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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

#491

Post by pradn »

dplgk wrote: post #250
aurareturn wrote: post #233

Earlier quoted context omitted.

This is wrong. First mover advantage is strong. This is why OpenAI is much bigger than Mixtral despite what you said. 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.

What is OpenAI's first-mover moat? I switched to Claude with absolutely no friction or moat-jumping.

Brand - it's the most powerful first-mover advantage in this space.

ChatGPT is still vastly more popular than other, similar chat bots.

Re: The impact of competition and DeepSeek on Nvidia

#492

Post by eru »

9rx wrote: post #481
eru wrote: post #471

Earlier quoted context omitted.

Technically, all the Nvidia stock (and virtually all stocks in the US) are owned by Cede and Co. So Nvidia has only one investor.[0] There's several layers of indirection between your Robinhood portfolio and the actual Nvidia shares, even if Robinhood mentions NVDA as a position in your portfolio. The ETF is just one more layer of indirection. You might like to read https://en.wikipedia.org/wiki/Exchange-traded_fund#…

> Technically, all the Nvidia stock (and virtually all stocks in the US) are owned by Cede and Co. Technically, but they extend ownership. An ETF is a different type of abstraction. Which you already know because you spoke about that abstraction in your original comment, so why play stupid now?

I have no clue what you mean by 'extend ownership', and it's supposed to be different from what ETFs are doing.

An ETF typically holds the underlying assets, and you own a part of the ETF.

Re: The impact of competition and DeepSeek on Nvidia

#493

Post by ksec »

kalleboo wrote: post #448
stevesimmons wrote: post #433

Earlier quoted context omitted.

In which countries? 3G networks in many European countries were shut off in 2022-2024. The few remaining ones will go too over the next couple of years. VoLTE is 5G, common throughout Europe. However the handset manufacturer may need to qualify each handset model with local carriers before they will connect using VoLTE. As I understand the situation, Google for instance has only qualified Pixel phones for 5G in 19 of…

> VoLTE is 5G Technically, on 5G you have "VoNR"[0], where VoLTE is over 4G. [0] https://en.wikipedia.org/wiki/Voice_over_NR

Which AFAIK is only a thing in China. Most network outside it are still stuck on NSA 5G, let alone VoNR.

Re: The impact of competition and DeepSeek on Nvidia

#495

Post by ttoinou »

xxpor wrote: post #255
dplgk wrote: post #250

Earlier quoted context omitted.

What is OpenAI's first-mover moat? I switched to Claude with absolutely no friction or moat-jumping.

What is Google's first mover moat? I switched to Bing/DuckDuckGo with absolutely no friction or moat jumping. Brands are incredibly powerful when talking about consumer goods.

But most of the money to be made in AI is B2B, no ? Not direct consumer products like ChatGPT being used by the public

Re: The impact of competition and DeepSeek on Nvidia

#496

Post by wetpaws »

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…

[dead]

Re: The impact of competition and DeepSeek on Nvidia

#497

Post by indymike »

eigenvalue wrote: post #446
indymike wrote: post #239

This story could be applied to every tech breakthrough. We start where the breakthrough is moated by hardware, access to knowledge, and IP. Over time: - 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…

Yes this is exactly right. All you need is the right incentives and enough capital and markets will find away to breech any moat that’s not enforced via regulations.

They'll even solve the regulations part most of the time as well. See Uber.

Re: The impact of competition and DeepSeek on Nvidia

#498

Post by litigator »

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…

Check out Anthonix on Twitter. He's already done what George Hotz is trying to do and he did it months ago. He's moved on from the RX 7900 XTX to MI300X and is setting some records. He had to write the majority of the code by himself but kept some of ROCm he deemed fit. He is always stirring George up when he has his AMD tantrums. Seriously though, how bad are AMD engineers if one person in their free time can make a custom stack that out performs ROCm.

Re: The impact of competition and DeepSeek on Nvidia

#500

Post by stormfather »

shwaj wrote: post #460
stormfather wrote: post #329

Earlier quoted context omitted.

I wasn't clear. Model weights aren't changing at inference time. I meant at inference time the model will output a sequence of thoughts and actions to perform tasks given to it by the user. For instance, to answer a question it will search the web, navigate through some sites, scroll, summarize, etc. You can model this as a game played by emitting a sequence of actions in a browser. RL is the technique you want to tr…

Makes sense, thanks. I wonder whether human web-browsing strategies are optimal for use in a LLM, e.g. given how much faster LLMs are at reading the webpages they find, compared to humans? Regardless, it does seem likely that Google’s dataset is good for something.

Take this example:

A human googles "how much does a tire cost?"

They pick out a website from search results, then nav within it to the correct product page and maybe scroll until the price is visible on screen.

Google captures a lot of that data on third party sites. From Perplexity:

Google Analytics: If the website uses Google Analytics, Google can collect data about user behavior on that site, including page views, time on site, and user flow.

Google Ads: Websites using Google Ads may allow Google to track user interactions for ad targeting and conversion tracking.

Other Google Services: Sites implementing services like Google Tag Manager or using embedded YouTube videos may provide additional tracking opportunities

So you can imagine that Google has a kajillion training examples that go: search query (which implies task) -> pick webpage -> actions within webpage -> user stops (success), or user backs off site/tries different query (failure)

You can imagine that even if an AI agent is super efficient, it still needs to learn how to formulate queries, pick out a site to visit, nav through the site, do all that same stuff to perform tasks. Google's dataset is perfect for this, huge, and unparalleled.

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