I’ve been doing this since the 80s when “tech bubble” meant the excitement around personal computers, not the multi-trillion dollar gamble we’re seeing today. Back when we had to physically load programs from floppy disks, the concept of burning billions on vaporware seemed absurd—until I watched it happen with 3D TVs, Segways, and a dozen other “revolutionary” technologies that promised to change everything while delivering almost nothing. The difference this time? The AI bubble isn’t just burning investor money—it’s burning actual economic value that will leave scars on the global economy.
The warning signs aren’t subtle if you’ve been around long enough. I remember the breathless predictions about 3D TVs back in the 2010s, how they’d revolutionize entertainment and become standard in every home. People lost their shit over the eventual failures all the time, but the demise of 3D TV didn’t bring down the whole economy. That’s what makes today’s AI spending so terrifying—when the bubble pops, it won’t just be investors losing money, it will be a systemic economic event.
Why AI Isn’t Like Other Tech Bubbles
Let’s be clear: Crypto had virtually no intrinsic value beyond speculation. AI, on the other hand, has real capabilities that are already transforming industries. The problem isn’t that AI will fail—it’s that the current spending is wildly out of proportion to actual revenue. I’ve seen this pattern before. It’s like how people think all music from the 70s is good, when reality is that we only remember the good stuff. The AI industry is in the middle of its “everything is golden” phase, where every half-baked solution gets funded because investors are chasing the next big thing.
The numbers are staggering. We’re talking about over $200 billion in funding for just a few AI startups, with nearly $700 billion in capex planned across major tech companies for 2026 alone. That’s more money than was spent during the entire dot-com bubble, and it’s being funneled into solutions that often can’t justify their existence beyond PowerPoint presentations. I’ve seen the same executives who laughed at 3D TV’s demise now making AI investment decisions with the same reckless abandon.
The 3D TV Lesson No One Learned
Remember 3D TVs? They were going to change everything. I remember standing in Best Buy in 2011, watching people flock to demo units like they were witnessing the future. The technology worked—sort of—but the market never materialized. What killed it wasn’t the technology itself, but the economic reality that consumers weren’t willing to pay premium prices for a gimmick they didn’t need. Today’s AI bubble faces a similar economic test, but with one critical difference: the stakes are orders of magnitude higher.
The AI industry has become expert at creating the illusion of progress. I’ve tested countless AI tools that claim to “revolutionize” workflows, only to find they’re barely better than existing solutions—and often significantly worse at basic tasks like arithmetic or Excel operations. The problem isn’t that AI can’t deliver value—it’s that the current generation of AI products is being sold as magical solutions that will replace human work entirely, when in reality they’re better thought of as sophisticated assistants that augment human capabilities.
The Math That Doesn’t Add Up
Let’s do some simple math. A $200,000/year engineer using an AI tool that provides 20-30% productivity gains might seem like a no-brainer. But when that AI tool costs another $200,000/year in infrastructure and subscription fees, the equation changes dramatically. I’ve seen companies proudly announce AI-driven “efficiency” measures that will “save” millions by replacing human workers, only to quietly reinvest those savings into even more expensive AI infrastructure that doesn’t deliver on its promises.
The current model is fundamentally broken. AI development requires unprecedented computational resources that few organizations can sustainably afford. I remember when we thought cloud computing was expensive—now we’re talking about data centers that consume more power than small cities, all to train models that often produce results no better than a skilled human with proper tools. The economics simply don’t scale, and when investors finally realize this, the correction will be brutal.
What Happens When the Bubble Pops
The dot-com bubble is the best comparison we have, but even that understates the potential impact. When the dot-com bubble burst, it primarily affected the tech industry and venture capital ecosystem. Today’s AI bubble has tentacles everywhere—from semiconductor manufacturers to data center operators to cloud services to countless specialized hardware providers. The interconnectedness means a collapse in one area will trigger cascading failures throughout the entire technology ecosystem.
I’ve been doing this since the 80s, and I’ve never seen a bubble this pervasive. Even during the housing crisis, the impact was contained to specific sectors. With AI, we’re talking about potential failures that could ripple through every aspect of the global economy. The warning signs are clear: companies reporting no measurable gains from AI investments, executives unable to define what “productivity” means in the context of AI, and a complete disconnect between what AI can actually do and what it’s being sold as.
The Humbling of Tech Bureaucrats
What will this humbling look like? When banks fail, it’s seldom the bankers who starve. The same will be true in the AI collapse. The executives who championed these initiatives will likely move on to the next big thing, leaving the wreckage for others to clean up. I’m stoked to watch tech oligarchs get humbled as well, but the reality is that the fallout will affect everyone from entry-level engineers to small businesses that invested heavily in AI solutions that ultimately don’t work.
The most disturbing aspect is how normalized this bubble has become. I’ve seen otherwise rational business leaders defend multi-million dollar AI investments with arguments that would have been laughed at during previous bubbles. The difference now is that AI has become so deeply embedded in corporate strategy that questioning it is seen as anti-innovation. This groupthink is exactly what happened during the dot-com era, and it’s what will make the eventual correction so painful.
The Real Value of AI (When It’s Not Overhyped)
Don’t get me wrong—AI has real value. The problem isn’t AI itself, but the current generation of AI products that promise to replace human intelligence rather than augment it. I’ve seen AI tools that genuinely make complex tasks easier, that help researchers process vast amounts of data, that assist doctors in diagnosing conditions. These are the applications that will survive the bubble—because they deliver tangible value that can be measured and justified.
The companies that will thrive after the AI bubble pops are those that treat AI as a tool, not a solution. They’re the ones who understand that AI is most powerful when it works alongside human expertise, not in place of it. They’re the ones who recognize that true innovation comes from solving real problems, not from implementing the latest technological fad. These are the lessons from history’s tech graveyards that no one seems to be learning.
Building a Post-Bubble Future
The collapse of the AI bubble won’t mean the end of AI development—it will mean the end of AI as a magic bullet solution to every problem. After the dust settles, we’ll see a more rational approach to AI implementation, one that focuses on genuine value creation rather than speculative investment. I’ve been through enough cycles to know that what comes after the bubble is often more interesting than what came before.
The companies that survive—and thrive—will be those that built real solutions on top of AI, not those that built AI solutions in search of problems. They’re the ones who understood that technology is a means to an end, not an end in itself. They’re the ones who learned from the tech graveyard that still holds the lessons we refuse to learn until it’s too late. And they’re the ones who will build the next generation of technology that actually works—not just for investors, but for everyone else too.