AI Startup Questions Fisher

AI Startup Strategic Questioning Framework

Framework for founders to navigate extreme uncertainty in the AI era by asking fundamental questions about defensibility, trust, timing, and what remains hard.

Core Philosophy

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.

Strategic Questioning Process

Step 1: Assess the Timing Question

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.

Step 2: Evaluate Defensibility

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?)

Step 3: Identify What Remains Hard

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

Step 4: Rethink Team and Hiring

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?"

Step 5: Challenge Your Go-to-Market

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?"

Question Framework Template

Use this template when evaluating any AI startup decision:

## Decision: [What you're deciding]

### Capability Questions
- What AI capabilities does this assume?
- How might those capabilities change in 6/12/24 months?
- What breaks if the assumption is wrong?

### Defensibility Questions
- 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?

### Trust Questions
- 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?

### Timing Questions
- 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?

### Team Questions
- What human skills does this require?
- How does AI augment vs. replace those skills?
- What happens to this role in 2 years?

Common Pitfalls to Question

The "AI Wrapper" Trap

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

The "Current Capabilities" Trap

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

The "Ignoring Trust" Trap

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

Example Application

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?"

The Meta-Question

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.