AI Product Management Journal

Explored how AI homogenization research (Cornell study, Google algorithm changes, HBR workslop) applies specifically to product development. The core tension: easy AI implementation is flooding markets with indistinguishable products, but the tests for "slop vs. value" differ significantly between B2B and B2C contexts. B2B Slop Problem: Products adding generic AI features that work identically across industries. No domain depth, no company-specific context, completely interchangeable with competitors. Key test: would this AI be equally useful to ANY company, or specifically to YOUR customers? B2C Slop Problem: ChatGPT wrappers with templates and branding. Users can't articulate why they'd pay when free alternatives exist. Generic "helpful assistant" personality that's indistinguishable from dozens of competitors. Core insight: Differentiation strategy diverges by market: B2B needs domain depth and context specificity (can't be dropped into competitor products) B2C needs either distinctive personality OR solving problems free tools don't solve The uncomfortable reality: Most AI features currently launching are slop - polished, but functionally identical to competitors. The antidote isn't avoiding AI, it's being ruthlessly honest about whether you're building something differentiated or just checking a box because competitors have it. Key question for both markets: If your biggest competitor (or ChatGPT for B2C) added your feature tomorrow, would your product still matter?

Explored the paradox of AI eliminating the "loneliness tax" of developing original ideas. Previously, half-formed thoughts had nowhere to go at midnight - friends are busy, colleagues have their own work, ideas would sit and either die or strengthen in silence. AI removed that friction. Now any idea can be explored for hours with an infinitely patient thinking partner. But this creates a new problem: opportunity cost. Key tension from Anne Bogel quote: "Mental energy is not a limitless resource. How we spend our days is how we spend our lives." When spending three hours refining one idea with AI, what's being sacrificed? What was gained: Fully explored ideas, multiple angles tested, sharper insights ready to share. What was lost: Quick decision-making about what deserves attention, the muscle of sitting with uncertainty, three hours that could have gone elsewhere. The old loneliness tax forced prioritization - only ideas worth bothering someone about got explored. The new frictionless tax means exploring everything but committing to nothing. Core question: Does AI make us better thinkers or more thorough overthinkers? Is the ability to think about anything making it harder to decide what's worth thinking about?

Explored the core tension in launching AI products: stakeholders want comprehensive AI that answers everything, but data reality means you can only answer one question well. The key insight is that narrow intelligence beats broad incompetence. V1 AI success isn't about perfect data - it's about: Picking ONE question users desperately need answered Achieving 70-80% accuracy on that specific question Being transparent about limitations Smart UI design that guides users toward what works The "blank text box" is a trap. It exposes every question your AI can't answer. Better to surface trending questions and example prompts that train users what's possible while keeping them in your AI's competency zone. Main tension: Most companies wait for perfect data and never ship. Smart ones ship narrow AI, learn from usage, expand iteratively.