Faster Code, Greater Risks: The Hidden Cost of AI Software Development

AI can generate software in minutes. Making sure that software is secure, reliable and ready for real-world use remains a much harder challenge.

Software development is entering a new phase. Tasks that once required hours of writing and refining code can now begin with a simple instruction to an AI assistant. Developers can generate functions, build interface components, draft database queries and explore unfamiliar technologies faster than ever.

For businesses, the attraction is obvious. Faster development can mean shorter delivery timelines, lower initial costs and more opportunities to bring products to market. For smaller companies and start-ups working with limited resources, AI-assisted development can make projects that once seemed out of reach more achievable.

But there is an important distinction between generating code and delivering dependable software.

A feature can appear to work while containing a security vulnerability. An application can pass a demonstration and still fail when hundreds of users access it simultaneously. A seemingly minor change can disrupt an existing integration, expose sensitive information or introduce a problem that only emerges months later.

These are not arguments against using AI. They are reasons to be more deliberate about how it is used.

As AI tools become increasingly capable of producing software, the question for businesses is no longer simply how quickly their development team can write code. It is whether that code has been properly tested, reviewed and prepared for the conditions in which it will operate.

The Productivity Gains Are Real. So Are the Trade-offs.

It would be misleading to dismiss AI coding tools as little more than a shortcut. Research suggests they can deliver meaningful productivity improvements in the right circumstances.

A study published in Management Science in 2025 examined three field experiments involving 4,867 developers at Microsoft, Accenture and a Fortune 100 company. Across the combined experiments, access to an AI coding assistant was associated with a 26.08% increase in completed tasks. However, the results varied between experiments, so the figure should not be treated as a guaranteed improvement for every developer or software project.

The finding supports a compelling case for AI-assisted development: it can help developers complete more work, particularly when used for suitable tasks.

Yet productivity is not the same as software quality, and completing more tasks does not automatically mean delivering more reliable products.

Generating a function is one part of development. Understanding its place within the wider application, checking its assumptions, validating its behaviour and ensuring that it does not introduce new problems are separate responsibilities.

AI can accelerate the first part without eliminating the others.

For example, an AI assistant might generate a payment-processing function that appears correct when tested with a standard transaction. That function may still mishandle a refund, calculate a rounding adjustment incorrectly or fail when a payment provider returns an unexpected response.

The code may look convincing. The underlying problem may remain invisible until a real customer encounters it.

The commercial value of AI-assisted development therefore depends not only on how much code it produces, but on how reliably that code performs.

When Working Code Is Not Secure Code

One of the most consequential risks is the assumption that code which works is necessarily safe to use.

Software security depends on decisions that may not be obvious from a feature’s visible behaviour. How does the application handle user input? Are database queries constructed safely? Can an unauthorised user access restricted information? Are passwords, authentication tokens and other sensitive values protected? What happens when an external service returns unexpected data?

An AI model can produce a functional answer without adequately addressing these questions, particularly when the original instruction does not specify the relevant security requirements.

Research published in Veracode’s 2025 GenAI Code Security Report illustrates the concern. The company evaluated code generated by more than 100 large language models across Java, JavaScript, Python and C#. In its defined test scenarios, 45% of the generated code samples failed security tests involving vulnerabilities covered by the assessment.

That result requires context. The research tested particular coding tasks designed to examine known security weaknesses; it does not establish that 45% of all AI-generated code in production is vulnerable. Nevertheless, it demonstrates that functional code generation alone is not a sufficient security measure.

Consider a web application that accepts information from users and stores it in a database. A generated implementation might produce the expected result during ordinary testing but handle user input unsafely, leaving the application exposed to a vulnerability such as SQL injection.

Similarly, an interface might display the correct information while failing to enforce the necessary permissions on the server. Hiding a button from an unauthorised user does not prevent that user from attempting to access the underlying operation directly.

These problems are not exclusive to AI-generated code. Experienced developers can make the same mistakes. The concern is that code can now be produced at a pace that makes weak review processes even more consequential.

If an organisation increases its development output without strengthening its security checks, it may simply introduce more code that needs to be examined and maintained.

Security must therefore be part of the development process from the beginning, rather than a final inspection carried out when a product is nearly ready to launch.

The Hidden Cost of Skipping Software Testing

Security is only one part of the problem. Reliability is equally important, particularly when software supports everyday business operations.

A software feature rarely operates in isolation. It interacts with databases, APIs, authentication systems, third-party services, existing business rules and other components that may have been developed years earlier.

A change that looks correct on its own can still break one of those connections.

Imagine an e-commerce business introducing an AI-assisted update to its checkout process. The new interface works, orders appear to complete successfully and the feature passes a basic demonstration. However, the update handles a payment failure incorrectly, records an order before confirmation or causes the inventory system to deduct stock twice.

The problem is not necessarily that the AI produced obviously poor code. It is that the change was not adequately tested against the wider system.

This is where a disciplined testing strategy becomes essential.

Unit testing checks individual functions or components against expected results. It can help identify incorrect calculations, unexpected outputs and failures in specific pieces of logic.

Integration testing examines whether connected components work together correctly. It is particularly important when software communicates with payment gateways, databases, external APIs or other services.

End-to-end testing evaluates complete user journeys, such as creating an account, placing an order or completing a payment. It helps reveal failures that may not appear when individual components are tested separately.

Regression testing checks whether a new change has disrupted functionality that previously worked. This matters because a seemingly local improvement can have consequences elsewhere in an established application.

Security testing looks for weaknesses that ordinary functional tests may not detect, including access-control problems, injection vulnerabilities and insecure handling of sensitive information.

No single testing method can identify every possible defect. Together, however, they provide a stronger basis for deciding whether a feature is ready for release.

The appropriate level of testing depends on the application, its risks and the consequences of failure. A minor internal utility does not necessarily require the same controls as a financial platform or a system handling sensitive customer information.

What matters is that testing is designed around the software’s actual responsibilities, rather than reduced simply because code was generated quickly.

Why Faster Development Can Still Lead to Slower Delivery

The pressure to move quickly can create another problem: teams may produce code faster than they can review, test and safely release it.

Google Cloud’s DORA research has examined the relationship between generative AI and software delivery performance. Its 2025 findings described AI as an amplifier of an organisation’s existing strengths and weaknesses. Teams with effective development practices are better positioned to benefit, while weaknesses in their processes can become more consequential.

An important distinction lies between individual productivity and the performance of the entire delivery system.

A developer may use AI to complete a task in half the usual time. But if the resulting change requires extensive manual review, triggers several failing tests or introduces a production incident, the overall time saved may be much smaller than expected.

The same issue can arise when developers submit larger changes because AI makes generating code easier. Large changes can take longer to review, make defects harder to isolate and increase the potential impact of a mistake.

This creates a familiar bottleneck. Code generation accelerates, but review queues grow. More changes are ready for testing, but the testing process cannot keep pace. Features appear to be progressing quickly until an unresolved issue delays a release or forces the team to revisit earlier work.

The solution is not to slow every developer down. It is to ensure that the surrounding engineering process can keep up.

Small, manageable changes are generally easier to review and troubleshoot. Automated checks can identify common problems early. Continuous integration can run tests whenever changes are submitted, while continuous delivery practices can make releases more consistent when the necessary safeguards are in place.

These practices are not new, but AI makes their importance harder to ignore.

The goal should be to improve the speed of the entire journey from an idea to dependable software—not merely the speed at which code appears on a screen.

The Long-Term Cost of Code That Nobody Fully Understands

There is another cost that may not become visible until long after a product launches: maintainability.

Software is rarely finished when its first version goes live. Businesses need to add features, respond to customer feedback, update dependencies, accommodate changes in external services and adapt to new security requirements.

Every implementation becomes part of the codebase that future developers must understand and maintain.

AI can help produce readable, well-structured code, but it can also generate unnecessary complexity, inconsistent patterns or abstractions that are difficult to justify. The outcome depends on the task, the instructions, the model and the developer’s review.

Problems emerge when generated code is accepted simply because it appears plausible or passes a narrow test.

A future developer may struggle to understand why a particular approach was taken. An apparently redundant condition may conceal an undocumented business rule. A dependency may be introduced without a clear reason. Error handling may be inconsistent with the rest of the application.

Over time, these decisions can make routine changes more expensive and increase the risk of introducing new defects.

This is often described as technical debt: the future cost created by decisions that make software harder to maintain or improve.

AI does not automatically create technical debt, just as human-written code is not automatically maintainable. The risk increases when teams prioritise immediate output over sound engineering decisions.

A responsible development process should therefore include more than generating and accepting code. Developers need to understand the implementation, remove unnecessary complexity, document important decisions and ensure that the code fits the architecture and conventions of the existing system.

The objective is not to reject every line that AI produces. It is to make sure the team can explain, maintain and safely change what it delivers.

What Businesses Should Expect From an AI-Assisted Development Team

For business owners, founders and technology leaders, the practical question is how to benefit from AI without allowing speed to undermine quality.

The answer starts with the development process, not the choice of AI tool alone.

1. Clear requirements before code generation

AI tools work within the context they receive. If the requirements are incomplete, the generated implementation may make assumptions about permissions, calculations, error handling or user behaviour.

A development team should establish what a feature must do, which conditions it must handle and how success will be measured before treating generated code as a finished implementation.

2. Human review with clear accountability

AI-generated code should be reviewed against the project’s requirements, architecture, coding standards and security expectations.

Reviewers should consider not only whether the code works, but also whether it is necessary, maintainable and appropriate for the application. Responsibility for approving and releasing the software must remain clearly assigned to people.

3. Automated tests and repeatable checks

Tests should be built into the development workflow rather than left until the end of a project. Automated checks can help identify defects consistently and provide early feedback when changes break expected behaviour.

For higher-risk systems, additional security testing and independent review may be appropriate.

4. Secure handling of data and dependencies

Teams should understand what information can be shared with AI tools, particularly when working with confidential source code, customer data, credentials or proprietary business information.

They should also review third-party dependencies, monitor known vulnerabilities and apply suitable access controls. AI-generated code is not exempt from the same security standards as code written manually.

5. Monitoring after release

Passing tests does not guarantee that software will behave perfectly in production. Real users, changing workloads and external services can expose problems that were not apparent during development.

Logging, monitoring, error reporting, backup procedures and a clear incident-response process help teams detect and address issues before they cause greater disruption.

6. Measure outcomes, not just output

The number of lines of code produced or features completed is not enough to establish whether AI is delivering value.

Businesses should also consider defect rates, time spent on rework, release frequency, production incidents, security findings and the effort required to maintain the software.

These measures provide a more meaningful picture of whether AI is improving delivery or merely moving effort from writing code to fixing it.

AI Is a Development Advantage, Not a Substitute for Engineering Discipline

The debate around AI in software development is often framed as a choice between embracing the technology and resisting it. That is the wrong question.

AI-assisted development can be genuinely valuable. It can help developers explore unfamiliar codebases, automate repetitive work, generate initial implementations and spend more time on complex problems. Used appropriately, it can improve the economics of building and maintaining software.

But a faster development process is only an advantage when the resulting product meets the standards its users and business depend on.

The evidence does not support treating every AI-generated line of code as unsafe. Nor does it justify assuming that increasingly capable models have made conventional engineering safeguards unnecessary. Productivity, security and reliability are related, but they are not interchangeable.

For businesses investing in software, the more useful question is not how much code a development team can produce in a day. It is how effectively that team turns ideas into secure, maintainable software that performs as intended in real-world conditions.

That requires clear requirements, thoughtful architecture, rigorous testing, human accountability and continued attention after launch.

At DibHub, we believe the real advantage of AI lies in combining its speed with sound engineering judgement. The aim is not simply to build faster, but to build software that businesses can trust, maintain and grow with confidence.

If your business is planning a new software product, modernising an existing platform or exploring AI-assisted development, start with the outcomes you need—and the quality standards the finished product must meet.

Because in software development, the fastest code to write is not necessarily the least expensive code to own.

Faique Ali Raza
Business Development Lead
DibHub