Overthinking doesn't look like procrastination. It looks like productivity. You're researching, reading, exploring prior art, comparing tools. It feels like work — because technically it is. But your project isn't moving forward. The code isn't shipping. And by the time you realize what's happening, you've spent hours (or weeks) going nowhere.
Kevin Lynagh, a software engineer and toolsmith, wrote a brilliant essay on this exact trap. His diagnosis is sharp: the root cause isn't laziness or lack of skill — it's unclear success criteria combined with an instinct to research before building.
This article breaks down why overthinking and scope creep are the silent killers of software projects, and what you can actually do about it.
The Two Outcomes of Every Project Idea
Every project idea tends to go one of two ways:
- You just do it. You build it, make minor revisions, and it turns out roughly how you imagined. You ship. You're done.
- You start researching. You look for prior art. There's a lot of it. The scope broadens. You start wondering if you should build on top of existing solutions. Or maybe you should just use the popular thing. But you could do better, if you invested more time. But you don't want to maintain something big. And now you've spent hours without shipping anything — or experiencing the joy of creating something.
Outcome #1 is obviously better. The question is: what determines which path you take?
Fuzzy Success Criteria Is the Root Cause
The pivotal factor is how well you've internalized your success criteria before you start.
When Lynagh wanted to build a kitchen shelf with a friend, the success criteria was crystal clear: make a shelf for my exact kitchen, and have fun doing woodworking with a friend. Done in a weekend. No overthinking.
But when he went down a rabbit hole researching structural diffing tools, he spent four hours reading about PhD-level tree diffing algorithms before remembering his actual goal: build a nicer diffing workflow for myself in Emacs. Estimated time: four hours. He spent those four hours researching instead of building.
The pattern is clear: fuzzy goals invite scope creep. When you can't articulate exactly what "done" looks like, your brain fills the void with more research, more comparison, more planning.
This is especially dangerous for founders and indie developers who wear multiple hats. You don't have a product manager telling you to stop researching and start shipping. You are the scope creep.
The Conservation of Scope Creep
Here's a particularly painful observation from Lynagh's essay: when he used an LLM to help build a fuzzy file search tool, the LLM found a great library (Nucleo) that had extra functionality — anchor-based matching. This triggered a chain of "what if" questions:
- What if anchors worked differently for file paths?
- What if we anchored to path segments instead of lines?
- What about handling slashes in anchored queries?
Several hours of design work and code later, he realized: he had never once wished his original tool had anchor functionality. He threw it all out.
This is what Lynagh calls a "conservation law" of software development: any increase in programming speed (from AI tools, better libraries, faster languages) will be offset by a corresponding increase in unnecessary features, rabbit holes, and diversions.
The LLM didn't cause the scope creep. But it accelerated it. Faster code generation means you can go down rabbit holes faster — and dig yourself deeper before realizing you should have stopped.
Why Smart People Are the Most Vulnerable
There's a painful irony here: the people most susceptible to overthinking-driven scope creep are often the most capable engineers.
Why? Because they can see the broader implications. They can imagine the edge cases. They can design elegant abstractions. And all of that capability becomes a liability when it's deployed against a problem that just needed a simple, direct solution.
Lynagh describes his own long-running interests — designing a programming language that fuses Clojure and Rust, building interfaces for hardware prototyping, creating a CAD language — as being stuck in the research phase for hundreds of hours. He's learned a lot by reading. But he hasn't shipped.
His self-diagnosis: "My inner critic (fear of failure?) is silencing my generative tendencies, keeping me from the much more enjoyable (and productive!) learning by doing."
How to Actually Ship: Practical Anti-Overthinking Strategies
1. Write Down Your Success Criteria Before Starting
Not in your head. On paper. One to three sentences that define "done."
Example: "A CLI tool that diffs two files by function/class, showing added/removed/changed entities. Must work with TypeScript and Python. I use it myself in Emacs."
If you can't write it in three sentences, you don't have clear enough criteria yet. Don't start building until you can.
2. Apply the YAGNI Rule Aggressively
"You Ain't Gonna Need It" isn't just a software design principle — it's a project survival strategy.
Every time you think "it would be nice if...", ask: "Do I have a concrete use case for this right now?" If the answer is no, skip it. You can always add it later. You can rarely undo the time spent building things nobody needed.
3. Set a Time Box
Before starting, estimate how long the task should take if you scope it correctly. Then set a hard deadline. If you estimated four hours and you're two hours deep into researching alternatives, you've already failed the time box. Stop researching and start building.
4. Default to Building, Not Researching
Research is valuable. But it has diminishing returns fast. The first 30 minutes of research will give you 80% of what you need. The next four hours will give you the remaining 20% — and probably introduce enough scope creep to kill the project entirely.
When in doubt, build first. You'll learn more from a working prototype than from reading ten blog posts about how other people solved similar problems.
5. Embrace the "Clueless 20-Year-Old" Mindset
Lynagh's closing advice: embrace your inner clueless 20-year-old and just do things. Some will turn out "obviously bad" in hindsight. But you'll come out ahead on net — because the experience of building, failing, and iterating teaches you more than the experience of endlessly planning and never shipping.
This isn't about being reckless. It's about recognizing that the cost of a bad first version is almost always lower than the cost of never shipping.
The Real Cost of Overthinking
Every hour spent researching instead of building is an hour you could have had a working prototype. Every feature added to the scope is a feature that delays the ship date. Every "what if" question that doesn't have a concrete user behind it is scope creep in disguise.
The projects that succeed aren't the ones with the most features or the most elegant architecture. They're the ones that shipped. That got into users' hands. That generated feedback and iteration data.
If you're building something — a startup, a side project, a tool for yourself — the single most impactful thing you can do today is clarify what "done" looks like, resist the urge to broaden the scope, and start building.
The enemy of shipped product isn't laziness. It's perfectionism in disguise.
FAQ
Q: How do I know if I'm overthinking vs. doing necessary research? A: If your research has a clear stopping point tied to a specific decision, it's necessary. If you're "just browsing" or keep finding new rabbit holes, you're overthinking. Set a time limit: 30 minutes of research, then decide and move on.
Q: What's the difference between good scope and scope creep? A: Good scope addresses a real user need you can articulate in one sentence. Scope creep addresses a hypothetical need — "what if someone wants to..." — without a concrete use case behind it.
Q: How do AI coding assistants affect scope creep? A: They accelerate it. LLMs can generate scope creep faster than you can think through whether you actually need it. Be extra vigilant about success criteria when using AI tools — they'll happily build features you never asked for.
Q: Is overthinking always bad in software development? A: No. For infrastructure decisions, security architecture, and data model design, careful thinking is essential. The key is knowing which category your current task falls into. Most feature work and prototypes benefit from moving fast, not thinking deep.
About the Author
Arkin is a startup founder and developer building Orbits Studio, a clip farming marketplace connecting everyday creators with brands. He writes about software engineering, startup lessons, and building in public.