Hong: DeepMind's Formal Math Slowdown Post-AlphaProof Was Non-Technical
Carina Hong · Scaling Past Informal AI - Carina Hong, Axiom Math · Jun 3, 2026 · at 6:18
Carina Hong, founder of Axiom Math, discusses why major frontier AI labs like Google DeepMind shifted focus away from formal mathematical verification.
0:00 / 0:12exact quote · 12.8s
▶ Watch the full episode on YouTube → 720p mp4 · rendered on demand · StarZero watermark
“After AlphaProof, kind of like, we didn't see a lot of the formal math you know, results or kind of progress from Google DeepMind, and that's actually because of reasons that are not necessarily technical.”
quote is from the automated transcript, cleaned for reading: filler sounds and stutters are removed, nothing is rephrased. names can be misheard (the analysis reads context, assessments check outside sources). how →
More from Carina Hong
Hong: Competitor's AI demo can be solved entirely by Lean's grind tactic
“We're talking about, for example, the grind tactic in Lean. It can currently handle a lot of mass proofs, like, at a very low level. And this is pretty shocking because I have seen, you know, actually another company working in the same space, like, you know, …”
Hong: Informal math systems will not achieve math AGI
“I'm going to say on the record, we do not believe that an informal math system is going to be the math AGI solution.”
Hong: Structured and formal data enables broad horizontal transfer learning
“If you have more structured and formal data, it's going to be a lot more horizontal than the specific vertical we are tackling.”
Hong: Formal verification in AI is about scaling superintelligence, not bug fixes
“It is not about, like, formal verification or verified AI to us. It's not just about handling or, like, kicking out the lousiness, the hallucinations, the mistakes. It's about scaling brilliance. It's about super intelligence.”