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sgt101 • yesterday at 8:18 PM • 1 reply • view on HN

It's friday night, I've had a beer (or three) and I was being glib and snarky with my comment above and you have replied in good faith and I feel that I owe you a good faith reply in return. Please accept apologies for any hurt caused by my comment or by this comment but I feel that there are serious issues to be addressed here.

I think that the first issue you raise is "why would they" and the answer is (IMHO) because there are a bunch of people who are trying to make money anyway that they can and as far as they are concerned it's just bad luck and social darwinism if they get the cash and other people are left holding the bag.

You are right that the products that have emerged from the AI boom have real value, but it's not clear that anyone can capture the value from consumers. LLMs are more or less commodity items. Listen, for sure, for serious, if you gave me $20m I could build a tier 3 LLM in six months. Fella, I am not boasting, I am a small fry old AI researcher and there are 100 like me in every bar in my town (London, near enough) but I know I could do that. I also know that the tricks to move from tier 3 to tier 1 are increasingly available *FROM THE FRONTIER MODELS THEMSELVES* . The number 1 use case at OpenAI for AI is... managing the clusters. And managing the clusters has been the hard bit since about 2017. This is not the case of a rare product that everyone wants, it's basically the same as Florida condos - yes a view over the ocean is great, yes a nice condo is good to live in, but also there is a lot of beach front in Florida and building condos is not rocket science (I am bearish on SpaceX too but for other reasons).

No one will give me $20M sadly, and if they did I'd probably abscond to somewhere I hoped was safe with it and spend the rest of my life raising litters of kittens. This is definitely a good hobby and is actually quite like managing big clusters, but with a better emotional balance.

Anyway, the next point is profitability.

These companies are not profitable. OpenAI is not profitable, Anthropic is not profitable. In fact they are burning cash and losing money hand over hand. They are subsidising subscribers and enterprises to use their models because if they don't, and they force market costs, then they won't have market share. Look to openrouter's stats if you don't believe me : https://openrouter.ai/rankings

The path for Amazon to be profitable was to use investor cash to build a modern last mile distribution and supply chain across the US and then the globe. The AI gamble was to create a capability to serve AI that could effectively monopolise the market and that they could raise the drawbridge like Google did with search. But Google created a market out of you and me for advertisers, where as OAI and Anthropic are asking you and me to buy their products. Brother, I will, but only if they are cheapest and best, which means that this will be a competition, and in a competition I do not care if you go bust and the data centre has to be auctioned to the lowest bidder because if that happens I will then see a cheap service come into use that asset to replace the leavered up investors who sadly lost their pants. If you have to sell off an Amazon distribution center you've got a big problem, an Amazon distribution center only has real value because of the last mile fulfillment and the global supply chain that's attached to the store. A data center for LLMs is not the same, tokens are tokens. I can buy them from Slough or Texas, the Texas ones might cost be less because gas is cheap there, but we're talking $1.40 per million vs $1.00.

That's it.

A $3Trn commodity service.


Replies

archagon • yesterday at 9:32 PM

Great comment.

Also, I could be wrong, but I see two additional problems with the AI business model:

* AI accelerationists believe that generative AI will lead to compounding software complexity that opens paths to incredible economic growth. But there's a good chance it will mostly just expand breadth-wise, i.e. flood the world with mediocre code that follows well-trod paths and ships a lot faster instead of inventing new abstractions or reifying concepts that actually allow for compounding complexity. I don't think consumers want to be flooded with more software. I think they actively don't want this.

* Unless software development is forever "finished" in its current state, or unless actual general intelligence is somehow invented, the training work can never stop. If the money runs out, current models will still be useful for a while, but eventually the industry will lap the training data and model output will degrade.