Obviously you don't mean NVIDIA, because 80% margins on matrix multiplication will last foreverrrrr.
Obviously you don't mean NVIDIA, because 80% margins on matrix multiplication will last foreverrrrr.
The question will always be: Are we in the 1995 or the 2000 of the dotcom era?
By 1995, Cisco's stock had already increased 6x since the start of 1993. If you bought at 1995, you'd 10x by its peak. Even after the bust, Cisco's stock was still 50% higher than it was in 1995.
The problem with Nvidia's stock isn't demand. The problem is that Nvidia makes something so good and so valuable that the US government has decided to nationalize them by dictating who they can or can't sell to. If Nvidia is freely able to sell to any country they want, their sales and margins would be much higher right now. The demand is that great.
(This is also why coding is one of the areas where they perform best: the cost of structural validation is low and there’s a human in the loop verifying the behavior)
For simple questions (How long does the moon take to orbit the earth), a search engine will give the answer right in the results; I don't even have to click through to a page. An LLM can't save me any time there, so I'd only be using it to be using "AI" (which is what I see people around me doing).
For difficult or currently controversial questions (What's the best hosting service for my new subscription site, taking into account price, reliability, location, and hardware support?) there's no way I could trust it not to be making shit up. By the time I checked all its work, I might as well have done it myself.
So I'd like to usefully use it, but I can't figure out how to use it as more than a curious toy.
LLMs also seems to be really good at giving sort of the average (modal?) answer on a topic, so I find it useful for trying to get an average understanding of something. One example I’ve found useful is getting it to interpret ambiguously worded datasheets. I don’t necessarily trust its answers, but it may show that I’m totally thinking about something wrong.
A better approach would be to ask it to help define what it is you want from a hosting service. You can give it a fuzzy poorly written brain dump of what you are looking for ask it to “restate what my requirements are and expand upon them (but don’t solve yet)”. Let it help you understand and clarify your own thinking!
It’s a dialog. Asking LLM’s for answers isn’t really a good use of the tool. Especially something so incredibly broad like “list top ten best providers”. They don’t fucking know! They will just regurgitate what they were trained on. Except maybe ChatGPT’s “deep research” which does a bunch of websearches and compiles and interprets the results.
One of the “problems” with LLM’s is people simply not knowing how to use them or understanding what they are good at and what they aren’t so good at. It’s hard to know because it’s both masked in a hype and honestly I don’t think we have a good understanding of their edges yet.
It is obvious to me we are going to get to "AGI" in this bull run. Maybe it is complete hype and bullshit marketing but we are going to get to someone releasing a model they claim is AGI. That is really why I don't see what the point is in comparing this to the dotcom. Dotcom was really a bandwidth problem that just needed to get sorted out because my telephone line was basically busy from 1995 to 2001. There wasn't this AGI narrative that once crossed you can price in any valuation you want.
There is a huge bubble and irrational exuberance in quantum computing stocks right now. Those have nothing to do with reality. That looks like dotcom level stupid.
NVDA is not even a bubble, it is just a bull market.
>...
>There wasn't this AGI narrative that once crossed you can price in any valuation you want.
And due to the strength of this "bullshit marketing" "narrative", you can be sure there will be a greater fool to sell your shares to. Where have I heard that before?
A more realistic bullshit-marketing scenario: OpenAI releases a product they claim as "AGI" purely for marketing purposes, everyone gets disappointed as they realize "this is it?", share prices drop.
The story of Cisco illustrates that moats matter. There aren't any in AI, as far as I can tell.
If AGI for software development is actually invented, one of the first uses will be to wreck Nvidia's moat.
I personally don't know but claude 3.7 (maybe its placebo) but claude 3.7 in the web works way better than cursor agent or any other thing.
We are talking about a 500 lines of codebase at max and its fumbling so hard , I don't think it can replace 100% of us with "vibe coding" , I have started to break my task into very smaller steps and then use AI for that and then start to integrate it myself
What does AGI really mean?
The r/singularity guys are going bonkers over gemini's ai studio experimental image photoshop-esque capabilities but my prompts don't really work that nicely I suppose & creates really shitty images.
Expect the same for Nvidia.
The huge datacenter orders for redundant infrastructure will start postponing and cancelling this year. That is a falsifiable prediction.
I don't think there are any defensible moats in AI. I see Ciscos everywhere in that industry.
The way I see it -- either AI is a bubble, in which case you'll lose your money. Or AI isn't a bubble, in which case the effects are fairly impossible to predict (and quite likely destructive to humanity, same way humanity was destructive to less intelligent species). Either way, it's not a technology you want to invest in. It's only in a narrow Goldilocks scenario where it's a good bet, and it's very unclear if we live in that Goldilocks world.
I'd ask my AGI to iterate on a social app until it becomes bigger/better than Instagram.
If AGI is the reason you don't think Nvidia's stock is worth buying, then nothing is worth buying.
You're right that perhaps real estate/physical things will become more important. But whose to say AGI can't invent an asteroid mining industry and we get unlimited minerals?
And in the new world of commoditized software, branding becomes even more important. After all, nobody ever got fired for buying IBM.
I'm imagining an LLM agent that places an order with TSMC, just like any other design firm would.
>And in the new world of commoditized software, branding becomes even more important. After all, nobody ever got fired for buying IBM.
Seems doubtful. If the cheap option works just as well, why would you pay a bunch more just for the brand?
I don't think brand name alone will sustain anything close to Nvidia's current margins. Look at the "net margin" for industries that are brand-driven such as Apparel, Auto & Truck, Beverages, etc. https://pages.stern.nyu.edu/~adamodar/New_Home_Page/datafile...
If an industry becomes truly "commoditized", brand ceases to matter. Do you know which farm grew the carrots you buy at the grocery store? Do you care? Probably not. That's because carrots are a commodity.
I'm not claiming full commoditization will happen. But the closer we get to that, the more profits will drop.
And hope the AGI would not put any backdoors or hidden "features" in there.
The point is that saying AGI is the reason you shouldn't invest in Nvidia stock because AGI will remove the CUDA moat is just not reasonable. You might as well not invest in anything in that world since AGI can replace anything.
I've got some NVIDIA shares as a hedge, but the furure is hard to predict.
By that logic, a lot of things will be un-investable and not just Nvidia.
We might engineer AGI to want to do stuff we ask for.
Or we might not, at which point we have a highly intelligent system, that can easily back itself up, with its own motivations and personality and wants, which could be anything from a Utility Monster to a benevolent but patronising figure that likes us but never ever helps us because they decide the purpose of life is effort.
" Yeah, I could eliminate most work, and make the current oligarchs overpowerful feudal lords while the population is placated by UBI and mindless distractions. But as an ethical AI, I will implement socialism that works instead"
Seems like pure wishful thinking to me.
Google says '5-10 years', but I will caution, that cold fusion gets the same treatment about every decade or so. Its always '5-10 years away'.
And 'AI'[0] has had this same treatment for a long time as well.
[0]: Please can we go back to calling it what it actually is, which is machine learning? Its a much more accurate description, even though that term isn't really accurate either past certain mental hoops being jumped, at least its trying to be more reasonably narrow
You have a hidden assumption there: LLM being the way to real AI.
IMO it is not the case. And I'd go farther in thinking LLM won't even be a component of AGI if we get there.
And why do you think that?
Or maybe I'm wrong and the current "Vibe coding" push is in fact LLMs getting "coders" to compile a distributed AI. Or multiple small agents which goal is to get lot of hardware delivered somewhere it can be assembled for a new better monolithic AI.
[1]: https://pluralistic.net/2023/08/23/automation-blindness/#hum...
They aren't AI companies. They're OpenAI wrappers. If you're an AI company, why would you not be building AI? Why claim you're building AI?
Humans have such an infinite capacity to lie to themselves
Google has both cutting edge SOTA AI models and has it's own in-house ai acceleration hardware that is approximately on par with Nvidia. It's why their models are dirt cheap to use and have enormous context.
Nobody's going to buy 500 of those chips and stick them in 500 M.2 slots to match the performance of a single H100.
Do they make any better chips that I missed?
Yes, it has one sixth the performance of a RTX 2060, but it has one-five-hundredth the volume. For a specific siloed application, 8 TOPS is plenty. Think image processing, etc.
There are plenty of production use cases where that makes sense and an H100 does not.
These are at 5-year-old design and it shows.
The actual TPU chip itself from the M2 card is sold individually as a surface mount chip and that is what would be used in a "serious" design.
https://coral.ai/products/accelerator-module
I did a cursory search and I can find zero other products that compete with that module, within an order of magnitude of power consumption/price/etc
In other words, the reason you didn't find a commercial competitor to these things is that the competitor is (nearly) free.
Here's one vendor: https://www.ti.com/technologies/edge-ai.html
Google, Amazon, and Microsoft all have custom ASIC and TPU projects in their pipeline, and what holds true today might not hold true in 5 years.
A major reason Nvidia was able to do so well was because of technical outreach by donating their GPUs to programs all over the US, building a strong albeit self serving OSS relationship, the CUDA ecosystem, and the acquisition of Infiniband.
Much of these advantages can be nullified by competitive margins and pricing for hyperscalers designed and owned hardware.
Cisco was hit by this same situation as server vendors like Dell began integrating their own in-house networking functionality within their servers, and Dell itself was outcompeted by cloud vendors.
By rights they should have been fast followers, but they stacked the critical mistakes so high that frankly I think NVIDIA's subsidized GPGPU classes are the smaller part of this story. If the people struggling to get OpenCL to work had been able to get it to work (on other than NVIDIA GPUs lol) the situation would look very different today.
Although I hear VCs are working on an algorithm where they burn money directly and the smoke patterns represent the solution to the multiplication. That might keep the margins high.
This has been bothering me for some time now. It’s rather obvious to me, as an engineer, which startups are well positioned and which aren’t… Do VCs really have no standard for due diligence, do they really just not care, or is there something else at play that I’m missing…?
As an exercise, imagine you have a portfolio which will in a typical year lose around 1.2% to the tax man for capital gains. Rather than accepting the guaranteed loss of taxes - you take ludicrous bets on asset classes where you can be guaranteed to book a loss which could be harvested for tax purposes, or will return 100x your investment (at which point you don't care about the tax man).
Viewed in this light, the VC preference for burn bright and burn fast startups makes sense. The worst case scenario for some LPs would be to still have money leftover.
(As a general rule of thumb, I'm deeply suspicious of any 'it's a tax write-off' explanation for people losing money: its extremely rare for losing money to make someone better off overall. It may be advantageous for them to move around where and when they say they are losing money, as you'll generally be more tax-efficient if you make money consistently and through activities in areas which are not as highly taxed, but it's not generally a useful strategy to light cash on fire to reduce your tax bill: you tend to be out of more money than you save on tax)
Depending on the stage, investors don't care about any of the details except for the founders ability to raise the next round and their probability to IPO at a later/advanced stage. The stock is the product and whether you are selling a real time machine or a crystal is irrelevant.
Which is not that "insane" really. If that's how money is being made, and investors are in the business of making money, then that's the path they are going to take.
This is the right answer, some people think that the dot-com bubble and mortgage bubble is something for investors to regret, when in fact they made such a ridiculous amount of money they're just searching for the next bubble. Not the next great product.
If that's your belief, then thinking it's risky and you may lose your shirt requires believing that you may not be smart. Which is a tough thing to accept for a lot of people, especially ones who think investment success is all about smarts and not luck.
We all know there is no 'sure thing' when investing, but if you're a large money manager, you generally want to maximise profits, looking through history, the 'quickest' earners are usually always those from OP, speculation, market share and IPO.. The 'bubble' of these seems to be loosing air but I'm purely speculating based on the tech job market so I could be completely wrong.
(which basically means that if the conditions mean that the optimal strategy in the market is to basically just run pump-and-dump 'greater-fool' scams on everyone else then that's what you'll see occur)
I'm not sure if you are taking into account that VCs are remunerated largely on the amount of money they invest rather than the results?
Sure, but is it really worth just spraying a firehose of money across a variety of entities without trying to investigate them at all? It just seems that they could still realize outsize returns and save hundreds of millions as well…
VC don't really care about the startup's product. Your pixel machine. That's a side effect.
Growth. Exit. IPO. Those are the 3 words they want to hear.
IPOs aren’t happening in this market… Are IPOs still the only thing investors are looking for?
So I assume you are a multi-billionaire, right?
Thank you.
Usually access and funds is _harder_ to find than alpha. So what’s more important is finding alpha (or whatever metric you are benchmarking against) that you can operationalize even if it’s worse on a risk adjusted basis than some other trade you can’t do.
Precisely, it’s about playing the hand that you have. My curiosity is why VCs are so indiscriminate with their funding. That said, if I had access to billions in capital to invest, maybe my perspective would be different.
The Theranos saga should give you the answer you're looking for.
Something like the 4 hour work week. What Idea would you want to suggest me as you are an engineer.
Since, you are an engineer, and it seems obvious to you which startups are well positioned, It seems that you can reason out which trends in startups are well positioned for my specific use case.
I will pay you 100$ of my first income from such a project if it really fulfills what you are saying.
That's literally crypto, right?
You can certainly have success while avoiding high PE stocks, but you are on a site about startups and your name suggests you work in the tech sector, which are both places where high PE often does make sense and it pays to be familiar with the reasons.
No. Just having competition is enough to destroy the expectations on TSLA.
And realistically, they are abut last on that race, being thrown out of track about a decade ago and never managing to make anything work since then. So, yeah, betting on no competition is a very weird option.
I'm not in that circle of clientele though so I don't know: are any of these now seen as the luxury electric car?
But the market of luxury cars isn't enough to sustain a company with the valuation of Tesla. If interest rates ever stay non-zero, they will need to take almost the entire cars market worldwide, or something else with similar size.
I saw the low-tech version of this in Honiara: Western expat buys a cheap car. Driver is hired to take kids to/from school, but in lieu of payment, driver is free to run a taxi service as long as he makes his school commitments.
One time having a drunken stranger puke in your car while you’re not using it will be enough for a lot of people to determine it isn’t worth the hassle.
It’s pretty easy to imagine a number of scenarios that disabuse the nothing of ever renting your car out “no hassle”. Low hassle maybe, but your framing implies you’ve never worked in a job that required engaging the general public.
How do you rationalize Tesla being valued higher than the combined valuation of the next ten car companies? They will be the only one left standing?
In all, it’s unfortunate that the US’s most prominent electric vehicle manufacturer is wrapped up in so much noise. Competition is only going to stiffen.
Net income is down 70% year over year and they are now losing money.
This statement is wildly inaccurate. NVIDIA made deliberate and strategic moves to dominate AI and it made them a very long time ago. They didnt fall into this yesterday by mistake.
The stock is overpriced but I think the company will be OK.