The thing is, companies like Google, Facebook, Amazon and Microsoft are already profitable, so it could lose them huge amounts of money, with no real meaningful benefit to user retention or B2B sales, but the companies as a whole would still be profitable. It could be a huge money black hole, but they continue to chase it out of unjustified FOMO and in an attempt to keep share prices high through misplaced investor confidence.

Apple’s share price has taken a pretty big hit from the perception that they’re “falling behind” on AI, even if they’ve mostly just backed away from it because users didn’t like it when it was shoved in their face. Other companies are probably looking at that and saying “hey, we’d rather keep the stock market happy and our share prices high rather than stop wasting money on this”.

The fang companies that are in on the llm hype are still lighting money on fire in their llm endeavors so I fail to see how the point that they may be otherwise profitable is relevant.

I should reframe what I said: there is not a single profitable AI-focused company. There are tons of already profitable companies that are now deeply embedding AI into everything they do.

This is an interesting take in that only doing one thing but doing it well has been, historically, how businesses thrived. This vertical integration thing and startups looking to be bought out instead of trying to make it on their own (obviously, VCs play a role in this) has led to jacks of all trades.

We’re only a few years off from platform-agnostic local inference at mass-market prices.

What makes you confident in that? What will change?

I have to think that most people won't want to do local training.

It's like Gentoo Linux. Yeah, you can compile everything with the exact optimal set of options for your kit, but at huge inefficiency when most use cases might be mostly served by two or three pre built options.

If you're just running pre-made models, plenty of them will run on a 6900XT or whatever.