> Nvidia wouldn’t say where this training data came from, but at least one report — and lawsuit — alleges that the company trained on copyrighted YouTube videos without permission.
Under the EU's AI act[1] there is now a legal obligation to disclose the source of the training data. Is this correct then that either the models cannot be used in the EU, or we'll get to know where the training data came from?
[1]: https://oeil.secure.europarl.europa.eu/oeil/en/procedure-fil... - "General-purpose AI systems, and the GPAI models such as ChatGPT they are based on, must meet certain transparency requirements including compliance with EU copyright law and publishing detailed summaries of the content used for training."
That would not work. It's the company who's responsible for compliance with EU laws and regulations in this case, not the user. So, if the company allows EU users they are de facto operating in the EU and thus out of compliance with the training data transparency law cited above.
> (a) providers placing on the market or putting into service AI systems or placing on the market general-purpose AI models in the Union, irrespective of whether those providers are established or located within the Union or in a third country;
Techcrunch didn't omit the lawsuit information which alleges Nvidia farmed YouTube to train this, I found this more useful than pure marketing spin on nvidia.com.
I don't think we can know about the long term, but I would assume that in the short-medium term, Nvidia can make a lot more money by continuing to sell its cards, where it's almost a monopoly, rather than competing in the overcrowded product space.
We don't yet know if there's actually any gold in the mine or not this time around, but we know for certain that there is money to be made selling pickaxes to the miners.
They lose their customers. TSMC succeeded because they didn't compete with their customers. When they started, they focused on making chips for others. Any other company that that had a Fab could offer the same service at the same quality back then. Yet folks choose TSMC because there was no conflict of interest. If Nvidia starts competing with their customers, that conflict of interest will force some of those customers to look else where. I suspect Nvidia is releasing these things to add value to using their GPUs, kinda like offering free tools for your OS, notepad, calculator, browser, etc
Nothing, in the same way that TSMC could cut Nvidia and OpenAI out.
Vertical integration is incredibly powerful, but it requires mastery of the whole stack.
Does Nvidia understand AI consumers the way OpenAI / Anthropocene does? Do they have the distribution channels? Can they operate services at that scale?
If they can truly do it all and the middlemen don’t add any unique value, Nvidia (or TSMC) can and should make an integration play. TBB I’m skeptical though.
Huh? Their position in the market is much better than that of OpenAI and Co. Selling hardware and services is sure business. Training models is by contrast a risky business, and even if you attain SOTA, you cannot relax because your competitors will be on your tail. From a purely pragmatic perspective they also do not have the manpower to compete seriously in that space.
The AI race is one of the most impressive examples of Capitalism making the market efficient. Or at least I've witnessed in my life.
We went from Google having complete control. To Open AI releasing GPT2 which really inspired a lot of people to try it. Then GPT3+ convinced the world to try it.
After that, Gemini, LLaMa, every type of fine-tune... The noteworthy thing is that LLaMA was good enough that ChatGPT had competition. Then within 1 year of that, we have a dozen companies with models that are good enough.
Good enough for what? I've been playing around with local models that often get mentioned here (llama3.3, mistral, etc.) and they routinely provide incorrect code that does not even compile or implements algorithms that have nothing to do with the task at hand, generate invalid JSON like '{ "foo": bar - 42 }', write nonsensical statements like "CR1616 has double the capacity of CR2032", etc. I'm yet to find a useful application for them that they can actually solve at least somewhat reliably.
Yeah, I would like to know this. From my perspective, even frontier models by the big players (4o, 3.5 Sonnet) can be unreliable at times, and are at best just walking the line of usefulness for a lot of "exact" tasks (for me: programming, approximation and back-of-the-envelope calculations, expertise on subjects I'm unfamiliar with, a better Google, etc.).
The only deal that would make sense for me is to get something more accurate, and these open models just go in the wrong direction. I've observed similar behavior to what you mention. I'd really like to know how people use them and for what tasks so that their performance is acceptable.
In agentic settings, cost and latency are also a large factor since tokens are consumed invisibly, so I think a lot of these systems are waiting for a trifecta of better accuracy, better cost and better latency to make them viable. It's unclear that this is coming, at least it hasn't been the trend so far.
Can it really be called efficient in the capitalist sense until they figure out how to actually turn this stuff into a viable business? Apparently OpenAI is even losing money on the new $200/month tier.
I believe they’re profitable on all unit economics other than the $200/month tier. The users who opt into this are the absolute “highest” users so you end up with an adverse selection issue with this plan.
I'm not sure how a few multinational mega corporations "competing" with each other is an impressive example of capitalist market efficiency. After all, this is isn't GPTx vs Gemini vs Llama vs Claude - it's Microsoft vs Google vs Meta vs Amazon. None of which are fair actors in the global marketplace.
What about the patient that got their misdiagnosed illness finally solved?
What is more important, the pains of Humans, or exhaustion of resources.
You want to go tell patients that rocks in the ground and unconcious trees are more important than their pain? Or tell a student that their educational future is less important than tress?
I think you could argue that competition indeed makes the market efficient, but that we shouldn't conflate that with capitalism itself. Capitalism, in my opinion, can at times prevent competition due to the required capital investment to compete. E.g. even OpenAI with their golden bullet couldn't get there without the capital investments from big tech? Might be wrong here of course.
I will agree that the free market has really shined here as far as product development is concerned. I have a hard time believing any government effort short of a war-time incentive would have produced anywhere near the same results.
That said, I think we're also going to see exactly where capitalism always fails - the negative externalities. If AI goes off the rails and ends up deliberately or incidentally wiping us off the globe, it's likely to be because of this relatively unregulated space race.
I have given up on AI folks using a scientific definition of “world model,” yet I am still amazed at how viciously dishonest NVIDIA is being here:
“Cosmos learns just like people learn,” the spokesperson said. “To help Cosmos learn, we gathered data from a variety of public and private sources and are confident our use of data is consistent with both the letter and spirit of the law. Facts about how the world works — which are what the Cosmos models learn — are not copyrightable or subject to the control of any individual author or company.”
Cosmos definitely does not learn facts about how the world works! This is just a ridiculous lie. It accumulates a bunch of data hopefully demonstrating how the world works, and hopefully some facts fall out of it. Given that this failed completely for Sora, which obviously knows nothing about physics, I am confident that Cosmos also knows nothing. It has no facts, just data. And unless they somehow integrated touch sensors it doesn’t even get physical data the same way toddlers do. So “learns just like people learn” is also a lie.
Some AI hype is people getting ahead of themselves and believing their own marketing. But here NVIDIA is just lying their asses off, presumably to stoke investor hype, but also because they’re trying to monetize a bunch of copyrighted data they stole. These are bad people.
Readit News
Under the EU's AI act[1] there is now a legal obligation to disclose the source of the training data. Is this correct then that either the models cannot be used in the EU, or we'll get to know where the training data came from?
[1]: https://oeil.secure.europarl.europa.eu/oeil/en/procedure-fil... - "General-purpose AI systems, and the GPAI models such as ChatGPT they are based on, must meet certain transparency requirements including compliance with EU copyright law and publishing detailed summaries of the content used for training."
> (a) providers placing on the market or putting into service AI systems or placing on the market general-purpose AI models in the Union, irrespective of whether those providers are established or located within the Union or in a third country;
https://www.nvidia.com/en-us/ai/cosmos/
Vertical integration is incredibly powerful, but it requires mastery of the whole stack.
Does Nvidia understand AI consumers the way OpenAI / Anthropocene does? Do they have the distribution channels? Can they operate services at that scale?
If they can truly do it all and the middlemen don’t add any unique value, Nvidia (or TSMC) can and should make an integration play. TBB I’m skeptical though.
one thing is not like the other.
We went from Google having complete control. To Open AI releasing GPT2 which really inspired a lot of people to try it. Then GPT3+ convinced the world to try it.
After that, Gemini, LLaMa, every type of fine-tune... The noteworthy thing is that LLaMA was good enough that ChatGPT had competition. Then within 1 year of that, we have a dozen companies with models that are good enough.
The competition has been the best type of brutal.
Otherwise, I just use the higher quality stuff online. I only use local stuff if I specifically don't want the data saved.
The only deal that would make sense for me is to get something more accurate, and these open models just go in the wrong direction. I've observed similar behavior to what you mention. I'd really like to know how people use them and for what tasks so that their performance is acceptable.
In agentic settings, cost and latency are also a large factor since tokens are consumed invisibly, so I think a lot of these systems are waiting for a trifecta of better accuracy, better cost and better latency to make them viable. It's unclear that this is coming, at least it hasn't been the trend so far.
https://finance.yahoo.com/news/sam-altman-says-losing-money-...
Its impressive.
(and if you want to see the flip side, our regulatory captured Medical industry still uses faxes)
I’d counter with “The competition has been the worst type of brutal.”
https://finance.yahoo.com/news/ai-destroyed-google-promise-c...
Tech’s quest for the best chatbot so they get all the grift dollars has torched climate change progress.
What is more important, the pains of Humans, or exhaustion of resources.
You want to go tell patients that rocks in the ground and unconcious trees are more important than their pain? Or tell a student that their educational future is less important than tress?
It was Capitalism, not market efficiency.
That said, I think we're also going to see exactly where capitalism always fails - the negative externalities. If AI goes off the rails and ends up deliberately or incidentally wiping us off the globe, it's likely to be because of this relatively unregulated space race.
Some AI hype is people getting ahead of themselves and believing their own marketing. But here NVIDIA is just lying their asses off, presumably to stoke investor hype, but also because they’re trying to monetize a bunch of copyrighted data they stole. These are bad people.
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