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Comment by gr_norm | Hacker News Reader

Agree, I don't necessarily see a strong argument favoring OpenAI or Anthropic here. In the interest of perspective, can anyone (perhaps playing devil's advocate) give one?

The open models are now good enough for what I want to do with them, let alone any future improvements. And factoring in efficiency gains, a model in the ~70b range starting to satisfy my needs would completely obviate the need to pay others for inference. This does not seem far-fetched to me, comparing with where open models were at this time last year. What am I missing?

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Seems to me OpenAI and Anthropic are kinda following the Apple business strategy. Those two offer a premium service that gets better results and works more seamlessly. I.e. the integration between Anthropic models and Claude Code is apparently nice and gets better results, and I've heard anecdotally that Codex is currently the best.

So just like in IPhone vs Android, you could end up with a situation where Chinese firms compete and get most of the revenue and usage with low profit margins but OpenAI and Anthropic capture a premium side of the market and still get a lot of profits.

> In the interest of perspective, can anyone (perhaps playing devil's advocate) give one?

I have numerous cases where Sol failed and only Fable could solve a problem. For example yesterday I was merging a Q2 curved with a Bezier curved face in 3D using OpenSCAD. I tried for over 2 hours with Sol 5.6 high and x-high.

Fable two-shotted it in about 30 minutes.

In my experience open models (or GLM, DS and Kimi) are radically worse than either of Claude or ChatGPT at these tasks.

I think there is a huge "long tail" of tasks like this where the frontier labs are ahead, and I think this long tail is quite important.

Same experience here.

I settled on Fable for design, Opus for build routine, and every now and then I'd try out the Chinese models. In my experience they do fine on small codebases, and quickly get confused on anything larger than 500k LOC.

My use case is: mature, very well documented, fully Ai written code, with about 1:10 ratio of prompts/specs to code, and codebase sizes >500k and <2m LOC. Once one achieves the state of good, comprehensive design documentation I can literally vibe code with single sentence prompts thanks to the extensive test coverage, ADRs, and tens of thousands of lines of specs. Fable/Opus works predictably well, the Chinese models are literally dangerous to the codebase.

Hardware and electrical costs including power usage and electric wiring/outlet costs of such machine.

Unless you are spending more than a max subscription (200 a month+) its cheaper to use the cloud.

But things are priced cheaper in the cloud now to lock you in and restrictions around hosted models are getting worse.

If you only have a $300 dollar laptop its probably not worth the upgrade.

I'm personally excited by local AI but the experience for the average isn't the same. I'm willing to get .5/s running on 10-15 years old machines but what I can do with it is limited.

> What am I missing?

Their marketing department :-) . I'm only half-joking; those guys are hard at work finding the best product-market fit for ChatGPT/Claude. "Product market fit" means "strongest revenue", which is not necessarily going to bring the best tool for you or me, but the one that can either get more consumers to shell off money, or more enterprises to cough money for licenses, and in both cases those consumer basis will be narrowed down to what legal and geopolitical circumstances allow OpenAI and Anthropic (and this is why they want to ban the competition!). It also means dark patterns and enshitification, of which I'm already seeing some both in the Codex interface (it was just renamed "ChatGPT"!!!) and in Claude Code (which also is just "Claude" now and can't '@' properly any longer). So in the medium run most people will be better off running an open source harness that can use any model.

coding on a laptop is only one use case

you can't create a new drug by running a model on a laptop. You can't serve a customer support bot running on a laptop. You can't generate video in bulk for many users on a laptop. So there is still a case for paying others for inference.

Does it justify the valuations? No idea, but some major use cases are still there. That's why they are rushing to implement, OpenAI creating a "deployment company", Anthropic having some pharma rumors, etc.

Surely all those use cases can be done right now without a closed model?

I think GP was asking specifically about the use case for closed models.

yes, but at that point even open weight models are not free. Can someone set up the best open-weight model for free and charge pretty much just for GPU usage+operations+margin for inference? yes. But they would have to keep up with how good the private models are. The bet is private models would sell in a similar way at a premium and invest in staying ahead. Is this bet going to work? no idea, don't shoot the messenger.

> yes, but at that point even open weight models are not free. Can someone set up the best open-weight model for free and charge pretty much just for GPU usage+operations+margin for inference? yes.

Or, the company itself can rent the GPU and host the model of their choice on it.

> But they would have to keep up with how good the private models are.

Why? The whole point of the thread is that the marginal gains of using private models is a fraction of the additional cost of using those private models.

IOW, if you really need that improvement over the open models, you can go ahead and pay it. What we are seeing though is that the improvement in general is barely noticeable but the cost is 4x as much.

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