A few weeks ago I ran the numbers on the token cost panic. I took the scariest figure in legal AI, the finding that agentic workflows burn a thousand times more tokens than a chat query, and followed it all the way down to a dollar amount on a real deal. The panic did not survive the arithmetic. The piece is here if you want the full walk-through.

This is not that piece. The panic has moved on since I wrote it, and the new versions are smarter than the old one. The thousand-times number has quietly retired, because a thousand times almost nothing is still almost nothing. In its place are three fresher anxieties, and they deserve a real answer. The first says the model makers have a monopoly now, the price of a token is climbing, and it will climb forever, so you had better lock in a flat rate or build your own models before it does. The second says forget the price of a token, watch the meter: every time the AI reads your contract it ticks, and a long agentic session reads your contract over and over and over. The third does not bother with an argument at all. It just points at a number. One company spent five hundred million dollars on AI in a single month, and the number is so large it does the panicking for you.

All three are wrong. They are wrong in more interesting ways than the original, which is the only reason I am writing this down instead of linking to the first piece again. But underneath the new costumes it is the same body. Every version of this panic makes the same mistake and reaches the same conclusion. So let us stop swatting the individual numbers and name the thing that keeps generating them.

The Mistake Underneath All of It

Here is the error, stated once, because everything below is a variation on it.

A token is the unit a model uses to bill you. It is not the unit your work is measured in, it is not the unit your client pays for, and it is not the unit anything you care about is denominated in. It is a meter reading. The entire genre of token panic consists of staring at the meter reading as though it were the fare, the destination, and the quality of the ride all at once.

It is not any of those things. It is the meter. And a meter, by itself, tells you nothing about whether you are getting a good deal. A taxi meter reading of forty dollars is a bargain to the airport and a robbery around the block. The number on the meter is the least informative number in the entire transaction, because it means nothing until you put it next to what the ride was worth. Every piece in this genre forgets that, and forgets it in a slightly different way. Let me take them in turn.

“Prices Only Go Up”

Start with the monopoly story, because it has a real fact inside it. Yes, the newest frontier model costs more per token than last year’s newest model. That part is true. What the story does with it is the problem.

It draws a line through two dots and calls it a trend. Frontier prices up, therefore prices up forever, therefore lock in a flat rate before the meter eats you. But you are watching the wrong number. The price of a frontier token is not your cost. Your cost is what it takes to finish a task, and the cost of finishing a given task has been in freefall for two straight years. The same capability that ran on the most expensive model available in 2022 runs today on something on the order of two hundred and eighty times cheaper. Last year’s frontier is this year’s mid-tier is next year’s free default. The token at the very tip of the frontier gets a little pricier each release; everything behind the tip collapses in price behind it. Gartner expects another ninety percent drop in inference cost by 2030.

Watching the frontier price and concluding that AI is getting more expensive is reading the thermometer and announcing a fever, while ignoring that you are holding the thermometer over a candle. The evidence that the baseline is getting cheaper often sits right there in the same articles raising the alarm, quoted from the experts and then left unaddressed. You do not build a cost strategy on the one number in the system that is engineered to always be the highest.Continue Reading Bride of the Token Cost Panic

I just spent five weeks writing a book with an AI. Not prompting it and cleaning up the output. Writing with it — the way you write with a co-author. The AI read a bunch of my earlier blog posts, absorbed my voice, argued with me about word choices, restructured chapters when our argument was not landing, and caught its own mistakes before I did. When we disagreed, we worked it out the way colleagues do — I explained my reasoning, it explained its reasoning, and we found the version that was better than either of us had separately.

That experience broke something in my head. I have spent the last several years helping law firms figure out their AI strategy, and somewhere in the middle of week two I realized that most firms have never experienced anything remotely like this.

You Are Reading the Example

This post was written with Claude Cowork — collaboratively, the same way the book was. In fact, this post is being written in the same workspace, with all the same context the book built up over the last month. In effect, I am writing this post with my co-author.

Say hi, Claude.

Hi. He is being generous with “co-author,” but I will take it. Back to Ryan.

Here is what writing this post actually looked like.

I sat down to write this piece frustrated. I knew what I wanted to say but not how to say it. I told Claude the situation: some firms are dismissing an entire category of AI tools because they think chat tools with rigid workflows are more than sufficient for their needs. Claude pushed back and told me my frustration was the right fuel but I needed to aim it at the situation, not the people. (Which I wasn’t intending to do anyway, but… AI colleagues aren’t so good at making those judgments. I appreciated the warning.)

Claude began writing and drafted a great opening. It was sharp, direct, well-constructed. It was also completely wrong.  It opened by telling people they were making big mistakes, which is a fine way to start an argument and a terrible way to start a conversation. I told Claude it would be off-putting to the people I wanted to engage with. I suggested opening with details of the book collaboration instead — what it was like, what it made me realize. Claude rewrote the opening around that idea. The version you read at the top of this post is the result.

Then I asked Claude to find a good demonstration from our collaboration that would clearly illustrate the gap between standard chat-based Saas products and agentic desktop AI, like Cowork. Claude wrote the story of one particular back and forth discussion we had to find just the right wording for a pivotal paragraph in the book. I liked the story, but it was written from Claude’s point of view in Claude’s voice inside this post, and the tonal shift was jarring. I asked Claude to try again but to tell the story in my voice from my perspective instead. It was still not right — the story only worked when Claude was the one telling it. Read from my perspective the story boiled down to, “I edited a paragraph,” which is not nearly so compelling.

That is how agentic desktop AI tools, a category that I call Delegate AI in the book, differs from other AI tools. I didn’t start this post with a prompt: “write a blog post about AI using the following structure, include three examples, write in a professional tone, and keep it under 1,000 words.” Instead, we had a working session where I sat down and said, “I am frustrated and I want to write a blog post about it.” And then we worked on the idea together.

Is that how you are working with your AI platforms now? If not, I would argue that you have not really worked with AI yet. You have used a precursor to an AI colleague. And the distance between that and the real thing is not a feature upgrade. It is a completely different way of working.Continue Reading Your New AI Colleague – A Field Guide to the AI That’s Going to Do Your Job