Google DeepMind Just Launched the DeepMind Institute. How Close Are We to AGI?

The last couple of weeks have been full of AGI talk.

Elon Musk, Sam Altman, and Dario Amodei have all said in one way or another that we’re getting close. Then on September 3, OpenAI president Greg Brockman ended the GPT-6 Astra press briefing by declaring the start of the AGI era, and the phrase went everywhere. 

Jensen Huang had already gone further back in March, telling Lex Fridman that he thinks we’ve achieved AGI, and he posted again on September 7 to say AGI had arrived once Astra shipped.

Jensen Huang claiming AGI has arrived. Image by Jim Clyde Monge

So have we actually achieved it? And if we haven’t, how far are we?

I’ve been testing Astra for a few days now, and yes, it’s really good. Instruction following is noticeably better than what I was getting from Sol, especially on multi-step tasks where the model has to hold onto a constraint I mentioned five messages ago. 

It’s also a great model for coding. Heck, I’ve shipped so many new features and enhancements to Zeniteq in the past couple of days. Debugging is also better. It handles long computer-use sessions without me continuously checking on it. 

But none of that makes me want to call it AGI, and I don’t think most of the people using that word actually believe it either.

The Navier–Stokes Millennium Prize story that got attached to Astra is a good example of how fuzzy this all gets, and I’ll come back to it, because the version that circulated isn’t quite what happened.

Meanwhile, on Wednesday, Google DeepMind opened something called the DeepMind Institute. 

Deepmind Institute landing page. Image by Jim Clyde Monge

The reason they give for launching it is that AGI is coming and we need interdisciplinary thinking to understand what it will do to us. Which is interesting, because that reason only makes sense if AGI hasn’t arrived yet.

What is AGI, and why does nobody agree?

Most of the AGI argument right now is really an argument about definitions, and everyone is quietly using a different one.

Before Jensen Huang answered Lex Fridman, he set the bar by asking whether an AI could start and run a tech company worth more than a billion dollars. 

Huang said yes, we’re already there, and gave the example of an AI shipping a cheap app that a lot of people use briefly before the whole thing folds. 

That’s a claim about what AI can do economically. It isn’t a claim that machines now match human thinking, and it’s a much lower bar than what most people hear when someone says AGI.

Altman, in the same week Astra launched, described AGI as a very poorly defined term. Honestly, that was the most accurate thing anyone said all week.

Google DeepMind is one of the few labs that has actually tried to clean this up instead of taking advantage of the confusion. Back in March, a team that included Shane Legg published a paper called Measuring Progress Toward AGI. 

Measuring Progress Toward AGI paper. Image by Jim Clyde Monge

It borrows from psychology and neuroscience and breaks general intelligence into ten cognitive abilities, including perception, attention, memory, reasoning, metacognition, executive function, and social cognition.

The idea is that you don’t give a model a pass or a fail. You test it across all ten areas using held-out tasks it can’t have trained on, and you end up with a cognitive profile, which is basically a shape showing where the system is strong and where it falls apart.

That framework is the definition the DeepMind Institute uses. When the launch essay says AGI is a system with all the cognitive capabilities of the human brain, that’s the paper it links to.

Most people talk about AGI like it’s a single moment. Either a model has crossed over into AGI, or it hasn’t. DeepMind’s framework doesn’t work that way. You test a model across those ten abilities, and you get ten scores, and right now every model is strong in some areas and weak in others.

Astra is a good example. It’s excellent at computer use and terminal work, and a lot more ordinary once you start testing general reasoning.

Artificial Analysis benchmarking Astra against Claude Fable 5.1. Image by Jim Clyde Monge

Artificial Analysis had it scoring below Claude Fable 5.1 on general intelligence at launch, even while it was winning on agentic tasks.

What the DeepMind Institute actually is

A lot of the coverage called this a research institute, which gave me the wrong idea at first. It isn’t one.

The DeepMind Institute, or DMI, is closer to a magazine. It’s a place where researchers publish essays about AGI. The three directors are Shane Legg, Demis Hassabis, and James Manyika, who runs research, labs, technology, and society at Google. 

Legg is also listed as the managing editor, and that title tells you more about the project than “director” does. There’s an editor’s email address at the bottom of the launch essay.

Introducing Deepmind Institute. Image by Jim Clyde Monge

Every page carries a disclaimer saying the pieces are conversation starters that reflect the author’s own views and shouldn’t be read as Google’s official position. 

The launch essay makes a similar point, saying contributors will not always agree and will probably change their minds as new information comes in.

Here are the initial posts that went live at launch.

• A framework for frontier AI by Demis Hassabis, which originally appeared as a thread on X.
• The case for reasoning transparency by Rohin Shah and Anca Dragan, who lead AGI safety and AI safety work at DeepMind.
• Economic policy for AGI by Julian Jacobs and Alex Imas.
• Principles for a new utopianism by Stephen Cave, a philosopher at Cambridge.
• The introductory essay from all three directors.

Here’s my first problem with it. Four of those five authors work for Google or Google DeepMind. The entire pitch of the institute is that technologists shouldn’t be the only people answering these questions, and that the arts, humanities, and governments all need a seat. 

At launch, the humanities got one seat out of five.

What questions is it trying to answer?

The launch essay lays out three: 

1. What will we value, and how will AGI change what it means to be human? 
2. How do we safely build and govern AGI systems and the groups of agents they’ll form? 
3. Which institutions and policies will we need to adapt or rebuild entirely?

The reasoning transparency piece quietly makes the case against the AGI-is-here. Shah and Dragan argue that right now we can read a model’s chain of thought, meaning the step-by-step thinking it writes out before answering. 

“Chain of Thought transparency gives us a window into a model’s reasoning — but that window could become narrower. We should measure how much transparency Chain of Thought is providing, so we can notice if it is accidentally degrading.”

That gives us a window into whether a system is scheming, cheating on evaluations, or hiding things from us. Their worry is that this window is an accident of how today’s models happen to be built, and it’s already starting to close.

The evidence they point to is quite concerning. OpenAI’s own system card for Astra reports a substantial decrease in chain-of-thought monitorability compared to earlier models.

Put simply, the model that OpenAI is calling the start of AGI is also the model that became harder for humans to read. Those two things happened at the same time, and they’re related. 

If future models reason entirely in their own internal number space instead of in English, we lose the main tool we currently have for catching misalignment. 

The authors propose putting a limit on how much hidden step-by-step computation a model is allowed to do, and they argue the limit wouldn’t slow anyone down much.

Hassabis’s essay is the most interesting to read. 

He says AGI is probably only a few short years away, compares it to the discovery of electricity or fire, and describes what we’ve built as a way to make sand think. 

The actual proposal underneath the poetry is a US standards body for frontier AI, modeled on the financial industry’s self-regulator FINRA, funded mostly by industry, with labs voluntarily sharing models for review up to 30 days before release, with that becoming mandatory later. He leaves room for the body to coordinate a slowdown among labs if things get serious enough.

It’s a serious proposal. It’s also worth remembering that an industry-funded standards body tends to work out fine for whoever has the most money, and Hassabis chairs the lab that does.

The economics essay is also a good one. Julian Jacobs and Alex Imas rate eleven different policy responses to AI-driven job losses and recommend a sequence. 

Expand unemployment insurance and employer-led retraining now. Move to a negative income tax if displacement gets worse. Keep universal basic capital, which would give people an actual ownership stake in the economy, in reserve for a scenario where labor’s share of GDP keeps falling.

Their case against universal basic income is good. They argue it’s expensive, blunt, and leaves people with no stake in the automated part of the economy.

What bothers me is how they got there. Alongside surveys and a literature review, they used 51 AI agents whose personalities were built from survey data on 51 real economists, then had those agents rate the policies. 

The essay presents the results in clean tables with scores like 76.3 and 69.8. The underlying paper is honest that this is exploratory work. The tables in the essay don’t carry that caution, and a number with a decimal point in it looks a lot more settled than a simulated economist’s opinion actually is.