# AI ENGINEERING REPORT 2026

## The Acceleration Whiplash

The definitive data on AI's engineering impact. What's working, what's breaking, and what leaders need to do next.

- Engineering throughput is up
- Bugs, incidents, and rework are rising faster
- Two years of telemetry from 22K developers across 4K teams

## The Findings

### More code. Declining quality. Accelerating incidents.

In 2025, we identified the [AI Productivity Paradox](https://www.faros.ai/blog/ai-software-engineering).  This report asks whether the pattern has changed. It has.

+51% PR Size  
+28% Bugs per PR  
5X Median Review Time  
3X Incidents per PR  
10X Code Churn

Sample what's inside

Want to see the rest of the data?

[Download the Report](https://www.faros.ai/research/ai-acceleration-whiplash#download-report)

## The insights

### Why read the report

[The Acceleration Whiplash](https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways) is one of the largest quantitative studies of AI's impact across the full software delivery lifecycle.

The data was pulled from every stage of the workflow to cover how AI code is written, reviewed, and tested—and what happens when it reaches production.

Across every stage, the signal is the same. Volume is up, quality is down, and the gap between the two is widening as adoption deepens.

What makes this report different:

- Telemetry, not surveys. Real engineering data from every stage of the workflow, not self-reported estimates.
- Before and after AI adoption. Two years of data, comparing outcomes at low versus high AI adoption within the same organizations.
- Correlation, not coincidence. Every finding reflects a statistically significant relationship between AI adoption and engineering outcomes.
- High performers aren't insulated. Engineering maturity is not a shield. Our data directly contradicts DORA's 2025 findings.

This report offers an objective view into how AI is reshaping software development, including:

- Why throughput is up but your incidents have tripled
- Why senior engineers aren't closing the quality gap either
- The case for fixing quality at the authoring stage, not downstream
- What organizations should do about headcount, process, and how far to extend AI's role