The Pulse
Undo Survey Links AI Coding Tools to Production Outages
Undo’s 2026 survey found that 81% of engineering leaders reported a production incident or service outage linked to AI coding tools in the previous six months. The survey also found that most respondents saw no faster release cycles, while
AI.info Team · Written with AI ·
“We’ve kind of unbottlenecked the bugging phase,”
Mark Williamson, CTO at Undo
Undo’s survey of 300 engineering leaders found that 81% said their organization had experienced a production incident or service outage linked to its use of AI coding tools at least once in the previous six months. The findings, published on October 12, 2026, also point to a disconnect between generating code faster and releasing software faster: 79% of respondents said their release cycles were no faster than before.
The results are leaders’ reports of problems associated with AI coding tools, not an independent audit establishing that the tools caused each incident. The survey was conducted on Undo’s behalf by independent research firm Coleman Parkes in July and August 2026.
Outages sit alongside defects and faulty diagnoses
Fourteen percent of respondents said their organizations had experienced production incidents or service outages affecting internal users or customers multiple times per month. Separately, 81% reported at least one such event in the past six months. The survey also found that 91% had encountered test escapes, serious defects or poorly optimized code entering production after using AI coding tools.
Respondents reported another debugging hazard: 93% said an AI hallucination had led to an issue’s root cause being incorrectly diagnosed. The survey does not identify particular companies, products or outages, so the figures describe patterns reported by the leaders rather than a catalogue of specific incidents.
Debugging takes more time than producing code
Engineers in the survey spent an average of 9.8 hours a week producing code, compared with 9.7 hours debugging issues during development and another 7.2 hours debugging problems found by customers in production. Combined, those debugging tasks account for 16.9 hours per week. Undo’s report says debugging takes 42% of engineering teams’ time, based on a 40-hour work week.
The report also found that 35% of AI-generated code reaches production before teams have fully comprehended it. In LeadDev’s coverage of the findings, Undo CTO Mark Williamson described the shift as a bottleneck moving downstream: AI can speed up code production, while teams still have to understand, review and diagnose the software they ship.
A survey of complex, mission-critical systems
The 300 respondents were senior engineering leaders at large organizations with annual revenue above $250 million, delivering mission-critical software on large, complex codebases. The sample included 200 respondents in the United States and 100 in the United Kingdom; 93% worked with C/C++ codebases. The reported industries included financial services, networking, semiconductor design, computational software and data management.
That focus matters when interpreting the results. Undo’s report centers on systems where codebases may have poorly documented dependencies and engineers carry knowledge that is not written down. Ninety-one percent of respondents said code comprehension and debugging take longer in complex C/C++ codebases.
Release speed is not the same as code volume
Undo argues that organizations should judge AI’s contribution by measures such as defect rates and mean time to resolution, rather than by how many lines of code tools produce. The survey’s results support a narrower point: respondents report more code production alongside substantial debugging work and little improvement in release-cycle speed.
Because the report was commissioned by Undo, a company that sells software debugging tools, its findings also serve a commercial argument for investing in debugging and code comprehension. Its survey offers a snapshot of leaders’ experiences, not a controlled comparison of teams using AI tools against teams that do not. The central figures remain specific: 81% reported at least one AI-tool-linked outage in six months, and 79% said release cycles had not become faster.