The 5 Learning Curves Of TypeScript (And Why You’ll Probably Stop At Curve 4)

As a former principal engineer and now an engineering manager who coaches others, I’ve noticed — you don’t “learn TypeScript” once. You climb it. Each step promises more safety and clarity – until it doesn’t. This is how I coach people through the curves.

Problem

You want TypeScript to save you from silly bugs without turning your codebase into a riddle.

Why It Matters

Used well, types make intent obvious, reviews faster, and refactors calm. Used badly, they become puzzles that only the original author can solve.

What To Do

Know the five curves. Climb deliberately. Stop before cleverness costs you.

Your Curve vs The Team’s

Your learning curve doesn’t have to match your team’s or the codebase’s.

  • Practise one curve ahead in sandboxes or spikes.
  • Match the repository’s current curve in PRs.
  • If you want to move the team up a curve, propose a small ratchet (config + lint) and a migration plan.

Curve One: Basic Syntax

At first, it’s all colons and question marks. Function arguments, variables, return types, you know – the basics. The feedback loop is quick, and it feels like writing smarter JavaScript.

Looks like

function add(a: number, b: number): number {
  return a + b;
}

type User = { id: string; name?: string };

Why it helps

  • Clearer intent for the next reader.
  • Autocomplete wakes up.
  • Low risk, quick wins.

Watch for

  • Blanket // @ts-ignore usage. Prefer // @ts-expect-error with a linked issue and a removal plan.
  • Boundary pressure (APIs, JSON) (using types to describe what is coming in, or not!). Use unknown + narrowing, runtime schemas (e.g. Zod), or specific helpers instead of any.
  • Migration drag. Enforce @typescript-eslint/no-explicit-any as error, with explicit allowlists for generated/legacy code only.

Curve Two: Trusting The Type System

You start treating types as contracts rather than hints. You write return types even when TS could infer them, to lock interfaces and help reviewers.

Looks like

type User = { id: string; name: string };

async function fetchUser(id: string): Promise<User> {
  const res = await fetch(`/api/users/${id}`);
  const data = (await res.json()) as User; // contract: callers get User
  return data;
}

Why it helps

  • Fewer “what does this return?” questions.
  • Safer refactors; your API surface is explicit.

Watch for

  • Type assertions as duct tape. Prefer decoding/validation at runtime if the source is untrusted.

Tip: Pair with lightweight runtime checks (e.g. a tiny zod schema) at the edges.

Curve Three: Codifying Discipline

Good habits drift. So you enforce them. Your team moves from “we usually do X” to “the linter enforces X”. Consistency scales.

Looks like

  • tsconfig.json: "strict": true, "noImplicitAny": true, "strictNullChecks": true.
  • ESLint rules for explicit returns, unused vars, consistent type imports.
  • ESLint: @typescript-eslint/no-explicit-any: error (with documented escape hatches).

Why it helps

  • Shared defaults; fewer style debates.
  • Missteps become compiler errors, not PR comments.

Watch for

  • Rigidity without relief valves. Enforce @typescript-eslint/no-explicit-any: error, but document escape hatches (generated/legacy code) and prefer unknown + narrowing over any.

Curve Four: Types As A Language Of Their Own

You step beyond nouns into modelling. Generics, discriminated unions, and mapped types express real domain states. Opinions form: type vs interface, when to reach for extends, how to build reusable patterns.

Looks like

// Discriminated union for async state
export type RemoteData<T> =
  | { status: 'idle' }
  | { status: 'loading' }
  | { status: 'success'; data: T }
  | { status: 'error'; error: string };

export function match<T, R>(
  rd: RemoteData<T>,
  cases: {
    idle: () => R;
    loading: () => R;
    success: (data: T) => R;
    error: (err: string) => R;
  }
): R {
  switch (rd.status) {
    case 'idle': return cases.idle();
    case 'loading': return cases.loading();
    case 'success': return cases.success(rd.data);
    case 'error': return cases.error(rd.error);
  }
}

// Reusable utilities
export type PropsWithout<T, K extends keyof T> = Omit<T, K>;
export type ValueOf<T> = T[keyof T];

Why it helps

  • Business logic becomes types you can reason about.
  • Illegal states become unrepresentable.
  • Reuse via small, predictable building blocks.

Watch for

  • Over‑abstracting early. Create a generic after you’ve seen the second duplication, not the first.

Curve Five: TypeScript As A Turing‑Complete Party Trick

Advanced conditional types, recursion in the type system, and deeply nested inference gymnastics. Impressive—and often brittle. The kind of code that shows up in Advent of TypeScript around Day 6.

Looks like

// Toy example: tuple length at the type level (don’t)
type Length<T extends unknown[], A extends 1[] = []> =
  T extends [infer _H, ...infer R] ? Length<R, [1, ...A]> : A['length'];

Why it sometimes helps

  • Library authors need precision without runtime cost.
  • Framework internals can justify the complexity.

Costs

  • Readability debt for everyday contributors.
  • Slower compiles and harder error messages.
  • Fragile across TS version bumps.

Why I Stop At Curve Four

In my opinion; I want 80–90% of the safety for 10–20% of the complexity. Curve Four gives you expressive modelling and good ergonomics. Curve Five often swaps clarity for cleverness. Unless you’re writing library internals or have a team fluent in the type calculus, stop one step earlier.

Pitfalls To Avoid

  • Types ≠ Tests: Types prevent a class of bugs; they don’t verify behaviour. Keep unit and integration tests.
  • Unvalidated Inputs: Static types don’t protect against runtime shape mismatches from APIs. Validate at the boundary.
  • Global any Leaks: One loose any in a shared util can silently undo safety elsewhere. Contain escape hatches.
  • Config Thrash: Changing strictness mid‑stream without migration time breeds “just suppress it” culture. Plan the ratchet.

How To Climb Deliberately

Before you start: align on the repo’s current curve and a target curve. Your personal learning can be one step ahead; commits should match the codebase until a migration is agreed.

  1. Adopt Curve One: Add basic annotations on public surfaces. Let inference handle locals.
  2. Lock Contracts (Two): Make return types explicit on exported functions/APIs.
  3. Enforce (Three): Turn on strict and add a minimal ESLint config. Fix violations incrementally.
  4. Model (Four): Introduce discriminated unions for state and small generics/utilities where duplication appears.
  5. Resist (Five): If someone proposes a recursive conditional type, ask: “Can we make illegal states unrepresentable without this?” Prefer runtime schemas or simpler shapes.

Conclusion & Key Takeaways

There are five stages. Where are you?

  • Go one step up from where you are today.
  • Your personal journey can be ahead of the team; experiment one step up, commit at the repo’s level.
  • If you’re on 4, probably chill: optimise for legibility, tests, and runtime validation.
  • Only reach 5 with a strong reason (library internals or a team fluent in advanced TS).

Immediate next step: choose the next curve and take one action now (e.g. turn on "strict", add explicit return types, or model one domain state as a discriminated union).