AI now writes 42% of committed code and is on track for 65% by 2027 — but review capacity hasn't caught up
Sonar's 2026 State of Code Developer Survey of over 1,100 professional developers finds AI now accounts for 42% of committed code, projected to reach 65% by 2027, while 38% of developers say reviewing AI-generated code takes more effort than reviewing a colleague's — a capacity gap, not just a trust gap, that founders commissioning software should be budgeting for.
14 September 2026
Sonar’s 2026 State of Code Developer Survey, covering more than 1,100 professional developers, puts a specific number on how much of the software being shipped today is AI-authored: 42% of committed code, with developers expecting that to reach 65% by 2027. Adoption is no longer the interesting variable — 72% of developers who’ve tried AI coding tools use them daily or multiple times a day. The interesting variable is what happens after the code gets written.
The bottleneck isn’t trust, it’s capacity
Most coverage of AI coding surveys leads with trust: developers don’t fully believe the code is correct. Sonar’s data confirms that (96% don’t fully trust AI output, only 48% always verify before committing) but the more operationally relevant finding is different: 38% of developers say reviewing AI-generated code actually takes more effort than reviewing code written by a human colleague. Sonar frames this as an “engineering productivity paradox” — code generation has accelerated sharply, but the review step it depends on hasn’t scaled with it, so the net gain is smaller than the generation-speed numbers alone suggest.
That distinction matters for anyone commissioning software rather than writing it. It’s not that AI-written code is assumed to be bad — it’s that verifying code you didn’t write, produced at a volume and speed no team previously had to review, is a distinct skill and a distinct time cost that a lot of build estimates don’t yet account for.
So what
If a development partner’s pitch is built entirely around how fast their AI tooling generates code, ask the follow-up question: what does the review process look like once nearly half the codebase is AI-authored, and who’s accountable for catching what a fast-but-imperfect reviewer misses? That’s the gap between a team that’s fast because it skipped a step and a team that’s fast because AI tooling removed genuine friction from a process that still has proper review built in. It’s the difference we build around — see our AI-assisted development approach, or get in touch if you want a project scoped with review capacity accounted for from the start, not bolted on after the fact.