The financial industry suffers from 'physics envy'—a desire for predictable laws like those in hard sciences. This leads to complex models that give an illusion of certainty in what is actually a complex, adaptive system, where such precision is impossible and often misleading.

Many accepted financial rules are not timeless. Stocks only began consistently outperforming bonds after WWII, and inflation-adjusted US home prices were flat for a century before 1997. This reveals that much financial advice is based on recent history, not immutable laws, making it a poor guide for the future.

Mathematical models like the Kelly Criterion are only as good as their inputs. Historical data, such as a stock market's return, isn't a fixed 'true' value but rather one random outcome from a distribution of possibilities. Using this single data point as a precise input leads to overconfidence and overallocation of capital.

In an era of high uncertainty, central banking has evolved. The focus is no longer on debating precise multi-year forecasts but on risk management through scenario analysis, evaluating how to respond to different potential states of the world.

Post-WWII, economists pursued mathematical rigor by modeling human behavior as perfectly rational (i.e., 'maximizing'). This was a convenient simplification for building models, not an accurate depiction of how people actually make decisions, which are often messy and imperfect.

The world has never been truly deterministic, but slower cycles of change made deterministic thinking a less costly error. Today, the rapid pace of technological and social change means that acting as if the world is predictable gets punished much more quickly and severely.

Unlike typical economic cycles with a clear baseline and tail risks, the current environment is defined by radical uncertainty. The combined unknowns of erratic economic policy and AI's transformative potential create a "flat distribution" where extreme outcomes like a depression or an industrial revolution are nearly as likely as a baseline scenario.

In stable markets, answering established questions works. During systemic shifts, like today's geopolitical and monetary changes, investors must first identify new, relevant questions. The greatest risk is perfecting answers to outdated problems, a common pitfall highlighted by financial history.

The period from 1870-1914 mirrors today's super cycle of innovation, wealth concentration, inequality, populism, nationalism, and geopolitical rivalry. This makes it a more relevant historical parallel for understanding current risks than the recent era of hyper-globalization.