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.

Traditional software relies on predictable, deterministic functions. AI agents introduce a new paradigm of "stochastic subroutines," where correctness and logic are abdicated. This means developers must design systems that can achieve reliable outcomes despite the non-deterministic paths the AI might take to get there.

Leaders often misunderstand AI's probabilistic nature, thinking it's a flaw that will be "fixed." Drawing parallels to chaos theory, the slight non-determinism is an intentional feature that enables creativity and requires building systems with guardrails and human oversight, not seeking perfect predictability.

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.

The future of AI is hard to predict because increasing a model's scale often produces 'emergent properties'—new capabilities that were not designed or anticipated. This means even experts are often surprised by what new, larger models can do, making the development path non-linear.

Standard benchmarks are too rigid. The future of model evaluation needs more open-ended, multi-agent scenarios like the "AI Village" project. Giving agents broad goals like "organize an event" reveals more about their "derpy" failure modes and real-world capabilities than constrained, benchmark-style tasks can capture.

When tested at scale in Civilization, different LLMs don't just produce random outputs; they develop consistent and divergent strategic 'personalities.' One model might consistently play aggressively, while another favors diplomacy, revealing that LLMs encode coherent, stable reasoning styles.

Instead of a single, all-powerful AGI emerging, the reality of AI is a "polytheistic" ecosystem of many decentralized models, each with different strengths. This framework challenges the notion of a single entity to control or fear and suggests a more complex, competitive landscape.

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.