Digital computing, the standard for 80 years, is too power-hungry for scalable AI. Unconventional AI's Naveen Rao is betting on analog computing, which uses physics to perform calculations, as a more energy-efficient substrate for the unique demands of intelligent, stochastic workloads.

The next frontier for Neuralink is "blindsight," restoring vision by stimulating the brain. The primary design challenge isn't just technical; it's creating a useful visual representation with very few "pixels" of neural stimulation. The problem is akin to designing a legible, life-like image using Atari-level graphics.

While current brain-computer interfaces (BCIs) are for medical patients, the timeline for healthy individuals to augment their brains is rapidly approaching. A child who is five years old today might see the first healthy human augmentations before they graduate high school, signaling a near-term, transformative shift for society.

We are building AI, a fundamentally stochastic and fuzzy system, on top of highly precise and deterministic digital computers. Unconventional AI founder Naveen Rao argues this is a profound mismatch. The goal is to build a new computing substrate—analog circuits—that is isomorphic to the nature of intelligence itself.

The team obsesses over perfecting the BCI cursor, treating it as the key to user agency on a computer. However, the long-term vision is to eliminate the cursor entirely by reading user intent directly. This creates a fascinating tension of building a masterwork destined for obsolescence.

A "frontier interface" is one where the interaction model is completely unknown. Historically, from light pens to cursors to multi-touch, the physical input mechanism has dictated the entire scope of what a computer can do. Brain-computer interfaces represent the next fundamental shift, moving beyond physical manipulation.

For frontier technologies like BCIs, a Minimum Viable Product can be self-defeating because a "mid" signal from a hacky prototype is uninformative. Neuralink invests significant polish into experiments, ensuring that if an idea fails, it's because the concept is wrong, not because the execution was poor.

Due to latency and model uncertainty, a BCI "click" isn't a discrete event. Neuralink designed a continuous visual ramp-up (color, depth, scale) to make the action predictable. This visual feedback allows the user to subconsciously learn and co-adapt their neural inputs, improving the model's accuracy over time.

Neuralink's initial BCI cursor used color to indicate click probability. As users' control improved, the design evolved to a reticle that uses motion and scale for feedback. This change was more effective because the human eye is more sensitive to motion than color, and it better supported advanced interactions.