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.