AI's Biggest Problem May Be Its Pace | The Professor Is In
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Why does a 1850s boot‑making case study matter for today’s AI debate?
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Saying we have an extraordinary new technology that can create greater abundance and we should stop using it feels totally upside down.
So you released a really fun diving in video this week in which you actually looked back in time at the Victorian boot making industry.
Yeah, fascinating, period. You might ask, why the heck do we care about making boots in the 1850s? And that's because it had an amazing technological transformation that meant the average worker could produce four times as much as they could before. Hey, I don't know if that reminds you of anything that you might be feeling a little bit of anxiety about right now.
We had some great questions that came out of this from our audience, and we're going to dig into those today. I'm Megan Connors.
I'm Justin Wolfers. This is the professor is in. Think about this as office hours. Megan is out there reading your comments so she can bring your comments to me. The professor's in. Let's go.
Yeah. So what we found or what you found is that the number of jobs, total number of jobs really didn't budge that much, even though nearly every other aspect of boot making in England at this time did change.
Let me pick up there, which is I didn't find it. Hilary Vupond, a brilliant young economic historian, has done this great deep dive into bootmaking in the 1850s. If you had told me eight days ago I cared at all about bootmaking, England or the 1850s, I would have said no. But she showed, this is I think it's where you're going, the total amount of employment didn't change even as this incredible labour-saving technology came along. That's the good news. Megan, do you want to tell people the other side of the coin?
I mean, the types of jobs totally changed. So, you know, certain elements of bootmaking did kind of go extinct and new jobs appeared in their place.
Absolutely. All right. I know you've got questions about all that.
So on that note, you know, we had a couple of different people ask what happened to pay? And I know you might not have the specifics in this circumstance, but what do we know about technological revolutions in general and their impact on wages?
Right. This particular study didn't have any pay data, so I can't say anything about Victorian England in the 1850s. And I know I'm going to surprise you, but I'm not actually an expert on Victorian England in the 1850s, even though I dress in linen. Okay, so what typically happens, it's not always the case that wages rise immediately. The period after the Industrial Revolution was when Charles Dickens started writing, and a Dickensian tale is full of young children labouring away in factories, in rags, barely able to make ends meet. So the immediate transition is not necessarily good news. What happens, though, over time is if the pie is bigger – And I don't want to say there's magic, which means the pie is always genuinely distributed in good ways.
But if the pie is big enough and humans are an important part of producing that pie, some of it comes back to roost. So the story on wages is, well, there's two stories. One is what's happening on average. On average, eventually, bigger pie, bigger servings to workers. In the short run, that may not be true.
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Chapters
8 chapters
1
Why does a 1850s boot‑making case study matter for today’s AI debate?
0:00–5:24
2
What happened to total employment when labor‑saving boot‑making technology arrived?
5:24–9:20
3
How did the shift from cord‑swainers to factory foremen illustrate occupational change?
9:20–13:05
4
Did wages rise immediately after the Victorian boot‑making boom, and what does history tell us about wages and new tech?
13:05–17:06
5
What can China’s rapid industrialisation teach us about wages and geographic mobility in AI‑driven growth?
17:06–23:20
6
Why is the pace of AI adoption a bigger economic risk than the technology itself?
23:20–32:12
7
What policy tools (taxes, hiring rules, unions) could slow AI’s disruptive speed without stifling innovation?
32:12–38:09
8
What practical advice should workers and students follow to stay relevant in an AI‑augmented future?
38:09–44:47
Speakers
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