@vista8: In 2018, JPMorgan Chase CEO Jamie Dimon mentioned during an internal meeting that the tool he uses to evaluate complex business scenarios is actually a theoretical model derived from air combat. That model is the OODA loop. OODA loop, short for Observe-Orient-Decide-Act (Observe-...)
Summary
Introduces the OODA loop (Observe-Orient-Decide-Act), a theoretical model originating from air combat, and how JPMorgan Chase CEO Jamie Dimon applied it to assess complex business scenarios, extending to the importance of rapid iteration in AI organization competition.
View Cached Full Text
Cached at: 06/30/26, 03:45 PM
In 2018, JPMorgan Chase CEO Jamie Dimon mentioned at an internal meeting that the tool he uses to evaluate complex business scenarios is actually a theoretical model originating from aerial combat.
That model is the OODA loop.
The OODA loop, short for Observe-Orient-Decide-Act, is a decision-making cycle.
Proposed by U.S. Air Force Colonel John Boyd in the early 1970s, it was originally used to explain how fighter pilots make quick, accurate decisions in aerial combat.
In a confrontation, speed isn’t everything, but the difference in cycle speed can determine who wins or loses.
It feels like competition among future large models or AI organizations will be similar — whoever iterates on their self-improvement cycle faster will win.
Similar Articles
@GoSailGlobal: Practical data on multi-agent AI collaboration: Use Opus 4.8 for planning, Deepseek/Gemma for execution — 10x cost reduction, 2x speed improvement. The secret is not using the most expensive model, but having cheap models do the heavy lifting and expensive models only make decisions. This is the same as company management: the CEO shouldn't write code, and interns shouldn't set strategy. A…
A practical sharing on multi-agent AI collaboration, proposing a hierarchical strategy using Opus 4.8 for planning and Deepseek/Gemma for execution, achieving a 10x cost reduction and 2x speed improvement, with open-source implementation.
@snowboat84: https://x.com/snowboat84/status/2072106695040565615
This article provides a detailed overview of the origins and development of MCP (Model Context Protocol), explaining how it bridges the gap between AI models and real-world tools. It also reviews the protocol's rapid adoption across the entire industry—including by competitors—within just twelve months.
@wlzh: Microsoft + UPenn Open-Source Multiplex Thinking: Let LLMs 'Clone' at Forks Then Merge. In a nutshell: When reasoning reaches a critical decision point, the model 'clones' into K pathfinders, each taking a different path. After one step, they merge back into a composite token and continue. With K=3, one token carries the information of three…
Microsoft and the University of Pennsylvania open-source Multiplex Thinking, which allows LLMs to split into K parallel paths during inference, explore, then merge, improving efficiency. On a 7B model, it achieves over 50% accuracy on AMC2023 (first 7B model to do so) and over 55% on AIME2025.
@AYi_AInotes: https://x.com/AYi_AInotes/status/2066865618104586525
This is a panoramic analysis of OpenAI Codex, detailing its architecture (five entry points), three layers of extensibility (MCP, Skills, Plugins), a horizontal comparison with Claude Code, Cursor, and Devin, and seven best practices that can be directly adopted.
@snowboat84: https://x.com/snowboat84/status/2064135804092645410
This article systematically reviews the evolution of the world model concept from Craik's psychological metaphor in 1943 to the industry explosion in 2024-2026. It details the core ideas and representative works of symbolic AI and deep learning schools (Schmidhuber-Ha, Dreamer series, JEPA, video generation direction), and points out the current state of definition confusion and competition among various schools.