Name: Could AI become smarter than humans? | Terms of Service
Uploaded: 2026-02-17T17:09:27.000Z
Duration: 26 min 56 s
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Future simple

It has been several, maybe two years now from every time I enter into a room and give a talk like this, I'll ask people, how many of you think AGI is coming really soon?

Basic quantifiers

The imperative

The question is, how many of you think we'll get AGI in the next few years being three.

I think Sam Altman only hangs out in rooms where the answer to that question is yes by everybody.

It is so great to be here at Ludlow House with the wonderful On Air Presents team and our guest, Nick Frost.

And also, it's so nice to have a live audience.

My name is Claire Duffy and on this show, Terms of Service, we talk about the latest advancements in technology and we try to help people understand how that tech is going to impact their lives right now so that we can all be smarter, more intentional tech users.

Today we're going to be discussing one of the spiciest, most talked about, but most controversial topics surrounding artificial intelligence.

And that is artificial general intelligence.

This is a not very well defined term, but generally refers to some theoretical future AI system that could be as smart as, if not smarter than, humans.

And the question in my mind is, can we actually build this technology?

Should we be trying to in the first place?

And what could it mean for all of us if we do?

So we have tonight, Nick Frost, thank you so much for being here.

He is an AI researcher and the co founder of the AI startup Cohere, which is last year reached a nearly $7 billion valuation.

And you have had this sort of interesting take about AGI.

There's many people in Silicon Valley, including OpenAI CEO Sam Altman, who have said that AGI is potentially around the corner.

You've said those comments are a disservice to the industry.

So I'm really excited to get into this with you and with all of you.

So, just to back up first, you studied computer science at the University of Toronto.

You worked under Jeffrey Hinton, who is now well known as one of the godfathers of AI.

You were his first hire at the Google Brain Lab in Toronto.

I think I got into it for probably the same reason most people got into it, especially around 10, when I got into it in 20.

2010, 2011, which is that it just seems kind of intrinsically Interesting.

Like there's something very human about wanting to create more intelligence.

I was reading, there was like, I was reading this Tolkien essay recently, and there's a line in it where he says it's.

It's intrinsically human to want to talk to trees, which I think is.

I was like, yeah, there's something really human about wanting to build stuff in our image and make.

So I think a lot of people got interested in AI just because it was cool to push the limits of what computers can do and envision what they might be able to do in the future.

Who I credit with kind of teaching me everything I know about machine learning, really didn't get into AI for that reason.

I mean, he kind of invented neural nets for the purpose of understanding how the human brain works.

And so when you started working with Jeff at the University of Toronto and then Google Brain, what did you think that you were building at the time?

And did you have a sense that AI was going to be as big a deal as it is today?

I remember back in 2013 or 2012 when I first started doing more research in machine learning.

This was a few years after neural nets had for the first time shown that they were good practically.

There was a thing called Alexnet, which was an image classifier, so it could take in a picture and tell you what was in the picture out of like one of a thousand different classes.

Was invented at University of Toronto under Jeff and Alex Kijinsky.

Which seems kind of pedestrian now, but at the time that was huge.

And it was the first time that neural nets, after being worked on for like 20 years, had done anything useful.

And I remember learning about that and being like, man, if only I had been here a little bit early.

I really missed the boat on this whole neural net thing.

And so that, yeah, that really stands out to me.

There was a moment where I was like, man, it's just, it's going to be downhill from here.

I'm still surprised at how good language models are.

And even when I left Google to found Cohere, an enterprise focused only language modeling company, even at that time, I was surprised with how good they are.

And I've continued to be surprised at how good language models are over the intervening six years.

So AGI, artificial general intelligence, is the thing that lots of People talk about.

I think you could ask 12 people and they might have a slightly different definition.

Artificial general intelligence just means a computer that you treat as a person.

And if you use a computer, like, if you use an AI system now, like, you use a language model for a while, pretty quickly you're going to realize it's really good at some things and really bad at some others.

And people who use language models, for the most part, don't interact with them like they would interact with a person.

They instead pretty quickly understand, hey, these are the things that's great at, and these are the things that it's not very good at.

So when I think of AGI, I think of, like, when will we have a technology that when you interact with, you expect it to behave as a person?

Like, I mean, you'd have a conversation like we're having now, and we both are functioning independently in the world, and I anticipate that you will go off and continue to do things after this podcast is done being recorded.

And none of that is the way I interact with the language model right now.

And why do you think that AGI is sort of the North Star that so many tech companies are building toward right now?

It's very human to want to talk to computers.

It's very human to want to build more humans.

That seems to be a motivation that is independent of economics.

There's the other one that it's a really compelling narrative for raising capital.

It's really, really compelling for venture capitalists.

If you say, hey, I'm going to build infinite digital people, what's wild is it turns out that's not even a good enough narrative anymore.

And now, now that narrative has moved on to, like, now I'm going to build a digital God.

Like, you know, artificial super intelligence, which is beyond that.

So you think of AGI and super intelligence as sort of two different things.

I think of them as both pretty poorly defined marketing terms.

And if I, like, if I was to carry on my, my definition of AGI by saying it's a.

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