Is the AI Bubble About to Pop?

DEEP DIVE
Is the AI Bubble About to Pop?

For the first time since GPT-3 went mainstream, it feels like the AI rocket ship might be running out of fuel.

ChatGPT-5 dropped last month and instead of the “world-changing” upgrade people were expecting, the reaction was… muted.

For a lot of founders and operators I spoke with, this felt like a plateau moment. The sense that AI might not be moving as fast as we thought.

So is this just a temporary slowdown? Or the beginning of an AI bubble starting to deflate?

Let’s break it down.

Another AI Winter?

This tweet nailed the current mood:

It’s not panic. It’s not despair. It’s something worse for a hype cycle: boredom.

And boredom is dangerous because it makes investors pull back.

You could see the signals almost immediately. Within days of ChatGPT-5, OpenAI started ramping up fundraising talks again.

That alone is telling. If a product is crushing, you don’t sprint back to raise cash right after launch.

Sam Altman has said the company expects to burn $150 billion between 2025 and 2030.

$150B.

That’s not “build a SaaS startup” money. That’s nation-state level spending. Which means to keep investors in, OpenAI had to raise their revenue projections sky-high:

One line stuck out to me: “new product, including free user monetization.”

Translation: expect ads inside ChatGPT free tier.

That doesn’t sound like a thriving business model. It sounds like they’re scrambling to squeeze every penny of ARPU.

Add to that: MIT found 95% of AI pilots are failing to generate ROI.

95%!

It’s not that companies aren’t trying. They are. But most projects flop because replacing jobs with random AI workflows isn’t as simple as dropping in an LLM.

The result is that for the first time since GPT-3’s breakout, enterprise AI adoption has started to decline.

That’s not the story of an unstoppable rocket ship. It’s the story of a market cooling down at best.

What That Means for You

Here’s where it gets tricky.

If you’ve actually integrated an LLM into your day-to-day workflow, you know the ROI is real.

• Writing a 10-page proposal in half the time.

• Using ChatGPT to do and summarize dense research that would’ve taken hours.

• Automating basic client comms so you can focus on higher-value conversations.

That’s value you can feel. Even if it doesn’t always show up neatly in a spreadsheet.

So LLM = positive ROI?

But, just like in a pint of beer, the SaaS layer built on top of AI is where the bubble lives.

Every day a new “AI-powered tool” launches. Most are wrappers on OpenAI’s API with a fresh coat of paint. They pitch as if they’ve built proprietary magic, but under the hood it’s the same GPT-4 you already have.

Even a lot of AI “agents” — the ones that demo as if they’ll run your business for you — fall apart when you actually use them.

And that’s where the slop piles up. Overhyped, under-delivering, and destined to fade once users realize the novelty doesn’t equal ROI.

What Survives When the Bubble Pops

The good news: AI itself isn’t going anywhere.

The bubble isn’t about LLMs. It’s about the frothy SaaS being built on top of them.

Here’s the more realistic trajectory:

• There won’t be a single “everything AI” platform. Just like you don’t go to one website for shopping, news, and entertainment, you won’t rely on one AI platform/model for everything.

• Specialized GenAI tools will survive. The ones that solve specific, painful workflow problems.

• The rest — the 95% of lookalike tools — will get washed out.

Figma didn’t win because it was “sexy design software.” It won because it solved the messy, boring, painful problem of design collaboration. AI will have its Figma moments too.

The Playbook for Businesses Right Now

So, what should you do in this awkward middle stage?

1. Master your LLM basics. ChatGPT or Claude should be second nature. Treat it like Excel: a tool every professional is expected to know.

2. Be skeptical of AI SaaS. Don’t buy tools because of shiny demos. Ask: does this save me time or money this week? If not, cut it.

3. Look for boring problems. The biggest winners will be tools that quietly fix headaches no one wants to deal with.

4. Experiment without over-committing. Play with new stuff, but keep your core stable. Build around proven LLM workflows first.

The bubble may pop. It probably needs to. But the fundamentals: LLMs that make you more productive, more creative, more efficient are here to stay.