When coding feels effortless, feature creep becomes inevitable. Our case study founder’s health app hit 60,000 lines because every user request sounded reasonable and AI delivered it in hours. The problem: you can’t launch a product with 100 partially working features. This guide distills the triage process we ran with that founder (and dozens of other AI-first founders) to cut scope, ship, and keep customers happy.
The "Five Years of Ideas" Trap #
AI lowers the cost of experimentation so much that founders build entire roadmaps before validating a single dollar of revenue. Signs you’re in the trap:
- Your Linear board has 200 tickets, half marked “in progress.”
- You can’t describe the core user journey in one sentence.
- You’re scared to launch because “just one more feature” feels necessary.
- Your app “looks good” but breaks in core flows (see the pre-launch testing checklist).
Why AI Makes Feature Bloat Worse #
- Low friction: Requests become working code overnight.
- No engineering pushback: AI doesn’t argue when you ask for the 37th dashboard.
- Illusion of progress: New screens feel tangible even if backend infrastructure lags.
- No effort accounting: Burndown charts don’t budge when AI silently builds features.
Need outside pressure to prioritize? Join Giga’s MVP focus sprint and we’ll co-own your launch scope for four weeks.
Framework: The Three-Feature Test #
- Define your “job to be done.” Example: “Help health coaches keep clients on track.”
- List every feature serving that job. Don’t filter yet.
- Force-rank features by revenue impact. Ask: “Would someone pay for this alone?”
- Pick the top three. Everything else becomes a post-launch follow-up.
If you can’t cut to three, your job definition is vague. Refine it until the top three jump off the page.
Case Study Example #
- Original backlog: AI summaries, coach dashboards, compliance reports, wearable integrations, B2B admin portal.
- Three-feature MVP: Daily accountability loop (push notifications + journaling), coach handoff workflow, exportable weekly report.
Launching with those three features closed his first paying customers.
The MVP Stack: Scoring Features by Value vs. Cost #
Build a table with columns: Feature, Customer Impact (1-5), Confidence (1-5), Build Cost (1-5), Maintenance Drag (1-5). Score each feature honestly. Prioritize high impact, high confidence, low cost items. Anything with high maintenance drag gets deferred until revenue justifies it.
The Launch Readiness Checklist #
Before you cut, align on what “ready” means. Use the checklist from a readiness checklist you can grab on gigamind.dev. The MVP must satisfy every item. Features that don’t contribute get cut.
Scope-Reduction Techniques #
- Time-box experiments. Give each optional feature 48 hours. If it’s not stable by then, sunset it.
- Introduce “Not Now” columns. Move features to a parking lot. Revisit once core metrics grow.
- Bundle adjacent features. Collapse similar requests into a single “delight” release later.
- Build manual fallbacks. If automation is flakey, provide a manual support flow temporarily.
- Use feature flags. Hide features from 90% of users until they’re reliable.
Communicating Your Cuts #
- Customers: Explain that trimming features improves reliability. Offer early access when features return.
- Investors: Share metrics you’re optimizing (activation rate, retention) instead of feature counts.
- Internal notes: Document why features moved to “Later” so you have context when revisiting.
Pair MVP Focus with These Resources #
- Grab the complexity wall playbook
- Download the solo founder decision guide
- Browse the full production guide for AI-built apps
Want help saying no? Schedule a Giga launch prioritization workshop and walk out with a ruthless, revenue-focused roadmap.
Cutting features isn’t about doing less—it’s about making the features you keep unstoppable. Shipping a reliable core earns you the right to add the rest later.