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- Hacker News
- > The subsequent bankruptcy of Jay Cooke & Company triggered the Panic of 1873, culminating in endless railroad bankruptcies across the country, a multi-year depression, multi-decade deflation, and, one could argue, the financial conditions that made Europe, four decades later, into a tinder box.
American history education needs some dire reform.
by petesergeant - Laughable to cite and reiterate the idea that Google is cooked where it comes to SoTA and AI in general when they operate, reliably and successfully for decades, one of the largest computing infrastructures on Earth and will likely continue to usefully serve the 90% of AI requests that don’t involve managing large codebases. Also seems strange to suggest that Google would need to Aquihire a company like Thinking Machines when it could spin up their AI model in a couple of weeks on its own TPUs if it felt like it. Demis likely wants to focus on his specific interest at the junction of biochemistry, neurobiology and computation which is more specific and unique to Demis than building a general purpose Q&A search model.by gizajob
- I actually agree with the article stating that not aiming for SOTA is actually a benefit for Google.
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.
by epolanski - Tend to agree with Ben's thesis RE Demis and DeepMind not really being focused on the agentic coding race. That being said, it remains to be seen whether Sergey and Koray can inspire the foot soldiers in the same way that Sama and Dario do. I'm not too optimistic, and that's to say nothing of the fact that Google cannot possibly hope to compete with these other companies on potential employee upside.by Dardalus
- I think the linked semi-analysis piece is about right. Google is more focused on protecting its 4.2 trillion dollar golden goose than pushing cutting edge which leads to a bureaucratic nightmare that researchers simply dont need to engage with when openAI/anthropic are offering even more money. Why waste time dealing with bureaucracy at google when you can be top dog at openAI or anthropic?by HDThoreaun
- > Google cannot possibly hope to compete with these other companies...
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.
by mrandish - I wonder why Google doesn't create an open-source CUDA alternative. Google released Kubernetes to stay relevant/competitive in the cloud wars, they were a distant third. They now have an opportunity to create an open source industry standard.
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.
- I see we have reached the stock market phase of Universal Paperclips.by Erikun
- Honestly, that game with a few changes would be very on the nose today :Dby doctorwho42
- Phew. I thought we were just about to leave it, albeit not quite getting to space, just dissolving the financial system so our AI overlords can make some more harvester drones.by Theodores
- Back in during the dotcom boom, there were multiple examples of companies being bought for ridiculous amounts. Often as the result of a bidding war.
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.
by alexpotato - IMO focusing on the hyperscalers is kind of misleading.
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.
by KaiMagnus - This is what so many people on HN and the market are constantly missing.
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.
by yaportmax - Nvidia has been playing a dangerous but profitable game since the Crypto boom.
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.
by dzonga - What's the danger? They slide back down to being just a gaming graphics card company with a $10 share price?by 2OEH8eoCRo0
- Nvidia sell iot boards with unified architecture. Would not be shocked if they launch pc/laptop/server boards at some point.by pletnes
- It seems they had a head start but are now facing stiff competition on all fronts. Software moat, GPU's for gaming, and its distant cousin datacenter compute. They rightfully invested their insane profits into many ventures, and how many of those have turned around into profit?
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.
by hermitShell - For awhile I've found two things hard to square, that the hardware and software making up current gen AI will bring us to a socioeconomic singularity, and the reality the thing they're mostly trying to emulate is a few pounds of meat and fat running on tens of watts equivalent. On one hand the current AIs are obviously super human in some tasks, get completely dunked on in others by far simpler organisms. My cat can catch a bug out of the air, Fable 5 in Cowork can lack the dexterity to make a slideshow because I had LibreOffice instead of Microsoft Office. Not even close to analogous, but point being they appear to have pretty fundamental differences in how they can interface with the world that the economic thesis seems to gloss over.by rcr-anti
- > a few pounds of … running on tens of watts equivalent.
Aren’t you already describing a laptop computer? Add a local LLM and you’re fully there
by hammock - You make a good point. Maybe the AI apocalypse will actually be when someone hooks up a cat brain to a super advanced AI.by Melatonic
- Another interesting discrepancy is that people think current GPUs are maybe capable of running AGI but they still can barely manage photorealistic rendering of a single room in realtime, or simulate something like a shirt thrown into a pile of laundry. They can generate a video of it based on millions of existing videos, but not do a real simulation of light and physics in realtime.
- More interesting take on Nvidia's position than I've come across before. One thing to be noted is 1) Nvidia is already making moves in robotics so even if their position in AI (moreso llms) diminished, they certainly have another big avenue arguably harder to just get into (although I'm not sure what efforts Google is doing for the tpu in robotics). Another point is Nvidia is still the main player in the west, that is, China certainly can and will create their own full stack without reliance on US companies. That puts Europe and other countries in an interesting, do you buy Nvidia because it's the only option or for security. That's to say I believe Nvidia's position relied on many different things being true at the same time, and we're moving towards an environment where those things are certainly being contested at (roughly) the same time.by tolugenius
- Google has their robotics models, for example:
https://deepmind.google/models/gemini-robotics/
Google is mostly the party behind the whole VLA principle.
by spwa4 - Even in the west, Nvidia's dominance is bound to weaken. There is a notable uptick of articles on HN about people running large models on AMD hardware. And while I don't know official sales figures, I know we have trouble getting our AMD system delivered
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.
by wongarsu - I'm just hoping that some day they can get back into the relatively small market of gaming gpus, even if for just nostalgia sake.by godshatter
- In many investment theses - like Nvidia's bet that demand for compute will keep growing - the first order assumption is usually correct. Yes, demand for more compute, chips, infrastructure is huge and each year some additional data centers will be built. Where such investment bets usually fail is in the second-order assumptions: Ie. the expectation of the growth of demand. This is where there's a high chance that the current expectations are likely exaggerated. So: demand is likely to persist for the foreseeable future but not increase every year. And that can upend the whole investment story. That can be enough to make these bonds a huge burden for Nvidia in the end. Not because people stopped buying more compute but because they stopped buying more every year.by jcfrei
- They also have to be feeling the heat of the ASIC vendors. AMD just acquired Taalas and they work with Cerebras all the time on special projects. ASICs outgun nVidia's chips by an order of magnitude.
- Plus on top of that 1st and 2nd order can be correct, but then the price is too high, meaning people lose money even if correct about the future, but over pay for it.by maerF0x0
- Each of the hyperscalers has put like 250B each in the last year for infra. That means that they need to be writing AI profits to the tune of 20B per year just to keep up with the cost of the cash they burned.
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.
by whatever1 - What makes this insanely hard to predict is that the compute needed for the same quality output has roughly gone down 90% every 18 months for ~5 years.
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.