Every round Anthropic raises twists the knife deeper in SBF. If only he could have survived the downturn his Antropic investment alone probably could have papered over the other loses.
FTX creditors should be seeing red. the trustee sold Anthropic out at the bottom. Same for crypto. Hindsight is 20-20, but imagine had CZ not made those tweets of divesting from the FTT token. FTX could have possibly weathered the final 3 months of the BTC bear market and then reaped the post-2023 AI and crypto bull market. Sam would have gone from pauper in jail to brilliant investor in Anthropic, mogul, and so on.
The compute moat is getting absolutely insane. We're basically at the point where you need a small country's GDP just to stay in the game for one more generation of models.
What gets me is that this isn't even a software moat anymore - it's literally just whoever can get their hands on enough GPUs and power infrastructure. TSMC and the power companies are the real kingmakers here. You can have all the talent in the world but if you can't get 100k H100s and a dedicated power plant, you're out.
Wonder how much of this $13B is just prepaying for compute vs actual opex. If it's mostly compute, we're watching something weird happen - like the privatization of Manhattan Project-scale infrastructure. Except instead of enriching uranium we're computing gradient descents lol
The wildest part is we might look back at this as cheap. GPT-4 training was what, $100M? GPT-5/Opus-4 class probably $1B+? At this rate GPT-7 will need its own sovereign wealth fund
That’s like being upset that you can’t dig your own suez canal.
So long as there is competition it’ll be available at marginal cost. And there is plenty of innovation that can be done on the edges, and not all of machine learning is LLMs.
My hope is that this hype cycle overbuilds nuclear power capacity so much that we end up using it to sequester carbon dioxide from the atmosphere once the bubble pops and electricity prices become negative for most of the day.
In the medium term China has so much spare capacity that they maybe be the only game in town for highend models, while the US will be trying to fix a grid with 50 years of deferred maintenance.
That’s probably what the companies spending the money think, that they’re building a huge moat. There’s an alternative view. If there’s a bubble and all these companies are spending these huge sums on something that ends up not returning that much on that investment, and the models plateau and eventually smaller, cheaper, self-runnable open source versions get 90% of the way there, what’s going to happen to that moat? And the companies that over spent so much?
If the diminishing returns that we see now continue to prove true, ChatGPT6 will already be financially not viable so I doubt there will be GPT7 that can live up to the big version bump.
Many folks already consider GPT5 to be more like GTP4.1. I personally am very bearish on Anthropic and OpenAI.
This is why Nvidia is the most valuable company in the world. Ultimately all these investment rounds for LLM companies are just going to be spent on Nvidia products.
Most of that power usage is moving data and weights into multiply accumulate hardware, then moving the data out. The actual computation is a fairly small fraction of the power consumed.
It's quite likely that an order of magnitude improvement can be had. This is an enormous incentive signal for someone to follow.
There is no generational differences between these models. I tested cursors with all different backends and they are similar in most cases. So called race is just a Wall Street sensation to bump the stock price.
I sincerely hope this whole LLM monetization scheme crashes and burns down on these companies.
I really hope we can get to a point where modest hardware will achieve similar results for most tasks and these insane amount of hardware will only be required for the most complex requests only, which will be rarer, thereby killing the business case.
I would dance the Schadenfreude Opus in C major if that became the case.
Bubble will only burst when market runs out of money or new model releases provide little to none improvements. Or someone actually creates a real AGI. No matter how low that chance is, the FOMO among investors must be crazy.
Their projections for ARR at the end of this year at a high of $9B[1] at the end of this year. And reported gross margins of 60% (-30% with cloud providers partnerships). All things considered, if this pans out, it's a 20x multiple. High yes, but not that crazy. Specially considering their growth rate and that too at a decent margin at gm level.
[1]: It was $3B at the end of May (so likely $250M in May alone), and $5B at end of july (so $400M that month).
I don't get the sky high valuation of LLM companies. I mean I get that these guys need a lot of money for compute to train the next generation of models. But Distillation does make it easy for other providers to replicate gains made by these providers at a much lower cost.
On a long enough timeframe, the open source models will catch up to the proprietary models and inference providers will beat these proprietary companies on price.
Impressive round but it seems unlikely this game can go on much longer before something implodes. Given the amount of cash you need to set of fire to stay relevant it’s becoming nearly impossible for all but a few players to stay competitive, but those players have yet to demonstrate a viable business model.
With all these models converging, the big players aren’t demonstrating a real technical innovation moat. Everyone knows how to build these models now, it just takes a ton of cash to do it.
This whole thing is turning into an expensive race to the bottom. Cool tech, but bad business. A lot
of VC folks gonna lose their shirt in this space.
Very happy for them - curious if the funding will help with the current capacity issues.
5 minutes into my first opus prompt on Claude Code on an empty repo, I've already been warned by Claude Code that I'm about to hit my opus limit despite not using it in 12 days.
Interesting that investors pay so many billions for a product that just iterates until something, somehow compiles but emits subtle garbage.
Intellectual engagement goes down, users get dumber and only look at quantity. China is taking first steps to continue its excellence. In the New York Post of all places:
Very interesting to see firms who already bet big on OpenAI (like Altimeter) on the list for this round. Anyone else remember when OpenAI told investors they couldn’t invest in competitors [1]?
79 comments
[ 3.3 ms ] story [ 81.8 ms ] threadWhat gets me is that this isn't even a software moat anymore - it's literally just whoever can get their hands on enough GPUs and power infrastructure. TSMC and the power companies are the real kingmakers here. You can have all the talent in the world but if you can't get 100k H100s and a dedicated power plant, you're out.
Wonder how much of this $13B is just prepaying for compute vs actual opex. If it's mostly compute, we're watching something weird happen - like the privatization of Manhattan Project-scale infrastructure. Except instead of enriching uranium we're computing gradient descents lol
The wildest part is we might look back at this as cheap. GPT-4 training was what, $100M? GPT-5/Opus-4 class probably $1B+? At this rate GPT-7 will need its own sovereign wealth fund
So long as there is competition it’ll be available at marginal cost. And there is plenty of innovation that can be done on the edges, and not all of machine learning is LLMs.
In the medium term China has so much spare capacity that they maybe be the only game in town for highend models, while the US will be trying to fix a grid with 50 years of deferred maintenance.
This article is a good example of the bear case https://www.honest-broker.com/p/is-the-bubble-bursting
And then we’ll realize we wasted an entire Apollo space program to build an over-complicated autocompleter.
If the diminishing returns that we see now continue to prove true, ChatGPT6 will already be financially not viable so I doubt there will be GPT7 that can live up to the big version bump.
Many folks already consider GPT5 to be more like GTP4.1. I personally am very bearish on Anthropic and OpenAI.
It's quite likely that an order of magnitude improvement can be had. This is an enormous incentive signal for someone to follow.
Does this really describe a "most" ør are you just describing capital?
The capitalization is getting insane. Were basically at the point where you ned more capital than a small nations GDP.
That sounds mich more accurate to my ears, and much more troubling
I really hope we can get to a point where modest hardware will achieve similar results for most tasks and these insane amount of hardware will only be required for the most complex requests only, which will be rarer, thereby killing the business case.
I would dance the Schadenfreude Opus in C major if that became the case.
I am very curious about the GAAP numbers here.
[1]: It was $3B at the end of May (so likely $250M in May alone), and $5B at end of july (so $400M that month).
This guy's analysis says they are bleeding out despite massive revenue
https://www.wheresyoured.at/anthropic-is-bleeding-out/
On a long enough timeframe, the open source models will catch up to the proprietary models and inference providers will beat these proprietary companies on price.
I got the impression that some people were reselling access and adding layers of fees to profit from the hype.
chat gpt 5 in codex is really good
so much that i stopped used claude code altogether
cheaper too
made me realize nobody has moat, coders especially will just go to whoever provides best bang for their buck.
Are they putting Canadian public funds into Anthropic?
With all these models converging, the big players aren’t demonstrating a real technical innovation moat. Everyone knows how to build these models now, it just takes a ton of cash to do it.
This whole thing is turning into an expensive race to the bottom. Cool tech, but bad business. A lot of VC folks gonna lose their shirt in this space.
5 minutes into my first opus prompt on Claude Code on an empty repo, I've already been warned by Claude Code that I'm about to hit my opus limit despite not using it in 12 days.
Intellectual engagement goes down, users get dumber and only look at quantity. China is taking first steps to continue its excellence. In the New York Post of all places:
https://nypost.com/2025/08/19/world-news/china-restricts-ai-...
"It’s just one of the ways China protects their youth, while we feed ours into the jaws of Big Tech in the name of progress."
That applies to individuals, but it probably also applies to companies. We're in an AI boom? Raise some money while it's easy.
[1]https://www.reuters.com/technology/openai-tells-investor-not...