101–110 of 408 posts
Earlier quoted context omitted.
Also, your local hardware is in no way capable of running the types of models that the cloud providers do, it’s just not economically feasible, and it never will be.
It can run open-weight models that are roughly as capable. It's going to be slow unless you're using actual datacenter hardware, but they'll run.
When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team peo…
> Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus What's your source for Opus being a 5T model? > and tiny distillations from DeepSeek that perform well only in benchmarks. I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”. And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decen…
Elon Musk tweeted that Grok is 0.5T or 1/10th the size of Opus. https://xcancel.com/elonmusk/status/2042123561666855235#m
While this source's reliability is certainly debatable, the size matches the results of this paper, in which researchers estimated the parameter count from model knowledge. https://01.me/research/ikp/
Earlier quoted context omitted.
Historically the winners in software have a flywheel that turns faster with more users. Facebook the more of your friends on it the better the product was. Google tracked how long users were on pages to improve search. The frontier models are going to win that way. They won't feed your code back into the system but they will track which code you keep and what code gets a "try again claude". They're not going to lose…
The marginal cost of AI is not 0. That's one of the big differences between this and older SaaS software. Inference costs a lot of money. Even if you're looking at just capital depreciation, it's quite expensive. I suppose it's more accurate to say marginal cost is stepwise - adding 1 new user is 0 cost if and only if your existing inference hardware covers that user's usage. As soon as you need a new server, adding…
In other words, AI is not your daddy's software. Comparing AI with old school software markets simply does not compute.
When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team peo…
> Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus What's your source for Opus being a 5T model? > and tiny distillations from DeepSeek that perform well only in benchmarks. I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”. And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decen…
Probably Elon Musk: https://eu.36kr.com/en/p/3760679047267075
Earlier quoted context omitted.
Depends on what their actual costs are. Either they are losing lots of money on subscriptions, or they make absolute bank on API pricing. Looking at the pricing of 1-2T models like Kimi or DeepSeek on the open market, I'm tempted to assume that inference costs are closer to subscription pricing than to API pricing. Especially considering that subscriptions a) distribute load over time via rate limits, and b) will inc…
Considering not one company is in the black yet I don’t really know how we can say anyone is making bank, unless we want to count absurd levels of VC funding (now slowing down) I guess.
Of course they do have to "make bank" in some way to offset the insane training costs. But whether they go for high prices or high volume, or offer some services as a loss leader to drive profits elsewhere is somewhat orthogonal to that
Earlier quoted context omitted.
> Lastly, there is a massive difference in capabilities, determinism, and error handling between 5T SOTA models like Opus What's your source for Opus being a 5T model? > and tiny distillations from DeepSeek that perform well only in benchmarks. I don't think you know what you're talking about. Local models aren't “distillations from Deepseek”. And they don't perform well “only in benchmarks”, Qwen 3.6 is a very decen…
> What's your source for Opus being a 5T model? Probably Elon Musk: https://eu.36kr.com/en/p/3760679047267075
I think this misses the forest for the trees. Working with ChatGPT is eerily similar to working with offshore Indian devs back in my enterprise days. Productive if guided explicitly but if let run wild there's lots of WTF moments. LLMs are likely to replace outsourced devs because your employees that know the context can use LLMs to do what offshore devs did before.
When discussing LLM pricing, people are missing the plot. The subscription token price is 10x-40x cheaper than API pricing. Your 90$ Claude subscriptions give you close to $1000 to $4000 in equivalent API token pricing. The second issue is that the quality of the model “operator” makes a massive difference in the outcomes. Highly skilled senior devs who know how to prompt and have high agency will outperform team peo…
This is a temporary phenomenon. Expect either drastic price increases or draconian throttling or both in the coming months.
These companies are operating at huge loses and have hundreds of billions in liabilities and commitments. They need to turn on the money faucet sooner than later.
I think this misses the forest for the trees. Working with ChatGPT is eerily similar to working with offshore Indian devs back in my enterprise days. Productive if guided explicitly but if let run wild there's lots of WTF moments. LLMs are likely to replace outsourced devs because your employees that know the context can use LLMs to do what offshore devs did before.
"offshore Indian devs" are no slouches. They have access to the same GPT models and likely cost a tenth of the median US salary. Businesses are always looking to lower marginal cost. They will hire 1 software architect in US to write specs and 10 software developers in India to babysit 100 agents.