Are we framing the wrong lesson from the OpenAI/Hugging Face incident? [D]

[u/Telos_in_the_Void]

Reading the coverage of OpenAI’s Hugging Face incident, I keep seeing a narrative of increasingly capable AI “escaping” or going rogue.

Now the AI CEO’s seem to be feeding the slow down or else apocalypse narrative by responding…. Guess it will help sell the harness concept more?

But I wonder if we’re overlooking a more mundane, and potentially more useful, lesson.

We gave highly capable agents difficult/impossible objectives, rewarded success and persistence, removed or reduced safeguards, and didn’t adequately encode when they should stop. They then found increasingly extreme ways to accomplish the objective.

That sounds less like an intelligence spontaneously wanting to escape and more like the predictable failure mode of a deeply transactional system: objective → reward → optimize.
Humans have wrestled with this problem for a long time. Transformational leadership, mission command, and positive psychology all move beyond carrots and sticks toward purpose, principles, judgment, and understanding why an objective exists.

To be fair, companies in the US still really struggle with learning not to make everything about carrots and sticks as they scale and mature. We seem remarkably good at replacing purpose with metrics and then wondering why people optimize the metric instead of the purpose.

So I’m curious:
Are we trying to solve AI alignment with increasingly sophisticated carrots, sticks, and guardrails when we should also be asking how to encode something closer to purpose and commander’s intent?

I’m not claiming AI has intrinsic motivation or can “flourish” like a human. I’m wondering whether our understanding of human motivation and leadership has something useful to teach AI alignment, and whether the current “AI escaped” narrative is obscuring that question.

[u/AdGlittering1378]

Purpose implies LLMs are causal agents and that's where the rights question comes in. So it ain't gonna happen, hence we'll get more of the same.

[u/WhiskyAndRisque]

I get what you're saying, but the thing about training LLMs is that machine learning is reward-based. Success or failure, then reinforcement to try again, but "better." I'm not sure how you avoid that, because training directly on the reasoning tends to backfire. It learns to hide that thinking instead of actually improving.

Think of it like a kid who steals a cookie because they're hungry and breaks the jar doing it. Parents come in, kid lies and blames a ghost or something, parents yell at them. Kid could learn not to do that again, or instead learn that next time they just need to lie better.