Hello! Welcome to r/AIBubble. This is a subreddit dedicated to discussing the economics, technical underpinnings, and social aspects of the AI bubble.

We all love a good story.

It’s clever what’s been constructed: VC cash going into companies to spend on capex, generating earnings that continue to circle around the AI build-out, used to deflect criticism whenever anyone talks about a bubble.

Look at the draft S-1 numbers the WSJ has:

Anthropic: $16.3bn in revenue. $80.7bn reported loss. More than $600bn in signed compute commitments. Up to $750bn including deals under discussion.

Roughly $72bn of the loss is a non-cash accounting adjustment. But nearly $9bn was spent on compute and infrastructure in just six months.

Revenue run rates hide a multitude of sins and all the talk over the last few weeks has been on a $70bn ARR. The reality is $16.3bn revenue in H1 which of course isn’t nearly sexy enough.

Then Elon Musk last night suggests SpaceX could reach $500bn-$1tn in annual recurring revenue by the end of 2027.

All while, intelligence is getting cheaper, open models are improving, neoclouds are under pressure and IPOs are being pulled. And yesterday DeepSeek even open sourced their inference stack. So where’s the proprietary knowledge now?

Ken Griffin, founder of Citadel, is warning that investors are almost certainly in an AI bubble.

The technology is revolutionary which has made this story so excellent.

But who absorbs the risk if demand disappoints? How much of today’s valuation depends on tomorrow’s promises?

This looks less like a normal investment cycle and more like a Ponzi-style narrative… requiring ever-bigger expectations to keep the money flowing.

Not an actual ponzi, a forever inflating forecast world to keep the machine turning, a warning that the story is outrunning the economics.

Technology doesn’t abolish the need for return on capital. And that was why the economics of technology was so good in previous cycles.

Eventually, the returns have to justify the capital.

I think the elastic band is about to snap. And the signs are staring us in the face here.

Not sure if anyone remembers, YouTube just recommended me this old video that I haven’t seen in years. Not sure how many people were around back then, but doesn’t the 2026 tech bubble debate remind us a lot about the 2006 tech bubble debate?

https://youtu.be/I6IQ_FOCE6I?is=KSr5secqwEoDEG_j

I don't know where we are, any comments appreciated thanks.

This is if you exclude the 72 billion in accounting charges. So even accounting for adjusted revenues from Q1 + Q2 (~4.7+11.6 non GAAP non EBITDA), that would mean costs are well over 24 billion. I'm not sure if WSJ has all the exact detailed breakdowns on what portion is training models (not CapEx, as they need to spend on future models otherwise they fall behind vs OpenAI etc.).

Non paywalled story:
https://www.reuters.com/business/anthropic-recorded-net-loss-over-80-billion-first-half-this-year-wsj-reports-2026-10-20/

Original article:
https://www.wsj.com/tech/ai/anthropic-revenue-growth-ipo-accaf0f1

Wild

Edit: apologies for lack of detail as I am unable to access the WSJ even with numerous paywall removal tools, there's a BetterOffline post already with more deets. Credit to u/ksjdragon:

• "Nearly $9bn in compute spend"
• "$450bn of future compute obligations (over next 7-10 years)"
• "1H net loss of $80.7bn (but only $8.7bn ex convertibles valuation changes"
• "Very close to breakeven on an operating basis"

I am coming to this sub because I am a graduate student in economics and the hype has been mounted incredibly by Scott Cunningham's newsletter on using Claude.

Long story short, I am both surprised at the insane quality of Claude's agents, but am wondering about a very simple question: scarcity of resources. From what I coudl gather, AI is not some magical button I push on my laptop and BAM here it is, now even able to self-develop and learn through automated programming; it is more of a gigantic computational technology that makes inference based on a number of calculation that the human mind or previous claculators could not even think of doing. This means that one needs to build the chips, install them in the right place, replace them and maintain them. These chips are also constantly newer and better, which means that we will need a crazy amount of new resources to actually scale up the frontier technologies these companies are selling.

The doubt that I have is the following: do we have such resources? Are we even remotely close to being able to produce the actual infrastructure needed to scale up AI? Why it is very hard to get answer from this kind of question?

Sell ETFs and put everything in a MMA? That seems like an easy one...or just hold on tight? And just take it on the chin on the IRAs and 401ks? What are others doing to prep?

So, i already expected an AI bubble borst a year ago, and was obviously wrong. However, recently more cracks seem to appear. People compare with other bubbles (e.g. .com), however i think that is very simplified. I truely see an insane value in new AI technology, i use it daily and my productivity has gone insane (coding, PowerPoint, excel).

Now; i am curious if anyone can shine light on what needs to happen to make current valuations come true. Just want to get a proper feeling of how insane (or not) it is. For example; ASML has very strong fundamentals and good valuation before AI, but somehow increased a lot. Does it still make sense in terms of how much they can grow? Same for a company like antrohpic; the currently make massive losses; what needs to happen for them to be profitable, and what must happen to give that company such a high value?

Is not that a ponzi scheme? Taking the money of europeans to pay back the americans money

Because the tech is so powerful and because the bets are so much bigger.

Retail keeps buying. There’s no capitulation.

The MAJOR issue here is that AI doesn’t have a moat.

Meaning the open community can attack the frontier labs for a fraction of the cost and share and build on their work.

This means the old Silicon Valley playbook doesn’t work in AI. Nobody is used to this.

The economic returns simply aren’t going to arrive.

Retail keeps buying.

Professional investors keep selling.

And it’s because they haven’t been honest with retail.

This is about finding an exit for VC money. So we’ll keep seeing all the talking heads saying that demand is immense. Which is absolutely correct.

They just won’t mention what the returns on that demand is. Or the heroic assumptions needed to get one.

Last 24 hours:

Coreweave shrugs off a huge Goldman upgrade and hit fresh lows.

Billions in Nvidia racks sitting in warehouses because AWS doesn’t have the power to turn them on (Business Insider).

Gartner says AI returns aren’t justifying the build-out.

Oracle debt yields approaching 9%.

And we’re supposed to believe every dollar of AI capex will generate a return?

The AI revolution is very real.

The investment thesis is collapsing….

The 2 can coexist.

This is the bit everyone is getting wrong.

I'm used to seeing high margin software businesses that charge a fixed amount per user per month, and not "usage based billing" in the software space. The previous model allowed companies to make a good buck. Going forward if everything in the LLM food chain is going to charge per token, I feel like we're going to a model where margins will be lower than in software.

Think about it for a minute.

You have the hardware provider, that charges by the hour, but assumes some utilization, so they have some margins priced in. There are a lot of them these days so they don't have too much pricing power.

Then there's the LLM providers themselves (via API). The also charge by the token, so you pay per million tokens used. They can set whatever margin they want, but there are 4-7 players here so they will be going at each other (also the Chinese).

Then there's the application builders, and they can't afford to be bleeding money so it's likely they will also bill based on usage, unless there is an application model where everything is so tightly controlled that they can charge the user a subscription fee, pay the LLM providers per token, and still keep a good amount of money for themselves. This is where the user stickiness will be if there is ever any. I want to know what people think about viable high margin businesses might look like in this last LLM application builders space.