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.

Beyond simple concept testing, AI simulations allow businesses to model downstream consequences. A car company can simulate how launching a new EV might change market perception of its entire gas-powered product line, revealing second-order effects that are impossible to test in the real world.

AI's greatest impact on economics will be the ability to run complex, agent-based simulations. This allows economists to model the dynamic, equilibrium responses of millions of economic actors to policy changes—like a Fed balance sheet reduction—providing a much richer understanding than traditional, static models allow.

Instead of debating which AI future will occur, a more productive approach is using scenarios to ask, 'What would we do in this future?' This shifts the conversation from arguing over predictions to identifying 'no-regrets' policies that are beneficial across multiple potential outcomes.

Simulations can be categorized in two ways. 'Convergent' simulations reliably reach a stable outcome despite small errors (e.g., network hub formation). 'Divergent' ones can have many results (e.g., elections). The value of the latter is mapping the range of potential futures, not a single prediction.

Customers like Starbucks don't just want a prediction that Frappuccino sales will fall. They want to know what actions to take to prevent that from happening. The true value of simulation AI is providing a causal model that allows businesses to test interventions and proactively shape their future, not just passively observe it.

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.

While simulation can disrupt the $100B market research industry, its true total addressable market (TAM) is far larger. The ultimate goal is to inform every decision made by humans, for humans, making the potential value proposition nearly boundless.

The AI 2027 team prioritized creating a highly detailed scenario, even if specific predictions were individually unlikely. This trade-off is valuable because it allows for a more thorough 'gaming out' of possibilities, providing specific hypotheses that can be debated and built upon, which is more useful than vague, high-level statements.