What Happened

  • The Dot-Com Crash refers to the collapse of internet-related stocks from 2000 to 2002 after an intense speculative boom.

  • In the late 1990s, enthusiasm for the internet drove massive demand for tech companies with little revenue and no profits.

  • IPOs doubled on their first day, startups raised capital on ideas alone, and valuations focused on “eyeballs,” page views, and user growth instead of fundamentals.

  • When the Federal Reserve raised interest rates and investors questioned sustainability, sentiment reversed.

  • The Nasdaq Composite fell nearly 80% from peak to trough.

  • Hundreds of companies failed, trillions in market value evaporated, and one of the most euphoric market periods abruptly ended.

What Drove the Crisis

  • Capital oversupply: Venture capital and public markets poured money into startups at unprecedented rates, encouraging growth over profitability.

  • Weak business models: Many companies had no clear path to cash flow and relied on advertising or customer acquisition strategies that never produced sustainable returns.

  • Valuations detached from fundamentals: Traditional metrics like revenue and earnings were dismissed; multiples implied unrealistic long-term expectations.

  • Rising interest rates and sentiment shift: Fed tightening reduced risk appetite. Investors realized many firms would never become profitable. Liquidity dried up and capital-dependent companies collapsed.

  • The decline reflected expectations outrunning economic reality, not a failure of technology itself.

Investor Lessons

  • Innovation can be real while valuations become irrational.

  • Cash flow, margins, and unit economics matter more than narratives or hype.

  • Rising rates expose weak business models dependent on cheap capital.

  • Market psychology can push prices to extremes, especially in new sectors.

  • Disciplined valuation and focus on durable economics help identify the companies that endure.

  • The firms that survived—like Amazon and Google—had real business models and long-term viability; the rest disappeared when funding dried up.