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I think Anthropic and OpenAI have found product-market fit

simonwillison.net

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Re: I think Anthropic and OpenAI have found product-market fit

#471

Post by janalsncm »

vlovich123 wrote: post #405
whatshisface wrote: post #173

Earlier quoted context omitted.

Here are a few thoughts: - The publicly available information about how inference costs compare to training costs is conflicted. EEs involved in datacenters talk about power usage spikes during training runs as if they were a major factor in the designs, but academic papers discussing cost-optimal scaling confidently treat inference-time compute as a major factor. - On the side of the balance indicating that training…

Small alternative potential future changes that alter this analysis: * At some point model capability reaches diminishing returns. Then inference >> training in the future but training >> inference now. It’s not a prisoner’s dilemma but a land grab to solidify market position and be one of the 2-3 firms left standing as dominant in the space. The model companies aren’t super sticky yet but they’re working on it. * ev…

The issue is that most tasks do not require frontier-level intelligence, but companies like OAI can really only profit off of the frontier. Capabilities from a year or two ago are so outdated that even OpenAI gives it away for free and there are many other models biting at their heels. In other words they are spending huge amounts of money to cash in on a depreciating asset.

So one possible future is that frontier-level training becomes so expensive and the use cases so sparse that it simply isn’t viable to keep going bigger.

Re: I think Anthropic and OpenAI have found product-market fit

#472

Post by wizzwizz4 »

j-bos wrote: post #441
alexpotato wrote: post #377

Earlier quoted context omitted.

I was in college in the late 1990s/early 2000s and I distinctly remember an econometrics professor state the following: "As cable TV and Pay Per View came out, there were studies done about how many movies people would watch if given unlimited access to films. The results were bandied about as proof that we should build out all this infrastructure to support this line of business. When the data was further analyzed b…

But isn't it wonderful that they did?

It's vaguely disturbing that people "watch" films 10-12 hours a day. Many of them are using it as a radio, for background noise, without really caring what the program is beyond vague genre, tuning in and out without particular regard to the plot… and yet we have all the cost of transmitting high-resolution video point-to-point.

Surely we could just put better stuff on the radio, and accomplish most of the same goals for a far lower price?

Re: I think Anthropic and OpenAI have found product-market fit

#473

Post by jonnat »

eqvinox wrote: post #86
simonw wrote: post #16

Earlier quoted context omitted.

I'm not sure what you're pushing back against here. I spent $200. If I had been paying API pricing it would have been $2,180.16. The article is about how enterprise customers get charged API pricing, which means if I had been employed by one of those companies I would have cost them $2,180.16. What am I missing?

Just because API pricing would've been $2180.16 doesn't mean that's the value of those tokens. For starters, you personally probably wouldn't have paid that. But also, sales price isn't value. This is like saying, oh, I saw this bar of gold somewhere for $10000 but got it here for $1000! So I got $10000 worth of gold for $1000! - no, the value of that gold is determined by its weight, which wasn't even mentioned. We…

No, value is determined by what participants in a market are willing to pay for something. The only reason you are able to say that the value of gold is determined by its weight is that gold is a commodity and no matter what you paid for it you'll find others willing to pay market price.

Simon is saying that companies are (today) willing to pay API prices for tokens which is as good as any determination of value.

Re: I think Anthropic and OpenAI have found product-market fit

#474

Post by regularfry »

cogman10 wrote: post #250
roncesvalles wrote: post #226

Earlier quoted context omitted.

Outsourcing of knowledge workers didn't work out because at large enough scales, the geographic arbitrage disappeared. Companies mostly always got what they paid for. The determinant of success was only whether the task needed American-tier labor or could make do with sub-American quality labor.

That's certainly part of it. But the other part that I've heard time and time again is that in order for outsourcing to be successful you basically needed an american engineer in the mix hand holding everything, clarifying requirements, and vetoing bad code. That part of dev work, the requirements gathering, attention to details, clarifying requirements, is something AI also struggles with. A lot of companies basical…

I think the mechanism here isn't that American engineers are magic. It's that you need that contextual knowledge really close to where the work is actually being done, so that the turnaround for questions, blockages, clarifications, "we've got no work to do", quality level-setting and so on is on the scale of minutes, not time-zones.

Re: I think Anthropic and OpenAI have found product-market fit

#475

Post by skybrian »

surgical_fire wrote: post #351
skybrian wrote: post #293

Earlier quoted context omitted.

There's a good reason to look at it separately: if inference is profitable then they make money (or at least lose less money) when they get more customers, because any fixed costs are spread across more usage.

Depreciation is part of the cost of inference. Inference happens in GPUs that have a relatively short lifespan. Those GPUs are very expensive. Inference is expensive because a GPU can only process a certain amount of requests in a given timeframe. Remember that Anthropic is constrained in compute. If they are constrained, it means that those GPUs are not idle. If they have more customers, they will need more GPUs. If…

Anthropic and OpenAI don't own data centers. Since they're renting GPU's, that's not depreciation. Paying rent is an operating cost.

Language models don't wear out the same way; upgrading is a choice.

Re: I think Anthropic and OpenAI have found product-market fit

#476

Post by jillesvangurp »

sourcecodeplz wrote: post #6

With deepseek and xiaomi mimo models slashing their prices 99%, I don't see a great future for openai / antrhopic with regards to their 1T valuations. Maybe 1T valuation will be the whole market, West + East.

Most of the corporate world in the EU or North America will be hesitant to rely on Chinese AI providers. There are some very real blockers for that for things like data security, compliance, etc. And recent geopolitics don't help.

Legalities aside, you need to look not at the model quality but at the infrastructure needed to scale these models from tens (now) to hundreds (soon) of millions of users. Only a handful of companies actually have the resources and funding to do that. That's what these huge valuations are based on. These companies are gearing up to scale to these levels. That's why they are spending on data centers. Whoever has access to those data centers gets to tap into the revenue stream of people using models running on those.

The market for frontier models is roughly split between OpenAI, Anthropic, and Google. And then you have companies like X/SpaceX, Amazon, and Microsoft being more successful with their infrastructure than their AI products and companies like Apple, Meta that have the money and the aspiration but are so far not really managing to be very successful with their AI strategies.

Deepseek is just very poorly positioned to capture a lot of the enterprise revenue in the EU or North America. But they might become very dominant outside the US/EU. And of course China itself is going to be a huge market and equally unlikely to want to be depending on US owner AI companies.

Re: I think Anthropic and OpenAI have found product-market fit

#477

Post by sailfast »

w29UiIm2Xz wrote: post #435
simplyluke wrote: post #305

Earlier quoted context omitted.

Yeah, that's the part that just seems to be wildly under-discussed to me. If open source models are ~3-6 months behind SOTA, and ~opus4.6 capabilities are good-enough for product market fit, do the frontier labs have half a decade to catch up on their prior burn? AI cost ballooning faster than companies can afford is becoming a very common topic in my circles right now. The era of "I'll pay infinitely more for margin…

If only the AI era was born in ZIRP.

Better now than ZIRP for me - at least people are asking timid questions about the unit economics and how long the runway is _early_ while also spending absolutely insane amounts of money on this bet. During ZIRP, these companies would have turned down any investor asking questions. Less contagion when rates aren't zero hopefully? :grimace:

Re: I think Anthropic and OpenAI have found product-market fit

#478

Post by stopachka »

FuriouslyAdrift wrote: post #246
trjordan wrote: post #11

They've got, ballpark, $5t to $10t to make back in the next 5 years, or the hardware buildouts will start getting written down. This means we're going to need $1t+ per year in spending, per year, on tokens. 200m knowledge workers in the world, 30m developers. We're talking about a world where you need 5% of every knowledge workers salary to go into tokens. 20% if you're a developer. That's a _huge_ shift. Most people…

I work for a tiny little company ($150MM annual rev with 9% net) and we are already looking at dropping $100k on hardware to run local models because, for us, they're "good enough." Our estimated spend for AIaaS would exceed that cost in less than a year. In a few years, there will be hardware capable of running frontier models good enough for most things at accessible prices for even tiny companies.

I don't quite understand, what would 100K buy you?

AFAIK you would get about ~5 concurrent users, with a max context window of ~128K tokens on the larger models.

This wouldn't be good enough for coding -- are you guys thinking of using it for something else?

Re: I think Anthropic and OpenAI have found product-market fit

#479

Post by goolz »

so_it_be wrote: post #156
bigbuppo wrote: post #136

Earlier quoted context omitted.

AutoCAD is still the budget-friendly CAD program it has always been. You don't build big boats in AutoCAD.

Except LLM's even with Vision are still useless at AutoCAD let alone Revit (please dont quote SCAD LLM's at me, useless). Knowledge based approaches still win. I might agree "AutoCAD" is the current level LLM's are at, but wait until your design departments discovers "Revit", its another ballpark (in wasted cots, engineers on site still get "clashes"). Revit costs are high, and the end results are marginally better -…

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Re: I think Anthropic and OpenAI have found product-market fit

#480

Post by repeekad »

sixhobbits wrote: post #216
aerhardt wrote: post #132

I find this analysis confusing. PMF for coding was likely reached some time last year. Profitability, which is different, we don’t know. The article kind of confuses both without making a strong economic case or using numbers in a compelling way. I don’t understand what the Uber case has to do with this either. The Uber COO clearly said that at least in terms of ROI he’s not seeing the results either. My take is the…

Pmf is this weirdly defined thing where "if you're not sure you have it then you don't". I think it was clearly useful for months to people who had tried it and taken the time to understand it, but now that knowledge has spread to the point where wallet holders are convinced it's not just passing fad or hype so now pmf can be "claimed". I agree it's weird to say "those people have pmf" though, usually it's something…

> clearly useful for people who took the time to understand it

people -> programmers, I haven’t met a non-developer who reports getting more time out of current AI platforms than they put in. If anything I’ve anecdotally heard the opposite, introducing AI at work creates so much slop (output) it takes more time to process it all without a tangible bump in overall productivity

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