According to Bloomberg, “Even CEO Shear has been left in the dark, according to people familiar with the matter. He has told people close to OpenAI that he doesn’t plan to stick around if the board can’t clearly communicate to him in writing its reasoning for Altman’s sudden firing.”
Evidence that Shear simply wasn’t told the exact reason, though the “in writing” part is suspicious. Maybe he was told not in writing and wants them to write it down so they’re on the record.
Sam’s latest tweet suggests he can’t get out of the “FOR THE SHAREHOLDERS” mindset.
“satya and my top priority remains to ensure openai continues to thrive
we are committed to fully providing continuity of operations to our partners and customers”
This does sound antithetical to the charter and might be grounds to replace Sam as CEO.
I do find it quite surprising that so many who work at OpenAI are so eager to follow Altman to Microsoft—I guess I assumed the folks at OpenAI valued not working for big tech (that’s more(?) likely to disregard safety) more than it appears they actually did.
https://twitter.com/i/web/status/1726526112019382275″Before I took the job, I checked on the reasoning behind the change. The board did *not* remove Sam over any specific disagreement on safety, their reasoning was completely different from that. I’m not crazy enough to take this job without board support for commercializing our awesome models.”
It seems to me that the idea of scalable oversight itself was far easier to generate than to evaluate. If the idea had been generated by an alignment AI rather than various people independently suggesting similar strategies, would we be confident in our ability to evaluate it? Is there some reason to believe alignment AIs will generate ideas that are easier to evaluate than scalable alignment? What kind of output would we need to see to make an idea like scalable alignment easy to evaluate?
“I think it doesn’t match well with pragmatic experience in R&D in almost any domain, where verification is much, much easier than generation in virtually every domain.”
This seems like a completely absurd claim to me, unless by verification you mean some much weaker claim like that you can show something sometimes works.
Coming from the world of software, generating solutions that seem to work is almost always far easier than any sort of formal verification that they work. I think this will be doubly true in any sort of adversarial situation where any flaws will be actively sought out and exploited. Outside of math domains I find it difficult to come up with examples of where verification is easier than generation, and easy to come up with the opposite.