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.

The discipline of writing down your thought process is crucial for decision analysis. AI now amplifies this by creating a searchable, analyzable record of your thinking over time, helping you identify blind spots and get objective feedback on your reasoning.

Frame AI as a fundamental productivity shift, like the personal computer, that will achieve total market saturation. It's not a speculative bubble but a new, permanent layer of the economy that will be integrated into every business, even a local taco truck.

During a fundamental technology shift like the current AI wave, traditional market size analysis is pointless because new markets and behaviors are being created. Investors should de-emphasize TAM and instead bet on founders who have a clear, convicted vision for how the world will change.

In the current market, AI companies see explosive growth through two primary vectors: attaching to the massive AI compute spend or directly replacing human labor. Companies merely using AI to improve an existing product without hitting one of these drivers risk being discounted as they lack a clear, exponential growth narrative.

Despite a long-standing data-science-driven investment thesis, Foresight Capital's founder Jim Tananbaum states that AI tools have not yet objectively led to increased investment returns. The technology is still maturing, highlighting a reality gap between the hype around AI in VC and its current practical impact.

The dominant market driver will transition from macro risks like tariffs and policy uncertainty to micro, asset-specific stories. The key focus will be on company-level analysis of AI capital expenditure plans and their impact on earnings.

The true economic revolution from AI won't come from legacy companies using it as an "add-on." Instead, it will emerge over the next 20 years from new startups whose entire organizational structure and business model are built from the ground up around AI.

An investor can have pages of notes yet still lack clarity. The most critical step is synthesizing this raw data by writing a cohesive narrative. This act of writing forces critical thinking, connects disparate points, and elevates understanding in a way that passive consumption cannot.