Did Mark Zuckerberg build one of the world's greatest companies through extraordinary skill, or was he simply in the right place at the right time? Using Meta's $77 billion Metaverse gamble as a starting point, Fraser, Nick, and Peter explore one of the most important questions in business, sport and life: how do we distinguish luck from skill?

The conversation ranges from chess and noughts-and-crosses, to Napoleon, David Bowie, Kubrick, Elon Musk and Einstein, examining whether repeated success is the strongest evidence of genuine ability. Along the way, the hosts discuss probability, decision-making under uncertainty, why outcomes alone can be misleading and whether "business genius" is often just survivorship bias in disguise.

Hello and welcome to the Cognitive Engineering Podcast brought to you by Aleph Insights and produced by me, Fraser McGruer. I'm here with Peter Coghill and Nick Hare of Aleph Insights. On this podcast, we take a look at a wide range of topics from an analytical viewpoint.

And today we're discussing the Metaverse. Nick Hair, real question, what's the Metaverse?

Yeah, it's actually not that easy to answer this precisely, but do you remember a company called Facebook?

I have heard tell of them, yeah.

Facebook used to be the Facebook. Yes, well, it used to be a website. Remember?

And so the Metaverse is actually a bunch of things. It's a persistent 3D world that you can go and live in.

This world.

No, an actual virtual world. The people in it are avatars, so you're actually a little sort of cartoon guy.

Okay.

And you can walk around and meet your friends and do all. Anything you can do in the real world, you'll be able to do in the Metaverse.

Rings bells.

Actually, it's got all kinds of exciting things like virtual workspaces, meetings, games, all of that kind of stuff accessible through.

Has a subset of things you find in the real world.

Yeah, a subset of the. Most of some of the more tedious things in the realm, like virtual meeting rooms.

Anyway, it's accessible through VR headsets and phones and through your PC.

Okay.

It's got a thriving economy of digital goods and services. It's going to be a successor to the mobile Internet. It's not just an app, Fraser. It's not just a platform.

Right.

Instead of just accessing the Internet through the mobile phone, which is what you're used to, this is just going to be a whole new way of interacting with the virtual world. You're putting on the headset and you'll be. You'll be.

I think it was envisaged to become the way that you interact with the main way.

Right. We'd forget about the old Internet because we'd just be living in the Metaverse. Everything you can imagine doing, you'd be able to do.

There's a scene where a woman is at a concert and she's bopping away to the live music and suddenly her virtual friend appears as a ghost next to her, joining in. But the friend is not wearing VR goggles or anything. The friend is just there as a. It's like that Star wars where you have a little.

The Star wars holograms. Yeah. So it was very light on how any of this would work.

Yeah.

A VR meeting application called Horizon Workrooms.

Yeah.

And various sort of slightly experimental technologies. What we have now, four years later, is some AI assistance. Of course, some smart glasses, a few wearables and. And a sort of mixed reality technology.

Okay.

Basically, Zuckerberg bet that the future of the Internet would be VR interaction, that VR headsets would become the new smartphone.

Yeah.

How much have they spent on this?

Well, it's not totally easy to work this out, but because they don't just have a line in their budget which says Metaverse, I'm going to take a guess. Go on, have a start.

Billion dollars.

If only.

Right.

So they've. Reality Labs for the last five years has lost between 10 to $20 billion a year.

Good Lord.

And they think that they have lost a total of $77 billion. So this is the difference between. It's not the amount spent as such, but it's a difference between, you know, income, revenue.

So what then they've ended up. I mean, they haven't got nothing for it because they've got some IP out of it and they've got some VR hardware, tech.

And I think, you know, they have, I guess they have a handful of users. I mean, it's. Virtually no one is on there. And so. So we're talking about $42 million a day that they have spent on this thing.

That's quite a lot.

That would get you in total. Right.

I really have struggled to work this out because if you want to get a decent senior ML researcher or engineer, computer vision engineer or something like that, you might be looking at half a million dollars a year. That's roughly how much these people cost. So you'd need. They must have been spending. They must have 30,000 of these people. 10, 20, 30,000 Of them.

What are they doing? Well, I mean, I think the, the.

Laughing all the way to the bank.

Well, indeed. So I think the main, the main consensus is, is no one wants this. And this is what everyone said at the time.

Everyone said no one is going to want this.

Like, do you want to wear a headset for three hours a day, knocking over your coffee and you know, having to take it off every time you want to go to the toilet?

But it allows you the chance to have like a virtual meeting.

Yeah.

With a bunch of people because they basically, it took them quite a long time to get legs. I don't know if you know this, but to begin with they couldn't do the legs.

They struggled to get the legs to work or they were worried that if there were legs then people might get sexually harassed or something. It's not clear, but it took them quite a long time to get the legs.

So John Carmack, who was, who was a guy who worked at Oculus before Facebook acquired it, he said, we've got a ridiculous amount of people and resources, but we constantly self sabotage and squander effort. There's no way to sugarcoat it. I think our organization is operating at half the effectiveness that would make me happy.

Some may scoff and contend we're doing just fine, but other others will laugh and say half ha. I'm at quarter efficiency. And Elon Musk said, I don't see someone strapping a frigging screen to their face all day. And I, I think that's it.

At the end of the day, VR users want games and simulators, but not virtual corporate meetings and floating legless avatars.

Yeah, meetings are unbearable enough without having a figure on your head.

And so, and I think ultimately there is, vibes wise, there's just, there's something so peak millennial cringe about the way everything, about the way it's been presented.

You know, the fact that Zuckerberg was sort of standing there saying this is the future of computing, but it's a sort of cartoon version of him with no legs in front of a shit looking Eiffel Tower. It's just incredibly cringe.

So the, anyway, the thing is there are dark rumors floating about now in Silicon Valley and elsewhere that Mark Zuckerberg might not in fact be the world's biggest business Genius. Some people are saying he might just have got lucky.

Well, here's the thing. I, I, yeah, I've made, I've made some mistakes in my time.

Right, Nick?

And you know, I feel comfortable saying that with you.

That's, it's good, you know, share it.

Yeah. But I don't think any of the mistakes I've made have cost. How many billions was it?

Yeah. $77 Billion. Yeah.

So actually, I'm kind of smarter and a better decision maker than Mark Zuckerberg.

Yes.

To be fair, most people are. It sounds like.

Well, I mean, but at the same time. But he did, they did invent Facebook.

Yeah.

So, you know, so the question is. Right.

And that's what we're really talking about here is can we actually objectively measure the amount of luck versus the amount of skill that has driven someone's success?

Yeah, yeah.

Someone's outcome or not just success, but failure. You know, could we say, well, this person's made terrible decisions or, you know, maybe they were just unlucky?

To what extent can we, how do we think about trying to tease those two apart? Are they mutually exclusive? And, you know, and so can we then apply that to, to the case of Mark Zuckerberg or other business.

Sure.

Purported business geniuses to find out? Well, actually, no, you know, we might be able to say, well, Jeff Bezos, that guy's actually a genius. Mark Zuckerberg is just a guy got lucky once.

Yeah.

Yeah.

Okay. So that's what we're going to do. We're going to figure out, how do we know if someone's been lucky or skillful?

I can think of no one luckier than Peter to go at this.

Okay, well, before we try to unpick luck versus skill, I thought it'd be fun to just for a moment assume that luck is real, is a real thing, a real thing in the universe, and to sort of reason about how it works.

Sure.

The physics of luck. So a question that springs mind to me, you know, is it a wave? Is it a particle?

So are you, Are you, Peter? Is it that we think it's something that inheres in a person?

Well, this is, yeah, this is good. That's a good question. Like, you know, is it, does it stick to things? There sort of some sort of field around objects?

Or is it some sort of particle that gets, adheres to your atoms in some way?

Could it be isolated?

Can it be isolated? Can it be concentrated?

Can we settle it?

Is it conserved? Can it be created or destroyed?

Right.

Okay. When you create and destroy. What does it turn into?

What's the answer? It feels like the basis of quite an interesting monograph.

Yeah. So particle or wave? Right. Okay. If it's particle model.

Yeah.

Luck is sort of, therefore localized and discrete. You can have it in one place, you may be able to transfer it.

That can explain things like the lucky rabbit's foot, you know, it contains luck particle.

There we go.

Right. And it can be physically transferred. So if I, If I give, if I have a lucky foot and I give it to Nick, he. He becomes lucky, I lose the luck. Right.

If it's a wave model, then you have luck fields which sort of. Which surround and possibly pass through people.

Okay.

Okay. So in which case you can amplify it or you can distort it. You cancel it out.

Like a really unlucky person could.

Exactly, yeah.

Ruin your.

You might have a.

Can we call them. Can we call. If they're particles, can we call them fortuitons?

Well, I mean, that's it really. I mean, the key questions really are. Is like a particle or a wave?

Right answer. No, something else.

Well, what is it?

I think I'd say it's a macro scale phenomenon.

All right. I think we're getting confused here between what we call something, what we label something and what it might actually be.

And you know, how we start to think about something is not necessarily accurate. So you're quite right. And when we, you know, in, you know, when we talk about this kind of thing, it's a. It's a noun, often an adjective. Right. And.

But maybe that's not a good way to see it. Maybe it's just.

It makes more sense to see it in terms of time and decisions and that those decisions have consequences which are good or bad, but also inexplicable sometimes. And yeah, I think that's all luck is. And it's not really a thing. Right. That can be. Have particles, waves, or even quantum mechanics.

But I mean, there is, there is a sense in which part, obviously, partly what we mean by luck is something to do with probability. And probability is about information ultimately. So it's about the things you don't know. And you know, we.

When you're, when you're exposed to that, there are things that you don't know about that are going to affect the outcomes. And so to that extent, you know, that is. That is going to be some sort of continuum and often is expressible, you know, in, in quantitative terms.

I mean, so I, I've had a look at or tried to think about how we might try and isolate specifically what we mean by luck versus skill. And, and I've thought about it in the context of games where we can actually kind of isolate the two.

So there are, there are games which are obviously totally luck based. Snakes and ladders, right? There's, there's absolutely nothing you can do. You are rolling the dice, see what happens. Doing the thing.

Maybe very good at rolling dice. I don't know.

Well, exactly.

I mean, and then you have games like Snap or chess which are 100% skill based in the sense that, you know, there is no, there's no randomness about the outcome whatsoever. And so you take those two. What does that mean? Right? Does that mean that chess is 100% skill based?

Well, even in, even in a game with no luck, like take chess versus noughts and crosses, they're both totally deterministic. There's no random outcomes in either of them. But it still kind of feels like there's more skill space in chess.

Like you could say chess is a game with more. A good chess player has more skill than a good noughts and crosses player does.

Right?

So we've got a situation where, okay, skill isn't. Skill is not something which is, which is on a continuum from entirely luck to entirely skill. Right?

It's not like, oh, well, as we reduce luck, we get more skill. It's not as simple as that. Because, because you can have, in both of these cases chess and naughts and crosses, you have zero luck.

But we still sort of think, well, there's differing amounts of skill. And likewise, you can arbitrarily add luck to a game. So I was thinking, you know, you imagine a situation where you have,.

I've.

Got a new exciting game to sell you. First we play a game of chess and the winner gets a point and then we roll a die and add that right?

Now if you've won the game of chess, you've got a point.

So if you, you know, if you, if we both rolled a five, but you'd won the game of chess, then that chess victory would give you the win in the game overall. You with me?

Maybe.

So now you can arbitrarily vary the number of points that the chess game gives you versus the number of points the dice give you. You could, for example, say, well, the chess game is worth five points and then we roll a die.

And now there's a very small chance that the die roll is going to make a difference. So that you could say there was more luck in that game.

But both Those games involve a game of chess, so they've got the same amount of skill as the chess does. So I think the odd thing here is luck and skill are not actually mutually exclusive. They're actually almost like two axes.

And you can have games that have a lot of luck and a lot of skill.

Right.

And games that have. Right, so. Well, I mean, I think that's counterintuitive to me. And so I sort of think, well, can we. Are there things which we can point to which are.

Which are like characteristics of the game which we might be able to then go and look at, you know, characteristics of business, for example.

So.

So when I say that chess has more skill than Orson Crosses, what do we kind of mean by that?

Yeah, yeah.

And what do we mean when we say that there's more luck, you know, in one game versus another? And. And one of them is about the sort of state space complexity. So the fact that.

So complexity.

Here we go.

Yeah.

So Noughts and crosses got about 5,000 distinct board states, whereas chess has 10 to the power 43.

It's a big number.

Big number. And go has about 10 to the 170. Right. But even then, I mean, the problem with state space numbers is that it doesn't necessarily mean that.

That the right. The right thing to do isn't actually super ultra straightforward.

So, like, you can imagine creating a game where every turn you had to do one of five things, and one of those things was obviously sensible and the other four were just stupid. You know, like say four of the choices that you take mean you lose straight away, and. And you can put.

You can string a billion of those decisions in a row, and this game is still no more skillful. Right. Just the number of state, of. Of states that that game can be in does not tell you how much skill is required to play it.

So it's not that either. It's not.

I think it's. I. I think it.

So just to summarize where I've. What I'm claiming.

Yeah.

Skill and luck are not actually mutually exclusive, but also they're very, very hard to define in terms of objective features of the system that you're looking at.

Well, we've not gotten to luck yet.

Right.

Well, although I think. I think, let's say it's the bit that you don't control. It's what can move you from one state space to another and you don't have control over.

I think the state space is an important component of assessing, measuring the level of skill required. It's not the whole answer, though, because your example of a very game that has a arbitrarily large number of states, but each state is obvious.

The next state is. The next position is obvious. That is a trivial result.

I think it's something to do with the breadth of available states that you can get into next and how computationally tractable that is for you to achieve.

Yeah.

Then you're relying. If you can't. What I'm getting at is if you can compute every single state and the relative value of the next state you can get into, that's trivial.

There's no skill there. That's just adding up.

But the skill comes in some sort of heuristic that you've developed to allow you to make the decision in a position of uncertainty, a position of not being able to compute all the states.

Yeah. To be able to navigate the number.

Yeah. So just.

You said navigate.

Yeah. So chess master is not computing all of the possible states for all of the moves ahead.

Right.

They're thinking maybe 15, 20 moves ahead, beyond that horizon. There's a sort of. They have a feeling about what the right decision is. And it's the skill. There's skill in being able to compute that. Yes. Okay.

But another skill is being able to go. That feels like a better direction.

Or is it.

I'm just wondering if the difference between, you know, a Grandmaster and, you know, two rungs down, whatever, is that the grandmaster can do 20, 25, whatever it is, moves ahead probably couldn't do that much, but someone else can do 10, 15, let's say. So before it becomes.

I think. I don't know. Well, if you think about how they. How you would. How you.

How they train chess AIs, which are now better than the best humans, it kind of corresponds to what Peter's saying in the sense that there is some tree search. So you literally compute all the possible states. You can be a few moves ahead, but you can't conceivably.

I mean, a number of states are just so big that that's not computationally tractable beyond a few moves. So what you then do is you also have a kind of policy engine which tries to evaluate, okay, let's say we look five, ten moves ahead.

So we know that there's several billion states we could be in, but we need to then evaluate how good those states you trick. So you train a thing that sort of says, okay, well, this board kind of looks like it's going to be a winner for black.

I'm white, I don't Want to, I don't want to make that move. Then I'll avoid that bit of the tree and go here. So you're not computing it through to the end.

What you're doing is combining tree search with understanding what features of the board make it likely to be winning. So I think, and I think here we've got.

This gives us something to think about in terms of the kind of real world business environment is a combination of, well, can I anticipate the consequences of what I do?

You know, is, is the system that I'm in sort of predictable or unpredictable, but to what extent can I try and look ahead, but then also sort of think, well, when I'm in that position, is it going to be good or bad for me? It feels like those are those.

That's not a million miles away from what we're talking about in terms of, in terms of, you know, what, what business decision making might look like and how we can evaluate the extent to which there's luck or skill involved.

I've kind of lost where we are with things. Oh yeah, we were talking about. Yeah, yeah.

So, well, we're still trying to pin down what we mean by these things. Right. So, I mean, look, so just summarize.

The moment we talk about luck and skill as if they're opposites, but no, they're not.

They're just different in some way. Yeah, yeah, yeah. So you said navigate earlier and I want to introduce an analogy which I think actually is not.

But it's not even an analogy, it's just a way of envisaging what we're doing when we're kind of making decisions in environments is if you imagine it's kind of like a, a landscape with rolling hills.

When you're playing a game or making a decision or interacting with a system, you're trying to find the route that gets you up to where, where you want to go.

Right.

So you're kind of trying to, trying to work out which direction to go in. And I think I'd, I'd say that the, the concept of a game which requires lots of skill puts you on a sort of narrow ridge where you are trying to.

You could, if you go either, if you make the wrong decision once you're toast, you go off the side and you're, you're suddenly in a very low part of the environment.

Whereas if, if the problem kind of space you're in is, well, actually you can kind of more or less go in any direction and you'll be going upwards, then that doesn't require much skill. Right. To navigate.

So I think, and it, and I think to some extent, you know, there are, there are situations in real life where there are some luck based bits or some, or rather let's say there are some bits where you have to be very skillful to navigate upwards. You've got to be constantly making the right decision. And there are some bits which might be more like a sunlit plateau.

Like for example, let's say you've got a billion pounds. It's quite easy to make ten million pounds if you've got a billion pounds. But if you've got nothing, it's very hard to make £10 million.

So that bit, getting up onto that plateau with a billion pounds from nothing, that requires a lot of skills. But when you're on that plateau actually it's probably a lot easier to keep going up.

So, so where I'm going with this is you are going to find on that plateau a combination of people who were very skillful and made the right decision all the way up and people who just YOLO'd it and, and just did random stuff. I've forgotten what YOLO means. You only live once. Okay. And, and there were maybe, there were maybe a million of those.

There are a million Mark Zuckerbergs and all 999,000 of them. 999 Failed and Mark Zuckerberg's didn't.

And it wasn't because he made the right, he did make the right decisions, but by accident because he was just picking a random direction like everyone else and his direction happened to lead to right place, right time. How do we really know that?

Sorry, I've missed.

No, we don't know it. What I'm saying is this is a way of characterizing, okay, the.

Because he might be the one brilliant guy.

Right? We don't know. We can't look at that outcome.

Yeah.

The only way we'd know whether that route upwards was a kind of just a lucky jump or a set of skillful decisions is by looking at what actually happened.

Yeah, yeah, yeah.

Sort of mean like so, so what I'm saying is, and I think this goes back to what I was saying about games is, is actually you, you, you, you.

It's very hard to just look at the structure of the landscape and say this is a skill or a luck based thing because you, because you know, at any given time I might play Magnus Carlsen at chess and just by accident make the set of moves that beats him. Do you know what I mean? And if There's.

If there's a billion people playing Magnus Carlsen at chess, one of them is going to be the person who accidentally makes the right move. Yeah.

Yeah. Well, it's interesting you should say that. It's an interesting way of looking at it. I mean, I used to play chess a lot.

I don't play it much at all now, but the person who really, really sort of properly taught me how to. Or rather taught me how to properly play chess.

Yeah.

I think I beat him once. Right. And maybe we had hundreds.

And then you retired.

Yeah.

And I was like, yeah, I'm done.

But what was.

What I used to really enjoy is we actually used to analyze, as we were going along often.

Yeah.

But we used to always analyze afterwards the different moves and invariably looking for my weaker moves. Right. And it was fascinating. It was. That was the most interesting point a bit.

And like, you know, just going over those and having a good chat about it, that was. But anyway, we did exactly what you're talking about, I think, you know, going back and looking, hey, where did it go? Right? Where did it go wrong?

Yeah, yeah. And sometimes you, you know, I mean, I. I used to play chess in. In primary school, you know that sometimes they would go, oh, it's.

It's really subtle what you did with the rook on turn three. And you're like, I had no idea.

I just.

I was just moving it there because. Because I wanted to take your bishop.

And it turns out, no, you accidentally set up some really impressive threat on rank 7 or something, and you had no idea.

And actually, you paid long, obviously, though.

Yeah, yeah, yeah, yeah, yeah, yeah, yeah.

But actually. And actually, this makes me think even closer to this. I remember a handful of occasions we had trouble identifying what was.

Because it's tempting to think of something as the wrong move. Right. Or the weak move.

Yeah.

And there are a handful of games we had trouble identifying. You know, I'd gone wrong, but it must have somewhere. And so my point is this. It's.

It.

It can be tricky to. To work out what's right, what's wrong.

Yeah.

And chess is a totally deterministic game. So in a sense it's like. Well, actually, what you did wrong was everything in some sense. But there are some moves.

And I think this comes back to this, this question of what makes chess complex is it's, you know, what.

And I suppose what it is that makes it not obvious whether you should do this thing or that thing, is that the states you want to be in, if you think about it as a big tree are scattered among multiple leaves. They're not all on one branch.

It's not obvious that you should always do this, you know, I mean, in a sense, I guess there are a small number of openings that we know work for white, and it says that's the sort of first few branches. We kind of know that the white winning positions are probably on those branches.

But pretty soon you're at situations where, well, there are going to be winning positions in all kinds of different directions. And I'm just going to have to keep steering in the right direction. I can't just keep going, oh, it's this way.

Yeah.

You know, and, and, and I think that's. Yeah. So.

Yeah. Yeah. And actually it's not. Maybe it is the same or not.

And it goes back to our other chess analysis when we're talking about, okay, I can compute this far, and after that, it's kind of feeling a bit. And one of the nice things about those opening moves is you don't have to think too much, actually.

It's not too computational, you know, that it works, and it's going to put you in a general kind of situation that's. That's going to be all right. So look, let's move along. Let's. We need to move towards wrapping this up. Before we do.

I know you've got something, Peter, but also, Nick, you've got a game or some practical thing. Tell us.

Well, it's more. Okay, so we've talked in theoretical terms, I think just to sort of summarize. Yeah, some. To summarize it. We, you know, the issue is.

Well, just looking at the landscape gives you a sense of how much skill do you need to navigate this landscape. And I think there are sort of characteristics of the game and the system. You know, the, the sort of sensitivity to, you know, to a bad decision if.

If it's possible to make a decision that really screws everything up.

But, you know, you've almost got to keep pressing the right button over and over again, and even then you haven't won versus well, it doesn't really matter what you do. You're going to kind of succeed anyway. With the first one is going to require more skill in general.

But at the same time, there's always the possibility that someone will luck their way through, you know.

Sure.

So just. So, so. So the thing is, in practice, what can we look at that gives us a sense of.

Of in a given real world situation of whether someone has been lucky or skillful? And so I've got A couple of, well, quite of related indicators that we can look at.

Okay. Just before we do.

Yeah.

I feel I should warn you, I used to be a grandmaster. Not in chess, in snout, in snap.

Right, right. Well, so the interesting thing about snap is it is 100% skill. There's no luck. Well, this is why I've just got to be faster.

Because we, we talked about chess and snap or you talked.

Basically they're both, you know, and I'm.

Just putting it out there.

Well, I think there are people who are consistently very good at rock paper scissors, for example, because they, they can read the opponent's intentions very quickly. So, so I mean, yeah, I, so, so the first one is repeated success.

If you do it twice, or dare I say three times, that is very good evidence that you're not just being lucky. Now that that kind of the thing is when you have a Facebook type situation that really does only happen kind of once.

It's not like, you know, you can, you. It's not like Mark Zuckerberg invented three things that got successively more successful and became Facebook. I love this because this is a.

Really good indicator of, you know, how much skill was involved and also how much luck was involved.

So let's, let's look at your likes of. In the music realm. Wright said Fred, right?

Yeah, yeah.

One hit wonders versus I don't know if you've heard of him, but it's this guy called David Bowie. Really good at reinventing himself and still being producing bangers. The Beatles, Mozart, people like that in film.

You know, your Kubricks and Spielbergs where it's like, well, that, you know, is Kubrick actually good or is he skillful? And you think, well, he's made the, you know, he's made like the best war film ever. He's made the best, best horror film ever.

He's made the best space film ever. He's got to be good, right? The mil in, in military affairs. Napoleon, right, Just, he just kept on winning those battles.

One of them you could have written off and said, well, he accidentally made the right move at the right time. But the, the is the repetition.

And then, you know, the likes of your sort of science people, Isaac Newton, John von Neumann, Einstein, you might think Einstein is a one hit wonder. He invented relativity.

I heard someone, someone once said that if Einstein had had not discovered the theory of relativity, he would have been the best scientist of the 20th century because of all this other stuff he did about the photoelectric effect and statistical physics, all this other stuff. So that Then implies. So I have this test which is remove the big success and ask what you're left with. I think that's. That's it, really. So I. And I.

So I. Doing. Applying that test, we go, Steve Jobs, right? You take out Apple. What have you got? Well, you've still got his thing called Next.

You still got Pixar. You've got his second go at Apple. You know, he's fired. And then he came back, Jeff Bezos, he actually was quite a hotshot before setting Amazon up.

He was VP of an investment company called D.E. Shaw by the age of 30.

And he started Amazon not because he liked books and just happened to want to set up a bookstore because he said, no, this is going to be big. It was a deliberate thing. I'm going to get into this. Elon Musk, get rid of PayPal.

You still got Tesla and SpaceX and Starlink and other things that are sort of. I would say, you know, they're not just things that were easy once you had a billion pounds. Exactly. They were. They were things that.

That actually were hard even if you have a billion pounds. So what Zuckerberg now, Peter, I think you're. You're a big. Mark Zuckerberg.

Massive.

You love that guy particularly.

But I, I think. I think we need to stick up from a little bit.

I think it's very difficult to retrospectively look at this one individual climbing this up to the plateau, as Nick said, because without engaging in counterfactual reasoning, you haven't got access to all, all of the things he had in his head and all the information he had available when he made the decisions. So you can't, you can't analyze, you can't. You can't pick it apart, decide if it was. If it was luck or, or skill.

And I think Mark does quite well with respect to the next test. If you take away Facebook, you know, the fact, just being lucky that Facebook was there, the right place.

Did you just call him Mark?

Yeah, apparently he's Mark now.

Yeah, I'm facing the tails with Big Zuck. Take away the fact that he was lucky to set Facebook up, he's made some pretty decent decisions there.

You know, the Metaverse business aside, he's made some good decisions. Acquisition of Instagram and WhatsApp have been extremely valuable and profitable for Meta.

There are acquisitions, though. It's like, what do you need to. How much skill do you need to just have loads of money and buy something?

Skill, I think, is in deciding that it was a good thing to buy yeah.

It's not quite the same though, is it?

Has he done some other stuff?

I don't know. But I mean, he's. Facebook is still highly profitable.

I don't understand how it can be. It baffles me. I hear what you're saying. It just. You. You know, Facebook has been so unbelievably insertified. I just don't understand. He still uses it.

Yeah. You know, neither I. But I'm just. I'm just trying to. Straw man.

Yeah. Pete is like one of those lawyers that you get in to defend a serial killer and it's like, yeah, I wouldn't be doing my job.

I'm the duty lawyer.

Yeah. I mean, he's got a nice wife, doesn't he? Mind you, lots of people have nice wives.

Yeah. I don't. I think we are clutching at straws if we're using that as evidence of his incredible business acumen.

But hey, do you know what I mean? Do you know we. Do you know who we need on this?

Mark Zuckerberg.

Mark Zuckerberg. Because he can tell us. We could ask.

We should probably invite him along.

Yeah, we should invite him. Let's do that. I mean, obviously you know him. You're on first name. Taz.

Yeah, I mean, I don't, I don't. Bear in mind, I. I don't think we should feel too bad about this because he's not. Yeah, he's not, you know, penniless.

He's not unsuccessful.

No, he's doing all right.

Yeah, I think it's fun. It's. He is now. Yeah, I think it's fun.

It's.

It, it's. You know, it's important to ask this question. Are you lucky? Are you skillful?

Because what we don't want to do is, is worship him as a business God if he just happened to be in the right place at the right time.

You know, for every, for every Zuckerberg, there's hundreds of thousands of non Zuckerbergs who haven't made it. But I mean, just Zuckerbergs. Yeah.

I mean, going back to the meta verse, the whole thing, the 70 odd billion dollars evaporated and not necessarily evaporated because there's loads of ip.

But I mean, being a billionaire, he lives in a different kind of world where the way you make, the way you make your next hundred billion is not by making small, well thought out decisions. You follow the sort of VP model where you take very large, very frequent big bets.

And if you only need one or two of those bets, out of 100 pay off and then you've made your money back in volume. So it's a different sort of standard for to us, mere failure.

You don't need to comparing the level of failure on the dollar value is not the right approach because there'll be lots of other things that, that he's done. And there are lots of other big failures the other VPs have backed that failed, but they're still, they're still rich.

Yeah. Yeah. Okay. So yeah, let's finish it off there.

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