In June 2021, GitHub announced Copilot, a kind of auto-complete for computer code powered by OpenAI’s text-generation technology. It provided an early glimpse of the impressive potential of generative artificial intelligence to automate valuable work. Two years on, Copilot is one of the most mature examples of how the technology can take on tasks that previously had to be done by hand.

This week Github released a report, based on data from almost a million programmers paying to use Copilot, that shows how transformational generative AI coding has become. On average, they accepted the AI assistant’s suggestions about 30 percent of the time, suggesting that the system is remarkably good at predicting useful code.

The striking chart above shows how users tend to accept more of Copilot’s suggestions as they spend more months using the tool. The report also concludes that AI-enhanced coders see their productivity increase over time, based on the fact that a previous Copilot study reported a link between the number of suggestions accepted and a programmer’s productivity. GitHub’s new report says that the greatest productivity gains were seen among less experienced developers.

On the face of it, that’s an impressive picture of a novel technology quickly proving its value. Any technology that enhances productivity and boosts the abilities of less skilled workers could be a boon for both individuals and the wider economy. GitHub goes on to offer some back-of-the-envelope speculation, estimating that AI coding could boost global GDP by $1.5 trillion by 2030.

But GitHub’s chart showing programmers bonding with Copilot reminded me of another study I heard about recently, while chatting with Talia Ringer, a professor at the University of Illinois at Urbana-Champaign, about coders’ relationship with tools like Copilot.

Late last year, a team at Stanford University posted a research paper that looked at how using a code-generating AI assistant they built affects the quality of code that people produce. The researchers found that programmers getting AI suggestions tended to include more bugs in their final code—yet those with access to the tool tended to believe that their code was more secure. “There are probably both benefits and risks involved” with coding in tandem with AI, says Ringer. “More code isn't better code.”

When you consider the nature of programming, that finding is hardly surprising. As Clive Thompson wrote in a 2022 WIRED feature, Copilot can seem miraculous, but its suggestions are based on patterns in other programmers’ work, which may be flawed. These guesses can create bugs that are devilishly difficult to spot, especially when you are bewitched by how good the tool often is.