A Field Guide to the Distributed AI Economy
About this guide
This is a field guide, in the naturalist's sense: a book you take into unfamiliar terrain to learn what things are, how they relate, and what to expect from them. The terrain here is a shift underway in enterprise AI, away from renting intelligence through vendor APIs and toward owning it as governed infrastructure, and toward an economy in which units of AI work, the workers that perform it, the organizational context that orients them, and the checks that verify them can all be published, installed, and exchanged.
Our primary source is a whitepaper: The Distributed AI Economy: Intelligence Hubs, Frames, Cogs, and Ops (OpenTeams/Nebari, July 2026), written by Travis Oliphant, the creator of NumPy, a co-founder of Anaconda, and CEO of OpenTeams, with his team. OpenTeams is building the architecture the paper describes, so the paper is an interested party's document. This guide treats it accordingly: as a serious piece of systems thinking worth learning from, whose claims deserve attribution, whose evidence deserves inspection, and whose bets deserve to be named as bets.
Three conventions keep those registers separate throughout:
Two smaller conventions also recur: Precedent boxes carry historical parallels (NumPy, shipping containers, TCP/IP), and In practice boxes carry illustrative worked examples, which are invented for teaching and marked as such.
The map
The guide is organized as five parts. Each section stands alone; read in order for the full argument, or jump to what you need.
A reading map of the guide. Part I, The Problem, covers rented intelligence and context as capital. Part II, The Execution Layer, covers Frames, Cogs, Ops, and the validation trio of Guards, Gates, and Tracks. Part III, The Ground It Stands On, covers the Intelligence Hub, the open-source commons it is assembled from, and the application through which people use it. Part IV, The Economy, covers the four-class market, why openness compounds, and the decade ahead. Part V is reference material: the stack drawn on one page, a glossary, an FAQ, and further reading. Each section builds on the ones before it; readers can also jump directly to the part that answers their question.
View as text diagram
PART I — THE PROBLEM 1. Rented Intelligence why consuming AI as a tenant fails 2. Context Is Capital the asset that leaks away PART II — THE EXECUTION LAYER 3. Frames organizational context as a versioned artifact 4. Cogs AI workers you can audit 5. Ops jobs you can install 6. Guards, Gates, Tracks how AI work gets verified PART III — THE GROUND IT STANDS ON 7. The Intelligence Hub the perimeter where it all runs 8. Assembled From the Commons open source, Nebari, and Nebi 9. The Doorway the application knowledge workers touch PART IV — THE ECONOMY 10. The Four-Class Market what trades, and on what terms 11. Why Open Compounds network effects and open standards 12. The Decade Ahead timing, value capture, open questions PART V — REFERENCE 13. Drawing the Stack the whole architecture on one page 14. Glossary 15. FAQ 16. Further Reading
Readers who came for the economics can go straight to Part IV; each of those sections opens with the prerequisites it assumes, linked back to where they are taught.