← The AI Daily Brief: Artificial Intelligence News and Analysis
The AI Daily Brief: Artificial Intelligence News and Analysis
What a $30B Hedge Fund Implosion Really Means for AI
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Today on the AI Daily Brief. Insane revenue growth, but also a hedge fund blow up. What is going on with AI in markets? 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 Blitzy, Retool and Airtable. To get an ad free version of the show, go to patreon.com aidaily brief or you can subscribe on Apple Podcasts. To learn more about sponsoring the show, send us a Note@ SponsorsIDailyBrief AI Two more quick notes before we dive in. First of all, today is one of those episodes where all of the stories and the headlines also fit the theme of the main. So it's going to be a main only. And second, your reminder to come check out the AI Summer Adventure. You can find it at Summer Adventure AI. It's a choose your own adventure style program where you can do projects at basically any level of AI learning. Go check it out. I'm excited to see what you do this weekend. But with that, let's talk some numbers. Today we have two stories that feel on first glance like they're telling totally different stories about the markets surrounding AI. On the one hand, we have just absolutely bonkers estimates and real numbers for AI lab revenue, which are in many ways genuinely hard to wrap your head around. And on the other side, we have the utter implosion of a Wunderkin led hedge fund that is surging renewed questions about the durability of AI markets. So let's figure out what stories these two very different events are telling and where they point for AI markets. Next, we're going to start on the revenue side, where both OpenAI and Anthropic appear to be having a resurgence in revenue growth. CNBC reported that during a Recent all hands, OpenAI CFO Sarah Fryer told staff that ARR annualized recurring revenue for July had exceeded the entire second quarter, adding and Q2 was no slouch. Now, without the full context, it is not exactly clear what Fryer meant, and the articles didn't do a lot to clear that up. But the takeaway certainly was that OpenAI had an absolute bonanza of a month. Then on Thursday, Axios reported that Anthropic was also seeing revenue skyrocket. Indeed, back of the napkin math put anthropic at a $71 billion run rate, up from 47 billion in May. When they last discussed revenue, this figure was based on a post from TAE Kim, who was referencing data from AI investment research platform Funda. Meanwhile, their data also showed that OpenAI was sitting just shy of 50 billion in arrow. Now obviously this data should be treated as a very rough estimate, but it seems directionally correct based on Fryer's comments. It also lines up with estimates from Semianalysis, who at the beginning of the month wrote that Anthropic is currently operating above 60 billion in ARR and looks set to end the quarter with $1 billion in profit. And some believe they will just keep going. In a recent blog post, Dwarkesh Patel wrote, Anthropic likely ends the year with 100 to 150 billion in revenue. Now on the one hand, those numbers seem absolutely gobsmacking, but if anthropic really jumped 10 billion in ARR in July alone, it doesn't seem impossible. Pointing out that Dwarkesh is in a position to have a lot of behind the scenes conversations, former Atlantic author Derek Thompson noted that if Anthropic can hit this mark, they will have eclipsed the revenue generating capacity of Tesla and SpaceX combined. For those not paying close attention, the surge also felt like it came out of absolutely nowhere. Just a week ago, the Wall Street Journal wrote an article about how corporate America had suddenly decided to stop blowing money on AI. That is obviously their words, not mine, and on the face of it, it seemed reasonable. So much of the media's story around enterprise AI for the past few months is CFOs trying to rein in token budgets and substituting expensive frontier AI for open source Chinese models. And so how the heck did these two companies have one of their best months yet? Two points that I've made repeatedly that I will use this as a chance to reinforce, which are honestly actually just part and parcel of the same point. And that is we are currently consuming a tiny, even vanishingly small percentage of the total possible demand for intelligence from AI. Yes, we have a very, very limited handful of companies who have a portion of their users that are deep enough that they actually have to do things like impose token limits, but the vast majority of the user base remains on the upswing with miles and miles of air above them. What's happening in the enterprise is not that companies have decided to stop spending, it's that they are seeing the early warning shots that what they can't do ultimately as AI gets to full mature scale, is simply deploy Fable 5 for every single problem they have. They are, in other words, looking to get out ahead of a problem which is primarily the domain of the future. All by creating more complex AI usage architectures that involve multiple different models and smart harness and provisioning arrangements. But in almost no cases are companies all of a sudden using less AI. Now it's my personal opinion that we are going to be on that upswing with miles of air ahead of us in terms of total intelligence demand for years to come. And the reason for that is simply physics. The speed at which demand will increase is faster and will be faster than our ability to bring more intelligence online. It turns out that building the entire slate of infrastructure needed to bring more intelligence online at this scale just takes longer than the demand grows. Which brings me to the second sub point that I'll make, which is that I believe that every single token that OpenAI or anthropic produce at almost any cost within the bands of where they are will be bought. The addition and presence of lower priced models and models that make different trade offs will be adding to the top line, not subtracting overall. Which is not to say that these companies are going to sit back and marinate only in their high priced offerings. In fact, we also just got news that OpenAI is slashing prices. Effective Thursday, the two smaller versions of GPT 5.6 saw a price cut with Luna down 80% to a buck 20 per million output tokens and Terra down 20% to $2 per million output tokens sold. Prices were held steady, but OpenAI introduced a new fast mode with a 2.5x speed boost. Now this shows some clear awareness that cost is a vector that they need to compete on, and that more efficient models are going to increasingly have a more complicated competitive landscape. But make no mistake, this is not a discount sale. Because OpenAI is having any problem selling intelligence, it's strategically leveraging their position to try to shore up a part of the market that could increasingly be a weakness. Now why all of these revenue numbers matter as more than just interesting headlines for podcasts is that demand for OpenAI and anthropic models is upstream of everything else in the AI economy. The equation pretty simply is OpenAI and anthropic revenue going up enough to justify increasingly large expenditures on new CapEx development, I.e. data centers and the finance that's requiring for that build out. Meaning of course, that the concern is on the inverse. If demand were to fall, none of those things would make financial sense and it could all come crashing down. But at the moment when it comes to the revenue, it is just nothing but air up there. Now close listeners will have heard me talk about before the fact that these massive revenue numbers and the shift that they represented from thinking about AI business models in terms of seats to instead thinking about it in the total addressable market for tokens is what got a lot of folks off of their Q4AI bubble worries that were such a big story last year. So where have the AI bears focused their concerns this year? To some extent the bear case hasn't changed. It's still based on things like concerns about Nvidia's circular deals, which, given recent talks for Nvidia to backstop $250 billion of OpenAI's data center demand, have come back louder recently. When it comes to the semiconductor trade, many if not most analysts still view the industry as cyclical with demand destined to crash, even though in that particular case I don't know that there's ever been quite as good an example of past results do not guarantee future performance. That is, the semiconductor trade now is totally and fundamentally different to what it was before the AI boom. Which to be clear, doesn't mean that it can't go badly. It's just not going to go badly for the same cyclical reasons that it did before. And indeed, there's the ever present argument that AI demand simply can't continue to grow and has perhaps reached the high water mark as cheaper Chinese models become good enough to substitute. Now, obviously that's just what I addressed. My position on that is pretty clear. However, very importantly, it is very easy for any of us who intersect primarily with one part or one theme of the market to think that that's the most important thing in markets. However, if you take a quick peek outside of the AI ecosystem, you'll notice that a lot of the bearish sentiment on AI right now has absolutely nothing to do with AI fundamentals. This week the Federal Reserve declined to raise interest rates, but many analysts now believe a rate hike is coming as soon as inflation picks up. The ongoing fracas of the Iran war is making investors extremely jittery, and from the beginning of the year where we saw macro conditions ripe for a boom in speculative tech stocks over the past couple of months the market has transitioned into a decidedly risk off mood. It is important for us to be able to distinguish when AI related market prices going down has to do with some change in belief about AI or whether they are just the biggest stocks being influenced by broader concerns. Overall, the Nasdaq is currently down slightly on the year and despite a strong recovery in July, is once again on the brink of a correction. The Mag 7 is basically flat over the past month, and the semiconductor index has taken a 23% drawdown from June highs. The software index is up 3% for the month, but that represents a rotation out of the AI trade and into the names that were beat up during the SAS apocalypse. And that's just the US market, which we are continuously surprised to discover is not the only market in the world. If you look abroad to South Korea for one globally interconnected example, that market is in absolute shambles. The major Korean index, known as the Kospi, is down 40% in a month, making it the worst stock crash in Korean history. Worse than the Asian financial crisis in the 90s, worse than the GFC in 2008. Now, the Korean stock market is composed very differently to the US. Rather than the major index tracking 500 stocks, it tracks 100, and only a tiny handful of them are large enough to matter. Right up the top of that list are Samsung and SK Hynix, the two major memory producers that make up around 50% of the index by themselves. By way of comparison, the Mag 7 are about 30% of the S&P 500. The other big difference is the behavior of Korean retail traders. Around 30% of the population actively trades, compared to around 0.2% in the US. They also notoriously love leverage, a word that we are going to talk about a lot in the latter part of this show, to the point that Korean regulators recently banned new leveraged ETFs to protect financial stability. According to Goldman Sachs, around 1.2 million Korean accounts were margin called this week, meaning that they had to deposit more money to stay afloat. That's around 3.4% of the adult population. As many as 360,000 accounts were liquidated, meaning they were forced to sell after failing to meet a margin call. It's always dangerous to try to say market moves are only about this thing versus that thing, and right now you're seeing a competition for interpretations around these particular moves. To some, it's a notoriously cyclical semiconductor industry experiencing a predictable crash as long term AI demand is questioned. But on the other hand, there is a very mechanical story playing out as a sizable chunk of the Korean population is forced to sell into a price crash. Those two interpretations have wildly different implications for what this all says about the state of AI markets in general. Another big story doing the rounds with AI bears is the problem of off balance sheet debt. As I've been discussing for months, we're at the phase where the hyperscalers are increasingly having to turn away from funding the infrastructure build out themselves towards instead turning to debt to fund that build out. Last week Nikkei Asia did a little digging and came up with some numbers around that. They believe that Hyperscalers are holding 1.65 trillion in data center debt. Debt which is largely being held by the companies not on their balance sheets, but in special purpose vehicles which are effectively shell companies spun up for the sole purpose of financing individual projects. Shocking. Horrifying. Terrifying, right? Well, if you listen to the AI bears, this has shadows of subprime. Rather than putting debt on their own balance sheet where they have to disclose it to the market, the hyperscalers are hiding it in shell companies. This debt is sliced and diced into structured credit products and sold off to insurance companies, private credit firms and pensions. If you're a California teacher, your pension is exposed to this dangerous and shadowy asset. But that's not really the full story and the comparisons to subprime tend not to make it past the surface level. In his fantastic newsletter Notes on the Crisis, Nathan Tankus wrote an extremely detailed comparison between hidden data center debt and and the CDOs that brought the financial system to its knees in 2008. And in this particular instance I do think it is worth noting the source Nathan is not a David Sacks Silicon Valley venture capitalist talking his own book. He's an extremely left leaning, extremely earnest markets commentator who's been writing this newsletter since COVID who if you have watched for the last five years or so as I have, is basically temperamentally incapable of making an argument that isn't just based on the best information that he can find, regardless of whose talking points it reinforces. In any case, when it comes to this comparison between the hidden data center debt and collateralized debt obligations, Nathan's core argument is that the hyperscalers are fundamentally a different type of borrower to the subprime borrowers. During the financial crisis, believing that hidden debt will once again break the economy requires you to bet that at least a couple of the hyperscalers go bankrupt, not struggle to maintain cash flows, not see their stock price cut in half, but actually default on their debts. That is quite a burden of belief given the overall strength of these borrowers. Now the other big difference is how this debt is being used. In the lead up to the financial crisis, subprime mortgages were repackaged as investment grade CDOs. They were accepted as basically the same as US government issued treasury bills for use in the interbank settlement system. This was the part of the system that did the most damage when it broke in 2008, much more so than the crisis being about home prices falling or the collapse of Bear Stearns or aig. Those were symptoms of the foundations of the financial system falling apart. This time around, no one is pretending that data center debt is the same as treasury bills. The debt is largely being sold to private credit firms and shoved onto the balance sheets of insurance companies and pension funds. Now, to be clear, it would be very bad for these organizations if the data center industry collapses and these debts default. But there, at this moment at least, are not the same sort of mechanisms for even those defaults, as improbable as they seem to cause a systemic crisis in any sort of manner, similar to the financial crisis of 2008. Now, just because there isn't systemic risk doesn't mean anyone should be flippant around either the current state of debt or the trajectory of debt. But I think that we should deal with it as it actually is, rather than plumbing for this sort of historical analogy, which is mostly interesting for grabbing headlines. One of the most important AI questions right now isn't who's using AI? It's who's using it? Well, KPMG and the University of Texas at Austin just analyzed 1.4 million real workplace AI interactions 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 us sophisticated that's kpmg.com us sophisticated Blitzi's understanding of massive code bases unlocks autonomous security fixes, modernization and new features. So what happens when there's no legacy code at all? Greenfield is supposed to be the easy partclean slate. No technical debt. But even Greenfield moves at human speed, one sprint at a time. Blitzi changes the unit of work from the developer to the project autonomously, planning, building, testing and validating entire applications from scratch. Hundreds of thousands of lines of production ready code. One Blitzi customer stood up a brand new application, 534,000 lines of code compressing a 65 week roadmap into two weeks. Another shipped an entire application with no front end engineer, Legacy or Greenfield? The answer is the same software at the speed of compute build. What's next@blizzi.com that's blitzy.com this episode is brought to you by Retool Generating a working app now takes about five minutes thanks to AI getting it safely into production with AUTH permissions, audit logs, security reviews. That part still takes time. That's the gap Retool closes. Build apps however you want natively in Retool with Claude code, codecs or any coding agent or by importing React you've already built it all, deploys into Retool and picks up governance automatically. 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It's time you add agents that feel like teammates. Hire yours at Hyper Agent built by the team at airtable. Claim your $1,000 in inference at hyperagent.com aidaily Brief. Now all of this was the background as we entered this week of tech earnings. The question on everyone's mind was whether any of the hyperscalers would pull back on capex and announce that AI spending was failing to deliver sufficient returns. Google had already gone first last Wednesday while growth was strong. They announced their first cash flow negative quarter in years as capex overtook profits. And that was the only message investors heard sending the stock price tumbling this Wednesday saw Meta and Microsoft Report on the same night met as earnings were very poorly received, with analysts remaining unclear on what the company's AI strategy actually was. Mark Zuckerberg tiptoed around the idea of renting out spare capacity, suggesting Meta was still better off keeping it for their own use. And as for AI sales, Zuckerberg presented a melange of options that to many didn't seem to really stack up. He is clearly still set on selling AI agents to consumers, what he calls personal superintelligence. But it's obvious that consumers by themselves can't justify hundreds of billions of dollars in CapEx, that is, those revenues are much more about seats than about aggregate tokens. Zuckerberg then listed options including enterprise AI spinning off their internal productivity tools as separate products and a constellation of new Vibe coded apps. However, he acknowledged that these would all require Meta to flex a quote different muscle and the stock immediately sank. Microsoft's earnings were a stark contrast. CFO Amy Hood presented a clear message of capital discipline. Earnings were strong, but the big takeaway was Hood's forecast that Microsoft would continue to be cash flow positive for at least the next year. Now this is a pretty big trade off for Microsoft. Azure is booming, reaching 100 billion in ARR for the first time and now representing around a quarter of forward revenue. By limiting their capex, they are also limiting growth. But it is very clear that they read the room correctly and that the market is not in the mood to want growth at all costs and handsomely rewarded Microsoft for their prudence. Now the great challenge for all of these companies continues to be how to get very short term investors on side enough to be able to continue to do what you need to do for the long term, while also not making too many concessions to what you need in the long term to compete. Rounding out the week was Amazon, who reported strong earnings and a slight capex bump. AWS sales are up 37% year over year and that seems good enough for investors to endorse this year's capex forecast, rising from 200 billion to 220 billion. CEO Andy Jassy explained the increase as increased costs rather than expanded scope, but he also defended the spending as clearly necessary. Jassy said even at that amount, we will still not have enough capacity to meet all the demand we have in 2026. And I believe this dynamic will also be true in 2027. In fact, the demand we already have for 2028 is striking. And this again, friends, is why the acceleration of revenue for Anthropic and OpenAI matters so much. Among other effects, it gives the hyperscalers the latitude they need to hike spending to match rising costs and keep the AI trade rolling. The hyperscalers say the demand is still there and it's still rising. Which brings us to perhaps the wildest story of the week. Tech earnings quickly took a backseat as the most famous AI hedge fund blew up. On Friday morning, it was reported that Leopold Aschenbrenner's fund Situational Awareness had been liquidated and taken over by Citadel. Now Leopold has had a crazy story. He, like me, was a refugee of FTX madness, and he, like me, found his way into AI, although intersecting with the industry in a very different spot. While he was originally at OpenAI, he left under some cloud of questions around whether it was disagreements in policy or him speaking too freely about internal matters. But where his story really picked up steam was the summer of 2024 when he dropped that namesake essay, Situational Awareness, a 165 page tome that woke many on Wall street up to how fundamentally powerful AI would likely change the world. Aschenbrenner used his newfound reputation to raise several hundred million dollars to start a hedge fund to bet on the rise of AI over the coming months. Several billion poured into the fund and Situational Awareness started posting sector leading returns. By the middle of 2025, many were viewing Situational Awareness as one of the most successful hedge fund stories ever and Ashton Brenner as a genuine wunderkind. As recently as the end of Q1, situational awareness was still riding high. The fund reported 439% net returns, which is, to use a highly technical phrase, absolutely insane for a hedge fund. Other funds copied their trades, meaning its downstream impact was a huge driver in the run up in neoclouds and semiconductors. A big part of the mystique was that Aschenbrenner was in his early 20s with no finance background. And yet, it turns out, even AI prodigies with incredible foresights can get smashed by leverage as well. Earlier this week, the crack started to show. It began with whispers that the fund had been margin called due to the sharp decline in semis. Then on Wednesday, the Financial Times reported that the fund was seeking additional capital. By Thursday morning, it was all over. Citadel securities, one of the largest trading firms in the world, had effectively bought out Situational Awareness. Now, given how much this is going to be seized in the narrative storytelling, it is important here to get technical into unpacking what really happened. By this year, Situational Awareness had grown gigantic. Sources say that they took in about 10 billion in investment capital and had grown that to around 30 billion in equity. However, Aschenbrenner was running the fund at 4x leverage, meaning that the fund had around 120 billion in positions. Even in the hedge fund world, that is pretty extreme and left the fund massively exposed to a drawdown. Leverage means using borrowed money to make a bigger bet than your capital would allow. So in the example of Situational Awareness, the fund has 30 billion of its own money. But at Forex leverage, it controls 120 billion of investments, making the other 90 billion effectively borrowed because the investments are four times larger than the fund's actual equity. And every market move is amplified fourfold. A 5% portfolio move shows up as 20% gains on the fund's 30 billion. But a 25% down move makes the fund 100% wiped out. But the question isn't just what's happening in the markets, but what the lenders expect from a collateral standpoint. A margin call. Is the lender saying that the cushion backstopping debt has gotten too small and a demand that the borrower either puts in more cash to increase that collateral or be a forced seller of their investments. So continuing with the situational awareness example, at their $120 billion position, with $30 billion of their own equity and 90 billion borrowed on leverage, the bank's 90 billion is protected by the fund's $30 billion cushion. Now, if the positions fall 10%, the portfolio loses 12 billion. The fund now only has 18 billion of equity, protecting the bank's 90 billion dollar loan. The banks may decide that that is too thin and demand billions in additional collateral. If the fund can't provide that cash, they have to sell positions and repay some borrowing. Now, critically, this can force the borrower to sell investments that they still believe in, possibly at terrible prices, simply because the bank will not keep financing them. And you can probably see how this becomes a vicious cycle. Positions fail, banks demand more collateral. The fund sells to raise cash. Those for sales push prices even lower. Prices going lower produces further losses and more margin calls. You can very quickly lose more than your equity because prices may move faster than positions can be sold. If that $120 billion portfolio plunges 30%, for example, it loses 36 billion, 6 billion more than the fund's 30 billion in equity. The banks would then be owed money that the fund no longer has. So in practice, they try to liquidate much earlier, precisely to prevent that outcome. And this is why you see this story happening so quickly. This wasn't some protracted month long drawdown. Stocks in Leopold's key area of bets had fallen by enough that lenders started calling in the positions. And as has happened so many times before, Leopold couldn't find enough investors to cover the difference and became a forced seller. Now, while in some ways this has the look of markets 101, there are some who believe that it's realistic to think that others in the market were pushing for this outcome. SEC registered funds are required to report their positions. So everyone knew situational awareness was massively long the AI trademark and knew the specific stocks that would hurt them most. In an interview with tbpn, Martin Shkreli discussed exactly how this works. Paraphrasing just a little bit, he said, I think some players were already positioning earlier in the week looking to do what they call shooting against a fund. If you know somebody has to liquidate, the best thing for you to do is sell all the positions you have in common and then start shorting everything they have. It accelerates the downfall. Very common and sadly, very Darwinian. Now, if one believes that this is at least part of the story, it is again another way in which weakness that we've seen in AI names may be driven as much by the structure of markets and in this case gamesmanship in the markets as opposed to AI fundamentals. Now, in terms of what happens next, the ultimate buyer of the Situational Awareness portfolio, Citadel is rumored to have gotten a 20 to 50% discount on the positions. That means there's not really a huge rush to sell it into the market. Now, no fund in the world has the ability to hold $120 billion in exposure indefinitely, but Citadel is certainly among the best place to sell it off slowly. Citadel also isn't a directional player. They make money by trading often and always try to maintain a neutral market position. That means that they were naturally hedged against this drawdown and likely aren't in the same vulnerable position as situational awareness was now. It is also worth contrasting this blow up with other famous hedge fund failures. When LTCM collapsed in 1994, they were trading currency and bonds on massive leverage. The reason that metastasized into the Asian financial crisis was not because a hedge fund blew up, but because but because the currency market couldn't absorb their liquidation. The Bear Stearns collapse in 2008 was a similar issue. They were trading subprime CDOs, but the core reason that caused contagion was because their corporate debt was being used as collateral across Wall Street. In both cases, the funds were touching important parts of the financial system, which allowed their liquidation to become a systemic shock. This blowup, on the other hand, looks a more like the archegos failure in 2021, which you've likely not heard of unless you're in the financial industry or were also doing a different daily podcast back then. That was a $10 billion blow up. So not quite as large a situational awareness. But it was similar in that it was trading Tech stocks on massive leverage. The blow up was painful and likely contributed to the bear market of 2022, but it didn't cause a systemic crisis. It did have downstream effects, with one of their prime brokers, Credit Suisse, having emerged into UBS after taking a massive loss. One of the big reasons that this time is different is how smoothly the Citadel purchase went. By all accounts, it sounds like the position is still above water and Citadel is likely well placed to ride it out. Ken Griffin has seemingly stepped into the role that Warren Buffett played in 2008 or that J.P. morgan played in the early 1900s, taking over distressed assets and absorbing the risk of a larger incident. Frankly, Wall street has just become much better at these private market takeovers. The distress was first publicly reported on Wednesday and by Thursday morning the deal was done. Ultimately, financial crises are never about equity drawdowns by themselves. It's when defaults start cascading through the collateral system that there's a major problem. I. E. Stock market crashes are not fun, but they are fundamentally a very different thing to a full on financial crisis. Kind of confirming all that is the early indications we have around how the market reacts from here. Now that the liquidation is over, there's no mechanical incentive to short the stocks. Some firms could decide to pressure test Citadel, but that's gone very poorly for everyone who's tried in the past. They're just far too big. Earlier in the week, some analysts were already calling the bottom. JP Morgan wrote on Monday that the market was flashing buy signals and was ready to rally and the market has bounced hard now that the situation is resolved. The Nasdaq was up 2.8% on Thursday, one of its strongest days in a month. The Korean market, where situational awareness had heavy positions, is absolutely ripping. The COPSE index ended the Friday session up 15% after cruising up as much as 17% earlier in the day. Now, of course at this stage this is just a relief rally after a major event, but it is entirely possible that the blow up of situational awareness could have actually marked a local bottom for the AI drawdown. There's a famous phrase that you might have heard some version of before. You may not be interested in politics, but politics is interested in you. That's kind of how I feel about AI markets. For many of us who are here around the AI Daily Brief, certainly some of you are professional investors for whom this is all obviously very mission critical and important for the average folks who are just trying to understand how AI is going to affect them. My argument is that at this point AI is so integrally tied to so many parts of the economy and so much of the current structure of the economy that even if you are not primarily an investor it is worth understanding what's going on. And right now if we have to sum this all up the story is one continued concerns around circular financing and just the nature of the debt in general. Behind the AI buildout which as I have said in the past are some of the best pressure release valves when it comes to whether an AI bubble would fully form. A very notable hedge fund that blew up not mostly because of AI fundamentals but instead because of tried and true issues of leverage. But behind it all a demand story for AI which does nothing but continue to grow. Hopefully you feel like you have a better sense of what's going on out there now. For now that's going to do it for today's AI Daily Brief. Appreciate you listening or watching as always and until next time peace.