That’s not true in general but the Cybertruck in particular is stupid expensive to manufacture. And then there’s the 4680 fiasco. I think it’s more of a personal political design than a realistic product.
They're not competing against each other, they're competing against free/local/open weight models that they're also lobbying very hard to regulate/ban.
As much as I hear this sentiment, and as much as all the smart software people around me echo the desire for this sentiment to be true; it feels like people say these things, but then at the same time say - well, Claude/Chatgpt is still marginally better for me now. But clearly they are catching up so maybe one day!!!
In that sense, I'm really not sure if Kimi/chinese models are really competing against Anthropic/OAI? Not to mention that for individuals, the subscription model is extremely different, and Kimi subs are (I think) worse value, and chinese API's maybe marginally end up cheaper than using american subscriptions.
The profit comes from the enterprise customers who have to pay for usage at API rates. Not the subsidized subscriptions, which both OpenAI have designed to be unavailable for large enterprises by withholding IAM and auditing features they need except on the usage based entperise plans.
As an individual I'm still paying for a ChatGPT Plus plan since $20/mo gives me a lot of value and I use many multiples of $20 at API rates per month. But in terms of capabilities, I am entirely satisfied when using my Opencode Go plan with open models like Deepseek Flash/Pro, Kimi and MiMo.
At this point keeping my ChatGPT subscription isn't because of a significant capability gap compared to open models. It's 90% about the value of the subscription priced well below API rates (10% the fun of trying latest models).
If OpenAI/Anthropic dropped their cheap subscriptions or drastically undermined the value proposition I'd be happy using open models exclusively and currently barely notice a difference switching back and forth for the vast majority of tasks.
I'm expecting the subs to increasingly provide less value as the US labs IPO and follow the SV pattern of moving from marketshare capture mode to profit extraction mode. Thankfully I'm already comfortable using OpenCode with open models and avoid lock-in to any specific harness so would be a very soft landing.
I guess I don't understand "enterprise psychology" but why are auditing features worth a 50x premium? I think we're currently blasting through $1000 worth of tokens a month per dev.
Because enterprises have compliance and certification requirements, meaning the enterprise is essentially held hostage by providers on price.
You could argue it's worth that much because of the value that compliance brings the enterprise, but it's just another gouging excercise. Certainly nothing unique to AI companies.
They aren't but both firms tell bigger/richer clients that the subs just aren't available to them no matter what. It's not a policy, it's an email that says "we're moving you onto API rates". They have no obligation to follow a systematic policy!
> I guess I don't understand "enterprise psychology" but why are auditing features worth a 50x premium?
Alternatively, one can say that the enterprise customers plus a metric shitload of VC and shareholder/investor money heavily subsidizes cheap personal plans.
It's the same playbook that Adobe used, provide cheap plans for academia and turn a blind eye towards piracy from non-commercial actors, get the young people hooked hard on Adobe's software and workflows, and they will demand that their employers purchase the nice, high profit commercial/enterprise plans.
They are of course are the ones paying for R&D of new models but its not exactly clear if gross margins on subscriptions are negative. It might be just normal market segmentation just like SaaS companies charging enterprise users many times more than private individuals.
DeepSeek announced that each GPU they have makes about $38 an hour in revenue during off-peak times. That's $331k a year, and DeepSeek is cheap.
I think that it's not only possible but likely that the frontier labs are profitable on an ongoing basis, and it's mainly the research and capex which make them look like impending disasters.
Realistically, OpenAI and Anthropic are each probably worth ~$1T each. Anthropic can already easily justify that based on revenue alone. OpenAI isn't far off. Consider how quickly these companies are growing and the fact that they maintain a small but significant edge, I don't think the bubble is going to pop. Just deflate a little.
> I think that it's not only possible but likely that the frontier labs are profitable on an ongoing basis, and it's mainly the research and capex which make them look like impending disasters.
And that is the much more relevant problem. Capable open-weight models from China, European labs or competitors with cash to burn (i.e. Meta!) erode all the moat that the major labs have, and tooling functionality like MCP, CLI interfaces or integration into third-party tools like IDEs quickly gets cloned.
Simply printing money by running inference does not justify valuations in the billions of dollars, because anyone can do so.
Let’s not forget that Luna max is actually extremely capable for the price, you can get a lot done on the $20 sub.
Haven’t tried haiku 5.5, I expect it to simply run out too fast, but they did change the price below 100k tokens recently; perhaps I should check it out.
Openrouter stats seem to indicate that there is a lot of demand for ‘good enough at a good price.’ After all, not everyone is decompiling PS5 games. In fact, most people are just speedrunning notetaking apps in JavaScript frameworks.
But, Openrouter stats can only measure the usage they see. I'm not sure it is really all that representative of total LLM usage across the market.
I think that's the difference here -- OpenAI and Anthropic are competing more with each other (and Google Gemini) than they are with open weight models. While people on HN are familiar with Kimi and GLM models, I'm not sure how many casual users of LLMs know about them. I think the competitive risk open weight models pose to OpenAI and Anthropic is much further in the future.
There is a lot of usage that's only known by Anthropic or OpenAI that's not visible to the rest of the world.
And the share of the tasks where cheap open model is enough only grows in time.
Frontier models just fight over smaller and smaller piece of the pie and I suspect them pushing for security work is way to open potentially infinite token sink as attackers pay for tokens so defenders need to spend even more tokens to defens
> most people are just speedrunning notetaking apps in JavaScript frameworks.
I feel seen. But seriously, my app, with my preferences, written by a semi-trustworthy entity that isn’t trying to shove ads down my throat. That’s really great.
Yep, this. I've seen it coined as "the age of personal software" and I totally agree.
I've built easily a dozen personal apps in the last few months that I have no plans on releasing or sharing, run locally, and solve very specific productivity (or one-off) issues for me.
I would never have spent the time building these apps in the pre-LLM days because the effort wouldn't have been worth the gains.
But now? Sure Claude, build me a MacOS app in Swift that does this exact thing I need. Less than an hour later, it's done.
i mean, i made myself a dvd player for my steam machine. i don't see how that makes anthropic money at real token costs.
when lyft line was heavily subsidized in SF by VC money, i used it almost daily to commute to work because it cost $2.50 each way. Now that it's $20, I don't use it.
> well, Claude/Chatgpt is still marginally better for me now
Enterprise doesn't care about marginally better. They care about cost.
Most organizations that run our self-hostable chat platform with cloud models are for most stretches running 6 months-1 year old models, and are strongly eyeing (or have already switched) to more commodity models for most of their workforce. Frontier models are mainly relevant for engineering tasks.
I'm pretty much weaned off closed-weight models at this point.
It's a very "The Year of Linux on Desktop" situation, in the sense that in any particular year it seems like Linux is close, but the OSS stuff requires a lot of configuration to get to feature parity, and the barrier keeps rising because the prop solutions are running as fast as they can to justify the cost premium.
Unlike Linux, the world is pretty literate about tech business models, so I think there's a way for the open weight world to avoid the situation Linux got into, where it only really caught up once the market was pretty much commoditization any-ways.
If anyone interested in PL theory (including adjacent fields) and/or inference engines is reading this wants to help make open weight models and associated ecosystems feature-competitive, we're hiring, drop me a line: nathan at nfulton org.
For enterprise customers the value proposition of self-hosting one of the open models is enormous. We're talking 10x cheaper (remember that enterprises pay API rates), plus more control on where the data goes (especially given that some frontier models do not support ZDR). And all those savings are lost revenue for the US AI Labs.
My issue with anthropic and openai is that if I use a model like e.g. Sonnet 5 I don't always get full Sonnet 5, sometimes it's dumber, sometimes it is smarter (mostly dumber though), while GLM, Kimi and others stay the same. So if I rely on AI for things I better make sure the platform is stable and does not introduce hidden changes.
All the money is in enterprise. Contrary to your experience in software development and other frontier knowledge tasks, 90% of enterprise ai tasks are pretty mundane so much so that GPT 4o was already plenty enough.
Is Starbucks competing against Pete's Coffee, or are they competing against your home brew? Is it McDonald's vs Burger King, or all fast food against home cooking?
Neither OpenAI or anthropic can afford to lose subscribers to the other or anybody else right before IPO. I tried both CC and codex recently, and I would say they are practically on par. Some things CC does better, some things codex does better. I've also seen them increase usage limits. The cheapest plan now feels very generous imo.
Price per task has been dropping exponentially for years. This isn’t a sudden new phenomenon. They’ve been competing with each other continuously. There is even more competition now than ever before.
I think this is a very classic "waterfall" world view.
In many cases, the problem can only be understood through many failed solution attempts. It could be because the actual problem is novel. It could be because there is a different way of thinking about the problem that is not apparent studying only what a customer does today with the limitations of the software that they have right now.
The point of "move fast, break things" is to address this fallacy that anyone can fully understand a problem before attempting the solution; it is often the case that in attempting a solution, one comes to understand the actual, valuable problem.
Waterfall means designing and building everything before rolling it out to users. Researching a problem before you start tackling it is just general good advice. You can be more effective while moving fast and breaking things while still taking some time to know where to move and what is ok vs not ok to break.
So what? The article describes the political implications of the Census Bureau errors. I cited the Census Bureau release itself so you can cross-check whether the factual assertions in the article are accurate.
> and also implies political causes.
> So it's useful to identify this as not something a journalist or credible outlet would actually put their name to.
> An independent analysis by the Pew Research Center and election data experts calculated exactly how those specific errors altered the final map. If the census had perfectly matched the PES estimates, the seats would have shifted as follows: [table showing apportionment errors cost Texas and Florida 1 seat each, and gave Minnesota, Rhode Island, and New York 1 undeserved seat each.
Fucking up 5 House seats for a decade seems like a firing-worthy offense.
> Sergey Brin and Larry Page came up with this precise algorithm, i.e., PageRank, which was one of the key algorithms that helped catapult Google into a household name and made them tons of money. Both Sergey and Larry were grad students at Stanford, so their coming up with such an amazing algorithm doesn’t seem surprising.
Inventor in the context of a U.S. patent has a very narrow and technical meaning, different from the broader concept of "coming up with something." Larry Page and Sergey Brin developed PageRank together as grad students in Stanford's Digital Library Project, led by Hector Garcia-Molina. This is simply undisputable and both are listed as co-authors of the PageRank paper [1]. The list of inventors on a patent, especially nowadays, should not be seen as a historical finding about credit.
Inventorship of patents has a clear legal basis, and is quite different from the basis of scientific authorship.
The system disincentifies adding "ghost" inventors since incorrect listing of the inventors can invalidate a patent.
So, yes, it's likely that the patent attorneys hired by Stanford advised that only Page should be listed as an inventor. Stanford gets its money via the assignment, not the inventor list.
That’s not true in general but the Cybertruck in particular is stupid expensive to manufacture. And then there’s the 4680 fiasco. I think it’s more of a personal political design than a realistic product.
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