AI Can Code Instantly. It Still Can’t Decide What to Build.
AI coding tools are extraordinary.
They’re also brutally honest.
They will build exactly what you tell them to, at incredible speed, without questioning whether it makes sense.
This is both their power and their danger.
I’ve watched founders use AI to generate complete applications in hours. Beautiful interfaces. Working features. Impressive demos. Then I ask: “Does this solve the problem you described?”
Long pause. “We’re not sure yet.”
That’s the trap. AI collapses the cost of execution, which makes clarity more valuable, not less.
When building was expensive and slow, bad ideas died naturally. You couldn’t afford to code something without thinking it through. The friction forced discipline.
Now you can build anything instantly. Which means you can build the wrong thing instantly.
And you do. Repeatedly. Until the pattern becomes clear: AI doesn’t eliminate the hard parts of product development. It just reveals them faster.
The Vibe Coding Illusion
“Vibe coding” sounds revolutionary. Describe what you want in plain English. Watch AI generate a working application. Ship in hours instead of months.
For personal projects and small tools, this is incredible. I’ve seen people build working prototypes of ideas they’ve had for years. No technical knowledge required. Just clear description and iteration.
But for real products? Products that need to scale, handle edge cases, integrate with systems, and not break when you add features?
Vibe coding creates a dangerous illusion: that speed equals progress.
Here’s what actually happens:
You describe a feature. AI generates code. It works in the demo. You ship it. Then users hit it with real data, unexpected inputs, and edge cases you never considered. Everything breaks.
You describe the fix. AI generates more code. That creates new edge cases. You describe another fix. The codebase becomes a patchwork of reactive solutions rather than coherent architecture.
Six months later, you can’t add features without breaking others. You can’t scale because the data model wasn’t designed for it. You can’t integrate with other systems because the architecture assumed isolation.
You have a prototype that looks like a product but behaves like a prototype under any real pressure.
Prompts Are Requirements in Disguise
The promise of AI coding is that you can describe what you want in natural language.
The reality is that natural language is dangerously ambiguous.
“Build a document sharing system where users can collaborate.”
AI will generate something. It might be beautiful. But consider what that prompt didn’t specify:
- Version control for document changes?
- Real-time or asynchronous editing?
- Conflict resolution when two people edit the same section?
- Permission models for who can view versus edit?
- File size limits and storage management?
- Offline access and sync behavior?
- Change tracking and history?
- Notification system for updates?
These aren’t technical details. They’re fundamental product decisions that determine whether the system actually works for real use cases.
AI can’t make these decisions. It guesses based on training data. And its guesses are optimized for demos, not production systems.
So you get code that technically implements “document sharing” but fails the moment users try to use it the way they need to.
I watched a founder use AI to build a financial dashboard for their SaaS product. Beautiful charts. Clean interface. Impressive visualization.
Then they tried to onboard customers. The dashboard displayed MRR, churn, LTV. But it didn’t handle:
- Multi-currency conversion
- Timezone differences for date ranges
- Refunds and credits
- Trial period accounting
- Partial month calculations
The AI gave them what they asked for: a dashboard that displays financial metrics. It didn’t give them what they needed: accurate financial reporting that handles real-world accounting complexity.
Where AI Coding Tools Actually Fit
Let me be clear: AI coding tools are remarkable. I use them daily. They’ve transformed how quickly we can iterate on ideas.
But they’re tools for execution, not strategy.
What AI is exceptional at:
- Generating UI components quickly
- Writing boilerplate code
- Translating specifications into implementation
- Handling syntax and simple logic
- Rapid prototyping and visualization
What AI still can’t do:
- Decide what features matter versus which don’t
- Surface edge cases you haven’t considered
- Design data models that support future requirements
- Make architectural decisions for scale
- Balance competing technical trade-offs
- Understand business context that determines priorities
Think of AI as an incredibly fast junior developer. Give it clear instructions, and it executes well. Ask it to make product decisions, and you get plausible-sounding choices that might be completely wrong for your context.
The Real Problems AI Accelerates (Not Solves)
Speed without strategy means building the wrong thing faster.
Consider the typical founder journey with AI coding tools:
Week 1: Describe the idea. AI generates a prototype. It looks great. Excitement builds.
Week 2: Show it to potential users. They ask questions you didn’t anticipate. “What happens if I delete this while someone else is editing?” “Can I export the data?” “How do I undo this action?”
Week 3: Prompt AI to add those features. Code complexity grows. Things start breaking in unexpected ways.
Week 4: Try to fix bugs. Each fix creates new issues because the foundation wasn’t designed for these interactions.
Week 8: Realize the architecture is fundamentally wrong. Need to rebuild from scratch with proper planning.
AI didn’t slow you down. It accelerated the discovery that you skipped the thinking phase.
This happens because AI tools assume clarity. They don’t create it.
Bolt, Cursor, Lovable, v0—these are all incredible at turning specifications into code. But if your specifications are vague or incomplete, they generate vague and incomplete systems.
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The problem compounds because speed creates false confidence. You built something in three days that would have taken three months. It must be real progress, right?
Not if you built the wrong thing. Then speed just means you discovered failure faster.
The 70% Problem
Engineering leaders have identified a brutal pattern in AI-generated code: tools rapidly generate the first 70% (often just the “vibe” or UI), but the final 30% becomes an impossible wall for non-technical founders to climb.
This isn’t about the percentage. It’s about what that 30% represents:
- Business logic that connects features
- Security that protects user data
- Error handling that prevents data loss
- Integration points that connect systems
- Performance optimization for scale
- Data validation that ensures correctness
The first 70% is what demos well. The last 30% is what actually works.
AI generates the demo. But demos aren’t products. And the gap between demo and product is exactly where founders get stuck.
I worked with a founder who built an “MVP” using AI in two weeks. They proudly showed me user authentication, profile management, dashboard, and data visualization.
I asked: “What happens if someone enters a date in the wrong format? What happens if two users try to edit the same record simultaneously? How do you handle failed payment processing?”
Blank stares. None of that was built because none of that was prompted.
The “MVP” was 70% complete. The remaining 30% would take months to figure out because it required understanding system-level behavior, not just feature-level implementation.
The Scope Creep That Nobody Sees
AI coding creates a subtle but dangerous form of scope creep.
Traditional scope creep: stakeholders keep adding features mid-build.
AI scope creep: the system accumulates fixes and patches that weren’t part of any coherent plan.
Because AI makes changes so easy, founders treat symptoms rather than addressing root causes.
The login flow breaks. Prompt AI to fix it. It works.
But the fix didn’t address why it broke. So three other places with similar patterns still have the bug. And when users hit those, you prompt more fixes. Each fix is fast. The cumulative complexity grows slowly and invisibly.
Six months in, you have a system held together by hundreds of small patches, none of which were architected as part of a coherent whole.
This is worse than traditional technical debt because it happens faster and feels frictionless. There’s no painful “we need to stop and refactor” moment. Just continuous small fixes that compound into structural chaos.
What Actually Needs to Happen Before AI Touches Code
If AI is an execution engine, what feeds it?
1. Clear problem definition
Not “build a project management tool.” But: “Marketing teams waste 5 hours weekly in status meetings because nobody knows what’s actually done. We need live visibility into who’s working on what so those meetings can be eliminated.”
That specificity determines what features matter and which are distractions.
2. Mapped user workflows
Walk through the complete journey from problem to solution. Every step. Every decision point. Every potential failure.
This surfaces the requirements that prompts miss. “User clicks save” isn’t enough. What if the network fails? What if they’re offline? What if someone else edited the same thing?
3. Explicit trade-offs
Every feature involves choices. Fast versus robust. Simple versus flexible. User control versus guided workflows.
AI can’t make these calls. You have to. Before prompting. Otherwise AI optimizes for what’s easy to code, not what’s right for your business.
4. Architectural decisions
How does this scale? What’s the data model? How do components interact? What’s the security model?
These decisions determine whether your system survives contact with real users or collapses under the first surge of traffic.
5. Definition of “done”
AI will keep generating code as long as you keep prompting. You need clear stopping criteria.
What does working mean? What’s the acceptance criteria? What’s the performance threshold? What edge cases must be handled versus which can wait?
The Framework: Clarity Before Velocity
Here’s what founders should do before using AI to build:
Run the Pre-Coding Audit:
- Can you explain the problem without mentioning your solution?
- Have you mapped the complete user workflow?
- Do you know which features are essential versus nice-to-have?
- Have you defined what “working” means with specific criteria?
- Do you understand the data model and how components integrate?
If any answer is no, you’re not ready to code. Slow down. Get clarity.
Because AI won’t fix ambiguity. It will compound it at machine speed.
Use AI for Iteration, Not Foundation:
AI excels at: “Here’s the specification, generate the implementation.”
AI struggles with: “Figure out what we should build.”
Get the specification right first. Use experience, customer research, and structured thinking to nail down what should exist. Then use AI to build it quickly.
Expect 70%, Plan for 30%:
Assume AI will get you most of the way there on UI and basic functionality. But budget time and expertise for:
- Integration complexity
- Edge case handling
- Security hardening
- Performance optimization
- Real-world data scenarios
The last 30% is where products succeed or fail. Don’t expect AI to handle it unsupervised.
Where Codalio Bridges the Gap
Codalio makes AI coding tools actually usable for serious products.
We provide the clarity layer that AI assumes exists but rarely does.
Before any code generation, our platform forces you through:
- Problem definition that surfaces real requirements
- User workflow mapping that reveals edge cases
- Technical scoping that makes architectural decisions explicit
- Trade-off documentation so you know what you’re optimizing for
Then we generate not just UI, but complete, structured scope that covers:
- Product logic
- Business rules
- Data models
- Component architecture
- Integration patterns
This is what AI coding tools need to work correctly. Clear, complete specifications.
We also handle the commodity 70% automatically—authentication, scalability patterns, standard infrastructure—so AI isn’t rebuilding solved problems. Your prompts focus on the 20-30% that’s actually unique to your product.
And because we maintain the connection between business intent and technical implementation, the code that gets generated actually delivers what you need. Not just what you asked for in a vague prompt.
AI collapses the cost of execution. Codalio provides the clarity that makes execution meaningful.
Ready to use AI responsibly? Start with structured clarity at Codalio.com—turn your idea into complete technical scope before generating a single line of code.
Resources
The AI Coding Assistant Reality Check - Theo Browne (t3.gg) Honest assessment of what AI coding tools actually do well versus the marketing hype. Theo breaks down the 70% problem with real examples from production systems.
Why “Vibe Coding” Fails at Scale - Martin Fowler Comprehensive analysis of AI-generated code quality from a software architecture perspective. Explains why systems that look fine in demos break under real usage.
The Hidden Cost of AI-Generated Technical Debt - Stack Overflow Engineering Blog Engineering teams document the maintenance burden of AI-generated code that wasn’t architected as a coherent system. Essential reading for founders using AI tools.
Learned something valuable? We regularly break down these patterns in Codalio’s newsletter. Subscribe to catch the common mistakes founders make and learn how to avoid them.
Subscribe now