👋 Hey {{first_name|there}},
Your engineers are shipping more code than ever, your AI adoption numbers look great in the board deck, and somehow, the features your customers asked for in Q1 are still not out. The speed is real. It's just landing in the wrong place.
📉 Where it's costing you
A VP of Engineering told me last month that her team's pull request volume was up something like 60 percent year over year. Then, almost as an afterthought, she added that the lead time for changes hadn't really moved. Same features, same quarter, same slips. She'd spent a real chunk of the budget on coding assistants, and every dashboard pointed up. The one number the CEO cared about time from idea to customer satisfaction.
This is the pattern now, and the data backs it up harder than I expected. Faros AI's 2026 telemetry across roughly 22,000 developers found output per engineer climbing while median PR review time went up several-fold, and incidents per change climbed right along with it. The 2025 DORA report, with around 5,000 respondents, ends up in the same uncomfortable spot: AI lifts throughput and, at the same time, tends to hurt stability. You're pushing more changes, faster, into a system that was never built to absorb them.
Here's the part nobody wants to say in the all-hands. Writing code was rarely your constraint. We covered this from the delivery side back in the 48-hour bottleneck audit and again when we mapped lead time versus cycle time. AI made the cheap stage cheaper. But the expensive ones review, integration, testing, and the plain wait for a human to make a call are right where they always were. Now they just have more work piling up against them.
🧭 The reframe
Most leaders think: AI makes the team faster, so delivery gets faster.
What's actually true: AI makes code generation faster, which just relocates the bottleneck to whatever stage comes next.
There's a study people keep citing, the METR randomized trial from mid-2025, where experienced developers expected AI to speed them up by around 24 percent and instead came out roughly 19 percent slower on the tasks measured. Small sample, open-source work, so I wouldn't treat it as gospel for your org. But the direction matches what the larger datasets show and, honestly, what you can feel walking the floor: the typing was never the hard part. Review is. Knowing whether the change is right is. Getting it safely to production is.
So when you pour AI into the part that was already fast, you don't shorten the line. You move the crowd to a different window. And if the only thing you're watching is that fast window code volume, acceptance rate, and commit counts, the metrics look terrific while delivery quietly stalls behind them. That gap between the dashboard and the actual quarter is what your board is starting to ask about.