# using [[update(🧬process, 📜product)]], Below are **emoji‑tagged versions** of each summary level. The coloured squares map every sentence to its eight‑module framework exactly as you specified earlier: |colour|module|meaning (NAIL → SCALE → SAIL roles)| |---|---|---| |🟪|**M1** S1‑2, S23‑24|Context & dynamic challenge| |🟩|**M2** S3‑4‑17‑18‑25‑26|Capability need & payoff| |🟧|**M3/M4/M7 core**|Methods, architecture, extensions| |🟦|**M2/M7 validation**|Links & empirical grounding| |🟥|**M3 & M8 vision**|Key principle & future platform| --- ### 32‑Sentence version (full SAIL granularity) 🟪 **1** Start‑ups that rely on complementary technologies must integrate their offerings with partners whose cost trajectories are highly uncertain. 🟪 **2** This uncertainty makes the market‑entry problem uniquely complex for new ventures. 🟩 **3** Entrepreneurship research usually recommends focused strategies because founders face budget and cognitive limits. 🟩 **4** Product‑development studies, however, argue for diversified investment across complements to hedge technological risk. 🟧 **5** To reconcile this conflict, the authors model the entry decision of a storage start‑up that must pair with one or more renewable‑energy technologies. 🟧 **6** They employ a system‑dynamics simulation centred on an energy‑storage firm integrating with wind (RA) or solar (RB) power. 🟦 **7** Two choices are pivotal: total integration spending and how that spending is split between complements. 🟦 **8** The model embeds learning‑curve cost reductions for storage and renewables plus competition from mature gas turbines. 🟥 **9** A causal‑loop diagram reveals reinforcing integration–learning feedbacks and balancing price–demand loops. 🟥 **10** Baseline runs show storage costs falling steadily while gas prices fluctuate. 🟧 **11** Consequently demand migrates from gas‑bundled renewables to storage‑bundled renewables over time. 🟧 **12** Integration investment substantially lifts the start‑up’s net present value (NPV). 🟧 **13** Directing all investment to a single complement (“focused” strategy) dominates a 50/50 split in the base case. 🟧 **S14** Focus still wins when the two complements are ex‑ante identical, because feedback loops amplify concentrated learning. 🟧 **S15** Monte‑Carlo experiments vary learning‑curve slopes and gas prices to inject risk. 🟧 **S16** Across 1,000 runs, an aggressive focused strategy yields the highest expected NPV. 🟩 **S17** Yet the no‑investment strategy minimizes bankruptcy probability, exposing a safety–return trade‑off. 🟩 **S18** Balanced strategies occasionally lead in market share, revealing potential objective conflicts. 🟩 **S19** Sensitivity tests vary market growth, integration cost, spillovers, and externality strength. 🟩 **S20** Greater externalities, higher uncertainty, or tighter time horizons strengthen the case for focus and aggressiveness. 🟩 **S21** Rising integration cost reduces the payoff to aggressiveness but leaves the focus advantage largely intact. 🟩 **S22** High spillover between complements erodes the benefit of focusing. 🟪 **S23** Longer horizons or larger equity injections can shift advantage toward balanced investment for share expansion. 🟪 **S24** Managerially, start‑ups should initially pick the most promising complement and invest deeply. 🟩 **S25** For storage firms that usually means wind integration before solar. 🟩 **S26** Policymakers may need incentives to prevent underinvestment in lagging complements such as solar. 🟧 **S27** The study bridges entrepreneurship and flexible design literatures by locating the mechanism in nonlinear learning externalities. 🟧 **S28** Its central insight is that concentrated learning loops outweigh portfolio hedging under typical start‑up constraints. 🟦 **S29** Limitations include a stylised model, single mature competitor, and industry‑specific parameters. 🟦 **S30** Nevertheless robustness checks suggest wide applicability. 🟥 **S31** Future work should examine industries without mature incumbents and alternative founder utility functions. 🟥 **S32** Overall, focused integration spending typically maximises start‑up value under technological uncertainty. --- ### 16‑Sentence version (SCALE granularity) 🟪 **1** Start‑ups integrating with uncertain complements confront severe strategic ambiguity. 🟩 **2** Entrepreneurship urges focus while design research urges diversification, creating a theoretical rift. 🟧 **3** The authors build a system‑dynamics model of an energy‑storage entrant pairing with wind or solar to explore the issue. 🟦 **4** Investment level and allocation drive learning‑curve cost dynamics against a gas benchmark. 🟥 **5** Reinforcing feedback loops dominate system behaviour and baseline runs show declining storage costs. 🟧 **6** Integration spending lifts NPV markedly. 🟧 **7** Focusing all funds on one complement beats splitting funds, even under symmetry. 🟧 **8** Monte‑Carlo analysis confirms aggressive focus maximises expected returns. 🟩 **9** Conversely, zero‑investment best preserves solvency while balanced approaches can win on share. 🟩 **10** Robustness checks across growth, cost, and externality parameters keep the focus result intact. 🟩 **11** Higher integration cost mainly tempers aggressiveness, not focus. 🟪 **12** Spillovers and abundant capital lessen the focus premium. 🟩 **13** Managers should specialise early—e.g., wind before solar—and governments may counterbalance neglected paths. 🟧 **14** The study bridges entrepreneurship and flexible design by highlighting nonlinear learning externalities. 🟦 **15** Limitations include stylised parameters, yet robustness suggests broad relevance. 🟥 **16** Hence focus under uncertainty remains the dominant start‑up strategy. --- ### 8‑Sentence version (NAIL → SCALE bridge) 🟪 **U1** Complement‑dependent start‑ups face high uncertainty over which technologies to integrate with. 🟩 **U2** Conflicting scholarly advice on focus versus diversification motivates this study. 🟧 **U3** A system‑dynamics model embeds learning curves and gas competition to examine the issue. 🟦 **U4** Reinforcing integration–learning loops drive falling storage costs and rising demand. 🟥 **U5** Focused integration spending outruns balanced spending on NPV, even when complements look identical. 🟧 **U6** Monte‑Carlo trials show aggressive focus wins on returns, while no spending minimises bankruptcy. 🟩 **U7** Sensitivity tests uphold the focus edge except under high spillover or abundant capital. 🟥 **U8** Early specialisation is advised, with policy incentives to offset inevitable underinvestment in the ignored complement. --- ### 4‑Sentence elevator‑pitch version 🟪 **V1** Start‑ups integrating with fast‑evolving complementary technologies face strategic uncertainty and competing doctrines on whether to focus or diversify. 🟧 **V2** A system‑dynamics simulation links integration spending with learning‑curve feedbacks that reshape costs and demand. 🟥 **V3** Across deterministic and stochastic scenarios, aggressive focus maximises expected NPV, whereas conservative spending best protects against insolvency. 🟥 **V4** Except when spillovers are large, this robustness suggests founders should specialise early while policymakers balance neglected technologies.