Comment by phoghed
16 hours ago
Lot of comments proclaiming it’s over for Anthropic and OpenAI, but none really explaining why. Many replies asking why and being wholesale ignored. Love to see it.
16 hours ago
Lot of comments proclaiming it’s over for Anthropic and OpenAI, but none really explaining why. Many replies asking why and being wholesale ignored. Love to see it.
The frontier labs are currently at point where they are essentially fighting for smaller and smaller piece of the pie.
The amount of tasks open models are just fine for increases every month, so there is just less and less tasks where you need frontier models.
This achieves 2 things:
* companies that might have reservations about chinese models having training backdoor are getting tools to train its own * the tricks deepseek uses to make building them faster can now be used by more companies, increasing competition even further.
So to answer the question: The pie is already shrinking, and that just makes it even faster
This assumes the set of tasks we want to use AI for is static. I don't think that assumption is true, it definitely hasn't been true up to this point.
What do you mean? Math, coding, and some computer use have been the main commercial usecases for LLMs for some years now.
3 replies →
> companies that might have reservations about chinese models having training backdoor are getting tools to train its own
This release is an inference stack, not training.
> the tricks deepseek uses to make building them faster can now be used by more companies, increasing competition even further.
But again, this is just inference, and specific to DeepSeek models. DeepSeek models are already dirt cheap on OpenRouter at pricing / speeds comparable to or better than DeepSeek's first party endpoint which uses this stack.
Yes, this repo will help others optimize inference, but it's nowhere near the hype being proclaimed in this thread.
This is a wrong mentality imo. Because we are nowhere near reaching a good quality and feature level on software in terms of the generally known theoretical ceilings.
For example you can use a database and see how it is nowhere near reaching any hardware limit. Or you can use some arbitrary software and see how it fails miserably once you start playing with options.
There is so much room for being better that I don’t think anything is justified for cutting including programmers.
Whether these companies will be able to sustain their business or weather software companies will prioritize paying LLMs instead of programmer I don’t know but the ceiling is way above
It's good that you can use an industrial hydraulic press to nail a picture on the wall but in general everybody is as happy to just use a hammer for it.
It's not just that a lot of tasks can be offloaded into these cheap models because they're already good enough for 90% of regular development stuff. What matters is the optics that the Chinese labs are chasing the US and burning all the bridges in favor of commoditization behind them. That's not a sane environment to be in if you're looking to be valued at +2T.
You're confusing software development use vs mass population use. And what you're claiming is entirely unsupported by the actual facts.
GPT's pie keeps getting larger, not smaller. Anthropic is growing hyper fast. DeepSeek and Kimi were supposed to put an end to that and the exact opposite has happened. The HN doomsayers are at 100% on being wrong spanning years at this point. It should be embarassing to be so wrong at some point.
The userbase that GPT has is not going to migrate to Chinese models. They don't even know where to find them to use them for the most part. They don't know their names. They don't know the difference between any of the models. They know: Google, GPT, Microsoft, Apple, Android, Samsung, etc.
GPT's foundation and future is as a service model. Anthropic is the frontier player that is potentially heavily threatened by China's models. It will require that Anthropic stay out in front and remain price competitive, and so far they're growing at the fastest pace of any start-up in world history.
Over maybe not. However, it does increase competition possibilities, right? Both of them have a pretty good head start though!
Still a vague argument. Why an open-sourced inference engine changes the game, drastically as some claimed?
Because when inference providers user/learn from this engine, their prices will go down, causing commercial API pricing from closed source providers to go down, hurting their net revenue.
My 2¢:
- It's been proven now that the "frontier labs" really have no technical moat. - DeepSeek has shown how cheaply inference can be done. - This creates a world where "intelligence" is a commodity and pricing is a race to the bottom.
This is not good for the multi-trillion-dollar hopes of the AI labs whose valuationa depend on them eventually reaching "ASI" or "AGI" or "RSI" or whatever they're currently clamoring about.
I personally believe this is a good thing. I wish it was coming from a US-based company, but DeepSeek is so far proving to be the OpenAI we all wanted.
I mean what’s to argue about really? If the product can do what I need it to do at lower cost and much lower exposure why wouldn’t I use it?
The vast majority of operations don’t need SOTA anyway.
How does this change that? You could already run open models with vLLM (note models, not just a single model like this release), which many companies do. DeepSeek’s fork of that will surely improve the self hosting landscape, but I don’t see how it it’s some “nuke” or “kamikaze” as the top comments are implying.
Also note that this is not a complete solution for self hosting. I assume that parts of this will be upstreamed to vLLM which this forked a while ago.
>The vast majority of operations don’t need SOTA anyway.
I agree. But people rarely pay only for the minimum they need.