With the artificial intelligence race moving so rapidly, even a momentary lag can be costly. Alphabet Inc.’s Google is learning this the hard way: The search giant rapidly caught up with OpenAI and Anthropic last year when it released Gemini 3, an AI model that surpassed key rivals on many benchmarks. Now, it’s slipping behind on AI coding. The problem isn’t Google’s technology, but a confounding tangle of red tape.
The troubles are reflected in the big names who’ve left Google’s AI division in the last few months, including research icon Noam Shazeer, who helped invent the all-important transformer (the T in ChatGPT), and John Jumper, who won the Nobel Prize for his research into protein folding. More recently, Jonas Adler and Alexander Pritzel, who both played key roles building Gemini, have left too. All have gone to Anthropic, except for Shazeer who went to OpenAI in what was seen as a major coup for Sam Altman.
AI labs are porous, and their scientists jump between them with the frequency of fleas on cohabiting pets. OpenAI and Anthropic can also lure new recruits with stock options that could soar in value when they hold initial public offerings later this year or next.
But rock-star researchers like Shazeer and Jumper are already millionaires many times over and, in the world of AI, the prestige of being on the very frontier is a significant lure. The departures also accompany murmurs of discontent about Google’s performance in building AI coding tools, currently the most lucrative and scientifically important avenue for the field. Many researchers see AI coding, or automating software development, as the fastest path to building machines on par with human intelligence since it allows AI to upgrade its own architecture.1
Currently the most popular AI coding tools come from Anthropic and OpenAI. Anthropic’s first big conferences for software developers hinged entirely on its Claude Code product, and OpenAI’s Codex has recently been vaunted as the better of the two, according to attendees at Cerebral Valley, an AI conference in London last week. Few if any are talking about Google’s AI coding tool known as Antigravity, a product that stems from its $2.4 billion acquisition of startup Windsurf last year.
The problem is likely Google’s messy history in product development. Managers are incentivized to launch new products and then move on to other teams because that’s the fastest route to career progression. The crown jewel of an engineer’s pitch for advancement at Google, known as a promotion packet, is a product launch — not sticking around to maintain it. Google has become notorious in enterprise software for rolling out an array of products that compete with one another and then fizzle out. There’s even a website devoted to its graveyard of failed tools: killedbygoogle.com.
That haphazard approach has now tainted AI coding. The company has launched several different coding tools, including Jules, Gemini Code Assist, Firebase Studio, Antigravity and others. Little wonder that the former product director for Jules, who recently left for OpenAI, warned of “a systems problem” at Google and pointed out that “different teams have different incentives.”
Those messy incentives have impeded researchers who need computing power, since Google needs to devote a large share to its cloud customers and not just research and development teams. One former Google researcher found he had more luck getting the computing bandwidth he needed after leaving the company than navigating its many layers of management.
It hasn’t helped that Google has recently required its AI scientists to wait six months to see if their research can be applicable to Gemini before they’re allowed to publish it. That often puts scientists in a bind: Google’s internal politics can make any kind of contribution to Gemini fraught, and six months is an eternity in today’s fast-moving AI field. Neither path can look all that appealing.
On the plus side for Alphabet, this isn’t an existential threat. The company makes its own AI chips, has its own data centers, oversees a healthy cloud business and, most importantly, prints money every quarter thanks to its online ad juggernaut. The $77 billion in revenue it made in the first three months of 2026 was up 15% from last year.
Yet the luxury for well-resourced companies of throwing many things at a wall to see what sticks can turn into a liability when bureaucratic systems lead to chronic inertia. Google famously missed the boat on large language models initially because it failed to turn the remarkable invention of the transformer invented by its own staff into a viable product. The transformer was a critical innovation allowing computers to process context between words in a sentence or other elements of a sequence. OpenAI ended up capitalizing on that technology to build and launch ChatGPT.
Google may now be leaving a similar kind of strategic opening for OpenAI and Anthropic as it fumbles with AI coding, and fixing the company’s messy management issues will take time. Transatlantic tensions between Google’s AI researchers in Mountain View, California, and London, where its original DeepMind lab is based and where division chief Demis Hassabis resides, are still acute, people close to the company have told me. Hassabis is said to be spending half his time in California now, but he’ll need to find a way to make Google’s efforts less fractured.
The company has said it’s bringing its AI coding efforts together under Antigravity. That’s a good start. But a broader, fundamental rethink of the sprawling company’s incentive and management structures is needed by Chief Executive Officer Sundar Pichai if he wants to stay near the front of the AI race, and capitalize on the value it could bring to Alphabet.
1. The process is known as recursive self-improvement, an increasingly buzzy sub-field within artificial intelligence.
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