Luis Garicano on the Economics of Artificial Intelligence

I don't think they can fix their two pieces of terrible software in 18 months, we all hit out.

And now the good fight with Yasha Monk.

What is the impact of artificial intelligence going to be on the economy?

Is it going to lead to huge economic growth or to mass unemployment?

Is it going to transform the world or turn out to be a hype that doesn't change the world nearly as much as some people are predicting?

To help answer this question or the set of questions, I have invited onto the podcast a really interesting economist and politician.

Luis Garicano is a professor at the London School of Economics.

He was also previously a member of the European Parliament in which he was the vice president of the Alliance of Liberals and Democrats for Europe.

We talked about why it is that we should believe that AI is making a lot of technological progress and continuing to make a lot of progress where people who checked out two years ago or three years ago are probably not fully aware of the extent to which artificial intelligence can now carry out a tremendous range of tasks that are key to the knowledge economy.

We also talked about the title of Luis's upcoming book, Messy Jobs, about why it is that the fact that AI can accomplish a lot of tasks doesn't necessarily mean that it can do all of the jobs which traditionally have carried out versus tasks.

We try to understand what the impact is if a new technology makes products a lot cheaper and means that a lot fewer humans are necessary in that sector, as was the case with agriculture in the past.

Who captures those benefits?

And what happens in one sector of the economy, bit and more and more and more sectors of the economy?

Finally, in the last part of this conversation we talked about Luis's advice for people who are worried about this transformation in the job market, particularly but not exclusively young people.

You are thinking about going off to college, going off to low school or business school or becoming a pilot.

Should you hold off on those things because you're worried that artificial intelligence may fundamentally transform the job market?

What kind of skills, what kind of careers should you invest in? How can you prove your own life for the economic ramifications of artificial intelligence?

And we also try to think through the phenomenon of bullshit jobs. Do they really exist?

And if they do, does their existence indicate that there's a lot of jobs that AI can take away?

Or does it suggest that if unproductive activities continue to be well remunerated in middle class jobs, if that was the case in the past, perhaps it'll also be the case in the future?

Here we answer to those two questions. Please become a paying subscriber. Please go to writing.

And support this podcast. Finally, one little technical note. We work really hard to get you the best audio quality we can.

Unfortunately, during this recording unexpectedly, they were murdering kittens and torturing AI systems in the apartment above me.

So ripping out pipes is something I felt at points as for the ceiling was about to cave in on my head.

Our excellent sound producer is doing what he can to minimize the impact of his own years episode.

But every now and again, you will hear some annoying background noises as I speak. I'm very sorry.

Luis Gallicano, welcome to podcast.

Thank you. Yes, it's really a pleasure to meet you.

There's many things that I would love to talk to you about, but the topic that I've been thinking about a lot is artificial intelligence.

And I've had conversations in this podcast about just the technology of it with people like Jeffrey Hinton.

I've talked about the dimension of existential risk with people like one of the co-authors of if anybody builds it, everyone dies.

You know, I thought about some of the broader public policy angles, but I haven't so far had a conversation really about the economics of artificial intelligence.

And I think it would be really interesting to try and get a handle on those questions.

I think we'll talk particularly on questions about the labor market, but before we get there, sort of what in general do you expect the impact of AI to be?

It's going to be major or middling or minor is it going to lead to just vast economic growth for some people are predicting or is it going to re-desimate the number of jobs that are out there for humans?

Is this going to be an economically revolutionary time or is it going to be one of many developments that are kind of interesting and we're following but ultimately not that consequential?

So I think that I don't have a crystal ball first of all, so it's it's it's it's and this many things is always it's always hard, but let me let me give you my best take based on what we see.

I think it's clear that a lot of knowledge work, even if the technologies talk tomorrow could be automated, the lot of knowledge tasks.

It's already very clear that, for example, tasks that are routine, tasks that have to do with diagnosis, writing, crafting documents, writing, doing research, for example, it does that the AI is already doing perfect coding work is really spectacular.

So in terms of is it going to be big? I think it's going to be huge. It's probably as big a revolution as the industrial revolution that's that's that's a very likely thing except that inside of our automated work is for cognitive work.

So I think that everything points out to to a large impact and also acceleration.

And there was there were people who were doubting there were people who were wondering if AI would be a big deal or not.

I don't think any of those people could be doubting given what we have observed.

Let's say in the last six or eight weeks, the explosion of new models the way they work.

Cloud code is is really kind of taking the world by storm. Everybody has noticed the software.

Firms value is plummeting in the stock market showing that people believe that many of the functions that many verticals, many servers that were actually accommodating for one particular usage can be replaced by AI.

So yes, a big deal and in many segments.

Second growth. Yes, I mean, if this is as big a deal, we'll see big productivity growth.

And an acceleration not, I mean, not the kind of growth that we'll talk about it for sure later, but not the kind of growth that many people in Silicon Valley predict because I think most economists think in terms of all rings and bottlenecks and weaklings.

Meaning you can you can invent as many compounds to solve cancers as we want if you need to go through years or clinical trials and regulatory approvals that's not going to suddenly accelerate massively.

So those weaklings will constrain growth everywhere with we'll talk about those for sure labor your third question.

I would say the evidence so far, I mean, maybe it's like the person falling through the window, but the evidence so far so good.

It's more complementing that replacing in the three areas where we expect largest impacts translators haven't dropped.

I mean, everybody thought translators are going to be decimated. Translation seems like a solved problem.

And yet the month for translators hasn't dropped by calling to world labor statistics, customer service agents, some people get fired, some people get rehired to do different jobs.

Again, the BLS doesn't seem much and even computer programmers, we are not seeing big jobs.

There was a couple of papers per year in the year.

Erick Rignosson has a paper with authors called Canaries in the coal mine, which was starting to see jobs in more exposed segments for more junior employees.

And yeah, we see a bit of that. But there's a lot of discussion on whether that has to do with with COVID and so on.

So for employment, at the moment, it looks like it assists more than more than replaces.

I mean, it's clear that it can do many tasks. My main quarrel with the Silicon Valley, the reputation of things is that they believe that if we can replace the task that can be more easily done by the computer, then the job is gone.

And jobs are even a radiologist only spending 30% of this time looking at scans.

The job of a radiologist is much more than just diagnosing scans. So all of those things happy to discuss.

Yeah, and I was in a meeting with Sam Oldman in I believe 2018. I value knew who he was at that time.

And I remember him pointing outside the window of his hotel in Silicon Valley, saying, you know, in three or five years, you know, there's going to be robots building homes here.

And none of that has materialized. So there's certainly a very real tendency of people in Silicon Valley.

Not just sometimes to over promise of the technology, but to underestimate the obstacles to real world adoption of technology.

So particularly evident as you've recently pointed out in something like house construction, wherever constraints just aren't actually the inability to build homes.

We know how to build homes. It's, you know, all of regulatory approval and zoning laws and the, you know, it's the nature of my neighborhood going to change and all of those kinds of things.

Two points about how about the comment you made one about the Silicon Valley position. I am very surprised that they are not just hyping the technology, which I understand you want to sell.

You want to sell enterprise subscriptions, but they are also hyping the risks of the technology and all the time threatening people with extension will take all these jobs.

I don't see the point of this tactic. I can see that if you want to justify their valuations, they need to say that.

That all these things are are incredibly transformative and they are transformative.

But I don't see why by this insistence, like the other day, most of us will demand the Microsoft.

The ex deep mine co funder and Microsoft AI head. He was saying to the FT that they're going to automate all white color works and jobs in 18 months.

And I was joking like, does anyone really believe that Microsoft will actually get outlook or work to work properly in 18 months?

I mean, I don't think that I don't think they can fix their two pieces of of turbos over in 18 months. We all hate outlook. We've hated it for 15 years.

I bet you will hate it in 18 months. So they're talking about automating complex jobs that they cannot automate their own software. I mean, that's just completely ridiculous.

Before we dive into all of this substance of this, I would just love for you to help us establish the premise that you're operating on because I think, you know, a lot of my listeners are tech forward, a lot of my listeners are not tech forward.

And I still find many people in conversation who've experimented with touchy when they came out three or so years ago and have gone back to use it every now and again, perhaps they use it instead of Google to search for certain things or if they have a translation need, perhaps they use it for very specific things.

You know, they're still convinced that it hallucinates a lot. They feel like a limitation is what you can do. A very strong part of that, I think, is that the most commercially used judge-pity products are not very good compared to some of competitors now and part because they route, you know, your request sometimes to repile for model and sometimes to read not very powerful model at all.

And I think part of it is that a lot of people use free versions of these AI tools, which are much less powerful than the ones for which you need to pay at least $20 a month.

And part of it is that probably only a fraction of people who listen to this podcast have used tools like Claude code. So just to motivate what we're talking about when you're saying, but there's been this tremendous progress over the last few months and more broadly over the last years.

What are we talking about these tools being able to do today? How is it that people are using them that are so different from, you know, what you might think if you're just using the free tier of judge-pity, let's say.

Okay, so let me give you a Claude code example and a deep research example. So a Claude code example would be the following. What is interesting is that the machine can talk to you and it can send tools.

She can put Python tools to work. So what do I mean? So let me let me explain to your to your listeners in a very clear way. So I did a paper.

I was a member of the European Parliament and after coming back to a back to academia to derive their attention to academia.

I wanted to do some research on how the narratives work in the European Parliament. I wanted to show there are no trade-offs in the narratives.

So, but basically what I did is I collected 46,000 speeches, all the speeches done loads is then putting in spreadsheet. Each speech goes to to the child GPT through API, which means it goes through a special buy.

It gets processed. It comes back into a spreadsheet. It gets classified in certain ways.

And then we analyze that classification with statistical tools.

That took six months. It's a lot of work getting each speech, sending it, bringing it back, et cetera, et cetera.

I had done this for climate. Now I decided with Claude code tools to do all the work, six months of work, to do all of these for AI.

How is the discourse in the parliament evolving on AI? Okay. So I told Claude code. I wrote this with text. Okay. It's no programming.

I told Claude code. Here is my directory. I put it in my directory where I had all these files. Figure out these files. You don't have to tell it that, but that's essentially what it does.

Write everything the same pipeline to get a speech, to send it there, to classify it, to analyze it.

But instead of for climate, like in the original files, do it for AI. So there's a lot of programs, Python programs, multiple Python programs I have to run.

Six months of work, six, 10 hours later, there was a proper analysis by Claude code on all of these with all in its directories, all the tables, every single one of the figures from start to it.

So basically the difference is you talk to it, but it can deploy all these tools. You can do all these things go over the web.

You can run code. The second thing that I would like to do is that this is something that if they are not using, they would enjoy very much using is the deep research tools. If research tools are fantastic.

So on the highest end research frontier of these models, you ask it to research a question and you say, you know, populism has been has been growing.

There are two explanations, there's cultural explanations, that economic explanations. I want you to give me a in depth literature review of all the evidence comparing and you can ask it to do it in your specific way.

The economic and the cultural theories and what's the evidence index going to spend a lot of time collecting hundreds of hundreds of references, classifies and tell you what's the problems of the right very good research report.

This is now better down what an R.A. could do over several months. So so these are two examples of things that are on the higher end, this reaches tools. Why are they useful for for the world? So think of a lawyer.

A transactional lawyer, it's basically what they are doing is they are comparing a situation to existing presence and existing case law drafting, for example, intellectual property lawyer or let's say a contract to buy your house.

They're going to to go and figure out similar contracts and it's going to be able to upload the knowledge that it has and and convert it into a contract.

Now, if a law firm has this Claude code and and incorporates all its contracts and it asks this system to use this knowledge to automate contract drafting and compliance and verification, etc.

It's definitely able right now without any question to do the complete task.

Yeah, and I have talked about this a little bit. I mean, I started to use Claude code. I guess about a month and a half ago now, I have basically no coding background.

You know, I had a couple of group lessons of C plus plus when I was in middle school and I, you know, did a little bit of programming in a statistical software and a gradian school that very limited.

I was mostly a political theorist and then I took, you know, a few weeks of CS 50, the famous online course and computer science just on ad ex.

It was a very good course that does 10 years ago. I mean, you know, you set me a kind of entry level coding task like doing and proving a number guessing game.

I would not have been able to do it, right? And now with this tool, I've been able to program five different things that are actually of use to me in a very concrete way.

And so it's just astonishing what it can do. I think more broadly for some of the just pitfalls that AI used to have until a few years ago.

I'm really there anymore, right? Like when touchy 3.5 launched, it didn't have an extended thinking modality. So basically the system was forced to think through the answer as it was talking.

It's as well, you know, I asked you a challenge in question and it's part of a game show where if you don't answer, if you don't start answering within one second and if you hesitate more than one second between any two words, you lose, right?

Like that answer is not going to be very coherent. Now the systems, if you're off the free tier, talk to themselves and they talk you through the process, but which they try to do the answer and they try one answer and then they check whether that makes sense.

And then they're like, no, actually, I made a mistake. I should do this. So by the time they give you output, they're just thinking through it in a much, much bigger way.

The problem of hallucinations, you know, I wrote a person's abstract a few days ago about asking Claude to write a publishable paper of political theory.

And you know, a number of senior colleagues in the field wrote to me after I published this saying absolutely this would have been published in the top journal, if it had been submitted.

And I looked through some of the references, not for every single one. And it wasn't hallucinating. It now knows by and large how to ensure that something actually exists.

And it flags. If it's uncertain, it told me, look, I put in the page numbers for the canonical translations of talkville. I'm not sure about this. Please go and double check from I don't have access to that for page. If I upload the PDF for that book, it'll do it for me, but without it, it can't know, right?

So it knows what it knows. It knows what does know. A lot of those problems have been fixed. All right. So now we go into the realm of economics.

I don't know whether we have reached superintelligence as to find Badaire Amade where we suddenly have the appearance of the whole country of geniuses.

But we certainly have the appearance of a whole country of middle class professionals, right? Suddenly, the number of people who can competely draft legal contract and do so in, you know, 10 seconds for very little money is vast.

It's the larger than it used to be in the past. So what does that do? What does that do first for growth? I mean, if our economic growth was in some ways constrained by human capital, some was constrained by the number of well trained people with access to a lot of knowledge, able to carry out that work.

Well, that should mean that we're going to really increase economic growth. Shouldn't it? Or is it more complicated than that?

No, I think that first order approximation is that you have an increasing productivity and that you have an increasing growth. I mean, I think that's a reasonable thing to start with.

There are a couple of three kind of caveats that I think are important in trying to figure out how big that is.

The first is, of course, organizations, organization of work is intensely human. And as you were hinting from from my from my recent post of on the on the London housing.

The reason 23 out of 25 bros of London are building zero housing this year in 2025, they built zero housing. There was zero housing starts wasn't at all technological.

And given them better technologies, not going to solve the problems with the neighbors with the nimbis with the greens with all the things that stop construction with the land, the regulation, the lawyers, all the things that stop construction that we already know.

So first are our organizations and human all to human obstacles that mean that even when the technologies there, there are many other aspects that have to collaborate.

And there are entire sectors, which have this Bommol characteristics, right. So so with them, Bommol, I don't know if your listeners had somebody discuss this and maybe you discuss it, but Bommol had this observation in the 60s that is an economist who observed that string famously that string quartet would still take one hour to play a most piece the same exact hours they would take 200 years.

200 years ago for people, you know that this is very old point because nowadays no economists will talk about string quartets.

That's right. So, so these people, these observation holds for a very large share of the economy that in a large share of the economy, hairdressers, cooks, technology doesn't play any role.

It's not just that there are bottlenecks, but it is that productive growth is very small because there's really no actual technology and no actual AI involved.

Now, what is interesting is that in the sector of the economy that enjoys the technological change as the prices drop, it's perfectly possible and we'll talk about demand elasticity in a second.

It's perfectly possible that people with association and that sector becomes smaller, for example, think of agriculture.

It became technologically fantastic, but it became smaller and smaller as I see it was more productive because people all stomachs didn't grow.

So, the amount of workers employed went down. Now, what that means is that the sector that has the technological expansion lowers, reduces its size and the other sector, the one with the violinist expands its size.

And as a result, the weighted average, not just how much is this growth, but how much is this growth, but average, but maybe the sector that's growing is getting smaller.

The other thing that I was referring at the start is the one way of thinking about this right is just that everything that can be automated does suddenly become plentiful.

That might not fully show up in GDP figures, but it does fundamentally remake the world.

When I think about the agriculture case, as a result of our successful mechanization of agriculture, that's become a much smaller part of the economy.

We're paying vastly less for food than we used to.

Perhaps you'll tell me your understanding of technical details better, that sort of underplays the degree of that change in the way that we track GDP.

But it does mean that whereas for most of human history, even people in affluent countries, if you weren't at the very top of a hierarchy, were deeply constrained on how much food they could consume.

And when I announced as a result and died earlier as a result nowadays, you know, if you are anywhere outside the bottom 20% of affluent to medium affluent country, food is not your primary expense.

It's a significant expense for you like nice food and you go shopping for nice things, but if all you want is to be able to feed yourself on ramen and a few supplements in such a way that you don't get malnutrition, that is going to be a tiny part.

Of your budget and that is a fundamental positive transformation of human life, even if it doesn't fully show up in GDP figures.

That is exactly right because the economy is like to talk about welfare as the sum of the consumer and produce surplus.

In this case, the consumers is really enjoying the biggest gain.

So a lot of what happens with AI that we are seeing our list already is that a lot of the gains are going to consumers and not showing up in this.

In this GDP figure. So let me tell you an example.

We have a dishwasher that is broken.

We take a picture. We are uploaded to judgey pd and say, what the hell is going on? It says, oh, this thing is stuck. You should just remove this and we remove it.

Now our welfare has gone up. We are happier. We just solve the problem in this washer.

Now there is a transaction that would have been some person comes to our house to fix the dishwasher that didn't take place.

So the GDP would have been higher if this person had come and we would have paid them.

But our welfare is increasing. And if we can diagnose our own illnesses and we can and we can know if our diet is good or bad with going to the addition.

If we can do our own contracts, all those things are increasing our welfare, but they are not indeed showing up in GDP.

In fact, some of them could produce the GDP. So it is true that a lot of the gains.

I was talking to a CEO from China who was telling me, I think a lot of the gains are being smoked in the corridor. And I said, what do you mean?

He says, like, well, I observe all these IT people and they are all more productive.

I'm like, oh, great. It's going to show up in better numbers at the end of the month. And I don't get better numbers.

So each person IT is more productive. They can do, they can solve problems faster, but then they are going home earlier or playing video games.

So all those gains that could happen and that could definitely not not increase GDP.

I think the other thing that I would, I would mention is that if it's within the short and the long run, imagine there are two sectors.

One sector sector A gets fully automated. So imagine we don't need, let's say, lawyers to put one example, which is not exact because lawyers have lots of regulatory power,

a lot of things they have, you need a lawyer, for example, for the court, but imagine these lawyers.

Let me do something that has capital. So let's say, let's do lawyers. It doesn't matter.

All the lawyers have to imagine we don't need any lawyers. We solve our legal problems. All the lawyers have to move to sector B,

or all the people in sector A that gets automated needs to move to sector B.

All the demand that is now like consumer surplus, we are not needing to spend money in legal problems.

We can go and spend it on the other sector at the moment we don't do that.

And all the capital has to be moved to the other sector. All of these things take time.

There is a moment when the GDP could be dropping because we don't consume legal or we don't consume these washroom repairs.

So in the meantime, when some tasks are automated, it could happen that in the transition, the capital needs to be relocated is written down.

The labor needs to be relocated. The demand needs to be relocated. There is not sufficient demand also.

So all of that transition could definitely not be like, oh well, we're just growing and growing.

I'm trying to figure out sort of what the aggregate effect of these changes might be.

On the one hand, you have traditionally agriculture as a huge part of human activity.

It mostly gets automated, the number of people working agriculture is now astonishingly low.

Output goes up a lot. As a result, food price goes down a lot.

So most of the consumer surplus is captured by humans and by consumers.

And so it's a very good thing. I guess one thing that I don't fully understand is what actually kind of provides the basis of negotiating power, the bargaining power of ordinary people, right?

In the agriculture world, the answer is that the production of agricultural products is now very cheap, but it turns out that humans are necessary for running all kinds of other elements of the economy.

Exactly. Yeah. And the agriculture, they're machines.

Yeah. And so there is a strong demand for human labor and that's what allows them to continue to consume a lot of things.

Now, you know, if we get and this still sounds a little bit like science fiction, right? But I'm just trying to imagine this this scenario, right?

Let's say we get to a world where AI can fully run agriculture. We don't need any humans in agriculture anymore.

And it can fully run the system that is needed in order to manage the agricultural system.

And it can, you know, fully run a couple of law firms that are needed to, you know, efficiently allocate capital to agriculture and make sure of the most efficient agricultural firm is, you know, telling the most land and so on and so forth.

And it may be that there's still all kinds of elements of a human economy where human work is needed, right?

And maybe that humans still prefer to have humans as teachers and that humans, you know, are continuing to be required in medical decisions.

Perhaps because we don't trust the air systems to do it or perhaps just because there's regulatory obstacles to fully automating those.

But like if a lot of the, you know, if all of the underlying productive processes that actually produce material wealth and along and human hands, or at least if they no longer require humans, you know, is that kind of perpetual

modular where the sort of circular economy of humans is enough to sustain affluence or does there need to be some kind of relation back to material production for for this whole construct to sustain itself, right?

All of the need for human labor is produced by the fact that it's extremely expensive to look after old people and the fact that, you know, stupid regulations weren't let us by houses and, you know, anybody who has capital is willing to pay a lot of money for a house because we need to live somewhere.

And, you know, some people who aren't really needed in the economy continue to have to be employed in human shape because of regulation.

Is that actually enough to sustain affluence within human workers if all of the actually productive processes can be done by non human workers?

Let me break it down into into into into a few parts. So the first demand the kind of association case that we discussing where the sector gets smaller doesn't necessarily have to be the case.

In fact, in many sectors, as technology gets better and they get more efficient, in fact, the sector grows in size.

This is called a jeven's effect after William Stanley even say English economies who observe that coal was getting machines using coal were getting more and more efficient and they were consuming more coal, rather than less.

Why? Because as they were getting better, they were using so many more things that the coal consumption was going up.

So in many sectors, think about health, think about energy. As things get more and more efficient, it's unlikely that the sector as a whole will shrink.

In fact, it's more likely that it could demand more humans and more it would grow in size in the amount of humans.

I would think the more elastic demand, the sectors that are likely to grow when the prices go down would be things like health and energy, for example, just to give to two simple examples.

Now, second thing that is really important is the idea of compliments and you were hinting at it clearly in your question.

There are many situations where the human is needed in a bottle neck. So even if the 99 first tasks can be automated if the 100th task needs a human.

99 are abundant but the scarcity is still the human and the human is going to get the rents and is going to get the labor.

Well, that depends on the human being's costs, right, which is to say that if that task is highly requires a very high level of qualification and you either need millions of humans to do that task because they are so productive, in which case a lot of people are going to be in relatively decent employment.

Before you need seven people to do it, but they have to be excellent, in which case those seven people are going to get huge rents, right.

Some of that economic gain is going to go to those people, but that doesn't mean that, you know, I mean let's say you have what is a 5% of a male workforce in the world being employed as drivers, some percentage like that.

That means that each of these drivers necessary for each of us riots and so, you know, the rent from the need for human drivers is very broadly distributed, right.

Each of these drivers probably not very affluent, but the rent for that activity is very brief.

Now we say like 10 people have to supervise all of the, you know, self-driving cars and let's say they have to be incredibly qualified.

Very few people who are able to do that, well, perhaps they're able to capture a lot of that rent, but that's only going to be 10 people who get that money, right.

All let's say that it needs, you know, a thousand people, but a million people are able to do the job, right.

Well, in that case, the wage for those thousand people is going to be really low because any one of them can be fired and there's, you know, 9,999 waiting outside the door willing to replace their positions.

Depends a lot of those kind of details, right.

So, yes, absolutely. So I'm writing a book on this point. It's called Messy Jobs and it's going to be, there's going to be out.

Well, it's going to be submitted in a couple of weeks, I hope, and out by May, let's hope.

And the argument of Messy Jobs is that there is a big difference between task and task and a job.

So, Geoffrey Hinton, who you have in your, in your podcast in the past, is famous for having said in 2016 that nobody should study the ideology because radiology was just an expert system that could scan photos.

And of course, any expert system was going to be better. It was going to be trained on hundreds of thousands, no hundreds of millions, millions of of breast cancer scans.

And it was going to be perfect at detecting those cancers.

The truth of the matter is the month for radiologists has never been higher. The salary is growing. The numbers are growing. It's the third highest salary for any medical profession in the US.

Why? Because the task is very different from the job. The technologist imagines the project manager is a guy looking at gun charts in the computer and imagines our ideologies is just looking at scans.

And only 30% of the time of radiologists is just looking at scans. They have to do the diagnosis plan. They have to talk to their colleagues. They have to talk to the patients. They have to do many other things.

So, I think that the first crucial obstacle to your, to your testopia is that automating parts of jobs, tasks is not automating the job.

And I invite all your listeners to think of what they did today. And think of which of the things they did today.

I went to a workshop. We had a job market seminar. I had a meeting with my colleagues. I had students working in. I worked on a paper.

All of those tasks can think about how many of these tasks you could replace with a machine. And you will discover that many of them can't definitely the task we are doing, which is having a human conversation or something can't.

So, the second idea is it's really very different a job and a task. And many aspects of the jobs can go without the bundle.

It will get re-bundled. It will, it will look different, but it will not go away. There are reasons for that. Part is the technology. You need to direct the AI. You cannot just let it do its thing.

Meaning, the AI is sick of panthic. It tends to agree with what you say. So, if you want to direct it in the direction left and say, yeah, the left is great, yeah, let's do the left.

And if you want to direct the direction right, it will say, yeah, the right is the best. You are smart. Your smartness right was the good way to go.

So, what you tell it is going to matter. And it means that that somebody is going to have to be access to adjustment.

But it's crucial to realize that this is not solved by AI being smarter and smart and smarter. Think of managing a family.

Managing a family, which all our audience is familiar with, everything that you do in the morning with the kids, moving around and deciding.

And a lot of that is not automatable because a lot of the knowledge of what's going on is tacit. It's in your head.

You know what's going on. And no machine can tell you whether the kid has to wear his boots or not, or whether the school is a day that they need to do this.

So that you're not going to have to be deciding all these things. So, authority and it's inherently human making the difficult decisions is inherently human.

Being a consultant who has power points, yes, it can be automated. But that's the consultant only to power points.

Or does he go to the, or she go to a company, listen to the workers figure out where the problems are, how to automate this bit, how to do this a little bit better.

A lot of that is tacit. So I would push back against the idea that entire jobs are going to be done autonomously. Yes, you're right.

Autonomy, the cars pass the autonomy threshold and they basically the cars can self drive.

And that means suddenly the supply is infinite. You can, you can have a lot of drivers like all the machines.

And that means eventually the boy just collapse. So that's a good example that you picked.

Is that a normal example? Is that an example where the task is very clearly defined?

It's very repetitive every day, going back and forth to end it all in the computer.

Is that the majority of jobs, my claim, the claim of this book of messy jobs is, if you think of elasticity, many things will grow in demand.

If you think of the complementarities, there is going to be scarcities, crucial, as you say, you're sure, but there are going to be many scarcities that human can exploit.

And this is without getting to the point that you were starting the question with, which is demand for human services, which actually I'm not sure is this that's large.

I'm not sure that people necessarily when they're old, they necessarily will want a person bossing them around and say, are we, are we well today?

I mean, I might prefer a robot who is like taking care of me.

Including a lot of the more intimate tasks that are involved in elder care and so on, right?

Like, would you rather have another human wipe your ass? Or would you rather have a machine wipe your ass?

Like, you certainly want some human company, right? I mean, once your ass is wiped, you'd love to be able to have a conversation with a human.

I agree, but you definitely don't need a person for that. You would rather have a person for that.

So, you know, I think I have a middle position in these debates.

As I have a position at this point, but we'll come to that.

I mean, just to push back on a couple of the things you said, and again, I'm not coming from a kind of maximum position.

I agree with you that a lot of the kind of predictions that all of the jobs are going to be gone in two years and just just a testament of people who haven't fought about politics and who haven't fought about the real world in many ways.

But some of the examples you gave, I'm a little bit less convinced of, right?

To give one example, you know, Kenny, I outsource the managing of a company of a family.

I mean, part of a family is just that, you know, you're negotiating between human beings.

You're trying to come up with a plan together, even via I can make a plan that is pirato superior to whatever plan you'd have.

You know, part of what it is to be a family is to make those plans together and say what do we do today?

So I agree that sort of on an emotional level, you might not be able to outsource this.

On a surely planning level, I think absolutely the things you talked about AI could outsource.

And in fact, I mean, many feminists would say, you know, that is what we've been arguing for for a very long time because it's often women who do the kind of emotional labor and the second shift and so on of, you know, keeping track of the fact that Timmy has to go to the dentist tomorrow and Tammy has to go to the lay the day after.

And, you know, have we made sure that the dress, you know, that she'll need to wear to her ballet class has already been washed, et cetera.

You know, it would take an invasion of privacy, like an AI that's part of all these conversations and that immediately notes down when Tammy says, I don't forget for my ballet practice next week, I need X or Y.

But like, can AI do all of those things? Absolutely.

And could it in fact, you know, save some marriages in the process of doing that? Probably yes.

So here's why I disagree. So there are information processing tasks and you're right. A lot of information processing tasks that we need to synthesize all this information.

We need to put it in a form that can be processed and we need to make a decision.

There are other tasks that have nothing to do with information. I mean, your wife or your kid is upset and why is he upset?

And someone needs to talk to the kid and someone needs to decide, yes, I had told you the optimal plan from the perspective of the family was that tomorrow you couldn't stay home and you were going with your friends, but I've listened to you and I decided that you stay.

A lot of it is not information processing is you understand the kids, you understand what a look means.

You understand when a look means from your wife or from somebody else, when a look means, yes, I will do it when they say yes, but in fact, I mean no.

And there is a lot of tacit local knowledge that goes in management and the family and in a business.

And we're talking politics, but we're not just talking politics, we're talking emotions, but not just talking emotion, we're talking local knowledge, interpersonal knowledge.

You know your wife or many years and you know when you can push and which she knows when she can push all of those things, you're saying, well, the machine could know.

I don't think it could know. I honestly don't think it could know you are the contractor.

You know, let's now go to the management, you're the contractor, you know which electrician is the one who is reliable and the one that played tricks on you last time.

And yeah, candy A now know whether you can use something to get this electrician to be on time or not.

I mean, we're talking about, I mean, just calling it intrusion, we're talking about the level of interpersonal and tacit knowledge that is unreal because also think about this.

A lot of the tacit knowledge on the jobs is knowledge that produce half that gives them power.

They're not going to be happy, just sharing with AI.

Oh, AI, you know, you should know that my colleague so and so has this problem with the boss and that he never wants to work with that boss.

No, I mean, this kind of stuff is going to remain on the heads of the human.

So I believe that yes, the information processing task will can and will be automated.

But a lot of it has to do with not just the emotional and social skills, but the tacit knowledge and the personal knowledge that probably the machine will never gain because it cannot capture it.

I have two kinds of different lines of questioning about this.

The first is just about if we go away from the extreme predictions, right?

If we recognize that clearly at this point, advanced AI tools are capable of doing a lot of the tasks involved in knowledge production.

That presumably means that some jobs are going to go away, right?

The idea that AI is incompetent, but it can't do all of those things.

AI is all a bubble. We agree with study, right?

I think we also agree on the other end that a lot of those real world frictions are very real.

That jobs are messy because the world is messy, right?

And that therefore the idea that, you know, the moment that Claude beats doctors in a bunch of medical, you know, in a bunch of stylized medical questions, which it more or less does now,

to expect that therefore tomorrow, there's no longer going to be any doctors, is really naive and doesn't understand the real world.

But what happens in that middle space, right?

What happens if suddenly the demand for white collar works is reduced by 25%, perhaps by 30%.

And it doesn't have to happen between today and tomorrow, it happens over the course of 10 or 20 years, right?

You just see a continuing gradual reduction in the demand for that kind of high-skilled work, you know, as existing firms automate outwork,

as firms that are too stubborn to do that, that are not able to do that, are outcompeted by new entrants to the market, which are AI native in the same way that in many areas of the economy,

it took internet native companies to outcompete old ones until you really saw some of those productivity gains come online, right?

That's going to be a significant process. It's not going to happen for one moment to the next.

But in a way that raises an even deeper and equally troubling possibility, namely that A, the job market is going to slowly slump for an extended period of time,

and B, that there's the famous summer-depocryphal boiling frog, right, that rather than, you know, everybody lost the job in the course of two months,

well, perhaps we would all organize and, you know, demand, I don't know what, some way of being made whole, right?

If this is just going to show up as, you know, decades in which we're bargaining power of ordinary people, diminishes and diminishes and diminishes,

because the demand for human labor just continues to fall in a messy, gradual haphazard way,

that could still be, you know, an incredibly painful period ahead for ordinary people.

So, you're more or less describing my scenario of transition between sector A and sector B,

and we know that in industrial revolution, what it's called was the, what it was called the Engels Post,

which is something between 1790 and 1840 or between 181950, where basically this was happening, workers, wages were stagnating or dropping,

and the workers were in trouble, and then GDP was multiplied by two over the following years,

over the 50 years to 1900. So, yes, it could happen that over a period of time the transition is hard.

Now, against that, I would say, now, human beings have been automating tasks for hundreds of thousands of years.

I mean, I think all the human existence has been automating things since the Abakus and since counting sticks to count.

So, there's something that we're used to doing. I would also say it is enormously promising to people

who are thinking of trying to stop this. I would say it's enormously promising to, for example,

if we have this counter-genius in the data center to have the cure of cancer,

or the cure of Alzheimer, or the cure of many things, if these technologies can really advance science by decades at the time,

so I would not think that the organising to stop it is the right thing to do.

I would think that the combination of Bommol sectors, sectors where nothing is going to happen because they are outside of this.

If you add up the sectors, the fact that you have Bommol sectors, sectors where nothing is happening because there's no technology

and everything from public sector jobs to arts, to, I mean, like the music, to the barbers and the hairdresses and the cooks and all of this,

you add, by the way, dog workers in the United States is a pet care in general,

it's like 1% of the population just doing all the pet care sector, which is a sector that we wouldn't imagine that.

And of course, it's not affected by AI.

If you add sectors with very elastic demand, which are sectors where, which are going to grow, like health and energy,

and then you add the messy jobs, the jobs where, even though the part of the task can automate it,

but there's many tasks that don't get automated and the jobs continues for managers to entrepreneurs,

then you have, and you add the complementarities, we haven't talked about this,

but there's this idea from David out there about the new middle class,

which is, you think of a nurse who is empowered with this genius on a box,

this nurse who can now diagnose really complicated illnesses, and she can, or he can hold the hand of the patient

and do all the other parts and now solve more problems.

Of course, as you said, then maybe everybody wants to be a nurse and we would have to think about the supply of nursing,

but you add up all these things.

I think you go away from the feeling that there's a cataclysmic change and you more go to,

yes, it's automation.

Yes, it's going to be a bigger revolution than what we've seen in the last 50 or 60 years in terms of automation.

It's more similar, maybe, to industrial revolution.

But no, I don't think it's going to cause widespread long-term unemployment.

We are going to be able to, from TikTokers and Instagramers to people walking dogs.

There is so many new jobs that we wouldn't even think.

I mean, this podcast there, who was going to tell you it would be a podcast there.

I mean, I don't know.

The vision of the future is that humans are going to be fine,

because we're still going to be TikTokers and Instagramers and dog walkers.

I'm a little bit skeptical about how, how, how, how...

I'm not saying that.

I was saying that.

I know you haven't.

The best care sector is 1% of the population.

That is not just dog walkers, it's nurses and people they get out of the beds and all these other things.

Let me ask you about the dog walkers.

I think one interesting thing that's happened over the last 10 years,

which just shows you how epistemically modest we should be about all of this,

is that I remember all of the conversations about all the drivers in the world are going to lose their jobs.

And somehow that was linked in the conversation about populism

to, you know, that's why the Midwest went for Trump.

I think there's never really a connection there.

And so they should all learn to code, right?

So now it turns out that AI is really good at coding,

but because of a set of technical issues that ended up being more hard to solve for a while,

now there's technical issues.

Armisticell, Waymo is very efficient and much safer than human drivers.

There's a lot of regulatory obstacles.

And so even for the number of rides that Waymo is offering is going up exponentially.

It's still a very small share of the market.

Most human drivers are still fine.

Again, this is going to take longer to play out than a lot of people think.

And we're now in a world where not as workers are seemingly about to lose their jobs,

but all of the mental, all of the manual traits are safe, right?

We're assuming a world in which the plumbers are still going to be fine to revert to you example.

The dog workers are still going to be fine.

Well, I watched as many other people on the internet, on the internet,

the quite remarkable display by Chinese robots for the annual Chinese state television gala,

the progress in their dexterity from a year ago to today is just astonishing.

We know that the ability to combine the manual dexterity of these machines

to visual processing and understanding of a world is going up very quickly as well.

So I am personally waiting for the chat GP 3.5 moment in robotics.

I think that it won't take very long for there to be some consumer product

that is actually usable. We're getting close to that.

And the applications in the industrial sector are likely to increase as well.

Again, I don't think it's going to happen tomorrow.

I think it'll take time to fully be implemented in the economy.

But when we're talking about a time scale over decades, right?

When we're saying, well, in 20, 30 years, the fact that more and more of these knowledge work tasks

are going to be automated because those skills can already be done by AI.

Perhaps it'll take a long time for firms to reorganize and for new firms to enter and so on.

But it's okay, because perhaps with all the, you know, in the pet care sector,

well, sure, that assumes that in 20 or 30 years,

we're still not going to have figured out household assistance

that if you're at the office or doing whatever you are during the day,

you can't have a little robot who walks your dog in your stead.

And that seems to me, given the rate of progress of this technology,

like a pretty significant background assumption.

I think that that is very, very correct.

I think that robots physical AI, let's say, is not that far.

What we have seen in the past is that the capital is in what we're calling

in the last supply. You can always invest more in capital.

And that means the capital, eventually the rents on the capital

get completed away and the robot gets sold at a competitive price.

And that means that people can use robots for care.

And remember, we have a lot of fatigued problems and growth,

population problems to pay our pensions.

And having robots could be a solution to all that.

It's like more population growth.

Now, in a world where this is completed away,

again, we're back to consumer gains.

So the capital doesn't earn extraordinary returns because there is infinitely

elastics of lab capital.

More people can invest in making more robots.

And what is the scarce resource?

Well, the scarce resource obviously is going to be land.

It's going to be energy.

But it's going to be whatever human labor is needed still.

And that human labor, it could be that we're working less hours.

It could be that we are able to enjoy more leisure.

And it could be that human labor is employed in a whole range of jobs,

which indeed you're right, we can't anticipate.

What we shouldn't imagine is that somehow there's an economy that works

with our humans because all the value is in the humans.

What does the economy generate value for?

If nobody's buying the products, the definition of value is something

that is worth more to humans than it costs to make.

That is what value means.

If there is no human who can buy stuff because they're all poor,

there's no value.

So it is the way the economy works that the return to capital gets pushed back

down to the normal return, to the competitive return,

and that the reins get captured by the scarce resources.

In this case, the complementary labor that is needed is still in those moments.

You're right.

Nobody can't anticipate what happened in 30 years time.

And yes, I agree that both physical roads and AI and cognitive AI

are going to be a big revolution.

I don't think we should be thinking of these as an apocalypse.

I think that there is a lot of complementarities.

There is a lot of scarcities that favor human labor still.

And there are a lot of areas where this doesn't really bind at all.

Tell me a little bit about the state of the empirical literature

and the fact that there's a real distinction in micro and micro studies,

a real distinction in studies that look at the extent to which particular task can be automated

and the extent to which the overall job picture has changed.

When I look at the fields that I know a little bit,

I worry that that is an indication of what is yet to come

rather than an indication of the fact that AI won't have a big impact.

Do you mention translations earlier?

Another thing I've been thinking about is index making and publishing.

There's all of those things where basically there's been no change.

So far as I can tell, my next book is going to be translated by human translators.

Well, perhaps they don't actually do it and they privately send it to Claude

and capture the consumer surplus by going out and having a nice vacation

where they tend to be working on the book.

But in terms of the actual economic flows, nothing has really changed.

I don't know how long it's going to continue to be the case.

It is very sticky and very complicated to change those processes.

Somebody needs to say, I'm going to be the asshole and fire over translators.

And I'm going to deal with a backlash of the agent saying,

my offer doesn't like the idea that it's AI rather than a human who's doing this.

And perhaps it's a newspaper story.

Your customers might be upset.

There's all kinds of arguments to be risk averse

from being the first mover to make that change.

But I will tell you that one of the things I've created for myself

with Claude code is a personalized translation tool

because I published my articles including some podcast transcripts

not just in English, but also in German and French.

And that is not just better than the offer shelf tools.

It is at this point better than all but the very best translators I've had.

The very best translators I've had who I'm deeply grateful for, particularly in France.

I think I'm still better, but 90% of the translators I've dealt with.

You know, professionals who've translated famous books by famous people

are significantly worse now, right?

For now, I agree, right?

Like if economists tell me, hey, actually,

tonsils haven't lost their job and none of us has changed that much.

I believe it, right?

I can see that.

If economists tell me and based on the fact that in the three years that AI has existed

in which two or diverse three years, it really wasn't that beloved yet

that it's coming to be.

No, I haven't seen too much of saying that.

And people have, you know, not yet integrated versus, versus processes sufficiently.

We can make predictions about the future.

I'll say, come back to me in 15 years and let's see where those translators still have jobs.

I don't think I don't think anybody's predicting that translators are still existing.

I said so far, so good.

Maybe like the person falling through the window.

So I do think jobs go away like newspapers are digital.

And there were lots of people in printing presses and paper and all the industry

and all the newspapers were out to my time.

Including my grandfather, his job was to, as a young man, to lay with newspaper,

you know, letter by letter and then later he helped to manage the printing site.

But yes.

Yeah.

Yeah, that's gone.

And that's been, that's been human history all the time.

I mean, the use has was the empirical evidence.

The empirical evidence up to now is positive.

So when they've done randomized control trials,

but they have given in a control setting, the micro evidence,

they've given a control setting the AI to a, it was a previous AI,

but customer service, customer support agents, what has happened is the least advanced customer support.

The most junior ones get a performance similar to the more senior ones.

When they've done it with writing tasks that worse writers get a performance similar to more better writers.

So it helps the least advanced people when they've given it to software programmers in three different tasks.

They've seen the software programmers were less good to get to program closer to the skill of the better software programmers.

So micro studies seem to be finding all the time,

complementarity is running substitution at the aggregate level.

There is much more confusion and much less clarity.

We don't see big jobs in demand.

There are some canaries, as I was telling you from that paper,

kind of in the comment, there is some preliminary evidence that,

hey, maybe there is some drops in junior jobs.

I think when we think that the research task, the power point task, the Excel task,

those are the issues to automate.

We have to imagine the junior lawyers, junior consultants, junior investment bankers,

will not be recruited as much because you can do the research task without the junior person.

Now it ends out the McKinsey class of this year is bigger than done before.

They keep hiring people.

It doesn't seem like they're hiring less.

So far so good.

I agree with you.

That is not the forecast of the future.

I don't mean to say a forecast of the future on the basis of the fact that we haven't seen much.

That's not the point.

I think the point, however, is there are indications that complementities are important,

that people who use this AI produce better, and that substitution is still limited.

But it's hard not to think that tasks that have to do with just basic, basic PowerPoint research,

or research, etc. is not going to be for the automated.

I don't think we want to make this 15-year forecast late.

Thank you so much for listening to this episode of The Good Fight in the rest of this conversation.

Lewis and I talk about what young people should do to future proof their careers.

This moment is disorienting for everybody.

It's particularly disorienting for people who are just trying to figure out

how they can have a meaningful and hopefully will renovate a job.

Not just for the next five years, but for the next 50 years.

Lewis gives some really interesting advice for how you can prepare yourself for a future in which messy jobs are key.

We also talk about the phenomenon of bullshit jobs.

My dear producer Leo thinks that a lot of jobs in the economy are bullshit.

I wonder whether that's true, and if it is true, whether that indicates that a lot of jobs can easily be automated and eventually going to go away.

Or whether that means that bullshit jobs somehow might persist even when more and more jobs in the world might turn out to be bullshit

because you could just have AI do it instead of you.

To see what Lewis thinks about this, how we puzzle through this question.

Please, become a paying subscriber, please set up the premium feed of his podcast in your favorite podcasting app.

By going to writing dot dasha mong dot com slash listen.

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