Sep 19, 2026 · 34 min · 10 segments
Artificial intelligence has become one of the defining technologies of our time. But what if the way we're thinking about AI – starting with the very idea of "intelligence" – is too narrow? Welcome…
Michael JordanGuestAshley KlimtHostSo Mako, your work has crossed boundaries between machine learning, statistics, economics, cognitive science, and other disciplines for decades.
When you look at where AI is today, which disciplines or ways of thinking do you believe need a much bigger seat at the table?
Well, it's mainly been driven by computer science, and that's not a bad thing because if you didn't have the infrastructure of the internet and the data gathering capability, none of this would have ever happened.
And that was, let's give credit where credit is due to computer science, largely, for building the internet and building these systems that capture all this data and so on.
But again, when you start to now solve new problems and think of transportation or finance or healthcare or anything that involves collectives of people and you want to build a system, it has got to be more like a market and it's got to be more like a a thriving uncertainty coping system.
And so you immediately need ideas from economics and in particular microeconomics.
Macro is just to stand back too far and kind of look at the thing in terms of geopolitical issues.
But microeconomics is how agents interact to bring value to everybody and to share things even if they have limited understanding.
Certainly, the companies don't talk about it and they don't necessarily develop their software with that in mind.
They don't think about it being as part of a federated network of like-minded systems and humans all mixed in with each other.
They don't think of the market that that's creating and the role of the producers and consumers and who are the producers, who are the consumers.
Are they all getting value out of it? Should they all be incentivized to participate, et cetera, et cetera.
Economics thinking is just absent and I think that is probably the hugest big issue.
People are building the systems and they're seeing that they're failing in all kinds of ways.
And the other one is really statistics, broadly speaking, or the management of uncertainty.
If you ask how sure you are of something, they'll give you a very compelling argument for why they're sure of something.
But if you continue the dialogue, you can push them in any direction you want to.
So Mako, your work has crossed boundaries between machine learning, statistics, economics, cognitive science, and other disciplines for decades.
When you look at where AI is today, which disciplines or ways of thinking do you believe need a much bigger seat at the table?
Well, it's mainly been driven by computer science, and that's not a bad thing because if you didn't have the infrastructure of the internet and the data gathering capability, none of this would have ever happened.
And that was, let's give credit where credit is due to computer science, largely, for building the internet and building these systems that capture all this data and so on.
But again, when you start to now solve new problems and think of transportation or finance or healthcare or anything that involves collectives of people and you want to build a system, it has got to be more like a market and it's got to be more like a a thriving uncertainty coping system.
And so you immediately need ideas from economics and in particular microeconomics.
Macro is just to stand back too far and kind of look at the thing in terms of geopolitical issues.
But microeconomics is how agents interact to bring value to everybody and to share things even if they have limited understanding.
Certainly, the companies don't talk about it and they don't necessarily develop their software with that in mind.
They don't think about it being as part of a federated network of like-minded systems and humans all mixed in with each other.
They don't think of the market that that's creating and the role of the producers and consumers and who are the producers, who are the consumers.
Are they all getting value out of it? Should they all be incentivized to participate, et cetera, et cetera.
Economics thinking is just absent and I think that is probably the hugest big issue.
People are building the systems and they're seeing that they're failing in all kinds of ways.
And the other one is really statistics, broadly speaking, or the management of uncertainty.
If you ask how sure you are of something, they'll give you a very compelling argument for why they're sure of something.
But if you continue the dialogue, you can push them in any direction you want to.
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