In this week’s Burn Rate podcast, a listener asked a question that’s on a lot of founders’ minds right now:
“What if the AI boom isn’t real? What if this whole thing corrects and takes my startup down with it?”
It’s a fair question. Everywhere you look, the numbers are dizzying. Data centers are being built at a scale Europe hasn’t seen in decades. Companies are throwing billions into capacity before they’ve figured out how to monetize it. Valuations float far above any connection to fundamentals. And across the continent, governments are suddenly “investing in AI”, sometimes with more enthusiasm than strategy.
So yes, the word bubble keeps coming up. But what does that actually mean for founders? And how can you build a company that doesn’t die when the market cools?
Let’s unpack that.
We’re Seeing Bubble Symptoms – But Not a Classic Bubble
Some of what’s happening looks undeniably frothy:
- Valuations that make no sense.
Many AI companies are priced as if infinite growth were guaranteed. - Capacity build-outs based on best-case fantasies.
Infrastructure is being planned for demand curves that only exist in pitch decks. - Circular risk-taking.
The same players that bear the bubble risk are also funding each other through strategic investments and giant pre-orders.
It’s the kind of loop that never ends well.
And yet: this isn’t 2000 all over again. There’s no mass overproduction, and no demand crash. In fact, the short-term problem is the opposite there isn’t enough capacity. Chips, energy, and cooling are scarce. Demand is real.
So yes, a correction will come. It may be harsh and wide. But a total collapse? Unlikely. The technology is too fundamental for that.
Who Gets Hit First When It Happens
Not all AI startups are created equal. When the air comes out, three groups will emerge:
1. The Casualties: Copycats and Wrappers
Startups with no real differentiation. Thin layers on top of existing models, no IP, no proprietary data will go first.
They exist because capital was cheap and APIs were new. When funding dries up, so do they.
2. The Survivors: Real Economics, Real Demand
Companies with strong unit economics, clear enterprise use cases, and revenue-driven value will stay standing.
If you generate income, not just savings, you’ll keep your customers and probably gain some of the weaker ones’ market share.
3. The Winners: Infrastructure Fixers
Anything that solves AI’s bottlenecks. Compute, cooling, networking, data management, security remains funded.
No matter how the hype cycles, money always flows to what keeps the system running.
How to Build a Startup That Survives the AI Correction
A few practical rules for founders who want to stay alive when things tighten.
1. Stay Lean Until You’ve Earned the Right to Scale
Don’t confuse activity with progress. Don’t hire 40 people before you have product–market fit.
The startups that survive a downturn aren’t the fastest they’re the most disciplined. Control your burn. Make every euro work twice.
2. Sharpen Your Differentiation
Ask yourself one question:
“Why can’t a well-funded competitor build this in six months?”
If you don’t have a strong answer, find one.
That could be proprietary data, a unique model, or deep integration in a vertical that actually matters.
Being “another AI feature company” is not a strategy.
3. Pick Markets That Don’t Disappear When Budgets Shrink
Some sectors always have money: healthcare, industry, defense, cybersecurity, compliance.
If your AI delivers tangible value in those areas, you’ll survive the winter.
Marketing tools and novelty chatbots won’t.
4. Raise Capital While You Can
If you have the chance to raise now: do it. Don’t wait for the market to “normalize.”
By the time it feels safe again, liquidity may be gone.
And when you do raise, spend the money to build moats, not momentum: proprietary data, deep integrations, distribution partnerships, compliance, reliability.
5. Don’t Bet on a Single Macro Story
If your business model only works when GPU prices fall or data centers expand forever, you’re exposed.
Build something that’s valuable even when compute is expensive and hype is gone.
Design for robustness, not perfection.
Max Meister 
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