(Many individual people are already saying that, but they aren't the people buying the GPUs for this in the first place. Steam engines weren't universally popular either when they were introduced to society.)
Either way that means a lot more NVDA hardware being sold. You still need CUDAs as rocm is still not there yet. In fact NVDA needs to churn out more CUDAs than ever.
A good fundamental analysis is probably very hard to get right, and the game is probably just guessing which way everyone else will guess the herd is going to jump.
Not sure what is supposed to happen to the inference demand but I guess that could be modeled as more of a long-run thing, as inference is going to be very coin-operated (companies need it to be net profitable now) whereas training is more of a build now profit later game.
I argue it doesn't apply to generative AI because its outputs are mostly no good, have no utility, or are good but only in limited commercial contexts.
In the first case, a machine that produces garbage faster and cheaper doesn't mean demand for the garbage will increase. And in the second case, there aren't enough buyers for high-quality computer-generated pictures of toilets to meaningfully boost demand for Nvidia's products.
Yes, the value of those only exists mostly if your internal team is too stubborn to change its opinion. But that seems to be the norm. And the value (those) consultants add is not that high in the first place! They don't have the internal knowledge of _why_ things are fucked up _your particular way_ anyways. That part your team has to contribute anyhow. So the value add shrinks to "throw ideas over the wall and see what sticks". And LLMs are excellent at that.
Yes, that doesn't replace a highly technical consultant that does the actual implementation. Yes, that doesn't give you a good solution. But it probably gives you 5 starting points for a solution before you even finish googling which consultancy to pick (and then waiting for approval and hoping for a goodish team). And that's a story that I can map to reality (not that I like this new bit of information about reality..)
If we accept that story about LLM value, then I think NVIDIA is fine. That generated value is far greater than any amount of energy you can burn on inferring prompts and the only effect will be that the compute-for-training to compute-for-inference ratio decreases further
That exec was hiring consultant and no longer is, in meaningful proportion, thanks to LLM?
There are basically two reasons for consultants:
Either you need some once-removed thing (be that you selling/buying some part to/from a competitor, accounting, lawyering). That part sensible people will not replace by LLMs. But here it isn't that you yourself lack the expertise at all. Here it is absolutely necessary, that someone else does the actual implementation.
Or you have some general "we need to do better" feeling. And here you have again two options: 1) you know _what_ your problem is and you just need the best solution there is. This is (obviously somewhat tongue-in-cheek) essentially corporate espionage. Again you cannot replace that with an LLM (or maybe you can I don't know), but you will pay a lot of money for it. Or 2) you don't know what the problem is. Now you are competing in finding an appropriate starting point with 20-somethings fresh from university that are not wanted in your organisation and therefore won't get access to the relevant information anyhow. So yeah, I'm willing to believe that the typical success rate of those consultancy projects is low to negative.
If you are only given a 3 week crash course in $BUSINESS, you won't be able to produce much more than a generic set of "have you thought about that?". And THAT is something that I believe LLMs to be reasonably good at. And they are dirt cheap and instantly available compared to any kind of human consultant.
Now I don't think that will necessarily a net-negative for consultants in general. I do think, similar to ATMs, that consultations are mostly becoming cheaper by that.
I don't know how things are in other white-collar industries (except wrt. creative jobs like copywriting and graphics design, where generative AI is even better at the job as it is at coding), but the incentives are similar so I expect most of the actual work is done by juniors anyway, and subject to replacement by models less sophisticated than people would like to imagine they need to be.
Its just that it costs too much to do that for the hoi polloi who think everything digital should be free forever.
But at some point, if the marginal product gets high enough, the world needs not as many, because money spent on other inputs/factors pays off more.
This is a classic problem with extrapolation. Making people more efficient through the use of AI will tend to increase employment... until it doesn't and employment goes off a cliff. Getting more work done per unit of GPU will increase demand for GPUs ... until it doesn't, and GPU demand goes off the cliff.
It's always hard to tell where that cliff is, though.
If every well funded start-up can have a shot, then they buy more GPUs and the big players will need to buy even more to stay noticeably ahead.
Really? Has anyone made a useful, commercially successful product with it yet?
People talk like the end user demand part of the equation is really solved when invoking an Econ 101 magical interpretation of the Law of Supply and Demand or Jevons Padadox.
Deep integration into iOS won't be tacked on in a rush to market addition to OS.
But to be clear, none of these things are commercial products. They're gimmicks. Google is an ads company, they make their money selling ads. Apple is a computer company, they make their money selling computers. These "AI products" are a circus sideshow.
The same is happening in enterprise tier products, Copilot 365 is still an extra SKU to count while Google Gemini Advanced has been integrated into the Workspace offering (i.e. they actually force you for an upsell of ~20% per user license for something we didn't ask, but I digress). At least that's a better alternative that paying +20 USD per license.
Prices need to and will go down, and business models will have to change and they are already doing so. But I'm not sure if OpenAI is really ready for that.
I gave it an easy one, “How many of the actors from the original Star Trek are still alive”. It gave me accurate information as of its training cut off date. But ChatGPT automatically did a web search to validate its answer. I had to choose the search option for it to look up later info.
With ChatGPT even when it doesn’t do a web search automatically, I can either tell it to “validate this” or put in my prompt “validate all answers by doing a web lookup”.
Then I gave it a simple compounding interest problem with monthly payments and wanted a month by month breakdown. DeepSeek used its multi step reasoning like o1 and was slower. ChatGPT 4o just created a Python script and used its internal Python interpreter.
Then DeepSeek started timing out.
This is the presentation of “what are some of the best places to eat in Chicago?”
https://chatgpt.com/share/6799510f-f4a8-8010-b80d-100c95d36d...
It doesn’t show on the shared link. But in the app it gives you a map of the restaurants or you can choose a list.
I can’t share a conversation with DeepSeek (?). But suffice it to say, the interface wasn’t as good.
I’m not saying the underlying technology of DeepSeek isn’t “good enough”. But the end user product is severely lacking.
The question is whether ChatGPT (the product) and running thr API is profitable or at at least whether the trend is that cost are coming down.
Aren't millions or even tens of millions students using ChatGPT for example? To me that sounds like a commercial success (and looks comparable with the usage of Google Search - a money printing machine for more almost 30 years now - in the first years)
And enterprise-wise - heard recently a VP complaining about entering expenses. As we don't have secretaries anymore in the civilized world, that means "Agents AI" is going to have a blast.
(i'm long on NVDA and wondering is it enough blood on the streets to buy more :)
It isn't because it's not making them any money. Having users doesn't mean you have a business. If you sell two dollars for one dollar having more users is not a blessing financially. Of course you could slap ads on it, like Google, but unlike Google openAI has no moat and there's already ten competitors. Competition eliminates profit and AI is being commoditized faster than pretty much anything else.
what was Google moat?
>and there's already ten potential competitors. Competition eliminates profit and AI is being commoditized faster than pretty much anything else.
we're discussing NVDA. Where are its competitors? ChatGPT having 10 competitors only makes things better for NVDA.
>Competition eliminates profit
Competition weeds out bad/ineffective performers which is great. History of our industry is littered with competition taking out bad performers, and our industry is only better for that. Fast commoditization of AI is just great and fits the best patterns like say PC-revolution (and like it i think the AI-revolution wouldn't be just one app/user-case, it will be a tectonic shift instead).
I read somewhere that OpenAI brought in $3.7 billion in 2024, and made a loss of $6 billion. So... no I don't think that is an example. They want to make a commercially successful product, but ChatGPT doesn't seem to be there yet.
> And enterprise-wise - heard recently a VP complaining about entering expenses. As we don't have secretaries anymore in the civilized world, that means "Agents AI" is going to have a blast.
We don't have secretaries because the word became unfashionable. They are called PAs or executive assistants or something like that now. They're still there, but if anything the need for them has probably been reduced with (non-AI) computers (calendars, contacts, emails, electronic documents, etc.) so I'm not sure that there is some enormous unmet demand for them.