Investors mistakenly believe that buying AI stocks is a direct bet on the technology itself. Dalio warns that, like past tech revolutions, the underlying technology will thrive, but most individual companies will fail due to intense competition. The investment risk lies in picking the few corporate survivors, not in the technology's potential.

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.

The massive capital influx into AI means much of the discourse is marketing disguised as education. To find the signal, analyze the speaker's incentives. Are they trying to raise capital and justify valuations, or are they providing a grounded, factual perspective on the technology's actual capabilities?

For breakthrough technologies like AI and quantum, traditional valuation is less important initially. Investors must buy into the narrative, long-term potential, and quality of the management team, much like early-stage seed investing. Near-term earnings are secondary to the transformative vision.

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.

For a proven, hyper-growth AI company, traditional business risks (market, operational, tech) are minimal. The sole risk for a late-stage investor is overpaying for several years of future growth that may decelerate faster than anticipated.

To move beyond FOMO-driven investment, AI21 Labs' CMO advises measuring AI's business impact across three pillars: its ability to scale growth, its power to improve decisions through faster analysis, and its capacity to help organizations avoid and plan for risks.

When investing in AI, the focus should be on companies building durable, multi-purpose infrastructure or solving real-world problems with a sustainable data flywheel. This approach is superior to backing firms with impressive tech demonstrations that lack a clear, defensible business model.

While many firms are just now reacting to AI's impact, major credit investors like KKR have been actively underwriting AI-driven business model risk for nearly six years. This proactive, long-term approach to assessing technological disruption is a core part of their due diligence process, not a recent development.