Taste in the age of AI and LLMs

Taste in the Age of AI and LLMs

The advent of Artificial Intelligence, particularly Large Language Models (LLMs), has fundamentally altered the landscape of output generation, making competent work widely accessible and inexpensive. This shift, noted as of April 3, 2026, has elevated human judgment, or “taste,” to a critical differentiator in a market saturated with generically polished content.

AI and LLMs are powerful tools for producing adequate first drafts, whether for landing pages, product memos, or pitch decks. However, this ease of generation means that merely competent output no longer provides a competitive edge. The real advantage now belongs to individuals and teams capable of discerning what is generic versus what is authentic, and what truly warrants further development. The challenge, however, is to not reduce human contribution to mere selection from AI outputs, but to integrate taste with a deeper understanding of context, genuine stakes, and a commitment to active construction.

The Shifting Value of Taste

In the context of AI and LLMs, “taste” is defined not by luxury or status, but by the ability to make distinctions under uncertainty. Meaningful work often lacks perfect data, requiring human judgment to decide, for instance, which sentence resonates with a customer or which design element transcends being merely polished to becoming memorable. Taste manifests in three key areas: what one notices, what one rejects, and the precision with which one can articulate what feels wrong. The ability to move from a vague sense of unease to a precise diagnosis, such as identifying a regulatory constraint obscured by marketing language, transforms taste into a valuable skill.

LLMs, functioning as pattern-compression engines, excel at absorbing and recombining vast amounts of data to produce statistically plausible outcomes. This inherent design means they tend toward the “safe center of the distribution,” resulting in work that is often familiar but lacks deep specificity. Consequently, AI-generated content frequently appears as polished but generic, such as landing pages with identical structures or product copy that could fit any application. This phenomenon creates a “crowded 7 out of 10 world,” where average output is abundant and no longer sufficient for distinction.

This environment highlights a new economic bottleneck: human judgment. Before AI, mediocre work often stemmed from resource limitations or execution skill gaps. Today, it frequently signifies that a creator stopped at the first acceptable, AI-generated draft. The scarce skill is now the ability to refuse, to identify content that, while superficially fine, is too generic, hides critical trade-offs, or fails to align with user needs or operating constraints.

Cultivating Judgment in an AI-Driven World

One of the most valuable, and often humbling, aspects of AI is its capacity to reflect the clarity of one’s own judgment. By prompting an LLM to generate multiple versions, for example, 10 to 20 variations of a homepage hero section, a pattern emerges: a few weak options, a large cluster of acceptable ones, and perhaps one or two closer to the desired outcome. The crucial insight comes from analyzing why most versions are still inadequate. A vague critique indicates underdeveloped taste, while precise identification of flaws reveals stronger human judgment, enabling effective use of the model rather than being passively led by it.

The division of labor between humans and AI can be summarized: while AI excels at generation, pattern matching, optimization toward a target, and scaling ideas into assets, humans remain essential for deciding direction, spotting generic content, validating the appropriateness of targets, and carrying real-world context, stakes, and consequences. AI generates options, but humans provide ownership.

To train taste, a practical loop involves selecting a high-leverage artifact, generating 10 to 20 AI versions, articulating specific reasons why each version “fails,” rewriting the strongest version with a hard constraint, and then deploying it to observe real-world outcomes. This iterative process aims to build a sharper rejection vocabulary, fostering the ability to quickly identify empty specificity, borrowed tone, or superficial confidence.

Beyond Taste: The Indispensable Human Element

While taste is increasingly valuable, it is not a complete solution. Reducing human involvement to simply selecting from AI outputs risks confining individuals to a narrow role, effectively making them discriminators in a machine-led process. Historically, significant work arises from co-creation under genuine constraints, where builders grapple with reality, collaborators, budgets, and the consequences of error. This friction generates depth.

Humans retain unique and indispensable capabilities that models cannot own, particularly concerning real-world consequences:

1. Holding the Stake: Real products are subject to consequences, such as trust, regulatory exposure, outage risk, and brand damage, which cannot be captured in a prompt. A model can suggest text, but cannot bear responsibility when that text leads to customer confusion or regulatory issues.
2. Working with the Truly New: Genuinely novel ideas often appear awkward or incomplete because they do not align with existing training data. Humans possess the capacity to tolerate this discomfort, nurturing fragile new concepts until they become viable.
3. Choosing Direction: The most significant decisions are directional, not merely cosmetic. These include defining the problem to solve, determining acceptable trade-offs, shaping company and product identity, and consciously deciding what not to optimize for. These are acts of authorship, not post-processing.

For builders, the implication is clear: avoid mistaking competent surface area for meaningful work. While AI democratizes the ability to ship polished products, it does not inherently provide specificity. The true competitive advantage lies in deep specificity, achieved by writing for actual human understanding, integrating domain constraints, designing for real-world conditions, and intentionally departing from the “canon” when context demands it. The market needs builders who leverage AI’s speed without sacrificing the critical specifics that foster trust and utility.

A more effective use of AI involves active shaping rather than passive selection. This entails using AI to rapidly explore design spaces, study existing best practices, and generate diverse alternatives, then applying human judgment to reject generic, dishonest, or context-blind outputs. The critical question for human creators becomes, “What am I adding here that the model could not have added on its own?” Answers should include real operating constraints, hard-won user truths, regulatory nuances, cultural details, strategic trade-offs, or a clear point of view one is willing to defend.

Ultimately, taste is not a distinct identity but a side-effect of meticulous attention to reality. It develops through studying strong work, generating numerous options, diagnosing failures precisely, shipping into real-world feedback loops, and maintaining close proximity to the domain. While AI and LLMs render the first draft cheap, they do not automate judgment, eliminate the need for ownership, or replace the fundamental task of deciding what should exist. The genuine advantage in the age of AI lies in utilizing models to efficiently discard the average, thereby freeing human judgment to focus on direction, specificity, consequence, and the courage to build truly original creations that transcend statistical norms.

What to Watch

The ongoing challenge for the AI industry and its users will be to continuously define and reinforce the unique human contributions in a world of abundant AI-generated output. Future success will depend on how effectively individuals and organizations cultivate a discerning judgment that complements, rather than merely consumes, AI capabilities, particularly in areas of high-stakes decision-making and genuine innovation.