The Great AI Standardization: A Blockchain Evangelist's Skeptical Take

Logic fails, but the narrative persists: two companies that rose on the promise of democratizing intelligence are now crafting the safety standards of the future hand-in-hand with the very institutions they once pledged to disrupt. The announcement that Anthropic and OpenAI will collaborate with the incoming Trump administration on AI model evaluation plans isn’t a technical milestone—it’s a political statement. Tracing the code back to its chaotic genesis, I see not a partnership but a power play, one that echoes the same centralizing forces blockchain was designed to dismantle. Let me set the context. Anthropic and OpenAI are the crowned princes of the AI arms race. One preaches “constitutional AI” and long-tail risk; the other accelerates toward AGI with open-source impulses (at least until the profit motive kicked in). Both now sit at a table with a government that, historically, views technology first as a lever for national power. The evaluation plan they’re drafting will likely define what “safe AI” means for the next decade—a definition that could become a de facto trade barrier, a barrier to entry for open-source models, and a bragging rights badge for those who can afford the compliance overhead. My background in blockchain decentralization—from auditing 50+ governance proposals during the 2020 DeFi summer to dissecting the moral ledger of smart contracts—has sharpened my nose for manufactured narratives. This “cooperation” smells like the same story told in DeFi: a supposed infrastructure upgrade that actually locks in existing power. Remember when “liquidity fragmentation” was the bogeyman that VCs used to push new protocols? Turned out the real fragmentation was between retail liquidity and insider knowledge. Here, the narrative is “AI safety standards are necessary for national security.” But the subtext is clear: these standards will be designed by those who already have the resources to meet them. Let me drop the first pillar of my analysis: the evaluation criteria themselves. Based on my experience reviewing institutional reports on crypto (where 80% missed the decentralized value proposition), I predict these standards will zero in on metrics that favor centralized, resource-rich actors—like red-team budget size, compute provenance, and training data transparency. Open-source models, built by communities with limited budgets and diverse sources, will struggle to prove compliance. This is the AI equivalent of a proof-of-stake validation gate that requires 32 ETH to enter: permissionless in theory, oligopolistic in practice. Where logic meets the absurdity of market hype, we must ask: what about the “community decision-making” that both companies claim to champion? OpenAI’s governance has already been criticized for its tight control by a few individuals. Anthropic’s “beneficial AI” mission is funded by a long-term trust that keeps decision-making opaque. In the silence between the block hashes, the voter turnout for meaningful governance is perpetually below 5%—and this AI evaluation plan will be worse. It’s a closed-door negotiation between a handful of CEOs and politicians. The very notion of “community alignment” is a farce when the community has no seat at the table. Now, the contrarian angle. Some argue that government involvement is necessary to prevent AI from spiraling into an uncontrolled arms race—a view I have some sympathy for. After all, I’ve seen what happens when unregulated code meets human greed: the 2022 FTX collapse and the LUNA debacle where “code is law” became an excuse for systemic failure. So maybe a government standard can serve as a floor, not a ceiling. Maybe it can force companies to disclose hallucination rates, bias benchmarks, and safety testing protocols. That would be a win for transparency, wouldn’t it? But here’s where pragmatism meets the harsh reality of institutional incentives. An evangelist who doubts his own gospel, I recognize that any standard written in collaboration with a single government—especially one with a track record of using trade policy as a weapon—will inevitably be partisan. The evaluation will likely include implicit requirements about data provenance (e.g., “must not use training data from certain foreign sources”) or compute origin (e.g., “must use chips from trusted vendors”). These clauses won’t be called protectionist; they’ll be called “national security safeguards.” And once adopted, they’ll raise the barrier to entry for any AI model developed outside the US sphere of influence. The result is a fragmented global AI landscape, much like the blockchain trilemma: you can have security, decentralization, or scalability—but not all three. Here, you can have safety, openness, or global reach—choose two, and the third is sacrificed. This is exactly the pattern I observed in DeFi during the 2024 institutional convergence. ETFs were hailed as validation, but the real effect was to channel capital into centralized products while leaving DeFi’s permissionless core starved of liquidity. Similarly, AI evaluation standards will be the ETF of AI governance: they’ll bring legitimacy to those who can pay the compliance tax, and they’ll sideline the open-source community that gave us Stable Diffusion, Llama, and countless innovations. The irony is that the very companies who benefited from open-source foundational work are now helping to build the walls that keep future competitors out. Let me zoom out to the philosophical level. Blockchain’s original sin was the belief that code is law—that smart contracts could replace trust. AI’s original sin is the belief that safety can be engineered by the same entities that profit from speed. Both are forms of hubris. The difference is that blockchain has a built-in check: sovereignty. If you don’t like the Ethereum governance, you can fork. If you don’t like Uniswap’s fee switch, you can route to a clone. With AI evaluation standards, the state becomes the enforcer. There is no fork from a regulation. You can’t hard fork your way out of a law requiring you to use a specific safety checklist that only three companies can afford to implement. That’s the true centralization risk: not in the code, but in the legal infrastructure that wraps around it. Signs to watch in the coming months. First, any language about “mandatory disclosure of training data” will be the smoking gun—it sounds pro-transparency, but in practice it can be used to blacklist models trained on publicly available data that happens to come from a geopolitical rival. Second, watch for exemptions: if the standards apply differently to closed-source vs. open-source models, you’ll know the fix is in. Third, monitor how the EU and China respond. If they adopt their own versions, we’ll see an AI balkanization that mirrors the internet’s splintering. If they reject the US-led framework, then the standard becomes just another tool for decoupling. My takeaway is not a summary but a forward-looking challenge. The news about Anthropic and OpenAI’s collaboration is not about safety; it’s about control. It’s about who gets to define the rules of the game, and who benefits from those rules. For the blockchain community, this should be a wake-up call: if we don’t build decentralized, verifiable, and truly permissionless AI evaluation tools—like on-chain audits for AI models—the state will define safety for us. And safety, defined by the state, rarely includes the freedom to innovate without permission. So here’s my rhetorical question for the architects of this plan: in the silent consensus of these closed-door meetings, who holds the minority report? Because if the answer is “no one,” then this isn’t an evaluation. It’s a coronation.