The AI industry's biggest vulnerability isn't the models themselves, it's the regulatory patchwork that makes securing them impossible.
The Summary
- Rhodium Group's Reva Goujon argues that fragmented US and international regulatory environments are undermining AI model reliability and security
- Bot hacks and security breaches aren't just technical failures, they're coordination failures between jurisdictions
- Better regulatory alignment could improve cutting-edge model safety without slowing deployment
The Signal
The conversation around AI safety has been stuck in a false binary: move fast and break things, or regulate everything into paralysis. Reva Goujon from Rhodium Group is pointing to a third path: coordinated guardrails that actually work across borders.
The timing matters. We're watching AI companies sprint to deploy increasingly capable models while security researchers demonstrate how easily bots can be manipulated or compromised. The problem isn't that companies don't care about safety. It's that they're navigating a dozen different regulatory frameworks, none of which talk to each other, and most of which were written before anyone understood what a foundation model actually does.
"Better coordination in regulatory environments could improve the reliability of cutting-edge models."
Here's what holistic actually means in practice:
- US federal agencies aligning on baseline safety requirements instead of each writing their own
- International frameworks that let companies comply once instead of fifty times
- Security standards that focus on outcomes (can your model be jailbroken?) rather than process (did you file the right paperwork?)
The fragmentation creates perverse incentives. A company that wants to do the right thing has to choose which jurisdiction's rules to follow first. A company that wants to move fast can jurisdiction-shop until they find the path of least resistance. Neither outcome makes models safer.
Goujon's argument lands differently in 2026 than it would have two years ago. We've now seen what happens when AI safety is treated as a compliance checkbox rather than a design principle. The bot hacks aren't sophisticated. They're predictable. And they keep working because the regulatory environment rewards companies that ship fast over companies that ship secure.
The Implication
If you're building AI products, this matters immediately. The regulatory coordination Goujon describes isn't hypothetical anymore. The EU AI Act, US executive orders, and bilateral agreements between major economies are all moving toward convergence. Companies that wait for perfect clarity will find themselves years behind companies that participate in shaping those standards now.
For everyone else, watch where the coordination happens first. Model security standards that work across borders will define which companies can actually deploy agents at scale. The winners won't be the ones with the most impressive demos. They'll be the ones whose models you can trust when they're making decisions without human oversight.