Ethan Mollick | The long way is the shortcut: experiments over AI playbooks
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What is the main topic discussed in this episode?
Nobody knows anything, right? Like I spend my time talking to the AI labs. I've like brief famous people. I talk to CEOs all the time and nobody knows anything.
I wanted to kick off with the fact that it's been almost exactly a year since you spoke to our founder, Joel, and obviously a lot has changed in the past year. So where are we in AI right now?
So you can kind of think about three eras of AI broadly. We talked about what I'd call the second era. The first era was machine learning writ large, right? Everybody was doing data analysis, and you had your beautiful data lakes, and you had your data scientists, and you were doing predictive modeling, and all that's still valuable, but that was sort of what AI meant prior to 2022. Then we had our chat GPT moment, and the last time we talked, we were still in the era that I will grandiosely call, after my own book title, co-intelligence, where you'd prompt an AI back and forth to get answers. And I think we have entered in the last few months, especially what we call the agendic era, where it's less about working back and forth with the AI through a chatbot interface, but more about assigning it to do long running tasks, starting to think about what it means to have organizations built around AI.
And I think that that has been the biggest change.
And could you tell us a bit more about why you think the co-intelligence era is over?
So I think that there is still a need for people in the loop, and there's lots of really interesting things to talk about, about how we incorporate people into AI. I think we still have to be centered around people. But the idea that every interaction is going to be you make your request, the AI tells you something, you do it in the world. You're like, I don't quite know how to do this. Can you make it easier for me? Maybe give me the code for this. That back and forth that was sort of the driver of like, let's talk to a chatbot, refine this email, do it better, is... was based on the idea that AI couldn't do anything, right? Like it had to be closely watched. It made lots of mistakes. There were lots of errors.
It couldn't take action in the world. Now hallucination rates have dropped. They're not zero, but they've dropped. Models can do multiple hours of work independently. That work is judged by independent people to be as high quality as experts work often. And that changes how you do things.
Has anything surprised you about what's gone on in the past year?
What are the three eras of AI and where are we now?
So I think there have been a couple of inflection points that you couldn't predict exactly when they happened that have occurred. I think a lot of us knew who were close in the space knew the agentic moment was going to happen. I think it was somewhat of a surprise that happened as quickly as it did and that the impact was as large as it was, right? So it went from AI as toy to, oh my God, cloud code is here and now we have to change how we do all coding and all work. And I think that has been a relatively rapid change. I've also been somewhat surprised in the last year about how much there was a pivot in early fall, even before that, with companies going from a conversation about, is AI worth doing, to that being answered and being like, how do we use AI?
I also think that just in a general purpose, I think the continued acceleration has been a little surprising. I think that we were all expecting AI to keep getting better, but there really has not been a lot of slowdown of exponential growth, which I think is interesting.
Why do you think it's interesting?
So everything is downstream of AI abilities, right? The jaggedness of those abilities, the fact that it's good at some stuff and bad at some stuff, is a, I think, it tells us what humans do, what our work should look like, where AI might fail, why it's problematic to integrate in. But as AI keeps getting better, the growing aspects of that frontier are what sort of determine how useful it is. So the fact that we've gone from You know, you can pick any scale that you want, right?
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Chapters
5 chapters
1
What is the main topic discussed in this episode?
0:00–2:28
2
What are the three eras of AI and where are we now?
2:28–5:32
3
Why does Ethan call current AI capabilities the ‘agentic era’ and how do agents change work?
5:32–18:43
4
What is the 'jagged frontier' and where do models excel or fail?
18:43–38:15
5
Why does Ethan compare working with AI to collaborating with 'wizards'?
38:15–54:03