Category: AI Economics

Proposes new valuation frameworks that adjust traditional multiples to reflect AI‑derived competitive advantages.

This article examines how advancing AI capabilities reshape venture capital (VC) investment patterns toward knowledge-intensive startups. We develop a framework linking AI-driven predictive analytics, automation potential, and scalability assessments to VC decision-making criteria. Through a mixed-methods analysis of recent investment data and expert surveys, we identify three core mechanisms: ...

This article investigates the pricing elasticity of AI-as-a-Service (AIaaS) offerings and how variable cost structures influence enterprise adoption rates. Using econometric analysis of API pricing data and adoption metrics across multiple industry segments, we quantify the sensitivity of demand to price changes in the AIaaS market. Our findings reveal that enterprises exhibit significant price...

The rapid expansion of AI workloads across distributed computing infrastructures has precipitated a critical environmental externality: uncontrolled carbon emissions from energy-intensive inference operations. This article designs a transparent ledger framework to systematically track, quantify, and mitigate carbon footprints of AI workloads spanning the full lifecycle from training through dep...

Foundation model providers are rapidly consolidating control over the most valuable AI assets — massive parameter counts, proprietary training pipelines, and exclusive access to high‑quality multimodal datasets. This convergence raises critical antitrust and governance questions: how concentrated is market power, what metrics can reliably capture that concentration, and how do these metrics evo...

Model version migration is a routine yet understudied cost center for enterprises deploying foundation models at scale. This article quantifies the economic impact of such migrations across three dimensions: direct migration effort, indirect operational overhead, and strategic opportunity cost. By analyzing migration records from 127 organizations, we find that average direct migration costs ha...

Anti-Money Laundering (AML) mechanisms are essential for maintaining financial integrity in the global banking sector. Traditional rule‑based transaction monitoring systems have long been employed to detect suspicious activity, yet they often generate high false‑positive rates, leading to operational inefficiencies and regulatory fatigue. Recent advances in neural network architectures and grap...

Enterprises increasingly deploy heterogeneous AI solutions across finance, operations, and customer service. While each AI component promises efficiency gains, the fragmented nature of these deployments makes it difficult to isolate the contribution of AI to overall return on investment (ROI). This article addresses the core challenge: how to attribute business value to AI when it operates as p...

Introduction The rapid diffusion of large language models (LLMs) across knowledge‑intensive industries has sparked intense debate about the extent to which specific tasks can be automated, augmented, or remain uniquely human. Existing surveys often aggregate diverse activities into broad categories, obscuring the nuanced dynamics that shape labor markets and wage trajectories. This article dise...

Enterprises increasingly deploy large language models (LLMs) as core components of their digital stack, yet the cost structure of provider access remains fragmented and opaque. This article investigates how organizations can systematically exploit price competition among LLM providers to achieve measurable reductions in inference spend. We frame the problem as a procurement challenge, map curre...