The data shows a single data point that reveals the entire strategic playbook. Of the five major frontier AI companies—OpenAI, Google, Meta, Microsoft, and Anthropic—only one refused to sign the open-source petition in July 2024. That company is Anthropic. This is not a minor stance. It is a calculated break from the industry consensus. And it demands an on-chain level of scrutiny.
Context: The AI Safety Governance Battle
The debate over open-source AI models has reached an inflection point. The underlying tension is binary: open weights promote innovation and democratization but sacrifice post-hoc security; closed weights enable centralized safety but limit competition and transparency. Supporters of open-source argue that transparency leads to faster bug fixes and broader access. Critics, led by figures like Anthropic CEO Dario Amodei, claim that once model weights are published, security controls become trivially removable—the asset becomes immutable, like a deployed smart contract that cannot be upgraded. The open-source petition, signed by OpenAI, Google, and SpaceX, advocates for unrestricted release of model weights below dangerous capability thresholds. Anthropic, alone, offers an alternative: a three-pronged proposal that includes restricting advanced chip exports, cracking down on industrial-scale distillation, and imposing mandatory safety tests for all sufficiently powerful models. This is not a compromise. It is a structural redesign of the AI ecosystem.
Core: Systematic Teardown of Anthropic’s Three Measures
Let us examine each measure as if auditing a protocol’s tokenomics. First, the chip restriction. Anthropic calls for limiting the flow of advanced semiconductors and associated manufacturing equipment to China. On the surface, this is a national security argument. But examine the incentives. Anthropic, alongside Google and Microsoft, has privileged access to NVIDIA’s H100 and B200 clusters. Restricting chip flows to potential competitors, especially Chinese AI labs, directly protects the compute-as-a-service moat that Anthropic relies on. The analogy in blockchain is clear: restricting ASIC supply to maintain proof-of-work dominance. The beneficiaries are incumbents. The victims are decentralized innovation.
Second, the distillation crackdown. Anthropic advocates for legal and technical measures to prevent “industrial-scale” model distillation—the process of training a smaller model to replicate a larger one’s behavior via API outputs. Distillation is the equivalent of a flash loan attack on API value. It allows third parties to extract the intelligence of a frontier model without paying for inference compute. Anthropic’s position is understandable: its API pricing relies on proprietary model quality. But framing distillation as a security threat conflates business defense with public safety. Distillation is also the primary mechanism through which open-source models achieve competitive performance. Cracking down on it would starve the open-source ecosystem of its most efficient learning pathway, forcing developers back to paid API calls or to less capable base models. In blockchain terms, this is akin to banning forking—restricting the ability to copy and modify code that has been publicly released.
Third, mandatory safety testing. Anthropic proposes that any model above a certain capability threshold—whether open or closed—must pass third-party evaluations for cybersecurity, biological weapon potential, and alignment. This sounds reasonable. But who sets the threshold? Who conducts the tests? The proposal lacks specificity. In practice, the testing infrastructure would likely be controlled by the same incumbents who lobby for it. Mandatory testing could become a certification cartel, much like how the SEC’s regulatory-by-enforcement approach creates uncertainty for smaller players. The cost of compliance—both financial and temporal—would be prohibitive for startups and open-source communities. The result is a higher barrier to entry that favors established players with legal and technical resources.
Based on my experience auditing protocols during DeFi Summer, I recognize the pattern: a proposer claims to solve a systemic risk while simultaneously erecting competitive moats. The 0x Protocol v2 audit taught me that routing logic can contain hidden incentives. Anthropic’s routing logic—chip, distillation, testing—also contains hidden incentives. The proposal is self-consistent: it protects Anthropic’s compute advantage, its API pricing power, and its regulatory-first-mover status. The open-source petition, by contrast, aligns with the interests of companies that benefit from a vibrant open-source ecosystem: Meta (via Llama) and OpenAI (via community goodwill). Anthropic chooses a different path because its business model depends on safety-as-a-differentiator.
Contrarian: What the Bulls Get Right
It would be intellectually dishonest to dismiss Anthropic’s position entirely. The bulls—those who support the proposal—raise valid points. First, the irreversibility of model weights is real. Once a model with autonomous capabilities is released, no patch can recall it. I witnessed this during the Terra/Luna collapse: an algorithmic stablecoin that was mathematically doomed, yet proponents claimed it was a black swan. Code does not care about marketing. Similarly, an AI model with self-improvement potential cannot be “un-learned.” Second, mandatory safety testing is a legitimate escalation from the current “voluntary commitments” framework. The U.S. government’s AI Safety Institute lacks teeth. Independent audits, if truly independent, could provide a floor for safety harms. Third, the chip restriction argument has geopolitical merit. Advanced AI training hardware is a critical national resource. Restricting its flow to adversarial nations is not necessarily anti-competitive—it is strategic. The problem is when security and strategy become indistinguishable from corporate lobbying.
Takeaway: Logic Outlives the Hype Cycle
Anthropic’s proposal is not about safety. It is about governance—who sets the rules, who enforces them, and who benefits. The three measures, if implemented, would create a new regulatory layer that advantages capital-rich, vertically integrated incumbents. The open-source community and developers in the Global South would bear the cost. The question we must answer is: do we want AI governance to be written by a single corporate actor, or do we want a transparent, auditable process similar to how blockchains implement governance through on-chain voting and multi-signature? Trust is verified, not given. We should apply the same scrutiny to Anthropic’s policy proposal as we do to a new DeFi protocol. Code speaks louder than promises—and here, the code is the proposal itself. The incentives are clear. The question is whether regulators and the public will recognize them before the system becomes entrenched.