There have been plenty of predictions that LLM tools will succeed in replacing software developers. For instance, Dario Amodei said back in May 2025 that AI might cut white-collar employment in half. (More recently, he said that LLMs could take over the majority, or nearly all, of software-engineering tasks by July 2026 to January 2027.)

Another news headline proclaimed that "The 'learn to code' era is over". In addition, roughly half of both regular adults and AI experts believe that AI will result in fewer software-engineering jobs.

However, in this analysis of Current Population Survey data, I found that the number of US software-development jobs has actually increased since 2022. Average estimated employment for developers in 2022 was around 2.2 million, whereas it's now closer to 2.6 million. (This latter value is based on data from January to September 2026.)

This suggests that, even though LLMs have been widely adopted, they have not yet proven effective as a replacement for real, human developers. They may very well be augmenting developers, but even then, recent research has questioned exactly how much they're helping companies ship software.

These results are based on information that millions of individual Americans provided about their current (or most recent) occupation, along with their current employment status. They are not based on online job postings (though that data, interestingly, shows a positive trend from May 2025 onward).

I aggregated and analyzed these IPUMS-provided Current Population Survey responses myself rather than taking them from pre-tabulated government statistics. Therefore, while sampling error will of course skew the results, I don't believe that a pro-industry bias on the part of the government could explain this increase. (If you'd like to download a copy of this dataset yourself, you can find the variables and samples that I used within this section of the README.

There are a number of caveats to mention, though. First, I found that that unemployment among developers has increased since 2022. (This suggests that, while demand for developers has increased, the supply of said developers has increased even faster.) This indicates that it's still harder to find a job in development now than it had been right before ChatGPT was increased.

Second, employment growth has been stronger for certain older age groups than for younger individuals. This, too indicates that younger job seekers are having a tough time out there.

Third, only a small portion of the U.S. takes the Current Population Survey in a given year, so there's plenty of sampling error in the results (as shown by the 95% confidence intervals in the article). Even so, its sample size (roughly 379,000 to 403,000 unique individuals each year from 2022 to 2025, regardless of occupation) is large enough for us to determine whether certain employment gains (or losses) are statistically significant.

Fourth, I'm only analyzing software-developer employment here--which is categorized separately from web developers, computer programmers [a strange distinction, I know], and many other related fields. You can explore trends for those additioal fields within this dashboard that I put together.

Finally, it's very possible that developer employment would have grown even faster if AI tools were not available. See this site for a discussion of this possibility.

At any rate, I hope these findings can help counter some of the hype out there regarding LLMs. I plan to update them periodically as new data becomes available--which will allow us to see if these positive trends for developer employment growth hold up in the coming years.

I've seen a lot of praise for SDD and even Kiro and similar projects launched for the new era of AI. But does anyone actually use this stuff and find it more helpful?

I can't imagine a person reviewing 1000s of specs and making sense of them, or even worse arguing that it's better than just writing code in a high level language.