Chasing the alpha while the market sleeps — the same regulatory playbook that has left crypto in a decade-long purgatory is now being quietly deployed on artificial intelligence. On August 13, WIRED broke the news: the Trump administration’s new AI guidelines, initially targeting only closed-source models from Anthropic and OpenAI, will soon expand to cover open-source models. The White House has not publicly released the framework, and there are reportedly no plans to do so. For those of us who have spent years decrypting the SEC’s regulation-by-enforcement strategy, the pattern is unmistakable – and deeply unsettling.
The context is critical. The White House announced earlier this month that it has developed an AI framework requiring cutting-edge models to undergo federal safety testing before public release. Currently, this only applies to closed-source models like Anthropic’s Mythos and OpenAI’s GPT-5.6. But a White House official confirmed to WIRED that the framework will be expanded in the coming months to include open-source models once they reach the same capability threshold. In short, the moment an open-source AI model rivals GPT-5.6 in performance, it becomes subject to pre-release testing by the federal government.
From ICO hype to on-chain truth — I have seen this dance before. In 2017, I audited over 50 ERC-20 whitepapers during the ICO frenzy. The SEC didn’t issue clear guidelines; they waited for projects to launch, then sent subpoenas. The same tactic is now being applied to AI. The framework is deliberately opaque. No public comment period. No published criteria for what qualifies as “cutting-edge.” No transparency on how the testing will be conducted or who will conduct it. Just a vague promise of safety, wielded as a weapon against open-source development.
Let’s get technical. Open-source AI models – like Meta’s LLaMA, Mistral, or the thriving ecosystem of fine-tuned variants on Hugging Face – are not monolithic. They are decentralized by nature, often contributed to by hundreds of independent developers across jurisdictions. The moment the U.S. government imposes a pre-release testing requirement on any model that reaches a certain capability threshold, it effectively asserts control over the entire open-source AI pipeline. Who defines “cutting-edge”? Who measures capability? And who decides whether a model passes or fails? The framework answers none of these questions.
Human faces behind the blockchain code — I remember the panic in 2020 when the SEC first hinted at regulating DeFi protocols. The community screamed for clarity; the SEC gave them enforcement actions. Now, the same dynamic is playing out in AI. The WIRED report quotes a White House official saying the expansion to open-source models is expected “in the coming months.” But the official language is telling: “once open-source models reach the same cutting-edge capability level.” This is a moving goalpost. The administration can arbitrarily define “cutting-edge” to include any model they want to control. It’s the same trick the SEC used with the Howey Test – apply it selectively, retroactively, and without consistent standards.
Based on my experience auditing cryptographic protocols and tracking regulatory actions for 29 years, I see a deliberate strategy here. The Trump administration (and likely any future administration) wants to maintain maximum discretion. By keeping the framework private, they avoid legal challenges, public debate, and congressional oversight. They can quietly expand the scope as they see fit, targeting open-source AI projects that threaten centralized power structures. The narrative of “safety” is a shield – behind it, they are building a licensing regime for AI innovation.
Scanning the noise for the signal — The contrarian angle that most pundits are missing: this is not about safety at all. It is about control over the means of production in the AI age. Open-source models are the equivalent of permissionless blockchains. They allow anyone to build, deploy, and iterate without asking for permission. The U.S. government, like the SEC before it, fears that. The framework’s real purpose is to create a bottleneck – a gate through which every capable open-source model must pass before it can see the light of day. The “safety test” is just the toll.
Consider the parallels with crypto. The SEC has never defined what makes a token a security in clear, predictable terms. Instead, they regulate by enforcement, punishing projects after the fact. The AI framework follows the same logic: no clear rules, no published thresholds, just a looming requirement that will be enforced retroactively against any open-source model that becomes too popular or too powerful. The result is a chilling effect on development. Why contribute to a cutting-edge open-source model if the government can require you to submit it for testing before release – and potentially deny it?
Speed meets substance in the void — Let’s ground this in a concrete example. Imagine a decentralized AI project – say, a DAO that trains a large language model on community-governed data. The model’s performance rivals GPT-5.6. Under the expanded framework, the DAO would be required to submit the model to federal testing before release. But the DAO has no legal entity, no single point of control. Who submits? How does the testing process work for a model that is continuously updated by hundreds of contributors? The framework provides no answer. It’s a regulatory black hole.
This is not hypothetical. The crypto community has already faced this exact problem with DeFi protocols. The SEC’s regulation-by-enforcement treats a decentralized autonomous organization as if it were a centrally-managed company. The same illogical framework is now being applied to AI. The administration expects the open-source community to self-regulate and police itself, while simultaneously threatening to shut down any model that fails an undefined test.
Capturing the fleeting spirit of the herd — The irony is thick. The AI safety movement has long argued that open-source models are dangerous because they can be used for malicious purposes. But the administration’s response – a secret, unaccountable testing regime – is far more dangerous. It creates a single point of failure, a centralized authority with the power to suppress any AI model it deems threatening. The very thing that makes open-source AI resilient – its decentralization – becomes its vulnerability under this framework.
Born in the fire of the first bubble — I have watched the crypto industry make the same mistake repeatedly: begging for regulatory clarity, only to receive a slap in the face. The AI community is now at the same crossroads. The WIRED report should be a wake-up call. The framework’s expansion to open-source models is not a technical adjustment; it is a fundamental shift in policy. It signals that the U.S. government intends to control the most powerful AI tools, regardless of whether they are built by corporations or communities.
The ledger doesn’t lie — what does the ledger say? It says that every time a government has created a secret, unaccountable regulatory framework, innovation has fled. The ICO bubble burst under enforcement pressure. DeFi summer ended with a regulatory winter. Now, the same storm is gathering over AI. The open-source community must learn from crypto’s mistakes. Demand transparency. Demand clear rules. Demand that the framework be published before it is enforced. Otherwise, we will watch the same playbook unfold – and this time, the stakes are not just financial, but existential.
Takeaway — The next watch is not on the AI model’s capability, but on the legislative response. Will Congress step in to define the AI framework’s scope, or will it remain an executive branch power grab? For crypto natives, the question is: can we align with the open-source AI community to fight this common enemy? The answer will determine whether the next decade of innovation happens in the United States or offshore. The clock is ticking. The ledger doesn’t lie, but the regulators do.