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.
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Many teams wrongly focus on the latest models and frameworks. True improvement comes from classic product development: talking to users, preparing better data, optimizing workflows, and writing better prompts.
Product managers should leverage AI to get 80% of the way on tasks like competitive analysis, but must apply their own intellect for the final 20%. Fully abdicating responsibility to AI can lead to factual errors and hallucinations that, if used to build a product, result in costly rework and strategic missteps.
Instead of waiting for AI models to be perfect, design your application from the start to allow for human correction. This pragmatic approach acknowledges AI's inherent uncertainty and allows you to deliver value sooner by leveraging human oversight to handle edge cases.
Product leaders must personally engage with AI development. Direct experience reveals unique, non-human failure modes. Unlike a human developer who learns from mistakes, an AI can cheerfully and repeatedly make the same error—a critical insight for managing AI projects and team workflow.
Dylan Field is skeptical that disposable, AI-generated apps will replace complex SaaS products. Real business software must handle countless edge cases, scale with teams, and support shared workflows—a level of complexity and institutional knowledge that today's agents are far from mastering.
The surprising success of Dia's custom "Skills" feature revealed a huge user demand for personalized tools. This suggests a key value of AI is enabling non-technical users to build "handmade software" for their specific, just-in-time needs, moving beyond one-size-fits-all applications.
Successful AI products follow a three-stage evolution. Version 1.0 attracts 'AI tourists' who play with the tool. Version 2.0 serves early adopters who provide crucial feedback. Only version 3.0 is ready to target the mass market, which hates change and requires a truly polished, valuable product.
Instead of building a single-purpose application (first-order thinking), successful AI product strategy involves creating platforms that enable users to build their own solutions (second-order thinking). This approach targets a much larger opportunity by empowering users to create custom workflows.
As AI makes it incredibly easy to build products, the market will be flooded with options. The critical, differentiating skill will no longer be technical execution but human judgment: deciding *what* should exist, which features matter, and the right distribution strategy. Synthesizing these elements is where future value lies.
The promise of AI shouldn't be a one-click solution that removes the user. Instead, AI should be a collaborative partner that augments human capacity. A successful AI product leaves room for user participation, making them feel like they are co-building the experience and have a stake in the outcome.
