Over the past 72 hours, two AI giants—Anthropic and OpenAI—announced a joint effort with the incoming Trump administration. They will co-author a plan for AI model evaluation standards. On the surface, this looks like responsible governance. A closer read of the announcement reveals something else: a classic regulatory capture play. The same companies that compete on safety rhetoric are now colluding with the state to define what “safe” means.
Verify the hash, trust no one.
Context
The AI industry thrives on hype cycles. The latest is “alignment” and “safety benchmarks.” Every lab wants a stamp of government approval. The incoming administration wants to project American leadership in AI. The result is a partnership that looks like progress but functions as a gatekeeping mechanism. Both Anthropic and OpenAI have massive compute resources, proprietary models, and established political connections. They are writing rules that will apply to everyone—including smaller open-source projects and decentralized AI networks that operate outside the traditional Silicon Valley orbit.
This mirrors what I’ve seen in DeFi. In 2017, while auditing the 0x Protocol v2, I watched a handful of centralized exchanges push for “security audits” that effectively excluded smaller tokens. The stated goal was investor protection. The actual outcome was market consolidation. Today’s AI evaluation plan is no different.
Core
Let’s tear down the proposal—what little is public—using forensic logic.
First, the standard itself. The evaluation criteria will likely require massive computational resources to train and test. Only companies with data center access can afford that. Smaller AI teams and decentralized projects (e.g., Bittensor subnets, Render compute markets) will be priced out of compliance. The barrier to entry becomes technical moat, not safety.
Second, disclosure requirements. The plan will almost certainly demand training data provenance, model weights, and red teaming logs. This sounds reasonable until you realize that proprietary models and open-source models are treated asymmetrically. A centralized lab can claim trade secrets and get exemptions. An open-source model must reveal everything—killing its competitive advantage. Complexity is often a disguise for theft.
Third, national security overlay. The Trump administration leans toward protectionism. The evaluation plan will likely include criteria that implicitly favor U.S.-based data and compute sources. Foreign models, especially from China, will face higher scrutiny or outright rejection. This turns a safety standard into a non-tariff trade barrier. The block chain remembers what humans forget—and on-chain data already shows that decentralized AI projects rely on global compute. They will be the first to break under such rules.
From my experience investigating the Terra/Luna collapse, I learned that tokenomics often hides Ponzi distribution. Here, the tokenomics is political. Anthropic gets a seat at the rule-making table because it spent millions lobbying. OpenAI gets influence because its CEO meets with Congress weekly. The actual technical standards are secondary. The intent is control.
Silence is the only honest ledger. The loudest advocates of “safety” are the ones who stand to gain the most from restricting competition.
Contrarian
What do the bulls get right? Clear standards can reduce fragmentation. A unified evaluation framework might accelerate enterprise adoption of AI. It could also provide a checklist for risk-averse institutions like banks or healthcare providers, which currently avoid AI due to liability concerns.
But this argument ignores a critical flaw: the evaluators are also the evaluated. Anthropic and OpenAI will be graded by a system they helped design. That is not oversight—it is self-regulation. In the crypto world, we call that “auditing your own token” and it always ends with a rug pull. Code does not lie; intent does. The intent here is to entrench incumbency.
Another bull point: government involvement ensures resources for long-term safety research. Yet the history of government-sponsored security standards—from TSA to PCI DSS—shows they quickly become ossified checklists that miss novel threats. Real safety comes from adversarial testing, not box-ticking.
Takeaway
This evaluation plan is a test case for the entire tech industry. If it passes, we will see similar “public-private” cartels in crypto, cloud, and biotech. The message is clear: cooperate with the state, and you get to write the rules that crush your enemies. Resist, and you become unregulated.
The only countermeasure is decentralized auditability. We need on-chain verification of evaluation results. We need zero-knowledge proofs that a model passes safety criteria without revealing its weights. We need open-source teams to build their own, global standard that no single government can capture.
Audit the edges, not just the center. The center is already compromised.
For every project building at the intersection of AI and blockchain, ask yourself: Whose standards will you be forced to follow? The answer depends on whether you act now or wait for the cartel to close the gates.