You’re knee-deep in a pile of AI-spit landing pages. Ten variations, each with hero images that pop, testimonials stacked just so, CTAs screaming ‘Sign up now.’ They all score a solid 7/10. Polished. Plausible. Forgettable.
Zoom out. This isn’t a glitch—it’s the new normal. AI and LLMs have slashed the price of competence to zero. A product memo? One prompt. Pitch deck? Minutes. The old moats—raw execution skill, grinding out drafts till they shine—crumbled overnight. What’s left standing? Taste. Sharp, untrainable judgment that sifts signal from the statistical sludge.
But hold up—don’t pat yourself on the back yet. Taste alone won’t cut it. We’re not here to play art director for machines. The real play? Fuse that eye with gritty context, brutal constraints, and the guts to build what no prompt could dream up.
Why Does ‘Good Taste’ Feel So Urgent Now?
Picture the pre-AI grind. Mediocre work screamed ‘no budget, no time.’ Now? It whispers ‘stopped at draft one.’ LLMs excel at pattern-crush: hoover billions of words, designs, interfaces, then remix into safe, familiar sludge. Landing pages swap logos but keep the same tired flow. Copy that fits any SaaS. Essays with crisp headings, zero soul.
“LLMs are extraordinary pattern-compression engines. They absorb huge volumes of language, design patterns, and interfaces, then recombine them at speed.”
That’s their superpower. And curse. They hug the median—statistically plausible, rarely piercing. Average used to be the moat; it took hustle to hit it. Now abundance clogs the middle. Crowded 7-out-of-10 hellscape.
Here’s my angle the original skimmed: this echoes the printing press revolution. Suddenly, books poured out—cheap, competent tracts flooding Europe. Quality? Middling. But it birthed critics, editors, tastemakers who didn’t just print; they curated, rejected, refined. Taste didn’t kill the press—it weaponized it. AI’s doing the same. Not ending human work, but demanding we evolve from generators to gatekeepers-plus.
One punchy caveat.
Taste thrives under uncertainty. No spreadsheet flags the killer sentence, the feature worth a sprint, the design that sticks. It’s what you notice first—the off-note phrasing. What you spike—the generic hero copy. And crucially, why: “This flops because it parrots every SaaS clone, dodging our weird regulatory bind.”
Vibe to verdict. That’s the leap.
What Does Taste Actually Mean in Tech?
Forget luxury vibes or Instagram aesthetics. Taste here is distinction amid fog. Humans layer in stakes: customer quirks, team bandwidth, market traps LLMs gloss over.
Fire up an AI mirror test. Prompt ten hero sections. Cluster emerges: duds, the meh bulk, maybe one near-hit. Skip ‘pick the best’—ask ‘why do nine miss?’
Vague gripe? Taste raw. Precise autopsy—“hides the trade-off, ignores user mental model”? You’re ahead. Now iterate: feed back, refine. AI amplifies clear judgment; it drags the fuzzy.
Think layers:
Generation? AI cranks variants.
You: pick direction.
Patterns? AI remixes norms.
You: nix the generic.
Optimization? AI tunes to specs.
You: vet the spec.
Scale? AI multiplies.
You: inject context, consequences.
Is Judgment the New Bottleneck—or Just Hype?
Corporate spin calls this ‘human-AI symbiosis.’ Cute. But peek behind: many ‘AI teams’ are prompt monkeys picking from slop. Real shift? Refusal as skill. “Fine, but too vanilla.” “Shiny UI, wrong user flow.” “Bold plan, ignores ops crush.”
Economic flip: first drafts free. Value cascades to critique, then build. The ones who stand out? Not fastest prompters. Builders who taste-test, reject 90%, then weld AI output to unpromptable truths—like your startup’s quirky pivot no dataset groks.
Risk if we slack? Become taste-testers in a machine factory. No stakes, no soul. Opportunity? Taste + constraints = originals. That niche LLM couldn’t hallucinate.
My bold call: this births judgment tools. Not more generators—augments for precision critique. Think AI that stress-tests your ‘why it sucks’ into data-backed evals. Or context-injectors pulling your CRM, roadmaps into prompts. Taste moat holds, but gets engineered.
Skeptical aside—companies hype ‘taste’ to dodge hard questions. Is your product defensible, or just pretty slop? AI exposes that fast.
Why Has AI Flattened Everything Else?
LLMs bias to the center because training does: vast webscrapes reward common over quirky. Success at average. Pre-AI, average demanded sweat—separated pros from hacks. Now? Everyone’s pro at meh.
Result: scarcity flips. Not making stuff. Saying no. Iterating past plausible to peculiarly right.
Cultivate it? Mirror drill daily. Generate, critique ruthlessly. Explain wrongs aloud. Build under constraints—no infinite revisions. Force choices.
Taste isn’t innate gift. It’s muscle, honed spotting what average conceals.
And yeah, it’s uneven. Designers often lead; PMs lag, mistaking buzzword bingo for judgment. Engineers? Gold if they pair code smarts with ‘does this solve real pain?’
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Frequently Asked Questions
What is ‘taste’ in the AI era for tech builders?
It’s the judgment to notice, reject, and diagnose under uncertainty—spotting generic AI output and steering toward context-specific gold.
Does AI make human taste obsolete?
Nope—AI commoditizes average; taste separates memorable from meh, especially with real-world stakes LLMs ignore.
How do I build better taste with AI tools?
Generate batches, critique precisely (‘why wrong?’), iterate. Train by rejecting 90% and explaining to sharpen your edge over the model.
