The claim that LLMs represent the next step in programming abstraction - following binary, assembly, C, and Python - appears everywhere online. One commentator argues this comparison fundamentally misunderstands how LLMs operate compared to traditional programming languages.

Previous language abstractions maintained deterministic behavior: given specific input x, they produced a single, predictable output y. Whether compiling assembly, C, or Python, the same source code always generated identical binary artifacts. LLMs break this pattern by producing probabilistic outputs - the same prompt can yield multiple different responses, making f(x) -> P(y) rather than f(x) -> y.

This probabilistic nature creates serious reliability problems. Instead of generating just the desired output, LLMs can simultaneously include unwanted elements - a TODO app prompt might accidentally expose credentials or create security vulnerabilities. Unlike deterministic compilation, you can't reliably test for the absence of harmful side effects.

The author emphasizes that developers need self-awareness about these differences rather than treating LLMs as simple abstraction layers. Understanding this distinction is crucial for anyone working with AI-assisted development tools.