Economics of foundation models

1 In epistemic communities and public discourse

Spamularity, dark forest, textpocalypse? See Spamularity.

2 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

3 Democratisation of AI

A fascinating phenomenon..

4 Art and creativity

For now, see timeless works of art.

5 Data sovereignty

See data sovereignty.

6 AI tech soap opera

See AI tech as tragicomedy.

7 Material basis of AI compute?

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

8 Incoming

• The Tweet that Sank $100bn

• The Hater’s Guide To The AI Bubble

• Brain Circulation: How High-Skill Immigration Makes Everyone Better Off

• Europe is losing its tech talent: the hidden salary crisis

• Ilya Sutskever: “Sequence to sequence learning with neural networks: what a decade”

• Why Quora isn’t useful anymore: A.I. came for the best site on the internet.

• What Will Transformers Transform? – Rodney Brooks

• Gradient Dissent, a list of reasons that large backpropagation-trained networks might be worrisome. Some interesting points in there, and some hyperbole. Also: If it were true that externalities come from backprop networks (i.e. that they are a kind of methodological pollution that produces private benefits but public costs) then what kind of mechanisms should disincentivise them?

• C&C Against Predictive Optimisation

• Stanford CRFM

  In this post, we evaluate whether major foundation model providers currently comply with these draft requirements and find that they largely do not. Foundation model providers rarely disclose adequate information regarding the data, compute, and deployment of their models as well as the key characteristics of the models themselves. In particular, foundation model providers generally do not comply with draft requirements to describe the use of copyrighted training data, the hardware used and emissions produced in training, and how they evaluate and test models. As a result, we recommend that policymakers prioritise transparency, informed by the AI Act’s requirements. Our assessment demonstrates that it is currently feasible for foundation model providers to comply with the AI Act, and that disclosure related to foundation models’ development, use, and performance would improve transparency in the entire ecosystem.

• I Do Not Think It Means What You Think It Means: Artificial Intelligence, Cognitive Work & Scale

• Groq Inference Tokenomics: Speed, But At What Cost?

• Invasive Diffusion: How one unwilling illustrator found herself turned into an AI model

• Cyborg Periods: There will be multiple AI transitions

• Bruce Schneier, On the Need for an AI Public Option

• Ecosystem Graphs for Foundation Models

• I Went to the Premiere of the First Commercially Streaming AI-Generated Movies

• Lower AI Costs Will Drive Innovation, Efficiency, and Adoption

• Spirals of Delusion: How AI Distorts Decision-Making and Makes Dictators More Dangerous (not convinced tbh)

• The Ghosts in the Machine, by Liz Pelly

9 References