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.

Unlike traditional deterministic products, AI models are probabilistic; the same query can yield different results. This uncertainty requires designers, PMs, and engineers to align on flexible expectations rather than fixed workflows, fundamentally changing the nature of collaboration.

The brain's hardware limitations, like slow and stochastic neurons, may actually be advantages. These properties seem perfectly suited for probabilistic inference algorithms that rely on sampling—a task that requires explicit, computationally-intensive random number generation in digital systems. Hardware and algorithm are likely co-designed.

The standard math curriculum is misaligned with real-world needs. Core rationality concepts, like Bayesian reasoning and distinguishing correlation from causation, are far more valuable for everyday decisions and citizenship than more abstract topics like trigonometry.

When an LLM is shown few-shot examples of a new task, it is performing Bayesian updating. With each example provided in the prompt, its belief (posterior probability) about the correct next token shifts, allowing it to "learn" a new pattern on the fly without changing its weights.

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.

While both humans and LLMs perform Bayesian updating, humans possess a critical additional capability: causal simulation. When a pen is thrown, a human simulates its trajectory to dodge it—a causal intervention. LLMs are stuck at the level of correlation and cannot perform these essential simulations.

Children are more rational Bayesians than scientists because they lack strong pre-existing beliefs (priors). This makes them more open to updating their views based on new, even unusual, evidence. Scientists' extensive experience makes them rationally stubborn, requiring more evidence to change their minds.

Future literacy requires understanding concepts beyond deterministic algorithms. As AI tools become more prevalent, users will need to grasp probabilistic and stochastic systems to effectively build with and manage them, recognizing that outputs are not always perfectly reproducible.