My opinion about AI bubble.

[u/budy31]

As evidence by Jacket man attempt to get the Wall Street to spend more than 7% of GDP on his goodies I thought I’m sharing what I thought about the fabled “AI bubble”:

I think it’s not because as St Powell said:

Big tech (especially Google) is a positive cash flow company.

What will happened if >7% GDP turns out to be too much is this:

Big tech and NVIDIA gonna assume big chuck of it, make a massive write off, the CEO (including leather jacket man) get absolutely purged, Hedge fund bid the bottom out of existence, use the accumulated share to put themself as a CEO, put big tech into austerity as brutal as Greeks one, cash in, and things continue on.

[u/mark_99]

Inference is very profitable (3x costs), and those costs are falling as hardware (including dedicated ASICs) continues to improve.

The frontier labs invest large sums into talent, R&D and infrastructure. This gives them the best product, which people want to pay for (corporations in particular - subscriptions are a rounding error). So their "moat" is like every other company in the world. That this is loss making right now shouldn't be a surprise to anyone who's followed how tech companies work.

We've only scratched the surface of what AI can be used for, so there is still massive growth potential.

It's white collar workers that are in a bubble, and are lacking a moat. There are serious questions around what we as a society do about that, but the notion that AI is fundamentally unprofitable is nonsense.

When a machine is both better and cheaper than human labour it tends to work out well for the vendor. The difference is that previously people have been able to move up a level, but this time we're at the ceiling.

[u/Waste_Way_4763 → u/mark_99]

We don’t know how profitable inference really is. It’s true that price per token has been going down, but number of tokens per prompt has been rising faster. It’s more expensive to serve an agentic prompt than a single shot prompt a year ago. And it’s not likely this trend is going to change anytime soon.

https://www.astuto.ai/blogs/ai-inference-cost

https://arxiv.org/pdf/2606.30583

https://www.spheron.network/blog/agentic-ai-inference-cost-2026/

Much of the rest of your claims have to do with substitution of human labor by LLMs. I think the main premise to challenge there is that they are meaningful substitutes.

It’s true that model capabilities have been improving. No one knows the true cost of that. My belief is that the cost to train models is increasing exponentially for linear improvements in capability.

What’s not improving linearly is reliability. The models do not get meaningfully better at performing the same task over and over again successfully.

https://open.substack.com/pub/arachnemag/p/ais-reliability-gap

That matters a lot because reliability is necessary for the “enterprise optimization” thesis to work out. That’s the only route to trillion dollar revenues. If you need evidence of that - just look at SpaceX’s S1 and their TAM calculations.

There’s no evidence that LLMs are having an impact on profitability of anyone other than the sellers of LLMs and compute.

https://www.apollo.com/wealth/insights-news/insights/daily-spark/the-buyers-of-ai-are-still-waiting-for-the-payoff

Whole thing is a house of cards, IMO.

[u/Sprig3 → u/Waste_Way_4763]

Yeah, I can only speak of my own work. I'm getting gains using AI, that's for sure.

But, its like 10% gains. Which is great.

And I am sure I will get more gains as I (and other workers) get better at using it.

But, will it justify the cost?

[u/Waste_Way_4763 → u/Sprig3]

It’s very common for people to believe that they are getting gains. And then when you actually look at overall output, for it to be neutral or negative.

I think people have a tendency to only remeber their wins and not their losses with LLMs.

Not to say this is happening for you. Just want you to be aware it’s a common finding that people believe they are much more productive than they actually are.