What A.I. Is Actually Doing to the Economy

Summary

Podcast Episode Summary: The Daily — "What A.I. Is Actually Doing to the Economy"
Date: July 27, 2026
Host: Zolan Kanno-Youngs (filling in)
Featured Guest: Ben Casselman (Chief Economics Correspondent, NYT)

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Overview

This episode explores the rapidly evolving impact of artificial intelligence on the U.S. economy and workforce. Host Zolan Kanno-Youngs and economics correspondent Ben Casselman delve into why AI’s effects are so difficult to measure, compare current anxieties to disruptions from past technological and economic shifts, and evaluate the challenges for policymakers and individuals in preparing for an uncertain future.

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Key Discussion Points & Insights

Current Mood: Anxiety & Uncertainty About AI

• AI as both tool and threat
  • Zolan opens by highlighting "conflicted" attitudes among workers: excitement for AI productivity, but fear of job losses.
  • Notable quote:
    • "AI is amazing and it's making me a better writer, but it's also taking my job away." — Unnamed speaker (00:57)
  • Many worry about their professions: "I really have to rethink what I do for a living and probably do else." (01:22)
• Polling Data
  • "About 70% of Americans think AI will lead to fewer jobs." — Zolan (02:25)

Measuring AI’s Economic Impact Is Murky

• Government Data Limitations
  • Job reports and economic data lack granularity to track the tech sector, let alone AI specifically.
  • "These industry categories were established literally decades ago... So you couldn't go and say what impact is this having on tech." — Ben (04:20–05:07)
• Private Sector Data: Contradictory Narratives
  • Firms like ADP, LinkedIn, Indeed try to fill the gap, but reach different conclusions:
    • Some see job losses in AI-exposed fields.
    • Others see job growth where AI is adopted fastest.
    • "There have been very credible reports...that show we're seeing losses of jobs for entry level workers in AI exposed occupations...And then somebody else...puts out a report...that says...the companies...adopting AI the most quickly are adding jobs more quickly..." — Ben (06:35–07:12)
• Corporate Announcements & Incentives
  • Companies like Amazon and Block tie layoffs to AI, but skepticism abounds.
  • "Companies are being rewarded by their investors for making big claims about AI...you are incentivized right now to say...AI has made me more productive." — Ben (08:10–08:55)
  • Zolan calls AI a "convenient scapegoat" for layoffs; Ben agrees, noting it's often a mix of causes. (09:01)

What Do We Know So Far About AI’s Impact?

• AI's effects are both rapid and subtle (so far)
  • "We know this is moving incredibly quickly...but whatever effect it's having, it's subtle right now." — Ben (10:12–10:56)
  • 200 economists recently warned of a possible "unprecedented transformation...larger than the Industrial Revolution, but unfolding over a vastly shorter period of time." (10:13)
• The J-Curve Effect
  • Technological adoption often causes initial productivity drops before dramatic gains ("J curve").
    • "We may right now still be in this scoop part of the J, where actually, if anything, it may be making us less productive in the moment..." — Ben (10:58–12:06)
  • Early-stage experimentation is common before widespread benefits are realized.
  • Zolan relates to this: using AI tools at work but often reverting to old methods. (12:06)

Lessons from Past Disruptions

1. The Internet Revolution

• Gradual, dispersed impact
  • Internet created new industries but also made others obsolete (e.g., typists, travel agents, bank tellers).
  • But this happened slowly and allowed workers to adapt.
    • "It wasn't like every company in this town all shuts down at once...it was spread out and gradual enough that people had an opportunity to react." — Ben (18:21–19:04)
  • The shift is remembered as positive precisely because of time to adjust. (19:19)
  • Notable quote:
    • "You had time to pivot. You had time to see the writing on the wall..." — Ben (18:23)
• Time as a buffer
  • "The real reason...seems to be time. It gave them time to adapt." — Zolan (19:04–19:22)

2. The "China Shock"

• Fast, localized devastation
  • Opening trade with China led to sudden, concentrated factory closures in industries like furniture manufacturing (Hickory, NC example).
  • "We see tens of thousands of jobs lost just in the Hickory area. In this one industry." — Ben (23:20)
  • Secondary effects rippled through entire communities: retail closures, lower property values, rising addiction, and political shifts. (24:39)
  • "That is exactly right. It was fast and it was concentrated." — Ben (22:19)
  • Notable quote:
    • "You can't even move out of the area because who are you gonna sell your house to?" — Ben (24:20)

Will AI Be More Like the Internet Or the China Shock?

• Pace and concentration matter
  • "If this plays out gradually...this transition...could be pretty good for the economy as a whole. If it happens...like that China shock example...that could be really painful." — Ben (26:14–27:33)
  • The greatest fear is not pure job loss, but rapid, unmanageable change.

What Should Policymakers Do?

• Measurement and policy remain behind the curve
  • "Policymakers are starting to grapple with those lessons, which is not the same as saying that they're acting on them." — Ben (27:52)
• Priorities
  • Find better ways to track AI-related changes in real time.
  • Strengthen safety nets like unemployment insurance, which performed poorly in the pandemic.
  • Consider overhauls or new programs (e.g., trade adjustment assistance, possibly even sovereign wealth funds or universal basic income).
    • "We need to develop new tools to allow us to measure this in more real time..." — Ben (28:31)
    • "None of that is anywhere close to an actionable policy right now..." — Ben (30:49)

What Can Individuals Do?

• Uncertainty is the only certainty
  • Advice is elusive because new AI-driven jobs haven't emerged yet.
  • "We don't have an answer now to that. There are no doubt going to be new jobs that are created through this AI innovation, but we don't know what they look like yet." — Ben (31:30–32:15)
  • Even what or whether to study is unclear—“those calculations are changing in ways that we don’t fully understand.” (31:30)
  • Zolan sums up: “For at least the time being, we will have to embrace the uncertainty.” (32:15)

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Notable Quotes & Memorable Moments

• "AI is amazing and it's making me a better writer, but it's also taking my job away." (00:57)
• "The thing we'll be talking about is AI and kind of the early signs of how it was affecting the economy." — Ben (02:48)
• "Companies are being rewarded by their investors for making big claims about AI." — Ben (08:10)
• "It may be making us less productive in the moment, even though it will have this big positive effect on our productivity..." — Ben (11:01)
• "That is exactly right. It was fast and it was concentrated." — Ben, on the "China Shock" (22:19)
• "We don't have an answer now to that. ... It's inevitable that people are going to really feel like they don't know what they should do." — Ben (31:31–32:15)
• "For at least the time being, we will have to embrace the uncertainty." — Zolan (32:15)

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Timestamps for Important Segments

• 00:34–01:30 — Worker anxieties and conflicted feelings about AI.
• 02:25–05:07 — Challenges in measuring AI’s current impact on the labor market.
• 06:05–07:21 — Contradictory private sector data and interpretations.
• 07:29–09:01 — Corporate announcements and skepticism about AI as a layoff cause.
• 10:13–12:06 — Big warnings from economists; introduction of the J-curve concept.
• 12:06–13:24 — Personal anecdotes about using (and struggling with) AI.
• 15:28–19:23 — Parallels to the 1990s internet revolution; time as a mitigating factor.
• 21:08–24:39 — China shock: rapid, concentrated economic pain and its ripple effects.
• 26:13–27:33 — Which historical model is most plausible for AI?
• 27:52–30:54 — Policy responses, past and present.
• 31:08–32:26 — The individual perspective and uncertainty about adaptation.

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Tone & Language

• Conversational, candid, and slightly anxious.
• Real-life examples and accessible analogies (e.g., the "J curve," comparing AI anxieties to Pokémon collecting).
• Heavy emphasis on uncertainty—acknowledging both the promise and risk of AI’s economic impact.

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Conclusion

The episode paints a nuanced picture: AI is transforming the economy, but its effects are hard to pin down amid insufficient data and competing narratives. Past disruptions (the Internet, the "China Shock") offer clues—suggesting that the pace and distribution of change may determine whether AI ultimately benefits or harms workers and society. For now, the lack of clarity—both on what jobs will be created, who is at risk, and how fast changes will come—means individuals and policymakers alike are stuck in a period of anxious uncertainty.

"For at least the time being, we will have to embrace the uncertainty." — Zolan (32:15)

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Transcript

[A]

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[B]

I want you to fill in the blank for me. Okay, so I feel blank about AI.

[C]

Ooh, I feel, ugh, mixed feelings about AI. I feel anxious about AI. It's a love hate relationship for sure. Completely conflicted.

[B]

AI is amazing and it's making me a better writer, but it's also taking my job away. From the New York Times, I'm Zolan Kano Youngs filling in as host. This is the Daily As AI becomes more advanced, people are getting increasingly nervous about how it could change the economy and their jobs.

[C]

I'm seeing a lot of job loss because of it.

[B]

It's a threat to my profession. I really have to rethink what I do for a living and probably do else.

[C]

Oh my God, this thing is going

[B]

to take my job.

[C]

And not only is going to, but actually did.

[B]

But for all the anxiety, what AI is actually doing to the economy remains pretty murky.

[C]

Seems like it's taking over being me.

[B]

Today, chief economics correspondent Ben Castleman on why AI's impact has been so hard to pin down and what we can learn from the tech disruptions of the past. It's Monday, July 27th. How we doing?

[C]

Doing well.

[B]

Appreciate you doing this.

[C]

Yeah. Excited to sit down for this. Excited to be hosted by you. I'm trying to accumulate as many daily hosts as I can.

[B]

Like Pokemon.

[C]

That's right. Exactly.

[B]

All right, I'm just gonna jump in.

[C]

Let's do it.

[B]

Ben, I am picking up on a lot of anxiety when it comes to how artificial intelligence will impact our economy. We know from polling that about 70% of Americans think AI will lead to fewer jobs. So as someone who talks to economists every day, how much of your time is being taken up by this question of how AI will impact the economy?

[C]

I think it is arguably the important question. We talk all the time about tariffs and oil prices and all of these shocks that are hitting the economy. And those are all, of course, incredibly important issues. But I Think it's very possible that if you and I are sitting here in five years or 10 years, looking back on this period, that the thing we'll be talking about is AI and kind of the early signs of how it was affecting the economy. I don't think we know what that conversation will look like. I don't think we know what big change we will have seen. But it certainly feels like this is the moment where it's all starting right?

[B]

This is interesting because sometimes I feel like we hear AI is gonna be a godsend to workers. It's gonna make us all 100% more productive or it'll wipe out all white collar jobs entirely. What do you make of those predictions?

[C]

So I think there are a couple of answers to that question. One is that it's early. We're still figuring out how to use this technology. It's still being rolled out, but beyond that we don't even really know with confidence what's happening now. Predicting the future is hard, but even knowing the immediate moment is difficult because our economic data really wasn't designed and isn't up to capturing a change that's happening this rapidly in anything close to real time.

[B]

Can you explain that a little bit more to me? Why don't.

[C]

We don't. Well, I mean, so let me take a simple example of this, right? We get the monthly jobs report and we talk about how many jobs were added or lost in the economy in a given month. If you go and look in that report the next time it comes out and you want to look at what happened with tech jobs, you will not find that anywhere in the report because we don't break out tech as its own industry. These industry categories were established literally decades ago. Tech is kind of sprinkled between a few different categories. Some of it's in the information sector, which also includes newspapers, includes us. Some of it is in professional services, some of it's in manufacturing. So you couldn't go and say what impact is this having on. On tech?

[B]

We're not even talking about AI, not even talking about how government data doesn't even track the tech industry.

[C]

I mean, if you get into the weeds enough and you look closely enough, you can start to tease it out. But there's not like a line in that report that says it. We've heard a ton about concern about what this means for recent college graduates. Well, we don't reliably track what happens with recent college graduates on a month to month basis. And our data infrastructure kind of isn't up to Doing that at the kind of speed that we want to know

[B]

right now, that does seem like a big oversight. So if government data isn't the resource, isn't the way that we're gonna get this clarity on AI, where did your search bring you next?

[C]

Well, I mean, so look, first of all, I wanna be careful saying oversight.

[B]

Yeah.

[C]

The economy changes quickly and we can't just sort of spin up a new measure every time something changes in the economy. Right. So there's a reason we kind of track this gradually over time.

[A]

Okay.

[C]

But we are seeing a lot of efforts to use data from the private sector to help measure this in something closer to real time. We have adp, which handles payroll for many big companies. They're putting out data and economists are analyzing that. LinkedIn. Indeed, other companies that collect and produce a lot of data about what's going on in the labor market, they're putting out reports and economists are diving into all that. The problem is they're all telling different stories. There have been very credible reports out recently from serious economists who have done careful work that show we're seeing losses of jobs for entry level workers in AI exposed occupations. And you look at that and you say, here it is. Here's the canary in the coal mine. Here's the sign of AI starting to wipe out jobs. And then somebody else comes along and puts out a report using good data, careful methods that says, aha. The companies that are adopting AI the most quickly are adding jobs more quickly than other companies.

[B]

So you may have different private sector companies trying to track AI, but they're coming up with takeaways that seem to be polar opposite at times.

[C]

That's right. Which I think maybe isn't that surprising considering how quickly this is moving and how quickly this field is developing.

[B]

But I do feel like there are some companies who are already raising their hands and saying AI is changing my business.

[C]

Amazon has just announced it's cutting 16,000 jobs worldwide.

[B]

It's the latest round of mass layoffs as the Seattle Amazon cut thousands of jobs in the past year. They cited AI Block the Payments company is another.

[C]

The company announced plans to lay off almost half its workforce. CEO Jack Dorsey pointed to AI saying intelligence tools have, quote, changed what it means to build and run a company.

[B]

Help me make sense of this because I do think we are seeing these big almost announcements that seem like warning signs from these companies. Companies.

[C]

That's exactly right. Right. We're hearing these big announcements of layoffs in some cases directly tied to AI. When I talk to economists, I Hear a lot of skepticism about those claims. You know, companies right now are being rewarded by their investors for making big claims about AI. Right. Any company that says, you know, we're making big AI investments, we're making big gains, their stock price goes up right there, the VC money floods in. And so if you're a CEO right now and you're thinking, maybe I overhired a little bit a couple years ago, maybe I need to make some cuts. Boy, you are incentivized right now to say, not like, oh, I screwed up. I hired too many people. It's, oh, AI has made me more productive.

[B]

Oh, this is interesting. So you're saying it's likely that AI could be a convenient scapegoat for these companies.

[C]

Yeah. Or look to be a little less cynical about it. In some cases, AI may be one factor among many. Your business has slowed down a little bit trying to, you know, rethink how you do things. And then AI is also creating some opportunities. But it's better to talk about the AI part of that than the other part of that. That's different from sort of saying, hey, I'm just going to, you know, lay off a whole bunch of workers tomorrow, because AI can do it today.

[B]

Okay? So when it comes to measuring AI, government data is outdated, private sector data is muddy. We can't fully trust the companies. So what do we know?

[C]

So I think we know two things. First, we know this is moving incredibly quickly. We know the technology is developing really rapidly. We know that companies are adopting it really rapidly. And when I talk to not just sort of the boosters in Silicon Valley, but talk to corporate leaders across industries, when I talk to economists. Right. There's sort of increasing confidence that this is going to have a real impact on our economy, our labor force, our lives.

[B]

Okay?

[C]

And, you know, we just actually, a few days ago, got a statement that was signed by around 200 economists in which they warn that this could be an unprecedented transformation of our economy, larger than the Industrial Revolution, but unfolding over a vastly shorter period of time. That's a pretty big deal. But the second thing we know is that whatever that effect is, it's subtle so far. If AI were already wiping out huge swaths of jobs, we would see that in our data. Right. Our data's not perfect, but we would know that. And so whatever effect it's having, it's subtle right now. And that may sound surprising. Right. I'm saying on the one hand, it's going to be this huge thing. On the other hand, we don't really see anything of it yet.

[B]

Yeah. How do you square those?

[C]

When you actually look at the history of this, it's not that surprising, because if we look at the way technology gets rolled out in an economy, it often follows a pattern that economists talk about as a J curve. Literally, it's just referring to the shape of the letter J. Right. It drops down and then it shoots up, following you. The idea here is when a new technology rolls out, companies initially have to try to figure out how to use it, Right? We're all trying to figure out how to use video conferencing technology. In the early days of the pandemic, we're all trying to figure out how to use the Internet. When we go back to the 1990s, and nobody's really sure how to use it, everybody's fooling around with it. They think it's going to be a big deal, but they don't know how yet. And then at some point, companies start to figure it out, workers start to figure it out, and all of a sudden, productivity starts to go up. But we may right now still be in this scoop part of the J, where actually, if anything, it may be making us less productive in the moment, even though it will have this big positive effect on our productivity and then maybe ultimately on our jobs down the road.

[B]

This kind of tracks as I'm even hearing you talk, because, look, I cover the White House, and I'm just starting to explore with some of these tools, and I'm not very good at it. I feel like at times I'll try to, like, look up an old speech or use AI to form a contact list, and eventually I get sick of it, and I actually just go back to making calls. But it sounds like what you're saying is we're just at the beginning of this process, and maybe over time, people like me, even novices with technology, will learn how to actually use these tools. And then hopefully, in my case, productivity will follow.

[C]

You start to figure it out as an individual, and I'm in the same place on this, by the way. And companies start figuring out.

[B]

Thank you for relating to me a little bit.

[C]

Thank you. But companies also figure out ways to reorganize work. They start to say, okay, you know, we don't need as much of this. We need more of this. New companies pop up that are built around this technology from ground zero. And all of a sudden, we start to have these real economic impacts that hit once we've sort of absorbed this technology. And that's where we could see big productivity gains, but also where these concerns about job loss and other disruptions start really taking hold.

[B]

So I guess the big question is like, what does the rest of this day look like?

[C]

So I think the best way to answer that question is rather than trying to predict the future is to look back at the past. And I've been spending a lot of my time looking back at one particular period, which is the 1990s.

[D]

Foreign.

[B]

We'll be right back.

[A]

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[B]

thedaily this podcast is supported by Audible.

[D]

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[B]

Okay Ben, as a 90s kid myself, I have a high top fade. I love the 90s. I don't want it to end, but tell me why we should look back at this time to understand something like AI.

[C]

So I'm a few years older than you. I think I remember.

[B]

I'm not gonna age you, Ben.

[C]

Yes, so I remember the 90s well. And this was a decade where we had two really big consequential forces hitting the economy at the same time.

[B]

It spans the globe like a superhighway. It is called Internet.

[D]

It feels a bit like Everyday human

[C]

fellowship, but it's bigger and more precise. The first of those is the Internet. This was the era of dial up, of aol, you've got mail, of kind of the early days of getting online.

[D]

It's tapped a yearning to connect, to talk with the world about art, music, sex, guitar, construction, conservative politics, grief.

[C]

And it's interesting to talk about that now in the context of AI, because we think back on the Internet, right, And we think of it as creating this whole new industry.

[A]

The marriage of mobile phone and Internet technology, which is working the world's stock markets into a fever of anticipation, setting

[C]

the stage for tech as we know it, setting the stage eventually for the mobile web and iPhones and apps and all of this, right? Every business, no matter how large, no matter how small, will be on the

[B]

Internet in the year 2000.

[C]

It created all these jobs, but it also wiped out lots of jobs. Goodbye. We used to have typists, we used to have typing pools. And then later word processors, right? Before word processor was a program, it was a job. We had whole categories of jobs that have been wiped out or dramatically reduced. We don't have nearly as many travel agents as we did before the Internet. Before we could all go and book our flights on Orbits or Expedia or whatever it is, right? We had to go to the bank a lot more before we could do it on our phones. We eliminated a lot of jobs, but we don't remember it as this mass job loss.

[B]

Why is that?

[C]

Because it happened gradually and it was diffused across the economy. This is not a story where one day they walk in and they fire the whole typing pool. It's not, you know, one day we stopped having travel agents. It was that as companies figured out how to use this technology in different ways, they started to change these jobs. People who were later in their careers had time to sort of wrap up their careers and retire. People who were earlier in their careers had time to learn new skills or to change direction, right? To say, hey, maybe this isn't the career that I want to go into. Maybe I should go in a different direction.

[B]

You had time to pick.

[C]

You had time to pivot. You had time to see the writing on the wall and to say, hey, maybe I should go to college, because that's the direction that the economy is moving in. It wasn't like every company in this town all shuts down at once, and now where am I gonna go? It was spread out and gradual enough that people had an opportunity to react. And so we don't have These mass job losses. We have job shifts that people are able to react to over time and to pivot into new areas that have more opportunity. And we look back now and we remember this as this period of growth and opportunity, even though it was a period of tremendous disruption and uncertainty in the moment.

[B]

Okay, this is fascinating. It seems like what you're saying is the reason we look back on the Internet revolution as not a time where jobs were lost and entire fields were made extinct. The real reason for that, the real factor seems to be. Seems to be time.

[C]

Right.

[B]

It gave them time to adapt.

[C]

It gave them time to adapt.

[B]

So now the question would seem to be just how fast is the AI transition going to play out, and will it be so fast that people can't pivot?

[C]

I think that's exactly a question, and it is what we'll learn over the next few years, but we don't know the answer to that yet.

[B]

Ben, it does seem to me, particularly as we sort of compare these two periods, like the Internet was a tool to workers, it's a tool to us now. Right. But when I talk to people in Washington, when I talk to just friends around the country, they're not worried about this as a tool. They're worried that they could be replaced by AI. So how do we make sense of that as we compare these periods?

[C]

That is the fear. I don't think we know yet that that's the reality.

[B]

Okay, okay.

[C]

AI at this point, for the most part, is a tool. It's a tool that workers are using in different ways. They're finding it more or less useful, but for the most part. Right. You're talking about starting to explore using AI in your work, but there's not an AI White House reporter right now. And so we don't yet know whether AI is really going to take my entire job or somebody's entire job.

[B]

I don't think AI could ever take your job or my job. Just gonna put that out there. But don't let me interrupt you as

[C]

we go through this part of the. As companies and workers start to figure this out, we'll get a better sense of whether does it actually replace some of these jobs, or does it just make all of us more productive? And I think that is sort of the core question right now.

[B]

Okay. I will say thus far, you are doing a great job of explaining this. Not a great job in lowering my blood pressure and anxiety levels on this

[C]

topic, but that was the good news conversation. We haven't even gotten to the bad news example.

[B]

What's the bad news example.

[C]

So this is the second big force in the 90s, or starts in the 90s, picks up steam in the 2000s, which is the so called China shock. By adding China to the WTO, we

[B]

strengthen the organization by further integrating China's 1.2 billion people and 1 trillion dollar economy into the world market network.

[C]

This is this period where we open up trade with China, of course, and all of a sudden we start to see this rush of competition that leads to huge job losses in particularly kind of the Southeast and the Midwest.

[B]

And here's what we know right now. Thomasville Furniture's plant C will close by mid July. The company will move that plant's production to a factory.

[C]

Crews demolished what was left of the old Pillowtex plant early this morning. It took more than a ton of dynamite. Cone Mills announced this morning it is shutting down two of the county's four plants. The closings of plants in Cliffside and Florence mean the loss of hundreds of jobs. But I think the critical thing to recognize there is that this was a fast shock.

[B]

We go back to time.

[C]

We go back to time, more than 800 jobs have been lost. And that's just since December. This is where we see entire factories close up, entire industries shut down in a matter of months or years. My whole family worked here and I got a job here and everything. It's a shame it's shutting down right now.

[B]

Things don't look too good. The unemployment rate in the county will probably approach 8 or 9, maybe even 10%. And as I think about this time period, Ben, I'm also thinking about entire sectors that just collapsed. Right. You mentioned before how the Internet revolution not only was gradual, but it was broad. I'm thinking about the town in small town America that relied on one factory. And during this time period, that factory went down and hundreds, if not thousands of people lost their jobs and entire communities basically collapsed. I mean, the ripple effects were really severe during this point in time.

[C]

That is exactly right. It was fast and it was concentrated. So take Hickory, North Carolina. This was a center of furniture manufacturing right in the 20th century. Well, we open up trade with China and we get flooded with cheap furniture from China. Hickory can't compete. The factories shut down. We see tens of thousands of jobs lost just in the Hickory area. In this one industry. Tens of thousands, tens of thousands. And what happens when that hits in one area? Well, think about all the other jobs that depend on that. Right. Those workers are shopping in the retail stores in the area. Right. They're sending Their kids to the schools in that area, they're eating at the restaurants in that area. All of those industries get hit as well when we get this kind of concentrated shock. You can't even move out of the area because who are you gonna sell your house to?

[B]

That's right. Why would I want to buy that home if I'm going to an area where the main industry that the community relies on has collapsed?

[C]

That's exactly right. And so we see this in Hickory, but we see this in communities all across the country that has these really powerful impacts and these lasting scars.

[B]

And Ben, I think we've reported on sort of either sides of this. Right. The stakes of this moment are really severe. We're not just talking about economic impact. We are also talking about communities that had addiction levels rise, rooted in that unemployment problem. This brings us to the opioid epidemic. We're talking about also an impact on our political system as well. The grievance filled politics that we have seen with a certain forgotten America rooted in this idea that these communities that relied on these certain industries found themselves without work because of this major disruption.

[C]

Yeah. And that was something that was happening in a relatively small industry in a relative handful of parts of the country. Think about what this could look like if it's something that is playing out across a much broader swath of our economy.

[B]

Right. So that's the scary version of how this AI thing could go down. You're suggesting it could look like a very quick collapse of industries that are vital to certain communities, vital to people.

[C]

Yes.

[B]

Do both of these models, the Internet revolution and the China shock, seem equally plausible here, Ben?

[C]

So I think there's a long term question and there's a short term question. When people in Silicon Valley talk about, you know, this is going to end work as we know it, we're all gonna be, I don't know, sitting on the beach while the robots do everything for us. Maybe that's possible, I don't know. But that feels like science fiction, feels

[B]

very sci fi movie. Right.

[C]

And when I talk to economists, they have a tendency to kind of roll their eyes about this. That's never the way this has worked out in reality. Right. If you go all the way back to the Industrial revolution, Right. We had people talking about like, aha, nobody's gonna have to work anymore. And like, guess what, we're all still working. The concern that I hear from economists is much more about this nearer term, these incredible disruptions. And so if this plays out gradually and if we have time for Sort of new industries to be created, new jobs to be created for us to figure it out and, you know, younger people to start to pivot, older people to wrap up their careers the way we did in the 90s, period, right then this transition, no doubt will be painful for some individuals, but could be, you know, pretty good for the economy as a whole. If it happens in a way that looks more like that China shock example, where we see whole categories of jobs wiped out more or less overnight, where it's not clear what direction you should go in, because all the other careers you can imagine are also getting wiped out. That is the kind of disruption that could be really painful, not just for some individual workers, but for the economy as a whole, with all of the kind of social and political implications that we were just talking about.

[B]

You know, even with the uncertainty we established at the top of this, you have, by bringing us to the 90s, outlined that there are some lessons from significant disruptions in the economy that we can draw from. We can draw lessons from the past. So are policymakers acting on those lessons?

[C]

I think policymakers are starting to grapple with those lessons, which is not the same as saying that they're acting on them. You know, we're starting to kind of see this process play out both at the political level and at kind of the Washington think tank policy level. We're starting to see some discussions in Congress and in state capitals, but I don't think we've seen anybody from either party kind of lay out a comprehensive plan that anybody thinks is really going to tackle this in a big way.

[B]

I think many would find that concerning. I mean, what should we be doing? When you talk to economists, what do they say?

[C]

Well, so when I talk to economists now, what I hear from them are a few sort of things that we really need to be tackling. One of them is this measurement question. Can we improve our ability to track this? Right. Can we develop new tools to allow us to measure this in more real time so that we actually know what is happening? So we know which workers are being affected and where they are and what is happening to them.

[B]

Identify who may need help the most, and then go and help those people? But first you gotta identify which sectors are gonna get hit.

[C]

That's exactly right. Then the question becomes, okay, now what do we do to help whoever is being affected here?

[B]

Right?

[C]

When I talk to economists and policy experts on this, one thing that they say is, look, given the uncertainty, step one is to shore up some of the existing systems that we have. You know, The US has an unemployment insurance system, but we all learned during the pandemic how rickety that unemployment insurance

[B]

system is, how many struggled to navigate that system and needed it and relied on it.

[C]

It was incredibly helpful for a lot of people. Right. But it was also a fundamentally pretty broken system in a lot of ways. We developed in the 1990s and further back trade adjustment assistance that was meant to help workers who were displaced by globalization, but it never really reached a lot of the workers who needed to benef. So there are a lot of policy experts now who are actually looking back at the lessons of that period and saying, how do we make sure that we design programs better this time around? And then there's, do we need to have some sort of totally new program that deals with AI specifically? Maybe that's the government taking stakes in companies to create some sort of sovereign wealth fund that allows everybody to benefit from AI. Maybe this is some form of universal basic income where instead of working, people are getting checks directly from the government. In this new world, none of that is anywhere close to an actionable policy right now, but it shows you sort of the extent to which people in Washington, people in Capitals, frankly, around the world, are starting to grapple with the potential impacts of this technology and the possibility that maybe we need to take a totally different approach to policy.

[B]

Okay, so we've been talking about what policymakers should do, but those listening might be wondering, what can they do? Right. What is the individual supposed to do? For those with kids, what are they supposed to tell them?

[C]

I think that that in many ways captures sort of what is scariest about this moment. If you think back to that 1990s period, there was a sense that you did know what you should do. You should go to college, you should pursue these new burgeoning careers. And look, that did not work out for everybody. We know that.

[D]

Yes.

[C]

But there was some sense that this is the direction the economy is moving in. Go that way and you can do. Okay, we don't have an answer now to that. In the same. There are no doubt going to be new jobs that are created through this AI innovation, but we don't know what they look like yet. I don't know what to tell somebody to go and major in today or even whether to go to college or not to go to college. Right. Those calculations are changing in ways that we don't fully understand. And because we can't give clarity, it's inevitable that people are going to really feel like they don't know what they should do. And where this is headed.

[B]

Well, it sounds like for at least the time being, we will have to embrace the uncertainty.

[C]

I don't know that we have much choice.

[B]

Thank you, Ben.

[C]

Thanks so much for having me.

[B]

I think AI has the chance to revolutionize everything.

[C]

We're not ready at all.

[B]

You know that one guy from office space who says, I'm a people person, we're all becoming that. I think we're just taking specifications from business people and feeding it to the AI these days.

[C]

Six months ago, I wasn't sure when I was going to retire, but AI

[B]

has actually accelerated that a little bit

[C]

because things are changing so much at work.

[B]

There's a part of me that's like, you know what? I'm ready to end right now.

[C]

I've got grandsons who are in fifth and seventh grade coming up, and I wonder what they're going to be doing when they get to college. Are they going to, you know, are certain choices going to be gone?

[B]

Well, in Brooklyn, we say, what are you gonna do?

[D]

You got.

[B]

It's, it's here. We'll be right back. This podcast is supported by Bombas. Comfort Logic is realizing the team behind your favorite socks is also behind your favorite sandals and slides. Cushy and lightweight, they're an easy way to float through the summer. From beach days to barbecues and everything in between. Head over to bombas.com NYT and use code NYT for 20% off your first purchase. That's b o m b a dash.com NYT code NYT at checkout. Watch disclosure day at home now with exclusive bonus features. If you found out we weren't alone, if someone showed you, proved it to you, would that frighten you? Now the truth belongs to 7 billion people in an original event film directed by Steven Spielberg, from a screenplay by David Koepp, starring Emily Blunt, Josh o', Connor, Colin Firth, Eve Hewson, and Colman Domingo. Find Disclosure Day on major digital platforms now with no subscription required. Also in theaters.

[A]

This podcast is supported by Memorial Sloan Kettering Cancer Center. What if cancer could be treated without surgery, radiation, or chemotherapy? At MSK, it's already happening. In a small, groundbreaking clinical trial, 100% of participants saw their cancer completely vanish using immunotherapy alone. This MSK Giving day, your gift will be triple matched to make the seemingly impossible breakthrough possible. Bring hope to people impacted by cancer. Give [e-mail] thedaily

[B]

here's what else you need to know today. On Sunday, firefighters continued to battle fast moving and fatal wildfires in Spain and France, forcing the evacuation of more than 300,000 people.

[C]

Saw the fire itself and it was an absolute conflagration. Yellow flames five, six stories high, whole area of forest on fire.

[B]

That the fires come amid extreme heat and dry conditions across Western Europe, with another scorching heat wave expected this week. And the Trump administration decided against a major escalation in the war with Iran. Over the weekend, the Times reports that President Trump was motivated to pause the military assault in Iran in part because of dwindling military stockpiles.

[C]

Is America backing away from a larger military attack because its military stockpile has been depleted?

[B]

Well, look, we have to take a step back here in that a lot of stockpiles were depleted, not only from what we gave Ukraine over the last however many years, four or five years, and the administration inherited. In an interview with Meet the Press, the US Ambassador to the United Nations, Mike Waltz was pressed on the voice vulnerability, knowledge.

[C]

They're depleted.

[B]

At this moment, I want to be crystal clear. The US Military, and I've verified this every which way, has everything that it needs to conduct this campaign as effectively as it needs to be. And I have to tell you, the people that are leaking this nonsense deserve to be in jail. Waltz also acknowledged that there might be more American lives lost as the fighting person. Today's episode was produced by Jack Desadoro, Diana Wynn and Eric Krupke with help from Caitlin o'.

[C]

Keefe.

[B]

It was edited by Annie Minoff and Paige Cowett with help from Patricia Willins, Fact Checked by Susan Lee and it contains music by Elisheba Itupe, Marian Lozano and Diane Wong. Our theme music is by Wonderly. This episode was engineered by Chris Wood. That's it for the daily. I'm zolan kano youngs. See you tomorrow.

[C]

This podcast is supported by bank of America Private Bank. You're cut from a different cloth and with bank of America Private bank, you have an entire team tailored to your needs, with wealth and business strategies built for the biggest ambitions like yours. Whatever your passion, unlock more powerful [e-mail] what would you like the power to do? Bank of America Official bank of the FIFA World Cup 2026 bank of America Private bank is a division of bank of America, NA Member FDIC and a wholly owned subsidiary of bank of America Corporation.