Nvidia’s $589B DeepSeek rout

https://finance.yahoo.com/news/asml-sinks-china-ai-startup-081823609.html

by rcarmo • 1 year ago

501 1060 1 year ago

finance.yahoo.com

nabla93• 1 year ago

Nvidia -13% in Frankfurt stock market just now.

Valuations of private unicorns like OpenAi and Anthropic must be in free fall. DeepSeek spends $6 million in old H800 hardware to develop open source model that overtakes ChatGPT. AI gets better, but profit margins sink with strong competition.

Chinese AI startup DeepSeek overtakes ChatGPT on Apple App Store https://news.ycombinator.com/item?id=42839656

Edit: Nvidia now -15% in Frankfurt.

gostsamo• 1 year ago

Consider that the chinese might be misrepresenting their costs. A newsletter was implying that they might do it to undermine the sanctions justifications.

Agree that the AI bubble should pop though and the earlier, the better.

nabla93• 1 year ago

Their model is open and they published paper describing it https://arxiv.org/pdf/2412.19437 The can't be far off, or it would be noticed.

Even if they are heavily government subsidized for energy and hardware, I don see how the cost of training in the US would be more than double.

esperent3• 1 year ago

DeepSeek spends $6 million in old H800 hardware to develop open source model that overtakes ChatGPT.

DeepSeek claims that's what they spent. They're under a trade embargo, and if they had access to any more than that it would have been obtained illegally.

They might be telling the truth, but let's wait until someone else replicates it before we fully accept it.

belter7• 1 year ago

They have a lot of H100: https://www.reddit.com/r/NVDA_Stock/comments/1iadc0s/evidenc...

senko• 1 year ago

There are already some (limited) reproductions that suggest they're not completely lying (ie that there are indeed perf benefits).

nabla93• 1 year ago

Huggingface is currently replicating it.

Replications of small models indicate that they don't lie any significant amount. The architecture is cheap to train.

Berkeley Researchers Replicate DeepSeek R1's Core Tech for Just $30: A Small Model RL Revolution https://xyzlabs.substack.com/p/berkeley-researchers-replicat...

maxglute9• 1 year ago

been obtained illegally.

PRC companies breaking US export control laws is legal (for PRC companies). Maybe they're trying to avoid US entity listing, lot's of PRC companies keep mum about growing capabilites to do so. But the mere fact Deepseek is publicizing means they're unlikely to care about the political heat that is coming and the ramifications. If anything, getting on US entity list probably locks in their employees with Deepseek on resume into PRC.

lmm6• 1 year ago

All of the western AI companies trained on illegally obtained data, they barely even bother to deny it. This is an industry where lies are normalised. (Not to contradict your point about this specific number)

nolok• 1 year ago

They were massively overhyped though, it feels more like a correction (and a partial one at that) than a fall.

benterix2• 1 year ago

Of course they are overhyped but in spite of this Altman is always asking for more money. And we know financially they are just burning money. So when someone finally brings a cheap but good model for the masses, this is where money should go. (This will also help all small AI startups.)

negamax• 1 year ago

The bigger correction will be in tech stocks that are overly exposed to datacenter investments to accommodate for ever rising AI demands. MSFT, AMZN, META they are all exposed

myth_drannon• 1 year ago

MSFT is down 7%

scrlk• 1 year ago

Not a good day for those who decided to hold 3x Nvidia ETPs - down 40% earlier.

chad1n• 1 year ago

This is not exactly right, they said they spent $6M on training V3, there aren't numbers out there related to the training of R1, I can feel it will be cheaper than o1, but it's hard to tell how much cheaper. I can guess that overall deepseek spent way less than openai to release the model, because I have the feeling that the R&D part was cheaper too, but we don't have the numbers yet. Anyway, we can assume that deepseek and Alibaba will try to get the most out of their current GPUs however.

kvgr• 1 year ago

Not sure if its as hot as it looks. Valuation of those private AIs is stupid. This looks like Chinese propaganda. Say we dont need that hardware, but it looks like they need it. https://wccftech.com/chinese-ai-lab-deepseek-has-50000-nvidi...

impossiblefork6• 1 year ago

It's a very strange result.

I believe that NVIDIA is overvalued, but if DeepSeek really is as great as has been said, then it'll be even greater when scaled up to OpenAI sizes, and when you get more out you have more reason to pay, so this should if it pans out lead to more demand for GPUs-- basically Jevon's paradox.

franktankbank• 1 year ago

The architecture doesn't keep yielding better results, Jevon's paradox doesn't apply.

lmm6• 1 year ago

If the top-tier premium GPUs aren't the difference-maker they were thought to be then that will hurt NVIDIA's margins, even if they make some of it up on volume.

alecco7• 1 year ago

The limit is high quality data, not compute.

nialv73• 1 year ago

You need to be prepared for the reality that naive scaling no longer works for LLMs anymore.

Simple question: where is GPT-5?

r00fus3• 1 year ago

For Oracle (another Stargate recipient) it was reversion to the mean. For Nvidia, it's a big loss - I imagine they might have predicated their revenue based on the continued need for compute - and now that's in question.

qwytw5• 1 year ago

overtakes ChatGPT

That's arguable, though. I mean it's much cheaper and reasonably competitive which is almost the same but IMHO DeepSeek seems to get stuck in random loops and hallucinates more frequently than o1.

SebFender2• 1 year ago

Taking out the popcorn early today.

mgh2• 1 year ago

Let's see how the open vs close ecosystem wins in AI

einrealist• 1 year ago

Nice to see a pullback within this AI bubble. Wake me when I don't have to double-check the results of generative AI, even with DeepSeek.

lm284692• 1 year ago

Wake me when I don't have to double-check the results of generative AI, even with DeepSeek

That's gonna be a looong nap

xyst• 1 year ago

Buy the dip

elaus• 1 year ago

Grab for the falling knife

testrun• 1 year ago

The problem is to know where the dip is.

nejsjsjsbsb13• 1 year ago

Or just do boring sound financial planning based on investment goals and retirement age!

sgt101• 1 year ago

hodl?

alecco7• 1 year ago

Hold the bag.

suraci5• 1 year ago

with what

Discordian93• 1 year ago

It's funny the bubble seems popping because the tech can be made better or cheaper instead due to loss of enthusiasm for the tech itself. AI is here to stay I suppose then.

InkCanon3• 1 year ago

If anything Meta and other foundation model companies should be dropping. What's the moat? DeepSeek is releasing stuff for free.

spacebanana72• 1 year ago

Meta should actually go up from this. If deep seek is perfect they don't need to pay for an expensive Llama team. And even if deep seek isn't perfect, the low cost training strategies they've invented could be used by Meta to reduce the cost of Llama training.

Although Meta develops models they don't sell them. So a world where foundation models are free is fine for them.

erredois• 1 year ago

Meta's main use of AI is in their own products, I don't think they should be affected. I would be more worried about companies that want to sell AI models and are not being efficient.

NitpickLawyer• 1 year ago

If anything Meta

I don't follow. Meta has been the only US big dog that released open-whatever variants of their models. They did that intending to minimise the gap between them and other big dogs. Their stated goal is to give open access to the community, while at the same time develop the models for internal uses (on their many platforms).

Meta doesn't sell API access. They are not losing on "cheaper" anything. If anything, they get to implement whatever others release under open terms into their stacks. And they still have all the GPUs to further train and serve on whatever improved stack comes next.

I don't see how meta loses here. In fact I think it is one of the only big players in this space that will come out better.

dogma11385• 1 year ago

Good time to buy then, I don’t understand how stupid some traders can be.

A more efficient model is better for NVIDIA not worse. More compute is still better given the same model. And as more efficient models proliferate it means more edge computing which means more customers with lower negotiating power than Meta and Google…

This is like thinking that if people need to dig only 1 day instead of an entire month to get to their nugget of gold in the midst of a gold rush the trading post will somehow sell fewer shovels…

diegocg• 1 year ago

Nvidia is way too overvalued regardless of deepseek or the success of AI. This is just some correction (not even too big even considering the current bubble), these traders are not stupid.

dogma11385• 1 year ago

NVIDIA has a P/E ratio of 56 that’s double that of the S&P 500 but half that of AMD and the same one as Meta.

And whether it’s overvalued or not isn’t relevant that selling a stock because the product the company produces is now even more effective is mind bogglingly stupid.

randomcarbloke2• 1 year ago

agreed but ASML has been chronically undervalued forever.

matwood6• 1 year ago

I agree with Aswath Damodaran here. NVDA is priced for perfection in AI, but also whatever is next.

In addition, IMO NVDA’s margins are a gift and a curse. They look great to investors, but also mean all their customers are aggressively looking to produce their own GPUs.

HPsquared• 1 year ago

In said gold rush scenario, the price of gold itself would have collapsed.

lovasoa• 1 year ago

And since the gold supply is limited, fewer shovels would indeed be required.

belter7• 1 year ago

NVDA is a totally manipulated stock. The company beat earnings in the last three quarters and the stock dropped 15% to 20% immediately after the results release.

Every single time...

sitkack4• 1 year ago

What do you think is going on here? Why is it dropping?

baal80spam2• 1 year ago

I don't think it's manipulated, just hot. As the old adage goes: "Buy the rumor, sell the news".

WithinReason2• 1 year ago

Indeed:

https://en.wikipedia.org/wiki/Jevons_paradox

In economics, the Jevons paradox occurs when technological progress increases the efficiency with which a resource is used, but the falling cost of use induces increases in demand enough that resource use is increased, rather than reduced.

belter7• 1 year ago

I beat your Jevons paradox and raise you the Theory of Constraints..

ReptileMan2• 1 year ago

Yes but it will also mean that people wouldn't need cutting edge NVIDIA chips - it will be able to run on older node chips. Or from different manufacturers. So NVIDIA wouldn't be able to command the margins they do now.

It may be great news for VRAM manufacturers tough.

sitkack4• 1 year ago

You are right, it is a Jevon's paradox thing. We were already going to spend all the gigawatts on AI, it is just more AI.

Etheryte• 1 year ago

While everything you say may be true, this shows a fundamental misunderstanding about how the modern stock market functions. How much value a company creates is at best tangential and often completely orthogonal to how much the stock is worth. The stock market has always been a Keynesian beauty contest, but in the past few decades, strongly shaped and morphed by attention economies. A good example of this is DJT, a company which functionally doesn't do much anything, but in the last year has traded at wildly differing prices. P/E, EBITDA, etc, are all useless metrics in trying to explain this phenomenon.

In other words, NVIDIA is in the red not because the company is suddenly doing worse, but because traders think other traders think it will trade down. That is a self fulfilling prophecy, but only so long as there is sufficient attention to drive that. The same works the other way around as well, so long as there is sufficient attention to drive the AI hype train upwards, related stocks will do well as well.

Cumpiler695• 1 year ago

In other words, NVIDIA is in the red not because the company is suddenly doing worse, but because traders think other traders think it will trade down.

Well put. People need to unterstand that some stocks are basically one giant casino poker table. There was a comment with a link here that a lot of Nvidia buyers don't even know what products Nvidia is making and they don't care, they just want to buy low and sell high. Insert old famous comment abut shoe shine boy giving investment advice to Wall Street stock traders.

Dalewyn3• 1 year ago

Put more simply: The stock market is not a reflection of the economy. Wall Street is not Main Street.

dogma11385• 1 year ago

I understand that part, hence why this is very much buy the dip for me.

api3• 1 year ago

Everything above the street level and physical economy is becoming gambling.

There has always been a component of gambling to all investing, but that component now seems to utterly eclipse everything else. Merit doesn’t even register. Fundamentals don’t register.

scotty79• 1 year ago

Markets can stay irrational longer than you can stay solvent.

mistercow• 1 year ago

I think it's because the media coverage is all focused on how this means the big AI players have lost their competitive advantage, rather than the other side of the equation.

But that's also dumb, because "huge leap forward in training efficiency" is not exactly bad news for the major players in even the medium term. Short term, it means their models are less competitive, but I don't see any reason that they can't leverage e.g. these new mixed precision training techniques on their giant GPU farms and train something even bigger and smarter.

There seems to be this weird baked in assumption that AI is at a permanent (or at least semi-permanent) plateau, and that open source models catching up is the end of the game. But this is an arms race, and we're nowhere near the finish line.

nejsjsjsbsb13• 1 year ago

Good time to buy is % of income via tax efficient methods into the in SP500 for most.

But I agree in the sense that Deepseek just creates more demand. Because people desire to get AI to do more work. This makes bang for buck greater opening new opportunities.

This sell off is like selling Intel in 2010 because of a new C compiler.

csomar2• 1 year ago

Not if the current architecture plateaus around the level of O1/3.5/R1. Then any further training is useless and inference is relatively cheap.

amazingamazing9• 1 year ago

maybe Nvidia is fine but I don't understand this logic. suppose it turns out GPUs are unnecessary at all, but they still provide a performance boost, but you can do everything you can do right now with CPUs w.r.t performance. would that be good or bad for Nvidia?

unless it can be said we need more performance than is currently possible, e.g. new demand, it would be catastrophic. it is unclear that throwing more compute actually expands what is possible. if that is not the case, efficiency is bad for nvidia because it simply results in less demand.

diggan3• 1 year ago

I could see arguments be made in both ways here. If GPUs end up being more efficient/powerful (like today) it could induce even more demand, but also if CPU gets within ~20% of how fast you can do something with a GPU, people might start opting for something like Macs with unified memory instead of GPUs.

Today a CPU setup is still nowhere near as fast as a GPU setup (for ML/AI), but who knows how it looks like in the future.

it is unclear that throwing more compute actually expands what is possible

Wasn't that demonstrated to be true already in the GPT1/2 days? AFAIK, LLMs became a thing very much because OpenAI "discovered" that "throwing more compute (and training data) at the problem/solution expands what is possible"

fintechie2• 1 year ago

I don’t understand how stupid some traders can be.

90% of traders lose money, so that's a data point...

You're trying to apply rational thinking but that's not how markets work. In the end valuations are more about narratives in the collective mind than technological merit.

ransom1538• 1 year ago

"dig only 1 day instead of an entire month"

You answered your own question. People do not dig in the Sacramento right anymore for gold, because, it is gone. If you can train models for 1/100 the cost, and you sell model training chips, you probably are not going to sell as many chips.

Jlagreen17• 1 year ago

That's why the shovel maker from back then are selling mining machines today.

Everyone here thinks Nvidia is dommed because of training efficiency.

But what has Nvidia been doing for the past decade? Correct increasing training and inferencing efficiency by magnitudes.

Try to train GPT4 on 10k of Volta, Ampere, Hopper and then Blackwell.

What has happened since then? Nvidia has increased their sales in magnitudes.

Why? Because thanks to improvement in data, in algorithms, compute efficiency ChatGPT was possible in the first place.

Imagine Nvidia wouldn't exist. When do you think the ChatGPT moment would happen on CPUs? LOL

Going back to my first sentence. Nvidia started also with small shovels which were GeForce cards with CUDA. Today Nvidia is selling huge GPU clusters (mining machines, yes pun intended ^^).

digitcatphd2• 1 year ago

This doesn't have anything to do with DS, despite what the media claims, it comes to a heightened sense of fear people have with playing with fire.

chvid• 1 year ago

It is a demonstration that progress is no longer due to llm scaling.

croes• 1 year ago

But a more efficient model doesn't need the top notch hardware

zozbot2346• 1 year ago

nVidia is also about HPC in general, not just AI. It's remarkably silly that the stock would plunge 13% just because someone made a more compute-efficient LLM.

csomar2• 1 year ago

Good time to buy then

No. The stock is still x10 after this dip from 2 years ago and x40 from a few years ago.

omgJustTest• 1 year ago

Good time to buy then, I don’t understand how stupid some traders can be.

Likely a "how solid is the technical moat" evaluation - this could be a one-off or could be that there are an avalanche of advancements to continue along the efficiency side of the process.

Given the style and hype of logic in the AI space, I fully believe resources are not well allocated in compute and _actual_ thinking as to how they are spent.

Deepseek's apparent 10x more efficient per inference token... implies a lot of other hardware meets the general use-case. We also know that reasoning should be about 10W for human speed-of-thought... maybe another 1-2 orders of power efficiency.

"Pre-Training: Towards Ultimate Training Efficiency

We design an FP8 mixed precision training framework and, for the first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model. Through co-design of algorithms, frameworks, and hardware, we overcome the communication bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap. This significantly enhances our training efficiency and reduces the training costs, enabling us to further scale up the model size without additional overhead. At an economical cost of only 2.664M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8T tokens, producing the currently strongest open-source base model. The subsequent training stages after pre-training require only 0.1M GPU hours." [1]

[1] https://huggingface.co/deepseek-ai/DeepSeek-V3

gadders• 1 year ago

>>I don’t understand how stupid some traders can be.

I believe the saying is "The market can stay wrong for longer than you can stay solvent."

llm_nerd• 1 year ago

the trading post will somehow sell fewer shovels

There is a point where there are enough shovels circulating that the demand for new shovels falters, even with zero drawback in the rush. And if so much gold was being mined that it overwhelmed the market and reduced the commodity price, the value of better shovels is reduced.

DeepSeek and friends basically reduce the commodity value of AI (and to be fair, Facebook, Microsoft et al are trying to do the same thing with their open source models, trying to chop the legs out of the upstart AI cos). If AI is worth less, there are going to be fewer mega capitalized AI ventures buying a trillion dollars worth of rapidly-depreciating GPUs in hopes at eeking out some minor advantage.

I wouldn't short nvidia stock, but at the same time there is a point where the spend of GPUs just isn't rational anymore.

And as more efficient models proliferate it means more edge computing which means more customers with lower negotiating power than Meta and Google

Edge compute has infinitely more competition than the data center.

fire_lake• 1 year ago

Fascinating.

I think Meta is a big winner from this - they still control the content and now mining it for value has been proven even cheaper by DeepSeek.

ppsreejith• 1 year ago

Agree. Apple should be as well. The only con I can think of would be their (Meta's) data center investments but seems this will make them more efficient?

rvz3• 1 year ago

Exactly this. This does not affect Meta as many people believe.

The startups that are in trouble are the ones that have been screaming for "AGI" and buying up GPUs and close-sourcing their models.

Open source was always going to win the race to the bottom. [0]

[0] https://news.ycombinator.com/item?id=41671582

sitkack4• 1 year ago

Yours is the correct take, it isn't about who did it, it was that it was done at all.

This shows how effective open source and open development can be.

Best way to get a correct answer is ... https://www.phind.com/search?cache=ws3qq1xl8hj4yx1izd5oeda0

p2detar• 1 year ago

Not sure I understand how exactly open source plays a key role here in terms of project development.

Looking at https://github.com/deepseek-ai, those repos have a bunch of of contributors but unless I'm wrong I don't see any significant contributions. What am I missing?

IAmGraydon2• 1 year ago

I'm not so sure about that. Deepseek puts their LLM (Llama) even further behind. It's basically at the back of the pack, signaling to the market that they don't have the top minds in the industry on board. Second, I'm not sure how a massive trove of misinformation is of much use or how it's of more use to them than it is to others. Can you elaborate on that?

buryat2• 1 year ago

What kind of quality of content can Meta theoretically mine with all the vast data that they collect? They have a good advertising and a content algorithms but does it approximate general intelligence? And the content you see on Facebook, Instagram, Whatsapp, that's just a lot of average content that might actually be great for AGI.

tartoran• 1 year ago

Propaganda. Pay to play

jamil7• 1 year ago

Shouldn't this news be bullish long-term for them?

blitzar6• 1 year ago

The $500 billion data center can now be $50 billion. That is excellent news, unless you were the company that was expected to sell $450 billion of GPUs with a 95% gross margin to that project.

Jlagreen17• 1 year ago

$50 billion can be afforded by WAY WAY more sites and companies so Nvidia will simply delivered to >10x more data centers. Or instead of shipping 100k GPUs to Meta, they will ship 100k GPUs to 10 different customers.

For Nvidia, it is great news because now finally, the concentration of GPUs at Hyperscalers will end and every Fortune company can finally get their local data center to train their AI Models.

Because if training AI models becomes more efficient and easier then the ones being that business are at risk so basically Big Tech. Nvidia isn't in that business but in the business of providing tools to train.

Fortunately, Big Tech can easily do something to prevent ANYONE for competing. They simply buy all available GPUs. Oh wait, haven't they been doing it for years? Excatly!

People really don't get what an arms race and market competition race is.

How do I prevent disruption? I simply buy all the tools the competition needs to disrupt me.

See, if Fortune 500 companies want to build large data centers but can't because all Hyperscalers buy the GPUs then eventually they will rent from cloud as otherwise they can't get the GPUs.

homarp• 1 year ago

see also https://news.ycombinator.com/item?id=42822162 "The Short Case for Nvidia Stock"

tobyhinloopen• 1 year ago

I was wondering why my whole portfolio was deeply red

pjc502• 1 year ago

The whole AI valuation was based on being able to rent-seek a significant chunk of all white collar salaries, with a permanent monopoly moat because nobody else would pay hundreds of billions to train models.

And yet again a cheaper Chinese product turns up and everyone loses their minds. Expect a ban incoming to preserve the AI valuations.

torginus• 1 year ago

Banning wouldn't work imo - now that the cat's out of the bag, with the architecture being open source, anyone can replicate their results to a compete with them for a relatively small investment.

fancyfredbot4• 1 year ago

The people writing market commentary are simply making it up. The news about DeepSeek is not new and doesn't reduce the value of ASML. People are selling now because they are scared because the number went down.

knoopx• 1 year ago

this, absolutely irrelevant to deepseek

sekai• 1 year ago

Andrej Karpathy was tweeting about DeepSeek a month (!) ago.

"DeepSeek (Chinese AI co) making it look easy today with an open weights release of a frontier-grade LLM trained on a joke of a budget (2048 GPUs for 2 months, $6M)."

https://x.com/karpathy/status/1872362712958906460

igleria7• 1 year ago

this same forum ignored deepseek one month ago, save for a few... open minded people.

eigenvalue4• 1 year ago

Yes exactly. The actual impetus this time was the article I posted here and how it got echoed and amplified by massive X accounts like Chamath and Naval.

FergusArgyll• 1 year ago

Yes! and this applies to all market commentary. Market goes down .7% and you have talking heads saying "fear of tariffs" "middle east tensions" "Hurricane season" whatever, next day market goes up .6% "talk of tax cuts" "Jobs numbers" whatever. There's no way anyone knows why "the market" behaves the way it does. The free market is the OG "Decentralized" project, it's 1 billion different decisions being made in a day each with their personal reasons. Yes, sometimes it's fairly obvious that something caused it (plane blows up, stock goes down) but that still doesn't explain the entirety of it

maxglute9• 1 year ago

ASML paper value is determined by equipment sales from projected compute supply/demand. CHIPS building redundant global fabs = glut from more excess capacity = less future sales. Stargate = excess demand from everyone spending 100s of billions of compute = need even more fabs = more future sales. Then DeepSeek = suddenly no need for that much future compute... if number of future compute demand relative to short term fab overcapacity is going down, then it's reasonable to sell. Relatively predictable Semiconductor market cycles due to cost of capex and time to build fabs / increase new wafers output to match future demand is a thing.

fancyfredbot4• 1 year ago

This would be an excellent explanation if DeepSeek had announced its model over the last weekend rather than weeks ago, and if R1 wasn't a COT reasoning model which needs a lot more inference time compute than other SOTA models like llama.

tmaly• 1 year ago

If you look at Intel, it is a cyclical business.

One could argue by extension that ASML is also cyclical.

IAmGraydon2• 1 year ago

The people writing market commentary are simply making it up.

Everyone except for you, right?

fancyfredbot4• 1 year ago

Oh no, I am making it up too.

rvz3• 1 year ago

Well this is very bad for the private AI companies but Big Tech will be fine, especially Meta and Apple.

Everyone else who over-leveraged into the private AI companies (Anthropic, OpenAI) are going to have their valuations under scrutiny.

romanovcode• 1 year ago

What does Apple have to do with AI

suraci5• 1 year ago

MSFT should be here instead of apple

rvz3• 1 year ago

Nothing. Apple (and Meta) are not directly impacted by AI even if the cost of training these AI models becomes cheaper and $0 free models get better.

It actually affects the frontier AI companies (OpenAI, Anthropic, etc) who directly make money from their closed models AND spend hundreds of millions on training these models.

Why pay $3/per million tokens (Claude 3.5 Sonnet) when DeepSeek R1 offers $0.14 / per million tokens and the model is on par with (OpenAI o1) and R1 itself is released for free?

$0 free AI models are eating closed AI models lunch.

piva00• 1 year ago

Nothing, and I think that was the point GP was making. Since not much of Apple's valuation is tied to AI hype they won't suffer (at least not close to as much) from the bubble bursting.

alecco7• 1 year ago

Big Tech is full of grifter CEOs.

fintechie2• 1 year ago

The parallels with the dotcom bubble are clear... Tech is magical. Tech becomes cheap/free. Stock holders panic.

For some time it's been clear that there's an AI bubble and this may be what finally pops it.

baobabKoodaa2• 1 year ago

According to you there's a bubble in AI and this... legitimate AI research breakthrough... may be the thing that pops the bubble?

Deegy• 1 year ago

Bubble's don't always imply fraudulent underlying tech.

The dot com bubble was a real thing, and yet the internet has gone on to be one of humanities most valuable innovations.

impossiblefork6• 1 year ago

I don't think there has to be an AI bubble, but valuations overall have to come down to something in accordance with the interest rates and expected long-term profit rates.

zo1• 1 year ago

Wow, literally bought more Nvidia shares last week. Just goes to show the stock market is 80% gambling. Me believing in the value of a company is overshadowed by the hype of growth and "future valuation".

asddubs2• 1 year ago

the price hasn't even dropped to as low as it was last week

qwytw5• 1 year ago

I don't think it has been this low (~$126) since October?

MadSudaca• 1 year ago

If this is gambling, what isn’t gambling? Sometimes things just don’t go the way you want.

A Chinese company coming up with a cheaper alternative to a cutting-edge technology out of nowhere, is an outcome that is hard to predict.

In hindsight, betting on Nvidia maintaining its monopoly on a resource crucial for such an important technology as AI, might not be the best of ideas, but then again, who knows.

rsanek7• 1 year ago

buying an individual stock is gambling. buying an broadly-diversified index is not.

nejsjsjsbsb13• 1 year ago

But it doesn't matter because you got them at a price you deemed they were worth based on some analysis right? :)

fvv• 1 year ago

based on meta declarations , amd is better at customized inference than nvda ,

meta uses amd mi300x for all inference and which bought 173.000 gpu ( 40% of their total gpu count ) according to that report https://www.cnbc.com/2023/12/06/meta-and-microsoft-to-buy-am...

gsky2• 1 year ago

It doesn't matter whose model is better. AI is here and it won't go anywhere. As a result companies that feed AI will see better valuations

Sol-• 1 year ago

I find it interesting because the DeepSeek stuff, while very cool, doesn't seem invalidate that more compute wouldn't translate to even _higher_ capabilities?

It's amazing what they did with a limited budget, but instead of the takeaway being "we don't need that much compute to achieve X", it could also be, "These new results show that we can achieve even 1000*X with our currently planned compute buildout"

But perhaps the idea is more like: "We already have more AI capabilities than we know how to integrate into the economy for the time being" and if that's the hypothesis, then the availability of something this cheap would change the equation somewhat and possibly justify investing less money in more compute.

Dalewyn3• 1 year ago

The stock market is not the economy, Wall Street is not Main Street. You need to look at this more macroscopically if you want to understand this.

Basically: China tech sector just made a big splash, traders who witnessed this think other traders will sell because maybe US tech sector wasn't as hot, so they sell as other traders also think that and sell.

The fall will come to rest once stocks have fallen enough that traders stop thinking other traders will sell.

Investors holding for the long haul will see this fall as stocks going on sale and proceed to buy because they think other investors will buy.

Meanwhile in the real world, on Main Street, nothing has really changed.

Bogleheads meanwhile are just starting the day with their coffee, no damns given to the machinations of the stock market because it's Monday and there's work to be done.

jart• 1 year ago

Not really.

The Magnificent Seven are the only thing propping up the whole US economy.

If they go down, you go down.

qwytw5• 1 year ago

Is it really related to China's tech sector as such, though? If this is true then Openai, Google or even many magnitudes smaller companies etc. can just easily replicate similar methods in their processes and provide models which are just as good or better. However they'll need way less Nvidia GPUs and other HW to do that than when training their current models.

lagrange77• 1 year ago

doesn't seem invalidate that more compute wouldn't translate to even _higher_ capabilities?

That's how i understand it.

And since their current goal seems to be 'AGI' and their current plan for achieving it seems to be scaling LLMs (network depth wise and at inference time prompt wise), i don't see why it wouldn't hold.

yo-cuddles5• 1 year ago

Probably not. If the price of Nvidia is dropping, it's because investors see a world where Nvidia hardware is less valuable, probably because it will be used less.

You can't do the distill/magnify cycle like you do with alphago. LLM models have basically stalled in their base capabilities, pre training is basically over at this point, so the news arms race will be over marginal capability gains and (mostly) making them cheaper and cheaper.

But inference time scaling, right?

A weak model can pretend to be a stronger model if you let it cook for a long time. But right now it looks like models as strong as what we have aren't going to be very useful even if you let them run for a long, long time. Basic logic problems still tank o3 if they're not a kind that it's seen before.

Basically, there doesn't seem to be a use case for big data centers that run small models for long periods of time, they are in a danger zone of both not doing anything interesting and taking way too long to do it.

The AI war is going to turn into a price war, by my estimations. The models will be around as strong as the ones we have, perhaps with one more crank of quality. Then comes the empty, meaningless battle of just providing that service for as close to free as possible.

If Openai's agents panned out we might be having another conversation. But they didn't, and it wasn't even close.

This is probably it. There's not much left in the AI game

esafak2• 1 year ago

So you're saying we're close to AGI? Because the game doesn't stop until we get there.

YZF2• 1 year ago

We don't know for example what a larger model can do with the new techniques DeepSeek is using for improving/refining it. It's possible the new models on their [own] failed to show progress but a combination of techniques will enable that barrier to be crossed.

We also don't know what the next discovery/breakthrough will be like. The reward for getting smarter AI is still huge and so the investment will likely remain huge for some time. If anything DeepSeek is showing us that there is still progress to be made.

Jlagreen17• 1 year ago

Your implication is that we have unlimited compute and therefore know that LLMs are stalled.

Have you considered that compute might be the reason why LLMs are stalled at the moment?

What made LLMs possible in the first place? Right, compute! Transformer Model is 8 years old, technically GPT4 could have been released 5 years ago. What stopped it? Simple, the compute being way too low.

Nvidia has improved compute by 1000x in the past 8 years but what if training GPT5 takes 6-12 months for 1 run based on what OpenAI tries to do?

What we see right now is that pre-training has reached the limits of Hopper and Big Tech is waiting for Blackwell. Blackwell will easily be 10x faster in cluster training (don't look on chip performance only) and since Big Tech intends to build 10x larger GPU clusters then they will have 100x compute systems.

Let's see then how it turns out.

The limit on training is time. If you want to make something new and improve then you should limit training time because nobody will wait 5-6 months for results anymore.

It was fine for OpenAI years ago to take months to years for new frontier models. But today the expectations are higher.

There is a reason why Blackwell is fully sold out for the year. AI research is totally starved for compute.

The best thing for Nvidia is also that while AI research companies compete with each other, they all try to get Nvidia AI HW.

jes51994• 1 year ago

You can't do the distill/magnify cycle like you do with alphago

are you sure? people are saying that there’s an analogous cycle where you use o1-style reasoning to produce better inputs to the next training round

gdiamos3• 1 year ago

This argument ignores scaling laws

JSR_FDED• 1 year ago

I saw the interview with Dario Amodei. Look at 0:48

https://m.youtube.com/watch?v=7LNyUbii0zw

He seems adamant that there are no diminishing returns to scaling AI.

I don’t want to stir up conspiracy theories but I do think that currently all the big AI players have a vested interest in the message that the current scaling paradigm is the right one, and that this is a supremacy issue wrt China. It drives so much investment and valuation that I doubt they can truly be objective.

igleria7• 1 year ago

I don’t want to stir up conspiracy theories but I do think that currently all the big AI players have a vested interest in the message that the current scaling paradigm is the right one, and that this is a supremacy issue wrt China. It drives so much investment and valuation that I doubt they can truly be objective.

500 Billion is a lot of money. Expect even crimes to be commited in order to make it happen.

Jlagreen17• 1 year ago

The issue is it feels like we came to a stop but Hyperscalers are simply waiting for Blackwell. That's all.

Why buy 100k Hoppers if 20k Blackwell offer the same compute so then it's better to buy 100k Blackwells right?

Backwell will increase cluster scaling easily by 10x performance and if you buy 10x of them then your compute on a cluster will be 100x than before. If it takes you to wait 6-12 months for that then so be it. You will easily make up the time in the end with the speed up.

igleria7• 1 year ago

I will never forget HN's not-made-in-the-US bias https://news.ycombinator.com/item?id=42514633

alecco7• 1 year ago

I think part of the problem was many/most previous models coming out of China were trained on eval data to cheat in the rankings. Quite an uphill battle for Deepseek.

blitzar6• 1 year ago

I think part of the problem was many/most previous models coming out of the US were trained on eval data to cheat in the rankings.

igleria7• 1 year ago

Assuming that is true, which I don't have a reason to believe otherwise, only gave an excuse to the average reader to disregard without reading more than the title.

You can see a similar bias in academia with work originating outside EU/USA.

before someone thinks something strange regarding me, I can only tell you I'm not chinese, but Argentinian :)

noxs• 1 year ago

this one too https://news.ycombinator.com/item?id=41999151

people are just more focusing on the political side of it

suraci5• 1 year ago

this happens in EVERY thead about Chinese tech news

it's always 'but tiananmen square'

2024user• 1 year ago

Which replies in particular? I read that thread and didn't see any bias?

junon3• 1 year ago

Not sure what the fuss is. I tried Deepseek earlier today for the first time and it was even worse than o1 when it came to reasoning skills and following my requests for how I wanted to engage with it.

o1 at least gives it to me straight. When I ask it to engage in more back and forth before assuming what I'm after, it tends to follow through. Deepseek seemed immediately eager to (very slowly) feed me a bunch of made up information thinking that's what I wanted.

I feel as though a lot of people get hung up on these sort of "micro benchmarks" whereas trying to get practical work done is severely under tested. I'm not a fan of openai at all but I don't have the spare compute to run anything locally so o1 suffices for now.

Still don't see how this is anything but a win for Nvidia though.

LittleTimothy• 1 year ago

The excitement isn't the capabilities of the model, it's how efficiently it was created. One of the major lessons in AI in the last couple of years was that scale mattered - you would want to throw more and more compute at a problem and that has turned into incredible share prices for Nvidia and incredible investments in data centre and energy generation. If it turns out that actually we didn't need quite such incredible scale to get these results and actually we were just missing some really quite basic efficiency optimizations then the entire investment cycle into Nvidia, data centres and energy generation is going to whipsaw in an incredible way.

nejsjsjsbsb13• 1 year ago

Which doesn't make sense. If anything it has the chance to make AI finally profitable and financially sustainable. Maybe that is the fear!

Balgair• 1 year ago

Essentially, Deepseek is showing that there is a lot of room for improvement with AIs. To paraphrase Orwell, AIs are a lot more like Alarm Clocks and a lot less like Manhattan Projects.

lm284692• 1 year ago

It can be a win for nvidia but bad for nvidia stock if the win is smaller than the initially expected win.

The business is doing good, the wannabe traders not so much

alecco7• 1 year ago

Deepseek shows you details of the reasoning so you can trust its answers more and you can correct it when it makes a bad turn.

junon3• 1 year ago

o1 also does this.

JofArnold• 1 year ago

R1 is the first model I've used that one-shotted a full JavaScript tetris with all the edge-case keyboard handling and scoring. It also one-shotted an AI snake game. With the right prompts I've found it consistently better than o1 and Claude 3.5 Sonnet.

rhubarbtree14• 1 year ago

This is an example of a recurrent phenomenon in politics. Trump comes in with big plans, this and that, threatening countries, deporting people, a clear agenda to take America to the hard right. Isolationism, crush China, screw every other country over, etc. etc.

And then - something completely unexpected, a total curveball - arrives a week into office and everything changes. Your agenda collapses and you enter reactive mode.

AI bubble could pop. Valuations drop. Crypto might get hit. Where is Project Stargate now? It might become a joke.

A more cynical observer might suggest that _the timing was no coincidence_.

suraci5• 1 year ago

I said in other threads about how the establishment passed Trump a ticking time bomb – if he were smart, he should've made it blow up early himself to avoid becoming the next Herbert Hoover

rhubarbtree14• 1 year ago

He's inherited a bubble. If he chooses to stoke that bubble...

refurb2• 1 year ago

Your agenda collapses and you enter reactive mode.

Where has Trump's agenda collapsed? I might have missed the press release.

And why would a curveball on AI throw off an agenda around trade, immigration and military engagement? I don't follow.

China could take the lead on AI and I don't see it would impact any of those things. Isn't DeepSeek open source? The US already has access to it, so what leverage could China possible have?

A more cynical observer might suggest that _the timing was no coincidence_.

Do you feel the Chinese government closely controls AI research and timed this response?

Any evidence for that?

rhubarbtree14• 1 year ago

Where has Trump's agenda collapsed? I might have missed the press release.

Well, it's too early to say if it has happened in this case. But we've seen it happen again and again, so it will not be a surprise if it happens.

And why would a curveball on AI throw off an agenda around trade, immigration and military engagement? I don't follow.

Well, trade restrictions against China have just backfired spectacularly. So further trade restrictions may not seem as good an idea. And to start trade wars, you need a strong economy. And at the moment America's economy is entirely driven by the AI bubble, as it is the value of the AI stocks that has separated the economy from the European trend.

It is very likely that military engagement will be driven by, or affected by, developments in AI. And there's no doubt that Taiwan's situation is heavily affected by chip production.

Do you feel the Chinese government closely controls AI research and timed this response?

No evidence, but I am not naive enough to think it's out of the range of possibility. I don't follow your reasoning, more likely the timing of the release is all they had to modify - which is quite trivial for any government. To not question that would be very naive. They have _every motive_.

canterburry• 1 year ago

Right now it seems like DeepSeek should splurge on some more GPUs from NVidia because all my requests are timing out.

alecco7• 1 year ago

Deepseek API works fine and it's dirt cheap.

niemandhier• 1 year ago

So a quant company releases a model and that causes stocks of an overvalued company to go down drastically?

Maybe this was the point of creating this model all along ?

alephnan• 1 year ago

OpenAI is not publicly traded

gsky2• 1 year ago

They must have shorted NIVIDA

yani• 1 year ago

Could NVIDIA be in hot water if it's proven they sold restricted hardware to Chinese companies?

alecco7• 1 year ago

H800s were made to match Biden's export restrictions. They were banned in late 2023 but a lot were sold to China. Having 2k is quite small compared to the bigger players like BABA (200k employees) and Tencent (100k employees). And those sure have access to the few H100s that were smuggled. But unlikely for a tiny company like High-Flyer/Deepseek (160 employees).

lmm6• 1 year ago

No, they're already too big to fail. They'll get a slap on the wrist at most.

dageshi• 1 year ago

Well this thread got nuked

pizza3• 1 year ago

Traders are saying not doing multitoken prediction, not using Sharpe ratio adjusted rewards, using reward models, and not compressing KV cache tokens by >90%, were supposed to be worth hundreds of billions of dollars of future expected revenue flow, at least according to other traders.

I say to the traders: you should have just stuck to reading arxiv, TPOT, and jhana twitter for the past 2 years, rather than listening to other traders, if you were trying to understand the utter spread of low hanging fruit that just hasn’t been picked up yet!

igleria7• 1 year ago

you should have just stuck to reading arxiv, TPOT, and jhana twitter for the past 2 years

Traders expect to be able to trade based on regurgitated headlines made by tech influencers who don't know ANYTHING about NOTHING.

djtango• 1 year ago

Who is jhana?

pizza3• 1 year ago

It’s a part of twitter that is nominally about advanced forms of meditation but is also a surprisingly overrepresented by ai researchers lol

nejsjsjsbsb13• 1 year ago

You need to be very solvent to stay rational.

danielmarkbruce• 1 year ago

The low hanging fruit thing is 100% correct. Anyone reading papers saw it everywhere, on every dimension. And it's not to say the authors didn't see it either, they did - they just had to get something out now. I'd guess folks in semi conductors saw the same things for ages.

msoad3• 1 year ago

We are at multiple trillion dollar investment territory, all purely based on the idea that "to make AI you need lots of GPU and power"

plaidfuji4• 1 year ago

The part of this that doesn’t jibe with me is the fact that they also released this incredibly detailed technical report on their architecture and training strategy. The paper is well-written and has a lot of specifics. Exactly the opposite of what you would do if you had truly made an advancement of world-altering magnitude. All this says to me is that the models themselves have very little intrinsic value / are highly fungible. The true value lies in the software interfaces to the models, and the ability to make it easy to plug your data into the models.

My guess is the consumer market will ultimately be won by 2-3 players that make the best app / interface and leverage some kind of network effect, and enterprise market will just be captured by the people who have the enterprise data, I.e. MSFT, AMZN, GOOG. Depending on just how impactful AI can be for consumers, this could upend Apple if a full mobile hardware+OS redesign is able to create a step change in seamlessness of UI. That seems to me to be the biggest unknown now - how will hardware and devices adapt?

NVDA will still do quite well because as others have noted, if it’s cheaper to train, the balance will just shift toward deploying more edge devices for inference, which is necessary to realize the value built up in the bubble anyway. Some day the compute will become more fungible but the momentum behind the nvidia ecosystem is way too strong right now.

gitfan866• 1 year ago

What has changed is the perception that people like OpenAI/MSFT would have an edge on the competition because of their huge datacenters full of NVDA hardware. That is no longer true. People now believe that you can build very capable AI applications for far less money. So the perception is that the big guys no longer have an edge.

Tesla had already proven that to be wrong. Tesla's Hardware 3 is a 6 year old design, and it does amazingly well on less than 300 watts. And that was mostly trained on a 8k cluster.

plaidfuji4• 1 year ago

I mean, I think they still do have an edge - ChatGPT is a great app and has strong consumer recognition already, very hard to displace.. and MSFT has a major installed base of enterprise customers who cannot readily switch cloud / productivity suite providers. So I guess they still have an edge it’s just nore of a traditional edge.

nejsjsjsbsb13• 1 year ago

The perception only makes sense if it is "that's it, pack up your stall" for AI.

I think what really happened is day to day trading noise. Nothing fundamentally changed, but traders believed other people believed it would.

polygamous_bat2• 1 year ago

The part of this that doesn’t jibe with me is the fact that they also released this incredibly detailed technical report on their architecture and training strategy. The paper is well-written and has a lot of specifics. Exactly the opposite of what you would do if you had truly made an advancement of world-altering magnitude.

I disagree completely on this sentiment. This was in fact the trend for a century or more (see inventions ranging from the polio vaccine to "Attention is all you need" by Vaswani et. al.) before "Open"AI became the biggest player on the market due and Sam Altman tried to bag all the gains for himself. Hopefully, we can reverse course on this trend and go back to when world-changing innovations are shared openly so they can actually change the world.

evanjrowley• 1 year ago

Exactly. There's a strong case for being open about the advancements in AI. Secretive companies like Microsoft, OpenAI, and others are undercut by DeepSeek and any other company on the globe who wants to build on what they've published. Politically there are more reasons why China should not become the global center of AI and less reasons why the US should remain the center of it. Therefore, an approach that enables AI institutions worldwide makes more sense for China at this stage. The EU for example has even less reason now to form a dependency on OpenAI and Nvidia, which works to the advantage of China and Chinese AI companies.

plaidfuji4• 1 year ago

I’m not arguing for/against the altruistic ideal of sharing technological advancements with society, I’m just saying that having a great model architecture is really not a defensible value proposition for a business. Maybe more accurate to say publishing everything in detail indicates that it’s likely not a defensible advancement, not that it isn’t significant.

kibibu3• 1 year ago

Even the "Language Models are Unsupervised Multitask Learners" paper was pretty open; I'd say even more open than the R1 paper.

tokioyoyo2• 1 year ago

Build trust by releasing your inferior product for free and as open as possible. Get attention, then release your superior product behind paywall. Name recognition is incredibly important within and outside of China.

Keep in mind, they’re still competing with Baidu, Tencent and other AI labs.

nejsjsjsbsb13• 1 year ago

I always thought AMZN is the winner since I looked into Bedrock. When I saw Claude on there it added a fuck yeah, and now the best models being open just takes it to another level.

AMZN: no horse picked, we host anything

MSFT: Open AI

GOOGLE: Google AI

AMZN is in the strongest position.

amluto2• 1 year ago

AWS’s usual most doesn’t really apply here. AWS is Hotel California — if your business and data is in AWS, the cost of moving any data-intensive portion out of AWS is absurd due to egress fees. But LLM inference is not data-transfer intensive at all — a relatively small number of bytes/tokens go to the model, it does a lot of compute, and a relatively small number of tokens come back. So a business that’s stuck in AWS can cost-effectively outsource their LLM inference to a competitor without any substantial egress fees.

RAG is kind of an exception, but RAG still splits the database part from the inference part, and the inference part is what needs lots of inference-time compute. AWS may still have a strong moat for the compute needed to build an embedding database in the first place.

Simple, cheap, low-compute inference on large amounts of data is another exception, but this use will strongly favor the “cheap” part, which means there may not be as much money in it for AWS. No one is about to do o3-style inference on each of 1M old business records.

mindwok• 1 year ago

You can also use Claude, Mistral, Llama and others on Google Vertex, similar to Bedrock.

fspeech5• 1 year ago

You are not taking into account why people are willing to pay exceedingly high prices for GPUs now and that the underlying reason may have been taken away.

reacharavindh• 1 year ago

What I’d like to know is.. If a good model can be trained with much fewer GPUs using a breakthrough technique, can the breakthrough technique be used by OpenAI, MSFT et al who has loads of GPUs to train a model that is orders of magnitude better than their state of the art today?

We’ve been getting the impression that the limiting factor was the number of GPUs right? If so, this reduces that bottleneck and frees them up to do even better right?

ReptileMan2• 1 year ago

From my understanding the limiting factor is the quantity and quality of data available for training.

afavour• 1 year ago

Seems bad! To me this highlights a failure in the US tech industry. Silicon Valley is theoretically a triumph of entrepreneurship, where the best and best thinking wins. In reality investors picked OpenAI as the winner right out of the gate, gave it more funding than most of us can imagine so that it could dominate the market, sat back and told themselves job done.

Meanwhile in another tech industry a startup had to think lean and innovate its way around resource restrictions. And OpenAI looks like a mess now.

whalesalad• 1 year ago

The innovators dilemma.

dralley2• 1 year ago

And this is why competition is a good thing. Silicon Valley keeps trying to create monopolies for the purpose of maximal profit extraction to the detriment of American technological development, or they use money and brute force as a substitute for actual innovation and get worse results.

nialv73• 1 year ago

Oh my, so much cope and denial here...

aurareturn15• 1 year ago

Deepseek should cause Nvidia and TSMC stocks to go up, not down. I'm buying more Nvidia and TSMC today.

1. More efficient LLMs should lead to more usage, which means more AI chip demand. Jevon's Paradox.

2. To build a moat, OpenAI and American AI companies need to up their datacenter spending even more.

3. DeepSeek's breakthrough is in distilling models. You still need a ton of compute to train the foundational model to distill.

4. DeepSeek's conclusion in their paper says more compute is needed for next break through.

5. DeepSeek's model is trained on GPT4o/Sonnet outputs. Again, this reaffirms the fact that in order to take the next step, you need to continue to train better models. Better models will generate better data for next-gen models.

I think DeepSeek hurts OpenAI/Anthropic/Google/Microsoft. I think DeepSeek helps TSMC/Nvidia.

NicoJuicy2• 1 year ago

Ugh. No, we can now run a decent model through CPU. Not an expensive video card.

Just try out the standard deepseek-r1 or even the deepseek-r1:1.5B through ollama.

No need for expensive hardware anymore locally. My PC ( without Nvidia card/expensive hardware) runs the a deepseek 1.5 b query fast enough - 2 - 9 seconds until it's finished.

aurareturn15• 1 year ago

The world isn't satisfied with a "decent model". The world is trying to reach AGI.

Further more, reasoning models require more tokens. The faster the GPU, the more thinking it can do in a set amount of time. This means the faster the hardware, the smarter the model output. Again, that reinforces the need for faster hardware.

More thinking = smarter models

Faster hardware = more thinking

Therefore, faster hardware = smarter models

Jlagreen17• 1 year ago

I would shoot myself if I had to wait 9 seconds for a query. I sometimes even would like to kill by browser taking seconds to open a page...

mullingitover4• 1 year ago

That’s a rational stance. However, the Buffet Indicator is flashing red, we’re in a dotcom-era sized bubble, and it only takes a little bit of ill-founded worry to kick off a serious panic.

aurareturn15• 1 year ago

But why would an AI breakthrough cause stocks to go down? It should cause it to go up. It means we should expect even more breakthroughs, better models, more LLM usage, etc.

bfeynman• 1 year ago

you're looking at it from economic theory not from stock market. NVIDIA's insane valuation right now was based on an almost exponential increase in demand for more and more of it. It's priced in that NVIDIA will continue that trend. DeepSeek proves that trajectory is no longer needed (not that it was ever cemented in rationalism), so anything less than the continued exponential growth would send stock down.

aurareturn15• 1 year ago

Jevon’s paradox suggests even more AI chips will be demanded after DeepSeek’s breakthrough.

aoeusnth1• 1 year ago

A huge leap in the cost effectiveness of AI capabilities only paves the way for faster timelines to ASI. I'm not sure why that would reduce the economic value of Nvidia. Pretty sure this is a reaction in the wrong direction. Nvidia should be popping.

j7ake• 1 year ago

This may be bad for tech stocks, but this is actually good for technology because the supposed monopoly in ML is deteriorating.

dboreham• 1 year ago

Because nobody would ever have imagined that clever people would optimize AI software so it doesn't need billions of expensive chips?

bee_rider4• 1 year ago

ASML plunge indicates a hysterical/irrational component to the response, right? They aren’t going anywhere. If it turns out training is easier than expected, they make the devices that make the devices that do inference too…

If the field is going to produce anything useful, cheap training gets us there faster.

maxglute9• 1 year ago

Not making the news in western media yet is PRC claims to have started mass producing their indigenous 28nm litho (70% of global wafer use) this month... the estimated cost is 1/30th of ASML machines. Extrapolate and they're on trend of produce 14nm machines at comparable fraction cost in next few years.

bee_rider4• 1 year ago

It seems hard to extrapolate there—the 30nm-10nm range is where Intel really started to start having trouble, right?

Anyway, this seems like a bigger problem for companies whose business model is actually selling those chips. It couldn’t be the case that much of ASML’s valuation is based on people continuing to use their old 28nm machines, right?

hintymad4• 1 year ago

I'm guessing the sentiment is not about training, but about the R&D capability of China. If Chinese can figure out how to build a good enough model faster and cheaper, they may be able to come up with an ASML competitor as well.

maxglute9• 1 year ago

PRC just announced mass producing 28nm litho that cost 1/30 ASML hardware. Easy to extrapolate where this goes especially mature nodes like 28nm still accounts fo over 70% of global wafer use.

fldskfjdslkfj3• 1 year ago

Not really, if demand for high end high margin chips goes down considerably than margins and demand will go down across the board.

apexalpha• 1 year ago

I think everyone was just a bit overpriced because of the AI hype and it's just letting out some air.

hn_throwaway_99• 1 year ago

ASML plunge indicates a hysterical/irrational component to the response, right

But don't forget about the hysterical/irrational component that also causes prices to go up when investors are all worried about FOMO. Of course, sure, ASML isn't going anywhere, but their stock price isn't based on them "sticking around", it's based on the idea that growing usage of AI will require exponentially more computing power over time, and DeepSeek kinda put a pin to that balloon.

fspeech5• 1 year ago

You need to also look at valuation and how much growth justifies that valuation.

russli1993• 1 year ago

Next episode in this is Chinese companies produces HBM3 (they are already shipping HBM2), 7nm class GPU products competitive against A100 from multiple vendors, EUV. The US will realize what a monster they created.

hintymad4• 1 year ago

I don't quite understand the sentiment. Lowered cost of training and inference means more companies can join the game, and therefore more demand for the hardware. Basically Jevon's paradox in play. What will more likely fall instead in the long-term should be OAI's valuation, if they don't have other killer products.

maxglute9• 1 year ago

IMO reaction to short term semi cycle - a lot of reshored/redundant fab expansions last few years outside of TW. If compute costs goes down 10-50x there may not be enough use case to fill Jevon in next infra hardware cycle of ~5 years. People are doing more with same, but maybe use case not sufficiently more to go fill/justify acquiring more hardware in near term time frame.

lostmsu• 1 year ago

I wish I would not already be so leveraged.

hintymad4• 1 year ago

Is it possible that the key here is all about where top talent goes? America's best minds from Math and Informatics Olympiads gravitate to Wall Street for financial gains. Similarly, DeepSeek's top talent are mostly IMO and IOI medalists and they originally aimed for finance too (The parent company of DeepSeek is a successful quant trader), but strict government regulations pushed them towards AI instead. Ironically, this unintended pivot turned out to be quite significant

Palmik• 1 year ago

Massive overreaction in my opinion (not financial advice).

People are forgetting how much compute is being used for inference. This is going to be further accelerated:

- Reasoning models are going to generate orders of magnitude more tokens.

- Synthetic an augmented data is more and more prevalent, and if you need to process pre-training scale dataset, you will need a lot of inference.

- etc.

dumbfounder• 1 year ago

No one will ever need more than 640k tokens.

physicsguy2• 1 year ago

Distillations are showing (and have been for a while) though that you don't necessarily need big hardware for decent inference. If that's the case, and you can run it on your Macbook trivially or even your smartphone.

fspeech5• 1 year ago

Inference can be done on much more diverse hardware.

mrbungie6• 1 year ago

When put into context one could say the OG overreaction was thinking 500B USD in infrastructure investments and restarting nuclear plants were rational decisions rather than working on efficiency.

scoofy• 1 year ago

I've been into investing for my entire adult life, and base my strategy mostly on John Neff's work on total return.

I have missed out on a lot of investments in the QE period, because many of them seem like "if this mid-level company becomes the biggest company in the world, you'll make a reasonable return," which has always seemed insane to me, but we've seen it happen again and again. I realize that we are probably in a place where insider trading is much more prevalent that we expect, and that the point of an IPO has been turned on it's head, but these type of potential blowups of high PE stocks is something I've never really come to terms with.

grajaganDev5• 1 year ago

This could be an extinction level event for some VCs.

echelon3• 1 year ago

Only if their portcos don't build products people buy.

Making a pure-research, foundation model company is silly. Make a product company that sells products.

grajaganDev5• 1 year ago

Google and Microsoft both bundled their AI offering into their office suites because they weren't getting traction as an add-on.

Startups don't have that option.

2OEH8eoCRo0• 1 year ago

Still overvalued IMO. Their market cap remains ludicrous.

m3kw92• 1 year ago

Everything is overvalued if you think in terms of earning multiples, p/s, p/e should be 1:1

andrewmcwatters• 1 year ago

Who's out here buying businesses for 1x the sales revenue volume? What a silly concept. If businesses could be so cheap, you'd just double down every single year until you owned every business on the planet.

dinkblam• 1 year ago

1:1? so that dividends cover the share price in the first year of ownership?

justahuman74• 1 year ago

Earning multiples choose an arbitrary time length of 1 year.

What you're really trying to purchase is a machine that creates more money than it uses. You need to guess at if that machine will do its job at an arbitrary point in the future, and how well it will do it. Those factors are only loosely correlated with current PE

phyzix5761• 1 year ago

Discounted future cashflows. If you buy an asset and every year it produces $100 profit for you it's worth more than $100. You're buying the ability to produce profits in the future not the profits its produced in the past. Those profits belong to the shareholders who have cashed that out already (through dividends or reinvestment).

saulpw• 1 year ago

No, that means that they're earning enough in one year to cover their entire valuation. You want something like 10:1 p/e which means that the next 10 years earnings are factored in to cover their present valuation.

talldayo3• 1 year ago

Yeah, just look at all the great commercial-quality alternatives to CUDA:

crickets

That demand is going to dry up any day now, mark my words. Tomorrow, even!

h1h1hh1h1h1• 1 year ago

So the Chinese graciously gift a paper and model which describes methods that radically increase the efficiency of hardware which will allow US AI firms to create much better models due to having significantly more AI hardware and people are bearish on US AI now?

baal80spam2• 1 year ago

Don't look for logic in the market, I suppose.

magic_hamster• 1 year ago

I think the idea that SOTA models can run on limited hardware makes people think that Nvidia sales will take a hit.

But if you think about it for two more seconds you realize that if SOTA was trained on mid level hardware, top of the line hardware could still put you ahead, and DeepSeek is also open source so it won't take long to see what this architecture could do on high end cards.

amazingamazing9• 1 year ago

there's no reason to believe that performance will continue to scale with compute, though. that's why there's a rout. more simply, if you assume maximum performance with the current LLM/transformer architecture is say, twice as good as what humanity is capable of now, then that would mean that you're approaching 50%+ performance with orders of magnitude less compute. there's just no way you could justify the amount of money being spent on nvidia cards if that's true, hence the selloff.

kibibu3• 1 year ago

DeepSeek is also open source so it won't take long to see what this architecture could do on high end cards

As far as I can see, the training code isn't open source. It's open weights.

bcrosby95• 1 year ago

If people are bullish on Nvidia because the hot new thing requires tons of Nvidia hardware and someone releases a paper showing you need 1/45th of Nvidia's hardware to get the same results, of course there's going to be pullback.

Whether its justified or not is outside my wheelhouse. There's too many "it depends" involved that, best case, only people working in the field can answer, worst case, no one can answer right now.

pokstad2• 1 year ago

Or you could argue you can now do 45x greater things with the same hardware. You can take an optimistic stance on this.

DiscourseFan• 1 year ago

No, because what this implies is that the Chinese have better labor power in the tech-sector than the US, considering how much more efficient this technology is. Which means that even if US companies adopt these practices, the best workers will still be in China, communicating largely in Chinese, building relationships with other Chinese-speaking people purchasing chinese speaking labor. These relationships are already present. It would be difficult for OpenAI to catch up.

brokencode• 1 year ago

What a stretch. One Chinese model makes a breakthrough in efficiency and suddenly China has all the best people in the world?

What about all the people who invented LLMs and all the necessary hardware here in the US? What about all the models that leapfrog each other in the US every few months?

One breakthrough implies that they had a great idea and implemented it well. It doesn’t imply anything more than that.

nine_k2• 1 year ago

the best workers will still be in China

This is quite an assumption.

But the majority of the AI R&D may be in China, with a high barrier for participation for outsiders, leading to an increasing gap. Whether this is so is not obvious.

bilbo0s• 1 year ago

I think it's probably more accurate to say that people are now a bit more bullish on what the Chinese will be able to accomplish even in the face of trade restrictions. Now whether or not it makes sense to be bearish on US AI is a totally different issue.

Personally I think being bearish on US AI makes zero sense. I'm almost positive there will be restrictions on using Chinese models forthcoming in the near to medium term. I'm not saying those restrictions will make sense. I'm just saying they will steer people in the US market towards US offerings.

paulddraper2• 1 year ago

US AI is only somewhat related though.

The subject is NVIDIA.

mullingitover4• 1 year ago

I'm almost positive there will be restrictions on using Chinese models forthcoming in the near to medium term.

If the models are open source, there are constitutional issues that would prevent restricting them unless we're going down the ridiculous path of classifying integers representing algorithms as munitions, like we tried with crypto.

nine_k2• 1 year ago

Not the AI proper, but the need for additional AI hardware down the line. Especially the super-expensive, high-margin, huge AI hardware that DeepSeek seems not to require.

Similarly, microcomputers led to an explosion of computer market, but definitely limited the market for mainframe behemoths.

paulddraper2• 1 year ago

https://www.reddit.com/r/investing/comments/1ib5vf9/deepseek...

They did it by using H800 chips, not H100 or B200 or anything crazy.

This means NVIDIA may not be the only game in town.

E.g. Chinese manufacturers.

paxys12• 1 year ago

You can be bullish about US AI but at the same time not believe that the industry is worth $10T+ right now.

linkregister• 1 year ago

the Chinese

Daya Guo, Dejian Yang, Haowei Zhang, et.al., quant researchers at High Flyer, a hedge fund based in China, open-sourced their work on a chain-of-thought reasoning model, based on Qwen and LLama (open source LLMs).

It would be somewhat bizarre to describe Meta's open sourcing of LLama as "the Americans gifting a model", despite Meta having a corporate headquarters in the United States.

culi7• 1 year ago

Thank you. The amount of casual sinophobia allowed on hackernews has been a real turn off. I find myself avoiding threads like these in anticipation of these comments

tester756• 1 year ago

US thought they were unparalleled and years ahead due to $$$ injection and HW,

but got caught by 200 people Chinese team that preferred clever approach instead of "let's put as much compute as we can on it"

Neonlicht2• 1 year ago

ASML warned that it was just a question of time for the Chinese to get their own tech industry up and running. The Americans were in denial.

Ultimately someone in America will get desperate enough and start a war when they still have a chance to win. See the Earth-Mars conflict in the Expanse.

skizm• 1 year ago

I like the chart Bloomberg has of the top 10 largest single day stock drops in history. 8 out of the 10 are NVDA (Meta and Amazon are the other two).

culi7• 1 year ago

Why mention it if you're not gonna link it?

dgfitz• 1 year ago

Because paywall.

Jach• 1 year ago

https://archive.is/Svzfz Paywall schmaywall

TeaBrain• 1 year ago

The chart is within the below linked article. The below link I've provided is a gift link, so you should be able to access the article through it.

https://www.bloomberg.com/opinion/articles/2025-01-27/deepse...

airstrike• 1 year ago

adjusted for inflation?

MichaelDickens• 1 year ago

Really it should be adjusted for global (or US) total market cap. Market cap tends to go up faster than inflation, so even if you adjust for inflation, it will still be skewed toward modern companies.

jandrese2• 1 year ago

I feel like this is a symptom of our broken economic system that has allowed too much cash to be trapped in the markets, forever making mostly imaginary numbers go up while the middle class gets squeezed and the poor continue to suffer.

A fundamental feature of a capitalist system you can use money to make more money. That's great for growing wealth. But you have to be careful, it's like a sound system at a concert. When you install it everybody benefits from being able to hear the band. But it is easily to cause an earspittig feedback loop if you don't keep the singers a safe distance from the speakers. Unfortunately, the only way people have to quantify how good a concert sounds is by loudness, and because the awful screeching of a feedback loop is about the loudest thing possible we've been just holding the microphone at the speaker for close to 50 years and telling ourselves that everybody is enjoying the music.

It is the job of the government, because nobody else can do it, to prevent the runaway feedback loop that is a fundamental flaw of capitalism, and our government has been entirely derelict in their duty. This has caused market distortions that go beyond the stock market. The housing market is also suffering for example. There is way too much money at the top looking for anything that can create a return, and when something looks promising it gets inflated to ridiculous levels, far beyond what is helpful for a company trying to expand their business. There's so much money most of it has to be dumb money.

danlugo92• 1 year ago

Or you could, you know, like just stop printing money and the problem goes away.

lvl1554• 1 year ago

TINA. Government forced everyone to save for retirement this way. There’s way too much capital trapped in SPY and that’s going to create distortions in price discovery and these abrupt corrections in individual stocks.

paxys12• 1 year ago

IMO this is less about DeepSeek and more that Nvidia is essentially a bubble/meme stock that is divorced from the reality of finance and business. People/institutions who bought on nothing but hype are now panic selling. DeepSeek provided the spark, but that's all that was needed, just like how a vague rumor is enough to cause bank runs.

onlyrealcuzzo3• 1 year ago

Hard to argue Nvidia is a meme stock and that Tesla is not a bigger meme stock.

If meme stocks were imploding, why is Tesla fine?

This is about DeepSeek.

paxys12• 1 year ago

What does any of this have to do with Tesla? Even if Tesla is a bigger bubble, not all bubbles have to pop at the same time.

coliveira• 1 year ago

Tesla is playing the political game with Trump. They're riding that wave. Musk always find some new reason for people to believe the stock.

pokstad2• 1 year ago

The market can stay irrational longer than you can stay solvent.

tgtweak3• 1 year ago

Hype buyers are also Hype sellers - anything Nvidia was last week is exactly what it is this week - DeepSeek doesn't really have any impact on Nvidia sales - Some argument could be made that this can shift compute off of cloud and onto end user devices, but that really seems like a stretch given what I've seen running this locally.

Analemma_• 1 year ago

I agree hype is a big portion of it, but if DeepSeek really has found a way to train models just as good as frontier ones for a hundredth of the hardware investment, that is a substantial material difference for Nvidia's future earnings.

zozbot2346• 1 year ago

The full DeepSeek model is ~700B params or so - way too large for most end users to run locally. What some folks are running locally is fine-tuned versions of Llama and Qwen, that are not going to be directly comparable in any way.

dgemm2• 1 year ago

I think less of that and more of real risks - Nvidia legitimately has the earnings right now. The question is how sustainable that is, when most of it is coming from 5 or so customers that are both motivated and capable of taking back those 90% margins for themselves

jhickok2• 1 year ago

Regarding their earnings at the moment, I know it doesn't mean everything, but a ~50 P/E is still fairly high, although not insane. I think Ciscos was over 200 during the dotcom bubble. I think your question about the 5 major customers is really interesting, and we will continue to see those companies peck at custom silicon until they can maybe bridge the gap from just running inference to training as well.

marcosdumay6• 1 year ago

They don't have anything close to the earnings to justify the price they have reached.

They are getting a lot of money, but their stock price is in a completely different universe. Not even that $500G deal people announced, if spent exclusively on their products could justify their current price. (Nah, notice that just the change on their valuation is already larger than that deal.)

belevme• 1 year ago

I don't think it's fair to say NVDA is meme stock, having reported 35B revenue last quarter.

master_crab• 1 year ago

True but with that revenue number it would mean that before today it was valued at ~100x revenue. That’s pretty bubbly.

danpalmer2• 1 year ago

I'd say it's a meme stock and based on meme revenue. Much of the 35B comes from the fact that companies believe Nvidia make the best chips, and that they have to have the best chips or they'll be out of the game.

DeepSeek supposedly nullifies that last part.

paxys12• 1 year ago

Nvidia's annual revenue in 2024 was $60B. In comparison, Apple made $391B. Microsoft made $245B. Amazon made $575B. Google made $278B. And Nvidia is worth more than all of them. You'd have to go very far down the list to find a company with a comparable ratio of revenue or income to market cap as Nvidia.

edm0nd• 1 year ago

Would now be a good time to buy into NVDA if you are long on it?

energy1232• 1 year ago

This is a cookie cutter comment that appears to have been copy pasted from a thread about Gamestop or something. DeepSeek R1 allegedly being almost 50x more compute efficient isn't just a "vague rumor". You do this community a disservice by commenting before understanding what investors are thinking at the current moment.

paxys12• 1 year ago

Has anyone verified DeepSeek's claims about R1? They have literally published one single paper and it has been out for a week. Nothing about what they did changed Nvidia's fundamentals. In fact there was no additional news over the weekend or today morning. The entire market movement is because of a single statement by DeepSeek's CEO from over a week ago. People sold because other people sold. This is exactly how a panic selloff happens.

spott2• 1 year ago

I wonder where they got 50… llama405 cost like 60M, which puts deepseek at closer to 10x…

codingwagie• 1 year ago

No the reality of AI models fundamentally changed

segasaturn4• 1 year ago

Correct, Nvidia has been on this bubble-like tragectory since before the stock was split last year. I would argue that today's drop is a precursor to a much larger crash to come.

KiwiJohnno3• 1 year ago

Not quite, I believe this sell off was caused by DeepSeek showing with their new model that the hardware demands of AI are not necessarily as high as everyone has assumed (as required by competing models).

I've tried their 7b model, running locally on a 6gb laptop GPU. Its not fast, but the results I've had have rivaled GPT4. Its impressive.

xiphias2• 1 year ago

I believe you that it had to do with the selloff, but I believe that efficiency improvements are good news for NVIDIA: each card just got 20x more useful

siwakotisaurav• 1 year ago

None of the models other than the 600b one are R1. They’re just prev gen models like llama or qwen trained on r1 output making them slightly better

donsupreme• 1 year ago

I've tried their 7b model

Anything other than their 671b model are just distilled models on top of Qwen and Llama using their 671b reasoning data output, right?

2-3-7-43-1807• 1 year ago

that's a Milchmädchenrechnung. if it turns out that you can achieve status quo with 1% of the expected effort then that just mean you can achieve approximately 10 times the status quo (assuming O(exp)) with the established budget! and this race is a race to the sky (as opposed to the bottom) ... he who reaches AGI first takes the cake, buddy.

subarctic• 1 year ago

If that's the case, then I have high hopes that the increase in efficiency will result in more demand, not less.

If only I could figure out how to buy NV stock quickly before it rebounds

khazhoux• 1 year ago

Not quite, I believe this sell off was caused by Shockley showing with their "transistor" that the electricity demands of computers are not necessarily as high as everyone has assumed (as required by vacuum tubes).

Electricity demands will plummet when transistors take the place of vacuum tubes.

ipv6ipv4• 1 year ago

To me this rings a lot like “640KB ought to be enough for anybody”

Similarly, as fast as processors have gotten, people still complain their applications are slow. Because they do so much more.

Generally applicable ML is still in its infancy, and usage is exploding. All those newfound spare cycles will get soaked up fairly quickly.

m00x2• 1 year ago

That's a pretty terrible take.

People who can use the 585B model will use the best model they can have. What DeepSeek really did was start an AI "space race" to AGI with China, and this race is running on Nvidia GPUs.

Some hobbyists will run the smaller model, but if you could, why not use the bigger & better one?

Model distillation has been a thing for over a decade, and LLM distillation has been widespread since 2023 [1].

There is nothing new in being able to leverage a bigger model to enrich smaller models. This is what people that don't understand the AI space got out of it, but it's clearly wrong.

OpenAI has smaller models too with o1 mini and o4 mini, and phi-1 has shown that distillation could make a model 10x smaller perform as well as a much bigger model. The issue with these models is that they can't generalize as well. Bigger models will always win at first, then you can specialize them.

Deepseek also showed that Nvidia GPUs could be more memory-efficient, which catapults Nvidia even further ahead of upcoming processors like Groq or AMD.

[1] https://arxiv.org/abs/2305.02301

datavirtue• 1 year ago

Exactly, and firing up reactors to train models just lost all its luster. Those standing before the Stargate will be bored with the whole thing by then end of the week.

rsanek7• 1 year ago

I've run their distilled 70B model and didn't come away too impressed -- feels similar to the existing base model it was trained on, which also rivaled GPT4

httpz• 1 year ago

Nvida has a P/E of 47. While it may be a bit high for a semiconductor company, it's definitely not a meme stock figure.

snailmailstare• 1 year ago

Yes and no, going from 47 to 50 would buy a few of the most popular meme stocks so there simply aren't enough people to make it a true meme stock with that market cap.

anon2913• 1 year ago

The forward PE is half that based on real numbers of future orders reported in company reports.

behringer• 1 year ago

Whenever the internet tells you to buy, it's a huge warning that a pump and dump is occurring.

digitcatphd2• 1 year ago

Exactly. Everything is massive kindle for a wildfire and it comes to show how tiny of a spark it takes. Yet - people keep adding more kindle.

827a• 1 year ago

I'm sorry, but this is just so, so wrong. Nvidia is an insane company. You can make the argument that the entire sector is frothy/bubbly; I'm more likely to believe that. But, here's some select financials about NVDA:

NVDA Net income, Quarter ending in ~Oct2024: $19B. AMD? $771M. INTC? -$16.6B. QCOM? $3B. AAPL? $14B.

Revenue growth, YoY? +93%. AMD? +17%. INTC? -6%. QCOM? +18%. AAPL? +6%.

Margin? 55%. AMD? 11%. INTC? -125%. QCOM? 28%. AAPL? 15%.

P/E Ratio? 46. AMD? 103. INTC? N/A, unprofitable. QCOM? 19. AAPL? 34. NFLX? 54. GME? 151.

Their P/E Ratio doesn't even classify them as all that overvalued. Think about that. Price to earnings, they are cheaper than Netflix, Gamestop, they're about the same level as WALMART, you know, that Retailer everyone hates that has practically no AI play, yeah their P/E is 40.

Nvidia is an insane company. Insane. We've had three of the largest country-economies on the planet announce public/private funding to the tune of 12 figures, maybe totaling 13 figures when its all said and done, and NVDA is the ONLY company on the PLANET that sells what they want to buy. There is no second player. Oh yeah, Google will rent you some TPUs, haha yeah sure bud. China wants to build AI data centers, and their top tech firms are going to the black market smuggling GPUs across the ocean like bricks of cocaine rather than rely on domestic manufacturers, because not even other AMERICAN manufacturers can catch up.

Sure, a 10x drop in cost of intelligence is initially perceived as a hit to the company. But, here's the funny thing about, let's say, CPUs: The Intel Northwood Pentium 4 was released in 2001; with its 130nm process architecture, it sipped a cool 61 watts of power. With today's 3nm process architecture, we've built (drumroll please) the Intel Core Ultra 5 255, which consumes 65 watts of power. Sad trombone? Of course not; its a billion times more performant. We could have directed improvements in process architecture toward reducing power draw (and certainly, we did, for some kinds of chips). But, the VAST, VAST, VAST majority of allocation of these process improvements was in performance.

The story here is not "intelligence is 10x cheaper, so we'll need 10x fewer GPUs". The story is: "Intelligence is 10x cheaper, people are going to want 10x more intelligence."

segmondy2• 1 year ago

Nah, this is not about Nvidia being a bubble. This is about people forgetting that software will keep eating the world and Nvidia is a hardware company no matter how many times people say it's a software company and talk about Cuda. Yes, CUDA is their moat, but they are not a software company. See my post on reddit from 10 months ago about this happening.

https://www.reddit.com/r/LocalLLaMA/comments/1c0je6h/comment...

"The biggest threat to NVIDIA is not AMD, Intel or Google's TPU. It's software. Sofware eats the world!"

"That's what software is going to do. A new architecture/algorithm that allows us current performance with 50% of the hardware, would change everything. What would that mean? If Nvidia had it in the books to sell N hardware, all of a sudden the demand won't exist since N compute can be realized with the new software and existing hardware. Hardware that might not have been attractive like AMD, Intel or even older hardware would become attractive. They would have to cut their price so much, the violent exodus from their stocks will be shocking. Lots of people are going to get rich via Nvidia, lots are going to get poor after the fact. It's not going to be because of hardware, but software."

A lot of people are saying that I'm wrong on other hardware like AMD or Intel, but this article by Stratechery agrees, all other hardware vendors are possibly relevant again. I didn't talk about Apple because I was focused on the server side, Apple has already won the consumer side and is so far ahead and waiting for the tech to catch up to it.

The biggest threat to Nvidia is still more software optimization.

Jlagreen17• 1 year ago

For 2 decades we were told how Apple will have to cut their margins due to competition and so on.

Today, it's simple. Apple has 25% unit share in smartphone markets and 75% profit share. Apple makes 3x the profit of ALL OTHER smartphone vendors combined.

And this is exactly where Nvidia's goal is. The AI compute market will grow, Nvidia will lose unit market share but Nvidia will retain their profit market share. Simple as that.

And by the way, Nvidia is way ahead in SW compared to alternatives. Most here have the DIY glasses on. But enterprises and businesses have different lenses. For those not being Tech they need secure and working solution with enterprise grades. Nvidia is among the few to offer this with Enterprise AI solutions (NeMo, NIMs, etc.). Nvidia's SW moat isn't CUDA, CUDA is an API for performance and stability. Nvidia's SW moat is in the frameworks for applications for many differnt industries and of course ALL Nvidia SW will require Nvidia HW.

A company using Nvidia enterprise SW solutions and consultancy will never use anything except Nvidia HW. Nvidia has a program with >10k AI startups being supported with free consulting and HW support. Nvidia is basically grooming their next generation customers by themselves.

You have no idea, many think Nvidia is only selling some chips and that's where they are wrong. Nvidia is a brand, an ecosystem and they will continue to grow from there. See gaming, much more standards and commodity in SW than AI SW. There is no CUDA, you can swap a Nvidia card with AMD card within a minute. So let me know, how come for 2 decades that Nvidia has continously 80-95% market share?

anon2913• 1 year ago

Nvidia's forward PE before this is 27, based off of real orders. Unless orders are being canceled, the stock price should be significantly higher

antigeox• 1 year ago

I just want to know if I can buy a gaming video card at a reasonable price or if i should hold off on it. I don't care about the AI shit. And yes I'd prefer nvidia because their closest competitor can't tape a box together nevermind develop and assemble a graphics card.

oblio• 1 year ago

Unlikely. Companies have doubled down on AI so expect the fall to be long and slow (think 2 years).

dtquad• 1 year ago

DeepSeek has humiliated the entire US tech sector. I wonder if they will learn from this, fire their useless middle management and product managers with sociology degrees, and actually pivot to being technology companies?

browningstreet• 1 year ago

"Again, just to emphasize this point, all of the decisions DeepSeek made in the design of this model only make sense if you are constrained to the H800; if DeepSeek had access to H100s, they probably would have used a larger training cluster with much fewer optimizations specifically focused on overcoming the lack of bandwidth."

https://stratechery.com/2025/deepseek-faq/

blitzar6• 1 year ago

Maybe you don't need a $5,000,000 car to build a charitable foundation.

66yatman• 1 year ago

Under deepseek R1 running on 6 Mac mini alone sure Nvidia can feel the pressure

lvl1554• 1 year ago

But Apple was up…indexing at its finest.

zozbot2346• 1 year ago

Not surprising there - a maxed out Mac Studio is a great AI homelab, giving you way more bang for the buck than nVidia offerings.

jhickok2• 1 year ago

Think that was the cause of their stock increase? I feel like investors use opportunities like this to pile money into safer bets rather than just bail on stocks altogether.

kiratp• 1 year ago

This is really dumb.

Deepseek showing that you can do pure online RL for LLMs means we now have a clear path to just keep throwing more compute at the problem! If anything we made the whole "we are hitting a data wall" problem even smaller.

Additionally, its yet another proof point that scaling inference compute is a way forward. Models that think for hours or days are the future.

As we move further into the regime of long sequence inference, compute scales by the square of the sequence length.

The lesson here was not "training is going to be cheaper than we thought". It's "we must construct additional pylons uhhh _PUs"

Markets remain irrational and all that...

aurareturn15• 1 year ago

Nvidia chip demand should increase from DeepSeek's release.

This market doesn't make any sense.

lerp-io• 1 year ago

good time to buy

zozbot2346• 1 year ago

Didn't DeepSeek also show that pure RL leads to low-quality results compared to also doing old-fashioned supervised learning on a "problem solving step by step" dataset? I'm not sure why people are getting excited about the pure-RL approach, seems just overly complicated for no real gain.

deepsquirrelnet• 1 year ago

If I’m understanding their paper correctly (I might not be but I’ve spent a little time trying to understand it), they showed you only need a small amount of supervised fine tuning “SFT” to “seed” the base model, followed by pure RL. Pure RL only was their R1-zero model which worked, but produces weird artifacts like switching languages or excessive repetition.

The SFT training data is hard to produce, while the RL they used was fairly uncomplicated heuristic evaluations and not a secondary critic model. So their RL is a simple approach.

If I’ve said anything wrong, feel free to correct me.

jaggs2• 1 year ago

The real hidden message is not that bigger compute produces better results, but that the average user probably doesn't need the top results.

In the same way that medium range laptops are now 'good enough' for most people's needs, medium range (e.g. DeepSeek R1x) AI will probably be good enough for most business and user needs.

Up till now everyone assumed that only giga-sized server farms could produce anything decent. Doesn't seem to be true any more. And that's a problem for mega-corps maybe?

jdietrich4• 1 year ago

medium range (e.g. DeepSeek R1x) AI will probably be good enough for most business and user needs

Except R1 isn't "medium range" - it's fully competitive with SOTA models at a fraction of the cost. Unless you need multimodal capability or you're desperate to wring out the last percentage point of performance, there's no good reason to use a more expensive model.

The real hidden message is that we're still barely getting started. DeepSeek have completely exploded the idea that LLM architecture has peaked and we're just in an arms race for more compute. 100 engineers found an order of magnitude's worth of low-hanging fruit. What will other companies will be able to do with a similar architectural approach? What other straightforward optimisations are just waiting to be implemented? What will R2 look like if they decide to spend $60m or $600m on a training run?

jaggs2• 1 year ago

Yes absolutely. I guess I meant medium range in terms of dev and running costs. R1 is a premium product at a corner store price. :)

People are also forgetting that High-Flyer's ultimate goal is not applications, it's AGI. Hence the open source. They want to accelerate that process out in the open as fast as they can.

Kon-Peki• 1 year ago

If you look at total volume of shares traded, this would be somewhere in the range of 200th highest.

If you look at the total monetary value of those shares traded, this would be in the top 5, all of which have happened in the past 5 years. #1 is probably Tesla on Dec 18 2020 (right before it joined the S&P500). It lost ~6% that day.

Don’t get me wrong, this is definitely a big day. Just not “lose your mind” big. It’s clear that most shareholders just sat things out.

kd913• 1 year ago

The biggest discussion I have been on having this is the implications on Deepseek for say the RoI H100. Will a sudden spike in available GPUs and reduction in demand (from efficient GPU usage) dramatically shock the cost per hour to rent a GPU. This I think is the critical value for measuring the investment value for Blackwell now.

The price for a H100 per hour has gone from the peak of $8.42 to about $1.80.

A H100 consumes 700W, lets say $0.10 per kwh?

A H100 costs around $30000.

Given deepseek, can the price of this drop further given a much larger supply of available GPUs can now be proven to be unlocked (Mi300x, H200s, H800s etc...).

Now that LLMs have effectively become commodity, with a significant price floor, is this new value ahead of what is profitable for the card.

Given the new Blackwell is $70000, is there sufficient applications that enable customers to get a RoI on the new card?

Am curious about this as I think I am currently ignorant of the types of applications that businesses can use to outweigh the costs. I predict that the cost per hour of the GPU dropping such that it isn't such a no-brainer investment compared to previously. Especially if it is now possible to unlock potential from much older platforms running at lower electricity rates.

chatmasta2• 1 year ago

Why is there this implicit assumption that more efficient training/inference will reduce GPU demand? It seems more likely - based on historical precedent in the computing industry - that demand will expand to fill the available hardware.

We can do more inference and more training on fewer GPUs. That doesn’t mean we need to stop buying GPUs. Unless people think we’re already doing the most training/inference we’ll ever need to do…

“640KB ought to be enough for anybody.”

Jensson3• 1 year ago

Historically most compute went to run games in peoples homes, because companies didn't see a need to run that much analytics. I don't see why that wouldn't happen now as well, there is a limit to how much value you can get out of this, since they aren't AGI yet.

theptip2• 1 year ago

I think training demand is what you might predict would plummet.

Inference demand might increase but you could easily believe that there’s substantial inelasticity currently.

diamond5592• 1 year ago

Over the long run maybe, but for the next 2 years the market will struggle to find a use for all this possible extra gpus. There is no real consumer demand for AI products and lots of backlash whenever implemented eg: that Coca Cola ad. It's going to be a big hit to demand in the short to medium term as the hyperscalers cut back/reasses.

egillie• 1 year ago

Could even argue that the price should go up, since the amount of with one GPU can do and its potential ROI just increased

jmward01• 1 year ago

So, what are investors thinking to warrant this? If it is 'DeepSeek means you don't need the compute' that is definitely wrong. Making a more efficient x almost always leads to more of x being sold/used, not less. In the long term does anyone believe we won't keep needing more compute and not less?

I think the market believes that high end compute is not needed anymore so the stuff in datacenters suddenly just became 10x over-provisioned and it will take a while to fill up that capacity. Additionally, things like the mac and AMD unified memory architectures and consumer GPUs are all now suddenly able to run SOTA models locally. So a triple whammy. The competition just caught up, demand is about to drop in the short term for any datacenter compute and the market for exotic, high margin, GPUs might have just evaporated. At least that is what I think the market is thinking. I personally believe this is a short term correction since the long term demand is still there and we will keep wanting more big compute for a long time.

amazingamazing9• 1 year ago

selling more does not necessarily mean you make more money. more efficiency could lead to less margins even if volume is higher.

moreover, even things are incredibly efficient, the bar to sufficiently good AI in practice (e.g. applications), might be met with commodity compute, pretty much locking nvidia out, who generally sells high margin high performance chips to whales.

acchow2• 1 year ago

But the SOTA models basically all suck today. If people don’t think they suck, definitely in 1 year they’ll look back and consider those older models unusably bad

jiggawatts3• 1 year ago

I recently went to the LLM chat arena and tried my "test input" against the latest frontier models that GPT 3 failed on. This test snippet simply repeats the same four-letter word in a paragraph many times using all of its various possible meanings simultaneously. The request to the AI is to put the meaning of each usage of the word next to it in brackets.

None of the frontier models can do this perfectly. They all screw up to various degrees in various interesting ways. A schoolkid could do this flawlessly.

This is not some contrived test with bizarre picture puzzles as seen in ARC-AGI or testing obscure knowledge about bleeding-edge scientific research. It's simple English comprehension using a word my toddler knows already!

It does reveal the fundamental flaw in all transformer-based models: They're just shifting vectors around with matrices, and are unable to deal with many categories of inputs that cause overlaps or bring too many of the tokens too close to each other in some internal representation. They get muddled up and confused, resulting in errors in the output.

I see similar effects when using LLMs for programming: They get confused when there are many usages of the same identifier or keyword, but with some subtle difference such as being inside a comment, string, or in a local context where the meaning is different.

I suspect this will be eventually fixed, but I haven't seen any fundamental improvement in three years.

fspeech5• 1 year ago

The key question is: has demand elasticity increased for Nvidia cards? An increase in elasticity means people are more willing to wait for hardware price to drop because they can do more with existing hardware. Elasticity could increase even if demand is still growing. Not all growths are equally profitable. Current high prices are extremely profitable for Nvidia. If elasticity is increasing future growth may not be as profitable as the projection from when Deepseek was relatively unknown.

bgnn4• 1 year ago

This totally makes sense, since Nvidia and ASML alike had hugely inflated market cap. This is more of a return to normal.

kristianp• 1 year ago

I found the Stratechery and Nadella's takes to be interesting. Also the fact that news orgs say things like:

"The situation is particularly remarkable since, as a Chinese company, DeepSeek lacks access to Nvidia’s state-of-the-art chips used to train AI models powering chatbots like ChatGPT."

Whereas they don't mention the fact that Deepseek still used Nvidia chips. news orgs are implying they didn't.

Stratechery points out that the reduced memory/communications bandwidth of the gimped H800 chips they have access to drove the MOE/MLA architecture developments to make their model possible on the less powerful chips.

Nadella (on X) points out that by Jevons paradox [1], AI usage (and NVidia chip usage) will increase because deepseeks has reduced costs.

One other point Statechery made is that DeepSeek likely distilled the output of leading models for V3 and R1. They have shown that they can replicate the leaders cheaply and quickly, but they can't produce a leading model without copying (yet).

[1] https://x.com/satyanadella/status/1883753899255046301

flashman4• 1 year ago

what people are neglecting about Jevons is that fuel efficiency led to increased use of coal because you could use coal to displace more expensive fuels

i wonder what is more expensive that cheap large language models can displace? is the problem with selling unreliable B.S. really its price?

noname120• 1 year ago

Time to buy some Nvidia

ozten2• 1 year ago

NVIDIA sells shovels to the gold rush. One miner (Liang Wenfeng), who has previously purchased at least 10,000 A100 shovels... has a "side project" where they figured out how to dig really well with a shovel and shared their secrets.

The gold rush, wether real or a bubble is still there! NVIDA will still sell every shovel they can manufacture, as soon as it is available in inventory.

Fortune 100 companies will still want the biggest toolshed to invent the next paradigm or to be the first to get to AGI.

culi7• 1 year ago

Yeah but NVIDIA's amazing digging technique that could only be accomplished with NVIDIA shovels is now irrelevant. Meaning there are more people selling shovels for the gold rush

ozten2• 1 year ago

CUDA begs to differ.

dragonwriter3• 1 year ago

What about DeepSeek negates NVidia’s advantages over other GPU vendors?

sho_hn• 1 year ago

DeepSeek's stuff is actually more dependent on nVidia shovels. They implemented a bunch of assembly-level optimizations below the CUDA stack that allowed them to efficiently use the H800s they have, which are memory-bandwidth-gimped vs. the H100s they can't easily buy on the open market. That's cool, but doesn't run on any other GPUs.

Cue all of China rushing to Jensen to buy all the H800s they can before the embargo gets tightened, now that their peers have demonstrated that they're useful for something.

At least briefly, Jensen's customer audience increased.

elihu• 1 year ago

Jevon's paradox would imply that there's good reason to think that demand for shovels will increase. AI doesn't seem to be one of those things where society as a whole will say, "we have enough of that; we don't need any more".

(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.)

int_19h6• 1 year ago

The other thing is that if this pushes the envelope further on what AI models can do given a certain hardware budget, this might actually change minds. The pushback against generative AI today is that much of it is deployed in ways that are ultimately useless and annoying at best, and that in turn is because the capabilities of those models are vastly oversold (including internally in companies that ship products with them). But if a smarter model can actually e.g. reliably organize my email, that's a very different story.

jcgrillo3• 1 year ago

AI doesn't seem to be one of those things where society as a whole will say, "we have enough of that; we don't need any more".

Really? Has anyone made a useful, commercially successful product with it yet?

puppymaster• 1 year ago

I also dont get how this is bearish for NVDA. Before this, small to mid companies would give up on finetuning their own model because openai is just so much better and cheaper. Now deepseek SOTA model gives them much better quality baseline model to train on. Wouldn't more people want to RAG on top of deepseek? or some startups accountant would run the numbers and figures we can just inference the shit out of deepseek locally and in the long run we still come out ahead of using oenai api.

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.

fabfoe• 1 year ago

Apparently it’s just Jevons, not possessive.

wisty3• 1 year ago

Yes, a global market of 5 big LLM (ChatGTP, Llama, Claude, Mistral, Qwen ... any other big ones?) is not exactly good for Nvidia.

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.

flashman4• 1 year ago

Jevons was talking about coal as an input to commercial processes, for which there were other alternatives that competed on price (e.g. manual/animal labour). Whatever the process, it generated a return, it had utility, and it had scale.

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.

mlyle• 1 year ago

When you get more marginal product from an input, it's expected you buy more of that input.

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.

addicted• 1 year ago

What you are missing is that it turns out the gold isn’t actually gold. It’s bronze.

So earliest, the shovelers were willing to spend thousands of dollars for a single shovel because they were expecting to get much more valuable gold out the other end.

But now that it’s only bronze, they can’t spend that much money on their tools anymore to make their venture profitable. A lot of shovelers are gonna drop out of the race. And the ones that remain will not be willing to spend as much.

The fact that there isn’t that much money to be made in AI anymore means that whatever percentage of money would have gone to NVIDIA from the total money to be made in AI will now shrink dramatically.

deeviant• 1 year ago

Wait, so AI might become 25x cheaper to train and run, and your thesis is... no one will make money on AI now?!?!

JumpCrisscross• 1 year ago

it turns out the gold isn’t actually gold. It’s bronze.

The gold is still gold. We just thought it was 10,000 feet down, which requires lots of shovels, when it was actually just under the surface.

UncleOxidant• 1 year ago

AGI would be El Dorado in this analogy?

buryat2• 1 year ago

El Dorado will be AGI with Humanoid robots and actual real pick-axes

avs733• 1 year ago

The thing with a gold rush is you often end up selling shovels after the gold has run out, but no one knows that until hindsight. There will probably be a couple scares that the gold has run out first to. And again the difference is only visible in hindsight.

medion• 1 year ago

Can anyone comment on why Wenfeng shared his secret sauce? Other than publicity, there only seems to be downsides for him, as now everyone else with larger compute will just copy and improve?

jes51994• 1 year ago

pure prestige play, I think

xdennis2• 1 year ago

Well, American investors seem to be shaking in their boots and publicizing this attracts AI investments in China because it shows China can out/compete with the US in spite of the restrictions.

seydor• 1 year ago

Is AI expanding horizontally or vertically? My understanding is that smarter models dominate over hordes of dumber ones

bodegajed• 1 year ago

The gold rush is over because pre-trained models don't improve as much anymore. The application layer has massive gains in cost-to-value performance. We also gain more trust from the consumer as models don't hallucinate as much. This is what DeepSeek R1 has shown us. As Ilya Sutskever said, pre-training is now over.

We now have very expensive Nvidia shovels that use a lot of power but do very little improvement to the models.

onlyrealcuzzo3• 1 year ago

This can all be true, and Nvidia's market cap can still go down A TON.

Nvidia's market cap is based on extreme margins and absurd growth for 10 years.

If either of those nobs get turned down a little, there can be a MASSIVE hit to the valuation - which is what happened.

flowerlad2• 1 year ago

A similar efficiency event has occurred in the recent past. Blackwell is 25x more energy-efficient for generative AI tasks and offer up to 2.5x faster AI training performance overall. When Blackwell was announced nobody said “great we will invest less in GPUs”. Deep Seek is just another efficiency event. Like Blackwell it enables you to do more with less.

numba8884• 1 year ago

Looks like panic sell off, but main question is:

do models with DeepSeek architecture still scale up?

If yes, then bigger clusters will outperform in near future. NVidia wins as tide rises all boats, and them first.

If not, then it's still possible to run several models in parallel to do the same, potentially big, job. Just like humans team. All we need is to learn how to do it efficiently. This way bigger clusters win again.

mrinterweb• 1 year ago

The vast majority of NVDA's value is based on the assumption they are the only game in town that can do AI. I'm still waiting for a competing tech to disrupt them further:

* Intel, AMD, etc. could start making competitive GPUs that push the price down

* A new ASIC chip specifically designed for LLMs

* A new training or LLM runtime algorithm that uses the CPU

* Quantum chip that can train or run a LLM

If Nvidia lost its AI dominance, where would its stock be?

blitzar6• 1 year ago

If Nvidia lost its AI dominance, where would its stock be?

Around the AMD (200bn) / Intel levels (100bn) which is a ~90% reduction in share price from todays close.

paxys12• 1 year ago

The thing with Nvidia is that it doesn't have a large "sticky" customer base that is guaranteed to spend money year after year on new products. If you look at other large tech companies with similar valuations (Apple, Microsoft, Amazon, Google, Meta), none of them are in danger of their core business disappearing overnight. In Nvidia's case, if large tech companies decide they don't need to continue loading up on AI chips and building larger data centers then they are back to where they were in ~2020 ($100-150B market cap from selling GPUs to gamers and professionals working on graphics-intensive apps).

0n0n0m0uz• 1 year ago

I am glad I saw this coming and profited nicely.

m3kw92• 1 year ago

What people are betting on by selling is there would be a short term pause or deceleration in the investment while they try to squeeze more from their current batch, could be some efficiency catchup

TheAlchemist3• 1 year ago

Nvidia valuation = $3000B

Nvidia profits during AI madness last year = $65B (last 4 quarters)

Nvidia profits during normal year = $5B

This stock could drop 90% from here, and still be expensive. The numbers are absolutely crazy and make no sense at all.

Stargate project is aiming to invest $500B over 4 years. Those $500B are a pipe dream, but let's suppose for a second that all of that $500B will be Nvidia profits and that we will have another Stargate project in 4 years, again resulting in a direct profits of $500B for Nvidia.

And you know what ? In that scenario Nvidia would STILL be overvalued by historical standards !

EDIT: Changed $30B from the fiscal year, to $65B for last 4 quarters.

epicureanideal3• 1 year ago

Exactly.

Also, what he’s saying is if 500/4 =125B were NVDA yearly profits (and of course really that would just be revenue, not profit), it’d still mean NVDA should be more like 1875B market cap at a more reasonable 15x price to earnings ratio. If I understand the previous poster correctly.

outside1234• 1 year ago

The Stargate project does not actual exist. It is just something they completely made up by lumping together data center investments from across the industry and rounded up to the nearest $500B

xbgaf• 1 year ago

Indeed, perhaps the plan was to pump certain stocks after they saw the unprecedented post election rally of Tesla and Bitcoin.

And these people complain about the Pelosi index, which is peanuts in comparison.

synergy202• 1 year ago

but you forgot the 80B from MSFT and 60B from META in 2025 alone? I assume quite a chunk of that investment will be going to NVDA.

Stargate wise, it's a joke, they have no money for their lip service.

paxys12• 1 year ago

Most of it will go towards energy. These companies are building literal power plants to power their data centers.

idiotsecant• 1 year ago

Agreed, it's very strange to me that this is being framed in the media as "CHINESE DEEPSEEK DESTROYS US STOCK MARKET". It's a good model, but not so wildly good that it suddenly destroys Nvidia somehow. The stock was (and remains) incredibly overvalued.

This smells a lot to me like someone with deep pockets is looking to get a bailout by framing this as some kind of Chinese threat.

Any time the entire media suddenly agrees on a very strange framing of a story you should immediately be suspicious.

dmix2• 1 year ago

The vibes market

breadwinner2• 1 year ago

Are you suggesting "AI madness" is a one time event that will not continue? AI is dramatically improving productivity and not just for developers. The "madness" will continue until every job is fully automated. That requires a lot of chips.

TheAlchemist3• 1 year ago

"AI", once we get there, is indeed the future and will continue to play an increasing role everywhere.

But what happened to Nvidia profits last year is a one time event and will get back to normal sooner or later.

gitfan866• 1 year ago

Your numbers are wrong. The last quarter profit was $19B and the projected profit is 21B next quarter. That is 84B/year profit with zero growth.

If META,STARGATE,xAI,etc.. all increased spending rapidly you could get to 200B profit rate in 2026.

3T / 200B = 15

That means they could return a 6.6 dividend which is higher than 10 year bonds and is in no way overvalued by historial standards.

All that to say, I sold all my NVDA last month because I don't think Wall St. is buying that everyone is going to actually invest that much.

TheAlchemist3• 1 year ago

You're right about the profit - I took last fiscal year. Still, it doesn't change anything in what I wrote.

What you just wrote is "IF the biggest companies on earth, and the US government decide to spend all of their money on a single chip maker, then you could get to 200B profit rate in 2026". I won't disagree with that.

raincole2• 1 year ago

And you know what ? In that scenario Nvidia would STILL be overvalued by historical standards !

It would makes it's PE ratio about 24 (3000/(500/4)).

AAPL's PE is about 36 at this moment.

So no, not "overvalued by historical standards".

rsanek7• 1 year ago

If you take a longer time frame, these PE ratios are indeed pretty high. Even 24 is much higher than the ~15 historical median https://www.multpl.com/s-p-500-pe-ratio

hindsightbias• 1 year ago

If this keeps up we’ll have to have a bailout.

synergy202• 1 year ago

It's said the AI war is a war between Chinese, be it US or China, be it hardware and software.

If that's true, normally, mainland will win, as the people over that side have more grit, are more eager to succeed, are working at 996 schedule, and have nothing to lose. They're hard to stop as long as their government will not interfere.

vcryan• 1 year ago

Gambling is hard

jmyeet• 1 year ago

What you find with any market news is that the optimism and pessimism are always overblown. Fear and greed. It happens all the time.

Look at Linux. For those old enough to remember, there was a time where many (including Microsoft) were worried it would destroy the company (eg [1]). There were complaints about the market destruction caused by Linux. What actually happened? Microsoft is bigger than ever even though Linux is on billions of devices worldwide.

IF DeepSeek's claims are real and this stands up, all that's happened is at worst the profit opportunity has simply move *as it was always going to do). This might be bad for OpenAI and Sam Altman but Big Tech will (IMHO) be fine.

Remember that training LLMs for chatbots, which is something people focus on, is just one narrow slice of the potential AI market. Recommendation engines, industrial/commercial applications, medicine, etc.

If there has been a aoftware breakthrough and training LLMs now costs a fraction of what it did last year, there's now an order of magnitude more potential appllications that have become economical.

Consider this: if we can do today with a model 1/10th the size of what we needed last week, what applications will there be for a model 10x DeepSeek-R1's size?

I'm also reminded of the invention of the cotton gin. This automated what used to be a highly manual process. At the time, there was concern this would diminish the need for slaves on cotton plantations. Instead the need exploded because cotton became so much cheaper [2].

Lastly, Stargate is largely meaningless. Companies spend a fortune on data centers. GPUs are just a fraction of that. A genuine software improvement just means you can do more with less.

My point is: don't panic. Unless you're an OpenAI investor, maybe.

[1] https://www.zdnet.com/article/microsoft-linux-is-a-threat-it...

[2] https://www.archives.gov/education/lessons/cotton-gin-patent

t_mann• 1 year ago

Curious thought: could those large price movements have something to do with the fact that DeepSeek is financed by a hedge fund (rather than the more typical VC)? It is unclear how DS will make money from its current strategy of sharing much of the secret sauce that went into training as well as releasing the results under permissive licenses. But if the play was "short major tech stocks and then release surprising results in a way that maximally undermines their current growth story", then it could make a lot more sense.

aptj• 1 year ago

Sounds plausible. Can someone tell us how they can hide/get away with such stock shorting?

thr0waway001• 1 year ago

Is this Monday Black-ish?

robjan• 1 year ago

Only for people who went all in on NVDA. The SP500 is just back to where it was last week.

cAIctus480• 1 year ago

A couple of things that caught my attention in context of the reactions from the AI community and investor community, but maybe im not fully read up. First, this seems to have been a surprise even amongst researchers but once released very transparent and open. Second, funded through a hedge fund.

dang• 1 year ago

Related ongoing thread:

The impact of competition and DeepSeek on Nvidia - https://news.ycombinator.com/item?id=42822162 - Jan 2025 (423 comments)

chenster• 1 year ago

Word of the day - Rout

yalogin• 1 year ago

Nvidia has gotten lucky repeatedly. The GPUs were great for PC gaming and they were the top dog. The crypto boom was such an unexpected win for them partly because Intel killed off their competition by acquiring it. Then the AI boom is also a direct result of Intel killing off their competition but the acquisition is too far removed to credit it to that event.

Unlike the crypto boom though, two factors make me think the AI thing was bound to go away quickly.

Unlike crypto there is no mathematical lower bound for computation, and if you see technology's history we can tell the models are going to get better/smaller/faster overtime reducing our reliance on the GPU.

Crypto was fringe but AI is fundamental to every software stack and every company. There is way too much money in this to just let Nvidia take it all. One way or another the reliance on it will be reduced

gradys• 1 year ago

Is it luck, or is scaling arithmetic genuinely a useful capability to offer the world?

nailer5• 1 year ago

Money isn’t fringe, and the target for crypto is all transactions, rather than the existing model where you pay between two and 3.5% to a card company or other middleman.

daveguy4• 1 year ago

Credit card companies averaged over 22,000 transactions per second in 2023 without ever having to raise the fee. How many is crypto even capable of processing? Processing without the fee going up? What fraud protection guarantees are offered to the parties of crypto transactions?

011000115• 1 year ago

the models are going to get better/smaller/faster overtime reducing our reliance on the GPU

Yes, because we've seen that with other software. I no longer want a GPU for my computer because I play games from the 90s and the CPU has grown powerful enough to suffice... except that's not the case at all. Software grew in complexity and quality with available compute resources and we have no reason to think "AI" will be any different.

Are you satisfied with today's models and their inaccuracies and hallucinations? Why do you think we will solve those problems without more HW?

dgemm2• 1 year ago

I think the point was not that we won't still use a lot of hardware, it's that it won't necessarily always be Nvidia. Nvidia got lucky when both crypto and AI arrived because it had the best available ready-made thing to do the job, but it's not like it's the best possible thing. Crypto eventually got its ASICs that made GPUs uncompetitive after all.

atrus• 1 year ago

Honestly, you'd be shocked at how much gaming you can get done on the integrated gpus that are just shoved in these days. Sure, you won't be playing the most graphically demanding things, but think of platforms like the Switch, or games like Stardew. You can easily go without a dedicated GPU and still have a plethora of games.

And as for AI, there's probably so much room for improvement on the software side that it will probably be the case that the smarter, more performant AIs will not necessarily have to be on the top of the line hardware.

dylan6043• 1 year ago

because that's what history shows us. back in the 90s, MPEG-1/2 took dedicated hardware expansion cards to handle the encoding because software was just too damn slow. eventually, CPUs caught up, and dedicated instructions were added to the CPU to make software encoding multiple times faster than real-time. Then, H.264 came along and CPUs were slow for encoding again. Special instructions were added to the CPU again, and software encoding is multiple times faster again. We're now at H.265 and 8K video where encoding is slow on CPU. Can you guess what the next step will be?

Not all software is written badly where it becomes bloatware. Some people still squeeze everything they can, admittedly, the numbers are becoming smaller. Just like the quote, "why would I spend money to optimize Windows when hardware keeps improving" does seem to be group think now. If only more people gave a shit about their code vs meeting some bonus accomplishment

amoss• 1 year ago

interesting take.

the aaa games industry is struggling (e.g. look at the profit warnings, share price drops and studio closures) specifically because people are doing that en masse.

but those 90s games are not old - retro has become a movement within gaming and there is a whole cottage industry of "indie" games building that aesthetic because it is cheap and fun.

tw1984• 1 year ago

Unlike crypto there is no mathematical lower bound for computation

this is what I feel, but is there any scientific proof on that?

brickfaced• 1 year ago

Yes, the human brain

eigenvalue4• 1 year ago

90% of the comments in this thread make it clear that knowing about technology does not in any way qualify someone to think correctly about markets and equity valuations.

cageface• 1 year ago

You could even say that overconfidence in abilities and knowledge outside of core expertise is the critical flaw of today's entire tech industry.

sympil5• 1 year ago

As a great example of this read Paul Graham’s essays that aren’t anout his core expertise.

ponty_rick3• 1 year ago

With the crowdstrike outage earlier last year it was incredible how many hidden security and kernel "experts" came out crawling from the woodwork, questioning why anything needs to run in the kernel and predicting the company's demise.

flashman4• 1 year ago

I think it's entirely possible that one categorically can't think correctly about markets and equity valuations since they are vibes-based. Post hoc, sure, but not ahead of time.

Rury2• 1 year ago

Most people don't care about the fundamentals of equity valuations is the crux of it. If they can make money via derivatives, who cares about the underlying valuations? I mean just look at GME for one example, it's been mostly a squeeze driven play between speculators. And then you have the massive dispersion trade that's been happening on the SP500 over the last year+. And when most people invest in index funds, and index funds are weighted mostly by market cap, value investing has been essentially dead for a while now.

talldayo3• 1 year ago

That means the remaining 10% are similarly disillusioned by the impression Apple or AMD could "just write" a CUDA alternative and compete on their merits. You don't want either of those people spending their money on datacenter bets.

10 years ago people said OpenCL would break CUDA's moat, 5 years ago people said ASICs would beat CUDA, and now we're arguing that older Nvidia GPUs will make CUDA obsolete. I have spent the past decade reading delusional eulogies for Nvidia, and I still find people adamant they're doomed despite being unable to name a real CUDA alternative.

seanmcdirmid• 1 year ago

Did ASICs beat CUDA out in crypto coin mining? Not the benchmark I really care about, but if things slow down in AI (they probably won’t) ASICs could probably take over some of it.

aurareturn15• 1 year ago

I think you’re wrong and Wallstreet got Deepseek’s impact wrong.

You say DeepSeek should decrease Nvidia demand. Wallstreet agreed today.

I say DeepSeek should increase Nvidia’s demand due to Jevon’s Paradox.

fldskfjdslkfj3• 1 year ago

The increase in efficiency is usually accompanied with the process of commoditization as stuff get cheaper to develop, which is very bad news for nvidia.

If you dont need the super high end chips than Nvidia loses it's biggest moat and ability to monopolize the tech, CUDA isn't enough.

golergka• 1 year ago

Will you post on HN in year describing how much money you earned by buying the stock today?

Aloisius• 1 year ago

Jevon’s Paradox

I've now seen this referenced two dozen times today which is well up from the 0 times I've seen it over the past year.

Is there some recent article referencing it that everyone is regurgitating?

raincole2• 1 year ago

I say DeepSeek should increase Nvidia’s demand due to Jevon’s Paradox.

If their claims were true, DeepSeek would increase the demand for GPU. It's so obvious that I don't know why we even need a name to describe this scenario (I guess Jeven's Paradox just sounds cool).

The only issue is that whether it would make a competitor to Nvidia viable. My bet is no, but the market seems to have betted yes.

spamizbad2• 1 year ago

Long term: Likely true. Short-to-medium term: Nope.

mitthrowaway22• 1 year ago

Jevon's paradox ony applies under certain conditions. It remains to be seen if it will hold in this case.

ojbyrne2• 1 year ago

A minor quibble: It’s Jevons Paradox - the last name of the economist who formulated it is “Jevons”

dumpsterdiver• 1 year ago

I’m also baffled by the reaction. Even with the ability to do more with less, the nature of the race still encourages everyone to do more with more.

vishnugupta2• 1 year ago

DeepSeek should increase Nvidia’s demand due to Jevon’s Paradox.

How exactly? From what I’ve read the full model can run on MacBook M1 sort of hardware just fine. And this is their first release, I’d expect it to get more efficient and maybe domain specific models can be run on much lower grade hardware sort of raspberry pi sort.

manojlds2• 1 year ago

With all the rate limits on Sonnet 3.5, people only want more of these models. We haven't even seen wide adoption yet.

sidkshatriya3• 1 year ago

No, nvidia's demand and importance might reduce in the long term.

We are forgetting that China has a whole hardware ecosystem. Now we learn that building SOTA models does not need SOTA hardware in massive quanties from nvidia. So the crash in the market implicitly could mean that the (hardware) monopoly of American companies is not going to be more than a few years. The hardware moat is not as deep as the West thought.

Once China brings scale like it did to batteries, EVs, solar, infrastructure, drones (etc) they will be able to run and train their models on their own hardware. Probably some time away but less time than what Wall Street thought.

This is actually more about nvidia than about OpenAI. OpenAI owns the end interface and it will be generally safe (maybe at a smaller valuation). In the long term nvidia is more replaceable than you think it is. Inference is going to dominate the market -- its going to be cerebras, groq, amd, intel, nvidia, google TPUs, chinese TPUs etc.

On the training side, there will be less demand for nvidia GPUs as meta, google, microsoft etc. extract efficiencies with the GPUs they already have given the embarrasing success of DeepSeek. Now, China might have been another insatiable market for nvidia but the export controls have ensured that it wont be.

bdelmas2• 1 year ago

I agree but in the short/medium term, I think it will slow down because companies now will prefer to invest in research to optimize (training) costs rather than those very expensive GPUs. Only when the scientific community will reach the edge of what is possible in terms of optimization that it will be back at pumping GPUs like today. (Although small actors will continue to pump GPUs since they do not have the best talents to compete).

boringg• 1 year ago

100%

Equity valuations for AI hardware future earnings changed dramatically in the last day. The belief that NVIDIA demand for their product is insatiable for the near future had been dented and the concern that energy is the biggest bottle neck might not be the case.

Lots to figure out on this information but the playbook radically changed.

wbsun• 1 year ago

I don’t understand why this is not obvious to many people: tech and stock trading are totally two different things, why on earth a tech expert is expected to know trading at all? Imagining how ridiculous it would be if a computer science graduate will also automatically get a financial degree from college even though no financial class has been taken.

vishnugupta2• 1 year ago

I’ve noticed this phenomenon among IT & tech VC crowd. They will launch pod cast, offer expert opinion and what not on just about every topic under the Sun, from cold fusion to COVID vaccine to Ukraine war.

You wouldn’t see this in other folks, for example, a successful medical surgeon won’t offer much assertion about NVIDIA.

And the general tendency among audience is to assume that expertise can be carried across domains.

nly4• 1 year ago

It's because software devs are smart and make a lot of money - a natural next step is to try and use their smarts to do something with that money. Hence stocks.

menaerus3• 1 year ago

People developing statistical models that are excercising the financial market at scale are the quants. These people don't come from financial degree background.

unsupp0rted2• 1 year ago

Tech people are allowed to quickly learn a domain enough to build the software that powers it, bringing in insights from other domains they've been across.

Just don't allow them to then comment on that domain with any degree of insight.

loeg• 1 year ago

Evergreen. The same observation applies to any subject you know anything about that is discussed on HN.

satvikpendem2• 1 year ago

"Briefly stated, the Gell-Mann Amnesia effect is as follows. You open the newspaper to an article on some subject you know well. In Murray's case, physics. In mine, show business. You read the article and see the journalist has absolutely no understanding of either the facts or the issues. Often, the article is so wrong it actually presents the story backward—reversing cause and effect. I call these the "wet streets cause rain" stories. Paper's full of them.

In any case, you read with exasperation or amusement the multiple errors in a story, and then turn the page to national or international affairs, and read as if the rest of the newspaper was somehow more accurate about Palestine than the baloney you just read. You turn the page, and forget what you know."

– Michael Crichton (1942-2008)

snake_doc2• 1 year ago

The other way is certainly also true. Your short piece is rational, but lacks insight into the inference and training dynamics of ML adoption unconstrained.

The rate of ML progress is spectacularly compute constrained today. Every step in today’s scaling program is setup to de-risked the next scale up, because the opportunity cost of compute is so high. If the opportunity cost of compute is not so high, you can skip the 1B to 8B scale ups and grid search data mixes and hyperparameters.

The market/concentration risk premium drove most of the volatility today. If it was truly value driven, then this should have happened 6 months ago when DeepSeek released V2 that had the vast majority of cost optimizations.

Cloud data center CapEx is backstopped by their growth outlook driven by the technology, not by GPU manufacturers. Dollars will shift just as quickly (like how Meta literally teared down a half built data center in 2023 to restart it to meet new designs).

optimiz3• 1 year ago

Everyone can say things that sound smart. When it comes to markets the only thing that matters is if your portfolio was green or red.

maybesomaybenot• 1 year ago

"knowing about technology" ... filter out the javascript programmers and that leaves about 15% of HN.

rhubarbtree14• 1 year ago

I think that’s correct, but it would be a more useful comment if you gave a few examples and explained the correct thinking.

eigenvalue4• 1 year ago

lol I posted the 12,000 word article that caused the sell-off:

https://x.com/doodlestein/status/1884712920543621148?s=46

sek• 1 year ago

The crash is absolutely rational; the cascading effect highlights the missing moat for companies like OpenAI. Without a moat, no investor will provide these companies with the billions that fueled most of the demand. This demand was essential for NVIDIA to squeeze such companies with incredible profit margins.

NVIDIA was overvalued before, and this correction is entirely justified. The larger impact of DeepSeek is more challenging to grasp. While companies like Google and Meta could benefit in the long term from this development, they still overpaid for an excessive number of GPUs. The rise in their stock prices was assumed to be driven by the moat they were expected to develop themselves.

I was always skeptical of those valuations. LLM inference was highly likely to become commoditized in the future anyway.

AnthonyMouse8• 1 year ago

It has been clear for a while that one of two things is true.

1) AI stuff isn't really worth trillions, in which case Nvidia is overvalued.

2) AI stuff is really worth trillions, in which case there will be no moat, because you can cross any moat for that amount of money, e.g. you could recreate CUDA from scratch for far less than a trillion dollars and in fact Nvidia didn't spend anywhere near that much to create it to begin with. Someone else, or many someones, will spend the money to cross the moat and get their share.

So Nvidia is overvalued on the fundamentals. But is it overvalued on the hype cycle? Lots of people riding the bubble because number goes up until it doesn't, and you can lose money (opportunity cost) by selling too early just like you can lose money by selling too late.

Then events like this make some people skittish that they're going to sell too late, and number doesn't go up that day.

camillomiller• 1 year ago

This is partially why Apple is the one that stands to gain more, and it showed. Their "small models, on device" approach can only be perfected with something like DeepSeek, and they're not exposed to NVIDIA pricing, nor have to prove investors that their approach is still valid.

bdelmas2• 1 year ago

The crash of NVIDIA is not about the moat of OpenAI.

But because DeepSeek was able to cut training costs from billions to millions (and with even better performance). This means cheaper training but it also proves that OpenAI was not at the cutting edge of what was possible in training algorithms and that there are still huge gaps and disruptions possible in this area. So there is a lot less need to scale by pumping more and more GPUs but instead to invest in research that can cut down the cost. More gaps mean more possibility to cut costs and less of a need to buy GPUs to scale in terms of model quality.

For NVIDIA that means that all the GPUs of today are good enough for a long time and people will invest a lot less in them and a lot more in research like this to cut costs. (But I am sure they will be fine)

simonebrunozzi• 1 year ago

Thanks. Given that you clearly belong to the top 10%, what's your take on the market?

mnky9800n4• 1 year ago

I feel like nvidia is still a good buy haha.

pdabbadabba• 1 year ago

Because your comment was posted 9 hours ago, I have no idea what view you think is wrong. Could you explain what the incorrect view is and — ideally — what’s wrong with it?

Xunjin• 1 year ago

Let's not forget, that also doesn't make you an expert in the history and society evolution, as we can all agree that a part of HN public still believe in “meritocracy” only and think that DEI programs are useless.

An experienced person on how is challenging to implement this in the Tech world: https://news.ycombinator.com/item?id=42658998

Nihilartikel• 1 year ago

I'm not convinced that this is more than the market having a jump-scare at an incremental leap in model building technique.

Say DeepSeek has worked out how to do more with less - that's great! I don't think it means that the market for Nvidia's silicon (or anyone else who can hop the CUDA moat) is going to shrink. I think that the techniques for doing more with less.. will be applied to _more_ hardware, to make even bigger things. AI is in its infancy, and frankly has a long way to go. An efficiency multiplier at this stage of growth isn't going to reduce the capital needed, it will just make it go further. There may be some concern about scaling the amount of training material, but I don't see that as the end of the road at all. After all, a human's mental growth is hardly limited by the amount of available reading material. After all written material is trained upon, the next frontier will just be in some other mimicry of biological metacognition.

At least that's my armchair analysis.

ezoe• 1 year ago

I really don't understand the market thinks Nvidia is losing its value.

If DeepSeek reduce the required computational resources, we can pour more computational resources to improve it further. There's nothing bad about more resources.

tonyhart7• 1 year ago

- "I really don't understand the market thinks Nvidia is losing its value."

because the less GPU need to train, the less money to be made

- "If DeepSeek reduce the required computational resources, we can pour more computational resources to improve it further. There's nothing bad about more resources."

thats why you are not hedgefund manager, these guys job is to ensure that the HYPETRAIN for company to buy as many nvidia gpu to sell no matter what, if we can produce comparable model without using B (as it stands billions of dollar), it means there are less billions of dollar to be made and the HYPETRAIN is near the end

skellington2• 1 year ago

Markets aren't rational.

NVidia is currently a hype stock which means LOTS of speculation, probably with lots of leverage. So, the people who have made large gains and/or are leveraged are highly incentivized to panic sell on any PERCEIVED bad news. It doesn't even matter if the bad news will materially impact sales. What matters is how the other gamblers will react to the news and getting in front of them.

aunty_helen• 1 year ago

Another thing, the markets had priced in X demand scaling @ ~145 and suddenly it's X/30 demand scaling and therefore the price should drop.

BoredomHeights• 1 year ago

The market might be right that Nvidia is overvalued, but if so I think only accidentally and not because of this news. Like you said, at least for now I think it's fairly clear that if a company has X resources and finds a way to do the same thing with half, instead of using less they'll just try to do twice as much. This could eventually changed but I don't think AI is anywhere near that point yet.

luxuryballs• 1 year ago

yeah I think people just got spooked and sold to take some profits they were going to take soon anyways

qqtt• 1 year ago

Well you have to keep in mind that Nvidia has a 3 trillion dollar valuation. That kind of heavy valuation comes with heavy expectations about future growth. Some of those assumptions about future Nvidia growth are their ability to maintain their heavy growth rates, for very far into the future.

Training is a huge component of Nvidia's projected growth. Inference is actually much more competitive, but training is almost exclusively Nvidia's domain. If Deepseek's claims are true, that would represent a 10x reduction in cost for training for similar models (6 million for r1 vs 60 million for something like o1).

It is absolutely not the case in ML that "there is nothing bad about more resources". There is something very bad - cost. And another bad thing - depreciation. And finally, another bad thing - the fact that new chips and approaches are coming out all the time, so if you are on older hardware you might be missing out. Training complex models for cheaper will allow companies to potentially re-allocate away from hardware into software (ie, hiring more engineering to build more models, instead of less engineers and more hardware to build less models).

Finally, there is a giant elephant in the room that it is very unclear if throwing more resources at LLM training will net better results. There are diminishing returns in terms of return on investment in training, especially with LLM-style use cases. It is actually very non-obvious right now how pouring more compute specifically at training will result in better LLMs.

msoad3• 1 year ago

My layman view is that more compute (more reasoning) will not solve harder problems. I'm using those models every day and when problem hits a certain complexity it will fail, no matter how much it "reasons"

plaidfuji4• 1 year ago

Here’s a take I haven’t seen yet:

If training and inference just got 40x more efficient, but OpenAI and co. still have the same compute resources, once they’ve baked in all the DeepSeek improvements, we’re about to find out very quickly whether 40x the compute delivers 40x the performance / output quality, or if output quality has ceased to be compute-bound.

BenFranklin100• 1 year ago

That seems to be the key question.

xbmcuser• 1 year ago

Yeah this was my first thought as well. If it got so efficient how good all the models will be 2-3 months from now

zeven7• 1 year ago

I've missed the stories on this until now. Is it known (and is there an ELI5) how they were able to do it so much more efficiently?

riffraff• 1 year ago

IIUC they released a paper, it's partially algorithmic improvements partially good old low level optimization.

rx_tx• 1 year ago

This article has good background, context, and explanations [1] They skipped CUDA and instead used PTX which is a lower level instruction set where they were able to implement more performant cross-chip comms to make up for the less-performant H800 chips.

[1] https://stratechery.com/2025/deepseek-faq/

gloflo• 1 year ago

Does line go up forever?

gdiamos3• 1 year ago

It goes up at least until LLMs match humans - ie until an LLM can write Windows

bmulholland• 1 year ago

Isn't the question closer to "/when/ does line stop going up?"

frontalier• 1 year ago

there's this and that little desktop computer they announced earlier this month - digits

they claim it's able to run models with 200B parameters on a single node and 400B when paired with another node

AnthonyMouse8• 1 year ago

If training and inference just got 40x more efficient

Did training and inference just get 40x more efficient, or just training? They trained a model with impressive outputs on a limited number of GPUs, but DeepSeek is still a big model that requires a lot of resources to run. Moreover, which costs more, training a model once or using it for inference across a hundred million people multiple times a day for a year? It was always the second one, and doing the training cheaper makes it even more so.

But this implies that we could use those same resources to train even bigger models, right? Except that you then have the same problem. You have a bigger model, maybe it's better, but if you've made inference cost linearly more because of the size and the size is now 40x bigger, you now need that much more compute for inference.

cyberax3• 1 year ago

DeepSeek is still a big model that requires a lot of resources to run

I can run the largest model at 4 tokens per second on a 64GB card. Smaller models are _faster_ than Phi-4.

I've just switched to it for my local inference.

mrtesthah• 1 year ago

Actually inference got more efficient as well, thanks to the multi-head latent attention algorithm that compresses the key-value cache to drastically reduce memory usage.

https://mlnotes.substack.com/p/the-valleys-going-crazy-how-d...

cyanydeez8• 1 year ago

I think what got cheaper are models with up to date information.

sghiassy6• 1 year ago

I don’t think that it got more efficient. It’s that smaller models can train via larger ones cheaply. Think teacher/student relationship

https://en.m.wikipedia.org/wiki/Knowledge_distillation

nejsjsjsbsb13• 1 year ago

That's a take I've seen in many HN comments

DebtDeflation2• 1 year ago

In the long run (which in the AI world is probably ~1 year) this is very good for Nvidia, very good for the hyperscalers, and very good for anyone building AI applications.

The only thing it's not good for is the idea that OpenAI and/or Anthropic will eventually become profitable companies with market caps that exceed Apple's by orders of magnitude. Oh no, anyway.

infecto3• 1 year ago

Yes! I have had the exact same mental model. The biggest losers in this news are the groups building frontier models. They are the ones with huge valuations but if the optimizations becomes even close to true, its a massive threat to their business model. My feet are on the ground but I do still believe that the world does not comprehend how much compute it can use...as compute gets cheaper we will use more of it. Ignoring equity pricing, this benefits all other parties.

matco11• 1 year ago

Can you guys explain what this would be bad for the OpenAI and Anthropic of the world?

Wasn't the story always outlined to be we build better and better models, then we eventually get to AGI, AGI works on building better and better models even faster, and we eventually get to super AGI, which can work on building better and better models even faster... Isn't "super-optimization"(in the widest sense) what we expect to happen in the long run?

paul_e_warner• 1 year ago

Yes, but I think most of the rout is caused by the fact that there really isn't anything protecting AI from being disrupted by a new player - They're fairly simple technology compared to some of the other things tech companies build. That means openai really doesn't have much ability to protect it's market leader status.

I don't really understand why the stock market has decided this affects nvidia's stock price though.

qwe----3• 1 year ago

Like everything, I expect improvement to be logarithmic

teleforce• 1 year ago

If training and inference just got 40x more efficient

The jury is still out on how much improvement DeepSeek made in terms of training and inference compute efficiency, but personally I think 10x is probably the actual improvement that's being made

But in business/engineering/manufacturing/etc if you have 10x more efficiency, you're basically going to obliterate the competitions.

output quality has ceased to be compute-bound

You raised an interesting conjecture and it seems that it's very likely the case.

I know that it's not even a full two years that ChatGPT-4 has been released but it seems that it take OpenAI a very long time to release ChatGPT-5. Is it because they're taking their own sweet time to release the software not unlike GIMP, or they genuinely cannot justify the improvement to jump from 4 to 5? This stagnation however, has allowed others to catch up. Now based on DeekSeek claims, anyone can has their own ChatGPT-4 under their desk with Nvidia project Digits mini PCs [1]. For running DeepSeek, 4 units mini PCs will be more than enough of 4 PFLOPS and cost only USD12K. Let's say on average one subscriber user pays OpenAI monthly payment of USD$10, for 1000 persons organization it will be USD$10K, and the investment will pays for itself within a month, and no data ever leave the organization since it's a private cloud!

For training similar system to ChatGPT-4 based on DeepSeeks claims, a few millions USD$ is more than enough. Apparently, OpenAI, Softbank and Oracle just announced USD$500 Billions joint ventures to bring the AI forward with the new announced Stargate AI project but that's 10,000x money [2],[3]. But the elephant in the room question is that, can they even get 10x quality improvement of the existing ChatGPT-4? I really seriously doubt it.

[1] NVIDIA Puts Grace Blackwell on Every Desk and at Every AI Developer’s Fingertips:

https://nvidianews.nvidia.com/news/nvidia-puts-grace-blackwe...

[2] Trump unveils $500bn Stargate AI project between OpenAI, Oracle and SoftBank:

https://www.theguardian.com/us-news/2025/jan/21/trump-ai-joi...

[3] Announcing The Stargate Project:

https://openai.com/index/announcing-the-stargate-project/

thw09j9m• 1 year ago

I think the big picture that many people are missing here is the motivation that all these AI/tech companies have for buying up so many GPU's in the first place: achieving AGI/ASI.

And while some still try to portray a dedication/duty to AI Alignment, I think most have either secretly or more publicly moved away from it in the race to become the first to achieve it.

And I think, given that inference time compute is so much cheaper than pre-training, the first to achieve AGI might have enough compute on hand from having been to first to build it that they would not need to purchase many more GPU's from Nvidia. So at some point, Nvidia's revenues are going to decline precipitously.

So the question is: how far away are we from AGI? Seems like most experts estimate 3-10 years. Did that timeline just shrink by 50x (or at least by some multiple) from these new optimizations from DeepSeek?

I think Nvidia's revenue is going to continue to grow at its current pace (or faster) until AGI is achieved. Just sell your positions right before that happens.

(Not an expert. Not even an amateur)

klodolph• 1 year ago

Seems like most experts estimate 3-10 years.

That’s spitting distance from “we don’t know”.

I think most of these valuations are made by people without the expertise to predict what the next one year of AI looks like, never mind 3-10 years.

YZF2• 1 year ago

It doesn't feel like DeepSeek has a big enough breakthrough here. This is just one of many optimizations we're going to see over the next years. How close this brings us to "AGI" is a complete unknown.

The large investments were mainly for training larger foundation models, or at the very least hedging for that. It hasn't been that clear over the last 1+ years that simply increasing the number of parameters continues to lead to the same improvements we've seen before.

Markets do not necessarily have any prediction power here. People were spooked by DeepSeek getting ahead of the competition and by the costs they report. There is still a lot of work and some of it may still require brute force and more resources (this seems to be true for training the foundation models still).

goatlover4• 1 year ago

Not that I believe it's likely to happen, but it seems incredibly fucking dangerous for there to be an ASI race with one winner. To the extent these companies believe it's possible, what are they hoping will be the outcome for humanity?

That they get to be the trillionaires with an untouchable moat? Wouldn't this be like creating a Kwisitz Hadarach thinking you can control it, to borrow a Dune reference?

DAGdug• 1 year ago

All the references to Jevon’s paradox fail to account for three things: 1. There’s no good forecasting model to account for how aggregate demand moves as a function of efficiency gains in this space 2. Aggregate demand isn’t the same as Nvidia’s share of market, which could drop if alternative paradigms for training or inferencing gain traction 3. Forecasting time horizons matter for discounted cash flow/valuation calculations, which nobody has a good basis for IMO, there’s just a lot of uncertainty, and it’s fair for the market to discount the optimistic trajectory aggressively based on net new info.

ponty_rick3• 1 year ago

Summarizing in Kevin Malone style - Nvidia good now. Later? No know.

Too• 1 year ago

Jeez chill... it’s just back to where it was 4 months ago and even after the drop it is still up 100% compared to this time last year! And it’s all fake inflated money.

This unprecedented growth simply couldn’t continue forever.

nicman23• 1 year ago

waiting on 1500 5090

robomartin• 1 year ago

I feel that one of the most fundamental aspects of business reality is being skipped in all of these conversations, on and off HN.

Everyone building an AI data center is likely using Nvidia technology. Sure, there's a 20% that is partially using other technology. The bulk of it is Nvidia.

If your project is in the planning for the next, say, two years, you have already placed your orders with Nvidia or are going to in the next few months.

Hardware has real lead times. You don't compile yourself 100K chips. They have to be made and you have to wait in line to get yours. For example, I remember when, during the pandemic, we had to place orders for chips with 40 to 50 week lead times.

This means you have to make decisions today (or you already made them months ago) to get in line.

Changes in training or inference efficiency should not change these orders or plans at all. If someone can train faster, they will benefit from the hardware in the pipeline. If they can make inference more efficient, they will be able to service more requests at reduced transactional costs.

The orders are in the pipeline and will continue to be added to the pipeline. Nvidia isn't going to be shipping half the hardware because Wall Street, overnight, panicked. What the grocery store owner does with their stock portfolio because they panic has nothing whatsoever to do with reality.

The same is true in the other direction. Wall Street has been going nuts with quantum stocks. Companies like Rigetti have exploded from nothing to see insane gains. This does not mean the company went from, well, shipping nothing to shipping real working solutions at scale.

Today's market reaction was nothing less than sheep running scared because someone when "boo!". It has nothing whatsoever to do with business realities on the ground. Go build an AI data center without Nvidia chips (or with 10x less chips) and see how that goes when everyone is loading-up with them.

qz_kb• 1 year ago

DeepSeek R1 just uses crappy PPO ("GRPO" is just using the sharpe ratio as a heuristic approximation to a value function) on top of distilled existing models, with tons of pipelining optimizations manually engineered in. I don't see this making leading edge research any less expensive, you won't get a "smarter" model - just a model that has a higher probability of giving an answer it could already give. If you want to try and do something interesting with the architecture the pipelining optimizations now slow down your iteration capability heavily.

The RL techniques present will only work in domains where you can guarantee an answer is right (multiple choice questions, math, etc.). It doesn't really present any convincing leap forward in terms of advancing the capability of LLMs, just a strategy for compute efficient distillation of what we know already works. The fact this shitty PPO proxy works at all is a testament to the fact that DeepSeek is bootstrapping its capability heavily off of the output of existing larger models which are much more expensive to train. What DeepSeek R1 proves is you can distill a ChatGPT et al. into a smaller model and hack certain benchmarks with RL.

If you could just do RL to predict the best next word in general this would have been done already - but the signal to noise ratio on exploration would be so bad you'd never get anything besides infinite monkeys at a typewriter. It's not a novel/complicated idea to anyone familiar with RL to try and improve probability of things you like, and whoever decided to do RLHF on an LLM surely thought of (and did) regular RL first - and found it didn't work very well with whatever pretrained model and rewards they had. it was like two weeks ago people were going crazy about O3 doing arc-agi by running the exact same kind of traces R1 is doing in "GRPO" at test time rather than train time. Doing this also isn't novel and also only helps on shitty toy problems where you can get a number to tell you good vs bad.

There is no mechanism to compute rewards for general purpose language tasks - and in fact I think people will come to see the gains in math/coding benchmark problems come at a real cost to other capabilities of models which are harder to quantify and impossible to generically assign rewards to at internet scale.

To explore the frontier of capability you will still need a massive amount of compute, in fact even more to do RL than you would need to do standard next token prediction - even if the LLM might have fewer paramters. You also can't afford to do all the optimizations as you try many different complex architectures.

divbzero2• 1 year ago

Greater efficiency of light bulbs has led to more light bulb use, not less. More efficient training of LLMs could just as likely lead to more chip use, not less.

gniv2• 1 year ago

But did it not lead to net less electricity used for lighting?

BobbyJo• 1 year ago

I'd wager there is more light today than before LEDs were invented. I'd wager a lot in fact.

divbzero2• 1 year ago

With more efficiency offset by more light bulbs, electricity used for lighting has been roughly flat since 2010: https://www.iea.org/data-and-statistics/charts/global-electr...

(For LLMs I wish that efficiency could lead to less electricity used for chips, but I think the best we can hope for is for electricity use to flatten out.)

jstummbillig• 1 year ago

Maybe, at some point.

From what I can tell there's are mostly two options: Either AI is and will be useless or it's severely undersupplied. People, even those deeply technical, where AI has the most impact right now, still widely argue about if AI even offers any value. Adoption is far from anything that is plausible, if (not when) it became clear that it does.

If you land on "does not", given the investments so far, commercial entities would obviously be overvalued already and any investment goes to 0 over time.

But if we land on "does", how could Nvidia not be anything other than undervalued right now? No matter what frontier model: I can look at my screen, LLM generated characters visibly appearing in chunks, depending on the model after initially waiting for 10-20 seconds, for even benign queries — because that is the best we can do right now. And that's while most people still argue if AI will actually do anything and humanity at large does not really use it, neither personally nor societally.

If AI does in fact do something valuable and that something gets better, everyone will want it and there will be demand for lots of chips.

light_hue_13• 1 year ago

Across 300 years of lighting technology decreases in cost consistently led to much larger increases in light use every time. Until about the past 50 years or so when increased light use started to fall behind the drop in costs.

https://www.researchgate.net/profile/Roger-Fouquet/publicati...

Cheaper models are good for industry. But the demand for better more reliable models is immense. The models won't stay cheaper.

sakopov• 1 year ago

Has the supposed DeepSeek training budget been verified in any way?

shahzaibmushtaq• 1 year ago

DeepSeek direct competitors[0] are OpenAI's models, Meta's Llama, Anthropic's Claude etc.

NVIDIA isn't even DeepSeek indirect competitor. And no investor/expert in their right mind would compare a software company to a hardware company.

[0] https://api-docs.deepseek.com/news/news250120

captainhulllo• 1 year ago

So the day this happened I didn't eat all day which was a weird thing. I don't know just one of those weird coincidences for no reason. Tomorrow I won't eat all day again and see what happens.

Anyway I'm homeless so the not eating all day maybe kinda possibly having something to do with wiping out 600 billion dollars in stock market valuation just on the off chance that it might based on nothing more than wishful thinking?

Nope. Not eating tomorrow. Wonder what will happen.

butterisgood• 1 year ago

One day does not a pattern make. Just bought some nvidia stock.

Guess we shall see how it pans out…

freddealmeida• 1 year ago

funny thing is they prob had a position to make bank off that. smart.

cultofmetatron• 1 year ago

I dont' know what the surprise is here. the human brain consumes about 20 watts. literally a rounding error compared to what chatgpt uses. so we already know that there was plenty of room on the table to improve.

incidentally, I love these kinds of market crashes. just moved a big chunk of my savings account into stocks last night :). Buy and hold. dont' sell during a dip lol

ludvigk• 1 year ago

Why do you think the human brain and chatgpt are in any way related?

Satam• 1 year ago

Because LLMs are based on the abstract ideas of neural nets from brains. Say what you wish, but some problems were completely unsolvable before we adopted this paradigm. On some level, we must've gotten some ideas close to the right ballpark.

thrance• 1 year ago

Maybe not that pertinent, but I remember someone computing joules per word written in humans vs chatgpt and found the former to be a bit higher.

lvl1554• 1 year ago

This is not a market crash. I don’t know how old you’re but you might have been conditioned to think this way by the market’s unrelenting march higher. But those of us from the dotcom days can tell you it crashes eventually and very painfully.

verytrivial3• 1 year ago

Is there evidence for the claim that the training was that much cheaper? Or is is 'apparent' by inspection of the verified ingestion architecture?

I can't seem to find any evience beyond the team's statements. What have I missed?

Jasp3r• 1 year ago

The hypothesis I have is that China has way more compute resources than they are willing to share.

Compute resources they officially should not access to given export bans, where mentioning them might lead to their export ban bypass getting rolled up.

verytrivial3• 1 year ago

Maybe. They're under no obligation to tell the truth about this.

Hypothetical: Take a large short position on NVDA, announce the market that you trained a massive model without using 10s of millions of rare-as-hen's-teeth NVDA-sentsitive resources. Settle postition then quietly settle giant compute bill. Difficult to know either way, but the market seems have taken the team at face value. I guess we'll know if and only if this reduced training cost methodology is replicated.

verytrivial3• 1 year ago

Well. https://www.tomshardware.com/tech-industry/artificial-intell...

orwin• 1 year ago

Is this linked to the fact that they bypassed CUDA, making Nvidia's moat way shallower that it was thought to be?

fcanesin2• 1 year ago

Nvidia doesn't have a monopoly on GEMM and GEMV. There will be dozens of hardware vendors. It is TSMC that should be the most valuable company in the world.

farseer• 1 year ago

By that logic TSMC is not the only one capable of 5nm. ASML should be the most valuable company in the world?

fcanesin2• 1 year ago

Certainly one of the most valuable, but I would still say TSMC as there are lots of other steps in the production besides photolithography (etching, ion implantation, vapor deposition, packing, ...).

mustaflex• 1 year ago

Not long ago Sam Altman was talking about how they were loosing money even on the paid version of chatgpt. Those incoming prices hikes going to be difficult to sell now.

m101• 1 year ago

1) to address frontier model company stock valuations (openai for instance): deepseek is creating stuff months after frontier companies. In the AI arms race it might make sense to burn billions to be 3 months ahead (think about how you use that superintelligence to prevent anyone else acquiring it)

2) to address Nvidia valuation: there is no cap to demand on intelligence (or, we're not close). People will never be satisfied with the intelligence achieved and just stop asking for more. So Nvidia will still sell the hardware as the demand side is uncapped.

Unrelated note that I was considering and would like an opinion on: Nvidia is the software play in AI, and TSMC the hardware play. Nvidia has competitors like broadcom/AMD/TPUs but beats out on software. TSMC will be frontier on manufacturing everyone's hardware.

sghiassy6• 1 year ago

I feel there’s a gap missing in this thread (or I may be the one missing it)

DeepSeek proved knowledge distillation works very well and cheaply https://en.m.wikipedia.org/wiki/Knowledge_distillation

But they didn’t show how to build a new frontier model cheaply.

So, you still need massive investments to build new frontier models. But the bad part, is they can be replicated cheaply

cubefox2• 1 year ago

This comment seems to be complete nonsense. See here https://arxiv.org/abs/2412.19437v1

sghiassy6• 1 year ago

The internet would be a little better, if people were a little nicer

gilbetron• 1 year ago

I think you are missing it:

https://stratechery.com/2025/deepseek-faq/

That has a great overview - this is a new model, but also a distillation. They used new techniques to make it really cheap (comparatively).

sghiassy6• 1 year ago

Thank you for the link. I have a lot of respect for Stratechery. I learned a lot, and agree, I’m the one who was missing it haha

d--b• 1 year ago

NVidia stock is valued at $118 today.

It was worth $50 in Jan 24.

While the drop may sound huge, we're pretty far from a crash here.

ubermonkey• 1 year ago

So I was aware of the basic story here from late yesterday, but I only actually looked at the numbers today. Focusing on the dollar figure here is misleading.

Basically, if you look at only the stories, you get a dire picture -- like, "how will they make payroll????"

However, if you look at the actual share price in context, you see . . . a not very interesting event.

Look here:

https://www.macrotrends.net/stocks/charts/NVDA/nvidia/pe-rat...

The graph definitely shows a share price adjustment, but it by no means erases the run-up Nvidia has enjoyed in the last 2 years. All that happened was the stock dropped back to it price of about the middle of last summer.

This may be old news to most of you, but: in markets we also track a figure called the P/E ratio, which is the ratio of the price of the company vs. its profits. Old-school manufacturing firms -- what used to be "blue chip" stocks -- would be in the 18-22 range here. Apple which absolutely PRINTS cash, is at a high-flying 38. NVidia's P/E is still 58.8.

The tl;dr here is that NVidia is still valued very, very highly. They're still what, the 3rd most valuable company in the world, with an enviable position in chipmaking. They still make money hand over fist. The weird part is that their firm is SO valuable that they could take a $600B haircut and it be almost a non-event.

coryfklein• 1 year ago

See the 2-year NVDA chart[0] for some perspective.

* Price has dropped to the level it was at four months ago in October

* The 1-year is up 142%

* The 2-year is up 530%

If you're just going by the market's assessment, this is not a "crash" and the "game has not changed".

[0] https://www.tradingview.com/x/b42zFn05/

Nvidia’s $589B DeepSeek rout

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