Developing exceptional taste requires learning from the best. A tactical method is to ask one skilled person for the 10 peers they admire most. Then, ask those 10 people the same question. The patterns that emerge will reveal the true masters, whose work and thinking you can then study.

Complex fields like shopper psychology, which often seem instinctive and chaotic, can be decoded and applied effectively by using a structured framework. This approach transforms vague feelings into concrete, understandable principles for analysis and action, removing guesswork from understanding consumer behavior.

As AI drives the cost of content creation to zero, the world floods with 'average' material. In this environment, the most valuable and scarce skill becomes 'taste'—the ability to identify, curate, and champion high-quality, commercially viable work. This elevates the role of human curators over pure creators.

Product 'taste' is often narrowly defined as aesthetics. A better analogy is a restaurant: great food (visuals) is necessary but not sufficient. Taste encompasses the entire end-to-end user journey, from being greeted at the door to paying the check. Every interaction must feel crafted and delightful.

Technical talent is not the primary driver of resonant creative work. The key ingredient is 'taste'—an unteachable ability to discern what will be emotionally pleasing and impactful to an audience. This intuitive sense separates good creators from great ones.

Teams can cultivate a shared sense of taste by encouraging constant and rigorous critique of both internal and external work. This process allows the team to self-regulate, learn from each other, and elevate their collective craft without top-down mandates.

To develop your "people sense," actively predict the outcomes of A/B tests and new product launches before they happen. Afterward, critically analyze why your prediction was right or wrong. This constant feedback loop on your own judgment is a tangible way to develop a strong intuition for user behavior and product-market fit.

To codify a specific person's "taste" in writing, the team fed the DSPy framework a dataset of tweets with thumbs up/down ratings and explanations. DSPy then optimized a prompt that created an AI "judge" capable of evaluating new content with 76.5% accuracy against that person's preferences.

The best AI models are trained on data that reflects deep, subjective qualities—not just simple criteria. This "taste" is a key differentiator, influencing everything from code generation to creative writing, and is shaped by the values of the frontier lab.