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- Hacker News
- Is this $673B coming mostly from BigAI?
Or from individuals and smaller companies?
by amelius - What if there are no memory chips for people to build hardware with, using nvidia components?by amelius
- There's been lots of investment in memory
SK Hynix (together with Samsung and Nvidia) claims $700B investment [1], Samsung itself is investing $70B, Micron $25B, Sandisk announced $31B today [2]
[1] https://asia.nikkei.com/business/technology/artificial-intel... [2] https://www.wsj.com/tech/kioxia-sandisk-to-invest-more-than-...
by dmix - A long time ago I put money into Qualcomm and then just dumped the remaining cash into Nvidia. That did well but wow do I wish I had done the reverseby Havoc
- Remember, Nvidia sells services too, not just GPUs. They also sell full racks of servers. This is just sales, not profits.by jedberg
- Their profits are incredible too, they have 62% net margin! Last quarter $60b profit on $96b revenue. Since the "AI bubble going to pop" terrible takes 2 years ago, NVDA has made $300b in profits.
- It’s crazy that their profit margin is above 50 %, before the AI craze I only knew such margins from drug trafficking cartels (supposedly).
- Well, being best of the best always pays off.
It's obviously hard to understand to people bad at everything.
by shuwix - Hermes is closeby FergusArgyll
- And Appleby intrasight
- The projected ai capex for this and next years is above 1000b/year. 673b/year doesn't sound so weird, if they manage to spent so much, obviously.by vb-8448
- I couldn’t find the 1000b number, but if that is “ai buildout capex” that means you think that 67% of every dollar spent on building data centers, training models, and running inference, is going directly to nvidia.
Which is just silly on the face of it. Data centers need concrete, copper, DRAM, SSDs, and labour. That alone will cost more than 33%.
by kennywinker - Capex covers a lot of non-gpu stuff, but yeah it doesn't sound crazy to me.by chermi
- Margin is insane. They made roughly double net income, pure profit, what Apple did (even if you take out the ~8B in paper gains from their investments in other AI shops) on 13B less revenue.by gigatexal
- As someone who uses AI all day it all makes sense. However, if AI is going to have serious impact white collar jobs as some people predict, the demand will decline. People without jobs won't pay for expensive subscriptions or API prices and the economy will be in recession.
For me the important question is where the economy will be in the next 5 years. Because if the economy is doing well, I have no doubt the AI demand will continue to sky rocket. I don't think it matters to Nvidia how uses their compute, closed or open models. The win either way.
by tinyhouse - Everyone looks way to narrow on nations.
AI compute and AI usage is a global thing.
Look at South Korea in the 70s and today. Technology helped South Korea to leapfrog many Western companies in a few decades.
It will happen again. Many 3rd world countries have better mobile phone networks than Europe while they basically have ZERO copper in the ground.
While in Western countries everyone is skeptical about AI, Asian countries are building robot armies to replace Western collar workers. So we think about regulation and bubbles while others think of how leapfrogging us. Invest and cost is 2 sides of the same coin. It's obvious what we see here and what others see around the globe.
by Jlagreen - Sold after the price rise from this announcement. They can make the sales projection, but it doesn't mean they'll hit it:
1. Small models are rapidly growing in capability, require less compute to train and serve
2. There are more suppliers now, both in China & the US (OpenAI even have their own inferencing hardware now)
3. Memory still constrains how much they can ship in the short term
by drbscl - >Small models are rapidly growing in capability, require less compute to train and serve
Must be very clear that China’s undercut strategy, which is a well-known and studied tactic that they’ve used for a long time, it is absolutely dominating this point.
Right now you can LLM, code, make songs, images, and esp video on gaming hardware in your PC that would’ve been absolutely datacenter shit last year.
So the question will be does the scaling continue to benefit efficiency or ability?
If ability (needs datacenter storage and performance), how much better can the code get? How much more realistic in the images videos get? There are definitely strides to be made everywhere, but man, just like the bottleneck wasn’t coding, I’m not sure the creation bottleneck is rendering.
- Every quarter I see a similar analysis, similar projection. Yet, they keep posting these insane numbers. Everyone knows it’s a bubble, the problem is determining the top. Nvidia is continuously showing the top is far far higher than everyone imagines.by shubhamjain
- Counterpoints:
1. Depending on the data source you look at, about 50 - 60% of people use AI at work but only for 5 - 15% of work hours. That leaves about 2x (from users) times 7 - 20x (from work hours) for growth. Furthermore agentic usage is much more token-intensive than regular prompts, that's another unknown multiple that will get applied.
Small models will make a dent for sure, but even they need to run on hardware. It's not clear how much their lower resource requirements will cancel out the scope for growth, but I think it will take time for that dynamic to play out; people are only just starting to ease up on tokenmaxxing. Anthropic revenues would be the canary in the coalmine, and thankfully they'll be IPO'ing soon.
2. All the relevant fabs (mainly, TSMC) are extremely capacity-constrained, so who actually gets the chips depends on who has the best vendor relationships... and who can pay the most for them. Even Apple, famed for its supply chain mastery, is having trouble these days.
I would assume TSMC will try to keep all its customers happy but will prioritize supplying the customer that will pay it the most money, and these days that's Nvidia. Simply because that's where ~all the AI boom money is flowing. Heck, you could even imagine some form of revenue share to keep the spice errr chips flowing...
3. Memory constraints affect all vendors, they will just pass those costs on to customers, like Nvidia with its recent 15% price bump. Notably the bump was announced BEFORE the earnings; I wonder if the effects of that was reflected in these projections.
Nvidia is in the same position with acquiring chip supply that Google is with acquiring search traffic: monopoly profits shared with suppliers make it very hard for other companies to compete.
by keeda - > Small models are rapidly growing in capability, require less compute to train and serve
According to Jevons' paradox a reduction in resource requirements (improved resource efficiency for the same payoff) leads to an increase in demand. This stops working when demand for compute is completely exhausted, but we are very far from that. There's even some very silly predictions floating around (see the latest Dwarkesh Patel podcast) that say compute will soon be most of the economy, even dictating market interest rates. Now, that has to be wrong, but the directional outlook is closer to correct than "very small and efficient models mean there will be ~0 demand for HPC-like compute".
by zozbot234 - Posts that question the AI Endsieg are flagged now. That is supreme confidence in the numbers.by ask1287
- This would require data centre cap-ex north of 1T and revenues from non AI companies in the same order of magnitude. Is it realistic to scale up data centre roll-outs? Are regular companies ready to re-allocate 1T? And this all happens in an environment where rates go up and many of the companies are not profitable?by heisenbit
- I have not been paying much attention to the whole circular deal thing that NVIDIA is supposedly doing. As in, they invest in their clients, who buy their products.
Can anyone who actually understands finance please explain a couple things to me?
1. Are those accusations are true in a significant way, and are actually a bad thing?
2. This claimed $673B in sales, how much of it comes from NVIDIA's own money, invested into their clients? Is there any way to know that?
by consumer451 - I posted this article a few days ago which goes into a lot of the details of the recent round of proxy borrowing that Nvidia went through with several large banks: https://www.sascha-steffen.de/updates/nvidia-500bn-ai-financ... https://news.ycombinator.com/item?id=49447878by rwmj
- If I give you 10 dollars, and then you put it in your pocket, and then you take it out again and give me 10 dollars back - no actual economic growth occurred. It's simply shuffling money around; the amount stays the same.by octaane
- What do you mean by “if true”? It’s a fact. It’s “only” bad if what they’re investing in goes south, because NVIDIA gets hit twice: it loses money on the investment and loses the GPU demand.
NVIDIA says it has invested nearly $50B in frontier labs. According to NVIDIA, “the AI labs for which NVIDIA expects to leverage its balance sheet should account for roughly one-quarter of NVIDIA’s business next year.”
To be clear, this doesn’t mean 1/4 of $673B is NVIDIA money.
by 0x457 - If we assume they've invested up to $70B in other companies, which is the estimated value of their equity investments, then that implies that a maximum of 10% of that estimated revenue demand is coming directly circularly... and that assumes these companies spend the entirety of their invested capital on Nvidia infr in one year which seems unlikely so probably much lower.
Still that's not to say these companies aren't leveraging the Nvidia capital with others' in a way that magnifies or multiplies some of the effect.
But it looks like a second order contributor unless Nvidia's actions are acting like a backstop that causes way more risk and leverage to pile up in a way that could come tumbling down
by redwood