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AI Startup Strategic Questioning Framework

AI 初创公司战略提问框架

Framework for founders to navigate extreme uncertainty in the AI era by asking fundamental questions about defensibility, trust, timing, and what remains hard.
帮助创始人在 AI 时代极端不确定性中，通过提出关于竞争壁垒、信任、时机以及哪些问题仍然困难的根本性问题来 navigating 方向。

Core Philosophy

核心理念

The AI landscape changes faster than traditional planning horizons allow. Stop planning for 6 months ahead—plan for 2-3 years when AGI may arrive. Embrace confusion as a signal that something interesting is happening.
Key insight: Founders must focus on everything while focus is everything. This paradox makes founders uniquely suited to grapple with AI's biggest questions.
AI 格局的变化速度已经超出了传统规划周期所能容纳的范围。不要只规划未来 6 个月——而要规划未来 2-3 年，因为 AGI 可能在那时到来。把困惑视为某种有趣的事情正在发生的信号。
核心洞察：当专注就是一切时，创始人必须关注一切。这种悖论使创始人天然适合应对 AI 领域最重大的问题。

Strategic Questioning Process

战略提问流程

Step 1: Assess the Timing Question

第 1 步：评估时机问题

Ask these questions about timing and market position:
  1. "What will AI be capable of in 2-3 years, not 6 months?"
  2. "Am I building for today's capabilities or tomorrow's?"
  3. "Is this the last window to build something that changes the world?"
  4. "What's my advantage if I can't see 5 years ahead anymore?"
Output: Document assumptions about AI capability trajectory and how they affect your thesis.
围绕时机和市场定位提出以下问题：
  1. “未来 2-3 年，而不是未来 6 个月，AI 将具备哪些能力？”
  2. “我是在为今天的能力而构建，还是为未来的能力而构建？”
  3. “这是否是打造一家改变世界的公司的最后窗口期？”
  4. “如果我再也看不到未来 5 年，我仍然拥有什么优势？”
产出：记录有关 AI 能力发展轨迹的假设，以及这些假设如何影响你的核心判断。

Step 2: Evaluate Defensibility

第 2 步：评估竞争壁垒

Challenge traditional moats with these questions:
  1. "What happens to my product when the next foundation model ships?"
  2. "Does my defensibility come from data, distribution, or something AI can't replicate?"
  3. "If AGI arrives in 3 years, does my company still matter?"
  4. "What do I have that a well-funded competitor with better models doesn't?"
Defensibility categories to examine:
  • Data network effects (does usage make the product better?)
  • Trust relationships (do customers need a human in the loop?)
  • Regulatory positioning (are there compliance moats?)
  • Integration depth (how painful is switching?)
通过以下问题挑战传统护城河：
  1. “当下一代 foundation model 发布时，我的产品会发生什么？”
  2. “我的竞争壁垒来自数据、分发渠道，还是某种 AI 无法复制的东西？”
  3. “如果 AGI 在 3 年后到来，我的公司是否仍然重要？”
  4. “面对资金更充足且使用更好模型的竞争对手，我拥有什么是他们所没有的？”
需要考察的竞争壁垒类型：
  • 数据网络效应（使用量增加能否让产品变得更好？）
  • 信任关系（客户是否需要 human-in-the-loop？）
  • 监管定位（是否存在合规方面的护城河？）
  • 集成深度（客户更换产品的成本有多高？）

Step 3: Identify What Remains Hard

第 3 步：识别仍然困难的问题

Focus on problems that stay hard even as AI improves:
  1. "What can't be solved by throwing more compute at it?"
  2. "What requires trust, accountability, or human judgment?"
  3. "What problems have regulatory or legal constraints AI can't navigate alone?"
  4. "Where do customers need someone to blame when things go wrong?"
Categories of durable difficulty:
  • High-stakes decisions requiring accountability
  • Relationship-dependent sales and partnerships
  • Physically constrained operations
  • Regulated industries requiring human oversight
聚焦那些即使 AI 持续进步也依然难以解决的问题：
  1. “哪些问题无法仅靠投入更多算力来解决？”
  2. “哪些问题需要信任、问责或人的判断？”
  3. “哪些问题受到监管或法律约束，无法仅靠 AI 应对？”
  4. “当事情出错时，哪些场景需要客户能够归责于某个具体的人？”
持久难题的类型：
  • 需要承担问责责任的高风险决策
  • 依赖关系的销售和合作伙伴关系
  • 受物理条件约束的运营活动
  • 需要人工监督的受监管行业

Step 4: Rethink Team and Hiring

第 4 步：重新思考团队与招聘

Question traditional hiring assumptions:
  1. "How does AI change what roles I need to hire?"
  2. "Should I hire fewer people and use AI for more?"
  3. "What human skills become more valuable, not less?"
  4. "How do I build a team that adapts as AI capabilities shift?"
重新审视传统招聘假设：
  1. “AI 会如何改变我需要招聘的岗位？”
  2. “我是否应该减少招聘人数，并利用 AI 承担更多工作？”
  3. “哪些人的技能会变得更有价值，而不是价值下降？”
  4. “如何打造一支能够随着 AI 能力变化而适应调整的团队？”

Step 5: Challenge Your Go-to-Market

第 5 步：挑战你的 go-to-market 策略

Question distribution and sales strategy:
  1. "Does my go-to-market depend on capabilities that will be commoditized?"
  2. "Am I selling AI or solving a problem that happens to use AI?"
  3. "What's my moat if the AI layer becomes interchangeable?"
  4. "How do I build trust with customers in a hype-saturated market?"
重新审视分发和销售策略：
  1. “我的 go-to-market 是否依赖于那些最终会被商品化的能力？”
  2. “我是在销售 AI，还是在解决一个恰好使用 AI 的问题？”
  3. “如果 AI 层变得可以相互替代，我的护城河是什么？”
  4. “如何在一个充斥着炒作的市场中建立客户信任？”

Question Framework Template

提问框架模板

Use this template when evaluating any AI startup decision:
markdown
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评估任何 AI 初创公司决策时使用此模板：
markdown
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Decision: [What you're deciding]

决策：[你正在决定的事项]

Capability Questions

能力问题

  • What AI capabilities does this assume?
  • How might those capabilities change in 6/12/24 months?
  • What breaks if the assumption is wrong?
  • 此项决策假设了哪些 AI 能力？
  • 这些能力可能会在未来 6/12/24 个月如何变化？
  • 如果假设错误，会导致什么后果？

Defensibility Questions

竞争壁垒问题

  • What's the moat if this works?
  • Can a better-funded competitor with better models replicate this?
  • What do we have that's hard to copy?
  • 如果这件事成功了，我们的护城河是什么？
  • 资金更充足且使用更好模型的竞争对手能否复制它？
  • 我们拥有哪些难以复制的东西？

Trust Questions

信任问题

  • Do customers need a human accountable for this?
  • What's the cost of AI being wrong here?
  • How do we build trust in an uncertain landscape?
  • 客户是否需要由某个人来承担这项决策的责任？
  • AI 在这里出错的成本是什么？
  • 如何在充满不确定性的环境中建立信任？

Timing Questions

时机问题

  • Why now and not 2 years ago or 2 years from now?
  • Is this a shrinking or expanding window?
  • What changes if AGI arrives in 3 years?
  • 为什么是现在，而不是两年前或两年后？
  • 窗口期是在缩小还是在扩大？
  • 如果 AGI 在 3 年后到来，会发生什么变化？

Team Questions

团队问题

  • What human skills does this require?
  • How does AI augment vs. replace those skills?
  • What happens to this role in 2 years?
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  • 这需要哪些人的技能？
  • AI 是增强还是取代这些技能？
  • 这个岗位在两年后会发生什么变化？
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Common Pitfalls to Question

需要警惕的常见陷阱

The "AI Wrapper" Trap

“AI Wrapper”陷阱

Ask: "Am I just wrapping an API that will be commoditized?"
Signs of danger:
  • Primary value is prompt engineering
  • No proprietary data or workflows
  • Switching costs are low
提问：“我是否只是在包装一个最终会被商品化的 API？”
危险信号：
  • 主要价值来自 prompt engineering
  • 没有专有数据或工作流
  • 更换成本很低

The "Current Capabilities" Trap

“当前能力”陷阱

Ask: "Am I building for GPT-4 or for what comes next?"
Signs of danger:
  • Product relies on current model limitations
  • No plan for capability improvements making features obsolete
  • Competing on capabilities that will be table stakes
提问：“我是在为 GPT-4 构建，还是为下一代模型构建？”
危险信号：
  • 产品依赖于当前模型的局限
  • 没有计划应对能力提升导致功能过时
  • 将很快成为行业标准的能力作为竞争点

The "Ignoring Trust" Trap

“忽视信任”陷阱

Ask: "Do I understand why customers might not trust pure AI solutions?"
Signs of danger:
  • Assuming AI accuracy alone drives adoption
  • Underestimating need for human oversight
  • Missing regulatory or liability concerns
提问：“我是否理解客户为什么可能不信任纯 AI 解决方案？”
危险信号：
  • 假设仅靠 AI 准确率就能推动采用
  • 低估对 human-in-the-loop 的需求
  • 忽视监管或责任归属问题

Example Application

示例应用

Scenario: Evaluating an AI legal document review startup
Capability Questions:
  • Current: AI can review documents but misses nuance
  • 2-year projection: Near-human accuracy likely
  • Risk: Accuracy alone won't differentiate
Defensibility Questions:
  • Data moat: Do we accumulate proprietary training data?
  • Trust moat: Law firms need accountability—who's liable for AI errors?
  • Integration moat: How deep are we in existing workflows?
Conclusion questions:
  • "Is our moat the AI or the trust relationship with law firms?"
  • "What do we offer when document review AI is commoditized?"
  • "Are we building a tool or a trusted service?"
场景：评估一家 AI 法律文档审查初创公司
能力问题：
  • 当前情况：AI 可以审查文档，但会遗漏细微之处
  • 两年预测：准确率可能接近人类水平
  • 风险：仅凭准确率无法形成差异化
竞争壁垒问题：
  • 数据护城河：我们能否积累专有训练数据？
  • 信任护城河：律师事务所需要有人承担责任——AI 出错时由谁负责？
  • 集成护城河：我们与现有工作流的集成有多深？
结论问题：
  • “我们的护城河是 AI，还是与律师事务所之间的信任关系？”
  • “当文档审查 AI 被商品化后，我们还能提供什么？”
  • “我们是在打造一种工具，还是一项值得信赖的服务？”

The Meta-Question

元问题

Always return to the foundational question:
"Everything's changing. How should that impact everything about my life—and my startup?"
This isn't a question to answer once. Revisit it regularly as the landscape shifts. The founders who thrive will be those comfortable operating in permanent uncertainty while still making decisive moves.
始终回到这个根本问题：
“一切都在变化。这应该如何影响我的生活——以及我的初创公司——的方方面面？”
这个问题不需要只回答一次。随着格局不断变化，应定期重新审视它。那些能够在永久不确定性中从容应对、同时仍能果断行动的创始人，最终才能蓬勃发展。