I see a key product challenge emerging as AI marches towards commoditization - how should we capture and price value? There's a new dynamic at play: direct substitution of labor value. AI substitutes creative and cognitive work directly. Take photography - when AI helps produce client-ready headshots in minutes instead of days, we must price-in that efficiency. There are three distinct value capture approaches: - Licensing (Perplexity) - Usage (Midjourney) - Outcome based (Headshots Pro) Outcome-based models promise the best value alignment between providers and consumers, yet pose real challenges. IMO, success hinges on trust and sophisticated measurement. Consider marketing agencies paying based on content performance, or architectural tools scaling with implementation success. We will need to move beyond consumption based pricing models for generative AI. - Value-Cost Decoupling: The fundamental disconnect between cost structures (token-based) and value delivery (outcome-based) requires new pricing paradigms that bridge this gap. - Successful pricing models must optimally distribute risk between providers and customers, acknowledging the inherent unpredictability of AI system usage. - As use cases mature, pricing models need to evolve from pure consumption metrics to outcome-based measures. - The gap between infrastructure costs and application value requires intermediary pricing layers that abstract complexity. The key challenge isn't technical implementation - it's building the measurement and attribution systems needed to quantify AI's actual business impact. But these models bring significant risks: - Attribution in collaborative work - Quality consistency - Outcome measurement delays - Variable customer expertise The future will likely blend approaches based on use case. Fascinating to be part of this evolution. *Originally published [here](https://x.com/barusebi/status/1870834458015752339)*