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

When? Forecasters say start of 2027 and some say late November 2026 yet i cannot see a major outbreak in the bubble
Please give your estimates

Guys,

I came across these two short documentaries. The first one is when the dot com bubble was hot 🔥. The second one is after the bubble burst. I will let you watch these two and decide for yourself. Top notch stuff in my opinion.

During market mania: https://youtu.be/uaK5tsH59UM?si=l10xyXrglOv3SflW

After the Bubble Burst: https://youtu.be/DSVPsP0Bfx0?si=WDcU02DNvNsvExhv

How can they still stay that hot if they continue losing a ton?

For a few years now the hyperscaler tech companies have been spending agressively, but it seems to me like spending this money is actually the easy part of the equation.

If we add up the contracted, not yet delivered cloud demand (Remaining Performance Obligations - RPO) of the hyperscalers (Microsoft, Oracle, Google and Amazon), we get a sum of about $2.3 Trillion (We still don't know if this will ever get paid but still RPO is a better estimate than a forecast & a big portion of it is from frontier AI labs like OpenAI and Anthropic). In order to serve this massive RPO, MSFT, GOOGL, AMZN, and META are spending about $660 Billion a year, moving from asset-light software companies into owners of physical plants, real estate, and equipment.

The real battle for big tech isn't buying servers anymore, but rather securing 24/7 power. That kind of energy commitment completely breaks traditional budgeting. As JPMorgan put it, "money can't buy you electric power" when a project's start date depends on a years-long grid-connection queue.

Now the interesting part is that if we follow the money trail down the supply chain, we can see exactly who is extracting the value. It starts with the regulated utilities like Southern, Duke and Dominion who are accelerating capex to build grid capacity. These guys face huge regulatory hurdles and political blowback from residential ratepayers over increasing electricity bills. To go around the regulations, NextEra for instance is using a hybrid strategy by restarting a nuclear plant (Duane Arnold) for Google.

Because the public grid is too slow, big tech are chasing independent producers like Constellation, Vistra, and Talen energy. These companies have 24/7 power and they spend almost nothing to expand. Amazon and Meta are locking up deals with Vistra, Microsoft contracted to restart the Three Mile Island in Pennsylvania, and Google signed for small modular reactors with Kairos.

One layer down, we arrive to the equipment makers like Vertiv, GE Vernova, Eaton and Caterpillar which are facing an increasing days inventory outstanding (DIO). Usually this is looked at as constraint on these companies, but as the gross margins also keep on rising for some of these companies, even hitting a record for Vertiv and GE Vernova, this is pure pricing power for them.

There are so many other layers that plays into this like, cooling systems, precision HVAC, advanced packaging & foundry equipment, copper mining & refining for power grids, etc etc. that won't fit into this single post.

The takeaway is big tech is bleeding money and the wealth is pooling at the bottom with nuclear operators collecting and hardware manfucaturers hitting record margins while the the payers wait for delivery.

If you're interested to read more, I have the full write up with plots & figures on my free substack:

https://secaura.substack.com/p/the-two-trillion-dollar-bottleneck

I came across this cool website/tool that tracks the spending and revenue from the top AI companies. Amazon is leading with most money spent ($330 billion spent and has only made $22 billion from it).

I recently started following the AI industry so this tool this gave me a visual perspective of how absurd all this spending is getting.

Even DLSS seems to be a blurry mess, halving framerates while eating NVRAM 🤡

Been sitting with an observation and want to know if it holds up.

The capex is undeniable: Microsoft, Amazon, Google, Meta are committing enormous sums to GPUs and accelerators. The standard explanation is AI demand plus long fab lead times. I think there's a simpler structural reason underneath.

Code is information. Once it's written, it copies at zero cost. Open source has been moving down the software stack for decades — operating systems, databases, dev tools, and now AI models. Every time a free equivalent appears, the pricing power of the proprietary version erodes. The advantage of "we own this code" gets weaker over time.

So the question is: if the code itself stops being the thing you can charge a premium for, where does the value go?

To the parts that can't be copied.

- The physical layer: fabs, advanced chips, memory, energy. You can open-source a design, but you can't open-source the wafer or the power.

- Proprietary data, regulatory licenses, compliance certs, distribution, the installed base.

- Operating expertise and time.

None of that is reproducible by releasing code. As software becomes a free utility, the rent migrates to the scarce physical and regulatory layer next to it.

If that's true, then the hardware build-out makes sense as a direct response: when the code is no longer the moat, you secure the layer that open source can't hand anyone. You don't need to out-build the open-source commons on software if you own the compute everyone has to run on.

A few things that push against this read:

- Most of the buying looks like genuine demand for AI inference and training, not a strategic pivot away from software.

- The lead times on fabs and accelerators force buying ahead regardless of strategy.

- The hoarding is largely hyperscalers positioning against each other, not against open-source developers.

- The heaviest buyers (Google, Meta, Microsoft) are also the heaviest open-source publishers. They use OSS to commoditize rivals, not flee from it.

So maybe it's just platform strategy plus AI demand. But the "value moves from code to compute because code is now free" story lines up with the spending in a way I can't entirely dismiss.

Where does this break down? Is the hardware accumulation really about securing the scarce layer, or am I over-reading a demand-and-supply-chain story?

AI is like nearly every powerful technology since the Industrial Revolution: It rewards individuals for behavior that collectively exhausts a shared resource (in this case, human attention). 

(Link to video of Elon Musk.)

My prediction: before the end of 2026, it will be clear that OpenAI will go bankrupt (hopefully without going public beforehand), with all domino's falling after that. And then we'll be hearing these lines (all starting with: "we could have never predicted this"):

"The safety concerns made us slow down, cause we are morally superior to China."

"The power grid buildout was too slow and so we got overtaken by China."

"The chip development wasn't as fast as was promised."

"The chip development was too fast and therefore the ones we were sold became obsolete too soon."

"We really expected the model development to improve exponentially." (Although this one is probably too hurtful for the egos.)

Other suggestions?

**Before you downvote ->** ***This is a humorous post...***

(one of)The world's worst late stage capitalism ideas.

Graphics cards(GPUs) have built-in AI token limits for offline AI models.

A graphics card comes with a set limit of AI tokens.

If you reach 7 million tokens you need a new graphics card. It's still functional for everything else but if any application or game uses AI that needs tokens it uses the GPU token budget. After the budget is reached the hardware disables Tensor Cores and CUDA cores or whatever the GPU uses for running AI models.

You get entry level amounts with 2 million or high end amounts with 10 billion. The graphics card is still ranked separately. You could purchase a 5060 with 10 billion tokens or a 5090 with 2 million. But at the end of the day once you reach your local offline token limit there is no more local AI support, and any game or application that needs any form of AI tokens will not be able to work.

A core element of the potential AI bubble is really just the cost of token supply — the "production" cost. If the tokens being offered end up more expensive than what it costs companies to produce them, then we're actually in relatively good shape, and Nvidia's circular economy financing plans probably work out.

Where do we stand on token economics?

Does anyone have good insights?

I kind of agree with the bubble concern. However, I find it quite fascinating to have the equivalent of 10 (or more) "experts" developing my own ideas iteratively — you still need human oversight, though.

Not too long ago, "computer" was actually a job title — rooms full of people doing calculations by hand. Now we simply do it in an Excel sheet with a few clicks.

The real question also isn't if there's a bubble, but rather how far along we are toward popping it.

A core element of the potential AI bubble is basically just the cost of token supply/'production' — because if the tokens being offered are more expensive than what it costs companies to produce them, then we're actually relatively in the clear, and Nvidia's circular economy financing plans are probably going to work out...

Where do we stand on token economics?

Does anyone have good insights?

Thanks.

In their quarterly press release last night, NVIDIA wrote that "now, compute is revenue".

Can anyone recall a claim this confident and arrogant from a public company in the past? Have you ever seen an example of a CEO literally equating their product to money in their customers' pocket? I have not, and I have been looking.

It's easy to say, "yeah, but they're crushing it, what company this size grows 100% y/y?". This is true. But I am more focused on the behavioral elements at play. NVIDIA knew they could make such a ridiculous statement because they knew that their audience already believed it! The reason no one is talking about this statement is because, for many people, it simply was not surprising. And yet it is without precedent in corporate history, and is on its face a totally absurd claim. Of course compute is not revenue, compute is compute.

The next logical step is this. If this many people believe this, and it simply isn't true, what does that say about the current valuation of NVIDIA stock and the other stocks in the AI ecosystem.

The best investment opportunities always come from falsely held beliefs in others.