- revenue growth at Anthropic slowing

- margins shrinking at Anthropic

- Google getting thwacked by missed earnings

- OpenAI missing funding target

- price war

- Microsoft revising capex projections down

- Plateauing of model capability improvement

What's weird to me is that you don't see a lot of people making deals that seem way too good to be true but yet the total numbers are absolutely gigantic. Throw on the fact that the stranded assets will have a shelf life, it really seems terrifying. Yet everything is being priced as pretty darn risky. I think maybe there can be a big slowdown without a dramatic pop. A sort of grind down. But there's not a lot of room for error to prevent a pop so I guess it will happen? I don't know its very weird.

Also of course this is all predicated on models not making another dramatic leap. The signals out of the cutting edge labs don't really suggest they have a model that would be as dramatic of a leap as Mythos was when it came out in the hopper. Their code quality seems about as mediocre as what the publicly available models can do. The OpenAI math results are very impressive but that's ripe picking for AI. Also they keep talking about a coordinated training slowdown which is textbook antitrust so they seem to want the government to force their hands off the "spend money on R&D" button.

So I don't really know. I guess I would say purely on vibes that this doesn't feel like a bubble but the size of the build out is just so massive that there really isn't a big window for a success case.

The web of circular financing that is going on leads to higher risk. If some ventures fail, and are unable to supply compute they are obligated to, or are unable to pay for compute they are obligated to buy, this could cause a series of financial problems for other related businesses.

I also think that some businesses may find that it might not (yet) be cost effective to focus AI use on every problem.

The pushback against data centers is getting bigger too. But america at least is usually pretty industry friendly so I'm not too sure how much it'll delay planned projects. I've read the limiting factor is sometimes just finding a source of power that can consistently deliver the electricity needed for the DCs that companies want to build.

Big enough companies who could be the best customers can run models on their infra for a fraction of the price. Eg: Revolut.

https://dealroom.co/news/169124-revolut-moves-2-000-engineer...

The biggest metric is that anthropic make ~$2 for every $1 of compute.

Two things:

1) it requires a massive money pit to make that compute worth anything

2) it requires charging a premium price

To compare to two previous bubbles; railways in the UK and USA.

running trains was really quite cheap and engines weren't expensive per passenger served. Compared to stage coaches, the number of staff needed was < 50%. the Massive fucking problem was that it needed GDPs worth of money to make the railway in the first place. Moreover as more railways were put in it significantly altered profitably of railways near them. Lines that were there for produce moving suddenly lost profitably because the produce they were moving fell out of fashion

Inflation, house prices, gas prices, interest rates, huge public distrust of government and corporations, votes to restrict data center construction, endless war in Iran, etc.

> pareto frontier gap decreasing by the month between open and closed models

Oh? This is the first I've heard that.

Some of these red flags aren’t real or backed up by much of anything.

- Is it uncommon or alarming for an IPO to be repriced?

- I’m not sure I see strong evidence to show that open weight models are making significant progress to catching up (and I’d argue that the model quality isn’t even where the business value is)

- What does “SpaceX disappointment” mean exactly?

- Are enterprise buyers buying raw tokens or are they buying productivity tooling for their employees? Not many companies switched to LibreOffice because it was cheaper than Microsoft Office.

- Look up BLS unemployment data, what job loss and entry level hiring collapse are you talking about?

- Does discounts on debt point to anything? Genuinely don’t know.

- What about the Chinese arms race is concerning? Isn’t an arms race evidence of value? There wouldn’t be an arms race if this technology wasn’t worth investing in. Doesn’t China’s desires to compete in this market bolster its merit?

- What is “possible downward spiral” and how do we know that’s real? Isn’t there a “possible downward spiral” with every industry at all times? This seems like pure speculation.

- job loss and entry level hiring collapse --> I will out that as in the group "Not a bubble" (Not that I am thinking it myself that it's not a bubble).

It looks like the end goal of replacing labor by capital is working.

Political resistance against data center build-outs

I generally agree with you, but have to get this off my chest.

> pareto frontier gap decreasing by the month between open and closed models

We need a temporary moratorium on the words "pareto frontier". Every new model is the pareto frontier by some combination of metrics. Like "Bayesian Priors", people love saying / tweeting it because it sounds Smart.

It's like watching car commercials where every single vehicle has won something from JD Power and Associates. Which, (a), who the hell are they? and (b) you can only pass out so many awards until the gluttony of awards proves the award is worthless.

This isn't targeted to you! It's a pet peeve.

Also - all the gee-whiz AI companies are headed by people who don't have great business backgrounds, either by people from tech backgrounds (chasing the last of the VC), or by people with the kind of "interesting" background who you wouldn't lend your own money!

> Am I missing anything?

- the general lack of merit with regard to most of the words in the article

- this post being front page

> - pareto frontier gap decreasing by the month between open and closed models

This is a problem for Anthropic and OpenAI, sure, but why does it matter for e.g. Nvidia (or any of its suppliers) who will use their GPUs for training?

We need to separate the model companies from the shovel-sellers in these "AI bubble" conversations.

Maybe this is what you meant by "Chinese arms race" but there is, though not quickly, a massive wave of increased hardware supply incoming. Again, let me mention this isn't coming soon in terms of the AI bubble conversation. But China is pushing for a home grown EUV and fabs that should start coming online and having an impact in 2030.

The reason that is important is it will fuel the competitiveness of on premises and local open weight models.

Though maybe it's less useful in the "bubble" conversation since over four more years you could fit in various booms and busts before the supply side significantly starts changing.

unless I am mistaken, you missed that the datacenter infrastructure being a one-time cost. nope. those servers have about a 5 year lifetime and then are likely to be replaced. The buildings aren't the expensive part. The GPUs are.

so I think that's one big miss from this blog, or I don't understand datacenters as well as I think I do (which is always a possibility! hopefully someone will inform me if I am wrong)

Musk's claim of Softbank defaulting on turbine orders for datacentres.

As the friendly article points out, the "AI is a bubble" hypothesis and "entry level hiring is collapsing because AI is replacing their jobs" hypothesis are not really compatible.

As always, reality is likely a muddled mix: there is real utility and there is wasted investment that will never pay itself back.