Not Once in 18,900 Tries | Michael Mauboussin on What History Says About AI Growth
TL;DR
Michael Mauboussin applies historical "base rate" analysis to AI growth projections, revealing that OpenAI's target of 108% annual compound growth has zero precedent in 18,900 firm-years of data, while warning that intangible-intensive business models create fatter tails of both spectacular success and catastrophic failure.
📉 Historical Base Rates vs. AI Growth Targets 3 insights
Unprecedented revenue growth requirements
OpenAI's forecast to grow from $3.7 billion (2024) to $145 billion (2029) implies a 108% compound annual growth rate—a feat no company in the $2-5 billion revenue range has achieved in 75 years of historical data covering 18,900 firm-years.
Statistical implausibility
This growth trajectory represents a 9.5 standard deviation event from the historical mean of 7% growth, and remains an outlier even after OpenAI revised 2029 targets upward to $185 billion (118% CAGR).
The three-legged execution stool
Achieving these targets requires simultaneously maintaining a competitive product, attracting elite talent, and burning through $218 billion in cash before reaching free cash flow neutrality—a combination of challenges no previous company has solved at this scale.
⚖️ Intangibles, Risk, and Market Concentration 3 insights
Fatter tails in intangible-heavy businesses
Companies intensive in intangible assets show similar mean returns to physical-asset firms but exhibit substantially higher standard deviations, creating more extreme outcomes—both spectacular winners and total failures.
The Enron cautionary tale
Enron grew 61% annually to reach $100 billion revenue by 2000 using an 'asset-light' intangible strategy, only to file for bankruptcy nine months later, illustrating that intangible-intensive models can collapse as rapidly as they ascend.
Magnificent 7 economic dominance
Today's largest tech companies comprise one-third of total market capitalization but generate roughly two-thirds of all economic profits, growing faster than historical large companies due to proprietary software creating unique moats that resist competitive diffusion.
🏗️ Infrastructure Reality and Project Risk 2 insights
Dismal big project base rates
Analysis of 16,000 large-scale projects reveals fewer than 50% finish on budget, under 9% hit both time and budget targets, and only 0.5% deliver on time, on budget, and with promised benefits.
AI data center delays mounting
Despite modularity advantages, 25% of AI data centers faced delays in 2025, with industry estimates suggesting 30-50% of 2026 projects will be delayed due to permitting, energy, and cooling constraints.
Bottom Line
Investors should anchor AI growth forecasts using historical base rates as a starting point, recognizing that while intangible assets enable unprecedented scale, they concentrate risk in fatter left-tail outcomes that demand probability-weighted analysis rather than linear extrapolation of current hype.
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