Why "AI Bubble" Critics Are Reading the Wrong Ledger
I've spent the last little while building AI tools that do cloud migration planning, talent assessments and development, organizational design, financial modeling, and strategic planning. Work that used to take teams of consultants months now takes hours. I've watched AI write production code that ships to users.
This isn't theory for me. It's Tuesday.
So when smart people tell me AI is a bubble, I listen. I take the critique seriously. But I've come to believe they're making a category error, and it's worth explaining why.
The Wrong Comparison
When people call AI a bubble, they're usually comparing AI company valuations to AI company revenues. By that measure, the numbers look stretched. They see the pattern from 1999 and conclude we're headed for the same crash.
But this comparison misses something fundamental.
They're valuing AI as a sector , like SaaS or social media. A set of companies selling products to customers.
They should be valuing it as a production technology , like electricity or computing. Something that reshapes how all work gets done.
That's a different kind of math entirely.
The Ledger They're Not Watching
Here's the question that reframes everything: How much does the world spend on cognitive labor?
Global labor compensation runs around $60 trillion a year. The cognitive and knowledge-intensive portion, the work AI is best positioned to touch, sits somewhere between $35 and $50 trillion of that total.
Current enterprise spending on generative AI? About $37 billion. Growing fast, but still tiny.
That's less than one-tenth of one percent of the labor value AI could eventually reach.
The bubble critics are looking at a technology that has captured almost none of its addressable market and calling it overvalued. They're watching the early frames of a film and reviewing the ending.
Why This Time Might Actually Be Different
I'm usually skeptical when people say "this time is different." It's the most dangerous phrase in investing. But the data from the last two years is hard to ignore.
The cost to run AI inference has collapsed. Stanford's AI Index reports a 280x reduction in the cost of GPT-3.5-level queries over roughly two years. That's not a typo. Two hundred and eighty times cheaper.
When something gets that much cheaper that fast, the economics of what's possible change completely. Tasks that couldn't justify the cost yesterday become trivial today.
Meanwhile, the capabilities themselves are accelerating. Epoch AI found that improvement rates nearly doubled around April 2024. The curve isn't just steep. It's getting steeper.
And then there's the recursive element that breaks historical comparisons.
This week, Anthropic released a product called Cowork. According to company reports, they built it in about ten days, with Claude doing most of the coding.
A production-grade product, built largely by AI, in under two weeks.
Steam engines couldn't design better steam engines. Electricity couldn't wire new factories. But AI can build AI. That feedback loop changes the adoption math in ways we don't have good historical models for.
The Jevons Question
There's a reasonable counterargument here. If AI makes cognitive work radically cheaper, maybe the whole pie shrinks. The $60 trillion wage bill becomes $6 trillion. Deflation wins. AI companies capture a percentage of a much smaller number.
The data so far suggests the opposite.
Despite costs falling by orders of magnitude, enterprise spending on generative AI more than tripled last year. Companies aren't pocketing the savings. They're finding new things to spend on that weren't economic before.
This pattern has a name: Jevons paradox. When you make a resource dramatically cheaper, you don't get proportional savings. You unlock demand that couldn't exist at the old price.
At $100 an hour, you hire a human to review important contracts. At a penny an hour, you review every contract. You analyze every log file. You tutor every student. You do work that was never worth doing before.
The pie doesn't shrink. It expands into territory that was previously too expensive to touch.
The Honest Bear Case
I want to be fair to the critics, because they have one argument that's genuinely strong.
Touching value isn't the same as capturing it.
AI could transform $40 trillion in cognitive labor and still generate thin margins if the technology commoditizes faster than anyone can build moats. The productivity gains might flow to customers as lower prices, not to AI companies as profits.
This is a real risk. It's the risk that matters.
But even conservative scenarios leave enormous runway. If AI vendors capture just 3-5% of the labor value they touch, that implies $1-2 trillion in annual revenue at maturity. We're at $37 billion today. That's 30-50x growth even if you're skeptical about capture rates.
The Cisco comparison is instructive here. Cisco in 2000 was a great company selling vital infrastructure. It was also wildly overpriced at 200x earnings. The stock took 25 years to recover its peak, despite the company's continued success.
That's valuation risk, not technology risk. Both can be true at once. AI can be transformative and some AI stocks can still be overpriced today.
But the ceiling question, whether AI will touch most of cognitive work, is increasingly settled. The open questions are timing and who captures what.
The Transfer
Here's the frame that makes sense of all this:
We're not watching value creation or destruction. We're watching value transfer.
When a task gets automated, it doesn't vanish. The output still exists. The work still gets done. But the line item moves. What used to sit under "Labor" on the ledger starts showing up under "Compute."
The bubble critics are watching the old ledger shrink and calling it a crash. They're missing the new ledger growing on the other side of the balance sheet.
This is what every general-purpose technology looks like from the inside. Heavy investment. Apparent overvaluation. Productivity gains that take years to show up in the official statistics. Economists called it the Solow paradox when computers were spreading everywhere but GDP wasn't moving.
The paradox resolved eventually. It always does. The question with AI is just how fast.
And there's reason to think fast. AI doesn't need new physical infrastructure the way electricity did. It rides on cloud and SaaS infrastructure that already exists. The installation phase that took decades for previous technologies might compress into years.
The Bottom Line
I'm not here to tell you AI stocks are cheap. Some of them probably aren't.
I'm here to tell you that the bubble framing is the wrong lens. It compares AI to tulips and dot-com stocks when it should be compared to electrification and computing. Productive capital, not speculative assets.
Current AI spending represents a tiny fraction of the cognitive labor it could eventually touch. The technology is getting cheaper and more capable at accelerating rates. The recursive loop, AI building AI, is now visibly active.
We are not at the peak of a bubble.
We are at the foothills of something much larger.
The critics are right that the journey from here will be volatile. They're right that valuations can get ahead of reality. They're right that capture is uncertain.
But they're reading the wrong ledger. And that mistake will be expensive.
The future is already here. It's just not evenly distributed.
And it's distributing faster than our intuitions can track.