Economics of foundation models

Microeconomic of compute

2023-03-23 — 2025-05-27

Wherein economies of foundation models are examined and the disproportionate energy and water demands of large-scale training, including data‑centre cooling and emissions accounting, are described.

agents
AI safety
bounded compute
collective knowledge
distributed
economics
edge computing
extended self
faster pussycat
incentive mechanisms
innovation
language
machine learning
neural nets
NLP
technology
UI
when to compute
Figure 1

Various different economic aspects of AI and foundation models are mentioned here. This is some kind of a hub page.

1 Knowledge collapse and the epistemic commons

What happens to collective knowledge when AI substitutes for individual learning? See Knowledge collapse and the epistemic commons.

2 In epistemic communities and public discourse

Slop, spamularity, dark forest, textpocalypse? See Spamularity.

3 PR, hype, marketing

George Hosu, in a short aside, highlights the incredible marketing advantage of AI:

People that failed to lift a finger to integrate better-than-doctors or work-with-doctors supervised medical models for half a century are stoked at a chatbot being as good as an average doctor and can’t wait to get it to triage patient

4 Democratisation of AI

A fascinating phenomenon..

5 Art and creativity

For now, see timeless works of art.

6 Data sovereignty

See data sovereignty.

7 AI tech soap opera

See AI tech as tragicomedy.

8 Material basis of AI compute?

Energy, water, minerals etc See material basis of AI economics.

9 Abstract economics of cognition in general

See economics of cognition.

10 Incoming

11 References

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