Re+: AX Thoughts

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.

My blog had 83 Chinese posts — all of them read like translated English. Stiff, formal, academic. So I studied China's best WeChat writers, distilled their craft into a 435-line writing system, and fed it to Claude as a system prompt. Then I rewrote every single post. This is the story of that guide.

One style guide fixed translation-ese, but a tech review shouldn't sound like social commentary. Different content needs different voices. I developed a systematic method for 'stealing' any author's writing style — collecting articles, dissecting patterns, and extracting a structured Style Profile. Here's the full process, demonstrated with a real example.

A style library, no matter how large, can't cover everyone's taste. Some users want to write like an author who isn't in the library. Some want the voice of one specific article, not a generalized style. The solution: real-time style extraction. Give the AI any reference article, and it clones the writing DNA on the fly.

Reactions to a three-hour interview with Zhang Yueguang (Miaoducamera creator, ~$40M raised for 'AI friends'). On why AI companions must evolve, why the feeling of being needed matters more than satisfaction, and what 'Dao rises, skill fades' means for products and teams. Not a systematic analysis — a few ideas from the podcast that happened to align closely with my own experience building AI companions.

MrBeast wrote 36 pages. Netflix distilled theirs to 5. Duolingo designed a 64-page illustrated book. All three generate over $1M per employee. All three are solving the same problem: how to transfer taste, judgment, and operating principles to another entity so it can execute autonomously. These handbooks are CLAUDE.md files for humans — and they reveal three distinct archetypes for how organizations might scale in the agentic AI era.

If employee handbooks are CLAUDE.md files for humans, what should the handbook of an AI-native company look like? What kind of people does it hire — and how does it screen for taste instead of skill? This piece designs the AI-native employee handbook from scratch: the talent profile, four screening methods, and the five sections every AI-native handbook needs — from mission to judgment frameworks to agent collaboration protocols.

Reactions to a podcast on AI-first org architecture. Use AI as a tool and you're racing the clock — 10x at best. Use it as a system and you rebuild the whole workflow, retreating to just two jobs: give intent, review output. But the review gate is drowning, and that's the part nobody wants to say out loud.

I listened to an episode on AI and wealth redistribution — the finance podcast 十分吸引 crossed with 听懂涨声 — and the best part was the history: how railroads, electricity, and the internet each reshuffled wealth. I dug a layer under their account and made it harder: the reshuffle runs one logic every time — technology rewires the economic network, the old bottleneck fails, a new one gets priced, and wealth follows the bargaining power. Railroads rewired the goods network, electricity the energy network, the internet the information network, AI the task network. The first three eventually grew a new middle class, because those production modes needed armies of ordinary people. AI is heavy-asset and dispersive, so it may run only the concentration half and skip broaden-the-middle.

Reactions to an interview with Tulong (Yang Ying) — Tsinghua biology, a Pittsburgh PhD decoding the brain for the US military, who came home and built four companies in a world of "accountants driving Ferraris and influencers showing up with gangster bosses." The title asks why she makes money at everything; she's the one who admits the jewelry line sold zero. What's actually valuable isn't a Midas touch — it's an operating system she can carry from neuroscience into skincare, books, and courses.

AI has driven the marginal cost of instruction to zero, and for the first time in the history of education the bottleneck has moved from 'who will teach' to 'why would anyone want to learn.' Starting from a community thread about why kids refuse to study, I traced the evidence across motivation science, learning science, and AI alignment: demand isn't found, it's ignited; 'you'll need it someday' loses to a discount curve; and a fully motivated student with an answer-giving AI scores 17% worse on exams than one with no AI at all. Education in the AI era is a double alignment problem — first get people to want to learn, then protect the cognitive labor that can't be outsourced.