There’s a quiet rite of passage every engineer goes through. You build something that already exists. You write your own search algorithm. You design your own auth system. You roll your own logging framework because the existing one feels too heavy.
And for a while, it’s exhilarating. You’re learning, stretching, discovering how the pieces actually work.
But there’s a difference between learning and shipping.
The Temptation to Reinvent
Every generation of engineers rediscovers the same truth: we love building things from scratch. We tell ourselves our use case is different, our system is simpler, our constraints are unique.
But the moment your code touches production when it has to handle real users, scale, security, and compliance you realize how deep the rabbit hole goes.
Here’s a short list of what you probably shouldn’t reinvent if your goal is to ship something that lasts:
- Search algorithms
- Encryption
- Authentication
- Credit card handling
- Billing
- Caching systems
- Logging frameworks
- CSV, HTML, URL, JSON, XML parsing
- Floating point math
- Timezones
- Localization and internationalization
- Postal address handling
Each one looks simple on the surface. Each one hides decades of hard-won complexity underneath.
Learn It, Don’t Ship It
You should absolutely build these things once.
Do it for the same reason musicians practice scales or pilots train in simulators. You’ll understand the invisible edges where systems fail, what tradeoffs libraries make, how standards evolve.
Build your own encryption to see why key rotation matters.
Write your own caching layer to feel cache invalidation pain firsthand.
Parse CSVs manually to understand why “CSV” isn’t a real standard.
You’ll emerge humbled, smarter, and far less likely to call something “trivial” again.
But then don’t ship it.
The Cost of Cleverness
Production is where clever ideas go to die.
The real cost of rolling your own isn’t just the initial build. It’s the invisible tax that compounds over time: maintenance, updates, edge cases, security audits, integration testing.
That custom auth system? It’ll need to handle password resets, MFA, SSO, OAuth, token expiration, brute-force protection, and GDPR deletion requests.
Your homegrown billing service? Get ready for tax handling, currency conversion, refund flows, audit trails, and legal exposure.
Most of us underestimate this cost by an order of magnitude. And that gap between what you think you built and what reality demands is where projects go to die.
The Wisdom of Boring Software
Mature engineering isn’t about novelty it’s about leverage.
When you use battle-tested libraries, you’re not being lazy. You’re standing on top of millions of hours of debugging, testing, and iteration that others have already paid for.
The best engineers I know are boring. They use Postgres, Redis, S3. They trust Stripe for billing, Auth0 for authentication, Cloudflare for caching. They’d rather spend their creative energy on business logic and user experience the parts that actually differentiate a product.
Boring software wins because it doesn’t collapse under its own cleverness.
Why This Matters Even More in the AI Era
Today, a new kind of abstraction has arrived: AI.
We don’t just import libraries anymore we import intelligence.
When you integrate AI into your workflow, you’re effectively outsourcing judgment, reasoning, and data handling to a black box that feels magical but is still software under the hood.
If you’ve never built or understood the underlying systems search, parsing, data handling, caching, numerical precision you’ll have no intuition for what the AI is actually doing. You’ll treat it as oracle instead of a tool.
Knowing how these fundamentals work grounds you. It helps you spot when the model hallucinates, when latency hides in API chains, when an embedding lookup behaves like a fuzzy search instead of real understanding.
The engineers who will thrive in the AI era aren’t the ones who blindly prompt. They’re the ones who know what’s happening behind the prompt.
Because AI systems don’t erase complexity they just bury it deeper.
And if you don’t know what lives underneath, you can’t debug, govern, or trust it.
When It’s Worth Reinventing
There are exceptions. Sometimes the act of rebuilding is the product itself.
Search at Google. Encryption at Signal. Auth at Okta.
If your business is the infrastructure, then yes go deep. Reinvent with intention. But if it’s not, your job is to assemble reliable systems, not to recreate them.
Learn enough to understand the tradeoffs, but don’t mistake knowledge for necessity.
The Real Lesson
Here’s the paradox: you can’t truly respect how hard these problems are until you’ve built them yourself.
So do it once. In a sandbox, on weekends, or as a thought exercise. Feel the pain, appreciate the elegance of the libraries you once dismissed, and move on.
That humility will make you a better engineer and a more trusted builder in the AI age than any clever homegrown library ever could.
Final thought:
Master everything. Ship selectively.
That’s the difference between engineering as craft and engineering as production.
And it’s the difference between using AI and actually understanding it.