On a 5K screen in Kirkland, Washington, four terminals blur with activity as artificial intelligence generates thousands of lines of code. Steve Yegge, a veteran software engineer who previously worked at Google and AWS, sits back to watch.
“This one is running some tests, that one is coming up with a plan. I am now coding on four different projects at once, although really I’m just burning tokens,” Yegge says, referring to the cost of generating chunks of text with a large language model (LLM).
Learning to code has long been seen as the ticket to a lucrative, secure career in tech. Now, the release of advanced coding models from firms like OpenAI, Anthropic, and Google threatens to upend that notion entirely. X and Bluesky are brimming with talk of companies downsizing their developer teams—or even eliminating them altogether.
When ChatGPT debuted in late 2022, AI models were capable of autocompleting small portions of code—a helpful, if modest step forward that served to speed up software development. As models advanced and gained “agentic” skills that allow them to use software programs, manipulate files, and access online services, engineers and non-engineers alike started using the tools to build entire apps and websites. Andrej Karpathy, a prominent AI researcher, coined the term “vibe coding” in February, to describe the process of developing software by prompting an AI model with text.
The rapid progress has led to speculation—and even panic—among developers, who fear that most development work could soon be automated away, in what would amount to a job apocalypse for engineers.
“We are not far from a world—I think we’ll be there in three to six months—where AI is writing 90 percent of the code,” Dario Amodei, CEO of Anthropic, said at a Council on Foreign Relations event in March. “And then in 12 months, we may be in a world where AI is writing essentially all of the code,” he added.
But many experts warn that even the best models have a way to go before they can reliably automate a lot of coding work. While future advancements might unleash AI that can code just as well as a human, until then relying too much on AI could result in a glut of buggy and hackable code, as well as a shortage of developers with the knowledge and skills needed to write good software.
David Autor, an economist at MIT who studies how AI affects employment, says it’s possible that software development work will be automated—similar to how transcription and translation jobs are quickly being replaced by AI. He notes, however, that advanced software engineering is much more complex and will be harder to automate than routine coding.
Autor adds that the picture may be complicated by the “elasticity” of demand for software engineering—the extent to which the market might accommodate additional engineering jobs.
“If demand for software were like demand for colonoscopies, no improvement in speed or reduction in costs would create a mad rush for the proctologist's office,” Autor says. “But if demand for software is like demand for taxi services, then we may see an Uber effect on coding: more people writing more code at lower prices, and lower wages.”