We’re Not in an AI Bubble. We’re Watching Big Tech Finally Spend Its Cash.

For the last year, the dominant narrative has been simple: AI is a bubble. Too much hype, too much capital, too many promises — and eventually, it all pops.

The warnings are everywhere. Nobel laureate Daron Acemoglu calls AI models “hyped up” and says “we’re investing more than we should.” Even Sam Altman admits investors are “overexcited” about AI. One analyst claims we’re in “the biggest and most dangerous bubble the world has ever seen.”

I don’t buy that framing — or at least, not the version that predicts a 2008-style collapse.

What we’re seeing right now isn’t a speculative bubble propping up weak fundamentals. It’s something very different: big tech companies finally deploying tens of billions of dollars in capital that’s been sitting idle for years — because until now, there was nowhere sensible to put it.

AI didn’t create the money. AI gave it a destination.

The Cash Problem Nobody Talks About

If you’ve never worked inside big tech, it’s hard to grasp the scale of the cash problem. Not revenue. Not valuation. Cash.

I’ve been at both Meta and AWS. At neither company did I ever feel like there was a lack of capital. It’s the opposite — you hear about it at All Hands, you can find documentation around it. When leadership talks about their spending, it’s a source of pride. It’s “look how much capital we are able to deploy to this cause.” It’s never a justification. If anything, employees would like more justification around it, but that information lives at the executive level and it’s not coming down.

Between roughly 2015 and 2022, companies like Meta, Google, Apple, and Microsoft accumulated enormous cash reserves. By the end of 2021, Big Tech held a combined $476.7 billion in cash, cash equivalents, and marketable securities. Alphabet consistently sat north of $100B. Apple peaked near $167B before aggressive buybacks. Microsoft hovered in the $80-130B range. Meta held $50-60B. Amazon reached $89B by Q2 2024. These are staggering numbers, but the important part isn’t the size — it’s that there were very few places to deploy it at scale without destroying returns or triggering regulatory scrutiny. You can’t buy meaningful companies without antitrust issues. You can’t launch moonshots at $50B scale without burning shareholder trust. You can’t dump tens of billions into financial markets without distorting them.

So the cash accumulated. Slowly. Relentlessly. By the late 2010s, big tech wasn’t constrained by capital — it was constrained by deployability.

What It Actually Looks Like From the Inside

Here’s something I’ve lived firsthand: the tension isn’t between “spend” and “don’t spend.” It’s between CapEx and OpEx.

I’m on a high CapEx team at AWS. We could get budget for infrastructure tomorrow. But if I could trade a million dollars of our CapEx budget for headcount, I would do it in a heartbeat — and we can’t. The money is there, but it’s locked into infrastructure spending rather than people. That’s not a bug in the system. That’s the system working as designed. Infrastructure is a depreciating asset — companies can write it off. Headcount is an ongoing operational expense that hits margins differently.

This is why the AI infrastructure spending feels so natural for these companies. The money was already earmarked for infrastructure. AI just gave it a more compelling label and a clearer return profile. AWS gets returns on a given server rack quickly — it’s always profitable to build data centers or add new buildings to existing ones. The demand is already there. Internally, we’re pushed to avoid certain regions because they have a capacity crunch. I’ve been through this before — at Meta during the pandemic, I was on the infrastructure teams that were kicking projects off of production and demoting them because we didn’t have enough capacity. When your monthly active users jump from 1.2 billion in 2020 to 2 billion in 2021, that increase in traffic is insane. The demand is real. It’s always been real.

Why This Is Nothing Like 2008

The 2008 bubble was built on leverage, fragility, and financial engineering. This moment is built on cash-on-hand and infrastructure investment. That difference matters.

In 2008, capital was borrowed, risk was hidden, and the system collapsed when liquidity disappeared. In 2024-2025, capital is owned outright, spending is discretionary, and no one is being forced to liquidate to survive.

When Meta spends $65-72B on capex in 2025, it’s not borrowing against the future — it’s spending money that already exists on its balance sheet. The combined numbers are staggering:

• Amazon: $100B+ planned for 2025 (up from $83B in 2024)
• Google: $91-93B in 2025 (up from $52.5B in 2024)
• Microsoft: $80B in fiscal 2025
• Meta: $65-72B in 2025

That’s over $320 billion in a single year — deployed into data centers, power infrastructure, and compute capacity.

And here’s what people miss about the timeline: when Amazon announces $200B in 2026 capital expenditure, that order was placed in 2024. These procurement cycles for server hardware go out months or years in advance. This isn’t reactive hype spending — it’s a supply chain that moves in years, not quarters.

Even skeptics acknowledge the structural difference. IMF Chief Economist Pierre-Olivier Gourinchas stated: “An AI investment boom may lead to bust, but not likely systemic crisis… Unlike the 2000 dot-com bust or 2008 financial crisis, AI investments are generally not debt-financed.”

The money is already there.

The Alexa Fumble and the Slow Mover Myth

There’s a narrative that AWS and Amazon were slow movers on AI. I think that’s true, but not for the reasons everyone thinks.

Amazon built Alexa and sold over 500 million units. They had an AI-ready piece of hardware in people’s houses first — before anyone else. They fumbled because they didn’t turn it agentic, didn’t invest in LLMs early enough, and didn’t see the interface shift coming. The hardware was there. The software vision wasn’t.

But calling Amazon a slow mover ignores the infrastructure reality. AWS has the most data centers and the most market share. The year-over-year growth isn’t as high in comparison to other companies, but it’s also not supposed to be given the size of it all. AWS and Meta know how to build data centers — it’s part of the core business. There are just a lot of permits and hoops to go through to break ground, and procurement of server hardware goes out months or years in advance.

The reason behind the capex isn’t speculation. It’s an infrastructure play. Infrastructure is a depreciating asset they can write off, and it’s something people will need. The question is whether we continue to build more efficient AI models and hardware to justify the capacity — and so far, the demand curve says yes.

AI as a Capital Sink, Not a Magic Wand

AI didn’t arrive as a miracle product. It arrived as an infrastructure excuse.

Training clusters. Inference fleets. Power contracts. Data centers. Networking. Cooling. Specialized hardware. Land. These are boring, real, industrial investments — the kind that absorb enormous amounts of capital without promising overnight returns. And that’s exactly what big tech needed.

Look closely at where the money is going: multi-year data center builds, power generation and grid commitments, long-term hardware supply chains, fiber and interconnects, custom silicon programs. These aren’t moonshots. They’re industrial-scale projects with decade-long horizons.

That’s why calling this an “AI bubble” misses the point. A bubble implies fragile demand and speculative excess. What we’re seeing is closer to a re-industrialization of the digital economy — funded by cash that’s been sitting on balance sheets for years, waiting for somewhere to go.

Where This Argument Could Be Wrong

I should be honest about the weaknesses in this framing.

“What if AI doesn’t deliver ROI?” If the productivity gains from AI infrastructure don’t materialize, big tech is left with expensive data centers that don’t generate sufficient returns. The spending is real, but the value capture isn’t guaranteed. That said, even skeptical scenarios suggest compute demand will continue growing — the question is rate of return, not total write-off.

“Isn’t this what people said about every bubble?” Yes. Every speculative excess looks like rational investment to someone. The difference I’d point to: this capital is owned, not borrowed; the spending is on physical infrastructure with residual value; and the companies deploying it are profitable on their core businesses regardless of AI outcomes.

“What about the smaller players?” My argument applies to big tech with fortress balance sheets. The AI startup ecosystem is a different story — many are burning cash on the assumption that scaling laws continue indefinitely. Some of those bets will fail. The distinction is between infrastructure builders and application gamblers.

“What about the API economics?” This one’s worth understanding. If you do the math on AI subscriptions, the compute costs mean they don’t make money off the subscriptions themselves. The play is loyalty to a specific model ecosystem — the real money is in API calls at scale. That’s a bet on adoption curves, not current profitability.

None of this means AI is a sure thing. It means the structure of this moment is different from 2008 — and that matters for how we think about systemic risk.

What This Means For Builders

Founders: The infrastructure buildout creates opportunity. The companies spending $320B need software, services, and tooling. That’s a procurement cycle, not a bubble pop. Build for the builders — but understand that the companies buying are looking for things that help them process data at scale, because humans don’t scale and AI isn’t deterministic enough yet. The gap between those two realities is where the opportunity lives.

Engineers: The layoffs are real — I’ve been through three rounds at Amazon. But from what I’ve seen, they’re not because of AI. They’re timed to earnings reports, and if you watch the pattern, each round waits for the previous severance period to end before announcing the next. There are a lot of AI initiatives inside these companies and a lot of teams being built around them, but they are not replacing engineers with AI. The message from leadership is “ruthless prioritization” — which gets old when you’ve just laid off 15,000 people and then tell the rest to be more efficient.

Investors: Watch capex efficiency, not hype cycles. The question isn’t “will AI work?” — it’s “what’s the return on this infrastructure at scale?” The winners aren’t necessarily the model makers; they might be the picks-and-shovels players.

This Is the Setup, Not the Punchline

If you’re looking for a clean pop — a moment where AI collapses under its own hype — you’re probably waiting for the wrong thing. The capital is real, it’s owned, and it’s being deployed into physical infrastructure that has value regardless of whether the current generation of AI models lives up to the hype.

The conversation I keep hearing — “AI is going to take our jobs” — is aimed at the wrong target. AI isn’t the existential threat to how people work. It’s a tool that shifts what work looks like, the same way every major technology has before it. The real disruption isn’t happening in your IDE or your inbox. It’s happening in warehouses, on highways, and in logistics networks — physical automation at industrial scale. And almost nobody is talking about it.

That’s Part II.