Two studies, two vantage points on AI code

The software industry has gone all-in on AI. Executives expect a productivity leap and a large return, and widespread adoption has followed. With AI now authoring the majority of the code at most tech organizations, the increased output feels like progress. Whether it performs like progress is the question two recent research efforts set out to answer, and they came at it from opposite directions.

New Relic’s 2026 State of AI Coding report surveyed 200 technology leaders about how AI-generated code is performing in their organizations. Faros’s AI Engineering Report 2026 (The Acceleration Whiplash) drew on two years of systems telemetry from 22,000 developers across 4,000 teams, pulling from version control, CI/CD, work management, and incident data. One study captures what leaders perceive; the other captures what the systems record. Read together, they paint a clear picture of AI’s current impact in software development: Output has increased, but production quality has declined. 

Where the two studies agree

Earlier this year, we exposed that AI has flooded the systems built around human-paced development and human-quality code with output they were never designed to absorb. Engineering throughput is up, and so are bugs, incidents, and the hidden costs accumulating at every stage downstream. This is the Acceleration Whiplash.

New Relic found familiar sentiment in their survey results. New Relic reported 78% of organizations saw production incidents climb, 86% saw senior-engineer rework grow, and 74% of leaders said at least a quarter of their org’s AI-generated code needs significant rework.

Faros’s telemetry showed the shape from inside the systems: the incidents-to-PR ratio more than tripled, bugs per developer rose 54%, and the ratio of lines deleted to lines added rose 861% under high AI adoption, as developers return to redo code that was accepted quickly the first time.

Oversight has loosened over the same window. New Relic reported 62% of teams often ship AI code without line-by-line verification; Faros’s telemetry showed 31.3% more pull requests merging with no review at all. Across both studies, the pattern is the same: dev roles are shifting from authoring code to reviewing and stabilizing it, bottlenecks are moving downstream into review and production, and the net positive business impact everyone expected remains elusive. AI velocity is here, but the improved business outcomes are still pending. 

The perception-reality gap found in New Relic’s survey

New Relic’s most striking survey finding highlighted leaders’ attitudes toward AI-generated code. A remarkable 94% rated AI-generated code as higher quality than human-authored code at the point of review; 61% of leaders rated AI-generated code as “somewhat higher quality” than human-authored code, and the other 33% rated it as “much higher.” That confidence held steady even as the same leaders reported the increased incidents, rework, and firefighting above. 

The people closest to the budget believe AI-generated code is excellent, while their own teams and systems struggle with what’s breaking downstream.

Why AI code looks better than it runs

The explanation lives in how AI writes. AI-generated code is both verbose and polished-looking. It is idiomatic, well-named, and pretty consistent with the code around it. It reads like the work of someone who understands the system, which makes it more challenging for engineers to spot issues during review. The gaps stay beneath the surface until the code runs against real traffic, real dependencies, and real edge cases—only to emerge later as a spike in incidents, bugs, and rework. 

AI’s impact on senior engineers

The massive amounts of AI-generated code moved bottlenecks downstream, and Faros’s data revealed where the burden usually lands: on senior engineers. Median time in PR review rose 441.5% under high AI adoption, as the weight of catching what AI gets wrong falls on the engineers equipped to catch it. Clean syntax and tidy structure hide misread requirements underneath, so catching them means reconstructing intent, which is high-intensity work senior engineers are uniquely suited for. We call this the senior engineer tax. As review consumes their hours, the architecture, mentorship, and technical strategy that compound across a team give way to it, and burnout and attrition follow at the level where replacing a single senior engineer runs anywhere from $150,000 to $300,000+.

The AI code quality mirage

This is the mirage. AI-generated code looks flawless at review and reveals its true state in production. And the same trick plays out one level up: increased AI usage looks like increased productivity, so executives keep pushing more AI into more of the work, while the delivered business value stays somewhere off in the distance. Even as AI coding costs are skyrocketing, both engineers and executives are reading the shimmer on the surface, and the surface looks superficially convincing.

The industry has placed its bet on the appearance of progress. AI has become a tool tech companies depend on, and it now sits squarely at the center of how software gets built. It is also, today, a tool still leaving much to be desired in terms of quality, consistency, and reliability. 

Faros’s data pinpoints these challenges as an authoring problem, not a review problem, so the fix belongs upstream at code generation; adding reviewers, gates, or QA just treats the symptom without addressing the root cause. Engineering companies should first pinpoint how AI is being used and where it could offer the most value. Then, they can improve code quality right at the source by equipping AI with richer context and guardrails, before finally monitoring and governing the entire AI-augmented pipeline.

The distance between how good AI code looks and how well it holds up is the space where engineering organizations are paying the cost right now, in incidents, in rework, and in the senior engineers spending their hours cleaning up after it. The measure that matters is the one underneath—what the code does once it ships, and whether the business is actually achieving better outcomes as a result.

Faros can help you maximize what your AI ships. Reach out for a demo to see how.