But the company doesn’t have the money. Stock value means investor valuation, not company funds.
Once a company goes public for the very first time, it’s getting money into its account, but from then on forward, that’s just investors speculating and hoping on a nice return when they sell again.
Of course there should be some correlation between the company’s profitability and the stock price, so ideally they do have quite some money, but in an investment craze like this, the correlation is far from 1:1. So whether they can still afford to build the data centers remains to be seen.
They’re not building them for themselves, they’re selling GPU time and SuperPods. Their valuation is because there’s STILL a lineup a mile long for their flagship GPUs. I get that people think AI is a fad, and it’s public form may be, but there’s thousands of GPU powered projects going on behind closed doors that are going to consume whatever GPUs get made for a long time.
Their valuation is because there’s STILL a lineup a mile long for their flagship GPUs.
Genuinely curious, how do you know where the valuation, any valuation, come from?
This is an interesting story, and it might be factually true, but as far as I know unless someone has actually asked the biggest investor WHY they did bet on a stock, nobody why a valuation is what it is. We might have guesses, and they might even be correct, but they also change.
I mentioned it few times here before but my bet is yes, what you did mention BUT also because the same investors do not know where else do put their money yet and thus simply can’t jump boats. They are stuck there and it might again be become they initially though the demand was high with nobody else could fulfill it, but I believe that’s not correct anymore.
Well, I’m no stockologist, but I believe when your company has a perpetual sales backlog with a 15-year head start on your competition, that should lead to a pretty high valuation.
I’m also no stockologist and I agree but I that’s not my point. The stock should be high but that might already have been factored in, namely this is not a new situation, so theoretically that’s been priced in since investors have understood it. My point anyway isn’t about the price itself but rather the narrative (or reason, as the example you mention on backlog and lack of competition) that investors themselves believe.
Right, and I mentioned CUDA earlier as one of the reason of their success, so it’s definitely something important. Clients might be interested in e.g Google TPU, startups like Etched, Tenstorrent, Groq, Cerebras Systems or heck even design their own but are probably limited by their current stack relying on CUDA. I imagine though that if backlog do keep on existing there will be abstraction libraries, at least for the most popular ones e.g TensorFlow, JAX or PyTorch, simply because the cost of waiting is too high.
Anyway what I meant isn’t about hardware or software but rather ROI, namely when Goldman Sachs and others issue analyst report saying that the promise itself isn’t up to par with actual usage for paying customers.
Those reports might effect investments from the smaller players, but the big names(Google, Microsoft, Meta, etc.) are locked in a race to the finish line. So their investments will continue until one of them reaches the goal…[insert sunk cost fallacy here]…and I think we’re at least 1-2 years from there.
Yeah, someone else commented with their financials and they look really good, so while I certainly agree that they are overvalued because we are in an AI training bubble, I don’t see it popping for a few years, especially given that they are selling the shovels. every big player in the space is set on orders of magnitude of additional compute for the next 2 years or more. It doesn’t matter if the company they sold gpus to fails if they already sold them. Something big that unexpected would have to happen to upset that trajectory right now and I don’t see it because companies are in the exploratory stage of ai tech so no one knows what doesn’t work until they get the computer they need. I could be wrong, but that’s what I see as a watcher of ai news channels on YouTube.
The co founder of open AI just got a billion dollars for his new 3 month old AI start up. They are going to spend that money on talent and compute. X just announced a data center with 100,000 gpus for grok2 and plans to build the largest in the world I think? But that’s Elon, so grains of salt and all that are required there. Nvidia are working with robotics companies to make AI that can train robots virtually to do a task and in the real world a robot will succeed first try. No more Boston dynamics abuse compilation videos. Right now agentic ai workflow is supposed to be the next step, so there will be overseer ai algorithms to develop and train.
All that is to say there is a ton of work that requires compute for the next few years.
{Opinion here} – I feel like a lot of people are seeing grifters and a wobbly gpt4o launch and calling the game too soon. It takes time to deliver the next product when it’s a new invention in its infancy and the training parameters are scaling nearly logarithmically from gen to gen.
I’m sure the structuring of payment for the compute devices isn’t as simple as my purchase of a gaming GPU from microcenter, but Nvidia are still financially sound. I could see a lot of companies suffering from this long term but nvidia will be The player in AI compute, whatever that looks like, so they are going to bounce back and be fine.
But the company doesn’t have the money. Stock value means investor valuation, not company funds.
Once a company goes public for the very first time, it’s getting money into its account, but from then on forward, that’s just investors speculating and hoping on a nice return when they sell again.
Of course there should be some correlation between the company’s profitability and the stock price, so ideally they do have quite some money, but in an investment craze like this, the correlation is far from 1:1. So whether they can still afford to build the data centers remains to be seen.
They’re not building them for themselves, they’re selling GPU time and SuperPods. Their valuation is because there’s STILL a lineup a mile long for their flagship GPUs. I get that people think AI is a fad, and it’s public form may be, but there’s thousands of GPU powered projects going on behind closed doors that are going to consume whatever GPUs get made for a long time.
Genuinely curious, how do you know where the valuation, any valuation, come from?
This is an interesting story, and it might be factually true, but as far as I know unless someone has actually asked the biggest investor WHY they did bet on a stock, nobody why a valuation is what it is. We might have guesses, and they might even be correct, but they also change.
I mentioned it few times here before but my bet is yes, what you did mention BUT also because the same investors do not know where else do put their money yet and thus simply can’t jump boats. They are stuck there and it might again be become they initially though the demand was high with nobody else could fulfill it, but I believe that’s not correct anymore.
Well, I’m no stockologist, but I believe when your company has a perpetual sales backlog with a 15-year head start on your competition, that should lead to a pretty high valuation.
I’m also no stockologist and I agree but I that’s not my point. The stock should be high but that might already have been factored in, namely this is not a new situation, so theoretically that’s been priced in since investors have understood it. My point anyway isn’t about the price itself but rather the narrative (or reason, as the example you mention on backlog and lack of competition) that investors themselves believe.
Why do you believe that? As far as I understand, other HW exists…but no SW to run on it…
Right, and I mentioned CUDA earlier as one of the reason of their success, so it’s definitely something important. Clients might be interested in e.g Google TPU, startups like Etched, Tenstorrent, Groq, Cerebras Systems or heck even design their own but are probably limited by their current stack relying on CUDA. I imagine though that if backlog do keep on existing there will be abstraction libraries, at least for the most popular ones e.g TensorFlow, JAX or PyTorch, simply because the cost of waiting is too high.
Anyway what I meant isn’t about hardware or software but rather ROI, namely when Goldman Sachs and others issue analyst report saying that the promise itself isn’t up to par with actual usage for paying customers.
Those reports might effect investments from the smaller players, but the big names(Google, Microsoft, Meta, etc.) are locked in a race to the finish line. So their investments will continue until one of them reaches the goal…[insert sunk cost fallacy here]…and I think we’re at least 1-2 years from there.
Edit: posted too soon
Yeah, someone else commented with their financials and they look really good, so while I certainly agree that they are overvalued because we are in an AI training bubble, I don’t see it popping for a few years, especially given that they are selling the shovels. every big player in the space is set on orders of magnitude of additional compute for the next 2 years or more. It doesn’t matter if the company they sold gpus to fails if they already sold them. Something big that unexpected would have to happen to upset that trajectory right now and I don’t see it because companies are in the exploratory stage of ai tech so no one knows what doesn’t work until they get the computer they need. I could be wrong, but that’s what I see as a watcher of ai news channels on YouTube.
The co founder of open AI just got a billion dollars for his new 3 month old AI start up. They are going to spend that money on talent and compute. X just announced a data center with 100,000 gpus for grok2 and plans to build the largest in the world I think? But that’s Elon, so grains of salt and all that are required there. Nvidia are working with robotics companies to make AI that can train robots virtually to do a task and in the real world a robot will succeed first try. No more Boston dynamics abuse compilation videos. Right now agentic ai workflow is supposed to be the next step, so there will be overseer ai algorithms to develop and train.
All that is to say there is a ton of work that requires compute for the next few years.
{Opinion here} – I feel like a lot of people are seeing grifters and a wobbly gpt4o launch and calling the game too soon. It takes time to deliver the next product when it’s a new invention in its infancy and the training parameters are scaling nearly logarithmically from gen to gen.
I’m sure the structuring of payment for the compute devices isn’t as simple as my purchase of a gaming GPU from microcenter, but Nvidia are still financially sound. I could see a lot of companies suffering from this long term but nvidia will be The player in AI compute, whatever that looks like, so they are going to bounce back and be fine.
Couldn’t agree more. There is quite a bit of AI vaporware but NVIDIA is the real stuff and will weather whatever storm comes with ease.