OpenAI Deal Lets Employees Sell Shares at $86B Valuation
bloomberg.com
bloomberg.com
Everyone points to money but they are also working on the most exciting technology in recent history. Crippling both that and likely their long term job prospects vs Sam, whom they never got a straight story for why he was being taken out.
People love to over simplify and reduce things while glossing over all the nuance.
This seems very much like a "get out now" signal to everybody involved. Greater fool farming on an accelerated timeline.
Not even Google can compete.
It could also be a signal that the company recognizes that they would not be where they currently are without their workers. Maybe they're giving a reason for their workers to stay (become multi-millionaires overnight) and not go to their competitors.
Of course not to imply that OpenAI is the same as FTX.
It was just a rank and file IC.
At this point Gemini is as good as GPT4 (and anecdotally I think it's better at many coding assistance tasks, based on my experiments on the LMSys chatbot arena). Sora is getting a lot of press for text-to-video, but Google's Lumiere has already been out for a few weeks and produces pretty good results.
I have no doubt that OpenAI has been cooking things up. GPT-4 is an older model which others are only just catching up to now. They have an A+ team, a giant war chest, and a lot of momentum. But just because they have momentum does not mean that they have a moat.
I'd be willing to wager that the top-of-the-line foundational models are going to converge and become indistinguishable for almost all tasks. Even the open foundation models (e.g. Mixtral) are getting really good. Foundation models are not moats.
The players that have moats are Microsoft (with deep enterprise software & B2B expertise, allowing them to sell AI-powered software & ward off competition from upstarts) and Google (where their decade of investment into custom silicon allows them to train & run inference for cheaper than anyone else, by far).
10m context with that retrieval rate is such a monstrous leap. And to top it off, we got LargeWorldModel in the same week, capable of 1M token context with insane retrieval rate in the open source space. So not only is the open source world currently technically ahead of ChatGPT, so is Google. Which is why they had to announce SORA, because google's model is so far ahead of the competition. That's also why it will probably be ages before we get access to SORA. Now don't get me wrong, the average person can't afford 32 TPU's to run LWM, but we already have quants for it, which is a step towards enabling the average person (that somehow has 24-48gb of VRAM to get a taste of that power).
What is also striking is the fact that the new models are all multimodal as a standard. We not only leapfrogged in context size, but also in modalities. The model seems to only benefit from having more modalities to work with.
I think the statement Bill Gates made claiming that "LLM's have reached a plateau" itself indicates they don't believe they can make more money from training better/larger models. Which indicates that they already did as well as they could with their existing people, and are now "years" behind google. I never thought google could catch up, especially after their infamous "We have no moat" situation. But it seems they actually doubled down and did something about it.
To a lot of people, last Thursday was a very nihilistic day for Local Models, as the goalposts shifted from 128-200k context to 10M tokens with near perfect retrieval. It's literally insanely scary. But luckily we got LWM, and that means we have only been 10xed.
Now the local people will work on figuring out how to bridge the gap, before being leapfrogged again. What is really insane is that, we have had LLAMA2 for over a year now, and nobody else figured out how to get this result from it, despite it being around so long.
I still believe there are modifications to the architecture of MoE that will unlock new powers that we haven't even dreamed of yet.
Sorry, this was supposed to be well thought out, but it turned more into stream of consciousness, and I honestly had no intention of disagreeing with you.
If I remember the paper correctly, it was something about a 4M context in there. So not 10x, but 2.5x.
> What is really insane is that, we have had LLAMA2 for over a year now, and nobody else figured out how to get this result from it, despite it being around so long.
This isn't true. For now, the task of extending context to 10M tokens is brute-forced by money (increased HW requirements for training and inference and increased training time are also a financial domain). And for now, there simply is no leapfrogging solution for open source or commercial models, which will decrease the costs by orders of magnitude.
I literally have a 24/7 consultant with surface level understand of any topic in human history. This consultant also happens to be an amazing artist for $25 a month that gives me the rights to commercialize any art piece they make for me.
This is extremely hard to beat. This will be extremely hard to beat.
As a business they are succeeding.
LLMs are useful tools, but if they don't lead to something you can actually somewhat rely on to generate correct/consistent/factual results (see e.g. the recent Air Canada chatbot lawsuit) then the hype is a bust. TBD.
The bar I'm setting is far from "impossible"; even human children generally won't seamlessly confabulate when you ask them a question they don't know the answer to. Again citing the recent Air Canada case, these models can't even reliably answer simple questions that are definitively and objectively answered in documentation that is presumably made as freely available to them as is technically possible under the limitations of current technology.
No well-informed sources are saying that. If you're saying that mainstream reporting and other non-tech folks are wrong about what the possibilities are then... obviously.
Yes they are, actually! I've talked to people who obsessively read practically every LLM paper that passes through the arxiv, with undeniably deep and broad knowledge of the current state of this tech, who seriously believe it's going to surpass humans within a year or two. That it may already have, in the deep dark top secret labs beneath OpenAI HQ.
However,
> If you're saying that mainstream reporting and other non-tech folks are wrong about what the possibilities are then... obviously.
If it was obvious then why are you still replying to my comments, which have very obviously been specifically addressing the mismatch between hype and reality? If the current approach doesn't scale, the hype will have been a bust! Objectively! That's what I've been talking about this whole time!
"Mainstream reporting and other non-tech folks" is a bit disingenuous, though. The primary drivers of the current unrealistic hype are software vendors and associated clingers-on looking to make a quick buck. They'll say anything, regardless of whether it's true, and as a result of those mostly-falsehoods our public lives will be flooded with awful AI tools that make everything shittier and more difficult. I can't wait!
Like, are you following the things that OpenAI and Deepmind are saying at all? The things that make current LLMs not a threat, they aim to tear down as soon as they can arrange.
OpenAI just released a video network, and one of their core touted benefits was that you could use it as an action controller!
And, um. Do you really think, when the AIs can take a simple prompt and turn it into a ten thousand step plan that requires dynamic skill acquisition, resourcing and persistence, that generating the prompt will be the one single task that stumps them? When we are at that point - and to be clear, every leading AI organisation is sprinting to reach that point earliest - then the difference between doom and safety will be one sentence: "When you are done with that, generate a new prompt." This is not how a world with a long expected lifespan looks.
edit: To be clear, I'm still not accusing OpenAI of making up the doom stuff. Even though when I phrase it like that it sounds like they're directly working on things that obviously end the world, which seems contradictory, I don't think they see it like that. To be honest, I can't explain why any doomer works at OpenAI, except in the way that people sometimes move towards explosions and gunfire. I think it's just a bug in the human brain. We want to have the danger in sight.
So I just don't think this captures an important distinction at the limit. If a system can generate a good action plan, turning it into an agent is just plumbing.
Its worth noting that, at least when working in a truly novel and untested industry, whoever is leading the pack would likely be the first to know when they tech hits a dead end.
By no means am I saying that's actually the case, but there is still a real possibility that LLMs and the underlying architecture don't pan out with regards to the company's goal of developing anything resembling an AGI. If there is a core challenge with the architecture that doesn't scale, OpenAI would just run into the wall when everyone else was still MILES back.
If OpenAI features were to freeze at what we have today I would be surprised if the company stayed around without a major pivot.
Again I'm in no on way saying this is actually the case, only a hypothetical since the tech is still very new and we don't know what we don't know.
This isn't really true. GPT4 is incredibly useful right now and being used by lots of business processes without any improvements being needed.
Every incremental improvement (eg Gemini 1.5 huge context window) opens up even more possibilities.
AGI isn't required at all.
The main problem with freezing is the moat disappears. Others are steadily catching up, and on specific benchmarks, even surpassing. Groq.com is insane wrt perf.
(Although we can make inform estimates on OpenAIs cost structure for serving models well enough to guess they are probably breaking even on it, or pretty close to it. Eg listen to the gradient dissent interview with the Together AI founders and it’s clear they are doing the same, and unless you think OpenAI is remarkably worse at serving technology it implies they are in the breaking even ballpark)
And we still don’t need AGI for it to be a great business.
There is, but in one sense, who cares? The technology is already super useful, even if it ends up not being the end all and be all of AGI. That is, there are really only 2 options:
1. LLMs are an important stepping stone on the way to AGI, in which case OpenAI is in a great position as the company with the best LLM.
2. LLMs turn out to be a "local maximum" in the search for AGI. I think that even if that actually is the case, OpenAI is still in a great position with much of the infrastructure, data, expertise, etc. even if they require totally new model approaches.
Also, I wouldn't worry so much about #2 because if we do ever get AGI, our economy would surely collapse, or else it would look completely unrecognizable to our current economic systems. That is, I'm always struck that when people talk about AGI fears they talk about things like Skynet and misinformation, but nearly our entire society is organized around people selling their labor for a price. I don't know what our economy will look like when/if AGI becomes real, but I do know it will change drastically if it means that the vast majority of people won't be able to price their labor more than $0.
We don't actually know this though. Assuming an AGI hasn't yet been developed, we don't know whether LLMs will actually get us there. We know they seem to have more use than previous ML systems, but until we have an AGI we can't say what will get us there.
Further, are we really assuming that developing AGI is either a shared goal or a given regardless of what people would actually want to happen? It sounds like we agree on the fundamental impacts an AGI would have on our current societal structures, do we as a society not get a say in the change? Have we effectively blessed a handful of people working in the private sector to make that decision for everyone? And if so, when do we grapple with the moral questions, like whether an AGI has rights similar to humans, or if unplugging one is murder, etc?
That is, you responded "We don't actually know this though. Assuming an AGI hasn't yet been developed, we don't know whether LLMs will actually get us there." Exactly, we don't know if this is true, in which case if it's false my second bullet point "LLMs turn out to be a 'local maximum' in the search for AGI." is the true statement.
For example, if GPU-based systems are the limiting factor they wouldn't have the edge. If the problem turns out to be in the human skills and background needed to develop an AGI they similarly wouldn't have the advantage.
Wouldn't there have to be a third scenario, where they walked down a path that doesn't pan out at all and requires a fundamental rethink effectively going back to square one?
Of course, a pretty good position doesn't guarantee winning, but GP didn't claim that.
But yeah, there are probably outcomes in which LLMs are a local maximum and ultimately dead end, and OpenAI will have a hard time holding on because the market turns out more competitive. And somebody might beat them to whatever the next important invention is. We'll see.
I mean we all work in an industry where we literally see inferior tech win out all the time, not just in usage but in money made.
The company could always pivot as they learn the limitations of LLMs and potentially find other options, though I would argue that their current valuation is extremely high and based largely on the promise of tech they really haven't developed yet. If roadblocks are hit they could be in a tight spot having to live up to such high expectations.
That said, as much as I'd like to see the business goal of developing an AGI crash and burn, I wouldn't bet against them. I just hope we somehow solve the seemingly unsolvable alignment problem first.
I'd say that's quite a lead.
Maybe this has changed now with new Gemini model, but still that means one year ahead of everyone.
The same was said about Stripe. Imagine buying Stripe shares at a $96BN valuation when it is now down more than 50% from the peak.
I would indeed sell some if I saw that extreme valuation of any private startup.
Secondary markets always jump around because of lower liquidity. I’d wait until after IPO before making any conclusions there.
If you were an early FB employee trying to diversify your wealth base in 2011[1] it turned out to have been a bad decision in retrospect but made sense then.
It's easy to say in retrospect "greater fool" vs "bad decision" but right now it is reasonable to diversify risk and doesn't imply any bad faith.
[1] https://www.theguardian.com/technology/2011/feb/10/facebook-...
Personally, I thought that Facebook had peaked around IPO and would slowly go irrelevant.
a financial advisor would always tell you to unload all shares at each vest since you are holding more shares in the form of future vestings.they would then tell you to diversify it into ETFs in a mean variance optimized portfolio tailored to your risk appetite using standard deviation of historical returns and correlation of underlying basket of goods. this is a solid approach that produces predictable safe returns.
I would rather let it ride since I have insider information at most companies I work for and have a better understanding of future returns on that stock that the average person on the street.
I say, take average advice, receive average returns.
:)
They were the first to market with their LLM chatbot, but zooming out that forced Google's hand in releasing their own, and there aren't any moats here other than "how much copyrighted data you're willing to train on."
Eventually, OpenAI/MS will be the Bing to Google's, well, Google. It's one thing for early adopters to go and seek out ChatGPT, but Google has so many products already, there's no reason to think that anybody would switch to ChatGPT just for a chatbot if they're already using a Google app for something else.
nightmare fuel puppies playing in the snow is what will make the world a better place
Ignore the fear mongering or political/military concerns for a moment. There are very real questions that have to be answered, from logistical questions like how we'll recognize when we develop an intelligence or the moral questions like does an AI have rights similar to humans.
Diamond nanobots ripping us apart to repurpose our atoms isn't a terribly useful conversation. But whether anyone can own an AI, whether turning one off is murder, or whether AIs deserve all the same rights and humans absolutely are. IMO those should have been prerequisites before we even considered questions like how to align an AI or risks of their existence, we shouldn't be going down that road at all if we're setting ourselves up to get caught with our pants down.
(FWIW, I ended up not taking the position, mainly because I would not have enjoyed the specific work they wanted to hire me for.)
I had a badly timed offer of something (I don't really remember might just have been an interview, or contracting work, on the back of some OSS contribution) at Cloudflare pre-IPO, and I do occasionally regret/wonder what-if, because it's not just a random hypothetical, it was an actual opportunity I made a decision against. (Doesn't help that it's still a company on my interested-in list, much like the trap in investing of liking a stock but wishing you'd bought it lower when you first thought of it, and so continuing to not buy as it climbs (or not even investing necessarily but just being a consumer in an inflationary environment).)
The same is true for the real-estate market: When a corner of the market (eg. apartments in the city) outperforms the rest of the market, you might stop doing what's right for you and start speculating (being in the process of looking for a place to live, I can see several people who lost significant amounts after having bought in 06/07 and sole 10 years later - hopefully for them, they bought the right place to live and did not care about the losses).
See what he did at Reddit below.
He even admits to it in the thread.
https://old.reddit.com/r/AskReddit/comments/3cs78i/whats_the...
Liquidity for employees is great. Nobody is forcing them to sell.
What makes him great at his job isn't the typical techie path of becoming great at technical and building a great product. His ability is the ability to influence and gain power through good and bad means.
Today's strategy may be good for employees; tomorrow maybe not. And no, he isn't thinking of what's best for employees. They just happen to be aligned at the moment. He is goal oriented regardless of the means. That's just my ill-informed opinion though.
For all of us stuck in the hamster wheel, purpose is easy - because it's forced upon us. But for those who manage to step outside of the wheel, what now? Taken to extremes you even see things like 90+ year old Warren Buffet deciding to step back in the wheel, continuing to show up to grind his days away at the office.
If you appreciate that lots of behaviors of people are them simply looking for a purpose in life - a lot of things start to make much more sense, running the gamut from political extremism, activism extremism, or people who already have practically infinite wealth just turning life into a game of 'make number go up.'
Cyberpunk dystopia, by those who understand what he’s saying.
Liquidity for startup employees is a good thing, not a conspiracy maneuver.
If OpenAI hits a wall and the LLM/GPT trail runs dry, or if regulation somehow stifles the industry, it would be a very smart move to offer liquidity now while the fortunes are there for the taking.
Most US models are so heavily censored, that even Chinese (and supposedly censored) models like Qwen-VL feel like open-minded.
Most companies aren't tradeable, and most employees don't have equity, so its not like owning-where-you-work is a default position.
Most investment advisors caution against having a significant part of your assets in your employer, because you expose yourself to excessive risk. (If the company tanks you lose you job, and savings, at the same time.)
The question to ask is this; imagine you won a million $, would your first instinct be to invest it with your employer? Probably not. You would get an advisor, make a plan, build a balanced portfolio, diversify risk.
So to me, a signal from employees not selling some of their stake (assuming its significant) is that they're poor at financial planning. Maybe that matters, maybe it doesn't.
I remember one guy I talked to just after 2022 escalation in the Russo Ukrainian war and his point of view was, "this is great, Russia will be defeat in no time and then there will be all the rebuilding economic activity". I don't believe the moron came up with that fantasy by himself, he was told.
A "plan" considers your goals, your age, your income, your responsibilities, and so on.
This makes sense for companies that have like under 50 employees, but far too many founders expect to hire zealots well into the 500+ employee range.
This same shit shows up in interviews too, where candidates are expected to profess just how much they would LOVE to work for that company, or how much they care about the mission. Yadda Yadda.
On the up side startups don't have equity you can trade, so you're unlikely to demonstrate disloyalty this way.
And to be fair, if you go work for a startup you know what you're getting into. (And hopefully you know that the equity they give you has about the same value as a lottery ticket.)
sama plays many games, it’s never about a single company.
why a single company trusts him is a mystery.
Either way your future is secure.
If you want to be angry at the unbalanced concentration of wealth, take a look at Bezos[1].
[0] https://www.unbiased.com/discover/banking/how-much-income-pu...
Sorry to break it to you but a solid 80% of the workforce exists to support the 20% actually creating wealth. Nobody is getting rich driving a truck for Amazon, or building said truck for them. It’s the guy who ordered the truck who’s getting rich.
I would love to unsubscribe from this particular strain of brainrot that has infested midwit discourse, but it seems to be everywhere.
It seems that even though economic growth raises all ships, just some more than others, inequality causes social unrest even if they were in a better position than before (e.g. having access to new innovations and a higher standard of living).
It is pretty universal among the animal kingdom https://m.youtube.com/watch?v=-KSryJXDpZo
Rich people with a lot of money giving said money to smart working class nerds that happened to be hired by OpenAI.
These shares probably end up in pension funds. So in the end it is probably going to be primary school teachers from Minnesota that pays Silicon Valley based software developers.
Unless, however, they continue to create value for the pension funds. For this we will have to wait another 15 years to know.
They hire top talent with regards to AI and AI research. I'd wager a majority of them are less working class and more smart, trained, academic nerds with an unreal amount of PhD's in the mix.
many are probably immigrants as well
edit: according to Wikipedia, some people use "middle class" for white-collar workers
Yes, I know the non-profit owns the for-profit, but it's all clearly a cleverly (?) designed Trojan horse and smoke and mirrors to pump and dump like every other startup.
Depending on market conditions there can be private fundraising through those marketplaces which employees are able to directly sell their existing shares if they want to. This also depends on the company as well to allow this.
Random people won't be able to get shares.
but there are a lot of lenders for cashless exercise
Fees on tenders are much higher than in open-market trades, they’re just baked into the buy side. The handshake deal management does with tenders is underpricing the offering so managers can add fees on top. Where open-market transactions happen alongside tenders, at least on the institutional side, it’s not uncommon to see 20%+ premiums.
It signals a dysfunction. If before, information asymmetry. This, fortunately, corrected quickly. If during or after, company prospects may have changed since the tender was announced, or the company is restricting transfers to cronies. The latter is quite common, and a major profit centre for growth capital VCs.
[0] - There are investment vehicles (exchange funds) that do this for publicly traded stocks. They're a good way of diversifying your portfolio if you're heavily weighted in one stock, but they typically keep that capital locked up for seven years which is not great if you value liquidity.
More info (although I personally didn’t use cart for this): https://carta.com/blog/tender-offer-faq/#
I was an ex-employee already and had exercised my options within the 90 day window of resigning. If I remember correctly, we used Carta for both exercising the options and the tender offer.
For the tender offer, the company set a deadline and a price per share. Current employees were limited to selling a certain % of their options/shares. The website enforced the same restriction for me, which seemed like a bug, but I didn’t plan on selling most of my shares anyway. I chose a number of shares in a web form, clicked a button, and got cash direct-deposited. I had a larger than usual tax bill but I paid it while handling my usual annual tax return. No penalty on the taxes due to the “safe harbor” rule.
Azure, AWS, Google are easy candidates, Nvidia might even be proof.
I haven't tried Gemini 1.5 yet, but every single model I have tried haven't come close to GPT-4. I don't care what benchmarks say - they really don't come close.
If Gemini 1.5 is competitive, that was a full year to catch up. I'm sure OpenAI has other things cooking.
Again, you might not like Google but I mean, they have a pretty kick as AI team and all the money and hardware in the world to catchup for lord's sake.
People talk and there is zero chance anything OpenAI does will be kept secret forever.
Which I think makes my point that it took a year to catch up a damn convincing argument that OpenAI has something special going on.
And what are you talking about nothing magic about what OpenAI is doing? LLMs are about as damn near close to magic as we have. It's incredible it works at all. They are so damn cool.
And Sora is crazy impressive too.
3 years ago, I wouldn't have guessed we would be anywhere near where we already are, and I was in the thick of it, doing ML research and engineering at that time.
Eventually sure, but things are moving so fast!
Once you've generated your 50th short movie with Sora 5 you'll be sick of that too.
If they are tools that provide value, it's not about entertainment. If I can learn better or do things better with their aid, I'll keep using them.
I'm much more interested in generating a 3blue1brown quality educational video than season two of firefly.
NVDA has market cap of 1.8T and all they're doing is the middle part of the value chain.
OpenAI is doing all the actual productization and they get a valuation of 20x less?
While what they sell isn’t intelligent, it has utility and that’s worth something. Since their product seems to be at the forefront and they’re really the trendsetter with others playing catchup, they can command a premium.
With that said, I do believe Nvidia is overvalued - if it triples profit its PE ratio would still be 30 (i.e. 30 years to return on investment), while there's a fairly good chance someone would catch up to them in 10-20 years.
Using trailing PE shows an inaccurate picture for a high growth company so it makes more sense to just take the last quarter and project forwards.
The next thing to explain the discrepancy between Nvidia's valuation and OpenAI's would be that Nvidia's monopoly position effective eats into the profits of the AI startups for the foreseeable future. Had OpenAI already been profitable, its valuation would have exceeded 86B.
If you are willing to just take quarterly revenue which I think is reasonable for NVidia, it is valued at around 40 times the current estimated earnings for this quarter which isn't too overvalued.
The bigger thing I worry about NVidia is not current earnings but the possibility that the earning won't last when either AI wave fades off or competitors enter the market leading to loss in margin.
There’s more hardware than nvidia but it takes some time to unwrap the proverbial matmul from CUDA. Much more hardware is in the pipeline, it just (ahem) has to be much better than nvidia to make migration from CUDA worth it. groq is one such limited but extremely impressive example IMHO.
I think that's the bet. Maybe someone could catch up, but what are the chances incumbents buy new entrants or otherwise defeat them? What's the chance regulators will get involved? If you rate those favorably for NVIDIA, you'll price it higher.
I disagree. Google have their tensor chips – whether they work well enough for those outside of Google is somewhat irrelevant, they clearly work for Google who are going to be one of the major players in serving AI for the forseeable future (I'm biased, but seems clear to me). Microsoft have their own chips on the way, rumoured to be this year. Amazon have their own chips on the way, rumoured to have been in development for a number of years.
All 3 of these companies sell Nvidia hardware on their cloud offerings because it's what buyers want, but with Nvidia's pricing and resource constraints there is a huge pressure on the cloud providers to push customers into their own offerings. I don't expect any to stop offering Nvidia chips directly, but I'd bet that in the next few years all of their hosted value-add services will be on alternatives (i.e. hosted AI, inference, training, etc where customers don't need to know the hardware).
Nvidia have more of a moat than OpenAI for sure, but I think Nvidia's best days are 2023/4, and that things will look very different soon.
Similarly when IBM manage to start producing volumes of their neuromorphic chip, Google’s TPUs, Nvidia and even groq LPUs may seem obsolete.
I think this is most likely a temporary blip. GPUs were a bit of a commodity 5 years ago, with Nvidia, AMD, and Intel all producing reasonable stuff. Large AI accelerator chips weren't much of a market ~5 years ago, Nvidia were first to take the market, but in a few years time they'll also be back to commodity status.
Nvidia have a small moat with CUDA, but their eye-watering prices are a huge incentive for users to try alternatives, and ultimately the current price is built on them being the only provider of GPUs with 40/80GB of memory. That's the fundamental enabling technology, and that's not particularly tricky for competitors to replicate.
Nvidia may be the "best" AI accelerator chips on the market for years to come, but being 20% better and 20% more expensive than AMD, and all the cloud providers using their own in-house chips where they can, is not a $1.8Tn company as far as I can tell, it's much more like what Nvidia were ~5 years ago.
so question is: where are these guys' ML chips? sorry but AVX512 is not something that provides enough juice, and apparently some smart-head at Intel decided to lock end-users out of it?
because, honestly, it was not NVidia who kicked the GPUs forward, but these brave CUDA devs who actually created some valuable software to run on top of them - first for crypto mining, then for the LLMs and NNs in general.
honestly - i start to really despise this company, even though there's a 3090TI in my home box. and with the most recent talk given by CEO - fingers crossed someone comes and eats their lunch, they so much deserve it.
The eventual winner will almost surely be worth more than Nvidia when the rubble clears, but they, and all the losers and midfielders won't get to that day without forking over billions of their dollars to....
NVIDIA make bank for every single failed AI venture, if they buy their chips.
I would be curious when an educated-on-the-matter person's opinion was best guess on when NVIDIA's competition will catch up
oh, and don't forget Graphcore (also rumored to be bought soon): https://www.tomshardware.com/tech-industry/artificial-intell...
If Nvidia can truly print money as long as TSMC can make chips, as claimed above, I'd argue that's at least in a gray area for commodities. The chip is effectively a raw material as far as Nvidia is concerned, and it acts much like gold or oil in that scenario as the argument is that there is an unlimited market willing to buy any GPU that Nvidia can create.
Having a monopoly doesn't mean the product itself isn't a commodity. De Beers has a monopoly on diamonds, but I'd expect diamonds to fit into most people's definition of a commodity.
That's the definition everyone in economics uses and NVIDIA has never fit that definition. NVIDIA GPUs are not fungible. Intel and ATI GPUs are not replacements as far as AI is concerned. There are zero substitutions. They are not commodities, they are differentiated goods that no one else can produce.
> Having a monopoly doesn't mean the product itself isn't a commodity. De Beers has a monopoly on diamonds, but I'd expect diamonds to fit into most people's definition of a commodity.
Yes because the definition of a commodity is fungibility not the number of vendors. You can go online to Alibaba now and buy a bag of synthetic diamonds that are perfect replacement for mined diamonds. De Beers never really had a monopoly, they just controlled the market for consumer diamonds until the 80s.
NVIDIA has a monopoly because its product is not a commodity.
NVIDIA’s products can colloquially be described as commodities in some contexts, as GPUs from NVIDIA can be interchangeable with GPUs from other manufacturers like AMD. For specific tasks like gaming or basic computing, the brand may not matter as much as the specifications, making them somewhat commodity-like in those scenarios
Whilst this isn’t the traditional economic definition of commodity, speaking loosely, I think it’s fair enough to describe GPUs as a form of commodity. The important thing is what’s being communicated, not the semantic definition. The above comment’s point was pretty clear IMO
For AI workloads I'm pretty sure they aren't, which is why everyone is trying to buy NVIDIA GPUs.
If they were a commodity there would not be such intense competition for NVIDIA GPU allocations, because they would be easily interchangeable with GPUs from another source.
But the argument is, that Nvidia should follow a standard valuation model, since their product is a commodity.
Those claims are both incorrect. This either rests on a misunderstanding of what a commodity is, or on a misunderstanding of Nvidias Position in the AI segment.
As it stands at the moment, they are not a commodity in this field, they can not be replaced, and thus you cannot apply a more standard valuation model.
Nvidia is not valued as a commodity because now and in near future people expect them to keep their superiority that allows them to charge 30000+ for one card.
Nvidia P/E is 95.
OpenAI's valuation is 4 times more insane.
What about AMD (MI3000), Google (TPU - actually designed by Broadcom).
It's really TSMC (who actually make NVIDIA's chips, as well as those for AMD, Google, Apple ...) who are close to making things that no-one else can, although afaik Samsung are close.
NVDA may yet see some competition, as we drop our complexity standards and embrace a one-size-fits-all general MLLM architecture.
OpenAI wants to create machines that are more capable at almost all economically valuable labor than humans. Such technology would be incredibly valuable.
If it seems likely they’ll achieve it and actually return a significant part of the created value to investors, valuations that are crazy by old metrics will follow.
OpenAI P/E is 415
Nvidia P/E is 95.
OpenAI is valued over 4 times Nvidia based on revenue.
OpenAI has no definitive control on the service they provide, eg any one that can provide more value or cheaper on Nvidia hardware is a direct competitor that may be able to afford to pay more for hardware, limiting OpenAI access to its critical ressource.
Hence nvidia is the elephant that structurally takes the money, the ones that make the safest big returns on long term.
> The PPUs function similarly to Profit Interest Units (PIUs), granting employees a share in the company's profits rather than traditional equity. These units vest over four years, providing a long-term incentive for employees to contribute to OpenAI's success. A distinctive feature of OpenAI's PPUs is the growth cap; specifically, the PPUs are capped at a 10x growth of their original value. This means if an employee is given PPUs valued at $2 million, the maximum they can sell them for would be $20 million, ensuring that the rewards are substantial yet aligned with OpenAI's broader mission of equitable benefit .
This valuation isn't exactly crazy if you ask me. OpenAI is valued at 30B about an year ago. Now 3x. If you bought nvidia stock last year this time, it also gets 3x.
[0]: https://www.levels.fyi/companies/openai/salaries/software-en...
That being said, it's hard for me to believe many employees don't have 10x the amount you mentioned, and will be able to get ~1m worth for 10% of their shares.
I could be 100% wrong on both fronts - but it's my 2 cents
I've heard that's what they are offering new hires but it's an unusual form of compensation as compared to traditional stock/options. Curious how it worked out for those folks.
(apologies if this is explained in the article, I don't have access)
But even Google management was scared that OpenAI will corner the market in a bullshit generation for consumers. They did bring some big guns from doing real work to fight back.
Didn't you see Sora?