Ken Griffin is skeptical of AI's role in long-term investing. He argues that since AI models are trained on historical data, they excel at static problems. However, investing requires predicting a future that may not resemble the past—a dynamic, forward-looking task where these models inherently struggle.

Public and expert opinions on AI are split between two extremes: it will either save humanity or destroy it. There is a notable absence of a moderate, middle-ground perspective, which is a departure from how previous technological shifts like the internet were discussed.

Most business professionals who are against AI haven't done their homework. Their opinion is a defense mechanism rooted in fear of financial loss and the unwillingness to put in the effort to understand the new technology. Vaynerchuk calls this a profoundly bad business strategy based on fear, not fact.

Pundits who were correct about past tech bubbles (like crypto) are now making confidently wrong predictions about AI. This "Gell-Mann Amnesia" effect, where expertise doesn't transfer between domains, creates confusing paradoxes and forces readers to question the credibility of sources opining outside their core expertise.

Citing Annie Duke, Oren Zeev highlights a critical cognitive bias for investors: the tendency to be "self-validation machines" rather than "truth seekers." Good decision-makers must possess the intellectual honesty to change their minds when presented with new data, rather than interpreting all new information as proof of their original thesis.

The reason smart AI experts continue to disagree on outcomes, despite new evidence, is that they operate from fundamentally different paradigms. One camp sees "always another bottleneck," while the other sees a pattern of overcoming past limitations. New data is simply used to reinforce these pre-existing worldviews.

Negative AI scenarios are more persuasive than utopian ones because of inherent cognitive biases. The "seen vs. unseen" bias makes it easier to visualize existing job losses than to imagine new job creation. The "fixed-pie fallacy" incorrectly frames economic growth and productivity gains as zero-sum.

Unlike previous tech shifts like cloud, AI is so disruptive that it creates a viable narrative for how incumbents could either massively win or be completely displaced. This complicates investment decisions across the software sector, as both optimistic and pessimistic outcomes are highly plausible.

The gap between AI believers and skeptics isn't about who "gets it." It's driven by a psychological need for AI to be a normal, non-threatening technology. People grasp onto any argument that supports this view for their own peace of mind, career stability, or business model, making misinformation demand-driven.