这是《前三次技术革命，红利最后都落到普通人头上》背后的完整框架。正文是给人读的一篇文章，把判断讲清楚就够了；这篇是给框架本身的——把'技术怎么洗牌财富'拆成第一性原理、一个财富捕获公式、六个传导机制、AI 的四层结构、一套九问研究法和几条可验证假说，方便你拿去套到下一个技术上，自己跑一遍。

The full framework behind Railroads, Electricity, the Internet Each Built a Middle Class. AI Might Not. The essay is the readable version; this is the machine — how technology reshuffles wealth, broken into a first principle, a wealth-capture formula, six transmission mechanisms, AI's four layers, a nine-question research method, and a few falsifiable hypotheses — so you can run it on the next technology yourself.

我用 TypeScript 写过几个 web 游戏，但总觉得天花板太低。想认真做一个、让 AI 写大部分代码，到底该选 Godot、Unity 还是虚幻？查了一圈才明白：AI 最擅长的引擎，不是训练数据最多的那个，而是它能整个读进眼里、自己跑起来、自己看着改的那个。

I built a few TypeScript web games but always felt the ceiling was too low. If I wanted to build a proper game and let AI write most of the code, should I pick Godot, Unity, or Unreal? After a lot of digging the answer surprised me: the engine AI is best at isn't the one with the most training data — it's the one AI can read entirely as text, run headless, watch, and fix on its own.

HBM isn't faster DRAM. It's DRAM rotated 90 degrees — stacked, drilled through with thousands of vertical wires, and bolted next to the GPU on a silicon interposer. That manufacturing nightmare is exactly why three memory makers just crossed a trillion dollars.

The same chip lives in your USB stick, your phone, and the petabyte SSDs feeding AI clusters. It got cheap by trading endurance for density — and that exact tradeoff is why AI is now dragging it out of cold storage and onto the hot path.

DRAM is a leaky bucket you refill thousands of times a second. That physical fragility is why it's fast, why it's volatile, and why it just hit a scaling wall at the exact moment AI demand exploded — sending RAM prices up triple digits.

I finance the machines AI runs on, and the single priciest, scarcest part inside the box isn't the logic die — it's the memory stacked next to it. This is why: inside a modern accelerator, raw FLOPS stopped being the binding constraint. Memory bandwidth did.

OpenClaw 中美接受度差异的 7 维度深度调研——群体心理学、文化结构、社会焦虑、传播机制、自媒体生态、美国免疫机制、历史类比，含 60+ 带来源引用和交叉验证评估。