UNLESS openAI/etc actually succeeds in making AGI that never hallucinates and goes over the current LLM limitations
as for google... well they own the web
(+google has plenty of other revenue sources, so it can just pay out its AI survival)
if you're a website owner, would you welcome chatgpt/etc's data-collection bots?
but... as for google's bots... you need your website to be on the Google search results...
Also, possible Apple since they haven't gone into deep debt to finance 'AI buildout'.
If not, I'd say they don't even count in the "everyone"-
That is insane if that is true, is that even legal?
Companies can go from looking really good to a complete financial mess almost overnight when all that leverage and self-reinforcing stuff unwinds. See last weeks headlines for one such scenario.
There's nothing really groundbreaking at all in there, just "chips are expensive, and open weights models hosted locally in enterprise could displace Claude/GPT"
It appears very unlikely we will ever see an IPO of OpenAI. Anthropic appears less doomed, but still iffy at best. Tons of other large, but little discussed, AI startups are just dead-companies-walking at this point.
The likes of AWS are showing good headline numbers but are taking out massive debt to build infrastructure that looks increasingly unneeded. Those with capacity are looking to offload it, quickly. Yes AWS has “committed contracts” for this capacity but if those commitments are with shaky AI startups then it’s mostly just fluff PR and these hyperscalers will get left holding the bag on all this debt.
On the plus side, our interest rates aren't 0% right now, so there's some room there.
On the down side, our national debt generation now exceeds 125% of GDP and bond rates are shooting up because nobody wants to buy our debt.
What do we have in the decade ahead? Robotics in every household, models 10x+ faster and more intelligent than today.
Really no significant impact in life sciences, R&D, and 'offline' world / robotics today as of yet, which is where most of the value will live.
If you want someone else to do household chores, hire someone. You can pay someone to do your house chores for years for the cost that these things will have initially.
Which are pretty useless for a lot of home layouts and degree of putting cords etc. away. I took a look a few years back and got a stick vac instead. (And have a monthly housekeeper who does a lot more than a robo-vac would.)
Movements of AI stocks shouldn’t be confused with “AI as a technology” and “AI as a business”. Market valuation is a different game.
What's very real is the rapidly growing amount of revenue for both OpenAI and Anthropic. That's already tens of billions per year and growing quite rapidly. Investments against that kind of revenue aren't completely horrible. To a point. But at the multi trillion dollar valuation level, of course there are going to be issues with living up to those expectations.
In my view some of the base assumptions are looking not so solid currently. It's not a given that OpenAI and Anthropic will end up with most of the revenue. The Chinese trust Silicon Valley just about as much as vice versa. Which is why they are doing their own models, chips, and data centers. This is driving a rapid commoditization for things like frontier models, open model weights, and chips. This in turn gives countries outside the US a lot of options to stay independent. Which burst the bubble that all that global revenue was going to flow towards Silicon Valley. Some of that still might. But that will have to happen based on cost and merit.
There are also geopolitical circumstances that cause most data center plans to be bottle necked on permitting, chip shortages, grid connectivity, availability of gas turbines, gas, solar panels, inverters, batteries, water, and other resources. As it turns out, you can't just willy nilly plan for hundreds of GW of data centers and expect those to pop into existence overnight along with all the needed infrastructure. Most of the announced/planned capacity for this will likely not be realized. Certainly not this decade. 5-10% by 2035 would be a lot given all the constraints and scarcity. No amount of reality distortion can change the physical constraints on this topic.
The good news is that most of the money needed for this hasn't been spent yet. And what has been spent won't be going to waste. Up and running data centers are a hot commodity right now. They won't be running idle if a bubble bursts. But probably investors dreaming of multi trillion dollar IPOs might be a bit more cautious now that SpaceX stock is trading well below its IPO value.
https://blog.google/company-news/inside-google/company-annou...
I saw it put quite well in a comment on reddit:
> In 2020, the gaming segment was 47% of revenue at around 8 billion. Today it's doubled to 16 billion, or 7% of revenue. That's right, data center went from 6 billion 2020 to around 198 billion today.
Even if gaming revenue doubles again when the AI bubble pops, their total revenue will still drop by something like 80%. I'm neither smart nor dumb enough to be confident about whether that's something Nvidia can survive.
Why? Depends totally on what kind of website you are running.
Yes the overall market will take a hit but, like a forest fire we need a healthy burn to just wipe out the weaker players so the older more mature trees can get on with it. Yes the big trees will get burned a bit but they’ll be fine in the long run.
We need a good brush fire to just wipe out all the iffy startups and investors that over-indexed here. Thats what people want with “let it burn.”
If models are becoming commodities, and the main bottleneck is actually serving them at scale, Anthropic does not appear particularly well positioned.
If AWS had a ~60-80% margin for decades, I see no reason why inference can't have a ~60-80% margin for quite some time.
The problem is, if costs continue to drop ~90% for the same level of quality every 18 months, demand is unlikely to grow 10x to keep the revenue stable.
Who knows. Jevon's paradox. But the cost/quality is dropping too fast that it's hard for me to imagine demand keeps up long term to keep revenues (and profits) GROWING.
This is the crux that needs to be substantiated. Without substantiation none of your other arguments hold up.
Frankly it’s the opposite scenario (that’s there’s all this demand) which is struggling for any hard evidence.
What I am aware of: the growing revenue numbers of the model providers (which might be slightly stale data), and the increasing prices for on-demand GPU compute (which is not stale data).
Go to OpenRouter and look at all of the unsubsidized providers.
Is this range just Silicon Valley or what is this? Even including just Europe, you're looking at a lower bracket of 10k. If you expand to the rest of the world... Or do you think rich cities in the USA, where developers make 100k+ per year, can alone sustain this industry?
€57-114k p.a. is well within the order of magnitude of yearly gross developer salaries in Western Europe (e.g. Germany).
Wasnt AI science fiction (research) for decades but just took couple of years after Chatgpt to become mainstream. Why do you that wont happen to robotics?
The Internet was a 'bubble' at one point, and after it crashed in 2000, it didn't go away, it continued to build out. We're still using the Internet after the Internet bubble popped.
Over 40 years, that's 7-20 hardware changes, that turns into between $420B and $1.4T of ongoing investment (not accounting for inflation). The $30B that is called "infrastructure" only accounts for 2-7% of the overall bill.
This is NOTHING like fiber buildouts because the fiber lasts the whole 40 years with ZERO replacements and very close to 100% of the cost is infrastructure rather than a tiny percentage.
We see a similar paradigm with lots of revolutionary technology. The initial promise is high, people get very excited, lots of money pours in, and.... 15-30 years go by before we start seeing real impact across the economy at large.
It's just a real slog to actually implement and roll out new tech.
So take robots: I can promise you that you won't see robotics in every household in the next decade (especially so if we exclude the current market of robot vacuums). Even if a company makes an incredibly capable robot "today" (and to be clear - they are not) it won't have time to scale out production, reduce costs, generate a used market that's accessible to less wealthy consumers, deal with regulatory hurdles and quality problems that only pop up in real-world usage, etc...
It's just slower than you're implying.
The change very well will happen (I'm inclined to agree that things are going to shift). That doesn't mean that the current investment is sane and will pay off.
So many historical examples of this, just two here real quick:
- Ford built his first automobile in 1896, founded a company in 1901, went out of business, got sued by ALAM, didn't build more than 10k Model T's until 1910, then only finally hit real scale (of low hundred of thousands of units) in 1913: More than a decade to "basic scale". Household ownership didn't hit 60% until 1929... 30+ years later.
- The initial web enthusiasm, followed by the dot-com crash in early 2000s...
I agree, but that doesn't mean there isn't a bubble.
It is possible we'll have all those changes and they generate a lot of revenue for very few players but, still, a lot of the remaining players fail.
In particular, I worry about robotics. It is clearly becoming a China-only game. The west just doesn't have the industrial manufacturing critical mass to play it.
simianwords•1h ago
> Yeah, they dropped the price, but count the number of tokens you're tossing into it and see if it's actually cheaper. It's good for marketing, but the rub is how much you're actually using.
The level of discourse is so horrible now, I don't have words. Are these the ones making predictions on AI bubble?
empath75•59m ago
People completely lack imagination about this stuff. The main problem right now with AI isn't even AI successfully producing code at a reasonable cost, it's human coordination and review that is the bottleneck.
HarHarVeryFunny•18m ago
Cost isn't just token price though - it's (number-of-tokens-used x price-per-token), and there are large differences in token efficiency between different models and harnesses. Increasingly we're seeing benchmark sites focusing on "cost per completed task" as a cost metric, and it's not always the cheapest tokens that win.
I agree that ultimately AI/coding cost is just part of the picture - at the end of the day it's about software development cost, which for time being involves humans.
InsideOutSanta•41m ago
simianwords•4m ago