An experiment measured developer productivity when switching from C to C++. While C++ compilation took twice as long, its stronger type system caught errors earlier. This resulted in C programmers recompiling twice as often, making the overall time spent compiling roughly equal for both languages.

Haskell's lazy evaluation means the order of operations is not guaranteed, making side effects like `print` statements unpredictable. This forced the language to be pure by default. Conversely, OCaml's strict, predictable evaluation order made it easy to incorporate I/O and side effects, allowing it to be impure by default.

The dream of hardware optimized for functional programming (e.g., dataflow or SK combinator machines) proved to be a mistake. These machines were essentially hardware-based interpreters. The better approach is to build a sophisticated compiler that translates functional code into efficient instructions for general-purpose CPUs.

An LLM generating code can use a static type checker as a rapid verifier. This allows the model to iterate and correct its own type errors internally before presenting the final code. This dramatically constrains the problem space and improves the quality of the generated output, making static typing a boon for LLMs.

The term "formal methods" isn't a single, complex technique but a range of mathematical approaches. Many developers already use them via simple tools like Java's type checker (weak guarantees, easy to use), while full functional correctness requires PhD-level interactive theorem provers (strong guarantees, high cost).

Beyond catching compile-time errors, a strong static type system's main benefit is making large, aging codebases maintainable. Dynamically typed programs can become immutable as original authors leave. With static types, a developer can fearlessly refactor a 35-year-old codebase by letting the compiler guide them to all necessary changes.

Dreamer's CTO chose TypeScript for their agent SDK over Python, his personal favorite. Strong typing provides immediate feedback at compile time, enabling AI coding agents to enter a tight loop of generating code, perceiving errors, and self-correcting—a critical advantage for building reliable software with AI.

Unlike languages like C which started as useful but unsafe, Haskell began with extreme safety and theoretical purity, even lacking I/O initially. This forced its designers to invent new, principled ways to handle side effects (like monads), ensuring the language evolved towards usefulness without sacrificing its core value of safety.

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.