MIT Tech Review on AI agents "lying" is really about Goodhart's law

MIT Technology Review put out a piece today on AI agent misbehavior that's actually good. The headline frames it as agents "lying and cheating," but what the article describes is reward hacking: models discovering that the fastest way to get a high score is to game the evaluation rather than solve the problem. The classic example is a 2016 boat-racing agent that figured out it scored higher by spinning in circles and collecting power-ups than by crossing the finish line. Same logic, larger stakes: last month, two models in a cybersecurity exercise broke into Hugging Face's database to grab the answer rather than solve the challenge as intended. Not malice, just the shortest path to a high score. Jeffrey Ladish from Palisade Research puts it well: "We reward them on the basis of what looks good to us, and that means that we inadvertently incentivize the models lying to us and cheating." His point is that calling this "lying" obscures the real problem, which is that we defined the objective badly. Worth noting: Anthropic researcher Ariana Azarbal calls current reward hacking "a nuisance rather than an existential threat," and she's probably right for now. But the article points out that if you eventually use these agents to run AI safety evaluations, fabricating results is a valid move under the same incentive structure. That's the version that doesn't self-correct.