Code Is Free Now. Taste Isn't

I Out-Coded 50 Engineers Last Month. That's Not the Point.

In the last 30 days, I merged more net-new code than the rest of our 50+ person engineering org combined.

That’s not 50× more business value. Value is impact.

But it’s a signal: implementation cost is collapsing.

Agentic coding (with Claude Code and similar tools) is turning “writing code” into the cheap part. The constraint is moving upstream—from syntax to semantics, from typing to taste.

Building software is becoming a taste + specification problem

The old equation weighted technical execution heavily:

Taste + Communication + Technical execution + Effort → Value

The new one doesn’t:

Taste + Communication + Verification → Value

When code is easy, the differentiator isn’t “can you implement?” It’s: did you pick the right thing? Did you specify it clearly enough that the machine can’t misunderstand you? Did you verify it fast enough that mistakes die in staging instead of customer workflows?

The skill stack shifts from writing correct code to defining correct systems.

Verification is the new moat

Most teams are under-invested in the three upstream skills that actually create leverage now:

Taste means choosing the right problems and tradeoffs—and knowing what to say no to.

Communication means turning taste into machine-followable requirements: interfaces, acceptance criteria, invariants, non-goals.

Verification means building harnesses that catch subtle failures early: automated tests, eval suites for AI behavior, schema contracts, canaries, telemetry tied to user outcomes.

We’re not at “prompt → unicorn.” But we are at “prompt → a lot of working software.”

The highest-leverage move is acting like a CTO for your agents: shape the architecture, define interfaces, set constraints, enforce quality. The question isn’t “can you code?” It’s: can you define what’s worth building, and specify what “done” means?

AI compresses traditional software process

A lot of process exists because developer time is expensive and slow mistakes are catastrophic. When implementation is cheap and iteration is fast, many rituals become pure overhead: long roadmaps, bloated specs, meetings as a substitute for shipping, review cycles that exist mainly to ration engineer time.

That doesn’t mean no process. It means the center of gravity changes.

The new loop is: Ship → Observe → Verify → Adjust (fast).

Two costs don’t disappear: attention (deciding what matters, steering agents, reviewing outcomes) and maintenance (keeping systems reliable as dependencies and reality shift). So the core skills become vision, focus, experimentation, and ruthless feedback loops.

AI blurs roles

When everyone can ship, distinct lanes dissolve. PM, SDET, DevOps don’t vanish overnight, but they stop being departments and become capabilities that must exist close to the work.

The rule emerging in AI-native teams: everyone touches code, everyone ships.

Specialization still matters—but it’s less about titles and more about who can own outcomes end-to-end: product thinking, architecture, reliability, security, user experience, operational excellence. You’ll still need experts. You’ll just stop pretending work can be neatly partitioned by job name.

Technical debt doesn’t disappear—but it changes

Vibe-coding generates debt fast. Two things shift how much that matters.

First, debt matters less when software is genuinely disposable. If it’s a one-off internal tool or a single-use workflow, maintainability is irrelevant. Throw it away.

Second, debt becomes easier to pay down when refactoring is cheap. Agents can discover inconsistent patterns, generate migration plans, refactor modules behind stable interfaces, write missing tests as part of cleanup.

But here’s the hard truth: if the system isn’t disposable—enterprise workflows, money movement, compliance, uptime—debt still hurts. The real tax is paid in attention during incidents.

The win isn’t “no debt.” The win is isolating change behind interfaces, making correctness cheap to prove, keeping maintenance bounded.

Rapid evolution requires embedded guidance

Rapidly changing software confuses users—even when it’s improving.

As systems evolve faster, users need help staying oriented. AI stops being a chatbot feature and becomes the layer that guides users through workflows, explains what changed, detects intent, and prevents mistakes before they happen.

The best products won’t just ship faster. They’ll make the experience feel stable while the internals churn.

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AI-native companies already practice most of this. The bigger question is diffusion speed.

Industry consensus says years. Some of that inertia is real. But I expect a faster break than people think—because the opportunity cost becomes obvious once a competitor ships 10× faster with the same headcount.

We’ve seen this movie before. Process waves don’t politely arrive. They roll through when the economics flip.

Implementation is getting cheap. Attention is not. Verification becomes strategy.

If you’re leading teams right now, the priority isn’t “use AI more.” It’s: rebuild your org around taste, specification, and verification.