Last week, a quiet announcement from Anthropic and OpenAI rippled through the policy corridors of Washington, D.C., barely registering on mainstream crypto radar. The two AI titans, often at odds over safety philosophy, jointly committed to developing a model evaluation framework in coordination with the incoming Trump administration. Compliance is the new currency — and this move marks its first major minting in the AI sector.
For those of us who have spent years mapping cross-border payment flows, the pattern is painfully familiar. When incumbent players align with state power to define standards, the result is almost always regulatory capture disguised as cooperation. The hollow resonance of digital ownership in art echoed through my mind as I read the press release: a promise of safety standards that may ultimately serve to entrench incumbents and marginalize decentralized alternatives.
Context: The collaboration targets an urgent gap in AI governance. Current safety benchmarks—like the Anthropic-led red-teaming frameworks or OpenAI’s preparedness evaluations—are fragmented. No unified standard exists for measuring catastrophic risks, bias, or alignment. The proposed plan aims to create a baseline that private companies, government agencies, and international partners can adopt. But the devil lies in the actors: two companies whose business models rely on closed models, massive compute resources, and favorable regulation.
Based on my experience auditing liquidity mining protocols during DeFi Summer 2020, I recognize the anatomy of this deal. When a project claims to be “working with regulators” to “ensure safety,” it often means they are designing moats that only their infrastructure can cross. The Anthropic-OpenAI pact is no different. By shaping the evaluation criteria, they can set technical requirements—such as mandatory disclosure of training data provenance, specific hardware architecture, or threshold compute budgets—that only their compute clusters can meet. Decentralized GPU networks like Akash or io.net, which rely on heterogeneous hardware and open frameworks, would face prohibitive compliance costs.
Core Analysis: Macro forces break micro promises. The macro force here is the weaponization of safety standards as non-tariff trade barriers. The U.S. government, under an administration prioritizing “American AI dominance,” can adopt these criteria as procurement mandates for federal contracts. Startups using open-source models or decentralized compute would be locked out of the most lucrative market: government and defense contracts. This replicates the pattern I saw in SWIFT remittances: centralized messaging systems claiming to be “secure” while extracting rents from those unable to afford compliance.
Moreover, the timing is tactical. The Trump administration is expected to promote deregulation for domestic tech giants while tightening scrutiny on foreign AI imports, especially from China. The Anthropic-OpenAI framework conveniently provides a technical justification for exclusion: “This model does not meet U.S. safety standards.” The result is a legitimized form of protectionism that does not look like protectionism.
But the crypto angle runs deeper. Tokenized compute markets, AI inference on-chain, and decentralized autonomous organizations (DAOs) that govern model training all rely on permissionless access. If evaluation standards mandate verifiable model identity, immutable logging, and auditable inference, these systems could be forced into compliance layers that undermine their core value propositions. DAOs conducting AI research would face legal exposure if their models are used by entities that violate the new standards—a liability that mirrors the unlimited personal liability risk I have warned about in DAO governance.
Contrarian Angle: The contrarian view suggests this cooperation is actually positive—a step toward predictable regulation that could reduce uncertainty for AI tokens and blockchain-based AI startups. After all, clear rules enable capital allocation. But this optimism ignores the power asymmetry. The standards are being drafted by the very companies that benefit most from them. OpenAI’s Sam Altman has repeatedly advocated for a “compute budget” licensing model, which would cap access to training resources. Such a cap would naturally advantage incumbents who already hold the largest compute reserves. Decentralized networks, by definition, cannot control who trains what.
Furthermore, the partnership may fracture the AI safety community. Anthropic’s “long-tail risk” focus and OpenAI’s “accelerationist” stance are incompatible. The resulting standard will be a compromise that satisfies neither side, creating a lowest common denominator that fails to address real risks while imposing bureaucratic costs. I recall a similar dynamic in 2021 when several DeFi protocols attempted to self-regulate stablecoins—the resulting “standards” were either ignored by dominant players or used to ostracize competitors. Regulation lags, capital moves, but here capital is not moving; it is entrenching.
Takeaway: The Anthropic-OpenAI evaluation plan is not about safety—it is about sovereignty. It signals that AI, like banking, will be captured by nation-state aligned incumbents long before decentralized alternatives can establish legitimacy. Crypto AI projects must now decide: fight to be included in the standard-setting process, or pivot to jurisdictions that reject this framework entirely. The next six months will determine whether blockchain-based AI remains a laboratory experiment or evolves into a parallel infrastructure that can survive the tightening grip of state-backed standards. The question is not whether the standards will come, but whose interests they will serve.
