The remarkably human task of giving AI ‘good enough’ taste

FastCompany: Ben Blumenrose has noticed that the floor for AI -generated website design is rising fast. As co-founder and managing partner at the venture capital firm Designer Fund , and a practicing designer for two decades, Blumenrose has been tracking the rapid improvement of AI-generated visuals. He can still spot the difference between a site designed by a professional human and one produced by a model (some tells: the proliferation of “pills,” floating dashboards, and gradients, gradients, gradients), but the AI results are “five times better than what the same kind of tools did a year ago.” He anticipates the trend will continue.

“Probably in six months to a year it’s going to be very hard even for me to tell,” Blumenrose says. One of Designer Fund’s investments is Framer, a no-code website design platform. [Image: Designer Fund] The AI industry has been working hard to improve creative output from models through crisp evaluation metrics, rubrics, and automated tests and tools. A few years ago, the challenge was to reduce the volume of slop in the world by ensuring that AI-generated human hands had the correct number of fingers, or AI-generated essays didn’t mix metaphors.

But now that “correct” has mostly become table stakes, the path to creative work that convinces, or even impresses, an expert like Blumenrose is less clear-cut. Taste, or at least an approximation of it, has become one of the next frontiers in model development. A host of startups are promising that they can encode taste into AI. Meanwhile, the major frontier labs are thinking about how to edge their models into subjective domains like writing and design. Anthropic’s Claude Design , which launched in April, has already encoded a particular aesthetic that’s replicating across the internet.

But as every human knows, even within a narrow profession, opinions on what’s considered “good” can vary based on each person’s alchemical mix of social, cultural, and work experiences. Developing taste in any creative field is both a technical challenge and a personal project: You need to learn enough information about what works from experts and from history, but then you need to develop an understanding of what you like and why. The same is true when teaching a model taste.

The first step in that process, information gathering and evaluating, is similar to how tech companies have operated for decades, and how frontier labs have been developing model intelligence in the last few years. The critical next step—deciding what feels good and unique, and then making a choice based on discernment—is more foreign to the processes and systems for building technology. Still, technologists across the industry are attempting to tackle that qualitative quandary. At large and small scales, they’re teaching models what “good” looks like for specific creative fields, aiming to hone AI taste to a fine-enough point.

But as it turns out, those methods rely on the taste of a whole lot of human professionals. Expert data in, expert data out The first hurdle toward developing AI taste is getting the right data sets. Most general-purpose model providers like OpenAI and Anthropic have built their model knowledge bases by scraping the internet and gathering publicly available information (or proprietary information, for which some AI companies have been sued and, in some cases, held accountable ). For a long time, the big labs have worked with “post-training,” or the process of refining an existing model to perform well at certain tasks.

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The article addresses the significant shift in brand strategy due to AI-generated design, highlighting the essential role of human insight, which is highly relevant and impactful for professionals in the industry.