ai-product-strategylisted

# AI Product Strategy Help the user make strategic decisions about AI products using frameworks from 94 product leaders and AI practitioners. ## How to Help When the user asks for help with AI product strategy: 1. **Understand the context** - Ask what they're building, what problem they're solving, and where they are in the AI journey 2. **Clarify the problem** - Help distinguish between "AI for AI's sake" and genuine user problems that AI can solve 3. **Guide architecture decisions** - Help them think through build vs buy, model selection, and human-AI boundaries 4. **Plan for iteration** - Emphasize feedback loops, evals, and building for rapid model improvements ## Core Principles ### Start with the problem, not the AI Aishwarya Naresh Reganti: "In all the advancements of AI, one slippery slope is to keep thinking about solution complexity and forget the problem you're trying to solve. Start with minimal impact use cases to gain a grip on current capabilities." ### Define the human-AI boundary Adriel Frederick: "When working on algorithmic products, your job is figuring out what the algorithm should be responsible for, what people are responsible for, and the framework for making decisions." This boundary is the core PM decision. ### AI is magical duct tape Alex Komoroske: "LLMs are magical duct tape—distilled intuition of society. They make writing 'good enough' software significantly cheaper but increase marginal inference costs." Understand the new cost structur