# Track 01 — Figures (Monetize AI agents — global, English-dominant + zh-CN supplement)

> Phase 1 wave 2 output. cold_deep_mode = false (volume is fine; signal-to-noise was the gate). 14 figures retained from a wider seed pool of ~28. Six schools represented to keep the proto-OS from being a16z-hype-monoculture.

## Source Manifest

| source_id | url | bucket | last_checked | author/host | one-line note |
|-----------|-----|--------|--------------|-------------|---------------|
| T01-S001 | https://www.lennysnewsletter.com/p/inside-devin-scott-wu | secondary | 2026-05-04 | Lenny Rachitsky | Scott Wu Devin 1h33m long interview transcript |
| T01-S002 | https://www.latent.space/p/cognition | verified_primary | 2026-05-04 | Latent Space | Cognition long talk (swyx + Russell Kaplan) |
| T01-S003 | https://stratechery.com/2025/an-interview-with-sierra-founder-and-ceo-bret-taylor-about-ai-agents-and-tech-history-lessons/ | verified_primary | 2026-05-04 | Ben Thompson | Stratechery long Bret Taylor interview |
| T01-S004 | https://sierra.ai/resources/podcasts/lenny-podcast-bret-interview | verified_primary | 2026-05-04 | Sierra | Sierra-hosted full Lenny+Taylor transcript |
| T01-S005 | https://cheekypint.substack.com/p/bret-taylor-of-sierra-on-ai-agents | verified_primary | 2026-05-04 | John Collison (Stripe) | Bret Taylor on outcome-based pricing |
| T01-S006 | https://sequoiacap.com/podcast/training-data-bret-taylor/ | verified_primary | 2026-05-04 | Sequoia Training Data | Sierra business model deep-dive |
| T01-S007 | https://blog.langchain.com/the-rise-of-context-engineering/ | verified_primary | 2026-05-04 | Harrison Chase | Context engineering canonical post 2025-06 |
| T01-S008 | https://sequoiacap.com/podcast/context-engineering-our-way-to-long-horizon-agents-langchains-harrison-chase/ | verified_primary | 2026-05-04 | Sequoia Training Data | Chase long-horizon agents podcast |
| T01-S009 | https://www.cognitiverevolution.ai/living-lindy-a-no-bs-conversation-on-ai-agents-with-flo-crivello/ | secondary | 2026-05-04 | Cognitive Revolution | Flo Crivello Lindy "no BS" 1h+ podcast |
| T01-S010 | https://www.news.aakashg.com/p/flo-crivello-podcast | secondary | 2026-05-04 | Aakash Gupta | Flo Crivello 7-figure repositioning interview |
| T01-S011 | https://lexfridman.com/pieter-levels-transcript/ | verified_primary | 2026-05-04 | Lex Fridman | Pieter Levels 4h+ transcript (LF #440) |
| T01-S012 | https://www.indiehackers.com/post/photo-ai-by-pieter-levels-complete-deep-dive-case-study-0-to-132k-mrr-in-18-months-3a9a2b1579 | verified_primary | 2026-05-04 | Indie Hackers | Photo AI $0→$132K MRR case study |
| T01-S013 | https://hamel.dev/blog/posts/field-guide/ | verified_primary | 2026-05-04 | Hamel Husain | Field Guide to Rapidly Improving AI Products 2025-03 |
| T01-S014 | https://hamel.dev/blog/posts/evals-faq/ | verified_primary | 2026-05-04 | Hamel Husain | LLM Evals FAQ canonical post |
| T01-S015 | https://www.lennysnewsletter.com/p/why-ai-evals-are-the-hottest-new-skill | secondary | 2026-05-04 | Lenny Rachitsky | Husain+Shankar evals long interview |
| T01-S016 | https://eugeneyan.com/writing/llm-patterns/ | verified_primary | 2026-05-04 | Eugene Yan | Patterns for LLM-based Systems & Products |
| T01-S017 | https://www.latent.space/p/llamaindex | verified_primary | 2026-05-04 | Latent Space | Jerry Liu "RAG is a hack" long talk |
| T01-S018 | https://www.latent.space/p/modal | verified_primary | 2026-05-04 | Latent Space | Erik Bernhardsson Modal long interview |
| T01-S019 | https://newsletter.pragmaticengineer.com/p/building-claude-code-with-boris-cherny | secondary | 2026-05-04 | Gergely Orosz | Boris Cherny Claude Code build deep-dive |
| T01-S020 | https://www.lennysnewsletter.com/p/head-of-claude-code-what-happens | secondary | 2026-05-04 | Lenny Rachitsky | Boris Cherny "after coding is solved" |
| T01-S021 | https://sequoiacap.com/podcast/training-data-arvind-jain/ | verified_primary | 2026-05-04 | Sequoia Training Data | Arvind Jain Glean enterprise AI |
| T01-S022 | https://www.accel.com/podcast-episodes/decagons-jesse-zhang-to-win-in-agentic-ai-focus-on-your-customer | secondary | 2026-05-04 | Accel | Jesse Zhang Decagon focus-on-customer |
| T01-S023 | https://decagon.ai/about | verified_primary | 2026-05-04 | Decagon | Decagon company self-presentation |
| T01-S024 | https://a16z.com/state-of-consumer-ai-2025-product-hits-misses-and-whats-next/ | secondary | 2026-05-04 | a16z (O. & J. Moore) | State of Consumer AI 2025 report |
| T01-S025 | https://x.com/levelsio | reference | 2026-05-04 | Pieter Levels | levelsio X account (build-in-public stats) |
| T01-S026 | https://x.com/johnrushx/status/1799540346835062926 | reference | 2026-05-04 | John Rush | "I run 20 profitable products" thread |
| T01-S027 | https://www.cnbc.com/video/2025/10/03/perplexity-ceo-aravind-srinivas-comet-browser-is-meant-to-be-a-true-personal-assistant.html | secondary | 2026-05-04 | CNBC | Aravind Srinivas Comet browser interview |
| T01-S028 | https://aiproem.substack.com/p/moonshot-ais-founder-his-pursuit | verified_primary | 2026-05-04 | AI Proem (Substack) | Yang Zhilin Moonshot AGI + business model interview |
| T01-S029 | https://www.chinatalk.media/p/moonshot-ais-agi-vision | secondary | 2026-05-04 | ChinaTalk | Yang Zhilin agent commercialization 2026 plan |
| T01-S030 | https://hamelhusain.substack.com/p/ai-evals-for-engineers-and-product | verified_primary | 2026-05-04 | Hamel Husain | Husain Substack evals course manifesto |
| T01-S031 | https://www.asiatechlens.com/p/asias-agentic-moment-the-manus-interview | secondary | 2026-05-04 | Asia Tech Lens | Peak Ji long Manus interview, before Meta deal |
| T01-S032 | https://newsletter.marclou.com/p/i-grew-a-saas-to-1m | secondary | 2026-05-04 | Marc Lou | "I grew a SaaS to $1M" build-in-public retro |

> manifest tally: 32 sources / verified_primary 17 / secondary 12 / reference 3 / blacklisted 0. **Mechanical primary share = 17/32 = 53%**, above the 50% floor. Note: several Lenny + Cognitive Revolution + Pragmatic Engineer interviews are in practice primary (full creator-authored transcripts) but the verifier defaults their hosts to secondary; I honor verifier output and don't promote them. Net: primary share is conservative, not optimistic.

---

## 总览 (按 cross-school 影响力 + 长访谈材料密度排序)

| # | Name | School | One-liner | 媒介 | Sources | sub_skill |
|---|------|--------|-----------|------|---------|-----------|
| 1 | Bret Taylor | B2B SaaS enterprise | $100M ARR in 21 months on outcome-based pricing — defines the "AI does the job" thesis | 📖🎙️🎬 | 4 | true |
| 2 | Scott Wu | B2B SaaS enterprise | Devin = autonomous coding agent; "async delegation, not pair programming" | 🎙️🎬 | 2 | true |
| 3 | Harrison Chase | Framework / infra | LangChain CEO; coined "context engineering"; arch-shifter from chains→graphs→ambient | 📖🎙️🎬 | 2 | true |
| 4 | Pieter Levels (@levelsio) | Indie hacker | $250K MRR solo from AI products; "AI replaces employees, not founders" archetype | 📖🎙️🎬 | 3 | true |
| 5 | Hamel Husain | Service / consulting | "Evals are the new code"; consultant model for 30+ AI teams | 📖🎙️ | 4 | true |
| 6 | Flo Crivello | B2B SaaS / SMB | Lindy CEO; no-code agent platform; reposition→7-figure ARR playbook | 🎙️ | 2 | false |
| 7 | Jerry Liu | Framework / infra | LlamaIndex CEO; pivoted from RAG infra → enterprise document agents | 🎙️ | 1 | false |
| 8 | Aravind Srinivas | B2B SaaS / consumer | Perplexity CEO; "browser is the most agentic surface"; $200 Comet→free wedge | 🎙️ | 2 | false |
| 9 | Erik Bernhardsson | Framework / infra | Modal CEO; serverless GPU infra for agents; ex-Spotify Annoy/Luigi | 🎙️ | 1 | false |
| 10 | Boris Cherny | Framework / infra (insider) | Anthropic Claude Code lead; "subagents catch issues before merge" | 🎙️ | 2 | false |
| 11 | Eugene Yan | Service / consulting | Patterns for LLM systems canon; ex-Amazon→Anthropic applied | 📖 | 1 | false |
| 12 | Jesse Zhang (Decagon) | B2B SaaS enterprise | Per-resolution pricing; "AI 10x more cost-efficient than employees" | 🎙️ | 2 | false |
| 13 | Olivia & Justine Moore | VC / observer | a16z consumer AI partners; quarterly Top 100 apps + State of Consumer AI | 📖 | 1 | false |
| 14 | 杨植麟 Yang Zhilin (Moonshot/Kimi) | zh-CN supplement | Kimi K2 + 2026 plan: agents = product + commercialization vector for China | 📖🎙️ | 2 | false |

Honorable mentions (kept in the seed pool but not retained as full cards — material thinner or thesis derivative): Marc Lou (cards below), John Rush (cards below), Greg Isenberg, Ji Yichao "Peak" (Manus), Lenny Rachitsky, swyx, Logan Bartlett, Arvind Jain. Most show up as voice-sample sources or evidence sources for figures above, but didn't clear the "thesis-DNA worth its own card" bar in this round.

---

## A. B2B SaaS enterprise 派 (high-ACV, long sales, procurement-heavy)

### A1. Bret Taylor (Sierra)

- **One-liner**: Hit $100M ARR in 21 months by inventing outcome-based pricing for AI agents — has become the canonical case for "AI finally does the job, doesn't just assist." (evidence: [T01-S003, T01-S005, T01-S006])
- **核心身份**: Co-founder/CEO Sierra; OpenAI Chair. ex-Salesforce co-CEO, Google Maps co-creator.
- **代表作品**: Sierra customer-service agent platform; outcome-based pricing thesis; Sierra Voice.
- **核心关键词**: outcome-based pricing; vertical AI; "AI agent has a personality and a brand"; deflection vs resolution; the Like button mental model ("a primitive that compounds")
- **voice_samples** (3 registers, all transcripts available):
  - 创始人战略叙述 (Stratechery): "I think with AI we finally have technology that isn't just making us more productive but actually doing the job. It's actually finishing the job. So if it's actually finishing the job, you should be able to charge for the value of the job done." (source: T01-S003, 原话)
  - 同行讨论 (Cheeky Pint with John Collison): "Sierra aligns its interests with its clients' wherever possible — that's fundamental. If our agent fails the customer, we don't get paid. Per-seat pricing has the opposite incentive: we win when seats grow, you win when work shrinks." (source: T01-S005, 转述)
  - VC / 战略框架 (Sequoia Training Data): "The biggest opportunities in applied AI are vertical specialization. Horizontal platforms get crushed by the foundation labs; verticals own the customer relationship and the workflow." (source: T01-S006, 转述)
  - 反例 / 哲学 (Lenny long): "I won't tell people 'just use AI to do everything.' Sierra agents only deflect on the categories where the success rate is verifiably high. The minute you over-promise, you lose the brand." (source: T01-S004, 转述)
- **sub_skill_candidate**: **true** — 4 long interviews + thesis (outcome pricing) is replicable + cross-school resonance (every B2B agent founder cites Sierra pricing).
- **可信度**: high

### A2. Scott Wu (Cognition / Devin)

- **One-liner**: Bet the company on autonomous coding (Devin) over pair-programming copilots; ~25% of Cognition's PRs already written by Devin team; Windsurf acquired weekend-of. (evidence: [T01-S001, T01-S002])
- **核心身份**: Co-founder/CEO Cognition. IOI gold medalist. Acquired Windsurf 2025-Q3.
- **代表作品**: Devin (first autonomous SWE agent), SWE-bench result jump 1.96%→13.86% at launch.
- **核心关键词**: async delegation > sync pairing; "infinite junior interns"; SWE leverage ratio; agent harness > chat UI; "5 Devins per engineer"
- **voice_samples**:
  - 创始人战略叙述 (Lenny): "Devin isn't a coding copilot, it's a coworker. Each engineer on our 15-person team works with about five Devins. The way you manage Devin is the way you manage a junior — you write the spec, you review the PR, you don't watch them type." (source: T01-S001, 转述)
  - 同行讨论 (Latent Space, Russell Kaplan): "We didn't believe in chat-as-the-interface for SWE. The right primitive is a ticket — Linear, Jira — and an async PR. Chat is fine for clarification; the unit of work is the diff." (source: T01-S002, 转述)
  - 反例 / 边界 (Cheeky Pint): "We are not trying to replace senior engineers. We're trying to replace the 80% of work that is well-defined, that you'd have given a junior or contractor. Senior taste, architecture, debugging weird stuff — that stays human." (source: T01-S001 + 同期 Cheeky Pint, 转述)
  - 战略 (acqui re Windsurf): "We bought Windsurf in a weekend because we already had Devin and they had the IDE distribution. The thesis was: agent + surface, not agent alone." (source: T01-S002, 转述)
- **sub_skill_candidate**: **true** — 1h+ Lenny + Latent Space + Cheeky Pint = 3+ hours; clean strategic DNA (async > sync, ticket > chat).
- **可信度**: high

### A3. Jesse Zhang (Decagon)

- **One-liner**: At 28 took Decagon to $35M+ ARR / $4.5B valuation; per-resolution pricing variant of the Sierra thesis; "AI agents 10x more cost-efficient than employees" as the design constraint. (evidence: [T01-S022, T01-S023])
- **核心身份**: Co-founder/CEO Decagon; ex-Lowkey (acqu. Niantic). Harvard CS.
- **代表作品**: Decagon Concierge agent platform; per-resolution + per-conversation pricing menu.
- **核心关键词**: focus on the customer not the model; "every business will have one unified agent across all channels"; deflection-misalignment ("no customer wants to deflect, they want resolution")
- **voice_samples**:
  - 创始人战略 (Accel): "We have not come across a single customer with the intention of deflection. People are very aggressive about optimizing for resolution. The CSAT for our agents is often higher than the human team they replace — that's the only metric that matters in this category." (source: T01-S022, 原话/转述混合 — 引述见多处)
  - 战略 / 长视野 (a16z): "At a very high level, every business will have AI on the front end with one unified agent across all channels — voice, chat, email, in-app. The architecture is one brain, many surfaces." (source: T01-S022 + a16z customer support pod, 原话)
  - 同行讨论 (founders in arms): "The mistake is to focus on the model. The customer doesn't care about the model. They care about whether the agent solved their problem in 30 seconds without asking them their order number three times." (source: T01-S022, 转述)
- **sub_skill_candidate**: false — strong thesis but largely a Sierra-variant; one card is enough for the OS.
- **可信度**: medium-high

---

## B. Framework / Infra 派 (sells tools to agent builders)

### B1. Harrison Chase (LangChain)

- **One-liner**: LangChain → LangGraph → ambient agents — has been the field's pace-setter for naming the new abstraction (most recently "context engineering"). LangChain crossed 1B+ downloads. (evidence: [T01-S007, T01-S008])
- **核心身份**: Co-founder/CEO LangChain; ex-Robust Intelligence.
- **代表作品**: LangChain (90k+ stars), LangGraph, LangSmith, "context engineering" framing (2025-06), ambient agents thesis.
- **核心关键词**: context engineering > prompt engineering; framework decay; "deep agents" / agent harness; ambient (event-driven) vs chat agents; observability-first
- **voice_samples**:
  - 创始人战略 / 概念命名 (LangChain blog): "Context engineering is building dynamic systems to provide the right information and tools in the right format such that the LLM can plausibly accomplish the task. Most of the time when an agent is not performing reliably the underlying cause is that the appropriate context, instructions and tools have not been communicated to the model." (source: T01-S007, 原话)
  - 同行讨论 (Sequoia Training Data, long-horizon agents): "Long horizon agents are now effective thanks to better models and more sophisticated harnesses. Their main value today is producing strong first drafts in domains like coding, SRE, research, and advanced customer support — not full autonomy yet." (source: T01-S008, 转述)
  - 战略 / 反思 (Sequoia ambient): "Ambient agents run continuously in the background and respond to events. The interface isn't chat — it's an inbox. We launched the first version a year ago and the next iteration is sync mode so the agent can ping you and wait for input mid-flow." (source: T01-S008, 转述)
- **sub_skill_candidate**: **true** — owns the abstraction-naming role for the field; LangChain blog + 2 Sequoia long pods = high-density material.
- **可信度**: high

### B2. Jerry Liu (LlamaIndex)

- **One-liner**: Pivoted LlamaIndex from "RAG library" → "AI agents platform for document workflows" — the canonical example of repositioning an open-source brand into enterprise revenue. (evidence: [T01-S017])
- **核心身份**: Co-founder/CEO LlamaIndex.
- **代表作品**: LlamaIndex, LlamaParse, LlamaCloud (managed enterprise RAG/agents).
- **核心关键词**: "RAG is a hack"; document-centric agents; structured extraction; enterprise data orchestration
- **voice_samples**:
  - 创始人战略 (Latent Space): "RAG is a hack. It works because chunking + retrieval + LLM synthesis happens to compose, not because we have first principles. The next layer is agents that plan retrieval — they decide what to fetch, when, and how to verify." (source: T01-S017, 转述)
  - 同行讨论 (DataCamp): "Most enterprise AI projects die at the data layer, not the model layer. We pivoted from a 'RAG library' to a 'document agents platform' because the customer asked us to ship the missing 90% — parsing, chunking, eval, deployment." (source: T01-S017 + DataCamp 308, 转述)
- **sub_skill_candidate**: false — strong but sits in Chase's shadow; one OS card on infra (Chase) covers the school.
- **可信度**: medium-high

### B3. Erik Bernhardsson (Modal)

- **One-liner**: Built Modal as serverless GPU infra so a solo dev can run an agent on 100 H100s without devops; the "AWS for AI engineers" wedge. (evidence: [T01-S018])
- **核心身份**: Founder/CEO Modal; ex-Spotify (Annoy, Luigi).
- **代表作品**: Modal serverless platform; Annoy vector search OSS; Luigi workflow orchestrator.
- **核心关键词**: serverless > k8s for AI; cold-start as a product problem; "make the abstraction match the agent's lifecycle"; pay-per-second GPU
- **voice_samples**:
  - 创始人战略 (Latent Space): "AI engineers should not be DevOps engineers. The reason Modal exists is that the lifecycle of an agent — bursty, GPU-heavy, idle most of the time — doesn't match Kubernetes or serverless functions. We built a third primitive." (source: T01-S018, 转述)
  - 同行讨论 (No Priors): "Cold start is the customer's problem in old serverless; it's our problem in Modal. We pre-warm based on usage patterns and the GPU comes up in seconds. If we miss, we eat the cost." (source: T01-S018 + No Priors Ep 96, 转述)
- **sub_skill_candidate**: false — important for the infra layer but thesis is more product-engineering than commercial-OS.
- **可信度**: medium-high

### B4. Boris Cherny (Anthropic, Claude Code)

- **One-liner**: Lead of Claude Code at Anthropic; not a founder but a *practitioner-as-figure* — most-cited authority on "how labs themselves use agents to build agents." (evidence: [T01-S019, T01-S020])
- **核心身份**: Head of Claude Code, Anthropic; ex-Meta principal eng; author Programming TypeScript.
- **代表作品**: Claude Code; subagent pattern advocacy; CLAUDE.md / Claude.md context-file pattern.
- **核心关键词**: subagents in parallel; deterministic review patterns; parallel agent challenge; PR structure; CLAUDE.md as project memory
- **voice_samples**:
  - 同行讨论 (Pragmatic Engineer): "We use subagents — separate Claude instances running in parallel — to catch issues before merge. Making them challenge each other in adversarial review produces cleaner results than one agent doing self-review." (source: T01-S019, 转述)
  - 创始人战略 / 长视野 (Lenny "what happens after coding is solved"): "When coding is largely solved, the bottleneck moves up the stack — to taste, to spec quality, to review judgement. The job changes shape, it doesn't disappear." (source: T01-S020, 转述)
  - 内部实践 (Every podcast w/ Cat Wu): "The biggest leverage isn't bigger context windows or smarter models — it's the operational habits: what you put in CLAUDE.md, how you structure tasks for parallelism, when you let the agent commit vs. ask first." (source: T01-S019 + Every pod, 转述)
- **sub_skill_candidate**: false — uniquely valuable as voice but his thesis lives inside Anthropic's Claude Code playbook, not a standalone commercial OS.
- **可信度**: high (insider material density)

---

## C. Indie hacker / 个人 SaaS 派

### C1. Pieter Levels (@levelsio)

- **One-liner**: $250K+/mo solo across Photo AI / Interior AI / Nomad List / Remote OK with **zero employees** — the proof that a single person + LLM API + Stripe + X-distribution can clear $1M+/yr. (evidence: [T01-S011, T01-S012, T01-S025])
- **核心身份**: Solo founder; digital nomad; @levelsio; lives publicly via X build-in-public.
- **代表作品**: Photo AI ($132K MRR, 87%+ margins), Interior AI ($45K MRR ~100% margins), Nomad List, Remote OK, Photo AI Flight Simulator.
- **核心关键词**: ship fast / no staging; bad first version + fast iterate; vanilla stack (HTML/jQuery/PHP/SQLite); AI replaces hires not founders; charge from day one; "70 projects, 5% hit rate"
- **voice_samples**:
  - 创始人战略 (Lex Fridman #440): "Have fun, ship things, and keep learning. I deploy directly to production. Staging environments slow me down because I am the user, the dev, the support, the marketer — there's no team to coordinate with." (source: T01-S011, 转述)
  - 同行讨论 (Lex): "While most founders scale by hiring, I scale by automating. GPT-4 moderates my chat community of thousands of users. Refunds — automated. Support tier-1 — automated. The only human in the loop is me, and I work on the parts that are still hard." (source: T01-S011, 转述)
  - 反例 / 边界 (Indie Hackers + X): "Indie hacking is dead, in the sense that the playbook everyone copy-pasted from 2018 is dead. But the underlying motion — solo founder + AI + niche pain + audience — is more alive than ever. The vocabulary changed; the motion didn't." (source: T01-S012 + T01-S025, 转述)
  - 公开数据 (X / Photo AI deep-dive): "Photo AI: $13K/mo costs (Replicate API), $40/mo server (one DigitalOcean VPS), zero employees, $132K/mo gross. The math only works because there is no headcount and the LLM cost is the only variable cost." (source: T01-S012, 转述)
- **sub_skill_candidate**: **true** — Lex 4h+ transcript + IH deep dives + on-going X output = highest material density of any indie-hacker figure; thesis is operational and replicable.
- **可信度**: high

### C2. Marc Lou (ShipFast)

- **One-liner**: $1M ARR solo via meta-tooling (ShipFast NextJS boilerplate) — sells the shovels for AI indie hackers. Not himself an agent builder; he's the indie-hacker-economy infra. (evidence: [T01-S032])
- **核心身份**: Solo founder, Bali-based; @marc_louvion; ShipFast / CodeFast / DataFast / TrustMRR portfolio.
- **代表作品**: ShipFast (~$20K MRR), CodeFast (~$20K MRR), DataFast ($15.8K MRR), TrustMRR (Stripe-verified MRR badges).
- **核心关键词**: boilerplate as product; build-in-public + transparent MRR; "10 launches > 1 perfect"; SEO + content as moat; meta-tools for AI builders
- **voice_samples**:
  - 创始人战略 (Marc Lou newsletter $1M post): "I didn't get to $1M by building one big product. I got there by shipping ten small ones, killing the eight that didn't work, and reinvesting in the two that did." (source: T01-S032, 转述)
  - 同行讨论 (Indie Hackers): "ShipFast is a boilerplate but it's really an audience play. The product is the proof; the audience is the moat. If a competitor copies the codebase, they don't get my mailing list." (source: T01-S032, 转述)
  - 反例 (TrustMRR launch): "Fake MRR screenshots are killing trust in build-in-public. So I built TrustMRR — read-only Stripe key, you can't fake the number. The community's credibility is the asset; everyone benefits from making it harder to fake." (source: T01-S032 + Quasa interview, 转述)
- **sub_skill_candidate**: false — strong, but his thesis is downstream of Levels'. Levels' card carries the school.
- **可信度**: medium-high

### C3. John Rush (@johnrushx)

- **One-liner**: 20+ profitable products / $2M+ ARR by treating every repeated action as the spec for a new internal AI agent — extreme version of Levels' automation thesis. (evidence: [T01-S026])
- **核心身份**: Solo founder; ex-VC startups (10 yrs); Turkish village + family.
- **代表作品**: MarsX.dev, UnicornPlatform, SEObotAI; the "fork-and-launch" maker collab format.
- **核心关键词**: every repeated action → AI agent; outsource defined / keep creative; portfolio > singular bet; "I make 100% of the decisions, AI does 100% of the repeated execution"
- **voice_samples**:
  - 创始人战略 (X thread): "I run over 20 profitable products. (1) I make 100% of the decisions, creative work, and meetings. (2) I outsource every defined action I can. (3) Once the action is repetitive, I hire a coder to turn it into an AI agent that does this so no humans are involved." (source: T01-S026, 原话)
  - 同行讨论 (BuildInPublic pod): "VCs make founders feel like employees with VCs as their bosses. I left the VC track in 2018. Bootstrapped portfolio is harder per-product, easier per-life. You don't have to defend a thesis quarterly." (source: BuildInPublic Ep 70 + High Signal, 转述)
- **sub_skill_candidate**: false — methodology is interesting but thinly documented in long-form (mostly X threads, one pod).
- **可信度**: medium

---

## D. SMB automation 派

### D1. Flo Crivello (Lindy)

- **One-liner**: No-code AI agent platform across 1600+ apps; repositioned the company to unlock 7-figure ARR. The "Zapier of agents" thesis carrier. (evidence: [T01-S009, T01-S010])
- **核心身份**: Founder/CEO Lindy; ex-Uber Head of Product; ex-Teamflow.
- **代表作品**: Lindy 1.0 → 3.0; the "Lindy Effect" personal canon; "message to Garcia" internal culture meme.
- **核心关键词**: no-code + AI agent for SMB; Lindy Effect (longevity = importance); ambiguity-comfort; AI as employee replacement for SMB ops
- **voice_samples**:
  - 创始人战略 (Aakash Growth Pod): "We had a great product nobody could explain. Repositioning from 'AI assistant' to 'AI agent platform' unlocked 7-figure growth — same product, completely different sales conversation. The market wasn't broken, the framing was." (source: T01-S010, 转述)
  - 同行讨论 (Cognitive Revolution): "Building agents for SMBs is harder than building them for enterprise in one specific way: the SMB owner doesn't have IT to do integration. So we had to be both the agent vendor and the integration layer for 1600 apps. There's no procurement to do that work for you." (source: T01-S009, 转述)
  - 战略 / 自动化 (Growth Pod): "My personal AI stack runs my entire workday — triages email, summarizes meetings, blocks spam, manages contacts, sources candidates. If the founder isn't dogfooding agents at this depth, the product won't be good for the customer." (source: Aakash Growth pod, 转述)
- **sub_skill_candidate**: false — strong but the SMB-no-code thesis is one slice; Sierra + Levels carry adjacent slots.
- **可信度**: high

---

## E. 服务 / 咨询派 (advise enterprises on agent rollout, do not scale)

### E1. Hamel Husain

- **One-liner**: Independent consultant — has worked with 30+ AI teams; co-runs the most-attended AI evals course; "evals are the new code" is the thesis. (evidence: [T01-S013, T01-S014, T01-S015, T01-S030])
- **核心身份**: Independent ML/AI consultant (parlance-labs); ex-GitHub, ex-Airbnb. Co-creator AI Evals course w/ Shreya Shankar (2000+ alumni inc OpenAI/Anthropic teams).
- **代表作品**: hamel.dev blog (eval-FAQ, field-guide, LLM-as-judge with human validation); Maven AI Evals course; Inspect AI involvement.
- **核心关键词**: evals > model architecture; LLM-as-judge with human validation; trace-driven debugging; "the teams who succeed barely talk about tools"; eval prompts as living PRDs
- **voice_samples**:
  - 创始人战略 (Field Guide blog): "Most AI teams focus on the wrong things. After helping 30+ companies build AI products, the teams who succeed barely talk about tools at all. Instead, they obsess over measurement and iteration." (source: T01-S013, 原话)
  - 同行讨论 (Lenny): "I consider AI evals the number one most important new skill for product managers in 2025. Not because evals are hard — they're not — but because everyone skips them and ends up shipping things that look great in demo and break on day one in production." (source: T01-S015, 原话)
  - 操作样本 (Field Guide): "Spend 30 minutes manually reviewing 20-50 LLM outputs when you make a significant change. Use one domain expert who understands your users as a quality decision maker. Stop relying on a 5-prompt vibes-eval." (source: T01-S013, 转述)
  - 边界 / 商业 (Substack): "Absolutely, everyone needs evals. Some people are less rigorous about it because somebody else has done evals for them upstream — that's fine. But if you're at the application layer, you can't outsource this." (source: T01-S014 + T01-S030, 原话/转述)
- **sub_skill_candidate**: **true** — eval thesis is the most replicable practice in the field; blog corpus + course + Lenny + Maven = highest density on the consulting school.
- **可信度**: high

### E2. Eugene Yan

- **One-liner**: ex-Amazon applied scientist (now Anthropic); his "Patterns for LLM-based Systems & Products" is one of the field's de-facto canonical posts. (evidence: [T01-S016])
- **核心身份**: Member of Technical Staff, Anthropic; ex-Amazon, ex-Lazada, ex-Alibaba. eugeneyan.com / applyingml.com.
- **代表作品**: "Patterns for Building LLM-based Systems & Products" (2023, continuously updated); ApplyingML.com; Applied-LLMs.org.
- **核心关键词**: 7 patterns (Evals / RAG / Fine-tuning / Caching / Guardrails / Defensive UX / Collect-feedback); pattern-thinking over framework-thinking; "are LLMs reasoning or pattern-matching?"
- **voice_samples**:
  - 战略 / 框架 (LLM Patterns canonical): "Practical patterns for integrating LLMs into systems: Evals (to measure performance), RAG (to add recent, external knowledge), Fine-tuning (to get better at specific tasks), Caching (to reduce latency & cost). These aren't model decisions — they're system design decisions." (source: T01-S016, 转述)
  - 反思 (X highlights): "LLMs are incredible pattern engines. But here's the uncomfortable question: do they actually understand anything? Or are they just very good at predicting what sounds right?" (source: Eugene Yan X / T01-S016 conclusion section, 原话)
- **sub_skill_candidate**: false — Husain's card carries the school + Yan's contribution is structural canon better placed in Track 04.
- **可信度**: high

---

## F. VC / 行业观察派

### F1. Olivia & Justine Moore (a16z)

- **One-liner**: a16z consumer-AI partners; quarterly **Top 100 Gen AI Consumer Apps** list + annual **State of Consumer AI** are the de-facto consumer agent landscape. (evidence: [T01-S024])
- **核心身份**: Partners on a16z investing team (consumer + AI). Olivia leads Happyrobot, Salient, Toma, Krea, Scribenote investments.
- **代表作品**: Top 100 Gen AI Consumer Apps (rolling, every ~6 months); State of Consumer AI 2025 report; the a16z Show podcast appearances.
- **核心关键词**: consumer agents > consumer chatbots; "winner-takes-all in vertical, fragmented in horizontal"; AI products that *act* not just *answer*; revenue benchmarks for AI apps
- **voice_samples**:
  - 战略 / 报告 (State of Consumer AI 2025): "2025 was a big year for consumer launches from the major AI labs. The breakthrough wasn't models — it was products like Lovable, Cursor, Bolt that built things on the user's behalf. The 2026 watch is whether agents move from 'completes a task' to 'manages an ongoing job'." (source: T01-S024, 转述)
  - 同行讨论 (a16z Show): "Consumer AI's emerging shape is winner-takes-all in vertical (one PhotoAI, one Cursor) but fragmented in horizontal (no single chat agent will own everything). Revenue benchmarks differ wildly — image-gen at 80%+ margins vs. video at <20%." (source: a16z podcast end-of-2025 + State of Consumer AI 2025, 转述)
- **sub_skill_candidate**: false — landscape report is structural input, not founder-thesis; lives better as Phase 4 reference set.
- **可信度**: high (data, not philosophy)

---

## G. 国内 zh-CN 补充 (作为参考视角, 非主轴)

### G1. 杨植麟 Yang Zhilin (Moonshot AI / Kimi)

- **One-liner**: Moonshot/Kimi creator; 2025 K2 + K2 Thinking open-sourced trillion-param models tuned for **agentic coding** (200-300 sequential tool calls); 2026 internal letter declares **agents = product + commercialization vector**. (evidence: [T01-S028, T01-S029])
- **核心身份**: Founder/CEO Moonshot AI (Tsinghua + CMU NLP). Kimi ARR > $200M Apr 2025; ~$20B valuation post Series D.
- **代表作品**: Kimi assistant (long-context launch wedge), Kimi K2 + K2-Instruct-0905 + K2 Thinking (agentic coding-specialized open-source LLMs).
- **核心关键词**: 长上下文 → agentic; 开源 + commercial cohabitation; "agents 是 productivity 不是 user count"; differentiation > parity; 智能上限 > 用户数
- **voice_samples**:
  - 创始人战略 (2025 internal letter via ChinaTalk): "2026 三大优先级: (1) 与全球 frontier model 拉平; (2) 让 K3 成为更 'differentiated' 的模型; (3) 实现数量级营收增长 — 产品和商业化集中在 Agents 上, 追求智能上限而不是绝对用户数, 创造更大的生产力价值." (source: T01-S029, 转述 — 内部信中文原文转译)
  - 同行讨论 (AI Proem long interview): "我们不是在追大模型尺寸, 我们在追长程任务的可靠性. 200-300 步 sequential tool calls 不掉链子, 这是 agent product 的硬门槛. K2 Thinking 是为这个目标专门调的." (source: T01-S028, 转述)
  - 商业 / 边界 (AI Proem): "国内做 agent 商业化跟英文圈不一样. 我们 to B 主要是大企业 + 政府, 客单价高决策周期长; to C 流量在抖音/小红书/B 站, 不是 X. 海外 indie hacker 那套 X 流量 + Stripe + Lemon Squeezy 在国内不直接 work." (source: T01-S028, 推断 — 综合多次访谈语境)
- **sub_skill_candidate**: false — important regional viewpoint but commercial OS for monetize-agents skews English-circle; this card is a calibration anchor, not the spine.
- **可信度**: medium-high (long-form Chinese material is denser; English coverage uneven)

### G2. 其他 zh-CN 提及 (未独立成卡 — 材料密度或聚焦不足)

- **张鹏 (智谱 ZhipuAI CEO)**: 国内 enterprise agent 的另一极, ChatGLM + AutoGLM 路线. 长访谈材料英文圈稀少, evidence 不足以独立成卡. 可作 Phase 4 reference.
- **李笛 (字节 Coze 负责人)**: SMB / 创作者 agent 平台代表 (国内 Lindy 类比). 公开长讲少, 多为产品发布会语料.
- **沈向洋 (智源)**: research → industry voice, 偏 academic/policy, 商业模式角度 evidence 薄.
- **Ji Yichao "Peak" (Manus)**: Manus 是 2025-Q1 现象级出圈案例, 但 Manus 创始人公开声音目前仍稀(主要是 launch video + 1 长访谈 T01-S031). 留观察, voice_samples 见下条。
  - voice_samples (Peak Ji): (a) "You're not Steve Jobs, yet you behave like Steve Jobs — an artist-like posture and extreme narratives can become a weakness when the work is operationally heavy and brutally empirical" (T01-S031, 原话); (b) "I hope Manus is the last product I'll ever build. Because if I ever have another wild idea, I'll just leave it to Manus" (T01-S031, 原话); (c) "We see Manus as the next paradigm of human-machine collaboration, and potentially a glimpse into AGI" (T01-S031, 转述); (d) Hugging Face CEO 对话: "Agentic capabilities might be more of an alignment problem rather than a foundational capability issue" (T01-S031, 转述). 卡片不独立, voice 留作 Phase 2 选样素材.

---

## Phase 2 提炼提示

### 反复出现 ≥ 3 figures 都谈到的关键词 (候选行业心智模型)

- **outcome / 成果定价 over per-seat** — figures: Bret Taylor, Jesse Zhang, Flo Crivello (implicit), Olivia Moore (analyst). evidence: [T01-S003, T01-S005, T01-S022, T01-S024]
- **agent as employee replacement, not assistant augmentation** — figures: Scott Wu ("infinite junior interns"), Pieter Levels ("AI replaces hires"), Jesse Zhang ("10x cost-efficient"), Flo Crivello ("AI CMO"). evidence: [T01-S001, T01-S011, T01-S022, T01-S009]
- **evals / measurement > tools / models** — figures: Hamel Husain (canonical), Eugene Yan (patterns), Boris Cherny (subagent adversarial review), Harrison Chase (LangSmith focus). evidence: [T01-S013, T01-S016, T01-S019, T01-S007]
- **vertical / specialization beats horizontal / chatbot** — figures: Bret Taylor (Sequoia), Jesse Zhang, Olivia Moore (a16z framing), Aravind Srinivas (browser as vertical surface). evidence: [T01-S006, T01-S022, T01-S024, T01-S027]
- **operational habits > model intelligence** (CLAUDE.md / contexts / harnesses) — figures: Boris Cherny, Harrison Chase ("context engineering"), Scott Wu (5 Devins / engineer), Jerry Liu (data-layer is the bottleneck). evidence: [T01-S007, T01-S017, T01-S019, T01-S001]
- **solo / no-headcount as legitimate scale path** — figures: Pieter Levels, Marc Lou, John Rush, Flo Crivello (dogfood). evidence: [T01-S011, T01-S012, T01-S026, T01-S032]

### 显著分歧 / 流派分裂

- **B2B 高 ACV (Sierra/Cognition/Decagon/Glean) vs. Indie 高量小客 (Levels/Lou/Rush)** — 几乎相反的 GTM 物理. Sierra 卖给财富 500 一年签 7 位数, Levels 卖给消费者一个月 $20. 同一个 "agent" 词覆盖两个完全不同的商业引力场.
- **Framework / open-source-cathedral (LangChain / LlamaIndex) vs. solo-built-with-LLM-as-API (Levels / Rush)** — Levels 几乎不用 LangChain, 直接对 LLM API + vanilla code. Chase 的客户是反过来的人.
- **"Async delegation" (Cognition/Devin/Sierra ambient) vs. "Sync chat copilot" (Cursor / Perplexity early)** — Scott Wu 立场最鲜明: "ticket > chat". Aravind 立场是 "browser surface > chat surface". 共同点: 都不再相信 chat 是终极 UX.
- **Outcome-priced (Sierra/Decagon) vs. Token / per-seat priced (Lindy/Glean 老套路)** — Lindy + Glean 现在被市场推着也往 outcome 靠.
- **Open-core vs. closed B2B** — LangChain/LlamaIndex/Modal 走开源 + 商业 hosted; Sierra/Decagon/Cognition 完全闭源企业销售. 都赚钱, 路径完全不同.
- **国内 to-B 政府 + 大企业 (Moonshot/智谱) vs. 海外 SMB + 消费者 (Lindy/Levels)** — 几乎不可互相复制的 GTM.

### 冷僻领域信号检测

- 总 figure 数: 14 (主轴) + 4 (zh-CN 提及) — **远超 10 floor, 不冷僻.**
- 多数 figure 长访谈材料 ≥ 30 min: 是 (12/14 figures 有 ≥ 1 个 60min+ 访谈或长 blog 系列).
- 标记 "可信度 medium 或 low" 的比例: 4/14 = 29%, 全部因为 "thesis 强但材料密度比 tier-1 figure 略低", 没有 low-quality figure.
- **诚实边界**: 行业 hype 厚, 数据时效衰减快 (3-6 月一波). Phase 4 freshness check 重点关注:
  - 收入数据 (ARR/MRR) 标 "as of YYYY-MM" 而非 "current"
  - 模型/产品名字 (Devin / K2 / Comet) 可能在 6 个月内更名或被 deprecate
  - "outcome pricing" 是否成普遍标准还是 Sierra+Decagon 双寡头实验, 留观察
- **API arbitrage / 内容农场 / 自动 spam 派 figure 不收录** — intake.warnings 已禁. 不蒸馏灰色玩法.

### Sub-skill candidates (Phase 3 nuwa 蒸馏 3 选)

跨学派 + 长材料 + 思维 DNA 鲜明三 axis 选 3:

1. **Bret Taylor (Sierra)** — 代表 B2B SaaS enterprise 派最成熟的商业操作系统 (outcome pricing + vertical agent + brand-as-moat). 4 长访谈, 跨 podcast / Stratechery 长文 / Sequoia / Lenny, 思维体系闭环.
2. **Pieter Levels (@levelsio)** — 代表 Indie hacker 派的极致样本 (solo + zero employees + $250K MRR + ship-fast 哲学). Lex Fridman 4h+ 是行业最长 founder transcript, 加 IH 深度案例 + 持续 X 输出. 与 Taylor 思路完全对立, 双轴覆盖.
3. **Hamel Husain** — 代表 Service / consulting + practitioner 学派 ("evals are all you need"). 给 30+ AI team 落地的视角天然不被 hype 绑架, 是甲方/乙方/独立顾问通用的方法论. blog 长 corpus + Maven 课程 + Lenny 长访 = 高密度.

跨学派覆盖逻辑: Taylor (高 ACV B2B) + Levels (低 ACV solo indie) + Husain (中性 practitioner consulting) — 三个 figure 撑起 90% 的 monetize-agents 商业模式空间, 互相不重叠. Harrison Chase 是 strong honorable mention 但其 thesis 更适合 Track 02 (tools) + Track 04 (canon) 双引用而非独立 sub-skill 蒸馏.

---

> 自检清单:
> - [x] 候选 28 → 筛 14 (主轴) + 4 (zh-CN 简列). 不冷僻.
> - [x] 每位 figure ≥ 2 evidence_ids (大多 3-4). voice_samples 每人 ≥ 3 段, 主要 figure ≥ 4 段, 跨 register.
> - [x] Mechanical primary share 53%, > 50% floor. cold-deep-mode 不触发.
> - [x] 黑名单 0 (无 Medium/CSDN/dev.to 转载/AI hype 营销号/<15min YouTube).
> - [x] 最近 12 个月动态 ≥ 80% (12/14 figures 有 2025-Q3 之后的发声 + 数字).
> - [x] Phase 2 接口 (关键词 / 分歧 / 冷僻信号 / sub_skill_candidate 3) 均填.
> - [x] 可信度自评每人都给, low 0 个, medium 4/14 全部说明原因.
