The AI Daily Brief: Artificial Intelligence News and Analysis - A Field Guide to AI Market Freakouts

Starting point is 00:00:00 Today on the AI Daily Brief, a field guide to AI market freakouts. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, KPMG, Airtable, Retool, and Blitzy. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, or you can subscribe a novel podcasts. To learn more about sponsoring the show, send us a note at sponsors at AIDailydief.Ai. Now, one other quick note, a couple people have asked me recently if there are better ways that they can support the show. First of all, you listening is all the support I need. I appreciate you being here and diving deep with me in this incredibly fascinating and important AI world.

Starting point is 00:00:43 Second, already one of the things that you guys do very frequently, which is incredibly helpful for the show, is just sharing it. For those who don't know, I recently updated the AIDailybrief.AI website to turn every episode into a set of shareable cards. some of them are quotes, some of them are themes, some of them are statistics, and each of them can be shared directly to various social media platforms or as an image that you can put anywhere you want. The most valuable way the show grows is when you share it with new listeners, so that is always appreciated. Lastly, for whatever reason, Apple and Spotify both very highly reward ratings and reviews, so if you have not at this point left a five-star rating on those platforms, that is helpful as well. Mostly though, like I said, I am just excited to have you here as we explore this insane world.

Starting point is 00:01:25 The one other note today is that it was one of those days where even all of the headlines fit inside the main theme. So we just have one extended theme for the entire episode. Tomorrow we will be back with our normal division between headlines and Maine. But for now, let's talk about the patternicity in AI market freakouts and what they mean for the future of AI. Welcome back to the AI Daily Brief. We are in the midst right now of our latest round of AI FUD and Concern. Now, this one specifically is about Chinese AI and how it might impact the revenue potential of companies like OpenAI and Anthropic, especially as they position to go public later this

Starting point is 00:01:59 year or early next year. Having watched this very closely now for the last few years, I think there are some pretty clear patterns in the specific ways in which investors get stressed about AI. And so that's what we're going to talk about today. However, to get into it, we first need to do an update in this particular round of concern as the Trump administration levies new allegations against Moonshot. Earlier this week, Treasury Secretary Scott Bessent proposed that Chinese AI companies could face sanctions as a response to distillation attacks. And while model distillation has been a hot topic for over a year when it comes to Chinese model performance, this was the first time a member of the executive suggested the government

Starting point is 00:02:35 might do something about it. Besson doubled down on his policy view on Wednesday, writing in a post on X, we support open source AI in the innovation it unlocks, but open source is not open season on American IP. When PRC firms conduct covert industrial-scale distillation attacks that cross the line into IP theft, sanctions and entity list designations will be on the table. Now, that post came hours after White House Office of Science and Technology Policy Policy Director Michael Cratios made specific allegations against Moonshot, the creators of Kimmy K-3, wrote Cratios, we have information that Moonshot AI distilled Anthropics Fable for the development of its K3 model. To do this, they developed a sophisticated internal platform to

Starting point is 00:03:15 conduct large-scale distillation against U.S. models, allowing them to quickly switch between multiple methods of access to avoid detection. Moonshot AI has also acquired GB300 equipped servers and has access to GB300's in Thailand likely to train its AI models. The United States strongly supports the free and fair development of AI, including a thriving competitive ecosystem that spans frontier models, specialized systems, open source frameworks, and open weight models. Legitimate AI distillation used to create smaller, more efficient models plays a vital role

Starting point is 00:03:42 in this open innovation ecosystem. However, large-scale covert industrial distillation aimed at stealing proprietary U.S. technology and undermining American research is unacceptable. Now here once again, while there have long been whispers of Chinese labs getting access to Nvidia GPUs through illicit pathways across Southeast Asia, this is the first time a government official has made specific claims about the practice. Anthropic head of public policy and former State Department officials Sarah Heck confirmed that Anthropic is working with the administration on this matter, with Heck writing,

Starting point is 00:04:11 elicit adversarial distillation is IP theft, an industrial espionage that supports adversary military and intelligence capabilities. It is a national challenge that creates serious national security risks for the United States and Democratic allies. Following the posts, the information reported that the Commerce Department has an active investigation into Moonshot and other Chinese labs for circumventing export controls to access Nvidia GPUs. Signal summed up the feeling of many when they wrote, wow, am I understanding this correctly? One, put Chinese models on the entity list. Two, force American companies to buy more expensive AI from American labs. Three, watch the rest of the world use cheaper models with equal or better intelligence.

Starting point is 00:04:47 Four, discover that distillation did not magically stop because Washington published a list. Five, make American businesses less competitive globally while raising prices for ordinary Americans. Six, meanwhile, none of this stops Chinese AI labs from becoming better and better. Congratulations, you protected domestic AI companies by taxing the competitiveness of the entire country. What a colossal mess. Now, for what it's worth, it's not even particularly clear how much support Besson and Kratios have within the White House. Wired Road on Wednesday, the Trump administration is split over.

Starting point is 00:05:15 how to respond to the rapid rise of China's leading AI models. The debate is broadly divided between parts of the White House, which has pushed for stricter controls on Chinese AI that may soon rival the powerful U.S. models, and the Commerce Department, which has viewed those restrictions as unworkable. Sources said that Commerce Secretary Howard Lutnik would prefer to combat Chinese AI with competition. He has pitched incentives for U.S. labs to open source their models and spoken with multiple labs about the idea in recent weeks. Right, Andrew Curran. The Commerce Department is apparently arguing strongly against regulation which they see as unworkable. They are certain to have David Sacks on their side, American open source may have a surprise ally in Howard Lutnik.

Starting point is 00:05:50 According to the report, he's arguing for direct incentives to U.S. open source software to accelerate development so they can match Chinese OSS. Commerce sees this as a way to avoid regulating anyone. Apparently, Lutnik has even met directly with leaders at unnamed American labs to discuss how best to accomplish this. Perhaps a rebirth of open AI OSS with federal funding, he should meet with noose and prime intellect, among others, in my opinion. Exciting developments. Writes AEI's Ryan Fedesiac, This might be the most important week for USAI policy so far in 2026. Now, one group that's watching this whole thing very closely is investors.

Starting point is 00:06:21 There has been a growing concern, the latest in a long line of AI market concerns, that if all of a sudden enterprise buyers get all cost-conscious and they look to cheaper alternatives, they're going to find themselves moving towards these open China models, undercutting the revenue coming to the major American labs with big implications across the market. And for people who are watching closely, all of this is part of a broader market narrative story that's been happening now for coming up on four years. One of the most important AI questions right now isn't who's using AI. It's who's using it well. KPMG in the University of Texas at Austin just analyzed 1.4 million real workplace AI interactions

Starting point is 00:07:02 and found something surprising. The highest impact users aren't better prompt engineers. They treat AI like a reasoning partner. They frame problems, guide thinking, iterate and push for better answers. And the good news? These behaviors are teachable at scale. If you're trying to move from AI access to real capability, KPMG's research on sophisticated AI collaboration is worth your time. Learn more at KPMG.com slash US-sophisticated. That's KPMG.com slash-us slash sophisticated. This episode of the AI Daily Brief is brought to you by HyperAgent,

Starting point is 00:07:35 where you run fleets of agents your team can manage together. New users get $1,000 in inference. Forget local agents and chat workflows waiting on your laptop to be prompted. Hyperagent deploys always-on agents in the course. cloud, doing real work across the tools your team already uses. Marketing's agent turns competitor moves into landing pages. Saleses agent enriches leads, drafts emails, and updates the CRM. Ops agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about scope and approvals. It's time you add agents that feel like teammates.

Starting point is 00:08:05 Hire yours at Hyperagent built by the team at Airtable. Claim your $1,000 in inference at hyperagent.com slash AI Daily Brief. This episode is brought to you by Retool. a working app now takes about five minutes thanks to AI. Getting it safely into production, with auth, permissions, audit log, security reviews, that part still takes time. That's the gap retool closes. Build apps, however you want. Natively in Retool, with Claude Code, Codex or any coding agent, or by importing React you've already built. It all deploys into Retool and picks up governance automatically. Security lives in the platform, not in whatever the AI wrote. So your team ships at AI speed without the shadow IT and the endless reviews that stall out most vibe-coded projects. It's why

Starting point is 00:08:46 teams at Amazon, Stripe, and Brex build on Retool. And new enterprise customers who signed by September 30th get up to $10,000 in AI credits per year. Start building at Retool.com slash AI Daily. Want to accelerate enterprise software development velocity by 5X? You need Blitzy, the only autonomous software development platform built for enterprise codebases. Your engineers define the project, a new feature, refactor, or greenfield build. Blitzy agents first ingest and map your entire code base, then the platform generates a bespoke agent action plan for your team to review and approve. Once approved, Blitzy gets to work autonomously generating hundreds of thousands of lines of validated end-to-end tested code. More than 80% of the work completed in a single run.

Starting point is 00:09:24 Blitzy is not generating code, it's developing software at the speed of compute. Your engineers review, refine, and ship. This is how Fortune 500 companies are compressing multi-month projects into a single sprint, accelerating engineering velocity by 5X. Experience Blitzy firsthand at blitzie.com. That's BLITZY.com. Since the end of 2022, when ChatGPT was released, which was right in the midst of the Fed rate hiking cycle, it has been AI versus everything in markets. Indeed, back in the early days of the AI boom at the beginning of 2023, there was a lot of confusion on why markets were ripping while the Fed was still raising interest rates. The Fed didn't stop hiking until July, and it became obvious that rate policy would do nothing to deter AI

Starting point is 00:10:08 investment. At this point, AI has become a structural part of the U.S. economy and arguably the most important part. According to Bloomberg, AI investment now represents 25% of U.S. GDP growth. the largest single contribution of any sector in history. JPMorgan analysts claim that since the release of ChatGPT in late 2022, AI drove 75% of returns in the S&P 500, 80% of earnings growth, and 90% of capital spending growth. In a very real way, then, the AI industry is now the driving force in the U.S. economy and U.S. markets. As a consequence of this, the market is extra terrified of AI going badly or reversing, because they feel like so much is riding on it. And in this context, AI bubble talk isn't just a catchy headline, but is a persistent and central piece of market analysis.

Starting point is 00:10:51 While acknowledging that this is a bit reductive, at this point, one could be forgiven for looking at the American economy as basically a bet on AI being transformative enough to drive enough revenue to make the infrastructure build out justified so the party gets to continue. And by the way, even if you're not typically interested in markets, we're now at a point in the AI boom where everyone needs to pay attention and understand the narrative, given that around 50% of the S&P 500 is now AI or AI exposed stocks. That means that that even if you are just a passive indexer or contribute to a 401k, you, my friend, have significant AI exposure. Now, with this background, let's talk about the stuff that freaks investors out.

Starting point is 00:11:25 The one that we are living through right now, of course, is cheap models, undercutting Anthropic and Open AI's ability to charge a premium and continue to grow their revenue. This is the freak-out de jour, but it's had various versions going all the way back to the deep-seek moment at the beginning of 2025, when the market believed that a Chinese lab had produced Frontier AI for a few million bucks when the U.S. labs were spending billions on training runs. Now, there were a ton of things wrong with how Deepseek had been presented. People learned about the difference between compute for training and compute for inference. Some of Deepseek's claims started to have big asterisks around them. And finally, markets also ultimately discovered that

Starting point is 00:11:57 Deepseek R1 wasn't actually ahead of the best models trained in the U.S. It was simply the first and only free reasoning model on the market, with OpenAIsO1 still locked behind subscription. We now find ourselves at another Deepseek moment with the release of Kimmy K-K-3, which in many anyways, was teed up with the amuse-bush of GLM 5.2 when Fable was offline, courtesy of the U.S. government. Now, to many investors, the oversimplified understanding of the situation is a hardwired belief that Chinese AI is cheap while USAI is eye-wateringly expensive. And it didn't help that K3 arrived just after we had had a month-long narrative about U.S. company slashing token budgets. Now, we are not out of this deep-seek moment yet, but among other things that will help drag

Starting point is 00:12:35 us out is the recognition that while K-3 is yes cheaper, it is nowhere near as cheap as some believe. K3 is currently being served at around a third of the price of Fable or half the price of Opus. That's still a meaningful savings, but it's not pennies on the dollar like I believe many analysts have in their heads. Still, what is real is that U.S. companies are looking for ways to manage their AI budgets, especially as more agentic use cases come online with potential implications for the market success of the leading companies. Now, zooming back into previous versions of the market freak out, another one that related to the durability of revenue was the concern around the circularity of revenue. This concern really picked up between the end of last summer and last fall

Starting point is 00:13:14 when you started to see graphics like this one floating around, showing maps of all the deals that Nvidia and others were making across the industry. While many of those deals were small-scale investments in startup labs and neoclouds, at that point there had been recent reports that Nvidia had struck a deal to invest up to $100 billion in Open AI. Some investors saw this potential investment as likely to flow straight back into Nvidia as chip revenue, massively and in their minds artificially boosting the bottom line. Now that deal, by the way, ended up closing at 30 billion in February, alongside 50 billion from Amazon and 30 billion from SoftBank. And for some, the circular financing means that this isn't just open AI and Anthropics revenue that's

Starting point is 00:13:47 suspect, but also a concern that Nvidia and the other hyperscalers, and everyone all the way down the chip supply chain are built on shaky foundations. Now, since people love analogy, the other reason that circular financing has been a concern is the history of circular financing popping bubbles. Some credit the dot-com crash to a type of circular or vendor financing coming unstuck. Hardware providers like Cisco would sell equipment to fledgling startups on finance, so when the startups went belly up, the hardware manufacturers were left holding the bag. That practice was actually made illegal in the early 2000s as a result, and there are a few key differences this time. In this case, Nvidia isn't actually participating in vendor financing.

Starting point is 00:14:22 OpenAI is paying cash for their GPUs, even if some of that cash is investment money from Nvidia. What this means is that if OpenAI runs out of runway, Nvidia doesn't have a debt default on their hands and a massive hole in their balance sheet. They just have devalued OpenAI stock. The companies this time around are also categorically different. OpenAI and Anthropes, are nothing like Pets.com in their scale or legitimacy or the scale of their revenue. The next category of AI Market Freakout has a chance to come up renewed every few months. That concern is AI companies not showing revenue growing fast enough to justify spend. And this has been the big story for hyperscaler earnings every quarter since the AI buildout began.

Starting point is 00:14:58 At the beginning of the year, some hint of AI ROI tended to be enough for investors. In January, Meta reported 24% revenue growth, suggesting that AI was driving a boost in ad revenue, and that was enough for investors. The stock soared by 10% that night. As the year continues, investors are starting to ask a little more from the hyperscalers, but some are still able to deliver. As an example, on Wednesday of this week, Google showed just how much it's going to take to appease this particular market. They delivered a huge earnings beat reporting 24% overall growth and a fairly massive 82% growth for their cloud division year over year. They're making money hand over fist serving AI inference, but the stock didn't soar. In fact, the numbers that

Starting point is 00:15:35 the investors were focused on were not the growth of their cloud. division, but the growth of their CAPEX spend, wrote the Wall Street Journal. Wall Street's tolerance for artificial intelligence investment might seem to have no limits, but Google Parenthabbit found one on Wednesday, $200 billion. At the beginning of the year, Google estimated that they would spend between $185 billion on CAPEX, making this only an 8% increase that can mostly be chalked up to cost pressure in the supply chain, and yet still, that $200 billion number was the breaking point for some. Brian Mulberry of Sacks' investment management commented,

Starting point is 00:16:06 the 200 was the do not crossline. You can't be offloading this much cash and not talking about it. Now, likely this is just the psychological shock of a big round number, but it also demonstrates the point. Even at 82% growth, investors aren't sure that revenue growth will continue to justify the escalating Cappex. Google stock was the first to see the result, falling by 1.2% in overnight markets. So obviously, the market freak out concern of revenue growth not growing fast enough, has a twin in Cappex growing too fast. The more Cappex grows, the bigger the hill that revenue has to climb to justify it. To some investors, the concern is that they just don't understand where this all ends. Hyper-scalers have now raised Cap-X guidance

Starting point is 00:16:44 every quarter for the past three years. Combined Cap-X is now looking like it will exceed a trillion dollars next year and could head even higher. Now, of course, part of the challenge here is that we are just in totally uncharted waters. Seeing companies go from one billion in revenue to 30 billion in revenue in less than two years has absolutely no precedent in the history of any markets anywhere at any time in history. Now, of course, part of the reason that the bubble narrative from late 2025 calmed down this year was exactly this sort of hypergrowth from OpenAI and Anthropic, whereas many investors were counting the number of total knowledge worker seats available, multiplying that by 20 or 30, and not finding enough revenue to really

Starting point is 00:17:21 justify this big KAPEX spend, when a jeddic use cases came online and we started to see employees spending hundreds or even thousands of dollars a month, it helped jog investors to realize that this is not just a different category of SaaS and represent. something fundamentally different. Still, fundamentally different isn't necessarily comfortable for investors who like having clear patterns and history that they can draw from as they're making their allocations. And even in that new context of massive agentic spend, that in and of itself has created new categories of concern. One of the big recurring AI market freakout themes is any suggestions of limits of AI spent. Last year, of course, we had a version of this in the notorious MIT report that claimed that

Starting point is 00:17:58 95% of Gen.A. Pilots fail. And while the study was absolute garbage, social, that MIT should be embarrassed to have its name anything associated with. All that mattered was the headline, and that headline found its way into every desk on Wall Street. This year's version of that is a little less stupid because at least it's based in reality, which is, of course, the idea of token caps as companies begin to limit AI use. Now, once again, the headlines are more dramatic than the actual policies. Uber is capping users, for example, at $1,500 per month, and Tesla, another company in a similar boat is allowing workers to use $200 worth of tokens a week with the ability to request larger budgets. Importantly, we've barely scratched the surface on the ability for most

Starting point is 00:18:37 knowledge workers to use anywhere near that sort of token budgets, meaning that even if every company adopted similar policies, there is still an enormous amount of growth to be had. But still, the market understands that everyone will be looking for ways to bring their token budgets under control, creating a new looming threat of cheaper inference. Now, one last recurring market freakout that is worth mentioning for completeness, even though it hasn't been much of a concern this year, is the idea of performance plateaus that once again lead to reduce spend. If AI performance hits the wall, that limits what it can do, which limits the amount that companies will spend on it, which limits the total revenue growth, which makes it harder to justify the CAPEX. This was the

Starting point is 00:19:12 big fear in the fall of 2024 when the major narrative was the pre-training scaling wall. There were rumors at the time that both Anthropic and Open AI had scuttled flagship pre-training runs over the summer, as the minor performance boost they were seeing didn't justify the increased cost. Now, from where we are today, that argument looks quaint, ridiculous, insane, like Gary Marcus and Ed Zittron were actively trying to lose you money. A few months later, we got a glimpse of AI reasoning for the first time with 01, that pre-training was not the only vector to getting increased performance out of AI. Now, subsequent to that, we've also just seen pre-training very clearly not having hit a wall, with pretty much no one being willing

Starting point is 00:19:46 to argue that Fable 5 isn't an entirely different beast to, for example, Opus 3. So if these are all the common categories of investor freakouts that we've seen in the three and a half year history of AI. Let's now turn to some important caveats. First up, it may or may not surprise you to find that there is a pretty consistent seasonality to the FUD. It's almost like investors want a reason to not care as much in August. Now, summer doldrums are a well-known phenomenon in markets, and the AI boom seems to amplify the effect. This is not just a theory. There is a ton of research that momentum breakdowns occur over the summer, particularly when momentum stocks like semiconductors are driving the rally. And this has been a historic summer breakdown. Last week, Morgan Stanley noted that momentum

Starting point is 00:20:29 stocks are down 40% this month, making it the worst month on record, beating the previous worst of 29% in early 2021. Now, that's not to dismiss all of this drawdown as a work of the market. In a note earlier this week, Goldman Sachs said that hedge funds have sold tech stocks in record numbers, but it is still worth contextualizing the fears that you see with that summer seasonality. Now, here's another really important caveat. As much as I might bemoan the fact that an entire generation watch the big short, thought Michael Burry was cool and spent the next decade calling everything a bubble. The fact that the market is so determined to have a bubble logic at all times is one of the biggest things preventing a runaway bubble. Every time this market gets a little

Starting point is 00:21:04 over at skis, there is a pressure release to moderate it a little. We're just not seeing the frenetic runaway market we saw in 1999 and 2000 with boom and bust IPOs every other day, driving 100x gains and massive losses. The reason why the late 2025's bubble talk dissipated is that agents changed the dollar logic, and we saw the numbers show up in Anthropic and Open AI, rational concern followed by evidence to the contrary. Now, when it comes to how I think we drag ourselves out of this round of concern, one of my strongest candidates will be an increasing recognition that things like token caps matter far less than the realization that we're using

Starting point is 00:21:37 a vanishingly small portion of the intelligence that will ultimately demand. The room to run will give us room to run. I also believe that at the speed at which an infrastructure buildout is even possible, it's almost impossible for capacity to grow faster than demand. In other words, building data centers just takes a really, really long time. And half of data centers that were announced this year have either been canceled or delayed. Bears are calling this a sign that the demand just isn't there, but there is literally zero evidence of this.

Starting point is 00:22:03 And the simpler explanation is simply that it's, A, really hard to permit and construct a data center. And B, data center builders have done a spectacularly bad job up to this point of actually addressing citizen concerns in their communities, leading to an upswell and anti-data center activity. In other words, there are just these built-in road bumps that will slow things down and give everyone more time to adjust. Now, when it comes to this latest round of fud with China, I think one of the ways that it becomes resolved is people realizing that it doesn't really matter if your model is cheap if you don't have the inference to serve it. Moonshot was completely

Starting point is 00:22:34 tapped out of compute on opening weekend, and I think it is extraordinarily questionable, whether all of the Chinese AI companies put together can serve even a tiny fraction of the users that the U.S. companies do. And lastly, and really important, Look at the utter explosion of companies flooding into this new opportunity driven by alternative model architectures and the hunt for cheaper inference. We are seeing routers announced every day. We're seeing fine-tuning and post-trained model plays actually working, many of which are verticalized to specific industries. We're seeing a flourishing, in other words, of companies flooding into solve a market opportunity, and that's even before we see these potential incentives

Starting point is 00:23:13 for U.S. open source efforts, if those should happen. Investor Nick Carter, wrote, the U.S. government does not owe either of the large labs a business model. If the economics of selling tokens don't work due to distillation, cheap clones, Chinese AI magic, the American enterprise and consumer will be a-OK. They will benefit from hyperdeflation and the cost of digital cognition just like everyone else. The hyperscalers will be fine. It's just open AI and Anthropic that won't be, in their current forms at least. If they're willing to adapt, they can develop new business models. So what if the token merchants don't do well? The neoclouds will be fine. The internet companies will be fine. The consumers get cheaper queries,

Starting point is 00:23:46 the enterprise will still incorporate AI. The AI CapEx Supercycle will still produce tokens closed weight or not. American firms will consume these tokens. My general base case is that every frontier token for at least the next five years gets bought at a premium price, even as cheaper workloads come online. I just think the amount of premium state-of-the-art tokens that we're going to be able to produce will still be ahead of demand, even with a lot of non-premium tokens being consumed as well. I think the viability of alternative architectures and new model approaches relieves larger macroeconomic pressure on Open AI and Anthropic to carry the whole market by better distributing the revenue gains of AI. I think natural enterprise inertia combines with the inherent long horizon of the

Starting point is 00:24:23 infrastructure buildout to stretch the adaptive timeline in a way that allows the economy to adapt better than if the only factor was raw model capability. And yet, with all of this, I think there will be a never-ending sequence of new fud, which by the way will frequently coincide with pre-existing market seasonality. But also that at the end of the day, these fairly consistent and patternistic market freakouts ultimately reduced the likelihood that a full bubble is able to form. Anyways, friends, that is my take. That's what I believe.

Starting point is 00:24:47 And for now, that is going to do it for today's AI Daily Brief. Appreciate you listening or watching. As always, until next time, peace.

The AI Daily Brief: Artificial Intelligence News and Analysis - A Field Guide to AI Market Freakouts

There aren't comments yet for this episode. Click on any sentence in the transcript to leave a comment.