MangoCoffee

its hyperscalers and neoclouds data center build out that ate those trillions.

a lot of those money is the commitment from US AI labs that's not yet spend until the data center is roll out.

you are conflating.

Deepseek and Muse are my go to for cheap. both work perfect for basic and mid tier tasks

Nvidia is also selling the shovle but they train their own LLM and by doing so they can fine tune their CUDA/Chip to have a better shovel than others.

Nvidia's own model does not have to beat OpenAI or any SOTA model. Nvidia just need to build up the in house expert and knowledge to make a better chip since hyperscalers and none hyperscaler like Meta is building their own AI chip now.

Apple can chose not to train their own model to learn how to sell a better shovel.

yes, this is my read as well. Nvidia does not care about how many LLM provider is out there as long as they pay the toll (Nvidia tax). for now, you can't beat the Nvidia CUDA/chip for training but for inference. that's where you can gain ground.

Hugging Face - distribution for model that you can run on your local Nvidia Spark

Neoclouds - Nvidia setup a 500 billion investment fund with Wall Street so Nvidia can sell chip and this news about buying a LLM start up. it seem like another customer for Nvidia.

correct me if i'm wrong but i remember i saw an interview with Jensen where he want more company to have their own model and country to have their own LLM model.

Nvidia doesn't make money from the gold rush. Nvidia made money from selling the shovels.

Nvidia is trying to increase its customer base. Look at the Mag 7, Amazon, Google, Microsoft, and even Meta are all working on their own inference chips. I don't think they can completely ditch Nvidia for LLM training, but they can make their own chips for inference. I believe that's also why OpenAI made its own.

No one wants to pay the Nvidia tax

isn't Sol became Terra? this is OpenAI tweet: https://x.com/OpenAI/status/2102460975790137662?s=20

Astra is the best. Luna is cheapest then it seems like Sol is the middle child like Terra.

maybe this is why Dario want to slow down AI development and all the big AI labs in the USA is singing the same song.

whey they all singing the same tune. it make me question what is their real motives.

they are afraid of Chinese good enough LLM model killing their margin. we already have story about US companies switch some task to use cheaper Chinese model hosted on Neoclouds.

>Nvidia’s financial engineering is partly a response to its biggest customers’ transformation into rivals. “Hyperscalers”, tech giants such as Amazon, Google, Meta and Microsoft, account for roughly half of Nvidia’s revenue

Companies just don't want to pay Jensen's tax. Hyperscalers might still pay Jensen's tax for LLM training but for inference. you don't have to. they are also betting on their own chip for training to replace Nvidia.

this is Nvidia panicking and doing vendor fiance to Neoclouds and buying Hugging Face. even none hyperscalers like Meta is betting on its own chip for AI inference.

it reminds me of a thread I read on PTT, Taiwan's Reddit. AI finally achieved what humans could not. Managers must give exact context for what they want, must pay exact wages (tokens), and can't delay salary payments (which seems to be a problem in China).

>I started using Spark 1.2 for development because if you're willing to let Meta train on your data it was dirt cheap

its free on opencode and i use it for personal projects. most of my personal projects are AI generated since its personal projects. nothing important are on them. it is hilarious if Meta is training their AI model with AI generated code.

>Musk admitted to distilling their models.

what's the moat for AI labs like OpenAI and Antrhopic as they seek trillion IPOs. a lot of people said Chinese models also distill US LLM models. if it is this easy to do. how do US AI labs justify asking for trillion?

A lot of the freakout and "bye" posts on the OpenCodeCLI subreddit due to DeepSeek raising prices.

Stripe is a middleman. So is OpenRouter. So is OpenCode. Unless you own the data center and the hardware, how cheap can a middleman's tokens really be compared to the hyperscalers, without massive model compression?

Even DeepSeek itself is raising token prices. How much margin is there for a middleman like Stripe buying tokens in bulk from a data center and reselling them? Last i check Stripe do not run or own physical data center

I doubt Stripe can do much better here.

OpenAI and Anthropic are both seeking trillion IPOs, while Chinese labs are pumping out open-weight models that are free for US providers to host and monetize.

These Chinese models cost less of US SOTA models to run, even if they are less capable. Providers can just run them, offer cheap tokens, and pocket the margin.

I just don't see how you justify a trillion valuation for US AI labs when the underlying models are being commoditized this fast.

agentic coding is real. If AI labs can take over the coding tool market, that's a billion+ market. LLMs work in coding, and AI labs can slowly expand into other white collar work. Vaporware? Hardly.

The American attitude is generally to let private companies build up a new industry so it can create jobs and pay taxes. However, in the LLM race, the Chinese open weight playbook pretty much killed that. China has basically commoditized LLMs. Chinese models are good enough, so the race has come down to who can offer the cheapest tokens.

US AI labs really rub me the wrong way, especially with the doom and scare tactics they use. Both Altman and Dario keep talking about how AI will replace workers and how we should regulate LLMs for national security, Dario’s main point.

LLMs are useful. We can all see that in agentic coding. But replacing everyone’s job? Hardly. And what’s with the scare tactic of trying to get the US government to ban foreign models?

LLMs are useful, and dare I say they’re on par with the internet. Making them cheaper and affordable is good for everyone. The fear mongering from Anthropic and OpenAI looks like an attempt to corner the US market into using only US models so they can keep the profits, especially since China has proven that LLMs are a commodity. US AI labs should work on making LLMs cheaper or better harness. Altman and Dario are not trustworthy.

LLMs is quickly became commodities. US AI labs like OpenAI is losing their moat. Hyperscalers and data center owners who can sell cheap token will win

this is why i think LLMs is a foundational technology like the Internet. there is no other technology on the horizon that have this much impact.

US's court can enforce IPs. Didn't Antrhpic just agree to pay book authors billion for the recent lawsuit settlement?

Yeah, I believe I’ve seen this topic posted on HN many times now.

Code has always been cheap, even before AI/LLMs. It’s your ability to understand your client’s problem and come up with a good solution that’s what gets you paid.

>open models is what will kill Anthropic and OpenAI

i doubt it. it cost money to train a model. we can see that with the price increase for Kimi3. Chinese AI companies is leaving a lot of money on the table for third party providers. you think they going to let go of those money that they can make. sooner or later they will want to collect. after all, none of Chinese open weight model is release by a none profit. its all for-profit companies that is releasing open weight model.

its ironic that IBM sold off its x86 pc/server to Lenovo and kept their big iron (mainframe) but now everyone is buying up pc/server due to AI boom. Dell's stock have been surging with the rest of AI stocks

its no difference than US team fixing slop written by outsource company from India. where do you think LLMs learn slop code from?

AI slop? Humans write slop too. We’ve all heard the stories, companies outsource projects to India, only to bring them back to the US for the local team to fix.

I saw it myself at a past job. We hired a consulting firm to convert a project. They outsourced it to India. In the end, we had to hire a US company to rewrite the whole thing from scratch.

Talk about slop!

Microsoft needs to offer cheaper option since they change to token based billing. GPT-5.4 used to be x1 for yearly subscriber but now it cost 6x. i run out the premium request for just couple prompts. Github copilot for $10 used to be the best value since you get all the US AI labs model for cheap.

>he could have rented GPUs the way Elon did at this point

"Meta building cloud business to sell excess AI capacity, Bloomberg News reports Meta building cloud business to sell excess AI capacity, Bloomberg News reports"

https://www.reuters.com/business/meta-sell-excess-ai-computi...

i like Chinese open weight model that offer cheap token but i only use it for my personal project.

China have a history of stealing IPs/trade secrets and Chinese court favored its own local companies. while US have a robust court that can enforce IPs. if you want to risk your company's IPs/trade secrets/data for some cheap token. Go ahead and use Z.ai's services.

to me its more about company's IPs/trade secrets. china have a history of stealing IPs and very poor IPs enforcement while US have an established history of protecting IPs and US court can enforce it but hey, cheap token is more important, right?

The AI race right now is in a sad state. Chinese's playbook is releases open weight models and trains them on their own chips.

Anthropic pushes fear and control. But the only way to win is by innovating. China is flooding the market with cheap, good enough models, while the U.S. is building a Chinese firewall.

Running LLMs locally for development doesn’t make sense to me. The hardware gets outdated in just a few years. Even hyperscalers replace their GPUs faster than they can buy them, plus the cost of running it locally, isn’t cheap. the cost saving just ain't there.