Money Talks: The AI Job Apocalypse is Avoidable

Summary

Slate Money – Money Talks: The AI Job Apocalypse is Avoidable
Host: Emily Peck (Slate Money, Axios)
Guest: Daron Acemoglu (Nobel Prize-winning economist, MIT professor)
Release Date: April 28, 2026

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Episode Overview

This special episode of Slate Money’s “Money Talks” features a deep-dive discussion with economist Daron Acemoglu on one of the most urgent questions in technology and economics: Will AI really lead to mass job loss, and is an “AI job apocalypse” inevitable? Drawing from his acclaimed research, Acemoglu argues that the direction of AI—and whether it is disruptive or empowering for workers—is a matter of choice, policy, and design, not destiny. The episode explores why the “job apocalypse” narrative dominates public conversation, how AI could instead augment work, and what can be done—by industry and policymakers—to encourage pro-worker forms of artificial intelligence.

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Key Discussion Points & Insights

The Job Apocalypse Narrative

[03:22]

• Emily Peck questions why the “AI job apocalypse” idea is so persistent and popular, citing recent industry warnings.
• Daron Acemoglu cautions, “We are moving in the direction of AI replacing people because that's what the tech companies are thinking about... Our point is that there's an alternative direction, but it's not one that's going to happen by itself. We need to target that in order for a more pro-worker direction to be a possibility.”

Quote:
“Artificial intelligence is quite different than human intelligence. And when two things are different, a natural way to combine them is in a complementary way, not try to replace everything that one does with one type of intelligence using the other type.”
— Daron Acemoglu ([03:39])

AI vs. Human Intelligence: More Different Than Better

[04:35] – [07:10]

• Acemoglu explains that the widespread belief that AI is “better” than humans is misguided. AI is just different.
• Example: Human children can recognize a cat after seeing it once or twice; AI needs thousands of labeled examples.
• Humans excel at:
  • Contextual understanding
  • Creativity and hypothesis generation
  • Social learning and trial and error
  • Combining multiple forms of reasoning and judgment

Quote:
“When models hallucinate, they hallucinate badly. When humans hallucinate, that's like when we're being creative, we think of new things and then we decide, ‘Oh, no, that's not a good direction. Let me experiment with this one.’”
— Daron Acemoglu ([06:39])

Why AI Tools Aren’t Designed for Human Partnership

[07:45] – [09:05]

• Today’s LLMs and other AI tools are engineered primarily for automation, not for worker augmentation.
• Acemoglu: “Imagine what you could do if [AI models] weren’t designed for automation, but they were explicitly designed for being complementary to you.”
• Current training focuses on autonomous task performance, not human-AI teaming.
• The tech industry’s obsession with AGI (Artificial General Intelligence) overshadows pro-worker approaches.

Underestimating Human Intelligence & Economic Incentives

[09:13] – [13:40]

• Two reasons for the automation-first focus:
  • Economic: Tech companies profit more by automating than by assisting workers; tax code advantages capital over labor.
  • Ideological: Many in tech underestimate human intelligence while being inspired by science fiction about machines replacing us.
    • Acemoglu references Alan Turing’s Imitation Game and science fiction as cultural drivers of AGI obsession.
    • The result is a mindset that “whole occupations” are replaceable, ignoring the complexity of real human work.

Quote:
“If you think of what most people do during the course of their occupations, there are so many complex judgment decisions, so many edge cases… All of those are very difficult things for artificial intelligence and very natural for humans.”
— Daron Acemoglu ([11:36])

Human Value & Vicious Cycles in Tech Industry

[13:40] – [14:33]

• Emily Peck notes that the tech industry tendency to devalue humanity is not new.
• Acemoglu calls this a “vicious circle”: “The moment you start thinking humans are worthless, you start treating them as worthless. And then their potential is not realized.”

What Real Pro-Worker AI Looks Like

[20:30] – [26:24]

• Policymakers, Acemoglu urges, should promote ways for AI to complement, not replace, workers.
• In education, AI often aims to automate teachers. A better model: AI as an assistant, helping teachers:
  • Analyze quiz results to identify struggling students
  • Suggest groupings and differentiated instruction
  • Leverage best practices from experienced teachers
  • Result: Not fewer teachers, but better-supported, more productive teachers and, potentially, more demand for skilled teaching staff

Quote:
“You don't need less of the teacher. You in fact, probably need more teachers... So you can see the big differences. In one case, you're amplifying what the teacher does, increasing the demand for the teacher, and you're actually going to get much better productivity outcomes.”
— Daron Acemoglu ([25:17])

• Historical parallel: Spreadsheets didn’t eliminate accountants; they made them more productive.

On Automation’s Dangers—And Its Limits

[27:48] – [29:34]

• Acemoglu: “I'm not anti-automation. I think automation is very important. Thank God we don't have to carry things on our backs anymore… But if all we do is automation, this is going to have horrible effects on workers.”
  • Automation without creating new, good jobs harms communities (citing research with Pascual Restrepo).
  • Even automating 10% of jobs would have “fairly large distributional consequences,” increasing inequality and decreasing purchasing power if rewards flow only to a few.

Why Don’t Firms Care About Job Loss Affecting Their Consumer Base?

[29:34] – [31:22]

• Automation increases productivity and returns to capital.
• If the gains go to a small elite, consumption is depressed (even if luxury spending goes up for a few), reinforcing inequality.

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Policy Recommendations for a Pro-Worker AI Agenda

[31:30] – [34:00]

• Step 1: Remove structural incentives for automation over augmentation
  • Reform the tax code (“Our tax code artificially encourages automation because we tax labor quite heavily… whereas we subsidize capital close to zero percent.”)
• Step 2: Encourage competition in tech (current levels of concentration stifle new, more pro-worker business models)
• Step 3: Leverage public policy and investment
  • Government’s role: Not to act as VC or pick winners but to catalyze new markets (e.g., competitions, subsidies for pro-worker prototypes)
  • Historical examples: DARPA “Grand Challenges” in robotics; government incentives in solar panels.

Quote:
"The government can provide certain subsidies or create tournaments or competitions... So we could do the same thing for pro-teacher technologies, pro-nurse technologies, pro-electrician technologies."
— Daron Acemoglu ([33:22])

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Memorable Exchange on AI Safety and Human Partnership

[34:20] – [35:11]

• On AI in warfare and critical systems:
  • Acemoglu: “If you actually let AI choose targets, it's just not up to that. Imagine we said… from now on, air traffic control is completely delegated to AI. Would you want to fly to Denver?”
  • Peck: “Absolutely not. Never again.”
  • Acemoglu: “No. So that should be a lesson about how we should try and strive to use AI.”

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Concluding Thoughts

[35:11] – [35:23]

• Much depends on shifting the AI narrative from science fiction and automation to a vision of human-machine partnership.
• Acemoglu: “So much of it is just… trying to change the story from, you know, science fiction to a different kind of fiction, at least.”

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Segment Timestamps

| Segment | Time | |-----------------------------------------------|------------| | The Job Apocalypse Narrative | [03:22] | | Humans vs. AI: Complementarity | [04:35] | | Why Automation-First in AI? | [07:45] | | Economic & Ideological Drivers | [09:13] | | Underestimating Human Value | [13:40] | | Pro-Worker AI in Education Example | [23:16] | | AI & Policy: Incentives, Competition | [31:30] | | Policy Levers for a Human-Centric AI Future | [33:22] | | Human-in-the-Loop: Safety, War, and Trust | [34:20] |

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Notable Quotes

• “Artificial intelligence is quite different than human intelligence... a natural way to combine them is in a complementary way.” — Daron Acemoglu ([03:39])
• “When models hallucinate, they hallucinate badly. When humans hallucinate, that's… creativity.”— Daron Acemoglu ([06:39])
• “The moment you start thinking humans are worthless, you start treating them as worthless.” — Daron Acemoglu ([14:19])
• “You don't need less of the teacher. You in fact, probably need more teachers...” — Daron Acemoglu ([25:17])
• “Our tax code artificially encourages automation because we tax labor quite heavily… we subsidize capital close to zero percent.” — Daron Acemoglu ([31:33])
• “Imagine we said… air traffic control is completely delegated to AI. Would you want to fly to Denver?” — Daron Acemoglu ([34:39])
• “So much of it is just… trying to change the story from, you know, science fiction to a different kind of fiction, at least.” — Emily Peck ([35:11])

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Summary in a Nutshell

The “AI Job Apocalypse” is not a foregone conclusion. The technology—and the economy—can be shaped by choices that value human intelligence, policy incentives, and public debate. With different design imperatives, business models, and supportive policies, AI can be made to amplify, not erase, human work.

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(This summary skips advertisements and non-content banter to focus on the episode’s core arguments and insights.)

Transcript

[A]

Hello and welcome to Money Talks special extra podcast from Slate Money where we chat with brilliant and interesting people. I'm Emily Peck, I'm a writer at Axios and co host of Slate Money. And I'm here today with a brilliant and interesting person, a Nobel Prize winner, Daron Esimoglu. Welcome to Money Talks.

[B]

Thank you Emily. It's my true pleasure to be here with you.

[A]

Daron is, as I said, Nobel Prize winning economist and a professor at mit. The reason I wanted to talk to you today was because of this paper that we talked about. It was recently, but it feels like a year ago.

[B]

Time flies by when you're having fun

[A]

with AI about how AI doesn't have to be apocalyptic for workers. It can be positive. So we're going to get into why AI doesn't have to take away all our jobs when we come back on Slate Money Talks. Slate Money is brought to you by Charles Schwab Decisions made in Washington can affect your portfolio every day. But what policy changes should investors be watching? Listen to Washington Wise, an original podcast for investors from Charles Schwab to hear the stories making news in Washington right now. Host Mike Townsend, Charles Schwab's Managing Director for Legislative and Regulatory affairs, takes a nonpartisan look at the stories that matter most to investors, including policy initiatives for retirement savings, taxes and trade, inflation concerns, the Federal Reserve, and how regulatory developments can affect companies, sectors and even the entire market. Mike and his guests offer their perspective on how policy changes could affect what you do with your portfolio. Download the latest episode and [e-mail] WashingtonWyse or wherever you listen.

[C]

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[A]

Darren what? Why do you think the notion that AI will replace people's jobs? I think the CEO of Anthropic has said it will create a job apocalypse. Why do you think that narrative has sort of taken hold not just among the AI industry, but it just seems like we see it everywhere now.

[B]

Well, I mean, let me first say that that talk isn't idle talk. We are going in that direction. We are moving in the direction of AI replacing people because that's what the tech companies are thinking about. That's what they're targeting. That's what their business model is, that's what their efforts are seeking to achieve. Our point is that there's an alternative direction, but it's not one that's going to happen by itself. We need to target that in order to that more pro worker direction to be a possibility. And the centerpiece of the argument is that artificial intelligence is quite different than human intelligence. And when two things are different, a natural way to combine them is in a complementary way, not try to replace everything that one does with one type of intelligence using the other type.

[A]

Could you unpack that? I mean, because stepping back, it seems like a lot of the things I'm reading now are like, yes, AI is different from human intelligence because it's in fact better than human intelligence. So it's actually pretty novel to argue. No, it's just, it seems like you're saying between the lines, it's not better, it's just different. So can you sort of, you know, unpack that? How is it different? Not just.

[B]

Well, if you look at how children learn, you'll see that it's different from AI and not inferior. Children don't have access to a huge training database.

[A]

Right.

[B]

Nor do they have billions of tokens in their minds. They cannot keep in their brain training weights and update them.

[A]

Right.

[B]

But they're doing a fantastic job of learning.

[A]

This was in your paper, right? You're like, my child didn't need to see a thousand cats before she understood what a cat was. It just took one or two, probably just one. And then they're good.

[B]

How do they do that? Well, they do that first of all, because our human brains work very differently and we would be hopeless, absolutely hopeless at taking something like a medium sized encyclopedia and find the relevant information in there in one hour or two hours or three days really. So we're not very good at taking large amounts of information and sifting through it and recognizing patterns. But we are very good at understanding the context, generalizing, developing hypotheses, extrapolating from a small number of things and then learning, continuing to learn by trial and error, by social learning, meaning other people are doing, being in arguments or being in discussions or imitating other people. So there are lots of things that humans do that seem to work extremely well in terms of very quick learning. And that kind of quick learning out of the box thinking is very important for creativity. The other thing that humans do very well is we are able to combine multiple types of reasoning for judging whether some new ideas are good or just are crazy. Whereas the artificial intelligence is not a one shot learner. It's not good at trial and error and it has no judgment. So when models hallucinate, they hallucinate badly. When humans hallucinate, that's like when we're being creative, we think of new things and then we decide, oh no, that's not a good direction. Let me experiment with this one. So there are many things in which the two types of learning could be very complementary to each other.

[A]

Yeah. So, okay, so now let's talk about the complimentary piece. Because I have to say, like even in just the past few weeks, I've been working more and more with Claude or Anthropic or whatever, and I'm finding it really useful. And I actually, I don't think it could take my job at all. It's just sort of helping me. It's almost functioning for me. Like when I call someone like you and ask a lot of questions and then go, you know, come back away from the interview and write something that helped me. But it's, I still need to do that thinking and writing and all that work on my own. I can't just, you know, publish what you say into the, into the world. I mean, maybe I could.

[B]

Great. And in fact, imagine what you could do if Claude and all of these other large language models weren't designed for automation, but they were in fact explicitly designed for being complementary to you. Right now they are explicitly designed for automation is for two different reasons. One is that their burning desire is for artificial general intelligence, let alone the fact that AGI, artificial general intelligence doesn't seem to be well defined, but there is just this singular obsession with reaching that in all of the leading AI labs. Second, the way that we train these models, we don't train them to work with humans. We train them to act autonomously, like the way that they are trained is. Okay, here are some questions. Are you going to get the right answer to these questions? How are you going to get scored when we ask questions and test you on these questions? Whereas if they are trying to be useful to humans, the way we should judge them is how can you team up with humans and be useful to them? It's not that we need to act like you are the lawyer passing the bar exam. How can you provide the useful type of information with the right context, with the right reliability to human lawyers?

[A]

Why the obsession with creating artificial general intelligence and not in creating partners for workers?

[B]

There are two broad reasons. And within each one of those broad reasons, we can also dig a little bit deeper. The first one is economic. The main companies at the forefront of AI have business models that are based on automation or things that don't involve human decision making centrally. They either make money from digital advertising or selling software or software services to large companies so that they can automate some of the tasks in their jobs. So that's the economic reason that they are targeting that. That's what they know in order to make money. Creating human complementary technologies has been very active during different periods of history. Certainly feasible, certainly potentially profitable, but you need to create a different business model, you need to market them differently, you need to babysit those models differently, etc. So that's the first economic reason and that there are additional subcategories there. Our tax code, for example, encourages this. Because we tax labor, we subsidize capital, so it makes it more profitable for firms for artificial, non natural reasons, that they should use algorithm for automation rather than rely on human workers and algorithms together. But there's also an ideological element. These companies have underestimated human intelligence and have, in my mind, been mesmerized by the potential of science fiction like machines to take over human tasks. These two are complementary, synergistic but distinct. Underestimating human intelligence shows itself in their thinking that you're going to be able to automate wholesale occupation with simple generative AI tool. The statements that there won't be any accountants left, there won't be any paralegals left, there won't be any customer service representative or interpreter, interpreters and translators or journalists left. Those are all statements about wholesale occupations being within reach of these Models. Whereas if you think of what most people do during the course of their occupations, there are so many complex judgment decisions, so many edge cases, so many places where you are routinely using combination of judgment and creativity, and you're doing the very difficult task of cognitive reasoning, interacting with the physical world. All of those are very difficult things for artificial intelligence and very natural for humans. So if we accept that, then it becomes much less likely that we're going to be able to automate wholesale occupations anytime soon. So we would be more in the realization that actually doing something complementary would be useful. Second, you know, from the time of Alan Turing's famous Imitation Game and all of the science fiction that followed, including all of the AI, remind us the Turing. So the Turing thing was, you know, started from the mathematical and philosophical endeavors that Alan Turing was pursuing. And at some point, he started arguing that because the human machine is a kind of computer, once we have a powerful enough computer that will be able to do everything that the human brain does, and then to prove or develop the argument that machines could think like humans, he also devised this Imitation Game, which then became part of our general culture when it became a movie. Where the Imitation Game is, you know, you're testing a AI model or a computer against a human, asking questions. And when you can distinguish that this is a machine, this is a human, then the machine has failed. Whereas the moment when the machine can imitate a human well enough that you cannot distinguish it from a human, that's when the machine has come to the thinking capabilities of humans. So that became the basis of the Turing Test, et cetera. So all of these, together with all of the science fiction that has been produced since then, in which machines quickly come to the same or superior capabilities as humans and then start threatening our civilization, have been the diet upon which many of the people in this industry have grown up. So they are naturally gravitating towards that kind of science fiction, I think, in their aspirations as well.

[A]

I think about this. I've been thinking about this so much since I first spoke to you. This idea that not only is there's this inspiration from science fiction, but also this idea that a lot of these researchers and tech industry folks are underestimating human intelligence, just like underestimating people's humanity. And just as someone who's been sort of like reporting and writing on the labor market for so long, it's actually not surprising at all, because this is how big tech and companies often treat humans as lacking in humanity, underestimating them, treating them often like machines, like that's not even a big tech.

[B]

It's a vicious circle. The moment you start thinking, and this is true both for tech companies and executives, the moment you start thinking humans are worthless, you start treating them as worthless.

[A]

Yeah.

[B]

And then their potential is not realized.

[A]

And now you're spending hundreds of millions of dollars to create these data centers and driving all this capital investment. Yes, I'm sorry, billions. Maybe even trillions. I think Sam Altman said at one point in these data centers and building this technology that will maybe but isn't yet be as smart as humans and systematically under investing in literal humans all the time in our education and workforce and policy. It's really kind of like blah. I don't have a specific question for you, but maybe that leads me to what I also wanted to talk about with you. And I'll ask you when we come back. Foreign.

[D]

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[C]

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[A]

so what I wanted to ask you was you talked a little bit about policy and you talked about the tax code, but what are some ways that policymakers could think about encouraging pro worker artificial intelligence? Because from what I hear when talking about policymakers and AI, all I really hear get talked about is sort of regulation or lately, you know, I don't even know picking favorite companies or stuff like that. But I don't hear anyone sort of putting forth a positive theory. Which is also why I liked what you guys wrote.

[B]

Thank you. Thanks Emily.

[A]

So what are some ways policymakers should be thinking about pro worker AI?

[B]

Well, let me first try to articulate how we could go to pro worker AI. And my belief, and this is just a belief, is that if we woke up tomorrow and 50, 60, 70% of the very talented AI engineers and creators working for these leading companies decided what we want is not the super duper large language model that can imitate human speech, but we want to create pro worker AI tools that expand what humans do, that create new tasks for them, that increase their ability to solve problems and make better judgments and decisions, be more creative, I think we would get there, there is enough talent, enough energy, enough unity in this industry that if they set themselves a feasible goal, and I very much believe, and we can talk about this, why pro worker AI is very feasible, it's actually much simpler to achieve than human level or near human level intelligence. In fact, it's within our possibilities. Even with the models right now, if they set their mind to doing that, we're going to get there. So the question is how can we change enough of the focus and prioritization of people in this industry? And there are two big levers for that. One is public pressure. So if the debate shifts, and that's why programs like this are very important, if the debate shifts to saying no, no, this discussion of whether we are good for humanity because we put some roadblocks against autonomous weapons systems, that's not the main area that we should be focusing on. We should be Focusing on whether you're actually furthering this pro worker agenda versus trying to just sideline humans, that would really have a big impact. Because I completely believe that many people within these organizations, especially in companies like Anthropic, really are well meaning. It's just that their well meaningness has been squeezed into this narrow space while we're going to AGI. So the only thing we can do is make sure that AGI, when it comes, doesn't kill us all. It's a very, very sad sort of space for us to be altruistic for the future of humanity, I would say yes. So how can we get there? I think public discussion, but also a little bit of policy push would help.

[A]

Before we talk about policy push, I think maybe I should ask you more about how AI would work in a complementary way to people in their jobs. You had some good examples in the paper, especially in the health care field. I think so, yeah.

[B]

I mean, I'm happy I can give examples from any field that you like. Let me give actually education, which I think is very easy to understand. Education is one of the areas where technological change has been very slow and there has been a lot of emphasis recently on using AI in education. But if you look at what most companies are trying to do is they're trying to replace teachers. They're trying to take things that teachers used to do and either centralize it or pass it to children by themselves or to children and their parents. So when you have automated grading, automated teaching plans, or you know, teaching that's by AI tools rather than teachers, those are all directly trying to replace teachers teaching roles. If you look at students having access to ChatGPT, you're trying to replace what teacher student interaction did in terms of teach students learning how to learn and how to write essays and how to develop their own reasoning skills. You're trying to do that with the students and perhaps students and their parents without with much less input from the teachers. None of that has proven very successful so far. And I think it's going to be very difficult to make those things become really transformative.

[A]

I cannot imagine, I mean, just having watched my kids suffer through online school in 2020, they didn't learn. I mean, nothing happened there.

[B]

But here is a different way to use AI. Imagine that we provide a background AI tool for the classroom which as a taking inputs, responses to quizzes from students or perhaps other things that students are doing during the class, understands which students are having trouble following which parts of the curriculum on the basis of that suggests to the Teacher ways of creating different groupings of students so that some groups can be taught at the right level and others can be given more advanced material, together with suggestions for overcoming some of the difficulties that students have. Because why can the AI tool do that? Because again, you fed in the right curricular material and you have fed in use cases from very experienced teachers dealing with specific types of problems. So the AI tool has more experience on some of these things than the technologies teacher can, but it cannot teach as well as the teacher, because there's a social aspect, there is an interactive aspect to it. So the AI tool and the teacher are together now achieving something that people have aspired for more than a century, which is individualized teaching. But you're doing it for much cheaper. But critically, you don't need less of the teacher. You in fact, probably need more teachers to do this. You need very skilled teachers. And you might need two teachers now for this class, because now you can divide the class into four groups and each teacher can take two of the groups. So you can see the big differences. In one case, you're amplifying what the teacher does, increasing the demand for the teacher, and you're actually going to get much better productivity outcomes. We know that from a few studies that have followed very costly individualized education programs. Now you can do that in a much cheaper way, in a much more effective way.

[A]

That doesn't sound that different from what we've seen in past technological advancements or shifts. Did you already mention accountants? I mean, I remember people always say when spreadsheets came out, you know, everyone said, oh, this is the end for whoever, accountants, financial analysts. But what actually happened was everyone just started using spreadsheets and started just doing a lot more stuff with spreadsheets and became more productive, right? Yeah.

[B]

I mean, there are some technologies that are flexible enough that you can use them in different ways. They will automate some tasks, but create complementarities in other tasks. Then there are other technologies that you can design them specifically to be complementary to workers. So many of the early manufacturing advances needed to be complementary to workers because there was definitely not the possibility of these machines acting autonomously. So you needed a lot of technical skills on the side of the workers so that they could work with these machines. So they needed to be upskilled at the same time as the machines were getting better. But you can also have technologies that are purely for automation. Robots. Great. They've actually improved productivity a lot in manufacturing. But they are an automation technology. They have been designed as an automating Technology. Perhaps in the future we may have core robots that work with workers, but that's not where we are. So. So technologies are a wide spectrum. Some of them are going to fall in the more automation category. Some of them are going to create new tasks or other complementarities to workers.

[A]

And yeah, we should say, and I should ask you, you, you're not anti automation. You're not, right?

[B]

I mean, I'm not anti automation. I think automation is very important. Thank God we don't have to carry things on our backs anymore.

[C]

Right?

[A]

Thank God for the washing machine.

[B]

Thank God that many people don't have to do the very dangerous tasks with machines that could take their fingers apart.

[A]

Yeah, right.

[B]

But if all we do is automation, this is going to have horrible effects on workers. Even moderate automation, if it does not, coupled with an effort to create jobs, good jobs for workers, can have pretty bad effects. For work I have done with Pascual Restrepo, for example, shows that robots had fairly negative effects in many communities, especially on blue collar workers, because they used to be essential for the production process and got paid quite well. And they lost those jobs because they were displaced by robots that did these things more cheaply. Firms benefited, but that led to 20, 30 years of lower wages, lower employment in those communities.

[A]

Right. And that's now, I mean, that's the

[B]

fear that's going to. Potentially what we are aiming for is something much bigger with AI. Now let me also say that the same things that I tried to argue earlier, that they are underestimating human intelligence, human capabilities, human dexterity, also means that the spread of AI models is not going to be quite as fast as what some people in the industry are thinking, even if they just aim for automation. So it's not like we're going to have a job apocalypse tomorrow. But Even if only 10% of jobs are automated away with AI, that's going to have fairly large distributional consequences.

[A]

So actually one more beat on the large distributional consequences. If 10% of jobs get automated away. I mean, if the goal is among these companies is to drive more and more profits, isn't it counterproductive to get rid of that many jobs because you're getting rid of your consumer base? I mean, isn't that sort of.

[B]

Well, it depends. So the consequence of large scale automation is a combination of three things. One is there is a productivity gain firms. I mean, there could be circumstances in which that's very trivial or even not present. But in most of the time there's either a small or a large Productivity gain, and that's additional earning power for somebody. Second, automation changes the balance between capital and labor. Labor earns less, capital earns more, so capital owners will have more money. Third, there is a greater inequality within labor. You know, when we had robots, the labor share went down relative to the capital share. But also engineers, technicians, managers did well. Yeah, blue collar workers didn't. So then the question is, one group is losing purchasing power, the other group is gaining purchasing power. Now if the people who are gaining purchasing power are Elon Musk, Sam Altman and Bill Gates, well, they have so much money they're not spending much of it. If on the other hand, if it's engineers and technicians, they could spend more now. They could start buying luxuries and a second home and very expensive wine, et cetera. It may not be what you want as a society that some people are hungry and the rest are buying, you know, vintage French wine, but the earnings might still translate into consumption. If it all goes to Elon Musk, well, He can't spend $3 million a day, so that might actually depress consumption.

[A]

Okay, so then get back to it. You talked about a policy push. How might a policy push towards pro worker AI? What would that look like?

[B]

So I would, I think there are two legs to it. One is get rid of existing distortions that make such an agenda difficult. And the other one is take facilitating enabling actions. In the first bucket, I would put the tax code. Our tax code artificially encourages automation because we tax labor quite heavily. More than 25%, payroll taxes, health care contributions, income taxes, whereas we subsidize capital close to zero percent. So that creates a very big wedge between capital and labor and makes it attractive but not productive for firms to use capital at the margin rather than labor. So we could have a more neutral tax policy where capital is not subsidized, and that would help. Second, we have a hugely concentrated industry in the tech field. More concentrated than, you know, many of the areas like steel, railways, oil were at the beginning of the 20th century, which started the progressive movement and all the antitrust actions. And that matters not just because it creates monopolies, but if we want new technologies often come from new entrants that have new business models and try different things. And that becomes harder and harder in a concentrated industry. So creating a more competitive environment and getting rid of the distortions is one thing. The second thing is we can also have the government encourage more pro worker areas. How would it do that? It would definitely be a horrible idea for the government itself. To become an entrepreneur or a venture capitalist and decide what technologies should be produced. But like what we have done in the area of solar panels or other technologies that are under supplied by the market, the government can provide certain subsidies or create tournaments or competitions where, you know, prototypes that are doing things that the market isn't doing could be rewarded. The government has done that very successfully. Even in the area of AI and robotics. The early challenges that the government had for robots that can do this or that under the auspices of darpa were very important in kickstarting big investments in robotics. So we could do that the same thing for pro teacher technologies, pro nurse technologies, pro electrician technologies. So there are a lot of things that with small amount of money but, but leveraging its leadership role, the government could move the dial.

[A]

It seems hard to imagine right now.

[B]

It seems many things seem hard to imagine.

[A]

Really hard to imagine. I mean, the one thing I was thinking of as, because as we're taping, we're in the middle of this war in Iran. And I was thinking war is generally advanced technology. And I was wondering, like, how the use of AI in this war is going to change or advance AI.

[B]

But I think if you're actually, if anybody is making note, the experience of using AI right now should encourage them to go in a more human, complementary direction, because they are seeing it every day that AI is providing useful information to humans. But if you actually let AI choose targets, it's just not up to that. Imagine, for example, we were running an experiment and we said, in this city, in Denver, from now on, air traffic control is completely delegated to AI. Would you want to fly to Denver?

[A]

Absolutely not. Never again.

[B]

No, but same thing.

[A]

Yeah.

[B]

Imagine you told the generals, from now on, AI is going to choose the targets. No human is going to interfere. But your life and your court martial is on the line. Would they agree to it? No. So that should be a lesson about how we should try and strive to use AI.

[A]

Yeah. So much of it is just, it's narrative. It's just trying to change the story from, you know, science fiction to a different kind of fiction, at least 100%. Well, thank you so much for coming on Slate Money. This has been great.

[B]

My pleasure. Anytime. Thank you, Emily.

[A]

Slate Money is brought to you by Charles Schwab. Decisions made in Washington can affect your portfolio every day. But what policy changes should investors be watching? Listen to Washington Wise, an original podcast for investors from Charles Schwab to hear the stories making news in Washington right now. Host Mike Townsend. Charles Schwab's managing director for legislative and regulatory affairs, takes a nonpartisan look at the stories that matter most to investors, including policy initiatives for retirement savings, taxes and trade, inflation concerns, the Federal Reserve and how regulatory developments can affect companies, sectors and even the entire market. Mike and his guests offer their perspective on how policy changes could affect what you do with your portfolio. Download the latest episode and [e-mail] WashingtonWyse or wherever you listen.

[E]

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