Despite vastly different approaches—one based on mutating a tape, the other on pure value reduction—Alan Turing and Alonzo Church's models of computation were proven to be equally powerful. This historical context reveals that the choice between paradigms is about usability and reasoning, not computational limits.

This approach contrasts with imperative languages where computation proceeds by mutating a state over time (e.g., a running total). Functional programming is more declarative, like a mathematical expression or a spreadsheet cell that calculates its value based on others, making it easier to reason about.

Most compilers use complex, untyped intermediate representations. GHC desugars Haskell into a tiny, statically-typed language called Core. This allows a type-checker to run after each optimization pass, immediately catching bugs in the compiler that would otherwise manifest as cryptic runtime segfaults in the final compiled program.

Amjad Masad draws a parallel between modern AI-powered coding in English and Grace Hopper's creation of the compiler. Both were forms of abstraction met with skepticism from purists who believed developers needed to work at a lower level (machine code then, traditional coding now).

Unlike software's daily compilations, hardware development allows only a few "compiles" (builds) in total. This necessitates a more conservative, upfront process focused on reliability and planning, as you can't ship over-the-air updates to fix physical products.

Bjarne Stroustrup advises against being "too clever." Manual optimizations from the 1990s are often "pessimizations" today because they constrain modern compilers, preventing them from applying more sophisticated optimizations tailored to new CPU architectures, caches, and memory access patterns.

Fal maintains a performance edge by building a specialized just-in-time (JIT) compiler for diffusion models. This verticalized approach, inspired by PyTorch 2.0 but more focused, generates more efficient kernels than generalized tools, creating a defensible technical moat.

Instead of using high-level compilers like Triton, elite programmers design algorithms based on specific hardware properties (e.g., AMD's MI300X). This bottom-up approach ensures the code fully exploits the hardware's strengths, a level of control often lost through abstractions like Triton.

To mitigate the risk of expensive physical failures, hardware control software company Revel developed its own programming language. A core feature is that if code compiles successfully, it is guaranteed not to crash at runtime. This design choice eliminates a common source of catastrophic errors in hardware operation.