Exactly. If open weight models are not cheaper than the big labs then the economic model of the big labs is in trouble. To survive the big labs need to be charging much much more but the market is dragging everything in the wrong direction for them.

It also further exposes the “we’re profitable this quarter” claims coming from big labs. These statements increasingly look like WeWork “community adjusted EBITDA” nonsense of “we’re profitable if you ignore all the reasons why we’re deeply unprofitable.”

I don't know about that. The frontier models have all sorts of strange regressions and quirks. They're more "intelligent" but, like hyper-intelligent humans who speak up without any skin in the game, I've found that they have a strong pull to add complexity to anything they touch. Truth is, I don't need frontier-level intelligence to write a CRUD app or debug my config file. The vast majority of software work is basic pattern matching.

Perhaps because they are dumber, they produce better results? IMO an excellent well-tuned harness combined with a "frontier minus x" model produces the highest quality result. DS4.1 and Qwen3.8, far from being a compromise, legit give me better results. For my personal definition of "better".

I think this is where Deepseek has nailed the mark; DS4.1 Flash is really, really fast, and really, really cheap. If you give it small, structured goals, it completes them crazy quick, at negligible cost. There's different vectors to differentiate along to stay in the conversation.

I've taken to using them as micro-review subagents at development milestones, where a "frontier - 1" model like Opus or Sol launches 10-15 of them on small review tasks that each run for ~10 minutes. Costs about $1 per cycle, and they usually catch something Astra or Fable didn't. Then the orchestrator validates each claim before passing it back to the planning session so we can fold the findings in.

Closed weight models are not usefully ahead of open weight models for coding anymore. "Fable, write a thousand file codebase from this one sentence prompt" is neat and I bet it required a lot of rollout compute to train, but my actual requirements are more detailed.

> To be honest, I don't know any companies using OW models at a large scale for their operations (agents or chat assistants).

Perhaps they simply don’t advertise it and investors (currently) love companies that spend heavily on frontier models. That said, OW models, especially when combined with RAG, work quite well, and given the current state of the industry, they may be the only sensible way to keep inference costs under control.

That is true if you're using them as chatbots but for something behind a product it's largely OK as long as it meets the requirements.

For chat I can see, less so for LLM use in automation for example, there it makes much less of a difference I can imagine.

I’ve found the opposite. Going from Opus 5 to Qwen3.8 flash has been a breath of fresh air.

This is nonsense and regardless your employer doesn’t care.

In my case they succeeded!

https://www.stoaexchange.com/ is an exchange for hardware itself and has market data for prices over time.

> HW clusters as the banks are not willing to give out loans against them

Which banks have analysts that understand the difference between H100 and A100? Do you have actual experience with being denied a loan based on this or are you just making things up?

Depends on the workload. H100 will never have the network performance of Vera Rubin. There's also token per watt, newer systems will beat the older systems.

Here he is. Shows up on time.