The End of Moore's Law for AI? Gemini Flash Offers a Warning

Posted by sethkim 7/3/2025

The End of Moore's Law for AI? Gemini Flash Offers a Warning(sutro.sh)

113 points | 75 commentspage 3

georgeburdell 7/3/2025|

Is there math backing up the “quadratic” statement with LLM input size? At least in the traffic analogy, I imagine it’s exponential, but for small amounts exceeding some critical threshold, a quadratic term is sufficient

gpm 7/3/2025|

Every token has to calculate attention for every previous token, that is that attention takes O(sum_i=0^n i) work, sum_i=0^n i = n(n-1)/2, so that first expression is equivalent to O(n^2).

I'm not sure where you're getting an exponential from.

fathermarz 7/3/2025||

Google is raising prices for most of their services. I do not agree that this is due to the cost of compute or that this is the end of Moore’s Law. I don’t think we have scratched the surface.

checker659 7/3/2025|

> cost of compute

DRAM scaling + interconnect bandwidth stagnation

Havoc 7/4/2025||

I don’t think it’s right to right a technical floor into this.

It could just as well have been Google reducing subsidisation. From the outside that would look exactly the same

timewizard 7/3/2025||

I've only ever seen linear increases. When did Moore's law even _start_?

recursive 7/3/2025|

1970

https://en.wikipedia.org/wiki/Moore%27s_law#/media/File:Moor...

guluarte 7/3/2025||

they are doing the we work approach, gain customers at all costs even if that means losing money.

ethanpailes 7/4/2025||

TPUs do give Google a unique structural advantage on inference cost though.

FirmwareBurner 7/3/2025||

Aren't all LLMs loosing money at this point?

simonw 7/3/2025|||

I don't believe that's true on inference - I think most if not all of the major providers are selling inference at a (likely very small) margin over what it costs to serve them (hardware + energy).

They likely lose money when you take into account the capital cost of training the model itself, but that cost is at least fixed: once it's trained you can serve traffic from it for as long as you chose to keep the model running in production.

guluarte 7/4/2025|||

Some companies like Google, Facebook, Microsoft, and OpenAI are definitely losing money providing free inference to millions of users daily. Companies where most users are using their API, like Anthropic, are probably seeing good margins since most of their users are paying users.

bungalowmunch 7/3/2025|||

yes I would generally agree; although I don't have a have source for this, I've heard whispers of Anthropic running at a much higher margin compared to the other labs

throwawayoldie 7/3/2025|||

Yes, and the obvious endgame is wait until most software development is effectively outsourced to them, then jack the prices to whatever they want. The Uber model.

FirmwareBurner 7/3/2025||

Good thing AI can't replace my drinking during work time skills

jjani 7/3/2025|

> In a move that at first went unnoticed

Stopped reading here, if you're positioning yourself as if you have some kind of unique insight when there is none in order to boost youe credentials and sell your product there's little chance you have anything actually insightful to offer. Might sound like an overreaction/nitpicking but it's entirely needless LinkedIn style "thought leader" nonsense.

In reality it was immediately noticed by anyone using these models, have a look at the HN threads at the time, or even on Reddit, let alone the actual spaces dedicated to AI builders.