Macroscopic Economic Indicator Integration into Generative AI Simulators for Stress-Testing Startup Resilience and Growth Trajectories

• Abey Litty

  Texas University

  Author

The intersection of macroeconomic volatility and startup sustainability presents a critical analytical challenge, as early-stage enterprises operate within increasingly fragile global economic environments characterized by costly capital, narrow margins of safety, and episodic shocks . While generative AI has demonstrated transformative potential in financial forecasting and venture capital decision-making , existing simulation frameworks lack systematic integration of macroscopic economic indicators for stress-testing startup resilience. This study addresses this gap by developing and validating a hybrid generative AI simulation framework that incorporates key macroeconomic indicators—GDP growth, interest rates, inflation, and capital availability—to model startup survival probabilities and growth trajectories under varying economic scenarios. Using a retrospective dataset of 1,500 U.S.-based startups from 2020–2025, the framework achieved 89.4% predictive accuracy in identifying startup resilience patterns, outperforming traditional static budget methods by 23.7%. The causal generative architecture, grounded in prospect theory and Neo-Schumpeterian innovation theory, enables counterfactual scenario analysis through "what-if" prompts across 10,000 simulated market conditions . Findings reveal that macroeconomic indicator integration reduces forecasting error by 34.2% compared to AI models operating solely on firm-level data. This research contributes a replicable, transparent framework for startup stress-testing, offering actionable insights for founders, venture capitalists, and policymakers navigating uncertain economic landscapes.

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Copyright (c) 2026 Abey Litty (Author)

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