Hacker News Reader

Comment by bwhiting2356 | Hacker News Reader

Amazon was founded in 1994

And who would have thought it was the online bookstore that would be the big survivor of the dotcom era? They were a comparatively small player relative to AOL/Yahoo/etc at the time of the dotcom bust. Which company is the 1994 Amazon of AI now?

Cohere is the forgotten AI company founded by the Attention is all you need team....

Apple.

As I recall, Amazon also famously didn't turn a profit for ages - but they were also capable of turning one much earlier than they did.

Are AI companies capable of turning a profit today if they turn some knobs?

Yes - IIRC, Amazon was profitable on books by 1996, with other sectors following as they expanded and it was clear that they could post profits any time they wanted by slowing expansion. It was surreal through the bubble years to see “analysts” equating them with companies which were losing money on every sale with no clear way to change that.

Exactly right. Even though ride sharing industry lost money in subsidy arms race and side bets it was likewise fundamentally sound in major metros since early on. Popular "analyses" kept equating Uber/Lyft with firms losing money on every sale with no path to fix it but the demand was always there as riders had already left taxis and transit on reliability and convenience grounds.

The narrative is that inference on existing models is profitable. All of the profits and many billions of additional capital invested go into training the next model, which is some multiple more expensive to train than the last. Each new model generation also leads to more revenue growth. Newer models are more compute-efficient when distilled (so could possibly be higher margin) but also they work on longer time-horizon tasks and can make greater use of test-time compute which increases token counts. So the inference ROI on each model can pay back the cost of training it, but future growth demands put all that money and more into training the next model. The numbers we’d need to prove whether this is true are not public, but it makes sense and fits what info we do have.

Theoretically, if training more expensive models stops resulting in better capabilities or isn’t economically viable, the labs can shift gears into making profit on old models. A lot of future growth is priced in so this would lead to a collapse in share price if it happens anytime soon.

There’s a story out that Anthropic might be profitable this quarter. This is in one sense bad news - it means that the company wasn’t aggressive enough about acquiring capacity last year, because they didn’t foresee how fast their inference business would grow. Anthropic is now forced to make suboptimal choices about serving existing users vs. training the next model (need to scrounge for capacity by paying other players like SpaceX). And as a Claude Code user I feel like I’ve been affected by that, what with the random outages and performance degradations.

Wait till people find out you can have the same or close to the same output at 1/100th of a price.

You cant possibly believe we'll be just spending more and more in tokens endlessly.

And if the margins are so good for anthropic they will collapse. There's too much competition in the field.

I don’t believe similar scores on small bounded tasks mean models are interchangeable. I’ve found that heavy token-burning workflows are good for my productivity (letting multiple sessions run async working of different stuff). Claude ultracode is an easy example to point to, but there are tons of harnesses out there doing similar things. I find using a higher quality model matters because it affects how far it can get unattended before heading the wrong direction. I’ve tried using the cheaper/faster models and it’s a real downgrade (or completely useless). A model that’s even smarter with longer time horizon would be even better for my productivity. I don’t think we are at the ceiling for model quality or price. My employer pays a lot for my tokens but it’s still a lot less than they pay me.

I agree Anthropic faces some risk they could get commoditized, but on the other hand if things go well they could end up leading adoption into more industries. There are upside and downside scenarios. Recursive self-improvement is obviously an important unknown and could lead to winner-take-all.

You can use Claude Code with DS4, I'm doing it as my daily driver.

There's the "how much of my company exists in a black box controlled by some asshole" angle as well, but in my mind the biggest issue is that current models are already capable of saturating a dev in like four hours.

There is a big difference between "Every customer is a loss" and "We are profitable and re-investing all of our money". Amazon continued to grow, and reinvested its revenue with solid business fundamentals.

Reply on news.ycombinator.com