Initially viewed as a growth driver, Generative AI is now seen by investors as a major disruption risk. This sentiment shift is driven by the visible, massive investments in AI infrastructure without corresponding revenue growth appearing in established enterprise sectors, causing a focus on potential downside instead of upside.

The Goldman Sachs CEO differentiates between two types of AI adoption. Giving employees AI tools to make them more productive is relatively easy. The much harder, yet more impactful, challenge is fundamentally re-engineering long-standing, complex processes like customer onboarding from the ground up.

The stock market's enthusiasm for AI has created valuations based on future potential, not current reality. The average company using AI-powered products isn't yet seeing significant revenue generation or value, signaling a potential market correction.

The massive spending on AI infrastructure may be a form of 'malinvestment,' similar to the telecom buildout during the dot-com boom. Rajan warns that while AI's promise is real, the transition from infrastructure creation to widespread, profitable use could be slow, creating a valuation gap and risk of a market correction.

Historical technology cycles suggest that the AI sector will almost certainly face a 'trough of disillusionment.' This occurs when massive capital expenditure fails to produce satisfactory short-term returns or adoption rates, leading to a market correction. The expert would be 'shocked' if this cycle avoided it.

Reporting from Davos reveals a disconnect between public AI hype and private executive sentiment. Tech leaders see enterprise AI adoption as "early and slow." The focus is moving from "panacea" solutions towards targeted, vertically-focused agents that can deliver measurable results, indicating a more pragmatic market phase.

The current era of broad enterprise AI experimentation will end. The CEO foresees 2026 as a "year of rationalization," where CFO pressure will force companies to consolidate AI tools and cut vendors that fail to demonstrate tangible productivity gains and clear return on investment.

The current AI hype is fueled by massive corporate spending on LLMs and chips. The entire bubble is at risk of unwinding when a critical mass of these companies reports that they are not achieving the promised ROI, causing a rapid pullback in investment.

History shows a significant delay between tech investment and productivity gains—10 years for PCs, 5-6 for the internet. The current AI CapEx boom faces a similar risk. An 'AI wobble' may occur when impatient investors begin questioning the long-delayed returns.