AI speeds first drafts of software but piles work onto testers
Reviewed by Helen Jones
On 24 September 2026, testing firm DeviQA published a survey of 4,000 quality-assurance professionals who recently tested software built with AI help. Sixty-five percent said new features now reach testing sooner, and nearly half said defects get fixed faster. The same group reported congestion, not a shorter job. Sixty-four percent said more features arrive at once, 55 percent said their testing queue has grown, and 52 percent reported more test-fix-retest cycles. Almost half said AI-built features sometimes pass the main path and still break something else. Fifty-six percent now spend extra time clarifying what the software was supposed to do, and 52 percent spend more time working out what a change actually touched.
Teams have treated AI as a way to ship the first version of work sooner. In software, that first version is a feature. In knowledge work, it is a draft, a brief, or a slide pack. The old assumption was that faster generation would shorten the whole chain. This survey shows the chain stretching at the handoff. Testers are not drowning because the model is slow. They are drowning because expected behavior, dependencies, and side effects were never written down. That is the same hidden checking cost that appears when a team pastes a task into a blank chat and then spends the saved time repairing tone, facts, and fit.
Analysis
Treat this as a trap to avoid, not a reason to pause AI. Before the next recurring AI-assisted task leaves your desk, add a one-page expected-behavior note: what must be true, what must not change, and who reviews the side effects. If that note cannot be written, the work is not ready to generate.
Source note
Pulse published by Collab365 Spaces, reviewed by Helen Jones on . Cite as "AI speeds first drafts of software but piles work onto testers", Collab365 Spaces. 1 source referenced.