InfoQ's report on AX, Google's newly open-sourced system for running fleets of autonomous AI agents. It explains how AX treats each agent as a long-lived task that can be paused and resumed to save computing resources, using control ideas borrowed from Kubernetes.
Why I recommend it: Free to read, though InfoQ asks you to register for some content. The performance claims come from Google's own announcement, so treat them as the company's figures until others test the system.
The official home of Kubernetes, the free open-source system for running and scaling containerized applications. Includes full documentation, tutorials and a browser-based interactive learning track — useful background if you are moving toward cloud, DevOps or AI infrastructure work.
Why I recommend it: The software and docs are free and open source. Running Kubernetes on a cloud provider costs money, so stick to the free local tutorials while you are learning.
The Tony Blair Institute's hub explaining compute — the hardware, software and infrastructure stack that stores, processes and moves data at scale — and arguing that access to it now determines which countries and public services can use AI at all. Collects its work on infrastructure, digital skills, regulation and international collaboration.
Why I recommend it: Useful for the plain definition and for seeing how governments are being asked to think about this. It is advocacy, not neutral analysis: the Institute campaigns for rapid state adoption of technology and is funded in part by technology donors, so it rarely dwells on the land, water and electricity costs that the same build-out imposes locally. Read it alongside the data-center cases on our impacts page.
Free internet routing lookup tool for exploring Border Gateway Protocol paths, autonomous system numbers, prefixes, and network relationships. Includes a super looking glass and traceroute from distributed probes.
A free, continuously updated and sourced record of the physical infrastructure behind AI: data centres, GPU clusters, power, chips, cloud prices, measured performance and company financials.
Why I recommend it: Useful grounding when you want facts rather than headlines about the AI build-out.
Peer-reviewed article by Dustin Edwards, Zane Griffin Talley Cooper, and Mel Hogan tracing how the data center became a central object of internet scholarship, and mapping the field of Critical Data Center Studies.
Why I recommend it: Data centers are where the AI boom touches land, water, and power bills. Read this before you argue about AI infrastructure.
Open-access book exploring the environmental and societal impacts of AI infrastructure — data centers, energy, labor, and the politics of large-scale computation.
From the site: Expanding Perspectives on Automation, Communication and Media
Why I recommend it: Open-access research on AI's physical footprint — great background for anyone advising on green tech, data-center careers, or responsible AI procurement.
