This is why Musk rushed consolidating xAI with Spacex and rushed to an IPO. And, why it was so important to him that Nasdaq and Russell rewrite their rules so he could dip into everyone's retirement accounts as quickly as possible. I don't think he's particularly bright, but he has an incredible cunning about how markets behave and how to manipulate those markets to his ends.

The writing was already on the wall about a lot of things WRT the AI market: 1. there's a limit to how much money will be thrown at it before investors expect a return, and that limit is fast approaching, 2. it's turning out "intelligence" is going to be a commodity and it's not much of a moat, 3. cornering the market on memory and GPUs (even if they sit in a warehouse for months or years before going online, as long as it drives up the price for competitors) only works if your competitors don't figure out how to do it more efficiently and on alternative platforms.

Spacex also soaked up a lot of the retail appetite for AI, even though it's an also-ran that barely qualifies, in terms of enterprise and government usage (and for good reasons, both technical and otherwise). Every other AI IPO is going to be facing a more pessimistic market, especially if we continue to be in a tough economic environment.

In short: They waited too long, now they're in a world where they've burned so much money people expect them to deliver something that no one in the AI industry probably can deliver: a monopoly-like hold on a huge percentage of the market, and to invent a bunch of new markets that aren't currently buying AI. They have to have a dominant position for this to work because they have to be able to set their price higher than is standard now, without everyone fleeing to other cheaper providers. And, when the market for memory and GPUs finally gets back to normal and supply catches up with demand, I'd wager a lot of companies, and even individuals, are going to get real serious about open models and self-hosting (unless prices remain competitive for the hosted models).

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handoflixue 2 hours ago | parent [-]

> 3. cornering the market on memory and GPUs (even if they sit in a warehouse for months or years before going online, as long as it drives up the price for competitors) only works if your competitors don't figure out how to do it more efficiently and on alternative platforms.

My understanding is that the GPU market is largely sold out for the next few years?

What exactly is the threat here? Like, which specific competitors, what efficiency improvements, what alternate platforms?

	▲	SwellJoe an hour ago \| parent [-]
		I didn't think I needed to spell it out for the HN crowd, since folks talk about the Chinese models every day. But, OK... At least three Chinese companies have bragged about their latest models being trained and/or served on Chinese-made GPUs. Literally within the past 24 hours, LongCat spent a large portion of their release announcement for 2.5 bragging about how efficient their training process is and that it runs on domestic (Chinese) GPUs: "LongCat-2.5 uses long-sequence training during both mid-training and SFT. With less than 80 GB of memory per domestic AI accelerator, the training system prioritizes memory efficiency and aligns its parallelism with the supernode architecture." https://longcat.ai/blog/longcat-2.5/ DeepSeek has repeatedly blown the curve in terms of how efficiently a model can be trained and served, Xiaomi and Z.ai aren't far behind, and have also discussed training and serving on home-grown hardware. I'm sure you can search Arxiv for all of the papers from these providers (DeepSeek, in particular, who document seemingly everything, even when they're doing something ahead of the US providers) as well as I can.