Bridging the Inferential Deficit
In an era of high-consequence shifts—from the Total Factor Productivity (TFP)-Stability Paradox to the evolution of Sovereign Risk—standard predictive models are insufficient. I build applications and technologies required to navigate this volatility by combining proven forecasting and simulation models with agentic inference. By layering autonomous with foundational forecasting and simulation approaches, we move beyond generative AI toward Decision Intelligence: the ability to recursively stress-test strategies against an evolving, multi-agent reality.
 
The TFP-Stability Paradox: Navigating the Friction Between Exponential Growth and Systemic Resilience
The coming decade will be shaped by a structural contradiction in the global political economy. Advances in agentic artificial intelligence are likely to produce a material acceleration in Total Factor Productivity (TFP), reversing a two-decade productivity slowdown. Yet this productivity surge is qualitatively different from prior technological revolutions: it is increasingly labor-decoupled. The TFP–Stability Paradox phenomenon occurs when rapid, automation-driven productivity growth simultaneously weakens the labor-based fiscal, political, and institutional foundations required to sustain long-run economic and social stability.
 
1. The Catalyst: Labor-Decoupled Productivity Growth
Recent advances in agentic inference systems—autonomous models capable of planning, coordinating, and executing complex tasks—are being integrated across the global value chain. Unlike prior waves of automation that primarily augmented human labor, agentic systems increasingly substitute for coordination, management, and decision functions traditionally performed by skilled labor. Under plausible adoption scenarios, this shift could raise annual TFP growth by 0.5–1.5 percentage points over the next decade, producing a large positive level effect on global output. Importantly, these gains are driven not by capital deepening or labor input, but by autonomous efficiency gains—TFP growth that is increasingly disconnected from broad labor participation. This represents a structural break from earlier productivity regimes, including the Industrial Revolution, which—despite dislocation—ultimately expanded the demand for human labor across new tasks, sectors, and income brackets.


2. The Stability Friction (The Paradox)
The paradox emerges because this specific surge in TFP is labor-decoupled. Unlike the Industrial Revolution, which augmented human capacity, the Autonomous Era replaces the "Coordination and Command" functions of the human labor force.

• The Selectorate Shift: Using the Revised Selectorate Model, we can mathematically demonstrate that when a state can generate massive wealth (TFP) without a broad, productive "Winning Coalition" (the labor force), the structural incentive for social stability and democratic governance erodes.

• The Transition Gap: The velocity of AI-driven TFP growth outpaces the ability of sovereign institutions to re-index labor, tax, and trade systems, creating a "Certainty Gap" that invites domestic volatility and geopolitical adventurism.

The Technical Framework: Building the Computational Engines of Strategic Foresight.

• Ensemble Forecasting: Implementing multi-model consensus to drive high-fidelity macro-economic and geopolitical projections, reducing variance in uncertain environments.

• Agent-Based Modeling & Simulation (ABMS): Constructing digital twins of global power dynamics where state and non-state actors interact within rigorous, physics-based constraints.

• Agentic Inference: Deploying autonomous reasoning layers that interpret simulation data and recursively optimize strategies against adversarial intent.

• Formal Game Theory: Grounding every agentic loop in empirical logic—from the Revised Selectorate Model for regime transition to nuclear escalation ladders.
   

Strategic Intelligence Workstreams: Building Predictive Digital Twins of the Dynamic Human World

• Capital Allocation & The "Certainty Gap": We utilize Agentic Reasoning to quantify the cost of inaction. By simulating crisis and conflict dynamics, we identify "Protected Value Pools"—sectors and geographies where sovereign risk is mitigated by physics-based constraints, allowing for aggressive capital deployment while competitors are paralyzed by uncertainty.

• TFP-Driven Profit Migration (The 2035 Transition): The $15T TFP-Stability Paradox isn't just a macro statistic; it represents a fundamental shift in industry margins.

  • For Capital-Intensive Industries: We model the point where AI-driven Total Factor Productivity (TFP) offsets labor volatility, identifying the specific "Efficiency Value Pools" in manufacturing, energy, and logistics.

  • The Margin Capture: Identifying which firms will retain TFP gains versus those forced to pass them to consumers through deflationary competition.

• Monetary Resilience & Balance Sheet Alpha: Through Ensemble Stress-Testing, we simulate USD devaluation and trade shocks to identify "Currency-Agnostic Value."

  • Value Pool: Designing "Anti-Fragile" corporate structures that capture arbitrage opportunities during global inflationary pressure and tariff-driven trade decoupling.

Techno-Geopolitical Moats: Using the Global AI Strength Forecast, we map the "Competitive Margin" of the AI Race. Strategic Advantage: We pinpoint the value pools within the semiconductor, energy-to-intelligence, and sovereign cloud stacks—identifying which companies are positioned to capture the rent of the new global compute-standard.

Global Leader and Builder of Agentic Decision Intelligence Systems
Amir Bagherpour is a Global Managing Director at Accenture for Agentic Modeling & Simulation, where he leads elite global teams of data scientists and engineers to architect the "Command Layers" of the modern enterprise. A pioneer in Agentic Decision Intelligence, Amir bridges the gap between frontier computational science and mission-critical execution for the world’s most complex organizations.
 
Amir is a Scientist-Builder who replaces speculative strategy with mathematical ground truth. He engineers the computational infrastructure for high-consequence decision-making, fusing Foundational Statistical Modeling and Formal Game Theory with Agent-Based Simulations (ABM). By constructing recursive, agentic loops, he resolves the 'Certainty Gap'—transforming volatile macro-economic and geopolitical variables into actionable, physics-based constraints.

Built a Predictive Analytics Startup
Amir Bagherpour built and cofounded giStrat, an analytics startup that developed a decision engine powered by game theory, machine learning, and predictive analytics. Under his leadership, giStrat’s solutions provided data-driven insights for commercial applications, national security, policy strategy, and crisis forecasting. His work at giStrat demonstrates his ability to transform complex data into actionable intelligence, bridging AI innovation with real-world impact.
 
Pioneered Mission Analytics
He was a pioneer in mission analytics at the U.S. Department of State, where he established the Department’s first enterprise analytics capability in the Office of the Secretary. He led an interagency team conducting over 100 conflict assessments, applying advanced analytics to support diplomatic negotiations and national security decision-making. His insights directly influenced U.S. foreign policy in regions including Latin America, Middle East & North Africa, East Asia, and Europe, solidifying his reputation as a leader in mission analytics for strategic decision-making.

Early Engineering & Technical Leadership
He began his career as a Mechanical Engineer at NSK Corporation, where he designed and integrated advanced motion-control systems for industrial applications. He also led technical training and product integration, contributing to over $7 million in revenue growth. This experience shaped his engineering-driven approach to analytics, fusing systems thinking with AI innovation to address complex, large-scale challenges.

Education & Military Service
Amir Bagherpour holds a Ph.D. in Political Science (Quantitative Methods) from Claremont Graduate University. For his dissertation he applied game-theory and agent-based simulation to forecast political transitions surrounding the Arab Spring. His dissertation accurately anticipated the outcomes of the Arab Spring. He earned an MBA from UC Irvine, focusing on macroeconomics and marketing, and a B.S. in Systems Engineering from West Point, where he was a Division I NCAA tennis player and boxing champion.

A former U.S. Army officer, he served honorably as as Armor Officer and led foreign military instruction for officers across different allied countries. His military experience instilled in him strategic leadership, operational discipline, and a mission-driven mindset, which he continues to apply in the fields of AI and analytics.

A Leader in AI-Driven Decision Intelligence
Amir Bagherpour is a recognized leader in AI and decision intelligence, driving data-driven transformation across industry and the public sector. His expertise in enterprise AI, strategic forecasting, decision science and mission analytics positions him at the forefront of AI-driven decision-making for national security, commerce, diplomacy, and global risk management.