// HACKER NEWS — CYBERSECURITY
Tokens Too Cheap to Meter
2026-09-16
• llms
• economics
The price of using machine learning intelligence is decreasing by several orders of magnitude a year and shows no signs of slowing.
We are likely to see LLMs integrated into every part of computing as infrastructure, not just as a product, in the next year or two.
We are likely to see LLMs running locally at current frontier-quality on commodity hardware in the next 3-6 years.
Starting very soon, we are likely to see quality and access become the limiting factor to AI 1 use, not sheer number of tokens.
Extraordinary claims require extraordinary evidence, so I collected a whole bunch of evidence.
AI can be either proprietary (such as GPT-6 Astra) or open weight (such as GLM-5.3-flash).
Open weight models can be either hosted (e.g. by Z.ai) or local.
Generally, models intended to be run locally will be much smaller, such as Muse Glimmer or Qwen3 Coder.
Improvements in one don't always affect improvements in the others.
GPUs are getting exponentially more efficient with every generation.
In the graph below (source), the X-axis is time and the Y-axis is power efficiency of the GPU itself.
Larger Y-axis numbers mean more efficient.
This is a logarithmic graph, which is to say that a straight line on the graph represents an exponential increase in efficiency.
In this particular case, the logarithm is 1.3, which means efficiency doubles about once every two years.
This is an increase in efficiency that we haven't seen since Moore's Law in the 1960s.
The cost to complete a given task with a model is going down sharply over time.