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Google's Internal Politics Leave It Playing Catch-Up on AI Coding
At Google, leaders are anxious about falling behind in the race to offer AI coding tools, especially as rivals like Anthropic PBC offer more effective and popular tools to businesses, according to people familiar with the matter. The search giant is now working to unite some of its coding initiatives under one banner to speed progress and take advantage of a surge in customer interest. In some corners of Alphabet Inc.'s Google, particularly AI lab DeepMind, concerns about the company's position are mounting, according to current and former employees and executives, who declined to be named because they weren't authorized to speak publicly. Businesses are just starting to realize that AI coding tools can enable anyone to build products by prompting a chatbot. But Google doesn't have a clear solution for them. Its Gemini model's capabilities are sprinkled across half a dozen different coding products with different branding, indicating how the company's lack of focus and competing internal efforts have hampered success, the people said. Even internally, some Google engineers prefer to use Anthropic's Claude Code, they said. More concerning, the people said, are the engineers who are struggling to adopt AI coding at all. Google has made some effort to reduce the internal confusion over priorities. Chief AI Architect Koray Kavukcuoglu is working with Google's main engineering team to unite the company's internal artificial intelligence coding tools in the coming weeks under Antigravity, a platform released last year, according to a spokesperson. DeepMind is also devoting more resources to AI coding by forming a new team led by research engineer Sebastian Borgeaud, according to a former Google employee. That new team was earlier reported by The Information. John Jumper, who won the Nobel Prize in 2024 alongside Google DeepMind Chief Executive Officer Demis Hassabis, is also at work on AI coding, according to a person familiar with the matter. Google was widely viewed as ascendant in AI late last year with the release of Gemini 3, a model that appeared to outperform rival services across a range of benchmarks. In recent months, however, Anthropic and OpenAI have gained business momentum by focusing on the lucrative market for products that streamline the process of writing and debugging code to speed up software development. "Coding is the single easiest way to actually make money," said Keith Zhai, co-founder of startup TinyFish, which makes web agents. Many engineers in the valley toggle back and forth between Claude Code and OpenAI's Codex to see which program will give them the best results, but Google often isn't in the conversation, he added. Google still has plenty of reasons to feel confident about its position: the company has made big strides in the quality of its foundation models, which underlie coding tools, and it has deep pockets and substantial computing power. "We've seen tremendous adoption of our internal coding tools such as Antigravity and others since introducing them over recent years, and their use has been turbocharging our model and AI tooling development," a Google spokesperson said in a statement. Meanwhile, Google has been eager to tout the speed of its internal culture change. Alphabet said in February that roughly 50% of new code at the company is written by AI. But Silicon Valley engineers are embracing AI coding so quickly that even a momentary lag in the market could be consequential. There is a growing conviction in the industry that coding is not just a lucrative early application of AI, but the key to building software that matches human capabilities, said Raj Gajwani, a former Google executive who is now chief business officer of startup OpenArt AI. "From a computer science point of view, if you win at coding this year, you get the raw data you need to win at model capability next year," he said. Google's emphasis on its own technology has also complicated the push to catch up. Most employees are banned from using competing tools such as Claude Code or Codex due to security concerns, but Googlers can request exceptions if they can demonstrate they have a business case, one former employee said. Some teams at DeepMind, including those working on the Gemini model, internal applications, and open source models, use Claude Code, according to three former employees. "You want the best people to use the best tool, even inside Google," one of the former employees said. Anthropic cut off OpenAI's access to its models last year, Wired magazine reported. Google has invested billions of dollars in Anthropic. A spokesperson for Anthropic did not immediately reply to a request for comment. In recent years, DeepMind has tried to tighten control over how its AI breakthroughs are woven into Google products. Last year, Google appointed Kavukcuoglu to a new position as chief AI architect, a role in which he is charged with folding generative AI into Google products. Yet confusion about who is leading the charge on AI coding persists. Along with DeepMind, Google Cloud, Google Core, Google Labs and Android are all pushing AI coding in different ways, one of the people said. Google released its Antigravity platform last year following the acquisition of talent and technology from startup Windsurf in a $2.4 billion deal. It joined a cluttered lineup of Google AI coding tools that includes Gemini Code Assist, Gemini CLI, AI Studio, Firebase Studio and Jules. Kathy Korevec, who oversaw Jules, jumped from Google to OpenAI earlier this month, according to her LinkedIn profile. In a post on social network X, Korevec wrote that Google had an opportunity to build AI developer tools that "feel cohesive, intuitive, and truly great to use. What I saw more often was fragmentation. Parallel tools. Overlapping surfaces. Smart teams solving similar problems in slightly different ways. That's not a talent problem. It's a systems problem." Korevec didn't immediately reply to a request for comment. Get the Tech Newsletter bundle. Get the Tech Newsletter bundle. Get the Tech Newsletter bundle. Bloomberg's subscriber-only tech newsletters, and full access to all the articles they feature. Bloomberg's subscriber-only tech newsletters, and full access to all the articles they feature. Bloomberg's subscriber-only tech newsletters, and full access to all the articles they feature. Plus Signed UpPlus Sign UpPlus Sign Up By continuing, I agree to the Privacy Policy and Terms of Service. Within the Googleplex, there is a philosophical clash between AI researchers who want to move as quickly as possible and more traditional senior engineers who have exacting standards for code quality, former employees say. AI usage is factored into performance reviews, according to a former employee. But engineers who try to use internal AI coding tools often hit capacity constraints due to competition for computing power, the former employee said. One of the executives who oversaw efforts to promote AI coding within Google, Brian Saluzzo, recently departed. Saluzzo did not immediately reply to a request for comment. Companies are still figuring out how to best incorporate AI into their workflows, and having a variety of products on offer gives Google more chances to see what sticks. But incumbent players like Google have only so much of an edge, said Deepti Srivastava, a former Google executive who is founder and CEO of AI startup Snow Leopard. "The market is moving too fast for the larger companies to think about it and then move," Srivastava said. "Speed is your only moat."
[2]
Google says AI now generates 75% of its new code, up from 25% in 2024
Serving tech enthusiasts for over 25 years. TechSpot means tech analysis and advice you can trust. Bottom line: Google said on Wednesday that about 75% of its new code is now generated by AI and then reviewed by engineers, a figure that marks a sharp increase from recent levels. In October 2024, Google said that about a quarter of its code was generated by AI. By late 2025, that share had doubled to 50%. The latest update shows AI is now a primary method for producing code at Google, rather than a supplemental tool. The shift is tied closely to Google's internal deployment of its Gemini models, which engineers are using to generate, refactor, and migrate code. The company has also pushed broader use of AI tools beyond engineering, tying their use in some cases to performance reviews. CEO Sundar Pichai described the shift as part of a broader change in how work gets done at the company. He said Google is moving toward "truly agentic workflows," in which engineers increasingly orchestrate systems that can carry out complex tasks with minimal intervention. He pointed to measurable gains in productivity from these approaches, adding that "Recently, a particularly complex code migration done by agents and engineers working together was completed six times faster than was possible a year ago with engineers alone." The focus on agentic systems points to a shift away from code-completion tools toward systems that can plan and execute development tasks independently. In practice, that includes codebase migrations, large-scale refactoring, and other operations that historically required sustained human coordination. The rollout of AI tools has not been uniform across the company. Some teams within Google DeepMind have been allowed to use Anthropic's Claude Code alongside Google's own models, a decision that has reportedly introduced friction among employees over tool standardization and strategy. Google's approach mirrors a broader shift across large tech companies toward treating AI-generated code as a core part of development. Microsoft has reported similar progress, with CEO Satya Nadella saying in April last year that 20% to 30% of the code in some projects was written by AI. Around the same time, CTO Kevin Scott said he believed 95% of code would be generated by AI within five years. Meta has also set formal AI usage targets within its engineering teams. Internal documents indicate that, as of the fourth quarter of 2025, the company aimed to have 55% of code changes in certain groups be agent-assisted. For the first half of 2026, some teams are expected to have 65% of engineers use AI to write more than 75% of their committed code. At Snap, the transition is already reflected in operating models. The company said earlier this month that at least 65% of new code is generated by AI under its current structure. These changes indicate a shift in the role of developers, with engineers increasingly supervising automated systems rather than writing most code themselves. At Google, where AI already produces most new code, that shift is well underway.
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Skeptical of AI-coded software? Google uses AI for half its code, and it's pushing for much more
The company only uses AI for about half of its code, according to February 2026 figures, while Anthropic uses AI for nearly all of its code. Coding platforms using specialized AI models and autonomous agents, like Claude Code, are taking off for both personal and enterprise workflows. Earlier this year, Spotify admitted that its best developers hadn't written a single line of code in months. Google is taking the threat seriously, and plans to ramp up its internal use of AI coding tools, according to a report by The Information. As part of the effort, the report claims Google is building a "strike team" of researchers and engineers to build better AI coding models, citing three sources with knowledge of the plans.
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Google Puts Together A-Team to Build Better Coding Models Than Anthropic
Google co-founder Sergey Brin is said to be involved in the project Coding seems to be the flavour of the month for artificial intelligence (AI) companies. Recently, OpenAI increased its focus on its coding platform Codex, and now Google appears to be shifting its focus as well. As per a report, the Mountain View-based tech giant's DeepMind division has created a specialised team that has been tasked with building AI models focused on coding. The aim is, reportedly, to close the gap between the company and Anthropic and is likely to secure a larger share of the lucrative high-ticket enterprise revenue. Google's A-Team Targets Anthropic's AI Models According to The Information, Google DeepMind has created a new team of researchers and engineers to develop the coding capabilities of future Gemini models and to build coding-focused large language models (LLMs) from scratch. This team is reportedly being led by research engineer Sebastian Borgeaud, who previously looked after pre-training of AI models. Some of the areas the team is focused on are said to be complex coding work, long-horizon programming, writing an entire software from scratch, and enabling the models to read files to contextually understand the requirements of the user. It appears that the company is aiming to develop an AI model capable of handling end-to-end coding tasks. Google's Co-Founder, Sergey Brin, and DeepMind's CTO, Koray Kavukcuoglu, are reportedly directly involved with the team. In an internal memo seen by the publication, Brin reportedly highlighted the need to "urgently bridge the gap in agentic execution and turn our models into primary developers." The Co-Founder is also said to have asked engineers working on Gemini to use internal agents for complex tasks. Google is reportedly pushing to improve the coding capabilities of its models because the executives believe that Anthropic's models are better at such tasks, and the Gemini maker does not want to be left behind. With the enterprise adoption of AI coding tools increasing, major AI players have switched their focus to developing models that can handle complex tasks autonomously. Just last week, OpenAI upgraded Codex with Computer Use and image generation capabilities, allowing the desktop app to access files, apps, and interfaces to write code, run programs, test software, and iterate on existing builds. The image generation feature also bolsters the coding tool's ability to handle frontend tasks.
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Google creates strike team to improve AI coding models, catch up with Anthropic: Report
The team is expected to focus on building AI systems that can handle complex, long-term coding tasks. Google has reportedly created a strike team to improve its AI coding models. According to a report by The Information, the move comes after growing belief within Google DeepMind that coding tools developed by Anthropic are currently ahead of Google's own Gemini models. AI-assisted coding has become one of the most competitive areas. Anthropic has been focusing heavily on this segment with its tool Claude Code. The company has even said that most of its code is now written with the help of AI. However, according to Google's CFO Anat Ashkenazi, AI currently writes around half of the company's code. The new strike team is expected to help improve that number while strengthening Google's AI coding capabilities overall. Also read: OpenAI releases Chronicle in Codex: What is it and how to use The strike team is led by Sebastian Borgeaud, who previously worked as the pre-training lead for Gemini at Google DeepMind. The group is expected to focus on building AI systems that can handle complex, long-term coding tasks. Also read: 'Legend': Sam Altman and other leaders react as Tim Cook steps down as Apple CEO Senior leadership is also said to be closely involved in the initiative, including Google co-founder Sergey Brin and DeepMind CTO Koray Kavukcuoglu. Google is also reportedly focusing on using AI internally. Until now, many of the company's AI models have primarily been designed for external developers and customers. Google is even tracking how often employees use these AI coding tools through an internal leaderboard. For those unaware, Claude Code is an AI-powered coding assistant by Anthropic that can read, modify and execute code on your computer via natural language commands.
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Google is assembling a specialized team to strengthen its AI coding capabilities after internal concerns that Anthropic's Claude Code outperforms its Gemini models. Despite AI now generating 75% of Google's new code, up from 50% in late 2025, the company faces internal confusion over fragmented coding products and struggles with adoption even among its own engineers.
Google DeepMind has formed a strike team of researchers and engineers to develop AI coding models that can compete with Anthropic's increasingly dominant Claude Code platform
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. The specialized team, led by research engineer Sebastian Borgeaud, aims to build AI coding models from scratch with capabilities spanning complex coding work, long-horizon programming, and writing entire software applications autonomously4
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. Google co-founder Sergey Brin and DeepMind CTO Koray Kavukcuoglu are directly involved in the initiative, underscoring the urgency within Google to catch up with competitors in the lucrative enterprise market for AI coding tools4
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Source: Digit
Despite Google's public confidence, internal anxiety is mounting across the company, particularly within Google DeepMind, where some engineers prefer using Anthropic's Claude Code over Google's own solutions
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. The company's Gemini AI models capabilities are scattered across half a dozen different coding products with inconsistent branding, reflecting a lack of focus and competing internal efforts that have hampered progress1
. While most employees are banned from using competing tools due to security concerns, some teams working on Gemini, internal applications, and open source models have secured exceptions to use Claude Code1
. This internal AI adoption challenge highlights a broader problem: even as businesses realize that AI coding tools can enable anyone to build products by prompting a chatbot, Google doesn't have a clear solution for them1
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Source: Gadgets 360
Google announced that AI generates 75% of its new code as of April 2026, a sharp increase from 50% in late 2025 and just 25% in October 2024
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. CEO Sundar Pichai attributed this surge to the deployment of agentic workflows, where engineers orchestrate systems that carry out complex tasks with minimal intervention2
. The company cited productivity gains, noting that a particularly complex code migration completed by agents and engineers working together finished six times faster than was possible a year ago with engineers alone2
. These systems now handle codebase migrations, large-scale refactoring, and other operations that historically required sustained human coordination2
.Source: TechSpot
Related Stories
Industry experts view AI coding as more than just a profitable early application—it's considered the key to building software that matches human capabilities
1
. "From a computer science point of view, if you win at coding this year, you get the raw data you need to win at model capability next year," said Raj Gajwani, former Google executive and current chief business officer at startup OpenArt AI1
. Keith Zhai, co-founder of startup TinyFish, noted that "coding is the single easiest way to actually make money," adding that many Silicon Valley engineers toggle between Claude Code and OpenAI's Codex to see which program delivers better results, but Google often isn't part of that conversation1
.To address internal confusion over priorities, Chief AI Architect Koray Kavukcuoglu is working with Google's main engineering team to unite the company's internal AI coding tools under Antigravity, a platform released last year
1
. Google DeepMind is also devoting more resources to AI coding, with Nobel Prize winner John Jumper reportedly working on code generation alongside the new team1
. In an internal memo, Sergey Brin emphasized the need to "urgently bridge the gap in agentic execution and turn our models into primary developers," asking engineers working on Gemini to use internal agents for complex tasks4
. The company is even tracking employee usage of AI coding tools through an internal leaderboard, in some cases tying their use to performance reviews2
5
. While Google still has deep pockets, substantial computing power, and has made significant strides in foundation model quality, Silicon Valley engineers are embracing AI coding so quickly that even a momentary lag could prove consequential in this rapidly evolving software development landscape1
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