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I've Been Through Three Tech Bubbles - This One's Different (And Worse)

AI investment growth chart

I've been through three tech bubbles now - dot-com, social media, and crypto. This AI spending acceleration feels different, and not in a good way.

Gartner's latest forecast predicts $1.5 trillion in AI spending for 2025 alone, surpassing $2 trillion by 2026. That's faster growth than any tech sector in history. During previous bubbles, you could at least point to actual users or revenue. With AI, we're spending trillions on infrastructure for products that barely work.

Here's what's actually happening: every company is terrified of being left behind, so they're throwing money at anything labeled "AI" without understanding what they're buying. I've consulted for Fortune 500 companies that allocated $50 million AI budgets without defining success metrics beyond "we need to do AI."

The infrastructure spending is real - data center investment is expected to hit $1 trillion through 2030. But most of these facilities will sit underutilized because the AI applications don't exist yet. It's like building highways for cars that haven't been invented.

The scary part is how detached this is from economic fundamentals. Google searches for "AI bubble" plummeted in the past month, which is classic bubble behavior - peak skepticism followed by complacency right before everything crashes.

I remember similar patterns in 1999 when everyone stopped questioning dot-com valuations. "This time is different" became the mantra. Same energy now, except instead of websites, it's ChatGPT wrappers burning through venture capital.

The difference between this bubble and previous ones: the infrastructure costs are front-loaded and massive. When dot-com crashed, companies lost websites and marketing budgets. When AI crashes, we'll have hundreds of billions in stranded data center assets and specialized chips nobody needs.

Most of these AI companies are just API wrappers around OpenAI or Anthropic. 90% of them will disappear, as detailed in AI bubble analysis, when the funding dries up, probably in the next 18 months when investors realize they're paying enterprise prices for chatbot features.

Tech investment vs government funding contrast

The government shutdown timing is almost poetic - while DC can't fund basic operations, Silicon Valley is planning to spend nearly $3 trillion on artificial intelligence infrastructure. The contrast shows how disconnected tech investment has become from actual economic priorities.

AI Startup Survival Odds Through 2027

Company Type

Funding Raised

Survival Probability

Why They'll Make It (Or Not)

OpenAI/Anthropic

$10B+

95%

Actual product with revenue

Enterprise AI Infrastructure

$100M-1B

75%

Boring but necessary plumbing

AI Chip Companies

$500M+

60%

Hardware is hard but demand exists

Industry-Specific AI

$50-200M

45%

Depends on vertical adoption rates

AI Dev Tools

$20-100M

40%

Crowded market, hard differentiation

Consumer AI Apps

$10-50M

25%

User acquisition costs are insane

ChatGPT API Wrappers

$5-25M

10%

Zero moat, easily replicated

AI Consulting Shops

$1-10M

5%

First to get cut when budgets tighten

The Questions Nobody's Asking (But Should Be)

Q

Is $2.8 trillion in AI spending actually justified by current capabilities?

A

Fuck no. Most AI applications today are glorified chatbots and image generators. We're building infrastructure for products that don't exist yet, hoping someone figures out profitable use cases later.

Q

How much of this "AI investment" is just API calls to OpenAI?

A

Probably 60-70%. Most "AI companies" are just wrapping ChatGPT with custom prompts and charging enterprise prices. When OpenAI raises API costs, these companies evaporate.

Q

When does the AI funding bubble actually pop?

A

My guess? Maybe another year before reality hits. Right now VCs are throwing money at anything with "AI" in the pitch deck. That ends when LPs start asking for actual returns.

Q

What happens to all this infrastructure when the bubble bursts?

A

Data centers and specialized AI chips become stranded assets, like abandoned shopping malls. The infrastructure is too specialized to repurpose easily, so you get billions in write-offs.

Q

Are any AI companies actually profitable?

A

OpenAI and Anthropic are getting close, mainly because they own the actual models. Everyone else is burning cash on customer acquisition while providing services that could be replicated by a teenager with API access.

Q

How is this different from the dot-com bubble?

A

The infrastructure costs are front-loaded and massive. Dot-com companies could fail cheap

  • just lose a website and some marketing spend. AI companies are burning through hundreds of millions on compute and talent before they even have a product.

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