When Google opens its doors for its annual developer conference, I/O, it does so as a clear third place in the foundation model race. A year ago, at Google I/O 2025, the situation looked very different: the company was still riding high from the launch of Gemini 2.5 Pro that March, and distinguishing among the top-tier large language models often felt like a subjective splitting of hairs.
But a foundation model's reputation these days rests largely on its coding capabilities, and for months Google's coding tools have been outgunned by Anthropic's Claude Code and OpenAI's Codex. Those systems are so dramatically superior to Google's own offerings that the company has reportedly had to allow some engineers at DeepMind, its AI division, to use Claude for their work—lest they fall farther behind.
While we will certainly be on the lookout for any efforts Google is making to claw its way back into the frontrunner position, we are also eager to see new developments in areas where Google shapes the cutting edge, such as AI for science. The company's moves there might receive less attention, but they will be no less consequential.
Here are two things to pay particular attention to over the next few days:
1. An Attempted Coding Comeback
Google is taking its AI coding crisis seriously. According to reporting from The Information, there is a new AI coding team at DeepMind. Reports also indicate that John Jumper, who shared a 2024 Nobel Prize in chemistry with DeepMind CEO Demis Hassabis for their work on AlphaFold, is lending his talents to these efforts. We will likely see a major new coding release at I/O, perhaps in the form of an update to the company's Antigravity agentic coding platform.
That said, we shouldn't expect an instant transformation. Googlers have access to internal models substantially ahead of those released to the public, yet they were reportedly fighting over access to Claude Code last month. Unless Google has made astonishing progress since then, reclaiming the coding frontier immediately remains a steep challenge.
2. Dominance in Science and Health
While coding might be DeepMind’s weakness, science is its conspicuous strength. It is the only frontier AI company to have earned a Nobel Prize. As LLMs dominate the AI-for-science landscape, Google has only solidified its lead. Last year, the company released multiple scientific AI tools, including the AI co-scientist, which formulates hypotheses and research plans, and AlphaEvolve, a system that iteratively discovers new solutions for mathematical problems.
[AgentUpdate Depth Analysis] The intense battle over AI coding tools signals a paradigm shift in the AI Agent ecosystem: we are moving rapidly from static code completion to fully autonomous, agentic workflows. Google’s rumored Antigravity platform represents its bid to catch up with Anthropic’s Claude Code. To succeed, Google must leverage its strength in massive context windows to enable agents to comprehend entire codebases seamlessly. Google’s current struggle underscores that raw model scale is no longer enough; the winner of the developer market will be determined by how intuitively an AI Agent integrates into real-world developer loops. Conversely, DeepMind's unparalleled success in scientific AI, like AI co-scientist, hints at a broader strategy. By translating scientific rigor and iterative hypothesis testing into software engineering, Google could potentially Leapfrog competitors, turning coding agents from pure syntax generators into rigorous software architects.
