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What AI Actually Replaces in Software Development (Part 2: The Reality)

#ai #career #management #softwaredevelopment

In Part 1, we established a core philosophy: AI Does Tasks. Humans Do Deals.

But what does this look like in your daily standup? This is not a future prediction. This is a role shift already in progress.

We are shifting from being "Coders" (producers of units of work) to "Validators" (signatories of outcomes). We are separating the execution from the commitment.

1. The Decomposition of the Workflow

AI is aggressively unbundling the development lifecycle. It is eating the "doing" and leaving you with the "deciding."

Code Generation & Refactoring

  • The Task (AI): Migrating a component from Class-based to Functional or converting an entire library from Moment.js to date-fns.
  • The Deal (Human): You own the technical debt. You evaluate if the migration is worth the regression risk and if the new dependency is safe for the long term.

Code Review (PRs)

  • The Task (AI): Catching syntax errors, suggesting better naming conventions, or identifying missing unit tests.
  • The Deal (Human): You audit the intent. You ensure the PR aligns with the product's long-term architecture. Does this change solve the right problem for the customer?

Testing & QA

  • The Task (AI): Generating 100% test coverage for a utility function or simulating 1,000 concurrent users.
  • The Deal (Human): You are accountable for the risk profile. You decide what not to test and judge if the release is stable enough for production.

2. From "Proposer" to "Decider"

We are seeing a new hierarchy in tools like Cursor, GitHub Copilot Workspace, and Devin.

In the old world, you spent 80% of your time typing. Today, the AI becomes the Proposer. It analyzes the issue, modifies files, and presents a solution. You become the Decider.

"I see the solution. It’s clever, but it introduces a race condition in edge cases. I will not sign off on this."

If you just "LGTM" an AI-generated PR without understanding it, you aren't an engineer—you're just a rubber stamp. And rubber stamps are easily replaced.

3. The Real-World "Task vs. Deal" Matrix

This matrix is the new reality of our work:

Category AI is doing this TODAY (Task) You must do this TODAY (Deal)
Security Automated patching of CVEs. Deciding if a "critical" patch is too risky to deploy mid-launch.
UX/UI Implementing a design system component. Judging if the flow feels "human" and intuitive for the user.
SRE Detecting a 500-error spike and suggesting a rollback. Navigating the fallout with stakeholders when a rollback loses data.
Documentation Summarizing what a function does. Ensuring the documentation actually helps a new dev understand why.

4. The New Skill: "Audit-ability"

If AI handles the execution, your primary skill is no longer
"How to write code," but "How to audit code."

AI optimizes for plausibility. Humans must optimize for consequences.

When an AI proposes a solution, you cannot just click "Approve."
You must interrogate it. Here are the questions that separate
validation from rubber-stamping:

The Validation Checklist

1. Problem-Solution Alignment

  • Does this solve the stated problem, or just the literal request?
  • What happens if the underlying requirement was misunderstood?

2. Scale & Performance

  • What breaks if this code runs 10,000 times per second instead of 10?
  • Where are the performance bottlenecks the AI didn't mention?

3. Failure Scenarios

  • If this fails in production at 2 AM, can we debug it with our current tools?
  • What's the blast radius if this goes wrong?

4. Maintainability

  • Can someone who didn't write this code understand it six months from now?
  • Is the AI introducing clever abstractions that will haunt us later?

5. Integration & Dependencies

  • What assumptions is the AI making about the rest of the system?
  • Did it introduce a new dependency that becomes a security liability?

6. Edge Cases & Assumptions

  • What edge cases did the AI not test?
  • What implicit assumptions will break in different environments (dev vs. prod)?

The Three Foundations of Audit-ability

To ask these questions effectively, you need:

  1. Deep System Knowledge: You must know the fundamentals
    better than ever to spot "hallucinated logic."

  2. Risk Assessment: You must develop a gut feeling for
    where things might break under load, at scale, or in production.

  3. The Courage to Say "No": An AI will always give you an answer.
    A human must have the courage to say, "This isn't good enough to ship."

The best validators don't just catch bugs—they catch *architectural

debt before it compounds*.

In Part 3, we will discuss Career Strategy: How do you get promoted in a world where "lines of code" no longer matter?

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