Prof G Markets - China Is Undercutting America’s AI Giants

Episode Date: July 28, 2026

Ed Elson is joined by Scott Singer to discuss why Chinese open models have been gaining momentum and whether or not the U.S. risks losing its lead in the AI race. Then, Vishy Tirupattur joins to unpack the debt behind the AI buildout and explain why investors should be paying attention to the off-balance sheet debt from the hyperscalers. Finally, Ed gives his take on China’s most valuable company. Scott Singer is a Technology and International Affairs fellow at the Carnegie Endowment for International Peace. Vishy Tirupattur is the Chief Fixed Income Strategist at Morgan Stanley. Subscribe to the Prof G Markets Youtube Channel  Check out our latest Prof G Markets newsletter Follow Prof G Markets on Instagram Follow Ed on Instagram, X and Substack Follow Scott on Instagram Send us your questions or comments by emailing [e-mail] Learn more about your ad choices. Visit podcastchoices.com/adchoices

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The president there is watch the show, sell, sell. Welcome to Profi Markets. I'm Ed Elson. It is July 28th. Let's check in on yesterday's market vitals. The major indices were mixed as chipstock sold off. Brent crude fell below $90 per barrel as the U.S. and Iran paused their attacks, the yield on 10-year treasuries declined.

A Chinese chipmaker CXMT soared more than 400% in its public debut, becoming China's new most valuable company, more on that later. And finally, Apple rose more than 1% to overtake Nvidia as the world's most valuable company. Okay, what else is happening? Chinese open models are gaining ground, and Washington and Silicon Valley are starting to notice.

In recent weeks, Chinese startup Z-I-E-Avety. AI and Moonshot AI have released models that are competitive with those from US Frontier Labs. The top five most popular models on OpenRouter, a marketplace that tracks usage across AI models are indeed Chinese. And when a rogue, unreleased open AI model hacked HuggingFace during a security test last week, HuggingFace used a Chinese openweight model to defend itself. They might have thought that American AI executives would use this opportunity to encourage a crackdown on Chinese open weight models. Instead, the opposite happened.

His first ever post on X, Jensen Huang shared a letter titled OpenWaights and American AI leadership. And within a couple days, 50 companies signed the letter, including Microsoft, Meta, Palanty, and IBM. Together, they argue that U.S. AI leadership depends on building not just frontier systems,

but a strong open ecosystem around it. And they warn Washington against, quote, premature restrictions that stifle competition and drive innovation overseas, which raises an interesting question, and that is why would US AI leaders seek to protect the very thing that supposedly might destroy them? Joining us to discuss this was speaking with Scott Singer, Technology and International Affairs Fellow at the Carnegie Endowment for International Peace.

Scott, thank you for joining us. I'd love to just start with the context here. This open versus closed weight debate that has taken over the world of AI has a lot to do with U.S. versus China, which is strange and interesting, but I would appreciate if you could just clarify why that is and how we got to this moment. I think it's really a lot of happenstance more than it was any initial intentional strategy or ideology. If we look at the history of AI model development, a lot of the initial general purpose frontier AI models that were coming out of places like OpenAI and then Anthropic,

were proprietary. The weights were not accessible to any sort of outside users. And so for China, there was a question of where and how they might be competitive on the global marketplace for advanced data models. And so having models that for the weights that are openly available and can be used by companies and startups and can be fine-tuned or adjusted so that they can fit whatever purpose they have, that was sort of the area where, you know, China couldn't necessarily complete. on having the most advanced capabilities, but they could have leaner, cheaper models that would be useful across the startup ecosystem

and for companies. One of the things that has caused a lot of controversy is this idea of distillation, and this is what Anthropic in open eye have accused many Chinese AI companies of doing, distilling their models, which has a relationship between being an open weight model as well.

What is distillation, and why does it matter in this conversation? Distillation is basically taken a much stronger model or a bigger model and using it to build a sort of smaller, cheaper model. And so that is, you know, the sort of crux of what's happening here. And you can, in an open model, you can take, in these cases, it's Chinese companies, but there's been allegations that American companies doing this as well, you know, taking what is American innovation, American products, and using that to power these Chinese products. And distillation is a super common

practice in AI. And on its own is not sort of something to, you know, be too focused on or see as especially malicious. But the question is really tied to intellectual property, trade secrets, as well as fraud. So the way in scale that some of the Chinese companies have been accused of sort of leveraging American IP in this case is through, for example, building up a bunch of fraudulent accounts, getting the outputs from the American AI models and then using that output to train their own models. And so there's this sort of thorny legal battle that gets at the heart of a sort of broader geopolitical question, which is basically, you know, what are the rules of the game in terms of this very technical process? And it is more representative of this sort of open versus closed battle,

even if the installation itself is sort of a widely accepted common practice. across AI. I mean, just from the perspective of a Chinese company, Anthropet releases its model, and then you submit like thousands of prompts to fine-tune that model and then eventually replicate that same model. And that is what many of these companies have accused companies like Kimi and the Kimi K3 model, which went viral and made a lot of headlines last week. That's what a lot of these Chinese companies are being accused of. Now, I mean, in the rules of, you know, clod and anthropics privacy policy, you're not allowed to do that. I guess the question for Chinese companies is, do they care about that? The answer is probably no. So then it seemed like we were

going to get some regulation because the administration seemed to have a view on this, and they were accusing these Chinese companies of distillation and saying that that was a problem, which is why I was quite surprised to see this open letter from Jensen Huang, co-signed by a lot of other American companies saying, no, don't restrict this, let this happen, distilling is okay, open source is okay, open weight is okay. Why do you think they are down with this? I think that there's maybe two things to point out. The first is that the American tech companies are not a monolith, and Nvidia itself benefits tremendously from, you know, exporting many chips to China. It can power, Chinese AI development.

And much of the American tech stack remains, even in the era of sort of great firewall tech decoupling, so much of U.S. tech is still integrated deeply with Chinese tech. And so I think in that sense, it is beneficial to companies like Nvidia to make sure that open way to access remains open. Startups in Silicon Valley, the A16s of the world, are running, you know, know, on a combination of American and Chinese models. And so American startups actually want to use a lot of these Chinese models because they're so malleable and adjustable. And so I would say that those are sort of the main factors that are really driving this sort of bifurcation between, on the one hand, you want, you know, the anthropics of the world that really have not much to gain and are getting their profits trimmed off by the fact they have to face competition from the smaller Chinese.

companies, but then you have so much of the U.S. that still benefits. Yeah, it seems as though this might be kind of an attack in so many ways on OpenAI and Anthropic, because, I mean, those are the two companies that really lose if the Chinese cheaper open weight models continue to gain market share because it puts pricing pressure on them. Might that have a role to play in why someone like, you know, Mog Zuckerberg at Meta, would be very excited about rolling back any restrictions on Chinese companies, because essentially it means that you kind of slow the role of Sam Altman and Daria Amadee. Are those the teams that are emerging in AI right now?

I think it's undoubtedly the case that for a company like Anthropic, which is exclusively building these proprietary close-source models, that having really strong competitive open-source models is not really where you are. want to be in terms of, you know, you already have razor-thin expectations in terms of the data center build-out. You're investing a ton in compute. And if you have your profit potentially undercut, you definitely don't want that. I think that there is just sort of a question for the entire ecosystem of how far is too far when it comes to restrictions. And I think that in general, there's going to need to be a complementary approach that includes a combination

of closed and open models. We see from, you know, instances like the OpenAI Hugging Face incident last week that there are going to be some serious risks, including the possibly that, you know, there are serious cyber incidents that lead to loss of developer control of varying degrees, and we also have concerns around misuse. And a combination of open and closed resource models

are probably going to be necessary to mediate the full range of concerns that we would have coming from those models. I think the other thing this brings up is just the rise of China in the AI race. And I know this is something that you spend a lot of time focusing on. I mean, where are we in that race? Where are we in terms of Chinese investment in AI, Chinese development AI, and then also Chinese adoption of AI just among the general populace? The numbers in China in terms of investment are not,

what they are or close to it in the U.S. But we do see a very powerful fast following strategy where China's not really trying to be at the frontier of capabilities or the most prominent of where, or the most elite of where U.S. model providers are. But they want to stay a few months behind it. If they can be maybe three, six, or nine months behind, then perhaps that's not really going to make or break

their competitiveness, especially if they continue to have access to U.S. models through distillation. And so what does this mean for China? It means that they have to operate and get the best they can within an environment where they don't have that many financial resources compared to the U.S. companies, where they have far less access to hardware to train on. You know, they're making the best of a strategy that has been sort of forced on them. And so I would bet on the U.S. position overall.

you know, the U.S. is the model provider of choice across, you know, OpenAI, Google, meta-anthropic, XAI. Like, if you look at the global diffusion of these models, there's just not that many countries and companies that are really uptaking outside of the startups that are really excited about the Chinese ecosystem. America is still really dominating here. but I think the question is just how far can China compete outside of its own borders. And one really interesting indicator of this is, like, for example, at the launch earlier this month of the World AI Cooperation Organization in China, and if you look at the list of countries that signed on to the list for whatever this international cooperation organization is going to be, it's not exactly a really big, powerful group.

You see absent on this list, you know, really none of the countries in the Middle East that have pretty excited about Chinese AI investment. And so it is to say that amidst this moment, or Kimi K-3 and Moonshot are getting deserved attention for the legitimate, really strong capabilities that their models have, there is just like an overarching gap between how strong the U.S. is, I would argue, and how strong the Chinese ecosystem is.

This seems to be one of the biggest concerns in the AI world right now is China going to beat us. Do you think that investors are too concerned about China, more concerned than they should be? I think it depends on what exactly you're concerned about. I think if you're concerned about, you know, potential losing profits because open source is a few months behind. I think that that's a legitimate concern. I would still bet if you are betting on whether or not the U.S. is going to have extremely capable general purpose capabilities first, and that that is what is going to power economic and strategic advantage in the long run,

then I would wholeheartedly bet on the U.S. ecosystem. But I think it's really at the profit margin level. It's, you know, what models are startups building on? And I think that there is a question in terms of the battle for the rest of the world. If you're sort of in the world post-worldly cooperation organization where most the world just thinks that U.S. models are better and more capable, then that seems like a fine world for the U.S. but it's an open question if China begins to more seriously aggregate up its compute to make a stronger play at general purpose capabilities in a more aggressive way than it is currently. So I think let's pay attention to market consolidation on the Chinese side.

Let's pay attention to just how far behind they are on capabilities and how successful they are on diffusion. This has been a massive priority for the Chinese. last year, they launched the AI Plus strategy, which is basically focused on embedding AI into practical, useful applications. And so far, it's really not clear exactly how successful this strategy is going to be. It seems like it's really sector-specific around how easy it is to embed AI into your ecosystem. In the U.S., of course, does this in some ways just through market pressure. We see our own economy fundamentally transformed by AI capabilities. And so even if it's less government-driven, I think that both societies are being dramatically transformed by the rapid adoption of AI.

All right. Scott Singer, Technology and International Affairs Fellow at the Carnegie Endowment for International Peace. Scott, we appreciate your time. Thank you. Thanks so much, Ed. After the break, a deeper look at AI debt. And for even more market insights, you can subscribe to my weekly newsletter, simply put, at simplyput.profgmedia.com. Support for the show comes from Noom. Most journeys don't travel in a straight line. There are ups and downs, peaks and valleys.

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The world's most valuable chipmaker is making its lenders nervous. The cost of ensuring Nvidia's debt against defaults just posted its biggest one-day jump on record. The spike followed reports that Nvidia has lost. lined up more than $750 billion in new AI commitments. That includes a $250 billion guarantee for OpenAIi and a $500 billion deal with S.K. Heinex's parent company. These developments have revived a fear that we've been discussing on this show for a long time, and that is circular financing. The worry is that deals like these create a loop. NVIDIA funds its own customers who spend that

money on VINC chips, making demand look bigger than it is, and investors are beginning to worry. The stock fell 5% on the news and it's now down over 16% from its May peak. But Nvidia isn't the only company under scrutiny when it comes to debt. Oracle's credit rating was downgraded this month to one notch above junk and its default insurance is now priced at levels last seen in 2008, which begs a question that the market is struggling to answer. How real is the demand for AI infrastructure and how risky is the debt that is financing it? here to help us discuss this, we're speaking with Vichy Tirupator,

chief fixed income strategist at Morgan Stanley. Vichy, thank you so much for joining us on the show. AI debt is suddenly the rage right now, and it's something we have been discussing at length on this show. It's something that a lot of people seem to be getting a lot more nervous about. We obviously had Oracle and their debt situation, which has gotten trickier, and we've seen that reflected in the bond markets. now it appears to be affecting Nvidia.

What do you make of all of this? What does it say about the AI credit markets at large? I think what's happening is that the market, a new sector is coming to the trade markets. If you look back, you know, just a few months ago, the AI ecosystem represented in the benchmark indices or in the investment rate market, you know, were under 3%. Today they are over 4, over 6%, more than doubled in a,

very short period of time. So when you have this much of supply and the expectation that there is a lot more supply ahead and is the crux of the Arjita that is in the market, if you step one more, one step back and examine it, it is really the expectations of the CAPEX from the AI ecosystem, the hypers included. There is a pretty substantial amount of upward revisions of CAPE. So beginning of the year, we thought that the, you know, the capex by the five hypers in this year would be something around $600 billion in 2026.

And we total number would be around $8.50 billion or so for next year. And we now think that this year number, 2026 CAPEX of these five hypers, would be, you know, greater than $800 billion. And the next year we're talking over $1.3 trillion. and similar magnitude in 28. So this is a substantial reset of expectations of CAPEX, and a good chunk of this CAPEX has to get funded through the trade markets. So if we think about from that perspective,

the expectations are constantly being revised as to incremental amount of supply that needs to be absorbed. So keep in mind that as you said, Oracle is at the low end of the credit. sector, but the rest of the hyperscale are very high quality. I mean, high quality Microsoft is AAA, Alphabet is AA and Amazon and META or AA plus. So much, much higher quality. But the expectation of a lot more supply in multiple currencies, in multiple maturities, in multiple forms, unsecured form, securitized form, public markets, private markets, all of this abundant of issuance expectations.

that are ahead is the source of this object. And if each time we have this expectations reset, then there is incrementally, the spread is getting wider. So in our mind, the one, the really credit spreads need to be wider to accommodate all of decisions. So not everyone is going to be equally wide.

The riskier names, so for example, credit risk lower the credit rating, and the greater the spread widening that you've seen. So as you mentioned, unsurprisingly, a name like Oracle is trading at a, you know, as of Friday, they were training, you know, well over 220 basis points in spread terms for their benchmark wants. That was, that's higher from a year today,

almost 65 basis points higher. But it's not uniformly like that. So the more trade worthy names are wider. but wider by less. Something we read recently from NICA Asia was this report that, and as you mentioned, these hypers, a lot of these names,

their credit ratings are in pretty good standing, and then when you look at their balance sheets, they're in pretty decent financial health. But we read this report from NICA Asia, which reported that Meta, Oracle, Alphabet, Amazon and Microsoft, all the hypers have roughly $1.7 trillion in debt that is off of their balance sheets,

that has been registered through these SPVs that get funded or that get financed by these private credit funds. And so a lot of the debt that is, you know, financing the AI boom, we don't really see much of because it's in these SPVs and they're not really taking much of a role in the larger names of the bond markets. Does that worry you? If so, why? And if not, why not?

And how much of attention should we be paying to that number, that $1.7 trillion number? From an investor perspective, you have to worry about all of the nuances associated with it. I think the traditional distinctions we've had between public and private, secure and secured, investment rate, high-eal, securityized, structured, all of these differences are sort of merging. These silos are merging. And we have bonds that are hard to fit into any one bucket. And more and more, investors are getting.

used to thinking about these bonds from, not just from one perspective, from a variety of perspectives. So almost all of these bonds are predominantly in institutional investor hands. And the distinction, each of these different forms have their own specific risk and return issues of conservation associated. So doing a deep dive on each of them is a now has become increasingly necessary. and as opposed to, you know, I look at trade rating by the bond, I look at trade ratings, yield, and by the bond, that's no longer going to be satisfactory.

So you need to understand where is the relative value and where is, what is the, is it secured, is it amortizing, is it, is there any residual value guarantee, is it tranched, all of these factors, but increasingly important. And in the market, people are paying a lot of attention to all this. So a lot more transactions will happen we expect in some of these SPV forms. That itself is, I'm not particularly bothered by it in itself. I think it's important for every investor to understand the needy-gritty if it is, you know, of all the structures, you know, what are the sorts of cash flows?

What's the timing of these cash flows? What are the factors that could risk, make, put these cash flows at risk? that understanding is essential. One of the concerns that I've voiced on this show earlier this week is that the more complicated these structures become, the more nitty-gritty, the more the lines between all of these different investment instruments are blurred, essentially, the more difficult it becomes to actually do that deep diving and to actually figure out what is credit-worthy and do the real hard work of underwriting. And that seems quite similar to what we saw in previous debt crises, like,

you know, I think of like CDOs as an example, where we kind of overcomplicated things at a financial level, and then people didn't really do, or underwriters didn't do their proper homework. Do you think that that is a risk, given the proliferation of SPVs, given the increased financialization and complexification of AI debt financing? Is that something that you're worried about? I mean, I think Yvans should always be concerned about complexity. Are you being rewarded? You do understand the complexity, and is there enough of a risk premium to offset the complexity? You know, the more complex a structure is, the liquidity is going to be, it is going to be less liquid than a plain banana.

It's going to be more liquid than a complex structure. And it is an index eligibility has a, it will be more liquid. So I think each of these components, I think the, you know, we've learned something. things from the financial crisis and other previous debt crises. I think if I compare this, probably a more appropriate comparison is looking at the telecom, which is a major Cappex boom in the late 90s and many of these companies. I think the big distinction between then and today is that bulk of the telecom related CAPEX, which involved laying all the fiber network, etc.

All of that stuff came from companies where the bulk of the debt of the companies was by issuers that were barely investment rate or below investment rate. Already starting point had a lot of outstanding debt. Starting point didn't have a lot of cash on balance sheet. You contrast that with where the bulk of spending is how issuance is happening raised from the much higher quality hypers that are both cash rich and do not have a starting point of debt is much, much lower. So some of this complexity is, this is, this is, this is,

what the more in the deeds you get into, there is rewards for institutional investors to take advantage of, you know, where is there is a relative value. So it's not I think that type of a understanding of the details very much matters.

And I am always concerned when someone says, especially a self-side analyst says, this time is different. You know, it's always we have to be cognizant of the nature of the complexity, the source of the uncertainty, have cash flows, and are you able to understand it and model them better?

To that point, if we see more debt issued under the SPV model off balance sheet of the hyperscalers, is that the thing to keep an eye on? If you're worried about the AI being a bubble, which a lot of people increasingly are, is that the number that we should care about? Because to that end, to your point, I mean, so far that the hypers, they're fine, their fiscal financial situations are in check, but we just don't know as much about the SPVs, and therefore maybe that's the thing that we should be keeping an arm. I think from the investors I speak to, as it might be an SPV, and it has either

a takeout or a back-end residual value guarantee from the hyperscalers, most of investors I know would actually consider that to be risk-offed hyperscale. So when people are adding up their total exposure to a particular hyperscaler, they're not only looking at their exposure or unsecured bonds or any public bonds, they're looking at what other ways is the exposure? You know, I'm buying this bond from an SVB, but ultimately there's a back-end, you know, a lease provision or some of the guarantee of various types of sorts

that is coming from a hyperscaler, then I would consider that bond to be, my exposure is not just my unsecured exposure, but also in this. So I think it is important to have systems and analytics that can actually look through all these structures and aggregate exposures in a manner that correctly reflects the overall exposure. And I think that's very much the direction of travel within the markets these days. All right. Vichi Tura Pouture, chief fixed income strategist at Morgan Stanley. Vichy, we really appreciate your time. Thank you.

Thank you, Ed. How did a company you've never heard of become China's most valuable company overnight? Two words, memory and hype. This week, CXMT, one of China's top memory chip producers, soared 466% on its stock market debut. Now, I made it one of the most successful IPOs in history, and it also made it more valuable than Tencent, the owner of TikTok. This company is now worth more than half a trillion dollars that's equal to Disney, Boeing and BlackRock combined. Why? Well, for one, business is booming. Memory prices are set to rise 130% this year, and as a result, the industry's revenues are set to explode over 140%.

Micron's revenues alone grew 346% last quarter. Its market cap is now about $1 trillion, making it one of the 15 most valuable companies in America. In sum, memory is the new gold. But we are now entering the hype phase of the cycle, where the stocks that produce these memory chips are now more sought after than the memory itself. At 49 U.N. per share, CXMT is now valued at 1,600 times earnings, which means that if you bought this stock, and if the company kept making as much as it makes today,

and if they decided to return all of their profits to shareholders, then in order to get your money back, you would have to hold the stock for 1,600 years. In other words, it appears that memory investors are beginning to lose their grip on reality. The numbers are increasingly taking a backseat to the narrative. And like meme stocks in 2021, the narrative is that memory is going,

to the moon. There's no question that CXMT is extremely well positioned in the industry right now. They're in the hottest market, and their market share keeps growing. But that's not the only thing that matters in investing. What also matters is the price. And at these levels, this price is not sustainable. Okay, that's it for today. This episode was produced by Claire Miller and Alice Weiss and engineered by Benjamin Spencer. Our video editor is Brad Williams, Our research team is Dan Chillon, Kristen O'Donoghue, and Mia Silverio, and our social producer is Jake McPherson. Thank you for listening to Profi Markets from Profi Media.

If you liked what you heard, give us a follow. I'm Ed Elson. I'll see you tomorrow. Running a business shouldn't feel like surviving a software group project. One app for accounting, another for inventory, another for sales, and somehow none of them talk to each other. That's where Odo comes in, an all-in-one business management software that brings every part of your business together.

From sales and accounting to inventory and marketing, all in one powerful platform. No messy integrations, no bouncing between tabs, and best of all, no spreadsheets. Stop managing software and start managing your business with one unified system. Try for free today at odu.com slash Vox. That's ODOO.com slash Vox.