2nd/10_Wiki/Topics/Domain_Programming/AI_and_ML/Startup.md

id, title, category, status, canonical_id, aliases, duplicate_of, source_trust_level, confidence_score, verification_status, tags, raw_sources, last_reinforced, github_commit, tech_stack

id	title	category	status	canonical_id	aliases	duplicate_of	source_trust_level	confidence_score	verification_status	tags	raw_sources	last_reinforced	github_commit	tech_stack
wiki-2026-0508-startup	Startup	10_Wiki/Topics	verified	self	Lean Startup Startup Methodology 스타트업	none	A	0.9	applied	startup lean mvp customer-development ai-startup		2026-05-10	pending	language framework english-korean lean-startup

Startup

매 한 줄

"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.

매 핵심

매 기본 정의 (Blank/Ries)

• Startup ≠ 매 small business. 매 "search for repeatable, scalable, profitable business model" 하 temporary org.
• Customer Development (Blank): Get-out-of-the-building, Customer Discovery → Validation → Creation → Building.
• Lean Startup (Ries): Build–Measure–Learn loop, MVP, validated learning, pivot or persevere.

매 stages

• Pre-seed ($100K~$2M): idea + founders. 2026 AI prototype demo 기본.
• Seed ($1M~$5M): PMF 탐색. 매 design partner 5–10 개.
• Series A ($5M~$25M): PMF 입증 + GTM scale.
• Series B+: scale operations, geographic/category expansion.

매 PMF (Product-Market Fit)

• Sean Ellis test: "How disappointed if product disappeared?" — 40%+ "very" → PMF signal.
• Retention curve flat → PMF. Churning curve → 매 not yet.
• Andreessen: "you can always feel PMF when it's happening."

매 2026 AI Startup wave

• Solo / 2-person AI startup: Cursor/Claude Code 로 1 founder 가 full-stack ship. ARR $1M+ < 12 개월 사례 다수.
• Vertical AI agents: 법률 (Harvey), 회계 (Pilot+AI), 의료 scribe (Abridge), 영업 (11x.ai).
• Foundation model wrapper risk: GPT-5 / Claude Opus 4.7 / Gemini 3 가 직접 feature 흡수. Moat = data/distribution/workflow integration.
• AI-native pricing: per-task, per-outcome (success-based), per-agent-seat. 매 traditional per-seat SaaS 의 위협.

💻 패턴

MVP 가설 worksheet (Lean Canvas style)

## Problem (top 3)
1. ...

## Customer Segments
- Early adopter: ...
- Mainstream: ...

## Unique Value Prop
"<X> for <segment> who <pain>"

## Solution (top 3 features)
1. ...

## Channels
- ...

## Revenue Streams
- ...

## Cost Structure
- ...

## Key Metrics (AARRR)
- Acquisition / Activation / Retention / Referral / Revenue

## Unfair Advantage
- ...

Customer interview template

# Discovery Interview (30 min)

## Warm-up
- Tell me about your role + day-to-day.

## Problem (no pitching!)
- Walk me through last time you <did task>.
- What was hardest part? Why?
- What did you do to solve it? (existing workarounds)
- How much time/money does <pain> cost?

## Solution probe (only after problem confirmed)
- If a tool did <X>, how would you use it?
- Who else needs to be involved in buying decision?

## Close
- Who else should I talk to?
- Can I follow up in 2 weeks?

MVP build (2026 AI startup stack)

# Day 0–3: validate pain via 10 interviews
# Day 4–10: prototype with Claude Code + v0 + Supabase
pnpm create next-app@latest mvp --typescript --tailwind --app
cd mvp
pnpm add @supabase/ssr ai @ai-sdk/anthropic
# Day 11–14: 5 design partner deploy via Vercel
vercel deploy --prod
# Day 15+: weekly Build–Measure–Learn cycle

Build–Measure–Learn loop

// 매 weekly cycle 의 instrumentation
import { track } from "@vercel/analytics";

export async function onUserAction(action: string, props: object) {
  await track(action, props);  // PostHog/Mixpanel/Amplitude
}

// Measure: cohort retention, activation funnel
// Learn: 매 weekly 5-customer call → hypothesis update
// Build: 매 1 hypothesis test per sprint

PMF metric dashboard (PostHog SQL)

-- Sean Ellis-style retention cohort
SELECT
  date_trunc('week', signup_at) AS cohort,
  COUNT(DISTINCT user_id) FILTER (WHERE active_in_week_4) * 1.0
    / COUNT(DISTINCT user_id) AS w4_retention
FROM users
GROUP BY 1
ORDER BY 1 DESC;
-- 매 30%+ flat W4 retention = PMF candidate

Pivot decision matrix

# 매 "pivot or persevere" — Ries
def pivot_signal(metrics):
    # No traction after 3 build-measure-learn cycles?
    if metrics.weekly_active_growth < 0.05 and metrics.cycles >= 3:
        return "PIVOT"
    if metrics.retention_w4 > 0.30 and metrics.organic_share > 0.20:
        return "PERSEVERE / SCALE"
    return "CONTINUE LEARNING"

Fundraising data room essentials (2026)

## Seed Data Room
- Pitch deck (10-12 slides, Sequoia/YC format)
- 매 financial model (3-year, monthly first 12mo)
- KPI dashboard (live link to Mixpanel/PostHog)
- Customer letters / testimonials (5+)
- Cap table (Carta export)
- 매 incorporation docs (Delaware C-Corp)
- IP assignment, founder agreements
- AI compliance: 매 SOC2 Type 1 progress, EU AI Act risk class

매 결정 기준

상황	Approach
매 Idea 단계	Customer Discovery 만, 매 build 전 10+ interview
Prototype 후 traction X	Lean iteration, 매 pivot 고려
매 Seed 단계, design partner 확보	Validation: contract / LOI 5+
Series A 준비	Repeatable sales motion 입증 (CAC payback < 18mo)
AI wrapper 우려	Workflow integration + proprietary data moat 의 강화

기본값: 매 Lean Startup + Customer Development 결합 — 매 Build–Measure–Learn weekly cadence.

🔗 Graph

• 부모: Business-Strategy
• 변형: Lean Startup
• 응용: MVP

🤖 LLM 활용

언제: 매 idea validation, customer interview synthesis, pitch deck draft, KPI dashboard SQL 작성, market sizing (TAM/SAM/SOM). 언제 X: 매 hard customer signal 의 대체 X — 매 LLM 가 진짜 customer pain 의 hallucinate 가능. 매 actual interviews irreplaceable.

❌ 안티패턴

• Build first, validate later: 매 6개월 build → 매 nobody wants. Customer dev 가 먼저.
• Vanity metrics: signup count, page view 만 추적 — 매 retention/revenue 의 무시.
• 매 founder–market mismatch: domain 의 unfamiliar — design partner 의 trust 약화.
• AI wrapper without moat: GPT-5 / Claude API call only → foundation model 이 흡수 시 사라짐.
• Premature scaling (Marmer): PMF 전 매 sales team 의 hire — 매 burn rate 폭주.

🧪 검증 / 중복

• Verified (Steve Blank "Four Steps to the Epiphany", Eric Ries "Lean Startup", YC startup library 2026).
• 신뢰도 A.

🕓 Changelog

날짜	변경
2026-05-08	Phase 1
2026-05-10	Manual cleanup — Lean/Customer Dev + 2026 AI startup wave 정리