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.

They've gotta hope they get to cheap AGI, though.

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.

Come to the counterrevolution; we have cookies : )

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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.

Its not clear to me that it needs to. If at the margins it can still provide an advantage in the market or national defense, then the spice must flow

Locally run video models that are just as good as today’s closed models are going to be the watershed moment

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

> 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.

> I can just pick an older model and often I can't tell the difference...

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.

I hope you're right.

The scaling laws already predict diminishing in returns

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.

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.

> So long as there is competition it’ll be available at marginal cost.

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".

As a fun comparison, because I saw the population is more or less the same.

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.

Country scale is weird because it has such a large range.

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.

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.

Not to mention water for cooling. Large data centers can use 1 million+ gallons per day.

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.

Off software codegen alone it is beyond useful

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.

> Once the model is out, no one would pay for it, right?

Well who does the inference at the scale we're talking about here? That's (a key part of) the moat.

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.

> 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?

Can you elaborate? The technology to build a human brain would cost billions in today’s dollars. Are you thinking moreso about energy efficiency?

Sentiment I have heard is manufactories do not want to increase die size because defects per die increases at the same time.

A couple of counter-arguments:

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.

it's difficult for me to imagine this level of compute existing and sitting there idle somewhere; it just doesn't make sense.

So we can at least assume that whoever is deciding to move the capacity does so at some business risk elsewhere.

A lot, but maybe a lot less than you expect.

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.

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.

SV playbook has been to make sustainable businesses. Uber makes profits, so do Google, Amazon and other big tech.

> 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.

There are other coins that are less unprofitable to mine (see https://whattomine.com/gpus ) but it's probably still not worth it.

Before your edit I was going to answer, sadly no, they can't even be repurposed for Bitcoin mining.

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