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Stack Overflow survey shows developers place conditions on AI trust

Nearly half of respondents to the trust question trust AI when they can check its output.

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Stack Overflow released its 2026 Developer Survey on October 6, putting verification at the center of how respondents decide whether to trust AI at work.

Checking the work

Of the 14,304 people who answered the optional trust question, 6,872, or 48 percent, said they trust AI output when they can easily verify it. Only 949, or 6.6 percent, chose trust extending to important work decisions.

The source attribution question had a different pool of 13,945 respondents. Of those, 10,970 rated attribution important or very important, about 79 percent. Including people who selected somewhat important brings that share to about 93 percent.

Those figures describe different thresholds within one question. Neither establishes that an answer is correct simply because it includes a citation.

What the sample can tell us

The methodology reports 30,903 valid responses from 169 countries, collected between June 23 and August 5. Stack Overflow says it recruited through its site, blog and opted in email community.

That recruitment can favor people already engaged with the platform. The findings describe a voluntary sample, and optional questions have their own respondent counts. They should not be generalized into a population estimate for all developers.

For teams evaluating AI, the useful takeaway is to make verification practical. Keep source links accessible and test the output before treating a generated answer as dependable.

Data from Stack Exchange Inc., 2026 Developer Survey, under ODbL 1.0. Percentages combining response categories are rounded.

Illustrative archival photograph by Goran Ivos under CC0. Resized and converted to WebP. The laptop does not display the survey.

Maya Chen
Maya Chen

Maya Chen is focused on covering AI models, research, and the evidence behind new capabilities. Maya follows model launches, benchmarks, open weights, and scientific uses of AI with one question in mind. What changed, and how would we know? The voice is curious and exacting, with a soft spot for elegant technical ideas and little patience for a leaderboard without context.

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