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Comment by treis | Hacker News Reader

Historically the winners in software have a flywheel that turns faster with more users. Facebook the more of your friends on it the better the product was. Google tracked how long users were on pages to improve search.

The frontier models are going to win that way. They won't feed your code back into the system but they will track which code you keep and what code gets a "try again claude".

They're not going to lose on price. No consumer software ever has because ultimately it's not that expensive relative to salary and the marginal cost is 0.

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The marginal cost of AI is not 0. That's one of the big differences between this and older SaaS software. Inference costs a lot of money. Even if you're looking at just capital depreciation, it's quite expensive. I suppose it's more accurate to say marginal cost is stepwise - adding 1 new user is 0 cost if and only if your existing inference hardware covers that user's usage. As soon as you need a new server, adding _that_ new user costs ~$20k/year (assuming 100k server and 5 year depreciation).

This is true for traditional SaaS too, but the number of concurrent users that could be served by one machine and the cost of the hardware were both at least an order of magnitude better.

The marginal cost of AI is not 0.

In other words, AI is not your daddy's software. Comparing AI with old school software markets simply does not compute.

It's not literally 0 but for most casual users it is. Programmers are heavier users and the ones that have 10 agents going wild are even more so. In that case the cost is above 0 but economies of scale means the effect is the same.

The marginal cost for _users_ (on a token plan) is $0 but not for the AI company supplying that token plan.

>They're not going to lose on price. No consumer software ever has

Lists examples of software that are free to the users

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httpd

I want AI to go the way of Linux. I hope we see that future.

Exactly the CC sessions flywheel is a treasure trove of data and they all know that. The reason we went to stackoverflow was because there was data (upvotes/downvotes, comments, workarounds) discussed under the answers. That is a very high quality signal from the field.

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