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Antigravity Agent
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---
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id: wiki-2026-0508-startup
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title: Startup
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category: 10_Wiki/Topics
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status: verified
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canonical_id: self
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aliases: [Lean Startup, Startup Methodology, 스타트업]
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duplicate_of: none
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source_trust_level: A
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confidence_score: 0.9
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verification_status: applied
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tags: [startup, lean, mvp, customer-development, ai-startup]
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raw_sources: []
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last_reinforced: 2026-05-10
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github_commit: pending
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tech_stack:
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language: english-korean
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framework: lean-startup
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---
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# Startup
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## 매 한 줄
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> **"매 startup = search for repeatable + scalable business model"**. Steve Blank 의 customer development + Eric Ries 의 Lean Startup 이 base. 2026 AI wave 에선 매 1인 founder 가 Claude/Cursor 로 prototype → seed 까지 < 30 일 의 cycle.
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## 매 핵심
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### 매 기본 정의 (Blank/Ries)
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- **Startup** ≠ 매 small business. 매 "search for repeatable, scalable, profitable business model" 하 temporary org.
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- **Customer Development** (Blank): Get-out-of-the-building, Customer Discovery → Validation → Creation → Building.
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- **Lean Startup** (Ries): Build–Measure–Learn loop, MVP, validated learning, pivot or persevere.
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### 매 stages
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- **Pre-seed** ($100K~$2M): idea + founders. 2026 AI prototype demo 기본.
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- **Seed** ($1M~$5M): PMF 탐색. 매 design partner 5–10 개.
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- **Series A** ($5M~$25M): PMF 입증 + GTM scale.
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- **Series B+**: scale operations, geographic/category expansion.
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### 매 PMF (Product-Market Fit)
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- Sean Ellis test: "How disappointed if product disappeared?" — 40%+ "very" → PMF signal.
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- Retention curve flat → PMF. Churning curve → 매 not yet.
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- Andreessen: "you can always feel PMF when it's happening."
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### 매 2026 AI Startup wave
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- **Solo / 2-person AI startup**: Cursor/Claude Code 로 1 founder 가 full-stack ship. ARR $1M+ < 12 개월 사례 다수.
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- **Vertical AI agents**: 법률 (Harvey), 회계 (Pilot+AI), 의료 scribe (Abridge), 영업 (11x.ai).
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- **Foundation model wrapper risk**: GPT-5 / Claude Opus 4.7 / Gemini 3 가 직접 feature 흡수. Moat = data/distribution/workflow integration.
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- **AI-native pricing**: per-task, per-outcome (success-based), per-agent-seat. 매 traditional per-seat SaaS 의 위협.
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## 💻 패턴
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### MVP 가설 worksheet (Lean Canvas style)
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```markdown
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## Problem (top 3)
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1. ...
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## Customer Segments
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- Early adopter: ...
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- Mainstream: ...
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## Unique Value Prop
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"<X> for <segment> who <pain>"
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## Solution (top 3 features)
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1. ...
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## Channels
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- ...
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## Revenue Streams
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- ...
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## Cost Structure
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- ...
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## Key Metrics (AARRR)
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- Acquisition / Activation / Retention / Referral / Revenue
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## Unfair Advantage
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- ...
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```
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### Customer interview template
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```markdown
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# Discovery Interview (30 min)
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## Warm-up
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- Tell me about your role + day-to-day.
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## Problem (no pitching!)
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- Walk me through last time you <did task>.
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- What was hardest part? Why?
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- What did you do to solve it? (existing workarounds)
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- How much time/money does <pain> cost?
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## Solution probe (only after problem confirmed)
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- If a tool did <X>, how would you use it?
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- Who else needs to be involved in buying decision?
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## Close
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- Who else should I talk to?
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- Can I follow up in 2 weeks?
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```
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### MVP build (2026 AI startup stack)
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```bash
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# Day 0–3: validate pain via 10 interviews
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# Day 4–10: prototype with Claude Code + v0 + Supabase
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pnpm create next-app@latest mvp --typescript --tailwind --app
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cd mvp
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pnpm add @supabase/ssr ai @ai-sdk/anthropic
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# Day 11–14: 5 design partner deploy via Vercel
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vercel deploy --prod
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# Day 15+: weekly Build–Measure–Learn cycle
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```
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### Build–Measure–Learn loop
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```typescript
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// 매 weekly cycle 의 instrumentation
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import { track } from "@vercel/analytics";
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export async function onUserAction(action: string, props: object) {
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await track(action, props); // PostHog/Mixpanel/Amplitude
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}
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// Measure: cohort retention, activation funnel
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// Learn: 매 weekly 5-customer call → hypothesis update
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// Build: 매 1 hypothesis test per sprint
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```
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### PMF metric dashboard (PostHog SQL)
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```sql
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-- Sean Ellis-style retention cohort
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SELECT
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date_trunc('week', signup_at) AS cohort,
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COUNT(DISTINCT user_id) FILTER (WHERE active_in_week_4) * 1.0
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/ COUNT(DISTINCT user_id) AS w4_retention
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FROM users
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GROUP BY 1
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ORDER BY 1 DESC;
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-- 매 30%+ flat W4 retention = PMF candidate
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```
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### Pivot decision matrix
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```python
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# 매 "pivot or persevere" — Ries
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def pivot_signal(metrics):
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# No traction after 3 build-measure-learn cycles?
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if metrics.weekly_active_growth < 0.05 and metrics.cycles >= 3:
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return "PIVOT"
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if metrics.retention_w4 > 0.30 and metrics.organic_share > 0.20:
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return "PERSEVERE / SCALE"
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return "CONTINUE LEARNING"
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```
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### Fundraising data room essentials (2026)
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```markdown
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## Seed Data Room
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- Pitch deck (10-12 slides, Sequoia/YC format)
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- 매 financial model (3-year, monthly first 12mo)
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- KPI dashboard (live link to Mixpanel/PostHog)
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- Customer letters / testimonials (5+)
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- Cap table (Carta export)
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- 매 incorporation docs (Delaware C-Corp)
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- IP assignment, founder agreements
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- AI compliance: 매 SOC2 Type 1 progress, EU AI Act risk class
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```
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## 매 결정 기준
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| 상황 | Approach |
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| 매 Idea 단계 | Customer Discovery 만, 매 build 전 10+ interview |
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| Prototype 후 traction X | Lean iteration, 매 pivot 고려 |
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| 매 Seed 단계, design partner 확보 | Validation: contract / LOI 5+ |
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| Series A 준비 | Repeatable sales motion 입증 (CAC payback < 18mo) |
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| AI wrapper 우려 | Workflow integration + proprietary data moat 의 강화 |
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**기본값**: 매 Lean Startup + Customer Development 결합 — 매 Build–Measure–Learn weekly cadence.
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## 🔗 Graph
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- 부모: [[Business-Strategy]]
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- 변형: [[Lean Startup]]
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- 응용: [[MVP]]
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## 🤖 LLM 활용
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**언제**: 매 idea validation, customer interview synthesis, pitch deck draft, KPI dashboard SQL 작성, market sizing (TAM/SAM/SOM).
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**언제 X**: 매 hard customer signal 의 대체 X — 매 LLM 가 진짜 customer pain 의 hallucinate 가능. 매 actual interviews irreplaceable.
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## ❌ 안티패턴
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- **Build first, validate later**: 매 6개월 build → 매 nobody wants. Customer dev 가 먼저.
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- **Vanity metrics**: signup count, page view 만 추적 — 매 retention/revenue 의 무시.
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- **매 founder–market mismatch**: domain 의 unfamiliar — design partner 의 trust 약화.
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- **AI wrapper without moat**: GPT-5 / Claude API call only → foundation model 이 흡수 시 사라짐.
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- **Premature scaling** (Marmer): PMF 전 매 sales team 의 hire — 매 burn rate 폭주.
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## 🧪 검증 / 중복
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- Verified (Steve Blank "Four Steps to the Epiphany", Eric Ries "Lean Startup", YC startup library 2026).
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- 신뢰도 A.
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## 🕓 Changelog
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| 날짜 | 변경 |
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| 2026-05-08 | Phase 1 |
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| 2026-05-10 | Manual cleanup — Lean/Customer Dev + 2026 AI startup wave 정리 |
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