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Compute inequality

As explained in Introduction to Generative AI, Second Edition

Compute inequality is the unequal distribution of access to computational resources needed to develop or use advanced AI.

Compute inequality is the unequal distribution of computational resources needed to develop or use advanced artificial intelligence. Access to training-scale models, multilingual datasets, and inference application programming interfaces is concentrated in a small number of countries and corporations. section 11.5

Unequal infrastructure limits who can shape, deploy, and benefit from AI. Investment may build national capabilities, but it does not automatically provide broad access; workers and regions supporting the ecosystem can remain overlooked. section 11.5

Responses discussed include:

  • Funding, education, and infrastructure-sharing through multilateral initiatives. section 11.5
  • Open-source alternatives, public laboratories, and safety-focused funding, despite resource and political constraints. section 11.5

How Introduction to Generative AI, Second Edition covers it

How deep it goes
Explains concentration, supporting labor, and proposed remedies, but stops short of measuring inequality or detailing infrastructure.
Examples
Uses concentrated access to models, datasets, and inference APIs, alongside data-center labor and infrastructure-sharing initiatives.
What you can do after
Evaluate how infrastructure, investment, and access policies distribute participation and influence across countries and organizations.
How it connects
Connects compute inequality with AI infrastructure, foundational models, multilingual datasets, inference APIs, open source, and AI governance.

Related concepts

This explainer was generated by Manning's knowledge graph from the text of Introduction to Generative AI, Second Edition. The section links point to the parts of the book it draws on.