The practice was known as “zaitech”
> zaitech - financial engineering
> In 1984, Japan’s Ministry of Finance permitted companies to operate special accounts for their shareholdings, known as tokkin accounts. These accounts allowed companies to trade securities without paying capital gains tax on their profits.
> At the same time, Japanese companies were allowed to access the Eurobond market in London. Companies issued warrant bonds, a combination of traditional corporate bonds with an option (the “warrant") to purchase shares in the company at a specified price before expiry. Since Japanese shares were rising, the warrants became more valuable, allowing companies to issue bonds with low-interest payments.
> The companies, in turn, placed the money they raised into their tokkin accounts that invested in the stock market. Note the circularity: companies raised money by selling warrants that relied on increasing stock prices, which was used to buy more shares, thus increasing their gains from investing in the stock market.
https://www.capitalmind.in/insights/lost-decades-japan-1980s...
OpenAI applies the same strategy, but they’re using their equity to buy compute that is critical to improving their core technology. It’s circular, but more like a flywheel and less like a merry-go-round. I have some faith it could go another way.
But we know that growth in the models is not exponential, its much closer to logarithmic. So they spend =equity to get >results.
The ad spend was a merry go round, this is a flywheel where the turning grinds its gears until its a smooth burr. The math of the rising stock prices only begins to make sense if there is a possible breakthrough that changes the flywheel into a rocket, but as it stands its running a lemonade stand where you reinvest profits into lemons that give out less juice
The other difference (besides Sam's deal making ability) is, willing investors: Nvidia's stock rally leaves it with a LOT of room to fund big bets right now. While in Oracle's case, they probably see GenAI as a way to go big in the Enterprise Cloud business.
That's only like 1/8th of the flywheel, though.
If they don't then they're spending a ton of money to level up models and tech now, but others will eventually catch up and their margins will vanish.
This will be true if (as I believe) AI will plateau as we run out of training data. As this happens, CPU process improvements and increased competition in the AI chip / GPU space will make it progressively cheaper to train and run large models. Eventually the cost of making models equivalent in power to OpenAI's models drops geometrically to the point that many organizations can do it... maybe even eventually groups of individuals with crowdfunding.
OpenAI's current big spending is helping bootstrap this by creating huge demand for silicon, and that is deflationary in terms of the cost of compute. The more money gets dumped into making faster cheaper AI chips the cheaper it gets for someone else to train GPT-5+ competitors.
The question is whether there is a network effect moat similar to the strong network effect moats around OSes, social media, and platforms. I'm not convinced this will be the case with AI because AI is good at dealing with imprecision. Switching out OpenAI for Anthropic or Mistral or Google or an open model hosted on commodity cloud is potentially quite easy because you can just prompt the other model to behave the same way... assuming it's similar in power.
Why would they run out of training data? They needed external data to bootstrap, now it's going directly to them through chatgpt or codex.
There are physical products involved, but the situation otherwise feels very similar to ads prior to dotcom.
That's capital markets working as intended. It's not necessarily doomed to end in a fiery crash, although corrections along the way are a natural part of the process.
It seems very bubbly to me, but not dotcom level bubbly. Not yet anyway. Maybe we're in 1998 right now.
It is at the very least highly debatable how much their core technology is improving from generation to generation despite the ballooning costs.
I don't tend to benefit from my predictions as things always take longer to unfold than I think they will, but I'm beyond bearish at present. I'd rather play blackjack.
In that sense it makes sense to keep spending billions even f model development is nearing diminishing return - it forces competition to do the same and in that game victory belongs to the guy with deeper pockets.
Investors know that, too. A lot of startup business is a popularity contents - number one is more attractive for the sheer fact of being number one. If you’re a very rational investor and don’t believe in the product you still have to play this game because others are playing it, making it true. The vortex will not stop unless limited partners start pushing back.
I wonder how they felt during the .com era.
https://time.com/archive/6931645/how-the-once-luminous-lucen...
The customers bought real equipment that was claimed to be required for the "exponential growth" of the Internet. It is very much like building data centers.
I’m thinking they eventually figure out who is the source of good data for a given domain, maybe.
Even if that is solved, models are terrible at long tail.
What _could_ prevent this from happening is the lack of available data today - everybody and their dog is trying to keep crawlers off, or make sure their data is no longer "safe"/"easy" to be used to train with.
Not? Money is thrown after people without really looking at the details, just trying to get in on the hype train? That's exactly how the dotcom bubble felt like.
Ugh I hate it so much, but you're right, it's coming.
The new OpenAI browser integration would be an example. Mostly the same model, but with a whole new channel of potential customers and lock in.
Even if that weren't true having your software be cheaper to run is not a bad thing. It makes the software more valuable in the long run.
2020: https://www.youtube.com/watch?v=rpiZ0DkHeGE 2019: https://www.cadtm.org/spip.php?page=imprimer&id_article=1732...
Things are worth what people are willing to pay for them. And that can change over time.
Sentiment matters more than fundamental value in the short term.
Long term, on a timescale of a decade or more, it’s different.
Nowhere near that level. There’s real demand and real revenue this time.
It won’t grow as fast as investors expect, which makes it a bubble if I’m right about that. But not comparable to the dotcom bubble. Not yet anyway.
I’ve made that mistake already.
I’m nervous about the economic data and the sky high valuations, but I’ll invest with the trend until the trend changes.
This is a pricey machine though. But 5-10 years from now I can imagine a mid-range machine running 200-400B models at a usable speed.
Capital markets weren't intended for round trip schemes. If a company on paper hands 100B to another company who gives it back to the first company, that money never existed and that is capital markets being defrauded rather than working as expected.
PE ratios of 50 make no sense, there is no justification for such a ratio. At best we can ignore the ratio and say PE ratios are only useful in certain situations and this isn't one of them.
Imagine if we applied similar logic to other potential concerns. Is a genocide of 500,000 people okay because others have done drastically more?
Even if the model training part becomes less worthwhile, you can still use the data centers for serving API calls from customers.
The models are already useful for many applications, and they are being integrated into more business and consumer products every day.
Adoption is what will turn the flywheel into a rocket.
If you have a better measure, share it. I trust data more than your or my feelings on the matter.
This boom is a data center boom with AI being the software layer/driver. This one potentially has a lot longer to run even though everyone is freaking out now. If you believe the AI is rebuilding compute then this changes our compute paradigm in the future. As well as long as we don't get an over leveraged build out without revenue coming in the door. I think we are seeing a lot of revenue come in for certain applications.
The companies that are all smoke and mirrors built on chatGPT with little defensibility are probably the same as the ones you are referring to in the current era. Or the AI tooling companies.
To be clear circular deal flow is not a good look.
I can see the both sides of bull and bear at this moment.
I'm commenting here in case a large crash occurs, to have a nice relic of the zeitgeist of the time.
Or not - there still knowledge in people heads that is not bleeding into ai chat.
One implication here is that chats will morph to elicit more conversation to keep mining that mine. Which may lead to the need to enrage users to keep engagement.
I've started to wonder why we see so few companies do this. It's always "evil company lobbying to harm the its customers and the nation." Companies are made up of people, and for myself, if I was at a company I would be pushing to lobby on behalf of consumers to be able to keep a moral center and sleep at night. I am strongly for making money, but there are certain things I am not willing to do for it.
Targeted advertising is one of these things that I believe deserves to fully die. I have nothing against general analytics, nor gathering data about trends etc, but stalking every single person on the internet 24/7 is something people are put in jail for if they do it in person.
Citation needed. This is completely untrue AFAIK. They've claimed that inference is profitable, but not that they are making a profit when training costs are included.
This can go either way. For databases open source integration tools prevailed, the commercial activity left hosting those tools.
But enterprise software integration that might end up mostly proprietary.
Power companies are even constructing or recommissioning power plants specifically to meet the needs of these data centers.
All of these investments have significant benefits over a long period of time. You can keep on upgrading GPUs as needed once you have the data center built.
They are clearly quite profitable as well, even if the chips inside are quickly depreciating assets. AWS and Azure make massive profits for Amazon and Microsoft.
That ultimately wouldn't be a big deal if the paper valuation from the trade didn't matter. As it stands, though, both parties could log it as both revenue and expenses, and being public companies their valuation, and debt they can borrow against it, is based in part on revenue numbers. If the number was meaningless who cares, but the numbers aren't meaningless and at such a scale they can impact the entire economy.
1) Google Search is now 99% crap that nobody wants, and even the AI answers are largely crap,
2) I believe somebody is going to eventually realize that search engines are stupid and improve on them. The whole idea of a single text box where you type some words and the search engine reads your mind to figure out the one thing you wanted, and then gives you one generic answer, is crap. We've just been blind to this because we don't see any other answer to realize we've been getting crap.
If I type in "when did MMS come out", Google will tell me when the candy product M&M's came out. But I wanted to know when the Multimedia Messaging Service was released. At some point somebody is going to realize that you can't actually tell what the hell the person wants from these simple queries alone. The computer needs to ask you questions to narrow down the field. That's sometimes what happens in ChatGPT, but it can be greatly improved with simple buttons/drop-downs/filters/etc. I think it'll also be improved by more dynamic and continuous voice input for context. (I notice Google Search now has audio input; I wonder if that came in after ChatGPT? Wayback Machine shows it starting in mid-2024) When they eventually implement all this, and people realize it's a million times better than what Google has, then Google will be playing catch-up.