Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. Welcome to Maren Talks Money,
Speaker 1: the podcast in which people who.
Speaker 2: Know the markets explain the markets.
Speaker 1: I am Meren Sumset Web and with me today is
Speaker 1: Kathy Would, CEO and CIO of investment management firm ARC
Speaker 1: Invest now ARC Invests.
Speaker 2: I think many of you will know.
Speaker 1: In disruptive and innovative technologies of all kinds, so we're
Speaker 1: going to talk about quite a few of those today, AI, robotics, cryptocurrencies,
Speaker 1: all this kind of thing. Kathy, Absolute pleasure to have
Speaker 1: you with us today in our London studio.
Speaker 2: Welcome, thank you, Maren, very very happy to be here. Right,
Speaker 2: we have got a lot to cover.
Speaker 3: Big ideas. There are a lot of big ideas out there.
Speaker 2: That's where I want to start with the Big Idea.
Speaker 1: So you've put out at the beginning of every year
Speaker 1: ago Big Ideas Report, where you talk about what's going
Speaker 1: on with a lot and this year one of the
Speaker 1: main idea in it was that there are this amazing
Speaker 1: convergence of different types of technology coming together to create
Speaker 1: a wave of tech innovation the likes of which we
Speaker 1: really haven't seen before, and the consequences is going to
Speaker 1: be enormous. So can we just talk through to get
Speaker 1: us started those technologies and the convergence that you're expecting.
Speaker 3: Sure, So, there are fifteen different technologies and they're included
Speaker 3: in the five major innovation or five innovation platforms that
Speaker 3: are evolving today. So AI the chatch GPT moment really
Speaker 3: did awaken investors to know something's going on here. So AI, robotics,
Speaker 3: energy storage, blockchain technology, and multiomic technology. And you're right,
Speaker 3: the fifteen technologies are converging. Two of the biggest trends
Speaker 3: out of this convergence have to do with AI applications,
Speaker 3: so embodied AI, the physical and the digital world coming together.
Speaker 3: So if we look at autonomous mobility, so robotaxis, that
Speaker 3: involves three of the major innovation platforms converging. So autonomous
Speaker 3: vehicles are robots, they will be electric, and they will
Speaker 3: be powered by AI.
Speaker 2: So AI robotic energy storage.
Speaker 3: Yes, and so if you think about those, those are
Speaker 3: three different s curves in the world of innovation slowly
Speaker 3: slowly than all at once, and they're beginning to feed
Speaker 3: each other. Tesla launched its robotaxi service in Austin in June,
Speaker 3: and we're seeing now a quickening of the pace. WEIMO
Speaker 3: is now moving more quickly into different cities. So there's
Speaker 3: a competitive dynamic that is going to accelerate the trend
Speaker 3: towards robotaxis in the United States. And because robotaxis are safer,
Speaker 3: we've just crossed the human threshold of safety and will
Speaker 3: continue too.
Speaker 2: That's true. That's true.
Speaker 3: So that's one and we think that's the biggest application
Speaker 3: of AI from a revenue generation point of view, other
Speaker 3: than the productivity tools that we're using right now. We're
Speaker 3: already yes, absolutely, and we're paying for them and we're
Speaker 3: going to pay more and more because they are that useful.
Speaker 3: The second major trend is in healthcare. It's the most
Speaker 3: profound application of AI, and it includes the convergence of
Speaker 3: sequencing technologies AI and Crisper gene editing. So sequencing, we
Speaker 3: have thirty five to forty trillion cells in our body
Speaker 3: generating data, and AI is a data project. Proprietary data
Speaker 3: is key to success. So sequencing technologies AI now that
Speaker 3: we can find mutations, which are programming errors in our body.
Speaker 3: We have six billion bits of code. If you think
Speaker 3: about three billion base pairs of DNA pairs, so six
Speaker 3: billion bits of code. We can now see when one
Speaker 3: of them has gone awry, when there's been a mutation.
Speaker 3: Now and we can see it because of AI. We
Speaker 3: couldn't have done any of this five, ten, fifteen years ago.
Speaker 3: Now that we can see it, we can use Crisper
Speaker 3: gene editing to reprogram the genome. And we're already seeing
Speaker 3: cures for beta thalocemia and sickle cell disease, and we're
Speaker 3: seeing revenue generation. Crisper Therapeutics is the company and Vertex
Speaker 3: it's partner, generating revenues and curing disease.
Speaker 2: Those are diseases.
Speaker 3: We think these technologies will impact common diseases in the
Speaker 3: not too distant future.
Speaker 1: And chrispre's one of your big holdings in the innovation
Speaker 1: ATIA it is it's I think it's.
Speaker 2: Our number two holding.
Speaker 3: It's earned it, yes, yes, yes, yes so. And one
Speaker 3: of the reasons it's up there is in its pipeline
Speaker 3: is a cure. Now it's in trials for bad cholesterol.
Speaker 3: Think about that. Cardiovascular disease is the biggest killer in
Speaker 3: the world. And what we're going to do here is
Speaker 3: shift sick care dollars. So just treating chronic disease shifting
Speaker 3: six sick care dollars or pounds or euros into really
Speaker 3: therapies that cure or prevent or into diagnostic tools that
Speaker 3: diagnose cancer in stage one or before stage one with
Speaker 3: blood tests. So we think healthcare is going to transform completely.
Speaker 2: Here, okay.
Speaker 1: The idea there being that people will end up with
Speaker 1: a much not necessarily a much longer life, but a
Speaker 1: longer health span, a longer period of healthy living.
Speaker 3: Yes, and we are writing a white paper about longevity.
Speaker 3: But you're absolutely right, maarn. The first order here is
Speaker 3: to extend healthy life, you know, increase the quality of
Speaker 3: life while we are alive.
Speaker 1: Yeah, and if that happens, that is transformative for public
Speaker 1: finances apart from anything else. Right, So the end of
Speaker 1: life that last ten years is the most expensive bit
Speaker 1: for any healthcare system and hans for any government.
Speaker 2: You can take that out suddenly. Possibly.
Speaker 1: The fiscal situations of the Western economies don't.
Speaker 2: Look quite awful.
Speaker 3: Absolutely, And the US is the worst that way, because
Speaker 3: we spend nearly twenty percent of GDP on healthcare. I
Speaker 3: think in the UK it's less than ten percent.
Speaker 2: Round eleven, but oh, you know, it's huge.
Speaker 3: Okay, So it's huge and in the US there's a
Speaker 3: lot because of our litigiousness, there's a lot of unnecessary spending,
Speaker 3: and so we will get those answers, yes spend and no,
Speaker 3: don't spend.
Speaker 1: Okay, So we have this convergence of these extraordinary technologies
Speaker 1: which gives us this acceleration in everything, right, acceleration and productivity,
Speaker 1: acceleration in GDP growth. I know you're very optimistic about
Speaker 1: growth across the board as a result.
Speaker 2: Of all these innovations.
Speaker 3: Yes, if you look in Big Ideas, you'll see there's
Speaker 3: a chart, a timeline, and it's in the section called
Speaker 3: the Great Acceleration. So our chief futurist, Brett Winton named
Speaker 3: his section the Great Acceleration, thinking people would understand that, no,
Speaker 3: we're not going into the Great Depression. We're going into
Speaker 3: the Great Acceleration. I have to explain that I've told him,
Speaker 3: because I didn't get so. Anyways, So the Great Acceleration.
Speaker 3: If you look, you'll find a GDP chart, and the
Speaker 3: timeline is broken down by tech breakthroughs, you know, tech revolutions.
Speaker 3: So if you look in the years from fifteen hundred
Speaker 3: to nineteen hundred, nothing much changed. And as best as
Speaker 3: Brett could ascertain with you know, consulting, academia and so forth.
Speaker 3: Global real GDP growth in that time period was about
Speaker 3: zero point six percent, so very slow. And then we
Speaker 3: have really the industrial revolution, first the railroads and then
Speaker 3: building on the railroads, we had telephone, electricity, and the
Speaker 3: internal combustion engine, and we had then a step function
Speaker 3: increase in growth to three percent real GDP growth globally
Speaker 3: for one hundred and twenty five years on average, with
Speaker 3: China joining in at the end. Now we believe with
Speaker 3: these five innovation platforms. So there were three major ones
Speaker 3: on top of railroads, there are five major ones on
Speaker 3: top of the internet. The Internet was sort of the prelude,
Speaker 3: much like the railroads were. So five innovation platforms which
Speaker 3: involve fourteen i mean fifteen different technologies which we think
Speaker 3: are going to take growth into the seven to eight
Speaker 3: percent range. Now many people think that sounds ridiculous, but
Speaker 3: that's not. The last technology revolution, which we don't think
Speaker 3: was as big as this one's going to be. We
Speaker 3: had a fivefold increase. We're saying maybe a two and
Speaker 3: a half fold increase, so we think we're being conservative.
Speaker 3: But the other thing to note is these technologies are deflationary.
Speaker 3: So if we were to enter this seven to eight
Speaker 3: percent growth range, it probably would be accompanied by deflation.
Speaker 3: And many people just don't believe that. But the deflation
Speaker 3: being caused by AI alone. AI training costs are dropping
Speaker 3: seventy five percent per year. AI inference costs, So what
Speaker 3: it costs to deliver an answer to your query, those
Speaker 3: are dropping eighty five to if you believe deep seek
Speaker 3: ninety eight percent per year cost. This is technology. It's
Speaker 3: called learning curves. So the more units produced in this
Speaker 3: case tokens in the world of AI, the lower costs go.
Speaker 3: It's the learning curves associated with technologies. So when a
Speaker 3: technology is mature, like the internal combustion engine autos, there
Speaker 3: isn't much unit growth, so the costs don't come down anymore.
Speaker 3: Right now, the unit growth in AI is exploding and
Speaker 3: therefore the cost and sort of the undertow, the deflationary
Speaker 3: undertoes we think are picking up here.
Speaker 2: Now.
Speaker 3: Of course, we have oil on the other side of this.
Speaker 3: We think this is temporary, better be temporary, right, and
Speaker 3: this deflation is being somewhat obfuscated, But there is a measure.
Speaker 3: It's in the blockchain world. It's called trueflation measures ten
Speaker 3: thousand items twenty four to seven.
Speaker 1: I keep an eye on that, and it's alf very,
Speaker 1: very different to the official inflation numbers.
Speaker 3: Yes, it was higher coming out of COVID. It got
Speaker 3: to eleven twelve percent, whereas the CPI in the US
Speaker 3: got to nine percent.
Speaker 2: Now it's one and a half percent.
Speaker 3: Whereas the inflation measures, especially with oil and tariffs, they're
Speaker 3: kind of sticky in this two to three percent range. Trueflation,
Speaker 3: we think is getting it right. It did drop below
Speaker 3: one percent, and now we've had a bit of a
Speaker 3: bounce because of energy prices.
Speaker 1: But even if then the oil price rise is temporary,
Speaker 1: Even if the att least is sorted out and everything's fine,
Speaker 1: old Press falls back down again. Still one of the
Speaker 1: main limitations to a lot of this is energy, right,
Speaker 1: and we know how energy intensive anything to do with
Speaker 1: AI is, so that still remains one of the limitations absolutely.
Speaker 3: And one of the limitations of course in the United
Speaker 3: States and elsewhere in the world is getting connected to
Speaker 3: the grid. It takes five years. So Elon has gotten
Speaker 3: very creative and brought in generators and batteries, and so
Speaker 3: he was able to get his Memphis data centers xais
Speaker 3: up in I think it was it was fewer than
Speaker 3: eight months, and most people thought it would take him years.
Speaker 3: Now he's thinking about data centers in space, and we're
Speaker 3: doing that work. In fact, we have published our SpaceX
Speaker 3: model and anyone can go to our site or to
Speaker 3: get hub and find it and if they disagree with
Speaker 3: our assumptions, they can change them and see a different number,
Speaker 3: which SpaceX is going to go public, So this is
Speaker 3: going to become more important. We are adding in data
Speaker 3: centers that will be additive to the model, and we
Speaker 3: do think it's possible.
Speaker 1: And if you have your data sense is in space,
Speaker 1: then they simply use solar energy solar and everything.
Speaker 2: Is very straightforward, very straightforward.
Speaker 1: And this brings into play reusable rockets that just go
Speaker 1: up and down lutely, and that's been the real breakthrough here.
Speaker 3: Without reusable rockets, it would be prohibitively expensive to put
Speaker 3: satellites into space. SpaceX has about two thirds of the
Speaker 3: satellites in space right now, and other rocket providers are
Speaker 3: attempting the reusable rocket space, but SpaceX is ten years ahead.
Speaker 1: Okay, So if we get to that point where most
Speaker 1: of the data centers are in space, and that takes
Speaker 1: away the energy limitation. It takes away the land limitation,
Speaker 1: the grid limitation.
Speaker 3: All of the litory, the bureaucracy, there's no bureaucracy, takes away.
Speaker 1: The regulatory I we know right now there is no
Speaker 1: bureaucracy or regulatory environment in space. But the more full
Speaker 1: space becomes, eventually there will have to be a regulatory
Speaker 1: regime around it.
Speaker 2: Right yes, yes, at place who own space. We don't
Speaker 2: know doing.
Speaker 3: Exactly that's that's going to be a company. I mean,
Speaker 3: it almost seems that this is a technology race, and
Speaker 3: that is why China is now racing to the moon
Speaker 3: and experimenting with reusable rockets as well. Okay, how far
Speaker 3: away do you think that is, by the way, before
Speaker 3: we move on, yes, orbital data centers, Yes, by our calculations,
Speaker 3: twenty thirty, twenty thirty one, So we still will be
Speaker 3: dealing with the bureaucracy and the regulation here. So yes,
Speaker 3: it's that time period is where we consider the energy drain.
Speaker 1: The shift comes at first and last time we talked
Speaker 1: to Kathy when we talked about the energy limitations on AI.
Speaker 2: We talked about nuclear energy and about how.
Speaker 1: That was the solution that we've already pushed that to
Speaker 1: one side because it doesn't matter anymore because we're going
Speaker 1: to space.
Speaker 3: What's interesting is the Trump administration recognizes that economic activity
Speaker 3: is energy transformed. Now, what has happened in the last
Speaker 3: fifteen twenty years was a movement towards more efficient energy use.
Speaker 2: So that was good, and that.
Speaker 3: Was very good, and we've become much more efficient. But now,
Speaker 3: because we believe GDP growth is going to accelerate, we
Speaker 3: need more more energy, and so nuclear absolutely is an answer.
Speaker 3: And I am shocked at the regulatory the speed of
Speaker 3: regulatory change in the United States. Now, what's interesting this
Speaker 3: is another race. China has about thirty nuclear plants big
Speaker 3: ones being built today. The US has zero, and so
Speaker 3: this is shocking to those of us who believe that
Speaker 3: real GDP growth is going to accelerate and we need
Speaker 3: the energy to do so. So we're now seeing some approvals,
Speaker 3: but we're also seeing breakthroughs in small modular reactors, and
Speaker 3: of course open AI with OClO really started that movement.
Speaker 3: In the public equity markets. There is a lot more
Speaker 3: going on in the private equity markets.
Speaker 2: It's interesting, isn't.
Speaker 1: That how as you say, we've been through this phase
Speaker 1: where the drive behind everything has been to try and
Speaker 1: use less energy.
Speaker 2: Yes, and that is not the way that wealthlies, isn't it.
Speaker 2: That's right.
Speaker 3: I do think going through that period was important because
Speaker 3: there was a lot of waste and we have gotten
Speaker 3: a lot better. But now we're going to have to
Speaker 3: step up energy production of all in order to accommodate
Speaker 3: this new world.
Speaker 1: Let's talk about the winners and the losers here, because
Speaker 1: it sounds like everything is becoming a little bit more clear.
Speaker 1: We have more of an idea of what the future
Speaker 1: is going to look like than maybe we did even
Speaker 1: a year or or two ago. As these things converge.
Speaker 1: But when you look at stock markets and listed companies,
Speaker 1: then you can now begin to.
Speaker 2: See who m win and who might lose.
Speaker 3: Yes, well, I have to start with Tesla. It's our
Speaker 3: largest position, at least in our flagship fund. Europe is
Speaker 3: really I think approaching ore and maybe it's because of
Speaker 3: your podcast, Mirion, but approaching our strategy in the correct
Speaker 3: way as a satellite strategy. So our largest position in
Speaker 3: the flagship strategy and in our AI and robotics strategy.
Speaker 3: I mean there's a bit of a competition there, but
Speaker 3: it's up there. It's Tesla because that is the largest
Speaker 3: AI project on Earth, and it embodies this idea of
Speaker 3: the convergence among technologies. And in fact, Elon very recently
Speaker 3: kind of giving us a clue that he was going
Speaker 3: to combine SpaceX and XAI. He said, you know, my
Speaker 3: companies are going to converge more than I ever thought.
Speaker 3: And we thought that was a victory for us because
Speaker 3: we've been using this word convergence for a while, and
Speaker 3: we've been describing Tesla in this way. And ultimately it
Speaker 3: could be that Tesla, XAI and SpaceX do get together,
Speaker 3: but we don't think yet, because I do think Elon
Speaker 3: wants to reward those of us who have been patient
Speaker 3: shareholders with the robotaxi takeoff, and now that analysts are
Speaker 3: being forced to look at this new world, they are
Speaker 3: seeing Tesla's model shift from sixteen percent gross margins in
Speaker 3: electric vehicles to ultimately with robotaxis just that opportunity segmented,
Speaker 3: that's eighty to ninety percent gross margins.
Speaker 2: It's a very competitive market, isn't it.
Speaker 3: It seems so there are a lot of companies trying
Speaker 3: to enter. We do think This is a winner take
Speaker 3: most market because the company to get passengers from point
Speaker 3: A to point B the safest, the fastest, and the
Speaker 3: least expensively. And that last one is where Tesla will
Speaker 3: ultimately win, will win the lion's share. It's a little
Speaker 3: bit like Uber and Lyft. There's a network effect, and
Speaker 3: so we think Tesla will win the lion's share. So
Speaker 3: if we're right, then globally, the revenue generation from autonomous
Speaker 3: taxis is going to skip globally, including China, is going
Speaker 3: to scale to north of ten trillion within the next
Speaker 3: five to ten years, and we're talking about only a
Speaker 3: billion dollars right now. And again, lions share will go
Speaker 3: to a few companies. The platform providers will get most
Speaker 3: of the profits. Tesla's a platform provider. Weimo is as well,
Speaker 3: but Tesla because it's vertically integrated. From a manufacturing point
Speaker 3: of view, we believe that by twenty thirty its costs
Speaker 3: will be fifty percent lower than Weimo's costs given what
Speaker 3: we know now, and so it will be able to
Speaker 3: pass those lower costs on and provide a less expensive ride.
Speaker 3: And then, of course China is where a lot of
Speaker 3: other companies are evolving so buydos Apollo, Pony AI we ride,
Speaker 3: and then of course Uber is partnering with a lot
Speaker 3: of auto manufacturers.
Speaker 2: Here in the UK, you have Wave.
Speaker 3: We had the pleasure yesterday of taking a ride in Wave,
Speaker 3: and I have to tell you I was truly impressed,
Speaker 3: very impressed. And you know we've been we cut our
Speaker 3: teeth on cyber Cab and way Mow and so forth.
Speaker 3: So here in London, the streets being as complicated as
Speaker 3: they are and pedestrians all over the place. I was
Speaker 3: truly impressed. And it is partnering with all the OEMs
Speaker 3: because there's a sense of urgency now. They all pulled
Speaker 3: away during COVID from these investments in the future, and
Speaker 3: now they realize they should not have done that. So
Speaker 3: now they're scrambling and Wave seems to be in the
Speaker 3: cat bird seat.
Speaker 2: Okay.
Speaker 1: Interesting and when is across the AI space, you know
Speaker 1: we've moved We've moved slightly on from the days of
Speaker 1: Mega seven, haven't we, And we sold all the disruption
Speaker 1: in the software space, etc.
Speaker 2: It's becoming more obvious.
Speaker 3: That not everyone's going to win in and this has
Speaker 3: been a wake up call. So we noted from our
Speaker 3: big ideas that if you split the tech stack into
Speaker 3: its three major components. So infrastructure, so that's data centers,
Speaker 3: chips and so forth. Platform as a service, which is
Speaker 3: really customized software often generated internally, but now pallentteer is
Speaker 3: turbocharging the customization of company data and company ontology. You know,
Speaker 3: every company has its own language, so that's in the middle.
Speaker 3: And then the third was applications, so infrastructure, platform as
Speaker 3: a service, applications, and we saw all of them growing
Speaker 3: very nicely over the past five years. But when you
Speaker 3: do the second derivative, we learned that wait a minute,
Speaker 3: platform as a service is capturing much more the incremental
Speaker 3: growth than applications. What's going on here It was that
Speaker 3: it was the SaaS copolps. It is palenteer and all
Speaker 3: of these customization efforts that are possible with AI now
Speaker 3: is taking share from sas, which is one size fits all,
Speaker 3: and so that was an important clue to us. So
Speaker 3: we did pull away in recognition, not understanding that there
Speaker 3: would be SaaS copolps, but just that there would be
Speaker 3: more opportunity in the platform and the infrastructure part of
Speaker 3: the tech stack.
Speaker 2: Some of that was a surprise to other people.
Speaker 1: Yes, when you look ahead, Now, what is the thing
Speaker 1: that you're most uncertain about that you look at? Because
Speaker 1: we've talked a lot about what you can see and
Speaker 1: what you can expect. What is it that you look
Speaker 1: at and then well, I just don't know how that's
Speaker 1: going to unfold. What are the big unknowns in these factors?
Speaker 3: Well, I think the most important question for us, thank you,
Speaker 3: is unit growth. What would impede the unit growth of
Speaker 3: these technologies? And we learned an important lesson in COVID.
Speaker 3: We had no idea that there would be that this
Speaker 3: supply shock would cause supply chain issues for three years,
Speaker 3: and that did slow down a lot of our technologies.
Speaker 3: So we learned an important lesson there anything, whether it's
Speaker 3: geopolitical risk. And sure, we have this Iran war and
Speaker 3: the question coming out of it is, oh, my gosh,
Speaker 3: is this another supply shock that's going to cause another
Speaker 3: few years? We don't think so, but it is the
Speaker 3: right question. One of the reasons we don't think so
Speaker 3: is what did we learn from COVID. We learned that
Speaker 3: just in time, so jit manufacturing had gone too far.
Speaker 3: We need buffers, So we need just in case. And
Speaker 3: I think that That's what we have done during the
Speaker 3: last few years, is we've started building in those buffers.
Speaker 3: But that's the most important unknown to us.
Speaker 1: And I suppose the other thing is that we're talking
Speaker 1: about supply crunches is the possibility of supply problems with
Speaker 1: that rare metals and various commodities, not just oil, right, yes,
Speaker 1: just about fossil fields.
Speaker 3: Yes, And I do think that President Trump, in his
Speaker 3: negotiations with President Jijiping, is focused on this quid pro quo.
Speaker 2: We have the oil that they.
Speaker 3: Need, especially if Iran and others energy is shut down
Speaker 3: in some way. I think China gets eighty percent of
Speaker 3: whatever flows through the Straits of Hormuz, right, so.
Speaker 2: We can be that fallback.
Speaker 3: They need to give their rare earths, and we in
Speaker 3: the United States need to deregulate. Why don't we have
Speaker 3: rare earths because in nineteen eighty our regulators decided that
Speaker 3: any waste from rare earths should be treated like nuclear waste.
Speaker 3: So everyone stopped. That is changing, yes, changing, yes, changing, yeah, yes.
Speaker 1: Well, at that brings me to the other limitation that
Speaker 1: you might find around pretty much everything, which of course
Speaker 1: is regulation. Yes, and you know, increasingly the whole conversation
Speaker 1: around AI gets quite.
Speaker 2: Emotional, doesn't that.
Speaker 1: Yes, And if you look on social media, you'll see
Speaker 1: these stories playing out of these terrible end games, one
Speaker 1: of them of course being mass unemployment, etc. So is
Speaker 1: there a concern that the regulatory build up around the
Speaker 1: use of AI might turn into a headwind.
Speaker 3: Well, in the United States, there was a regulatory build up.
Speaker 3: There was an executive order which President Trump rescinded first thing,
Speaker 3: and now we're seeing in New York ridiculous legislation, so
Speaker 3: you know, being proposed saying no corporation can use large
Speaker 3: language models. It's like, that's crazy. If New York wants
Speaker 3: to continue the exodus from New York to my state
Speaker 3: of Florida or Texas or other more business friendly, capital
Speaker 3: friendly states, that's the way to do that. That's the
Speaker 3: way to do it. And I think this mass unemployment
Speaker 3: that is it is the unions who are behind this legislation.
Speaker 3: Mass unemployment, I think is exaggerated. There will be displacement,
Speaker 3: and in fact, already we're seeing in the cohort sixteen
Speaker 3: to twenty four years old, that unemployment rate is going up.
Speaker 3: It's at nine and a half percent in the US
Speaker 3: versus four point four for the overall unemployment rate. So
Speaker 3: those entry level jobs, those are disappearing and giving way
Speaker 3: to AI. What's happening in the US, though, interestingly, is
Speaker 3: despite what we just heard from Metaplatform laying off twenty
Speaker 3: percent of its workforce to afford the capital spending, we
Speaker 3: are seeing more people holding their jobs. They're they're not layoffs,
Speaker 3: and so we're already starting to see entry level jobs disappearing.
Speaker 3: But those who are at work are taking up that
Speaker 3: challenge and saying, I'll run with this AI. I know
Speaker 3: in our firm everyone's really excited about what we can do,
Speaker 3: and in fact, everyone's asking for bigger budgets so they
Speaker 3: can do more. So I think the saying goes that
Speaker 3: those who lose their jobs will it won't be because
Speaker 3: of AI. It will because be because of someone else
Speaker 3: using AI and becoming much more productive. So that's a
Speaker 3: clarion call for everyone to just, you know, really delve
Speaker 3: into this opportunity. It is a huge opportunity for everyone
Speaker 3: to increase productivity. And we you know the history of
Speaker 3: technology is net job creation.
Speaker 2: Long term.
Speaker 3: Sure, short term.
Speaker 2: Some iceplace out with a lost generation along the way.
Speaker 3: Yes, well, but they have an opportunity. You know what's
Speaker 3: so wonderful about this AI opportunity or technology opportunity.
Speaker 2: This isn't going.
Speaker 3: To coders and developers. Sure the developers, the most productive
Speaker 3: ones are benefiting. It's going to almost anyone. If you
Speaker 3: speak a natural language, you can vibe what's called vibcode,
Speaker 3: vibe code with chat, GBT or claud and start your
Speaker 3: own business, solve a problem that you see out there.
Speaker 3: And I'm counseling young people especially to do that because
Speaker 3: maybe it takes six months on average to find a
Speaker 3: job if you're unemployed in the US, the median is
Speaker 3: three months. But if you look for the job, but
Speaker 3: in the meantime try and start your own business using AI,
Speaker 3: you're going to be become much more attractive to employers
Speaker 3: out there, anyone with AI initiative. And again this is
Speaker 3: all natural language, so it's about initiative, not girl exactly exactly,
Speaker 3: and creativity and imagination.
Speaker 1: Speaking of which, the big IPAs that everyone is talking
Speaker 1: about SpaceX anthropic open AI. Are you still expecting those
Speaker 1: this year or do you think maybe the Middle East, etcetera.
Speaker 2: Will have pushed it out a bit.
Speaker 3: I think the reason we'll see more this year than
Speaker 3: otherwise might have been the case is because of the
Speaker 3: midterm elections. If the Republicans lose the House, it could
Speaker 3: be that will be in a more stringent regulatory environment.
Speaker 2: Yes, they might come forward, right right, right. Interesting. Let
Speaker 2: me then connect with question evaluations.
Speaker 1: Now, one of the things that is often talked about,
Speaker 1: of course, is the high valuations, and of course lots
Speaker 1: of the companies that you invest in. Yes, and I
Speaker 1: suspect that your view, obviously is that your companies will
Speaker 1: grow in to that valuations. The other view, of course,
Speaker 1: is that they will collapse into that that valuation, into
Speaker 1: the correct valuation.
Speaker 3: Yes, yes, it's a very good contrast to help me
Speaker 3: explain what we're doing. Yes, our companies do sell at
Speaker 3: premium multiples. Why because we want them to sacrifice short
Speaker 3: term profitability and invest aggressively to capitalize on these really
Speaker 3: massive opportunities. So, while they do have premium multiples right now,
Speaker 3: our assumption is that that valuation will compress to a
Speaker 3: market multiple during the next five years, and our analysts
Speaker 3: and portfolio managers must believe that the revenue growth and
Speaker 3: margin expansion is going to overwhelm that headwind, and you're right,
Speaker 3: grow into the valuations is another way of saying it.
Speaker 3: But to visualize it mathematically, we're saying, no, that is
Speaker 3: a big headwind for the next five years, and our
Speaker 3: analyst and portfolio managers must believe that revenue growth, margin
Speaker 3: expansion will overwhelm the valuation compression and deliver a minimum
Speaker 3: hurdle rate of return of fifteen percent at a compound
Speaker 3: any rate during the next five years.
Speaker 2: Okay, and if that happens, everything really is fine. Yes, yes, yes, yes, Okay.
Speaker 1: Let me ask you one last question because our listeners
Speaker 1: will not forgive me if I do not bitcoin. The
Speaker 1: one thing we haven't talked about is the monetary system
Speaker 1: and possible changes within.
Speaker 3: Are you still fully confident in your view here? Yes,
Speaker 3: on bitcoin. Absolutely. Bitcoin we believe is going to be
Speaker 3: the most important crypto asset for three reasons. It's really
Speaker 3: three big ideas in one. The first is it's a
Speaker 3: new technology. We have now currency native to the Internet,
Speaker 3: which is going to take out a lot of friction
Speaker 3: from the buying process, especially as we move to agentic AI,
Speaker 3: where you know, machines will be will be buying from
Speaker 3: other machines. Right, So that's the first thing. The second
Speaker 3: is it is a global monetary system and we're beginning
Speaker 3: to see it react to the events in the Middle East,
Speaker 3: and it has stabilized after something called a flash crash
Speaker 3: from last October which caused autod leveraging, which caught a
Speaker 3: lot of people off sides. We think that's all washed through.
Speaker 3: We think Iran now is taking center stage and that
Speaker 3: Bitcoin will serve its role as a store of value.
Speaker 3: We just hit twenty million bitcoin minted ever and we're
Speaker 3: going only to twenty one million gold. With the increase
Speaker 3: in price, I guarantee you miners have accelerated their search
Speaker 3: for more gold and will be producing more. Bitcoin is
Speaker 3: mathematically metered and its growth rate has already slipped below
Speaker 3: that of gold. And we also think with the huge
Speaker 3: intergenerational wealth transfer taking place, that bitcoin will be the
Speaker 3: store of value of choice. And then the third big
Speaker 3: idea is bitcoin and we wrote this paper in twenty sixteen.
Speaker 3: I'm very proud of our team. Bitcoin the first of
Speaker 3: its kind in a new asset class. If you look
Speaker 3: at bitcoin, it's correlation relative to other assets is very low,
Speaker 3: even to gold. Many we call it digital gold.
Speaker 2: But if you look at.
Speaker 3: The correlation since twenty nineteen, really since institutions started thinking
Speaker 3: about this. The correlation between gold and bitcoin is zero
Speaker 3: point one four, hardly any correlation. And if you look
Speaker 3: at a matrix I put it in my New Year's
Speaker 3: letter in January, we have a matrix you'll see the
Speaker 3: correlation with other assets is very low.
Speaker 2: So what does this mean?
Speaker 3: Acid allocators are looking for low correlation assets because if
Speaker 3: they in the satellite strategy that we offer, if they
Speaker 3: put a lower correlation asset in their mix, their risk
Speaker 3: adjusted returns go up over time.
Speaker 2: So maybe everyone should just hold bitcoin and gold and
Speaker 2: be done with it if you want a store of value.
Speaker 1: Yes, Kathy, thank you very much for joining yesterday.
Speaker 2: Really appreciate it.
Speaker 3: Thank you very much.
Speaker 1: Thanks for listening to this week's Merin Talks Money.
Speaker 2: If you like us, show, rate, review.
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Speaker 1: sending your questions or comments to Merrin Money at Bloomberg
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Speaker 2: You can also follow me and John on Twitter. I'm
Speaker 2: marins w and.
Speaker 1: John is John Underscore Stepic. This episode was hosted by
Speaker 1: Me Maren's amazep web. It was produced by some Asadian
Speaker 1: roses and sound designed by Blake Maples, and many many
Speaker 1: things to Kathy with