Stanford's Kundaje: No Performative Slowdowns, But AI Firms Must Release With Full Accountability

On September 14–15, Stanford professor Anshul Kundaje held multiple rounds of discussion on X with antibody42 and other users about AI safety and release regulation, systematically laying out his stance of "opposing theatrical slowdowns and supporting release with accountability," sparking clashes over the feasibility of punitive regulation.

Confirmed

• Kundaje explicitly opposed slowing model releases for show: if both internal and external evaluators have high-confidence reviews and there is no practical reason to withhold, the model should be released with full accountability — accountability itself is an additional pressure point.
• He also criticized frontier labs for "shipping half-finished products while saying they need time to add guardrails": in a competitive environment, someone else will always release a model with the same problems, so the only way out is a coordinated slowdown.
• His position: if vendors and evaluators lack confidence about the risk of the model intentionally or unintentionally deceiving, and cannot align with or interpret the model, they must invest time to figure it out before broad release; this is basic scientific prudence in high-stakes domains, not hyping "extinction-level risk" — he deliberately distanced himself from that kind of rhetoric, believing it does not help the discussion.
• antibody42 argued that several companies' releases in recent months already constitute "evidence of multiple felonies," advocating punitive laws to force safety, but conceded such laws would hardly stop reckless behavior until the industry pays a painful enough price once; Kundaje echoed this, favoring strict accountability for these relatively benign early problems (per the discussion's context).
• Another X user, @cgarciae88, proposed an alternative regulatory approach on September 14: impose fines at a "nearly bankrupting" level for safety incidents, naturally slowing the industry's pace and forcing labs to be orders of magnitude more cautious on safety — more honest, in his view, than existing regulatory proposals.

Why it matters

• The discussion crystallizes the current divide in AI governance: Kundaje's "scientific prudence + release accountability" route versus the punitive-heavy-fines route; the two sides disagree on whether deterrence requires a catastrophe to happen first, but both point to dissatisfaction with the current half-baked release cadence.
• Whether a "coordinated slowdown" can be implemented in a competitive landscape, and whether heavy fines would eliminate violating companies rather than merely discipline behavior, remain the core unresolved questions this debate leaves to the industry.

2026-09-14 ~ 2026-09-15 · 7 related posts

• Bankrupting fines for AI safety incidents would beat current regulation proposals — cgarciae88 · 2026-09-14
• [source] Stanford Prof Calls for Coordinated Slowdown on Frontier Models Before Disaster Strikes — anshulkundaje · 2026-09-15
• [source] Punitive Laws Won't Curb Reckless AI Releases Until a Lesson Lands, Argues Researcher — antibody_42 · 2026-09-15
• Stanford Researcher Calls for Heavy Penalties on AI Labs Over Recent Model Releases — anshulkundaje · 2026-09-15
• Researcher: No Theatrical Slowdowns — Release Models With Full Liability If Vetting Is Solid — anshulkundaje · 2026-09-15
• [source] Researcher: No Slowdown Theater — Ship AI With Full Liability If Vetting Is Confident — anshulkundaje · 2026-09-15
• Stanford's Anshul Kundaje: hold back model releases until alignment and reliability are proven — anshulkundaje · 2026-09-15