Why Finishing with AI Feels Harder Than Starting - My Confessions
AI tools make starting projects lightning-fast, but why does finishing feel so brutal? Explore the 80/20 challenge of building with AI, tool fatigue, and why execution still needs the human touch
We live in an age where AI and no-code tools can take your idea from napkin sketch to working prototype in hours. But here's the punchline nobody talks about: the last 20% — the finishing touches, integrations, edge cases — that part still hurts. It's messy, ambiguous, and deeply human.
I've started more AI projects than I can count. Each began with momentum and magic. But many still sit in some corner of Replit, Lovable, Bolt.new, Supabase and a lot more… unfinished.
As someone who's built small products like tools.thetoolnerd.com, resume creator, SOP writer, biography app etc., I've lived this paradox dozens of times. The excitement of seeing your idea come to life in minutes, followed by the brutal reality of making it actually work for real users.
No Time to Read? Here's the Scoop
Starting is Easy, Finishing is Brutal
AI tools like lovable, bolt.new, ChatGPT, Claude etc. can build blazing-fast MVPs — but adding logic, polishing UX, and handling real-world edge cases? That still needs you.
Tool Overload = Burnout
Toggling between 5 tools, 3 LLMs, and 2 frameworks can feel like productivity — until it becomes chaos.
Everyone Has a Shelf of Half-Done Projects
You're not alone. Most builders I know have 5–10 projects that almost made it.
The Future: Every Business = A Software Company
Just like every business today needs marketing, soon every company will have small in-house AI/software teams — not for scaling big products, but for building internal tools and experiments.
Execution Isn't Just Coding
Shipping something usable — with auth, analytics, payments, and polish — is still hard, even with AI. We need better tools that don't just generate code, but help us finish.
The 80% Illusion
So I recently started working on a Resume Creator app on Bolt.new with ridiculous excitement. Within 12 hours, I had the UI looking sleek, integrations humming, and I was feeling like a wizard. The idea was brilliant (at least in my head) — a voice-powered app where you could have a natural conversation in any language to create your resume. Just talk, and boom, a professional resume ready.
Then the voice integrations became difficult. Nothing worked properly. I tried everything — LiveKit, Google Gemini realtime, OpenAI realtime, ElevenLabs. I wanted this seamless mix of chat and voice with shared context, where users could switch between typing and talking without losing the thread.
Cue the debugging marathon. Many iterations, countless experiments. I'd fix bugs in Cursor, then jump to Replit for the backend, then get frustrated and stop. Then I'd get excited again and integrate Supabase for database management. The cycle repeated until... I just didn't continue.
Another project like that is a biography app - same problem with voice, design consistencies, you fix one and other feature disappears.. many such stories.
The pattern is always the same. Fast start, exciting progress, then the reality of making things actually work hits like a brick wall.
Why Does It Feel So Hard to Finish?
Because AI gets you 80% of the way — and that 80% is fast, flashy, and exciting. But the rest?
Building that login system that doesn't break
Writing perfect validation logic for edge cases
Fixing responsiveness for mobile (because yes, people will use your tool on their phones)
Integrating five APIs that don't want to talk to each other
Adding proper error handling so your app doesn't crash when something goes wrong
That's the grunt work — the boring stuff that separates "demo" from "product".
And ironically, it's the stuff AI still struggles with. AI excels at creating, not at the tedious work of making things bulletproof.
During my 100 days of GenAI learning challenge, I probably started 15 different projects. Comic strips, story books, AI kartoons, research agents — you name it. But only 3 of them ever made it to a state where I'd feel comfortable sharing them publicly.
The difference? Those 3 projects, I forced myself to sit through the boring parts. I debugged the authentication flows. I handled the error messages. I made sure they worked on different devices.
The Tool Juggling Act
Another thing that makes finishing brutal? Tool fatigue.
When I start a project, I'm excited to try the latest and greatest. "Oh, this new AI model just dropped! Let me integrate that. Wait, there's a better deployment platform? Let me migrate everything over."
Before you know it, you're managing code across 3 different platforms, using 5 different AI tools, and you've forgotten why you started the project in the first place.
I learned this lesson the hard way. My most successful projects have been the ones where I picked a simple stack and stuck with it. Replit for development and hosting, Claude for AI assistance, and that's it. No fancy integrations, no cutting-edge frameworks. Just simple tools that work.
The Real Challenge: Execution in an AI World
The truth is, building with AI has taught me that execution is still a very human skill. AI can generate code, suggest solutions, and even debug simple issues. But it can't make the hundreds of small decisions that turn a prototype into a product.
Should this button be blue or green? How do we handle users who forget their passwords? What happens when our third-party service goes down? These decisions require context, empathy, and judgment that AI just doesn't have yet.
When I built my SOP Generator tool, the AI nailed the core functionality in the first attempt. But it took me weeks to add proper file upload validation, improve the user interface based on feedback, and handle all the weird edge cases that real users discovered.
The irony is that these finishing touches — the unglamorous 20% — are often what determine whether people actually use your product or just play with it once and forget about it.
But Wait, Before You Give Up on AI...
Now, before this sounds like I'm bashing AI or saying building with AI is wrong — let me be crystal clear: that's absolutely not the case. The rate of growth we've seen in the last couple of years is tremendous. Mind-blowing, actually.
The tools and applications that were struggling with basic tasks just a few months back are now doing fantastically. Remember when AI couldn't handle simple coding tasks without hallucinating? Now we have tools like Cursor, Bolt.new, and Lovable that can build entire applications from a single prompt.
I'm very sure that going forward, the challenges I'm facing today will be solved by LLMs and cutting-edge models that are coming out. The voice integration nightmares I faced with my Resume Creator? The authentication headaches? The deployment complexities? These are temporary growing pains.
There are predictions by CEOs from OpenAI and Anthropic that 80-90% of coding will be written by AI by the end of this year. That's a big claim, isn't it? But looking at the trajectory, it doesn't seem impossible anymore.
So what does this hold for us as builders? I think we're in this fascinating transition period where the tools are incredibly powerful but still need that human touch to cross the finish line. But that gap is closing fast.
So, What's the Takeaway?
Use AI for what it's good at: blasting through blank pages, generating scaffolding, getting feedback on ideas. But don't forget that finishing still needs you.
Don't feel bad if you have a graveyard of half-finished projects. Most builders I know are in the same boat. The key is recognizing that starting fast doesn't mean finishing is automatic.
My advice? Pick one project from your collection of almosts. Just one. And commit to finishing it. Not perfectly, not with every feature you originally imagined, but to a point where someone else could actually use it and find value.
That's where the real learning happens. Not in the exciting first 80%, but in the grind of that final 20%.
Because in a world where everyone can start with AI, the people who actually finish will be the ones who stand out.
The Future of Tech Teams: Micro Teams in Every Company
Here's something I've been thinking about a lot lately. We're heading toward a world where every company, no matter how small, will have an internal "AI dev" — not to build the next unicorn, but to create workflows, automate internal reports, build small client-facing tools, and enhance customer experience.
It'll be like having a designer on staff. Expected. Essential.
The beauty of AI tools is that they're democratizing this capability. You don't need a computer science degree to build useful software anymore. But you still need the persistence to finish what you start.
