>we don't want a hard dependency on another multi-billion dollar company just to write software

One of two main reasons why I'm wary of LLMs. The other is fear of skill atrophy. These two problems compound. Skill atrophy is less bad if the replacement for the previous skill does not depend on a potentially less-than-friendly party.

Another aspect I haven’t seen discussed too much is that if your competitor is 10x more productive with AI, and to stay relevant you also use AI and become 10x more productive. Does the business actually grow enough to justify the extra expense? Or are you pretty much in the same state as you were without AI, but you are both paying an AI tax to stay relevant?

Open models keep closing the eval gap for many tasks, and local inference continues to be increasingly viable. What's missing isn't technical capability, but productized convenience that makes the API path feel like the only realistic option.

Frontier labs are incentivized to keep it that way, and they're investing billions to make AI = API the default. But that's a business model, not a technical inevitability.

>perhaps we can come up with something like the "linux/postgres/git/http/etc" of the LLMs

I fear that this may not be feasible in the long term. The open-model free ride is not guaranteed to continue forever; some labs offer them for free for publicity after receiving millions in VC grants now, but that's not a sustainable business model. Models cost millions/billions in infrastructure to train. It's not like open-source software where people can just volunteer their time for free; here we are talking about spending real money upfront, for something that will get obsolete in months.

Current AI model "production" is more akin to an industrial endeavor than open-source arrangements we saw in the past. Until we see some breakthrough, I'm bearish on "open models will eventually save us from reliance on big companies".

This is part of the reason why I'm really worried that this is all going to result in a greater economic collapse than I think people are realizing.

I think companies that are shelling out the money for these enterprise accounts could honestly just buy some H100 GPUs and host the models themselves on premises. Github CoPilot enterprise charges $40 per user per month (this can vary depending on your plan of course), but at this price for 1000 users that comes out to $480,000 a year. Maybe I'm missing something, but that's roughly what you're going to be spending to get a full fledged hosting setup for LLMs.

I'm hopeful that new efficiencies in training (Deepseek et al.), the impressive performance of smaller models enhanced through distillation, and a glut of past-their-prime-but-functioning GPUs all converge make good-enough open/libre models cheap, ubiquitous, and less resource-intensive to train and run.

Open models are only near SOTA because of distillation from closed models.

The lock in is so incredibly poor. I could switch to whatever provider in minuets.

But it requires that one does not do something stupid.

Eg. For recurring tasks: keep the task specification in the source code and just ask Claude to execute it.

The same with all documentation, etc.

What open models are truly competing with both Claude Code and Opus 4.7 (xhigh) at this stage?

Who’s your “we,” if you don’t mind sharing? I’m curious to learn more about companies/organizations with this perspective.

I’m imagining a (private/restricted) tracker style system where contributors “seed” compute and users “leech”.

> open models

Google just released Gemma 4, perhaps that'd be worth a try?

Any recommendations on good open ones? What are you using primarily?

or just use codex

> I think that's the way forward. Actually it would be great if everybody would put more focus on open models,

I'm still surprised top CS schools are not investing in having their students build models, I know some are, but like, when's the last time we talked about a model not made by some company, versus a model made by some college or university, which is maintained by the university and useful for all.

It's disgusting that OpenAI still calls itself "Open AI" when they aren't truly open.

I'm increasingly thinking the same as our spend on tokens goes up.

If you have HPC or Supercompute already, you have much of the expertise on staff already to expand models locally, and between Apple Silicon and Exo there are some amazingly solutions out there.

Now, if only the rumors about Exo expanding to Nvidia are true..

Opencode go with open models is pretty good

> we don't want a hard dependency on another multi-billion dollar company just to write software

My manager doesn't even want us to use copilot locally. Now we are supposed to only use the GitHub copilot cloud agent. One shot from prompt to PR. With people like that selling vendor lock in for them these companies like GitHub, OpenAI, Anthropic etc don't even need sales and marketing departments!

My understanding is that the major part of the cost of a given model is the training - so open models depend on the training that was done for frontier models? I'm finding hard to imagine (e.g.) RLHF being fundable through a free software type arrangement.

I can recommend this stack. It works well with the existing Claude skills I had in my code repos:

1. Opencode

2. Fireworks AI: GLM 5.1

And it is SIGNIFICANTLY cheaper than Claude. I'm waiting eagerly for something new from Deepseek. They are going to really show us magic.

>perhaps we can come up with something like the "linux/postgres/git/http/etc" of the LLMs: something we all can benefit from while it not being monopolized by a single billionarie company

Training and inference costs so we would have to pay for them.

Is that why they are racing to release so many products? It feels to me like they want to suck up the profits from every software vertical.

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