The joint statement landed like a sanctioned transaction: OpenAI and Anthropic, two of the most capitalized AI labs on the planet, urged the U.S. government to subject all advanced AI models to national security review. Their argument is familiar to anyone who has watched crypto regulation unfold: foreign competitors, specifically Chinese AI firms, pose an existential threat to American technological sovereignty. The models, they claim, should be treated like nuclear materials—controlled, inspected, and restricted at the border of trust.
I read this as a cross-border payment researcher who has spent decades watching how capital flows follow regulatory fences. The language is new. The playbook is not. In 2017, I audited ICOs that promised decentralized finance but begged for government approval when their liquidity pools evaporated. In 2022, I watched Terra-Luna collapse because its algorithmic stablecoin was built on a feedback loop that ignored the one thing regulators love: systemic risk. Now the same pattern emerges in AI. The players are different, but the move is identical: use national security as a moat to protect a business model under siege.
Liquidity evaporates faster than hype. But in AI, the liquidity is attention, compute, and talent. And the hype is the belief that any model—open or closed—can survive without state backing.
Context: The Security Narrative as a Trade Barrier
Let’s strip the jargon. OpenAI and Anthropic are not primarily concerned about rogue AI destroying humanity. They are concerned about open-source models from China—like those from Baidu, Alibaba, Huawei, and a swarm of startups—that are closing the performance gap while being freely available. If a company can deploy a Chinese open-source model at near-zero cost, why pay OpenAI’s API fees? The answer, they hope, is that the U.S. government will make it illegal to use those models.
The request is framed in the language of national security: Chinese models could be backdoored, could train on biased data, could be used by the People’s Liberation Army. These are plausible risks. But they are also the same arguments used to justify the trade war on Huawei, the sanctions on Tornado Cash, and the banning of certain crypto exchanges from the U.S. market. In crypto, the effect was clear: offshore exchanges thrived, decentralized protocols became harder to shut down, and the U.S. ceded leadership in innovation to jurisdictions like Singapore and the UAE.
Code is law until the wallet is empty. Once the wallet is empty, the state steps in to fill it with compliance costs.
The historical precedent is damning. In 2020, when DeFi summer exploded, protocols like Uniswap and Compound were hailed as unstoppable. Then the SEC started classifying tokens as securities. Then the OFAC sanctioned Tornado Cash. Each action was justified by a perceived threat: investor protection, money laundering, national security. The result was not the death of DeFi but its migration to permissioned, regulated versions—and a thriving gray market that operates outside U.S. jurisdiction.
Now the same mechanism is being applied to AI. The request for “national security review” is a request for a licensing regime. Only models that pass a government audit—essentially, models built by American firms with American capital and American data—will be allowed to operate in the U.S. market. This is not about safety. This is about creating a regulatory moat that excludes Chinese competitors and, incidentally, open-source projects that cannot afford compliance.
Core: The Fragmentation of Intelligence
Based on my experience mapping cross-border capital flows in Latin America after the 2024 ETF approvals, I can tell you that regulation rarely stops technology. It redirects it. When BlackRock launched its Bitcoin ETF, I predicted that institutional money would flow into regulated venues while retail would continue using offshore exchanges. The same will happen with AI.
The U.S. will build a walled garden of “approved” models. Inside the garden, OpenAI and Anthropic will thrive, charging premium prices for compliance-washed intelligence. Outside the garden, a parallel ecosystem will emerge—decentralized AI protocols, tokenized compute networks, and open-source models hosted on blockchain-based storage. The very architecture that AI companies are trying to suppress will become the refuge for those who cannot or will not submit to review.
I have seen this before. In 2026, when I audited the payment layer of a leading AI-agent platform, I discovered that its fee-burning mechanism created deflationary spirals during high-demand periods. The team was so focused on technical novelty that they ignored basic economic sustainability. The same trap awaits regulators who think they can inspect their way to safety. A model trained on censored data will be brittle. A model forced to pass a political litmus test will produce mediocre outputs. The real innovation—the kind that trumps regulatory capture—will happen in the gray zone: on-chain AI models that are transparent by default, governed by smart contracts, and verified by decentralized validators.
Regulation lags, but penalties lead. The penalty here is not just fines but a loss of competitive edge. If the U.S. locks down its AI ecosystem, China and the global South will accelerate their own, unencumbered by the same constraints. The result will be two AI worlds: one slow, safe, and expensive; the other fast, risky, and ubiquitous. The irony is that the slower world will eventually be outrun, just as regulated crypto exchanges lost trading volume to decentralized protocols during the 2021 bull run.
Contrarian: The Decoupling Thesis Is Flawed
The conventional wisdom is that AI models are too complex to be decentralized. They require massive datasets, GPUs, and engineering teams—resources that only nation-states and large corporations can muster. I disagree. The same argument was made about crypto in 2013: only banks can process payments. Then Ethereum showed that smart contracts could run on a global computer. Then rollups proved that you could scale without sacrificing security.
Today, decentralized AI startups are already experimenting with tokenized compute markets (e.g., Akash, Golem) and model training on distributed networks. The quality is lower, but the trajectory is clear. If the U.S. imposes a national security review on AI models, it will inadvertently accelerate these experiments. Developers who want to avoid the compliance headache will turn to decentralized alternatives, just as DeFi developers turned to offshore smart contracts after the SEC’s crackdown on EtherDelta.
Volatility is the fee for entry. The fee for a regulated AI model is submission to state oversight. For many, the fee is too high.
Moreover, the call for review might backfire on OpenAI and Anthropic themselves. Once a government review mechanism exists, it can be turned on anyone. A future administration might decide that models trained on unvetted internet data are a national security risk—even if they were built in Silicon Valley. The same logic used to exclude Chinese models could be used to demand that OpenAI open its training data to public scrutiny. This is the classic risk of inviting the state to manage innovation: you cannot control the state’s appetite.
Takeaway: The Inevitable Gray Market
The most important insight from this episode is that regulatory capture is a lagging indicator of technological stagnation. OpenAI and Anthropic are not acting from strength; they are acting from fear. They see the open-source wave rising, and they are trying to build a wall before it drowns them. But walls in digital markets are porous.
I predict that within 18 months, we will see a thriving gray market for AI models that bypass U.S. review. These models will be hosted on IPFS, accessed via VPNs, and paid for using stablecoins. They will be used by companies in the global South that cannot afford the compliant version. They will be used by researchers who want uncensored outputs. And they will be used by the very developers who are now signing the national security letters, because when the wallet is empty, even the most principled firm will look for cheaper compute.
Trust is deprecated; verify everything. The only verification that matters in a fragmented world is code that runs on an immutable ledger. The sovereign individual will choose a model not because it is approved by Washington, but because its code is auditable and its governance is decentralized.
The AI cold war is not about intelligence. It is about control of the channels through which intelligence flows. And in that war, the side that builds the most open, permissionless infrastructure will win—regardless of how many national security reviews are demanded.
Liquidity evaporates faster than hype. But intelligence, once open-sourced, never returns to its bottle. The regulators are already too late.