10 myths about AI and software development

AI is often described in extremes. Either it will replace every software developer, or it is only useful for small experiments and simple applications. Neither view reflects how responsible software teams use it in practice.

At TailoredByte, we build dedicated software for manufacturing, logistics, food, and energy companies. AI helps our engineers work faster, but it does not replace architectural decisions, security review, testing, or accountability. Below are ten common myths and what the available research actually supports.

1. AI writes the code and nobody checks it

Responsible development does not end when code is generated. AI output enters the same repository, pull request process, test suite, and review flow as code written manually. GitHub reports that more than 12,000 organisations use Copilot code review in pull requests, but automated review is designed to support human reviewers, not replace them.

Developers themselves are cautious. Stack Overflow's 2025 survey found growing AI use alongside lower trust in the accuracy of AI generated answers. In a well run team, AI accelerates the first draft. Engineers remain accountable for every change that reaches production.

2. Software built with AI is automatically less secure

The risk is real, but the conclusion is too broad. In a controlled Stanford and UC Berkeley study, participants using one AI coding assistant created less secure solutions on most tested tasks and were sometimes more confident in incorrect code. Veracode's 2025 report also found security weaknesses in a substantial share of generated samples across many models.

This does not mean AI assisted code is inherently insecure. It means security cannot be delegated to a prompt. Static analysis, testing, manual review of access control and data handling, and a secure development lifecycle remain essential. AI can assist with tests and pattern detection, but engineers must validate the result.

3. AI written code is a black box

Code is only a black box when a team cannot inspect or trace it. AI assisted code can be versioned, reviewed, documented, and tested in exactly the same way as other code. The important question is whether the team understands the module and records the architectural decisions behind it.

AI can also help make legacy systems easier to understand. IBM reports that its mainframe modernisation tool reduced the time needed to understand complex COBOL applications in one specific public sector case. That example does not apply automatically to every system, but it shows how AI can support comprehension when experts verify its output.

4. AI is only useful for simple projects

Enterprise work includes documentation, refactoring, integration, and maintenance, not only writing new features. McKinsey found meaningful productivity gains in some of these activities, including documentation and refactoring. However, benefits fell sharply on the most complex tasks, and less experienced developers could be slower when using AI.

Complexity does not make AI useless. It makes experienced engineering judgment more important. The value depends on the task, the existing system, the quality of the requirements, and the people guiding the work.

5. Dedicated software is always slow and expensive

Traditional ERP projects can be long and difficult. Industry benchmarking cited by zConsulto describes implementation timelines commonly ranging from six to 18 months, with many projects exceeding their original budget. That is one reason generic software often becomes a compromise between the tool and the real operation.

AI assisted development can shorten selected parts of delivery, such as documentation, early prototyping, and test preparation. With a tightly defined problem and phased implementation, a working prototype can often be validated in weeks. This does not remove the need to scope integrations, security, change management, or long term maintenance.

6. Every system change becomes another expensive project

Poor architecture and weak documentation make even small changes costly. Stripe's 2018 Developer Coefficient report remains a useful illustration of this problem: developers spent significant time on maintenance, debugging, and correcting poor code. It should not be treated as a current measurement of the whole market.

The answer is maintainable design. A modular system, current documentation, tests, and clear ownership reduce the risk that one change will affect unrelated workflows. AI can help draft and test an isolated change, but the benefit still depends on the system's architecture and the review process.

7. AI replaces programmers, so quality will drop

AI can reduce repetitive drafting. It does not replace understanding business needs, designing a reliable architecture, deciding tradeoffs, reviewing code, or taking responsibility for production outcomes. Those are the activities that determine quality.

The labour market also does not support a simple conclusion that AI has already replaced engineers. Recent hiring data has been mixed and influenced by many factors. What is clear is that complex systems still require experienced people who can recognise an incorrect answer, even when it is presented confidently.

8. Generic tools with AI are always enough

Low code, no code, and off the shelf platforms are useful in the right situation. But as a process becomes more specific, integration heavy, or subject to demanding performance, security, and compliance requirements, platform constraints may become limiting. The outcome depends on the platform and the use case.

AI has not removed the tradeoff between speed and fit. A dedicated solution becomes worth considering when the process, controls, and integrations are important enough that a generic tool creates more work than it removes.

9. AI cannot help with legacy systems

Legacy environments often contain exactly the kind of documentation gaps and repeated patterns that AI can help engineers explore. IBM's examples in COBOL to Java modernisation show potential productivity and comprehension gains in a specific mainframe context.

The lesson is not that every old ERP can be modernised automatically. AI output must be checked against the code, data, and business rules. Its value is helping experts map an unfamiliar system and focus their time on decisions that need human verification.

10. You can prompt AI and skip engineers entirely

A chatbot can produce working looking code, but it cannot guarantee that the code is secure, maintainable, compliant, integrated correctly, or aligned with the way a business operates. The research above points in the same direction: AI is most useful when it works inside a structured engineering process.

AI is a force multiplier for people who can define what correct looks like and verify the result. It is not a substitute for that experience.