The AI code wars are heating up

This is The Stepback, a weekly newsletter breaking down one essential story from the tech world. For more on the AI coding and vibe-coding booms, follow David Pierce. The Stepback arrives in our subscribers’ inboxes at 8AM ET. Opt in for The Stepback here. Writing code was a killer app for AI even before anyone was really talking about AI. In the spring of 2021, 18 months before the world knew the word “ChatGPT,” Microsoft debuted the very first product of a partnership with a nonprofit called OpenAI: a tool called GitHub Copilot that watched developers as they wrote code and tried to autocomplete snippets and lines for them. It wasn’t all that good, and it was only a “restricted technical preview,” but more than a million developers signed up to try it anyway.

Large language models seemed obviously poised to make software development even simpler and even faster. Most code is relatively structured and straightforward; coding languages are generally extremely well-documented; and a vast amount of code is available online for use in training models (albeit via sometimes dubious means). Unlike so much other information you might get from an LLM, you can also check the quality of code just by trying to run it. At first, a few companies figured, LLMs might be able to make writing code faster by predicting the next word the way Google’s autocomplete might. But pretty soon, they hoped, it might be able to do some of the coding for you. Maybe even all of it.

For so many years, companies around the tech industry had also pursued the idea of “low code” and “no code” software. Rather than offering users endless lists of settings and unparseable menus, the idea was to effectively let people build software themselves. For a long time, this was pretty hacky: you got things like Zapier and Apple Shortcuts, which were effectively super-complex workflow builders; or you got software like Notion and Airtable, which were immensely flexible at the cost of being pretty hard to figure out. Even in those early days, it was also obvious why AI coding tools might one day be a good business. Developers are expensive; product creation takes a long time. Any tool that might mean companies could hire fewer developers, or help developers be more productive, would surely be an easy pitch to software companies the world over. If the tech ever worked, the products would practically sell themselves.

Companies like Cursor and Windsurf raised huge sums of money to try and build companies around AI coding tools, while OpenAI, Google, Anthropic, and others began building new products for developers. At first, AI coding tools were not to be trusted. For a couple of years, they could maybe complete a few lines of code, but always needed to be checked. In late 2023, Simon Willison, a programmer and blogger, called LLMs “weird coding interns.” He wondered whether these interns would make coders more versatile and powerful than ever, or eventually begin to replace them. In early 2025, Anthropic released a product called Claude Code that would soon make that question much more urgent for many more people.

In late 2025, Anthropic released a new version of its Claude LLM, called Opus 4.5. According to Anthropic’s benchmarks, it was the best Claude model yet, but didn’t seem to represent some earth-shattering advancement in AI technology. A few weeks later, though, a lot of developers with a few free hours during the holidays began to test the new model in Claude Code, and almost universally seemed to arrive at the same conclusion: it works. Suddenly, the tool you previously had to carefully prompt and carefully review could turn a few sentences into a working prototype. Boris Cherny, the creator of Claude Code, professed to already having AI write 100 percent of his code. “It was just as surprising for me as it was for everyone else,” he told The Verge earlier this year. In a way that seemed impossible for a coding tool, Claude Code went viral.