Google DeepMind

The specification change that matters is the context window moving from 32,000 tokens to 128,000 for smaller models and up to 256,000 for larger ones, because it changes which problems are solvable without infrastructure. A 32,000-token limit means retrieval, chunking and index management; at 128,000 many tasks fit whole and the workarounds become unnecessary. The deployment range runs from a browser with zero ongoing server cost through local runtimes to one-click hosted endpoints, and the strategically significant detail is interface compatibility — a local model speaking the same protocol as hosted APIs is substitutable without changing application code, which makes placement an operational choice rather than an architectural commitment. That is what makes open weights competitive: not being better, but making switching free.

Olivier / Google I/O

The distinction Reese draws early is the one that matters: an embodied reasoning model is not a vision model bolted to a robot but the logic unit of the system, fine-tuned on robotics data for spatial understanding, and reasoning about a scene's physics rather than its pixels. That collapses the seam between perception and planning where most traditional robotics failures lived, because the planner no longer receives categories with everything uncategorisable discarded. The browser-based demonstration carries an argument about access as much as capability, since robotics has been gated on hardware and a physics engine in a browser moves the constraint from equipment to ideas. The session's sharpest moment is its last: a model that hallucinates in software produces a strange recipe, and the same error rate attached to something exerting force is a different category of event — a gap of orders of magnitude, not an increment.

Paul Reese / Google I/O

The anecdote that opens the panel does the work: a robot asked to pick up the extinct animal selected a dinosaur toy, with nothing in its training data connecting the phrase to the object. That transfer from language models into machines with hands is the premise of the current wave. What the practitioners then describe is where it stops. Physical intelligence is about exerting force and using a body to do it, which is knowledge about consequences — the one thing a corpus of internet images contains almost nothing about. The humanoid question gets an honest treatment: not that human shape is optimal, but that the world is already built for it, plus a development-loop argument about collecting data and deploying on the same hardware. The most useful passage is scepticism about the field's favourite shortcut: generated video looks realistic and does not hold up for dexterous manipulation, because looking right and being physically consistent are different properties.

Kenjiro / Google I/O

The observation that organises this session is not about capability but about attention. Engineers increasingly file a ticket rather than opening an editor, and the code comes back — which changes what the surrounding tools are for. If the agent works while you do something else, the conversation between you is no longer a workspace; it is the mechanism by which the agent surfaces a question it cannot resolve alone. Interfaces built for continuous conversation optimise for flow, and interfaces built for interruption should optimise for the opposite. A runtime constraint follows immediately: an agent that starts a long-running job cannot block until it finishes, which turns out to be a workflow-engine problem rather than a model one. The panel's closing formulation — that deciding what to build is the hard skill and always was — reads as reassurance and functions as a warning, since that judgement is downstream of exactly the work now being delegated.

Tulsi Doshi / Google I/O

Four months after Davos, Hassabis put a sharper number on the same forecast: AGI around 2030, give or take a year, arriving gradually rather than as a single moment. His test for it is concrete — a model with a 1901 knowledge cutoff that could produce Einstein's 1905 insights — and by that standard current systems plainly fail. The interview is more useful than the Davos panel on two fronts. First, competitive position: he argues Google's advantage is being the only organisation holding the full stack from chips to billion-user products, citing 900 million monthly users on the Gemini app. Second, method: the AlphaFold story of choosing to fold every known protein at once rather than run a request service is his working example of what acceleration should look like. He closes on a warning aimed at the Bay Area — that direction matters more than velocity, and that the current frenetic pace is not conducive to the deep work the next advances require.

Demis Hassabis / Google I/O

Four of Google's model, product and search leads on what changes once agents run for hours rather than seconds, and the most quotable argument comes from Dean: the constraint is moving out of the model and into the tools around it. By Amdahl's law, an agent spending half its time in tools built for human-speed interaction cannot gain more than a doubling however fast the model becomes — which reframes a great deal of current infrastructure work as latency debt. Their internal response is concrete: rewriting Python tooling into Go, framed as a fully specified translation task rather than an open prompt, produced order-of-magnitude speedups overnight. Reid supplies the counterweight from Search, where acceptable latency turns out to scale with how much work is being taken off the user rather than being a fixed budget. Woodward's detail is the quietest and perhaps the most telling: teams that have stopped writing product documents for humans and now write context files for models to act on directly.

Koray Kavukcuoglu / Google I/O

A year after their first joint appearance, the heads of Anthropic and Google DeepMind returned to a shared stage and disagreed mainly about speed. Amodei held to a horizon of one to two years for systems that outperform humans across most cognitive work, resting the claim on a self-improvement loop that runs through code; Hassabis kept to the end of the decade, arguing that verifiable domains like coding and mathematics automate far earlier than natural science, and that the capacity to pose a new question rather than answer an existing one is still missing. The exchange is most useful where they converge: both accept the loop is the variable that decides everything, both are sceptical of doomerism without dismissing the risk, and both want more time than the competitive dynamic allows. Amodei's chip-export argument and Hassabis's call for minimum international safety standards are the two concrete policy asks.

Demis Hassabis / World Economic Forum Annual Meeting