A key difference is that the cost to execute a cab ride largely stayed the same. Gas to get you from point A to point B is ~$5, and there's a floor on what you can pay the driver. If your ride costs $8 today, you know that's unsustainable; it'll eventually climb to $10 or $12.

But inference costs are dropping dramatically over time, and that trend shows no signs of slowing. So even if a task costs $8 today thanks to VC subsidies, I can be reasonably confident that the same task will cost $8 or less without subsidies in the not-too-distant future.

Of course, by then we'll have much more capable models. So if you want SOTA, you might see the jump to $10-12. But that's a different value proposition entirely: you're getting significantly more for your money, not just paying more for the same thing.

> Their pricing models are simply not sustainable. I hope everyone realizes that the current LLMs are subsidized, like your Seamless and Uber was in the early days.

If you run these models at home it's easy to see how this is totally untrue.

You can build a pretty competent machine that will run Kimi or Deepseek for $10-20k and generate an unlimited amount of tokens all day long (I did a budget version with an Epyc machine for about $4k). Amortize that over a couple years, and it's cheaper than most people spend on a car payment. The pricing is sustainable, and that's ignoring the fact that these big model providers are operating on economies of scale, they're able to parallelize the GPUs and pack in requests much more efficiently.

I'm not sure. I asked one about a potential bug in iOS 26 yesterday and it told me that iOS 26 does not exist and that I must have meant iOS 16. iOS 26 was announced last June and has been live since September. Of course, I responded that 26 is the current iOS version is 26 and got the obligatory meme of "Of course, you are right! ramble ramble ramble...."

I've been explaining that to people for a bit now as well as a strong caution for how people are pricing tools. It's all going to go up once dependency is established.

The AWS price increase on 1/5 for GPU's on EC2 was a good example.

Yep. The goal is to build huge amounts of hype and demand, get their hooks into everyone, and once they've killed off any competition and built up the walls then they crank up the price.

The prices now are completely unsustainable. They'd go broke if it weren't for investors dumping their pockets out. People forget that what we have now only exists because of absurd amounts of spending on R+D, mountains of dev salaries, huge data centers, etc. That cannot go on forever.

The pricing will go down once the hardware prices go down. Historically hardware prices always go down.

Once the hardware prices go low enough pricing will go down to the point where it doesn't even make sense to sell current LLMs as a service.

I would imagine that it's possible that if ever the aforementioned future comes to pass that there will be new forms of ultra high tier compute running other types of AI more powerful than an LLM? But I'm pretty sure AI at it's current state will one day be running locally on desktops and/or handhelds with the former being more likely.

We're building a house on sand. Eventually the whole damn thing is going to come crashing down.

It would mean that inference is not profitable. Calculating inference costs show it's profitable, or close to.

>I hope everyone realizes that the current LLMs are subsidized

This is why I'm using it now as much as possible to build as much as possible in the hopes of earning enough to afford the later costs :D

> I hope everyone realizes that the current LLMs are subsidized, like your Seamless and Uber was in the early days.

A.I. == Artificially Inexpensive

> I hope everyone realizes that the current LLMs are subsidized

Hell ya, get in and get out before the real pricing comes in.

Wait for the ads

On the bright side, I do think at some point after the bubble pops, we’ll have high quality open source models that you can run locally. Most other tech company business plans follow the enshittification cycle [1], but the interchangeability of LLMs makes it hard to imagine they can be monopolized in the same way.

1: I mean this in the strict sense of Cory Doctorow’s theory (https://en.wikipedia.org/wiki/Enshittification?wprov=sfti1#H...)

Except most of those services don't have at-home equivalents that you can increasingly run on your own hardware.

They just need to figure out KV cache turned into a magic black box after that it'll be fine

The results are repeatable. Models are performing with predictable error rates on the tasks that these models had been trained and tested.

AI is built to be non-deterministic. Variation is built into each response. If it wasn't I would expect AI to have died out years ago.

The pricing and quality on the copilot, codex (which I am experienced in) feels like it is getting worse, but I suspect it may be my expectations are getting higher as the technology is maturing...