History shows the ultimate beneficiaries of technological waves are often not the initial darlings. Facebook and Google became internet giants long after the dot-com bubble. This suggests investors should be wary of paying high valuations for today's hyped AI companies, as the true long-term winners may not even exist yet.

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.

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.

Like containerization, AI is a transformative technology where value may accrue to customers and users, not the creators of the core infrastructure. The biggest fortunes from containerization were made by companies like Nike and Apple that leveraged global supply chains, not by investors in the container companies themselves.

Sam Lessin predicts massive losses for seed VCs backing companies branded as "AI businesses." These ventures are too capital-intensive and commoditizable to generate traditional venture returns, even if they become massive. AI should be a tool, not the business model itself.

Marks warns against conflating a technology's societal impact with its investment potential. Fierce competition among AI service providers or their customers could pass all productivity gains to consumers through lower prices. This would result in little to no profit for the underlying companies, echoing a similar warning from Warren Buffett during the dot-com era.

Instead of betting on unknowable AI winners, a better strategy is to find quality companies the market has written off as "losers" due to AI fears. Similar to the unloved "old economy" stocks during the dot-com bubble, these perceived victims could offer significant upside if the disruption threat is overblown.

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.

Unlike previous tech waves, AI's core requirements—massive datasets, capital for compute, and vast distribution—are already controlled by today's largest tech companies. This gives incumbents a powerful advantage, making AI a technology that could sustain their dominance rather than disrupt them.