Good dev knows: post #274 — TG.ME

📹 Interesting talk on how the ROI on the AI adoption might be measured in tech companies.

💡My insights

1️⃣ AI can lead up to 25% increase in productivity (10% median)

2️⃣ AI works best if your workload is following the best SE practices (tests, modularity, types)

3️⃣ You can measure AI adoption effect just using the git history and comparing past vs present

4️⃣ They provided example of one company of our size, after AI adoption
* number of PR grew up 📈 , but code quality decreased 📉
* Rework type of code changes increased x2.6 (a lot)
* Effective output gain only +1% 🤯

What does it all mean? 🤔

It does not mean that we will stop using AI, but that we will use it more consciously and improve in the areas where we are lagging. The fact that code is now cheaper is not the final answer. We have gained a new tool, and this tool helps (as proven by +25%), but achieving those +25% (or even +10%) is not a default outcome.
YouTube
Can you prove AI ROI in Software Eng? (Stanford 120k Devs Study) – Yegor Denisov-Blanch, Stanford
You’re investing millions in AI for software engineering. Can you prove it’s paying off? Benchmarks show models can write code, but in enterprise deployments ROI is hard to measure, easy to bias, and often distorted by activity metrics (PR counts, DORA)…
👍7
January 7, 2026 1.1K 5 2