For the past 18 months, AI excitement has created a rising tide that boosted fortunes for all major tech companies. This is changing. In the next year, their strategic bets, investments, and results will diverge dramatically, revealing clear winners and losers as "the tide goes out for some people."

Similar to the dot-com era, the current AI investment cycle is expected to produce a high number of company failures alongside a few generational winners that create more value than ever before in venture capital history.

The current AI investment frenzy will create a paradox: significant layoffs as companies use AI to become more efficient, coupled with immense wealth concentration. This will create a class of "haves and have-nots" and set the stage for major antitrust battles against newly public AI giants by 2027-2028.

For current AI valuations to be realized, AI must deliver unprecedented efficiency, likely causing mass job displacement. This would disrupt the consumer economy that supports these companies, creating a fundamental contradiction where the condition for success undermines the system itself.

The enormous capital bets made on AI infrastructure and frontier models are reaching a breaking point. As not all these gambles can pay off, 2026 is anticipated to be a year of reckoning and chaos, leading to a significant industry shakeout where some high-profile players will fail.

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.

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.

The dot-com era saw ~2,000 companies go public, but only a dozen survived meaningfully. The current AI wave will likely follow a similar pattern, with most companies failing or being acquired despite the hype. Founders should prepare for this reality by considering their exit strategy early.

The common goal of increasing AI model efficiency could have a paradoxical outcome. If AI performance becomes radically cheaper ("too cheap to meter"), it could devalue the massive investments in compute and data center infrastructure, creating a financial crisis for the very companies that enabled the boom.