S&P500 had a rule from 2017 to 2023 that prevented companies with dual classes of shares (the sort that allow them to maintain founder control- like what GOOG and META did) that went public after the rule was instituted from ever being in the index. To be clear, META and GOOG were both in the index, but it was to prevent new companies from coming along and doing it. (I think it was related to SNAP going public?)
They removed it largely because investors wanted higher returns, and the tech companies that had such dual classes (1) were doing really well, and the S&P ended up caving on that rule.
1: Perennial hot button around here Palantir did this in a more extreme fashion than most. The three founders F class shares will always be at 49.9999% of the votes and the early investors B class shares have 10 votes each as compared to the publicly traded A class shares 1 votes.
There is a lot resting on Starlink, 11 gigadollars in direct revenue that accounts for fully 60% of SpaceX's total revenue of 18 gigadollars. It's hard to see how that level of revenue can sustain a 1 terradollar valuation.
Like, TSLA had 94 gigadollars in revenue last year, and it's a 1.2 terradollar company, and most outside analysts are frankly skeptical of that multiple. SpaceX is trying to get a similar valuation on a fifth of that revenue.
Interesting to see if Claude Code gets a lot better with a complete set of all jira tickets along with the integration to see the associated actual PR's, the linking of issues... it would depend on who owns the Atlassian data, of course. But that could be the last best set of programming data out there, if you had the complete Atlassian cloud-hosted archives.
Jet fuel in particular is more complicated than that. At the moment, most of the shipping passing through the straits are coming to and from Iran. I believe only a few ships for other countries have transited, none of them tankers- the GCC countries are not willing yet to acknowledge Iran's control over the Straits, since doing so would be to admit that this war was a giant catastrophe.
Iran, for sanctions related reasons, is unable to make international grade jet-fuel. Only the GCC countries can (in the Persian Gulf). And so not a single tanker of jet fuel has transited the Straits of Hormuz to Europe since this incredibly dumb war started. Iran does export raw crude to China, which refines it to international grade jet fuel, and China is getting some shipments from Iran, but China's raw crude imports have dropped, and they have responded by ending jet-fuel exports to the rest of Asia.
My understanding is that Europe can produce jet-fuel from the North Sea deposits, but they rely on imports because it is not sufficient for their consumption (My memory is that 'domestic production' was on the order of 60% of consumption). So as long as the Straits are blocked to GCC traffic there will be problems for European commercial aviation, getting worse over time.
Is there a cite for that explanation? That doesn't sound right to me. My understanding is that almost all Hormuz oil is crude, the refineries are elsewhere.
Which part? That GCC countries export refined Jet-A?
Kuwait was responsible for 15% of seaborne jet fuel exports in 2025 (1), something like 10% of the world's total exports. In 2024, Bahrain exported 20 million barrels of jet-a (2). South Korea, #1 in the world, exported 90 million barrels in 2025- all by sea- (3), so Bahrain isn't a dominant player, but it's still an important amount.
Obviously most of ROK's oil was crude imported to South Korea for re-export elsewhere, but the GCC has spent the last few decades trying to get up the value chain of petro-chemicals and capture more of the value themselves.
Yeah, those number seem cherry-picked. The fact that refineries exist in gulf isn't saying that refinery capacity doesn't exist elsewhere to manage the crude that is transiting the straight. It doesn't mean they do either, but I'd want to see a deeper analysis than any of that stuff you're linking.
Supply chain management is hard, but it's not nearly as fragile as people tend to fool themselves into thinking. How many chip or egg shortages have we lived through which showed up as pretty routine price disruption? And that's especially true in areas like fuel, which everyone recognizes as national security issues worthy of careful study and planning.
My gut says that's bunk, basically. Europe isn't running out of fuel.
I know that people keep saying "we're early on here", but I take it as a negative signal that people keep thinking we are in the early innings here. Compared to previous generations of technology change, a great deal of time has passed, it should be a bit disconcerting that no one seems to have found a way to make money out of this yet.
Look at previous killer apps- they came out quickly and were raking in money very quickly. The Apple II went on sale on June 10th, 1977. Visicalc went on sale October 17th, 1979- 860 days separate the two. Apple IPO'd in 1980 with a 21% operating margin! Netscape Navigator 1.0 released December 15th 1994, Amazon.com made its first sale July 16th 1995- 214 days later. AMZN IPO'd May 15th 1997, 883 days after Netscape 1.0 released to the public (they had raised <10 million dollars to that point, but chose not to have a profit because they kept re-investing all of their profit into expanding the business).
We are already 1232 days since ChatGPT 1.0. So we're about 50% farther along than either of those killer apps. No one has figured out as good a business model for Generative AI as either of those were.
To use the other great technology transformation of the past 50 years, cell phones, I have a bit of trouble figuring out the right comparison to ChatGPT 1.0. I can work backwards from today to ChatGPT 1.0 opening up to the public, that's about the difference from the iPhone 3G (the first one with an appstore, the real killer app) to the launch of the Motorola Razr, to give you an idea of how fast mobile technology moved.
Do note that the Razr and the iPhone, like Visicalc, the Apple II, and Netscape 1.0 were hugely profitable for their companies, in a way that no one has demonstrated with Generative AI. Amazon is a bit of a special case, but they were not raising money, they were just re-investing cash that was being thrown off not as profits but into expanding the business. I don't believe that any AI company is generating cashflow the way that Amazon was in 1997, and the other companies mentioned here were GAAP-profitable.
Is it actually profitable? That the presumed market leader, Anthropic, changed their business model just today to kill off their buffet monthly plans and switch to a la carte for Enterprise makes me doubt they are making money off of selling tokens to software developers.
There is some revenue in copywriting, translation and generating images. But that is probably 20 per month per seat enterprise plans with limited use. With the possible cost of interface varying enough to have actual marginal costs...
On top of that, the APIs/Tools/Function Calls into the real world don't exist yet. But consumer products are going to start eventually exposing functionality to these LLMs. By that time, I wonder if we'll all have an edge-inference box sitting in every one of our houses that we buy from a consumer products company like Apple or from Amazon, or directly from OpenAI or Anthropic. These little brains will be the low latency central nervous system of a lot of things in our homes, and gateways to the larger models in the cloud. Or at least that's how I imagine it sorting out in the future.
Previous generations of technological change of the calibre we are told AI will be also required major changes to the real world and new products to be built: new cell towers had to be constructed, fibre cables laid, data centers built, personal computers produced, warehouses established. And software needed to be fundamentally rewritten to support each of these generations too. And yet the companies doing that in those previous generations managed to produce huge profits significantly faster than Generative AI has.
That's my biggest concern with it, I don't see the business case closing anywhere, and without businesses that actually make money all the technology in the world doesn't actually do anything.
> And yet the companies doing that in those previous generations managed to produce huge profits significantly faster than Generative AI has.
Have you considered a simple answer to this inconsistency? The market and investors does not demand that these AI companies make a profit. The only reason companies are expected to make profits is because either those who own shares in the company expect it, or those willing to invest in a company expect it.
> There just isn't enough compute right now to realize the larger monetization strategies.
How can this be relevant? Why isn't the compute we have available right now sufficient for turning a profit?
Is this another one of those "We lose money on each sale but make it up in volume" things?
I mean, if much much larger investments are needed before current LLM providers can turn a profit, that's not a good indicator that they have any sort of sustainable business, is it?
Comparing the IPO market today to the IPO market in the late 90s is not very instructive. You could have IPO'd a lemonade stand in 1998 and raised $10 million.
I'm using that only for AMZN because they seem to have made a choice to not turn a profit and instead to expand their business. The other companies I mentioned were directly profitable by this point in their respective revolutions, except for Amazon, where I'm using the IPO as proof that they had a sustainable business, even if it wasn't precisely profitable- they were generating enough cash to be profitable, they just chose to reinvest it into the business. I don't see any evidence that any of the major Generative AI companies are in that position or the position that Apple, Netscape, Motorola etc. were in.
And that's the weird one, all of the other examples I provided were booking real profits by this point in their technology cycle.
I think that fact that IPOs have grown slower over the years is more about larger VC markets where they can fund valuations up to hundreds of billions rather than something to do with adoption.
As you note, Netscape and Amazon IPOed fairly quickly.
Google took 6 years (1998 to 2004)
Facebook took 8 years (2004 to 2012)
Alibaba Group took 15 years (1999 to 2014)
Claude Code is at $30B annual recurring revenue, and it launched in Feb 2025, and OpenAI at $25B (although they measure partner revenue differently). By comparison the iPhone make $630M revenue in the 12 months after it was launched.
> Claude Code is at $30B annual recurring revenue, and it launched in Feb 2025, and OpenAI at $25B (although they measure partner revenue differently). By comparison the iPhone make $630M revenue in the 12 months after it was launched.
What does revenue have to do with it? Companies usually want to IPO with a decent profit margin showing on the books, revenue doesn't usually come into it.
The "official" value of a stock is it is the current best guess of the market for all future earnings until infinity discounted back to the present at some discount rate (to account for the time value of money). That price to earnings rate is 1, because it's the definition. The "E" in PE ratio, however, is for a different time period: traditionally just the trailing 12 months (or previous completed FY- for high growth companies you will sometimes see "last month's revenue multiplied by 12" or other guesses).
This calculation is why "growth" companies dominated the stock market during the 2010's: with the Zero Interest Rate Policy that most of the developed world had, the discount rate that the markets used ended up being basically zero. In which case a market player is indifferent between a dollar in 2020 and a dollar in 2040. So if a company had a 10% chance of being worth a trillion dollars in 2040, that was worth (0.1 * 1 trillion=10 billion dollars). But with a more traditional 4% discount rate then a dollar in 2040 is worth less than half of a dollar in 2020, and that means your 10% chance of being worth a trillion dollars in 2040 has less than half of the value. Even if nothing else changed about your business, just the discount rate changing halved the value of your company.
In order to call it a NASDAQ 100 Tracking Fund you need to pay the NASDAQ a licensing fee (same with S&P500, Wilshire 5000, etc.). The contract you have with NASDAQ will determine exactly how much freedom you have to change rules and still call it a NASDAQ 100 fund. I've never seen a licensing agreement, don't know anything about how they would typically read.
There is also the concept of "Index Tracking Error". No fund can perfectly mimic the index, and that is expected and understood, but the goal is generally to have the tracking error <0.1%- 1% would be a bad track. And so an index fund could take the risk that they will have a tracking error and delay picking up SpaceX even after it joins the official index, but then if it goes up they will look worse relative to their real competitors, the other NASDAQ 100 tracking index funds. If SpaceX goes down, of course, they will have positive tracking error, but I'm not sure how much potential investors would value that. SpaceX would be something like 4% of the NASDAQ 100 at it's announced expected market cap, so a 10% movement by SpaceX would be enough on its own to get you into the notable tracking error range if you didn't have any exposure to it.
S&P500 held fast to their rules on consecutive quarters of profitability and forced TSLA to meet them (must be profitable in qX + sum to net profit over the past year). If they hold to them this time, SpaceX would need to be profitable over a year while public to enter the index.
They have instituted rules and gone back on them eventually (most notably for several years they had a "no going public with different classes of voting shares designed to allow control forever, if IPO is after today" rule that they eventually dropped) but they are generally pretty good about following rules.
And not normal for a company that has been at it this long.
The Apple II went on sale on June 10th, 1977. Visicalc went on sale October 17th, 1979- 860 days separate the two. ChatGPT was opened to the public on November 30th, 2022, which was 1219 days ago- almost 50% more time has elapsed than between the Apple II and Visicalc.
Visicalc is often described as the killer app of the first generation Personal Computer(1). It was the product that drove them into every small business in the country, that blew up sales of personal computers and brought them out of the realm of hobbyists into enterprise. And, honestly, I think Visicalc and spreadsheets are still a greater benefit than what I've seen out of generative AI today. And that happened a lot faster than where we are today with generative AI. Apple had enormous actual profits by 1980 (Apple IPO'd in 1980 with a 21% operating margin). So I think that a lot of the "just got to give it more time" argument misses that the previous computer based revolutions that we know about productized and threw off gobs of cash a heck of a lot faster than this one has.
If the end result of this is "certain classes of white collar workers are 10-25% more productive" (which is the best results I can extrapolate from what I've seen so far) then it's really hard to imagine how OpenAI can return a profit to their investors.
>If the end result of this is "certain classes of white collar workers are 10-25% more productive" (which is the best results I can extrapolate from what I've seen so far) then it's really hard to imagine how OpenAI can return a profit to their investors.
If we take this as face value, and say that the absolute best case scenario is there are literally no other uses for AI but helping programmers program faster, given 4.4 million software devs, with an average cost to the company of $200,000 (working off the US here, including benefits/levels/whatever should be close), those 4.4 million devs with 20% productivity would save roughly 176 billion dollars a year.
Some companies will cut jobs, some will expand features, but that's the gist. And it's hard not to see the magnitude of improvement that's come in just 3 years, though if that leads to a 'moat' is yet to be seen.
> If we take this as face value, and say that the absolute best case scenario is there are literally no other uses for AI but helping programmers program faster, given 4.4 million software devs, with an average cost to the company of $200,000 (working off the US here, including benefits/levels/whatever should be close), those 4.4 million devs with 20% productivity would save roughly 176 billion dollars a year.
I don't think that's necessarily out of line with struggling to return a profit to investors though: an individual company is only ever going to capture a tiny fraction of the productivity improvements it enables its customer base to make[1], its own cost base is unusually high for tech, and investors are seeking a 10x+ return on an $852B valuation for a company that isn't even the market leader in that segment (which isn't the only segment, but it's the optimum B2B one). You can have a great business with a great value proposition and a sustainable moat and still not generate the desired returns on investment at a $852B valuation.
[1]and that's productivity improvements over the best-known free models, not productivity improvements over reading StackOverflow
Sorry, I forgot that for many engineers this is, in fact, their first time going through a technology cycle like this, and so would need more explanation. I am too young for Visicalc myself, but the cycle that I saw while I was in high school- the dot-com bubble- doesn't have convenient, easy to mark out dates like the PC does.
Thinking... Thinking...
Tim Berners-Lee proposing HTTP in 1989 is kinda like the original Attention is All You Need paper, I guess? Netscape 1.0 release in December 1994 is ChatGPT 1.0? And then Amazon.com opened up to the public in July 1995 and then IPO'd in May 1997 (after raising less than 10 million dollars in two funding rounds). But once again we have the business side of these previous cycles moving much faster than this one.
WOW. That does really drive home the perspective. I was an adolescent during those years and it did seem quick then, but that's an insane pace in retrospect.
Amazon is perhaps a counter-example to your point, though, to be fair. It seems to me they did a lot of spaghetti throwing while making accounting losses for a good number of years. Granted, they did it on OpenAI's dining budget.
I took it the other way, spreadsheets shook up the world way more than AI has (to date) - it's possible that history will look back and count AI as the bigger "thing" but if I had to pick a killer app, VisiCalc and computer spreadsheets in general would beat ChatGPT.
VisiCalc was "the" killer app for early micros, but being able to edit a written text on screen and then print it out with letter-like quality was nothing to sneeze at, either. This was plausibly a key gain in efficiency for the service sector, perhaps comparable to the 10%~25% that's now being talked about re: LLM's (which is huge on a secular basis).
IMO, the AI companies are trying to be both T-Mobile and Google Doc at the same time. Even Apple is struggling with being both the platform and the product. The issue with OpenAI is that the platform has no moat (other than money) and the product can be easily copied. In the game console world, the platforms have patents and trademarks, and games are not easily produced.
I'm a retired engineering manager so judge me appropriately :-) I've had 1000 good chats with ChatGPT on a wide range of topics. I build personal Excel and Access applications but not any real programming. I don't need workflow automation although I will dabble with Codex. I'm curious why I should abandon what works for Claude.
You shouldn't. No need to rush to buy a TI-84 to do simple arithmetic. I don't use either because I can learn just find from docs and textbooks. And I don't have that many problems to solve with computing.
The Apple II was so simple (by today's standards) that it came with a complete printed circuit diagram. Visicalc was so simple it was written by two guys in a year.
AI is so many orders of magnitude more complex that the comparison is not really useful.
This complexity requires a lot of money- from investors- to sustain. If those investors don't see a return on their investment before they get too anxious, then no more money will be invested and the business is dead. So that would suggest that there will be even less patience from the money than the investors in Apple had. If you are correct that this greater complexity actually makes it harder to productize, then it is hard to see how frontier model generative AI will be viable under a VC funded domain.
It is entirely plausible to me that there are great technologies that are impossible to reach via the normal means of VC/investor financed capitalism. I certainly have encountered market failures requiring extremely patient money (usually in the form of government subsidies) to produce a useful product that eventually does have market value. That has worked many times in the past. But so far generative AI has not had that, and looking at my non-technology friends, I very much doubt that there would be much support among them for government subsidies of AI companies. AI companies have made too many people unhappy, served as too much of a punching bag, to be in a good position politically for that.
In June 2022 the Oracle acquisition of Cerner (a EMR now billed as Oracle Health) closed, so that would be after the 2022 date and before the 2023 date. Cerner was 28,000 employees.
If they do cut back to their size before the acquisition, while continuing to try and support the EMR, they will be doing a lot more with fewer employees.
They removed it largely because investors wanted higher returns, and the tech companies that had such dual classes (1) were doing really well, and the S&P ended up caving on that rule.
1: Perennial hot button around here Palantir did this in a more extreme fashion than most. The three founders F class shares will always be at 49.9999% of the votes and the early investors B class shares have 10 votes each as compared to the publicly traded A class shares 1 votes.
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