AI Is Booming—And The Bust Could Be Even Bigger

In the 1960s, IBM embarked on what Fortune Magazine called the $5 Billion Gamble. It was a bet-the-company investment on a scale nobody had ever seen before. The payoff was the legendary System/360 mainframes, which revolutionized computing and set the stage for two decades of IBM dominance.
That $5 billion would be roughly the equivalent of $50 billion today, but even that princely sum is dwarfed by the $364 billion that tech giants are expected to invest in artificial intelligence this year. And the spending won’t stop there. McKinsey projects that building AI data centers alone could demand $5.2 trillion by 2030.
Today, the AI investment boom is probably the single biggest factor propping the US economy. However, there is cause for concern. Throughout our history, great technological advances have led to overinvestment, crowding out of traditional industries and, eventually, a collapse triggering economic upheaval. Indicators suggest that’s where we’re headed now.
The Booms And Busts Of Railroad Barons
In terms of economic impact, the closest comparison to the AI boom was the railroads in the 19th century. Then, like now, there was a revolutionary technology with unprecedented potential for impact. The railroads promised to connect production to markets like never before in human history.
Another striking parallel was government support and subsidy for investment. The Pacific Railroad Acts of 1862 and 1864 authorized vast land grants and the issue of government bonds to finance the construction of railroad infrastructure. These effectively guaranteed profits for private investors, while the public bore the risks.
Railroad barons such as Cornelius Vanderbilt, Jay Gould and Leland Stanford made enormous fortunes and came to dominate the era. They created huge monopolies that stifled competition and squeezed farmers and small businesses. If the local railroad wouldn’t give you a rate, you couldn’t get your goods to market and you were, effectively, ruined.
Greed, arrogance, and overinvestment fueled massive and repeated boom-and-bust cycles. The panics of 1873 and 1893 led to crushing financial crises followed by years-long economic depressions and political instability. As historian Richard White explains in Railroaded, while eventually railroads would be valuable for America, the corruption, monopoly power, public cost, and repeated crashes were unnecessary and avoidable.
The Second Industrial Revolution
After the panic of 1893, hundreds of railroads went bankrupt, which created an opportunity for financiers like J.P. Morgan. As industries consolidated, and competition decreased, stability returned and the Gilded Age roared back to life. The locus of power shifted to Wall Street as Morgan and his colleagues organized the American economy into great trusts such as U.S. Steel, International Harvester, and General Electric.
It was in this environment that the Second Industrial Revolution took hold. Driven by technological breakthroughs in electricity and internal combustion in the 1880s, it fueled the emergence of entirely new industries, such as automobiles and radio. By the 1920s, the electrification of factories powered a productivity boom.
Much like today’s AI boom, the Second Industrial Revolution seemed to change everything. The confluence of electricity and internal combustion, along with the secondary innovations they spawned, led to mass manufacturing and mass marketing. Improved logistics reshaped supply chains and factories moved from cities in the north—close to customers—to small towns in the south, where labor and land were cheaper.
These were genuine innovations and the resulting improvements in productivity, combined with lax regulation and easy credit, led to overinvestment and an enormous bubble, which eventually popped. The stock market crash of 1929, along with the poorly advised Smoot Hawley tariffs that led to the Great Depression of the 1930s.
The pattern mirrored the railroad busts of the 1800s: Genuine innovation, poor government regulation, overinvestment, boom, bust and, inevitably, horor and misery.
The Dotcom Boom And Bust
I was working on Wall Street in 1995 when the Netscape IPO hit like a bombshell. It was the first big internet stock and, just like that, a tiny company with no profits was worth $2.9 billion. Soon, productivity growth—depressed since the early 1970s—began to surge. Economists explained that certain conditions, such as negligible marginal costs and network effects, would lead to “winner take all markets” and increasing returns to investment.
Venture capitalists saw how this could make them rich beyond their wildest dreams and began raising massive amounts of capital to finance the dot-com boom. Calls for deregulation increased, even if it meant increased disruption. Most notably, the Glass-Steagall Act, which was designed to limit risk in the financial system, was repealed in 1999.
Companies such as Webvan and Pets.com, with no viable business plan or path to profitability, attracted hundreds of millions of dollars from investors. In a sign of the times, America Online (AOL), merged with Time Warner, the biggest and most prestigious media company on the planet to create a $350 billion megagiant that would straddle both the old and new worlds.
By 2000, the market peaked, the bubble burst, and the AOL–Time Warner merger became a cautionary tale. While some of the fledgling Internet companies, such as Cisco and Amazon, did turn out well, thousands of others went down in flames. Other more conventional businesses, such as Enron, WorldCom and Arthur Anderson, got caught up in the hoopla, became mired in scandal and went bankrupt.
Like the railroads and the Second Industrial Revolution, bust followed boom but, this time, there was no depression. While some prestigious companies failed, investors lost money and genuine malfeasance was exposed, the Internet economy wasn’t quite big enough to pose a systemic risk. The recession that followed was relatively mild by historical standards.
Is This The Perfect Storm?
Looking at the AI boom from a historical perspective, the similarities to earlier technological cycles are striking. We see the same excitement, the same calls for government regulators to get out of the way and let the technological and market forces do their work. We also see the same pattern of massive overinvestment. The only part we haven’t seen is the bust…yet.
There are also signs that this particular cycle has the potential to be worse than anything in living memory. As investor Paul Kedrosky points out, the size of investment in data center infrastructure has already surpassed that of the dotcom boom and is beginning to approach levels last seen during the railroad frenzy of the 19th century.
And for all the hype and hoopla, we’re not seeing much of a boost in productivity growth. A study by the St. Louis Fed suggests a 1.1% increase in aggregate worker productivity, with much of that concentrated in the tech sector. A paper by Nobel laureate Daron Acemoglu, looking at total factor productivity (TFP), a measure which takes use of capital into account, sees a 0.66% increase over 10 years, translating to a 0.064% increase in annual TFP growth.
Finally, there are signs of growing systemic risk. Kedrosky notes that, increasingly, tech giants are choosing to finance much of their infrastructure build-outs with Enron-like special purpose vehicles, which cost more but keep the debt off their balance sheets. That risk, in turn, is increasingly being passed to more traditional investors such as REITs.
So, whether you like it or not, we’re all deeply invested in this AI boom and there will surely be rough waters ahead. We need capable governance if we’re going to navigate the rapids and not end up crashing on the rocks. Who, if anyone, is at the helm?
Greg Satell is Co-Founder of ChangeOS, a transformation & change advisory, a lecturer at Wharton, an international keynote speaker, host of the Changemaker Mindset podcast, bestselling author of Cascades: How to Create a Movement that Drives Transformational Change and Mapping Innovation, as well as over 50 articles in Harvard Business Review. You can learn more about Greg on his website, GregSatell.com, follow him on Twitter @DigitalTonto, watch his YouTube Channel and connect on LinkedIn.
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I’ve been saying to my tech colleagues for the last 18 months that AI is over-hyped and over-inflated. Specifically that the LLM and Generative AI models are over hyped and over worked. Small scale SLM models designed for specific purposes such as those used in medical and pharmaceutical research are entirely different beasts. The threat that LLMs pose to IPR is also not-negligible and may pose quite distinct threats to the success of AI..
My prediction, pace Drucker & Taleb, is that we shall see a bust no earlier than late summer 2026 and no later than late summer or early autumn 2027. That bust *may* end up being influenced by how the mid-term election campaigns are going. If Trump and Congress look vulnerable to a Blue resurgence and the techbros feel vulnerable then things may change, perhaps even in very dramatic ways.
Once again, brilliant observations.
Hard to know what to address with systemic changes and risk, especially where AI is concerned. Is it chips? Power? Servers? Software? SaaS?
As the internet hit hard in the mid-90s, I quickly knew it was the culmination of everything I’d worked on within my career to that date (computing, desktop publishing, video, audio, graphic design, et al) and knew I had to find a strategic play that encompassed it all.
In the late 90s I chose to go after Vignette to see if I could land a gig and did. At the time, due to the rush toward all things dotcom, the company was the fastest growing software infrastructure firm for the WWW. Enjoyed the growth and rode out a year or two after the dotcom bust. Damn, was that sobering, but luckily I rode it out and bought a lot of Apple (instead of Vignette or their partner’s stock) so all the crash did was for me to find a new gig as it made my job there and my vesting Vignette stock worthless. (I ended up at Lawson Software as VP for strategic alliances). As an old mentor of mine always said, “I’d rather be lucky, than smart” and often thank Steve Jobs for our retirement.
That street-smart throwaway ‘lucky’ line did define my personal involvement in tech as I could see things coming but almost always my timing was a year or so ahead of when various confluence of events ‘hit’ hard in the market. It’s why this post I found so insightful.
The issue now is that it’s much, much harder to ‘see’ the future and connect the dots with fundamental stuff like blockchain/crypto, government investment, global politics and, especially, where A.I. is headed and where it will be in a year or two.
As an investor, I can pick-n-choose what I think are key investments in our portfolio but, I must admit, even Nvidia’s explosion was initially a surprise to me as was how fast A.I. came on the scene.
Add-in all the current flux and uncertainty in the current administration, tariffs and worldwide confusion and rage toward the U.S., — along with what I’m seeing as intentionally inflicted chaos to what end, I’m uncertain — and it feels like we’re living in a house-of-cards.
That said, your referencing the Railroad boom and bust was interesting but didn’t address something that, IMHO, is at the core of what’s taking place now with A.I. and the one place actual money is being made vs. “hope investing” in A.I. software and that place is infrastructure.
At one point early on in railroads, businesses, warehouses and thus towns sprang up (or went bust too) if not near the railroad line. When I read “The Box that Changed the World: Fifty Years of Container Shipping” it was an “Aha!” moment for me.
The infrastructure of the Interstate Highway system, the container, proliferation of semi-trailer trucks enable goods to go anywhere, just like packets move information via the internet.
Businesses needing raw materials or other supply-chain goods could locate anywhere as long as they had docks for trucks to unload.
So I guess I’m focused on infrastructure as it relates to A.I. but still haven’t come up with a fundamental hypothesis on how this monumental shift will play out. Is a coming crash just a temporary lull and power plants, chips, servers, racks and other infrastructure be the bigger loser than A.I. software companies in a crash?
Or will infrastructure build-out continue, then until some years after an A.I. software crash, be a baseline upon which the next phase of A.I. will be deployed?
Those types of thoughts drive my investing (or lack thereof) and the way I see where A.I. is headed and so on.
Keep up the great analysis, Greg, and sharing it with the rest of us.
As good a bet as any Robert. We shall see…
Good points Steve. The thing that really worries me about the AI infrastructure buildout is that roughly 60% of the AI investment go to chips that have a (mostly) 3 year horizon. So it’s not like during the dotcom buildout where a ton of money was invested in dark wire that ended up being useful a decade later.
Greg