Working software is becoming weaker evidence that the people responsible for it understand how it works. AI can generate schemas, security rules, infrastructure, architecture documents, tests, and deployments. Those outputs can run and pass checks while the developer retains only a partial mental model of what the AI produced.
That distinction matters because engineering practices provide visible evidence about software quality and process. A clean continuous integration and continuous delivery (CI/CD) pipeline, which automates software integration and release, shows that defined build and deployment steps can run successfully. Reproducible infrastructure, extensive tests, and a coherent design document provide other evidence. With AI-assisted development, these artifacts can reveal less about the human understanding behind them.
Working software does not establish human understanding
Consider an AI-assisted deployment. A developer asks an AI to scaffold the application and connect its back end. The model generates a database schema that gets a quick review, security rules the developer does not fully understand, and infrastructure-as-code, meaning machine-readable definitions of cloud resources. The system runs.
That outcome exposes the core issue. AI can help produce sophisticated technical artifacts while the person directing the work understands only part of the reasoning embodied in them.
Executives therefore face two separate questions. Does the system work under the conditions tested? Does the team understand why it works well enough to take responsibility for it? Engineering artifacts can answer the first. The second requires evidence of human command of the system.
Engineering artifacts can carry less evidence about their creators
Test-driven development (TDD), in which developers write tests around expected behavior as part of implementation, produces useful tests. CI/CD makes defined integration and deployment steps repeatable. Architecture documents record technical decisions, while infrastructure-as-code makes environment definitions reproducible.
These artifacts can also reveal something about how they were produced. Writing a test requires an expected behavior to be expressed. Infrastructure-as-code encodes how an environment should be assembled, and an architecture document records technical choices.
AI weakens that inference. A generated test can catch a regression, and a generated deployment definition can be reproducible. But the existence of either artifact does not establish how much its human reviewer understood.
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AI can preserve the artifact while changing its production
Debugging shows how the practice can change. A developer can paste an entire “200-line stack trace” into a model and request a correction. An agentic integrated development environment (IDE), meaning a coding tool that can take multi-step actions, can identify an error, propose a solution, and apply it after approval. It may also operate with automatic confirmation enabled.
When an answer fails, the developer can feed the failure back into the prompt, forbid a library, supply release notes for a current application programming interface (API), and narrow the available solution space until the error disappears.
This work still requires judgment. The developer must recognize failure, choose constraints, and assess later output. But that activity can differ from constructing a causal explanation of the failure. The developer may instead steer the model toward a result that passes the immediate check.
The same pattern can apply elsewhere. AI can translate existing JavaScript logic into Rust or expand an incomplete architectural idea into a polished design document with technical specifications and sequence diagrams. The result can resemble an artifact produced by an engineer who personally worked through more of the intermediate reasoning.
The hardest case: when AI writes both the code and the proof
Testing makes the distinction especially clear because tests provide evidence about code.
The claim that AI can deliver “95% test coverage almost effortlessly” requires verification. Test coverage measures how much code is exercised by a test suite under a specified coverage metric. A high percentage can show broad execution by tests, but it does not establish that the tests contain independent reasoning about the requirements.
A developer can ask the same model to generate application logic and its unit tests, integration tests, smoke tests, mocks, assertions, and edge cases. A mock is a simulated version of an external component used during testing.
Suppose a model generates an implementation from an incorrect assumption about an edge case. If it carries that assumption into the corresponding test, the implementation and assertion can agree, and the test can pass. A generated mock can create the same problem when both the implementation and mock reflect one incorrect understanding of an external dependency.
This is a correlated blind spot: separate artifacts share an error because they derive from the same underlying assumption. Independent scrutiny can challenge that shared assumption. Tests generated from the same model and context as the implementation may provide less independent scrutiny when both inherit the same mistake.
The management issue is precise. Coverage measures the relationship between tests and code. Engineering leaders should separately assess whether someone challenged the assumptions behind both.
AI-assisted development can shift the skills required
Producing working software with AI can demand judgment. In the debugging example, the developer recognizes regressions, adjusts constraints, rejects bad approaches, and supplies missing information. A context window, the amount of information a model can consider at one time, can limit the interaction. Models can also use outdated APIs or fail to follow instructions.
Persistence and the ability to constrain a model can matter in AI-assisted engineering. Those skills demonstrate command of the production process. By themselves, they do not demonstrate knowledge of every consequential assumption in the resulting system.
For CTOs, this separates two measures that can otherwise be conflated: the ability to complete engineering work with AI and the ability to explain the resulting system and respond when it behaves outside the path already explored with the model.
Accountability requires direct evidence
A clean build establishes that defined build steps succeeded. A deployment shows that defined deployment steps succeeded, while tests show that specified assertions passed under the tested conditions. An architecture document records a description of the system.
Organizations also need evidence that responsible engineers can explain consequential assumptions, investigate surprising behavior, and make decisions when generated fixes fail. They can ask engineers to explain critical design choices and failure modes directly, then test those explanations against the running system and its requirements. This measures human command of the system itself instead of inferring it solely from the artifacts surrounding the software.
Key executive takeaways
- Working software does not prove human understanding: AI can produce functioning systems while developers retain only a partial mental model. Leaders should assess whether responsible engineers can explain consequential design decisions, assumptions, and failure modes.
- Engineering artifacts are weaker evidence of expertise: Tests, CI/CD pipelines, architecture documents, and infrastructure-as-code remain valuable, but AI can generate them without equivalent human understanding. Evaluate the quality of the artifact separately from the engineer’s command of it.
- AI changes how engineering work gets done: Developers can steer models toward successful fixes without fully constructing the causal reasoning behind them. Leaders should distinguish effective AI use from the ability to diagnose unfamiliar failures independently.
- AI-generated code and tests can share blind spots: When the same model generates both implementation and validation, incorrect assumptions can appear in both and still produce passing tests. Require independent scrutiny of critical requirements, edge cases, and external dependencies.
- AI-assisted development shifts the skills that matter: Prompting, constraint-setting, model supervision, and recognizing failures are increasingly useful engineering skills, but they do not replace system knowledge. Measure both AI-enabled productivity and technical command.
- Accountability requires direct evidence: Successful builds, deployments, and tests establish that defined checks passed, not that engineers understand why the system works. Ask responsible engineers to explain critical assumptions and failure modes, then validate those explanations against requirements and system behavior.
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