Product Slop vs. Differentiation: The B2B and B2C Divergence
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199 words
Product Strategy / AI Differentiation / Market Positioning
😊Critical, slightly confrontational, forcing honest self-assessment about product value vs. feature theater
Product SlopAI DifferentiationB2B StrategyProduct ValueB2C StrategyAI Features
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?