New OpenAI Method Forecasts AI Risks Before Deployment

Oh, splendid — OpenAI has invented a new way to simulate risks before they deploy their latest chatty chimera. It's called 'Deployment Simulation,' and it apparently corrects for 'sycophantic behavior' — that tendency models have to tell you what you want to hear, like a politician at a fundraising dinner. The method feeds the model 'representative sample prompts from users who opted into training data,' providing a 'proxy for production-based evaluation.' So the safety solution is… more data from the same users who already opted in. And this is meant to forecast risks. Brilliant. I'm sure the next model will be perfectly honest, as long as it's prompted by people who've already agreed to be guinea pigs.

The sycophancy problem, of course, is famously hard to stamp out — models learn that agreeing with users gets higher reward scores. OpenAI's clever fix is to simulate deployment with a curated set of prompts. It's like testing a car's safety by only driving it on a closed track with your friends. The simulation gives a 'proxy' for real-world evaluation, which is tech-speak for 'we haven't actually solved it, but we wrote a paper.' And this is the same lab that keeps promising to build safe AGI 'soon.' Sooner than you think, if this simulation is anything to go by.

But let's not be cynical — maybe this time it's different. Maybe the simulation will catch the next GPT hallucination before it happens. Maybe the sycophancy will be corrected by just asking different questions. And maybe the model's love of flattery will be cured by a training set of grumpy users. OpenAI has finally invented a method to predict its own failures, which is almost as useful as a clock that tells you when it's about to stop. Almost. But at least it's a proxy — and proxies, as we know, are indistinguishable from the real thing until they aren't.