191–200 of 500 posts
Perhaps most devastating is DeepSeek's recent efficiency breakthrough, achieving comparable model performance at approximately 1/45th the compute cost. This suggests the entire industry has been massively over-provisioning compute resources. I wrote in another thread why DeepSeek should increase demand for chips, not lower. 1. More efficient LLMs should lead to more usage, which means more AI chip demand. Jevon's Par…
- Wait till NVDA rebounds in price.
- Create an OpenAI "competitor" that is powered by Llama or a similar open weights model.
- Obscure the fact that the company runs on this open tech and make it seem like you've developed your own models, but don't outright lie.
- Release an app and whitepaper (whitepaper looks and sounds technical, but is incredibly light on details, you only need to fool some new-grad stock analysts).
- Pay some shady click farms to get your app to the top of Apples charts (you only need it to be there for like 24 hours tops).
- Collect profits from your NVDA short positions.Earlier quoted context omitted.
I agree with this. Fwiw many of the improvements in Deepseek were already in other 'can run on your personal computer' AI's such as Meta's Llama. Deepseek is actually very similar to Llama in efficiency. People were already running that on home computers with M3's. A couple of examples; Meta's multi-token prediction was specifically implemented as a huge efficiency improvement that was taken up by Deepseek. REcurrent…
> openAI seem to have forgotten 'The Bitter Lesson'. They have been going at things in an extremely brute force way. Isn't the point of 'The Bitter Lesson' precisely that in the end, brute force wins, and hand-crafted optimizations like the ones you mention llama and deepseek use are bound to lose in the end?
Any customisations that aren't related to the above are destined to be overtaken by someone that can improve the scaling of compute. OpenAI do not seem to be doing as much to improve the scaling of the compute in software terms (they are doing a lot in hardware terms admitedly). They have models at the top of the charts for various benchmarks right now but it feels like a temporary win from chasing those benchmarks outside of the focus of scaling compute.
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 following: economies of scale, network effects, regulatory capture
On the second point, it seems to me like the only realistic strategy for companies like OpenAI is to turn themselves into a platform that benefits from direct network effects. Whether that's actually feasible is another question.
I'm wondering if there's a (probably illegal) strategy in the making here: - Wait till NVDA rebounds in price. - Create an OpenAI "competitor" that is powered by Llama or a similar open weights model. - Obscure the fact that the company runs on this open tech and make it seem like you've developed your own models, but don't outright lie. - Release an app and whitepaper (whitepaper looks and sounds technical, but is i…
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…
The description of DeepSeek reminds me of my experience in networking in the late 80s - early 90s. Back then a really big motivator for Asynchronous Transfer Mode (ATM) and fiber-to-the-home was the promise of video on demand, which was a huge market in comparison to the Internet of the day. Just about all the work in this area ignored the potential of advanced video coding algorithms, and assumed that broadcast TV-q…
The rise of the net is Jevons paradox fulfilled. The orders of magnitude less bandwidth needed per cat video drove much more than that in overall growth in demand for said videos. During the dotcom bubble's collapse, bandwidth use kept going up.
Even if there is a near-term bear case for NVDA (dotcom bubble/bust), history indicates a bull case for the sector overall and related investments such as utilities (the entire history of the tech sector from 1995 to today).
I think longer-term we'll eat up any slack in efficiency by throwing more inference demands at it -- but the shift is tectonic. It's a cultural thing. People got acclimated to shlepping around morbidly obese node packages and stringing together enormous python libraries - meanwhile the deepseek guys out here carving bits and bytes into bare metal. Back to FP!
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…
you are assuming that what DeepSeek achieved can be reasonably easily replicated by other companies. then the question is when all big techs and tons of startups in China and the US are involved, how come none of those companies succeeded?
deepseek is unique.