I trace the policy memo, not the press release.
When the Washington Post reported that the Trump administration is privately negotiating a framework for ‘American open-source AI models,’ the initial reaction was a collective shrug from the crypto-native AI builders I monitor. Another political gesture, they muttered. Another committee. But I’ve been reading the tea leaves of regulatory capture since my 2018 0x protocol audit taught me that every new rule is a vector for rent extraction. This framework is not about empowering open collaboration. It is about drawing a fence around the most valuable digital asset of the decade: the right to call your model ‘open source.’
Let me be precise. The framework, as currently whispered, aims to define what constitutes an American open-source AI model — a classification that will likely dictate access to federal contracts, export licenses, and liability shields. The bulls will tell you this creates ‘regulatory clarity’ for the AI industry, boosting valuations and domestic innovation. They are half-right. Clarity is a commodity, and like all commodities, its distribution will be rigged.
Context: The Hype Machine and Its Blind Spot
The AI narrative in 2025 is a familiar one. Venture capital is flooding into every vertical that can attach the letters ‘AI’ to its pitch deck. Open-source models — led by Meta’s Llama family, Mistral’s releases, and the emerging Chinese challengers like Qwen and DeepSeek — have become the infrastructure layer for thousands of startups. The promise is democratization: anyone can download weights, fine-tune them, and deploy without asking permission.
But the promise has a hidden liability. Unlike a truly permissionless blockchain, the distribution of AI weights is a one-way valve. Once you release a model, you cannot revoke it. The Trump framework is a response to this asymmetry. The stated goal is to ‘protect national security and promote American leadership.’ The unstated goal is to create a moat — a government-backed seal that separates ‘safe’ American models from ‘untrusted’ foreign ones.
This is where my skepticism crystallizes. As someone who dissected the Terra-Luna collapse and saw how algorithmic stability was sold as ‘innovation’ until the seigniorage mechanism imploded, I recognize the pattern. A politically convenient definition of ‘open source’ will be weaponized to exclude competitors, not to enhance security.
Core: Systematic Teardown of the Framework’s Mechanics
To understand where the rigged game begins, I analyzed the likely architecture of this framework based on the policy signals leaked so far. The Washington Post report mentioned discussions with industry leaders — Meta, OpenAI, Anthropic, and others — and a potential timeline of an executive order or a National Institute of Standards and Technology (NIST) guideline.
Here is the skeleton:
- Definition of ‘Open Source’ – The framework will probably not adopt the formal Open Source Initiative (OSI) definition. Instead, it will craft a new category called ‘American Verified Open Source.’ This will require the model to be partially transparent (weights and inference code) but will allow restrictions on commercial use, data provenance, and hardware sourcing. The result: only models trained on US soil, using US-manufactured chips (or those of exempted allies), with documented training data free of Chinese or adversarial sources, can qualify. This is the digital equivalent of a ‘Buy American’ clause for AI.
- Compliance Burden – To obtain verification, a model must undergo a battery of tests: red-team evaluations aligned with NIST’s AI Risk Management Framework, bias audits, content safety filters, and export control checks. The cost of these tests will be significant — hundreds of thousands of dollars per model. Only well-funded organizations or Big Tech subsidiaries can afford the process. Independent open-source projects without corporate backing will be priced out of the ‘verified’ ecosystem, essentially becoming second-class citizens in the US market.
- Government Procurement Preference – The real teeth come from procurement rules. Federal agencies (DoD, HHS, DOE, NASA) will likely be required to use only verified models for any AI-related contracts. This instantly creates a captive market for Meta’s Llama, Mistral’s models (through their US affiliates), and any startup that can afford the compliance toll. Conversely, any model that lacks verification — including the majority of globally developed ones — will be locked out of the richest government customer.
- Export Control Extension – The most insidious aspect is the tie-in to export controls. Currently, the US restricts the shipment of advanced GPUs to China. This framework extends the wall to the software layer. A verified model could have clauses in its license that prohibit deployment in sanctioned countries. It could also require a ‘watermark’ or cryptographic signature in the model output to trace the origin. In effect, the US government gains a kill switch on the most powerful open-source models.
Based on my experience auditing the 0x protocol vulnerability, I can tell you that the devil is in the nonce handling. Here, the nonce is the framework’s definition of ‘open source.’ If the nonce is misconfigured — if the definition is too restrictive — the entire ecosystem breaks down. The entire promise of open-source AI is that a developer in Jakarta or Nairobi can take a model and improve it. A verification regime that limits that to American infrastructure is not a security measure; it is a friction tax on global innovation.
Let me provide a concrete example. Suppose a South Korean startup fine-tunes Llama 3 for medical diagnostics. Under the current structure, they can deploy it freely. Under the verified framework, Llama 3 is already verified (Meta will pay for it). But the fine-tuned version, which includes new layers and data, might require its own verification. The cost and time — months — could kill the startup. The incentive shifts: instead of building on any open model, developers will gravitate to a handful of pre-verified canonical versions, centralizing the ecosystem around a few gatekeepers.
This is the same fragility I flagged during DeFi Summer 2020, when I calculated that unsustainable leverage loops were inevitable because the governance was centralized in a few smart contracts. The framework is replicating that leverage, but instead of borrowed tokens, it uses borrowed trust.
Contrarian: What the Bulls Got Right
I am not blind to the counterargument. The proponents of the framework — including many AI safety researchers I respect — argue that without such guardrails, open-source models will be used for bioweapons, disinformation, and surveillance. They point to the EU AI Act and China’s regulations as existing models that classify AI risks. The framework, they say, is America’s chance to set a global standard that balances innovation with accountability.
And they have a point. The current unregulated open-source environment does create genuine risks. A malicious actor could download an unrestricted model, strip safety filters, and weaponize it. A unified verification system could, in theory, provide a safety net by requiring minimal red-teaming and use restrictions.
Moreover, the framework could create a new market for verification services — AI auditors, compliance consultants, testing platforms. This could be a legitimate business sector, just as smart contract audits became a necessary cost of doing business in DeFi after the DAO hack. In that sense, the framework is not purely extractive; it is a response to a real market failure.
But the devil is in the execution. The problem is not the concept of verification; it is the political capture of the definition. If the framework is written by the tech giants themselves — as the Washington Post report suggests — it will inevitably reflect their interests. They will define ‘open source’ in a way that makes their own models the only viable option, while branding competitors as ‘unverified’ or ‘risky.’ The result is not a safety floor but an entry barrier.
I’ve seen this before. In 2021, I exposed the Quantum Cat NFT scam by tracking wallet flows. The developers used a simple backend swap and redirected minting fees. The industry’s solution was not to enforce better auditing but to create a ‘verified’ badge on OpenSea that cost projects $10,000. The badge didn’t prevent the next rug pull; it just made it more expensive to scam. The framework will do the same for AI — a gilded cage for innovation.
Takeaway: The Real Audit is Not the Code, It’s the Incentives
When the yield is too high, the exit is rigged. The Trump AI framework offers a yield of ‘security’ and ‘leadership’ that is too high not to be the source of future exploitation. I do not propose we abandon all regulation. I propose we audit the regulators.
The question I leave you with is not whether open-source AI needs guardrails. It does. The question is who designs the guardrails and for whose benefit. If the answer is ‘the same incumbents who already control the cloud infrastructure and the largest models,’ then we are not building safety; we are building a monopoly with a government seal.
I will continue to trace the wallets, the nonces, and the policy memos. Because every piece of code is a hypothesis about trust. And the Trump framework is a hypothesis that trust can be centrally managed. My eleven years in this industry — from the 0x vulnerability to the Terra collapse to the AI-agent fraud rings of 2026 — have taught me that centralization of trust is the most fragile architecture of all.