Openness is Disruption: Examining the Seven Forces of the Cryptocurrency Era

Let’s begin with the central question: In today’s crypto landscape, how many truly on-chain projects possess the resilience to survive market cycles and remain relevant for decades? Not just those with potential or current popularity, but those with enduring, compounding advantages that allow them to stand firm over the long term.

Honestly, the number is extremely limited.

This isn’t surprising. Early stages of any technological revolution are marked by constant change. The internet’s early days were no different—rapid experimentation, frequent shakeouts, and many large companies lacking staying power. As economic activity shifted from the physical world to digital realms, it took time to recognize where lasting advantages would emerge. Today’s crypto sector is experiencing similar growing pains.

Hamilton Helmer’s “Seven Powers” framework offers one of the clearest models for understanding how moats are formed. Helmer defines “power” as a durable structural advantage that enables an enterprise to consistently generate outsized returns, even against formidable competitors. When he introduced the “Seven Powers” in 2016, the framework was rooted in traditional software and legacy industries—cryptocurrency wasn’t mentioned.

Now, it’s imperative to systematically examine how “power” manifests within crypto’s new paradigm. With this perspective, let’s analyze each of the seven powers to determine which remain effective, which have evolved, and which have faded in a fully on-chain world. This analysis uncovers the deeper rules behind power formation in crypto: Lasting advantages are built around mechanisms that enable trusted and scarce ownership in open, online environments. [1]

Power One: Scale Economies

Scale economies occur when increased output leads to lower unit costs. Typically, substantial fixed costs are spread across a growing sales base.

Helmer uses Netflix as an example: By shifting from licensing content to producing originals (such as the reported $100 million “House of Cards”), Netflix spreads high upfront production costs across a vast subscriber base. Each additional subscriber lowers per-user costs, giving Netflix more pricing flexibility. New entrants trying to replicate Netflix’s content library face prohibitively high costs unless they reach similar scale.

The essence of scale economies is high fixed costs distributed over a large customer base. However, software (except large AI models) usually doesn’t require overwhelming build costs. In crypto, development costs are even lower because projects can reuse existing code—most code and APIs are public. This enables competitors to copy or use fully on-chain software at nearly zero cost, erasing the moat.

However, another more nuanced type of scale economy exists—Lindy Effect economies.

Early depositors confront smart contract risks, low liquidity, and unproven records, demanding high returns. Over time, as protocols demonstrate reliability and assets under management grow, perceived risk falls and required yield thresholds drop. Lower yield requirements mean cheaper capital, making it easier to attract liquidity—this is genuine scale economy.

Aave exemplifies this. In overcollateralized lending protocols, the cost of capital is simply the annualized yield (APY) needed to attract deposits. New protocols lacking Lindy Effect often spend millions in subsidies to meet depositor demands. Aave, having survived multiple cycles without major fund losses, has earned the Lindy Effect. On Ethereum mainnet, it maintains about $5 billion in USDT deposits at 2.7% APY and $4 billion in USDC at 3.34% APY. That means Aave’s cost of attracting deposits is lower than the US government’s! [2]

By comparison, Hyperlend—a fork of Aave v3 on Hyperliquid—can only attract around $20 million in USDT at 3.02% APY and $35 million in USDC at 5.16% APY. Aave draws over 100 times the deposits at much lower APY, demonstrating the Lindy Effect economy. [3] This gives Aave a moat, letting it either charge higher profits or maintain low margins to pressure competitors.

Power Two: Network Effects

Network effects occur when each additional user increases the product’s value for all users. [4]

This is especially potent in platform businesses (like Amazon and Uber), where users on one side attract users on the other (cross-side effects), and in social graph businesses (like Facebook and LinkedIn), where users attract others on the same side (same-side effects). The moat is the “critical mass”: Without enough users, a competitor’s network holds little value, and reaching that threshold is extremely costly.

On-chain development disrupts this logic. Traditional networks rely on proprietary state—your Facebook friends list or Uber driver interactions. Platforms gain power by controlling access to these proprietary states. On public blockchains, information is open, so platforms can’t enforce such control.

Still, the on-chain world can foster other forms of network effects that don’t depend on closed systems.

First is the liquidity network effect. In many on-chain protocols, each additional unit of liquidity benefits all participants through better pricing, deeper markets, and higher coordination success. Liquidity-driven pricing advantages are well understood and serve as the moat for many protocols. Coordination benefits are less recognized; Pump is a prime example.

Pump relies on coordinated liquidity network effects in primary issuance. Investors prefer allocating funds where other investors are active and quality projects launch. Project teams seek access to this concentrated capital pool. These launches depend on “critical mass”: Either sufficient liquidity ensures success, or failure is complete. Pump’s joint curve mechanism formalizes this network effect moat. Tokens failing to attract enough liquidity never “graduate” to AMMs and are deemed failures. Each new unit of capital locked in the joint curve increases the probability of future launch success, making Pump more useful for all and reinforcing its liquidity-driven moat.

Second is the decentralized network effect. For protocols like Bitcoin that aim to create non-sovereign money, fault tolerance, state integrity, and protocol immutability are vital. If a protocol can be arbitrarily changed or halted, it’s not a credible non-sovereign currency. For these protocols, every new collaborative participant (miner, investor, developer) strengthens robustness and the non-sovereign claim.

This effect doesn’t apply universally. For application chains like Hyperliquid, the core value lies in the application itself, not neutral state management. Increasing decentralization offers limited user experience improvements, so decentralized network effects are not significant. [5]

Power Three: Counter-Positioning

This is the most counterintuitive of the seven powers. It arises when a newcomer adopts a more efficient business model (such as higher margins), but incumbents refuse to imitate because it would harm their core business and cause significant profit loss. The newcomer’s moat is built on the collateral damage incumbents must bear.

Helmer cites Vanguard as an example. When Vanguard entered, asset management was dominated by active managers like Fidelity. Vanguard tracked market indices, eliminating active management and advisor costs, offering similar returns at ultra-low fees. Incumbents could copy this, but doing so would cannibalize their high-margin active management business and fee structure—imitation would be self-destructive. This collateral damage allowed Vanguard to grow and redefine the industry.

Crypto’s unique nature again transforms counter-positioning. In traditional industries, incumbents can cite execution risk, technical complexity, or uncertainty about scaling new models as excuses to avoid direct competition. On-chain, these excuses rarely hold, since challengers’ business logic and cash flow are public.

On the other hand, on-chain challengers can more easily execute counter-positioning. On-chain attacks are irreversible and mistakes costly, so incumbents are often less nimble than traditional firms. Thus, shifting to new business models brings greater collateral damage, making counter-positioning more effective. While it’s difficult to compare crypto’s counter-positioning to other industries, it certainly exists.

A clear example is Morpho versus Aave. Aave pools all collateral in a governance-driven fund and uses existing liquidity to list new assets. Morpho does the opposite, offering isolated markets so experienced lenders can directly manage risk, but sacrificing pool liquidity. Although Aave can see Morpho’s success on-chain, copying the model would disrupt Aave’s pool design and governance economics, and introduce smart contract risks—making imitation costly. This collateral damage gives Morpho the window needed to take root and thrive.

Power Four: Switching Costs

Switching costs are the significant expenses users incur when moving to another provider or application.

The Apple ecosystem is a textbook example. By controlling hardware and OS, Apple built a “walled garden” where leaving the platform causes real headaches: data loss, device incompatibility, and more. Most users stay to avoid these high costs, allowing Apple to charge more for add-ons and services than competitors.

Apple demonstrates that switching costs depend on users being entangled with closed, proprietary platforms. When the underlying platform is fully open and publicly accessible, competitors can replicate the same foundation, dramatically lowering switching costs and eroding moats. This dynamic makes switching costs especially hard to maintain on-chain.

Thus, for fully on-chain businesses, switching costs as a source of power are greatly diminished. In permissionless environments, users are rarely locked in—a single wallet can natively access all protocols.

Still, a weaker form of switching cost persists, rooted in operational security and smart contract risk. While funds and users can move freely, every new protocol requires fresh due diligence. Over time, verified reliability accumulates value. In this way, the Lindy Effect and scale economies create a form of switching cost based on risk and trust, not technical lock-in, but still real and meaningful.

Power Five: Brand

Brand power means a seller can charge a premium for products that are objectively identical, due to the seller’s reputation and history—not superior product features. “Objectively identical” is key: If the product is genuinely better, that’s product differentiation, not brand power. [6] Brand power is when customers pay more for a product solely because it comes from a certain brand.

Helmer cites Tiffany as an example. Its diamonds are nearly indistinguishable from those sold by other jewelers but command much higher prices. Here, the product is the brand itself. People want to say their engagement ring is from Tiffany, as it signals status and taste. Another example is Advil versus generic ibuprofen—same ingredients, but many pay more for Advil because the brand stands for trust.

These cases show that brand is a critical source of power for commoditized businesses. Many on-chain projects fit this mold: core protocol software is open, easily copied, and essentially commoditized. In this environment, brand is one of the few ways to capture lasting value. Brand power is most evident in exclusivity, social signaling (e.g., CryptoPunks, BAYC), and trust/security (e.g., Uniswap versus its forks).

Crypto’s provenance strengthens brand power in ways other industries cannot. Traditional brands fight counterfeiting because if users can’t distinguish real from fake, brand value is diluted. Crypto is inherently equipped with proof of origin—authenticity is obvious. Anyone can copy code, but not the social signals and trust a brand has built; a quick check on a block explorer reveals the “real” product. For example, no amount of copy-pasting can convince an on-chain-savvy user they own a real CryptoPunk, allowing the NFT series to charge more for functionally identical products.

Power Six: Resource Control

Resource control means a company gains priority access to a valuable asset that can independently create value. With exclusive control, competitors cannot replicate the product, allowing higher prices or profits.

The simplest examples are exclusive control over physical resources (like rare minerals) or intellectual property (like patents or proprietary data).

On-chain, proprietary information cannot be used to create resource control, since code and data are open and replicable. However, crypto achieves asset scarcity, which itself can be a form of resource control (similar to physical resources).

One example is native issuance. Blockchains that natively issue assets (like SOL on Solana, ETH on Ethereum) effectively monopolize access to those assets, as it is difficult to use them securely and trustlessly elsewhere. Thus, Ethereum and Solana essentially dominate the DeFi markets for ETH, SOL, and other native assets.

Power Seven: Process Power

This is the rarest of the seven powers. It comes from deeply embedded, continuously improved, hard-to-copy organizational processes and knowledge—so even if competitors know what you do, they can’t replicate it.

Helmer cites Toyota as an example. Its production system embodies decades of tacit manufacturing knowledge. Competitors can tour the factory, but even with years and huge investments, they can’t copy it; General Motors tried and failed.

Process power fails when outputs are replicable. Even if GM doesn’t understand Toyota’s process, if it can copy Toyota’s cars directly, the process advantage is moot. On-chain, this is a problem: the final product—the protocol itself—can be instantly copied. When outputs are directly replicable, the process advantage collapses. Thus, openness largely erodes process power as a defensive force.

Internet-Borne Assets

Looking back at the seven powers, openness has eliminated many mechanisms traditional businesses use to accumulate power:

• Scale economies: Software and APIs are open and replicable, erasing fixed cost advantages from proprietary code or infrastructure.
• Classic network effects: Social graphs are open and replicable, letting competitors bypass cold start challenges.
• Counter-positioning: Business logic and cash flow are transparent on-chain; incumbents cannot hide behind opacity or execution risk.
• Switching costs: Users are no longer trapped by proprietary platforms; legacy lock-in friction is gone.
• Resource control: Information is open and replicable; proprietary data and trade secrets are no longer scarce.
• Process power: Outputs are instantly replicable, diminishing the importance of underlying processes. [7]

This openness explains why so few on-chain projects endure across cycles. Early internet businesses faced similar challenges but ultimately solved them by controlling information and redefining property rights. Blockchains, by design, remain open. So the core question is: How does power form in this new medium?

The answer begins with crypto’s fundamental breakthrough: enabling internet-borne assets for the first time. Bearer assets—like cash or physical stocks—are owned by whoever holds them; possession equals title, and assets cannot be owned by multiple parties simultaneously.

On the old internet, bearer assets were impossible: once information was open, it could be copied at zero cost, destroying scarcity. Blockchain, through cryptography, distributed systems, and economic incentives, gives information the properties of bearer assets—ownership is determined by cryptographic possession, not centralized custody. This enables scarcity in an open system.

Consequently, holders can freely transfer digital assets, and developers can openly build any logic to enhance their utility—creating a landscape similar to the freedoms of physical asset ownership. When value in the form of internet-borne assets is directly controlled by end users, no intermediary can seize, misappropriate, or freeze it. This makes ecosystems easier to form, as developers can freely compose and innovate atop other protocols. Traditional internet giants built on information control cannot adopt this model without abandoning their “rent-seeking” control points, undermining their core business—a powerful form of counter-positioning.

This counter-positioning not only creates advantages over traditional internet businesses, but internet-borne assets also help on-chain protocols accumulate power among themselves. Two mechanisms are especially critical:

First, scarcity. Internet-borne assets are inherently scarce due to exclusive ownership; they cannot be duplicated or reused. This scarcity gives economic weight to stores of value. We see this in liquidity network effects: Capital allocated to one protocol cannot simultaneously support another, so advantages accumulate around a single coordination point. Brand power also relies on asset scarcity—provenance depends on it; without scarcity, everything is fungible and brands lose meaning.

Second, security. If possession determines ownership, any hack that transfers assets is catastrophic. Protocols that endure cycles and protect user assets in adversarial environments earn ever-growing reputational capital over time. As seen with Lindy Effect economies and switching costs, reliability itself becomes a moat. In crypto, security is not just a cost—it is a source of power.

Conclusion

Cryptocurrency is still in its infancy. We’re still discovering how lasting advantages will ultimately take shape in this new paradigm. Internet-borne assets have rewritten the rules of online ownership, altering how power is accumulated. As with any new medium, truly epoch-defining structures take time to emerge—but they will inevitably be built around the system’s unique strengths, not in opposition to them.

Disclaimer:

1. This article is republished from [Foresight News]. Copyright belongs to the original authors [Daniel Barabander (@ dbarabander), Elijah Fox (@ PossibltyResult)]. If you have concerns about this reprint, please contact the Gate Learn team, and the team will promptly address your request according to established procedures.
2. Disclaimer: The views and opinions expressed in this article are solely those of the authors and do not constitute investment advice.
3. Other language versions of this article are translated by the Gate Learn team. Unless Gate is cited, reproduction, distribution, or plagiarism of the translated article is prohibited.