Speaker 1As the anthropic IPO inches closer, discussions of an AI bubble are also getting a lot louder. The concerns range from GPUs sitting in Amazon warehouses unused, to the quality of NeoCloud debt, to the concentration of enterprise AI revenue. In this episode, we're going to go through all of those concerns, talking about which are in fact most concerning, and for those that aren't, what would make them more concerning in the future. 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, Robots and Pencils, CDATA, and Blitzy. To get an ad-free version of the show, go to patreon.com slash ai-dailybrief, or you can subscribe on Apple Podcasts. And if you want to learn more about sponsoring the show, send us a note at sponsors at ai-dailybrief.ai. We're doing something we haven't done for a while, which is an AI bubble episode. Now, recently, the AI Daily Brief was released, and we're going to be talking about the AI bubble getting louder that the AI bubble has reached the end of the road. On Wednesday, Americans for Financial Reform produced a report warning that the bubble is, in their words, primed to pop. Senior policy analyst Maya Jenkins warned, if nothing changes, working people will pay a high price for an AI crash. Policymakers should be taking urgent action to reduce the risks. The bubble discourse has, of course, evolved quite a bit since its dominant place in the narrative last year. And there have been many things that people have been pointing to as warning signs over the past month. What we're going to talk about today is the AI bubble. Today is which of these signs matter most, what the reasons for optimism, or at least reduced fear are, and what things in each of those cases, if we saw them happen, would make me more concerned. First, though, a bit of background. We're coming up on a three-week downtrend for tech stocks, with the Nasdaq losing 3% on Thursday following a relatively flat week. Some blamed Meta's Wednesday night earnings call, which made it clear that the next leg of their AI build-out would be partially funded by equity dilution. This sell-off, though, has been happening for weeks. And it's not just the AI bubble. It's the AI bubble. It's the AI bubble. In their latest trading flow report, Goldman Sachs reported that hedge funds sold tech stocks at their most rapid pace in at least a decade last week. We also got an interesting change in behavior during the Thursday session. For the past few months, Treasury bond rates have been surging, dragging up interest rates on commercial bonds and making data center financing more expensive. On Thursday, the ultra-long 30-year U.S. Treasury bond dropped to a one-month low of 5.38%. Now, this is early, and none of the shorter-dated bonds have seen falling rates so far. This is indicative of a classic flight-to-safety reaction in the markets. In other words, more money flowing out of risky assets like the AI trade and into the safe havens of Treasuries, which could spell more trouble ahead. Certainly, the past few weeks have also seen quite a bit of bearish analysis. One big example was Gartner, who latched on to stumbles in the neocloud sector to claim that 80% of neoclouds will go bankrupt by the end of 2030. Their report argued that $3.5 trillion in AI infrastructure spending would be subject to an impairment by the end of 2030. The report also argued that $3.5 trillion in AI infrastructure due to a lack of demand. Gartner's chief forecaster John Lovelock told CNBC on Monday, At the moment, we have more compute capacity than we have demand, and we have a lot of the tokens and processing power being given away in freemium solutions. Everyone in the AI race is running for a last-man-standing position. Pretty much every market that Gartner is tracking in our AI race is likely to collapse down to a duopoly. To put some numbers around this, Gartner estimated that through 2030, the owners of these data centers would need to generate $8.5 trillion in revenue to cover what they have announced around capital spending, financing costs, and a return on invested capital. On the other side of the equation, they argued, though, they believe that total enterprise and consumer AI revenue is forecasted to not reach more than $3.6 trillion. Quite a big gap if Gartner are right. That, meanwhile, kicked off a frenetic week-long news cycle around AI bubble fears. Citadel CEO Ken Griffin was one of many offering his opinion. At a Wall Street conference on Tuesday, he proclaimed that we are, quote, almost certainly in the middle of a bubble. His argument? hinged on uncertain ROI, causing a rethink on AI spending. Griffin commented, Corporate America has been very uneven in its ability to extract value from its AI models. I think all of corporate America is looking at their bills from Anthropic and OpenAI and going, OK, we're going to need to think about how we're sure we're getting productivity out of this. Which is not to say that bubble calls have been consensus. On the other side of the argument, leaders from private equity firm Blackstone were out in force this week. Now, the grain of salt here is, of course, that Blackstone has massive equity and debt positions in the neocloud sector, including extremely early involvement in Corweave and Firmus. But last week, veteran executive Josh Kyra said that he isn't concerned about data center debt. Because hyperscalers are the tenants, Kyra claimed that this debt is ultimately backed by the most creditworthy companies in the world. His phrase. In a Bloomberg interview on Thursday, Blackstone president John Gray spoke directly to Ken Griffin's bubble comments. Gray said, When you think about a bubble, you think about building capacity and then hoping the demand comes. With AI, you're building capacity and you're hoping you can keep up with demand. Now, the thing to be skeptical here is that every talking head you see analyzing markets has a dog in the fight. I mentioned that Blackstone has positions in companies like Corweave and Firmus. But on the flip side, Ken Griffin profits most when there's volatility in the market. But there is a through line to this discourse that, as you'll see, I think is pretty much the only thing ultimately that will matter, which is the question of demand. In many ways, everything else is just a proxy fight for the big question of how much AI is there going to be and whether the value of that demand is ultimately higher than the amount being spent on the infrastructure. Now, as we dig deeper into some of the big events that have shaped markets over the past couple of weeks and try to sort through the signals that matter from those that don't, I think it is worth being somewhat precise about our terms. When we use big apocalyptic terms like AI bubble, we lose nuance. And people who are talking about a bear market in stocks of the kind that takes stock markets down 20 or 30 percent are talking about something quite different than a generational bubble burst like the dot com crash. Which saw over 75 percent wiped off the Nasdaq across two years. Stock market crashes, in other words, are not the same as a credit or financial crisis. You can have one without the other. And of the two, a stock market crash is far less damaging. While absolutely painful, sometimes extremely so for those invested in the markets and especially those who rely on their investments in markets like those living off fixed income retirement accounts. If there's not a systemic problem for banks in the broader financial system, markets can recover naturally. A credit crisis on the other hand, where mass defaults and contagion create impairments throughout the economy, is quite a different problem and one that can require central bank intervention. Now, if there's one segment of the AI trade that's been the locus of concern this month, it's the neoclouds. We had the firm as IPO in Australia getting canceled after investment banks couldn't find enough buyers at their desired price. Then a portion of core we've debt traded down to distressed levels, implying a reasonable chance of default. Microsoft then pulled out of a planned data center in the UK because of power delays, leaving end scale high and dry. And more recently, we're starting to see the big players in AI ride to the rescue. Anthropic is rumored to be in talks for a new $45 billion contract with end scale. Goldman Sachs has upgraded core we've stock to a buy, saying that some concerns over their debt are overblown and the stock has become oversold. And Nvidia is rumored to be in talks to invest hundreds of millions into firmness to help finance them after that failed IPO. It would certainly be going too far to say that the neocloud jitters are overblown. It is absolutely an area of the market to watch. And investors are keeping a very close eye on it. But it's also important to keep the numbers we're talking about in perspective. End scale is targeting a $30 billion IPO. In an industry where the key firms are worth around $10 trillion as a collective, a $30 billion company is a relative minnow. From strictly an economic perspective, it's likely the entire neocloud sector could go to zero without it having much of an effect. Other than, of course, the narrative effect, which would be gigantic. And that's why the big players are coming together to backstop the guys. According to the information, Nvidia has already provided $170 billion in support for the neoclouds through credit guarantees and data center rental backstops. The frontier labs and hyperscalers have also shown they're willing to put multi-billion dollar contracts in the headlines if anyone gets too shaky. The entire industry has a huge vested interest in making sure these companies survive and their data centers get built. So what then would be the most worrying concerns to watch for? If a neocloud gets into trouble and nobody rides to the rescue, that would be a more concerning signal. If distress started to spread from Coreweave's own bonds to project debt for data centers leased to Meta, Microsoft, or Google, that might suggest that the markets doubt the tenant, not just the neocloud. Finally, if a hyperscaler or lab walks away from capacity because it doesn't need it, that would be huge. Remember, Microsoft's UK exit was blamed very specifically on power delays. A cancellation blamed on demand would be a totally different story. One story that's getting a ton of attention from certain critics this week is that Amazon has a huge pile of GPUs gathering dust in a warehouse. On Tuesday, Business Insider wrote, Under a new early rack receipt process, AWS expects to receive roughly $5 billion in NVIDIA's GB300 server racks at U.S. facilities before final deployment. The company projected roughly $3 billion more in GB300 racks each month for the foreseeable future. GB300s are the current top-of-the-range AI chips, the latest in the Blackwell series. Sources said that Amazon has room for more racks, but they don't have the power to turn them on. According to emails viewed by Business Insider, at least one team at AWS did not have a clear forecasted path to getting the new racks installed. An Amazon spokesperson said it's routine to stage hardware ahead of construction, and that being prepared to plug in GPUs once the power is available is best business practice. The article also noted that Amazon is attempting to secure scarce GPUs ahead of the competition. The critics' take here this week has been the idea that NVIDIA is somehow forcing hyperscalers to take delivery of GPUs, even if they don't have the power to use them. fear of losing allocation to future chips. There have been direct claims that Nvidia is channel stuffing or forcing sales to realize revenue more quickly. Basically that the ruthless and powerful Jensen Huang has all the hyperscalers under his thumb. As the logic among those critics goes, this means that some amount of Nvidia's revenue is fake, demand for GPUs and therefore AI is softer than it appears, and the whole bubble is getting ready to fall apart. I have a hard time coming to the same conclusion. There's no reason to doubt Business Insider's reporting. They're very well sourced at AWS, and this has clearly been a month-long investigative piece to find out how many GPUs are actually being installed. But the Occam's razor explanation here is not a lack of AI demand. It's that there's a lack of powered up data centers. Amazon, even if Jensen was a ruthless businessman, would not be ordering tens of billions of GPUs if the long-term plan is just to warehouse them indefinitely. It would make much more sense that Amazon is genuinely concerned about future access to chips. Currently, GPUs are still being priced at a gigantic premium, with old hardware selling above its original price. Meanwhile, old hardware is still in active use, reducing the harm in storing GPUs for a few months. Frankly, among a lot of things that we'll talk about today, this is one story that feels like gasping at straws, looking for any possible sign that the AI infrastructure build-out is falling apart. It's leaning a bit too hard on the comparison to dark fiber during the dot-com era, but that was an issue of too little demand for already installed infrastructure. There are plenty of implications and issues with the challenges of getting data centers built and power connected, but those issues don't have anything to say, ultimately, about the demand for GPUs or AI in general. Again, just going by the simplest available explanation, Occam's razor, if AWS has to warehouse GPUs, it seems more likely to be a positive sign that demand is sky-high than a nefarious plot to boost Nvidia earnings. And frankly, even if that is the case, it just demonstrates that Nvidia still has all the pricing power in the world. Quietly bubbling away in the back of everyone's mind, though, is the question of where all the data-centered debt is sitting. This gets back to that idea of the difference between a stock market crash and a more systemic issue. Especially after the experience that people lived through in 2008, every time there's any sort of market wobbles, the natural question is to ask if and how contagion can flow. In this case, if the AI bubble bursts, is it going to blow up the pension system or major insurers like we saw during the pandemic, or is it just going to blow up the stock market? That is a notoriously difficult question to answer definitively. And this is an area where we should absolutely tread with humility. What we know for sure is that a significant amount of data-centered debt has been collateralized and sold to insurers and pension funds. The information reports that a collection of giant insurance firms bought more than $740 million worth of CoreWeave debt the last time they sold bonds. They listed American General Life Insurance, Fidelity, and Guarantee Life and Security Life of Denver, all of which have portfolios managed by Blackstone. In the abstract, that seems like a big number. But realistically, $740 million spread across these large portfolios is a rounding error. It is still meaningful that they're carrying data-centered debt, and it would leave a hole if that debt defaults, but it certainly so far doesn't look like the potential of a mass impairment we saw following the GFC. A new study from KPMG in the University of Texas at Austin found that when people work with AI, similar skills don't guarantee similar outcomes. Researchers studied more than 500 early career professionals and found that the best performers consistently amplified the value of AI by guiding, evaluating, and refining its outputs. These top performers, called AI amplifiers, weren't defined by what they knew alone, but by how they worked with AI. Learn more about what separates AI amplifiers from everyone else at kpmg.com/us/aiamplifiers. At this point, it's no longer a question of whether companies are actively using AI. Using it well, on the other hand, is a whole different story. Robots and Pencils, though, is a company that I can point to that is actually built for this time. They're an applied AI engineering firm working directly with clients on problems that matter to the business, not experiments that live in a slide deck. Every engagement starts by working backwards from the outcome a client actually needs. If you're trying to tell real AI engineering apart from noise in this space, that's the difference maker. Head to robotsandpencils.com. This episode is brought to you by CDATA. Companies rolling out AI keep hitting the same wall. Token spend is climbing, security is holding agents back, and no one knows how AI is truly being used. The root cause? AI is cut off from the systems and context your business runs on. That's why CDATA built Connect AI Gateway. One control point between your AI and your systems. Built on the connectivity layer powering Palantir, Google, Salesforce, and hundreds more, each agent gets exactly the data and tools it needs. It governs down to the record and lets you finally see your orgs completely be able to use it in one place. So, let's get right into it. Accurate answers, a fraction of the token cost, and a gateway that learns your business with every interaction. See how at cdata.ai. Blitzy's understanding of massive codebases 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 part. Clean slate, no technical debt. But even Greenfield moves at human speed, one sprint at a time. Blitzy 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 Blitzy 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 at blitzy.com. That's B-L-I-T-Z-Y dot com. Thanks for watching, and I'll see you next time. Another pocket of risk to be aware of is the growing pairing of insurance firms with private equity. The canonical example is Apollo Capital, which merged with an insurance firm called Atheny in 2022. This is a classic financing strategy pioneered by Warren Buffett. The insurance company gets favorable tax treatment, and the investment firm gets a steady flow of capital. This is a complex corner of finance that is certainly beyond the scope of this show. But one important thing to understand is that these aren't major insurers like State Farm, providing auto and home insurance. Instead, they're often in the business of offshore insurers that mostly sell life insurance and annuities. It's a real risk, and they could definitely become impaired during an AI credit default, but they're mostly a risk for the wealthy people who invest in annuities. Finally, there's the pension funds, which definitely have exposure to the AI sector. How could they not? Their job is to invest in stocks and bonds, and for the past four years, the bulk of the returns have come from AI. The Wall Street Journal reports that AI drove $138 billion in gains across 13 public pension funds since 2022. Last week, a report from Americans for Financial Reform stated: "Speculative data center debt is being repackaged and sold to pension and retirement funds, university endowments, and other institutional investors in a manner eerily reminiscent of the subprime bubble. Private equity firms have purchased life insurers and loaded them up with risky debt, exposing policyholders and the public to losses and raising the threat of financial contagion." That report was largely interested in arguing against an AI bailout, which many are certain the Trump administration will indulge in at the earliest opportunity. But understanding not just how it is similar to the subprime bubble but how it is different, is essential here. Everyone remembers the slices of mortgages chopped up and sold to pension funds as CLOs during subprime, depicted as a Jenga stack in the Big Short. There is less discussion about why that was actually a problem. It wasn't just about chopping up the debt and combining it into a CLO. The problem was that those CLOs were rated investment-grade debt. They were treated as being just as safe as Treasury bonds. This time around, data center debt is being treated as corporate bonds, sometimes investment-grade, sometimes not, and definitely less than that. This is not the case here. In the subprime, the debt was used to obfuscate how risky it was. This time around, no one is pretending that data center debt is as safe as houses. But again, I want to not just focus on why things are maybe worth a little pause in the stress right now, but what could in the future be more cause for concern. One cause for concern would be that if we saw a disclosed exposure jump by orders of magnitude or concentrating in a handful of PE-owned insurers. 740 million spread across big portfolios is thin. But if that jumps to tens of billions sitting in 2 or 3 insurers, that could be a different story. Another concern would be if data center debt started to get repackaged into securities that were rated higher than the loans underneath them. And again, the point here is not that these things are impossible. It's that they're not the things that are happening yet. Now going back to the idea of a bailout, another big topic of discussion of late is the idea that the large AI players are willing to pay for the loans that they're being paid for. They're willing to pay for the loans that they're being paid for. This discussion came back with a vengeance after Anthropic and then Elon Musk said that they would gift equity to the public through two very different mechanisms. Now there is a whole very important policy discussion about whether this is actually appropriate in a democratic society, whether it's a good way to redistribute gains, or possibly a glaring sign that billionaires and mega cap companies need to be paying more in taxes. But that's not the particular can of worms that we're opening today. There is clearly a solid argument that giving the public a direct stake in the company that they're paying for risks making them too big to fail. And to be clear, when we say too big to fail, we're using a specific term from the last 15 years that refers to institutions that the government feels obligated to come in and protect, regardless of what would otherwise happen to them in the free markets. This is an area where I'm perhaps more cynical than some of you, and forgive me if so. Realistically, I think if your concern is that we're going to get another group of too big to fail companies, in my opinion, that ship has already sailed whether or not they give shares to the public. At this stage in the history of the relationship between the U.S. government and markets, it seems massively unlikely to me that the government would just stand around while a trillion-dollar company blew a giant hole in their economy. The government didn't let it happen with GM in 2008, and certainly the norms of how the government engages with markets have gotten a lot weirder since then. On the other hand, as for the argument that these companies are engineering a bailout ahead of a market crash, that's something I just don't buy. Regardless of what anyone wants to say about Anthropic, it's pretty clear at this point that they have a genuine belief in their mission, and the simplest explanation, once again, is that they want to establish a giant public benefit fund to further that mission. As for Elon Musk, you have the convergence of the fact that it would be entirely consistent if he did believe that this sort of giving is the best way to impact public benefit, but also there's the fact that, more cynically, he might be reading the room that some sort of redistribution is going to happen, and he thinks if he gets out ahead of it, he might be able to control it rather than be subject to it. And there's also the more armchair psychologist assessment that the man loves being loved, and there's no easier way to being loved than to give $11 billion to the American public. This is one that, at the moment, I don't think is worth spending a ton of additional time on, but I think it's worth spending a ton of additional time on. But for the sake of completeness in terms of what to watch for, if you start to see AI companies begin to ask for explicit federal guarantees, or loan programs, or financing backstops before there's a crisis, that could cause, let's say, a different conversation here. I think of all the warning signs that people are bringing up, the one that is most interesting and deserving of deep consideration is the question of revenue quality. Although even within this, there's a bunch of different versions of it that I think have very different dynamics. On the one hand, you have NVIDIA, who reported that just two customers made up 39% of their Q2 revenue. On the other is the Wall Street Journal's recent leak of Anthropix's IPO prospectus, which noted that a fifth of Anthropix's revenue in the first half came from their top two customers. Relatedly, RAMP data has shown that the top 1% of businesses account for 80% of spending volume for OpenAI and Anthropix through RAMP's platform. So how are these different? Well, NVIDIA has a highly concentrated customer base because there's only a handful of companies in the world that are building multibillion-dollar data centers. And some of this might even apply to the Anthropix story. A large chunk of their revenue is likely coming from the hyperscalers, because that relationship is that of a wholesaler and a distributor, rather than a vendor and customer. Revenue quality becomes an issue when there's a big risk that one of your largest customers could pull the plug. For hyperscalers reselling Claude tokens, that seems very unlikely to happen in the short term. A much bigger concern, I think, is this narrow distribution of power users making up the bulk of enterprise AI use. As always, we need to contextualize RAMP data, recognizing that they service a very, very slim part of the market, extremely forward-looking companies, extremely tech-advanced companies, many of them from the tech sector. And yet it's still worth noting that RAMP found that 80% of the top 1% of AI customers are VC-backed startups. Some amount of that volume is wrapping frontier tokens in a service and providing a VC subsidized discount, which is, of course, the business model for some of the agent startups. And we can also presume that a lot of the token volume is for internal use, which is also funded by VC dollars. Where this becomes a problem is if VC money dries up, and these startups hit the end of their runway. At the moment, AI venture is still a raging bull market, but that can always change. The bigger risk, and really the big question here, is how much of AI spending is permanently going to be subject to power laws without broadening out? In other words, is this incredible concentration likely to hold as the demand profile for the entire AI industry, even as buyers mature? Or is it simply an indicator of the difference between companies at different stages in their adoption cycle? If the startup spending around $9,000 per employee per month are the top of the chart now, at some point in a decade, are all enterprises spending $9,000 per employee per month? And if not, how close to those sort of numbers are they going to get? Ramp has seen businesses across all cohorts triple their spend per employee since the summer. Businesses not in the 1% but in the 10% went from around spending $200 per employee to around $800 per employee, growing faster than the median. That suggests an adoption curve that is expanding out across the economy. But there's certainly no guarantee there. Another curveball, which gets into our next and final line of discussion, is that this particular risk gets far more tangible after the major labs go public. At the moment, this risk is based on data from Ramp, the often misunderstood Silicon Data Token Index, and various other unofficial sources that have a tiny sliver of the full picture. Once Anthropic and OpenAI go public, they'll have nowhere to hide. Their fully audited financials will be reported quarterly, and their CEOs will be grilled by equity researchers on earnings calls. If they're not growing and improving their revenue quality, everyone is going to know about it. And of course, clearly, the rapidly approaching Anthropic IPO is a big part of the reason that the AI bubble is back in the discourse. Frothy markets often top on a catalyst that breaks the suspension of disbelief, and for many, the Anthropic IPO seems likely to fit that bill. What we've learned so far about Anthropic's financials have, compared to at least what many people were thinking, looked less than stellar. The headline that Anthropic made an $80 billion loss in the first half went viral on social media, even though this was an accounting loss and their true cash loss was $8.7 billion. Still, we're seeing valuations soften as Anthropic enters the final stretch. OpenAI's fundraising is expected to close soon, and the information reports that the valuation will come in at $1.25 trillion. Earlier reporting had it at $1.4 trillion. Now, I don't know if you can consider the difference in an early leaked reported number and the actual factual final number as being clearly a valuation downgrade as opposed to just missed reporting, but that's certainly how investors are looking at it. While some of Anthropic's investors were originally pushing for a $2 trillion valuation in the press, more recently there's been reporting that another group is pushing for $1.5 trillion. Meanwhile, the rest of the IPO market is completely frozen, with less than $600 million raised so far this month. West Riggs, the head of equity capital markets at Truist Financial, said, "People are truly paralyzed. The market has to sort itself out." The U.S. capital markets transaction leader at Deloitte & Touche said, "There is a stalemate on valuation, and rationalizing that gap is what needs to happen before the broader market can move forward." Essentially, companies looking to IPO are asking for more than investment banks will give them. This could mean neoclouds are unable to go public and consequently unable to access their next tranche of funding, leaving private equity holding the bag. But ultimately, as with any market or market psychology issue, this one is only resolved by the next thing happening. In Bloomberg Opinion this week, Chris Hughes wrote about the long list of problems with the AI trade but concluded, "Some of the questions currently weighing on the market will be resolved over time, but if Anthropic could just get on with its IPO, that would give everyone one less thing to worry about." And I think that there is real risk here. Not for some sort of systemic contagion or anything like that, but from the standpoint of confidence in the AI trade. If Anthropic prices their IPO at $1.5 trillion after investors splashed around the $2 trillion number for months, that's not great, but ultimately probably not all that concerning. If, on the other hand, Anthropic prices at $1.5 trillion and we still see a weak reception in the market, that will confirm a lot of people's worst fears about the state of the AI trade. The point of today's episode is neither to definitively try to deny any causes for market concern, nor to surprise you and come out saying that some AI bubble burst is inevitable. Instead, what I've tried to do is look at each of the different sub-components of the narrative in turn and talk about where I think they are today and what would make me more concerned going forward. One thing that's undeniable is that the narrative that the AI bubble is about to burst has been a recurring feature of this market cycle, going back to the beginning of the generative era. It seems to come up around this time of year. In 2024, we saw a massive concern about AI scaling hitting a wall after reports that multiple labs were seeing a slowdown in model improvement. But then, just shortly after that, we got the first reasoning models. And farther after that, advances in chips with more memory that allowed multi-trillion parameter models to be trained. Then, of course, last year, there was a concern that enterprises weren't seeing returns from AI and wouldn't continue paying. This, of course, embodied in the bane of my existence, the MIT report about the 95% of generative AI pilots failing. Spoiler alert: Enterprise spend did not dry up. Instead, it boomed and is still growing at around 50% a quarter. Now, previous bubble calls being wrong does not mean that all bubble calls in the future will be wrong as well. It just means we'd all be better served carrying around bags of salt at this point to take with all of these big bombastic headlines. With the anthropic IPO coming up, we will get a much more definitive answer soon. The impacts of whatever that shows remain tantalizingly and frustratingly in the future. And for now, all we can do is speculate and debate. Hopefully you've got a bit more fodder for that now. And that's going to do it for today's AI Daily Brief. Appreciate you listening or watching, as always. And until next time, peace!
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