Transcription
There isn't a great deal of value in something that benefits the next handful of years at the expense of all future years. Um, and so I think what, you have a question of time horizon here? Yeah. Do you? Yes. You can you can save money in the short run. But if it's going to have a real long term cost to your ability to, um, operate your business, then that that's a false economy.
Welcome to Marin Talks Money, the podcast in which people who know the markets explain the markets. I am Merryn Somerset Webb, 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 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 crucially, for society. I mean, it's a really interesting we can put in a link complete, can't we Tom, at the end and people can read it for themselves as well. Oh, absolutely. Yeah. Okay, great. I know that wasn't supposed to say that. I'm supposed to say before I start talking to. I'm supposed to say, Tom, welcome to Merryn Talks Money. Thank you very much for having me. It's great to be here.
Well, I'm really interested in this. I can't tell you how much it resonated with me. I mean, normally when we're talking about AI, we talk about what we talk about the investment. We talk about. Is there a data center bubble? Uh, we talk about how it's going to take everyone's jobs. When you talk 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. Well I guess it's a topic that um, is is at the top of people's minds at the moment. Um, you know, in one way or another, we're all being affected by this technology, thinking about what it what is, what it's doing to society. And, um, I guess from books that I'd read, I just started wondering about whether we were focusing on the wrong question. Um, because, you know, this is very obvious, you know, is, are our jobs all going to be automated, automated away by this technology? Um, and, you know, we can be we can get into that. But the more I thought about it, the more it seemed to me, actually, that wasn't the issue. Um, it because it it assumes sort of stasis that assumes that, um, you know, humans would stay the same and this technology would get better and we would be displaced. But actually and all the evidence suggests that, ah, it it changes the way we think. So if there isn't some sort of stable balance between us and us, as in the machines we adopt. So the simple example, um, for me, you know, I used to be able to I've lived in Edinburgh for 30 years, and, you know, I used to be the case that I could think, right, I wanted to go to this place, and I, and I visualize where I was going in my head and I'd set off. Um, but just because I turn the satnav on every time I go somewhere in the car now, and it's got to the point where I actually can't do that anymore, and it's I haven't suddenly lost that skill, but it's neglect and and it's sort of a 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 mindless use of AI does the same thing.
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 around 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, this democracy, etc., etc. 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 rouse 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, this intellectual shift led to a biological shift. Yeah. That's right. And I think I find this fascinating because I think, you know, certainly, I had had thought about, you know, changing physiology as a being, a function of evolution. You know, it happens across generations. Um, but I was reading a book called The Weirdest People in the world that got me thinking about this. And we had the author of that on the podcast a while back. And it's a fascinating book. It's a good book. Um, but, you know, he talks about this way, that sort of culture hijacks your physiology and changes changes the way you think. And, um, in particular, this learning the skill of, of, um, reading and writing. You know, it it caused these changes in the brain. Um, you know, we we repurposed this area of the brain that was, was used for recognizing faces. And there's a bit of a trade off there. You know, you do become less good at that, but, you know, in, in, in return, you get this incredible skill, which is which is so useful. Um, but it's just a really stark example of, you know, the way that you use technology. In that case, writing reshapes your physiology. Um, and, and so we shouldn't, you know, it is absolutely possible for a cultural technology to reach inside your skull and change the the organ that makes you human.
It's interesting because we think that we tend to think that that is you say that's evolutionary and it should take thousands and thousands of years. They should be generations of tiny, tiny little bits of change until the result appears. But this can happen in no time at all. And in a reading spread quite slowly. Right. Not everybody learning to read in a year, but to everyone who can read now has access to AI immediately. So it's much faster, right? Yeah. Yes, I think that's right. So so I think what you can what what you can see is that, um, you get these this sort of, um, these cultural changes operate in the same way, Um, that, um. Um, genetic changes work. So. And the three factors of variation, transmission and selection. Um, so, you know, if you think about, um, if you go back to the weirdest people in the world, the example there is the Catholic Church and how, you know, that controlled variation and idea, is it control transmission, the way ideas spread and it controlled selection. Um, now, if you if you if you apply that to what's going on with in the world today, um, you know, AI, um, we can supercharge variation because, you know, the number of ideas that you can test, that you can generate, you know, grows exponentially and ideas that humans just wouldn't have got to. Um, it completely changes transmission of ideas. Um, so, you know, if if a million children, um, ask their parents, why is the sky blue? They'll get close to a million different answers. Um, but because we're all start, it started using 2 or 3 of these really dominant systems. Now everybody's getting some variation of the same answer. And yes, those systems are built on, you know, the whole corpus of human knowledge. Um, but the centralization is is incredible. You you change the transmission mechanism for ideas. Um, and then, you know, these, these AI algorithms that power media sites now completely control selection. You know, which ideas get amplified? Um, which which quietly disappear. And so we've allowed technology to, to, to dominate those three key vectors of cultural evolution, variation, transmission selection. Yeah.
And you say as well that you now understand why Elon Musk bought Twitter. At the time, it was challenging to understand the economic rationale. Um, now that you we can debate whether that has changed subsequently. Um, but but when you think of it in terms of the impact that it has on the cultural evolution and shaping ideas. You can see why that would be a valuable asset to somebody. Hmm. Interesting. Okay, so it sounds when you put it like that, it sounds as though, uh, civilization itself will be transformed by these three things. Well, I think that, um, 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, you know, how these tools are used, where they used, um, you know, do do we passively accept our fate with this, or do we even proactively seek to control it?
Okay, well, let's look at what happens when we allow it to happen passively. Right. Um, so one of the things you 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 assistants at all. Some are able to use search engines and some are 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. Yeah, absolutely. So what you saw was that, um, if you looked at, um, what happened to the people, um, if, if, if they were given I, they would essentially outsource the writing process. And the result was that about a over 80% of of the 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. So the effort that goes into the durable learning that writing an essay had just been bypassed entirely. Um, and and it's and that was completely different in the group that you search the, the group that that wrote themselves. So know if, if you just employ these tools to do something you would have done otherwise done yourself, and you would gradually lose the capability to do that. You will never develop the skill to do that. And but if you engage, um, positively with these systems so 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. You know, I want to understand why this happened. I don't understand the relationship between, you know, factory, in fact, to be you, you actually interact with it and think about the output and engage with it. Then you get the same results, you know, cognitively as if you if you just handwritten the essay. So it's that there's something about that effortful interaction that, that um, um, that seeking to gain knowledge that is, is the crucial part in actually developing the cognitive skills. And if you if you passively let the AI do it, then it's not simply as bad as you never learn it. Um, but actually you you will start to mistake, um, your ability to use eye tools with your mastery of the subject that you are. Try and and so you will gain no, um, um, knowledge or little knowledge and skill in the, in the subject that you're focusing on. You would just get better at using AI, but you will confuse that with thinking that you're good at the actual topic.
Okay. So as you would put it, you've made it. You've made a trade, but you haven't recognized that. You've made that trade, you've traded, um, efficiency, saving time, etc.. Um, the result has been that your brain has become weaker, actively weaker. It's worse than it was. Yeah. That's that's right. It's it's because you you I think it's called the Dunning-Kruger effect. This sort of, um, this, you know, as, as you put more effort into something, you know, if you, if you, um, um, you, 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 when you start using these tools and that's that's the real weakness, the real interruption of the learning process. Mhm. Mhm. And you end up without that base knowledge for yourself. And so that brings us to the world of work and the entire world. Right. Um there's been so much talk about AI removing the bottom level of jobs so I can do the simple stuff. I can do anything an intern could do. It could do anything a junior could do. I mean, you can argue about whether it can and can't an extent which makes mistakes. And we've done podcasts on that, of course, but for the moment, let's just take it as read that it is possible on the AI to remove these lower levels of jobs. That then turns into a real problem, because if the next level up, um, 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. Yep, yep. Absolutely right. So I think in one way of, of thinking about this technology is that it's not it's tied that lifts all boats. In some ways you can think of it as as a force multiplier. And what I mean by that is, you know, if you are experienced in a topic and have have a great deal of knowledge, then these tools can massively enhance your productivity. Um, but, you know, a key part of it, as you highlight, is you have to be able to understand, interpret the output of, of these systems. And if you know, if you were a scientist who's never struggled through a statistical analysis manually, then you're probably not going to spot the results, um, that are conceptually meaningless, that that are being spat out by a machine. It's that prior mastery, um, which allows that, you know, 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 have not gained the skills prior to using these tools, you don't have the ability to do that. You have a there's a completely different power balance between the AI system and the human. Mhm. Mhm.
So a quote from 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 tomorrow. And you talk about that as well in terms of productivity. And 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. Yeah. That's that's it. So so you get this 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 can, 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. And then even worse, you get the this confident trap that you talk about as well. I mean, I was really struck for which read absolutely horrified by a study you talk about from last year in The Lancet, which tracked, um, and endoscopes. How do you say that word? Endoscopy. Yeah, endoscopes. Endoscopes. I can't even say, look, here we are. I'm. I'm destroyed by AI already. And, um, anyway, they, um, went through periods of working with AI when they hadn't been working with AI and the results were appalling. Yeah. That's right. So you saw, um. If you had these experienced practitioners, um, using AI systems, um, and then after you took their AI systems away, um, there was one key indicator, um, for example, where the detection rate fell 21% for the same group before and after they, they'd had access to these AI tools. Um, but then there was this, and I think this is probably the more worrying result was that, um, people tend to trust the output from the AI. Um, and so when, when the experimenters went in and um, um, 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 was not, even when they were experienced and should know even when they were experience. Yes. Yeah. Well that's and AI literacy was not um was not sufficient to protect against that. So knowing that the two is fallible did not prevent overreliance. Yeah. And we've seen we've seen this happened in these studies in the in the medical industry are really intrigued. 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 I am producing false stuff, false quotes, false cases, false precedent. Yeah. And it's back to this. You know, you if you get good results from the system, um, then you fall into this pattern of, you know, not questioning the output. Um, and, you know, that's, that's true of experienced professionals as, as it is and it's and, and is 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. 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 didn't use their found foundational knowledge to spot mistakes. Yeah. And that's and this is, you know, this back to this sort of erosion of human scale or changing, changing the way. Um, that there just isn't this stable balance between us and the machines is, you know, as you as you use these tools as, as they become more and more effective, um, your relationship with them changes. And, you know, there is we have this tendency to, to in it when they confidently say things about the world to accept it is true. We give them a false, um, and, um, an inappropriate amount of respect.
So what do we do? Well, I and I think that they, um. I think there's, there's a couple of things. So the, there is some evidence that in, in sort of expert domains, um, that, you know, if you use these tools deliberately, they, they can make you better. So, you know, one one example of that was in the, in the game go, where AI famously beat a human player. Um, but what you've seen and subsequent to that, um, that sort of landmark event was that human players have learned from the AI, they it's developed novel strategies, novel ways of going about the game. And you've seen an improvement in human performance. And you do see that result in sort of expert fields where, you know, these these tools are used in a very deliberate way to be complimentary to the human behavior. And so it's you know, I think the key to it is do you, um, do you actually deliberately and thoughtfully engage with these, with these, with these systems or do you, you know, do you, 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 and human capability will atrophy. Yeah. And the worry is that we are seeing that. I mean, 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 that's happening. And you write about that in this, in this paper as well about, um, the hollowing out of white collar professions. So it looks like we are already taking the wrong path here and that we, we, you know, we should have just as many junior, junior lawyers, accountants, doctors, customer services, people, whatever it is, we should have as many as we have before for the knowledge building. Well, I think I think that's the real challenge, right. Because people look at this technology and say it can allow me to reduce costs. And, you know, I, I can, I can, you know, um, I don't have to hire junior people. I don't have to train them. I can use these systems, but and and there's real pressure to do that. But I, I just think that is a very dangerous path to go down, you know? So, um, I, I think there's real value in having humans learning to do these, these tasks, you know, which have white collar profession, you know, and having them do it without access to these systems. Um, and so that you, you, you then retain that ability to, you know, oversee, override these systems when, when they're not producing the right answers. Um, but you have to invest in the human capital first. Um, so I guess the, the analogy would be with pilots. Um, you know, 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. And I mean, this is why everyone has a bias towards older pilots, right? I mean, I don't know about you, but when I got on an aeroplane airplane and I see that the pilots are kind of 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, um, without autopilot before, uh, before now. Absolutely. And it applies. And, you know, an accountant who's prepared hundreds of tax returns by hand can spot the error in an AI generated filing. Um, the the AI, the doctors who's made the diagnoses without AI can can override the confident but wrong prediction. Um, with in the right circumstances, with the right support. Um, and so I think know what that says to me is that, you know, you professional bodies, universities, employers, they need to preserve those training pathways to, to building expertise, even when the AI makes that look slow and inefficient, because, you know, that's going to be the straight the AI. Yeah, yeah. So salespeople trying to cut costs and a tough sell in the world where you're 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 the competitors are saying, so that we're going to fire everybody around down at the bottom, or certainly not hire any more. And I'll 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 in a lot of companies, I think so. And the and the challenge with it comes that it's happening so fast. You know, in previous transitions, you've had time to work through these issues. And, and and the challenge here is that we don't have a lot of time because this is coming at us so quickly. Um, but it's, you know, I, I do think, you know, as, as, you know, the way that Scottish Mortgage invests over really long time periods and, you know, it's, you know, there is 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, and so I think what you have a question of time horizon here is you. Yeah. Do you. Yes. You can you can save money in the short run. But if it's going to have a real long term cost to your ability to, um, operate your business, then that that's a false economy.
Okay. So let's 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 mean, I, 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, you know, we have a huge shortage by the way, of craftsmen in the UK. So this is a 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. Um, what else? How do you look at your your path as a young person? Well, I, I guess the way I frame it is if the, the people who will thrive and not those who use AI the most, but those who can still think without it. Um, and so I guess, you know, the message is don't take the easy path. Don't, um, you know, take the shortcuts to productivity that these tools give you. It's it's putting in the hard yards. It's doing the things that you don't want to do that effortful struggle, um, and generate, you know, um, build investing in yourself and your own capabilities. Um, that will be most important over the long run. Um, and at the company level. So that's, that's individuals. But what does a company need to do? I think we we've 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. But I do think it's it's really hard because, you know, you have this, um, this constant battle for survival in the corporate world. You have to be cost effective. But if you, you know, if it it seems to me very shortsighted if you if you allow the current generation of experienced professionals and you get a huge productivity bump from these tools and but you don't think about how how you replace them. You know, how how the next set of, um, you know, the next the next generation of professionals will actually operate. Um, because because we all we can all see the flaws. And in these tools, you know, a deep understanding of how they work and ability to, you know, their own set of, of cognitive skills which will be necessary for the next generation, which which weren't necessary for our generation, you know, the the ability to orchestrate these tools, the ability to break down tasks and, and in such a way that you can use these tools to answer the sub questions and, and recompose that. And for me, you know this, there's a whole set of different cognitive abilities which are necessary to use these tools effectively. But it's don't neglect the 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 because otherwise it's it's a false productivity gain.
And what about investment implications? Honestly I don't. I don't really think this that there is a particular investment implication from this. You know, I went into it because it's something I was interested in and I was interested in how it was affecting me. I was thinking about, you know, what it means for my kids. Um, I think that the, the sort of narrower investment standpoint, um, you know, it is this is this really tricky balance between, um, driving productivity and, uh, making sure you have the capabilities for the future. Um, I, you know, and I, I wouldn't extend it beyond that because I, you know, in a way, it's the it's the inverse. It's the, the, you know, the gains that people are making today are coming at the expense of, of people. So that's you know, it. It's not something that, you know, from a, from just my cold investment, a hard nosed sort of view of the world, the, you know, you can really take into account. Yeah. Yeah. Um, how nervous does it make you? Because when? Well, if I think people listening to this conversation will come out of the other end thinking 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 and there will be a small group of people who will not and who will engage with it. And the other way that you suggested using it as a, as a, uh, 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, very unequal outcomes, even more so than the world we live in now. Yeah. Um, yes, I, I completely agree with you. So and that's come back to the title of the paper. AI isn't coming for your job. It's coming for your mind. Uh, if if you if you passively accept that outcome, then you know, the question is what what what job are you suitable for in this world where, you know, there's there's some set of activities and I think I'm a useful model for this is don't thinking, don't think of AI replacing jobs, think of I replacing tasks. You know. And a job is composed of of multiple tasks. Um, now it's, it's if I will replace will replace humans doing some of those tasks so that that is inevitable. Um, but it's like, what, what's your worth as a human worker? Well, it's sort of depends what you've what you've let AI do do your mind and, but, but I think that the to your inequality point, um, you know I, I do take the example of, of Meta the, the owners of Facebook, you know, the it was, it was last summer that they were going, you know, they, they went through this hiring spree, um, uh, for, for their, their superintelligence, their AI systems unit. And the rumors were that they were paying, you know, individuals hundreds of millions of dollars to come and do this. And the rationale was that, you know, you're spending billions of dollars on these systems. Um, you 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, it that sort of is dwarfed by the amount that you're spending on the hardware. So, you know, within that, companies suddenly get this, um, you know, this, this, um, big discrepancy, this big inequality between those small number of individuals and, and, you know, the engineers across the rest of the firm. But but what you what what you also see is that, you know, in, in other parts of the business, you're reducing the number of people because you're getting efficiency from using these tools. Um, and so you're laying off people. And so, you know, you go from this world where there was there was greater equality in the workforce than what people were getting paid to. Suddenly you're paying people hundreds of millions of dollars and and firing people at the same time. Um, and you get that within a single company, but then you start to get that across companies, you know, which are the ones that have been able to adopt these tools and, and, um, embrace them and, and win competitively. And, and so you get the inequality between these two companies. And then, you know, it's in between countries as well because you know, you know who's who's AI we're using, you know, in, you know, in the UK where we mostly using AI systems from the US and, and, and from uh, from China and, and and cheap power then becomes another really important factor in that. And we have very expensive power. So you can you can see these, these full lines of inequality and widening rapidly. Yeah, yeah. Um, final thought. Um, 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 it gives you once you leave for actively paying nine percentage points of extra income tax every year, etc. and the question now is, are you learning anything valuable at university? And I hear more and more from students at their exams or at home, open book, 24 hours, etc. and a lot of them will then use I for that, I'm sure. At which it 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. I think that's a real risk. Um, I'd say the my my point of optimism on this is, you know, I think if you look back to some of these previous technological waves, you know, if you take social media, you know, if again, from, from, for, for our generation, you know, we were hopeless users of those systems initially and people made all sorts of faux pas, etc. but then the next generation who had grown up with those tools, you know, 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, you know, I, I observe, um, similar pattern with I in my own children is that I used to worry about are they 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 not because I'm leaning on them the whole time, but I'm just watching what they do, and and they don't sort of pass it. And AI outsource the task. They're actually, you know, these are fantastic tools for learning if you use them appropriately. Um, and so, you know, it's it's back to that sort of personal responsibility point. Yes. You can go and, you know, um, skive your way through university and, and outsource all the learning and, you know, and to come out with your worthless to one. Yeah. Yes, exactly. Or you can have a very different experience and you want to. You know, you want to have a much better experience because effectively you have a an always on, infinitely patient tutor who can answer you every question. Um, you know, it sometimes incorrectly. Yeah. And but I do I do think that you have to learn how to think fundamentally to be employable. You have to be able to think for yourself. Yeah. And that's what you need to go to university to learn to do. 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 a really, really interesting Tom. Thank you. Thanks, Merryn, for having me.