Speaker 1: All right, welcome back everyone. I'm here with Ryan. We're
Speaker 1: going to be discussing AI once again. We actually discussed
Speaker 1: this in our pilot episode, which I believe was in November,
Speaker 1: And obviously this is a developing story all the time.
Speaker 1: There's a lot to talk about. But I guess, first off, Ryan,
Speaker 1: looking back on what we discussed in November, what you
Speaker 1: remember of it, how do you think that's worked out
Speaker 1: as far as where we said this was going what
Speaker 1: to expect.
Speaker 2: I think we've been vindicated so far. The hype cycle continues,
Speaker 2: the same faces at the helm. You know, the original
Speaker 2: open AI group still quarreling. Elon Musk is going at
Speaker 2: it with Sam Altman on on X and you know
Speaker 2: open ayes under severe pressure. But I guess we'll get
Speaker 2: into the numbers of it as far as we can
Speaker 2: work out, because there's a huge amount of obviousigation in
Speaker 2: this market. But the build out is absolutely remarkable. The
Speaker 2: numbers that are being talked about, the you know, the
Speaker 2: demands that is being calculated based on labor displacement. It's
Speaker 2: being pitched as a battle for global supremacy between America
Speaker 2: and China particularly, and you know, a new economic paradigm,
Speaker 2: which Musk at least is saying we'll be work free
Speaker 2: and abundant. So yeah, that's uh, let's get into it.
Speaker 1: I guess yeah. The numbers are staggering. We went over
Speaker 1: some of them the last time we spoke. I mean,
Speaker 1: six hundred and fifty billion is the numbers. Six hundred
Speaker 1: and thirty billion direoboats of capex that's just going to
Speaker 1: be spent by four companies on AI this year, Amazon, Microsoft, Alphabet,
Speaker 1: Meta and obviously these numbers, you know, they just keep
Speaker 1: blowing up during unbelievable territory. But there's no sign of
Speaker 1: it's slown down. And there's definitely more of a sense
Speaker 1: now of people recognizing that this is some kind of bubble.
Speaker 1: But I guess the question is how does that come
Speaker 1: to an end? Because I think a lot a lot
Speaker 1: of people have a sense, okay, it's a bubble to degree,
Speaker 1: but there is a worthwhile product there, something will come
Speaker 1: of it, some kind of productivity gains. We just don't
Speaker 1: know how that will manifest or where the money needs
Speaker 1: to go to benefit from that. But also last time
Speaker 1: we discussed this, we talked about China, which sometimes doesn't
Speaker 1: get talked about so much in these discussions, and you
Speaker 1: were kind of bullish on Chinese manufacturing and China's potential
Speaker 1: to catch up, and that's suddenly something that's coming to
Speaker 1: the fore neat. There was a couple of models released
Speaker 1: in the last couple of weeks from China that again
Speaker 1: has caused alarm in Silicon Valley because they're so close
Speaker 1: to the top models being it out by entropic open Ai,
Speaker 1: and the head of strategy for open Ai actually had
Speaker 1: an interesting post I think he made on x where
Speaker 1: he warned that these open source models being put out
Speaker 1: by the Chinese that this was going to lead to
Speaker 1: an era of AI communism, I think he called it,
Speaker 1: And he was basically warning people of this terrible potential
Speaker 1: future where you can just access AI models for free.
Speaker 1: I'm not sure why he thought that would be such
Speaker 1: a terrifying prospect of people, that you would be able
Speaker 1: to access these models for free, rather than the brilliant,
Speaker 1: liberal capitalist future of paying a subscription to scam Altmann
Speaker 1: to access the same tech. But apparently this to him
Speaker 1: is a dangerous specter of AI communism. Interestingly enough, I
Speaker 1: think David Sachs, who's the AI advisor to the US
Speaker 1: government actually pushed back on that and warned about regulatory captures.
Speaker 1: So there's there's some interesting dynamics happening there as far
Speaker 1: as how these tech companies now are actually going to
Speaker 1: have to deal with this tread that you warn't a
Speaker 1: bode of China coming in and eating their lunch. And
Speaker 1: it's suddenly seemed to look more realistic.
Speaker 2: Yeah, I mean, let's go back to the beginning of AI.
Speaker 2: Twenty fifteen, Open AI is born. It's a collaboration between
Speaker 2: likes of Vilo mass Musks, Sam Altman, a bunch of
Speaker 2: other tech nerds, mathematicians, whatever. Twenty nineteen, that project, which
Speaker 2: was meant to benefit all humanity, open source AI for all,
Speaker 2: becomes a private company because they realized that to scale
Speaker 2: this up into something brilliant, something resembling a brain, is
Speaker 2: going to require a lot of computing power. And then
Speaker 2: along comes in Nvidia, who historically were just a company
Speaker 2: that provided graphics for gamers like me when I was
Speaker 2: a teenager, and then became a bitcoin mining company or
Speaker 2: you know, bitcoin mining compute provider. You know, the the
Speaker 2: tech optimists of AI decided that they could do brilliant
Speaker 2: things with GPUs, and that's you know, that's when the
Speaker 2: kind of American rent seeking model was born that they
Speaker 2: were going to, you know, create all these data centers,
Speaker 2: create these huge clusters of compute, and then they were
Speaker 2: going to sell it back to consumers enterprises after they've
Speaker 2: scraped all of Western civilization by training their models on
Speaker 2: Reddit and you know, every document, every pdf on the Internet.
Speaker 1: Because I read it was the pinnacle of civilization.
Speaker 2: I mean, to be fair to them, they have or
Speaker 2: here's an exact sample, anthropic. They were actually scared of
Speaker 2: copyright infringement for all the sources they effectively stole, so
Speaker 2: they used their seed capital to buy millions of physical books,
Speaker 2: tore them apart by removing the bindings and copy in
Speaker 2: every page. So they have a phenomenal database of real
Speaker 2: world books. But I think AI is bundled together in
Speaker 2: this kind of very vague tech optimism, and I think
Speaker 2: we should separate the kind of processes that are actually
Speaker 2: going on here. So you've got the LLM, which is
Speaker 2: basically predicting the next word, and it happens to be
Speaker 2: great for coding. So you've got these two frontier labs,
Speaker 2: Open AI and the spinoff of open Ai because Oltman
Speaker 2: and Amedi fell out. Anthropic and Athropic is the market
Speaker 2: leader encoding. You know, they you know, have basically taken
Speaker 2: the software development well by storm. I think pretty much
Speaker 2: everyone's using them.
Speaker 1: I should point out, actually, since we last spoke, I
Speaker 1: was banned by Claude. I really, I don't know if
Speaker 1: I told you this, I guess not yet. So I
Speaker 1: I'd only used Claude a couple of times, wasn't really
Speaker 1: feeling chat GPT, but so I decided to test out
Speaker 1: the you know, every want to talk about who great
Speaker 1: Claude is compared to chat GUPT. So I bought the
Speaker 1: premium subscription. Immediately banned, and I got an email from
Speaker 1: Claude saying that their algorithm had I forget how they
Speaker 1: worded it. It was signals. That was it. That there
Speaker 1: were It's algorithm had generated signals associated with me as
Speaker 1: a user. And on the basis of this, decided that
Speaker 1: I may be in violation of their term or I
Speaker 1: shouldn't be using their product, and banned me. And then
Speaker 1: you know, centered to review human reviewed it and yes
Speaker 1: I'm banned. So that was that was the extent of
Speaker 1: the explanation. I got as there are signals associated with
Speaker 1: you as a user that means you can't use Claude AI.
Speaker 1: So I haven't. I haven't been able to experience and
Speaker 1: of any of the Entropic models. But this is there's
Speaker 1: your future, you know. I guess I'll be cheering on
Speaker 1: for the Chinese open models. I guess we're facing.
Speaker 2: You're banned from vibe coding now if that was interest
Speaker 2: of yours? So yeah, where to go next? Yeah, Andthropic
Speaker 2: is brilliant coding every development.
Speaker 1: But and Tropic does seem like the most liberal of
Speaker 1: all these companies in terms of most the other tech
Speaker 1: companies seem pretty amoral. But wasn't Entropics kind of modus
Speaker 1: operando when they started out? It's like, oh, we're going
Speaker 1: to be ethical AI.
Speaker 2: Wasn't that kind of the Well yeah they said that,
Speaker 2: but I just think it's pr no.
Speaker 1: But but I'm I think maybe they're their idea of
Speaker 1: ethics is I thought Drew's posted something that he was
Speaker 1: asking what the different types of head speech laws are
Speaker 1: in Europe and a Claude wouldn't tell them, you know,
Speaker 1: bands me, so I think, like, you know, their idea
Speaker 1: of ethics is you know, even society leftism'm.
Speaker 2: I mean, I basically think they're all in it together.
Speaker 2: They have a pr mantra, and then in reality they're
Speaker 2: ducing their own stock so they can cash out. So
Speaker 2: Amadi at Davos earlier in the year, he basically announced
Speaker 2: that twenty percent of the workforce was going to be
Speaker 2: unemployed in a couple of years. And then he announced
Speaker 2: Mithos and said this is so good. Everyone's going to
Speaker 2: have their cybersecurity compromise, and this could bring down the
Speaker 2: world economy. So he's fallen out with Department of Defense,
Speaker 2: like the American government basically hate him. They basically said
Speaker 2: that there had to be severe restriction on the use
Speaker 2: of myth foss because it was such a risk. And
Speaker 2: you've even got the likes of Alex carp A Palenteer
Speaker 2: coming out and saying, this guy is a clown. He's
Speaker 2: you know, scaring the voters. He's putting us in danger
Speaker 2: of you know, electing communists and Luddites and all this
Speaker 2: kind of red scare stuff.
Speaker 3: So there doesn't seem to be that.
Speaker 2: Much consensus between the various oligarchs associated with AI. They're
Speaker 2: all kind of feuding with each other and they're trying
Speaker 2: to reduce their own stock, and you know, Sam Altman
Speaker 2: says that AGI is coming. There are different, so many
Speaker 2: different layers to this. And then you've also got the
Speaker 2: hyperscalers who own equity in the frontier labs open Ai
Speaker 2: and and Thropics, so they're incentivized for these two ipo
Speaker 2: at very high evaluations. Meanwhile, there's Nvidia in the backgrounds
Speaker 2: there just trying to ship out as much Compute as
Speaker 2: possible to you know, all all buyers, which is basically
Speaker 2: just the hyperscalers, and to keep everything afloat, they're doing
Speaker 2: all these really dodgy circular finance deals, very dodgy accounting
Speaker 2: where they're putting things on the revenue statements that really
Speaker 2: shouldn't be there, and they're hiding the debt and so
Speaker 2: much is obviouscated. But if you look at the raw
Speaker 2: numbers and what these companies are saying, they're basically selling
Speaker 2: Compute at a loss, and Thropic and open Ai are
Speaker 2: selling at a loss. And you know, you've got all
Speaker 2: these stories of enterprises token maxing. They're trying to get
Speaker 2: productivity out their workers, they end up spending too much,
Speaker 2: they cancel it that happened to Uber and they're having
Speaker 2: to go through so many rounds of raising new capital.
Speaker 2: Open Ai has you know, raised capital from Massi Yoshi
Speaker 2: Son to the tune of about sixty five billion dollars.
Speaker 2: His last investment was we work lost about sixteen billion
Speaker 2: dollars on that, So I wouldn't say he's a very
Speaker 2: good investor himself. And now the hyperscale is a juice
Speaker 2: in the high whole cycle. There are plans for huge
Speaker 2: amounts of data centers all across America, one hundred plus gigawatts.
Speaker 2: They've already broken ground on twenty and the kind of
Speaker 2: predictions they're making are you know, quite phenomenal.
Speaker 1: Yeah, one hundred and fifty gigawatts and just proposed new
Speaker 1: data centers across the US. One of the but, you know,
Speaker 1: one of the limitations on this is the compute. But
Speaker 1: one of the limitations as well is just what is
Speaker 1: the actual limit of an LM as far as intelligence?
Speaker 1: And this is kind of a lot of what I
Speaker 1: discussed when we covered this topic last is the way
Speaker 1: people misunderstand intelligence, the embodied nature of intelligence, and not
Speaker 1: kind of accepting that LLM is especiically an advanced predictive
Speaker 1: text model that can't jump into what is, you know,
Speaker 1: genuine learning. I think since then, though, I think there
Speaker 1: are people in the field that are kind of putting
Speaker 1: this out there of you know, the AGI thing has
Speaker 1: been very overhyped, and it often comes with you know,
Speaker 1: this can give us great productivity gains, and it can
Speaker 1: be very useful in specific fields like coding directed by humans.
Speaker 1: But a lot of this kind of sci fi AGI hype,
Speaker 1: you know, where Elon Musay in artificial general intelligence was
Speaker 1: just around the corner for years, I think you see
Speaker 1: some more mature voices kind of damping that. Jan Lakun,
Speaker 1: who's the head of AI development at Facebook, says, look,
Speaker 1: llms are not going to develop.
Speaker 3: A g I.
Speaker 1: They are very useful in a certain way, certain limited way,
Speaker 1: but we have kind of reached the limits of this paradigm. Like,
Speaker 1: if there's going to be another leap in AI, it's
Speaker 1: going to be from some kind of model that isn't
Speaker 1: just an LLM. And I think that's clear. You know,
Speaker 1: we get we're obviously in this race of new AI
Speaker 1: models coming out all the time, and some of the
Speaker 1: releases have been pretty impressive, but it is kind of
Speaker 1: you know, it's a more advanced version of the same thing,
Speaker 1: and a lot of the core problems that were there
Speaker 1: with lllms are still there. You know, these models still hallucinate.
Speaker 1: You know, you still can't rely on it on supervised
Speaker 1: for serious work. Right, it really is still basically a
Speaker 1: guide because it does make mistakes. It's still hallucinates. They
Speaker 1: haven't been able to solve that. There's increasingly a problem
Speaker 1: of AI critting this ecosystem of unsourced or badly sourced
Speaker 1: and just fake material. Some people have been doc coumenting this.
Speaker 1: You know that a YouTuber uses AI, it throws him
Speaker 1: out a fake statistic or a fake scientific fact. He
Speaker 1: puts it in his YouTube video, the YouTube video gets
Speaker 1: a million views. That then becomes a source on this
Speaker 1: maybe niche topic that other lllms use people posted on Reddit,
Speaker 1: and then it kind of enters into this ecosystem of
Speaker 1: knowledge within the AI. Sometimes you have you know, one
Speaker 1: AI is sited in Krokipedia. We're not really sure where
Speaker 1: Rokipedia is getting the information from that itself as LLM generated.
Speaker 1: So like you're running into limits with these lms where
Speaker 1: they can't really self correct. It's still very much human
Speaker 1: guided and there's been all sorts of hype about all
Speaker 1: these are learning in new ways that we hadn't expected,
Speaker 1: and that we're moving into this new kind of paradigm
Speaker 1: where they learn in ways similar to neural networks and
Speaker 1: it's not just the standard token prediction. And as far
Speaker 1: as I can tell, this is all nonsense. I mean,
Speaker 1: antime I've looked into these, it's it's kind of a
Speaker 1: slide of hand trick where it's like, you know, one
Speaker 1: extra step between the same kind of token prediction that's
Speaker 1: always been happening. So it seems like we're kind of
Speaker 1: trapped in this paradigm where they're going to add more
Speaker 1: compute and it may look somewhat more impressive, But as
Speaker 1: far as breaking into that next step where it gives
Speaker 1: you something like, you know, scientific discoveries, which we still
Speaker 1: don't get from these lllms, you know, again they're helpful
Speaker 1: their replace jobs and work when you kind of know
Speaker 1: what the parameters are and it can do it using
Speaker 1: existing material that's out there and put it together. But
Speaker 1: as far as any kind of intelligence breakthroughs, you know
Speaker 1: that they're going to be providing incredible medical discoveries or
Speaker 1: things that are really just going to you know, advanced capitalism,
Speaker 1: like like Musk says, that make us all post scarcity.
Speaker 1: I mean, it's just not there in this LLM paradigm.
Speaker 1: And you know, someone like Jean Lacun acknowledges that, and
Speaker 1: he kind of says it with the suggestion that something
Speaker 1: that's going to come after it, that's going to be
Speaker 1: the real deal. But you know, I'm not sure. I'm
Speaker 1: not sure that there's just this bridge. Everyone is assuming
Speaker 1: that and them gets so complicated and then we get
Speaker 1: this kind of self sufficient component in general intelligence.
Speaker 2: Well, I can't even get my Gemini to make a
Speaker 2: basic meme and it hallucinates all the time, So I
Speaker 2: have to be quite careful about the sourcing. But let's
Speaker 2: talk about another of the big powerbrokers of Ai Demissabis
Speaker 2: of Google Deep Mind.
Speaker 1: He was.
Speaker 3: Talking about AGI as.
Speaker 2: You know, this brilliant you know, something that's equal to
Speaker 2: human intelligence. That's now the new definition. The previous cope
Speaker 2: was there's going to be recursive learning, it's going to
Speaker 2: become super intelligent and be able to solve anything. That's
Speaker 2: now been dialed back down to oh AGI means human
Speaker 2: level intelligence across a number of domains, you know exactly
Speaker 2: as a intelligent person, I might correct you a little
Speaker 2: bit there. Demester Sarvice with Google Deep Mind did solve
Speaker 2: the protein folding problem and he won the.
Speaker 3: Nobel Prize for Chemistry for that.
Speaker 2: But the way that works different to an LM is
Speaker 2: they use the GPU compute to do what they call
Speaker 2: reinforcement learning, where they basically give it like a biological problem,
Speaker 2: chemical problem, run like, you know, all these huge amounts
Speaker 2: of computations, and then they gradually work out how the
Speaker 2: you know, natural biology works and.
Speaker 1: They were able to discover right, But that's the point.
Speaker 1: Did the did the LLLM intelligently make a scientific discovery
Speaker 1: intuite and how to you know, synthesize these different fields
Speaker 1: and theories, or did someone that had this proposition using
Speaker 1: to process a ton of data and then.
Speaker 2: Yeah, they knew where there were unknowns that were hard
Speaker 2: to record, and then they were able to use the
Speaker 2: compute to solve the unknowns which were already known byrilogists.
Speaker 2: But Hasabis is now talking about the use cases being
Speaker 2: things like automated labs and we're going to use AI
Speaker 2: to you know, solve scientific problems. So overall, the projections
Speaker 2: have been downgraded significantly, and there have been signals among
Speaker 2: these people to you know, stop telling the public they're
Speaker 2: going to be unemployed and things like this. And you know,
Speaker 2: the AI build out doesn't get that much attention in
Speaker 2: the mainstream.
Speaker 3: It's kind of you.
Speaker 2: Know, talked about on finance podcasts and you know, tech
Speaker 2: nerd podcasts, but it doesn't receive much attention and you know,
Speaker 2: in the news or politically. But yeah, Hasabis is probably
Speaker 2: the most trustworthy figure.
Speaker 1: I would say, Well, it's interesting how there is this
Speaker 1: kind of leftist, populist sentiment of anti AI prolog erics.
Speaker 1: You know, those kinds of people are especially disliked, the
Speaker 1: ailig erics. But also this like anti data center trend,
Speaker 1: which is kind of it's kind of cross left and
Speaker 1: right wing. You know, like you've got this guy James
Speaker 1: Fishback who's running in Florida as a Republican and he's
Speaker 1: kind of griperus candidate, but but he's he's he's doing
Speaker 1: this populous like I'm going to ban data centers in Florida. Yeah,
Speaker 1: it's it's kind of it's kind of like a slopuloust
Speaker 1: slopulous meme like the it's kind of like the you know,
Speaker 1: the the most populist elements of the left and right
Speaker 1: are both doing this out a center thing. And sometimes
Speaker 1: it's not really clear why the general public poses these
Speaker 1: data centers. A lot of times, I mean, I think
Speaker 1: they have a sense that makes things more expensive and
Speaker 1: maybe it's going to be damaging for the infrastructure around.
Speaker 1: But there hasn't really been a well articulated political case.
Speaker 1: I mean, it's basically just nimbism, isn't it. Like, let's
Speaker 1: banner in our state and you know, still have the
Speaker 1: gains of AI productivity, but is built it somewhere else?
Speaker 2: I think there's the cost concern of increasing electricity prices.
Speaker 2: There's the environmental concern, you know, things like you know,
Speaker 2: the ugliness of a data center, the water usage, et cetera.
Speaker 3: But yeah, there are.
Speaker 2: Thousands of these things planned all across America. There's been
Speaker 2: a lot of resistance to it. These tech bros are
Speaker 2: even suggesting that the Chinese might be involved in leading
Speaker 2: this kind of movement, and Sam Altman has two attempts
Speaker 2: on his life this year. You know, a lot of
Speaker 2: people are very skeptical about it, and you know, the
Speaker 2: tech bros are blaming the likes of Amma Day for,
Speaker 2: you know, saying that they're going to be unemployed, and
Speaker 2: there is this big question that if the predictions do
Speaker 2: come to pass and it really is as intelligent and
Speaker 2: disruptive as they are claiming, and as it needs to
Speaker 2: be for all the debt that's being accumulated, then yeah,
Speaker 2: people will be unemployed and we will be in a
Speaker 2: you know, semi communistic economic paradigm.
Speaker 3: I mean, we should, we should talk about it.
Speaker 1: I mean, well, one question is what's what's funding all this?
Speaker 1: Then you know, you can get into the numbers, but
Speaker 1: I mean, I mean, the essential thing is that the
Speaker 1: US economy basically is not growth outside of AI companies,
Speaker 1: Like that's entirely what's driving everything, and even growth in
Speaker 1: other areas of the economy of the economy you can
Speaker 1: say is very downstream of the growth generated by the
Speaker 1: promise of AI. But it's basically the entirety of what
Speaker 1: the US economy is built on right now in serious trouble.
Speaker 1: But there's like certain there's certain signs that investors are
Speaker 1: getting a little bit concerned about how deep they are
Speaker 1: in this. And the AI companies have used all sorts
Speaker 1: of interesting account and tricks to kind of hide how
Speaker 1: deep they're spending is. But I think currently the number
Speaker 1: is that they're they're spending one point four trillion this
Speaker 1: year and all sorts of expenditure, and the revenue is
Speaker 1: less than half that. So I guess at this point,
Speaker 1: if we are looking at a serious correction that is
Speaker 1: at some point going to come somehow, who is initially
Speaker 1: going to bear the brunt of that? Like, where is
Speaker 1: the majority of this funding coming from?
Speaker 2: Well, there's different types of funding. So the hyperscale is
Speaker 2: the big tech companies. They've basically issued a lot of debt.
Speaker 2: Google recently did an equity raise, which implies that they
Speaker 2: don't think their stock is particularly good value. They raised
Speaker 2: eighty five billion dollars with that, So yeah, they've created
Speaker 2: all these bonds equity raises. Other people talking about equity raises.
Speaker 2: Open Ai got funding from Massiyoshi Son Andthropic got funding
Speaker 2: from Amazon. Microsoft has seed funding in open Ai, and
Speaker 2: then Google also has interest in the Frontier Labs. So
Speaker 2: that's kind of mostly for purchasing compute. But there are
Speaker 2: also these other vehicles which are off balance sheet called
Speaker 2: SPV special purpose vehicles, and these are mainly funding the
Speaker 2: data center build out, and that usually involves a minority
Speaker 2: stake by a hyperscaler around twenty percent, but then they
Speaker 2: keep the debt obligations off their balance sheet. And it's
Speaker 2: basically private credit that is providing all the additional funding
Speaker 2: um so basically American boomers. They're also Japanese banks like
Speaker 2: Mitsubishi also funding a lot of this. And the scale
Speaker 2: of the buildout is remarkable. They're talking about ten trillion
Speaker 2: dollars by twenty thirty, and that implies needing to be
Speaker 2: taking two trillion dollars revenue or one and a half
Speaker 2: trillion dollar dollars revenue by that time. And it's not
Speaker 2: clear where the demand is coming from. I mean just
Speaker 2: two examples from the current day space. It bought a
Speaker 2: load of compute power they don't have a use for
Speaker 2: it and sold it to Anthropic Meta. Same thing. They
Speaker 2: bought a load of compute sold it to Athropic. So
Speaker 2: open Ai and Anthropic are the only ones buying the
Speaker 2: compute power that's already been built. They're selling their tokens
Speaker 2: at a loss. Their revenues aren't particularly impressive. Well, we
Speaker 2: don't know exactly how much Anthropic is really making because
Speaker 2: again there's clever accounting there and the likes of Amazon,
Speaker 2: who are one of them.
Speaker 1: Do we know it seems like it's hard to get
Speaker 1: numbers on what I need these companies exactly are making
Speaker 1: and losing on AI because a lot of time, you know,
Speaker 1: Alphabet and Amazon and so on, it's not like very neatly.
Speaker 2: It's not clear for the hyperscalers at all. But okay,
Speaker 2: let's take open AI. Thirteen billion revenue last year, predicted
Speaker 2: twenty five billion this year. They lost twenty two billion
Speaker 2: last year, and five billion of that was marketing like
Speaker 2: TV adverts, trying to get people to pay for chat GBT.
Speaker 1: But you know, I believe I've seen a projection that
Speaker 1: they need to have one hundred billion in revenue by
Speaker 1: the end of this decade.
Speaker 2: Oh, I would have thought more, because we discussed before
Speaker 2: that Ortman's predicting a break even in twenty thirty and
Speaker 2: they're talking about spending like seventy eight billion in twenty
Speaker 2: nine or thirty.
Speaker 3: Yeah, these numbers.
Speaker 1: One hundred billion was the number I saw, And then
Speaker 1: there are projections that they won't be doing ten percent
Speaker 1: of that.
Speaker 2: And we don't know how much of this revenue is
Speaker 2: real because the hyperscalers that have equity in them are
Speaker 2: reducing the numbers. Amazon burnt through a huge amount of
Speaker 2: tokens because they're a stakeholder in anthropic, so it makes
Speaker 2: sense to duce evaluation. Same thing with holders in U
Speaker 2: SpaceX a Google being one of them. I believe, you know,
Speaker 2: they want people to believe in the fantasy so that
Speaker 2: their own you know, equity is they can dump it
Speaker 2: on the market, you know, in a years time when
Speaker 2: those shares are locked.
Speaker 3: I mean, let's talk about SpaceX.
Speaker 2: Five percent of the float was iPod two trillion dollar
Speaker 2: valuation got squeezed up mainly mainly options buyers up to
Speaker 2: three trillion dollars.
Speaker 3: This company lost six billion.
Speaker 2: Dollars last year and it's been valued at the same
Speaker 2: price as you know, a legacy company like Amazon, which
Speaker 2: you know controls logistics all over the Western world, is
Speaker 2: involved in you know, web services, and it has all
Speaker 2: this infrastructure, and you know SpaceX. These satellites fall out
Speaker 2: of the sky after five years because they're in such
Speaker 2: low orbit. The the starlink business is only going to
Speaker 2: make sense if starship works and they can take tons
Speaker 2: of these satellites up at the same time, so there
Speaker 2: are so many doubts over whether the numbers are going
Speaker 2: to add up. It's all the can being kicked down
Speaker 2: the road and a lot of.
Speaker 1: Yeah I found a deal on I mean, his ability
Speaker 1: to spin an arrative about a company and get people
Speaker 1: behind it is you know, like when you compare the
Speaker 1: valuation to the revenue there, it's just insane.
Speaker 2: Well, yeah, think about the clout he built by purchase
Speaker 2: an X you know, that would cost him fifty billion.
Speaker 2: But the equity he's been able to create since out
Speaker 2: of being able to influence elections, being able to co
Speaker 2: opt the right wing populist movement, get people buying his
Speaker 2: companies is remarkable. And you know, society is just so
Speaker 2: involved in the these hype trends. Same thing with The Odyssey.
Speaker 2: Everyone thinks this is a great film. It's just crowd
Speaker 2: following madness. No one really knows what's going on.
Speaker 1: Yeah, but that's one thing that's different about this bubble actually.
Speaker 1: I mean if you look at something like, you know,
Speaker 1: the classic example is of a bubble, right, an investor
Speaker 1: bubble is chulip Mania, right, Or you could look at
Speaker 1: the dot com bubble where you suddenly had a huge
Speaker 1: uptick in ordinary retail investors that own stocks, like the
Speaker 1: amount of US households that own stock went up by
Speaker 1: like eight ten percent in a few years in the
Speaker 1: late nineties every two thousands. And then the housing crisis
Speaker 1: as well, ordinary people getting involved in this on the
Speaker 1: belief that housing prices just go up. Housing is an
Speaker 1: appreciate and assen and everyone was kind of in on it.
Speaker 1: So those were you know, retail lead or heavily retail
Speaker 1: involved booms with with ordinary people trying their money in,
Speaker 1: and that was kind of a sign that something was wrong.
Speaker 1: But the AI bubble is is something different. You know,
Speaker 1: you're you're the the you know, uneducated boomer in the
Speaker 1: US is not throwing a lot of money at AI
Speaker 1: stock like people were with pets dot com or business
Speaker 1: dot com during the dot com bubble, right, Like you
Speaker 1: discuss some of it there, But this is very much
Speaker 1: institutional lead and money between these AI AI companies and
Speaker 1: huge investors. So what does that mean for you know
Speaker 1: the nature of it as a as a bubble, Like
Speaker 1: have we seen a bubble like that before? Because there
Speaker 1: isn't even really an entry point for a lot of
Speaker 1: people that wanted to invest in this. Like if you
Speaker 1: were super bullish and AI, I mean you can't really
Speaker 1: you can't invest publicly in open AI or on tropics.
Speaker 1: What would you be doing ob secondary things like you'd
Speaker 1: be buying Microsoft stock because they use up in the
Speaker 1: heavily and you know, I guess Amazon, Stark and so on.
Speaker 1: But Tesla, which you can't really directly buy your short
Speaker 1: day itself read.
Speaker 2: Well, yeah, that that that's the thing. It's you know,
Speaker 2: it's had these seed rounds. Early investors have you know,
Speaker 2: bought the equity. But now Anthropic Open Ai lining up,
Speaker 2: I pos so Anthropic are expected to float later this
Speaker 2: year over a trillion dollars. Open Ai pushback to next
Speaker 2: year also over a trillion dollars. And I think normal investors,
Speaker 2: boomers with assets are going to get left hold in
Speaker 2: the bag. All these billionaires are going to cash out.
Speaker 2: And if you are invested in a tracker like NASDAK,
Speaker 2: you're going to be owning SpaceX and you're gonna be
Speaker 2: owing all the hyperscalers at rich valuations. And even worse,
Speaker 2: if the demand isn't there and you put a load
Speaker 2: of your family wealth into these special purpose vehicles. You
Speaker 2: were the creditor for the data center build out. Again,
Speaker 2: you're going to lose all your money. And this has
Speaker 2: been built up so big and the projections are so large,
Speaker 2: it really has to work in a phenomenal way to
Speaker 2: even get a return on capital. And that's probably a
Speaker 2: good place to bring China in because China has a
Speaker 2: very different model. It's not rent seeking. They are engineering
Speaker 2: with much less compute available and they're producing models that
Speaker 2: are ninety five percents good for a tenth or less
Speaker 2: of the price.
Speaker 1: Yeah, well, you know, talking about what, how are these
Speaker 1: companies actually going to make money? I mean we I
Speaker 1: think discussed us in the previous show. We did, but
Speaker 1: Sam Altman taller investors or people that were interested in
Speaker 1: opening that the plan was to build super intelligence and
Speaker 1: ask it to make money. You know, like that's the
Speaker 1: kind of promises you're getting. But I mean, essentially the
Speaker 1: hope is that they can just make a ton of
Speaker 1: money on subscriptions. And you know, there has been a
Speaker 1: there's is a constant increase in people buying subscriptions to
Speaker 1: a I But obviously to get to the numbers they're
Speaker 1: talking about talking into the hundreds of billions just in
Speaker 1: subscriptions and do like a multiple x of Netflix subscriptions.
Speaker 1: I mean, that is a huge adoption rate. And to
Speaker 1: do that while there are all these AI models out there,
Speaker 1: you have to have a huge advantage, right, I mean,
Speaker 1: who's who's gonna what average person is paying X number
Speaker 1: of dollars per month for chat GPT if they can
Speaker 1: get a free model, that's ninety percent of away there.
Speaker 1: So you know, the whole thing was contingent on these
Speaker 1: handful of USAI companies having this huge kind of frontier
Speaker 1: advantage over anyone that attempted to open sources or do
Speaker 1: it elsewhere, you know, with the support of the US
Speaker 1: government and the huge data center build out and the
Speaker 1: technological advantage and so on. But yeah, like you said,
Speaker 1: these companies now are ninety ninety five percent the way
Speaker 1: there with the recent releases. You know, I've seen they
Speaker 1: have all sorts of ways of estimating, you know, where
Speaker 1: these models are in terms of intelligence. But they said,
Speaker 1: you know, until these recent releases, the best Chinese eye
Speaker 1: models were about six months off the top performing US models,
Speaker 1: And with the recent releases, they think there's you know,
Speaker 1: negligible a few weeks difference. So like looking ahead to
Speaker 1: twenty thirty, how are they going to As I talked
Speaker 1: about earlier, the llms are kind of at the limit
Speaker 1: of their cycle of what this paradigm can produce, and
Speaker 1: now beyond that it's just more compute, more data. How
Speaker 1: are they going to make a further leap that would
Speaker 1: justify creating those huge amounts of subscriptions that they need.
Speaker 1: I mean, I just don't see it. And even with
Speaker 1: the business adoption, what you're actually seeing again, a lot
Speaker 1: of this was just contingent on There'll be one or
Speaker 1: two big companies that have the best model and then
Speaker 1: everyone's going to flock to them and paid subscriptions. But
Speaker 1: the way it's being rolled out with businesses is a
Speaker 1: bit different, where they're taking this kind of you know,
Speaker 1: this kind of like cafeteria approach where they take a
Speaker 1: bit from every model. Like I saw Pinterests achieved huge
Speaker 1: advances where it used this kind of hybrid model, or
Speaker 1: it used some things like open a eye for certain aspects,
Speaker 1: but then it actually took one of the Chinese open
Speaker 1: source models and used that for most of it, tweaked
Speaker 1: a little bit for its own company, and so it
Speaker 1: was this kind of hybrid of Chinese open source models
Speaker 1: and then some of the private US models. But it
Speaker 1: seems like that increasingly is the direction things are going.
Speaker 1: And if you look at actually startups in the US,
Speaker 1: I think many, if not the majority of them now
Speaker 1: are actually being run on these Chinese open source models.
Speaker 1: So already the adoption is there. You know what, This
Speaker 1: was maybe more abstract when we talked about this almost
Speaker 1: a year ago, but now like they're becoming serious players
Speaker 1: in the America, are not just worldwide but actually in
Speaker 1: the US, where you would think and traffic and opening
Speaker 1: I would still have an enormous advantage.
Speaker 2: Well, they were talking about huge adoption of chatbots that
Speaker 2: hasn't really come to pass.
Speaker 3: Now the new cope is agentic ai.
Speaker 2: That you're going to have agents doing all your daily
Speaker 2: tasks for you, and everyone's going to pay fifty one
Speaker 2: hundred dollars a month and they're going to reduce their
Speaker 2: workload and stress and all this stuff. I just don't
Speaker 2: think that's reality. You know, they're losing, you know, so
Speaker 2: much money on the tokens they're providing at the moment,
Speaker 2: and I think it's just new cope in terms of
Speaker 2: Chinese AI. Yes, American companies are adopting it because it's
Speaker 2: a lot cheaper and open source. So examples they're Airbnb
Speaker 2: and pinterest company.
Speaker 3: I'm invested in them. I think is rather a good investment.
Speaker 1: Are you supposed to add some this isign investment advice
Speaker 1: that people always do?
Speaker 3: I don't know what. Yeah, no investment advice.
Speaker 1: I think that's that's I never really understood why that's
Speaker 1: a rule anyway.
Speaker 2: I mean, I mean, you just can't go and you
Speaker 2: don't tell people to buy it because it's going to
Speaker 2: go up.
Speaker 3: But you can say.
Speaker 2: I own this dog because you know so. Yeah. Andrews
Speaker 2: and Horowitz. Earlier in the year, they reported the over
Speaker 2: eighty percent of AI startups pitching to them were using
Speaker 2: Chinese models, again presumably because of cost. And China is
Speaker 2: kind of lining itself up to really undermine the American
Speaker 2: rent seeking model. They're you know, providing these open weight models.
Speaker 2: Three big companies, the Kimmi model, the Quinn model by
Speaker 2: Ali Barbar, and Deep Seek, and all of them are
Speaker 2: performing really well, you know, companies using them in different ways.
Speaker 2: But it's not really a rent seeking model. It's more
Speaker 2: of a Chinese state influence model, and they I think
Speaker 2: they're aiming for soft power by taking this approach away
Speaker 2: because they can't compete on the big compute model rent
Speaker 2: seeking model. I mean, they may catch up eventually, you know,
Speaker 2: I do believe in Chinese engineering. I think they're very
Speaker 2: good at engineering. I think they will be able to
Speaker 2: reverse engineer the stuff that they do have, and I
Speaker 2: wouldn't be surprised if they're on par with the American
Speaker 2: chip makers within three years.
Speaker 1: But there have been reports recently that the Trump administration
Speaker 1: is looking at more serious regulations to try and stop
Speaker 1: these Chinese models coming into the market and disrupting the
Speaker 1: US system. And I think, based on what we know
Speaker 1: about this Trump administration, I mean that would seem likely.
Speaker 1: I would say, you know, the kind of the kind
Speaker 1: of like haphazard kind of economic nationalism Trump has pursued,
Speaker 1: where you know, he'll just wake up and draw on
Speaker 1: a bunch of tariffs or you know, band trade with
Speaker 1: a country or something. I mean, it seems very likely
Speaker 1: with all they have invested in Aii, they're not just
Speaker 1: going to allow us to continue. So I think they
Speaker 1: will try and respond to some way. But then The
Speaker 1: question is what can they even do Because it's one
Speaker 1: thing to say, Okay, TikTok is processing people's data in China,
Speaker 1: it's Chinese owned, We're going to just take control of
Speaker 1: TikTok and give it to Larry Ellison. But this is
Speaker 1: something different because these are open source models, right. It's
Speaker 1: not like, you know, it's not like Pinterest is using
Speaker 1: this Chinese company and sending people's data there or whatever.
Speaker 1: You know, you just take the model. I mean, it's
Speaker 1: basically just taking an open sourced algorithm on the Internet
Speaker 1: and using it yourself. So how do you even regulate that?
Speaker 1: That seems like that seems like a nightmare, especially when
Speaker 1: when they're already released. And then if you were to say, okay,
Speaker 1: well future models, we're not going to let them be
Speaker 1: open sourced and enter the US market. I mean, I
Speaker 1: don't see how you could possibly enforce that. So it
Speaker 1: seems like they're they're in a pretty difficult position there.
Speaker 2: I mean, I'm not sure if the technicality technicalities, how
Speaker 2: difficult it would be to enforce such a ban, but
Speaker 2: I do think they will be forced into that position
Speaker 2: because if the if the American AI bubble crashes completely
Speaker 2: I mean, that's just monumental for the world economy. And
Speaker 2: imagine how indebted and how broke America will be if
Speaker 2: this build out completely flops and America is just provided
Speaker 2: and China is providing, you know, all the technology for
Speaker 2: a fraction of the price, and that gets mass adoption.
Speaker 2: I mean, you know, so much power has been handed
Speaker 2: to the likes of Sam Moltman and Dario Amaday to
Speaker 2: call the shots here how big this is going to be.
Speaker 2: And you know, there's all this crowd following all these
Speaker 2: predictions by the banks that are exaggerated saying how big
Speaker 2: it's going to be. I mean, some of some of
Speaker 2: the bank's price predictions on SpaceX stock is insane. You know,
Speaker 2: some of the banks have ten trillion dollar predictions on
Speaker 2: a company again that lost six billion dollars last year.
Speaker 2: It's it's just it's just you know, mass delusion.
Speaker 1: Well, I would imagine another way that would maybe hit
Speaker 1: the Chinese companies is to say that there's huge abuses
Speaker 1: going on with intellectual property, because I mean, you mentioned earlier,
Speaker 1: was it entropic that's just scanning through these books and
Speaker 1: destroying them. It's just like a brilliant brilliant example of
Speaker 1: why people kind of hite these these AI companies. But
Speaker 1: I mean, I assume we don't know what models like
Speaker 1: Deep Secret being trained on. But I mean I feel
Speaker 1: like we could be pretty confident they're not really respecting
Speaker 1: Western intellectual property law and they're probably just tearing through
Speaker 1: everything they can get their hands on. And I'd imagine
Speaker 1: there's a case that could be made that you know,
Speaker 1: this is very unfairly profit and from intellectual property in
Speaker 1: the West, and that that violates all sorts of you know,
Speaker 1: World Trade Organization rules and so on that have been
Speaker 1: used before to to force China to lessen some of
Speaker 1: its status and with companies. But again, how do you
Speaker 1: enforce that to stop at just being open source center
Speaker 1: in the market. I'm not I'm not sure how you
Speaker 1: can do that. And obviously, I mean the you know,
Speaker 1: to even go that route of challenging foreign AI companies
Speaker 1: on that based on what we know about what the
Speaker 1: exists in US AI companies are being traded on. I mean,
Speaker 1: you know, I don't think that you know, there's no
Speaker 1: respect for intellectual property with this stuff. I mean, they've
Speaker 1: in the past valued at whatever like five I think
Speaker 1: there was a case where they valued it at five
Speaker 1: thousand dollars per book that they trended on, and they
Speaker 1: were taking these kinds of like in the authors and
Speaker 1: secondary books and so on. But I think it's it's
Speaker 1: very clear in the way they process data that they're
Speaker 1: eating up huge amounts of intellectual property. And obviously, you know,
Speaker 1: there are thousands millions of you know, artists, scientists, everyone
Speaker 1: else data that are not being credited and paid for
Speaker 1: the work. And that's you know, that's just too fair
Speaker 1: a gun. There's no there's no correct in that.
Speaker 2: I mean, it's basically a steal first, deal with the
Speaker 2: consequences later kind of model by a lot of these guys.
Speaker 2: And yeah, I mean the latest cope is that the
Speaker 2: Chinese have been stealing all the weights by setting out
Speaker 2: thousands of accounts and working out how the models work,
Speaker 2: by you know, culminating all the data and then reverse
Speaker 2: engineering it as far as possible to create their own models.
Speaker 2: I mean, it wouldn't be any different from what the
Speaker 2: Chinese have been doing for the last decades. So it's
Speaker 2: you know, not as if it was unpredictable, and you know,
Speaker 2: that's just the world we live in. Now information is
Speaker 2: very democratized. I think there's huge decentralization going on in
Speaker 2: you know, all different fields, particularly military, particularly you know, intelligence.
Speaker 2: You know, any which way you spin this, intelligence will
Speaker 2: be decentralized by AI.
Speaker 1: But I do think you can make a political case.
Speaker 1: I mean, this is obviously AI is coming to the
Speaker 1: form of politics now. And you hear people on the
Speaker 1: left and the right talking about data centers and talking
Speaker 1: about Palente or an oracle and these kinds of things,
Speaker 1: and everyone knows who's some oltmeners and so on, and
Speaker 1: people follow this drama. But like I said, it's a
Speaker 1: kind of a misdirected, vague populism that you know, they're
Speaker 1: taking their data and making society worse. But I do think,
Speaker 1: I do really think you can make a case on
Speaker 1: the basis that basically their business model is them opped
Speaker 1: up the public commons of knowledge of intelligence, scientific discoveries, Wikipedia,
Speaker 1: you know, read out where people were just posting publicly,
Speaker 1: just scraping the Internet, taking open source books that were
Speaker 1: out of copyright and so on. But basically the collective
Speaker 1: pool of human knowledge over thousands of years, that's what
Speaker 1: they used as the substrate for these models. Plus you
Speaker 1: can also say, you know, the original science, the original
Speaker 1: theories behind LLMS that was open source, you know, like
Speaker 1: I think some of the most influential work and it
Speaker 1: came throughe like Princeton University, and these were scientists doing
Speaker 1: open academic work. And then obviously you can look at
Speaker 1: I mean, just look at the technology and how much
Speaker 1: the US government and DARPA and NIH and so on
Speaker 1: were involved in the development of all of the important
Speaker 1: hardware for this kind of thing. But you know, I
Speaker 1: think on the base of that, you can say, like
Speaker 1: the substrate for this is, yeah, the collective pool of
Speaker 1: human knowledge and thousands millions of individuals, scientists, authors, philosophers,
Speaker 1: whatever that contributed and did it not expecting that this
Speaker 1: would be used for private profit and it is, and
Speaker 1: yes there is you know, there is a kind of
Speaker 1: private innovation there in terms of obviously the way they
Speaker 1: scrape it and processed and presented for a user. So
Speaker 1: you know, it's not like it's something like someone just
Speaker 1: you know, grabs a piece of land full of oil
Speaker 1: and takes one hundred percent of profits or something like that.
Speaker 1: Like there is a contribution there that you don't want
Speaker 1: to totally disincentivize. But I think you can definitely say
Speaker 1: that this is a unique form of rent extraction if
Speaker 1: these companies make monopoly profits, and therefore there has to
Speaker 1: be a way to somehow decide what chunk of that
Speaker 1: properly belongs to the public, as haven't been built on
Speaker 1: this public commons of human knowledge, and so that we're
Speaker 1: just taking that back, whether that's somehow tax and windfall profits,
Speaker 1: some kind of distribution of stock that would go into
Speaker 1: a sovereign wealth fund, these kinds of ideas are being
Speaker 1: talked about. No, obviously, I actually think I mean, I
Speaker 1: think if you did that and you say, okay, windfall
Speaker 1: profits beyond a certain point are going to be heavily
Speaker 1: taxed in a way other corporations aren't because of this
Speaker 1: public element, Well, I mean, I personally think these companies
Speaker 1: will actually never reach that. So it's probably not going
Speaker 1: to be a problem anyway, because most likely, Yeah, what's
Speaker 1: going to happen is the open source of this stuff
Speaker 1: is kind of just going to nuclear profit margins of
Speaker 1: these companies.
Speaker 2: Well yeah, open AI were trying to make the US
Speaker 2: government responsible for their debt, so they're already the hints
Speaker 2: of this kind of thing.
Speaker 3: But yeah, I agree with you.
Speaker 2: It's essentially scraping Western civilization, creating a hive mind out
Speaker 2: of Western people and selling it, selling it back, selling
Speaker 2: it back to people. Maybe, you know, give a few
Speaker 2: scraps of UBI to all the people that became unemployed
Speaker 2: if it works out.
Speaker 3: I mean, the numbers that they're.
Speaker 2: Talking about in terms of disruption unemployment are remarkable. McKinsey
Speaker 2: is predicting between four and eight hundred million people unemployed
Speaker 2: in the next few years. A more modess essment by
Speaker 2: Goldman Sex was three hundred million, and World Economic Forum
Speaker 2: has they predict that there will be basically as many
Speaker 2: jobs made as created.
Speaker 3: So they're sitting around neutral and.
Speaker 1: Very skeptical how they calculate any of this.
Speaker 2: So I mean McKinsey has really lost its reputation. I
Speaker 2: mean a lot of these tech bros openly mock them.
Speaker 3: Now, yeah, I.
Speaker 1: Mean, well, because these projections are really built on speculative
Speaker 1: like what can super intelligence do? And you know, fifteen
Speaker 1: years kind of thing. But I mean if you look
Speaker 1: at the actual surveys of existing companies, I mean, I
Speaker 1: saw when they surveyed six thousand CEOs in Anglosphere countries
Speaker 1: like Australia and the UK America, and ninety percent said
Speaker 1: AI has had no impact on employment, and eighty nine
Speaker 1: percent pretty much the same numbers, so that it's had
Speaker 1: no impact on productivity. So apparently ten percent of companies
Speaker 1: are noticing anything at all. You know, I assume it's
Speaker 1: a lot of software developers. Certainly it's been impactful there,
Speaker 1: but you know, as far as it's rolling out to
Speaker 1: the general economy, I mean, it seems like it's very speculative,
Speaker 1: and any numbers on it, I just wonder where people
Speaker 1: are pulling them out of.
Speaker 2: Yeah, exactly, I think there was You know, most of
Speaker 2: the revenues are created by software developers in these enterprises,
Speaker 2: and many of those have found out that it wasn't
Speaker 2: actually that productive. It worked out for some companies not others.
Speaker 2: There was all this talk of job losses being created
Speaker 2: by AI, but there is an incentive for CEOs to
Speaker 2: sack staff and make it look as though there's disruption
Speaker 2: there and they don't need as many staff to increase
Speaker 2: their margin and get their stock compensation. This is what
Speaker 2: capitalism does. It creates all these incentives that don't serve
Speaker 2: the collective. Whole everyone's out for themselves. Everyone's due in
Speaker 2: you know, their own book. And you know, meanwhile, the
Speaker 2: same people are buying bunkers in New Zealand and islands
Speaker 2: and stuff because you know, they're probably smart enough to
Speaker 2: see the doom coming if it doesn't work out.
Speaker 1: So I guess so that you know our message, after all,
Speaker 1: this is a smash capitalism, tax the rich.
Speaker 3: I mean what we've just said there.
Speaker 2: The natural conclusion of this theft that has occurred, the
Speaker 2: scraping of Western civilization, is that it shared among the worst.
Speaker 2: And I think you know, these people, the likes of Teal,
Speaker 2: the likes of carp they know that the people out
Speaker 2: there are not having a very good time of things.
Speaker 2: They know that they feel they're being completely robbed of.
Speaker 2: You know, in many cases they're birthright. They're living, you know,
Speaker 2: paycheck to paycheck. I mean, so much of us spending
Speaker 2: is just the top ten percent and everyone else is
Speaker 2: living paycheck to paycheck. Loads of people don't have medical insurance.
Speaker 2: And you know, people like Mam Danny breaking through Green
Speaker 2: Party in the UK. There is a very leftist communist
Speaker 2: vibe starting to emerge, and these incredibly rich people are
Speaker 2: looking at it and saying okay. Sam Altman says, Oh,
Speaker 2: maybe we could give five percent equity to the US government.
Speaker 2: Amiday says, oh, maybe we'll have an AI tax just
Speaker 2: to like, you know, give some of these back to
Speaker 2: the people. But it's a question that will have to
Speaker 2: be reckoned with if it does work out. If it
Speaker 2: doesn't work out, then boomers and Japanese banks and so
Speaker 2: on are going to lose a lot of money. Everyone
Speaker 2: that's got an index for the S and P five
Speaker 2: hundred or Nasdaq, particularly exposed to you know, going to
Speaker 2: get wrecked. I could see fifty percent plus drawdowns in
Speaker 2: the next two three years on these indexes because they're
Speaker 2: already very.
Speaker 3: The earnings ratios aren't particularly good.
Speaker 2: I mean, I'm not a particular fan of him, but
Speaker 2: Warren Buffett is like nearly one hundred percent cash at
Speaker 2: the moment. I think he's got like some Google a
Speaker 2: few other bits and pieces. But you know, when legendary
Speaker 2: investors like that are sitting in cash, you know you
Speaker 2: should be thinking about it.
Speaker 1: All right, Well, I guess we can wrap up there
Speaker 1: for second show. And it's so fair. I think the
Speaker 1: projection from the original episode was already good as far
Speaker 1: as the you know, Chinese eye becoming the other big
Speaker 1: character in this. Maybe the next one we do on
Speaker 1: this will be after the bubble pops and we can
Speaker 1: we can look back in these predictions. There'll be a
Speaker 1: lot to discust.
Speaker 3: Then.
Speaker 2: Yeah, it could be apocalyptic and we'll be streaming from
Speaker 2: the bunker somewhere.
Speaker 1: Streaming too analog radio or something.
Speaker 3: Yeah.
Speaker 1: Yeah, all right, that's it from now. Everyone take care,