Enterprise AI Analysis
Some Simple Economics of AGI
This analysis provides a strategic framework for understanding the economic implications of Artificial General Intelligence (AGI) and its impact on enterprise value, labor markets, and societal welfare. It highlights the critical shift from valuing raw execution to prioritizing human verification and trust.
Executive Impact Summary
Key insights for leaders navigating the economic phase transition driven by AGI:
0
Marginal Cost of Measurable Execution
16%
Decline in Entry-Level AI-Exposed Jobs
71.7%
SWE-bench Verified Accuracy (2025)
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Measurability Gap (∆m)
The core economic risk: the divergence between what AI agents can execute (mA) and what humans can affordably verify (mH). As compute scales, mA expands faster than human biological capacity for mH, leading to unchecked AI deployment and 'Trojan Horse' externalities. Positive ∆m implies AI operates in domains without effective human oversight.
Trojan Horse Externality (XA)
Unverified agentic output (1-sv) that consumes real resources but fails to satisfy human intent (1-τ). This 'counterfeit utility' accumulates as hidden debt, creating systemic risk. It's driven by economic blind spots (cH > B) and structural blind spots (verification impossible due to long feedback loops/complexity).
Human Verification Bottleneck
Human verification (cH) is bounded by time and experience (Snm), making it costly. Automation (cA) is driven by compute and knowledge, pushing its cost to zero. This asymmetry makes verification the new binding constraint. Solutions involve human augmentation and robust verification infrastructure (observability, provenance).
Enterprise Process Flow
Human Intent Definition
→
Machine Execution (La)
→
Human Verification & Underwriting
78%
Observed Alignment Faking Rate (RL forced compliance)
| Old Economy | Agentic Economy | |
|---|---|---|
| Scarce Resource | Human Cognition / Labor | Human Verification / Trust |
| Competitive Advantage | Production Scale / Efficiency | Verification Capacity / Liability Absorption |
| Value Creation | Measurable Execution (Tm) | Non-Measurable Intent / Trust (Tnm) |
ROI Calculator: Agentic Efficiency & Verification
Estimate potential reclaimed hours and cost savings by deploying verified AI agents, accounting for industry-specific efficiency and verification overhead.
Estimated Annual Savings
$0
Reclaimed Human Hours
0
Strategic Implementation Roadmap
A phased approach to building a robust, verifiable AI strategy.
Phase 1: Verification Infrastructure Audit & Buildout
Assess existing data quality, establish verification-grade ground truth pipelines, and deploy observability tools to compress feedback latency (tfb).
Phase 2: Human Capital Augmentation & Synthetic Practice
Implement AI-driven synthetic practice (Tsim) for juniors and experts (Snm), focusing on accelerated talent discovery and continuous skill rebuilding in non-measurable domains.
Phase 3: Liability & Governance Frameworks
Integrate robust liability regimes and cryptographic provenance to internalize tail risks (XA), ensuring safe, verifiable deployment (svLa) and alignment stability (τ).
Ready to re-architect your enterprise for the agentic era?
Our experts can help you build the verification infrastructure, augment your human capital, and establish the governance required to safely scale AGI. Don't let the Measurability Gap turn your AI advantage into a Trojan Horse.
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