“When Claude Code and some of these other tools come out, you get something like an 800% increase in the amount of lines of code written. … To the point where if you actually look at real releases of the world, it goes up 20%. So from 800% to 20%, right?”
The Data Exchange with Ben Lorica · AI Research & Frontier Labs · October 2026
Thompson was explaining why AI adoption lags what the tools can do, citing recent MIT research on coding tools. When one step of a process is automated, another becomes the bottleneck, such as senior engineers who have to review all the extra code.
Transcript
…drops from year to year. And so, that, you know, it seems like as I think as you would expect, as people build better tools, better platforms in order to make AI easier to implement, more and more people are using it as they build it into stuff that people are already using. That helps it as well.
So, the desire is there. It's just that the infrastructure or the data pipelines or whatever it is are not yet in place. But assuming they're in place, people are willing to adopt.
…can write. And it's amazing how frustrating it is for our senior people who have to review code because they're getting lots and lots of stuff in there where people are, people who don't know better are just handing them stuff and like, yeah, that's pretty good. And to give you a sense of the scale of this, so Mert Demerer and some co-authors did some work at MIT recently. And they showed that when Claude code and some of these other tools come out, you get something like an 800% increase in the amount of lines of code written. And then you get many, many fewer, like you still get an increase, but much, much smaller increase in the number of commits that people are doing, the number of files that are created, et cetera. To the point where if you actually look at like real releases of the world, it goes up 20%. So from 800% to 20%, right? And that's really telling you something is going on there where like there are…
very similar to the mathematician's observation of generation is cheap, verification is getting easier, but the acceptance and validation in the analog and software would be deployment is not keeping pace. So let's pivot to your other paper so that we can wrap up. So the other paper, AI scale and…
So, I mean, it also, it is also empirical in the sense that we're going to ground it. But yeah, I mean, I think the biggest contribution here is theoretical. I agree with that.
And so give us a high-level description of the findings.
More from The Data Exchange with Ben Lorica
