🔮 The state of the AI economy

2020-12-30 · Source: Exponential View · Field: Finance & Economics — Economic Analysis & Policy, Capital Markets & Investment Management · Depth: Intermediate, medium

Summary

The "State of the AI Economy" report provides a first-of-its-kind bottom-up, deduplicated analysis of consumer and enterprise AI spending, revealing the generative AI economy generated \$110 billion in sales over the past 12 months. The annualized revenue run rate currently exceeds \$175 billion, growing approximately three times faster than previous IT waves like mobile or the Internet. The report's methodology meticulously tracks end-customer spending, avoiding double-counting by modeling private AI companies (e.g., OpenAI, Anthropic) and triangulating data from public hyperscalers (Amazon, Google, Microsoft). It clarifies that AI-attributable revenues for hyperscalers currently cover depreciation expenses for infrastructure, which is depreciated over 6-14 years. Furthermore, the analysis indicates that a 10% reduction in token prices leads to a 12-18% increase in token usage, suggesting rising total spend. This initial version excludes internal AI uplift, efficiency savings, professional services, and Chinese market data.

Key takeaway

For investors evaluating AI market opportunities, recognize the generative AI economy's rapid growth to a \$175 billion annual run rate, significantly outpacing prior tech waves. Your investment models should account for the current revenue coverage of AI infrastructure depreciation and the elasticity of demand, where falling token prices drive increased overall spending. Focus on companies demonstrating strong end-customer revenue capture, as this indicates sustainable growth in a market still in early scaling phases.

Key insights

The generative AI economy is a rapidly growing $110B market, with revenues covering infrastructure depreciation despite falling token prices.

Principles

• Do not double-count value in supply chains.
• Lower token prices increase total AI spending.
• Depreciate AI compute assets over 6 years, other infrastructure over 14.

Method

Proprietary AI economy model built on bottom-up, deduplicated end-customer spending. Triangulates public disclosures, well-reported leaks, and self-reports with confidence scores for auditable financial plans.

In practice

• Focus on end-customer spend for market sizing.
• Factor in 6-year GPU depreciation for CapEx.
• Anticipate increased usage with token price drops.

Topics

• Generative AI Economy
• AI Market Sizing
• AI Infrastructure Investment
• Token Economics
• Hyperscaler AI Revenue
• Demand-Side Analysis

Best for: CTO, VP of Engineering/Data, Entrepreneur, Investor, Executive, Director of AI/ML

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Editorial summary, takeaway, and curation by AIssential. Original article published by Exponential View.