The issue isn't that there aren't good business models or value creation, it's that anything related to AI currently has valuations that are unsustainably high given the current limits of the technology. That leads to economic activity that just couldn't exist without those valuations. And once the hype cools down the valuations will go closer to reality, leaving a lot of companies unviable, and many more will have to severely cut back spending to remain viable.
Or maybe the entire AI market pulls a Tesla and just stays at valuations that aren't justified by normal market fundamentals. Or maybe the technology adapts fast enough to keep up with the hype and can actually deliver on everything that's promised. This doesn't have to come down, it's just very likely that it will.
That doesn't mean much does it? Oracle is a huge company. Not just a cloud either. Companies often offer discounts or promotions; so? There could be plenty of managed services, managed databases, CRM and many more that make up for it.
Whilst I'm not sure if Oracle's stock price is right - the memo was more like a way to pressure the stock down for whatever reason.
September 2020: 2020 Tech Stock Bubble (Sunpointe Investments, tech in general)
https://sunpointeinvestments.com/2020-tech-stock-bubble/
August 2017: When Will The Tech Bubble Burst?" (NY Times)
https://www.nytimes.com/2017/08/05/opinion/sunday/when-will-...
March 2015: Why This Tech Bubble is Worse Than the Tech Bubble of 2000 (Mark Cuban, bubble is social media)
https://blogmaverick.com/2015/03/04/why-this-tech-bubble-is-...
May 2011: The New Tech Bubble (Economist, bubble is "web companies")
https://memex.naughtons.org/where-angels-dare-to-tread-the-n...
And of course I haven't even bothered listing all the people who said cryptocurrency is a bubble. That's 15+ years of continuous bubble-calling.
At some point you have to say that if the thing supposedly inflating the tech bubble changes four or five times over a period that lasts a big chunk of a century, then maybe it's not a bubble but simply that economic growth comes from only two sources: a bigger population and technological progress. If technological progress becomes concentrated in a "tech industry" then it's inevitable that people will start claiming there is a "tech bubble" even if that doesn't make much sense as a concept. It's sort like claiming there's a "progress bubble". I mean, sure, there can and will be bankruptcies and retrenchments, as there always are at the frontier of progress. But that doesn't mean there's going to be a mega-collapse.
So if the action of datacentre building shows up as essentially the only GDP growth, but what later happens in the datacentres fails to take its place or exceed it, there will be a dip.
Whether LLMs grinding away can prop up all GDP growth from now on remains to be seen. People use them when they're free, but people also collected AOL discs for tree decorations because they were free.
There's obviously evidence people use LLMs. That's not necessarily the same as people paying a noticeable fraction of all their money to use them in perpetuity. And even if "normal" people do start taking out $50 subscriptions as a matter of course, commoditisation could push that price down as could "dumping" of cheap models from overseas. A breakthrough in being able to run "good enough" models very cheaply, or even locally, would also make expensive cloud AI subscriptions a hard sell. And expensive subscriptions are the only way this pans out.
It hasn't yet been shown that AI is a gas that will fill all the available capacity, and keep it filled. If bread were 10 times cheaper, would you buy 10x as much? That has more or less happened to food availability in the West over the last 200 years and OpenBread and BunVidia don't dominate the economy.
None of that is sure to happen, and maybe the AI hype train is right and huge expensive LLMs specifically drive a gigantic productivity boom¹ and are worth, say, 0.2*GDP forever. But if it isn't, and it turns out $5 a month gets you all people actually need, it's going to be untidy.
¹: in which case, why is GDP not growing from the AI we already have?
1. LLM's are so economically unfeasible that companies won't be able to make a profit and investing in datacenters will turn out to be a bad bet because AI companies themselves are a bubble
2. LLM's will become so cheap that datacenters will be useless and people will just use local models so investing in datacenters is a bad bet
I see both positions in this thread so which one is true?
The two positions there aren't really different, they're mostly that the profitability of AI can be eroded from several sides. One: the cost to run (power and hardware) being high and bring unable to recover it from revenue. Or two, commoditisation and efficiencies (which can also be operational convenience rather than only about power) driving down costs and therefore also revenue, and being unable to compensate by selling more AI more cheaply. Three: AI didn't actually help as many people make money as hoped and thus they don't want to pay, also depressing revenue.
In the middle is the three-axis happy AI place where costs are not too high, but also AI is too hard to have someone else do it cheaper, and it's useful enough to be paid for.
My guess is AI ending up roughly as impactful overall as cloud computing. A big industry, makes a lot of money, touches and enables very many businesses but hasn't replaced the entire economy, profitable especially if you can stake out a moat, with low-margin battlegrounds for the price-sensitive.
Maybe it just works out like CPUs or as you said cloud computing? CPU's got cheaper and demand increased and more people use them but everyone still made a profit.
Another good example is railways in the US. That was an huge, huge boom, around a fifth of GDP. No one knew what the railway-based economy of the 1900s would look like but it was surely going to be spectacular. All that money! The speed! The cargo! All those people! Railways absolutely were a commerce multiplier, and made stacks of revenue very quickly and got investment from around the world to build build build. But, eventually, the (over)building was done, there were bankruptcies and consolidations and it ultimately did not become the dominant industry. And yet, it's still a big industry that enables a lot of other economic activity. Trains are still expensive to operate, but moving goods is pretty cheap. Obviously there's a natural physical monopoly at play there that AI doesn't have so again, who knows.
Which leads to another thought the AI investors, both commercial and national, maybe should eventually have: is there an automobile or airliner to their railway?
Will LLMs create a significant shift in productivity where its usage will create enough overall value to the economy to justify the hoovering of capital from other industries?
Those are the unknowns, I don't think many people are saying that LLMs have no value like NFTs, it's that the money being pushed onto this novelty is such an absurd amount that it might pull down everything else if/when it's discovered that there won't be enough value generated to compensate for trillions of USD in investments. Hence the comparison to the dotcom bubble, we came out of that with the infrastructure for the current internet even though it was painful for a lot of people when it crashed, will we have a 2nd internet-esque revolution after this whole thing crashes?
The technology is definitely valuable, and quite fantastic from a technical standpoint, is it going to lift all the other boats in the rest of the economy like the internet did though? No one can tell that yet.
This is what I want to challenge. At what point do you think people will pay more than it costs? Lets try to come up with a number because the price of LLMs have dropped more than 30 times in the last 2 years.
It may continue to drop and AI companies will continue to be in loss because the new things will be unlocked due to new efficiences and the same debate over LLM economics will continue.
I think it is already profitable and people are more than willing to pay for the actual costs.
If people are willing to pay for the costs, where are the profitable AI companies?
At the moment everyone is trying their best to implement it, but it remains to be clear if it actually increases a company's profitability. Time will tell, and I think there are a lot of things obfuscating the reality right now that the market will eventually make clear.
Additionally the economics of training new models may not work out, another detail that's currently obfuscated by venture capital footing the bill.
People also like pizza. How many million weekly active consumers of pizza? how about rice?
Really what people like here is cheap stuff and having a job that pays money to buy it. chatgpt so far loses boatloads of money. Soon they jack up prices, add adds, and people realize that it was all trained on them & threatens their job. So really right now chatgpt is sweating hard to make itself too big to fail.
800m total users, 25m paying customers... Most people use free accounts and would likely never pay any substantial amount of money for them
https://www.theverge.com/openai/640894/chatgpt-has-hit-20-mi...
I think it will only be economically sound as a business if you're Google and can start serving ads OR when we switch over from GPUs to wildly more efficient TPUs/ASICs
All the data center CapEx is going into compute that will be obsolete once that happens
LLMs are very useful, I can’t see myself walking back to the old way of doing things. But the amounts invested expect major breakthrough that we are not anywhere near. It’s a gamble and that’s what innovation is; but you gamble on a small portion of your wealth. Not your house and certainly you do not gamble a huge country like the US on a single thing.
Why do you automatically assume that people won’t pay for it?
But there is a big difference here compared to most software companies. The product does cost significant money per additional customer and usage.
There is a real product here. And you can likely earn money with it. But the question is "how much money?", and whether these huge data center investments will actually pay off.
I keep hearing this but this is very unlikely to be true. The cost of LLMs have gone down by more than 30 times in the past 1 year. How much more should it go down until you consider it economically feasible?
You can also do a simple analysis on the Anthropic Max plan and how it successively gets more and more limited, they don’t have the OpenAI VC flow to burn so I believe it’s a indicator of what’s to come, and I could of course be wrong.
If you want to question to on the fundamental economics of LLm themselves then how efficient should LLMs get till you decide that it’s cheap enough to be economically viable? 2 times more efficient? 10 times? It has already gotten more than 30 times over last 2 years.
I don’t think it’s a matter of efficiency at current pricing but increased pricing. It would be a lot more sane if the use cases became more advanced and less people used them, because building enormous data centers to house NVIDIA hardware so that people can chat their way to a recipe for chocolate cake is societal insanity.
This is not true for any LLM and not just Claude.
> I don’t think it’s a matter of efficiency at current pricing but increased pricing.
I don't know what this means - efficiency determines price.
> It would be a lot more sane if the use cases became more advanced and less people used them, because building enormous data centers to house NVIDIA hardware so that people can chat their way to a recipe for chocolate cake is societal insanity.
Do you think same thing could have been said during the internet boom? "It would be more sane if the use cases become more advanced and less people used them, because building enormous data centers to house INTEL hardware so that people can use AOL is societal insanity".
Efficiency doesn’t determine price, companies does. Efficiencies tend to give more returns, not lower prices.
Internet scaled very well, AI hasn’t so far. You can have millions of users on a single machine doing their business, you need a lot of square footage for millions of users working with LLM’s. It’s not even in the same ballpark.
Did we build many single company data centers the scale of manhattan before AI?
Then I think we agree that while the cost remained the same, the performance dramatically increased.
FWIW Sonnet 3.7 costs 2.5x as much as GPT-5 while also being slightly worse.
As for OpenAI I don’t think anyone is working on the API side of things since GPT-5 has had months of extreme latency issues.
I think it will only be economically sound as a business if you're Google and can start serving ads OR when we switch over from GPUs to wildly more efficient TPUs/ASICs
All the data center CapEx is going into compute that will be obsolete once that happens
That alone will be a monumental shakeup for the industry.
0.
Ferrari is a luxury sports brand. What's the point of it if it flooded the streets?
How to say you don't own a Ferrari without saying you don't own a Ferrari.
It’s actually quite interesting to see these contradictory positions play out:
1. LLMs are useless and everyone is making a stupid bet on it. The users of llms are fooled into using it and the companies are fooled into betting on it
2. Llms are getting so cheap that the investments into data centers won’t pay off because apparently they will get good enough to run on your phone
3. Llms are bad and they are bad for environment, bad for the brain, bad because they displace workers and bad because they make rich people richer
4. AI is only kept up because there’s a conspiracy to keep it propped up by Nvidia, oracle, OpenAI (something something circular economy)
5. AI is so powerful that it should not be built or humanity would go extinct
B) You're missing a few things like:
1. The hardware overhang of edge compute (especially phones) may make the centralized compute investments irrelevant as more efficient LLMs (or whatever replaces them) are released.
2. Hardware depreciates quickly. Are these massive data centers really going to earn their money back before a more efficient architecture makes them obsolete? Look at all the NPUs on phones which are useless with most current LLMs due to insufficient RAM. Maybe analogue compute takes off, or giant FPGAs, which can do on a single board what is done with a rack at the moment. We are nowhere near a stable model architecture, or stable optimal compute architecture. Follow the trajectory of bitcoin and etherium mining here to see what we can expect.
3. How does one company earn back their R&D when the moment it is released, competition puts out comparable models within 6 months, possibly by using the very service that was provided to generate training data.