This "via negativa" approach, inspired by Sun Tzu and Charlie Munger, posits that the easiest way to improve returns is by systematically avoiding common mistakes. Instead of trying to be brilliant, investors should focus on not doing "dumb stuff," as it's easier to identify what leads to failure than what guarantees success.

David Kaiser's system doesn't try to predict cyclical peaks. Instead, it mitigates the risk of buying hot cyclical stocks by owning a diversified portfolio and rebalancing consistently. This structural approach ensures that if the model over-allocates to a sector at its peak, the error is contained and corrected relatively quickly.

Inspired by Charlie Munger, this investment strategy is built on three common-sense pillars: maximizing earnings growth, maintaining valuation discipline, and focusing on downside risk. The goal is reliability and avoiding major mistakes rather than chasing spectacular, high-risk wins.

David Kaiser suggests that as AI becomes ubiquitous in investing, a "tiptoes at a parade" problem emerges where no one gains an edge. By intentionally not using AI to constantly evolve his process, he believes his firm can be differentiated. The alpha may lie in the systematic, old-school approach that AI-driven consensus overlooks.

Methodical Investment's David Kaiser suggests that the primary benefit of a rules-based system isn't just performance, but the psychological comfort it provides. It establishes a clear process (if X happens, do Y), removing emotional decision-making and making strategy easier to communicate, especially during volatile periods.

Moving from science to investing requires a critical mindset shift. Science seeks objective, repeatable truths, while investing involves making judgments about an unknowable future. Successful investors must use quantitative models as guides for judgment, not as sources of definitive answers.

Quoting G.K. Chesterton, Antti Ilmanen highlights that markets are "nearly reasonable, but not quite." This creates a trap for purely logical investors, as the market's perceived precision is obvious, but its underlying randomness is hidden. This underscores the need for deep humility when forecasting financial markets.

Absolute truths are rare in complex systems like markets. A more pragmatic approach is to find guiding principles—like "buy assets for less than they're worth"—that are generally effective over the long term, even if they underperform in specific periods. This framework balances conviction with flexibility.

The highest-performing strategies often have extreme volatility that causes investors to abandon them at the worst times. Consistency with a 'good enough' strategy that fits your temperament leads to better real-world results than chasing perfection.