The AI Daily Brief: Artificial Intelligence News and Analysis - The Truth About the AI Bubble

Episode Date: September 21, 2025

Is AI in a bubble, or just experiencing one of history’s biggest booms? This episode breaks down Azeem Azhar’s 5-guage framework for evaluating whether AI is bubble territory—or still solidly in growth mode. From trillions in CapEx spending and surging enterprise demand to valuation heat and funding quality, we examine the five gauges that separate hype from reality. The data suggests AI is not yet in bubble territory, but pressure points remain.Source: https://www.exponentialview.co/p/is-ai-a-bubbleBrought to you by:Is your enterprise ready for the future of agentic AI?Visit AGNTCY.orgVisit Outshift Internet of AgentsTry Notion AI today with Notion 3.0 https://ntn.so/nlwKPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.kpmg.us/AIpodcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy.com - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ to build enterprise software in days, not months Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Vanta - Simplify compliance - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://vanta.com/nlw⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Agent Readiness Audit from Superintelligent - Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://besuper.ai/ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? [e-mail]

Today we are looking at the best analysis that I have yet found on whether AI is in a boom or a bubble. So what's your guess about what it's going to say? 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, notion, and agency.org. To get an ad-free version of the show, go to patreon.com slash AI Daily Brief, and to learn about sponsorship opportunities to get in front of this, audience of millions of brilliant AI practitioners, email us at sponsors at AIdailybrief.

Today we are talking about the big question, at least from a macro perspective, is AI a bubble? This, of course, is a question that has been lurking for some time. One could go back to last summer, the summer of 2024, to see some of the first places where this conversation broke into the mainstream. We had the Goldman Sachs report, Gen AI, too much spend, too little benefit. Sequoia's AI's $600 billion question, which was very comfortable calling it an AI bubble. Of course, subsequent to that, we've had hundreds of billions of dollars of market cap added, tens of billions in new revenue realized, hundreds of millions of new users, basically to the extent that it was a bubble

then, it has done nothing but gotten bigger, or maybe that bubble talk was premature and was about something else. For my money, at least part of the bubble conversation has always been connected to the uncomfortable weight that this market and economy has put on Gen. ChatGPT, launched at almost the exact same time that Jerome Powell started the fastest rate hiking cycle in history. And for most of that hiking period, it was AI enthusiasm versus the world when it came to Wall Street performance. In the wake of the initial disappointment around GPT5, there was an interview with Sam Altman, where he was widely interpreted as having said that there was a bubble, even though he couched his language a lot more than the headline suggested. And really, it's just a question

that's never fully gone away. Now, functionally, does this matter to those of us who are just using these tools or figuring out how they are going to impact our businesses? The short answer is no. If you have used these tools for your work or your personal life, especially if you are here listening to this show, you will have undoubtedly come to the conclusion that they are immensely powerful and are likely to reshape much of what you do. Ethan Malick recently tweeted, A third of American adults use AI many times a day to almost constantly and another third several times a week. I can't usefully add much to discussions evaluation bubbles, but if Bubble means a disappointing technology that is overhyped and not useful, that doesn't match the data.

And so for our purposes, let's clear out that side of the bubble conversation. I've spent enough time on this show recently talking about the shift in sentiment around GPT5, around why capabilities actually are continuing to increase, about how many different areas beyond just core chat interface AI has still to explore and how fast those areas are evolving. Things not only like nanobanhas image generation, but also world models. Let's instead then talk about the other part of the bubble question, which is specifically about markets. And for that, we're going to turn to Azim Ashar to creator of exponential view, which is both podcast and substack.

I've known Azim for more than a decade since back in our social entrepreneurship days when that whole movement was first starting. And he's always been a really interesting and comprehensive and dispassionate thinker when it comes to the future of technology, an optimist but never a huckster, and always wanting to look at things in full terms. And so when I saw that he had written a long-form piece called Is AI a Bubble, I knew for sure that this was going to be this week's Long Read Sunday slash Big Think episode. As has been our style lately, we're going to do a combination of reading excerpts plus discussing, but in this case I am going to try to get through the majority of the argument because I think part of what makes it powerful is its comprehensiveness. Asim writes,

A month ago, I set out to answer a deceptively simple question. Is AI a bubble? Since 2024, people have been asking me this as I've spoken at events around the world. Even as Wall Street bankers largely see this as an investment boom, more people are asking the question in meeting rooms in conference halls in Europe and the U.S. The best way to understand a question like this is to create a framework, one that you can update as new evidence emerges. This essay is that framework. Five gauges to weigh Gen A.I. against history's bubbles. Azim starts historically. Bubbles, he writes, are among the oldest stories of capitalism. Their parables have excess, belief, and collapse, but bubbles are not just financial phenomenon. They are cultural artifacts. They return again and

again as morality tales about greed and folly. Tulip Mania, often misremembered as a frenzy of bankrupt weavers and drowning merchants, was less disastrous than legend suggests. It was confined to wealthy merchants and left the Dutch economy largely unscathed, but the myth has endured and that is the point. Bubbles become stories we tell ourselves about the dangers of optimism. Some bubbles are financial. The South Sea frenzy of the 1720s, the roaring stock market of the 1920s, Japan's real estate boom of the 1980s and the housing crash of 2008. Some are technological. In the 1840s, railways were hailed as the veins of a new industrial body, and they were. But a body needs only so many veins and tracks were soon laid in places commerce could not sustain.

Telecoms in the 1990s promised a wired utopia, only for 70 million miles of excess fiber to lie dark underground. The dot-com boom gave us a vision of a new economy, much of which did eventually materialize, but not before valuations evaporated in 2000. The funny thing is, there doesn't seem to be an academic consensus on what an investment bubble is. Nobel laureate in economics Eugene Fama has gone so far as to say that they don't exist. Asim's goal with this piece is to go beyond the I know a bubble when I see it kind of idea. He suggests that the two key pieces are when stock markets become overvalued and collapse, and then secondly, whether the quantity of productive capital, such as the money going into CapEx or VC, also collapses.

To get specific, he writes, we see a bubble as being a 50% drawdown from the peak equity value that is sustained for at least five years. In the case of the U.S. housing bubble and the dot-com, that trough was roughly five years long. Full recovery to pre-bubble peaks took 10 years for U.S. housing and 15 for the dot-com. Alongside, we would expect a substantial decline in the rate of productive capital deployed once again 50% from peak. Ultimately, he says, a bubble means a phase marked by a rapid escalation in prices and investment, where valuations drift materially away from the underlying prospects and realistic earnings power of the assets involved. Bubbles thrive on abundant capital and seductive narratives, and they tend to end in a sharp

and sustained reversal that wipes out much of the paper wealth created on the way up. A boom, by contrast, can look very similar in its early stages with rising valuations and accelerating investment. But the crucial distinction is that in a boom, fundamentals eventually catch up. The underlying cash flows, productivity gains, or genuine demand growth rise to meet the optimism. Booms can still overshoot, but they consolidate into durable industries and lasting economic value. Now, I will say, and a little editor's note here, this is kind of becoming the clever person's interpretation or analysis of what's happening now. It is almost kind of what Sam Altman was

saying. The clever feel-good at dinner parties take is, well, yeah, of course, the investment guys are getting a little over-exuberant and maybe spending on things that they shouldn't, and a lot of the things that they're investing in now aren't going to be useful in the future, but the underlying technology is real and it will be world-changing just on a longer time scale than we think. Now, there is nothing wrong with this point of view. It's a very reasonable, nuanced point of view. I just basically have an allergy to this type of clever analysis that, again, mostly serves to make people feel nuanced and sophisticated in conversation with their peers. And editor's note, back to Azeem. He writes, between the boom and the bubble lies a gray zone, periods of exuberance

when it is genuinely hard to tell whether capital is building the foundation of a new economy or merely inflating prices that will not be sustained. And that, of course, gets us to the question of is AI bubble. So what are the gauges that he is applying to actually look at this? There are five. Gage one, economic strain. Is investment now large enough to bend the economy? Gage two, industry strain. Are industry revenues commensurate with deployed CAPEX? Gage three, revenue growth. Is revenue rising or broadening fast enough to catch up? Gage four, valuation heat. How hard are valuations? Compared to history, are stocks excessively overpriced? And gauge five, funding quality. What kind of money is funding this, is it strong balance sheets or fragile, flighty capital? What Azim does with the rest of

the essay is to look at each of these five areas and put them on a green, yellow, red kind of scale, where ultimately he argues that two reds equals trouble when it comes to bubble analysis. The first gauge is economics train. Asim writes, the investment underway is vast, with Morgan Stanley expecting $3 trillion in AI infrastructure spend by 2029, but it has not yet reached the runway extremes of history's great blowouts. But as Azizier, Seem points out it's not just the sheer magnitude that is the question. The other big factor is what he calls dependence. He points out that in the U.S., a third of GDP growth right now can be traced to data center construction. You might remember this chart I shared last month that showed that

the pace of data center construction was about to overtake the pace of general office construction. Now, when it comes to the economy's overall dependence on this particular area, Asim writes, it's not inherently bad, but it may be dangerous if the momentum falters. An economy leaning this heavily on one sector for growth can find the ground falling away faster than expected. And by the way, I think that this is actually why the bubble narrative is so ever-present, and part of why people can't just be excited about what's happening. There is actually an interesting argument that people are so concerned about their analysis that this is a bubble, that they're not recognizing that AI is one of very few technology

changes that actually has the chance to create jobs in the short term, even as it's going through the phases of creative destruction. Creative destruction is, of course, the phase of famous idea from Joseph Schumter that new technologies, while inevitably creating new things, wreak a path of destruction in terms of old processes and old jobs and old roles and old industries even that get replaced and competed away. In the process of creative destruction, we usually see the destruction before the creative. One of the reasons that there's anxiety around AI and agent-related job loss is that it's much easier to see the one-to-one replacement effects for certain highly capable agents on jobs that exist today than it is to imagine five years out

what new industries are going to be created by the new capabilities that these technologies represent. This is sort of the normal pattern of new technology shifts. However, with all of this CAPEX spend, with data center construction, there are big sectors of the economy, things like construction that are going to be forced to hire new people, upskill them in new ways, in ways that could be value accretive in the short term. But again, people are so concerned that it's over-exuporin and it's all going to go away, that they're not really letting themselves get excited about those short-term gains. Anyway, coming back to Azeem, he writes,

the surge of Cappex poured into the physical infrastructure that AI demands is an act of optimism. This is what Cappex is. Money spent today in the belief that it will become a funnel of revenue tomorrow. If it's well placed today, it will eventually lead to productivity gains and economic expansions. AI data centers are not just factories

for a single product, they are infrastructure. Microsoft, OpenAI, and the U.S. government all see it this way. They see compute as a foundational utility of the 21st century, no less critical than highways, railways, power grids, or telecom networks were in earlier eras. To build such infrastructure inevitably requires historic sums, on a par with the railway or electricity buildouts of the past. Asim points out that in sheer terms, AI data centers are likely to be among the largest infrastructure buildouts in modern history. However, he writes, useful though infrastructure is. Especially when private capital gets involved, things can get divorced

from reality. The financing structure matters as much as the technology itself. He then points out the difference between the way that railways were funded versus electricity and road systems. Railways were largely private and had a number of different investment bubbles, whereas, as he puts it, electricity and road systems benefited from greater public investment and coordination, and were less prone to speculative excess. A boom becomes dangerous when the resources at demand start to bend the whole economy around it. Wages get sucked into one sector, supply chains reorient to serve it, and capital markets grow dependent on it. The snapback is vicious when expectations break. Consequently, he suggests that a good way to gauge the economic strain is to look at investment

as a share of GDP. He calls this a crude but telling ratio, showing how heavily the economy leans on one technological bet. By this measure of past bubbles, the railway bubbles were the heaviest. In the U.S., railway spending peaked at about 4% of GDP in 1872, which was just before the first crash in that area. On the other end of the spectrum, the telecom boom of the late 1990s topped out at around 1% of GDP. Asim writes, the AI buildout sits in the middle zone. Around 370 billion is expected to flow into data centers globally in 2025, with perhaps 70% earmarked for the U.S. or roughly 0.9% of American GDP. Goldman Sachs projects spending will climb by another 17% in 2026. My own forecast are in line with this view, annual capex of 800 billion by 2030, perhaps 60% of the U.S.,

which would bring the American share to 1.6% of 2025 GDP. So, for his economic, strain gauge. He places the green segment as a technology representing up to 1% of GDP, the yellow as up to 2% of GDP, and the red as above 2%. Generative AI today at 0.9% is still in the green, although it could be heading into the yellow soon. The biggest caveat with the economic strain analysis here is that the depreciation of AI CAPEX could be much faster than the comparative depreciation of railway track or telecom fiber. GPUs, he writes, by contrast agent dog years, Their useful life for frontier applications such as model training is perhaps three years, after which they are relegated to lower intensity tasks.

Roughly a third of hyperscalor capex is going into such short-lived assets. They remain, in theory, monetizable in years five and six. The rest goes into shells, power, and cooling that lasts two or three decades. Adjusting for asset life makes the AI buildout look even more demanding. Unlike railroads or fiber, the system must earn its keep in a handful of years, not generations. Interestingly, he says, while the negative implications of that are clear, there is also an optimistic case. He writes, Shorter depreciation cycles may impose financial discipline on incoming investors.

During the railway mania, decades-long asset lives masked the weakness of many business models. Companies could stagger on for years before insolvency. In AI, the flaws may surface quickly, forcing either rapid adaptation or rapid failure. Ultimately, he concludes, the strain is noticeable but not yet unbearable. Venture funding in the AI application layer, while noisy, remains modest compared to the telecom mania of the 1990s. That suggests that there may be running room before the cycle overheats. What if AI wasn't just a buzzword, but a business imperative? On You Can with AI, we take you inside the boardrooms and strategy sessions of the world's

most forward-thinking enterprises. Hosted by me, Nathaniel Wittamore, and powered by KPMG, this seven-part series delivers real-world insights from leaders who are scaling AI with purpose, from aligning culture and leadership to building trust, data readiness, and deploying AI agents. Whether you're a C-suite executive, strategist, or innovator, this podcast is your front-row seat to the future of Enterprise AI. So go check it out at www.kpmg.org.us slash AI podcasts or search you can with AI on Spotify, Apple Podcasts, or wherever you get your podcasts. AI changes fast. You need a partner built for the long game. Robots and pencils work side by side with organizations to turn AI ambition into real human impact. As an AWS-certified partner, they modernize infrastructure, design cloud-native systems, and apply AI to create business value, and their partnerships don't end at launch.

As AI changes, robots and pencils stays by your side, so you keep pace. The difference is close partnership that builds value and compounds over time. Plus, with delivery centers across the U.S., Canada, Europe, and Latin America, clients get local expertise and global scale. For AI that delivers progress, not promises, visit robots and pencils.com slash AI Daily Brief. Chatbots are great, but they can only take you so far. I've recently been testing Notion's new AI agents, and they are a very different type of experience. These are agents that actually complete entire workflows for you in your style,

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Every boom he writes needs to prove that the money poured into new equipment is starting to earn its keep. In any growth stage, it is unlikely that revenues will cover investment, but they should be non-zero. This gauge looks at the ratio of CAPEX to revenues. Now, there have been a number of different estimates of how to look at Gen. revenues. Some of the most common that you see, especially when people are trying to say that there's a bubble, is just adding up the revenue of OpenAI Anthropic and a handful of other startups, usually pointing to a number that comes to between 15 and 18 billion, and saying something to the effect of how could that possibly justify, the hundreds of billions being spent in infrastructure.

Azim's estimates are over 60 billion this year, and even that, he says, could undercount the value. He writes, meta, for example, has suggested that the technology has increased conversions on its platform by about 3 to 5%. Indirect effects like this may help explain why some analysts, such as Morgan Stanley, peg 2025 revenues far higher at $153 billion. And yet it's undeniable that CAPEX intensity is also increasing. He writes, in 2021 before ChatGBTGBT, hyper-scalers invested about 44% of their operating cash flow in CAPEX. By 2024, that had risen to 68% and in 2028, it will be higher still. But these firms can't absorb this shift by replatforming, with structurally higher capital intensity driving growth and efficiency gains. This dynamic has been

in place for a decade already. Between 2015 and 2018, he writes, Microsoft Azure's CAPEX represented between 70 and 90% of revenues. It was an investment in the future. He continues, this makes for an interesting comparison to earlier boom cycles. The railroads are particularly pertinent. The railroad's direct revenue contribution was tiny compared to the value the railroads created in U.S. economy. Railway bubbles were always tethered to the reality of cash flow. The bonds issued to finance new track and rolling stock had to be serviced out of passenger fares and freight revenues. Whenever CAPEX outpaced earnings, this train showed. The manias of 1873, 1883, and 1887, all followed the same pattern, a sharp decline in the ratio of annual revenues to capital spending and in some

cases outright revenue contraction. At the height of the U.S. railroad expansion in 1872, CAPX was around two times revenues. In the late 1990s telecom bubble, CAPX amounted to just under four times revenues. By contrast, today's Gen A.I. Boom runs on roughly 60 billion in revenues, against about 370 billion in Global Data Center Cappex, a Cappex to revenue ratio of six times the most stretched of the three. On the industry's strain gauge then, Railways sat healthily in the green, Gen A.I was in the yellow, but Gen A.I is in the yellow nearing red. Still, he says, it's not quite a warning sign, not least because Gen A.I has people clamoring for access to AI data centers. One report suggests that enterprise customers are committing to capacity before data center

are even built. What is driving that is usage, and with that comes astonishing revenue growth. So of these indicators, jumping ahead a little bit, this is the one that is the closest to red. When push comes to shove, Azeem does not argue that any of the gauges are yet in the red when it comes to AI. For my money, I think that Azeem and his team are undercounting revenue. I think that part of why there is such incredible demand is those second order effects like the ones he talked about at meta, and my guess is that Morgan Stanley's estimate is much closer to the reality, which would push this number down even farther. Still, all of this is pre-lawful. to gauge three, revenue growth. He writes, the problem in the railroad in telecom booms was not

sector strain per se, but that revenues ran out of momentum. Investment expects a return. After the railway bubble burst in 1873, revenue declined by 3% year over year. Telecoms did slightly better, declining 0.5%. Before the crashes, revenue growth was hardly explosive. Railways in 1873 expanded 22%, enough to double in three years. Telecom in the late 1990s managed only 16%, a doubling time of just over four years. By contrast, Gen AI revenues are still accelerating. By our estimates, Gen AI revenues will grow about twofold this year. And this is likely a conservative forecast. Citi estimates that model makers revenue will grow 483% in 2025. Open AI forecasts annualized growth of about 73% to 2030, while analysts like Morgan Stanley estimate this market could be as large as

$1 trillion by 2028, equivalent to compound growth of 122% a year over the period. This puts generative AI in his estimation very, very much. squarely in the green when it comes to revenue growth, given that basically every estimate has at the very minimum, Gen. AI doubling every year. Asim continues, in my conversations with large companies, I get the strong sense that they can't get enough of this technology right now, and this likely supports the strong growth rates. IBM's CEO survey shows that GenAI is already expanding IT budgets, with 62% of respondents indicating that they will increase their AI investments in 2025. If you listen to Friday's show, you will have heard me talk about KPMG's latest Pulse

survey, where the anticipated investment among their 130 survey companies with a billion dollars in revenue each was $130 million over the next 12 months, which was up from 112 earlier in the year and 88 in Q4 of last year. Ultimately, he points out that we are still at the, quote, foothills of enterprise use. For now, firms can barely secure enough tokens to meet their needs. He also notes that the consumer side tells a parallel story. U.S. consumers, he writes, already spend around $1.4 trillion a year online. This could plausibly double to $3 trillion by 2030 if it grows at 15 to 17% a year, and it has grown at more than 14% a year since 2013. Against this backdrop, a Gen AI app sector rising from today's 10 billion to 500 billion

within five years looks less far-fetched. Exponential growth rates of 300 to 500% are already visible in mid-sized startups and the large model providers, suggesting that even a small reallocation of consumer digital spending could drive revenues into the hundreds of billions. Taking together, he says, these signals point to an industry still in strong assent, unlike the relatively meager revenue growth that preceded the railway and telecom busts. If GenAI revenues were to grow at even half the pace of last year, then on my conservative forecast, they would reach $100 billion by 2026, covering about 25% of that year's CAPEX. Gage 4 is called valuation heat, and as he puts it, is all about the mood of the market.

He writes, this is often where bubbles reveal themselves most clearly, how exuberantly investors are pricing the sector regardless of fundamentals. And this is almost a default part of new technology cycles. He writes, as Carlotta Perez has argued for decades, financial markets tend to overshoot in the early installation phase of each technological revolution, pouring in capital far beyond what near-term revenues justify. The frenzy looks irrational in the moment, but it is the mechanism by which society lays down the new infrastructure. The challenge is whether the frenzy can evolve into the deployment phase, when the infrastructure becomes universal and delivers real productivity

gains. Now, obviously, the story that we all know about this one is the dot-com bubble. companies raising tens or hundreds of millions of dollars on absolutely no revenue and no business model to speak of, ultimately led to the bubble popping, and a long, slow rebuild where so many of the ideas that were initially present actually came to fruition backed by real revenue and real growth. As an even importantly makes the point, what's going on in Gen.A.I. does not compare to this. The key measure here is the price earnings ratio or PE a shorthand for how many years of current profits and investor is effectively paying for. A high PE means companies are betting on rapid future growth, but too high for too long, investors might be buying into a fantasy. This was the case in the dot-com

era. At the peak, the NASDAQ traded at a PE of about 72. One detailed study estimated that internet stocks alone carried an implied PE of 605. In other words, investors were willing to pay for more than six centuries of current earnings. The issue wasn't that the demand disappeared. Amazon's revenues grew from $2.76 billion in 2000 to $3.12 billion in 2001, but that no company could grow fast enough to justify those sky-high expectations. In other words, he writes, fundamentals improved, but expectations collapsed. Today, he says the picture is much calmer. The NASDAQ PE is about 32, half of the dot-com era. The broader tech market is higher than the long-run average, but nowhere near dot-com territory. Ultimately, he puts this category in the green as well.

Prices, he says, haven't yet broken free of gravity in the way that dot-com valuations did. A couple things to note here, you will sometimes see people point to the incredibly high valuations that startups are getting in the venture realm as evidence of bubble. I think there are a couple things that make that not all that concerning when you look at it in this overall context. The first and most obvious one is that venture dollars ultimately are a tiny, tiny portion of the financial markets. In 2024, U.S. venture investment deals were worth $215.4 billion. Last week, after Oracle announced its projections for the coming years, the company added $244 billion to its market cap, about $30 billion more than all of VC in 2024. The point is

that VC is just ultimately relatively small and has an outsized impact on our imagination as compared to what it does in our markets. Second, its impact is even lessened because VC is meant explicitly to take risky bets. Venture capitalists are well prepared as are LPs for the idea that most of their investments will fail. That's just built into the industry. And finally, while valuations are high and moving quickly, the rate of revenue growth among many of these companies is unlike anything we've ever seen in startups before. One may quibble about how durable that revenue is. There is a huge amount of, for example, what some have called curiosity revenue, where enterprises and consumers try interesting new products but then don't stick around,

that maybe are warping some of those growth numbers. But still, taken as a whole, companies are making more and faster in this technology shift than just about any we've ever seen before. All of which finally brings us to gauge five, funding quality. Funding quality, as Eam writes, is not a standard metric but a composite judgment. It asks who the money is coming from, how it is structured, and whether the capital is willing to wait years for returns or rather chase quarterly pops. Low-quality capital, in short, is short-termist, undisciplined, and debt-laden. It rushes and flees quickly.

High-quality capital is more patient, better underwritten, and able to withstand volatility. Every bubble has its signature weakness, invariably rooted in how it was financed. Railways were fueled, by speculative retail investors with little capital behind them. By the early 1870s, funded debt averaged 46% of total assets among American railroads. when overbuilding meta-credit squeeze, financing evaporated. Dot-com firms a century later were a little sturdier. Venture Capital was a boutique business in 1995, with only $5.3 billion deployed. By 2001, more than $237 billion had been poured into startups, often by new and inexperienced

managers. The frenzies spilled into public markets. IPO volume between 1999 and 2000 ran six times above historical averages. Companies went public with little revenue. One other aside note on the manager thing, by the way, not only do you not have new and inexperienced managers running around venture capital right now, you actually have the exact opposite. Alpils have been starved of liquidity for so long that the entire industry has been forced to start to use secondaries as a mechanism to get some liquidity, and many, many funds

in the wake, especially of rising interest rates in the post-ZERP era, have shuttered their doors and not been able to raise again, meaning the crop of people that are around now are a lot more battle-hardened, even relative to venture capital terms. Anyways, back to Azim, he continues, telecoms in the late 1990s leaned on mountains of cheap debt. U.S. and European carriers doubled and quadrupled their leverage in just a few years. Deutsche Telecom and France Telecom together added $78 billion in net debt between 1998 and 2001, when revenues failed to keep pace, defaults rippled through the sector. In each case, the capital that fueled the boom proved ephemeral, but the degree of fragility

differed. Railways and telecoms were most exposed to credit crunches with debt ratios ballooning. Dotcoms were hostage to market mood with equity values evaporating. On this front, today's AI boom looks sturdier. Microsoft, Amazon, Alphabet, meta, and Nvidia are minting extraordinary cash flows, easily enough to bankroll their own buildout, for now. But investment needs are racing ahead. Morgan Stanley reckons total global data center CAPEX will hit $2.9 trillion between 2025 and 2028. Hyperscalers can cover perhaps half of that from internal cash. The rest must come from private credit, securitized finance, and new operators. Governments have also pledged $1.6 trillion in sovereign AI investments, and Gulf Capital is seeking

new opportunities. Here is where the risk creeps in. Morgan Stanley itself points to a $1.5 trillion gap that will need to be plugged by debt markets and asset-backed securities. The sums are enormous. $800 billion from private credit, $150 billion in data center ABS, and hundreds of billions more in OEM loans and vendor financing. That $150 billion alone would triple the size of the data center securitized markets almost overnight. And not every borrower looks like Microsoft. So the point for him is that right now, as much as you could say companies are spending too much on CapEx? They're spending their own money on CapEx. They're not going into debt or finding weird novel instruments to do so. The question is how long that can persist. As Azim sums up, the foundations

are stronger than in past bubbles, but the superstructure is starting to resemble the old pattern. Esoteric debt structures, concentrated counterparties, and hardware that may not hold value are reappearing. If Gen. AI revenues grow tenfold, creditors will be fine. If not, they may discover that a warehouse full of obsolete GPUs is a different thing to secure. So where does that net out? Simply put, we are in boom territory, not bubble. Of Azim's five gauges, four of them, economic strain, revenue growth, valuation, heat, and funding quality are all in the green right now. The closest to red is industry strain, which is again a measure of CAPEX investment divided by revenue, where he is purposely going with a relatively modest or conservative revenue number.

The inescapable conclusion is AI is not a bubble. But of course, that doesn't mean that it won't become one. The pressure points that Azeem considers worth watching include more and more of the economy relying on AI, basically if investment climbs towards 2% of GDP, a sustained fall in enterprise or consumer spending, especially if it's followed by a shrinking order backlog for companies like Nvidia, valuations jumping from where they are at a PE ratio of 32 right now to up to 50 or 60. And lastly, if a greater and greater portion of CAPEX starts to be covered off of the balance sheet. He concludes, my current heuristic is that if two of the five gauges head into the red, you're in bubble territory. Time to sell up, buy the VIX, and take some deep breaths.

Gen A.I. isn't there yet. Racing fast, the engine is whining, but not overheating. How long would it take for two gauges to get into the red? I've toyed around with combinations and most scary scenarios take a couple of years to play out, and not all scenarios are scary. That said, so many macro factors from a recession in the U.S. to rising inflation, a challenging interest rate environment, and domestic or international politics could dampen spirits. While we might not be solidly in bubble land, it would be hubristic to assume the AI investment cycle is immune to those exuberant dynamics. And this, I think, is a great take. One does not need to shout bubble left and right just because things are moving fast, in some cases faster than we've ever seen. Things moving fast

does not a bubble make on its own. At the same time, we don't need to be polyanish about the risks. companies are betting hundreds of billions of dollars on a future which none of us can know exactly how it's going to play out. There are many, many ways for that story to play out in the future that leads to bubble dynamics. Neither do we need the clever but ultimately fake nuance of, well, sure, we're in a bubble now, but that doesn't mean that there won't be value later. What the data suggests, at least if you agree with the Zim's interpretation, which of course is totally reasonable for one not to,

is that at this moment we are firmly not in bubble territory. And thanks to this framework, we have a much better system for tracking to see how things are changing over time. Big ups to Azim at Exponential View for this piece. And 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.