Sam Altman — In Conversation: Lessons on Building OpenAI

Altman's Stanford Human-Centered AI symposium conversation (2024, ~90 minutes) was his most candid academic-audience appearance — fielding questions from researchers including those skeptical of OpenAI's safety approach rather than the supportive startup-world audiences he usually addresses. His answers were notably more qualified than in commercial settings: he acknowledged that current interpretability tools are insufficient for verifying alignment in frontier models; that RLHF produces models that are "aligned to human preference" in a narrow sense that doesn't necessarily mean "aligned to human values" in the deeper sense; and that the argument "we must race because someone else will if we don't" is a reasoning pattern that should be examined critically rather than accepted as a given. His most revealing answer came on the question of what would cause him to stop: "if I genuinely believed we were building something that would harm humanity, I would stop." The follow-up — "how would you know?" — produced a careful and honest response: evaluations, red-teaming, and external review are necessary but not sufficient, and there is irreducible uncertainty about whether current safety methods are adequate. The academic context produced the closest Altman has come to publicly acknowledging that the safety-and-speed-are-compatible thesis might have limits. Reading this against Super Intelligence shows the gap between his public advocacy and his private uncertainty.