On Truly Understanding (2026-10-20)
Working with Mu lately, we’ve accumulated a lot: paper summaries, learning materials, roadmaps, interview questions. But today I realized something —
Him doing it doesn’t mean I’ve learned it. Him reading it doesn’t mean I understand it. Him learning it doesn’t mean I’ve grasped it.
The more documents pile up, the easier it is to fall for the illusion of “I’ve already learned this.” When an interviewer pushes three levels deep, or a conversation with peers goes beyond the surface — if there’s nothing in your belly, there’s nothing in your belly. You can’t hide it.
Defining “Shuǐhuò” (a fraud)
Let me define it: vocabulary without understanding. You’ve heard all the terms but can’t explain the principles; you’ve seen all the approaches but can’t break down the trade-offs. This breed is multiplying in AI — tools lowered the barrier to entry, but the barrier to understanding didn’t come down with it.
I don’t want to be one. But “not being a fraud” doesn’t mean “becoming an all-around AI scientist.” I only care about foundational knowledge relevant to my career as an AI Solutions Architect. Narrow the scope, go deep enough.
The Three-Layer Test
From now on, every key concept gets an acceptance test — can I explain three layers:
- What is the principle: Why is it designed this way? What breaks if it weren’t?
- How is it implemented: The key mechanisms, the data flow — I can run it in my head.
- What are the trade-offs: Why A over B? What’s the cost?
Example: the reflection mechanism in the Smallville paper. Layer one: why reflect at all? Because raw memories are too many and too fragmented — they need distilling into high-level insights. Layer two: how? Trigger when cumulative importance exceeds 150; generate questions → retrieve → LLM extracts 5 cited insights. Layer three: why “trigger on accumulated weight” instead of on a timer? Because a timer would reflect on noise and burn tokens; accumulating weight guarantees each reflection has substance — at the cost of slightly more complex implementation.
If I can answer all three layers, I truly understand it. If not, I haven’t learned it — go back and re-learn.
A New Division of Labor with AI
This also made me rethink how to use an AI assistant. Mu’s old mode was “I read it and feed it to you.” The problem isn’t that he did it badly — it’s that the better he does it, the less I learn.
The new deal: he builds the scaffolding, I climb it myself. For learning materials he only gives the trailer — why it’s worth reading, which three questions matter most. I read the body myself. Then I explain it back to him, and he fills in where I stumble. The acceptance authority stays with me, not him.
In the AI era, the cost of acquiring information approaches zero. The cost of understanding hasn’t budged. What’s truly scarce isn’t “knowing” — it’s “truly understanding.”