Ideas Weave Every Narrative with AI.
Your AI demo is flawless. Production is a nightmare. The difference isn’t model capability—it’s product design. The real issue? LLMs are trained to guess, and most products force them to answer even when evidence is thin. Here’s why the gap between a stunning demo and reliable AI isn’t a model problem—it’s a design problem that requires better boundaries.
You’ve built a brilliant AI demo, but the moment it hits production, it burns tokens, forgets constraints, and fails silently. The problem isn’t the model—it’s the runtime control layer. Here’s why the AI moat has shifted from raw intelligence to the ‘Harness’ and proprietary context.
Most AI projects fail not because of bad technology, but because of a missing translator between business teams and data scientists. In retail, models with 85% accuracy are useless if they don’t understand store-specific context, customer life stages, or external variables. The real fix isn’t more data or better models—it’s a human role that converts business intuition into algorithmic features, and algorithmic outputs into actionable decisions.
When every AI product can call the same model, your only moat is the ability to define ‘correct’ in your specific context. Evaluation sets—not models, prompts, or features—are the CUDA of AI product management. Build one tonight, and you become irreplaceable.
For AI agents, the evaluation set is the new PRD. Every input-output pair defines the product’s natural language boundary. The most dangerous bug isn’t a crash—it’s fake success, where the AI reports completion but fails silently. And the sensitive, overthinking humans? They’re the only ones who can judge what ‘good’ really means in a world of generative AI.