1. Lisp: The Foundational Thinker
It’s no secret that Patrick Collison is a long-time Lisp admirer. As a teenager, he created his own Lisp-style language called Croma. Lisp is one of programming’s oldest and most influential high-level languages, and its DNA can be found in countless
modern successors. What makes it a 'Collison' language is its emphasis on foundational concepts. Lisp treats code as data (a concept called homoiconicity), allowing for powerful metaprogramming that other languages struggle to replicate. It encourages a first-principles approach to problem-solving, which aligns perfectly with Collison’s focus on deep, structural thinking over superficial trends. Exploring Lisp isn't about chasing the latest framework; it's about understanding the very nature of computation, a pursuit that feels central to the Collison ethos.
2. Ruby: The Pragmatic Workhorse
Stripe was famously built on Ruby, a choice that speaks volumes about pragmatism in the face of immense technical challenges. In the early 2010s, Ruby, particularly via the Rails framework, was the king of developer productivity. For a startup trying to solve a complex problem like online payments, the ability to build and iterate quickly was paramount. While some might see Ruby as less academically pure than Lisp or Haskell, its selection for Stripe’s core infrastructure highlights a key Collison trait: a relentless focus on getting the job done effectively. Even as Stripe has grown and introduced other languages like Java and Scala for specific needs, its massive Ruby codebase remains a testament to the power of choosing the right tool to build a lasting foundation, a decision that values results over dogma.
3. Rust: The Systems Builder
Collison's interest lies not just in software, but in building robust, enduring systems—the invisible infrastructure that powers progress. Rust embodies this philosophy in code. Developed to solve problems of memory safety and concurrent programming that have plagued languages like C and C++, Rust allows developers to build high-performance, incredibly reliable systems without a garbage collector. Its core features, like the ownership and borrow checker, enforce safety at compile-time, eliminating entire classes of bugs. This focus on correctness and performance makes it ideal for the kind of foundational services—operating systems, browser engines, and critical financial infrastructure—that align with a long-term, architectural view of technology. For anyone who appreciates building things that are made to last, Rust is a natural fit.
4. Haskell: The Principled Purist
If Lisp is about foundational thinking, Haskell is about principled execution. It's a purely functional programming language, which means it treats computation as the evaluation of mathematical functions and avoids changing state and mutable data. This approach can be mind-bending for programmers used to imperative languages, but it offers incredible benefits in clarity, predictability, and correctness. The appeal for a Collison-type thinker is the intellectual rigor it demands. Haskell forces you to be explicit about side effects (like writing to a database or the screen), leading to code that is easier to reason about and test. It represents a commitment to a powerful, coherent idea, even if it has a steeper learning curve—a trade-off between immediate ease and long-term structural integrity that is a recurring theme in building ambitious things.
5. Julia: The Language of Progress
Collison is a co-founder of the "Progress Studies" movement, which seeks to understand and accelerate scientific and technological advancement. The Julia programming language feels like a tool purpose-built for this mission. Designed to solve the "two-language problem" in scientific computing, Julia offers the high-level syntax and ease of use of Python but with performance that rivals low-level languages like C or Fortran. This makes it ideal for complex modeling, data analysis, and large-scale numerical simulation—the very work that underpins scientific breakthroughs. Its growing adoption in academia and research for everything from climate science to machine learning makes it the language for those who want to build the tools that solve the next generation of hard, technical problems.
