcity, everything collapses into fungibility and brand cannot form. 

Second, internet bearer assets mean that security over those assets becomes a source of Power. If possession determines ownership, then any exploit that transfers possession is catastrophic. Protocols that survive — that protect user property across cycles and adversarial conditions — earn credibility that compounds over time. As we saw with economies of Lindyness and switching costs, reliability itself becomes a moat. In crypto, security is not just a cost center. It is a source of Power.

Crypto is still early. We’re still learning where durable advantage actually forms in this medium. Internet bearer assets change the rules of ownership online, and with them, the ways Power can compound. As with every new medium, it will take time for the true generational structures to emerge — but they will be built around the unique features of the system, not in spite of them.

Thank you to Proph3t (MetaDAO), Jackson (Azura), Charlie (Felix), Nick (Derive), Paul (Fomo), Karthik (Sorella), and Chad (BlueYard) for their thoughtful feedback on this article.

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