For founders, AI tools are excellent for quickly building an MVP to validate an idea and acquire the first few customers—the hardest step. However, these tools are not yet equipped for the large-scale, big-picture thinking and edge-case handling required to scale a product from 100 to a million users. That stage still requires human expertise.
Related Insights
The barrier to building AI products has collapsed. Aspiring builders should create a one-hour prototype to focus on the truly hard part: validating that they're solving a problem people actually want fixed. The bottleneck has shifted from technical execution to user validation.
While many new AI tools excel at generating prototypes, a significant gap remains to make them production-ready. The key business opportunity and competitive moat lie in closing this gap—turning a generated concept into a full-stack, on-brand, deployable application. This is the 'last mile' problem.
AI doesn't replace business fundamentals; it accelerates them. The most successful founders apply timeless frameworks for building valuable companies—like achieving product-market fit—but use modern AI tools to run experiments and learn at a massively compressed time and cost.
AI coding tools can rapidly build the first 70% of an application, but the final 30%—the complex, unique features that define your vision—will consume the vast majority of your development time. This is a critical reality check for anyone starting with these tools.
Non-technical founders using AI tools must unlearn traditional project planning. The key is rapid iteration: building a first version you know you will discard. This mindset leverages the AI's speed, making it emotionally easier to pivot and refine ideas without the sunk cost fallacy of wasting developer time.
AI can generate hundreds of statistically novel ideas in seconds, but they lack context and feasibility. The bottleneck isn't a lack of ideas, but a lack of *good* ideas. Humans excel at filtering this volume through the lens of experience and strategic value, steering raw output toward a genuinely useful solution.
The ease of AI development tools tempts founders to build products immediately. A more effective approach is to first use AI for deep market research and GTM strategy validation. This prevents wasting time building a product that nobody wants.
Early versions of AI-driven products often rely heavily on human intervention. The founder sold an AI solution, but in the beginning, his entire 15-person team manually processed videos behind the scenes, acting as the "AI" to deliver results to the first customer.
Teams that become over-reliant on generative AI as a silver bullet are destined to fail. True success comes from teams that remain "maniacally focused" on user and business value, using AI with intent to serve that purpose, not as the purpose itself.
Jason Fried argues that while AI dramatically accelerates building tools for yourself, it falls short when creating products for a wider audience. The art of product development for others lies in handling countless edge cases and conditions that a solo user can overlook, a complexity AI doesn't yet master.
