What information are you trying to either ask for or share with us?
The original article is pretty clear, agent swarms and token spend are AI automating AI, and the RSP attribution condition specifically excludes your capital thrown at training concern?
I'm not sure I follow. Are you saying a hardware shortage is going to restrict AI development? Because it seems to me like it would just push us towards an even greater focus on efficiency, which is still pushing the Pareto Frontier.
But, then as now, what they didn’t have was profits.
But that didn’t stop the AI winter from coming. Because a technology being transformative does not protect it from over-investment. History perhaps suggests the opposite is true.
Nor did it prevent the AI companies of the 80s from going bankrupt when they got undercut by the commoditization that naturally came with the maturation of the technology.
Given how "cost per capability " is still exponentially decreasing and only needs to stay on trend for a few months longer before nobody needs to pay anyone more than a few cents a month for something close to general intelligence - I am with you there.
I just hope it isn't the USA's Darien Scheme. If the entire world basically gets what is now frontier AI capability for very little cost, at the US private equity and taxpayer's expense, and given the state of the US current account right now, then that is pretty much it for the Pax Americana.
We're coming up on four years since ChatGPT launched, and the only thing LLMs seem to excel at is the movement of imaginary little green pieces of paper.
We're getting the Claude efficiency talks at work. We're looking at all sort of optimizations, including local models and graphify, to reduce spend. There are people dedicating their nights and weekends to dropping token consumption and cache rebuilding. A few of these will likely catch on and be brought into common use. That will relieve at least a bit of the pressure, and enable some of the scaling to continue.
Not at all. Have you tried local AI lately? Qwen 3.8 is legitimately much better than SoTA from 1 year ago (Opus 4.1). You can run it on a consumer GPU.
Qwen Next Flash runs on a DGX spark well and is as good as some things you need datacenter class GPU's to run even today.
Check out Artificial Analysis if you haven't. Sometimes you don't realize how fast these things are moving, but to compare things from a year ago to today is just wild.
They aren't just getting better, they're getting more efficient by many orders of magnitude.
the same thing you are:
> if I may ask?
asking.