231–240 of 500 posts
Their software platforms and CUDA are a very strong moat against everyone else. I don't see any beating them on that front right now.
The problem is that I'm afraid that all that money sloshing inside the company is rotting the culture and that will compromise future development.
- Grifters are filling out positions in many orgs only trying to milk it as much as possible.
- Old employees become complacent with their nice RSU packages Rest & Vest.
If there's a real threat to their position at the top of the AI offerings will they be able to roll up the sleeves and get back to work or will the organizations be unable to move ahead.
Long term I think it's inevitable that China will take over the technology leadership. They have the population and they have the education programs and the skill to do this. At the same time in the old western democracies things are becoming stagnant and I even dare to say that the younger generations are declining. In my native country the educational system has collapsed, over 20% kids that finish elementary school cannot read or write. They can mouth-breath and scroll TikTok though but just barely since their attention span is about the same as gold fish.
DeepSeek just further reinforces the idea that there is a first-move disadvantage in developing AI models. When someone can replicate your model for 5% of the cost in 2 years, I can only see 2 rational decisions: 1) Start focusing on cost efficiency today to reduce the advantage of the second mover (i.e. trade growth for profitability) 2) Figure out how to build a real competitive moat through one or more of the foll…
First mover advantage acquired and keeps subscribers.
No one really cares if you matched GPT4o one year later. OpenAI has had a full year to optimize the model, build tools around the model, and used the model to generate better data for their next generation foundational model.
Earlier quoted context omitted.
Deepseek is unique, but the US has consistently underestimated Chinese R&D, which is not a winning strategy in iterated games.
There seem to be a 100 fold uptick in jingoists in the last 3-4 years which makes my head hurt but I think there is no consistent "underestimation" in academic circles? I think I have read articles about the up and coming Chinese STEM for like 20 years.
Great article. > Now, you still want to train the best model you can by cleverly leveraging as much compute as you can and as many trillion tokens of high quality training data as possible, but that's just the beginning of the story in this new world; now, you could easily use incredibly huge amounts of compute just to do inference from these models at a very high level of confidence or when trying to solve extremely…
> If most of NVIDIAs moat is in being able to efficiently interconnect thousands of GPUs nah. it moat is CUDA and millions of devs using CUDA aka the ecosystem
Earlier quoted context omitted.
Ahh got it, thanks for the pointer. I am surprised there is enough correlation there to allow an entire GPU to be specialized. I'll have to dig in to the paper again.
It does. They have 256 experts per MLP layer, and some shared ones. The minimal deployment for decoding (aka. token generation) they recommend is 320 GPUs (H800). It is all in the DeepSeek v3 paper that everyone should read rather than speculating.
Earlier quoted context omitted.
If you watch this video, it explains well what the major difference is between DeepSeek and existing LLMs: https://www.youtube.com/watch?v=DCqqCLlsIBU It seems like there is MUCH to gain by migrating to this approach - and it theoretically should not cost more to switch to that approach than vs the rewards to reap. I expect all the major players are already working full-steam to incorporate this into their stacks as…
> I don't think this seems particularly bad for ChatGPT. They've built a strong brand. This should just help them reduce - by far - one of their largest expenses. Often expenses like that are keeping your competitors away.
This is a step function in terms of efficiency (which presumably will be incorporated into ChatGPT within months), but not in terms of end user experience. It's only slightly better there.
- Competition gets crucial features into cheaper hardware
- Work-arounds for most IP are discovered
- Knowledge finds a way out of the castle
This leads to a "Cambrian explosion" of new devices and software that usually gives rise to some game-changing new ways to use the new technology. I'm not sure where we all thought this somehow wouldn't apply to AI. We've seen the pattern with almost every new technology you can think of. It's just how it works. Only the time it takes for patents to expire changes this... so long as everyone respects the patent.
DeepSeek just further reinforces the idea that there is a first-move disadvantage in developing AI models. When someone can replicate your model for 5% of the cost in 2 years, I can only see 2 rational decisions: 1) Start focusing on cost efficiency today to reduce the advantage of the second mover (i.e. trade growth for profitability) 2) Figure out how to build a real competitive moat through one or more of the foll…
Theres a lot more still to unpack and I don’t expect this to stay solely in the tech realm. Seems to politically sensitive.