Transcription
The US will invest well over a trillion dollars in AI, artificial intelligence, in the next couple of years alone. It's the transformative technology of our age. But has the US already fallen behind China in the global AI race? And if it has, did that lead to yet another great financial crash? This is the Andrew Neil report.
"Well, I expect most of the companies, the American ones, to go bust."
"Oh. And we will see I think maybe you know one in 10 of the companies surviving."
"The Chinese are the winners."
"Yeah. And that's they're not necessarily behind the Americans either. I I wouldn't like to be an investor in AI right now, let alone somebody who started one of these companies."
The scale of US investment in AI is eyewatering. It propels economic growth in America. It leaves Europe in the dust, but not China. Indeed, USI stocks are starting to shake a little as the Chinese threat starts to materialize. This month saw the release of KK3 from a Chinese company called Moonshot. It's a powerful AI model that's cheaper than many of its US rivals. But China's not always worrying AI in America. So this week, the Andrew Neil report is going to take a deep dive into the challenges facing America's AI revolution: China, debt, and energy. Then speak to Professor Steve Keane of University College London and a global authority on such matters to get the measure of the problems. Let's start with China. Perhaps the biggest threat to American AI companies is that they're undercut by cheaper, what you could call commoditized AI from the likes of Deep Sea in China. That would really threaten the implicit pricing models behind today's equity valuations. They wouldn't be able to charge the prices they hope for their services, which are propelling the size of the valuations. Now, if it's true that, for example, Deepseek V4 can achieve 80 to 90% of the performance of Anthropic's Claude at 10% of the cost, then you can see American AI has a problem. And let's just look at the current valuations. Let's chart, start with China's two biggest moonshot AI. That's valued around $31 billion. Deepseat, that's valued around $71 billion. A lot of money. But look at the two American giants. OpenAI, valued at $852 billion. Anthropic, valued at $965 billion, almost a trillion. So this means that US AI firms are currently valued 10 to 25 times more than their Chinese competitors, despite similar capabilities. I think that's what's known as systemic risk.
So much for China. Then there's the financing problem. Now, a recent study by Nikai, the Japanese stock exchange, revealed that five US tech giants have hidden off-balance sheet debt of $1.65 trillion. Much of it accumulated via what's called private credit. Now, the opaque financing of AI investment by private credit sources. They're not banks, they're not bonds, it's not equity, but various sources of largely unregulated credit. That's a potential threat not just to the expansion of AI, but to the wider financial system. Now, some fear that the next financial crash will be caused by a meltdown in private credit, bringing AI investment down with it. Now, nobody even knows just how much private credit there is in the AI investment boom. Just that there's a lot. Private credit as a source of investment finance has grown since regulations following the great financial crash of 2008. They put greater restrictions on bank lending and other forms of finance. It's essentially private credit, a way around these regulations. Even the banks are now lending via private credit vehicles. Private equity and private credit are intimately intertwined. Now, by early 2025, it was reckoned that private credit lending to the technology sector in America stood about $450 billion. That was up $100 billion on the year. It's probably doubled since then as the AI spending boom has gathered speed. Now, one of the risks is that private credit is so deeply embedded in loans to software companies, perhaps as much as $500 billion a year. And yet, these are the very companies thought most at risk from AI, which can develop its own software. So the more private credit invests in AI, the more it might be undermining its previous investments. The global private credit market is now estimated to be over $3 trillion. We don't actually know how much. That's because of the lack of transparency, but it's a lot. And some estimate that outstanding private credit to AI firms will be at least $600 billion by 2030. Now, of course, private credit is only one source of finance fueling the AI boom. Corporate bonds, banks, equity, other sources are putting in even more. But the fear is that private credit is the most vulnerable, if AI doesn't start generating the revenues that will be needed to service this debt and pay it back. As Nikkei discovered, private credit is most often involved in off-balance sheet joint ventures, special purpose vehicles, and the like, somewhat shadowy financing mechanisms, often first to combust when things go wrong, bringing down not just AI, but companies throughout the whole financial system.
And what could go wrong? Other than China? Well, there might not be enough energy to power all these AI data centers springing up, even in energy-rich and energy-cheap North America. Hyperscalers, chipmakers, large AI complexes, they now have a valuation at a jaw-dropping $20 trillion. And that is predicated on a quick and massive build-out of AI infrastructure to generate the revenues, to justify the investment, to pay back the debt, service the debt, above all in data center hubs. But the electrical infrastructure needed to sustain these hubs, well, it just isn't there and it won't be anytime soon. Start with the fact that the US power grid hasn't been upgraded since the 1970s. And it's an immense job to begin now. And the lack of energy isn't actually the primary problem. It's the global bottlenecks of transformers, substations, switch gear, transmission lines needed for that upgrade, plus the acute shortage of skilled labor in the United States to install and run these things. Now, the data center appetite for power and water is insatiable. People don't realize just how much. Let's just take the hub, the data hub at Hayes County in Texas. It's a good example. Texas, of course, you don't get more energy, Rachel, than that. But that hub can use up to 10 million gallons of water a day for cooling and power generation. And in the process, it's depleting what's known as the Edwards Aquifer, which is essential to the Austin-San Antonio corridor. You can't do both. You can't keep the water running to the people and give AI all the water it needs. Now, this electricity crunch may well be the biggest danger to AI of all. The data companies, let's stick with Texas because, as I say, you don't get more energy, uh, uh, so energy-intensive than that. Data companies have asked the Texas power system operator, it's called ERCOT, for grid connections to 200 projects in the years ahead. Now, that's a total request amounting to 446 gigawatts by 2023. Let me just put that in context. It's five times the current peak power for the entire state of Texas. It's 13 times the UK's average use of power for the whole country. Now, expansion on this scale and on this timeline is simply impossible. So massive investment is taking place in AI infrastructure that won't come on stream for years because of power shortages. Now, think what that will do to cash flows. Now, of course, hyperscalers, they can build their own generating plants. They've got a ton of money, but that can't happen quickly enough to reduce or service the fast-mounting burden of opaque AI debt. Let's look at the new Vogal nuclear power plant in Georgia. Georgia being an area where a lot of data centers have been built. It took seven years from conception to operation for that nuclear plant. Not just the British or the French that are slow at building. Cost more than doubled from $12 billion to $30 billion. So that's not a quick way forward. Okay, I hear you say, what about small modular reactors? The SMRs, Rolls-Royce being one of the world leaders, fair enough. But none yet exists or operates in the West. None is likely to come on stream much before the second half of the 2030s. So that isn't a way out either. What about these amazing US frackers? Look at the oil and gas they've got out of the ground, particularly in Texas. They can frack till they drop. But that's no solution. If you get the gas out the ground, but there are no gas turbines to generate the electricity. The waiting list for heavy-duty turbines used in combined cycle plants is now seven years. Some hyperscalers are trying to jump the queue using their deep pockets to get ahead of the game, but with limited success. 90% of gas turbines worldwide are dominated by three companies: Siemens, Germany; GE Vernova, America; Mitsubishi, Japan. Japan, of course, as it does in most things, has tried to replicate these gas turbines, but it isn't easy. They've found it difficult to develop complex metal alloys needed to withstand temperatures of 815° centigrade. So the triopoly, if I can call it that, it has boosted combined global production of turbines to 45 gigawatts. That's way below AI energy demand. They're not inclined to invest in more capacity. They fear an AI bust and they'll be lumbered with excess capacity. Plus, they're already making a fortune from the premium pricing of gas turbines. So, the companies in the vanguard of AI, the technology of tomorrow, might be most at risk from an old-fashioned energy crunch. China, private credit, energy shortages, each one separately, or all three taken together, could stop the AI revolution in its tracks. Not so much the technology. I think that's here to stay, but the companies behind the technology. And we've been here before. Every new technology sees its Praetorian guard go bust, even while the technology marches on. The great railway bust in America in the 1870s, the electricity revolution at the end of the 19th century, the rise of aviation in the early part of the 20th century, the dot-com collapse at the very beginning of the 21st century. Each saw companies in the vanguard of that new technology of the day go bankrupt, often causing financial meltdown in their wake, even as the technology itself powered on. In the immediate years ahead, that is now the risk facing those in the forefront of AI. It isn't just a problem for them. It's a problem for all of us if they bring down the financial system in their wake. So hold on to your hat. Keep out a weary eye. We could be in for a bumpy ride for the rest of the decade and into the 2030s. This is the Andrew Neil Report.
Let's speak now to Professor Steve Keane. He keeps an eye on all these matters. He's at University College London. Professor, thank you for joining me. If I can, let me start with China. What is your overview of where we are with this Chinese-American competition in AI?
"Well, it's interesting in that what China has done is prepare the ground for something like AI before it actually came along. So, China has built, rebuilt an enormous energy transmission system throughout the country. Uh, we're using 800 volts rather than 400 volts, which has, in technologically, means you have a higher amount of capacity to push energy from one part of the country to another. Uh, and what it means is the people in China basically take energy as something which is going to be available almost whatever you do with it. America is still stuck with the old 400-volt system, a power volt system. They can't transfer power easily, and therefore they've got these energy shortages in places like Texas, as you're explaining. So, in some ways, China set the ground for the demands of AI before AI came along. And then a major part of the, the entrepreneurial side of Chinese industry, which is massively underestimated by America, has focused on reducing the costs of doing the processing involved in running these AI. So, uh, the basic story is there's enormous amounts of multiplication matrix mathematics going on to enable these machines or data systems to communicate with you as if you're talking to a human. And, and the calculations themselves are immensely expensive in terms of energy. You've got some of the models involving, let's say, they say two trillion parameters. That's two trillion numbers that need to be multiplied against each other multiple times to converge on some advice to you on whatever you're looking at. Whether that's how to send up your father-in-law in a birthday card or how to solve an unsolved conundrum in mathematics. So the energy needs are enormous. And what the Chinese have done as well is say that this is something which should be open and available to everybody, rather than restricted and only available to people who purchase a particular proprietary system. Uh, so they're going open source, and that's one of the major challenges of AI. You can actually see what the parameters are. You can potentially load it on your own hardware. You don't need a data center necessarily, whereas you do for the American systems. So I think at a global level, it's the Chinese who were set up to win this contest. And of course, that's not at all what the American entrepreneurs who dived into AI thought was going to happen."
"Indeed. And it's not what's behind the valuations of AI companies in America either. Just before I move on on on other Chinese issues, just to be clear, it's interesting what you said there that the the kind of energy constraints I was talking about in my monologue that AI in America is about to hit. By and large, you're saying they don't exist in China."
"No, that's right. They the higher volt. This is again one of the advantages of China being run by engineers rather than economists and lawyers. And I think that's the reason why everybody in the Politburo, not everybody, but the vast majority, have engineering backgrounds. They're aware of the advantages of different, uh, transmission systems in a way that somebody doing an economics degree, you know, PPE course, has no idea. And they then say, well, we need energy for an advanced economy. How do we distribute the energy more easily? We need to go to higher voltage, and that's what they've been doing. So the energy has been built there, I suppose, almost as a matter of course. And then when you talk to Chinese entrepreneurs, they are fairly, they don't worry about the availability of energy. Water is a different story, but they don't worry about the availability of energy. So that combination, having available energy to start with, and then producing systems which don't need the same amount of energy in terms of data centers, gives the Chinese an enormous advantage. And the Americans, I think, you're finding out what happens when you have a centrally planned versus a non-centrally planned system. And in this case, the central planning means the energy is already available, or it's not there for the disaggregated American system."
"In general, is it true to say that although the American AI systems may be more sophisticated, that the the Chinese AI models are much cheaper and have 85-90% of the capacity of the most sophisticated American models? So if you, if you combine cost with capacity and capability, the Chinese are the winners."
"Yeah. And that's they're not necessarily behind the Americans either. There's a lot of extremely clever engineers. Uh, you know, they often did their PhDs in America and went back to China. Uh, they're outstanding intellects. And a lot of it is how do you manage to do these matrix mathematics more efficiently? How can you cut down the load? Where, where do you get points where there's overkill versus where you can stop and get most of the answers you need? And therefore, if you do that, you get more time to actually train the results and you get a higher quality result. So, uh, that the Chinese have been out-innovated the Americans. In some ways, the Americans have done what they seem to do as a species, that is, they use brute force to overwhelm the rest of the world. The Chinese have used intelligence rather than brute force this time round."
"So what is the likely American response to be? I know that the Trump administration has tried to limit the export of some of the most sophisticated chips, uh, to China, which chips which China, at the most sophisticated level, does not produce itself. It does need these imports. But am I right in thinking that nothing America has really done to try and dampen the Chinese AI projects has had much of an impact?"
"No. In fact, in fact, in some ways, it spurred the Chinese to innovate more. So, for example, the bans on chips meant some, and and not being able to get, you know, machinery from ASML to build, uh, chips in their own country, whereas they sell those to Taiwan and then they're on-sold to America. This has forced the Chinese to innovate different ways to produce, uh, very, you know, low, low micron chips to get enormous amounts of processing power in small, small bundles. So the the bans have actually encouraged innovation by the Chinese. And I think in some ways, I think this has got a historical parallel because every Chinese school student learns that the opium wars, learns that they were effectively raped by the West, that they can't rely upon. They can't trust the West. And so anything like putting up a, a barrier saying you can't have this technology actually inspires the Chinese to say, we'll replace it ourselves and become self-sufficient because our mistake in the previous times was to lose our self-sufficiency against the West."
"Reuters has been reporting this week that talks are to begin between China and America on AI. The American side to be led by the Treasury second secretary, Scott Bassant."
"Is that likely to lead to anything, or is continued competition between the two superpowers of AI just likely to continue?"
"I I doubt it because what the Chinese and the EU, even made a speech on this front just recently, they want it to be open source. They want the stuff to be publicly available. Treat, effectively treat AI as a public utility, not as a source of private profit. Now, when you do private profit, you make everything proprietary. You try to restrict what's available. And that's what the Americans have done. And in a typical, you mentioned the railways boom beforehand, a typical boom and bust cycle in capitalism, which Schumpeter explained extremely well. All the firms go in thinking they're going to be the dominant party. So you get massive overinvestment. The overinvestment extends the technology through a whole of society. When the technology comes out, it starts undercutting and destroying other, previous sectors. So of course, railways destroyed carriages in that sense. And then the, the technology permeates society when the bust occurs. Now, I think we're close to that point. It will permeate through society after a bust occurs, but the scale of that financial bubble, as you were saying, in America is absolutely enormous. So these Americans are in a desperate situation. They've, their their costs are sometimes 10 times their revenue. They're running up massive losses. Uh, they think we've got to make sure that we get the revenue that's going to come out of this when the crash occurs. I think they're seeing the inevitability of a drop in the price of tokens coming up at some point. So they're trying to make it proprietary. And in that sense, what you've got is the Chinese are voting for making this open and available to everybody. The Americans are trying to restrict it for their own benefit. The Chinese can just keep on going. They don't have to cooperate. So I think we ultimately, the only resolution for this is the one the Chinese want, where AI becomes a public utility provided to everybody and it's open access rather than restricted and privatized."
"Is the at at base is the problem that now faces American AI, one of the major problems is that the the valuations we currently see?"
"Typically hyped up, almost like the valuations of a bubble."
"Yeah. But they are predicated on when this is up and running and we're in the market, we're going to be able to charge an arm and a leg for this wonderful technology, but it didn't take into account that they are likely or could well be undercut when it comes to price by the Chinese. And therefore, if that's the case, the the pricing models holding up these valuations, well, they collapse, do they not?"
"They're going to collapse. I completely agree. And if, like a factor of 10 fall, the costs and the pricing is quite likely. And I saw one commentator saying that a lot of those data centers are going to turn into pickleball halls at some point."
"They might make more money out of pickleball."
"Be more fun at least anyway. But yeah, I I think this is a huge overinvestment. It's what typically happens in any transformative technology coming in. There will be overinvestment first of all and a boom and bust caused by that cycle. But the double what's going to double this up is you mentioned private credit. Now, a lot of this, people think private organizations give money to somebody else, money that already exists. That's less dangerous than if those private, private credit have then taken the money they've pulled and then gone to a bank and levered it up. Now, most likely they have done that. We're not getting any transparency on that at all. So as well as the amount of money is vaster than is actually being saved to be reinvested by the Microsofts and the and the Facebooks and so on. Uh, and it's also been levered. And that's exactly what gave us the for the global financial crisis, excessive private sector leverage. So we're getting that again, plus a huge scale bust, uh, and at the same time as the unavailability of many of the physical inputs that are necessary, the Strait of Hormuz affecting the supply of sulfur dioxide and therefore of cable, of copper for cabling inside these data centers. So it's in many ways, it's the it's the biggest transformative technology challenge we've faced, coming at a time when the resources, the availability of resources to produce the ordinary goods and services we expect are themselves in challenge. So I I wouldn't like to be an investor in AI right now, let alone somebody who started one of these companies."
"No, I can understand that. And I want to come on to the financial implications and financial consequences in a minute because that is my next big topic. But before I do, let me just finish off in the China-American dynamic. I, if everything you say is largely true, but also I guess one should never underestimate the resilience of American enterprise, its ability to adapt, and to go in different directions. It's very agile. But even taking that into account, how, where do you see things going in the next couple of years with AI into the next decade, let's say, given the China-American dynamic that you've just outlined for us?"
"Yeah. Well, I expect most of the companies, the American ones, to go bust, uh, because the difference between their revenue and their cost is huge. Five to 10 times cost being five to 10 times revenue. Remember, remember the internet bubble, you and I obviously lived through that one, and you remember Pets.com, uh, uh, and I think it was valued at more than the entire pet industry in America. Uh, there, there were Amazon was valued at more than the transportation, you know, the airline systems. Valuations get crazy, and then they crash. Uh, after the, the, you know, putting in the technology transforms society, but doesn't lead the profit that the initial people who rush in expect. So we're getting that in on steroids this time round. And we will see, I think maybe, you know, one in 10 of the companies surviving. But the wildcard, as it was discussing now, is that there's another country that's in this game in a way that America hasn't experienced before. So when the telecommunications bubble or the internet bubble occurred, it was exclusively American technology doing it. This time round, they've done it, they've overinvested, they've overborrowed, they're levered, they're going to crash. And on top of that, they might lose all the competition to China. Anyway."
"Let me move on to the whole financial implications of what's going on. Put aside that, let's supposing there wasn't a China problem to worry about. Uh, but, but that all, all, let's freeze that for the minute and isolate the American AI, even within its own ecosystem. Uh, this massive, this multi-trillion dollar investment in AI and associated technology and technology companies, of course, like all these things, is being done on debt, and particularly the new kid on the block compared with previous decades is this thing called private credit, which, you know, I think four, five, six years ago, most of us didn't talk about at all. It's a, it's become a new thing to take into account, even if it was there before, it's now at scale. I emphasize in my monologue that the private credit itself was a potential systemic risk in the financing of American AI. Do you agree with that?"
"Absolutely. Like this is why we, we ignore the private sector. We, we get paranoid about the level of government debt and never look at the level of private debt. Now, I'm the exact opposite. I have come from the perspective of what's called Minsky's, Minsky's financial instability hypothesis, and that argues that it's the dynamics of private debt that caused booms and busts, and the government sector basically picks up the pieces afterwards. And that's why I saw the global financial crisis coming because I looked at the mere level of American private debt was already about 150% of GDP in 2005. It was growing at up to 16% per annum. I said that growth rate of growth can't be sustained. When it turns negative, there'll be a financial crash, and that's what's happened. So private credit coming from banks at that stage went from being plus 15% of GDP in 2006 to minus 5% in 2009. And that 20% of GDP turnaround in bank-created credit is what caused the global financial crisis. Now, what we are talking about here is that a lot of large holders of large amounts of money, so the Microsofts and the, and the Facebooks with enormous cash reserves, they've then pulled that into their own distribution of private credit, and they're lending out money that they've had idle, in a sense, on their bank balances. That itself is not as dangerous as bank money because you're not creating new money. But I'm sure, knowing how the financial sector behaves, they saw a potential profit. They probably levered up the amount of money that has been saved by these institutions. And so you might, the amount of money might be, you know, three or five times what we're seeing in terms of the private credit pools because of the leverage from bank loans as well, which are likely to be off-book vehicles. So you can't actually see them in the recorded data. So we're going to get a triple whammy: overvaluations that mean you're going to have mass bankruptcies, and the bankruptcies cascading through the financial system, the loss of the money that these huge companies have poured in. So the Microsofts and the and the Facebooks will find they haven't got the cash reserves they used to have. They'll try to find some way to recuperate those. And the, the loss of money that's going to come out of this, the bankruptcies that will flow through the system as well. So we are facing a gigantic financial crisis. And again, it's being made even worse by the strait of wars and everything that's happening in the Middle East."
"Yes, indeed, which, uh, drags everything down. Uh, although private credit is opaque, and as you say, it's off-balance sheet, it's in these special purpose vehicles which are quite hard to be able to penetrate."
"We are right in thinking that although it's not a majority of the debt going into AI, it is a big enough chunk if it goes sour to cause problems that private credit is a big player in the expansion of AI."
"Absolutely. And, and again, it's the enthusiasm. This is part of what capitalism's vitality comes from, the things that also cause it crisis, because people see a potential enormous profit, and everybody who sees it dives in, and they get funding, then others get funding in the same area. So you get massive overinvestment as a natural part of the way a capitalist economy functions, and then you get a slump in the aftermath. So that's that's the standard picture. Schumpeter covered that brilliantly back in 1907's theory of economic development. That's the best explanation for this boom and bust cycle in technological innovations and capitalism. But we're added to it again because we have an enormous level of leverage of the private sector with private debt that was built up in the subprime bubble and the telecoms bubble and bubble before that. So with a high level of private debt, we already had fragility that way, because to service your private debt, you've got to be making a sale out of cash flow. Now, if your cash flow is negative, which is the case for these companies, you've got to go back and borrow again to remain in business. So all these things mean it's, we've got a, a Schumpeterian downturn on steroids hitting us with the AI sector. And the ultimate winners, as you say, potentially being China rather than America. There's going to be lots of bankrupt billionaires in America coming out of this."
"Now, we talk about the opaque nature of private credit, and it certainly is. But it's not entirely opaque, professor, is it? We do know, I think, tell me if I'm wrong, that it actually comes from institutions that we know very well. It actually comes from banks. Banks have been using it as a way around the regulations. Private equity has been involved in it as well. You mentioned it. See, Microsoft and other companies are going in through the, putting their money in through the private credit route. There is a kind of, there's a kind of doom loop blowback danger here, is there not?"
"There is, and this, this happens all the time. This is one reason I emphasize we have to study far more the private financial system than we do the government financial system, because it's the private financial system, at least, that has these booms and busts. And by ignoring that, mainstream economics completely ignores private debt. By ignoring it, we're not seeing what actually causes the real booms and busts. And this is a bit like a wakeup call coming along saying, 'Hello, you've been ignoring me for two for a hundred years. See if you can ignore this one.'"
"Well, of course, we always think we can, cuz as we always know, this time will be different."
"Hope springs eternal in the human breast, you know that wonderful line. And that's, and capitalism amplifies that, which is one of its great creativities, but it also has the great downside. And that we, we need to manage the financial sector and not allow these booms and busts to be as extreme. And when we obsess about government debt and don't look at the private sector, we take our eyes off what really matters. So I, I hope this is a wakeup call to people to say, take a look at the level of private debt. Both what the banks do and what private non-financial institutions like the very well-cashed up, or once they were well-cashed up. Now they've blown all their money. So the cash might get distributed, but we're going to see so much wreckage as a result of it."
"So you and I can sit here and we can see the structural imbalances. We can see the risks. Uh, we can see the perhaps dark clouds gathering on the horizon. Um, and you saw that in the run-up to the great crash in 2008. Others saw it with the dot-com boom. People saw it even with the great railway crash in 1873."
"We saw it in the run-up to 1929."
"But that's right. But what triggers it? What, what causes there? I can see the dark clouds. What causes the thunderstorm? Talk us through how this manifests itself."
"That's a good question. Mainly, it is that you take out, you take on debt, and therefore you've got to service the debt to be able to remain in business. So when you're building the data centers and laying out the systems and so on, you don't actually have the cash flow yet. And so you, you've got a timing issue. Can you get the cash flow to the level that it makes your investment profitable before you accumulate too much losses in the meantime? Now, what happened in every of those bubbles you've mentioned, the time element broke. So like in 1929, people don't realize, but the downturn in private credit-based demand in America began in 1927, two years beforehand. So suddenly the cash wasn't turning up to finance all the C market purchases. People were running up losses servicing their, uh, margin debt at the time. They could get away with it until suddenly people were forced to sell, and then bang, when some people are forced, the whole system cascades and collapses. So we really need to have an overview of the financial sector and not let these bubbles get as extreme. And what happens after every one of these crises, mainstream economics comes out and basically sort of tells us, don't worry about private debt. Get obsessed about government debt as well. We take our eye off the private sector, and they do the damn same damn thing again because, you know, hope springs eternal in the human breast, and the, the bankers all dive into exactly the same obsession at the same time. One of my best friends in this whole, there is an economist and financier called Richard Vague. I'd love you to have a talk to Richard as well. He was a banker in the in the 1980s in America in Texas and saw the massive investment in oil rigs at the time. And then when the, when all the banks did it, the oil price collapsed from $40 to $10. Uh, his company, his bank collapsed. He bought out the consumer wing and became a very successful banker. And he said, 'This obsession of driving into what's currently hot is a natural thing for bankers, and unless we restrain it, we're going to continue getting booms and busts.'"
"I'm going to ask you, it's probably an impossible question, but I'll ask it anyway. When does the bubble burst?"
"I'd say the next two years. I can't be more precise than that. You can say it's going to inevitably going to happen, but as to when, timing is always the hard bit, but certainly I think the next two years, it's got to come crashing down. And when you take together the China competition, the China threat, if I can put it that way, the private credit risk we've been talking about, the energy crunch that will probably stop a lot of the, even if they've got the money, will stop these data centers from coming online. Add that all up. It's a kind of triple whammy of bleakness, is it not?"
"It is. At the fourth, we're witness being the ridiculous war in Iran, when and Ukraine for that matter. We're damaging our physical facilities at the same time as we're causing a financial bubble. So that, that's what actually worries me in many ways more than just the AI, in terms of the most immediate crisis we face, because we're destroying so many of the channels of producing goods and services that are necessary to service the debt that private enterprises have. So there's going to be a whole range of companies who suddenly can't sell the products they need to sell to service their debt, and we'll get bankruptcies popping up all across the system, and they're going to precede what happens with AI."
"And you're quite right to emphasize what's going on in the Gulf. The war is not coming to an end anytime soon. The Strait of Hormuz has closed again effectively to shipping. It's been a kind of slow car crash on the world economy. If you take what we've been talking about AI and this continued threat to the global economy, the prospects for the global economy, which in some ways have proved quite resilient since we came out of the pandemic, the prospects as we approach the end of this decade, sounds to me from everything you're saying, that the global economy is heading for some trouble. I certainly, and I expect famines as well as just economic downturns to come out of this crisis, because we've cut off, you know, almost one-third of the world's fertilizer supply as well. So all these things are building up on us over time. And what has stopped them manifesting them immediately is that we had reserves elsewhere that could be depleted while the flows of goods were stopped. Now, those reserves are heading down. China is now rebuilding its oil reserve, which is the biggest on the planet. So that means we're likely to see the flows cut out, and when they do, the system, it's like suddenly you run out of lubricating oil in your engine, and that's one thing we are going to run out of. Bang, the engine seizes up. There's nothing you can do about it. So I think we're facing a time like that, and it's as scary as hell, because there was no reason for this to occur in the first place. These are two effectively wars of choice that have accelerated the decline of the physical capacity of our economy to service its financial obligations."
"Professor Keane, we'll leave it there. Uh, I think I need a stiff drink after our discussion. Many of our viewers and listeners will too, but I'm very grateful to you for joining me. These are big issues, well above and more important than the day-to-day who's in, who's out of party politics. So it was good."
"Absolutely. Who cares which cat runs Number 10?"
"Yes, exactly. So I'm delighted that we're able to get this talk through these things with you. Professor Steve Keane, University College of London."
"Well, that's it for today. Thanks for joining me. The main podcast will be with you every Thursday. Make sure you follow and subscribe so you never miss an episode. And head to our YouTube channel for video clips throughout the week. If you're enjoying the show, even if it makes you a little gloomy at times, please do leave us a rating on Spotify or Apple Podcasts, 'cause it really does help us. But for now, bye-bye."