AI AND COMPLEX CODE

The most surprising capability of current LLMs isn't writing boilerplate. It's understanding complex, low-level code that most developers would struggle with.

I've watched Claude trace through cryptographic implementations, identify subtle timing vulnerabilities, and explain memory layout issues in C code. This isn't pattern matching on Stack Overflow answers. It's genuine comprehension of how bits move through a system.

This matters because the bottleneck in security-critical code has always been the scarcity of people who can review it. There are maybe a few thousand developers worldwide who can properly audit a TLS implementation. AI doesn't replace them, but it dramatically expands the pool of people who can engage meaningfully with that code.

The same applies to kernel code, compiler internals, database engines, and embedded systems. These domains have always been talent-constrained. AI assistance makes them more accessible without lowering the bar on correctness.

The risk is false confidence. An AI can produce a plausible-looking explanation of complex code that happens to be wrong in subtle ways. The reviewer still needs enough expertise to catch these errors. But as a force multiplier for existing expertise, it's remarkable.