AI has taken the hard part of building software and made it feel easy.
But it hasn't touched the harder part of understanding if the software will make money.
If you've ever shipped a product or owned a software business, you know this: writing code isn't the real bottleneck for business, of-course the teams could be faster, could have less technical debt and so on.
The bottleneck is figuring out what problem is worth solving, validating that customers care, and adapting when the market laughs at your neat little roadmap.
The big question is : How long does you software take to make the first money?
That's the real unhappy path of product development. And here's the irony: AI has smoothed the "happy path" of building so much that we risk confusing motion for progress.
The core challenge of product development—understanding what users actually need and validating that solutions address real problems—remains as difficult as ever.
While artificial intelligence has revolutionized how we write code, it hasn't touched the fundamental human work of discovering genuine user needs, validating solutions through real-world testing, and adapting products based on market feedback.
This distinction matters now more than ever, as AI-powered development tools create a dangerous illusion of progress that threatens the very foundation of successful product building.
The Trap of Vibe Coding
2025 gave us a phrase that spread like wildfire: "vibe coding." Andrej Karpathy coined it, and the premise is as simple as it is intoxicating: describe what you want in natural language, and the AI writes the code.
Startups are spinning MVPs in days, and new AI users claim "I just vibe the feature, and it shows up."
It feels like magic. Faster prototypes, democratized access, the barrier to building nearly gone.
That's the happy path.
But here's the trap: faster output doesn't mean better outcomes. You can build the wrong thing with unprecedented efficiency. AI has automated construction, not discovery.
The promise is intoxicating: rapid prototyping, democratized development access, and the ability to build MVPs in days rather than months. With the majority of developers now using AI coding tools daily or weekly, and a significant portion of new startups operating on codebases that are heavily AI-generated, the trend seems unstoppable.
But this seductive simplicity masks a fundamental trap.
What the Tools Actually Do
AI coding platforms like Cursor, Lovable, and Replit have all cracked the how to build challenge—but none of them address the what to build challenge.
Cursor speeds up developers who already know what they’re doing, but it collapses under complex, multi-layered projects and offers zero help in understanding user needs.
Lovable makes spinning up MVPs easy for non-technical founders, but it stops at surface-level prototypes. It doesn’t tell you if the product solves a real problem, and what steps to take to get closer to solving user needs.
Replit lowers the barrier to coding and collaboration, but at scale it introduces technical debt, performance issues, and still leaves discovery untouched.
The pattern is clear: these tools amplify execution but ignore validation. They accelerate ideas into code, but they don’t answer whether those ideas matter to real users. Which means if your product direction is off, AI won’t fix it—it’ll just get you lost faster.
The Dangerous Illusion of Progress
Here's where the unhappy path sneaks in.
When code generation is frictionless, teams mistake activity for value. They see more features, more prototypes, more output—and assume they're moving forward.
But output ≠ outcome.
You can now generate bad products faster than ever. The cost of creation has dropped, but the cost of wrong creation is still painfully high.
Think of it like speeding down the wrong highway. The faster you go, the further you get from where you need to be.
When development becomes frictionless, teams often mistake increased output for increased value. The ability to generate code quickly creates a dangerous illusion that problems are being solved at the same pace. However, building software that works and building software that addresses real user needs are fundamentally different challenges.
Software waste is prevalent when user needs are unclear. Without deep understanding of customers' problems, teams end up building impressive features that nobody wants or needs. As product experts observe, "It's not enough to build great software that solves problems: the problem needs to be tied to business objectives, and the solutions can't hide in the shadows."
While vibe coding makes creating software easier than ever before, it has not made solving user problems any easier. The most successful companies—Airbnb, Dropbox, Slack—didn't succeed because they could build faster. They succeeded because they focused specifically on understanding customer pain points and designing solutions around those pain points. Each success story began with deep user understanding, not technical capability.
User research is crucial for validating product market fit. Understanding customer needs and pain points requires comprehensive research methodologies including surveys, interviews, and usability testing. These insights cannot be automated away by AI tools, no matter how sophisticated they become.
Five Unhappy Path Traps of AI-Driven Development
Like strategy, AI-powered building has its own unhappy path traps. If you don't plan for them early, you'll only react once you're already in trouble.
Here are the five teams could fall into most:
Illusion of Validation – "It works, so it must be right"
Teams assume that because they can demo an app quickly, it means the problem is solved. But functioning software ≠ validated need.
The speed of AI development creates a false sense of validation. Teams see working prototypes and assume they've solved customer problems, when in reality they've only demonstrated technical feasibility. This trap is particularly dangerous because it feels like progress—stakeholders see demos, features ship quickly, and metrics show increased output. But customer satisfaction, retention, and genuine problem resolution remain untested.
Feature Creep on Steroids – "It's so easy, why not add it?"
When features cost nothing to build, scope balloons uncontrollably. Before long, you're shipping Frankenstein products instead of focused solutions.
AI tools remove the traditional friction that naturally limited feature scope. When adding functionality requires only a natural language description, teams lose the discipline that scarcity imposed. Projects using vibe coding experience significantly more feature expansion than traditional development, with the average project growing to include multiple times more features than initially planned. This feature bloat occurs because the reduced friction in adding new capabilities doesn't come with corresponding increases in user research or market validation.
Silent Security Debt – "AI wrote it, must be fine"
AI-generated code often imports insecure patterns. Teams skip reviews and pay the price when vulnerabilities surface in production.
The most critical risk in vibe coding is the introduction of security vulnerabilities. AI models learn from vast repositories of public code, including insecure patterns, and often suggest code with known vulnerabilities including SQL injection, improper authentication, and insecure file handling. Since many vibe coding practitioners are not experienced developers, there's significant risk that security problems will go undetected.
Context Collapse – "AI lost the plot"
Tools struggle with large, complex projects. They lose context, break architecture, or create messy dependencies. Suddenly, your "fast build" becomes a slow maintenance nightmare.
AI tools excel in isolated scenarios but struggle with system-wide coherence. As projects scale beyond simple prototypes, the tools lose architectural context, create inconsistent patterns, and generate code that works in isolation but breaks system integrity. This creates technical debt that compounds over time, eventually slowing development more than traditional approaches.
Skipped Discovery Work – "We'll validate later"
The speed of delivery tempts teams to punt on user research. But "later" rarely comes, and you end up optimizing for output instead of outcome.
Each of these traps is an unhappy path. And like all unhappy paths, you don't fix them by reacting late—you design your process to anticipate them early.
The Moving Target of User Needs
When AI can spin up features in minutes, it’s easy to lose sight of whether those features still solve the problems that matter.
To navigate this shifting landscape, teams must treat product–market fit as an ongoing discipline, adapting their vibe-driven speed to the rhythms of user discovery and validation.
Pre-PMF: Discovery with Rapid Prototypes
Vibe coding shines brightest in the discovery phase—letting you translate hypotheses into clickable prototypes almost instantly. But speed alone won’t uncover genuine pain points. Instead, weave AI-powered prototyping into a structured discovery loop:
Customer conversations before you “vibe” a feature. Use AI to mock up screens that bring your assumptions to life in user interviews.
Hypothesis-driven prompts. Frame each prompt around a specific problem statement (“Show me a booking flow for busy parents”) to guide both coding and validation.
Rapid usability tests. Deploy AI-generated prototypes to a small cohort, gather real-time feedback, and refine your prompts—don’t wait to write a single line of production code.
By front-loading vibe coding with disciplined customer research, you turn lightning-fast prototypes into purposeful experiments rather than vanity demos.
Post-PMF: Sustaining Fit at AI Speed
Once you’ve found an initial fit, the challenge flips to staying aligned as markets shift—and vibe coding’s velocity can either help or hurt. To prevent runaway feature bloat or misaligned pivots:
Continuous feedback gated by AI prompts. Before “vibing” new features, validate impact metrics (e.g., engagement, retention) and tie each prompt to a concrete user insight.
Iteration sprints with built-in checkins. Use AI to spin up A/B variations quickly, but schedule regular human review sessions to interpret data and decide which versions deserve further investment.
Technical and validation debt rituals. Just as you track code debt, log “validation debt” on features added without fresh user input. Prioritize overdue research before unlocking the next round of AI-generated enhancements.
In a world where the cost of building new functionality approaches zero, the true differentiator is not how fast you can vibe—that’s table stakes—it’s how rigorously you tether each AI-driven feature to evolving user needs.
By embedding research checkpoints before and after every vibe coding iteration, teams can harness AI’s speed without sacrificing the empathy and insight that drive lasting product–market fit.
The Hidden Debts
When development feels free, hidden debts pile up.
Technical Debt: AI-generated code often lacks coherence and maintainability. You feel the pain when you scale.
Feature Bloat: Adding features becomes too easy. Suddenly your MVP is Frankenstein.
Security Debt: AI models pull from public repos, including insecure patterns. Vulnerabilities like SQL injection slip in without warning.
Validation Debt: The biggest one. Teams skip the hard work of talking to customers because "we can just build and see." But by the time you've "seen," you've already wasted cycles.
These debts don't show up on your Jira board. They hit when it's too late.
While vibe coding can accelerate initial development, it often creates technical debt that becomes problematic at scale. AI-generated code frequently lacks optimization, architectural coherence, and maintainability. This creates a compounding problem where initial speed gains are eventually offset by maintenance overhead.
Vibe coding can actually hinder the feedback loop process by making it too easy to implement changes without proper validation. When modifications can be made instantly through natural language prompts, teams may skip crucial validation steps that ensure changes actually improve user experience.
The most effective approach combines rapid prototyping capabilities with rigorous user research methodologies. This means using AI tools for quick iteration while maintaining strong discipline around user validation and market feedback.
The Balanced Path Forward
So what does the happy path of AI-assisted product building look like? It's not about rejecting speed—it's about pairing speed with discipline.
Keep User Research Sacred
Run interviews. Do usability testing. Capture feedback. AI can't empathize for you.
Regardless of development speed capabilities, maintain consistent user research practices:
Regular user interviews: Schedule ongoing conversations with customers to understand evolving needs
Usage analytics: Implement comprehensive tracking to understand actual user behavior patterns
Feedback systems: Create multiple channels for users to provide input on product direction
Build Quality Gates
Security reviews, performance tests, and structured user validation must remain checkpoints.
Establish checkpoints that prevent rapid development from bypassing critical validation:
Security reviews: Mandatory security assessments for all AI-generated code
Performance testing: Regular evaluation of system performance under realistic load conditions
User testing: Structured validation of new features with real users before release
Balance Speed with Sustainability
Manage technical debt like financial debt—pay it down before it spirals. Document AI code. Keep humans in the loop for architectural oversight.
While embracing AI development tools, maintain focus on long-term sustainability:
Technical debt management: Regular assessment and remediation of accumulated technical debt
Documentation requirements: Ensure AI-generated code includes proper documentation for future maintenance
Architecture oversight: Human review of system design decisions to ensure scalability
AI is an accelerator, not a compass. You still need to steer.
So, what does it all mean..
Vibe coding represents a powerful tool for accelerating software development, but it amplifies both good and bad product decisions. When teams have clear understanding of user needs and market demands, AI can help them build solutions faster than ever. However, when teams lack this fundamental understanding, AI simply helps them build the wrong things more efficiently.
No amount of coding efficiency can substitute for the careful, iterative process of discovering genuine user needs, validating solutions through real-world testing, and adapting products based on market feedback. The most successful teams will be those that combine the speed of AI-assisted development with the rigor of user-centered design principles. They will use vibe coding as a powerful tool for rapid iteration while maintaining disciplined approaches to user research, market validation, and iterative improvement.
As we embrace these new development capabilities, we must remember that determining what we should build remains fundamentally a human challenge that requires empathy, curiosity, and deep engagement with the people whose problems we're trying to solve.
The future belongs not to those who can code fastest, but to those who can understand users deepest.