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
The whole LLM era is horrible. All the innovation is coming "top-down" from very well funded companies - many of them tech incumbents, so you know the monetization is going to be awful. Since the models are expensive to run it's all subscription priced and has to run in the cloud where the user has no control. The hype is insane, and so usage is being pushed by C-suite folks who have no idea whether it's actually benefiting someone "on the ground" and decisions around which AI to use are often being made on the basis of existing vendor relationships. Basically it's the culmination of all the worst tech trends of the last 10 years.
In a previous generation, the enabler of all our computer tech innovation was the incredible pace of compute growth due to Moore's Law, which was also "top-down" from very well-funded companies since designing and building cutting edge chips was (and still is) very, very expensive. The hype was insane, and decisions about what chip features to build were made largely on the basis of existing vendor relationships. Those companies benefited, but so did the rest of us. History rhymes.
This is very pessimistic take. Where else do you think the innovation would come from? Take cloud for example - where did the innovation come from? It was from the top. I have no idea how you came to the conclusion that this implies monetization is going to be awful.
How do you know models are expensive to run? They have gone down in price repeatedly in the last 2 years. Why do you assume it has to run in the cloud when open source models can perform well?
> The hype is insane, and so usage is being pushed by C-suite folks who have no idea whether it's actually benefiting someone "on the ground" and decisions around which AI to use are often being made on the basis of existing vendor relationships
There are hundreds of millions of chatgpt users weekly. They didn't need a C suite to push the usage.
> All the innovation is coming "top-down" from very well funded companies - many of them tech incumbents
What I always thought was exceptional is that it turns out it wasn't the incumbents who have the obvious advantage.
Take away the fact that everyone involved is already at the top 0.00001% echelon of the space (Sam Altman and everyone involved with the creation of OpenAI), but if you had asked me 10 years ago who will have the leg up creating advanced AI I would have said all the big companies hoarding data.
Turns out just having that data wasn't a starting requirement for the generation of models we have now.
A lot of the top players in the space are not the giant companies with unlimited resources.
Of course this isn't the web or web 2.0 era where to start something huge the starting capital was comparatively tiny, but it's interesting to see that the space allows for brand new companies to come out and be competitive against Google and Meta.
> All the innovation is coming "top-down" from very well funded companies - many of them tech incumbents
The model leaders here are OpenAI and Anthropic, two new companies. In the programming space, the next leaders are Qwen and DeepSeek. The one incumbent is Google who trails all four for my workloads.
In the DevTools space, a new startup, Cursor, has muscled in on Microsoft's space.
This is all capital heavy, yes, because models are capital heavy to build. But the Innovator's Dilemma persists. Startups lead the way.
This is only if you ignore the growing open source models. I'm running Qwen3-30B at home and it works great for most of the use cases I have. I think we're going to find that the optimizations coming from companies out of China are going to continue making local LLMs easier for folks to run.
> The whole LLM era is horrible. All the innovation is coming "top-down" from very well funded companies
Wouldn't it be the same for the hardware companies? Not everyone could build CPUs as Intel/Motorola/IBM did, not everyone could build mainframes like IBM did, and not everyone could build smart phones like Apple or Samsung did. I'd assume it boils down the value of the LLMs instead of who has the moat. Of course, personally I really wish everyone can participate in the innovation like the internet era, like training and serving large models on a laptop. I guess that day will come, like PC over mainframes, but just not now.
Any stall in progress either on chips or smartness/FLOP means there's a lot of surplus previous generation gear that can hang and commoditize it all out to open models.
Just like how the "dot com bust" brought about an ISP renaissance on all the surplus, cheap-but-slightly-off-leading-edge gear.
IMO that's the opportunity for a vibrant AI ecosystem.
Of course, if they get to cheap AGI, we're cooked: both from vendors having so much control and the destabilization that will come to labor markets, etc.
Nevertheless, prices for LLM at any given level of performance have gone down precipitously over the past few years. Regardless of how bad it seems the decisions being made are, the decision making process both is making an extreme amount of money for those in the AI companies, and providing extremely cheap and high quality intelligence for those using their offerings.
What's the counterfactual? Where would the world be today? Certainly the present is not an optimal allocation of resources, uncertainty and hysteris make it impossible. But where do you think we'd be instead? Are you assuming all of those dollars would be going to research otherwise? They wouldn't; if not for hype "ai" LLMs, research funding would be at 2017+/- 25% levels. Also think of how many researchers are funded and PhDs are trained because of this awful LLM era. Certainly their skills transfer. (Not that brute forcing with shit tons of compute is
standard "research funding").
And for the record I really wish more money was being thrown outside of LLM.
How can you dismiss the value of the tech so blatantly? Have you used Opus for general questions and coding?
> no idea whether it's actually benefiting someone "on the ground"
I really don't get it. Before, we were farmers plowing by hand, and now we are using tractors.
I do totally agree with your sentiment that it's still a horrible development though! Before Claude Code, I ran everything offline, all FOSS, owned all my machines, servers etc. Now I'm a subscription user. Zero control, zero privacy. That is the downside of it all.
Actually, it's just like the mechanisation of farming! Collectivization in some countries was a nightmare for small land owners who cultivated the land (probably with animals). They went from that to a more efficient, government controlled collective farm, where they were just a farm worker, with the land reclaimed through land reform. That was an upgrade for the efficiency of farming, needing fewer humans for it. But a huge downgrade for the individual small-scale land owners.
It's not clear to me that each new generation of models is going to be "that" much better vs cost.
Anecdotally moving from model to model I'm not seeing huge changes in many use cases. I can just pick an older model and often I can't tell the difference...
Video seems to be moving forward fast from what I can tell, but it sounds like the back end cost of compute there is skyrocketing with it raising other questions.
We do seem to be hitting the top of the curve of diminishing returns. Forget AGI - they need a performance breakthrough in order to stop shoveling money into this cash furnace.
> Anecdotally moving from model to model I'm not seeing huge changes in many use cases.
Probably because you're doing things that are hitting mostly the "well-established" behaviors of these models — the ones that have been stable for at least a full model-generation now, that the AI bigcorps are currently happy keeping stable (since they achieved 100% on some previous benchmark for those behaviors, and changing them now would be a regression per those benchmarks.)
Meanwhile, the AI bigcorps are focusing on extending these models' capabilities at the edge/frontier, to get them to do things they can't currently do. (Mostly this is inside-baseball stuff to "make the model better as a tool for enhancing the model": ever-better domain-specific analysis capabilities, to "logic out" whether training data belongs in the training corpus for some fine-tune; and domain-specific synthesis capabilities, to procedurally generate unbounded amounts of useful fine-tuning corpus for specific tasks, ala AlphaZero playing unbounded amounts of Go games against itself to learn on.)
This means that the models are getting constantly bigger. And this is unsustainable. So, obviously, the goal here is to go through this as a transitionary bootstrap phase, to reach some goal that allows the size of the models to be reduced.
IMHO these models will mostly stay stable-looking for their established consumer-facing use-cases, while slowly expanding TAM "in the background" into new domain-specific use-cases (e.g. constructing novel math proofs in iterative cooperation with a prover) — until eventually, the sum of those added domain-specific capabilities will turn out to have all along doubled as a toolkit these companies were slowly building to "use models to analyze models" — allowing the AI bigcorps to apply models to the task of optimizing models down to something that run with positive-margin OpEx on whatever hardware that would be available at that time 5+ years down the line.
And then we'll see them turn to genuinely improving the model behavior for consumer use-cases again; because only at that point will they genuinely be making money by scaling consumer usage — rather than treating consumer usage purely as a marketing loss-leader paid for by the professional usage + ongoing capital investment that that consumer usage inspires.
The raw model scale is not increasing by much lately. AI companies are constrained by what fits in this generation of hardware, and waiting for the next generation to become available. Models that are much larger than the current frontier are still too expensive to train, and far too expensive to serve them en masse.
In the meanwhile, "better data", "better training methods" and "more training compute" are the main ways you can squeeze out more performance juice without increasing the scale. And there are obvious gains to be had there.
> Anecdotally moving from model to model I'm not seeing huge changes in many use cases. I can just pick an older model and often I can't tell the difference...
Model specialization. For example a model with legal knowledge based on [private] sources not used until now.
The wildest part is that the frontier models have a lifespan of 6 months or so. I don't see how it's sustainable to keep throwing this kind of money at training new models that will be obsolete in the blink of an eye. Unless you believe that AGI is truly just a few model generations away and once achieved it's game over for everyone but the winner. I don't.
It is being played like a winner-takes-it-all right now (it may or may not be such a market). So it is a game of being the one that is left standing, once the others fall off. In this kind of game, speeding more is done as a strategy to increase the chances of other competitors running out of cash or otherwise hitting a wall. Sustainability is the opposite of the goal being pursued... Whether one reaches "AGI" is not considered important either, as long as one can starve out most competitors.
And for the newcomers, the scale needs to be bigger than what the incumbents (Google and Microsoft) have as discretionary spending - which is at least a few billion per year. Because at that rate, those companies can sustain it forever and would be default winners. So I think yearly expenditure is going to be 20B year++
They are only getting deprecated this fast because the cost of training is in some sense sustainable. Once it is not, then they will no longer be deprecated so fast.
> “There's kind of like two different ways you could describe what's happening in the model business right now. So, let's say in 2023, you train a model that costs 100 million dollars.
>
> And then you deploy it in 2024, and it makes $200 million of revenue. Meanwhile, because of the scaling laws, in 2024, you also train a model that costs a billion dollars. And then in 2025, you get $2 billion of revenue from that $1 billion, and you spend $10 billion to train the model.
>
> So, if you look in a conventional way at the profit and loss of the company, you've lost $100 million the first year, you've lost $800 million the second year, and you've lost $8 billion in the third year. So, it looks like it's getting worse and worse. If you consider each model to be a company, the model that was trained in 2023 was profitable.”
> ...
>
> “So, if every model was a company, the model is actually, in this example, is actually profitable. What's going on is that at the same time as you're reaping the benefits from one company, you're founding another company that's like much more expensive and requires much more upfront R&D investment. And so, the way that it's going to shake out is this will keep going up until the numbers go very large, the models can't get larger, and then it will be a large, very profitable business, or at some point, the models will stop getting better.
>
> The march to AGI will be halted for some reason, and then perhaps it will be some overhang, so there will be a one-time, oh man, we spent a lot of money and we didn't get anything for it, and then the business returns to whatever scale it was at.”
> ...
>
> “The only relevant questions are, at how large a scale do we reach equilibrium, and is there ever an overshoot?”
That's why wealthy investors connected to the AI industry are also throwing a lot of money into power generation startups, particularly fusion power. I doubt that any of them will actually deliver commercially viable fusion reactors but hope springs eternal.
Imagine solving energy as a side effect of this compute race. There's finally a reason for big money to be invested into energy infrastructure and innovation to solve a problem that can't be solved with traditional approaches.
Continuing to carve out economies of scale in battery + photovoltaic for another ten doublings has plenty of positive externalities.
The problem is that in the meantime, they're going to nuke our existing powergrid, created in the 1920's to 1950's to serve our population as it was in the 1970's, and for the most part not expanded since. All of the delta is in price-mediated "demand reduction" of existing users.
> 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.
For what it is worth, $13 billion is about the GDP of Somalia (about 150th in nomimal GDP) with a population of 15 million people.
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.
Also not all compute was necessary for the final model, a large chunk of it is trial and error research. In theory, for $1B you spent training the latest model, a competitor will be able to do it after 6 months with $100M.
Not only are the actual models rapidly devaluing, the hardware is too. Spend $1B on GPUs and next year there's a much better model out that's massively devalued your existing datacenter. These companies are building mountains of quicksand that they have to constantly pour more cash on else they be reduced to having no advantage rapidly.
I saw a story posted on reddit that U.S. engineers went to China and said the U.S. would lose the A.I. game because THE ENERGY GRID was much worse than China's.
That's just pure insanity to me.
It's not even Internet speed or hardware. It's literally not having enough electricity. What is going on with the world...
Assuming the best case: we're going to need to turn this productivity into houses or lifestyle improvement, soon... or I'm just going out with Sasquatch
I feel like it's pretty settled that they are a little bit useful, as a faster search engine, or being able to automatically sort my emails. But the value is nowhere near justifying the investment.
In this imaginary timeline where initial investments keep increasing this way, how long before we see a leak shutter a company? Once the model is out, no one would pay for it, right?
Whatever happens if/when a flagship model leaks, the legal fallout would be very funny to watch. Lawyers desperately trying to thread the needle such that training on libgen is fair use, but training on leaked weights warrants the death penalty.
In this imaginary reality where LLMs just keep getting better and better, all that a leak means is that you will eat-up your capital until you release your next generation. And you will want to release it very quickly either way, and should have a problem for a few months at most.
And if LLMs don't keep getting qualitatively more capable every few months, that means that all this investment won't pay off and people will soon just use some open weights for everything.
You can't run Claude on your PC; you need servers. Companies that have that kind of hardware are not going to touch a pirated model. And the next model will be out in a few months anyway.
gpt-oss-120b has cost OpenAI virtually all of my revenue, because I can pay Cerebras and Groq a fraction of what I was paying for o4-mini and get dramatically faster inference, for a model that passes my eval suite. This is to say, I think high-quality "open" models that are _good enough_ are a much bigger threat. Even more so since OpenRouter has essentially commoditized generation.
Each new commercial model needs to not just be better than the previous version, it needs to be significantly better than the SOTA open models for the bread-and-butter generation that I'm willing to pay the developer a premium to use their resources for generation.
There’s the opportunity cost here of those resources (and not talking only about the money) not being spent on power generating that actually benefits the individual consumer.
You think any of these clusters large enough to be interesting, aren't authorized under a contractual obligation to run any/all submitted state military/intelligence workloads alongside their commercial workloads? And perhaps even to prioritize those state-submitted workloads, when tagged with flash priority, to the point of evicting their own workloads?
(This is, after all, the main reason that the US "Framework for Artificial Intelligence Diffusion" was created: America believed China would steal time on any private Chinese GPU cluster for Chinese military/intelligence purposes. Why would they believe that? Probably because it's what the US thought any reasonable actor would do, because it's what they were doing.)
These clusters might make private profits for private shareholders... but so do defense subcontractors.
One more unimpressive release of ChatGPT or Claude, another 2 Billion spent by Zuckerberg on subpar AI offers, and the final realization by CNBC that all of AI right now...Is just code generators, will do it.
You will have ghost data centers in excess like you have ghost cities in China.
Barely 50 years ago computers used to cost a million dollars and were less powerful than your phone's SIM card.
> GPT-4 training was what, $100M? GPT-5/Opus-4 class probably $1B+?
Your brain? Basically free *(not counting time + food)
Disruption in this space will come from whomever can replicate analog neurons in a better way.
Maybe one day you'll be able to Matrix information directly into your brain and know kung-fu in an instant. Maybe we'll even have a Mentat social class.
> Barely 50 years ago computers used to cost a million dollars and were less powerful than your phone's SIM card.
Fifty years ago, we were starting to see the very beginning of workstations (not quite the personal computer of modern days), something like this: https://en.wikipedia.org/wiki/Xerox_Alto, which cost ~$100k in inflation-adjusted money.
Yeah, no hate for kung fu here, but maybe learning to better communicate together, act in ways that allows everyone to thrive in harmony and spread peace among all humanity might be a better thing to start incorporating, might not it?
>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.
I really have to wonder, how long will it be before the competition moves into who has the most wafer-scale engines. I mean, surely the GPU is a more inefficient packaging form factor than large dies with on-board HBM, with a massive single block cooler?
And distillation makes the compute moat irrelevant. You could spend trillions to train a model, but some companies is going to get enough data from your model and distill it's own at a much cheaper upfront cost. This would allow them to offer them for cheaper inference cost too, totally defeating the point of spending crazy money on training.
Labs can just step up the way they track signs of prompts meant for model distillation. Distillation requires a fairly large number of prompt/response tuples, and I am quite certain that all of the main labs have the capability to detect and impede that type of use if they put their backs into it.
Distillation doesn't make the compute moat irrelevant. You can get good results from distillation, but (intuitively, maybe I'm wrong here because I haven't done evals on this myself) you can't beat the upstream model in performance. That means that most (albeit obviously not all) customers will simply gravitate toward the better performing model if the cost/token ratio is aligned for them.
Are there always going to be smaller labs? Sure, yes. Is the compute mote real, and does it matter? Absolutely.
What’s the hardware capability doubling rate for GPUs in clusters? Or (since I know that’s complicated to answer for dozens of reasons): on average how many months has it been taking for the hardware cost of training the previous generation of models to halve, excluding algorithmic improvements?
The other problem is that big companies can take a loss and starve out any competition. They already make a ton of money from various monopolies. And they do not have the distraction of needing to find funding continuously. They can just keep selling these services at a loss until they’re the only ones left. That’s leaving aside the advantages they have elsewhere - like all the data only they can access for training. For example, it is unfair that Google can use YouTube data, but no one else can. How can that be fair competition? And they can also survive copyright lawsuits with their money. And so on.
> 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.
I'm curious to hear from experts how much this is true if interpreted literally. I definitely see that having hardware is a necessary condition. But is it also a sufficient condition these days? ... as in is there currently no measurable advantage to having in-house AI training and research expertise?
Not to say that OP meant it literally. It's just a good segue to a question I've been wondering about.
I'm not an expert at how private investment rounds work, but aren't most "raises" of AI companies just huge commitments of compute capacity? Either pre-existing or build-out.
So far it doesn't seem like winner-take-all, and all the major players (OpenAI, Anthropic, xAI, Google, Meta?) are backed by strong partnerships and a lot of capital. It is capital-intensive this round though, so the primary producers are big and few. As long as they compete, benefits mostly go to other parties (= society) through increased productivity.
Could they vastly reduce this cost by specializing models? Like is a general know everything model exponentially more expensive than one that deeply understands a single topic (like programming, construction, astrophysics, whatever)?
Is there room for a smaller team to beat Anthropic/OpenAI/etc. at a single subject matter?
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.
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.
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.
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?
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.
You'd be competing with ASIC miners, which are 100x more cost effective per MH/s. You don't need 100,000GB of VRAM when mining GPU, therefore its waste.
This is why I keep harping on the world needing China to get competitive on node size and crashing the market. They are already making energy with solar and renewable practically free. So the world needs AI to get out of the hand of the rich few and into the hands of everyone
It's the SV playbook: invent a field, make it indispensable, monopolise it and profit.
It still amazes me that Uber, a taxi company, is worth however many billions.
I guess for the bet to work out, it kinda needs to end in AGI for the costs to be worth it. LLMs are amazing but I'm not sure they justify the astronomical training capex, other than as a stepping stone.
Why would a global taxi/delivery broker not be worth billions? Their most recent 10-Q says they broker 36 million rides or deliveries per day. Even profiting $1 on each of those would result in a company worth billions.
Deepseek story was way overblown. Read the gpt-oss paper, the actual training run is not the only expense. You have multiple experimental training runs as well as failed training runs. + they were behind SOTA even then
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 round started at $5bn target and it ends at $13bn. When this sort of thing happens it's normally because the company wants to 1) hit the "hot" market, and 2) has uncertainty about their ability to raise revenues at higher valuations in the future.
Whatever it is, the signal it's sending of Anthropic insiders is negative for AI investors.
Other comments having read a few hundred comments here:
- there is so much confusion, uncertainty, and fanciful thinking that it reminds me of the other bubbles that existed when people had to stretch their imaginations to justify valuations
- there is increasing spend on training models, and decreasing improvements in new models. This does not bode well
- wealth is an extremely difficult thing to define. It's defined vaguely through things like cooperation and trade. Ultimately these llms actually do need to create "wealth" to justify the massive investments made. If they don't do this fast this house of cards is going to fall, fast.
- having worked in finance and spoken to finance types for a long time: they are not geniuses. They are far from it. Most people went into finance because of an interest in money. Just because these people have $13bn of other people's money at their disposal doesn't mean they are any smarter than people orders of magnitude poorer. Don't assume they know what they are doing.
I may agree if it was a 20% dilution round, but not if they are increasing from 3% to 7% dilution. Being so massively oversubscribed is a bullish sign, bad companies would be struggling to fill out their round.
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.
I was convinced of this line of thinking for a while too but lately I'm not so sure. In software in particular, I think it's actually quite relevant what you can do in-house with a SOTA model (especially in the tool calling / fine tuning phase) that you just don't get with the same model via API. Think Cursor vs. Claude Code -- you can use the same model in Cursor, but the experience with CC is far and away better.
I think of it a bit like the Windows vs. macOS comparison. Obviously there will be many players that will build their own scaffolding around open or API-based models. But there is still a significant benefit to a single company being able to build both the model itself as well as the scaffolding and offering it as a unit.
My gut feeling is that Claude Code being so popular is:
- 60% just having a much better UX and having any amount of "taste", compared to Cursor
- 39,9% being able to subsidize the raw token costs compared to what's being billed to Cursor
- 0,1% some magical advantage by also training the model
Claude Code is just much _pleasant_ to use than most other tools, and I think people are overly discounting that aspect of it.
I'd rather use CC with slightly dumber model, than Cursor with a slightly better one; and I suspect I'm far from being the only one.
I think we underestimate the insane amount of idle cash the rich have. We know that the top 1% owns something like 80% of all resources, so they don't need that money.
They can afford to burn a good chunk of global wealth so that they can have even more global wealth.
Even at the current rates of insanity, the wealthy have spent a tiny fraction of their wealth on AI.
Bezos could put up this $13 billion himself and remain a top five richest man in the world.
(Remember Elon cost himself $40 billion because of a tweet and still was fine!)
This is a technology that could replace a sizable fraction of humamkind as a labor input.
But in the consumer market segment, for most cases, its all about who is cheapest (free preferably) - aside from the few loonies who care about personality.
The true lasting economic benefits within enterprise are yet to play out. The trade off between faster code production vs poorer maintained code is yet to play out.
And unfortunately, the amount of money being thrown around means that when the bottom falls out and its revealed that the emperor has no clothes, the implosion is going to impact all of us.
It's going to rock the market like we've never seen before.
Hope it stays long enough to build up serious electricity generation, storage and distribution. Cause that has a lot of productive uses, and has historically been underdeveloped (in favor of fossile fuels). Though there will likely be a squeeze before we get there...
Why is this downvoted when it's spot on.. if reality < expectations so much money is sitting on extremely quickly depreciating assets. It will be bad. Risk is to the downside.
I'm not so confident in that yet. If you look at the inference prices Anthropic charges (on the API) it's not a race to the bottom - they are asking for what I feel is a lot of money - yet people keep paying that.
To answer: no, and even if it was a “yes” it wouldn’t affect the argument I was making. I’ll explain.
I was wondering how long it would take for this kind of meta-critique would pop up. Meta critiques are interesting: some people use them as zingers, hoping to dismantle someone else’s entire position. But they almost never accomplish that because they are at a different level of argument: they aren’t engaging with the argument itself.
Meta-critiques are more like an argument against the person crafting the argument. In this sense, they function not unlike ad hominem attacks while sneakily remaining fair game.
Lastly, even if I was a hypocrite, it wouldn’t necessarily mean that I was wrong — it would simply make me inconsistent in the application of a principle.
I don’t interpret the above as mean-spirited comment, but it does miss the point of the example I gave; namely, people second-guessing a market (or information heavily influenced by markets, like a new funding round) tend to lose. (Of course there are examples in the other direction, but they are less common and do not deserve equal emphasis.)
In general, a market synthesizes more information than any one individual, and when they operate well it is unlikely for an individual is going to beat them.
This is a well known general pattern, so if someone wants to argue in the other direction, they need to be ready to offer very strong evidence and reasoning why the market is wrong — and even when they do, they’re still probably going to be wrong.
I see what you are getting at, but it is important to understand the context for my example and the argument I’m making.
I’ve explained various points at length in other comments: (i) why I selected this example (simply to show that folk wisdom or common sense is less reliable than market-driven valuations) (ii) how a funding round is influenced by markets even though it isn’t directly driven by a classic full market mechanism.
Something I haven’t said yet would be a question: how can an outsider rigorously assess the error in a funding round or acquisition? To phrase the question a different way: what price or valuation would an oracle assign based on known information?
One might call this ex-ante rationality. Framing it this way helps remove hindsight bias; for example, a subsequent failure doesn’t necessarily mean it was mispriced (sp?) at the time.
So many negative comments here! The fact that one of the top players in a new market segment with significant growth potential can raise $13B at a 20x revenue valuation is not the bubble indicator you think it is.
It's at least possible that the investment pays off. These investors almost certainly aren't insane or stupid.
We may still be in a bubble, but before you declare money doesn't mean anything any more and start buying put options I'd probably look for more compelling evidence than this.
Most investors I've heard talk about the AI bubble have mentioned exactly that they know it is a bubble. They are just playing the game, because there is money to be made before that bubble bursts. And additionally, there is real value in these companies.
I would assume the majority of investors in AI are playing a game of estimating how much more these AI valuations can run before crashing, and whether that crash will matter in the long-run if the growth of these companies lives up to their estimates.
That sounds very cynical and knowing which is obviously great, but not super interesting. Do you think the investors are insane or stupid? Do you think this is a bubble and that it's about to burst? I'm interested to know why.
I see two of nineteen investors were also invested in FTX (Insight and Ontario teachers). With hindsight that's a bad investment although they probably recovered their money here so probably not their worst. Does this actually tell you they are stupid or insane?
I think that's one possible interpretation but another is that these funds choose to allocate a controlled portion of their capital toward high risk investments with the expectation that many will fail but some will pay off. It's far from clear that they are crazy or stupid.
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).
In Jan, when deepseek launched, Dario Amodei had to disclose they spent about $10M to train the last generation of models (his arguments was deepseek was on the curve, not breaking it).
They earned $250M in May based on ARR, and about $400M in july. Model training is going to be amortized over multiple years anyway. I am not privy to how much they spent, not going to comment on that. GM was public news, and hence I got that.
Re Zitron's analysis, I don't find them to be reliable or compelling.
exactly. what are people who make these investments even betting on? it certainly is not revenue or dividends. so it can only be a bet the stock will go up faster than other less risky stocks.
and we continue to pretend that market generates any semblance of value.
But if you're an investor who doesn't care about the long-term value of the firm, all you care about is maximizing your return on future sales of the shares of stock.
Doing proper intrinsic valuation with technology firms is nigh-on impossible to do.
Throwing money and compute at AI strikes me as a very short-term solution. In the end, the human brain does not run off a nuclear power plant, not even when we are learning.
I expect the next breakthroughs to be all about efficiency. Granted, that could be tomorrow, or in 5 years, and the AI companies have to stay all at in the meantime.
This is roughly where I am on the matter. If the energy costs stay massive, your investment in AI is really just a bet that energy production will get cheaper. If the energy costs fall, so does the moat that keeps valuations like this one afloat.
If there's a step-function breakthrough in efficiency, it's far more likely to be on the model side than on the semiconductor side. Even then, investing in the model companies only makes sense if you think one of them is going to be able to keep that innovation within their walls. Otherwise, you run into the same moat-draining problem.
Yeah. Electricity production that we also need for electrifying all of our transports and industry because of climate change. Good luck getting that cheaper.
That said innovation on the model side is more likely to come from a 10B-funded startup that still has some money to spare on the brightest researchers on top of giving them all the data and compute they want to play with.
the human brain can't run off a nuclear power plant b/c it was too hard for evolution to figure out, but we figured it out. No reason running on nuclear power plant won't give much higher intelligence.
But if we could drink a bottle of oil and become 10x smarter for 1 hour, it would be really cool. There just wasn't any use for that in the savannah, or indeed many bottles of oil.
I'm not sure quantum computing is the solution, but it strikes me that a completely new compute paradigm like quantum computing is probably what is necessary - which is orders of magnitude more efficient and powerful than today's binary compute.
There's a big issue with a lot of thinking about these valuations, which is that LLM inference does not need the 5-nines of uptime guarantees that cloud datacenters provide. You are going to see small business investors around the world pursue the following model:
- Buy an old warehouse and a bunch of GPUs
- Hire your local tech dude to set up the machines and install some open-source LLMs
- Connect your machines to a routing service that matches customers who want LLM inference with providers
If the service goes down for a day, the owner just loses a day's worth of income, nobody else cares (it's not like customers are going to be screaming at you to find their data). This kind of passive, turn-key business is a dream for many investors. Comparable passive investments like car washes, real estate, laundromats, self-storage, etc are messier.
I use OpenAI's batch mode for about 80% of my AI work at the moment, and one of the upsides is it reduces the frantic side of my AI work. When the response is immediate I feel like I can't catch a break.
I think once the sheen of Microsoft Copilot and the like wear off and people realise LLMs are really good at creating deterministic tools but not very good at being one, not only will the volume of LLM usage decline, but the urgency will too.
Yeah these things take time to play out. So I always just say, the large populous of people will finally realise fantasy and reality have to converge at some point.
Readit News
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
How do you know models are expensive to run? They have gone down in price repeatedly in the last 2 years. Why do you assume it has to run in the cloud when open source models can perform well?
> The hype is insane, and so usage is being pushed by C-suite folks who have no idea whether it's actually benefiting someone "on the ground" and decisions around which AI to use are often being made on the basis of existing vendor relationships
There are hundreds of millions of chatgpt users weekly. They didn't need a C suite to push the usage.
What I always thought was exceptional is that it turns out it wasn't the incumbents who have the obvious advantage.
Take away the fact that everyone involved is already at the top 0.00001% echelon of the space (Sam Altman and everyone involved with the creation of OpenAI), but if you had asked me 10 years ago who will have the leg up creating advanced AI I would have said all the big companies hoarding data.
Turns out just having that data wasn't a starting requirement for the generation of models we have now.
A lot of the top players in the space are not the giant companies with unlimited resources.
Of course this isn't the web or web 2.0 era where to start something huge the starting capital was comparatively tiny, but it's interesting to see that the space allows for brand new companies to come out and be competitive against Google and Meta.
The model leaders here are OpenAI and Anthropic, two new companies. In the programming space, the next leaders are Qwen and DeepSeek. The one incumbent is Google who trails all four for my workloads.
In the DevTools space, a new startup, Cursor, has muscled in on Microsoft's space.
This is all capital heavy, yes, because models are capital heavy to build. But the Innovator's Dilemma persists. Startups lead the way.
Wouldn't it be the same for the hardware companies? Not everyone could build CPUs as Intel/Motorola/IBM did, not everyone could build mainframes like IBM did, and not everyone could build smart phones like Apple or Samsung did. I'd assume it boils down the value of the LLMs instead of who has the moat. Of course, personally I really wish everyone can participate in the innovation like the internet era, like training and serving large models on a laptop. I guess that day will come, like PC over mainframes, but just not now.
Any stall in progress either on chips or smartness/FLOP means there's a lot of surplus previous generation gear that can hang and commoditize it all out to open models.
Just like how the "dot com bust" brought about an ISP renaissance on all the surplus, cheap-but-slightly-off-leading-edge gear.
IMO that's the opportunity for a vibrant AI ecosystem.
Of course, if they get to cheap AGI, we're cooked: both from vendors having so much control and the destabilization that will come to labor markets, etc.
And for the record I really wish more money was being thrown outside of LLM.
> no idea whether it's actually benefiting someone "on the ground"
I really don't get it. Before, we were farmers plowing by hand, and now we are using tractors.
I do totally agree with your sentiment that it's still a horrible development though! Before Claude Code, I ran everything offline, all FOSS, owned all my machines, servers etc. Now I'm a subscription user. Zero control, zero privacy. That is the downside of it all.
Actually, it's just like the mechanisation of farming! Collectivization in some countries was a nightmare for small land owners who cultivated the land (probably with animals). They went from that to a more efficient, government controlled collective farm, where they were just a farm worker, with the land reclaimed through land reform. That was an upgrade for the efficiency of farming, needing fewer humans for it. But a huge downgrade for the individual small-scale land owners.
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Anecdotally moving from model to model I'm not seeing huge changes in many use cases. I can just pick an older model and often I can't tell the difference...
Video seems to be moving forward fast from what I can tell, but it sounds like the back end cost of compute there is skyrocketing with it raising other questions.
Probably because you're doing things that are hitting mostly the "well-established" behaviors of these models — the ones that have been stable for at least a full model-generation now, that the AI bigcorps are currently happy keeping stable (since they achieved 100% on some previous benchmark for those behaviors, and changing them now would be a regression per those benchmarks.)
Meanwhile, the AI bigcorps are focusing on extending these models' capabilities at the edge/frontier, to get them to do things they can't currently do. (Mostly this is inside-baseball stuff to "make the model better as a tool for enhancing the model": ever-better domain-specific analysis capabilities, to "logic out" whether training data belongs in the training corpus for some fine-tune; and domain-specific synthesis capabilities, to procedurally generate unbounded amounts of useful fine-tuning corpus for specific tasks, ala AlphaZero playing unbounded amounts of Go games against itself to learn on.)
This means that the models are getting constantly bigger. And this is unsustainable. So, obviously, the goal here is to go through this as a transitionary bootstrap phase, to reach some goal that allows the size of the models to be reduced.
IMHO these models will mostly stay stable-looking for their established consumer-facing use-cases, while slowly expanding TAM "in the background" into new domain-specific use-cases (e.g. constructing novel math proofs in iterative cooperation with a prover) — until eventually, the sum of those added domain-specific capabilities will turn out to have all along doubled as a toolkit these companies were slowly building to "use models to analyze models" — allowing the AI bigcorps to apply models to the task of optimizing models down to something that run with positive-margin OpEx on whatever hardware that would be available at that time 5+ years down the line.
And then we'll see them turn to genuinely improving the model behavior for consumer use-cases again; because only at that point will they genuinely be making money by scaling consumer usage — rather than treating consumer usage purely as a marketing loss-leader paid for by the professional usage + ongoing capital investment that that consumer usage inspires.
In the meanwhile, "better data", "better training methods" and "more training compute" are the main ways you can squeeze out more performance juice without increasing the scale. And there are obvious gains to be had there.
The companies doing foundational video models have stakeholders that don’t want to be associated with what people really want to generate
But they are pushing the space forward and the uncensored and unrestricted video model is coming
Model specialization. For example a model with legal knowledge based on [private] sources not used until now.
Or, as in the case of a leading North American LLM provider, I would love to be able to choose an older model but it chooses it for me instead.
And for the newcomers, the scale needs to be bigger than what the incumbents (Google and Microsoft) have as discretionary spending - which is at least a few billion per year. Because at that rate, those companies can sustain it forever and would be default winners. So I think yearly expenditure is going to be 20B year++
From Dario’s interview on Cheeky Pint: https://podcasts.apple.com/gb/podcast/cheeky-pint/id18210553...
The problem is that in the meantime, they're going to nuke our existing powergrid, created in the 1920's to 1950's to serve our population as it was in the 1970's, and for the most part not expanded since. All of the delta is in price-mediated "demand reduction" of existing users.
For what it is worth, $13 billion is about the GDP of Somalia (about 150th in nomimal GDP) with a population of 15 million people.
The GDP of the Netherlands is about $1.2 trillion with a population of 18 million people.
I understand that that’s not quite what’s meant with ‘small country’ but in both population and size it doesn’t necessarily seem accurate.
California (where Anthropic is headquartered) has over twice as many people as all of Somalia.
The state of California has a GDP of $4.1 Trillion. $13 billion is a rounding error at that scale.
Even the San Francisco Bay Area alone has around half as many people as Somalia.
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.
Most things are not perfect competition, so you get MR=MC not P=MC.
We're talking about massive capital costs. Another name for massive capital costs are "barriers to entry".
That's just pure insanity to me.
It's not even Internet speed or hardware. It's literally not having enough electricity. What is going on with the world...
Like sure it saves me a bit of time here and there but will scaling up really solve the reliability issues that is the real bottleneck.
And if LLMs don't keep getting qualitatively more capable every few months, that means that all this investment won't pay off and people will soon just use some open weights for everything.
Well who does the inference at the scale we're talking about here? That's (a key part of) the moat.
Each new commercial model needs to not just be better than the previous version, it needs to be significantly better than the SOTA open models for the bread-and-butter generation that I'm willing to pay the developer a premium to use their resources for generation.
You think any of these clusters large enough to be interesting, aren't authorized under a contractual obligation to run any/all submitted state military/intelligence workloads alongside their commercial workloads? And perhaps even to prioritize those state-submitted workloads, when tagged with flash priority, to the point of evicting their own workloads?
(This is, after all, the main reason that the US "Framework for Artificial Intelligence Diffusion" was created: America believed China would steal time on any private Chinese GPU cluster for Chinese military/intelligence purposes. Why would they believe that? Probably because it's what the US thought any reasonable actor would do, because it's what they were doing.)
These clusters might make private profits for private shareholders... but so do defense subcontractors.
One more unimpressive release of ChatGPT or Claude, another 2 Billion spent by Zuckerberg on subpar AI offers, and the final realization by CNBC that all of AI right now...Is just code generators, will do it.
You will have ghost data centers in excess like you have ghost cities in China.
> GPT-4 training was what, $100M? GPT-5/Opus-4 class probably $1B+?
Your brain? Basically free *(not counting time + food)
Disruption in this space will come from whomever can replicate analog neurons in a better way.
Maybe one day you'll be able to Matrix information directly into your brain and know kung-fu in an instant. Maybe we'll even have a Mentat social class.
Fifty years ago, we were starting to see the very beginning of workstations (not quite the personal computer of modern days), something like this: https://en.wikipedia.org/wiki/Xerox_Alto, which cost ~$100k in inflation-adjusted money.
I really have to wonder, how long will it be before the competition moves into who has the most wafer-scale engines. I mean, surely the GPU is a more inefficient packaging form factor than large dies with on-board HBM, with a massive single block cooler?
Labs can just step up the way they track signs of prompts meant for model distillation. Distillation requires a fairly large number of prompt/response tuples, and I am quite certain that all of the main labs have the capability to detect and impede that type of use if they put their backs into it.
Distillation doesn't make the compute moat irrelevant. You can get good results from distillation, but (intuitively, maybe I'm wrong here because I haven't done evals on this myself) you can't beat the upstream model in performance. That means that most (albeit obviously not all) customers will simply gravitate toward the better performing model if the cost/token ratio is aligned for them.
Are there always going to be smaller labs? Sure, yes. Is the compute mote real, and does it matter? Absolutely.
When you consider where most of that money ends up (Jensen &co), it's bizarre nobody can really challenge their monopoly - still.
I'm curious to hear from experts how much this is true if interpreted literally. I definitely see that having hardware is a necessary condition. But is it also a sufficient condition these days? ... as in is there currently no measurable advantage to having in-house AI training and research expertise?
Not to say that OP meant it literally. It's just a good segue to a question I've been wondering about.
So we can at least assume that whoever is deciding to move the capacity does so at some business risk elsewhere.
Nice timing? I am sure they have scored a deal with the selling of personal data
Is there room for a smaller team to beat Anthropic/OpenAI/etc. at a single subject matter?
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
Is it?
Seems like theres a tiny performance gain between "This runs fine on my laptop" and "This required a 10B dollar data centre"
I dont see any moat, just crazy investment hoping to crack the next thing and moat that.
And then we’ll realize we wasted an entire Apollo space program to build an over-complicated autocompleter.
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.
how so? deepseek and others do models on par with previous generation for a tiny fraction of a cost. Where is the moat?
This article is a good example of the bear case https://www.honest-broker.com/p/is-the-bubble-bursting
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.
You'd be competing with ASIC miners, which are 100x more cost effective per MH/s. You don't need 100,000GB of VRAM when mining GPU, therefore its waste.
It still amazes me that Uber, a taxi company, is worth however many billions.
I guess for the bet to work out, it kinda needs to end in AGI for the costs to be worth it. LLMs are amazing but I'm not sure they justify the astronomical training capex, other than as a stepping stone.
> LLMs are amazing but I'm not sure they justify the astronomical training capex, other than as a stepping stone.
They can just... stop training today and quickly recuperate the costs because inference is mostly profitable.
Doesn’t explain Deepseek.
Edit: for the curious, no. An H100 costs about ~25k and produces $1.2/day mining bitcoin. Without factoring in electricity.
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.
Whatever it is, the signal it's sending of Anthropic insiders is negative for AI investors.
Other comments having read a few hundred comments here:
- there is so much confusion, uncertainty, and fanciful thinking that it reminds me of the other bubbles that existed when people had to stretch their imaginations to justify valuations
- there is increasing spend on training models, and decreasing improvements in new models. This does not bode well
- wealth is an extremely difficult thing to define. It's defined vaguely through things like cooperation and trade. Ultimately these llms actually do need to create "wealth" to justify the massive investments made. If they don't do this fast this house of cards is going to fall, fast.
- having worked in finance and spoken to finance types for a long time: they are not geniuses. They are far from it. Most people went into finance because of an interest in money. Just because these people have $13bn of other people's money at their disposal doesn't mean they are any smarter than people orders of magnitude poorer. Don't assume they know what they are doing.
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.
I think of it a bit like the Windows vs. macOS comparison. Obviously there will be many players that will build their own scaffolding around open or API-based models. But there is still a significant benefit to a single company being able to build both the model itself as well as the scaffolding and offering it as a unit.
0 - https://x.com/thisritchie/status/1944038132665454841
1- https://docs.anthropic.com/en/docs/agents-and-tools/tool-use...
Claude Code is just much _pleasant_ to use than most other tools, and I think people are overly discounting that aspect of it.
I'd rather use CC with slightly dumber model, than Cursor with a slightly better one; and I suspect I'm far from being the only one.
They can afford to burn a good chunk of global wealth so that they can have even more global wealth.
Even at the current rates of insanity, the wealthy have spent a tiny fraction of their wealth on AI.
Bezos could put up this $13 billion himself and remain a top five richest man in the world.
(Remember Elon cost himself $40 billion because of a tweet and still was fine!)
This is a technology that could replace a sizable fraction of humamkind as a labor input.
I'm sure the rich can dig much deeper than this.
And if it does? What happens when a sizable fraction of humamkind is hungry and can't find work? It usually doesn't turn out so well for the rich.
This ignores differential quality, efficiency, partnerships, and lots more.
But in the consumer market segment, for most cases, its all about who is cheapest (free preferably) - aside from the few loonies who care about personality.
The true lasting economic benefits within enterprise are yet to play out. The trade off between faster code production vs poorer maintained code is yet to play out.
It's going to rock the market like we've never seen before.
Narrow point: In general, one person’s impression of what is crazy does not fare well against market-generated information.
Broader point: If you think you know more than the market, all other things equal, you’re probably wrong.
Lesson: Only searching for reasons why you are right is a fishing expedition.
If the investment levels are irrational, to what degree are they? How and why? How will it play out specifically? Predicting these accurately is hard.
Not to be mean, but aren't you being a little hypercritical here, bringing up your bespoke example of YouTube?
To answer: no, and even if it was a “yes” it wouldn’t affect the argument I was making. I’ll explain.
I was wondering how long it would take for this kind of meta-critique would pop up. Meta critiques are interesting: some people use them as zingers, hoping to dismantle someone else’s entire position. But they almost never accomplish that because they are at a different level of argument: they aren’t engaging with the argument itself.
Meta-critiques are more like an argument against the person crafting the argument. In this sense, they function not unlike ad hominem attacks while sneakily remaining fair game.
Lastly, even if I was a hypocrite, it wouldn’t necessarily mean that I was wrong — it would simply make me inconsistent in the application of a principle.
In general, a market synthesizes more information than any one individual, and when they operate well it is unlikely for an individual is going to beat them.
This is a well known general pattern, so if someone wants to argue in the other direction, they need to be ready to offer very strong evidence and reasoning why the market is wrong — and even when they do, they’re still probably going to be wrong.
Google also bought Motorola for 12 billion and Microsoft bought Nokia for 7 billion. Those weren't success cases.
Or more similarly, WeWork got 12B from investor and isn't doing well (hell, bankrupt, according to Wikipedia).
A lot of that was patent acquisition rather than trying to run those businesses so it's hard to say a success or not.
I’ve explained various points at length in other comments: (i) why I selected this example (simply to show that folk wisdom or common sense is less reliable than market-driven valuations) (ii) how a funding round is influenced by markets even though it isn’t directly driven by a classic full market mechanism.
Something I haven’t said yet would be a question: how can an outsider rigorously assess the error in a funding round or acquisition? To phrase the question a different way: what price or valuation would an oracle assign based on known information?
One might call this ex-ante rationality. Framing it this way helps remove hindsight bias; for example, a subsequent failure doesn’t necessarily mean it was mispriced (sp?) at the time.
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Example: I could also make up dozens of reasons why something permitted by the laws of physics seems ridiculous.
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It's at least possible that the investment pays off. These investors almost certainly aren't insane or stupid.
We may still be in a bubble, but before you declare money doesn't mean anything any more and start buying put options I'd probably look for more compelling evidence than this.
What a minute. Isn't this the very definition of a bubble?
Answer: It's easy to pick and choose to prove one's point.
Softbank has been doing well lately by the way:
https://www.ebc.com/forex/softbank-stock-price-jumps-13-afte...
I'm sure this exact sentence was said before every bubble burst.
I would assume the majority of investors in AI are playing a game of estimating how much more these AI valuations can run before crashing, and whether that crash will matter in the long-run if the growth of these companies lives up to their estimates.
I think that's one possible interpretation but another is that these funds choose to allocate a controlled portion of their capital toward high risk investments with the expectation that many will fail but some will pay off. It's far from clear that they are crazy or stupid.
[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/
They earned $250M in May based on ARR, and about $400M in july. Model training is going to be amortized over multiple years anyway. I am not privy to how much they spent, not going to comment on that. GM was public news, and hence I got that.
Re Zitron's analysis, I don't find them to be reliable or compelling.
and we continue to pretend that market generates any semblance of value.
They they achieve AGI or a close approximation, and end up wealthier than god.
That's basically the bet here. Invest in OpenAI and Anthropic, and hope one of them reached near AGI.
Doing proper intrinsic valuation with technology firms is nigh-on impossible to do.
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I expect the next breakthroughs to be all about efficiency. Granted, that could be tomorrow, or in 5 years, and the AI companies have to stay all at in the meantime.
If there's a step-function breakthrough in efficiency, it's far more likely to be on the model side than on the semiconductor side. Even then, investing in the model companies only makes sense if you think one of them is going to be able to keep that innovation within their walls. Otherwise, you run into the same moat-draining problem.
That said innovation on the model side is more likely to come from a 10B-funded startup that still has some money to spare on the brightest researchers on top of giving them all the data and compute they want to play with.
- Buy an old warehouse and a bunch of GPUs
- Hire your local tech dude to set up the machines and install some open-source LLMs
- Connect your machines to a routing service that matches customers who want LLM inference with providers
If the service goes down for a day, the owner just loses a day's worth of income, nobody else cares (it's not like customers are going to be screaming at you to find their data). This kind of passive, turn-key business is a dream for many investors. Comparable passive investments like car washes, real estate, laundromats, self-storage, etc are messier.
I think once the sheen of Microsoft Copilot and the like wear off and people realise LLMs are really good at creating deterministic tools but not very good at being one, not only will the volume of LLM usage decline, but the urgency will too.
[0] https://sfcompute.com/