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AI's $600B Question

397 points by fh973 2y ago

mupuff1234 2y ago
> I expect this will lead to a final surge in demand for NVDA chips But other than FOMO why would someone buy better chips when they don't actually know what to do with their old ones?
- alecco 2y ago
  H100s are in very high demand and usually running work 24/7. So much that the energy cost is a big factor. The B100 halves the energy costs, among many other things.
  - technotony 2y ago
    If they are in such high demand, then are they just being priced below cost? why else is the capex so much higher than the revenues? These things must depreciate over only 3-5 years (or less?)
    - alecco 2y ago
      No. Some people say H100s are priced at 1000% of cost and Nvidia could charge even more but then they would really push their large clients (Microsoft, Meta, Amazon) to build competing hardware. And even at the current price they are trying already.
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jfghi 2y ago
Reminds me of the gnomes from South Park.
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threeseed 2y ago
> Founders and company builders will continue to build in AI—and they will be more likely to succeed, because they will benefit both from lower costs and from learnings accrued during this period of experimentation Highly debatable. When we look back during the internet and mobile waves it is overwhelmingly the companies that came in after the hype cycle had died that have been enduring.
- malfist 2y ago
  Let's see: Microsoft Windows: wasn't close to the first OS Microsoft Office: wasn't close to the first office editing suite Google: Wasn't close to the first search engine Facebook: Wasn't close to the first social media website Apple: ~~First "smart phone"~~ but not the first personal computer. Comments reminded me that it wasn't the first smartphone Netflix: Wasn't close to the first video rental service. Amazon: Wasn't close to the first web store None of the big five were first in their dominate categories. They were first to offer some gimmick (i.e., google was fast, netflix was by mail, no late fees), but not first categorically. Though they certainly did benefit from learnings of those that came before them.
  - AlexandrB 2y ago
    > Apple: First "smart phone" but not the first personal computer Was it the first smartphone? I would call phones like the Palm Treo and later BlackBerries smartphones. There were even apps, but everything was lot more locked down and a lot more expensive.
    - seanmcdirmid 2y ago
      First modern smartphone (capacitive touch screen/multi-touch/form factor), but not first smartphone.
    - dvt 2y ago
      > There were even apps, but everything was lot more locked down and a lot more expensive. And just plain... bad. The entire experience didn't have that "feel" that Apple turned into reality. It's comparable to today's AI landscape—the technology is pretty neat, but using it is a complete slog.
neaanopri 2y ago
This seems about as bearish as a VC is allowed to be. My takeaway is: Sell! Sell! Sell now before it's too late!
throwaway42668 2y ago
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- deleted 2y ago
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beejiu 2y ago
It interests me that the $200-600 billion number seems to be all-derived from GPUs. Are LLMs/AIs totally dependent on GPUs? I read last week (https://news.ycombinator.com/item?id=40787349 https://news.ycombinator.com/item?id=40787349) that there is research ongoing to run LLMs on FPGAs at greater energy efficiency. I'm reminded of Bitcoin/crpyto, which in its early history was all operated on GPUs. And, then, almost overnight, the whole thing was run on ASICs. Is there an intrinsic reason something similar couldn't happen with LLMs? If so, the idea of a bubble seems even more concerning.
- cma 2y ago
  Nvidia and AMD bought out the big FPGA makers.
  - jamessinghal 2y ago
    AMD bought Xilinx, but Intel recently spun off Altera.
    - cma 2y ago
      Opps, yeah was thinking of Intel's Altera buy, not Nvidia, which I guess is now undone.
- wmf 2y ago
  These numbers are just the hole from GPUs that have already been bought/ordered. Today's GPUs will inevitably be replaced by something, whether it be better GPUs, NPUs/TPUs, ASICs, or FPGAs. As chips get cheaper in the future the hole will grow at a slower rate but it only grows.
- sason 2y ago
  There is a fairly new ASIC named "Sohu" that is purpose-built for transformers. They have some bold claims that are impressive if true. I found a short discussion[2] you may find useful. [1]: https://www.etched.com/ https://www.etched.com/ [2]: https://www.lesswrong.com/posts/qhpB9NjcCHjdNDsMG/new-fast-transformer-inference-asic-sohu-by-etched https://www.lesswrong.com/posts/qhpB9NjcCHjdNDsMG/new-fast-t...
ryandrake 2y ago
Others are saying this article is bearish, but then... > A huge amount of economic value is going to be created by AI. Company builders focused on delivering value to end users will be rewarded handsomely. Such strong speculative predictions about the future, with no evidence. How can anyone be so certain about this? Do they have some kind of crystal ball? Later in the article they even admit that this is another one of tech's all-too-familiar "Speculative frenzies." The whole AI thing just continues to baffle me. It's like everyone is in the same trance and simply assuming and chanting over and over that This Will Change Everything, just like previous technology hype cycles were surely going to Change Everything. I mean, we're seeing huge companies' entire product strategies changing overnight because We Must All Believe. How can anyone speak definitively about what AI will do at this stage of the cycle?
- glitchc 2y ago
  Whenever I hear the AI hype cycle, I'm always reminded of expert systems and how they were going to revolutionize the world.
  - awahab92 2y ago
    there were probably people who doubted electricity, vaccines and indoor plumbing.
    - ryandrake 2y ago
      There were also people who doubted the Segway, Magic Leap, Theranos, 3D TVs, Windows Phone, and Google Glass. I think doubt is OK, at least it is before any particular technology or product has actually proven itself.
      - fragmede 2y ago
        the gap between participants in this conversation is that some have had it proven itself for themselves and others around them, and others have not seen that same proof.
dvt 2y ago
> In reality, the road ahead is going to be a long one. It will have ups and downs. But almost certainly it will be worthwhile. The biggest takeaway from this piece is the stark realism of this article (maybe a bit too bearish, imo) compared to the usual Sequoia VC-speak. Maybe FTX did teach them something, after all.
- mike_d 2y ago
  VCs in general hate core AI because it is dominated by large players that don't need VC money to be successful. If someone was taking Sequoia money to buy GPUs they'd be singing a completely different tune.
- smeeth 2y ago
  I don’t think FTX is the right contrast here. This document is mostly about business realism: revenue, margins, etc. VCs did get burned by speculative investing in the 2019-21 period but FTX wasn’t like the others. At the time of its collapse, FTX was profitable and had $1+ bn in revenue, its doom had nothing to do with product market fit, revenue, margins, etc. Quibi might be a more relevant example of a learning opportunity.
  - dvt 2y ago
    > I don’t think FTX is the right contrast here. Comparing this article to the (now deleted) SBF profile is night and day.
    - smeeth 2y ago
      I know, my point is that I fail to see how the FTX collapse would lead them to a tone change. > Maybe FTX did teach them something The new tone is all about business fundamentals and the FTX collapse had literally nothing to do with business fundamentals. If this is the lesson they learned they learned the wrong lesson.
openrisk 2y ago
> Those who remain level-headed through this moment have the chance to build extremely important companies. But in which sector will these extemely important companies be active? Adtech? Knowledge management / productivity tools? Some completely new category? What is an undeniable fact is the drastic commodization of the hardware / software stack for certain classes of algorithms. How is this technological development going to be absorbed and internalized by the economy feels still rather uncertain.
- sumedh 2y ago
  > But in which sector will these extemely important companies be active? I dont think anyone knows the answer to that question.
lqprag 2y ago
As always, free analysis by VCs and investment banks is not necessarily for the reader's benefit. So we have this bearish piece and the previous bearish Goldman Sachs piece. While I agree with their analysis in this case, there is a lingering doubt that some banks might just want to tank Nvidia a little in order to go long. Or something like that.
Animats 2y ago
"Unlike the CPU cloud, which became an oligopoly, new entrants building dedicated AI clouds continue to flood the market. Without a monopoly or oligopoly, high fixed cost + low marginal cost businesses almost always see prices competed down to marginal cost (e.g., airlines)." So why aren't there more entrants in the CPU cloud area? The technology is a commodity. Google and Amazon don't make CPUs.
- wmf 2y ago
  The secret sauce in the cloud is not hardware but control plane, networking, and storage software.
  - falcor84 2y ago
    And battle-tested SREs
    - ukuina 2y ago
      Battle-scarred*
  - mcast 2y ago
    And IP space.
    - wmf 2y ago
      The clouds are buying IPs and there's nothing stopping you from doing the same.
- mike_d 2y ago
  > So why aren't there more entrants in the CPU cloud area? Because the market is saturated with players. AWS, Azure, GCP, Alibaba, IBM Cloud, Digital Ocean, Tencent, Oracle Cloud, Huawei Cloud, that Dell/VMware thing, Linode/Akamai, HP, Scaleway, Vultr, GoDaddy, OVH, Hetzner, etc.
  - 2y ago
mike_d 2y ago
The calculations here seem to depend on a few false assumptions: 1. That all datacenter GPUs being purchased are feeding AI. You might be able to argue that some are or a lot are, but you don't know how many just looking at Nvidia sales numbers. I know of at least two projects deploying rows of cabinets in datacenters full of GPUs for non-AI workloads. 2. The assumption that pay-for-an-API is the only AI business model. What we now call "AI" has been driving Google's search and ad businesses for nearly a decade, sooo AI is already doing $300B/yr in revenue? There is no way for this guy to quantify how AI is solving problems that aren't SaaS. David Chan, if you are reading this feel free to email me if you want a fact check for what will surely be the third installment in the series.
- jerrygenser 2y ago
  Yes but Google hasn't needed gpus to power search. It's used cpu even for ML inference and training before deep learning.
LarsDu88 2y ago
According to Jensen it takes about 8000 H100s running for 90 days to train a 1.8 Trillion param MoE GPT-4 scale model. Meta has about 350,000 of these GPUs and a whole bunch of A100s. This means the ability to train 50 GPT-4 scale models every 90 days or 200 such models per year. This level of overkill suggests to me that the core models will be commoditized to oblivion, making the actual profit margins from AI-centric companies close to 0, especially if Microsoft and Meta keep giving away these models for free. This is actually terrible for investors, but amazing for builders (ironically). The real value methinks is actually over the control of proprietary data used for training which is the single most important factor for model output quality. And this is actually as much an issue for copyright lawyers rather than software engineers once the big regulatory hammers start dropping to protect American workers.
- VirusNewbie 2y ago
  Llama isn't even in the same stratosphere as the big models when it comes to coding, logic, and other interesting tasks that I think are commercially viable.
- laweijfmvo 2y ago
  A lot of those GPUs are for their 3B users to run inferencing, no?
  - LarsDu88 2y ago
    True that, but I think in a very short amount of time, using dedicated general purpose GPUs just for inferencing is going to be mega overkill. If there's dedicated inferencing silicon (like say the thing created by Groq), all those GPUs will be power sucking liabilities, and then the REAL singularity superintelligence level training can begin.
    - campers 2y ago
      Etched is another dedicated inference hardware company that recently announced their product. It only works for transformer based models, but is ~20x faster than a H100
Kon-Peki 2y ago
Many of the winners haven't even been formed yet. And most of the losers have already lost, they just don't know it yet.
shitpostbot 2y ago
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Timber-6539 2y ago
The problem with AI is that it's very much a gimmick and consumers don't have much use for gimmicks. Certainly not when it works half the time and hallucinating the other half. All it takes is an NBER announcement to pop this mania.
matchagaucho 2y ago
There's a growing divide between inference GPUs and training GPUs. Investors first need to ask what that ratio might look like in 10 years. 10-to-1? 100-to-1? Inference-to-Training Assuming for each NVDA training GPU sold there are 100 open source / commodity GPUs doing inference, who owns and supplies those data centers and hardware?
dzink 2y ago
AI is dangerous to VCs. The returns are going to chip-makers and employers, including single founder startups, who don't have to hire a lot of people, and additionally get productivity they never thought possible. A surgeon I know uses AI every day - to translate, to explain, to figure out problems, to write. They wouldn't have paid someone to do that, but now they get that output in seconds. This is a time to solve all kinds of problems we didn't think possible - because AI has made the enterprising among us instantly smarter. All the fodder about AGI being a next step is smoke and mirrors - for everyone using OpenAI knows they don't need any more niche tools as their one $20 subscription is doing more for them every day. AGI is here. Experts can correct AI generated mistakes, but those are getting less and less too. The real benchmark is: Name how many people you know who can out-do ChatGPT on a question. You won't bother to check LinkedIn for that. The gains are aggregating towards Chips, Clouds and Entrepreneurs. The VCs, since A16Z's original AI blog post (all expense, little return, echoed this Sequoia post but did it but years ago), know they are not needed as much anymore. Fewer VCs will beat the market when founders can grow startups without raising too much money (they don't need to hire as many people). Hiring needs lead to PR waves which require VC funding. Valuation is not a big deal for founders making money either, so they may not even disclose how successful their companies are. Bragging about your gains only invites competition. So other than ponzi-type ventures where you need to attract the dinner to serve dinner, you won't hear much about the good ones. A different era indeed. The tech giants are in for a lot of change as well. Those who have distribution may try to push their models to the masses to be the point of reference, but that can get expensive, especially for those who don't charge. AI will help improve AI performance as well and that means cheaper better performance with time. What's most needed in this era are people who know what the world needs that hasn't been invented yet. They need to be inventing and monetizing it. Little stopping you now.
- nojvek 2y ago
  Well written argument. The holy grail of AI is still in future. Where it can interact with software tools like we do. Competent AI Agents will be a huge productivity unlock.
- threeseed 2y ago
  This is the sort of grifter/influencer style comment that we saw in crypto and now pervasive in AI. Not just that AI is a great tool that will over time have a significant impact on society. It’s the breathless hype e.g. we have AGI today, society will be instantly smarter because of it and every tool and employee will be impacted. The FOMO i.e. you must jump on board now or be left behind. And the complete lack of any data or evidence.
Ekaros 2y ago
Has anyone asked will AI actually very soon be commodity tech? With too many players competing with each other driving prices to bottom? Is there actually meaningful value to extract, after you have spend money on hardware and power? Modern web is full of examples of platforms that really don't make money...
pedalpete 2y ago
I think this is the correct take. My understanding of the article is that huge investments in hardware, mostly to NVIDIA, and spending by major tech companies is currently defining the market, even if we include OpenAI, Anthropic, etc. It is FAANG money they are running on. I put this as equivalent to investing in Sun Microsystems and Netscape in the late 90s. We knew the internet was going to change the world, and we were right, but we were completely wrong as to how, and where the money would flow.
- npalli 2y ago
  The better analogy is the massive investment in fiber optic cable in the late 90s. All the companies in that line (Global Crossing, Worldcom etc.) went bust after investing 10s of billions but the capacity was useful (with a >90% drop in price) for future internet services. Around 2000 when the bubble was bursting only 5% of capacity was being used but proved to be useful to get all internet-first companies like the Googles, Amazon, NetFlix's going.
  - cruffle_duffle 2y ago
    Good old JDS Uniphase was one of the first individual stocks I bought. I mean it had to go up right? Fiber and dark fiber and the continual threat of the internet collapsing due to load… better buy that stock!
    - elphinstone 2y ago
      Worldcom here. Ah, the Enrons of the internet.
  - pedalpete 2y ago
    I initially was agreeing with you, but I don't see NVIDIA, AWS, Microsoft, etc going to zero (and Worldcom was unraveled by accounting fraud). Sun Microsystems sold to Oracle for $7B, and Netscape was acquired by AOL for $10B.
    - 2y ago
nabla9 2y ago
38 OECD countries have combined population 1.38 billion, GDP $49.6 trillion $600 billion is $434 per person, $36 per month per person, 1.2% percent of GDP. If 75% of the spending goes to increasing productivity, I could see it. Get rid of $450 billion in labor costs (12 - 15 million work years). Cut worked hours in call centers, customer service, many services, menial programming jobs, ... But I don't see it happening fast enough to pay for current investments.
light_hue_1 2y ago
This is not an AI or ML problem. This is an Nvidia, Google, Microsoft, Meta, Tesla, etc. problem. Most AI startups aren't building massive data centers so they're unaffected. Most money isn't spent on compute in most startups. Only a few companies spend big. It's obviously a terrible idea to invest massive amounts into compute when Nvidia's profit margins are so astronomical if you need ROI in the long term. The massive corporations won't get their money back for these investments; but they don't have to.
sadhorse 2y ago
I recall Marvin Minsky saying that 1 GigaFlops would suffice for doing most of what humans do. Current AI is addiction to more data, more compute but no smarts. At least they gave us cheap compute, soon the right people will figure out how to produce real AI, not this travesty.
sheeshkebab 2y ago
The only thing AI needs to do to demonstrate its value is replacing jobs at scale. Until it happens, it’s all capex and opex bs and hobby projects.
- dbish 2y ago
  Yes, and I think we're on the way to that happening in chunks of the economy, but it's not going to be overnight, there are still a number of improvements to make along the way. I am willing to bet my time working on it that we will be replacing many jobs in the next few years.
  - sheeshkebab 2y ago
    Maybe Musk will show to all how it’s done starting with robotaxis, however looking at current state I wouldn’t bet on it, at least not anytime in near future. Buying more gpus will definitely not be the thing that solves this.
the__alchemist 2y ago
About a week ago, [Angela Collier posted a video on this topic](https://www.youtube.com/watch?v=dKmAg4S2KeE https://www.youtube.com/watch?v=dKmAg4S2KeE) highlighting the "malicious optimism" of AI hype. She used comments by the Zoom CEO as a basis, but it generalizes.
andsoitis 2y ago
"Consider how much value you get from Netflix for $15.49/month or Spotify for $11.99. Long term, AI companies will need to deliver significant value for consumers to continue opening their wallets."
julianozen 2y ago
ELI5, why are GPUs not a commodity piece of hardware that any sophisticated chip manufacturer can't also produce? This is one of the first time in the tech industry where the value was fully reaped by the hardware itself and not by the differentiated software that ran on top of it
- alberth 2y ago
  Intel for a few decades didn’t do to shabby.
  - julianozen 2y ago
    Yes, they did have great run, but I think over the long run the OS they were tied to was more important
- ineedaj0b 2y ago
  they are very small, very advanced, and production yields are poor. The equipment is also stupidly expensive and takes reams of smart people just to run. Silicon is currently the most advanced tech humans have ever made and those GPUs are on the cutting edge of that
- paulmendoza 2y ago
  Amazon has a some AI chips they created themselves. Same with Tesla. They are both behind Nvidia and probably won’t catch up BUT raw performance isn’t always the most important part. Often it’s the cost per compute unit and companies are quickly getting close to Nvidia.
  - julianozen 2y ago
    Can more less sophisticated chips perform as well as fewer more expensive chips?
_xnmw 2y ago
Maybe because the revenue isn't directly attributable to AI itself, but is realized in the cost savings and productivity improvements in already existing revenue streams? That's where AI has been useful to me. I can't put a number on how much AI has made me exactly, but it has certainly helped all aspects of my bootstrapped startup.
- alok-g 2y ago
  Any product should bring benefits to both the producer and the consumer. For the case where a company is using their own AI for their own cost reduction and productivity improvements, they can keep doing that but not offer to another party. If they offer to another party, and that party is having benefits (like you have said), the price should be such that a part of the consumer benefit is shared with the producer resulting in benefits for the producer. The real challenge here is because of price wars, i.e., too much competition already with producers willing to take a hit on profitability in anticipation that they will be able to do so later after creating a moat above and beyond competitors. Or they think that it will strenghen their overall bigger offering by adding an otherwise lossy feature. In a nutshell, even if there's a lot of value for the consumers, it must result in a win-win for a new product to be sustainable in the market.
- dmix 2y ago
  > but it has certainly helped all aspects of my bootstrapped startup. Well if there's value to you then how much did you pay for it and would it realistically cover operating cost once VC cash dries up? That's the only question.
barnabyjones 2y ago
I think there is far too much focus on potential productivity gains, and not nearly enough on entertainment value, which still has a ways to go but much higher potential compared to serious problem solving, which may or may not be be feasible with these models. Nobody has really figured out yet a good way to integrate generative multimedia together in a way that makes a hit, but surely someone will in the next few years. Right now there are utterly half-assed apps like Linky making good money, and that's just midjourney and CAI glued together awkwardly. I don't know what a genuine hit product is going to look like, or if it'll even be an app, but the potential for generative multimedia is so much higher that what we're seeing now.
mark_l_watson 2y ago
Context: I have been paid to work as an AI practitioner since 2982, nothing special about me except I have worked with amazing people. I have lived through at least two “AI winters.” The author of this blog makes a great argument that there is a risk of AI investments not paying off because if a revenue gap: The author argues that the gap between the revenue expectations implied by the AI infrastructure build-out and actual revenue growth in the AI ecosystem has increased from $200B to $600B. This is due to factors such as the subsiding of the GPU supply shortage, growing GPU stockpiles, and the dominance of OpenAI in AI revenue. The author also notes that the $125B hole in AI revenue has now become a $500B hole. However, my experience with previous AI winters is not relevant here because now is the first time in history that there is a possibility of what we used to call “real AI” and now call AGI. No investor wants to miss out completely on the creation of near limitless wealth.
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artisin 2y ago
LLMs are undeniably remarkable, but their wheels start to fall off once you go beyond the basics and a few party tricks. The cynical part of me thinks this tracks pretty well with our society's tendency to favor appearance over substance. Yet, if my burger order gets messed up, I don't fault the restaurant; however, if an LLM messes up my order, the same cannot be said. As a seasoned human, I feel confident saying that we humans love blaming other humans, whether it's justified or not. So while I initially thought customer-facing roles would be front and center in the "AI revolution." Today, I tend to think they'll be bringing up the rear, with entertainment/smut applications at the forefront along with a few unexpected applications where LLMs operate behind the scenes.
- deleted 2y ago
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- bamboozled 2y ago
  Even smut seems to be having a pretty slow take off, I had a go of an AI porn generator for fun recently, it was nightmare fuel. I know it's "almost right and it will get better" but not being "there" yet just isn't good enough when it comes to accurately depicting penetration.
joshuakarl 2y ago
Data center GPUs are not only used for LLMs. Meta makes hundreds of billions and invests tens of billions on GPUs, using them directly in its core business, not just for cute chatbots. OpenAI is not the clear winner in revenue generation from "AI" if we define AI as deep learning models trained on GPUs.
surfingdino 2y ago
> Investment incineration Just two words to describe all the idiocy of the current wave of AI offerings. > A huge amount of economic value is going to be created by AI. Company builders focused on delivering value to end users will be rewarded handsomely. We are living through what has the potential to be a generation-defining technology wave. No. It's crap no matter how much money you throw at it. It will end in tears, because there is no standard, no operating system, no protocol, only a handful of APIs controlled by a couple of companies. They are not building networks, but hubs with a single point of failure--the API provider. The internet is such a world-changing force because it is built on top of TCP/IP, which allows the rest of the internet to survive even if a part of it goes down. When an AI API provider shuts down, all those bullshitters repackaging LLMs will be left holding the bag, or rather their investors will be. In a way, the coming AI bubble burst is going to be a self-fulfilling prophecy--AI will make its creators redundant. > But we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world. That delusion says that we’re all going to get rich quick, because AGI is coming tomorrow, and we all need to stockpile the only valuable resource, which is GPUs. No, the delusion is that Gen AI is good for anything. It is not. Soon, the $600B question is going to be, "where's the money gone?"
timestap 2y ago
Are there any case studies of companies/cloud vendors using non-Nvidia GPUs for inference at scale? Assuming startups like Etched (with its recent massive funding) could shrink CapEx quite a bit (and make it not such a large revenue shortfall)
arisAlexis 2y ago
The article doesn't say anything. First is says AI is meh for profit and it ends with AI is the most important tech ever and will generate tons of money. I think if it wasn't written in the sequoia blog it would have gotten very little attention.
- demaga 2y ago
  It's all relative. The author does appreciate AI and its potential value. He simply argues that this value won't be as high as $600B.
ksec 2y ago
This article and most of these thesis assume one thing, you need to make a return of investment from AI while using OpenAI revenue as an anchor to measure it. What if these AI investment were only to protect or strengthen their current business? AI in Windows, macOS, Adobe, iPhone, Facebook, Instagram may not bring in any additional revenue. But it add additional value to their current product line, making competition harder, further hardening their moat. Nvidia or Jensen is also smart to play the national security card. Does the European want their model to be all US based. Are the answer culturally correct? Just like how every single country invested in their own Telecom or Internet infrastructure, if this pitch were even half as successful, do these numbers we are looking at even matter when it is spread out across G7 or G20? While I believe we are still far, or at least 10+ years away from AGI, the current form of AI still has a lot of improvement incoming and are already bringing in real world benefits and value to a lot users. The adoption curve will accelerate once it is integrated into Windows, Office and Mac. So even if we are in a bubble, I still think we are very early in the curve before it burst.
barbequeer 2y ago
> AI's $600B Question What do you get if you multiply six by seven
padiyar83 2y ago
I think we are applying the wrong model here. We should not try to force-fit an app store model. A more applicable model is that of a decentralized internet and intranet. As in the case of intra-net, even though there were no visible benefits to the end-user, it did help the business itself improve margins by making information broadly available. Then just like the internet I would see these AI optimisations to work with one another Ex: It's totally possible that a model from Amazon and DHL talk to each other to optimize package delivery given a constraint like a truck parked on a loading dock and unable to move. #1 Just like the intranet there are a lot of productivity gains that firms like Tesla, Meta, Google and Amazon can gain internally by optimizing their own workflows. That in itself should justify the investment. Granted some of these optimisations will use their own chips instead of Nvidia, but Nvidia will get a lion-share of this. #2. Then there are other verticals - pharma, oil and gas, logistics who can optimize their internal workflows to gain productivity. It just helps them improve their margins. No end user benefit may be realized and that’s fine. #3. Nation states are buying GPUs too. Ex: Falcon2 was trained on a cluster owned by a middle eastern country. Nation states see something larger at stake here than just releasing an app. This does not have to even be a profitable endeavour.

397 points by fh973 2y ago

> I expect this will lead to a final surge in demand for NVDA chips But other than FOMO why would someone buy better chips when they don't actually know what to do with their old ones?

alecco 2y ago
H100s are in very high demand and usually running work 24/7. So much that the energy cost is a big factor. The B100 halves the energy costs, among many other things.
- technotony 2y ago
  If they are in such high demand, then are they just being priced below cost? why else is the capex so much higher than the revenues? These things must depreciate over only 3-5 years (or less?)
  - alecco 2y ago
    No. Some people say H100s are priced at 1000% of cost and Nvidia could charge even more but then they would really push their large clients (Microsoft, Meta, Amazon) to build competing hardware. And even at the current price they are trying already.
- deleted 2y ago
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jfghi 2y ago

Reminds me of the gnomes from South Park.

deleted 2y ago
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threeseed 2y ago

> Founders and company builders will continue to build in AI—and they will be more likely to succeed, because they will benefit both from lower costs and from learnings accrued during this period of experimentation Highly debatable. When we look back during the internet and mobile waves it is overwhelmingly the companies that came in after the hype cycle had died that have been enduring.

malfist 2y ago
Let's see: Microsoft Windows: wasn't close to the first OS Microsoft Office: wasn't close to the first office editing suite Google: Wasn't close to the first search engine Facebook: Wasn't close to the first social media website Apple: ~~First "smart phone"~~ but not the first personal computer. Comments reminded me that it wasn't the first smartphone Netflix: Wasn't close to the first video rental service. Amazon: Wasn't close to the first web store None of the big five were first in their dominate categories. They were first to offer some gimmick (i.e., google was fast, netflix was by mail, no late fees), but not first categorically. Though they certainly did benefit from learnings of those that came before them.
- AlexandrB 2y ago
  > Apple: First "smart phone" but not the first personal computer Was it the first smartphone? I would call phones like the Palm Treo and later BlackBerries smartphones. There were even apps, but everything was lot more locked down and a lot more expensive.
  - seanmcdirmid 2y ago
    First modern smartphone (capacitive touch screen/multi-touch/form factor), but not first smartphone.
  - dvt 2y ago
    > There were even apps, but everything was lot more locked down and a lot more expensive. And just plain... bad. The entire experience didn't have that "feel" that Apple turned into reality. It's comparable to today's AI landscape—the technology is pretty neat, but using it is a complete slog.

neaanopri 2y ago

This seems about as bearish as a VC is allowed to be. My takeaway is: Sell! Sell! Sell now before it's too late!

throwaway42668 2y ago

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beejiu 2y ago

It interests me that the $200-600 billion number seems to be all-derived from GPUs. Are LLMs/AIs totally dependent on GPUs? I read last week (https://news.ycombinator.com/item?id=40787349 https://news.ycombinator.com/item?id=40787349) that there is research ongoing to run LLMs on FPGAs at greater energy efficiency. I'm reminded of Bitcoin/crpyto, which in its early history was all operated on GPUs. And, then, almost overnight, the whole thing was run on ASICs. Is there an intrinsic reason something similar couldn't happen with LLMs? If so, the idea of a bubble seems even more concerning.

cma 2y ago
Nvidia and AMD bought out the big FPGA makers.
- jamessinghal 2y ago
  AMD bought Xilinx, but Intel recently spun off Altera.
  - cma 2y ago
    Opps, yeah was thinking of Intel's Altera buy, not Nvidia, which I guess is now undone.
wmf 2y ago
These numbers are just the hole from GPUs that have already been bought/ordered. Today's GPUs will inevitably be replaced by something, whether it be better GPUs, NPUs/TPUs, ASICs, or FPGAs. As chips get cheaper in the future the hole will grow at a slower rate but it only grows.
sason 2y ago
There is a fairly new ASIC named "Sohu" that is purpose-built for transformers. They have some bold claims that are impressive if true. I found a short discussion[2] you may find useful. [1]: https://www.etched.com/ https://www.etched.com/ [2]: https://www.lesswrong.com/posts/qhpB9NjcCHjdNDsMG/new-fast-transformer-inference-asic-sohu-by-etched https://www.lesswrong.com/posts/qhpB9NjcCHjdNDsMG/new-fast-t...

ryandrake 2y ago

Others are saying this article is bearish, but then... > A huge amount of economic value is going to be created by AI. Company builders focused on delivering value to end users will be rewarded handsomely. Such strong speculative predictions about the future, with no evidence. How can anyone be so certain about this? Do they have some kind of crystal ball? Later in the article they even admit that this is another one of tech's all-too-familiar "Speculative frenzies." The whole AI thing just continues to baffle me. It's like everyone is in the same trance and simply assuming and chanting over and over that This Will Change Everything, just like previous technology hype cycles were surely going to Change Everything. I mean, we're seeing huge companies' entire product strategies changing overnight because We Must All Believe. How can anyone speak definitively about what AI will do at this stage of the cycle?

glitchc 2y ago
Whenever I hear the AI hype cycle, I'm always reminded of expert systems and how they were going to revolutionize the world.
- awahab92 2y ago
  there were probably people who doubted electricity, vaccines and indoor plumbing.
  - ryandrake 2y ago
    There were also people who doubted the Segway, Magic Leap, Theranos, 3D TVs, Windows Phone, and Google Glass. I think doubt is OK, at least it is before any particular technology or product has actually proven itself.
    - fragmede 2y ago
      the gap between participants in this conversation is that some have had it proven itself for themselves and others around them, and others have not seen that same proof.

dvt 2y ago

> In reality, the road ahead is going to be a long one. It will have ups and downs. But almost certainly it will be worthwhile. The biggest takeaway from this piece is the stark realism of this article (maybe a bit too bearish, imo) compared to the usual Sequoia VC-speak. Maybe FTX did teach them something, after all.

mike_d 2y ago
VCs in general hate core AI because it is dominated by large players that don't need VC money to be successful. If someone was taking Sequoia money to buy GPUs they'd be singing a completely different tune.
smeeth 2y ago
I don’t think FTX is the right contrast here. This document is mostly about business realism: revenue, margins, etc. VCs did get burned by speculative investing in the 2019-21 period but FTX wasn’t like the others. At the time of its collapse, FTX was profitable and had $1+ bn in revenue, its doom had nothing to do with product market fit, revenue, margins, etc. Quibi might be a more relevant example of a learning opportunity.
- dvt 2y ago
  > I don’t think FTX is the right contrast here. Comparing this article to the (now deleted) SBF profile is night and day.
  - smeeth 2y ago
    I know, my point is that I fail to see how the FTX collapse would lead them to a tone change. > Maybe FTX did teach them something The new tone is all about business fundamentals and the FTX collapse had literally nothing to do with business fundamentals. If this is the lesson they learned they learned the wrong lesson.

openrisk 2y ago

> Those who remain level-headed through this moment have the chance to build extremely important companies. But in which sector will these extemely important companies be active? Adtech? Knowledge management / productivity tools? Some completely new category? What is an undeniable fact is the drastic commodization of the hardware / software stack for certain classes of algorithms. How is this technological development going to be absorbed and internalized by the economy feels still rather uncertain.

sumedh 2y ago
> But in which sector will these extemely important companies be active? I dont think anyone knows the answer to that question.

lqprag 2y ago

As always, free analysis by VCs and investment banks is not necessarily for the reader's benefit. So we have this bearish piece and the previous bearish Goldman Sachs piece. While I agree with their analysis in this case, there is a lingering doubt that some banks might just want to tank Nvidia a little in order to go long. Or something like that.

Animats 2y ago

"Unlike the CPU cloud, which became an oligopoly, new entrants building dedicated AI clouds continue to flood the market. Without a monopoly or oligopoly, high fixed cost + low marginal cost businesses almost always see prices competed down to marginal cost (e.g., airlines)." So why aren't there more entrants in the CPU cloud area? The technology is a commodity. Google and Amazon don't make CPUs.

wmf 2y ago
The secret sauce in the cloud is not hardware but control plane, networking, and storage software.
- falcor84 2y ago
  And battle-tested SREs
  - ukuina 2y ago
    Battle-scarred*
- mcast 2y ago
  And IP space.
  - wmf 2y ago
    The clouds are buying IPs and there's nothing stopping you from doing the same.
mike_d 2y ago
> So why aren't there more entrants in the CPU cloud area? Because the market is saturated with players. AWS, Azure, GCP, Alibaba, IBM Cloud, Digital Ocean, Tencent, Oracle Cloud, Huawei Cloud, that Dell/VMware thing, Linode/Akamai, HP, Scaleway, Vultr, GoDaddy, OVH, Hetzner, etc.
- 2y ago

mike_d 2y ago

The calculations here seem to depend on a few false assumptions: 1. That all datacenter GPUs being purchased are feeding AI. You might be able to argue that some are or a lot are, but you don't know how many just looking at Nvidia sales numbers. I know of at least two projects deploying rows of cabinets in datacenters full of GPUs for non-AI workloads. 2. The assumption that pay-for-an-API is the only AI business model. What we now call "AI" has been driving Google's search and ad businesses for nearly a decade, sooo AI is already doing $300B/yr in revenue? There is no way for this guy to quantify how AI is solving problems that aren't SaaS. David Chan, if you are reading this feel free to email me if you want a fact check for what will surely be the third installment in the series.

jerrygenser 2y ago
Yes but Google hasn't needed gpus to power search. It's used cpu even for ML inference and training before deep learning.

LarsDu88 2y ago

According to Jensen it takes about 8000 H100s running for 90 days to train a 1.8 Trillion param MoE GPT-4 scale model. Meta has about 350,000 of these GPUs and a whole bunch of A100s. This means the ability to train 50 GPT-4 scale models every 90 days or 200 such models per year. This level of overkill suggests to me that the core models will be commoditized to oblivion, making the actual profit margins from AI-centric companies close to 0, especially if Microsoft and Meta keep giving away these models for free. This is actually terrible for investors, but amazing for builders (ironically). The real value methinks is actually over the control of proprietary data used for training which is the single most important factor for model output quality. And this is actually as much an issue for copyright lawyers rather than software engineers once the big regulatory hammers start dropping to protect American workers.

VirusNewbie 2y ago
Llama isn't even in the same stratosphere as the big models when it comes to coding, logic, and other interesting tasks that I think are commercially viable.
laweijfmvo 2y ago
A lot of those GPUs are for their 3B users to run inferencing, no?
- LarsDu88 2y ago
  True that, but I think in a very short amount of time, using dedicated general purpose GPUs just for inferencing is going to be mega overkill. If there's dedicated inferencing silicon (like say the thing created by Groq), all those GPUs will be power sucking liabilities, and then the REAL singularity superintelligence level training can begin.
  - campers 2y ago
    Etched is another dedicated inference hardware company that recently announced their product. It only works for transformer based models, but is ~20x faster than a H100

Kon-Peki 2y ago

Many of the winners haven't even been formed yet. And most of the losers have already lost, they just don't know it yet.

shitpostbot 2y ago

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Timber-6539 2y ago

The problem with AI is that it's very much a gimmick and consumers don't have much use for gimmicks. Certainly not when it works half the time and hallucinating the other half. All it takes is an NBER announcement to pop this mania.

matchagaucho 2y ago

There's a growing divide between inference GPUs and training GPUs. Investors first need to ask what that ratio might look like in 10 years. 10-to-1? 100-to-1? Inference-to-Training Assuming for each NVDA training GPU sold there are 100 open source / commodity GPUs doing inference, who owns and supplies those data centers and hardware?

dzink 2y ago

AI is dangerous to VCs. The returns are going to chip-makers and employers, including single founder startups, who don't have to hire a lot of people, and additionally get productivity they never thought possible. A surgeon I know uses AI every day - to translate, to explain, to figure out problems, to write. They wouldn't have paid someone to do that, but now they get that output in seconds. This is a time to solve all kinds of problems we didn't think possible - because AI has made the enterprising among us instantly smarter. All the fodder about AGI being a next step is smoke and mirrors - for everyone using OpenAI knows they don't need any more niche tools as their one $20 subscription is doing more for them every day. AGI is here. Experts can correct AI generated mistakes, but those are getting less and less too. The real benchmark is: Name how many people you know who can out-do ChatGPT on a question. You won't bother to check LinkedIn for that. The gains are aggregating towards Chips, Clouds and Entrepreneurs. The VCs, since A16Z's original AI blog post (all expense, little return, echoed this Sequoia post but did it but years ago), know they are not needed as much anymore. Fewer VCs will beat the market when founders can grow startups without raising too much money (they don't need to hire as many people). Hiring needs lead to PR waves which require VC funding. Valuation is not a big deal for founders making money either, so they may not even disclose how successful their companies are. Bragging about your gains only invites competition. So other than ponzi-type ventures where you need to attract the dinner to serve dinner, you won't hear much about the good ones. A different era indeed. The tech giants are in for a lot of change as well. Those who have distribution may try to push their models to the masses to be the point of reference, but that can get expensive, especially for those who don't charge. AI will help improve AI performance as well and that means cheaper better performance with time. What's most needed in this era are people who know what the world needs that hasn't been invented yet. They need to be inventing and monetizing it. Little stopping you now.

nojvek 2y ago
Well written argument. The holy grail of AI is still in future. Where it can interact with software tools like we do. Competent AI Agents will be a huge productivity unlock.
threeseed 2y ago
This is the sort of grifter/influencer style comment that we saw in crypto and now pervasive in AI. Not just that AI is a great tool that will over time have a significant impact on society. It’s the breathless hype e.g. we have AGI today, society will be instantly smarter because of it and every tool and employee will be impacted. The FOMO i.e. you must jump on board now or be left behind. And the complete lack of any data or evidence.

Ekaros 2y ago

Has anyone asked will AI actually very soon be commodity tech? With too many players competing with each other driving prices to bottom? Is there actually meaningful value to extract, after you have spend money on hardware and power? Modern web is full of examples of platforms that really don't make money...

pedalpete 2y ago

I think this is the correct take. My understanding of the article is that huge investments in hardware, mostly to NVIDIA, and spending by major tech companies is currently defining the market, even if we include OpenAI, Anthropic, etc. It is FAANG money they are running on. I put this as equivalent to investing in Sun Microsystems and Netscape in the late 90s. We knew the internet was going to change the world, and we were right, but we were completely wrong as to how, and where the money would flow.

npalli 2y ago
The better analogy is the massive investment in fiber optic cable in the late 90s. All the companies in that line (Global Crossing, Worldcom etc.) went bust after investing 10s of billions but the capacity was useful (with a >90% drop in price) for future internet services. Around 2000 when the bubble was bursting only 5% of capacity was being used but proved to be useful to get all internet-first companies like the Googles, Amazon, NetFlix's going.
- cruffle_duffle 2y ago
  Good old JDS Uniphase was one of the first individual stocks I bought. I mean it had to go up right? Fiber and dark fiber and the continual threat of the internet collapsing due to load… better buy that stock!
  - elphinstone 2y ago
    Worldcom here. Ah, the Enrons of the internet.
- pedalpete 2y ago
  I initially was agreeing with you, but I don't see NVIDIA, AWS, Microsoft, etc going to zero (and Worldcom was unraveled by accounting fraud). Sun Microsystems sold to Oracle for $7B, and Netscape was acquired by AOL for $10B.
  - 2y ago

nabla9 2y ago

38 OECD countries have combined population 1.38 billion, GDP $49.6 trillion $600 billion is $434 per person, $36 per month per person, 1.2% percent of GDP. If 75% of the spending goes to increasing productivity, I could see it. Get rid of $450 billion in labor costs (12 - 15 million work years). Cut worked hours in call centers, customer service, many services, menial programming jobs, ... But I don't see it happening fast enough to pay for current investments.

light_hue_1 2y ago

This is not an AI or ML problem. This is an Nvidia, Google, Microsoft, Meta, Tesla, etc. problem. Most AI startups aren't building massive data centers so they're unaffected. Most money isn't spent on compute in most startups. Only a few companies spend big. It's obviously a terrible idea to invest massive amounts into compute when Nvidia's profit margins are so astronomical if you need ROI in the long term. The massive corporations won't get their money back for these investments; but they don't have to.

sadhorse 2y ago

I recall Marvin Minsky saying that 1 GigaFlops would suffice for doing most of what humans do. Current AI is addiction to more data, more compute but no smarts. At least they gave us cheap compute, soon the right people will figure out how to produce real AI, not this travesty.

sheeshkebab 2y ago

The only thing AI needs to do to demonstrate its value is replacing jobs at scale. Until it happens, it’s all capex and opex bs and hobby projects.

dbish 2y ago
Yes, and I think we're on the way to that happening in chunks of the economy, but it's not going to be overnight, there are still a number of improvements to make along the way. I am willing to bet my time working on it that we will be replacing many jobs in the next few years.
- sheeshkebab 2y ago
  Maybe Musk will show to all how it’s done starting with robotaxis, however looking at current state I wouldn’t bet on it, at least not anytime in near future. Buying more gpus will definitely not be the thing that solves this.

the__alchemist 2y ago

About a week ago, [Angela Collier posted a video on this topic](https://www.youtube.com/watch?v=dKmAg4S2KeE https://www.youtube.com/watch?v=dKmAg4S2KeE) highlighting the "malicious optimism" of AI hype. She used comments by the Zoom CEO as a basis, but it generalizes.

andsoitis 2y ago

"Consider how much value you get from Netflix for $15.49/month or Spotify for $11.99. Long term, AI companies will need to deliver significant value for consumers to continue opening their wallets."

julianozen 2y ago

ELI5, why are GPUs not a commodity piece of hardware that any sophisticated chip manufacturer can't also produce? This is one of the first time in the tech industry where the value was fully reaped by the hardware itself and not by the differentiated software that ran on top of it

alberth 2y ago
Intel for a few decades didn’t do to shabby.
- julianozen 2y ago
  Yes, they did have great run, but I think over the long run the OS they were tied to was more important
ineedaj0b 2y ago
they are very small, very advanced, and production yields are poor. The equipment is also stupidly expensive and takes reams of smart people just to run. Silicon is currently the most advanced tech humans have ever made and those GPUs are on the cutting edge of that
paulmendoza 2y ago
Amazon has a some AI chips they created themselves. Same with Tesla. They are both behind Nvidia and probably won’t catch up BUT raw performance isn’t always the most important part. Often it’s the cost per compute unit and companies are quickly getting close to Nvidia.
- julianozen 2y ago
  Can more less sophisticated chips perform as well as fewer more expensive chips?

_xnmw 2y ago

Maybe because the revenue isn't directly attributable to AI itself, but is realized in the cost savings and productivity improvements in already existing revenue streams? That's where AI has been useful to me. I can't put a number on how much AI has made me exactly, but it has certainly helped all aspects of my bootstrapped startup.

alok-g 2y ago
Any product should bring benefits to both the producer and the consumer. For the case where a company is using their own AI for their own cost reduction and productivity improvements, they can keep doing that but not offer to another party. If they offer to another party, and that party is having benefits (like you have said), the price should be such that a part of the consumer benefit is shared with the producer resulting in benefits for the producer. The real challenge here is because of price wars, i.e., too much competition already with producers willing to take a hit on profitability in anticipation that they will be able to do so later after creating a moat above and beyond competitors. Or they think that it will strenghen their overall bigger offering by adding an otherwise lossy feature. In a nutshell, even if there's a lot of value for the consumers, it must result in a win-win for a new product to be sustainable in the market.
dmix 2y ago
> but it has certainly helped all aspects of my bootstrapped startup. Well if there's value to you then how much did you pay for it and would it realistically cover operating cost once VC cash dries up? That's the only question.

barnabyjones 2y ago

I think there is far too much focus on potential productivity gains, and not nearly enough on entertainment value, which still has a ways to go but much higher potential compared to serious problem solving, which may or may not be be feasible with these models. Nobody has really figured out yet a good way to integrate generative multimedia together in a way that makes a hit, but surely someone will in the next few years. Right now there are utterly half-assed apps like Linky making good money, and that's just midjourney and CAI glued together awkwardly. I don't know what a genuine hit product is going to look like, or if it'll even be an app, but the potential for generative multimedia is so much higher that what we're seeing now.

mark_l_watson 2y ago

Context: I have been paid to work as an AI practitioner since 2982, nothing special about me except I have worked with amazing people. I have lived through at least two “AI winters.” The author of this blog makes a great argument that there is a risk of AI investments not paying off because if a revenue gap: The author argues that the gap between the revenue expectations implied by the AI infrastructure build-out and actual revenue growth in the AI ecosystem has increased from $200B to $600B. This is due to factors such as the subsiding of the GPU supply shortage, growing GPU stockpiles, and the dominance of OpenAI in AI revenue. The author also notes that the $125B hole in AI revenue has now become a $500B hole. However, my experience with previous AI winters is not relevant here because now is the first time in history that there is a possibility of what we used to call “real AI” and now call AGI. No investor wants to miss out completely on the creation of near limitless wealth.

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artisin 2y ago

LLMs are undeniably remarkable, but their wheels start to fall off once you go beyond the basics and a few party tricks. The cynical part of me thinks this tracks pretty well with our society's tendency to favor appearance over substance. Yet, if my burger order gets messed up, I don't fault the restaurant; however, if an LLM messes up my order, the same cannot be said. As a seasoned human, I feel confident saying that we humans love blaming other humans, whether it's justified or not. So while I initially thought customer-facing roles would be front and center in the "AI revolution." Today, I tend to think they'll be bringing up the rear, with entertainment/smut applications at the forefront along with a few unexpected applications where LLMs operate behind the scenes.

deleted 2y ago
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bamboozled 2y ago
Even smut seems to be having a pretty slow take off, I had a go of an AI porn generator for fun recently, it was nightmare fuel. I know it's "almost right and it will get better" but not being "there" yet just isn't good enough when it comes to accurately depicting penetration.

joshuakarl 2y ago

Data center GPUs are not only used for LLMs. Meta makes hundreds of billions and invests tens of billions on GPUs, using them directly in its core business, not just for cute chatbots. OpenAI is not the clear winner in revenue generation from "AI" if we define AI as deep learning models trained on GPUs.

surfingdino 2y ago

> Investment incineration Just two words to describe all the idiocy of the current wave of AI offerings. > A huge amount of economic value is going to be created by AI. Company builders focused on delivering value to end users will be rewarded handsomely. We are living through what has the potential to be a generation-defining technology wave. No. It's crap no matter how much money you throw at it. It will end in tears, because there is no standard, no operating system, no protocol, only a handful of APIs controlled by a couple of companies. They are not building networks, but hubs with a single point of failure--the API provider. The internet is such a world-changing force because it is built on top of TCP/IP, which allows the rest of the internet to survive even if a part of it goes down. When an AI API provider shuts down, all those bullshitters repackaging LLMs will be left holding the bag, or rather their investors will be. In a way, the coming AI bubble burst is going to be a self-fulfilling prophecy--AI will make its creators redundant. > But we need to make sure not to believe in the delusion that has now spread from Silicon Valley to the rest of the country, and indeed the world. That delusion says that we’re all going to get rich quick, because AGI is coming tomorrow, and we all need to stockpile the only valuable resource, which is GPUs. No, the delusion is that Gen AI is good for anything. It is not. Soon, the $600B question is going to be, "where's the money gone?"

timestap 2y ago

Are there any case studies of companies/cloud vendors using non-Nvidia GPUs for inference at scale? Assuming startups like Etched (with its recent massive funding) could shrink CapEx quite a bit (and make it not such a large revenue shortfall)

arisAlexis 2y ago

The article doesn't say anything. First is says AI is meh for profit and it ends with AI is the most important tech ever and will generate tons of money. I think if it wasn't written in the sequoia blog it would have gotten very little attention.

demaga 2y ago
It's all relative. The author does appreciate AI and its potential value. He simply argues that this value won't be as high as $600B.

ksec 2y ago

This article and most of these thesis assume one thing, you need to make a return of investment from AI while using OpenAI revenue as an anchor to measure it. What if these AI investment were only to protect or strengthen their current business? AI in Windows, macOS, Adobe, iPhone, Facebook, Instagram may not bring in any additional revenue. But it add additional value to their current product line, making competition harder, further hardening their moat. Nvidia or Jensen is also smart to play the national security card. Does the European want their model to be all US based. Are the answer culturally correct? Just like how every single country invested in their own Telecom or Internet infrastructure, if this pitch were even half as successful, do these numbers we are looking at even matter when it is spread out across G7 or G20? While I believe we are still far, or at least 10+ years away from AGI, the current form of AI still has a lot of improvement incoming and are already bringing in real world benefits and value to a lot users. The adoption curve will accelerate once it is integrated into Windows, Office and Mac. So even if we are in a bubble, I still think we are very early in the curve before it burst.

barbequeer 2y ago

> AI's $600B Question What do you get if you multiply six by seven

padiyar83 2y ago

I think we are applying the wrong model here. We should not try to force-fit an app store model. A more applicable model is that of a decentralized internet and intranet. As in the case of intra-net, even though there were no visible benefits to the end-user, it did help the business itself improve margins by making information broadly available. Then just like the internet I would see these AI optimisations to work with one another Ex: It's totally possible that a model from Amazon and DHL talk to each other to optimize package delivery given a constraint like a truck parked on a loading dock and unable to move. #1 Just like the intranet there are a lot of productivity gains that firms like Tesla, Meta, Google and Amazon can gain internally by optimizing their own workflows. That in itself should justify the investment. Granted some of these optimisations will use their own chips instead of Nvidia, but Nvidia will get a lion-share of this. #2. Then there are other verticals - pharma, oil and gas, logistics who can optimize their internal workflows to gain productivity. It just helps them improve their margins. No end user benefit may be realized and that’s fine. #3. Nation states are buying GPUs too. Ex: Falcon2 was trained on a cluster owned by a middle eastern country. Nation states see something larger at stake here than just releasing an app. This does not have to even be a profitable endeavour.