Picture this: you're holding a device containing billions of precisely calibrated numbers, each one crucial to its operation. Now imagine a cosmic ray streaks through the atmosphere, passes through your roof, through your computer, and flips a single bit in one of those numbers. Now imagine that the device is Large Language Model - what happens next?
Most likely, nothing at all.
This isn't science fiction. Cosmic rays flip bits in computer memory all the time (Cosmic Rays are something I worried about a lot when first launching Sigstore's Transparency Log), and yet when impacting large language models running on servers around the world, they continue to function perfectly. The reason why reveals something really interesting about the similarities between artificial neural networks and biological brains.
The Architecture of Redundancy
When we think about precision engineering, we usually imagine systems where every component matters. Remove one gear from a Swiss watch, and it stops ticking. Change one line of code in a program, and it might crash entirely. But neural networks operate on entirely different principles, and understanding why requires us to peek inside the mathematical machinery that powers modern AI.
A large language model like GPT-5 contains somewhere between hundreds of billions and trillions of parameters. These aren't just storage slots for data, they're the learned connections between artificial neurons, each one encoding a tiny fragment of knowledge about language, reasoning, and the patterns hidden in human communication. When you ask a model to complete a sentence or solve a problem, you're watching these billions of numbers collaborate in ways that even their creators don't fully understand.
But here's the fascinating part: most of these parameters aren't irreplaceable specialists. They're more like members of a vast crowd, where losing any individual voice barely affects the overall conversation.
When Numbers Go Wrong
To understand just how robust these systems really are, researchers have conducted what can only be described as digital vandalism experiments. They deliberately corrupt random parameters in trained models, essentially breaking parts of the AI's "brain," then measure what happens to its performance.
The results are counterintuitive. You can corrupt thousands, even tens of thousands of parameters in a billion-parameter model, and it will still generate coherent text, answer questions correctly, and perform complex reasoning tasks. It's as if you took a massive orchestra and randomly muted dozens of musicians, only to discover the symphony sounds virtually identical.
... continue reading