Is AI taking software engineering jobs?

1. What the doom loop is

Daniel Chait runs Greenhouse, which more than 7,500 companies hire through, and he named the pattern. Candidates use AI to find and apply to more jobs and to tailor every resume to the posting. Recruiters, now looking at four or five times the applications they saw a couple of years ago, point an off-the-shelf model at the inbox and ask which 20 of the thousand to call. That makes it harder to get seen, so candidates send more. Both responses are individually reasonable and jointly ruinous.

2. Engineers are the heaviest auto-appliers

By Greenhouse’s own data, software engineers automate applications more than any other group and send the most. Chait is careful that this does not make them the villains: they are early adopters doing what the tooling makes easy. It does mean engineering roles feel the signal collapse first and hardest.

3. The signal collapsed before the demand did

Chait’s read is that the number of postings is not the binding problem. When every resume is tailored by a model to the job description, resumes converge, and a thousand near-identical applications carry less information than a hundred varied ones. Layer on the trust problem. Greenhouse’s survey found 91% of recruiters have spotted candidate deception and 41% of candidates admit to using prompt injection to get past AI filters, with resume hacks like white-fonting and injection up 500%. Screening turns more defensive in response, which attenuates the signal further.

4. The job did change, which is a separate claim

Chait grants the part that is real: software engineering looks different than it did three years ago, and different than it did sixty days ago. That is a change in what the work is, and it shows up in what teams hire for. It is a separate question from whether the roles exist, and conflating the two is what turns a matching failure into a story about replacement.

Why it matters

If the problem were demand, the fix would be waiting. Because it is signal, the fix is anything that puts information back in the channel: a targeted application instead of a hundred automated ones, and a screening process that rewards evidence over keyword overlap. Both sides are currently optimizing against a system that stopped carrying information.