Can the AI Boom Survive a Margin Squeeze?

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Last night I was walking around West Village back from dinner and I heard just overheard with no context someone said oh it's the big guys building data centers something along that line in other words this is very much in the zeitgeist you know so I think People say that buy the news, sell the rumor. And, you know, or when your barber starts pitching you a stock in, you know, in something, it's time to get out.

Welcome to Merrin Talks Money, the podcast in which people who know the markets explain the markets. I am Merrin Somerset Webb, and this week I'm speaking with Steve Hu. Steve is head of research at Silicon Data, a firm founded in 2024, which aims to revolutionize compute markets by providing price transparency and GPU performance data to traders, financial institutions, and builders. Before he took on that role, he was a quantitative researcher on the Bloomberg Index Research Team responsible for systemic equity and multi-asset strategy research. And of course, he has been on this podcast before. And that is why I wanted to speak to him. He's previously recommended reading material to us that has gone viral later. And also,

he really is an expert on everything digital. So I'm hoping he'll be able to guide us through what is going on at the moment. Now, during our conversation, we get into everything from the price of GPU rental to whether restaurants are going to be overwhelmed. Are your agents trying to get the best tables? But before all that, we start off where we left off last time we had Steve on the show with situational awareness. Steve, welcome to Merrin Talks Money.

Yes, that's right. Thank you for having me back, Merrin.

It's a pleasure. Anyway, back then. The last thing that Steve said to us that we should all read is something called situational awareness, which is kind of a pamphlet, kind of a, I would go so far as to call it a book. I think at the time you said, you know, we should read this pamphlet. But in fact, it's a good 200 pages long. And that gave a sort of sense of what the author thought was going to happen in the AI marketplace. It was written in 2024 or 2023 originally.

I think that's right. Yeah, around then.

Anyway, you recommended it. I had a skim through it and thought, wow, okay, that's kind of interesting. And then about a year off was the whole thing went completely viral, right?

And by the.

Beginning of this year, everybody was raising situational awareness and getting themselves quite upset about the whole thing. And I wanted to start with this because it is fairly extraordinary that the AI landscape is unfolding almost exactly the as this pamphlet slash book suggested, in that we're now at the bit where the extraordinary power of what is being developed is being recognized. And the conversation has moved on from, oh gosh, we can use this to help organize our email to, okay, well, this is a massive military asset and needs to be contained slash regulated by government. Well, we're at that bit, right?

Yeah, indeed. The implications, I think, are enormous. And this is regardless of how this whole investment cycle transpires, going to be a transformative technology that's going to shape and change society and our world beyond recognition. Yeah.

So we're mostly interested in the investment cycle, of course, because that's what the podcast is for. But obviously, as a background, the way that it's changing the world is kind of important. But before we get into all that, tell me about your new role, Steve, because you used to be a colleague of ours here and then off you went. Is that all of that?

That's right.

Yeah.

So I left Bloomberg in May to join a startup that also happens to be founded by a former Bloomberg colleague, Carmen Lee. And the company is called Silicon Data. It is a data company that tries to bring data transparency to the AI CapEx industry. And we are launching benchmark indices on GPU rental rates and GPU token prices. And we have also announced plans of launching futures contracts and options on the CME pending CFTC approval.

Explain to us about that a little more. What are these indices you're launching? On GPU pricing and GPU.

So GPU rental prices. So essentially, how much does it cost to rent a H100? So all these, you know, GPUs, Nvidia GPUs that are running in data centers that produce, you know, AI tokens from which we run these models, they are all named after famous, former physicists, right? So Hopper 100, Blackwell, you know, 200, you know, and Ampere 100 was a chip that's launched in 2020. So we have data that tracks how much it costs to rent an hour of these GPUs around the globe from a designer, whether it's sitting in Western Texas or Finland or Southern Europe somewhere or Asia or Middle East. And we aggregate these numbers into a single, you know, apples to apples comparable benchmark number. The same way you can think of

it is almost like creating a single-bedroom rental apartment index for New York City, right? So you aggregate all the rental agreements from across the city, whether it's in Soho, Harlem, Brooklyn, and so on and so forth. You control for all of them. Variables like square footage, you know, term of the length of the agreement and so on. And you make all these rental prices, apples to apples comparable, and you create a singular index so that people can track it.

And this is an important variable if you are a cloud provider, if you are one of the data centers that's providing the compute, this is a source of revenue for you. And if you are one of the AI labs, whether it's OpenAI, Anthropic, one of the Neo labs, or one of the inference platforms that post-train these open models that allow people to, say, serve Chinese models, this is a major cost for you, right? And we, Carmen, had this vision that eventually we're going to enter a very capital-intensive part of the cycle. There's going to be a lot of people with a lot of exposure to AI, and there's going to be need for hedging. There's going to be need for... hedging on the revenue side, on the cost side, and there's going to be traditional financial

instruments that help solve this problem. That means futures, that means options, which is the reason why we have created these trusted benchmarks against which futures contracts on compute as a new commodity will settle. And we have to believe that this will be one of the biggest, if not the biggest commodity rivaling Okay, interesting.

So we've got basically what you're looking at here is the price of renting compute from a data center. You've got suppliers, their revenue, and then you've got everybody who is using AI in anything is one way or another, either one step or two steps removed from the price of renting compute. the chips inside the data centers, right?

So that's really interesting information telling us what might or might not be going on in the AI boom or the AI bubble. What is that information telling you right now?

So right now we are in this very interesting era where we have this, you know, very, I think, well cited chart on our Twitter account that just poses price of AI tokens or how much you can charge for the models. with against how much it costs to rent this different type of chips. And you see this interesting pattern where since about the end of May, the token index or the expenditure rate, the effective price paid for the models has been coming down pretty significantly, almost half of where it was back in the middle of the year.

In the meantime, rental rates for these chips have been going up rapidly, continuously. So The analogy I like to give is almost a bit like if you are inventing the car for the first time, there's a luxury car, say Mercedes, then there's a cheaper car, say Toyota, Corolla. In the beginning, everyone was buying their Mercedes. These are these frontier models, the top-of-the-line models from Cloud or OpenAI.

But eventually, people realized that some of these non-frontier models, the cheaper models that have less capability but still evolving over time, are saturating most of the everyday use, and they're using a lot more of that. So everyone's buying a ton and ton of Corollas, right? A lot of Toyotas all over. So the thing is, you can have this proliferation of car usage, and every car burns gasoline or benzene, I guess, wherever, I guess, even in the UK, that would drive up consumption of the underlying input. So the same analogy applies in AI, in that the proliferation of AI and everybody using it these cheaper models to power their agents, right? And to read their emails, to respond to things, that all consume compute, right? Even if

it's not necessarily using the highest margin and the most profitable models, the most powerful models coming from the frontier labs.

Okay, so you've got the cost going up, but the price coming down.

If you put it that way, yes, there is a certain sense that's the case.

Okay, that seems problematic for some part of the market.

Indeed, for some part of the market, it could be problematic, yes. In fact, interestingly, through sheer coincidence, the time when the token index peaked was also around the time when, according to some measurements, The anthropic revenue growth also slowed and came to grow, you know, sort of either flatlined or, you know, sort of much slower than it was growing in the beginning of the year. So something tentatively related, but not conclusive by any means.

Yeah, I mean, this does seem to be one of the bigger problems for the anthropics and the open AIs, which is that cheaper, simpler models work generally. as well for the things that most of us do. Your early adopters are going to be very different to the people who are going to use your product long term. So the majority of us are always going to use, I'm guessing, I'm speaking for myself here, always going to use cheaper, simpler models. So what does that mean? for the anthropics and the open airs long-term?

Well, long-term is a long time away, but.

All right, medium-term, short-term.

I suspect this is going to create some questions surrounding their upcoming IPOs and the, I think, high, shall we say, valuations, relatively high valuations. The last time I think I heard numbers anywhere between 1.5 $ 0. 5 to $ 2 trillion for Anthropic and OpenAI, I think, a tad lower than that. I mean, these are still non-public startups, young companies, but growing extremely rapidly, scratching the soul. Anthropic has gone in almost no time at all from having very little revenue to $ 60 billion of annualized revenue run rate. and projected to go much higher based on the extrapolated growth previously. So if there are signs that the growth rate is indeed slowing, and there could be structural reasons for why, for competitive reasons,

that it may not grow as rapidly as people have previously thought, and that will put into question some of the valuations and rosier hopes for their long-term value.

But it's also the case that at an IPO, the buyers at IPO don't want to pay a price that reflects every single piece of potential growth already priced in, right? The buyer at IPO wants to buy at a price that gives them something. And the problem possibly with these IPOs, particularly with Anthropic and OpenAI, is that We look at them now and I think increasingly people don't think of them as an opportunity for new investors to get a slice of the growth. They look at them and think of them as fabulous exit opportunities for people who already own the shares. And that's not what the market is supposed to do or it's not what people expect the market to do. You get an IPO, you buy something that has growth left in it. And even if these

companies grow, if you've paid in advance for all the growth, what's in it for you? And so you get the sense that the market is beginning to shift the way they're thinking about these IPOs.

Oh, you ask very good questions. Yeah, I think those are questions that people should be asking.

Steve, before we go any further, it occurs to me that we have been talking about tokens, tokens this, tokens that, tokens this. And, you know, there is a tendency for all of us at this point in the cycle, we've been talking about things for a while now, not to define our terms. So would you mind just for our less experienced listeners explaining what we mean by a token?

We've all used ChatGPT, right? And how you use it is you enter a bunch of texts, say, you know, sort of, uh, help me, uh, plan a little trip to, you know, uh, North England. This, you know, coming weekend with my family is going to give you back an itinerary telling you, go here, go here, go here. And if you are here, you check out this restaurant and that.

So a token is basically a length of text that you're entering. So if you're entering a long prompt, a long set of conditions about what you want, there's a large number of tokens. And if the output is a large output, long output, that is a large number of tokens too. Now, it doesn't necessarily have to be the case that your input or output are just text, right? Increasingly, we're seeing people inputting a screenshot into a prompt to say, hey, explain how this works or what is in the picture or whatever. Nevertheless, the computer is basically, the AI is translating the text, you know, the picture into essentially text or tokens, right? And that's the only way the computer is able to think. Okay.

So the way that an LLM, a large language model, what we all think of when we talk about AI these days, although obviously it's not, there's more than that to it these days. But nonetheless, an LLM is what we think of when we think AI. So you give it a prompt, ask it a question, and then you keep developing that. You ask more and more questions, you put in more and more prompts. The reason it becomes so expensive or historically expensive. has been expensive, is that the tokens are rerun each time, right? So it's not like you're starting from where you left off. The model has to rerun every token. And that's what historically built the price up.

Well, a core challenge with AI, with LLM, has been managing memory and long context, you know, exactly as you say, right? When you are in a conversation, you know, with an AI person, just like we know in a conversation with a human being, you don't want to start over. You want people to remember the context of where you got to, where you are, so that the next sentence makes sense within the context of what you were talking about. But of course, as the conversation gets longer and longer, the amount of information that has been talked about, the amount of memory, things that needs to be memorized, it just becomes bigger and bigger, right? And at some point, you run out of room to store that memory, but also

memorizing it. So there's all these attempts of essentially compressing the memory, right?

Yeah.

You know, it's a little bit not so different from when we human beings talk to people, we don't remember every single detail in a conversation that's been mentioned. We make mental notes of the key points that have been touched upon, right? And we make recall. And what a computer is able to do even better, increasing better, you know, algorithms is actually able to sort of make that recall better.

even more uh refinely being able to actually go back oh actually let's dial back to that moment when we talk about something in the greater detail stuff that you may not remember very well and that makes ai super powerful and in terms of the practical implications part of the reason why we have seen such an explosive growth in the stock prices of memory and you know storage stocks of such as sandisk or micron in the us or sk hynix and samsung in in uh In Korea, it's exactly that, right? We suddenly have an exercise where the demand for memory and demand for storage of data has just gone exponential, right? Every single conversation everybody's having is generating tons and tons more of stuff that needs

to be remembered and stored down. So that's the reason why Korea is suddenly seeing such intense attention from across the globe.

That's not particularly comfortable, by the way, but thank you for that explanation. Very useful. Can we go back to data centers? We were talking about the cost of GPU and rental going up. Is there still, despite this massive CapEx boom, is there a shortage of data centers? I mean, we're seeing, as you know, protests against data centers, people protesting, People don't really like to have them built near them.

We've seen some cancellations most recently in the UK, a big cancellation because they can't get connected to an energy supply, etc. And that's a big problem, globally connecting your data center to an energy supply. So is there still a shortage despite all this capex? And is that what those GPU rental prices are reflecting?

So shortage is, you know, a complicated concept, right? So As it stands today, everybody is able to get on an AI and get some output from it. But, you know, shortage is mostly reflected in terms of the anticipated growth in demand of a lot more people using a lot more AI and the ability to supply into it. And to that extent, on the reasonable estimates, you know, of everybody just using, I think, AI more meaningfully, as you said earlier, The average person is not using AI in a very, very high power way, right? Everyone is using it mostly as like a glorified search. But there is actually a lot more ways you can actually use it in an agentic way where it's continuously running. And that is actually a lot more demand for compute. And we

multiply that across the global population, especially within developed economies. And that would imply a level of demand that is not currently matched by the growth in supply. So to that extent, yes, there is a shortage. In data centers, not necessarily.

But it's a forecast shortage as opposed to a current shortage.

Of course, of course. It's a forecast shortage. Nevertheless, I think it's a reasonable forecast given the recent trend. I mean, I do think people are going to be using more AI and it's going to proliferate, especially with more, I think, products that are built with better product market fit, with good understanding, anticipating consumer needs, like the latest product from Meta, Muse, and so on. There's going to be more of those and combine that with the fact that there is a shortage in supply on data centers because of the shortage of just sheer physical constraint of power, right? The lack thereof, right?

There's not enough sites for us to build large data centers. We're having to build smaller ones in different corners of the world and contending with energy shortage. I think local political pushback from local residents is, which I think is maybe a little bit overblown in terms of the actual net impact.

Is there a case to be made for demand not developing? in the way you expect? I mean, I was surprised by a stat, I don't even know if it was true, but it rang a bit true, that more people pay for weight loss drugs in the US than pay to use AI in any way. Fascinating at this point in the cycle. And the other thing to say on that is that you know, look at Muse and the other agents that people are starting using fairly regularly. Is it sustainable for everyone to use an AI agent in this way?

And that we're already seeing, for example, local government getting clogged up by people having their agents complain about their parking tickets and potholes, planning getting clogged up with masses of objections generated by agents and even restaurants that are hard to get into being overwhelmed by... By phone calls and emails from agents. And there comes a point when rather, and this is at the very beginning of this cycle, there comes a point when instead of reducing friction in our lives, if everyone's using agents to do all these things, the world just collapses. I mean, how does this actually work if already when only a tiny percentage of the population is using an agent to sort their email box,

to do their shopping, to do their restaurant renovations, to complain to the council, to do their taxes? Tiny, tiny already is overwhelming some of our institutions and our businesses. How does that play out?

So you bring up two main points that are both issues are very good. So let me address each of them. The first one you, you, you raise is regarding demand growth and how reliable is it? Um, in the, it is the case when I say demand, we are looking at the current condition of how much, um, you know, uh, LGBT and tropic is selling, you know, they are sort of monthly plans at, uh, you still a fixed price or you can eat, you know, kind of a plan. They, they throttle usage and they give you an amount of credit at which point you run out. Muse, by the way, is free. So, um, At current prices, demand is growing in a certain way. It is unknown at this point how much people are going to be willing to pay

for AI per usage, especially if it's going to be mediated. It sounds as though that Mela and Zuckerberg have been talking about the fact that they want to make this widely available to people either for free or at very low cost, and they will find ways of monetizing it via other means such as e-commerce and collecting a fee that way, and so on. I don't think the exact channel of the economics of how AI companies will be paid while demand grows has been fully articulated. I think there are competing narratives. So you are right to question or ask the question that what is going to be the growth of demand for AI if costs start going up, right?

I don't mean to say that nobody's paying for AI. That's clearly not the case. I'm just saying that how AI will be monetized, I think there are competing narratives and also depends a little bit on whether or not you're looking at consumer use case or enterprise, right? Enterprises are clearly paying very large bills. And so enterprises clearly are paying these models by the token prices and that are, you know, publicly known. And then there is cashing, there are ways in which you get some discounts. But generally speaking, yeah, there is an anticipation that this is going to be a metered usage for a lot of it. You know, the question remains, as to whether or not once this proliferates, you know, the usage will keep up, right? And that

is part of the discussion when we are talking about the economics insofar as who actually captures the rent. If you invent this extraordinary thing and, you know, open AI, you know, anthropic, and then do you actually sort of manage to recuperate, right, those initial discoveries, the training runs, uh, that came up with all these models, uh, if, uh, subsequently there are models that are based on these models, uh, distilled versions, either, you know, themselves distilled cheaper versions of those models or, you know, others, uh, um, such that they are sold at a much cheaper price and people optimize on cost and quality, then, you know, so that's the open question.

Okay, second bit. Second bit.

Yes.

Obviously, this is, you know, the use case for AI is now, or for LLMs at least, is very clear. We know what they can do and we know what they can't do. And we know that we need new types of models to do the things they can't do. For example, you know, precision, audit, compliance, regulatory stuff, et cetera.

We know that. So the use case is certain. But when you are going back to my question about retail agents, agents working for ordinary people, and the way this can very quickly overwhelm the systems that we already have in place.

Indeed, right. If you have proliferation, everybody just activates agents and, hey, go pick up, you know, go find me a reservation, go find me a ticket, go do whatever. On the receiving end, you know, you're going to find yourself overwhelmed with all the agentic requests And we're going to find ourselves in a situation where everyone can generate a ton of AI output, AI slop, but we don't have the ability to process it all, right? And in the enterprise context, I think it's a similar thing going on where you can have enormous efficiency gains where someone's generating tons and tons of new PPTs and presentations or code and whatnot.

But then you have other parts of the business that actually process that output to actually take it into a finished product to turn that into a business outcome and revenue. And I think that analogy applies and that is a very big open question as to what happens You know, I don't have good answers for you. I suspect some of the questions and pain points you're pointing out are going to actually surface in the coming months.

Yeah, I mean, I have this vague worry that we end up with everyone has an agent trying to make them a reservation at the new great restaurant in London. The new great restaurant in London then has to, you know, create a big pile of agents to deal with the agents bothering it for reservations.

Either that or they shut down agentic inbound, you know, altogether. Or you've got to basically force a new type of restaurants that is AI native that can actually handle these things. So you have to be a new type of business. sort of, you know, they say shortage or whatever is always the mother of innovation. I mean, there's got to be, yeah, there's got to be things that we've got to give, right?

Exactly.

Anyway, so we've talked about a lot of things. We've talked about a lot of the uncertainties. We've talked about who's using, who's demanding, et cetera.

Yeah.

Bubble or boom.

I think the question is not so much that whether it's a bubble or not. I mean, every single investment cycle, every Quebec cycle has always gone through the boom and bust. The question is a matter of timeline. The market has already topped in the local fashion in the middle of the year, right? So there is definitely a sense in which people, a lot of the questions you were just raising with regard to ROI, with regard to how does this investment produce a return and how do these frontier labs actually justify their valuation, which has been the source for a lot of their CapEx spending commitments because they've been selling shares, right? They've been raising money from VCs and other investors in order to raise capital, which in

turn to acquire, compute, and build data centers or commit to renting data centers. So that whole narrative, that whole cycle, cyclical narrative, could be put into question if more and more people are going to start questioning, you know, some of the questions that you're just raising and good answers are not forthcoming. So I do think we are in an environment of the market where these questions are growing louder and we're not early in this trade. I think last night I was walking around West Village back from dinner and I heard, just overheard with no context, someone said, oh, it's the big guys building data centers or something along that line. In other words, this is very much in the zeitgeist. So I think people say that buy the news,

sell the rumor, or when your barber starts pitching you a stock in something, it's time to get out. I mean, I think there's some truth to it in that we are probably in a pretty feverish moment of the market that some of the smarter money has already either taking chips off the table or having their chips taken off the table for them. Also, I do think this is an important point to make, is that nothing ever happens in a straight line. Even if this was the end of the bubble, we can have extremely violent counter rallies as uncertainties regarding whether or not this really was the end can actually cause that to happen, number one. Number two is that I think There's a lot of people in the bearish circles. Some of them actually sort of

have this position that this whole thing is vaporware and it's all useless. And it's just been a con job that none of this is ever going to be useful to anybody. Nobody actually wants it. I think that is fundamentally untrue.

That's a man who's never going to get a restaurant reservation again.

Yeah. he's short in the market, in which case he might very well just, yeah. But I do think this is a fundamentally transformative technology. I do think it's going to reshape society. And I eventually think the ball is that people who are bullish on the technology are going to be proven right. And there is a chance in which things work out right in that you find products with good product and product market fit you have an acceleration of adoption and people actually do want to pay for things that give them utility. I want to be a little bit contrarian just because these days, it seems to have become very fashionable. I have not met a single person who doesn't think this is just a repeat of the dot-com bubble that we're staring at within the

next 12 months, some sort of spectacular crash and blow up of the US tech bubble and stock market. And we're going to repeat the 2010 I think there is a chance in which the mismatch, I think this is fundamentally a problem of potential mismatch of timing, not necessarily there won't be demand for it, but rather the demand could show up slower than some of the most bullish people in SF seem to think. They seem to think that because it works for coding, all the programmers adopt AIT, rapidly overnight. This is going to happen everywhere. Ignoring a lot of the practical bureaucratic hurdles to adoption that could slow things down such that the supply could actually show up or show up in 2027, 2028 and then you

find yourself not having as much demand because enterprise adoption wasn't as rapid and maybe consumer adoption is not as rapid because you didn't find the right product market fit for the products that actually really took off the same way iPhone took hold with mobile. So I think that remains to be seen. This risk of an air pocket is very much real, and we are clearly living through it in different manifestations. But I think there's also a risk of of a right tail risk by things that really do go right, in which case we can actually see a pretty violent melt up as well.

Okay, Steve, so you've given me two options here. One is that we're closer to the end than the beginning, and the other is that we might have a massive melt up. Which one do you think is most likely? Melt up or closer to the end than the beginning?

The funny thing is, those two things don't have to necessarily be.

They don't have to be mutually exclusive. They could both happen.

Indeed. You know, what happens if the market melts up and then crashes? You know, you're not necessarily going to be nimble enough to time yourself perfectly to, you know, catch one and then boy the other.

So.

I'm sorry, that's a little bit of unsatisfactory answer if you are looking for me to give you like a tummy signal.

Yes, I am.

Okay, so what do we do? What do we do? We're an ordinary retail investor. We're like everybody. We are overweight, probably by mistake, overweight US tech, by mistake, overweight AI. What do we do now? Is the answer just to rebalance, pull back? But then, of course, is everything AI now? Is there a way out? Is there a way that we should be investing right now to protect ourselves a little?

Well, first of all, I am not here to give anybody any investment advice. No, no, this is not advice. In general, if we are just chatting casually, I think It is fair to say there are plenty of things that are not directly AI-related. You know, energy, you know, like traditional fossil energy stocks and, you know, oil companies.

They are not AI, right?

Yeah, okay. Thank you. Now, the other thing I wanted to ask you was about this business that is all over the news this week, about Anthropic is saying that they intend to give 5% of their shares to into a foundation that will support people who are affected by AI. So it should be nice to people who've been affected by it. Maybe they lost their job or to look into this kind of thing, et cetera. And then at the same time, we've got Elon Musk saying that he's going to give a SpaceX share to every kid in America.

And all this looks to me a bit like, well, we better give it away before they bring in a wealth tax. What does it look like to you?

I did not make that connection. I knew that anthropic has always had this longstanding worry, very vocal, you know, about the danger of AI poses on society, which is a bit ironic because, you know, they are the one actually that's spearheading or, you know, at the forefront of inventing this technology. So it's almost as though like, oh, we'll have this product. I look, look, you know, look, the danger is actually, you know, posing. So, Yeah, it is a bit interesting, I think, from a behavioral point of view. But I also kind of appreciate from their perspective, I think, in a much more charitable fashion to understand that there are other people developing this technology. One of the many competing forces, this is going to be developed.

So they want to pay their own, make their contribution to help society deal with the consequences of I don't know what 5% of their market cap, you know, put aside towards, you know, how this exactly will pan out. I mean, you know, there's not a lot of details yet as to how the program will work. But I do think the end goal and the question as to how this will, big society that I have no doubt.

I think AI is going to be hugely transformative in good and potentially bad ways as well. Not just only in the popular narrative of, you know, disrupting jobs, but also this idea that you suddenly have a technology that enables everybody to do things that previously could not, right? You know, and there could be all sorts of risks unleashed onto the system. that we previously didn't have the capability of dealing with. So, yeah, I mean, within that context, I don't know what the 5% share will do. But it's good that somebody is funding somebody to study these issues.

And I hadn't heard about the SpaceX one. So I'll have to learn more about that.

The SpaceX one is interesting. I mean, it smacks to me a little bit about, you know, buying indulgences. We may destroy the human race, but at least, you know, we paid up a little up front.

Yeah. You know, the one question I've got to ask you, maybe we'll have to do this on a follow-up conversation. So what this all looks like from the perspective of a European person, a British person, you could looking, you know, from across the Atlantic, I think this seems mostly of an American activity, right?

Yeah, we're just irritated we're not going to get a SpaceX share.

Why can't my kids have SpaceX shares too? What's with all the borders?

I don't know. Anyway, listen, the last question I must ask you before we go, because I've had you far too long, is, so last time I said to you, what should we read? You said read situational awareness. And boy, were you ever right. So now I'm going to say to you, Steve, what should we read?

I think people should read some poetry. rediscover our humanity oh.

God you know that it's an awful time on the other day said something very similar i said what should we renew the philosophy philosophy get ready get ready for your existential crisis and you're clearly in the same zone anyway indeed so thank.

You so much for joining us today steve we really.

Appreciate it thank you very much for having marine always a good time and i hope the next one won't take us another two years It won't.

It won't. Thanks for listening to this week's Merrin Talks Money. If you like our show, rate, review, and subscribe wherever you listen to your podcasts. And keep sending questions or comments to merrinmoney at bloomberg.net. You can also follow me and John on Twitter or X. I'm at MerrinSW and John is John underscore Stepak. This episode was hosted by me, Merrin's Unset Web. It was produced by Sam Asadi and Moses Andam and sound designed by Blake Mapleson.

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