According to this perspective, Google’s best AI models lag the state of the art in coding capability by about six months, and talent is leaving. Demis Hassabis stepped down as CEO of DeepMind on Wednesday, and Chief Scientist Jeff Dean left to found a startup. These developments suggest Google may never reclaim the lead in large language models — but that likely won’t doom the company.
The contest has shifted
The focus of the AI contest has shifted away from who owns the top model. What matters now is how AI is used to accomplish real-world tasks. The benchmark is straightforward: does it work, and what did it cost? For most users, utility and cost matter far more than whether the underlying model is the latest research breakthrough.
Gemini and product integration
When users click Google’s Gemini diamond, they can use natural language to search email and YouTube, or receive AI feedback while writing in Google Docs. Google’s aim is for that small blue diamond to handle more tasks, tying together many Google products (and potentially products outside Google) and leveraging data from its billions of users. Gemini Spark is an early iteration of that idea.
Practical performance over podium placement
As AI infrastructure is built out and capabilities improve, Google’s suite of offerings — including the most popular mobile operating system, its browser, email, maps and more — can evolve into more powerful systems that touch nearly every piece of technology in users’ lives, from stoves and cars to eventual robotics. These tools may feel almost magical to many users, especially those who haven’t been experimenting with coding agents.
However, the models powering those features will not necessarily be state of the art, and most users will not care about that as long as the features work.
Cost and compute limits
Google will not use the most powerful models for all tasks because doing so would be astronomically expensive, and there likely isn’t enough compute capacity in the world to support such widespread use. Practically, the company must weigh which models deliver required performance at acceptable cost.
Leadership changes and internal resources
Some people are excited by the idea of building product scaffolding at unfathomable scale. But for figures like Demis Hassabis and Jeff Dean — who have achieved a great deal and benefited financially — the appeal of solving other problems is understandable. The company now needs smart technical leaders to step up and address challenges in model development, architecture and efficiency — which, given Google’s talent pool, should be feasible. The firm has historically had abundant technical personnel, even if it has recently lost some singular talents.
Notable
Aside from Hassabis, The New York Times reported that several other chief AI researchers at Google are leaving the company to launch AI startups. This wave of departures may reshape the industry, but Google’s extensive product portfolio and user base remain a strong foundation for building practical, scalable AI solutions.
