31–40 of 88 posts
Earlier quoted context omitted.
I really want to get in the mindset of people who are like "AI is totally going to fail! Haha! Now let me just use AI to write a piece about it..."
It's not inconsistent if you think of it as the finances falling apart regardless of how useful the tool is.
Interesting piece, I just wish the author had presented the data and their thesis instead of making Claude vomit out 20 pages of trash around it.
Are the contracts actually take-or-pay-style? What are the terms? What are the amounts of compute and money involved for each future time period? What happens if the datacenter costs spiral upwards? What happens to the datacenter investment if the buyer goes bankrupt, goes public, or gets acquired (potentially by the datacenter owner)?
I personally think the datacenter build-outs are going to generally be a big swing and a miss. The problem 1-2 years ago was making models good enough to be useful for a variety of tasks. Now we have that. The next problem is making the models efficient enough to run a profitable business. Recent Chinese lab model releases (because they're already constrained on compute resources) and OpenAI price cuts on Luna seem to indicate that this transition to chasing efficiency has already started.
With AI it comes down to whether the large companies orders and building of datacenters aligns with token demand. There is years worth of lag there so they kinda have to front load this by necessity
Interesting piece, I just wish the author had presented the data and their thesis instead of making Claude vomit out 20 pages of trash around it.
Earlier quoted context omitted.
"The underlying product, the model keeps improving and therefore increases its value." That's not exactly true. Yes, the fundamental capabilities of the models do seem to be growing dramatically, but the economic value of any particular model may be steady, or even falling, because of commoditization, or other issues external to the model itself. Without a moat, improvement in model capability does not necessarily tr…
The economic value may be real but the profits may not be. One thing that's clear is that there is no leading vendor in this space and there may never be one. To some extent premium models can charge a premium price but it's going to be a competitive market and the likes of Anthropic and OpenAI will not be able to sustain monopoly pricing.
It's entirely possible for this tech to be humanity altering in the long term while we are also in a huge bubble that could pop at any moment. In that way the housing analogy is apt. The utility of the houses themselves didn't change, the problem was purely with financial markets until eventually those markets made it everyone's problem.
It would be nice if the article cited the actual contracts the labs have signed so that others could also read them and draw their own conclusions (maybe it does and I got tired of the slop-like writing style too early?). Are the contracts actually take-or-pay-style? What are the terms? What are the amounts of compute and money involved for each future time period? What happens if the datacenter costs spiral upwards?…
In that world, what does Oracle do with a bunch of data centres which 1. It needs to pay for and 2. nobody needs. This is what the article is about: building supply far in advance of demand.
I get the structural comparison they are trying to make. But mortgages are not a frontier AI lab. They try to draw a comparison to the valuation of the real estate and the valuation of the hyper scalers in the markets. I would argue that the demand and valuation of a house is less elastic than AI. While a house’s value may continue to appreciate in the market there is an upper bound for the price of a house set by pe…
2. A model's value seems to be depreciating at an unbelievable rate. The most expensive top SOTA models (GPT-5, Opus 4.1) a year ago are far less capable than GPT-5.6 Luna. Compared to when those models were new, Luna costs 85% less than GPT-5 and 98% less than Opus 4.1. That's good for us consumers, but if a lab stumbles for 6-12 months, a lot of their value goes away. Especially with open models only months behind the SOTA closed models.
I stopped there. I just resist reading slop.
It would be nice if the article cited the actual contracts the labs have signed so that others could also read them and draw their own conclusions (maybe it does and I got tired of the slop-like writing style too early?). Are the contracts actually take-or-pay-style? What are the terms? What are the amounts of compute and money involved for each future time period? What happens if the datacenter costs spiral upwards?…
> The next problem is making the models efficient enough to run a profitable business. In that world, what does Oracle do with a bunch of data centres which 1. It needs to pay for and 2. nobody needs. This is what the article is about: building supply far in advance of demand.