Azeem Azhar's Exponential View
Showing you my AI chief of staff (OpenClaw practical guide)
05 Mar 2026
Transcript generated automatically by AI and may contain errors.
Chapter 1: What is the AI chief of staff introduced in this episode?
Two days ago, Goldman Sachs' chief economist said that AI investment had added, in his words, basically zero to US GDP in 2025. But here's the thing. They're looking at companies across the economy, but they don't look at people like me. A small number of us have already deployed what amounts to a five, maybe a 10-person team working round the clock on our behalf. I'm not special.
Chapter 2: How does my AI chief of staff operate overnight?
I'm just early. The gap between the people who've started and the people who haven't started is widening every week. Is this going to make me worse at certain things?
Chapter 3: What hardware and software support my AI chief of staff?
Am I going to think less carefully before delegating because the system is so capable? Am I sharpening my judgment or losing the muscle that that judgment requires? Welcome to Exponential View, the show where I explore how exponential technologies, in particular AI, are reshaping our future.
And it does feel like that future is coming ever closer and becoming more like a discussion of the present. Now, each week, I'll share some of my analyses or speak with a guest to make light of a particular topic. But this week, I want to show you something. What you're seeing on the screen right now is a piece of software.
Chapter 4: How do I communicate with my AI assistant?
It is a knowledge dashboard that I use to track what I need to do each day. It brings together material from my internal systems, from our CRM system, from our research engine, from data.
x from my email from other sources and it ranks the things that it thinks are important for me and given my work a lot of the things that are important for me are outward facing they're not necessarily about projects and things that are happening internally this is a live knowledge dashboard
Chapter 5: What are some real-world examples of my AI assistant's capabilities?
The thing about this is it's live now, right now, and it runs on a Mac Mini in my studio, just over there. You can't see it. It's in an equipment cabinet. This didn't exist eight days ago. I haven't written a single line of code of it. It was put together... by six AI agents overseen by a super agent, or perhaps I should say it was one AI agent overseeing six sub-agents.
And they built it overnight over the course of a couple of days with my feedback.
Chapter 6: What are the costs associated with using an AI chief of staff?
They argued about the database schema at three in the morning. They wrote tests for each other's codes. They deployed it and I woke up and it was running the first time it was running.
Chapter 7: How does my AI assistant's personality specification work?
It was a bit ugly. It was a bit shonky as any new piece of code, but ultimately, It works, and I've iterated a couple of times, and it's something that I now use every day. Now, that super agent, the primary agent that orchestrated and coordinated all of that is called R. Minnie Arnold. Now, the R comes from Isaac Asimov's novels. In those novels,
robots that were intelligent were given that moniker R for robot, R. Daniel Olivo. And it's what we use when we are naming agents of the type that I've just described today with an exponential view. What about the name Arnold?
Chapter 8: What practical tips can I implement this week using AI?
In the Terminator films, in the second Terminator film, Arnold Schwarzenegger comes back to protect humanity from the even more Terminator-y Terminators. And so our mini Arnold, because my agent is not as big as Mr. Schwarzenegger, is sitting in that Mac Mini and doing all of that work.
I've been running our mini Arnold or RMA, as I will call it during the course of this conversation, for about a month. And it has really changed the way I work physically. more than any single tool since the web browser. I can't overstate it. You're going to hear me talk about it now for 25 minutes, but it has changed the way I work. And what is it?
Well, Armony Arnold is, for want of any kind of better word, definition, phrase, the academics can argue about this, It is an AI agent. It is the AI agent that does things that are similar to the AI agents that were imagined back in the 80s and the 90s. Think about Alan Kay's famous knowledge navigator. And what I've experienced over the last three or four weeks is that
A lot of the discussion about AI agents, especially when we think about it in economic terms, perhaps might have missed the point entirely. Two days ago, Goldman Sachs' chief economist, Jan Hatzius, said that AI investment had added, in his words, basically zero to US GDP in 2025.
And earlier in the week, there was the paper from the NBER that suggested that 80% of American, British and Australian companies were reporting no productivity gains. And that headline is repeated in lots of other places. There was a PC magazine that said the AI agent hype is real. The productivity gains aren't. Now, there are many ways to interpret that number.
Of course, not least, it means that 20% of firms just three years after the chat GPT moment do claim to see productivity benefits. And that concords with our proprietary tracking of US public companies' Gen AI claims. It lines up with what our friend Eric Brynjolfsson at Stanford University is starting to say about the productivity data that is becoming visible.
But here's the thing, those numbers measure a particular type of real thing. And perhaps they're measuring something that is less important. Maybe they are measuring the wrong thing. They're looking at companies across the economy. Sometimes they look at individual companies, but they don't look at people like me and they can't capture what is going on there.
The revolution isn't merely general intelligence. One of the things I've observed is that when the cost of delegation falls below the execution cost for a growing fraction, of what we call knowledge work, when that cost falls by an order of magnitude, you do much more of it. And that's basically the whole story. What RMA has done is it has made it really, really easy for me to just buy
mark orders sometimes into a system in parallel and get lots and lots and lots of work done. If you think about a big company, they're running pilots, they're building governance frameworks, and they're hiring chief AI officers, a job that exists today and will not exist in five years. But They have to contend with all the issues of big companies. And that's what turns up in the statistics.
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