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.
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The AI boom is fueled by 'club deals' where large companies invest in startups with the expectation that the funds will be spent on the investor's own products. This creates a circular, self-reinforcing valuation bubble that is highly vulnerable to collapse, as the failure of one company can trigger a cascading failure across the entire interconnected system.
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.
Following George Soros's theory of reflexivity, markets act like thermostats, not barometers. Rising AI stock prices attract capital, which further drives up prices, creating a self-reinforcing loop. This feedback mechanism detaches asset values from underlying business fundamentals, inflating a bubble based on pure belief.
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.
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.
Current AI spending appears bubble-like, but it's not propping up unprofitable operations. Inference is already profitable. The immense cash burn is a deliberate, forward-looking investment in developing future, more powerful models, not a sign of a failing business model. This re-frames the financial risk.
The memo flags deals where money is "round-tripped" between AI players—for example, a chipmaker investing in a startup that then uses the funds to buy its chips. This practice, reminiscent of the 1990s telecom bust, can create illusory profits and exaggerate progress, signaling that the market is overheating.
While AI investment has exploded, US productivity has barely risen. Valuations are priced as if a societal transformation is complete, yet 95% of GenAI pilots fail to positively impact company P&Ls. This gap between market expectation and real-world economic benefit creates systemic risk.
Leaders from NVIDIA, OpenAI, and Microsoft are mutually dependent as customers, suppliers, and investors. This creates a powerful, self-reinforcing growth loop that props up the entire AI sector, making it look like a "white elephant gift-giving party" where everyone is invested in each other's success.
The AI infrastructure boom is a potential house of cards. A single dollar of end-user revenue paid to a company like OpenAI can become $8 of "seeming revenue" as it cascades through the value chain to Microsoft, CoreWeave, and NVIDIA, supporting an unsustainable $100 of equity market value.
