# W2-이론: Technology S-Curves and Promise Precision **발송일**: Friday, November 8, 2024 (오후 2-4pm) **Subject**: [Theory] Technology S-Curves and Promise Precision --- Dear Scott, Your S-curve paper provides the commitment mechanism I need to explain *why* vague promises work during uncertainty. --- ## Your Framework In "The Paradox of the Technology S-Curve: Exploration versus Exploitation in the Face of Technological Uncertainty," you resolve a fundamental tension in innovation strategy. **The Paradox You Address:** - If technology trajectory is "given" (exogenous) → entrepreneurial strategy doesn't matter - If trajectory is "choice-based" (endogenous) → why do we see systematic S-curve patterns? **Your Resolution:** You reconceptualize the S-curve not as a technological given but as an **envelope of potential outcomes** reflecting entrepreneur choices in exploration versus exploitation. Key insight: The S-curve emerges from strategic commitment decisions under uncertainty, not from physical laws of technology. **Your Critical Finding:** **Staged exploration may stall innovation due to the replacement effect** → commitment becomes strategically important even under uncertainty. This challenges the naive "learn before committing" intuition in entrepreneurship. --- ## Connection to Strategic Vagueness **My Extension:** Replace "technology choice" with "promise precision": | Your Framework | My Framework | |----------------|--------------| | Exploration vs. Exploitation | Vagueness vs. Precision | | Technology trajectory uncertainty | Market demand uncertainty | | Replacement effect stalls innovation | Pivot costs stall execution | | Commitment enables focused learning | Precision enables resource mobilization | | S-curve as envelope of choices | Optimal precision as function of V/i | **The Parallel Trade-off:** **In your model**: Entrepreneur chooses between: - **Exploration** (preserving option value, delaying commitment) - **Exploitation** (committing to a trajectory, enabling focused progress) **In my model**: Entrepreneur chooses between: - **Vagueness** (preserving strategic flexibility, high variance promise) - **Precision** (committing to specific targets, low variance promise) --- ## Why Commitment Matters Despite Uncertainty **Your Key Insight:** Commitment is valuable *despite* uncertainty—not just *after* uncertainty resolves. Why? The **replacement effect**: If you keep options open too long, you never invest enough in any single path to reach the steep part of the S-curve. You get stuck in the slow-progress region because you're constantly hedging. **Applied to Promises:** If you keep promises vague too long, you never mobilize enough resources to execute effectively. Stakeholders need something concrete to commit to. But if you commit too early (make precise promises before market validation), you face high pivot costs when feedback suggests a different direction. --- ## The Replacement Effect in Strategic Vagueness **Your mechanism**: Staged exploration → replacement effect → innovation stalls **My application**: Excessive vagueness → mobilization failure → execution stalls **But also**: Premature precision → pivot costs → flexibility lost **The Tension:** ``` Vague promises: + Preserve flexibility (low pivot cost) - Hinder mobilization (stakeholders can't commit) Precise promises: + Enable mobilization (stakeholders can commit) - Create rigidity (high pivot cost if wrong) ``` **The Solution (from your logic):** Optimal precision is NOT "stay vague until certain" (that's the replacement effect trap). Optimal precision is: **Commit when the value of focused execution exceeds the cost of reduced flexibility.** This is exactly what my V/i ratio captures: ``` τ* = √(V/4i) Where: - V = value of mobilized resources (your "focused learning" benefit) - i = integration cost (your "commitment cost") ``` --- ## Integration Cost as Commitment Cost **Your framework** emphasizes: Different technologies have different commitment costs. **My extension**: Different venture types have different integration costs: - **Hardware/chip firms**: High integration cost - Physical prototypes expensive to change - Manufacturing setup asset-specific - Long development cycles - → High i → Low τ* → Stay vague longer - **Software/API firms**: Low integration cost - Code easy to refactor - Cloud deployment flexible - Fast iteration cycles - → Low i → High τ* → Can commit earlier **This explains the moderator in Model 2:** ``` β₇·(Vagueness × SeriesB × High_Integration_Cost) > 0 ``` Hardware firms benefit MORE from vagueness during shakeout because their replacement effect (cost of committing to wrong path) is higher. --- ## S-Curve Envelope Applied to Venture Outcomes **Your insight**: The technology S-curve is an envelope—different strategic choices yield different trajectories within that envelope. **My application**: The venture success curve is an envelope—different promise precision levels yield different funding trajectories. ``` High precision at Series A: → High mobilization (steep early growth) → But locked into specific path → If market shifts, replacement effect hits hard → Series B failure Low precision at Series A: → Low mobilization (slow early growth) → But flexibility preserved → Can pivot toward emerging dominant design → Series B success (among survivors) ``` The 2021-22 AI/ML boom → 2023-25 shakeout provides a natural test: - Precise promisers: Committed to specific AI architectures too early - Vague promisers: Kept options open, pivoted toward GPT/transformer dominance **Your S-curve logic predicts**: Vague firms should show **delayed but ultimately higher** Series B success. --- ## Why This Matters for Entrepreneurial Strategy **Your paper challenges**: "Just keep learning, don't commit until certain" **You show**: That strategy leads to stagnation (replacement effect) **I extend**: "Just stay vague, don't commit until validated" also leads to failure (mobilization failure) **The synthesis**: There's an **optimal timing for commitment** (in your case, to technology; in my case, to promises). - Too early → locked into wrong path - Too late → replacement effect / never mobilize **The formula captures this**: τ* = √(V/4i) - When V low (early stage, little to gain from mobilization) → τ* ≈ 0 (stay vague) - When V high (validated traction, resources multiply impact) → τ* > 0 (commit) - When i high (hardware, high pivot cost) → τ* lower (stay vague longer) - When i low (software, low pivot cost) → τ* higher (can commit earlier) --- ## Three Questions for Your Intuition 1. **Does the replacement effect apply to promises?** Or is there a different mechanism explaining why excessive vagueness fails? 2. **Is "integration cost" the right analog to your "commitment cost"?** Or should I think about the cost structure differently? 3. **In your framework, commitment timing is key. In mine, it's promise precision.** Do you see other domains where this "optimal specificity" logic applies? --- Looking forward to discussing when convenient. No rush before our meeting. Best, Angie --- ## 작성 가이드 (당신이 채울 때) **핵심 구조:** 1. "Your Framework" (S-curve paradox 설명) 2. "Connection to Strategic Vagueness" (table로 parallel 보여주기) 3. "Replacement Effect" (핵심 메커니즘 연결) 4. "Integration Cost" (moderator 이론적 근거) 5. "Why This Matters" (entrepreneurial strategy 함의) 6. Questions (답장 유도) **이 메일은 Scott에게만 보냄** (Charlie 포함 안 함) **톤:** - "Your insight challenges the naive view..." - "I extend your logic to a new domain..." - Respectful but confident **만약 당신이 replacement effect를 잘못 이해했다면:** - "I may be stretching your replacement effect logic too far here..." - "Please correct me if this application doesn't hold..." **길이:** - 이 버전 ~750단어 (약간 길지만 괜찮음) - S-curve는 복잡한 개념이라 설명 필요 - 너무 짧으면 shallow하게 보임 **핵심 메시지:** "Your commitment mechanism explains when precision becomes valuable"