Some Thoughts on AI – Matt Bruenig Dot Com

Matt Bruenig Dot Com

Some Thoughts on AI

I am a weird social position when it comes to the discourse over large language models or artificial intelligence more generally.

Politically, I align with the socialist left and have since I was a teenager. On the internet, I am most well-known as a left-wing economic policy guy who runs the People’s Policy Project think tank and helps left-wing politicians — like Bernie Sanders and Zohran Mamdani — with their policy agendas. I also practice union-side labor law, maintain the NLRB Research database, and publish the NLRB Edge newsletter, which I’m told has been helpful in a small way to the labor movement.

But I am also really into coding, mostly as a hobby and as something I can use for my various other projects, but not as something I have ever wanted to make a specific career out of. I used to be a lot better at it. When I was a teenager, I got really into Linux and Bash scripting and even contributed some system scripts to a couple of Linux distributions (my most lasting contribution was this rankmirrors script which is still used by the Archlinux package manager). Later on, I got very interested in Python, especially when I wanted to learn statistical programming. I can still do both reasonably well, but, as the saying goes, if you don’t use it, you lose it, and that has happened to some extent with me as I spend most of my work time on legal work and public writing and most of my free time on my kids.

For various reasons — some understandable, others not — there is a lot of reflexive opposition to AI/LLMs on the left. But even though I am of the left, this has not been my reflexive response to the technology, perhaps because of my hobbyist coding background. Instead, when it was first introduced a few years ago, I was very intrigued by it. As it has gotten better, that intrigue has only grown. What generative AI does and how it does it is genuinely amazing, and I have found so many ways to make use of it, both to speed up work I already do and to enable me to do work I didn’t previously have the time or ability to do.

What I Do

For example, I have not written any statistical programming code in many months and I don’t see why I would ever write any such code ever again. I know how to do it, but I don’t need to. Instead, I can take my extensive knowledge about the various public microdata sets and simply prompt an LLM harness, like Claude Code, to do it for me, after which I can eyeball it to make sure it looks right. This is not complicated coding by any means, but it reduces an hour-long task to a few seconds.

For another example, my NLRB Edge publication tracks all new documents put out by the agency using scrapers and the NLRB Research database that I used AI coding assistants to help construct (I did a lot of this before those assistants got as good as they are now, but even in their more error-prone days, they were helpful). Those new documents are then summarized by an LLM and posted to the NLRB Edge newsletter, which has 13,000 subscribers.

More recently, I spent a lot of time with Claude Code and the Opus 4.5 model working through how to reliably grab down election, case, and docket data from the NLRB website, which is not straightforward because the NLRB website is a mess and there are a bunch of frustrating technical problems with the parts of the website that serve up this kind of information. Claude Code also struggled to figure out solutions to these problems. At one point, it even sharply concluded that the NLRB had done “sloppy database work,” which is the meanest I have ever seen it be. But by bouncing ideas back and forth with Opus 4.5 and having it quickly write code to test the ideas, eventually I was able to figure out how to produce the clean, comprehensive, self-updating datasets I wanted.

For legal research, the LLMs have proven less helpful than I once thought they would be because they all have limited “context windows,” which refers to how much material you can feed it to help it answer your prompt. But even there, I have figured out ways to make it useful by narrowing the legal documents I want to get information from through a more conventional search first and then feeding only those matches to the LLM (this is called RAG). Another, even more useful path has been to take a large chunk of cases where I think the information might be and have an LLM go through them one at a time and summarize them, and then feed all of the summaries back to the LLM and prompt it from there (this is called Map/Reduce). I’ve used this approach to generate a legal reference book about the NLRB that summarizes the most-cited points of NLRB case law (to be released soon).

Even in my free time with my kids, it has provided amusement in the form of making simple javascript games with them, which we have posted at GamesAxolotl.com.

Skepticism

The skepticism I’ve read about AI/LLMs comes in various forms and it’s important not to lump it all together. These forms are listed below:

One of the ironies of the left-tinted skepticism of AI is that it seems in part to be related to dislike of the tech sector, which also is very reasonable. But the sector that is going to be most shook up by this is the tech sector. The labor that is being replaced is tech labor, coding labor. Whether this results in a total decimation of tech head counts (same output but now done by LLMs) or in an expansion of tech output (more output enabled by LLMs) is hard to say at this point. But if you don’t like the tech sector, a new technology that makes it so that coding is trivially easy seems like exactly the thing you might invent to stick it to them!

For me, ultimately what I like is the technology, not any particular story I have about what its ultimate distributive results will be. It reminds me of what it was like when I first figured out that you can use a computer, not just to run other people’s software, but to write your own, to have it do whatever you want to do. It reminds me of what it was like when I first got into open source software and the free software movement where you could read any code you wanted and therefore, with enough time, figure out how to do whatever you wanted. This is all of that on steroids and it’s very nice to have a tool that enables you to do a lot more of what you want to do than you could before.

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