The hype and potential bubble in AI are concentrated in private markets, evidenced by vendor financing and easy credit for any AI-linked venture. In contrast, public markets are viewed as more realistic, and the high concentration in top tech stocks is not statistically correlated with poor forward-looking returns.
Like the dot-com era, many overvalued AI startups will fail. However, this is distinct from the underlying technology. Artificial intelligence itself is a fundamental, irreversible shift that will permanently change the world, similar to how the internet and social media became globally dominant despite early market bubbles.
Mark Cuban argues the AI bubble isn't in public markets like the dot-com era. Instead, it's the unsustainable, winner-take-all spending race between a few large companies building foundational models. This creates an opportunity for disruption by more efficient technologies.
The massive capital expenditure in AI is largely confined to the "superintelligence quest" camp, which bets on godlike AI transforming the economy. Companies focused on applying current AI to create immediate economic value are not necessarily in a bubble.
David Kostin argues public AI stocks aren't in a bubble because earnings growth has matched price increases. The real bubble is in private markets, driven by George Soros's "reflexivity" theory, where a recursive loop of new capital inflates valuations to unsustainable levels.
The key signal for an AI bubble isn't just stock market commentary. It's the transition of data center buildouts from being funded by free cash flow to being funded by debt, particularly from private credit firms. This massive, less-visible market is the real stress test for AI's financial stability.
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 most immediate systemic risk from AI may not be mass unemployment but an unsustainable financial market bubble. Sky-high valuations of AI-related companies pose a more significant short-term threat to economic stability than the still-developing impact of AI on the job market.
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.
Contrary to fueling hype, public offerings from companies like OpenAI would introduce real financial data into the market. This transparency could ground the "AI bubble" conversation in actual performance metrics, clarifying the significant information gap that currently exists for investors.
When capital flows in a circle—a chipmaker invests in an AI firm which then buys the investor's chips—it artificially inflates revenues and valuations. This self-dealing behavior is a key warning sign that the AI funding frenzy is a speculative bubble, not purely market-driven.