Interesting read. I agree completely with their Introduction, that the definition of AGI is constantly shifting, and this leads to endless (and useless) debates.

What I find cool about the paper is that they have gathered folks from lots of places (berkley, stanford, mit, etc). And no big4 labs. That's good imo.

tl;dr; Their definition: "AGI is an AI that can match or exceed the cognitive versatility and proficiency of a well-educated adult."

Cool. It's a definition. I doubt it will be agreed on by everyone, and I can see endless debates about just about every word in that definition. That's not gonna change. At least it's a starting point.

What I find interesting is that they specifically say it's not a benchmark, or a test set. It's a framework where they detail what should be tested, and how (with examples). They do have a "catchy" table with gpt4 vs gpt5, that I bet will be covered by every mainstream/blog/forum/etc out there -> gpt5 is at ~50% AGI. Big title. You won't believe where it was one year ago. Number 7 will shock you. And all that jazz.

Anyway, I don't think people will stop debating about AGI. And I doubt this methodology will be agreed on by everyone. At the end of the day both extremes are more ideological in nature than pragmatic. Both end want/need their view to be correct.

I enjoyed reading it. Don't think it will settle anything. And, as someone posted below, when the first model will hit 100% on their framework, we'll find new frameworks to debate about, just like we did with the turing test :)

>Paper claims definition of AGI >Look inside >No definition of AGI.

It's giving... SAT scores meets venture capital.

Desperate to run without even knowing how to walk.

I think a lot of this is all backwards. People think AGI is taking something dumb, like an LLM, and sticking on learning, like a software upgrade.

I think it's the other way around: you build a system that first and foremost _learns_ as part of its fundamental function, _then_ you train it in the domain you want expertise.

You're not going to get expertise in all domains all the time, just like with people. And you're not going to get a perfect slave either, just like with humans. You'll probably get something more like in between a human and machine. If that's what you really want, great.

To put this another way, if you neglect your kids, they're still going to learn things, just probably not things you want them to learn. If you neglect your language model it's just not going to do anything.

Ugh just looking at their list, this paper gets a hard no from me. Intelligence isn't mastery of some arbitrary list of mathematical subjects. It's the ability to learn and apply these subjects (or anything else) after minimal exposure to the topic.

For a bar as high as AGI (and not just 'the skills of an educated person,' which is what this paper seems to be describing), we should include abstract mathematical reasoning, and the ability to generate new ideas or even whole subfields to solve open problems.

Here is a definition of AGI in 2025: Hype.

> .., Eric Schmidt, ..

Right. That explains it.

> Last, we deliberately focus on core cognitive capabilities rather than physical abilities such as motor skills or tactile sensing, as we seek to measure the capabilities of the mind rather than the quality of its actuators or sensors.

seems pretty unfair to exclude motor skills, especially given 1) how central they are to human economic activity, and 2) how moravec's paradox tells us they are the hard part.

it's giving...SAT scores meets venture capital.