What If AI’s Biggest Impact Isn’t Jobs, But Minds?

Summary

Podcast Summary: Merryn Talks Money – “What If AI’s Biggest Impact Isn’t Jobs, But Minds?” Host: Merryn Somerset Webb (Bloomberg)
Guest: Tom Slater (Baillie Gifford, Scottish Mortgage Investment Trust)
Date: May 11, 2026

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Episode Overview

This episode explores a thought-provoking question: Is artificial intelligence (AI)’s most profound impact really on our jobs—or is it reshaping our minds in ways we haven’t yet fully acknowledged? Host Merryn Somerset Webb sits down with Tom Slater, manager of the Scottish Mortgage Investment Trust, to discuss his recent essay, “AI isn’t coming for your job, it’s coming for your mind.” The conversation moves beyond typical headlines about job displacement and market valuations, digging deep into how AI is rewiring human cognition, learning, and societal structures.

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Key Discussion Points & Insights

1. Traditional Narratives vs. Deeper Concerns

• The Investment Obsession ([03:10])
  • Merryn notes that discourse about AI typically centers on investments, job loss, and economic restructuring.
  • She asserts the most important effect might be “what's happening in your head”—the unseen rewiring of our cognitive patterns.

2. From Skill Loss to Mind Reshaping

• Sat Nav Example ([03:49])

  • Tom reflects on how reliance on sat navs eroded his navigational abilities, not from active loss, but neglect—an allegory for how outsourcing thinking to AI may cause cognitive atrophy.
  • Quote: “Each time I've not made that effort, that piece of my brain has become less utilized, has shrunk, and now I can't do it.” — Tom Slater [04:37]

• Reading, Writing, and the Brain ([05:14])

  • Refers to “The Weirdest People in the World” book: Literacy didn’t just enrich lives; it actually rewired brains, thickening connections between hemispheres.
  • Quote: “This learning skill of reading and writing... caused these changes in the brain. We repurpose this area of the brain that was used for recognizing faces… It’s just a really stark example of the way that you use technology… writing reshapes your physiology.” — Tom Slater [06:40]

3. The Mechanisms of Cultural Evolution

• Variation, Transmission, Selection ([07:40])

  • Cultural changes act like genetic changes: the spread of AI supercharges idea variation, transmission, and selection.
  • Centralization through a handful of AI systems could standardize (and narrow) how knowledge is spread.
  • Quote: "We've allowed technology to dominate those three key vectors of cultural evolution. Variation, transmission, selection." — Tom Slater [09:06]

• Control Over Ideas ([09:14])

  • Discusses why Elon Musk’s acquisition of Twitter—controlling a major transmission platform—has outsized impact in the context of shaping minds and cultural evolution.

4. The Passive vs. Active Use of AI

• MIT Study on Learning with AI ([10:02])

  • Participants writing essays with AI failed to retain knowledge ("over 80%... couldn't provide a single correct quote from the essay they'd just written").
  • Engaging AI as a research tool rather than a replacement helps retain learning.
  • Quote: “If you passively let the AI do it… you will start to mistake your ability to use AI tools with your mastery of the subject.”—Tom Slater [11:42]

• Dunning-Kruger Effect Amplified ([12:36])

  • Users think they're knowledgeable simply because they efficiently use AI, not because they've mastered the underlying subject.

5. The Workforce and Foundational Skill Loss

• Impact on Junior Roles ([13:05], [13:54])
  • If AI eliminates foundational tasks (e.g., intern/junior roles), future professionals may lack core competencies needed to evaluate AI output—a fragile workforce ensues.
  • Quote: “If you are experienced in a topic and have a great deal of knowledge, then these tools can massively enhance your productivity. But... you have to be able to interpret the output.” — Tom Slater [13:54]

6. Productivity Paradoxes & Lasting Effects

• One-Time Productivity Windfall ([17:38], [18:08])

  • Immediate productivity is boosted; underlying human capability erodes, undermining sustainability.
  • Quote: "AI reliably improves immediate task performance while degrading the underlying human capabilities that produce that performance.” — from Tom's essay, read by Merryn [17:39]

• The Confidence Trap in Medicine and Law ([18:39], [19:09])

  • Study of endoscopists: Prolonged use of AI led to a 21% drop in detection accuracy after AI was removed.
  • Even knowing AI is fallible didn't stop overtrust.
  • Legal parallels: Lawyers have relied on AI-generated (and sometimes fictitious) references.

7. What Should We Do? Individual and Institutional Responses

• Expert Deliberate Use vs. Passive Outsourcing ([21:41])

  • Positive examples exist: Go players learned novel strategies from AI, outperforming previous levels. Deliberate, interactive use builds capacity.
  • But “if you don't tightly control that, people will just let the machine do it and human capability will atrophy.” — Tom Slater [22:53]

• The Risk of Hollowing Out Professions ([22:53], [23:25])

  • Eliminating junior positions for cost savings hollows out future expertise.
  • Analogy: Pilots must learn to fly manually for emergencies, not just rely on autopilot.

• Advice for Young Entrants ([27:09])

  • “The people who will thrive are not those who use AI the most, but those who can still think without it.” — Tom Slater [27:10]
  • Don’t shortcut the slow, effortful learning process; master the fundamentals before using AI to “multiply” your skills.

• Corporate Responsibility and Training ([28:04])

  • Organizations must keep training pathways open, despite short-term cost incentives to automate early-career work.

8. Inequality and Societal Impacts

• Widening Inequality ([30:30])

  • “You get that within a single company, but then you start to get that across companies... and then it's between countries as well.” — Tom Slater [32:24]
  • Highly-paid AI engineers vs. mass layoffs; disparities extend to nations based on AI ownership, talent, and resource access.

• Educational Dilemmas ([32:48])

  • If university assessments move online (and AI-assisted), and students do not engage meaningfully, education risks losing value entirely.
  • Tom offers a note of cautious optimism: digital natives may yet evolve smarter, more constructive AI usage.

9. Final Thoughts & Takeaways

• “You have to learn how to think fundamentally to be employable. You have to be able to think for yourself and that's what you need to go to university to learn to do.” — Tom Slater [35:06]

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Memorable Quotes & Moments (with Timestamps)

• On Mind Rewiring:
  “A cultural technology can reach inside your skull and change the organ that makes you human.” — Tom Slater [06:58]

• On Over-Reliance:
  “AI reliably improves immediate task performance while degrading the underlying human capabilities that produce that performance.” — Merryn quoting Tom [17:39]

• On Individual Success:
  “The people who will thrive are not those who use AI the most, but those who can still think without it.” — Tom Slater [27:10]

• On Inequality:
  “Suddenly you’re paying people hundreds of millions of dollars and firing people at the same time. And you get that within a single company, but then you start to get that across companies... and then it's between countries as well.” — Tom Slater [32:24]

• On Education’s True Purpose:
  “You have to learn how to think fundamentally to be employable. You have to be able to think for yourself and that's what you need to go to university to learn to do.” — Tom Slater [35:06]

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Important Segment Timestamps

• [02:07] Introduction to Tom Slater and topic
• [03:49] The cognitive impact of technology (sat nav as analogy)
• [05:14] How reading and writing rewired brains (cultural technology example)
• [07:40] Mechanisms of cultural evolution (variation, transmission, selection)
• [10:02] MIT essay study: dangers of passive AI use in learning
• [13:54] Impacts of AI on foundational job skills and workforce structure
• [17:38] Productivity paradox—AI’s short-term gain, long-term atrophy
• [19:09] Evidence from medicine: loss of skill and trust in AI
• [21:41] Productive uses of AI: deliberate learning from AI
• [27:09] Advice for young people: build primary knowledge before using AI
• [32:24] Growing inequality within and between companies/countries
• [35:06] The ultimate employability metric: thinking for oneself

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Conclusion & Core Takeaway

The message threaded throughout the episode is clear and urgent:

“Keep learning to think for yourself.” — Tom Slater [35:14]

AI is not merely a threat to employment—it poses a deeper challenge to human cognition and societal structures. Whether as individuals or institutions, our survival and flourishing in an AI-enabled world depend on our willingness to engage critically, build foundational skills, and control—not cede—our intellectual destinies.

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Read Tom Slater’s essay for further insight (link in show notes as referenced).

Transcript

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Bloomberg Audio Studios Podcasts Radio News.

[Marion Somerset-Web]

Welcome to Marant Talks Money, the podcast in which people who know the markets explain the markets. I am Meron Somerset Web and this week I am speaking with Tom Slater of Baillie Gifford. He is an investment manager inside the Private Companies team. He's head of the the US equities team. But crucially, I think most of you will know him as the manager of the Scottish Mortgage Investment Trust, which I strongly suspect most listeners hold. And by the way, I also hold I invited Tom on specifically because I wanted to talk about an essay he's published recently called AI isn't coming for your job, it's coming for your mind. It's about how AI is reshaping the way we think at a fairly extraordinary speed and what that means for us, for workers in general, for Companies, and especially for society. It's a really interesting. We can put in a link, can't we, Tom, at the end, so people can read it for themselves as well?

[Tom Slater]

Oh, absolutely. Yep.

[Marion Somerset-Web]

Okay, great. Anyway, I wasn't supposed to say that. I'm supposed to say. Before I start talking to you, I'm supposed to say, tom, welcome to Marantalks Money.

[Tom Slater]

Thank you very much for having me. It's great to be here.

[Marion Somerset-Web]

I'm really interested in this. I can't tell you how much it resonated with me. Normally when we're talking about AI, we talk about the investment, we talk about, is there a data center bubble? We talk about how it's going to take everyone's jobs and we talk about. About how it will reshape the economy. And while there are occasional conversations about how it might actually reshape our brains and even our consciousness, that is not people's focus. And actually, the truth is, it's probably the most important thing of all. It's an awful lot more important than the valuations of AI companies and AI adjacent companies. Right. What's happening in your head? So let's talk about how you started thinking about that.

[Tom Slater]

I guess it's a topic that is at the top of people's minds at the moment. In one way or another, we're all being affected by this technology, thinking about what it's doing to society. And I guess from books that I'd read, I just started wondering about whether we were focusing on the wrong question, because this is very obvious. Are our jobs all going to be automated away by this technology? And we can get into that. But the more I thought about it, the more it seemed to me actually that wasn't the issue because it assumed sort of stasis. It assumed that humans would stay the same and this technology would get better and we would be displaced. But actually all the evidence suggests that it changes the way we think. So there isn't some sort of stable balance between us and us and the machines we adapt. So a simple example for me, you know, I lived in Edinburgh for 30 years and used to be the case that I could think, I want to go to this place and visualize where I was going in my head and I would set off. But just because I turn the sat nav on every time I go somewhere in the car, now it's got to the point where I actually can't do that anymore. And I haven't suddenly lost that skill, but it's neglect. And each time I've not made that effort that. That piece of my brain has become less utilized, has shrunk, and now I can't do it. And I think mind disuse of AI does the same thing.

[Marion Somerset-Web]

Tom, in the beginning of your paper, you talk about how our brains literally rewired when we learned to read, so you say, 200, 200 years ago. So only about 12% of the world's adults could read. Today, it's about 87%. And we all look at that and we go, well, this is all about education. And that's amazing because it leads to different lives, leads to democracy, et cetera, et cetera, all these things. But it wasn't really just about that. It was about our brains rewiring. And I'm really interested in this bit because I actually got some rows on social media about this recently about whether if you read very fast, which I do, that affects your ability to recognize faces, because it's the same part of the brain. And what you say here is that. Is that the connection between the hemispheres thickened. I'm not quite sure what that means. I'm going to leave you to explain that. But this intellectual shift led to a biological shift.

[Tom Slater]

Yeah, that's right. And I find this fascinating because I think, certainly I had thought about changing physiology as being a function of evolution. It happens across generations. But it was reading a book called the Weirdest People in the World that got me thinking about this.

[Marion Somerset-Web]

And we had the author of that on the podcast a while back.

[Tom Slater]

It's a fascinating book.

[Marion Somerset-Web]

Yeah, such a good book.

[Tom Slater]

But he talks about this way that sort of culture hijacks your physiology and changes the way you think.

[Marion Somerset-Web]

And.

[Tom Slater]

And in particular, this learning, this skill of reading and writing, it caused these changes in the brain. We repurpose this area of the brain that was used for recognizing faces. There's a bit of a trade off there. You do become less good at that. But in return, you get this incredible skill which is so useful. It's just a really stark example of the way that you use technology. In that case, writing reshapes your physiology, and so we shouldn't. It is absolutely possible for a cultural technology to reach inside your skull and change the organ that makes you human.

[Marion Somerset-Web]

Interesting, because we tend to think that, as you say, that's evolutionary and it should take thousands and thousands of years. There should be generations of tiny little bits of change until the result appears. But this can happen in no time at all. And reading spread quite slowly. Right. Not everybody learned to read in a year, but everyone who can Read now has access to AI immediately. So it's much faster. Right. And what is it doing?

[Tom Slater]

I think that's right. So I think what you can see is that you get this sort of. These cultural changes operate in the same way that genetic changes work. The three factors are variation, transmission, and selection. If you think about, you go back to the weirdest people in the world, the example there is the Catholic Church. And how that controlled variation and ideas, it controlled transmission the way ideas spread and controlled selection. Now, if you apply that to what's going on in the world today, AI, we can supercharge variation because the number of ideas that you can test, that you can generate, it grows exponentially. And ideas that humans just wouldn't have got to, it completely changes transmission of ideas. If a million children ask their parents, why is the sky blue? They'll get close to a million different answers. But because we're all started using two or three of these really dominant systems, everybody's getting some variation of the same answer. And yes, those systems are built on the whole corpus of human knowledge. But the centralization is incredible. You've changed the transmission mechanism for ideas. And then these AI algorithms that power media sites now completely control selection, which ideas get amplified, which quietly disappear. So we've allowed technology to dominate those three key vectors of cultural evolution. Variation, transmission, selection.

[Marion Somerset-Web]

Yeah. And you say as well that you now understand why Elon Musk bought Twitter.

[Tom Slater]

Yeah, it was quite. At the time, it was challenging to understand the economic rationale. Now we can debate whether that has changed subsequently. But when you think of it in terms of the impact that it has on cultural evolution and shaping ideas, you can see why that would be a valuable asset to somebody.

[Marion Somerset-Web]

Interesting. Okay, so it sounds, when you put it like that, it sounds as though civilization itself will be transformed by these three things.

[Tom Slater]

I think that it does bring change. And the question to my mind is, are we going to passively and unthinkingly let this change happen, or do you have a debate about how these tools are used, where they used? Do we passively accept our fate with this, or do we proactively seek to control it?

[Marion Somerset-Web]

Okay, well, let's look at what happens when we allow it to happen passively. Right. So one of the things you look at is this MIT study that looked at brains of participants while they wrote essays. So some of them wrote essays with absolutely no assistance at all. Some were able to use search engines, and some were able to use AI assistance. And that was an experiment that showed us what will happen if we don't bring some focus into this conversation.

[Tom Slater]

Yeah, absolutely. So what you saw was that if you looked at what happened to people if they were given AI, they would essentially outsource the writing process. And the result was that about over 80% of that group that used AI to write the essay couldn't provide a single correct quote from the essay they had just written a few minutes earlier. So the effort that goes into the durable learning that writing an essay had just been bypassed entirely. And it's. And that was completely different. In the group that you searched, the group that wrote themselves, if you just employ these tools to do something you would have done otherwise done yourself, you will gradually lose the capability to do that. You will never develop the skill to do that. But if you engage positively with these systems, you don't, you have the essay to write. You don't just say to the AI, write the essay, but you go and research the question using AI as a tool. I want to understand why this happened. I don't understand the relationship between factor A and factor B. You actually interact with it and think about the output and engage with it. Then you get the same results cognitively as if you'd just handwritten the essay. So it's that there's something about that effortful interaction that, that seeking to gain knowledge that is, is the crucial part in actually developing the cognitive skills. And if you passively let the AI do it, then it's not simply as bad as you never learn it, but actually you will start to mistake your ability to use AI tools with your mastery of the subject that you are trying. And so you will gain no knowledge or little knowledge and skill in the subject that you're focusing on. You'll just get better at using the AI, but you will confuse that with thinking that you're good at the actual topic.

[Marion Somerset-Web]

Okay, so as you would put it, you've made a trade, but you haven't recognized that you've made that trade. You've traded efficiency, saving time, etc. And the result has been that your brain has become weaker, actively weaker. It's worse than it was.

[Tom Slater]

Yeah, that's right. It's because you, I think it's called the Dunning Kruger effect. This, sort of this, that as you put more effort into something, if you are, you don't know what you don't know effectively. And as you put more and more effort into it, you become more aware of how little you know of the topic, but you completely break that effect, that understanding of the limitations of your own knowledge when you start using these tools, and that's the real weakness, the real interruption of the learning process, and

[Marion Somerset-Web]

you end up without that base knowledge for yourself. And so that brings us to the world of work or and in fact the entire world, right? There's been so much talk about AI removing the bottom level of jobs. So AI can do the simple stuff. It can do anything an intern could do, it could do anything a junior could do. You can argue about whether it can and can't and the extent to which it makes mistakes. And we've done podcasts on that, of course. But for the moment, let's just take it as red that it is possible for AI to remove these lower levels of jobs. That then turns into a real problem. Because if the next level up, if the job market starts at the next level up, that base knowledge is never embedded and created. You have to judge whether what your agent has told you is correct or not. But you no longer have the foundational skills to be able to do that.

[Tom Slater]

Yeah, absolutely. So I think one way of thinking about this technology is that it's not a tide that lift all boats. In some ways you can think of it as a force multiplier. And what I mean by that is if you are experienced in a topic and have a great deal of knowledge, then these tools can massively enhance your productivity. But a key part of it, as you highlight, is you have to be able to understand, interpret the output of these systems. And if you're a scientist who's never struggled through a statistical analysis manually, then you're probably not going to spot the results that are conceptually meaningless that are being spat out by a machine. It's that prior mastery which allows you to genuinely evaluate what's coming out of these systems rather than just giving it a superficial check. And so for those that have not gained the skills prior to using these tools, you don't have the ability to do that. There's a completely different power balance between the AI system and the human.

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[Marion Somerset-Web]

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[Marion Somerset-Web]

So a quote from your paper the central paradox is this AI reliably improves immediate task performance while degrading the underlying human capabilities that produce that performance. You get better results today but become less capable of tomorrow. And you talk about that as well in terms of productivity and that we get a productivity revolution up front while undermining the foundations of that productivity enhancement. So it can't continue indefinitely unless it's used in a different way.

[Tom Slater]

Yeah, that's it. So you get this one time benefit if you like, that you have a workforce who's been through the effortful struggle to gain capability and knowledge and mastery and then can use these tools. But unless you invest in the next generation and ensure that they have those skills. They don't simply use these tools or they don't simply oversee these tools, then they can never have that same relationship with them and you don't get the same productivity benefit.

[Marion Somerset-Web]

And then even worse, you get this confident trap that you talk about as well. I was really struck for which Reid absolutely horrified by a study you talk about from last year, year in the Lancet, which tracked endoscopists. How do you say that word?

[Tom Slater]

Endoscopists?

[Marion Somerset-Web]

Endoscopists. Words I can't even say, look, here we are, I'm destroyed by AI already. And anyway, they went through periods of working with AI when they hadn't been working with AI, and the results were appalling.

[Tom Slater]

Yeah, that's right. So you saw, you had these experienced practitioners using AI systems, and then after you took the AI systems away, there was one key indicator, for example, where the detection rate fell 21% with the same group before and after they'd had access to these AI tools. But then there was this, and I think this was probably the more worrying result was that people tend to trust the output from the AI. And so when the experimenters went in and tweaked the AI to give confident but incorrect interpretations, what you saw was that human performance collapsed. So humans were much more likely to accept that the answer that they were being given was right, even though it

[Marion Somerset-Web]

was not even when they were experienced

[Tom Slater]

and should know even when they were experienced. Yes. And AI literacy was not sufficient to protect against that. So knowing that the tool is fallible did not prevent over reliance.

[Marion Somerset-Web]

I've seen. We've seen this happen. These studies in the medical industry are really interesting, but we've seen this happen in real life in the legal industry, haven't we? We've seen lawyers, actually on several occasions. In fact, we've had examples of this in Scotland, where I live, seen lawyers actually relying on AI and producing false stuff, false quotes, false cases, false precedents.

[Tom Slater]

Yeah. And it's back to this. You. If you get good results from the system, then you fall into this pattern of not questioning the output. And that's true of experienced professionals as. And it's. And it's a problem that only gets worse if people don't. Don't get that sort of underlying experience and understanding before they start using the tools.

[Marion Somerset-Web]

Yeah. So we could end up in a position where most people do not have the foundational knowledge required to spot errors or to really see what is right and what is wrong. And even people with Solid foundational knowledge can still fall into these traps where they don't use their foundational knowledge to spot mistakes.

[Tom Slater]

Yep. And that's. And this is this back to this sort of erosion of human skill or changing the way that there just isn't this stable balance between us and the machines is as you use these tools, as they become more and more effective, your relationship with them changes. And there is, we have this tendency to, to when they confidently say things about the world to accept it as true. We give them a false inappropriate amount of respect.

[Marion Somerset-Web]

So what do we do?

[Tom Slater]

I think there's a couple of things. There is some evidence that in sort of expert domains that if you use these tools deliberately, they can make you better. One example of that was in the game Go, where AI famously beat a human player. But what you've seen subsequent to that sort of landmark event was that human players have learned from the AI. It's developed novel strategies, novel ways of going about the game, and you've seen an improvement in human performance. And do you see that result in sort of expert fields where these tools are used in a very deliberate way to be complementary to human behavior? And so I think the key to it is do you actually deliberately and thoughtfully engage with these systems or do you just allow them to take over tasks that people are doing? And all the evidence suggests that if you don't really tightly control that, people will just let the machine do it and human capability will atrophy.

[Marion Somerset-Web]

Yeah. And the worry is that we are seeing that and the big conversation of course at the moment is around employment and about the disappearance of junior jobs, which we talked about just now. But that's not theoretical, that's happening. And you write about that in this paper as well, about hollowing out of white collar professions. So it looks that we are already taking the wrong path here and that we should have just as many junior lawyers, accountants, doctors, customer services people, whatever it is, we should have as many as we had before for the knowledge building.

[Tom Slater]

I think that's the real challenge. Right. Because people look at this technology and say it can allow me to reduce costs and I don't have to hire junior people, I don't have to train them. I can use these systems and there's real pressure to do that. But I just think that is a very dangerous path to go down. I think there's real value in having humans learning to do these tasks, whichever white collar profession, and having them do it without access to these systems. And so that you then retain that ability to oversee override these systems when they're not producing the right answers, but you have to invest in the human capital first. So I guess the analogy would be with pilots. Pilots still have to learn to fly manually before they. They can learn to use autopilot, because if the autopilot at some point fails, they need to be there to. To land the plane.

[Marion Somerset-Web]

This is why everyone has a bias towards older pilots. Right. I don't know about you, but when I got on an airplane and I see that the pilots are maybe over 45 or better still over 50, I'm thrilled, absolutely thrilled, because I know that it means that they've got a strong base of knowledge about how to actually fly a plane, because they probably had to do it for a good while without autopilot before now.

[Tom Slater]

Absolutely. And it applies. And an accountant who's prepared hundreds of tax returns by hand can spot the error in an AI generated filing. The doctor who's made the diagnoses without AI can override the confident but wrong prediction in the right circumstances with the right support. And so I think what that says to me is that professional bodies, universities, employers, they need to preserve those training pathways to building expertise, even when the AI makes that look slow and inefficient. Because you're not going to be able to orchestrate the AI. Yeah.

[Marion Somerset-Web]

Tough sell to people trying to cut costs.

[Tom Slater]

Yes.

[Marion Somerset-Web]

And a tough sell in the world where let's say you're the company who says I have to preserve this and I'm going to keep hiring and we're going to learn this properly and then we'll use AI later and your competitors are saying so that we're going to fire everybody around down at the bottom, or certainly not higher anymore and our cost be lower and our margins will be higher. And as you say, that's going to be a bit like a sugar rush. Right. That'll work for a certain period and then it won't work anymore. But that transition period of learning how we work with AI is going to be tricky for a lot of people and a lot of companies.

[Tom Slater]

I think so. And the challenge with it comes that it's happening so fast. In previous transitions, you've had time to work through these issues. And the challenge here is that we don't have a lot of time because this is coming at us so quickly. But I do think as the way the Scottish mortgage investors over really long time periods and there isn't a great deal of value in something that benefits the next handful of years at the expense of all future years. And so I think what you have a question of time horizon here is yes, you can save money in the short run, but if it's going to have a real long term cost to your ability to operate your business, then that's a false economy.

[Marion Somerset-Web]

Okay, so let's think about what we can do both as individuals and as companies. If you are a young person entering the workforce today, what do you need to do? What are the skills you need to acquire and how do you need to look at your employment? I know there is one path to take which is simply to say I'm not getting involved in this and I'm going into, I'm going into a craft, I'm going into roofing or stone carving or we have a huge shortage by the way of craftsmen in the uk. So this is a fantastic thing if people are decide to make their choice in that direction as well. But nonetheless that's not going to absorb, that's not going to absorb a million kids a year. What else, how do you look at your path as a young person?

[Tom Slater]

I guess the way I frame it is the people who will thrive are not those who use AI the most, but those who can still think without it. And the message is don't take the easy path, don't take the shortcuts to productivity that these tools give you. It's putting in the hard yards, it's doing the things that you don't want to do. That effortful struggle and generate, build investing in yourself and your own capabilities that will be most important over the long run. And it's master a task first before you start utilizing these tools to make yourself more productive at it and at the company level.

[Marion Somerset-Web]

So that's individuals. But what does a company need to do? I think we've slightly come up with the answer to that. They need to keep paying up to make sure that their employees have a base of knowledge and skill beyond that.

[Tom Slater]

Well, I do think it's really hard because you have this constant battle for survival in the corporate world. You have to be cost effective. But it seems to me very short sighted if you allow the current generation of experienced professionals to get a huge productivity bump from these tools. But you don't think about how you replace them. You know how the next set of the next generation of professionals will actually operate because we can all see the flaws in these tools, a deep understanding of how they work and ability to. There's a whole set of cognitive skills which will be necessary for the next generation, which weren't necessary for our generation. The Ability to orchestrate these tools, the ability to break down tasks in such a way that you can use these tools to answer the sub questions and recompose that information. There's a whole set of different cognitive abilities which are necessary to use these tools effectively. But it's. Don't neglect the basics, the fundamental understanding of the job that you're trying to do and the ability to solve problems and investing in people with the capability to do that, because otherwise it's a false productivity game.

[Marion Somerset-Web]

And what about investment implications?

[Tom Slater]

Honestly, I don't really think this. There is a particular investment implication from this. I went into it because it was something I was interested in. I was interested in how it was affecting me. I was thinking about what it means for my kids. I think that this sort of narrower investment standpoint, it is this really tricky balance between driving productivity and making sure you have the capabilities for the future.

[Marion Somerset-Web]

How nervous does it make you? Because I think people listening to this conversation will come out of the other end thinking to themselves that it's almost a given that the majority of people will use AI passively in the dangerous way that you have suggested that this will be the default. This is what most people will do. And there will be a small group of people who will not and who will engage with it in the other way that you suggested, using it as an assistance, a learning tool in which, with which they engage but still think for themselves. So it rather sounds as though that leads us into a world of very unequal outcomes, even more so than the world we live in now.

[Tom Slater]

Yes, I completely agree with you. And that's come back to the title of the paper, AI isn't coming for your job, it's coming for your mind. If you passively accept that outcome, then the question is, what job are you suitable for in this world where there's some set of activities? And I think a useful model for this is don't think of AI replacing jobs, think of AI replacing tasks. And a job is composed of multiple tasks. Now, AI will replace humans doing some of those tasks. That is inevitable. But it's like, what's your worth as a human worker? It depends what you've let AI do to your mind. But I think that to your inequality point, if you take the example of Meta, the owners of Facebook, it was last summer that they were going, they went through this hiring spree, their super intelligence, their AI systems unit. And the rumors were that they were paying individuals hundreds of millions of dollars to come and do this. And the rationale was that you're spending billions of dollars on these systems. What you want is a small group of engineers who can really keep that whole system in their head. And even if you're paying them hundreds of millions of dollars, that sort of is dwarfed by the amount that you're spending on the hardware within that company. You suddenly get this big discrepancy, this big inequality between those small number of individuals and the engineers across the rest of the firm. But what you also see is that in other parts of the business, you are reducing the number of people because you're getting efficiency from using these tools. And so you're laying off people. And you go from this world where there was greater equality in the workforce and what people getting paid to. Suddenly you're paying people hundreds of millions of dollars and firing people at the same time. And you get that within a single company, but then you start to get that across companies, which are the ones that have been able to adopt these tools and embrace them and win competitively. And so you get the inequality between companies, and then it's between countries as well, because whose AI are we using? In the uk, we're mostly using AI systems from the US and from China. And cheap power then becomes another really important factor in that, and we have very expensive power. So you can see these fault lines of inequality widening rapidly.

[Marion Somerset-Web]

Yeah, yeah. Final thought. Kids going to university these days, we talk about this a lot on the podcast because of the extraordinary cost of student loans and the handicap that gives you once you leave, effectively paying 9 percentage points of extra income tax every year, et cetera. And. And the question now is, are you learning anything valuable at university? And I hear more and more from students that their exams are at home, open book, 24 hours, et cetera, and a lot of them will then use AI for that, I'm sure, which sounds like it makes the entire university experience not just pointless, but worse than pointless. So it's only worth going at all if you engage with your learning in the way that we've just discussed. So for an awful lot of people, this may be a pointless expense.

[Tom Slater]

I think that's a real risk. I'd say my point of optimism on this is, I think if you look back through some of these previous technological waves, if you take social media again, for our generation, we were hopeless users of those systems initially, and people made all sorts of faux pas, et cetera. Then the next generation who'd grown up with those tools were very, very smart at how to use them and didn't encounter the same sort of set of issues. They were native, they were digitally native. And I observe similar pattern with AI technology in my own children. I used to worry about are they doing exactly what you said, are they taking their schoolwork and just outsourcing it? But I actually see them using these tools to. Not because I'm leaning on them the whole time, but I'm just watching what they do and they don't outsource the tasks. They're actually, these are fantastic tools for learning if you use them appropriately. And so, you know, it's back to that sort of personal responsibility point. Yes. You can go and sky your way through university and outsource all the learning and end up with lots of tasks.

[Marion Somerset-Web]

You don't come out with your worthless two one.

[Tom Slater]

Yeah. Yes, exactly. But you can have a very different experience and you want to have a much better experience because effectively you have an always on infinitely patient tutor who can answer your every question sometimes incorrectly. But I do think that, you know, you have to learn how to think fundamentally to be employable. You have to be able to think for yourself and that's what you need to go to university to learn to do.

[Marion Somerset-Web]

Okay, there we go. There's your takeaway, everyone. Keep learning to think for yourself. Tom, thank you so much. That was absolutely, absolutely fascinating. And just to repeat to everybody, we will put the link in the show notes so you can go and read this for yourself. It's really interesting. Tom, thank you.

[Tom Slater]

Thanks Marin for having me.

[Marion Somerset-Web]

Thanks for listening to this week's Marin Talks Money. If you like our show, rate, review and subscribe wherever you listen to podcasts and keep sending questions or comments to marinmoneyloomburg.net you can also follow me and John on X or Twitter. I'm at marionsw and John is johnstepic. This episode was hosted by me, Marisam Zepp. Web was produced by Sama Saadi and Moses Andam Sound designed by Blake Maples and Aaron Casper. A special thanks of course, to Tom Slater.

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