Last week, I wrote about the mindset that computer systems can be understood, and behaviors can be explained, if we’re willing to dig deep enough into the stack of abstractions our software is built atop. Some of the ensuing discussion on Twitter and elsewhere lead me to write this followup, in which I want to run through a few classes of systems where I’ve found pursuing in-detail understanding of the system wasn’t the right answer.
Machine understanding
What do artificial intelligence (AI) systems “understand”? This question arises not only in assessing a system’s intelligence but also in evaluation practices to ensure the safe and responsible deployment of AI. Drawing on scholarship from philosophy and cognitive science, and informed by current practices in AI, we develop a framework for asking more precise questions and making more precise claims about machine understanding. We conceptualize understanding as a relation between a system (S) and a target of understanding (T), and we discuss how to specify the relation, the system, and the target, offering a landscape of options in each case. Our goal is not to defend a particular account of understanding, but to provide conceptual tools for those working to assess or advance machine understanding.
Computers can be understood
Computers and computer systems are build up from deterministic, comprehensible, building blocks. Their operations and behaviors can be understood and reasoned about. I relate my personal beliefs and mindset on this point, and explore some manifestations and ramifications of this philosophy.
Complexity Explorables
A collection of interactive explorable explanations of complex systems in biology, physics, mathematics, social sciences, epidemiology, ecology and other fields....
Summary: Thinking in Systems (by Donella H. Meadows)
The main argument of the book: Everything in this world is made of systems. While we cannot fully understand systems, we can observe them…
20 Things I've Learned as a Systems (Over) Thinker
This list has received positive feedback.
Understanding is the new bottleneck
Agents can write code faster than we can absorb it. Here's why it still matters for humans to understand what they build — and some techniques for doing that efficiently: explainer docs, quizzes, micro-worlds, and shared spaces.
Understanding is the new bottleneck
Agents can write code faster than we can absorb it. Here's why it still matters for humans to understand what they build — and some techniques for doing that efficiently: explainer docs, quizzes, micro-worlds, and shared spaces.
Understanding is the new bottleneck
Agents can write code faster than we can absorb it. Here's why it still matters for humans to understand what they build — and some techniques for doing that efficiently: explainer docs, quizzes, micro-worlds, and shared spaces.
The Systems Thinker
The Systems Thinker works to catalyze effective change by expanding the use of systems approaches. All articles are available free of charge in an effort to expose as wide of audience as possible. Browse, share with others, save your favorites and tell others about this valuable resource!
How to safely think in systems.
While there are a decent number of folks out there modeling systems, there’s a much larger group of folks who think of themselves as systems thinkers but utilize techniques like modeling rather casually (a polite way of saying that they don’t use them). Even with modeling tools available, I’ve often taken the shortcut of intuitive modeling, which over time has given me quite the education in making well-intentioned reasoning errors.
Joshua Gu on Twitter / X
Recent agentic systems (Claude Code, Codex, RLM, etc.) push context out of the prompt and into the environment (e.g., as files). This helps them maintain long-term knowledge about their goals and functionality.🚨 While this is a good idea, we show a surprising result: systems… pic.twitter.com/XHFVaDcr4l— Joshua Gu (@astrogu_) May 20, 2026
How to Make Sense of Any Mess
Information Architecture for Everybody. Based on a book by Information Architect, Abby Covert.
Learnable Programming
Here's a trick question: How do we get people to understand programming?