Public market investors feel compelled to buy into major AI IPOs, even if they doubt a company's fundamentals. The strategy is driven by market dynamics: the expectation of a 'pop' from massive retail investor demand forces funds to participate to avoid underperforming their benchmarks.
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Unlike past platform shifts that caught many off-guard, the AI wave is universally anticipated. This 'consensus innovation' intensifies all existing competitive pressures, as every investor—from mega-funds to accelerators—is aggressively pursuing the same perceived opportunities, pushing factors like Power Law belief to an extreme.
A market bifurcation is underway where investors prioritize AI startups with extreme growth rates over traditional SaaS companies. This creates a "changing of the guard," forcing established SaaS players to adopt AI aggressively or risk being devalued as legacy assets, while AI-native firms command premium valuations.
Unlike past speculative bubbles, the current AI frenzy has near-universal, top-down support. The government wants domestic investment, tech giants are in a competitive spending arms race, and financial markets profit from the growth narrative. This rare alignment of interests from all major actors creates a powerful, self-reinforcing mandate for the bubble to continue expanding.
Today's massive AI company valuations are based on market sentiment ("vibes") and debt-fueled speculation, not fundamentals, just like the 1999 internet bubble. The market will likely crash when confidence breaks, long before AI's full potential is realized, wiping out many companies but creating immense wealth for those holding the survivors.
Current AI investment patterns mirror the "round-tripping" seen in the late '90s tech bubble. For example, NVIDIA invests billions in a startup like OpenAI, which then uses that capital to purchase NVIDIA chips. This creates an illusion of demand and inflated valuations, masking the lack of real, external customer revenue.
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.
The current AI investment frenzy is a powerful feedback loop. Silicon Valley labs promote a grand narrative to justify huge capital needs. Simultaneously, Wall Street firms earn massive fees by financing this buildout, creating a shared, bi-coastal incentive to keep the 'super cycle' narrative going, independent of immediate profitability.
Contrary to the popular VC idea that IPO pops are 'free money' left on the table, they actually serve as a crucial risk premium for public market investors. Down-rounds like Navan's prove that buyers need the upside from successful IPOs to compensate for the very real risk of losing money on others.
The current market is unique in that a handful of private AI companies like OpenAI have an outsized, direct impact on the valuations of many public companies. This makes it essential for public market investors to deeply understand private market developments to make informed decisions.
In the current AI hype cycle, a common mistake is valuing startups as if they've already achieved massive growth, rather than basing valuation on actual, demonstrated traction. This "paying ahead of growth" leads to inflated valuations and high risk, a lesson from previous tech booms and busts.
