Precision vs. Accuracy

This transcript discusses how precise calculations can create a false sense of security when predicting market sizes and company valuations, using Bill Gurley's critique of a professor's Uber valuation analysis as the central example.

The Precision vs. Accuracy Problem in Valuations

• Bill Gurley (legendary VC, early Uber investor) critiqued a professor's analysis claiming Uber wasn't worth $17 billion
• The professor (Aswath Damodaran from NYU Stern) was technically right - Uber wasn't worth $17B, it was worth 10x more
• Precise calculations give a false sense of security in valuations
• Precision: forecasting down to the second decimal point
• Accuracy: actually being correct in the prediction
• You can be precisely wrong - having detailed calculations that miss the mark entirely

Two Key Mistakes in Market Predictions

1. TAM (Total Addressable Market) mistake

   • Assuming the future market will look like the past market
   • Failing to account for how new products/services change market dynamics
   • Example: AT&T paid McKinsey to forecast cell phone market
   • McKinsey predicted 900,000 cell phone users by a certain date
   • Actual number was 9 billion - they were off by over 1000x

2. Market penetration mistake

   • Underestimating how new services create entirely new use cases
   • For Uber:
     • More convenient than taxis (precise timing, wider availability)
     • Lower price points enabled new use cases
     • Some people stopped buying cars altogether (unlocked part of car ownership market)
     • Expanded into rental car market territory
   • Result: The actual market was closer to a trillion dollars, not the hundred billion initially calculated

Application to AI Market Predictions

• AI will follow similar pattern of creating new markets
• People will pay AI for tasks they wouldn't have hired humans to do
• Examples:
  • Building small personal apps (piano tracking, health tracking)
  • Having AI analyze blood results instead of hiring a concierge doctor
  • Creating a market for services that didn't previously exist

The Shopify Example

• When Bessemer invested in Shopify, they predicted a potential $400M exit
• The company is now worth $130 billion
• Even Shopify's own employees and advisors thought it would be worth at most $50M
• Shows how even top investors (top 1%) struggle to predict future outcomes for obvious winners