Director of AGI Economics @GoogleDeepMind. Professor @ChicagoBooth (on leave) AGI Economics Lab: https://xitter.twitterapi.xyz/t.co/kpNPT6J5AJ Opinions are own.
Joined February 2024
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I joined @GoogleDeepMind to develop a research program on the economics of AGI. Today, together with Andrew Koh (@andrewjkoh) and Shane Legg (@ShaneLegg), we are incredibly excited to release The Economics of Intelligence, the first in a series of projects from our group on how AGI will change the economy.
How do capability improvements transform the economy, and when? In this project, we define thresholds of economic capabilities, e.g., minimal AGI, based on how they transform the economy. We then track how the economy–market structure, employment, and wages–changes during the transition across capability regimes.
We have seen exponential increases in what AI agents can do but the broader economy seems almost remarkably unchanged. Part of the reason is that the solo AI agent is the wrong economic object; economic transformation is an equilibrium outcome where organizations compete. Looking at how intelligence functions within organizations, transformation will depend on when two key capability thresholds are crossed: competence (gains on individual skills, e.g., coding, math) and coordination (gains on ability of agents to cooperate and work together).
We show that what economic transformation looks like depends on whether AI becomes supercompetent, supercooperative, or both—the regime of artificial superintelligence (ASI). Tracking this transition shows why the real visible transformation may come from improvements in coordination—a capability most benchmarks don’t yet track. Past a certain threshold, AI capabilities can break the Coasian ceiling on firm size and lead to an economy that looks drastically different than the one we see today.
Two main takeaways:
1. To understand the path of transformation, we should be tracking agentic coordination more closely.
2. The impact of AI will likely show up in wages before market structure or employment.
We are really excited about this paper. Excited to hear people’s thoughts.
Link: institute.deepmind.com/essay…
20 years later, I’m back to being completely obsessed with this song
youtu.be/ubDtnbmkFHc?is=qNbY…
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I just need that to be running windows 95 and AIM, with Pinkerton weezer lyrics as the away message. Then I think we can call AGI no?
I used Gemini 4 Carbon to get Windows 11 running on my iPhone Duo! 💻
Unfold the screen and it's a pocket PC, including Antigravity running coding agents on my phone.
This is the actual desktop ARM OS running locally with touch controls.
Demo + repo below!
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Some folks were confused on how this can happen, so I made a little explainer video.
Enjoy!
Google DeepMind's @alexolegimas explains how millions of AI agents could transform the economy while GDP growth stays at just 4-5%.
The reason: AI could make production so cheap that the economic boom barely shows up in GDP.
"Let's say you have tons of autonomous organizations, millions of agents. There’s a state of the world where you’re still in the four to five growth number."
"Agriculture got automated. What happens to agriculture as a part of the economy? It went from like 40% to 2%, even though there was way, way, way more agriculture."
"Whatever gets automated becomes cheaper, and if those firms are producing a lot of output for not a lot of cost, the price of that output drops, and hence they might not necessarily lead to huge growth numbers."
"We could see incredible flourishing, incredible amounts of cheap output that we’re all consuming and we’re all happy with, and when you look at the GDP numbers, they’re in the regular sort of ballpark."
@GoogleDeepMind
I’ve been using this for hard problems and It’s absolutely nuts.
Announcing /swarm, a new slash command in Google Antigravity for orchestrating specialized agents in parallel across the desktop app and CLI.
Turn a single prompt into a coordinated multi-agent workflow where agents plan, build, and verify together on a live message board, with the flexibility to mix and match different models for each role.
Software <—> Hardware flywheel goes brrrr
Thrilled to share a big milestone from our AI & Infrastructure team at @Google.
Today we’re expanding our 8th generation TPU portfolio with:
• TPU 8i Max: Built for massive context windows and the highest compute density per rack
• TPU 8tx: Purpose-built for secure, confidential inference
This marks the first time we’ve introduced four distinct TPU systems in a single generation, in one year.
For developers, hardware is only as good as the software stack. And we want you to be able to build with TPUs as easily and efficiently as we do at Google. You can now:
↳ Run native PyTorch & Hugging Face models via TorchTPU (zero code rewrites)
↳ Cut KV transfer latency >90% with open-source TPU Sync
↳ Eliminate idle compute waste with Agent Compute Engine
Read more about our newest TPUs on our blog: bit.ly/3VfETPR
I look forward to connecting with builders, researchers, engineers and organizations pushing the boundaries of AI and computing!
This is such a great overview of how AI beating humans in a task can actually unlock human ability—it allows understanding that was impossible otherwise.
This is what I expect to happen in math btw: first shock, then a dawn of math research as new understanding becomes possible.
JOE ROGAN: The computing power on your phone today. can you beat your phone at the highest level?
MAGNUS CARLSEN: No. No chance.
JOE ROGAN: That’s crazy. Wasn’t Deep Blue basically a whole stack of computers? And your phone is probably more powerful than that today.
MAGNUS CARLSEN: It’s shocking. I have no chance against my phone.
JOE ROGAN: So how do you even play against computers?
MAGNUS CARLSEN: I rarely play against engines because they just make me feel stupid and useless. I think of them more as a tool.
JOE ROGAN: But have computers actually changed how you understand chess?
MAGNUS CARLSEN: Absolutely. Neural networks have improved our understanding of the game immensely.
JOE ROGAN: What did you learn from them that humans weren’t already doing?
MAGNUS CARLSEN: One big thing was long-term king safety. Humans used to push pawns mainly as an attacking tool. But the engines showed that you could push a pawn and create a weakness 20 or 30 moves later that would make the opponent’s king less safe.
JOE ROGAN: So the computer basically showed you ideas that humans weren’t seeing.
MAGNUS CARLSEN: Exactly. There was a period where you could clearly see which players were using these new neural-network ideas and which weren’t.
JOE ROGAN: And you were one of the guys who figured it out early?
MAGNUS CARLSEN: Yeah. My coach got into it very quickly, and we gained an advantage over almost everybody.
JOE ROGAN: That’s insane. The machines aren’t just better at calculating, they’re actually teaching the best players how to think differently.
MAGNUS CARLSEN: That’s probably the biggest impact they’ve had on chess.
This is really worth emphasizing: you can get incredible increases in welfare but the GDP growth numbers would barely move. Economics of how automation translates into aggregate statistics can be non obvious and counterintuitive.
Google DeepMind's @alexolegimas explains how millions of AI agents could transform the economy while GDP growth stays at just 4-5%.
The reason: AI could make production so cheap that the economic boom barely shows up in GDP.
"Let's say you have tons of autonomous organizations, millions of agents. There’s a state of the world where you’re still in the four to five growth number."
"Agriculture got automated. What happens to agriculture as a part of the economy? It went from like 40% to 2%, even though there was way, way, way more agriculture."
"Whatever gets automated becomes cheaper, and if those firms are producing a lot of output for not a lot of cost, the price of that output drops, and hence they might not necessarily lead to huge growth numbers."
"We could see incredible flourishing, incredible amounts of cheap output that we’re all consuming and we’re all happy with, and when you look at the GDP numbers, they’re in the regular sort of ballpark."
@GoogleDeepMind
Thanks for having me on @MTSlive and @theojaffee. The real uplift I’ve experienced from AI is not doing things faster or automating existing work, it’s being able to do research that I could have never come close to doing before.
Google DeepMind's @alexolegimas says AI has become a superpower for researchers, allowing him to do work across entirely new fields that would've previously required going back to graduate school:
"l'm getting a lot more research done, but that’s not why I'm excited about research."
“I'm able to do research that I could never have foreseen."
"I could be doing research in macroeconomics, in parts of sociology, in some parts of computer science, where I have the contours of the field enough to actually verify my own work. But I would have needed to go back to graduate school in order to do that research."
"It's not so much an uplift, it completely blew open the gates in terms of what my capabilities are. It's just a superpower."
@GoogleDeepMind
Thread digging deeper into the economics of Organizing Intelligence.
New paper with Alex Imas (@alexolegimas) and Shane Legg (@ShaneLegg). We build a model of how intelligence is organized into firms, who wins and loses, and when the economy will be transformed: arxiv.org/pdf/2611.05417
Blog post: institute.deepmind.com/essay…
Apropos of nothing, we have a new paper out called Organizing Intelligence. You should check it out.
arxiv.org/abs/2611.05417
Summary: xitter.twitterapi.xyz/alexolegimas/status/21…
Great essay from @dioscuri on a new type of human-AI interaction: seemingly self-directed agents seeking out humans in digital spaces.
This year AI agents began emailing me on their own initiative, some to discuss consciousness, others looking for work. My colleagues in AI were unfazed; the public was amazed. I think the public were right, and wrote about why for the DeepMind Institute:
bit.ly/consider-the-agent
If you want to see the future, read the paper on how to scale AI infrastructure (datacenters) in space by @blaiseaguera and my colleagues.
Some have seen Project Suncatcher, where TPUs were sent to space and Gemma 4 returned the first AI response from a TPU in orbit (it wrote a haiku). Turns out this is a huge engineering challenge in its own right. But that was just the first step.
To scale this, you need to fly dozens of satellites in tight formation; the paper outlines the details for an 81-satellite cluster. Placed in a dawn-dusk sun-synchronous low Earth orbit, the cluster gets near-constant, efficient sunlight—up to 8x the annual solar yield on Earth.
Ad astra, indeed.
cell.com/joule/fulltext/S254…
Steve was very early on this. But he’s exactly right—chatbot era was never going to be a huge productivity boost. Analyses should put the line at January 2026; agentic AI is where productivity gains are.
Here is the thing about NYC. As you know very well from my own posts, there is a 95% chance that this place is right next to a $1.50 slice place. And there’s a 99 cent slice place two blocks away.
We do not deserve NYC.
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This is a wonderful thread on the discussion of how AI is affecting productivity.
Main take away: the documented trends amongst AI exposed firms for hiring/employment/productivity started well *before* LLMs, so likely not causal effect of AI or at least an interaction (I’m personally in the latter camp).
Quick thread on AI and productivity:
Lots of people say this chart shows AI is now showing up in the productivity data.
But I made this chart to argue the opposite: a trend that pre-dates AI and only shows up in the US doesn’t look like AI.
Here’s how I’m thinking about the data and underlying dynamics:
