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No, says software developer Quentin Merle: AI coding tools may make code easier to produce, but they do not eliminate the need to understand, verify, and maintain it. His case is best read as an engineer’s opinion—not a study proving what will happen to developer jobs.
What Merle means by “Are we doomed?”
In his DEV Community essay, “Are we doomed? A developer’s manifesto on AI,” Merle argues that developers are not doomed by AI coding tools. He sees AI as another layer of abstraction: it can reduce the amount of syntax a person has to write and help with tasks such as brainstorming, drafting, debugging, and learning. But producing code is not the same as delivering software that behaves correctly in a real system.
Merle compares current predictions about AI replacing developers with earlier claims that content management systems would make developers obsolete. His point is that complexity becomes more visible when software has to work with existing systems, handle production conditions, and remain maintainable. That is an analogy and an interpretation, not a data-backed comparison of technology-driven job changes.
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Why generated code still needs engineering judgment
Merle’s central distinction is between code that has been generated and software that has been checked. A model can produce a plausible implementation, but a developer still needs to determine whether it fits the intended design, works with surrounding systems, and can be maintained. Treating output as understood and tested simply because it looks convincing is the risk he emphasizes.
Use checks that do not depend on the model’s confidence
Merle recommends a deterministic software harness: a compiler, linter, and independent test suite can catch defined classes of errors without asking the same model to vouch for its own answer. These checks do not prove that software is free of defects, but they make important parts of verification repeatable. The engineer must still decide what the software should do and whether the checks cover the relevant behavior.
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Merle captures his position this way: “The value of an engineer today does not lie in writing a poetic prompt, but in the ability to design a deterministic software harness to neutralize the entropy of the machine.” That is his prescription, not a demonstrated guarantee that developers who follow it will keep their jobs.
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In Merle’s framing, AI reduces some of the effort involved in writing code while making the work of evaluating and integrating that code more important. Developers still need to understand requirements, judge trade-offs, review changes, and take responsibility for what is shipped. The essay does not show that these responsibilities are immune to automation; it argues that producing code alone is an incomplete picture of software engineering.
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When Merle suggests hosted or local AI
Merle proposes choosing where AI runs according to the sensitivity of the information being used. His division is a suggested workflow, not a security assessment of any particular service, local model, or deployment.
| Approach | Uses Merle suggests | Trade-offs he emphasizes |
|---|---|---|
| Hosted model | Brainstorming, drafting or debugging, public documentation, and rapid prototyping | He emphasizes capability, speed, and large context. How submitted data is handled depends on the service and arrangement; the essay does not verify a provider’s retention or security practices. |
| Local model | Personally identifiable information, production logs containing IDs, and confidential internal scripts | It can give an organization more control over where data is processed, but Merle highlights hardware demands and performance limits. The essay does not evaluate a particular setup’s security. |
“Local” or “air-gapped” is not, by itself, proof that sensitive information is protected. Merle’s recommendation is about the processing boundary; organizations still need to assess how their own systems, access controls, and data flows are configured.
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How junior developers can use AI without skipping learning
Merle rejects the idea that junior developers are automatically an endangered species. He argues that AI can support learning when a beginner asks for explanations, inspects the generated code, and works to understand why it behaves as it does. Copying output without understanding it may make a task appear finished while leaving the developer less prepared to debug or extend the code.
This is a learning recommendation, not evidence of measured career outcomes. A useful distinction is whether AI is helping a learner build an explanation they can defend or merely supplying an answer they cannot evaluate. The former fits Merle’s argument; the latter sidesteps the engineering knowledge the essay says remains important.
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What the manifesto does—and does not—establish
Merle’s essay offers a clear professional stance: use AI where it helps, keep independent checks in the workflow, and do not confuse generated code with verified software. It does not provide controlled benchmarks, a security audit, or employment data to establish how AI affects performance, data exposure, or developer demand.
One employment claim needs particular care: the essay says that developers adopting this rigor will become more technically mature over three years than earlier developers were after ten. That is Merle’s prediction, not a measured result. The same caution applies to broad claims in the essay about autonomy, agent loops, data retention, liability, market incentives, hardware performance, and employment.
Merle writes, “This is not a taking of sides, but a reflection at a given point in time, a manifesto.” That framing is useful: the essay argues for a way to think about AI-assisted development, rather than settling what the technology will mean for every developer.
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