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The impact of competition and DeepSeek on Nvidia

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Re: The impact of competition and DeepSeek on Nvidia

#221

Post by jerjerjer »

tw1984 wrote: post #200
fairity wrote: post #194

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…

> DeepSeek just further reinforces the idea that there is a first-move disadvantage in developing AI models. 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.

We have one success after ~two years of ChatGPT hype (and therefore subsequent replication attempts). That's as fast as it gets.

Re: The impact of competition and DeepSeek on Nvidia

#222

Post by BlackSwanMan »

brandonpelfrey wrote: post #157

Great article. I still feel like very few people are viewing the Deepseek effects in the right light. If we are 10x more efficient it's not that we use 1/10th the resources we did before, we expand to have 10x the usage we did before. All technology products have moved this direction. Where there is capacity, we will use it. This argument would not work if we were close to AGI or something and didn't need more, but I…

[dead]

Re: The impact of competition and DeepSeek on Nvidia

#223

Post by [deleted] »

tw1984 wrote: post #200
fairity wrote: post #194

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…

> DeepSeek just further reinforces the idea that there is a first-move disadvantage in developing AI models. 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.

[deleted]

Re: The impact of competition and DeepSeek on Nvidia

#225

Post by [deleted] »

2-3-7-43-1807 wrote: post #106
Herring wrote: post #33

Earlier quoted context omitted.

He's setting up a case for shorting the stock, ie if the growth or margins drop a little from any of these (often well-funded) threats. The accuracy of the article is a function of the current valuation.

> The accuracy of the article is a function of the current valuation. ah ... no ... that's nonsense trying to hide behind stilted math lingo.

[deleted]

Re: The impact of competition and DeepSeek on Nvidia

#226

Post by segasaturn »

vonneumannstan wrote: post #213
TwoFerMaggie wrote: post #137

Earlier quoted context omitted.

This reminds of the joke in physics, in which theoretical particle physicists told experimental physicists, over and over again, "trust me bro, standard model will be proved at 10x eV, we just need a bigger collider bro" after another world's biggest collider is built. Wondering if we are in a similar position with "trust me bro AGI will be achieved with 10x more GPUs".

The difference is the AI researchers have clear plots showing capabilities scaling with GPUs and there's not a sign that it is flattening so they actually have a case for saying that AGI is possible at N GPUs.

Sauce? How do you even measure "capabilities" in that regard, just writing answers to standard tests? Because being able to ace a test doesn't mean it's AGI, it means its good at taking standard tests.

Re: The impact of competition and DeepSeek on Nvidia

#227

Post by p0w3n3d »

jnwatson wrote: post #169
p0w3n3d wrote: post #165

> which require low-latency responses, such as content moderation, fraud detection, dynamic pricing , etc. Is it even legal to give different prices to different customers?

Of course it is. That how the airlines stay in business.

However imagine entering a store where the camera looks up your face in shared database and profiles you as a person who will pay higher prices - and the prices are displayed near you according to your profile...

Re: The impact of competition and DeepSeek on Nvidia

#228

Post by wolfhumble »

onlyrealcuzzo wrote: post #203
tomrod wrote: post #158

Earlier quoted context omitted.

Conversely, how much larger can you scale if frontier models only currently need 3 consumer computers? Imagine having 300. Could you build even better models? Is DeepSeek the right team to deliver that, or can OpenAI, Meta, HF, etc. adapt? Going to be an interesting few months on the market. I think OpenAI lost a LOT in the board fiasco. I am bullish on HF. I anticipate Meta will lose folks to brain drain in response…

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.

Re: The impact of competition and DeepSeek on Nvidia

#229

Post by latchkey »

breadwinner wrote: post #26

Great article but it seems to have a fatal flaw. As pointed out in the article, Nvidia has several advantages including: - Better Linux drivers than AMD - CUDA - pytorch is optimized for Nvidia - High-speed interconnect Each of the advantages is under attack: - George Hotz is making better drivers for AMD - MLX, Triton, JAX: Higher level abstractions that compile down to CUDA - Cerbras and Groq solve the interconnect…

George is writing software to directly talk to consumer AMD hardware, so that he can sell more Tinyboxes. He won't be doing that for enterprise.

Cerbras and Groq need to solve the memory problem. They can't scale without adding 10x the hardware.

Re: The impact of competition and DeepSeek on Nvidia

#230

Post by pjdesno »

AlanYx wrote: post #209
pjdesno wrote: post #176

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…

Another aspect that reinforces your point is that the ATM push (and subsequent downfall) was not just bandwidth-motivated but also motivated by a belief that ATM's QoS guarantees were necessary. But it turned out that software improvements, notably MPLS to handle QoS, were all that was needed.

Nah, it's mostly just buffering :-)

Plus the cell phone industry paved the way for VOIP by getting everyone used to really, really crappy voice quality. Generations of Bell Labs and Bellcore engineers would rather have resigned than be subjected to what's considered acceptable voice quality nowadays...

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