Nvidia's Risky Business
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Google's limitation is that they still don't offer TPUs in a PCI-E card/dev board that people can plug in to their PC for local development and sane low level API to develop against, instead you have to go through their cloud and their full software stack which greatly limits ecosystem growth. The minute that Google figures that out, that's when Nvidia's dominance would be challenged.
Management likes it because it removes software developers from the loop.
The basic problem is that CUDA has become something of a Schelling point. If you want to train a model right now, the highest performance you can get is almost certainly on CUDA. From the basic general matrix multiply operation, to specific NN architectures, CUDA is going to have incredibly optimized implementations out of the box. And it's going to make multi-GPU training so much easier. And all the dependencies you build on (those layers you import from PyTorch or Transformers or whatever) are going to work optimally right away on CUDA. And that weird random repo that you found with a unique optimizer--it runs on CUDA too. And now the cool new implementation that you're about to release is also going to be built for CUDA.
It's so tempting to think "Just write replacement software", but you also need to transition the entire ecosystem in large part to match CUDA's effectiveness, and you need to get comparable performance out of your chip/library combo as NVIDIA can get out of its cards with CUDA.
There's a whole story here to how effective NVIDIA has been at navigating this. Very early on, they heavily prioritized PyTorch and TensorFlow, getting involved in the projects as much as they could and making sure they always ran best on CUDA. But the TLDR is that yes, you're right, another company could write a CUDA competitor. But actually replacing CUDA is a much larger task.
I'm personally hopeful that with the rise of coding agents, we see more movement on this front with other projects moving into view. It will take some time for any ecosystem to start to emerge that can dislodge CUDA for researchers who don't want to dive that deep into the stack, but hopefully we start to see some momentum build.
“CUDA is the worst development ecosystem in existence. Except for all the others.”
Looking at software more specifically the Linux foundation reported based on software dev salaries in 2008 it would be 1.4 billion to only write the Linux kernel.
Up until about 2023 there wasn’t enough money involved to have any reason to make a real CUDA killer even if you could get it adopted.
Replacing CUDA with another framework has much lower motivation. That advantages of the new framework must cover the switching costs and the risk of such a switch. All while CUDA continues to evolve and allow access to additional features.
Apple and Microsoft had something of a captive userbase. New vendor on the block trying to replace CUDA does not.
And the people they were selling that to, ( It's free and open souce now! ), were a very different group to the market they left behind on .NET Framework, who are often still struggling to make the transition now.
Had they actually killed off .NET Framework, it would have been a different story, much more like the VB6/VBA to VB.NET transition, which so few people bothered with that VB.NET died out, because if you had to retrain that much, you figured you might as well go to C# or a instead, or indeed a completely different language entirely.
I briefly worked professionally on a VB.NET project, but outside that job I've never met anyone else who can say the same. I've met a few who went straight from VB6 to C# though.
Largely the same market (Enterprise) but not the different segment (web as opposed to Windows/WinForms).
I ported about 15 years of projects from various versions of .Net to .Net Core whilst they were developing (and sent feedback to the team - they were asking us to do that) and the process was pretty reasonable. You were only really stuck if you were using something very very Windows specific (certain image processing libraries iirc) and even then it was largely manageable.
The old full-fat framework is, AFAIK, still supported, as there's a whole lot of legacy code which is Windows specific which is still expensive / hard to port over.
Between MSMQ, WCF over named pipes, MSDTC, and MSI installers, there's a lot to replace that is hard to provide the same guarantees or performance with straight replacements, if they even exist.
The end goal, being on modern dotnet, is better, but it's difficult to get there with a phased approach without accepting a temporary worsening, which is often hard to sell.
Especially while Framework is still supported.
I was under the impression that AI was supposed to remove software-moats, let us all ask it to write our custom MS Word for us for instance?
AMD has had years to try and counter it, but just has not. Google is kinda trying to do an end run around it with TPUs but they are still niche high end stuff with limited availability.
Its really just CUDA, and CUDA can be seen as somewhat akin to C for assembly used by Nvidia's gpus- In many ways a wrapper around the low level hardware that often has those details bleed through.
Again, "CUDA" isn't a programming language, it stands for Compute Unified Device Architecture; "C/C++ for CUDA" are the high-level languages that compiles to PTX and then SASS as well CPU orchestration code via NVCC.
And to be honest, pretty much everything you can do in CUDA C/C++, you can also do in HLSL/GLSL compiled to SPIR-V, as long as the Vulkan hardware extension is available.
Maybe I wasn't being precise enough with my language for this forum, and also my last hands on experience with it was roughly 6 years ago, maybe it's gotten better. But it was much less (and forgive the imprecision!) python/pytorch-like where you say hey take this big blob of data and just slice and dice it on your many cores, and more like ok, here is the data, let's cudamemcopy it in these size chunks over to the gpu itself, to be used by this block of threads and run these commands (kernel in cudaspeak) on it. Much more painstaking and micromanagey of the resources.
Pytorch IMHO feels like a proper abstracted API that hides the details and lets you just unleash the fury at the cost of some efficiency, while the cuda api itself, similar to working with C, forces you to really think about the low level details. I have a heavy backend and systems development background, and while it wasn't really intimidating to me, it was like wow you really have to have a deep working knowledge of how these things work and it felt like a step back in time IMHO.
I doubt that's going to satisfy you but I think it gives a clearer picture of what using cuda is like if you typically use higher level languages and haven't touched C since college.
It's pretty heretical for me to say this, but a lot of GPU compute complexity that Nvidia is doing in CUDA is unnecessary and is by the simple fact that to do anything meaningful you have to either use their library or handle allocation/scheduling yourself. Imagine if JavaScript required you to handroll part of the V8/Node's JIT compiler, allocator and scheduler yourself every time you just want to make a webpage, that is essentially what CUDA is doing.
The actual "program" that runs on the GPU, the compute shaders in PTX/SPIR-V, are very low level but pretty straight forward once you get down to it.
[1] https://developer.nvidia.com/nccl [2] https://pytorch.org/blog/torchcomms/ [3] https://rocm.docs.amd.com/projects/rccl/en/latest/
Ironically, there was an open source project that was making great progress on CUDA compatibility on AMD hardware. AMD hired the lead developer, and then he shut down the project.
It doesn’t really make sense for AMD themselves or most use cases, though; any compatibility shim just adds problems on top of problems, and for AMD, entrenching a competitors technology even more never really seemed like a great idea.
They don't know what good developer experience is, how do you expect them to deliver it to other people?
I think a simple reason why it’s been hard to unseat in Nvidia is first mover advantage. A lot more water has flown through Nvidia pipes than TPUs or AMDs chips for that matter.
TPUs and AMD chips aren’t priced cheaper than NVIDIA (at least for my purposes training models). So there hasn’t been an impetus for me to venture there and use those chips.
Anecdotally, folks I know who have tried using TPUs and AMD chips have hit more issues with the underlying drivers than with NVIDIA chips. That costs time and money to fix.
Eventually the other chips will go through enough iterations and stability will be reached
What do you gain by not using CUDA vs what do you risk?
Nvidia's sells hardware yet their market cap is about the same as Google's.
How much value could Google get by selling hardware too? Google'd be selling to competitors, so difficult to capture much of the value and would decrease Google's value as an AI company. Maybe a child company?
I do agree that it’s really not great, and I also have never been a strong believer in the CUDA moat overall; as the need for GPUs moves from research to production (inference), companies are plenty willing to build software from scratch anyway (and we see this with AMD GPUs being in plenty high demand in the datacenter and enthusiast market now).
AI is supposed have solved the "coding problem". But shouldn't translating a program from one platform to another be an even easier, more mechanical, task for the AI?
Which AI? LLMs are coding facilitators and code producers.
A problem is solved when the solution is reliable. Non-deterministic Neural Networks are not reliable. In fact,
> more mechanical[] task
that suggests an expectation of process and procedure, which is still not a capability of current architectures.
Sure, you can ask a brains-deficient operator to perform a huge task, but then you'll have to check the whole product, and that remains not cheap.
"AMD and Anthropic also formed a multiyear engineering partnership to optimize ROCm using Claude"
FROM: https://finance.yahoo.com/markets/stocks/articles/ex-amd-exe...
And where are the warranties that the solution built be reliable (and optimal, etc.)?.
This is actually a corollary to the point I was making about "CUDA" usually also including a ton of the included kernels and not just referring to a crappy programming environment; translating mid-level C that does math between two runtimes wouldn't be hard for an LLM, but translating "doBigDNNThingNVidiaGaveMeInAKernel()" to "doBigDNNThingByHandBecauseAMDDoesntSupportIt()" isn't a rote translation at all.
Of course, once you accept that it's _not_ "why don't you just translate it," you _can_ iteratively use an LLM to implement the ThingNVidiaGaveYouInAKernel, but it probably isn't well-trained, yet, on low-level AMD optimization tricks, so the kernel you end up with will likely be slower than the CUDA one.
I wonder if this points to a deeper limitation of AI, it can not do coding tasks it has not seen in its training material. Or could it possibly "generalize" to accompllish something like this anyway?
I’m not familiar with this field, but to my brain, https://www.amazon.com/s?k=Google+Coral seem to show me several such options.
1) We don't know how long that trend will continue, but you do know where to look for when it may end (if smaller sized models continue to compress the knowledge effectively of larger models).
2) We don't know when the appetite for higher cost models might go down and by how much if smaller models get "good enough" and price becomes far more important.
It is entirely possible that 5 years from now, there's >100x LLM inference going on - but demand for AI chips (including memory) is only 2x or less.
It is also entirely possible that at some size - LLMs pick up some emergent capability that doesn't scale well to smaller sizes - and that there's an incredible boost to demand to get that capability.
It's just very hard to predict.
The harder thing to forecast for me is if we hit a wall on increasing efficiency, either on the model weights side or silicon side, with current approaches. If we have to switch to something like burning the model weights into silicon to continue to make gains, then the current math on general purpose accelerators might be upside down.
> If we have to switch to something like burning the model weights into silicon to continue to make gains
I think that's already being considered semi-seriously [0][1]
[0] https://taalas.com/products/
[1] https://ir.amd.com/news-events/press-releases/detail/1296/am...
I've been keenly interested in the ability to run local models, but the hardware is just not there. Consumer RAM speeds and capacity will have to significantly increase before local models will be able to perform as well as even the lowest end GPT-5.6 Luna model.
This is on the backdrop of RAM becoming prohibitively expensive. And without the speed and quantity of RAM, it becomes impossible to generate tokens at interactive speeds, regardless of model. There is a fundamental dependency between calculating all of the active params with the given RAM speed.
Even with a model that has been quantized all the way down to Q4, the DGX/RTX Spark chip with 128GB of RAM can only generate ~18 tokens/sec for a MoE model with only 30B active parameters. There haven't been any broadly useful models below 30B active parameters. And that is for a $5000+ piece of hardware that will be one of the best for running on-device models.
I really want to buy instead of rent my AI, but the economics are truly terrible.
If you can integrate AI accelerators into consumer cards (you can), you can have local AI for "reasonably" cheap. This is Nvidia's long term goal if you listen to what Jensen has to say.
The limitation is entirely on memory right now. Just a few years ago we could of been strapping 80-100GB to cards for under $200 (BoM).
You can't save yourself rich.
I buy that. Jevon's Paradox, sure.
> and we are not going to run out of economically useful things to do with it anytime soon on the demand side
This I don't buy. Not fully, at least. Whether or not there's demand for LLMs in some particular field is one thing, whether or not there is a sustainable business model to be built out of that demand is another thing entirely.
There is a staggering amount of money pouring into startups looking for novel use cases for LLM-based agents. As usual, 99% of them will fail, but those other 1% are going to have to look harder and harder to find a novel use case that can actually be served profitably.
First of all, there's only so many places where a chatbot is going to sell. But, that also seems to be the only interface anyone can come up with that allows a user to steer an agent through a long-running task reliably. I'd love to be proven wrong here.
Also, if current trends plateau and large datacenters are still needed for complex tasks, that would stimy growth of LLM usage across entire industries.
But, if present trends continue, then local inference will become feasible for most tasks. That would lower the barrier to entry across tons of heavily-regulated and/or cost-sensitive industries. But, widespread local inference will almost certainly come with a painful market correction centered around hyperscalers, which would itself dry up the pool for ventures into new markets.
Now for every human replacement, that is 1 unit less of communication and bureaucratic burden (HR, middle management etc) that the org requires.
I guess I don't know what to say except that my experience is the polar opposite of yours.
I moved to a new state at the beginning of the year. Needed a new doctor, needed to schedule apartment tours, needed to talk to my employer about insurance and relocation stuff, etc etc. Lots of chatbots, a handful of humans. Humans consistently did what I needed them to do, the chatbots just didn't. I could list examples but I'd be typing all night.
And yknow what, my one call with Comcast to get my internet set up was downright pleasant. The rep was knowledgeable and a good conversationalist.
However, at some point AI may be good enough for most people and then it makes sense to make an ASIC for the model (or group of models); and at that point you don't need Nvidia.
I suppose this scenario will happen in various moments at different levels.
Railways were also the future, but that didn't stop a rush to build out (often subsidized) lines that were ultimately uneconomical (either because they were corrupt or the planned settlements never arrived).
If AI is similar, then there's going to be a long slowdown on compute spend until the surplus is worked through. A good historical analogy could be the fiber optic buildouts of the late 1990s. The demand for data never really went down much, but the industry eventually commodified and took down some large companies (Nortel, especially)
Compare to just paying for an internet connection, you have bandwidth but not sure what you can do with it that is valuable.
Let's say you use AI to produce software. There;s no limit as to how high the quality you want your software to have. And how fast you want your project to be complete. There's plenty of room for higher quality, and more performant AI. As AI becomes chepaer people will use more of it, they're not going to say "We have enough AI".
Compare to railroads. Yes you pay for the distance travelled but there's a limit to how much people wwill want to travel, how it will benefit them.
It is entirely possible that 5 years from now, there's >100x LLM inference going on - but demand for AI chips (including memory) is only 2x or less.
Jevons paradox should win out for a long time for AI.
When internet connections got faster than 56k modems, we didn't use the same amount of bandwidth but faster. We used more bandwidth doing things like 4k streaming. I see the same in AI inference. If AI inference is that much more efficient, it will just enable more use cases for AI.
See for example, internet traffic over time: https://encrypted-tbn0.gstatic.com/images?q=tbn:ANd9GcS779VS...
Even after so many years, internet traffic continues to grow at an increasing rate.
We are not there. But they better figure it out soon. The cash flows dried up, and everyone is taking debt to support the capex. Google for the first time in its public history is cash flow negative. Amazon too.
AMD's software story is still a lot worse than Nvidia's. But patching up vllm to run one or two models you care about on AMD hardware is a much easier proposition than using them in most other fields of AI.
Trust me guys, it's over!
You can both become a company that supplies 80% of the world with your type of product, and then still have your stock go down in value.
All it takes is over evaluation by the stock market. Then a course correction from unsustained growth on growth (second order). So even if you continually replace YoY 80% of the world's hardware on a rotating business, but you don't increase market share or increase demand (aka growth)... Your business looks stagnant to the stock market, and there isn't really anything you can do about it. The best you can do is track inflation +/- 2%.
And that's why a lot of older established companies were dividend stocks. You don't expect to growth anymore, but that's not where the value is anymore... The value is in the reliable sales that will happen after infinitum because your company controls a majority share of the business... And that's ok! Unfortunately, silicon valley has created a philosophy of 'you gotta expand into new fields or your on the decline' - aka neo-monopolization
I’m not predicting Nvidia will MBA themselves to death in the near future but I think there’s a tendency to overstate how profitable companies will stay. The more money Nvidia makes, the more motivated their competitors will be to get a piece of that market and the more customers will be looking for alternatives like the push into TPUs which the article discussed.
The current administration is definitely corrupt enough that you could imagine an anti-competitive deal of some sort but I don’t think there’s a way for even that to change matters because key competitors are well-connected American companies willing to play that game, too.
Your margin is my opportunity - Jeff Bezos
The root comment in this thread was about Nvidia hedging their bet on lost AI market share. They recognize that a reduced pace in training and inference competition will undercut their business, but CUDA isn't a one-trick pony for LLMs alone. TPUs are - you can't even reuse the same architecture for training and inference, they're separate ASICs unlike CUDA cores/ALUs. Veterans of crypto mining will tell you that the ASICs lost in the end, as Nvidia was evolving their hardware faster than the ASIC manufacturers could iterate. When the crypto acceleration landscape diversified away from ETH/BTC into altcoins, Nvidia was still there making money hand-over-fist from mining hardware.
I guess you could argue that robotics, world models or computer vision won't be a trillion-dollar market. But Nvidia is positioned to be the first mover in all of these markets, and none of their competitors are even coming close to the integrated stack that they sell consumers.
AWS begs to differ. They originally split between `Trainium` and `Inferentia` but now support both with `Trainium`
Nonetheless, TPU architectures are still a systolic array, and have their own limitations for scalability and flexibility. CUDA is no silver bullet, but it satisfies the demands of the edge and research customers very well.
How much money was in it for the first decade or so? I think AMD was asleep at the switch but e.g. Apple just did their own thing for the parts which they prioritized.
My understanding is also that Anthropic and OpenAI have also worked to decouple themselves so I think it’s likely that the CUDA moat is going to be less of a barrier than it used to be from the perspective of guaranteeing Nvidia profits.
Once Apple fully left Khronos, AMD played the smartest card they had; they architecturally split RDNA and CDNA into separate product lines, so they could optimize them independently. This staunched the bleeding, and gave AMD a datacenter presence that Apple Silicon could only dream of. Still not a scalable architecture, but better than nothing.
https://deepmind.google/models/gemini-robotics/
Google is mostly the party behind the whole VLA principle.
The decision to scare the shit out of normal people early on was a short-term decision that will cost everyone dearly (it led non-technical people to believe in things about LLMs that just aren't true—even worse, they've now made difficult business decisions on bad information with seemingly limited consideration of secondary effects).
The amount of short-sightedness alone should give pause, but at least in America, we seem to have collectively lost our minds in worship of the almighty dollar.
Judgment day approaches.
Aren’t you already describing a laptop computer? Add a local LLM and you’re fully there
but now I think they probably have bitten more than they can chew.
Apple already proved with their unified memory - that as long you have the capacity you can run capable models locally - thereby goes demand for inference if everyone is running some model locally.
For training - Chinese models have proved that you don't need the latest & greatest in Nvidia hardware. Same as TPUs.
only time will tell.
The absolute fastest desktop Mac GPUs cannot beat an Nvidia laptop GPU in prefill or inference speeds. Apple Silicon is a non-entity for professional datacenter deployment and arguably unusable for frontier models at agentic context sizes. AMD is Nvidia's primary worry, and they're not doing much better in terms of GPGPU SOC compute.
You can believe all you want that the dinky little jetson boards were desktop grade when historically the ARM SoC portion of a jetson board couldn't even keep up with broadcom/rockchip SoCs. It's taken until recently for the actual arm compute portion of Nvidia SoC's to be worth a damn at all, and they still fall far behind Apple let alone the rest of the pack like Qualcomm/Samsung.
You can believe all you want that good single-core performance will corner the edge compute market. It hasn't, Graviton has more buy-in than any Apple Silicon chip ever got.
Nvidia dropping from being a $5 trillion company to a $242 billion company would be 1929 levels of bad.
Global economy end of days stuff, especially since Nvidia can't crash that hard without a lot of other stuff crashing with it.
And I am pretty sure that their value will drop in 5 to 10 years.
https://stockanalysis.com/quote/lon/VWRA/holdings/
That is an astounding figure and means markets are highly imbalanced, unless you think Nvidia represents 5% of the global economy.
That’s still a lot, but “the global economy” over the horizon where those earnings are not discounted to 0 contains a lot more than what’s listed in VWRA today.
This consolidation is scary. I've started putting a little bit in emerging markets and China. I cannot trust the US long term, hopefully I'm wrong!
They are also a robotics AI company with Omniverse. They are also an AI company with Nemotron. They are also a bleeding edge network equipment company after the Mellanox aquisition.
They stand to make a lot of money if they succeed in every venture. Good for Jensen taking risks and driving innovation, I hope they succeed in chewing even 50% of what they bit off.
This is the inherent downside of such a rapid rise to being the world's most valuable company AND still being considered a growth stock. At their massive scale, the number of new adjacent businesses that have both sufficient size and potential growth is limited.
> I hope they succeed in chewing even 50% of what they bit off.
Anything approaching that is vanishingly unlikely. They're being forced to play the game more like a VC. The question is if a few unicorn winners can offset dozens of losers. The challenge is that, unlike a VC, their bets are much more correlated around AI.
Yes, for programmers and tech companies AI is kinda boring now, but AI integration in general is still kind of uncharted territory.
There are so many small companies and individuals just getting started with AI today and I believe a large the customer base (and revenue) is still untapped. Hell, I’m discovering new use cases regularly still and the average mismanaged 30 people whatever SaaS vendor probably didn’t even get started yet.
Jason Kottke almost didn't found his blog in 1998, famously quoted as saying: "I thought I was too late, that no one would be interested." Needless to say, the internet was a tiny joke in 1998 compared to what it is now.
We are just barely scratching the surface of what's possible with AI, both in terms of the leading edge and in the 'torso' of the economy (the portion you're describing).
Folks from Silicon Valley working in AI-forward companies have a skewed perception of how many people have adopted this technology so far. Codex recently celebrated hitting 10 million users. This is a great milestone and all, but to put it in context, Microsoft office has a billion users. Sure, many people use Claude Code and or some other harness and the growth is staggering, but the overall scale is tiny compared to software as a whole. Costs of serving and usage are still very high, prohibitively so for many, so we aren't even close to market saturation.
And even at the leading edge, people who do work in those AI-forward companies; models are still slow, require hand holding, and produce suboptimal outcomes sometimes. Imagine the value when instead of needing to prompt it once per 30 mins, you prompt it once per day. Then once per week. Then once per month. Imagine all this running not on 3 trillion parameter models, not on 10 trillion, but 100 trillion. What kind of computer infra will be needed then? Certainly more than we have today.
Then, move five years forward from that year and look back. In every case, people think, "Hah! The internet was so simple then!"
In 2031, I suspect we'll say the same about 2026.
So much of the dotcom bust was essentially: "anything internet will work", but even after it was pretty easy to find different ways to publish web pages or migrate to new forms of social media, video, short-video, etc.
It's orders of magnitude harder to automate work, which is the business value proposition of AI (whether you are displacing worker or adding breakthrough capacity), and it's not clear the LLM hyperscalers will get the value-add from that.
On the consumer side, it might be a race to the bottom: same ad profits, but now you need AI to produce it.
Even back then, some economists used the "hidden wallet auction" as an example of how this could happen.
To summarize:
- there is a wallet
- you don't know how much is in the wallet
- you bid on amount to buy the wallet
- if you get the highest bid you win
- crucially, if you lose then you still have to pay
This is often cited as a game that you do not want to play b/c it's a. hard to predict the upside, b. the downside is huge.
That being said, people still got into these auctions and because of sunk cost fallacy, decided to keep bidding even if they might lose.
The hyperscaler race feels a bit like the above but no one seems to ant to admit it.
Or the companies spending trillions of dollars can do a Manhattan Project (Or X-Prize) and let a thousand startups work on it. One will succeed. Between Google, Amazon, FB, Microsoft, Apple, AMD, Qualcomm, Intel (and dozens of other companies) there is enough economic incentive to do it. Also, isn't this what AI is supposed to be extremely good at, CUDA experts can continue to write CUDA (without having to learn anything new), a translation layer will rewrite it. If software can be one-shot from markdown files, this can't be impossible.
It's possible Google has intentionally decided to take a more conservative blended approach than purely competing at the bleeding edge of the frontier. If so, they obviously have no incentive to state it publicly but the recent departures and financials are consistent with the idea. It also makes sense that a company so much bigger, longer-term and (somewhat) more diversified than pure-play frontier labs would play the game to align with their strengths (capital, balance sheet, breadth, etc).
In their position, why not take an 'arms supplier' strategy in the near-term while drafting behind the frontier labs as a fast follower in AI, essentially betting the AI race is more akin to the Indy 500 than a quarter-mile drag race. If they're wrong and it IS more like a drag race, Google is in a better position to absorb and adjust than a frontier lab, for whom current valuations and capex spending requirements nearly require this to be a relatively short, winner-take-all race.
Gemini is already good for enterprise users, most companies I know are using Gemini 3.1 to interact with their email and sheets and creating presentations on the fly.
American history education needs some dire reform.
Counterpoint: xAI pooped out a frontier model based on nothing but capital and one man's desire to push a right-wing political narrative. Google has the talent, and the money, and the experience, they just need some leadership.
Perhaps this has something to do with the economic dislocations and world wars between the 1870s and today?
Building a business model on the belief that “this time is different” always finds storms on the horizon.
The current setup can’t sustain a downturn, even if yes 20 years from now point B is likely to be higher than present.
That’s the danger. Those that are going to get wiped out by the AI bubble burst aren’t wrong about AI being huge long term, they just put themselves in a position to not survive the storms that happen between points A and B.
Over those years, my NVDA stock has been by far my biggest winner. I'm now up more than 1500% on it.
Let the dooming continue
Disappointed by the lack of Tom Cruise.
1. Circular investment/spending.
2. Too much capital in the system, so returns cannot be hit regardless because the barrier is too high. (Evidence being every capital cycle in history)
My guess is at least another 2 years, as most people don't use AI yet, or maybe more precisely, AI is not used in the underlying workflows (which are invisible to the consumer) that make up most people's jobs.
Who knows if I am right. My original post was just pointing out that what you say is about timing, not whether it's true or not, because of course its true.
What a weird reason to stop reading an article.
Bayesian inference and all....
ps. Being influential in Silicon Valley just means you are influential, it does not mean substantial. Leopold is still influential and gets money thrown at him at $100s of million despite having no substance.
They are the marketing wing of the AI ecosystem. Their recent article on how SpaceX would drive 500B in data-center revenue was ludicrous-mode. Lets revisit this in a few years.