Don't dismiss a model because its output is a wide, uncertain distribution. This is often the correct answer, as it accurately reflects the state of knowledge and prevents acting on a false sense of certainty from intuition. The model's value is in defining the bounds of what's possible.

A powerful scenario planning technique involves identifying future driving forces, choosing two that seem completely unrelated (e.g., economic growth and climate change), and analyzing the four extreme combinations. This forces your team to consider non-linear futures and develop more robust, resilient strategies.

Human wisdom derives from a single lifetime of experience. AI will achieve a superior form of wisdom by simulating billions of potential future scenarios and identifying the statistically optimal paths. This predictive power, already matching elite human forecasters, will be its core advisory function.

AI's predictive power is based on identifying patterns in historical data. While effective when the future resembles the past, this makes it inherently unable to account for new inventions, crises, or paradigm shifts not represented in its training text. It predicts from old maps, not what will come next in a new world.

A key risk in deploying AI is its inability to generalize to 'long-tail' or out-of-distribution events. Models trained on vast but finite data often fail when encountering novel situations common in the open-ended real world, such as a self-driving car mistaking a stop sign on a billboard for a real one.

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.

Citing historical failures like David Ricardo's on automation, individual AGI forecasts are deemed useless. A better approach is to model potential scenarios (e.g., labor share collapses) and then identify the crucial, currently missing data (like consumer demand elasticities) needed to determine which scenario is likely.

The most likely future is a "weird" state we can't easily classify as good or bad. Rather than comparing today to a hypothetical endpoint, we should focus on evaluating the desirability of the path, or trajectory, we are on.