DevOps / SRE / Platform · 09.08.2026, 14:10 UTC
AI coding got faster. Why didn’t engineering?
| Schweregrad | info |
|---|---|
| Kategorie | DevOps / SRE / Platform |
| Quelle | The New Stack ↗ |
| Veröffentlicht | 09.08.2026 UTC |
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AI is great at making individuals faster, but the surrounding systems are then slowing everything right back down. This result — or, rather, lack thereof — is amplified by company size and pull request size. To the point that, while AI investment has increased 28 times for most companies, and especially those with more than 99 engineers, velocity measures are stagnant and even down.
Such is the finding of the recently released State of AI Impact in Engineering from DX, which measures engineering organizations across speed, effectiveness, quality, and impact.
Justin Reock, deputy CTO of DX, tells The New Stack, “It is concerning because, when the cost has gone up 28x — which, literally, the only exponential metric is cost — and velocity is not exponential, we’re not shipping exponentially more.”
While AI spend continues to skyrocket, the innovation ratio — the allocation of engineering effort spent on new feature work versus maintenance, toil, and operational overhead — remains flat. This means AI is not freeing up engineers’ time to spend on interesting business solutions, according to the report.
How is the industry spending so much more money on agentic and AI developer tools, while also doing layoffs, to no avail? Read on as we dive into these disturbing metrics.
Is developer experience trending down?
“It is possible that we’re still in this inflection point where a lot of this saved time is still just being spent on tech debt, backlog stuff that may not necessarily be tagged as a new feature,” Reock says, still hopeful that the gap between the AI cost and …