Patterns

For over a year, I've been building systems using off-the-shelf large language models—products of companies backed by billions in funding. Every interaction costs me per token, every prompt requires careful engineering, and every deployment depends on massive data centers running somewhere in the cloud. As I watch these systems strain against their limitations, requiring endless tweaks and workarounds to achieve seemingly simple tasks, a thought keeps recurring: we've been thinking about computing all wrong.

Digital computing isn't the destination—it's the bootstrap. We created digital computers not because they're the ideal form of computation, but because they were the first type we could control reliably. They're the scaffolding that lets us build something better.

This realization comes at a critical time. The race toward artificial general intelligence through digital computing has revealed a fundamental problem: scaling digital systems to human-level intelligence requires astronomical resources. The largest AI models now consume megawatts of power, demand complex cooling systems, and cost hundreds of millions to train. This isn't sustainable or democratically accessible.

Natural intelligence—the kind that evolved in biological systems—operates in the analog domain. It processes patterns continuously through massively parallel neural networks, integrates information across multiple scales, and achieves remarkable capabilities with minimal power. The human brain performs incredible feats of cognition while consuming about the same power as a dim light bulb. This gap between natural and artificial approaches points to something important: our current path might be fundamentally misaligned with the nature of intelligence itself.

Enter resonant crystal computing—a technology I've been developing. Instead of forcing the world's inherently analog patterns through digital bottlenecks, my approach processes electromagnetic fields directly. The architecture stores information as distributed resonant modes across the entire array, similar to how holograms store interference patterns. This distributed storage provides natural fault tolerance and, uniquely, allows the system to be physically subdivided while maintaining functionality at lower resolution.

This distinction matters because it changes what we're trying to achieve. Instead of pursuing human-level intelligence through brute force computation and ever-larger parameter counts, we can take a more direct path—one that mirrors how intelligence naturally emerges. The goal shifts from maximizing raw computational power to understanding and implementing the fundamental patterns of intelligence itself.

My crystal resonant pattern transformer (RPT) architecture embodies this shift. It processes information through continuous field interactions rather than discrete digital steps. The distributed nature of its pattern storage provides natural fault tolerance and graceful degradation. Instead of building massive data centers and complex distribution networks, we could train a single large system and subdivide it to serve many users. Each subdivision maintains complete pattern recognition capabilities, just at reduced precision—often more than adequate for practical applications.

The theoretical performance numbers are compelling. While a modern GPU-accelerated inference server—the kind that costs thousands per month to run in the cloud—typically processes around 62,500 inferences per second on a large language model, my calculations show that crystal-based designs could handle a million patterns per second while consuming a fraction of the power. Based on the underlying physics, a single cubic centimeter should be able to store 10^18 patterns—that's a billion billion patterns in a space smaller than a sugar cube. Most remarkably, these projected capabilities would persist even when the system is subdivided. Mathematical models indicate that cutting it in half would maintain complete functionality at 71% resolution, and each quarter would still operate at 50% resolution. The system would gracefully adapt its precision to available resources rather than failing outright.

Some will object that analog systems can't match the precision of digital computation. They're right—and that's exactly the point. Most real-world problems don't require perfect precision. They require good pattern recognition, sensible responses, and adaptive behavior. Natural intelligence proves this every day.

I've spent years working with digital systems, pushing them to their limits, and watching them consume ever-increasing resources for diminishing returns. The future of computing doesn't lie in more powerful digital systems—it lies in returning to nature's approach, armed with the precision tools and understanding we gained from the digital bootstrap. Now it's time to build something better.