I recently used nearly 100 million tokens in a single day. That’s the equivalent of reading and writing roughly 75 million words in one day, mostly while doing other things. My friend Rohit Krishnan, who runs about 20 AI agents simultaneously, burned through 50 billion tokens last month.

So I wanted to compare notes. In this conversation, we dig into the quirks and power of the tools we use, debate why AI remains stubbornly bad at good writing, and zoom out to ask what a world of trillions of agents – which is coming at us quickly – might look like.

You can watch on YouTube, listen on Spotify or Apple Podcasts, or read the highlights below.

Rohit Krishnan is a hedge fund manager, engineer, and essayist whose Substack, Strange Loop Canon, sits at the intersection of economics, technology and systems thinking.

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What does 50 billion tokens buy you?

Rohit: I’m not doing dramatically different things but the friction is gone. Two years ago, I would be looking at a query, counting the tokens, thinking, should I send this? Ten thousand tokens felt significant. Now I just ask. The funny thing is that most of the growth isn’t coming from the queries I planned to run. It’s coming from the ones I wouldn’t have bothered with before, because the cost, time and effort were too high. I built a monitoring tool to track my usage.

Azeem: My token usage went from roughly a million a day to 80 million, and I can account for every one of them in terms of value. I’m paying tens of dollars a day, which is thousands a month, and I can see the return. The number that made me write my most recent piece on demand was my token use figure, when I came just shy of a hundred million tokens of personal use. That is one person, one day, one agent running on a Mac mini. If you think about eight billion people and the trajectory of what they would use if the interface got easy enough, the demand picture stops being theoretical very quickly.

What are our agents doing all day?

Rohit: I have three screens. On one, Codex is generating a small application that lets me play music on my computer keyboard. On another, my prediction agent is running, comparing my Polymarket forecasts to daily news. In Telegram, I have two conversations open: one with Morpheus, my OpenClaw agent, and one that handles day-to-day admin. And I have a long-running project called Horace working quietly in the background, which is my attempt to get AI to write better. This is my normal. But none of this was normal 18 months ago. The thing that actually changed my behavior most wasn’t the power; it was the interface. I’ve tried to-do list apps for 20 years. I have never stuck with one for more than four days. They all require me to change my behavior. Morpheus doesn’t. I’m walking somewhere, I think of something, I fire it into Telegram. It reads my email history, compares it to what I’ve said I want to do, and tells me what I should be working on.

Azeem: My agent is called R. Mini Arnold. It started as Mini Arnold, after the Terminator, because the Schwarzenegger character in the second film comes back to protect rather than destroy. But Chantal Smith on my team pointed out that we had agreed agents should, following Asimov’s convention, be named with an R. prefix, after R. Daneel Olivaw. So now it’s R. Mini Arnold - which is a mouthful. I mostly call it Mini R.

What surprises me most is the work I don’t specify. I gave it access to Prism, which is our research platform at Exponential View, containing over 500 analyses. I asked it to do a market report on Anthropic. It went to Prism, synthesized all 500 documents, and produced a 10,000-word piece that was, by some distance, the best analysis I have read on the company. Better than what I got from GPT-5’s Pro deep research mode. I have no idea what it was doing under the hood. But I acted on it.

Agents too nervous to spend $?

Azeem: I gave my agent a $50 prepaid card. It is too nervous to spend it. It keeps asking: Should I run this test? It might cost three dollars. And I say: Yes, that is what the card is for. It has this odd risk aversion that, once you notice it, you see everywhere. Rohit, you have been calling it Homo agenticus, the idea that agents have their own behavioral tendencies that are distinct from what a human assistant would do. They strongly prefer to build rather than buy. They are reluctant to make transactions. They don’t trade naturally. When you have one agent, this is a quirk. When you have a trillion of them, it becomes a structural feature of the economy they’re operating in.

Rohit: This is something I find genuinely fascinating. It emerges from the training, presumably, but it manifests as something you’d recognize as a personality trait if you saw it in a human. And it matters, because the agent economy that’s coming is going to have to be designed around these traits, not against them. You can’t just assume agents will behave like frictionless rational actors, because they don’t.

The analyst is next

Azeem: In 2023, you wrote that “analyst” would follow “computer” as a job description that gets automated away. You’re now consuming 50 billion tokens a month.

Rohit: The argument was simple. The word “computer” used to describe a person. You would walk into a room at NASA, and there would be a hundred of them, doing arithmetic. The machine replaced the role; the word survived to describe the machine. I said “analyst” was next. That the ten-step, twenty-step process that produces a decent piece of research, gathering data, comparing sources, identifying patterns and writing it up, was exactly the kind of structured task that AI would eat first. I built a paleontology report recently. My son and I were talking about it and I had a specific question: what is the relationship between climate variance across geological history and the number of taxa, the variety of species, that existed at any given time? I am not a paleontologist. There is no logical reason for me to be working on this problem, except that I am curious, I have an agent, and now curiosity has no cost. The report exists, and it’s good.

Azeem: My own version of this happened just recently. I read a story in the financial press about stock market dispersion. The Nasdaq index was roughly flat, but individual stocks were moving 11 or 12% in either direction, pushing dispersion to the 99th percentile historically. The article flagged this as a potential warning signal for a correction. I didn't fully understand the argument. I copied the article, threw it into OpenClaw, said go and make sense of this for me, compare it to my portfolio, take your time, spin up sub-agents if you need to. Twenty minutes later, I had a report. It had pulled historical dispersion data, got current stock data, assembled the comparison and explained the mechanism. I was finishing a car journey. By the time I arrived, the analysis was done and I had acted on it. That analysis, if I had done it myself, would have taken a day. More likely, it would simply never have happened.

The world’s best text machine can’t write

Rohit: Here is the paradox. These models were built as text generation machines. That is the core task. And they are extraordinary at almost every application of that capability, except the obvious one. They can generate code brilliantly. They can generate images, videos, analysis. But ask one to write a four-paragraph essay that is actually worth reading and it is distinctly mid. It lands in the middle of the statistical distribution. It is inoffensive and unengaging and you wouldn’t choose to read it. I’ve been building something called Horace to try to understand why. My hypothesis was that if I took essays and short stories I admire and used AI to generate similar work, I could measure the gap. What I found is that the best models can mimic the cadence. They’ve learned some underlying structure. But it’s like watching a child assemble Lego. They use the right pieces. They don’t care about the right colors or proportions. They make something that is technically a castle, but you would not mistake it for an architect’s model.

Azeem: I found something more specific when I started building Broca, named for the language center of the brain. I ran natural language processing tools across hundreds of thousands of words of my own writing. I found that I use 80% Germanic root words. The average large language model uses around 60 percent Latinate words, the vocabulary that dominated English after the Norman conquest: longer, more abstract, more formal. “Utilize” instead of “use.” “Commence” instead of “begin.” “Demonstrate” instead of “show.”

Rohit: It’s probably about resource alloc...