AI won't replace designers because it lacks taste and subjective opinion. Instead, as AI gets better at generating highly opinionated (though not perfect) designs, it will serve as a powerful exploration tool. This plants more flags in the option space, allowing human designers to react, curate, and push the most promising directions further, amplifying their strategic role.

Once AI coding agents reach a high performance level, objective benchmarks become less important than a developer's subjective experience. Like a warrior choosing a sword, the best tool is often the one that has the right "feel," writes code in a preferred style, and integrates seamlessly into a human workflow.

In the age of AI, the new standard for value is the "GPT Test." If a person's public statements, writing, or ideas could have been generated by a large language model, they will fail to stand out. This places an immense premium on true originality, deep insight, and an authentic voice—the very things AI struggles to replicate.

The term "data labeling" minimizes the complexity of AI training. A better analogy is "raising a child," as the process involves teaching values, creativity, and nuanced judgment. This reframe highlights the deep responsibility of shaping the "objective functions" for future AI.

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.

The effectiveness of an AI system isn't solely dependent on the model's sophistication. It's a collaboration between high-quality training data, the model itself, and the contextual understanding of how to apply both to solve a real-world problem. Neglecting data or context leads to poor outcomes.

Teams that claim to build AI on "vibes," like the Claude Code team, aren't ignoring evaluation. Their intense, expert-led dogfooding is a form of manual error analysis. Furthermore, their products are built on foundational models that have already undergone rigorous automated evaluations. The two approaches are part of the same quality spectrum, not opposites.

As models mature, their core differentiator will become their underlying personality and values, shaped by their creators' objective functions. One model might optimize for user productivity by being concise, while another optimizes for engagement by being verbose.

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.