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.

Figma CEO Dylan Field argues that while AI can quickly generate "good enough" results, this baseline is no longer sufficient. As AI floods the market with generic software and designs, true differentiation will come from human-led craft, taste, and pushing beyond the initial AI output.

AI can pattern-match, but it lacks the personal history, cultural nuances, and real-world experiences that inform great design. This 'lived context' allows designers to create products that resonate deeply on a human level, a task AI is far from achieving.

Developers fall into the "agentic trap" by building complex, fully-automated AI coding systems. These systems fail to create good products because they lack human taste and the iterative feedback loop where a creator's vision evolves through interaction with the software being built.

Many aspiring creators quit because their creative taste exceeds their technical skill, causing frustration. Figma's CEO suggests AI's most exciting potential is bridging this gap. It allows creators to rapidly generate and sample the possibility space, helping them achieve their vision almost instantly and overcome the initial skill barrier that stifles creativity.

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.

Current benchmarks focus on whether code passes tests. The future of AI evaluation must assess qualitative, human-centric aspects like 'design taste,' code maintainability, and alignment with a team's specific coding style. These are hard to measure automatically and signal a shift toward more complex, human-in-the-loop or LLM-judged evaluation frameworks.

According to Dreamer's CEO, the biggest capability missing from LLMs is "taste." By default, AI-generated applications and UIs are generic and identifiable by the model that created them. It requires extensive human effort in prompt engineering and templating to create delightful, non-generic user experiences.

AI coding tools generate functional but often generic designs. The key to creating a beautiful, personalized application is for the human to act as a creative director. This involves rejecting default outputs, finding specific aesthetic inspirations, and guiding the AI to implement a curated human vision.