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If this really is an AI bubble, it’s still missing a key ingredient: inflation in expectations.

What it’s not:

→ Not CPI Higher prices for goods and services — a macro issue, not a bubble trigger.

→ Not wages Labor cost pressure that hits margins, but doesn’t usually end asset booms.

What it is:

→ Narrative inflation The slow shift from “will this pay off?” to “of course this pays off.”

How bubbles actually form???

Historically, asset bubbles don’t peak just because capital spending accelerates. Large investment cycles can run for years without breaking markets, especially when the payoff is still uncertain and debated.

The turning point?

You’d see it in valuations first.

That’s usually how these things end — not with spending slowing, but with prices getting ahead of any believable outcome.

❗AI hasn’t reached that stage yet.

→ There’s still argument in the numbers.
→ Still friction in the assumptions.
→ Still doubt doing real work.

And until that changes, the bubble case remains unfinished.

If this period gets labeled in hindsight, it will probably read as a hybrid rather than a clean bubble.

→ An earnings illusion
Short-term profit strength helped by timing, accounting, or credit effects.

→ A valuation expansion
Multiples rising on future promises rather than current cash flow.

That setup echoes the late ’90s.

The problem for the “AI already peaked” argument is timing. If this were truly the end, markets would already look like 2000. Instead, behavior still resembles 1996 — when the infrastructure was being built, skepticism still existed, and excess was discussed more than it was expressed.

Bank of America’s equity derivatives team put it plainly: avoiding an AI bubble may be unlikely, but the core trade still appears to have room to run into 2026.

That’s not euphoria.
It’s excess being anticipated — not realized.

And until behavior changes, calling this late-cycle remains premature.

The old “Fed model” — comparing equity earnings yield to bond yields — still isn’t flashing dot-com levels of irrationality.

Today’s reading:
→ ~0.35% nominal, 2.6% real

Dot-com extremes:
→ −2.75% nominal, −0.4% real

Lower readings mean investors are choosing risk over safety.

We’re not there.

Translation:
Risk appetite is elevated — not unhinged.

None of the hyperscalers are trading anywhere close to the kind of numbers that defined the dot-com peak.

Back then, Cisco Systems topped out north of 130× forward earnings — a level that only works if the future arrives immediately and flawlessly.

Today’s leaders don’t look like that.

By forward P/E:

…are expensive, sure — but not detached from reality, either relative to their own history or to the broader S&P 500.

That’s the part that keeps the “this is just 2000 again” argument from sticking.

Where things do start to stretch is free cash flow.

On that metric, most of these names are sitting at fresh valuation highs.

And that’s not a coincidence.

Capex is doing something awkward right now:

So on paper, profits look fine.
On the balance sheet, the money is already gone.

That disconnect is doing a lot of the market’s work right now.

Right now, AI spending is doing two jobs at once:

As Ryan Cummings (Stanford Institute for Economic Policymaking) points out, AI-related revenue is still being dwarfed by AI capex.

That’s not a red flag by itself — it’s typical for early-stage platforms.

But it does explain why markets haven’t gone full-bubble.

Skepticism still exists.

And skepticism keeps bubbles alive.

The market isn’t treating AI capex as a guaranteed win.

It’s treating it as a pending claim.

A bet that:

Until that chain tightens, enthusiasm has a ceiling.

Which is why valuations feel stretched — but not unhinged.

Markets no longer reward AI spending for its own sake.

Investors want proof that AI is moving from: “We’re building it” → “People are using it”

That’s why some of the cleanest demand signals aren’t hyperscalers — they’re enablers.

Two names matter here:

If AI adoption stalls, these numbers will show it first.

It’s a market suspended between:

And that doubt is doing something important:
keeping valuations in check — for now.

Real bubbles don’t pop because capex is too high.
They pop when everyone stops asking whether the returns will show up.

We’re not there yet.

That’s why the AI trade hasn’t peaked.
And that’s why, if history rhymes, there may still be air left to inflate it.

Prices move constantly.
Volume tells you whether the market actually believes the move.

A rally on heavy volume signals conviction — broad participation backing higher prices.
A rally on light volume signals caution — fewer buyers, weaker follow-through.

That’s why volume matters most at key moments:

Volume doesn’t predict direction.
It confirms — or questions — what price is already doing.

Translation:
Price shows what happened.
Volume tells you how much it mattered.

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