The Benefits of Bubbles

It’s funny to remember that a decade ago there were enough people convinced we were in a bubble that I felt compelled to write an Article entitled It’s Not 1999; that was right then, and it’s obviously right now, when we have a clear counter-example: this is a bubble.

How else to describe a single company — OpenAI — making $1.4 trillion worth of deals (and counting!) with an extremely impressive but commensurately tiny $13 billion of reported revenue? Sure, the actual number may be higher, but that is still two orders of magnitude less than the amount of infrastructure OpenAI has publicly committed to buy over the coming years, and they are not the only big spenders. Over the past week every big tech company (except Apple) has significantly expanded their capital expenditure plans, and there is no sign of anyone slowing down.

This does, understandably, have people wringing their hands. What goes up must come down, which is to say bubbles that inflate eventually pop, with the end result being a recession and lots of bankrupt companies. And, not to spoil the story, that will almost certainly happen to the AI bubble as well. What is important to keep in mind, however, is that that is not the end of the story, at least in the best case. Bubbles have real benefits.

Financial Speculation and Physical Capacity

The definitive book on bubbles has long been Carlota Perez’s Technological Revolutions and Financial Capital.¹ Bubbles were — are — thought to be something negative and to be avoided, particularly at the time Perez published her book. The year was 2002 and much of the world was in a recession coming off the puncturing of the dot-com bubble.

Perez didn’t deny the pain: in fact, she noted that similar crashes marked previous revolutions, including the Industrial Revolution, railways, electricity, and the automobile. In each case the bubbles were not regrettable, but necessary: the speculative mania enabled what Perez called the “Installation Phase”, where necessary but not necessarily financially wise investments laid the groundwork for the “Deployment Period”. What marked the shift to the deployment period was the popping of the bubble; what enabled the deployment period were the money-losing investments.

In the case of the dotcom bubble, the money-losing investments that mattered were not actually the dotcom companies that mark that era in Silicon Valley lore: yes, a lot of people lost money on insane IPOs, but the loss was mostly equity, not debt. Where debt was a problem was in telecom, where a host of companies went bankrupt after a frenzied period of building far more fiber than could ever be justified by current usage, fast though it may have been growing. That fiber, however, became the background of today’s Internet; the fact that it basically existed for free — because the companies who built it went bankrupt — enabled the effectively free nature of the Internet today.

The Conditions for Cognitive Capacity

Late last year Byrne Hobart and Tobias Huber made a new contribution to our understanding of bubbles with their book Boom: Bubbles and the End of Stagnation. While Perez focused on the benefits that came from financial speculation leading to long-term infrastructure, Hobart and Huber identified another important feature of what they called “Inflection Bubbles” — the good kind of bubbles, as opposed to the much more damaging “Mean-reversion Bubbles” like the 2000’s subprime mortgage bubble. First, here is Hobart and Huber’s definition of an inflection bubble:

Inflection-driven bubbles have fewer harmful side effects and more beneficial long-term effects. In an inflection-driven bubble, investors decide that the future will be meaningfully different from the past and trade accordingly. Amazon was not a better Barnes & Noble; it was a store with unlimited shelf space and the data necessary to make personalized recommendations to every reader. Yahoo wasn’t a bigger library; it was a directory and search engine that made online information accessible to anyone. Priceline didn’t want to be a travel agent; it aspired to change the way people bought everything, starting with plane tickets.

If a mean-reversion bubble is about the numbers after the decimal point, an inflection bubble is about orders of magnitude. A website, a PC, a car, a smartphone — these aren’t five percent better than the nearest alternative. On some dimensions, they’re incomparably better. A smartphone is a slightly more convenient tool than a PC for taking a photo and quickly uploading it to the internet, but it’s infinitely better at navigation. A car is not just slightly faster and more reliable than a horse (although in the early days of the automobile industry, it was apparently common for pedestrians to yell “Get a horse!” at passing motorists); cars transformed American cities. Modern-day Los Angeles is inconceivable on horseback. The manure problem alone beggars the imagination.

This is what makes inflection bubbles valuable:

The fundamental utility of inflection bubbles comes from their role as coordinating mechanisms. When one group makes investments predicated on a particular vision of the future, it reduces the risk for others seeking to build parts of that vision. For instance, the existence of internet service providers and search engines made e-commerce sites a better idea; e-commerce sites then encouraged more ad-dependent business models that could profit from directing consumers. Ad-dependent businesses then created more free content, which gave the ISPs a better product to sell. Each sector grew as part of a virtuous circle.

What I love about this formulation from a tech perspective is that it captures the other side of the dotcom era: no, Silicon Valley didn’t produce any lasting infrastructure (unless you count a surplus of Aeron chairs), but what the mania did produce were a huge number of innovations, invented in parallel, that unlocked the following two decades of growth.

First, the dotcom era brought nearly the entire U.S. population online, thanks to that virtuous cycle that Hobart and Huber described in the above excerpt. This not only provided the market for the consumer Internet giants that followed, but also prepared an entire generation of future workers to work on the web, unlocking the SaaS enterprise market.

Second, the intense competition of the dotcom era led to one of my favorite inventions of all time, both because of its impact and because of its provenance.

Microsoft famously saw Netscape, the OpenAI of the dotcom era, as a massive threat; the company responded with Internet Explorer, and a host of legally questionable tactics to spur its adoption. What is forgotten, however, is that Microsoft was at that time actually quite innovative in terms of pushing browser technology forward, driven by the need to beat Netscape, and one of those innovations was XMLHttpRequest. XMLHttpRequest, introduced with Internet Explorer 5 in 1999, allowed Javascript to make asynchronous HTTP requests without reloading the page; previously to change anything on a webpage meant reloading the entire thing. Now, however, you could interact with a page and have it update in place, without a reload.

What makes this invention ironic is that this was the key capability that transformed the browser from a media consumption app to a productive one, and it was the productivity capabilities that began the long breakdown of Microsoft’s application moat. Once work could be done in a browser, it would be done everywhere, not just on Windows; this, in the long run, created the conditions for the smartphone revolution and the end of Windows’ dominance. This was, to be clear, but one of a multitude of new protocols and innovations that made today’s tech stack possible; what is important is how many of them were invented at once thanks to the bubble.

Third, the cost and complexity of serving all of these new use cases drove tremendous innovation on the backend. The Nvidia of the dotcom era was arguably not Cisco, but rather Sun: a huge percentage of venture capital went to buying Sun SPARC/Solaris servers to run these new-fangled companies. Solaris was the most advanced operating system in terms of running large websites, with the most mature TCP/IP stack, multithreading, symmetric multiprocessing, etc. Moreover, the dominance of Solaris meant that it had the largest pool of developers, which meant it was easier to hire if you ran Solaris.

The problem, however, is that SPARC servers were extremely expensive, to the point of being nearly financially impractical for the largest-scale web applications like Hotmail or Yahoo. That’s why the former (in its startups days) ran its front-end on free software (FreeBSD) on commodity x86 hardware from the beginning, and why the latter made the same shift as it exploded in popularity. Both, however, had custom-built back-ends; it was Google, founded in 1998, that built the entire stack on commodity x86 hardware and Linux, unlocking the scalability that was critical to the huge growth in the Internet that followed.

This entire stack was the product of a massive amount of uncoordinated coordination: people came online for better applications that ran on hardware powered by software built by a massive array of companies and individuals; that all of this innovation and invention happened at the same time was because of the bubble.

Oh, and to return to Perez: all of this ran over fiber laid by bankrupt companies. What Perez got right is that bubbles install physical capacity; what Hobart and Huber added is that they also create cognitive capacity, thanks to everyone pulling in the same direction at the exact same time, based not on fiat, but on a shared belief that this time is different.

Is AI Different?

This question — or statement — is usually made optimistically. In this case, the optimistic take would be that AI is already delivering tangible benefits, that those benefits are leading to real demand from companies and consumers, and that all of the money being spent on AI will not be wasted but put to productive use. That may still be the case today — all of the hyperscalers claim that demand for their offerings exceeds supply — but if history is any indication we will eventually overshoot.

There is, however, a pessimistic way to ask that question: will the AI bubble be beneficial like the positive bubbles chronicled by Perez and Hobart and Huber, or is it different? There have been reasons to be worried about both the physical buildout and the cognitive one.

Start with the physical: a huge amount of the money being spent on AI has gone to GPUs, particularly Nvidia, rocketing the fabless design company to a nearly $5 trillion valuation and the title of most valuable company in the world. The problem from a Perez perspective is that all of this spending on chips is, relative to the sort of infrastructure she wrote about — railroads, factories, fiber, etc. — short-lived. Chips break down and get superseded by better ones; most hyperscalers depreciate them over five years, and that may be generous. Whatever the correct number is, chips don’t live on as fully-depreciated assets that can be used cheaply for years, which means that to the extent speculative spending goes towards GPUs is the extent to which this bubble might turn out to be a disappointing one.

Fortunately, however, there are two big areas of investment that promise to have much more long-term utility, even if the bubble pops.

The first is fabs — the places where the chips are made. I’ve been fretting about declining U.S. capacity in this area, and the attendant dependence on Taiwan, the most fraught geopolitical location in the world, for years, and for much of that time it wasn’t clear that anything would be done about it. Fast forward to today, and not only are foundries like TSMC and Samsung building fabs in the U.S., but the U.S. government is now a shareholder in Intel. There is still a long path to foundry independence for the U.S., particularly once you consider the trailing edge as well, but there is no question that the rise of AI has had a tremendous effect in focusing minds and directing investment towards solving a problem that might never have been solved otherwise.

The second is power. Microsoft CFO Amy Hood said on the company’s earnings call:

As you know, we’ve spent the past few years not actually being short GPUs and CPUs per se, we were short the space or the power, is the language we use, to put them in. We spent a lot of time building out that infrastructure. Now, we’re continuing to do that, also using leases. Those are very long-lived assets, as we’ve talked about, 15 to 20 years. And over that period of time, do I have confidence that we’ll need to use all of that? It is very high.

Amazon CEO Andy Jassy made a similar comment on his company’s earnings call:

On the capacity side, we brought in quite a bit of capacity, as I mentioned in my opening comments, 3.8 gigawatts of capacity in the last year with another gigawatt plus coming in the fourth quarter and we expect to double our overall capacity by the end of 2027. So we’re bringing in quite a bit of capacity today, overall in the industry, maybe the bottleneck is power. I think at some point, it may move to chips, but we’re bringing in quite a bit of capacity. And as fast as we’re bringing in right now, we are monetizing it.

As I noted yesterday, this actually surprised me: I assumed that chips were in short supply, and the power shortage was looming, but actually power is already the limiter. This is both disappointing and unsurprising, given how power generation capacity growth has stagnated over the last two decades:

At the same time, this is also encouraging: the fastest way to restart growth — and hopefully at an even higher rate than the fifty years that preceded this stagnation — is to have massive economic incentives to build, combined with massive government incentives to eliminate red tape. AI provides both, and my hope is that the fact we are already hitting the power wall means that growth gets started that much sooner.

It’s hard to think of a more useful and productive example of a Perez-style infrastructure buildout than power. It’s sobering to think about how many things have never been invented because power has never been considered a negligible input from a cost perspective; if AI does nothing more than spur the creation of massive amounts of new power generation it will have done tremendous good for humanity. Indeed, if you really want to push on the bubble benefit point, wiping away the cost of building new power via bankruptcy of speculative investors — particularly if a lot of that power has low marginal fuel costs, like solar or nuclear — could be transformative in terms of what might be invented in the future.

To that end, I’m more optimistic today than I was even a week ago about the AI bubble achieving Perez-style benefits: power generation is exactly the sort of long-term payoff that might only be achievable through the mania and eventual pain of a bubble, and the sooner we start feeling the financial pressure — and the excitement of the opportunity — to build more power, the better.

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I’ve been less worried about the cognitive capacity payoff of the AI bubble for a while: while there might have been concern about OpenAI having an insurmountable lead, or before that Google being impregnable, nearly everyone in Silicon Valley is now working on AI, and so is China. Innovations don’t stay secret for long, and the time leading edge models stay in the lead is often measured in weeks, not years. Meanwhile, consumer uptake of AI is faster than any other tech product by far.

What is exciting about the last few weeks, however, is that there is attention being paid to other parts of the stack, beyond LLMs. For example, last week I interviewed Substrate founder James Proud about his attempt to build a new kind of lithography machine as the center of a new American foundry. I don’t know if Proud will succeed, but the likelihood of anyone even trying — and of getting funding — is dramatically higher in the middle of this bubble than it would have been a decade ago.

It was also last week that Extropic announced a completely new kind of chip, one based not on binary 1s and 0s, but on probabilistic entropy measurements, that could completely transform diffusion models. Again, I don’t know if it will succeed, but I love that the effort exists, and is getting funding. And meanwhile, there are massive investments by every hyperscaler and a host of startups to make new chips for AI that promise to be cheaper, faster, more efficient, etc. All of these efforts are getting funding in a way they wouldn’t if we weren’t in a bubble.

Hobart and Huber write in Boom:

Not all bubbles destroy wealth and value. Some can be understood as important catalysts for techno-scientific progress. Most novel technology doesn’t just appear ex nihilo, entering the world fully formed and all at once. Rather, it builds on previous false starts, failures, iterations, and historical path dependencies. Bubbles create opportunities to deploy the capital necessary to fund and speed up such large-scale experimentation — which includes lots of trial and error done in parallel — thereby accelerating the rate of potentially disruptive technologies and breakthroughs.

By generating positive feedback cycles of enthusiasm and investment, bubbles can be net beneficial. Optimism can be a self-fulfilling prophecy. Speculation provides the massive financing needed to fund highly risky and exploratory projects; what appears in the short term to be excessive enthusiasm or just bad investing turns out to be essential for bootstrapping social and technological innovations…A bubble can be a collective delusion, but it can also be an expression of collective vision. That vision becomes a site of coordination for people and capital and for the parallelization of innovation. Instead of happening over time, bursts of progress happen simultaneously across different domains. And with mounting enthusiasm…comes increased risk tolerance and strong network effects. The fear of missing out, or FOMO, attracts even more participants, entrepreneurs, and speculators, further reinforcing this positive feedback loop. Like bubbles, FOMO tends to have a bad reputation, but it’s sometimes a healthy instinct. After all, none of us wants to miss out on a once-in-a-lifetime chance to build the future.

This is why I’m excited to talk about new technologies, the prospect for which I don’t know. The more I don’t know projects there are, the more likely there is to be one that succeeds. And, if you want an investment that pays off not for a few years, and not even for a few decades, but literally forever, then your greatest hope should be invention and innovation.

Stagnation: The Bubble Alternative

Hobart and Huber actually begin their book not by talking about ancient history, but about this century, and stagnation.

The symptoms of technological, economic, and cultural stagnation can be detected everywhere. Some of the evidence is hard to quantify, but it can perhaps best be summarized by a simple thought experiment: Will children born today experience as much change as children born a century ago—a time when cars, electrical appliances, synthetic materials, and telephones were still in their infancy? Futurists and science-fiction authors once prophesied an era of abundant energy due to nuclear fission, the arrival of full automation, the colonization of the solar system, the end of poverty, and the attainment of immortality. In contrast, futurists today ask questions about how soon and how catastrophically civilization will collapse.

There is a science-fiction innovation that has been hovering around the edges of the tech industry for the last decade: virtual and augmented reality. It hasn’t gotten far. Meta has, since it started breaking out Reality Labs financials in Q4 2020, recognized $10.8 billion in revenue against $83.2 billion in costs; the total losses are far higher when you consider that the company bought Oculus VR for $2 billion six years before that breakout. Apple, meanwhile, announced the Vision Pro in 2023, launched it in 2024, and has barely talked about it since — and certainly not on earnings calls.

Both companies would argue that the technology just isn’t there yet, and to the extent AR and VR are compelling, it’s because of the money and time they have spent developing it. I wonder, however, about a counter-factual where AR and VR were developed by a constellation of startups, not big companies: how much more innovation might there have been? Or, perhaps the bigger problem is that there was not — and, given that all of the investment is a line item in large company budgets, could not be — a bubble around AR and VR.

More generally, tech simply wasn’t much fun by the time 2020 rolled around. You had your big five tech companies who had each carved out their share of the market, unassailable in their respective domains, and the startup industry was basically itself another big tech company: Silicon Valley Inc., churning out cookie-cutter SaaS companies with a proven formula and low risk. In fact, it’s the absence of risk that Hobart and Huber identify as the hallmark of stagnation:

Of course, the causes of stagnation are complex. But what these symptoms of stagnation and decline have in common is that they result from a societal aversion to risk, which has been on the rise for the past few decades. Societal risk intolerance expresses itself almost everywhere — in finance, culture, politics, education, science, and technology. Broadly, there seems to be a collective desire to suppress and control all risks and conserve what is at the expense of breaking the terminal horizon of the present and accelerating toward what could be.

This is why Hobart told me in a Stratechery Interview that Boom was ultimately an exhortation:

What I took away is your book was much more of a sociological exposé, like spiritual almost, and I shouldn’t say almost because you were actually quite explicit about it. It’s like you were seeking — the goal of this book, it feels like — is to call forth the spirit of the bubble as opposed to have some sort of technocratic overview. You give us useful history, but there’s not really charts or microeconomics, it’s an exhortation. Is that what you were going for?

Byrne Hobart: Yes, it is an exhortation. We do want people to pick up a copy and quit their job halfway through reading it or drop out of school and start something crazy. I don’t want to be legally liable if you do something sufficiently crazy, and I think that the spiritual element is something that we did want to talk about in the book, because I think if you — you can apply this totally secular framework to it, and it’s perfectly valid. Of course, if it is a mostly materialist framing of things, then it has a lot more real world data because it’s all reliant on that real world data, but if you have the belief or at least suspicion that all of us are unique and special, and there is something that we are, if not put on this Earth to do, at least there are things that we are able to do that other people wouldn’t do as well, that part of our job is to find those things and do them really well. Bubbles play into that in an interesting way because they tell you it’s time, it’s like you wanted to do this kind of thing.

What is fascinating about the AI bubble is that there is at its core a quasi-spiritual element. There are people working at these labs that believe they are building God; that is how they justify the massive investment in leading edge models that never have the chance to earn back their costs before they are superceded by someone else. That’s why they push for policies that I think are bad for innovation and bad for national security. I don’t like these side effects, to be clear, but I appreciate the importance of the belief and the motivation.

And, I must say, it certainly is fun and compelling in a way that tech was not a few years ago. Bubbles may end badly, but history does not end: there are benefits from bubbles that pay out for decades, and the best we can do now is pray that the mania results in infrastructure and innovation that make this bubble worth it.