”Given that most people have most of their money in index funds”

”Most people” is doing a lot of heavy lifting there; 52% in the US and just 25-30% globally invest their money

> world does not allow perfect competition

What does this have to do with anything?

Plenty of asset managers construct indices to save fees.

In the short term there are distortions and inefficiencies. It may feel like free market is done .

However in the long term, economics usually finds the most efficient way.

Maintaining inefficient structures like tariffs or monopolies becomes more and more expensive and eventually untenable and disruptions will occur.

There are a few things to consider if you are in the investment space:

- Growth rate: you can't compare them to the average single digit growth companies or dividend focused companies. Most of these tech companies revenue are still growing at double digit with good moat. Pe is a good measure but it's not absolute. If you believe they sustain their growth then it's a good bet. And you can choose not to buy in their growth stories too. At the end of the day investment is about judgement call

- History benchmark: some of their pe is at historical low. So they are actually cheaper than before.

- Pe ttm and forward pe: how much pe ttm are they at? how much forward pe are they projecting? If forward pe is significantly lower, that means the current analysts consensus is that they will grow in future

- Pe is the a number but it's not everything. You need to consider multiple things to decide if that's undervalued for you. It's highly subjective as different interpretations are common.

- This post is about if you want to play the gaap game with private tech companies. My point is that there are still many public companies that are cheap at certain point. You just need to be patient and be willing to research and wait. For example, meta at around 500 was a buy for me, but since then it has rebounded it's still good but not as undervalued as a few days ago

> Just because they aren't regulated doesn't mean they are literally free to do whatever they want to close the round.

What makes you think their public announcements are aligned with what they give prospective investors?

it would be fraud only if they're also telling their investors the same numbers.

You can't put anthropic and openai in the same basket regarding ethics. One accepted Department of War's conditions and the other not.

What origins of ethics?

You use the term piracy, which potentially hints at ur biases.

American IP laws aren’t universal, and last I checked neither is it popular in Silicon Valley.

Institutions surrounding dealing with IP Piracy is an American strong arm attempt to own the unownable and to use Russel conjugates to make the flagrant attempt seem just.

And who would be buying this from them? Let's say you're anthropic, would you give money to your competitor?

I'll also add that Google is already a player in that space so more likely to easily sell it off.

this isn't credible though. them not being able to use all their compute likely means that the ai bubble has popped, so they won't be getting a good price on it.

In principle yes, but all metrics so far suggest they are losing money every user interaction. There is very little network effect with these tools so It's not like they can start cutting back on staff and feature deployment.

lol that’s a line so incredibly naive it hurts.

One does not “simply” reduce spending.

  Whats worse is they mostly seem to have retail market name recognition which is arguably the hardest, or maybe the impossible market to make money from.

Apple is another one that focuses almost exclusively on retail and is also one of the most profitable in history.

2000s, 2010s, and 2020s. This is how tech companies work, especially in a new industry.

OpenAI and Anthropic have positive gross margins for inference.

Uber generates about $1b in profit yearly now.

How did Uber somewhat break even? They lost $34b before making a profit.

Uber was only on a path to monopoly in the US, not world wide. It’s lost to local competitors in most countries. And it can get disrupted by self driving cars soon.

OpenAI’s SOTA LLM training smells like a natural monopoly or duopoly to me. The cost to train the smartest models keep increasing. Most competitors will bow out as they do not have the revenue to keep competing. You can already see this with a few labs looking for a niche instead of competing head on with Anthropic and OpenAI.

Worse, Google can afford to outspend them in this game and basically run them both out of money.

It seems like it is comparable based on what you just said.

> It's well know everyone is making great money on inference.

That is not, in fact, "well known", but based entirely on the announcements of the inference providers themselves who also get very cagey when asked to show their work and at least look like they're soliciting a constant firehose of investment money simply to keep the lights on. In particular there's a troubling tendency to call revenue "recurring" before it actually, you know, recurs.

Google has already surpassed them both in all areas except coding. People on HN only look at benchmarks, but Gemini's multimodal understanding, things like identifying what a plant is, normal user use cases (other than chatting), integration with other tools, is much better.

It's believable that Meta, ByteDance, etc. can catch up too. It is not certain that scaling will meaningfully increase performance indefinitely, and if it stops soon, they surely will. Furthermore, other market conditions (US political instability) can enable even more labs, like Mistral, to serve as compelling alternatives.

Uber, TSMC, etc. have strong moats in the form of physical goods and factories. LLMs have nothing even remotely comparable. The main moat is in knowledge, which is easy to transfer between labs. Do you think all the money that goes into training a model goes into the actual final training run? No, it is mostly experiments and failed ideas, which do not have to be repeated by future labs and offshoots.

Google has completely caught OpenAI. Anthropic has a better coding model, but I'm sure Google is working on that too.

>Industries always consolidate and winners emerge.

no, most industries just sell boring generic products, a few industries favor monopolists. Semiconductors are one of them but LLMs are also as far removed from that business as is physically possible.

TSMC makes the most complicated machines humans have ever built, a LLM requires a few dozen nerds, a power plant, a few thousand lines of python and chips. That's why if you're Elon Musk you could buy all of the above and train yourself an LLM in a month.

LLMs are comically simple pieces of software, they're just big. But anyone with a billion dollars can have one, they're all going to be commoditized and free in due time, like search. Copying a lithography machine is difficult, copying software is easy. that's why Google burrowed itself into email, and browsers, and your phone's OS. Problem for openai is they don't have any of that, there's already half a dozen companies that, for 99% of people, do what they do.

The barrier to replicating TSMC isn't just cost, it's supply chain, geopolitics, and talent.

Only one company on Earth can make the UV lithography machines TSMC buys for their highest end fabs, and they're not selling to anyone else.

The PRC tried to brute force this supply chain backed by the full might of the Party's blank check, all red tape cut, literally the best possible duplication scenario, and they failed.

OpenAI is great at attracting people who say "yeah, sure, I'll give you capital at some point in the future" who then never actually give them the capital (or at least haven't yet).

If I remember correctly, Facebook took 10 years raising money before going ipo.

Could be wrong though.

If your shop is unprofitable for years with no chance to recoup any of the costs, you close it, as your investments run out, and investors and banks stop giving you money as you keep losing them.

US tech companies just continue operating because "revenue and growth".

Its simply not going to happen. People like Nadella call it 'tacit knowledge' - the reality is the work people do is much broader than what is producible by LLMs alone. Without the human, there is no work done. Unlike classic machinery, LLMs are not comparable in that you cant simply reduce labour input by X and be fine. Sure in the short term the consequences will not show up, but in the long term they will.

Altman and co. get down on their knees and pray that proposition is only transitory in the short run.

LLMs wont disappear, but they wont be large profit generators either. Especially not so whilst there is fierce competition and every dollar of profit is re-invested. The value of an asset is derived upon its potential cash return, net of reinvestment, taxes et al.

Altman is hoping to survive long enough to finance R&D to figure out how to encode the entirety of what humans do, to be able to come good on the asinine aspirations he has put forth that justify its valuation. But it will end in disaster.