The ledger remembers what the hype forgets. This week, two of the most heavily funded AI labs—Anthropic and OpenAI—announced a collaboration with the incoming Trump administration to design a national framework for evaluating frontier models. On the surface, it reads as a responsible act of self-regulation. A mature industry stepping up to meet the demands of national security. But I have spent enough time auditing smart contracts and tracing governance tokens to recognize the familiar structure of a gatekeeping mechanism being erected under the guise of safety. What we are witnessing is not a technical standard. It is a political fence built around the most valuable data moats in the world.
Let me ground this in what I saw during the ICO audit trail of 2018. When EtherCity promised immutable land titles via smart contracts, the vulnerabilities were hidden not in the code but in the governance layer—who controlled the oracle that validated land ownership? That project collapsed when investors realized the illusion of decentralization was merely a marketing front. Today, the same pattern repeats. Anthropic and OpenAI are not offering a neutral evaluation plan. They are positioning themselves as the gatekeepers of what constitutes a safe, acceptable AI model. The standard they co-create will inevitably favor their own architectures, their own safety budgets, and their own political alignments.
Context: The Governance Vacuum That Became a Power Vacuum Since the Biden administration’s executive order on AI in 2023, the U.S. government has been searching for a credible industry partner to operationalize safety testing. The EU’s AI Act looms as a regulatory hammer. China is racing ahead with its own state-led standards. The vacuum was obvious. What is less obvious is why Anthropic—a company that publicly criticized OpenAI’s closed-source approach—would join hands with its rival to hand a blueprint to a new administration. The answer lies in the same cynical utility filter I apply to every protocol token airdrop: when the rules are being written, you damn well better be at the table. Otherwise, your product becomes non-compliant overnight. Both labs understand that the next government will likely enforce some version of mandatory red-teaming and model evaluations. By volunteering to co-author the test, they ensure the test is calibrated to their strengths.
Core: A Systematic Takedown of the Evaluation Framework The announcement lacks specifics, but the implications are clear from the participants. Anthropic has long championed its “Constitutional AI” approach, which embeds safety rules directly into the training process. OpenAI, despite recent pivots toward more open research, has proprietary systems like the GPT-4 family that rely on reinforcement learning from human feedback. Any evaluation framework that emerges from this collaboration will almost certainly reward heavy investment in alignment research and punish lightweight, open-source or decentralized approaches. This is no accident.
From my experience analyzing DeFi liquidity traps, I saw how a small group of large holders could set governance parameters that made smaller participants economically unviable. Here, the pattern is identical. The evaluation metrics will likely include compute cost, maximum harm thresholds, and transparency requirements that only well-funded labs can meet. A university research group using a fine-tuned Mistral model will not be able to pass the same red-team gauntlet that OpenAI’s $5 billion compute stack can. The standard becomes a barrier to entry. Not because the smaller model is unsafe, but because the test is designed for the industrial scale of the incumbents.
Let me be specific about the risk of weaponization. The evaluation plan is being developed in the context of a new administration that has signaled hostility toward Chinese technology. If the standard includes requirements like “training data provenance” or “chip sourcing audibility,” it becomes a non-tariff trade barrier. I have seen this in crypto regulation—when compliance burdens are tailored to exclude specific jurisdictions, the outcome is the same regardless of the stated intent. Utility vanished before the mint even cooled. The AI standard will become a tool for economic nationalism, not a genuine safety framework.
Contrarian: What the Bulls Got Right I do not cover the story; I follow the code. And the code here is not entirely malicious. Both Anthropic and OpenAI have genuine safety teams that understand the risk of uncontrolled model proliferation. A voluntary, industry-led standard is preferable to a chaotic patchwork of state-level laws or a federal agency with no technical expertise. There is also a strategic argument: by cooperating with the government early, these labs can steer policy away from draconian measures like mandatory kill-switches or training data seizures. The collaboration could indeed prevent a worse outcome. But that is the same logic that every protocol used during the crypto policy debates of 2021—co-opt regulation before it becomes hostile. The result was the same: the big players got their licenses, and the small players were squeezed out.
Takeaway: The Illusion of Unified Safety The real question is not whether the evaluation framework will be effective. It will be effective at doing exactly what its architects intend: consolidating power. The next time you hear about an AI safety standard, ask yourself who sits on the committee and whose compute infrastructure the test is optimized for. Silence in the code is the loudest confession. Anthropic and OpenAI are not solving the safety problem. They are solving their market positioning problem. And they are using the state as their enforcer. The ledger of public trust will remember this long after the hype of the announcement fades.