“A lot of the value of AI is not in doing the same thing faster. It's doing a harder, more valuable thing that you wouldn't have even thought about. And if your productivity measures are the logical equivalent of counting how many widgets are coming out of the factory, and then you start producing super gold-plated deluxe widgets, there's no way to measure that.”
The Pragmatic Engineer · Hands-On Engineering Podcasts · October 2026
Context
Winters is reconciling two facts: research hasn't shown AI productivity gains above roughly 20–30%, yet engineers report 10x experiences. His answer is that the metrics count output, not the value of what is now possible, such as writing a JPEG parser in a couple of weeks.
Transcript
…for instance, it is entirely possible at this point. And I hang out in security spaces and do a lot of low-level C ⁇ library stuff. You can write a JPEG parser in a couple of weeks. With whatever additional features you need. That would be insane to say that word sentence out loud three years ago.
…they can relicense. And suddenly, the game was completely changed. If this person would have gone ahead with, let's say, the layoffs, this engineer might have just got it. And no one might have known that the game could be changed because of this. AI makes things crazy things sometimes possible.
Right, exactly. And so I think that this is the insight that tries to bridge the gap between, I have not seen a research-based number that says productivity post-AI is up more than like 20 or 30%. But then everyone talks about like these 10x sort of experiences. I super don't think it's actually 10x, but yeah. But like, how do you bridge that gap? And I think it's exactly this thing of a lot of the value of AI is not in doing the same thing faster. It's doing a harder, more valuable thing that you wouldn't have even thought about. And if your productivity measures are the logical equivalent of counting how many widgets are coming out of the factory, and then you start producing super gold-plated deluxe widgets, there's no way to measure that or track that over time because we just don't have the tracking of what's the value of this component or feature. And that also matches with, you know, talking with senior engineers that are like, yes, we're up 70% on amount of code. We're up 30% on outages. Number of features is the same. The thing that we Can't know for sure is: are the value of those features up? Because what's how do you, how do you do…
One challenge that AI is giving is entering the market for new graduates. There's now, there seems to be expectation companies are very eager to hire senior engineers who know how to use AI and they have taste and judgment. And we have these things thrown around. It's not deliberate, but whenever I…
I will riff a little on my bona fides there to like to add some weight to this. So after the book, I got recruited into the ACM IEEE AAAI work on setting the curriculum requirements for an undergraduate CS degree. So this is like the professional societies saying these are the things that we need…
Okay.
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