A reported survey of 23 startups found that every respondent said AI-assisted code had caused a problem at least once. That is a striking result, but it is not an estimate of the share of developers industry-wide: the survey was small and startup-focused, and its article does not establish a representative sampling frame or independently validate incidents.
What the 100% figure means—and what it does not
Dimitris Kyrkos’s April 1, 2026, DEV Community article reports that none of 23 startup respondents chose “No, never” when asked whether AI-assisted code had caused problems. In that survey, 100% therefore means all respondents reported at least one problem. It does not mean that every developer who uses AI has encountered an incident, nor that every AI-generated change causes one. Read Kyrkos’s article on DEV Community.
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The article describes problems or issues, but does not provide a severity scale or incident taxonomy. It does not establish how many reports involved minor defects, production outages, security breaches, or other outcomes. Its result is self-reported, and the available article text does not establish the questionnaire, survey field dates, or independent incident validation. Treat the headline as a report about these respondents, not a universal industry incident rate.
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The figures below are Kyrkos’s article’s account of responses from the same 23-startup survey. They describe reported habits and perceptions, not controlled groups or measured security outcomes.
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| Survey topic | Reported result |
|---|---|
| AI use in core workflow | 47.8% used AI tools daily; another 34.8% used them several times a week; 4.3% rarely or never used them, according to Kyrkos’s report of the 23-startup survey. |
| Frequency of AI-related problems | 78.2% said they faced problems occasionally or all the time, according to Kyrkos’s report of the 23-startup survey. |
| Review behavior | 52.2% described themselves as cautious and said they reviewed code carefully; 34.8% said they mostly trusted AI under deadline pressure, according to Kyrkos’s report of the 23-startup survey. |
| Response to security-tool flags | 69.6% said they investigated further when a security tool flagged AI-generated code, according to Kyrkos’s report of the 23-startup survey. |
| Proprietary data concerns | 43.5% said they were not very or not at all concerned about sharing proprietary data with AI models, according to Kyrkos’s report of the 23-startup survey. |
These results should be read as separate self-reported behaviors. For example, the security-tool response does not show how many vulnerabilities were found or fixed, and the data-concern response does not establish what information respondents actually shared or which model policies applied.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why verification is the practical issue
Kyrkos argues that deadline pressure can weaken review discipline and that developers’ work is shifting toward auditing AI output. The survey figures illustrate that concern: 34.8% reportedly mostly trusted AI under deadline pressure, while 52.2% described a cautious review approach. But the survey does not demonstrate that deadlines caused trust or that AI use caused a particular rate of defects. It records respondents’ reported practices, not a causal test.
A separate 2026 academic pilot offers adjacent context, not corroboration of the developer survey. In a study of human problem-solving on mathematical and analytical tasks, the authors reported that reliance on generative AI was highest on difficult tasks while verification confidence declined where performance was weakest. They describe important limitations: a convenience sample in an academic setting, self-reported confidence, no control condition, a task scope outside software development, and too little time to assess long-term skill changes. Read the study abstract on arXiv. It raises a broader question about checking AI-assisted work, but says nothing directly about software incidents.
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How to interpret the headline as a developer
- Use it as a prompt to check your workflow, not as a forecast. The 100% result applies only to the 23 respondents described in Kyrkos’s article.
- Separate defect occurrence from severity. The survey’s broad term “problems” cannot tell you the likelihood of a production outage or security breach.
- Notice the pressure point. The reported contrast between cautious review and trust under deadlines makes review practice worth discussing, but does not prove a causal relationship.
- Keep security and privacy claims narrow. Reported follow-up on security flags and reported concern about proprietary data are attitudes or behaviors, not evidence of breach rates or safe data handling.
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