OCaml's success lies in its dual nature. It offers the elegance of a fine functional language while also providing the imperative power and predictable execution cost model of a systems language. This rare combination is highly valuable for applications like high-frequency trading and network programming.

For a project with a massive user base like TypeScript, a full rewrite is toxic because it inevitably introduces breaking changes. The team chose to port the existing code to Go, preserving the exact algorithms and behavior to ensure backward compatibility and keep the ecosystem happy.

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.

Go was selected for the native port because its built-in garbage collection could handle the TypeScript compiler's many circular data structures. Rust's borrow checker would have required a fundamental redesign of these structures, violating the team's "port, don't rewrite" principle.

The idea for TypeScript came when a team wanted to transpile C# to JavaScript solely to get better tooling. Anders Hejlsberg realized the core problem wasn't JavaScript, but its poor developer experience. This led to improving JavaScript with a type system rather than trying to replace it.

The primary motivation for rewriting the TypeScript compiler in Go wasn't just the 2-3x performance gain. It was to escape JavaScript's single-threaded model and access shared memory concurrency, which is essential for utilizing modern multi-core CPUs and couldn't be achieved with web workers.

Some companies intentionally choose a more complex or niche programming language like OCaml not just for its technical merits, but as a hiring strategy. It acts as a filter, attracting fewer applicants overall, but those who do apply often have more interesting and diverse technical backgrounds.

AI models are most effective with languages they have seen the most in training data, like JavaScript and Python. This creates a feedback loop where AI makes developers more productive in these languages, further cementing their dominance and raising the barrier for new niche languages to gain traction.