Unlike scientific fields that build on previous discoveries, philosophy progresses cyclically. Each new generation must start fresh, grappling with the same fundamental questions of life and knowledge. This is why ancient ideas like Epicureanism reappear in modern forms like utilitarianism, as they address timeless human intuitions.

Unlike ancient Greek philosophy where ethics, metaphysics, and logic were deeply interconnected, modern philosophy is largely separated into distinct, specialized fields. For example, the Stoics believed their ethics were a direct consequence of their understanding of the world's nature (metaphysics), a link often lost in modern discourse.

A key measure of philosophy's historical success isn't solving its own problems, but rather birthing new academic fields. Disciplines like mathematics, physics, economics, and psychology all originated as branches of philosophical inquiry before developing into their own distinct areas of study, a point Bertrand Russell made.

True scientific progress comes from being proven wrong. When an experiment falsifies a prediction, it definitively rules out a potential model of reality, thereby advancing knowledge. This mindset encourages researchers to embrace incorrect hypotheses as learning opportunities rather than failures, getting them closer to understanding the world.

Today's AI models are powerful but lack a true sense of causality, leading to illogical errors. Unconventional AI's Naveen Rao hypothesizes that building AI on substrates with inherent time and dynamics—mimicking the physical world—is the key to developing this missing causal understanding.

Current AI can learn to predict complex patterns, like planetary orbits, from data. However, it struggles to abstract the underlying causal laws, such as Newtonian physics (F=MA). This leap to a higher level of abstraction remains a fundamental challenge beyond simple pattern recognition.

To make genuine scientific breakthroughs, an AI needs to learn the abstract reasoning strategies and mental models of expert scientists. This involves teaching it higher-level concepts, such as thinking in terms of symmetries, a core principle in physics that current models lack.

While a world model can generate a physically plausible arch, it doesn't understand the underlying physics of force distribution. This gap between pattern matching and causal reasoning is a fundamental split between AI and human intelligence, making current models unsuitable for mission-critical applications like architecture.

Current LLMs fail at science because they lack the ability to iterate. True scientific inquiry is a loop: form a hypothesis, conduct an experiment, analyze the result (even if incorrect), and refine. AI needs this same iterative capability with the real world to make genuine discoveries.