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AI can make a first draft of code faster to produce. It does not, by itself, make software useful, safe, or inexpensive to own over time. The distinction matters: a quick tool for one task may be good enough for a short life, while software that handles important data or must keep working needs problem analysis, testing, maintenance, and someone accountable for change.
What “code is cheap” means
In his January 10, 2026 essay, “Code Is Cheap Now. Software Isn’t,” Chris Gregori argues that AI has lowered the friction of producing code, but code generation is not the same as understanding the problem the software should solve. A prompt can yield a plausible implementation before anyone has established what users need, which cases matter, or what the system must do when conditions change.
That changes where effort is most visible. A working demo may arrive quickly; deciding whether it behaves correctly across real situations, fits into existing systems, protects its data, and can be changed safely remains engineering work. The speed of the first draft is therefore not a measure of the total cost of owning the result.
Why the first working version is not the whole product
Requirements and edge cases
A happy-path demonstration shows that one sequence can work. A dependable tool must also account for invalid input, interrupted work, unusual but legitimate cases, and what users should see when something fails. Those needs come from the actual task and its consequences; generating more code does not decide them.
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Integrations and changing dependencies
Software often relies on systems it does not control. Gregori offers illustrative scenarios—not measured incident data—in which a bank changes its CSV export, a website changes its DOM, or users need offline support and reliable synchronization. Any of these changes can turn a previously working integration into a maintenance task.
Data, security, and user experience
Gregori names maintenance, edge cases, mounting UX debt, and data ownership as continuing costs. A tool that stores or moves data raises questions about who can access it, where it lives, and what happens when it is corrected, migrated, or deleted. A technically functional interface can also accumulate friction if users cannot understand its behavior or recover from mistakes.
Jan Jikeli’s enterprise commentary, published January 30, 2026 and updated April 15, 2026, adds concerns that grow with organizational scale: compliance, security, legacy systems, team turnover, and operational failure. These are professional observations, not a comparative study showing that AI-written code is inherently defective.
When a small, short-lived tool is enough
Not every useful piece of software needs the same engineering investment. Gregori distinguishes task-specific “personal software” from systems expected to persist, evolve, or serve a broader product or organization. A one-off internal helper may be a sensible solution if its purpose is narrow, its lifetime is limited, and failure has low consequences.
A practical way to choose the level of care is to consider the tool’s intended lifetime alongside the consequence of failure. Then account for how much important data it handles, how many external systems it depends on, whether security or compliance controls apply, and who will maintain it. This is a way to organize the examples in these sources, not a formally validated scoring model.
| Question | Limited-lifetime tool | Durable production system |
|---|---|---|
| How long must it work? | For a defined task or short period; retirement is acceptable. | It is expected to remain available, evolve, and outlast individual contributors. |
| What happens if it fails? | The inconvenience or loss is limited and understood. | Failure may disrupt important work, users, or operations. |
| What does it connect to? | Few dependencies, with a straightforward way to stop using the tool. | Integrations and data flows require monitoring and adaptation as other systems change. |
| Who owns it? | A named user or team can accept its limits and retire it. | There is an ongoing responsibility for testing, security, support, and maintenance. |
These are decision prompts, not a rule that every prototype must become a production platform. A low-consequence tool can remain deliberately simple; the important thing is not to mistake a useful first version for a durable service without making that choice consciously.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What engineers and teams should focus on
AI assistance does not remove the need for engineering judgment. It can shift time away from typing routine code and toward clarifying requirements, evaluating generated changes, testing behavior, and managing dependencies and operations. Gregori puts the responsibility plainly: “AI often feels powerful because it hides the complexity, but as an engineer, your job is to manage that complexity, not ignore it.”
For coding agents working in large codebases, Markus Eisele’s WeAreDevelopers World Congress 2026 Europe session listing, dated July 10, 2026, recommends making intent explicit, constraining changes, assigning small tasks, and reviewing the result. Its session description advises teams to “treat generated code like a pull request from a teammate you don’t fully trust yet.” That is guidance from the listing, not a claim about an independently verified talk.
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- State the intended behavior. Describe the user problem, expected inputs and outputs, relevant failure cases, and what is out of scope.
- Limit the change. Give an agent a small, bounded task and identify the files or interfaces it should affect where practical.
- Inspect the result. Review the diff for unintended behavior, assumptions, data handling, and compatibility with the surrounding system.
- Test what matters. Check ordinary use as well as the edge cases and failures that matter for this tool’s purpose and consequences.
- Assign ownership. Decide who will respond when dependencies, external interfaces, requirements, or users’ needs change.
These steps do not guarantee correctness; they make generated changes easier to understand and govern. The right depth depends on what the software does and what failure would mean.
The real distinction: generating code versus owning software
Gregori’s central warning is not that every small tool needs enterprise-grade architecture or that AI-generated code cannot work. It is that code is only one part of a software system. Once the system is used, someone must understand its behavior, handle the cases it missed, respond to changing integrations, and make future changes without breaking what already works.
The sources offer an argument and practical recommendations, not a measured comparison of software built with and without AI. They do not establish a universal productivity multiplier, failure rate, or cost saving. The useful conclusion is narrower: faster code production can reduce one kind of effort, but it does not settle whether a solution fits its purpose, risk, and expected lifetime.
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