ChatGPT Replacing Programmers: What Still Needs Human Judgment

ChatGPT can assist with programming tasks, but generating code does not establish that it can replace the person responsible for a working software system. Requirements, security, review, and maintenance still need explicit ownership. A useful assessment asks which tasks an assistant can attempt and what evidence a person needs before accepting the result.

A record in fenbs can keep review lessons, human decisions, and unfinished checks available as assistants change. That helps a team remember why a change was accepted without treating generated output as proof that the work is complete.

Which programming tasks can an assistant help with?

Try clearly bounded work whose output you can inspect: explaining a function, suggesting a test, investigating an error, or proposing a small change. OpenAI’s code review documentation↗ (opens in a new tab) describes using ChatGPT or Codex to investigate code changes and prepare review feedback. It also directs users to inspect changes and run relevant checks.

The smaller the task, the easier it is to compare the proposed result with a clear expectation. That does not guarantee correctness. A short permission check can carry more risk than a much larger cosmetic change, so assess consequences as well as size.

Why does a convincing demo leave the question unanswered?

A demo can show that a feature works for the inputs used in the demonstration. It does not establish behavior under missing data, repeated requests, unauthorized access, or an unavailable dependency. A successful example is evidence for that example, not for every condition the system will face.

Imagine an assistant builds a reservation form. The happy path might display a confirmation, while two simultaneous requests still book the same slot. Someone must define whether that is allowed, examine where the conflict is prevented, and check the behavior under contention.

That example is hypothetical, but it illustrates the missing work in a simple “can it code?” comparison. Software also involves finding the right problem, maintaining agreements with users, and deciding what level of failure the organization can accept.

What must people specify before code is written?

Write the intended behavior in terms a reviewer can evaluate. Name who may perform the action, what must happen on success, and what must happen when the action cannot be completed. Include any data that must be preserved and any action the assistant is not authorized to take.

• Scope: the behavior to change and the files or components likely involved.
• Constraints: compatibility, accessibility, privacy, and security requirements that apply.
• Examples: expected results for ordinary, invalid, and unauthorized requests.
• Acceptance: the review and checks needed before a person accepts the change.
• Responsibility: who resolves ambiguity and who approves release.

Do not let an assistant silently settle an unresolved business rule. If the team has not decided whether a reservation can be changed after payment, ask the responsible person. Record that choice in fenbs Decisions and rules so the next assistant does not reopen it from incomplete context.

What evidence should a reviewer require?

Require evidence appropriate to the change: the actual code difference, results from relevant tests, and an explanation of remaining uncertainty. A statement that tests passed needs to identify which tests ran. If the assistant could not run them, that limitation should remain visible.

Check tests against the requirement rather than only against the new implementation. Code and tests can agree while both encode the wrong rule. For the reservation example, define what should happen when two people request one remaining slot before reviewing a test that accepts both requests.

The NIST Secure Software Development Framework↗ (opens in a new tab) treats secure development as a set of practices across the software lifecycle, including responding to vulnerabilities and their causes. Its scope is a useful reminder that producing source code is only part of maintaining dependable software.

NIST’s reference model discussion of AI↗ (opens in a new tab) describes reviewing AI output through established testing, security validation, and approval processes. Use your organization’s actual requirements to choose those checks; an assistant’s confidence does not replace them.

What changes for beginners and experienced developers?

For a beginner, an explanation is useful only if you can test your understanding. Ask why a change works, predict a result before running it, and make a small variation yourself. If you cannot explain a piece of generated code, treat that as a learning task before using it in a system others rely on.

For an experienced developer, the review burden may shift toward checking larger amounts of proposed work. Keep changes small enough to inspect, and reject unrelated additions that make evaluation harder. Saving typing is useful only when the resulting change remains understandable and maintainable.

For a manager, compare outcomes on representative work rather than counting lines generated. Useful questions include whether the change met the requirement, how much review it needed, and what defects or uncertainty remained. Those observations support a local staffing decision more directly than a dramatic coding demonstration.

How can a team assign work without losing responsibility?

1. Choose one task with clear expected behavior and a person responsible for acceptance.
2. Give the assistant the relevant requirements, current decisions, and boundaries on actions.
3. Ask it to make a focused proposal and report what it changed and checked.
4. Review the result, verify the important behavior, and resolve open decisions with the responsible person.
5. Record the outcome and a lesson when the process exposed a gap. Repeat with a different task only when the first result is understood.

Keep release authority explicit, especially where a tool can run commands or change a live system. The ChatGPT and Codex guide discusses the coding product relationship; Copilot versus ChatGPT addresses a separate tool comparison. Neither choice removes the need to define who accepts the work.

How to do this with fenbs

1. Create a free account and add a software project with the current goal and tasks.
2. Write each task’s implementation and verification steps in its plan. Keep the problem distinct from the proposed solution.
3. Record choices people have made in Decisions and rules, including who decided. Leave unresolved choices open for a person.
4. Connect a supported assistant through Integrations and ask it to read project context before starting.
5. Save progress with completed work, actual checks, remaining risks, and the next step. Record a lesson if review found a mistake worth avoiding.

fenbs is free for one person, no credit card. It does not run your test suite or certify that code is secure. If you only need a one-off code explanation, a chat may be enough; fenbs is useful when decisions, review lessons, and progress must survive repeated sessions.

How should you read claims about replacing entire roles?

Separate an observed task result from a claim about employment. An assistant completing one well-specified change does not establish how an organization will staff design, operations, support, or future maintenance. Equally, one failed attempt does not prove a tool cannot assist with narrower work.

The programmer workflow guide covers day-to-day use, while the AI at work hub covers other roles. For your own work, keep a concrete record of what the assistant attempted, what a person verified, and who owns the result. That is a firmer basis for decisions than an unsupported prediction about every programmer’s future.