131–140 of 965 posts
Earlier quoted context omitted.
Every company that I've worked with that provided models internally did so through LiteLLM and offered both Anthropic and OpenAI models so it was trivial to switch between them.
Most companies just get you a Claude team sub and maybe a couple of skills.
The people who are most afraid of Chinese models are the VCs who poured into Anthropic and OpenAI at astronomically high valuations. Anthropic is valued at $1.2T and OpenAI is targeting $850B. These astronomical valuations were built on the premise that these labs would generate massive profits from premium API pricing, but the Chinese labs are completely undercutting this strategy by releasing excellent open models…
Its the user base (with ads and upselling) and proprietary wrappers which will make money for typical customer.
Even enterprise customers arent going to be spending a lot on tokens. Once labs no longer have to subsidize trainings tokens costs will drop 10x and once models get burned on chips costs will drop 10x more and you physically won't be able to burn significant number of tokens unless you're deliberately trying to.
Earlier quoted context omitted.
What makes the Internet raw data in a different way? wasn't it mostly worked on by people first?
There is value add in AI irrespective of how the data got to what it is. Literally the biggest thing of our generation - AI - is the living embodiment of that 'value add' writ large. 'What is the difference' - is the AI you use all day, in comparison to 'all the world's data' you can use for stuff and do 'whatever' with it, but are not likely to come up with something hugely useful otherwise. Maybe, not likely, if yo…
Earlier quoted context omitted.
Sure, let's have a look... > I highly doubt that Chinese models are cheaper to serve on a marginal cost basis, they just seem cheaper because Anthropic and OpenAI are so supply constrained that they are charging far more than they would if there were sufficient supply to meet the demand for intelligence. [emphasis mine] I guess I'm missing the part of this article where they bring hard numbers in to back up the argum…
>What work was attempted? https://cursor.com/evals shows the previous generation of open models (Kimi K2.7) trading blows with the others, cost effectively Because you're comparing retail price whereas the parent commenter (and the article) is talking about marginal (ie. inference) costs. American labs are providing a premium product and they're charging accordingly. Meanwhile for chinese models they're open weight s…
But if we have to look at what we think margins might look like, DeepSeek continues to host v4 Flash at the existing price despite competitors beating it in price (https://openrouter.ai/deepseek/deepseek-v4-flash), so there's at least one example of a Chinese lab charging a predetermined price despite competition. And no one but Moonshot is hosting Kimi K3 yet (https://openrouter.ai/moonshotai/kimi-k3). Perhaps there's room in the market for those who release their models to make margin on them.
And I believe my Composer example speaks for itself. The open models are behind but there's tangible proof they can be tuned for pareto frontier efficiency. See "Cost per Task" at https://artificialanalysis.ai/agents/coding-agents.
The people who are most afraid of Chinese models are the VCs who poured into Anthropic and OpenAI at astronomically high valuations. Anthropic is valued at $1.2T and OpenAI is targeting $850B. These astronomical valuations were built on the premise that these labs would generate massive profits from premium API pricing, but the Chinese labs are completely undercutting this strategy by releasing excellent open models…
The (quite excellent) article discusses several of your points. If you haven't read it, I recommend it. - Commodity market profitability is determined by marginal cost of production. LLMs have marginal cost; traditional software does not. - Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category…
> Models are not free. Downloading them is free. Running them is not. This has manufacturing economics, not software economics; the idea that they are "free" is an economic category error as it relates to their actual use
I think a large part of manufacturing economics is illiquid overhead and the cost of expertise to set up and run your manufacturing line. Compute economics don’t have the same illiquidity nor do they require the same expertise or even specialized infra (current temporary chip shortage aside).
The implications of this are small players (e.g. your uncle running an inference server out of his garage) have comparably efficient marginal costs as big players. Compare this to actual manufacturing where small players have essentially no access to the manufacturing facilities of the big players.
Additionally, big players with a lot of compute who are not meaningfully in inference today (e.g. Amazon) have a fairly straightforward glide path to utilizing that compute to compete.
> This is because US labs are leading on cost efficacy of inference ($/task)
It’s possible, but I would need to see better data on this.
>A big part of training now is optimizing token efficiency. It's hard to distill token efficiency; that is perhaps why Chinese LLMs are so inefficient.
I think it’s fair to assume this is true, but also token efficiency is not a meaningful competitive moat. It’s not like these are secrets the Chinese will never figure out, it’s a fairly active research space and the outcomes are quantifiable.
Earlier quoted context omitted.
Good. Over the past few years, VCs have proven that they’re warmongering psychopaths. Hopefully China puts every last one of the Palantir/Flock/Anduril class out of business.
[flagged]
> It’s striking the extent to which Claude Code and Codex are proving to be quite sticky; whichever harness you start working with is likely to be the one you stick with, and that figures to be even more the case with non-technical users. My experience has been quite the opposite. I was using Claude Code almost exclusively this winter/spring and swapped to Codex earlier this summer. It took no time whatsoever to swit…
Have you ever worked with a non-programmer and helped them setup their AI workflows? You install MCP connectors, specific skills, work around model/harness quirks, set security boundaries etc. It's a lot of work, and most people will never want to change it once they have it working.
> It’s striking the extent to which Claude Code and Codex are proving to be quite sticky; whichever harness you start working with is likely to be the one you stick with, and that figures to be even more the case with non-technical users. My experience has been quite the opposite. I was using Claude Code almost exclusively this winter/spring and swapped to Codex earlier this summer. It took no time whatsoever to swit…
For personal use I agree. For companies, these decisions are very sticky. Companies go through a lot of red tape to get anything purchased and approved, then they discourage change because it's a lot of work. So the product that gets a foothold in a company sticks for a long time. Then a couple years later a sales person convinces an exec that they can save some money by switching, so the switching game begins. Not n…
There is no “Chinese LLM”. Each “lab” is distinct and their models behavior is as unique as those from OpenAI and Anthropic
Somehow a certain set of labs are all releasing open weights and a certain other set of labs are closed weights.
The people who are most afraid of Chinese models are the VCs who poured into Anthropic and OpenAI at astronomically high valuations. Anthropic is valued at $1.2T and OpenAI is targeting $850B. These astronomical valuations were built on the premise that these labs would generate massive profits from premium API pricing, but the Chinese labs are completely undercutting this strategy by releasing excellent open models…
Correct. These chinese labs has proven that having just the model is not a moat, and the safety concerns were all just attempts at regulatory capture.
This is why labs like OpenAI and Anthropic are panicking and are racing to the exit before their valuations start being questioned.