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In a survey of 23 startups reported by Dimitris Kyrkos, every respondent said AI-assisted code had caused a problem at least once. That is the source of the “100%” headline—not evidence that every developer who uses AI will experience an incident. The survey reports respondents’ own answers, and it does not establish how common AI-related problems are across the wider software industry.
What the 100% figure measures
Kyrkos’s DEV Community article, posted April 1, 2026, reports that none of the 23 startup respondents selected “No, never” when asked whether AI-assisted code had caused problems. Put precisely: every respondent in this small survey said they had encountered a problem at least once. The article describes “problems” or “issues,” but provides no severity scale or incident taxonomy.
The finding does not show that 100% of developers generally have AI-related incidents. The available article text does not establish a representative sampling frame, survey field dates, the questionnaire in full, or independent validation of incidents. It also does not report measured production outages, security breaches, or defect rates. Read Kyrkos’s article on DEV Community.
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The same 23-startup survey offers context for the headline. Its figures describe separate self-reported behaviors, not controlled groups or measured outcomes.
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| Survey response | Reported result |
|---|---|
| Used AI tools daily in core workflow | 47.8% of respondents |
| Used AI tools several times a week | 34.8% of respondents |
| Rarely or never used AI | 4.3% of respondents |
| Faced AI-related problems occasionally or all the time | 78.2% of respondents |
| Described themselves as cautious and said they reviewed code carefully | 52.2% of respondents |
| Mostly trusted AI under deadline pressure | 34.8% of respondents |
| Investigated further when a security tool flagged AI-generated code | 69.6% of respondents |
| Were not very or not at all concerned about sharing proprietary data with AI models | 43.5% of respondents |
Every percentage in this table is Kyrkos’s report of answers from the same 23 startup respondents. The article does not provide enough detail to infer how respondents interpreted each question or whether the results represent other teams.
Why deadline pressure matters—and what the survey cannot prove
Kyrkos argues that deadline pressure can weaken review discipline and shift developers’ work toward auditing AI output. The reported responses give that argument context: 34.8% said they mostly trusted AI under deadlines, while 52.2% described cautious review. But these are self-reports, not evidence that deadlines caused a particular coding problem or that one group had more incidents than another.
The survey also separates two issues that are easy to conflate. The reported 69.6% who investigated security-tool flags describes a follow-up behavior; it is not a measure of vulnerabilities found or prevented. The 43.5% who expressed little concern about sharing proprietary data describes reported concern, not proof that respondents disclosed such data or suffered a privacy breach.
What separate research says about verification
A 2026 academic pilot study by Matthias Huemmer, Franziska Durner, Theophile Shyiramunda, and Michelle J. Cummings-Koether examined generative AI and problem-solving in an academic setting—not software development or coding incidents. Its abstract says reliance was highest on difficult tasks while verification confidence declined where performance was weakest. The authors characterize verification, rather than solution generation, as a bottleneck in their human-AI problem-solving study. Read the study abstract on arXiv.
That result is adjacent context, not confirmation of Kyrkos’s 100% figure. The pilot’s stated limitations include an academic convenience sample, self-reported confidence, no control condition, mathematical and analytical tasks, and insufficient time to assess long-term skill changes. It cannot establish software-team incident rates.
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How to read the headline responsibly
- Keep the sample attached: say “all 23 startup respondents in Kyrkos’s survey,” not “all AI-assisted developers.”
- Keep the outcome modest: the reported outcome is that respondents said AI-assisted code had caused problems at least once; severity and consequences are not specified.
- Separate experience from frequency: respondents’ reports of ever encountering a problem do not quantify how often problems happen per developer, project, or line of AI-generated code.
- Separate checks from results: reported review behavior and security-tool follow-up do not show that defects were detected, fixed, or prevented.
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