The Shape of The AI Economy

2024-05-13 · Source: The Business Engineer · Field: Finance & Economics — Economic Analysis & Policy, Capital Markets & Investment Management, Artificial Intelligence & Machine Learning · Depth: Intermediate, extended

Summary

The "State of the AI Economy" report by Exponential View rigorously addresses the opaque demand side of the AI market, revealing a \$110 billion trailing 12-month deduplicated GenAI revenue, now annualized at \$175 billion. This growth is 3x faster than prior IT waves, compounding on existing infrastructure. While the AI economy is still small, representing 0.42% of US GDP, it generates an invisible \$3-4 billion/month consumer surplus. The report details a \$2 trillion cumulative CapEx through 2026, with \$848 billion in 2026, showing current revenue covering annual depreciation. Token volumes exceed 30 quadrillion per month, with prices collapsing from ~\$17 to ~\$2 per million tokens, yet demand elasticity (1.2-1.8) ensures total spend rises. Value is shifting up the stack from hosting (89% to 82%) to models (8% to 11%) and apps (3% to 7%), with labs vertically integrating to counter competition and commoditization of older frontier models.

Key takeaway

For investors evaluating AI companies, recognize that while GenAI revenue is real and growing rapidly, the market's financial stability depends on sustained token demand elasticity. Your investment thesis should account for the ongoing shift of value up the stack, favoring firms with proprietary data or vertical integration strategies that can withstand intense competition and the rapid commoditization of frontier models. Be wary of generic middle-layer plays.

Key insights

AI demand is revenue-validated and growing exponentially, but its economic sustainability hinges on token price elasticity.

Principles

• AI growth compounds on prior tech waves.
• Consumer surplus is a significant, unmeasured value.
• Value migrates up the tech stack over time.

Method

Exponential View built bottom-up financial models for 1,000+ firms, tracing revenue to primary filings and deduplicating flows across the AI stack (apps, models, hosting) to accurately measure real demand.

In practice

• Monitor token price elasticity for market health.
• Evaluate AI investments beyond direct revenue.
• Consider vertical integration to defend margins.

Topics

• AI Economy
• Generative AI Revenue
• AI Infrastructure CapEx
• Token Economics
• AI Stack Dynamics
• Consumer Surplus

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

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Business Engineer.