DeepMind’s cadence right now is wild—new drops practically daily. In this conversation, Demis Hassabis connects the dots: “thinking” models (Deep Think), world models that capture physics, and a path toward an omni model that unifies language, vision, audio, and interactive behavior. As an AI practitioner, I buy the core thesis: pure next-token prediction has hit diminishing returns; reasoning, tool-use, and grounded physical understanding are the new scaling dimensions.
I especially agree with the framing of thinking as planning—AlphaGo/AlphaZero DNA brought into the LLM era. The key is not the longest chain of thought, but the right amount of thought: parallel plans, prune, decide, iterate. That’s how strong engineers work, and it’s how models should spend compute. My caveat: “thinking budgets” still pay a real latency/energy cost. Until tool calls and sandboxed execution are bulletproof, deep reasoning will remain spiky in production.
The world model agenda resonates. If you want robust robotics or assistants like Astra/Gemini Live, you need spatiotemporal understanding, not just good text priors. Genie 3 is a striking signal: it can generate coherent worlds where objects persist and physics behaves sensibly. I’m enthusiastic—and I still want tougher tests than “looks consistent.” Sim-to-real is notorious; we’ll need evaluations for controllable dynamics, invariances (occlusion, lighting, continuity), and goal-conditioned behavior before I call it solved.
Hassabis is refreshingly blunt about jagged intelligence. Yes, models ace IMO-style math yet bungle simple logic or even chess legality. Benchmarks saturate (AIME hitting ~99%); we need new stressors. I like Game Arena with Kaggle—self-advancing tournaments give clear, leak-resistant signals and scale with capability. Where I push back: games aren’t the world. Outside well-specified payoffs, reward specification gets messy. The next wave of evals should be multi-objective and long-horizon—measuring planning, memory, tool reliability, and safety traits (e.g., deception) under distribution shift, not just single-shot accuracy.
Another point I applaud: tools as a scaling axis. Let models reason with search, solvers, and domain AIs (AlphaFold-class tools) during planning. The open question—what becomes a built-in capability versus an external tool—is empirical. Coding/math often lifts general reasoning; chess may or may not. My hesitation: as “models become systems,” provenance and governance get harder. Developers will need traceable tool chains, permissions, and reproducible runs—otherwise we ship beautifully wrong answers faster.
Finally, the omni model vision—converging Genie, Veo, and Gemini—feels inevitable. I’m aligned on direction, wary on product surface area. When base models upgrade every few weeks, app teams must design for hot-swappable engines, stable APIs, and eval harnesses that survive version churn.
Net-net: I’m excited by DeepMind’s trajectory—reasoning + tools + world modeling is the right stack. But to turn wow-demos into trustworthy systems, we must grow our evaluations just as aggressively as our models. Give me benchmarks that span days, not prompts; measure alignment under ambiguity; and prove sim-to-real. Do that, and an omni model won’t just impress us—it’ll hold up in the messy, physical, human world it aims to serve.
