Risk-Adjusted Valuation of Agentic-AI Firms: Hierarchical Cohort Unit Economics, Reliability Learning, Conformal Forecasting, and ES-Based Downside Mapping

Abstract

This thesis examines whether agentic-AI firms can be valued adequately using the roll-up discounted cash flow frameworks commonly applied to SaaS businesses. It argues that such firms present valuation challenges that are not well captured by conventional approaches based on blended churn assumptions, smooth margin expansion, and discount-rate-based risk adjustment alone. Because agentic-AI businesses frequently exhibit usage-linked monetization, reliability-sensitive retention, and asymmetric operating downside, their economics are more naturally understood through a framework that models forecast uncertainty and tail risk explicitly. To study this problem, the thesis develops a risk-adjusted valuation framework comprising four components: cohort-level unit economics, a time-varying reliability signal, conformal predictive intervals, and downside valuation through Expected Shortfall. The framework is implemented in a synthetic environment designed to generate cohort heterogeneity, reliability learning, regression shocks, reliability-linked tail events, and evolving cost structures. Forecasts and valuations are evaluated under a strict time-cut protocol that excludes look-ahead bias. The empirical pipeline is fully reproducible and generates the synthetic panel, roll-up series, forecast intervals, valuation distributions, robustness results, tables, and figures. The empirical findings are mixed but informative. In the current implementation, the benchmark roll-up model attains stronger empirical coverage at the nominal 80% and 90% levels, whereas the reliability-aware framework produces slightly narrower intervals and materially different left-tail valuation statistics. These results indicate that explicit conditioning on reliability materially affects downside valuation even before the full hierarchical model is estimated. At the same time, the remaining valuation error indicates that improved state estimation and model-consistent uncertainty calibration are necessary before the proposed framework can be regarded as superior to a disciplined roll-up benchmark. The principal contribution of the thesis is therefore methodological rather than dispositive. It establishes a testable end-to-end framework for calibrated forecasting and tail-aware valuation of agentic-AI firms, shows that explicit reliability modeling materially changes downside valuation, and identifies the precise components, most notably latent-state estimation and model-consistent uncertainty calibration, that must be strengthened before broader claims of superiority can be sustained.