The US Treasury Secretary just proposed treating frontier AI like a financial security.
The trap isn't the regulation itself—it's the illusion of infinite growth that comes with institutional capture.
On paper, Scott Bessent's plan sounds mature. Create an independent agency modeled after FINRA (Financial Industry Regulatory Authority) to oversee ‘frontier AI models’. Audit safety, enforce compliance, issue licenses. It’s the same playbook that turned the 1930s securities markets into the modern, trusted financial system. But as someone who spent 2017 auditing ICO tokenomics in Buenos Aires, I’ve seen this movie before. The promise of safety is always a Trojan horse for centralizing power.
Let’s zoom out. The macro context is everything. We’re in a sideways market—liquidity is rotating, not expanding. The Federal Reserve’s balance sheet is flat, M2 growth is anemic. In such an environment, the last thing innovators need is a new layer of compliance costs. Yet here comes the Treasury Secretary, borrowing a framework from the 1930s to manage a technology that moves at the speed of neural firing. The dissonance is deafening.
Bessent’s core insight is valid: frontier AI models pose systemic risks, much like the 2008 financial crisis or the 2022 Terra/Luna contagion. In my post-mortem of that crash, I mapped how algorithmic stablecoin failure triggered margin calls across centralized exchanges—a liquidity cascade that destroyed $60 billion in hours. AI risks are similarly interconnected. A single unaligned model could trigger automated trading, misinformation, or critical infrastructure failures. The risk of “model contagion” is real.
But here’s where the proposal goes wrong. By placing this new agency under the SEC’s orbit—or even just using FINRA as a template—Bessent ensures that the regulators will be lawyers and finance people, not engineers. The compliance culture will prioritize paperwork over actual safety. I saw this in 2020 when I modeled the yield farming incentives of Compound and Aave. The “liquidity mining” programs looked like DeFi innovation, but they were ponzi-like structures dependent on constant new capital. The SEC missed it because they were looking at securities laws, not tokenomics. Now they want to apply the same tired lens to AI.
The core analysis must cut through the hype. Let’s examine three dimensions: feasibility, cost, and unintended consequences.
Feasibility. Defining “frontier AI” is the first nightmare. Is it by parameter count? FLOPs? Compute budget? Or by application domain? If you set a threshold at 10^26 FLOPs (the current bar for frontier), you catch models like GPT-4 and Claude 3 but miss smaller, fine-tuned models that could be weaponized. The definition will be contested, lobbied, and eventually captured by incumbents. My 2017 ICO analysis taught me that any metric becomes a compliance barrier once it’s codified.
Cost. Compliance doesn’t come free. The SEC’s own estimate for Reg A+ compliance is $50k-$100k per filing. For frontier AI, expect millions. This will crush open-source and academic projects. Remember the 2022 Terra collapse? The cost of auditing Anchor Protocol was zero—because decentralization was the entire point. Now imagine requiring every AI model to undergo a federal audit before release. The speed advantage of open-source innovation vanishes.
Unintended consequences. The biggest blind spot: this regulation will drive AI development underground or offshore. Just as crypto exchanges moved to the Bahamas and Singapore after the SEC’s crackdown, AI labs will set up in jurisdictions with lighter rules. The US will lose its leadership. And the models trained abroad won’t necessarily be safer—they’ll just be invisible to US regulators. Chaos is just data that hasn't been collected yet.
Now, the contrarian angle—and where crypto fits in. Most analysts will see this proposal as a positive for “institutional adoption.” A regulated AI market, they argue, will attract pension funds and sovereign wealth. That’s true. But it’s also the decoupling thesis I’ve been tracking since the 2024 Bitcoin ETF inflows. Institutional capital follows compliance. It doesn’t follow innovation. The trap is mistaking safety for health. A regulated AI sector becomes a slow, expensive, and centrally controlled ecosystem—perfect for incumbents, terrible for breakthroughs.
The real opportunity lies in decentralized AI networks that operate outside the new regulatory net. Projects like Render (decentralized GPU rendering) or Fetch.ai (autonomous economic agents) don’t fit the definition of “frontier AI” because they are composable, permissionless, and asynchronous. The regulation defines a model; it doesn’t define a network of agents. In my 2026 hypothesis on AI-crypto compute markets, I argued that blockchain’s trust layer is the only way to verify AI outputs without centralized oversight. This proposal accelerates that thesis.
Decentralized AI will become the escape hatch. When centralized models are bogged down by compliance paperwork, open networks that use zero-knowledge proofs for inference verification will offer a faster, cheaper alternative. The yield will shift from regulated safety to unregulated efficiency. And the macro watchers—people like me—will track the migration of compute liquidity from compliant clouds to decentralized renderers. That’s where the next liquidity pulse will come from.
Takeaway: In the next cycle, the winners won’t be the best AI models. They’ll be the ones that exist outside the regulatory net. Bessent’s plan is a gift to crypto—if we have the vision to see it. The illusion of infinite growth through regulation is just that: an illusion. Real growth comes from the chaos of permissionless innovation. And chaos, as always, is just data that hasn’t been priced in.