In the first two articles we talked about how to think about AI and how to read the bill. A large language model is a language processor — it takes language in and gives transformed language back. And the meter it runs on is the token, the little chunk of text every prompt and every response is measured in.

In this article we are going to make practical application where the stakes are unmistakable: a small law practice. If you don’t run a firm, stay with us anyway — we are working through law as an example precisely because it’s unforgiving. We will answer two questions every business owner eventually asks as they are the same in any field: What do I actually use this thing for? And how do I know the answer is any good? In a law office, getting the second one wrong gets you sanctioned. The discipline that protects a litigator protects everyone.

In the first article in this series, we landed on a mental model that cuts through most of the confusion about AI: a large language model is a language processor. It takes language in and gives you transformed language back, the way a food processor takes in vegetables and hands you back something chopped or blended to spec.

That framing answers what the tool is for. This article answers a question that arrives the moment you start using it for real work: what does it cost to run, and how do I tell a good deal from a bad one?

AI is the biggest revolution since the Industrial one!!! AI is overhyped and the bubble is just about to pop!!! Which is it? It can’t be both, or can it? Even if that’s a bit unclear at the moment, it is very clear that billions of dollars are being poured into it. Billions upon billions even while 95% of generative AI pilots are failing according to an [MIT report](MIT report: 95% of generative AI pilots at companies are failing | Fortune).