From idea to deployed app in 30 minutes – this isn't science fiction anymore.
The last time I did production code was in 1996. But that may change now.
This week, I built a social media advocacy reach calculator using Lovable, an AI-powered development platform. What would have traditionally taken weeks of back-and-forth with engineering, requirement documents, and multiple sprint cycles was completed in 30 minutes with just 5 iterations.
The result? A pixel-perfect app that does exactly what I envisioned.
Here is that app:
https://ziply-amplification.lovable.app
It’s not really production app for customers. Rather it is my marketing tool that I can use with customers to show the ROI of Ziply (my current startup).
Now, I could have asked an engineer to build it. But then it would have taken time away from building the core product. I have been in this situation so many times before.
I want to build a custom demo. Or a landing page. Or an ROI calculator. Or create synthetic test data. All non production stuff. I always hesitated to “waste” precious engineer resources.
But that changes now.
This experience crystallized something I’ve been sensing for months: we’re witnessing a fundamental shift in how product managers and founders approach product development. The traditional barriers between ideation and execution are crumbling, and the implications are profound.
Three Ways AI Development Tools Are Transforming Product Management
1. From Specification to Experimentation: The Death of the PRD
Product Requirements Documents (PRDs) have long been the sacred text of product management – detailed blueprints that attempt to capture every edge case, user flow, and technical consideration before a single line of code is written. But AI development tools are making this artifact obsolete.
Instead of spending weeks crafting comprehensive specifications, product managers can now engage in rapid experimentation cycles. With platforms like Lovable, you can describe your vision in natural language, see it materialize in minutes, and iterate based on what you actually see rather than what you imagine. This shift from specification-driven to experimentation-driven development means PMs can validate assumptions faster, uncover edge cases through actual usage, and pivot based on real user feedback rather than theoretical scenarios.
The traditional waterfall approach of “think, document, build, test” is being replaced by “think, build, test, refine” – compressing what used to be months of planning into hours of doing. This doesn’t eliminate the need for strategic thinking, but it dramatically reduces the cost of being wrong about implementation details.
2. Democratizing Technical Validation: Every PM Becomes a Prototype Engineer
Historically, product managers have been translators – converting business requirements into technical specifications that engineers could implement. This translation layer often introduced miscommunication, delayed feedback loops, and limited the PM’s ability to truly understand technical constraints and possibilities.
AI development tools are changing this dynamic by giving PMs direct access to functional prototypes. When I built my reach calculator, I wasn’t creating a clickable mockup or wireframe – I was building a working application with real calculations, responsive design, and production-ready functionality. This means product managers can now validate technical feasibility, user experience, and business logic simultaneously.
The implications extend beyond just faster prototyping. PMs can now engage in technical discussions with engineering teams from a position of understanding rather than assumption. They can demonstrate exactly what they’re envisioning, identify technical challenges early, and make more informed trade-offs between features and complexity. This creates a more collaborative relationship between product and engineering, where both sides are working from the same concrete reference point.
3. Redefining the Minimum Viable Product: From Concept to Market in Days
The concept of MVP has been stretched and misused over the years, but AI development tools are bringing us back to its true essence – the smallest possible product that can validate core hypotheses with real users. When you can build functional applications in minutes rather than months, the entire calculus of what constitutes a “minimum” viable product changes.
Traditional MVP development required careful prioritization of features, often resulting in products that felt incomplete or compromised. With AI development tools, PMs can build more complete initial versions that better represent the final vision while still maintaining the speed and cost benefits of rapid iteration. This means you can test more sophisticated hypotheses earlier in the product lifecycle.
Moreover, the reduced cost of experimentation enables product managers to test multiple variations of their core concept simultaneously. Instead of committing to one approach and hoping it works, you can build and test several different implementations of the same idea, learning from real user behavior across different approaches. This parallel experimentation was previously impossible due to resource constraints but is now becoming a standard part of the product development toolkit.
Embrace AI Development or Fall Behind
The companies and product leaders who will thrive are those who recognize that AI development tools aren’t just about building faster – they’re about thinking differently. The ability to rapidly prototype, test, and iterate fundamentally changes how we approach product strategy, user research, and market validation.
My recommendation: Start experimenting with AI development tools today, not as a replacement for your engineering team, but as a new capability that enhances your product management toolkit. Use these tools to validate concepts before involving engineering, to create more concrete specifications that reduce miscommunication, and to engage with users through functional prototypes rather than abstract descriptions.
The future belongs to product managers who can bridge the gap between strategy and execution, and AI development tools are the bridge. The question isn’t whether this transformation will happen – it’s whether you’ll be leading it or scrambling to catch up.
Your biggest enemy will be inertia and sunk cost fallacy (we already spent money on Agile training and tools).
In other words, that mastery you have on Python syntax or SQL queries is no longer a selling point. Just like typing on a typewriter is not a selling skill anymore. It was when I started my career and took typing lessons.
