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.

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.

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.

> 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.

Why is opus 5.5 suddenly a lot dumber? Who knows.

Have you considered you are reading comments from different people?

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?

If prices were not being significantly lowered by Anthropic and OpenAI then you’d probably see more people using the Chinese models

The frontier labs think the switching threats are credible or they would not have cut prices.

Competition in a duopoly looks very different from competition with a large number of suppliers.

nah if not for open models they'd just raise the price in unison as any self respecting oligopoly would.

They'd compete on model quality, sure, but without competition from open models there is no reason to compete on price

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.

Borrowing money is not the only way to go into debt. They reportedly made binding and non-cancelable commitments. Once those commitments come due then they will be indebted to their counterparties until they pay up or the courts sort it out.

This is comparable to how, if I sign a 12 month non-cancelable lease and then don’t pay the rent, I will be in debt to my landlord.

Yes.

Goverment bailing they out will be using people tax to rescue the companies that absorbed most of the 801k money so they can get people out of work... it's the ultimate slap to the face... I would strongly resist this, I would vote if they implode to give to whoever owned shares via 801k their money, to people and let them (openai, anthropic, et al) eat shit... I would love to buy one of those gpus to run an open model at discount price...

LLMs? No. LLM companies? Yes. It's just math, data centers, and mass copyright infringement. The only scarcity is Nvidia, who just passed Apple in market cap, somewhat proving your point but not in the way you meant it.

Maybe not, but it is probably less likely that Apple will face stiff competition from freely available alternatives, given that AOSP has been a thing for the iPhone’s entire lifespan and nonproprietary Android phones have failed to capture significant market share.

I don't think we have seen an Apple of AI yet. I wouldn't describe the frontier that way, would you?

Are you avoiding Google and SpaceX in that math?

Amazon were investing in goods that lasted longer. It's an open question how long the GPUs will be worth anything, and training costs are the real killer.

Z.ai, who make GLM are public and their books are really, really, really bad.

I am technically not saying that. I am taking off from the parent comment and speculating.

Their data from September is leaked via IPO preview, and October is not yet complete, so how can someone even project that?

// I am a techie, not a finance guy, so apart from public chatter, no inside knowledge.

And I do feel their IPO is being rushed before anyone has enough information to make up their mind. The VCs or private equity have given them a lot of money and want to cash out. When the revenue does slow down, any investor should pause to take a look at whether they can right the ship, or has the landscape itself changed.

My reason is their revenue chart is flattening.

https://techstartups.com/2026/10/15/anthropics-q3-revenue-hi...

I don't have the numbers in front of me but could have sworn I had seen quarterly revenue growth rates dropping drastically over the last year or so. Still a strong growth rate, but much lower than prior growth rates.

Edit: Data because silent down votes are useless, Anthropic Q2 2026 grew 147% while Q3 grew 45%

The US can make it difficult, but obviously not air tight. However no enterprise will touch it, just fringe hacker types.

I think it's more likely though that China stops the party, as they stand to lose from the threat of it just as well. And China can just flip the switch to stop new releases.

It may solve the problem of anthropic and open ai going out of business with all major consequences that would have for the rest of the US.

Banning open weight models in the US would provide the rest of the world with a huge business advantage since these models are much more cost-effective than US models.

“Ban open weight models” Lol how. How do you even prove a model that I’m hosting is open weights?

Presumably, legally operating companies aren't the dominant threat though. So banning open weight models doesn't solve anything.

> If you are a legally operating company in the US just saying it's banned is enough, the threat of enforcement is enough pressure.

Is there precedent for the US actually "banning" software? I know for example the Kaspersky ban and tiktok ban, but both of those were either banning the sales or banning the service. IIRC neither made the actual software illegal.

What is an example of a specific software being illegal to possess? What is an example of a specific software being illegal to use if it is not used to break other laws (copyright, etc.) or talks to banned service providers?

Closest thing I can think of is restricting the export of cryptography in the 90's, but that was not even close to what you are suggesting.

Thanks for the suggestion, but that’s not a fit for me.

I’m writing my own harness to learn. And I’m writing it in zig.

But, I’ll absolutely add maki.sh to my reading list. So, that’s a win.

I am thinking about it but I think forking will miss the point. I'd rather do in a way when there is a prevalence of various types of agentic/harness SDKs for people to make their own apps (aka harnesses) from.

> "What kind of hardware... "

At home, I mostly use openrouter.

A few bucks will realistically last you forever. I also bought a DGX Spark at launch. I've learned a ton. but it's not usually my daily driver. I do run overnight tasks on it, like "read this book and write up notes on each chapter..."

In retrospect, given the dirt cheap prices of apis and or renting hardware, buying hardware for inference was likely a mistake (but not enough of a mistake to sell it now).

Take a look at the docs for https://github.com/niko1221/strata for some ideas, particularly https://github.com/Niko1221/Strata/blob/main/docs/MODELS.md#... .

I think building a OpenAI compatible RAG-pipeline that also perhaps includes the anthropic sdk let's you have the best of both worlds. You can build it in a way that a local agent can escalate to a frontier model when needed and also substitute prompts data that you want to hide from being "public". For example. I think that is meant by harness here.

And with some smart feedback loops, you will see that for home assistant type tasks local AI can pretty quickly fulfil all requirements. For quick responses or voice analysis you might want to start with an external provider.

Otherwise, I think for complex home automation around 24-32gb vram are a realistic house number, but more experienced users will probably need less here.

People say that tools are called by the model. That isn't entirely correct. The caller calls the tools, the model just formulates the syntax for it. So the model part can be externalised without you needing to compromise you system fully (just to the degree the tools would allow anyway)

Schaefer says they write their own harness, not necessarily training or running a local model.