DeepMind CEO Demis Hassabis pushes back on claims by Meta's Yann LeCun that he, Sam Altman, and Dario Amodei are fearmongering to achieve AI regulatory capture
Ng states that the idea that artificial intelligence could lead to the extinction of humanity is a lie being spread by big tech in the hope of triggering heavy regulation that would shut down competition in the AI market. … X: Yann LeCun / @ylecun : Altman, Hassabis, and Amodei are the ones doing massive corporate lobbying at the moment. They are the ones who are attempting to perform a regulatory capture of the AI industry. You, Geoff, and Yoshua are giving ammunition to those who are lobbying for a ban on open AI R&D. If your fear-mongering campaigns succeed, they will *inevitably* result in what you and I would identify as a catastrophe: a small number of companies will control AI... Jeremy Howard / @jeremyphoward : Whilst @geoffreyhinton is a *brilliant* scientist, scientists are *not* the people to teach us about risk management. The project manager for the Nuclear Information Project has already warned us about these “muddled analogies”, leading to a “calorie-free media panic”. [image] Mark Chen / @markchen90 : Let me get something straight. The folks who have been worried about AI safety consistently since 2015 — 3 years before GPT and 7 years before ChatGPT — have been using it this whole time as a tool for regulatory capture? Pedro Domingos / @pmddomingos : The psychology of AI alarmists: Elon Musk: Savior complex. Needs something to save the world from. Geoff Hinton: Ultra-leftist, world-class eccentric. Yoshua Bengio: Hopelessly naive idealist. Stuart Russell: His only impactful application ever was to nuclear test monitoring.... Sriram Krishnan / @sriramk : Realizing how important it was for @ylecun and team to get llama2 out of the door. A) they may have never had a chance to later legally B) we would have never seen what is possible with open source ( see all the work downstream of llama2) and thought of LLMs as the birthright of 2-4 companies. Clem / @clementdelangue : IMO compute or model size thresholds for AI building would be like counting the lines of code for software building. Regulation based on this will most likely be easily fooled, create hurdles/worries for companies to compete on bigger models (so concentration of power) and slow... Forums: r/singularity : Google DeepMind boss hits back at Meta AI chief over ‘fearmongering’ claim
Context & Ripple Effects
The dispute follows LeCun's earlier argument that regulating AI research would entrench incumbent platforms rather than protect competition, a position captured in his warning against regulating AI R&D. Hassabis's rebuttal makes the divide explicit: the disagreement is not only over AI risk, but over whether risk-based governance is a public safeguard or a competitive barrier.
It also sits alongside calls for rapid government action from figures such as Yoshua Bengio, whose case for public protection contrasted with LeCun's more permissive view of research. The argument matters because the credibility of safety advocates is becoming part of the policy fight itself.
First-order effects
- Hassabis, Altman and Amodei must defend safety advocacy against the claim that it is self-interested lobbying, while LeCun and Meta sharpen their case for keeping AI research open.
- The public policy debate becomes more polarized: proposals framed as frontier-model safeguards can be challenged simultaneously on safety grounds and on their effects on market entry.
Second-order effects
- AI labs and policymakers face greater pressure to distinguish rules aimed at dangerous deployments from restrictions on research, model access, or training capacity.
- Open-model advocates gain a clearer competitive narrative against concentrated frontier labs, while safety-focused labs have an incentive to make their governance proposals more specific and auditable.
Third-order effects
- If this divide persists, AI governance may be shaped as much by disputes over who gains from compliance as by agreement on technical risks, raising the importance of rules that do not simply privilege well-resourced incumbents.
- The sector could settle into competing institutional models—more controlled frontier development versus broader research access—rather than a single shared definition of responsible AI.
The trend: AI safety is evolving from a technical-risk discussion into a contest over market structure, legitimacy, and which labs get to help define the rules.