China Is Undercutting America’s AI Giants

The US stock market started history's greatest way of a wealth creation, from factory

“workers in Detroit to farmers in Omaha, anyone can own a piece of the great American”

companies. But today, our most innovative companies are staying private longer, which means every day Americans are missing out, until now. Introducing BCX, the public ticker for private tech, now available wherever you buy stocks. Visit getVCX.com for more info. That's getVCX.com.

Carefully consider the investment material before investing, including objectives, risks, charges, and expenses. This and other information can be found in the funds perspective. At getVCX.com, this is a paid sponsorship.

Recommendations can be amazing. I mean, maybe someone recommended that TV show you've been

obsessed with lately. But when it comes to home projects, it's different. If you don't like a show, you might lose a few minutes. If you hire a friend, a friend, of a friend, to fix a leaky ceiling, you could end up with a flooded kitchen.

“Maybe I know a guy just isn't enough for your home. That's why thumbtack works so well.”

They'll match you with a top-rated local pro, and you can see photos of past work, credentials, and reviews all right in the app. For your next home project, try thumbtack. Higher the right pro today. [Music] Welcome to Profty 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 tenure treasuries declined. The Chinese chipmaker CX&T sold more than 400 per cent 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 start up ZAA and Moon Shot AI have released models that are competitive with those from U.S. Frontier Labs. The top five most popular models on open router,

a marketplace that tracks usage across AI models are indeed Chinese. And when a rogue unreleased open AI model hacked, hugging face during a security test last week, hugging face used a Chinese open weight 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. It's first ever post on X.

Jensen Wong shared a letter titled "Open Wates an American AI Leadership." And within a couple days, 50 companies signed the letter, including Microsoft, Meta, Palante, 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 won Washington against quote, premature restrictions that stifle competition and drive innovation overseas. Which raises an

interesting question. And that is why would U.S. 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 you 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 inthropic 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 AI 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 they can fit whatever purpose they have. That was sort of the area where China couldn't have so to compete on having the most advanced capabilities. But they could have leaner cheaper models that would be useful across a startup ecosystem for companies.

And this is what I'm throw up. You know, OpenAI 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 master in this conversation?”

The solution is basically taking a much stronger model or a bigger model and using it to

build a 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 Chinese companies, but there's been allegations that American commissioners 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 into actual 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 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 distillation itself is sort of a widely accepted common practice across AI. I mean, just from the perspective of a Chinese company, and thought that 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 Kimmy and the KimmyK3 model, which went viral in middle order 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, clawed in 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 lesson from Chancellor Huang, co-signed by a lot of other American companies saying, no, don't restrict this. Let this happen. Distilling is okay. Open sources okay. Open weight is okay. Why do you think they all down with this?

I think that there is maybe two things to point out. The first is that the American tech companies are not a monolith. In Nvidia itself benefits tremendously from, you know, exporting many ships to China, it can power Chinese edit out. And much of the American tech stack remains even in the era of sort of great firewall tech decoupling. So much of US 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 weight access remains open. Start-ups in Silicon Valley, the A16Zs of the world are running, you 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 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 US that's still benefits. Yeah, it seems as though this might be kind of an attack in so many ways on open AI 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 price and pressure on them.

Might that have a role to play in why someone like, you know, Magzuckaburg, 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 Darryr Amade is that kind of, there's just 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

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 a 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 close and open models. We see from, you know, instances like the open AI, hugging phase incident last week that they're going to be some serious risks, including the possibility that, you know, there are serious cyber incidents that lead to loss of developer control of varying degrees. And we also have

concerns around this use. And a combination of open and close source models are probably going to be necessary to mediate before range of concerns that we would have coming from those models. I think the other thing is brings out 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 close to it in the US. 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 the most elite of where US 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 US 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 US 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 I would bet on the US position overall. The US is the model provider of choice across open AI, Google, Meta, and Thropic, XII. If you look at the global diffusion of these models,

there's just not that many countries using companies that are really up taking 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-Aid Cooperation Organization in China. And if you look at the gusts 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 not of the countries

in the Middle East that have been pretty excited about Chinese investment. And so it is to say that amidst this moment, or 10K, 3 in 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 the 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-world acquisition organization where most of 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

was pay attention to just how far behind they aren't 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 a 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, it 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 the AI. All right,

it's called saying a technology and international affairs fellow at the Carnegie Endowment for International Peace. Scott, we appreciate your time. Thank you. Thank you so much Ed.

Off for the break, a deeper look at AI debt. And for even more markets in sites, you can subscribe to my

weekly newsletter simply put at simply put.profgmedia.com. Support for the show comes from noon. Most journeys don't travel in a straight line or ups and downs, peaks and valleys. Way lost can feel the same way. Often losing the weight is only half the

“battle, keeping it off is the other half. That's why noon combines GLP wood medication and”

behavioral support designed to help people build healthier routines and sustain progress over time. noon found that 37% more weight is lost when using noon that just taking GLPs on their own. And the weight is sustained too. That's because noon uses a psychology-based science-back program that pairs your medication with daily lessons and food logging to support weight loss that lasts. Plus, you can get personalized weight loss plans and expert coaching designed to maximize your

success with GLP ones while helping you develop sustainable habits to live better and longer. The noon GLP one program starts at $39 in a delivered year door in as little as seven days. Start your GLP one journey today at noon.com that's n-o-o-m.com, noon, lose for keep. Initial one month description from $39 plus to acts and $99 plus to acts for month thereafter. Cost of medication not included final pricing depends upon program selection.

Support for the show comes from so-fi. Your home might be one of the biggest assets you own, but a lot of people don't realize you can actually put that equity to work without refinancing your existing mortgage. That's where so-fi home equity can help. Whether you're planning a renovation, consolidating high interest debt or covering a major expense, so-fi offers home equity solutions designed to help you access your home's value while keeping your current mortgage interest rate

“exactly the same. If you want to lump some with predictable monthly payments,”

so-fi home equity loan lets you borrow up to $750,000 or 85% of your home's equity with a fixed interest rate or, if you prefer more flexibility, a so-fi heloc lets you access funds that you need them during a three-year drop period, so only borrow what you need when you need it. If you've built equity in your home, it could be one of your most valuable financial tools. Head to so-fi.com/proxyhome to explore your options and check your rate with no impact to your

credit score. That's so-fi.com/proxyhome. So-fi bank n-a, n-m-l-s, six-nine, six-eight, nine-one. Remember FTIC offers loans directly or through spring EQ, a state license lender. N-m-l-s, one-four, six-four, nine-four, five, terms and conditions apply so-fi receives a fee for loan's broker to spring EQ. Running a business shouldn't feel like surviving a software group project, one-up for accounting, another for inventory, another for sales, and somehow,

none of them talk to each other. That's where Odu comes in, and 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/box. That's OduO.com/box. We're back with Proftly Markets. The world's most valuable chipmaker is making its lender's nervous. The cost of ensuring Nvidia's debt against defaults just posted its biggest one-day jump on record. Despite follow reports that Nvidia has lined up more than $750 billion

in new AI commitments, that includes a $250 billion guarantee for Open AI, and a $500 billion

discussing on the 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 Nvidia 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 pay 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 seeing in 2008. Meg's 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 Vishy Tirupatoor. She fixed income strategist at Morgan Stanley. Vishy,

thank you so much for joining us on the show. AI debt is suddenly the rage right now, and it's something we've 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 and new sector is coming to the”

trade markets. If you look back just a few months ago, the AI ecosystem represented in the benchmark indices were in the investment trade market. We're under 3% today, they're 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 it's the crux of the the auditor that is in the market. We should step one more one step back and examine it. It is

really the expectations of the CapEx from the AI ecosystem, the hyperscalers included, there is a pretty substantial amount of upward revisions of CapEx. So beginning of the year, we thought that the you know the CapEx by the five hyperscalers in this year would be

“something around $600 billion. So in 24/6 and we thought that the number would be around $850”

billion or so for next year. And we now think that the this year number, 24/6 CapEx from these five

hyperscalers 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 be funded to the trademark. So if we think about from the 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 with the as you said,

Oracle is at the low end of the Great Spectrum, but the rest of the hyperscalers are very high quality. And in high quality, Microsoft is triple A, alphabet is double A and Amazon and Meta or double A plus. So high in much higher quality, but the expectation of a lot more supply in multiple currencies, in multiple mutualities, in multiple forms, in unsecured form, securedize form, public markets, private markets, all of this abundance of issuance

expectations that are ahead is the source of this budget. And if each time we'll have this expectations reset, then there is incremental inputs getting wider. So in our mind, the one, the ability, credit spreads need to be wider to accommodate all of decisions. So not everyone is going to be equally wider. The risk here needs. So for example, credit reset, lower the credit rating and the greater the spread widening that you've seen. So as you mentioned,

and surprisingly, a name like Oracle is trading at a, you know, as a Friday, they were trading, you know, well over 20, 20 basis points in in in spread terms for their benchmark once. So that was, that's higher from year to day, almost 65 basis points higher, but it's not nearly for me like that. So the more trade worth it means are wider, but wider by less. Something we read recently from Nicaria Asia was this report that, and as you mentioned,

these hyperscalers, 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.

Alphabet, Amazon, and Microsoft, all the hyperscalers 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 in the in the larger names of the bond markets. Does that worry you if so why? And if not, why not? And how much of tension should we be paying

“to that number, that 1.7 trillion dollar number? From an investor perspective, you have to worry about”

all of the new offices associated with it. I think the traditional distinctions we've had between

public and private, it's a cure and a cure, it must admit how you use the cured eye structure,

all of the differences are sort of merging, these silos are merging, and we have bonds that it hard to fit into anyone 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 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 now has become increasingly necessary, and as opposed to a look at trade rating by the bond, a 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 little bit of value, and where is what”

is secured, is it amortizing, is it, is there any residual value, guarantee, is tronced all of these factors, or increasingly important, and the market people are paying a lot of attention to all this. So a lot more transactions will happen, where expect in some of these FPV folks, that itself is not particularly bothered by it itself. I think it's important for every investor, to understand the nearly greedy, if it is, you know, of all these factors, you know, what are the

source of cash flows, what's the timing of these cash flows, what are the factors that

go, let's 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 crisis, I think

of like CDOs and 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 SBV is given the increased financial ization and complexification of AI debt financing? Is that something that you're worried about?

“I mean, I think eventually always be concerned about complexity, are you being rewarded?”

Are you doing understand the complexity? And is there enough of a risk premium to offset the complexity? You know, the more complex the structure is, the liquidity is going to be, it's going to be less liquid than a plain vanilla bar. It's going to be more liquid than a complex structure. And it is an index eligibility, it will be more or less more liquid. So, I think each of these components I think we've learned some things from the financial crisis, and other previous crisis.

I think for compare this probably a more appropriate comparison is looking at the the telecom, which is the major CapEx boom in the late 90s, and many of these companies, and the big distinction between then and today is that bulk of the telecom related CapEx, which involved laying all the fiber, fiber debt, etc, all of that stuff came from companies where the bulk of the debt of the companies were by issuers that were barely in investment

weight or below investment weight. All that is starting point had a lot of outstanding debt. Starting point didn't have a lot of cash on balance sheet. You can't trust that with the bulk of the spending is how issuants happen to raise from a higher, much higher quality hyperscanners, that are both cash-rich and do not have a starting point of debt as much much lower. So, some of this complexity is this is what the more in the weeds you get into,

So, it's not in, you know, I think that type of understanding of the details very much

matters. And I am always concerned when someone says, "especially a self-centered honest says,

“it's different." You know, it's always, you have to be a common sense of the nature of the complexity,”

the source of the uncertainty of cash laws, and all you're able to understand it, and model them 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 above all, which a lot of people increasingly are, is that the number that we should care about because to that end is to your point, I mean, so far that the hyperscalers, they're fine,

they're fiscal financial situations are in check, but we just don't know there's much about the SPVs, and therefore maybe that's the thing that we should be giving an eye on. I think from the investor size P2, there might be an SPV, and it has either a take-out or a back-and-sidual value guarantee from the hyperscalers, most of investors I know would actually consider that to be risk-orthed hyperscaler. So, when people are adding up their total exposure to

a particular hyperscaler, they're not only looking at them exposure on non-secured bonds or highly, you know, any public bonds, they're looking at what other ways is the exposure.

You know, is this a, am I buying this bond from an SPV, but it's ultimately, there's a back-and,

you know, a lease provision, or a some of the guarantee of various types of sorts, that are, that is coming from a hyperscaler, then I would consider that bond to be

“my exposure is not just my unsecured exposure, but also this in this. So, I think it is important”

how systems and analytics that can actually look through all these stockings, and an 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, Vishitura Pator, chief fixed income strategist at Morgan Stanley. Vishit, 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, sword 466% on its stock market debut. That made it one of the most successful IPOs in history, and it also made it more valuable than 10 cent the owner of TikTok. This company is now worth more than half a trillion dollars. That's equal to Disney, Boeing, and Black Rock 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%. Microns revenues alone grew 346% last quarter. This market cap is now about one trillion dollars, making it one of the 15 most valuable companies in America. In some, 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 UN per share, CXMT is now valued at 1600 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 1600 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, an engineered by Benjamin Spencer. Our video editor is Brad Williams. Our research team is down to the lawn, Cristina Donahue, and Mia Savario, and our social producer is Jake McPherson. Thank you for listening to Proftory Markets from Proftory Media. If you liked what you heard,

give us a follow. I'm Ed Alson. I'll see you tomorrow.

for accounting, another for inventory, another for sales, and somehow, none of them talk to each other.

“That's where Oru comes in, and 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 odio.com/box. That's odio.com/box.