Ethan Mollick

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673 appearances 1 recordings 1 series first heard Jul 2026 last heard 9 Jul

Ethan Mollick’s voice in public audio — every appearance, attributed to the second.

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Recordings per month over the last 12 months — 1 in all, peaking in Jul 2026 with 1.

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Strange Loop Podcast · Ethan Mollick | The long way is the shortcut: experiments over AI playbooks · 9 Jul 2026 podcast

And so I think we're going to have to think about those aspects.

I think the bottlenecks are often about the complexity of tasks on a jagged frontier.

And there are some areas that AI can do well and some it can do badly.

And the bottlenecks become the things AI can do badly or only does badly is where human efforts needed the most.

Obviously.

I mean, we can have a super intelligent machine and we might already have them in some ways, but we can have super intelligent machines and very few people's work will change the next day because it's still jagged.

And it's just even jagged in the physical world.

Like somebody is attending the meeting, someone's going to something, there's tons of unwritten material.

You can't just drop in, you know, the smartest person you can imagine, and if you drop them into a job,

and even a thousand of them into a job, you're still going to have a lot of trouble making things operate, right?

So, there is obviously some issue, you know, big picture issue for two years from now.

But let's talk about five, seven years from now, which is actually when all the AI labs are sort of saying jobs get destroyed.

There's sort of an emerging economic consensus.

We don't know the answer, but the consensus is around something called the O-ring model of jobs, which is grimly named after the Space Shuttle Challenger disaster, where thousands of parts worked well in the Challenger, but the O-ring failed, right?

In an O-ring theory of jobs, we do many different tasks.

And some of those tasks are really important in a way that is not just 10% better, 20% worse.

But if they go badly, the entire job falls apart, or the entire work stream falls apart.

And what happens is those become the, to use a Claude term, the load-bearing pieces of the entire job organization.

And by the way, we can see this happening.

Even before the pandemic, but certainly during it, there was a huge demand for software developers.

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