Despite seeing 100x revenue multiples reminiscent of 2021, VCs are not accelerating their fund deployment or rushing back to fundraise. This more measured pace indicates a potential lesson learned from the last bubble, where rapid deployment led to poor vintage performance and pressure from LPs.

Unlike the leverage-fueled dot-com bubble, the current AI build-out is funded by the massive cash reserves of big tech companies. This fundamental difference in financing suggests a more stable, albeit still frenzied, growth cycle with lower P/E ratios.

Harvey, an AI startup for the legal industry, exemplifies the hyper-growth funding environment for top-tier AI companies. The company raised capital three times in less than a year, with its valuation climbing from $3 billion (Sequoia) to $5 billion (Kleiner Perkins) and finally to $8 billion (a16z).

Vincap International's CIO argues the AI market isn't a classic bubble. Unlike previous tech cycles, the installation phase (building infrastructure) is happening concurrently with the deployment phase (mass user adoption). This unique paradigm shift is driving real revenue and growth that supports high valuations.

The startup landscape now operates under two different sets of rules. Non-AI companies face intense scrutiny on traditional business fundamentals like profitability. In contrast, AI companies exist in a parallel reality of 'irrational exuberance,' where compelling narratives justify sky-high valuations.

For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.

Contrary to common belief, the earliest AI startups often command higher relative valuations than established growth-stage AI companies, whose revenue multiples are becoming more rational and comparable to public market comps.

The CEO of Numeral notes that in the current fundraising climate, startups must heavily feature AI in their pitch to secure investor meetings. Furthermore, landing a major AI lab as a customer has become a key signal for VCs, leading to valuation multiples as high as 100-200x revenue for some companies.

Unlike the dot-com era funded by high-risk venture capital, the current AI boom is financed by deep-pocketed, profitable hyperscalers. Their low cost of capital and ability to absorb missteps make this cycle more tolerant of setbacks, potentially prolonging the investment phase before a shakeout.