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Tinley Park · October 13, 2026Ollama Decision Models: The End of Jev?!
The video explores how Ollama, a local large-language-model runtime, can be used to build decision models, and questions whether making AI more accessible and efficient locally signals the end of Jevons Paradox in computing and AI resource consumption.
The outline
Every point the video makes, in the order it makes them.
- 01
Ollama as a local LLM runtime
The video introduces Ollama as a tool for running large language models locally, likely as a foundation for the decision-model discussion.
- 02
LLM-powered decision models
It covers how decision models can be constructed or augmented using locally hosted language models via Ollama.
- 03
Jevons Paradox in AI
The video frames Jevons Paradox—the idea that efficiency gains increase rather than decrease resource use—in the context of AI and computing.
- 04
The 'End of Jev' thesis
It questions whether running models locally and more efficiently through Ollama could break the Jevons cycle by reducing overall demand for compute.
- 05
Promotional and resource links
The description directs viewers to the creator's consulting services and two books (The Python Bible and The Algorithm Bible) for further learning.
A decision model running locally on consumer hardware with predefined output structures removes the per-call API cost from a solo builder's shipping calculus. The Jevons question the title raises is practical: when inference gets cheaper and faster locally, the likely outcome is more structured AI features shipped per week, not fewer. The barrier between prototype and production for tasks like classification or routing narrows when the runtime is a local install rather than a cloud dependency.