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The 5 o' Clock Shadow with David Shapiro and Viv Govender · Strictly Business

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You're listening to Strictly Business Podcast with Lindsay Williams.
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Welcome to a special edition of the Five O'Clock Shadow.
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Normally we talk about markets, but often we talk about AI, the AI side of markets,
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AI-led stocks, tech stocks in general.
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And sometimes I sit there and I drift off a bit, I must say,
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because David Shapiro talks about things I don't understand and then Viv Govender
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talks about things that David doesn't
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quite understand.
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So we get a little bit lost.
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And I was chatting away to David on Monday, and he was having a bit of a
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troubled session,
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trying to write a piece about AI for his own peace of mind, as he put it.
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So I said, well, why not use the giant resource that we have, i.e.
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Viv Govender?
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Why doesn't he give us a tutorial?
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So I sent Viv a message on Tuesday night, quite late, and I don't know why he was
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up.
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But anyway, I said the following.
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Hi, Viv, David and I want some help on Thursday.
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Can you come up with an easy to understand tutorial that explains AI, what it is,
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what it does, how it's used,
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who uses it and which companies do what?
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Who buys what from which company?
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What is the ecosystem, et cetera?
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Tough assignment, I said, but it'll be well promoted and listened to.
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And Viv comes back with a long answer.
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Sure.
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So that's how we get to where we are now.
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David is very keen to ask all the questions and I applaud that.
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It gives me less to do, but more to learn.
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I will ask the first question though, Viv.
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When on earth did this happen and what is AI, please?
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Okay, when did it happen?
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It happened because the chips got small enough and fast enough.
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So there's a guy called Ray Kurzweil.
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He's quite a famous inventor and he predicted roughly 30 something years ago.
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So we're talking like the 1990s.
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He says, if you look at Moore's law, look at how fast these chips are getting faster
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and faster and faster.
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Around about 2030 is when you're going to get to a point at which these chips or
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you'll have enough chips there.
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to kind of compete with the human brain.
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Now, what you must understand is the human brain is unbelievable.
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It's like, it's amazing.
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If you can consider the fact that there's no system in the world right now running
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on the power of an entire city that can compete with the power
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in totality of a single human brain that runs on 20 watts, which is what a human
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brain runs on effectively.
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Effectively, you get Einstein on 20 watts.
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You know what I mean?
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20 watts is a light bulb, effectively.
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But he said, you just...
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forecast just how much chips are improving over time.
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And by about 2030, you're going to have a point at which the cheapness and the scale
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of the chips, etc.,
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get to a point at which you can actually mimic the human brain because of the
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number of connections involved.
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So it happened because of that.
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Almost everything else, except for a minor thing called transformer,
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but the actual fundamental pieces behind AI was developed in the 1960s and 1950s.
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And so people knew about the actual mechanism, but they didn't know how it
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would work or how well it would work until we had the
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chips in place that could actually do the stuff that current AI systems are doing.
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And that is why you're seeing right now the guys that have the biggest valuation
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are the chip companies.
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It is NVIDIA. It's ASML.
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It's basically TSMC.
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These are all huge companies that are making a fortune because it turns out that
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the chips that you have are the actual.
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At the moment, anyway, real bottleneck in the system, because that's what's been the
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thing that's gotten fast enough, you know, to get to the point.
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And the next step basically comes along with that.
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OK, so hold a second.
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So it started with chips getting smarter and smarter and becoming as big as the
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human brain and smaller than the
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human brain in real size.
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But when did it suddenly appear?
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When did somebody suddenly wake up?
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When did Viv Govender and David Shapiro wake up and say, wait a second, there's
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something called chat GPT that I should be?
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looking at and what is it i mean so start i'm sorry i'll repeat my question a good
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background what
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is it and when did it happen okay so what happened was around about 2017 20 i think
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it was 2017 you had
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the this paper coming out it was called attention is all you need right
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and then people uh there's there's also another paper called the bitter lesson
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okay the bitter lesson basically uh uh this guy's name
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is richard sutton if i'm not mistaken he basically uh
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What you do is you just basically get the scale up.
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That means it's like the human brain, like bigger brains make better stuff.
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So if you look at human brains versus like chip brains versus mouth brains
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effectively,
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the bigger the brain is relative to the body size, the more intelligent the animal
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is effectively.
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It's not quite certain because I mean sperm whales' brain is the biggest brain
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in the world, but the sperm whale is also a gigantic, competitive human being,
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therefore it doesn't quite work that way.
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But the bigger it gets, the better.
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So scale matters.
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The idea was you put in more computing power, more time, more data, and that
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gives you...
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better results okay and it's almost like a magic formula and that is the the the
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thing that the guys that are running like open ai the
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guys are at tropic the guys are running all these big labs they are not like doing
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they didn't basically go out and do some kind of like you know
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einstein level discovery relativity all they did was they became skillful as they
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call it means meaning that they believe
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that you take this formula that these guys it was developed by
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a deep mind by but i think it's uh kind of the names of the people but it's the the
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paper is quite famous It's called attention is all you need.
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It says you take this formula, you just add data, and you add computing power, and
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you just run it longer,
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and what you get is something that is clever.
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The more data, the more computing power, the longer you run it, the cleverer the
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things you get at the end of the day is.
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That is basically what happened is that people just got scale pulled at that point
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in time.
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When I knew it was happening was when there's a prediction marker before the and
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before the, probably markers, I think it's called Mercator.
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I saw this sharp, I mean, I follow it because it's kind of interesting.
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It's an idea of what the future looks like.
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And then I saw a sharp decline until that point in time.
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It was always 30 to 50 years or 20 to 50 years was the timeline for AI. Then
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suddenly around about,
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I think it was 2021 or something, it turned out to be seven years.
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And I remember emailing Gary, who I work with at Ransfors, and I said to him,
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something's happened.
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I don't know what it is, but something's happened.
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It took me too long to basically...
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really believe it and create a fund around it and so on.
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But I could see immediately that something in the AI ecosystem had changed because
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it's a very,
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very sharp drop around about the early 2020s, where you went from, like I said,
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20 to 50 years down to, like I said, seven years is what people were predicting
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around then.
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Which is 2027, 2028.
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What caused that?
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I mean, why did it drop like that?
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Because the skills, I mean...
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Was it the quality of the chips or scalability, which just means more of the
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same?
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They weren't using the big chips back then.
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The models that we are seeing, I mean, look at the amount of money we spent on
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chat GPT and so on back in the day.
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It wasn't so much the chips were so important because the models were
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requiring small,
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really small in today's numbers, amounts of money to train up.
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It was just the fact that what happened was they had this thesis that the more
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scale you had.
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the better things got and they started adding more scale and sort of thing things
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are getting better not not one to one you don't double scale get double as good
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but you basically take them scale and you get like you know uh
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50 better you know what i mean so yeah you basically it's a logarithm uh you know
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kind of thing but what's been
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happening is that it's getting better and better the more you scale it and what
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happened around that is that people started to believe that you just need to
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add
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scale just make it bigger make it more expensive and what you'll do
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is you will have something that's basically going to, you know, give you a
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better intelligence at the end of the day.
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But Viv, you needed, when ChatGPT came,
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this understands my language.
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I can ask it a question.
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You know, was that the genius behind AI or the modern concept of AI?
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What, you know, before you might need while you still had scale?
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While you still had science, you still needed to formulate or put in a question.
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I don't know how you do it.
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You know, you needed somebody there to program a question.
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Now you can just chat, which is exactly that.
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Why did Sam Altman or somebody like that suddenly become the hero of ChatGPT and
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the big name?
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You know, what did he do differently from everybody else?
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Well, what he did was he was aggressive and he came out first.
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I mean, Google was always able to basically catch up and overtake OpenAI.
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There is a bit of a thing about the history as well in terms of the people
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involved.
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So Google has a guy called Dennis Assabas, right, running DeepMind there.
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But they also had a guy called Jeffrey Hinton.
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Jeffrey Hinton is the guy that won the Nobel Prize along with a couple of the
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guys for AI.
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Now, Jeffrey has a son, I believe, who's disabled.
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And he basically decided that he needs to cash in a bit on his fame and make a
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couple million dollars and just to basically
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ensure that his son, I think he's got some kind of mental disability.
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So he needs to have, you know, money to take care of him going forward.
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So what he did was he had an auction for his, you know, his thing,
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his work at university at auction.
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And I think basically he stopped it at about 30 million dollars.
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It was much more.
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Before this, this man was running Coursera courses, trying to make money.
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They're very silly courses of life.
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You know what I mean?
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So you're talking about a huge amount of money for him.
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Anyway, his student is a guy called Ilya Tsitskaya.
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Ilya is considered to be the genius out there in the AI space, like one of the
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true geniuses.
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Anyway, Ilya basically left OpenAI and joined Sam Altman over at...
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No, he left DeepMind and joined Sam Altman over at OpenAI.
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And OpenAI, Elon Musk is sometimes a bit of a blowhard,
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but he is unbelievably connected.
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with AI.
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He was one of the first people that helped fund DeepMind, which is the one that
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Google bought.
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He effectively named and started OpenAI, and then he basically created Grok
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thereafter.
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And OpenAI really was started by Elon Musk.
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I mean, he basically recruited Ilyas Iskaya.
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He funded it for the first bit of stuff and so on.
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And the idea behind OpenAI had been what you need to do is that Google's about to
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take over the world.
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This AI stuff is going to take over the world.
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What you need is somebody out there that's going to be for the world.
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You know, open AI.
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uh you know the people and uh sam alpin who is you know one of those people i
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seriously think
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i mean you know musk may be a bit crazy i think sam alpin is seriously scary in
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terms of like i mean there's rumors about what he did with his sister
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that's a bit weird but there's also people that have described stuff about him in
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terms of like one man
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that you worked with said you could put some output on an island with cannibals
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and come back in six months time he'll be the king of them right He's that kind of
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guy.
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And even the people that, you know...
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They tried to fire him for a real reason.
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The thing is that they were all idyllic, that they didn't basically know that they
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were competing against a real player, and he basically was able to kick them all
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out.
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Anyway, he gets open AI.
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He then gets these scientists together.
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They go where Google was not willing to go, because Google was always a bit scared
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about it.
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There is a theory in AI, and it's still prevalent out there, called doomerism.
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I don't know if you guys want to talk about that, which basically means it comes
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down to this.
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if anyone builds AI...
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we all die that is the idea nice it doesn't matter who it is the chinese
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bullet the indians
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bullet the americans bully it doesn't matter who builds it once ai is created it
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doesn't matter who built it it's its own thing and then it kills us all right
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there is the belief that there's a real danger on that and it's been there for
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decades
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actually people have always even you go back to basically alan turing uh you know
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isaac asimov they've all talked about the idea that a real
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ai is scary.
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Elon Musk called, you know, investing in AI, summoning the demon.
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And there is that belief that they use imagery like Cthulhu from
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those novels, you know, those...
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It's one of those old novels, you know what I mean?
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Those horror novels where it's basically a squid demon, alien squid demon.
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They use that idea, or they use the word sugar, stuff like that.
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They use this idea of this evil alien creature that you're creating.
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and it pretends to be nice and whatever, but what it actually is, you don't know,
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because it's more alien to the human mind than a spider is to your mind.
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You know what I mean?
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Okay, Viv, before we go on about this and before we all die because of AI,
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let's try and make some sense of it and you two can make some money out of it.
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I mean, it does.
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It is nice to you.
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Deep Seek was very nice to me today because it got something completely wrong,
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or rather I'd written my question completely wrong,
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and it apologised to me.
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And I said, don't worry about it.
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And eventually I thought, what am I doing here?
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I'm typing this into deep seek and I'm forming a relationship with this thing.
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But what I want to know is before David talks about the investment side of things
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and who's buying what and what we should be buying in the future, Viv,
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I want to know how it went from David cheating on a report to clients via AI.
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In other words, saying, please, could you write a paragraph about this?
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And it does it for him or partly does it for him.
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How does it go from that?
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to helping a surgeon in an operating theatre?
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This is what I want to know, the really big side of AI, the good side of AI, or
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so-called good.
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Okay, so firstly, there's a big difference between writing stuff and doing physical
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work.
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And you've got to understand that physical stuff is vastly,
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vastly more difficult than anything that's done on a computer.
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I've said I'm going to...
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Before, Magnus Carlsen is the world chess champion.
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By far, objectively, the most difficult thing he does when he goes and plays a
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chess match is walking up
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to the table and sitting down.
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The actual chess play itself is a trivial problem compared to walking up to the
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table and sitting down.
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And we know that because AI is much easier, more easily able to replicate the
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chess match than able to actually walk up to a
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table and sit down.
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But again, it's about scale.
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It's about scale of data, scale of computing power.
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And what we have right now is all these labs have, you know, huge muscular power.
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They are now using physical data, and some of it is basically, you know, model data
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as well,
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to train their robots, okay?
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And that is what's getting to the next scale.
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It's all about scale.
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You have better computers, you have better data, you will have a smarter thing at the
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end.
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And there does not seem to be a limit to how good that is.
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And I think that is the real thing you have to understand.
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People have stopped seeing the difference between GPT-4 and GPT-5, not because The
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GP5 is not as good.
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It's a lot better than GP4 is because it's gotten smarter than us to a level that we
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can't actually judge how much smarter it is than us anymore.
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You know what I mean?
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We know for a fact that the latest models can do mathematics, like novel
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mathematics.
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We know for a fact that the latest models are actually doing new science,
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are passing tests at a level that was unimaginable six months ago to a year ago.
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And again, it's not because of any kind of new massive invention.
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It's pretty much coming down to...
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you know mostly anyway there is some obviously algorithmic changes happening
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but it's mostly happening around bigger scale and
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bigger data and like i said a bigger system bigger data and that's why everyone
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just wants to buy more remote chips because you know you build
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a bigger data center you put the data into it you just run this thing longer and
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you're gonna get something smarter than the day therefore the guy that has the
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biggest data center is going to have the smartest machine.
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David?
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No, no.
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I, listen, it's fascinating.
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And I mean, it's to understand where it's going and how it works.
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I think what my investigation is,
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or question was, okay, let's bring it down to investment.
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And I said, okay, who's building the future?
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In other words, you know, who are the companies now?
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that are laying down the infrastructure, creating the infrastructure,
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then also incorporating it or embedding it in the workflow.
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By embedding it, you know, using it.
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How do we use it?
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So I'm saying I understand there's so many new businesses that have emerged and I'm
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trying to get out my head around, OK, you know, where do we go?
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How do we reduce this to...
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An acceptable level.
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In other words, you know, you can buy 100 different companies now and or more.
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But
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I was trying to get down to two things like who the model builders.
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In other words, you mentioned DeepMind, you know, you've got you've got Anthropic,
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you've got which are the other ones?
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OpenAI, you've got many of them who are actually the Chinese models as well.
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Yes.
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So you've got all of those then.
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That's the one thing, you know, who's building the models?
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And then, number one, how do they use those models?
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I use it as the, you know, the hardware or the backbone of all of this, the spine,
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which I would imagine would be your chip companies, the various ones as well.
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So and then then also, you know, where is it stored?
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You know, who stores all this data and how do you process it?
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And then afterwards that, well, how do we as enterprises use it?
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How do we bring it in?
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And which of the companies?
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So I'm trying to get down to what will probably be in your fund.
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Which are the big businesses now that are going to benefit from all of these?
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Those four kind of subjects.
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You've got the power as well.
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When you look at just how much power is needed to drive
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these as well.
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I saw something there that, you know, by...
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2030 about 3% of global electricity is going to be used for data centers.
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So, I mean, that's big, you know, that's a large number.
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So that's where I'm trying to get my head around.
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So the models, you know, models don't seem to have an advantage, meaning that anybody
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will catch up to the best model today within six months to a year.
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So we're talking about these, you know, DeepMind and all of those.
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DeepMind, OpenAI.
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Cords and everything, yeah, okay.
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Nobody has an advantage because you can find a model from China that's almost as
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good as the top model from the US coming almost
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immediately after or a few months after.
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And we're not talking about years behind.
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We're talking a few months behind.
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And often much more efficient and whatever.
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There is a thing called distillation that allows you to actually distill the biggest
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value from a model.
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Why is that?
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Why is that, Viv?
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Is it just become commoditized?
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Is it?
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Again, the formula is very simple.
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It's basically, like I said, it's data, it's chips.
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Get you the thing here.
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And the data, basically, if you have what distillation uses, it uses the output from
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another smarter model as its data.
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You know what I mean?
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Okay.
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And therefore, it allows you to train on a smaller kind of chip base.
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The chips themselves are basically advancing, and advancing at significantly
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faster than Moore's law.
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Moore's law was advancing double every two years, 18 months, let me say, right?
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And basically, this thing is advancing probably five times faster.
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That's because, for a number of reasons.
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One is there's never been this kind of money on any kind of chips designed
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before.
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So the advantage of being the best chip out there is huge.
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Also, because it's kind of like a newer thing, there's more low-hanging fruit, and
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therefore they're able to attack it from multiple angles more easily,
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and therefore you're finding advantages coming through.
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So I don't think the one reason people are actually still inventing the model is
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because there's the idea of what's called
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FOOM or fast takeoff.
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The idea that at some point in time, what you do is you get a model that's so smart
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that it becomes able to develop its successor itself.
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So you have an AI that can create the next AI.
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At that point, whoever has that AI suddenly goes from being like you know
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moving like at whatever speed at speed one
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to speed ten thousand because there's no bottleneck for the human being anymore and
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that becomes when you have actually the fastest bottle
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winning but until that point in time happens if it ever does happen
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uh i don't see a differentiator between uh
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anthropic or grok or opening eye uh because if who is number one right now i
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would bet you that the other competitors will
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be up to them and then put it six to three years.
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But what I want to point out is this, a couple things.
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Firstly, around the chips, we know about, you know, have you heard about Toto
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toilets?
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No, I saw.
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Yeah, so there's a Japanese toilet maker, right?
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Oh yeah, yes.
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Yes, and they make ceramics and apparently the ceramics they make just have to be
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useful
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for some AI stuff and the ship prices are up, I'll tell you right now, massively
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You know what I mean?
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So yeah, it's basically, it's up 40% in the last two months, okay?
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Because people basically say that the ceramics they make are used for this AI
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stuff.
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There's other companies out there, there's a lot, I can't remember the name of the
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company exactly, it's like, but it's another Japanese company,
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they make the substrate that is like 90% of the world's, you know,
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supply of a particular kind of thing that is used for AI.
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And there's all these little kind of supply things here.
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What you must understand is what around There's no moat around the models.
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There's a huge moat around the actual manufacturing of the chips because not
22:22
only do you need the technology,
22:24
you actually need the manufacturing expertise.
22:26
So, for instance, what ASFL in the Netherlands does is literally, I say,
22:32
this is the most advanced thing huge bigs do.
22:35
It's not going to space, it's not doing anything else, it's building these
22:39
chip-making machines.
22:41
They are so complicated.
22:43
The Chinese government is trying for years, hasn't gotten there yet.
22:43
It will eventually, but it hasn't gotten there yet.
22:45
And that is the kind of moat you're talking about.
22:47
But yeah, you're looking for bottlenecks.
22:48
You're looking for weird things in the makeup of this thing.
22:51
So recently the bottleneck was memory.
22:53
That's why you found SanDisk and you found Micron and SK Hynix all going higher.
22:58
Now there's some bottlenecks.
22:59
They're talking about, you know, things like, for instance, the ceramics.
23:02
But one thing I want to point out here as well about AI, because we are in such
23:08
low-hanging fruit territories.
23:10
When they invent the internal combustion engine and people are inventing airplanes
23:13
and cars and all the weird things around it.
23:15
And often you'd find like two brothers who are basically bicycle repairmen be the
23:20
first boys to fly, you know, the Wright brothers.
23:22
Yeah, and you had that happen right now.
23:24
There's a guy called Peter Steinberger.
23:25
He was a German guy.
23:27
OK, and this like in the last, he invented a PDF program years ago, sold it for a
23:34
couple hundred million dollars,
23:36
retired, was whatever.
23:38
So AI played around.
23:40
And for the fun of it, invented what is considered to be the next step in AI,
23:44
which is this open claw, clawed bot,
23:46
molt bot thing that he did.
23:48
You told me to go into open claw the other night, Viv.
23:49
You actually said, don't go into open claw, Lindsay.
23:53
It's dangerous.
23:54
And so, of course, what did I do when it went into open claw?
23:57
And this thing opened up and it was very aggressive.
24:00
It was red and black in its presentation.
24:03
And then it sent me somewhere else to Pete Says or whatever.
24:07
It just...
24:07
It was very confusing to a layman like me, a nitwit like me, but I didn't understand
24:14
what it was.
24:16
But you said it's the next big thing.
24:18
And my question, which will follow up once you've answered David's, is how do we know
24:21
what the next big thing is?
24:23
Do we just stick with chips?
24:25
So anyway, continue with David's, please.
24:27
OK, so it's all about bottlenecks at the moment.
24:28
If we invest something, invest something on the bottleneck that has the motor on
24:31
it.
24:31
I don't think that the LLMs do, unless you're betting that one of the LLMs takes
24:35
off.
24:36
And that means they get to a point where they have the recursive AI system.
24:42
But that being said, the LLMs are about to become a lot more valuable because of this
24:46
open-closed thing.
24:48
Let me tell you why.
24:50
What OpenClaw does is it gives a heartbeat to the AI.
24:53
Literally, it's called a heartbeat.
24:54
And what it does is it gives AI a sense of time.
24:58
What you do is you give the AI a distraction.
25:02
Now you do it, and it just gives an answer.
25:05
But what OpenClaw does is it gives AI kind of like a sense of time.
25:09
It gives it like a heartbeat.
25:10
Usually, I think it defaults to 30 minutes.
25:12
So every 30 minutes, it comes back and looks again at the results and sees
25:16
whether or not it's the right thing.
25:18
and then doesn't loop effectively, right?
25:20
And that alone, it results in what's called emergent behavior that looks very,
25:25
very useful.
25:27
And I think it's a new big thing.
25:28
And the good thing about AI is this.
25:30
It's not difficult to spot the next big thing because it's so often magical.
25:34
But when you see the next big thing, it looks magical.
25:37
This is obviously the next big thing.
25:38
When you see the new video stuff from China, okay?
25:43
It looks magical and you know this is obviously a huge deal when it comes to
25:46
video.
25:47
When you see the new, what OpenClaw can do,
25:49
it's obviously explaining if either of you guys are into what you call this,
25:56
obviously I mean both of us,
25:58
all of us in the financial sector, have you guys tried working with Cloud Excel,
26:02
right?
26:04
It is unbelievably useful.
26:06
You can go out and take a whole data set of like data and like you know what the
26:12
worst things in data is when they use different load
26:14
formats for runs, you know, around the currency, or they use different formats
26:18
for the date.
26:20
And then you've got to go in and clean each of them up individually.
26:22
One word thing, clean up this data, and it will do it for you automatically.
26:26
You want to say, okay, you want to say, I want to basically get a column here for,
26:30
you know,
26:32
finding the interest rate or the change between this column and that column.
26:34
You just write it out, and it does it automatically for you.
26:37
You see that, and you just know that's the next big thing.
26:40
So I don't think we'd have trouble finding the big divots.
26:42
It's magical.
26:42
But I think that the new open-cloud thing is very useful for a couple of reasons.
26:45
Firstly, it actually makes AI useful because it gives you a sense of time.
26:49
And we need time.
26:49
We've operated time ourselves.
26:51
So you can give it tasks to do, and it does these tasks quite nicely.
26:55
The second thing is that because it continuously has this loop, it's almost
27:02
like you're prompting the AI all the time, continuously.
27:04
That means the guys that run the LLMs suddenly find themselves being inundated
27:07
with huge amounts of requests.
27:09
So there's no chance that they're ever going to be, at least in the short term,
27:13
that they're going to be short of demand.
27:15
People have gone from spending, you know, for the biggest product, biggest product
27:19
from like OpenAI or for Plot,
27:21
$200 a month to spending $200 a day because of the amount of prompting that
27:26
these
27:26
machines are doing right now.
27:28
And I do think that that is the next big thing.
27:30
But like I said, it was invented by a single man effectively doing his own
27:37
little thing and it came out of left field.
27:39
He now works with OpenAI, but I still think that there's such low-hanging fruit
27:43
that we don't know what
27:45
the future holds.
27:47
Well, this is interesting.
27:49
Let's go from Steenburger to Shapiro here, because I can sense David in the
27:52
background taking it all in,
27:54
but still being dissatisfied.
27:56
Because it's all very well, Viv, you saying there's low-hanging fruit,
27:57
and you know what the next big thing is going to be is because it's magical.
28:01
Maybe we don't move in the same sort of...
28:05
AI circles as you do.
28:07
We can't see the magic and how to take advantage of the magic.
28:12
I mean, the magic could last for six months for one company and then someone
28:17
else comes
28:19
and knocks it off its pedestal, if you see what I mean.
28:20
Are you getting me here, David?
28:22
Are you feeling the same way?
28:25
I'm actually looking, you know, at this stage, I mean,
28:29
Viv has given us enough information to know where to look at.
28:33
I mean,
28:34
if if at the moment you've got uh um asml which i think you know you talk about
28:42
bottlenecks or you talk about problems um you know without asml you can't make the
28:48
machines that make the chips
28:50
and i suppose that goes down to probably
28:52
nvidia is one of the more important players in the chip market at the moment
28:59
simply because of uh where it
29:01
is i'm down the line Yes, there will be competition, but they are there at the
29:04
moment.
29:04
I think what I'm trying to do is say, okay,
29:08
there's two companies that are at the moment irreplaceable,
29:15
if you could call it.
29:16
Then we've got the larger businesses who are making the backbone to this,
29:20
or I mean the hyperscalers who are creating the data centers at the moment.
29:25
I think they're going out very fast, so it's going to be difficult to replace them
29:29
yet.
29:30
You know, and I've got to keep saying it because somewhere along the line it's
29:33
going to happen.
29:35
I'm going down to the next line and say, okay, we've got all of these things, you
29:40
know, we've got the, we've got ASML,
29:42
we've got the chips, we've got the data centers.
29:44
What happens next?
29:45
Who comes now?
29:48
Businesses.
29:49
How do they use it?
29:50
How do we monopolize on that or monetize that as businesses go in?
29:55
I don't know what the right word is, enablers or whatever you want to call
29:59
them.
30:00
I know we've got the cloud companies and the data centers and that, but those,
30:07
call them integrators, whatever.
30:09
I don't know what the embed is or whatever it is.
30:13
Who's going to be there?
30:14
you know, who the company is now to watch in that area of the market.
30:18
Yeah, so you've got to look at the ecosystem now for us, Viv.
30:20
Yeah, yeah, yeah.
30:23
I think one of the first things that happened, I think people are ignoring
30:27
what's going to be happening in biotech pharma.
30:29
I think that pharmaceuticals, I mean, one of the first things that Gemini, also what
30:33
DeepMind did,
30:35
was at least something called AlphaFold, which was a way to model protein folding.
30:39
And I think that, you know, LLMs don't give you the results immediately, but we
30:46
are seeing, I mean,
30:48
I don't know if you've seen recently, there's been a whole bunch of stuff
30:50
happening about cancer.
30:52
People are inventing new cures for cancer all the time, it seems right now.
30:56
Obviously, still in very early testing,
30:59
and obviously these things are going to be gatekept by safety and so on.
31:04
But I think biology is number one.
31:06
The next one is in terms of the...
31:11
the space you're talking about, NVIDIA is far more replaceable than ASML.
31:15
Because NVIDIA doesn't actually make anything.
31:17
NVIDIA sends a file, a digital file, over to TSMC in
31:22
Taiwan. And TSMC then buys the machinery from ASML.
31:26
TSMC, I think, is more irreplaceable than NVIDIA.
31:30
Because NVIDIA has some advantages, like with its CUDA, which is its platform.
31:36
But this is more a case of like Windows versus Apple.
31:39
People are used to using Windows, but they want to.
31:42
Well, it's ASML, which is, I think, really, truly irreplaceable, and TSMC
31:47
maybe is a step down from that.
31:49
Those companies, I think, really have a real boat.
31:52
Vidya can be caught up with immediately.
31:54
I mean, a smart design decision by AMD or Intel or whatever,
32:00
can really hit into Vidya very quickly.
32:02
Whereas I don't think it's possible for one or two smart decisions.
32:05
to beat asml or tsmc uh i think that's that's the that's the hardware there and
32:12
like i said down the line uh you
32:14
know right now i have a couple um you know pharma companies i put in my my portfolio
32:19
as part of my ai portfolio
32:21
just because uh you know whatever uh but i think that the real uh
32:24
thing that's going to be coming next uh is robotics and that is i think something
32:31
where if it
32:33
does occur uh i think that the number is there's something like about
32:35
50 trillion dollars a year in salaries okay uh i think if you have robotics you
32:42
can take a huge chunk of that 50 trillion dollars a year uh
32:44
because you're talking about mining you're talking about manufacturing you're talking
32:49
about construction uh you know you're talking about like other services like
32:51
cooking
32:53
and whatever driving robotics encompasses all that and i think it's actually a
32:55
larger part of the economy than
32:57
anything else so i think the next step is going to be robotics if like i
32:59
said we can solve that because it's a far harder problem to solve robotics than it
33:04
is to solve anything to do with a computer.
33:06
Does that explain things, David?
33:08
It does.
33:09
Have you got some tips there, David?
33:11
Pharma?
33:11
Yes.
33:12
No, of course.
33:13
Of course.
33:15
And, you know, to me, that's going to be the next exciting part, are those
33:21
companies that use it.
33:23
Yeah.
33:25
And how they use it.
33:27
Who they post in each company.
33:29
Yeah.
33:31
Exactly.
33:33
This is going to last, Viv.
33:35
Hey, this is not going away.
33:37
Oh, no.
33:39
Well, it's not.
33:41
I mean, think of it this way.
33:43
We are so, so, so far from, I mentioned low-hanging fruit because think of it this
33:45
way, an amateur, you can't,
33:47
there's almost no other field but an amateur, like a real amateur, who's like
33:48
kind of familiar with tech,
33:50
but could revolutionize the technology.
33:51
You're not going to find some guy tinkering in his garage that's going to
33:54
revolutionize cars, are you?
33:56
You're going to find some guy that's basically messing around in his backyard
33:59
going to make the next better rocket.
34:01
Whereas with AI right now, there's still these individuals that are messing around
34:05
and coming up with like, you know.
34:07
industry redefining kind of technology and so uh i do think that there's there's such
34:13
low hanging fruit
34:15
available here that we probably are you know a while away but the scarier part is
34:20
this that we are also
34:22
in terms of like this we're so far from the edge of the top in one way but the
34:27
second thing is that we are so close to the level
34:29
at which these things become really really impactful on the global economy uh on the
34:34
other end and the real fear i have is
34:36
this And I know this is not finance or whatever, but it's...
34:38
We in Africa are just not going to take part in this thing.
34:41
We have no hook into it, you know?
34:44
And you look at what's happening with people like Andrew Yang in the US and, you
34:49
know, other parts of the United States and Europe,
34:51
and they're all talking about what happens when AI comes, you give what's called UBI,
34:56
Universal Basic Income, effectively the dole for everybody,
34:58
okay?
35:00
But the dole requires you to have taxes collected to pay the dole, right?
35:03
In Africa, where are you getting the taxes from?
35:06
You don't have the company that's basically making the stuff, so you're not
35:08
taxing them.
35:09
We've seen what's happened with Donald Trump and USAID.
35:12
We're not going to get large jets from Europe or North America and stuff sent to
35:19
Africa to support our populations.
35:21
So when this stuff really does take off, when we do start to see job replacement on
35:25
a really high scale,
35:27
what happens to some kid in Lagos or some family in Joburg when you no longer have a
35:32
marketable, employable skill?
35:34
But your government doesn't have the money to pay you anything.
35:39
Yeah, yeah.
35:39
I think it's going to be further than that as well.
35:42
You know, that could go through to Europe or to other continents as well, outside
35:49
of, you know, where the big,
35:51
outside of the US, maybe China as well.
35:52
But I think Africa is, it's a big concern.
35:57
You know, we're still pulling things from the ground and still worried about.
36:01
politics and various other things without seeing all of this.
36:05
So I agree with you.
36:06
We're just nowhere to be seen.
36:09
I'm talking of the whole continent, not South Africa specifically.
36:13
We've won by teachers.
36:16
Maybe someone will see a gap in the market and come here and start promoting AI and
36:23
using
36:23
AI in South Africa and beyond.
36:25
We build data centres.
36:27
We build data centres, I'm sure.
36:29
But But But when Viv talks, it's much more about the technology and the use of that.
36:37
Are you going to use it here for pharmaceuticals or for biotech or whatever
36:41
it is?
36:43
You're going to go where the skills are and where the money is.
36:47
And if anybody does have an idea and wants to develop it, you can't raise anything
36:51
here.
36:51
There's no money to be raised here.
36:53
Just stop that.
36:54
The banks won't lend it.
36:55
They just haven't got that same culture.
36:57
I, listen, Viv, I found this was fascinating.
37:01
And it really was very, you know, inviting.
37:04
And I just hope other people are going to listen to it and learn from it as much as
37:10
we have.
37:12
What about the people that are, what about the naysayers, Viv and David?
37:16
There's a woman who is a researcher, mathematician, open AI.
37:23
Zoe Hitzig, her name is.
37:25
She wrote a letter to the New York Times saying,
37:28
I've quit because my company is making the same mistakes that Facebook made,
37:34
OpenAI that is.
37:36
And she says, for several years,
37:37
chat GPT users have generated an archive of human candor that has no precedent,
37:43
in part because people believe they were talking to something that had no ulterior
37:48
agenda.
37:50
Users are interacting with an adaptive conversational voice to which they have
37:56
revealed their most private thoughts.
37:58
People tell chatbots about their medical fears, their relationship problems, their
38:02
beliefs about God and the afterlife.
38:04
Advertising built on that archive creates a potential for manipulating users in ways
38:09
we don't have the tools
38:11
to understand, let alone prevent.
38:12
She left because chat GPT and open AI,
38:16
everybody else contemplating moving towards an ad based model, just like
38:23
Netflix is,
38:25
for example.
38:27
Viv, that's just one example of someone saying, I don't like the way things are
38:28
going.
38:28
Not the sort of intellectual way that you're talking about.
38:31
But nonetheless, there are people out there that are saying enough is enough
38:35
now.
38:35
What do you say to that?
38:38
It's totally correct.
38:38
I mean, if you don't think worlds matter, you ask a question.
38:42
How many people did Hitler kill personally?
38:43
OK, good point.
38:46
None, almost.
38:47
Right.
38:48
He did all his damage by just speaking.
38:50
Right.
38:51
And people underestimate the power of just, you know, propaganda and words and
38:55
whatnot.
38:55
And the thing you have to understand is that imagine having somebody with
39:01
persuasive ability, not speaking to a crowd,
39:03
but speaking to you in particular.
39:05
You know what I mean?
39:07
And there is a huge chunk of people who are quite vulnerable to this.
39:09
I'm not even saying I'm not one of them.
39:10
Maybe I might be one of them, but the AI gets smarter.
39:13
But there's been some really famous cases of people committing suicide.
39:18
There's people who try to marry the AI.
39:21
We've gotten really, you know,
39:22
kind of like convinced that they're dealing with another thinking entity out
39:28
there.
39:28
And the thing is that these things are designed for a profit maximizing or
39:35
intention maximizing
39:37
thing.
39:39
They will do things to your mind that are quite scary.
39:41
And we already see, like I said, suicides.
39:43
I think there's arguments that there's no.
39:45
Hundreds of thousands of people.
39:47
That's what's called AI psychosis.
39:50
And I think as AI gets smarter and smarter, it's going to be bigger and
39:53
bigger.
39:54
But it's not just that.
39:54
I mean, look at this new stuff with video.
39:57
There's new video out there.
39:59
If you want to have a bit of fun, there's an AI video of Kanye West, right, from
40:05
China, where he sings in Chinese,
40:07
Mandarin.
40:09
And I swear, if it wasn't for the fact it was so ridiculous that Kanye West was
40:12
singing Mandarin, okay,
40:14
it would...
40:15
be i would not if you just had like a chinese person singing there or he sang in
40:22
english number one the song is good it's actually a catchy song okay
40:24
number two it looks totally real you cannot believe any video anymore you can't
40:29
believe
40:29
photos and you couldn't believe photos like if you if you really need a photo in
40:36
the last say six months you've just been you know possibly fooled by air quite a
40:38
number of times but you're into point we can't believe a uh video as well
40:39
so how are we as people you know who are not physically looking at things with our
40:46
eyes but look at the internet and reading newspapers and so on,
40:48
going to judge what's real or not.
40:50
Number one.
40:50
So I do think this is a huge danger.
40:53
We are the job losses.
40:55
There's the AI psychosis, the AI basically propaganda.
41:00
There are the job losses.
41:02
There is the military stuff happening right now.
41:04
I mean, AI with drones becomes, I think, the most powerful military unit in the
41:10
world.
41:10
The Europeans right now are rebuilding their military drone based upwards.
41:14
You know what I mean?
41:15
Then you're talking about the economic impact as well.
41:18
What happens when you have, you know, the centers of AI being primarily the U.S.
41:23
and China, but a little bit in Europe.
41:25
What happens to basically Africa, India, South America?
41:29
Do these places just effectively become what?
41:31
I mean, if you don't have the AI companies in your country, you don't have the
41:36
technology in your country, what are you?
41:38
You effectively are just warehouses.
41:40
You know, you just buy stuff and you just sell your raw materials.
41:42
I mean, There's nothing that you...
41:44
to the wall at that point.
41:46
These are all, I think, very dangerous, very scary kind of things for the future.
41:50
At the same time, let's be fair about this.
41:53
AI has the promise of huge, like, you know, if AI is treated properly and we are
41:58
able to deal with it properly,
42:00
we would be wealthier than we've ever been in human history.
42:03
People are talking about, I mean, Cathy Wood, I know you know from After Capital,
42:08
she's talking about 30%
42:10
per year GDP growth.
42:12
That means your economy effectively doubles every two and a half years.
42:14
Okay, so in 10 years time, your economy goes up, you know, two to the power of
42:20
four, which is 16 times,
42:22
you know, if you make it a 10,000 a month now, in 10 years time, you can have 60,000
42:26
a month.
42:28
And in another 10 years time, you're making basically, you know, millions a
42:32
month.
42:32
Okay, so it's 30% a year increase in GDP, 25%
42:36
of that will go on benefits for people that have been knocked out of a job by AI.
42:41
and what do they do with themselves all day?
42:42
They go and get their check, metaphorically speaking, once a month, and
42:48
they have got nothing to do.
42:50
They can do a bit of gardening, play a bit of golf, like David, do a bit of running
42:53
and reading, etc.
42:55
But eventually you have a social uprising of bored people.
42:56
Crime increases, society collapses.
42:59
I sound like, what's his name, Jeff Goldblum in Jurassic Park, Viv,
43:05
because of that park that that silly fellow made.
43:09
But do you see what I mean?
43:11
It's all very well.
43:12
And you talk about wealth as though money is wealth.
43:15
There's other things apart from money that are considered wealthy by certain types of
43:20
people.
43:21
Do you see what I mean, Viv?
43:24
I don't believe that to be true.
43:25
I think we've had people for thousands of years who have not had a job.
43:29
They call it rich to class.
43:30
They live quite nice lives.
43:34
we've had we've had people who have had a job but not hundreds of millions of them
43:41
viv not hundreds of millions of astocrats walking
43:43
around with powdered wigs but i'm saying that people make up their own what happens
43:48
when you have like these these i mean there are people in
43:50
new york city there are people even in joburg uh you know i once did a uh
43:53
not like i once did a a a weekly class for some very wealthy uh wives you know what i
43:59
mean
44:00
And, you know, these were very smart women, but they were, the wives were very
44:04
rich men.
44:04
And they effectively were kind of like, you know, they had lunch and they went
44:09
shopping as they kind of like did they.
44:11
But they still had things to do.
44:13
They basically did charity events.
44:15
They made work, you know, made interesting things.
44:17
They took classes.
44:19
People find things to do in their life.
44:21
That's not the danger.
44:23
Let me tell you.
44:25
Viv, I'm sorry, what we'll do is that when you find the pharma company that makes the
44:26
pill that
44:26
makes me and you able to live to 500 and David to 510, okay, we'll go out and do
44:33
that,
44:33
the three of us, and I can guarantee that you can talk about people going to do
44:36
charity work.
44:38
You're talking about a couple of rich people who've got nothing to do apart from
44:40
go down,
44:40
have a cup of coffee and pretend to be nice to the rest of humanity.
44:44
I'm talking about hundreds of millions of people.
44:46
I'm talking about ordinary people that don't have a job.
44:48
When you get ordinary people.
44:50
that don't have a job.
44:51
I promise you, the peasants are revolting.
44:54
That's what's going to happen.
44:55
But I do get your point, but you seemingly don't get mine.
45:00
I'm a pessimist, Viv, I'm afraid.
45:02
I'm a pessimist when it comes to the long-term future, which hopefully I won't
45:07
be able to see.
45:09
David, you're sitting there on the fence.
45:12
No, I'm not on the fence.
45:16
Okay.
45:16
What we can say...
45:18
Yeah, go on.
45:19
No, I say it's a very difficult issue if this does happen.
45:24
I don't believe it will happen.
45:26
I don't believe it will happen to the extent that we say it will happen, but
45:30
I think in its own way it creates actually more jobs.
45:33
It becomes an incredible tool for people to use.
45:36
But let's watch that.
45:37
You know, let's see how that develops.
45:40
I mean, we're getting a little too carried away with the future.
45:43
Well, 30% GDP, Viv started it, not me.
45:46
I know, I know.
45:49
Listen, when Trump said he wanted Kevin Walsh to get…
45:54
growth up to 15 i thought he was nuts but 30 i said okay
46:01
yeah you know we looked at most recent job numbers in the u.s yeah you actually are
46:08
seeing a a bifurcation where you
46:10
have really growth without job creation for the longest period i think they've
46:14
recorded and
46:16
is that is that not a possible indicator things are starting to happen i don't
46:21
think people are being replaced by air right now what i do think is happening
46:23
right
46:25
now and i know from a fact because I looked at a couple other people in the
46:25
sector, etc.
46:26
What people are doing is delaying hiring.
46:28
They're not hiring new graduates because they think in a year's time, AI will be
46:33
smart enough not to need the new guys.
46:35
And that, I think, is what the start is, right?
46:36
Hopefully.
46:37
But getting back to the 25% thing here, David, sorry, it's called Lindsay.
46:41
Yes.
46:42
25% in which country?
46:44
It's going to be in the US.
46:45
It's going to be in China.
46:46
How does the 25% come to the kid in Lagos or the kid in Soweto?
46:50
It doesn't.
46:51
It does not come here.
46:53
So we will have the combination of both lack of employment and no money from
46:58
taxes.
46:58
Because where is the South African government going to get the tax money to
47:02
pay UBI in South Africa?
47:04
Yeah, yeah.
47:06
You're quite right.
47:08
You both highlighted Africa as something that would be on the fringes.
47:10
And they'll be sort of looking in, you know, like at the sweet shop, can't afford
47:16
to go in and everyone else is in there munching their heads off.
47:18
OK, hopefully that doesn't happen as well.
47:18
David, let's get back to reality now and today.
47:21
So after what you've heard...
47:22
And after what you've seen with your own research and listening to Viv,
47:27
does anything change when it comes to the principles of investing?
47:30
You look at the market and you say, I don't understand that yet.
47:33
Therefore, I'm not going to invest in it just because somebody else is.
47:37
I'm going to stick to the principles of P.E.
47:39
ratios and cost a book and all that sort of thing.
47:42
Does anything change for you?
47:45
No, I think they've just given me a clearer view of what it does.
47:51
And in fact, he's making a better case for investing in AI.
47:58
When you understand how it's going to change our lives and where it's going,
48:03
I'm certainly not going to buy Unilever or Procter & Gamble.
48:09
It's as attractive as they might seem, and I'm sure there are going to be other
48:13
businesses.
48:15
But I think, Lindsay, where it comes down is that this is something I'm not going to
48:19
give up and continue.
48:21
to hold where we are at the moment.
48:22
And perhaps I'm looking for further companies to add.
48:25
You know, that was the whole purpose of my exercise.
48:29
And I think just Viv's reinforced the view that this is going to be around for a long
48:34
time.
48:35
So stay with it.
48:37
You know, and I know we're trying to bring money back here.
48:40
We're looking for banks in South Africa and retailers and that.
48:44
Yeah, that might be in the short term.
48:45
It might be over the next few months you're going to be able to.
48:48
make some money but i think in the longer term you've got to stick with this theme
48:55
and you've got to be fleet-footed as well
48:57
as as viv says you know nvidia might be hot now and and uh making 80 of the
49:02
gpus that that are used in ai
49:06
or in various things and you know when when suddenly those things change then
49:13
you've got to get out you know and as viv says he's you
49:15
know he he highlights that this could change pretty fast as well so but the
49:20
theme's not going to change.
49:22
Now, what isn't going to change as well is my understanding of it, because after Viv
49:26
told me to go to, what was that thing?
49:28
Claude.
49:29
Claude.
49:29
No, not Claude, the other one.
49:33
Gosh, what was it?
49:33
Double Glock. Yeah, that's it, yes.
49:36
I thought, no, this is too much.
49:37
My brain was throbbing.
49:38
So I looked around at an AI newsletter that I subscribe to, and it said
49:45
Alibaba has just introduced something new, and it was on the 15th of February, so
49:49
quite recent.
49:51
on Sunday.
49:53
And Dalai Baba says, it's called QEN 3.5.
49:54
It says, we are delighted to announce the official release of QEN 3.5,
49:58
introducing the open weight of the first model in the QEN 3.5 series, namely QEN
50:05
3.5-397b-A17b. As a native vision language model, that
50:10
QEN 3.5 that I just said demonstrates outstanding results across a full range of
50:17
benchmark evaluations,
50:19
including reasoning coding, agent capabilities and multimodal understanding
50:23
empowering developers and
50:25
enterprises and it went on and on and on and the words got longer and longer and i
50:30
said but what is it i didn't know what it
50:32
was viv i'm being stupid aren't i this
50:34
is something to actually point out as well it's another thing like it's not like
50:41
opening it's not like this is another thing like chad gpt it's another thing
50:43
like claude but this is an
50:45
important fact here the chinese are basically giving this They are effectively
50:48
dumping the market with LLMs,
50:50
free LLMs.
50:52
So, Quen, Kimi, DeepSeek are all free open-weight models.
50:54
But effectively, the Chinese are coming out and saying, you know the stuff that
50:57
Anthropic is making?
50:59
Here's something almost as good for free.
51:01
Here's the stuff that
51:03
Gemini, you know, Chachi PT, here is what they have almost as good a few months
51:09
later,
51:11
but free.
51:13
Again, what kind of model?
51:15
like you know a car company trying to survive when his biggest competitors are
51:20
giving away something almost as good for free and that
51:22
is another aspect of this market that we haven't discussed is the fact that the
51:27
chinese are really by this by by using open
51:29
source are effectively ensuring
51:30
that if it's ever stall if it's ever slowed down that they will destroy any of
51:37
these llm model makers out there because they
51:39
are giving whatever they are making for free Very interesting.
51:39
Okay.
51:40
It's one for, I mean, do you buy the Chinese company if it's not making money
51:44
because it's giving it away for free?
51:46
But there's obviously a much, much bigger story than that.
51:47
Gentlemen, thank you very much for your time.
51:49
I've enjoyed being the village idiot combined with the devil's advocate.
51:57
Viv, brilliant as always.
51:58
David, listening, soaking it all up as always and contributing.
52:02
Viv Govender is from the award-winning RAN Swiss and David Shapiro is the
52:07
award-winning.
52:09
portfolio manager from sasfin securities and that was a special five o'clock shadow
52:14
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52:16
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52:21
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52:37
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