Trump’s Ukraine Flip, Nvidia’s $100bn OpenAI Deal & HSBC’s Quantum Breakthrough

Hello and welcome back to the Market Maker podcast, and we've got 3 topics we're going to discuss in this conversation. Number 1, Trump just flipped his stance on Ukraine saying that Kiev can retake all of its land. Defense stocks, particularly in Europe, have surged. Uh, Rheinmetall, the German arms manufacturer, the 5th largest European arms manufacturer in fact, is up nearly 2,000%. since the war between Russia and Ukraine began. So we're going to unpack why are they such an outlier? What's happening to defence names as NATO's pledge to boost defence spending continues? Second story, of course, Nvidia, they have planned $100 billion investment into OpenAI alongside its ongoing deal-making spree. We've also had news this week about Alibaba, last week Intel signalling a strategy to cement their dominance as the indispensable engine. In this global AI race. And then HSBC just claimed a world first in quantum computing. So look, everyone's talking AI. I want to talk a little quantum for the backend of this episode. And the reason why is HSBC, they're boosting bond predictions by 34% accuracy, they said. And in the world of finance where 1% significantly moves the dial, they put out there in a demo test, 34%. It's experimental, but even if part of this is real, it could dramatically reshape finance competition on Wall Street and careers in finance. So I want to kind of loop it back into that side as well. Quick shout out to Saab, our new podcast researcher doing an awesome job for us. So Saab Ertam, thank you very much. Then also final one is we're on 821 Spotify ratings. I would very much love to get that to 1,000 by year end. Year end target, 1,000 locked in. Let's go. So over to the first story then. So Piers, Trump, bit of a surprise. He has done, well, is it a surprise? I don't know. It's, he's done a U-turn again. Trump said Ukraine could retake all occupied land. That obviously reverses earlier suggestions that Kiev should give up territory. So First of all, what was the market reaction to that?

Yeah, well, is it a surprise? I mean, I guess the thing about Trump is he just keeps flip-flopping and, you know, is prone to changing his mind seemingly on the fly, you know, every day or two or whatever. So, I mean, it's an unsurprising surprise if you see what I mean, but it is a massive flip and U-turn on his whole stance around the Ukraine situation. Of course, he's been trying to broker the ceasefire and the deal. But Putin seemingly not playing ball. So it looks like, in my opinion at least, a part of this is about him trying to, it's a veiled threat to Putin to say that, look, if you weren't prepared to come to the table to broker a deal, well then, all right, game's back on. But of course, Trump wants to avoid getting boots on the ground involved in this kind of thing. And so he's pointing towards the Europeans And it's their responsibility and really blaming the Europeans for not getting, doing their share in this whole situation. And so what's the market reaction to this? Well, the most standout market reaction was the European defense companies and they've marched higher. Part of this is because NATO's pledge to increase defense spending to 3.5% of GDP. So we know that the Europeans have been woefully off that sort of pledge for many years. And Trump has been kind of banging them for that for a long time. And so commitments from European governments to spend more on defense, of course, is good news if you are in the business of supplying military equipment and defense items right now. There's one standout though, player who's benefited more than anyone else from this, and this is a company, a German company called Rheinmetall. Now they're up, they're actually since the Ukraine-Russian war began, they're now up 2,000%, their share price.

That is, yeah, I saw that and I saw a chart and it was a whole cluster of these European defense names because there's quite a few and they've all gone up dramatically. But this German firm was up way over and above everyone else. So why is that? Why are they up 2,000% when everyone else is only up about 200% to 400%? Yeah.

So this is where you've got to, if you kind of just go below that first layer. So the layer one is, ah, well, more defense spending, right? Who are all the defense companies? Right. Let's buy all of them. That's kind of layer one, right? If you then go one layer down, well, this is where you can find some massive outperformance because what do Rheinmetall specialize in? They're Europe's biggest ammunition producer. Now there's two interesting things. To that. Firstly, the EU and NATO have named munitions resupply as their top short-term defence need, number one. Number two, you can bang out these munitions like just turn on the tap and it doesn't take long to manufacture munitions. You know, if you're in the business of building, you know, high-tech kind of missiles, or let's say high-tech Aeroplanes, right? I mean, these take a long time. You can't just turn the tap on and off with that stuff. So Rheinmetall are seen to be here perfectly positioned to benefit in the straightaway short term from the defined kind of top sort of priority to kind of re-load on munition side. So yeah, they're the ones that are going to benefit the most and in the shortest term. And so it's kind of an ammunition pure play, if you like. And so They're top dog and that's why investors have been buying those shares like they're going out of fashion and 2000% return. I mean, that war has been going on unfortunately for like 4 years now. So it's a 2000% return over a 4-year period and seems like that upside's still on the go.

Yeah, I guess one of the things is the, the long-term tailwinds here that they're going to benefit from? Because when you said there about 3.5% of GDP that NATO's pledging, assuming though that that pledge is several years, well, I don't actually know what the defined timeline is, or is it until we decide to change again in the future? So they'll continue doing that.

Yeah. Well, there are risks to this. It's all very well and good that your policy is, right, we're going to aim to move towards spending 3.5% of GDP on defense. The problem is all of these governments, maybe with the exception of Germany, to be fair, all of these governments have got a massive debt problem. They're trying to figure out ways of spending less, not more. So that's obviously the key barrier here or the key risk to all of this policy. It's kind of execution risk and whether it's possible or not.

Yeah, it's interesting as well. Just on the final point on this before we move on, I think the Trump angle politically where it's, you know, he went in, I think he was going into some of these big confrontations like with China or with Putin and he was trying to strong-arm a deal. But as you said, the Putin interactions have kind of happened, have kind of been and gone, and seemingly the war is continuing at a pace that hasn't really changed. Yeah. And so I just wondered whether the calculations have been that, yeah, this, now he's got more into it, it's just avoiding it becoming his war. Let's just be a strong figurative person within the talks. However, this is really Europe's problem so that it doesn't really come to get him back later down the line.

Right. And look, Putin, I mean, apparently economic sit— the economic situation in Russia is deteriorating and it seems like the speed of deterioration has picked up a little bit. So that's obviously, that's bad news for Putin and his war, right? And so he needs an off-ramp where he can somehow get out of this and still claim a victory. And maybe Putin's just now threatening that by saying, look, maybe this war's still on, maybe Ukraine can regain territory. That is a risk, Putin. So come to the table now and do a deal.

Yeah. Interesting. All right. Well, look, let's move on, talk NVIDIA, 'cause this was a big deal and I think it came a little bit of as a surprise as well. So the headline was that NVIDIA and OpenAI announced a landmark partnership with plans for NVIDIA to invest up to $100 billion. So I guess a couple questions here. What exactly is the partnership? How are they putting the funds in? How does this work in practice?

So this is a letter of intent, okay? Has been signed. So this is NVIDIA saying, well, and OpenAI of course, agreeing a deal at a top level in principle. The deal is $100 billion of investment, okay? So this is NVIDIA investing $100 billion into OpenAI. But look, this is going to be staged over time. This is one key part of this deal. The overall idea is this is to help NVIDIA, sorry, OpenAI, you know, build out 10 gigawatts of AI data center capacity. So obviously on one side, OpenAI's growth ambitions, I mean, at the moment they've got about 700 million weekly active users on ChatGPT. Obviously each person that's using this, that requires compute, right? You need a data center to be able to handle their, questions and then do the thinking and then fire back the answer, right? So the more people you have on the platform, the more compute power you need. Also, as they're upgrading, they're going through the generations. What are we on? ChatGPT-5 now? Each new version is better, meaning it needs more compute power anyway. So you've got this exponential compute demand as these ChatGPTs get more advanced and the number of new users increases at the same time, right? So it's about how can we How the hell can we meet this demand? And so this is why you're getting all of these kind of data center build-out deals being done. 'Cause don't forget, OpenAI has already done a $500 billion Stargate deal with SoftBank and Oracle, okay? With the US government getting tied in. So this is just another deal. Now, the way it's structured, their plan is to build out 10 gigawatts, right? But they're gonna kind of do it 1 gigawatt at a time. So initially it'll be $10 billion of the $100 billion. $10 billion will be upfront and land in OpenAI's bank account on the signing of the deal. So that's the first point. It hasn't been signed yet. A letter of intent is right. We've agreed in principle, and apparently it was just Jensen and the OpenAI CEO. His name has just completely escaped me. Chris Altman. Sam Altman. Sorry. So they apparently, they brokered this deal together, like literally CEO to CEO, no bankers, no advisors. Like they've been talking for a while. Wow.

Can you imagine these egos, the size of these egos?

Yeah. And they both went, they actually both, where they finalized the letter of intent was when they were here in the UK. 'Cause Trump did a state visit and he brought a load of his big guns with him, including Jensen and Sam Altman. And apparently on the outskirts of all of this stuff that was going on, they finalized this deal, just the two of them apparently. So look, stuff needs to happen now. The lawyers get involved, obviously M&A advisors get involved before it actually gets signed. But if it gets signed, $10 billion in the bank account for OpenAI. So that's number one, right? And then they're gonna do this over time. A 1 gigawatt sort of build out each, you know, a time times 10, right? Now apparently, well, so here, what does Nvidia get? Well, they get an equity stake in OpenAI. Now, how much for the $100 billion? Well, we don't know yet because it's gonna be in stages and OpenAI's gonna be growing and their valuation's gonna shift over time. I guess what we do know is that right now they're worth about $500 billion based on their last funding round. So $10 billion upfront would get NVIDIA a 2% equity stake. Okay. The beauty of this for NVIDIA though, This money's coming back. It's like, here's $10 billion. Oh, but by the way, a condition of the deal is you've got to spend that $10 billion on buying my chips. So this money is going to do an about-turn and temporarily be in OpenAI's account before it now comes back into NVIDIA's. We'll talk about this in a bit more. It's kind of a circular deal. Is this like double counting from a kind of revenue point of view? So some people are being pretty critical about this is a bit dodgy, but it's not dodgy from a legal sense, but it's like, is it kind of overinflating NVIDIA's revenue growth if essentially they're growing it with their own cash? So there's that kind of angle to it. But look, what do they get out of this? And this isn't just chips, by the way. So this is OpenAI will be spending this money on NVIDIA's next-gen Vera Rubin platform. Okay, so this isn't just their chips. This is a wider platform for these data centers. One risk is this Vera Rubin platform ain't finished yet and it hasn't been shipped. They're still in the design phase. It's due for delivery autumn 2026, but their Blackwell chip, That was late and they shipped that a few months after they were kind of saying they were going to. So there are risks here in terms of can they deliver and ship Vera Rubin in time? But look, if that, if, if they do, well then fine, right? This data center buildout cracks on and each gigawatt then will release another $10 billion of transfer from NVIDIA to OpenAI. So that's the kind of deal at the top level. Oh, and by the way, just finally, just on this first phase of the conversation, it's not just 10 billions coming back, right? Because NVIDIA have guided, and this was earlier in the year, right? For every 1 gigawatt of data center buildout, it costs about $50 to $60 billion. Okay. That's for the total spend, about 35. So just over half of that is typically spent on NVIDIA hardware. Okay. So if you add it all up, if they're doing 10 gigawatts, then they're going to end up spending about $400 billion on NVIDIA stuff. So this is NVIDIA saying, here's $100 billion. We will finance your data center build-out ambitions. We'll take an equity stake for that. Thanks very much. Oh, and by the way, you've got to buy our stuff and we're basically gonna 4x our investment in terms of direct revenue back, you know, over this 10-gigawatt build-out cycle.

Sounds like a cheeky little PE deal going on here to some extent, but like what, So I think Nvidia, what they generated just over $70 billion in free cash flow over the last 4 quarters. I mean, which is just insane.

Wow.

So is this way of operating, is this like a new paradigm now where it's like, actually this is quite unique in a scenario where yeah, they do have financial firepower. This isn't about I'm gonna buy you.

Yeah.

It's an invest play, right?

I mean, if you think about this from, and there's a few angles here, right? It's like, can they afford it? Is this, does this put at risk NVIDIA's business? Are they taking on too much risk here? That's kind of one angle. Another one is, well, what does regulator think about this? Are there any antitrust risks and, and so on? But they definitely can afford it. I mean, as you're saying, they are just spewing out free cash flow and actually $72 billion over the last 4 quarters. But if you look at this fiscal year we're in now, of course they're growing, right? Every quarter they're growing faster. So in the fiscal year we're in now, they're expected to churn out $100 billion of free cash flow. So that kind of covers, that kind of covers this like in one fell swoop, just in 12 months worth of business, right? So they can definitely afford it. Don't forget in parallel, NVIDIA issued a $50 billion share buyback program last year. This year they've upped it to $60 billion. They got so much cash, they just don't know what to do with it. Okay. So they can definitely afford this. On the antitrust thing, it's quite clever because obviously the antitrust police kind of get involved when the big guns are doing full acquisitions where they are buying entire companies and then the regulator's like, whoa, whoa, whoa, hang on. On. You're getting far too much control here of a particular situation, and this isn't good for competition or for price points to consumers and all the rest of it. But here, look, this is NVIDIA just chipping away, buying initially a 2% stake, right? That's not a big enough stake for NVIDIA to be able to control anything that OpenAI does. And OpenAI have got plenty of other investors, Microsoft, Oracle, and so on, right? So I think this is an interesting play where it kind of falls under the radar, I would say, at least for now, the sort of antitrust police. And really it's more of a kind of, I guess it's a compute land grab for NVIDIA. You know, they're saying, well, look, OpenAI, you know, they're a different vertical to us. You know, NVIDIA, they are the, they're the kind of infrastructure build out for AI, right? OpenAI, well, they're the product that's then the app that sits on the top of this stack. They're the consumer-facing entity, right? At least for now, they're the biggest. So NVIDIA are saying, look, let's lock them into our vertical infrastructure stack so that we're kind of making sure we've got a, we've got a continued majority share in that compute land grab that's kind of going on all over the world. And we'll come onto it. But NVIDIA, they're not just investing in OpenAI, right? There's deals going on all over the place here, and it's all about trying to maintain their kind of dominant market share on that compute side.

Yeah, I just find working in the strategic department of NVIDIA at the moment must be such a brain box of intelligence going on and how to maximize this because they announced that partnership with Alibaba to bring its physical AI development tools. So thinking things like autonomous vehicles, robotics, smart spaces and into Alibaba Cloud. And again, it's, I guess like you were saying, it's this, you know, you think about China, this is a major global distribution channel for your GPUs, your AI software stack. So it's all kind of, it's all sort of feeding into this same theme, isn't it?

Yeah. That, so that, that AI is global, right? I mean, all right, we spend most of our time talking and thinking about the US and fine, they are, leading this race, but obviously it's a global thing. And so if Nvidia can get a big slice of some of the action in the China market via their Alibaba tie-up, well then obviously that kind of makes a lot of sense from that sort of global perspective. Yeah.

And also like, yeah, I was just going to say like a bit of detail there, like locking in the timing's interesting because on the Alibaba side, there's this QN3 Max model rollout there in the midst of at the moment. And it claims to outperform rivals. And this is obviously a key use case of AI. It's encoding in autonomous agents tasks. And so if they could get in now and dominate what is China's top AI players and secure a critical role empowering their cloud systems, I mean, it's a stroke of genius.

Yeah. And like they've also taken up a $500 million, or they're in talks of doing a $500 million, taking a $500 million stake in Wavy, which is a UK-based kind of sort of car, basically a brain, like it's like rivaling Tesla's kind of autonomous vehicle sort of brain, if you like. And they're taking a stake here in the UK. So again, that's like just trying to diversify outside of, obviously Nvidia's big customers are the superscalers, they're the Microsofts and the Amazons and the Metas and and the Googles, right? So it's just trying to diversify themselves away from just those kind of big tech superscalers and be a bigger, wider part of this broader AI future that's obviously sprouting out in many, many directions.

One of the big things here, the risk factors you said about on the regulatory front. So also what I find is really fascinating about NVIDIA's approach is they invested $5 billion Intel. So they've struck a partnership to co-develop some custom GPUs that could easily integrate into NVIDIA's GPUs. Now, what was, I think, really clever about this is that the deal— so the context, the US government said it's going to take a 10% stake in Intel. This was like a month ago. That deal converted nearly $9 billion in promised grants and equity and made the Commerce Department Intel's largest shareholder. So this latest agreement NVIDIA's done, it, I think it just shows how a lot of these executive leadership teams, they're viewing efforts to help Intel as a little bit of a, let me help you trump, you've got objectives and ambitions, let me align with you. And, and you know, one of Jensen Huang's top priorities of course is he wants to export Blackwell chips to China.

Yeah.

And so how does he get favorable terms? Well, look, if I scratch your back, you scratch mine. So again, it makes a lot of sense.

100%. This is a big, you know, Jensen is playing at the top political table. You know, he was part of Trump's party on this state visit here over to the UK. He is 100% playing a political game here. And that China export sort of ban, I mean, basically they're doing it, they're negotiating, but basically the, what Jensen's hoping for is Trump to agree that they can can start shipping Blackwell chips, but not full Monty Blackwell. Apparently they need to be 30% less capable than the full Monty Blackwell. I think this is the number that's being negotiated and Trump's going to say, okay, if you kind of dumb these down a touch, fine, you can send them. And of course, don't forget also Trump's done a deal with Jensen where they're going to get, I can't remember the figure, was it 30% of revenue? From chips getting sold to China goes to the US government. So this is getting crazy political. And look, we could have a whole conversation on a tangent here about the US government's role in essentially what are private and public companies and whether or not that is a good direction for the US government to be taking. That's a whole different conversation. But right now, Jensen's gotta pay the politics game. And you know what? Looks like he's playing it pretty damn well, to be fair.

He's at the top table. So the, well, maybe just to pull all of this together then, there's kind of 5 components of it. This particular story to summarize, because it is a really fascinating one. So number 1 is the partnerships, many of these different firms we said. So NVIDIA investing in partners like OpenAI, Alibaba to secure chip demand, reinforce its role at the backbone of this build-out, this infrastructure for AI. Number 2, compute power. That's obviously the priority here. You've, you said this nearly every episode. You just can't keep up pace at this point. It's the new battleground. It's kind of massive energy financial costs shaping the, the competition landscape. The circular loop, I guess there's pros and cons here. There's the, the funding customers who reinvest in your demand, makes a lot of sense. But also I can see the point. I'd love to get people's opinion in the comment section about capital recycling rather than true underlying growth and value. And then number 4, the political levers we've just been talking about, you know, the Intel State UK investments alignments, US UK government. Why do they do this? Well, they want to win favor, they want to reduce kind of regulatory friction headwinds. And then finally, interestingly for the investment bankers listening is NVIDIA avoids large-scale acquisitions. So one of the stats I saw here, and a shout out again to Saab, our researcher, he said the 2020 acquisition of Mellanox is so far NVIDIA's only M&A deal valued over $1 billion.

Yeah.

I mean, we're talking here with this latest pop this week, NVIDIA's a $4.5 trillion company, and we've been used to seeing big tech like Facebook back in the day gobbling up the Instagrams, the WhatsApps. These guys have only spent, they've added on one deal over $1 billion and here we are. Yeah. It's amazing.

A $4.5 trillion company that spews out $100 billion of free cash flow every 12 months, and yet they've only ever spent a billion or more on one acquisition. And that was 5.5 years ago, which was before this AI kind of accelerated boom even began. Of course, this is pre-ChatGPT. I'm going to put two other for thoughts on this to kind of wrap. Number one, is this, and I was reading the Lex column in the FT, which is, I'd recommend.

Let me guess, they're gonna be critical. Let me guess, they're gonna be critical.

Well, that's it. So you gotta understand their flavour. You gotta understand their flavour.

They need to look, the sun's shining. I know it's cold, but it's not raining. Come on, guys.

They're basically saying, look, this circular thing as in OpenAI, here's $100 billion. Oh, you gotta give it, give it us back though. And that'll be, we'll mark that up revenue. They're like, come on. They're basically calling this, and I'll quote, this is partly theater to keep the flywheels spinning. So to keep this whole kind of story about the AI revolution going, obviously NVIDIA needs to carry on growing. So how are they going to achieve that? They're going to fund other people to then self-fund their revenue growth. So that's one, I'd say that's one angle here.

Welcome to something that's been happening through the history of time. It just happens to be in the context of AI. I mean, I love it when market traditionalists, it's kind of like market traditionalists sit here, the crypto crew and NFT group sit here. This AI one's kind of somewhere in the middle and it's like as soon as it's threatening traditional landscape of finance, they're like, oh no, it's all bad. But yeah, go on. You have a second point.

Yeah. Well, you're right. This isn't new 'cause Lex were referencing a, a Nortel Lucent deal in the early 2000s. I won't go into it now, but they're basically saying it's the same again. This isn't good. The other part is, is really out of their, well, it's kind of out of Nvidia's control to a degree. For all these, you can build all the data centers you want, right? But is there enough power? Is there enough electricity? Number one, is there enough energy? And number two, is there enough infrastructure? To get the energy to the data center. So there are big power and energy infrastructure challenges here. So again, and partially this is about the government and partially this is about the regulatory environment around getting permits to increase energy production, but also permits to transition this power through the grid and through power lines. So all of this stuff is a definite risk to this huge ambition of like building, fine, 10 gigawatts of data centers. Yeah, go for it. But is it going to even be possible from a power point of view? So that's another risk to factor in.

And just to round up, your man Sam Altman and his relationship as chair just so happens to be chairman of Oklo. That's that nuclear technology fusion power company. So he has got this all worked out, but such as the Altman and the Musks of the world. They're certainly, they've got all bases covered here to meet your second point. But look, just quickly to wrap in about 5 minutes, just a story that I thought, I didn't want to go into it too much, but just as a headline, HSBC, they've achieved a breakthrough in deploying quantum computing in financial markets. And I thought this is interesting because it felt like quantum was coming up and then the AI's just kind of superseded it. Right. And it's gone backward a little bit. So this was an interesting development 'cause I remember watching a documentary about HSBC and IBM, IBM being one of the forefront kind of, well, at least US companies in the quantum space. And this was years ago they were talking about it, but basically IBM's Heron quantum processor, and they're using it to improve bond price predictions. So one of the things here, and the reason why it stood out this week, is the bank applied quantum processing to an anonymized set of European bond trading data, and they found it can significantly enhance the efficiency of the market with a 34% improvement in predicting how likely a bond will trade at a given price. Right now, I've always struggled to understand conceptually what is quantum computing. So this is the best effort that I can do for anyone who's still Anthony Cheung, uh, was in that camp I was in not so long ago. So a classical computer works in bits. Each one is either a 0 or a 1, and it tests solutions at a, uh, at a time in a sequence. A quantum computer uses qubits, which thanks to superposition can be 0 to 1 at the same time. Now, my first question was, what the hell is a superposition? So the easiest analogy I could come up with is think about if you flick a coin. Now, a normal computer is like flipping a coin and you have to wait for the coin to land.

Right.

Before you can then determine, oh, it was a heads or a tails. So you have to flick it, it spins and it lands, and then you pick it up and flick it. And it's one sequence after another, but each event has to occur in a structure. In a quantum computer, think about it again, me flicking the coin. but while it's in the air, it's at any point in time, it's rotating through both heads and tails at the same time. So this in-between state, if you like, is the superposition. All right. You still with me, Leonardo DiCaprio?

I mean, I like it.

I was just thinking that—

It's the best description I've heard. Carry on.

I'm thinking Inception here. Um, but quantum computers can, can basically use that state. The superposition to explore many possibilities at once instead of waiting for the coin to land. So it means that you can solve problems exponentially faster. Like when I mean fast, I mean, you know, dramatic.

You saying Bolt?

Yeah. You saying Bolt fast. So tying this back then to markets, what, you know, when I was reading this, I was like, okay, so what does this mean for financial markets? You mentioned the bond pricing. So First of all, who's looking at this? HSBC's been a bit of a front runner. They've been doing this for several years. JP Morgan, Goldman's, they are actually all investors in quantum themselves in-house.

Yeah.

So the ways to think about this are risk management. So think about if you were testing scenarios, but on an instant basis. Imagine if a piece of news broke and you could run it into your system and automatically then get outputs of it might mean this or that correlated effect or second, third order impacts. And they happen immediately as trading signals, for example, in an automated way. Price prediction accuracy, obviously, could that— this is what this data was saying. But I know you had a third one as well that some of these banks look at.

What, with regards to what, what, as in price prediction or—

As in fraud, wasn't it like fraud?

Oh, I see.

Yeah.

Well, fraud detection. And security. Yeah. I mean, analyzing patterns and kind of building new encryption and just kind of massively upping the game with regards to fraud detection, which of course can save them a lot of money. So yeah, that's another angle to it.

Yeah. And so when I was thinking of this, I was thinking, okay, so thinking about a lot of our audience, well, what does this mean? Like, why should a student care about this? And it's this kind of intersection with tech and finance. And I think most students have got a finger on the pulse. You know, the world's moving in a way where quantitative researchers, analysts, these are all quite key. So typically programming skills, advanced mathematics, statistics, leaning towards AI, machine learning, these types of principles. I thought myself when I read this, I was like, oh my God, like what about people like me who don't have any of these skills? Because I'm not a mathematician by trade. And so what I thought was interesting, and just to finish and share, I was just, you know, talking to AI going, you know, this is the scenario, like, what type of roles can I do then within a financial institution that, yes, we've talked about OpenAI and we're talking about all these, it's all very sophisticated, like frontier technology. But what about, where does that leave then the human element? And I thought this was interesting from 5 different roles that you could think of. The first one is product and strategy. So there's one thing building this stuff out, but like you said, the Huang and Altman mindset of, okay, how do I engineer situations, the strategic elements of deal-making and strategy creation, like that's very much still led by experience and organizing teams, getting input, stuff like that. So, you know, deciding where quantum should be applied, where it should be applied is just as important as being sure it can actually work and do its thing. Absolutely. So strategic priorities. Client-facing sales roles. So, you know, I won't mention who, but I'm doing some actual coaching on public speaking and how to communicate with a fairly sophisticated group of high-end quants. Because they're struggling to really articulate to PMs in a way that the PM can understand very quickly. So again, being able to understand this stuff, maybe not be a technical person who's, you know, an engineer, for example, but enough where you can articulate it to other people. And also to clients, obviously external. Then there's regulatory risk oversight. That stuff's only going to get more onerous as technology becomes more influential. Project management, you know, coordinating all these people. And then communication and education. You know, what about these bits about internal comms, your staff training, you know, these translators, I think is going to become a really important skill as well. So look, overall, I don't want people to be spooked too much. They've got to go away and do a PhD in maths. That for sure is going to lock you in on that OpenAI or what was it, that $100 million salary at Facebook. Sure. But there's still a place for me in this world, I'm thankful to say. But, uh, so we'll wrap it up there. Um, Piers, pleasure as always. Thank you very much. Any comments that people have, just let us know. We'd love to know, and we'll see you next week.

Thanks a lot. Catch you later.