The code bleeds, but the liquidity stays cold.
Two AI labs—Anthropic, OpenAI—shake hands with the incoming Trump administration. They promise a joint model evaluation plan. The press calls it a breakthrough in responsible AI. I call it a backroom deal dressed as public safety.
I’ve seen this script before. In 2020, when Uniswap V2 liquidity pools flooded with retail capital, the first thing the big players did was design “oracle safety standards.” Criteria that just happened to favor their own nodes. The result? Small LPs bled. The liquidity stayed—but only for those who wrote the rules.
This AI evaluation plan is the same. A Trojan horse wrapped in a safety label. Let me show you the architecture.
Hook: A Crisis You Didn’t See Coming
On the surface, this is a photo-op. Two rival labs agreeing to a common framework. But the real signal is in the timing. Trump’s second term hasn’t even started, and the AI giants are already kneeling to the flag. They’re not doing this out of altruism. They’re hedging against an administration that has promised to “drain the swamp” of tech billionaires. They’re trying to buy influence before the regulatory guillotine drops.
The headline writes itself: “Anthropic and OpenAI Partner with Government on AI Safety.” Beneath it, a quieter truth: they’re building a moat. Setting baseline requirements that only they can meet. Killing the open-source competition before it scales.
Context: The Protocol's Weakest Point
Let’s look at the players. Anthropic—the “safe” lab, built by ex-OpenAI defectors, obsessed with constitutional AI and red-teaming. OpenAI—the “accelerate” lab, now a capped-profit behemoth, pushing GPT-5 while bleeding talent. They spend more time fighting each other than building. Yet here they are, united.
Why now? Because the Trump administration’s “America First” doctrine has a clear target: non-US AI. Chinese models are eating into benchmarks. DeepSeek, Qwen, and a dozen open-source alternatives are closing the gap. If the US government can define what “safe” means—and make it expensive to verify—then foreign models can be excluded without ever being named.
This is the same crypto playbook from 2023. Remember the “proof-of-reserve” mandates after FTX? The big exchanges wrote the standard. Smaller exchanges couldn’t afford the audits. They died. Not because they were insolvent, but because the cost of compliance was a weapon.
Core: The Order Flow You Can’t See
I’ve spent 9 years reading smart contract patterns. I know when a parameter is a backdoor. This AI evaluation plan is still a draft, but the architecture is clear:
- Verification Burden: The standard will require continuous red-teaming, adversarial testing, and compute transparency. Who has the hardware to run a full red team? Anthropic and OpenAI. The open-source community does not. The moment a new LLaMA variant releases, it won’t be “government-approved” until it passes a test only two labs can afford.
- Data Sourcing Requirements: The plan could mandate traceable training data—every source must be citable. That kills synthetic data pipelines and federated learning models that rely on privacy. Again, Anthropic and OpenAI control the data contracts. They feed their own models on exclusive deals with Reddit, news guilds, and academic journals.
- Latency as a Feature: The evaluation rubric will likely include “real-time intervention” capabilities—the ability to shut down a model mid-inference if it misbehaves. That requires a backdoor. A kill switch. In 2026, I worked with a Dublin-based AI startup to integrate autonomous agent payments. We used ZK-proof authentication to avoid exactly this—a centralized kill switch that could be weaponized. The government plan will require the opposite. A leash.
This is not safety engineering. This is vendor lock-in. The same way DeFi protocols used “liquidity mining rewards” to funnel TVL into their own pools, these labs are using “safety standards” to funnel trust into their own infrastructure.
Contrarian: The Blind Spot Everyone Misses
Here’s the counter-intuitive truth: This collaboration is a sign of weakness, not strength.
Anthropic and OpenAI are terrified. They have zero moats. Their models are approaching performance plateaus. The marginal gains from bigger compute are shrinking. The differentiation is gone. They’re both burning cash on inference costs while open-source alternatives achieve 90% performance at 10% cost.
So they run to the state. They ask the government to build a wall. But walls have gates, and gates have guards. Once you invite the state into your product design, you forfeit control. The same way DeFi protocols that partnered with centralized stablecoin issuers later found themselves frozen during OFAC sanctions.
Incentives align only when the risk is priced in. The risk here? A future administration—say, a 2028 Democrat with a populist bent—could use that same kill switch to shut down AI development entirely. Or demand full audit access that exposes trade secrets. The labs are betting on Trump’s loyalty to tech. But volatility is the only constant truth. Political winds shift faster than GPU demand.
Also, this plan will crater community trust. The open-source ecosystem will view it as a cartel move. AI developers will fork everything. They’ll hide training methods. The “safety standard” will become a joke—a compliance checkbox that nobody respects. Sound familiar? That’s exactly what happened with DeFi’s “audit pyramid.” Every protocol pays for a security audit, but the audits are often rubber stamps. The code still bleeds. The liquidity stays cold.
Takeaway: Actionable Signals
Don’t trade the narrative. Trade the technicals. Here’s what I’m watching:
- The “Red Team” Requirement: If the standard mandates a specific vendor for adversarial testing (e.g., only labs with government security clearance can perform audits), that’s a hard floor for small players. Price in a valuation gap between compliant and non-compliant tokens.
- The “Open Source” Exemption: If the standard carves out models released under a permissive license, then Meta and Mistral win. If it doesn’t, then open source dies in the US. Bet on the former—european regulators will likely push for open exemptions.
- The “Compute Transparency” Data: If the plan requires publishing total FLOPs and power consumption per model, that’s a weapon for shorting energy tokens tied to AI farms. Transparency cuts both ways.
Bottom line: This isn’t about safety. It’s about market control. Two labs and a president want to rig the game. But rigged games fail when the players exit. Watch the talent flows. Watch the fork activity. Watch the liquidity.
When the leverage snaps, the silence is loud. The silence here? No open-source developer signed on. No academic lab endorsed it. This evaluation plan has no teeth until the community decides it does. And the community has a long memory.
Audit trails don’t lie, but they can be gamed. So don’t audit the standard. Audit the incentives. The ones who wrote this plan are the ones who stand to gain the most. That’s not a signal. That’s a red flag.
The code bleeds, but the liquidity stays cold.