Uber didn't have ever-increasing costs though.

Try doing some inference with local models.

I'd be surprised if they're making money on inference just from that. There's no way someone paying $20 p/m and using it all day is not spending way more on even just the electricity for tokens, let alone the capex.

I don't really get the last bit. It's hard to imagine what a new fangled "frontier model" could do that would blow anyone out of the water. Like what does this look like? Really good benchmarks? Who cares about that anymore?

How? They're already burning $2 bills to make $1, court documents shown that Anthropic has already been lying around revenue (claimed to have made $19 billion when it's actually $5 billion to date [1]).

Not hard to believe they're lying about other things when they've been lying about the capability of their products since inception.

[1] https://www.reuters.com/commentary/breakingviews/anthropic-g...

So just ignoring the link entirely, cool cool cool

And what possible moat. It isn't hard to foresee that in just a couple of years, models outpacing the latest frontier tech we have today will run on consumer hardware. With open source workflows anyone can pull in to run, providers won't see a penny.

Another scenario is that dense models get replaced entirely, in which case the likelyhood of OpenAI and co pioneering the concept is pretty slim. They will be left with billions worth of infrastructure which cost them 10 times that 2 years earlier, faced with the reality touched by the article: liquidate.

If a major model provider were to just halt progress on developing new and improved models, the open weight alternatives would catch up in a couple years.

They would have a period of great margin, followed by possibly zero margin as enterprises move to free options.

They would have to come up with a lot of great products around the inferior models to justify charging at that point.

There are companies that already do nothing but serve tokens using models trained by others. Just running infrastructure and collecting a reasonable fee for their troubles. It's only a bad strategy if you want to claim to investors that you'll gain monopoly market share if only they could give you a few more billion dollars.

i don't think it will work, it's too easy to switch models. When google comes out with a new model people will just switch. I think Google wins in the long run, they have the money to just wait until everyone else goes bankrupt and they also have the Apple contract and therefore the mobile market.

What's interesting to note is that the "intelligence" labs can squeeze out of an H100, an almost 4 year old GPU, is dramatically higher than what they got out of it in 2022.

It hints that once these labs get a good enough "everyday model", they can work on efficiency so they can serve these models on old hardware. Which is almost certainly already happening.

> So if the money starts drying up, then they’ll naturally slow down because they’ll have to figure out how to do more with less.

Meanwhile companies like Google will keep investing on training...

Anthropic's CEO has suggested all AI companies should slow down training but obviously this is only beneficial for companies that can't afford to keep training.

> UI/UX conventions that help users get what they’re trying to accomplish in fewer steps

If we can expect the past 15 years of software UI/UX history to continue, it's more likely they'll spend the money on making the UI/UX more confusing, removing features, and making basic tasks take more steps than they do today.