After Vibe Coding: What Developers Who Succeed With AI Do Differently

In Why Vibe Coding Fails for Most Developers, I argued that AI does not magically close skill gaps. When vibe coding works, it works because the developer already knows how to operate under ambiguity. When it fails, AI simply accelerates confusion.

This follow-up is not about who is ahead or behind.

It is about what is different in how developers who are succeeding with AI actually work, and how those behaviors can be adopted deliberately.

The difference is not intelligence.
It is an operating model.

The Persistent Problem: AI Makes Bad Habits Faster

Many developers assume AI will fix what is fundamentally a sense-making issue.

They ask for code instead of questions.
They want answers instead of evaluations.
They seek completion instead of clarification.

This mirrors what the Level Up article “I Screened 30 Developers. Here’s Why 90% Can’t Handle Real-World Coding” surfaced so clearly. The candidates did not fail because they forgot syntax or algorithms. They failed because real systems are ambiguous, partially specified, shaped by legacy constraints, and full of competing signals. The hard part was not writing code. It was deciding what to do next with incomplete information.

AI does not solve that.
It hides it temporarily.

When AI produces plausible code, many developers stop thinking. Everything looks fine until the system shows up. Production load, side effects, evolving requirements, or unclear ownership quickly expose the gaps.

Developers who are doing well with AI do not stall at that moment.
They operate differently from the start.

The Core Shift AI Introduces

AI did not change what software development is.
It changed where leverage lives.

Before AI, leverage often came from:

• Framework fluency
• Writing code quickly
• Knowing the “right” patterns

Now those are cheap!

What is not cheap is:

• Framing the right problem
• Choosing which assumptions to test
• Running fast feedback loops
• Making decisions with incomplete information

Developers getting real leverage from AI are not better at typing. They are better at learning faster than uncertainty grows.

Why This Way of Working Feels So Hard

If thinking in loops, exploring ambiguity, and using code to discover truth feels unnatural, there is a reason. Most developers were never trained or rewarded to work this way.

They were trained to emphasize correctness within defined constraints, not judgment under uncertainty.

In practice, many engineering environments reinforce this by rewarding:

• Correctness against predefined criteria
• Clean starting states over inherited complexity
• Forward progress over understanding consequences
• Abstractions that hide system behavior until it fails

Production systems operate very differently. They are shaped by history, tradeoffs, and incomplete information. Ownership is often diffuse. Constraints and requirements tend to surface through use, failure, and change rather than clean handoffs.

In that environment, skills optimized for correctness against defined inputs stop being sufficient. What replaces them is judgment under uncertainty, and many engineers were never trained to exercise it explicitly.

They learned how to solve problems.
They were never taught how to decide.

Why Clarity Gets Mistaken for Competence

A common belief follows naturally from that training:

“If the requirements were clearer, I could do this.”

That sounds reasonable. It is also backwards.

In real systems, clarity is not a prerequisite. It is an outcome.

Developers who wait for fully defined tickets, finalized acceptance criteria, or pre-approved architectures are not being lazy. They are operating exactly as their environment has taught them to operate.

In enterprise systems, the hardest unknowns are rarely requirements. They are architectural in nature. How systems integrate, where state lives, what failure looks like, and which constraints actually matter are not fully knowable upfront. These realities surface only through interaction with the system.

In the production experiment described in What 65% AI-Written Code Taught Us About “Good Enough”, the team did not succeed because they had perfect clarity at the start. They succeeded because they used AI to explore architectural options, test assumptions against real constraints, and converge on a solution that fit the system they actually had.

Clarity was not a prerequisite. It was the result of disciplined exploration.

AI does not replace judgment here. It lowers the cost of reaching it by making architectural exploration faster and more concrete.

If that feels risky, it is. That is the job.

Why Safety Wins Over Outcomes

Layered on top of this is how most teams reward behavior.

Many developers learn quickly that the safest path is to:

• Avoid breaking things
• Avoid visible failure
• Avoid irreversible decisions
• Avoid owning outcomes

This leads to familiar patterns:

• Premature abstraction
• Extra layers “just in case”
• Endless alignment
• Reluctance to choose

The result is paralysis that looks like professionalism.

Teams that make progress do not avoid risk entirely. They choose where to take it.

In practice, this means designing work so uncertainty surfaces early and cheaply, before decisions become irreversible. It means shaping delivery to generate feedback while the blast radius is still manageable.

In the Barazany example, the team did not push half-understood designs into production recklessly. They used AI to explore solution space, validate constraints, and narrow options before committing. AI accelerated feedback without eliminating responsibility.

Progress comes from reducing uncertainty faster than it accumulates. Safety often comes from avoiding decisions. Outcomes come from learning sooner.

Why Framework Fluency Is Not Enough

Modern development has over-indexed on frameworks.

Many developers can assemble systems from well-known pieces. Far fewer can explain:

• Why the system is structured the way it is
• Where state actually lives
• How failure propagates
• What changes under load or latency
• Which constraints are real and which are imagined

Framework knowledge helps you build.
Systems understanding helps you recover, adapt, and evolve.

That gap only shows up when something goes wrong, which is exactly when exploration and judgment matter most.

The Missing Ingredient: Taste and Judgment

Underlying all of this is something rarely taught explicitly: taste.

Taste and judgment are what separate plausible output from durable solutions.

They show up in moments like these:

• Recognizing that a generated abstraction will complicate integration six months from now, even though it looks clean today.
• Choosing a simpler data flow because it makes failure modes observable, even if the alternative appears more elegant.

In the Barazany experiment, the delivered system was “good enough” not because standards were lowered, but because the team exercised judgment about what needed to be correct now and what could evolve later. That judgment determined where rigor mattered most.

Taste comes from owning systems long enough to experience the consequences of earlier decisions. Without it, every option looks equally viable and decision-making slows to a crawl.

No tutorial teaches this. Feedback does.

Why AI Exposes This So Quickly

AI accelerates everything that came before.

It can write boilerplate better than most developers. It can scaffold systems instantly. It can explore multiple solutions in parallel. What it cannot do is decide what matters, which assumptions to test, or which tradeoffs to own.

If a developer’s primary value was speed of execution, AI compresses that advantage. What remains is problem framing, tradeoff navigation, system-level thinking, and ownership of consequences.

This is why AI makes the gap feel sudden. It is not creating new problems. It is removing the scaffolding that hid old ones.

So What Actually Works?

If developers were trained on toy problems, rewarded for compliance, and shielded from ambiguity, then no amount of better prompts or tooling will fix this on its own.

The ambiguity that matters most in enterprise software is not about requirements. It is about architecture.

It is about integration boundaries, data ownership, operational behavior, and how the system responds to change and failure. These uncertainties are not resolved by more precise specifications. They are revealed through interaction with the system.

AI helps not by eliminating ambiguity, but by making it cheaper to explore responsibly. It allows engineers to model architectural alternatives, surface hidden constraints, and reason about tradeoffs before committing.

Used well, AI supports more robust systems because it accelerates understanding of how the system actually behaves.

What works instead is a system of work that:

• Treats ambiguity as something to explore, not eliminate upfront
• Trains people to think in feedback loops, not linear steps
• Uses code as a probe for learning, not just a delivery mechanism
• Encourages reversible decisions and fast feedback
• Externalizes thinking so judgment can be examined and improved

That is the operating model developers who are succeeding with AI are converging on, whether they name it or not.

Exploration Requires Context and Discipline

Working in loops does not mean exploring everything indiscriminately.
Effective exploration is constrained by context.

In real systems, feedback loops only work when they are fed with the right inputs: known constraints, prior decisions, blast-radius awareness, and an understanding of what is safe to probe versus what must remain stable. This is where context engineering matters. It shapes the loop so exploration produces learning instead of noise.

Clarity does not come from wandering. It comes from generating hypotheses and killing them quickly. Sloppy exploration is not the unlock here. Relentless elimination is. High-leverage engineers move fast because they narrow the solution space aggressively, not because they keep options open.

The same is true of patterns. Patterns are snapshots of past constraints, not universal truths. Any sufficiently old codebase is full of contradictions. The skill is not strict adherence or reflexive rejection. It is learning how to navigate those patterns, filter signal from residue, and choose a path that fits the current system. AI helps tremendously here, but only if the engineer knows how to drive it.

This operating model does not reward chaos. It rewards disciplined exploration, fast pruning, and judgment applied at the right points in the system.

In environments where failure is expensive, loops slow down, move earlier, or shift into simulation and review. What changes is the mechanism, not the mindset. The work is still about exploring assumptions and narrowing uncertainty before committing to irreversible decisions.

This Shift Is Already Visible

This shift is already showing up in real delivery work. In a production experiment described in What 65% AI-Written Code Taught Us About “Good Enough”, a team shipped a complex feature where the majority of the code was AI-generated. The outcome was not lower quality or reduced rigor. It was faster convergence on the right solution because the engineers focused on understanding constraints, exploring architectural options, and deciding when the system was good enough to meet real business needs. The leverage came from judgment and exploration, not from writing more code.

The same shift is visible from the tooling side. In I Helped Build Your IDE. Here’s What Will Replace It, Dave Griffith explains why traditional IDEs are giving way to orchestration-focused tools. Editors were designed for an era where typing, navigating, and refactoring code by hand was the core activity. As AI absorbs more of that execution work, tools are evolving toward context management, coordination, and validation. The interface is changing because the job has changed.

Both perspectives point to the same conclusion. When execution becomes cheap, judgment becomes the bottleneck. Developers who get real leverage from AI are not those who generate the most code, but those who can explore solution space responsibly, work within constraints, and decide when convergence is sufficient.

How Developers Excelling with AI Actually Operate

They think in loops, not steps!

A common question in day-to-day work is:
“What is my next task?”

Developers who use AI effectively ask something else:
“What feedback loop am I running?”

They move continuously through loops like:

• Hypothesis → test → revise
• Build → observe → adapt
• Ask → challenge → refine

AI accelerates these loops, but thinking in loops is a human choice.

Actionable habit:
Before coding, write down what you are trying to learn.
After coding, ask what the system just taught you.

They treat code as a byproduct, not an endpoint

Many developers treat code as the deliverable.

Developers succeeding with AI treat code as a tool for revealing truth. They write code to:

• Test assumptions
• Observe real behavior
• Expose edge cases
• Learn where their mental model is wrong

This shifts the goal from perfect code to clear understanding.

Actionable habit:
Ask “what question does this code answer?” before worrying about polish.
Refactor after the behavior is understood, not before.

They use AI as a partner in reasoning

Less effective use of AI looks like faster autocomplete.
More effective use looks like cognitive collaboration.

Developers getting real value from AI use it as:

• A parallel reasoning partner
• An adversary that challenges assumptions
• A simulator for failure modes
• A generator of alternative framings

They do not ask AI to confirm they are right. They ask it to push back.

Actionable habit:
Ask AI to critique your approach before finalizing it.
Treat disagreement as signal, not friction.

They collapse ambiguity early

Many developers wait for ambiguity to disappear.

Developers who work well with AI surface ambiguity immediately because that is where decisions live.

A common pattern looks like:

• Expose uncertainty early
• Generate alternatives quickly
• Evaluate empirically
• Decide and move

Actionable habit:
List unknowns explicitly before coding.
Use AI to explore options, then commit to one.

They bias toward reversible decisions

AI speeds everything up, but decisions still carry consequences.

Developers using AI effectively constantly ask:

• Can I undo this?
• Can I observe the impact quickly?
• Can feedback turn this into data?

If yes, they move.
If not, they slow down and seek clarity.

Actionable habit:
Label decisions as reversible or irreversible.
Move quickly on the former and deliberately on the latter.

They externalize their thinking

One of the biggest differences is visibility.

Developers succeeding with AI insist on external artifacts:

• Diagrams
• Hypothesis statements
• Notes tied to decisions
• Tests that express intent

This is not overhead. It turns ambiguity into shared reality.

Actionable habit:
Write assumptions down before coding.
Let AI reason over those assumptions directly.

What Actually Holds People Back

This is not about intelligence or experience.

The most common blockers are:

• Fear of being wrong early
• Over-identification with code quality
• Waiting for instruction instead of deciding
• Confusing framework fluency with system understanding

AI exposes these patterns faster than before. That can feel uncomfortable, but it is also an opportunity to change how you work.

Closing Thought

The previous era rewarded execution in low-ambiguity environments.
The AI era rewards decision-making under uncertainty.

AI does not replace judgment.
It amplifies it.

If you stop treating code as the goal and start treating it as a mechanism for discovery, AI becomes a multiplier rather than a crutch.

That is how developers are finding real leverage with AI today.