On Functional Programming and if it Even Matters Anymore
On Functional Programming and if it Even Matters Anymore
Functional programming is a paradigm near and dear to me (if I had to pick one language to use for the rest of my life it would be F#). However, I can't help but wonder if AI-assisted coding is reducing its relevance. I will first explore what functional programming is (while denoting distinctions that are similar but sometimes mistaken as core concepts -see DDD). Then I want to explore a larger question that has been bouncing around my head:
Was functional programming designed for human cognition, or for universal program correctness?
Because depending on the answer, AI could either make functional programming more important than ever or quietly obsolete.
What Functional Programming Actually Is
At its core, functional programming treats code as data. The fundamental unit of code is a function. A function has input and output. Functions can themselves be inputs or outputs to other functions. From this simple idea, a surprising amount of software design philosophy emerges.
In functional programming, programs are built by composing transformations of data rather than by issuing sequences of instructions that mutate shared state. Instead of telling the computer how to step through a process, we describe how values transform into other values.
This pattern is extremely powerful because it makes behavior composable. When functions behave predictably, they can be rearranged, substituted, and reused without requiring global knowledge of a system.
One of the most practical examples of this is in testability of mission-critical systems. By passing functions as dependencies, we can abstract behavior away from implementation. When writing automated tests, we can easily substitute or mock these functions, leading to more robust and isolated test suites.
However, testing is really just a surface-level manifestation of something deeper. Functional programming attempts to minimize hidden state and implicit behavior. The more a system can be expressed as transformations of data, the easier it becomes to reason about locally. Developers can understand pieces of a system in isolation without needing to mentally simulate the entire application.
Concepts Often Paired With Functional Programming (But Not The Same Thing)
Before going further, it is important to define what functional programming is not. This matters, especially if we want to discuss whether these patterns remain relevant in an AI-assisted coding world. We need to know what we are actually evaluating.
Type Systems
As someone who worked extensively in F# with an extremely powerful type system, I sometimes forget that this is not the default experience in most programming environments.
Strong type systems often appear alongside functional programming languages, but they are not the same thing. Type systems enforce constraints about how data can be structured and how it flows through programs. They can prevent entire classes of bugs by making illegal states impossible to represent.
Examples of languages that combine functional programming with strong type systems include:
There are also languages that incorporate functional ideas without always emphasizing strong static typing in the same way:
Type systems and functional programming historically travel together because both attempt to reduce ambiguity. Functional programming reduces behavioral ambiguity by eliminating hidden side effects. Type systems reduce structural ambiguity by restricting how data can be used.
But they are still separate ideas.
Domain-Driven Design (DDD)
Many teams that embrace functional programming also embrace Domain-Driven Design. However, DDD is not a core tenet of functional programming itself.
DDD focuses on modeling software around domain language, bounded contexts, and explicit representation of business rules. It is fundamentally about aligning software structure with real-world concepts. Functional programming often pairs well with DDD because functional patterns encourage explicit modeling of state transitions and transformations. But again, these are complementary philosophies, not identical ones.
Why Functional Programming Historically Matters
Functional programming is often praised for elegance, but I think its real value is more practical. It dramatically reduces cognitive load.
When programs rely heavily on shared mutable state, understanding system behavior requires tracking how data changes across time and across multiple parts of the application. This becomes extremely difficult in large systems.
Functional programming attempts to limit that complexity by encouraging referential transparency. If a function receives the same inputs, it produces the same outputs. This property allows developers to reason about systems in smaller, more predictable pieces.
This is where functional programming begins to reveal its deeper purpose. It is less about how computers execute instructions and more about how humans understand systems.
Now For The Question: Does This Even Matter Anymore?
The central idea I keep coming back to is this:
If the primary way developers interact with code becomes AI-assisted tools, one of the main benefits of functional programming (making code easier for humans to reason about) may become less relevant.
Historically, functional programming helped developers manage complexity that exceeded human cognitive limits. But AI tools can already analyze entire repositories, generate integration tests, refactor complex dependency graphs, and reason across system boundaries in ways humans struggle to do.
If AI can reliably track global state, simulate execution paths, and validate correctness through massive automated testing, functional programming may start to look like a set of constraints designed for human limitations rather than fundamental properties of good software.
This leads to the philosophical fork that makes this topic fascinating.
Was Functional Programming Designed For Humans, Or For Correctness Itself?
If functional programming exists primarily to help humans reason about complex systems, then AI may reduce its necessity. AI does not experience cognitive overload in the same way developers do. It can explore larger state spaces, track side effects across thousands of files, and validate behavior through simulation.
In that world, the need for strict functional structure may diminish. AI might comfortably maintain systems filled with mutation and implicit state because it can continuously verify behavior through testing and analysis.
However, there is a powerful counterargument.
AI systems are fundamentally probabilistic. They generate solutions based on likelihood rather than proof. They are capable of producing internally inconsistent or subtly incorrect implementations while still appearing highly competent.
Functional programming (especially when combined with strong type systems) introduces deterministic constraints. It creates explicit contracts that define what is allowed and what is impossible. These constraints act as an external correctness oracle that AI cannot simply approximate or guess around.
From this perspective, functional programming might become more valuable, not less. It could serve as a structural interface between probabilistic intelligence and deterministic software guarantees.
A Possible Middle Ground
The future may not be that functional programming disappears or dominates. Instead, its role may shift.
Rather than being primarily an implementation style chosen by developers, functional programming might evolve into a modeling discipline. Developers may spend less time writing function implementations and more time defining invariants, transformations, and domain constraints. AI systems could then generate imperative or optimized implementations that satisfy those models.
In that future, functional programming would survive not as a coding preference but as a way of specifying truth about systems.
I'm not sure how this will all play out, but it is fascinating to think about.