Our perception of sensing then reacting is an illusion. The brain constantly predicts the next moment based on past experiences, preparing actions before sensory information fully arrives. This predictive process is far more efficient than constantly reacting to the world from scratch, meaning we act first, then sense.

While LLMs dominate headlines, Dr. Fei-Fei Li argues that "spatial intelligence"—the ability to understand and interact with the 3D world—is the critical, underappreciated next step for AI. This capability is the linchpin for unlocking meaningful advances in robotics, design, and manufacturing.

A "software-only singularity," where AI recursively improves itself, is unlikely. Progress is fundamentally tied to large-scale, costly physical experiments (i.e., compute). The massive spending on experimental compute over pure researcher salaries indicates that physical experimentation, not just algorithms, remains the primary driver of breakthroughs.

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.

Long before Einstein's relativity, scholars like Pierre-Simon Laplace and John Michell theorized about "dark stars." They reasoned that if a star were massive enough, its escape velocity could exceed the speed of light, trapping light and rendering it invisible. This early concept was based entirely on Newton's laws of gravity, demonstrating remarkable scientific foresight.

The Standard Model of particle physics was known to be incomplete. Without the Higgs boson, calculations for certain particle interactions yielded nonsensical probabilities greater than one. This mathematical certainty of a flaw meant that exploring that energy range would inevitably reveal new physics, whether it was the Higgs or something else entirely.

World Labs co-founder Fei-Fei Li posits that spatial intelligence—the ability to reason and interact in 3D space—is a distinct and complementary form of intelligence to language. This capability is essential for tasks like robotic manipulation and scientific discovery that cannot be reduced to linguistic descriptions.

Fears that the Large Hadron Collider could create a world-ending black hole were mitigated by a simple astronomical observation: Earth is constantly bombarded by cosmic rays creating collisions with far greater energy than the LHC can produce. Since the planet has survived billions of years of these natural, high-energy events, the risk from the collider was deemed negligible.

Current multimodal models shoehorn visual data into a 1D text-based sequence. True spatial intelligence is different. It requires a native 3D/4D representation to understand a world governed by physics, not just human-generated language. This is a foundational architectural shift, not an extension of LLMs.