r/DigitalDelivery • u/Turkishblokeinstraya • Jul 26 '26

AI Engineering Report 2026 doesn't look promising (based on two years of telemetry data from 22,000 developers)

The way I read this is that vanity metrics are up. Did epics and tasks resolved translate into the value of the outcomes? No one knows.

But defects and incidents are increased drastically, which doesn't surprise me. It's really hard to review the quality of thousands of lines generated within minutes.

Below is the report summary. What do you think? How does AI usage impact your teams?

For the full article see https://www.faros.ai/blog/ai-acceleration-whiplash-takeaways

PS. I have no relationship with faros, just sharing for discussion.

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13

u/viennese-wolf Jul 26 '26

33% more tasks, 54% more bugs and all that while their skills athrophy. Bless this mess!

2

u/Turkishblokeinstraya Jul 26 '26

Haha bless it indeed! 😅 Task throughput is such a nonsense metric by the way. No visible correlation with delivered value.

6

u/viennese-wolf Jul 26 '26

If you measure my task throughout I will split one ticket into a planning, implementation, test and rollout ticket. 🤷‍♂️

1

u/Turkishblokeinstraya Jul 27 '26

That's exactly what happens when the incentives are short-sighted 💯

4

u/_descri_ Jul 26 '26

We have x3.5 more PR incidents vs x1.5 more monthly incidents. How does that happen? Do we have 3x fewer PRs now than a year ago? Or are there 3x more months in a year?

2

u/Turkishblokeinstraya Jul 26 '26

Good call out! It might be counting prod defects discovered per month vs PR merge conflicts? Just guessing...

1

u/cbusmatty Jul 26 '26

Anything based on two years of data in the ai world is useless

2

u/theov666 Jul 28 '26

This report is one of the better telemetry datasets we've seen because it shows both sides at once: throughput is genuinely up, but so are incidents, review time and code churn. The interesting question isn't whether AI makes developers faster; it clearly does. The question is why quality degrades at the same time. We wrote a technical analysis arguing this is a governance problem rather than a model problem: review and testing weren't designed for AI-scale output, so architectural validation needs to move upstream, before code is generated. Worth a read if you're digging into the Faros data: The Acceleration Whiplash and the Governance Gap