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