What You’ll Find This Week

This is Part 3 of our 4-part series on The Age of Outsourced Reality. In Part 1: When Thinking Becomes Optional, Reality Becomes a Product, I looked at what happens when AI makes answers cheap and humans stop doing the messy middle work that builds true understanding. In Part 2: When Reality Becomes the Exception Handler, I looked at how that levels up to the entire org.

This week, I’m exploring what happens when GenAI overwhelms us with choice and how that washes out the value of one of our most human traits: taste.

Read on to find out...

Here’s what you’ll find:

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This Week’s Article

In Part 1, I made a simple point: when AI hands you packaged answers, you stop doing the messy work that builds judgment. The output still ships, but the scrutiny that makes your judgment trustworthy starts to disappear.

In Part 2, that habit scaled to the org-level. Simulation moved from informing decisions to steering them. Digital twins turned into decision systems. Reality became the exception handler.

Part 3 is what happens next. When output becomes cheap, selection becomes the control point.

AI can generate 100 options in 10 seconds, whether that’s copy, code, mockups, strategy, positioning, naming, or feature lists.

That doesn’t make you faster by default. It just moves the bottleneck. Execution stops being the constraint, but choice paralysis takes its place.

Once decision-making becomes the constraint, the organization reaches for the safest shortcut: the option that looks most defensible, the output that resembles what already worked, the template that won’t get you yelled at, the version that can be explained with a chart.

That’s the point where selection stops being an act of judgment. It becomes an act of compliance. And that’s how taste gets outsourced.

Taste shows up in the obvious places first. Color palettes. Fonts. Products you like. Brands you trust. The things that feel right before you can explain why.

And that all counts.

But taste runs deeper than aesthetics. Underneath the color choices and the cool factor, taste is learned selection under uncertainty. It’s how humans make calls before the numbers can justify them. It’s how founders spot the non-obvious opportunity, how product leaders know which feature to kill based on gut feel before the data comes in, and how a venture team chooses the odd idea that doesn’t look right yet but feels directionally true.

That makes taste one of the last genuinely human advantages. It’s built through exposure, repetition, friction, failure, memory, and point of view. It isn’t bestowed. It’s earned.

And it is deeply human. Taste shapes identity because it reflects what we notice, what we value, and what we are willing to stand behind. If readers want to go deeper on that relationship between taste, identity, and aesthetic judgment, this is a useful place to start:

Because every real innovation looks a little wrong before it looks inevitable. Taste is what lets a human back that idea anyway. It lets you make a call before the evidence is obvious. And once an organization stops trusting that kind of judgment, it doesn’t just get more efficient. It gets more average.

Taste doesn’t disappear in one dramatic moment. It gets replaced step by step, the same way decision systems got promoted in Part 2.

Generative AI makes optionality cheap and fast. You can ask for five versions, or fifty, or even five hundred. The machine doesn’t argue, it just keeps spitting out choices. But when you're mired in choices, the work shifts from creating to sorting.

We like to think more options means more creativity. In practice, creativity thrives in constraints. More choice just increases the pressure to pick quickly.

When you’re staring at too many options, pattern recognition takes over.

The quickest move is to do what worked last time. The second quickest is to do what looks familiar enough to pass.

That’s how defaults start to feel safe. They’re easy to explain, easy to defend, and easy to mistake for good judgment.

Once selection gets difficult, the organization stops relying on human judgment and starts relying on metrics that can stand in for it.

Judgment gets translated into proxies: engagement, conversion, time on page, velocity, NPS, adoption, retention.

Those proxies are useful until people start treating them like evidence of what’s good.

They can tell you what performed. They can’t tell you what’s original, what matters, or what’s worth building next.

Over time, the question stops being “is this interesting?” and starts being “will this pass?”

The system stops acting like a tool for generation and starts acting like a filter for selection. The work that survives is the work that feels familiar, defensible, and easy to justify. The weird idea doesn’t lose because it’s bad. It loses because it asks for more belief than the system allows.

At that point, you’re not using AI to expand creative range. You’re using it to narrow the field of what gets chosen.

Taste gets built the hard way: through contact with reality.

You see real work. You do the work yourself. Reality pushes back. Something fails. Something surprises you. You start noticing patterns other people miss. Over time, that becomes a point of view. And once you have a point of view, you can make original decisions that don’t need consensus to feel right.

Over time, that chain looks like this:

Exposure → Reps → Feedback → Point of View → Taste → Original Decisions

Exposure is contact with the real thing. Real customers. Real constraints. Real products in the market. Real consequences.

Reps are what happens when you have to make the call yourself. Not select from outputs. Not curate. Decide. Ship. Miss. Adjust.

Feedback is reality pushing back. Customers ignore it. Pilots fail. Systems break. Something you were sure about turns out to be wrong.

Point of View is what survives that friction. It’s the set of judgments you’ve earned the hard way.

Taste is point of view under pressure. It’s how you choose when there isn’t enough proof yet.

Original Decisions are the bets that come out of it. They’re the bets that create new categories, new products, and new businesses instead of slightly better versions of what already exists.

AI breaks this chain in the places that matter most.
Generative AI can give you outputs without reps.
Metrics can give you feedback without context.

And once the system starts rewarding what’s familiar and defensible, point of view gets flattened into pattern recognition. The organization still produces work. It just stops producing the kind of judgment that makes original work possible.

This is why taste matters so much in an AI-saturated environment. It’s not a decorative layer on top of the work. It’s the part that decides whether the work becomes different enough to matter.

When a team has too many plausible options, the fight is rarely about what’s best. It’s about what can survive review. The idea that feels familiar, benchmarks well, and comes with a clean explanation gets through fastest. The weirder idea has to carry a heavier burden. It has to be defended harder, modeled earlier, and justified longer. Most teams don’t kill that idea because it’s bad. They kill it because it asks for more belief than the process allows.

Once everyone is using the same models trained on the same corpus, the center of gravity gets stronger. The same phrases, structures, aesthetics, and strategic instincts keep resurfacing. You still get variation, but it’s variation inside a narrowing band. The work looks polished. It feels competent. It just stops feeling distinct.

Real innovation always looks a little unjustified at first. That’s part of the job. But once teams are trained to defend every decision with precedent, metrics, or model-legible evidence, novelty starts to read like recklessness. Not because the idea is wrong, but because it arrives before the system has language for why it might work. Over time, the organization doesn’t just avoid bad bets. It avoids unfamiliar ones.

That’s how taste collapses inside a system. Not all at once. Through review cycles, approvals, benchmarks, and a hundred tiny decisions that favor the work that already makes sense. The company still produces. It just produces inside a shrinking field of acceptable choices.

When taste gets outsourced, the first thing you lose is not innovation. It’s discernment.

You stop practicing the messy, human work of noticing what has life in it, what feels dead on arrival, and what’s actually worth standing behind. Over time, that doesn’t just flatten the work. It flattens the people making it. Taste is one of the ways humans become themselves. It’s how we learn to notice, how we build conviction, and how we develop a point of view that isn’t borrowed.

Once that starts to disappear inside an organization, the brand starts losing the thing that made it feel alive.

It gets smoother. Safer. Easier to scale.
It also becomes less itself. More beige. More copy-paste ready.

That’s why growth so often sands off the very qualities that made a company distinctive in the first place. The systems get tighter. The outputs get cleaner. The edges get rounded down. What looked like refinement from the outside was often the slow loss of human judgment on the inside.

The company can still generate. It can still optimize. It can still ship. But it starts losing the distinctiveness that made people care in the first place.

Originality gets filtered out long before it has the chance to become innovation.