AI coding tools have changed the way developers approach software development.
Tasks that once took hours to get started can now have a working first version in minutes. Developers can use AI to generate functions, find potential fixes, create test cases and understand unfamiliar codebases without spending as much time starting from scratch.
At first, this sounds like a major productivity boost.
But there's something teams need to consider: writing code is becoming faster, but reviewing, testing and making sure that code actually works still takes time.
A developer might generate several changes in a single day. However, every change still needs to be reviewed, tested and checked before it can become part of the application.
When the amount of generated code increases, the pressure doesn't necessarily disappear. It can simply move to another stage of development.
Instead of waiting for developers to finish writing code, teams may find themselves waiting for reviews, QA checks and approvals.
And that's where a new engineering bottleneck begins to emerge.
1. Faster Code Generation Doesn't Automatically Mean Faster Delivery
Let's look at a typical development scenario.
A team is working on a new feature. One developer uses an AI coding assistant to generate the initial implementation. Another uses AI to write supporting functions, while someone else creates test cases with the help of AI.
Within a few hours, the team has produced a considerable amount of code.
But there's still a problem.
The pull requests are waiting for review.
The reviewer needs time to understand the generated code. QA needs to check whether the feature behaves as expected. Edge cases need to be tested, and some of the generated tests may need further work.
The team has written more code, but the feature isn't necessarily any closer to being released.
That's an important distinction.
Software development involves much more than writing code. It also requires:
- Understanding what the requirement actually involves.
- Planning and designing the solution.
- Implementing the required functionality.
- Reviewing the code for potential issues.
- Testing expected and unexpected behaviour.
- Fixing defects and making necessary changes.
- Integrating and releasing the final implementation.
AI can speed up certain parts of this process, but it doesn't remove the responsibility of checking whether the final result is reliable.
2. What Does Verification Debt Look Like?
Most developers are familiar with technical debt. It usually refers to shortcuts or design decisions that create additional work when software needs to be maintained or improved later.
Verification debt is a different challenge.
It builds up when code is being produced faster than a team can properly review and validate it.
Here are a few situations where this can happen.
More Pull Requests, But Fewer Completed Reviews
Developers are submitting changes more frequently, but reviewers still have limited time.
As a result, pull requests start accumulating. Some remain open for longer than expected, while others receive less attention because everyone is trying to keep the work moving.
Tests Are Being Generated, But Confidence Is Still Missing
AI can help create test cases, but having more tests doesn't automatically mean better testing.
A test might check whether a function produces the expected result without considering invalid inputs, unusual conditions or interactions with other parts of the application.
- The test exists, but does it actually cover the risks?
- That's a question teams still need to answer.
- Problems Are Being Discovered Much Later
An AI-generated implementation might work perfectly for the example used during development but behave differently under another condition.
If the issue isn't identified during review or testing, it could make its way into a later stage of development.
Fixing it at that point may require additional time and effort.
Nobody Has a Clear Picture of What's Still Waiting
One developer is waiting for a code review. QA is waiting for an updated build. Another developer is working on a related bug.
All these activities are connected, but their dependencies might not be visible to everyone.
When tasks and updates are spread across different tools, understanding what's holding up delivery becomes more difficult.
The result is a growing list of work that looks almost finished but still needs someone's attention.
3. Why AI Can Make This Problem Easy to Miss
The speed of AI-assisted development can sometimes give teams the impression that everything is moving faster.
A developer completes several assignments in a short period, and it feels like productivity has improved significantly.
But the number of completed coding tasks doesn't tell the entire story.
A feature that's been implemented but hasn't passed review isn't necessarily ready for release.
A bug that's been assigned but not fixed is still unfinished work.
And a test generated by AI doesn't automatically prove that the implementation is behaving correctly.
This is why measuring AI productivity purely through the amount of code produced can be misleading.
Teams should also pay attention to what happens after implementation.
For example:
- How long do pull requests wait before someone reviews them?
- How often does code need to be sent back for changes?
- Are defects being discovered after implementation?
- How much work is waiting for QA?
- Which dependencies are preventing tasks from moving forward?
Looking at these details gives engineering teams a better understanding of whether AI is genuinely improving delivery or simply increasing the amount of work waiting to be verified.
4. How Engineering Teams Can Keep Verification Under Control
The solution isn't to stop using AI coding assistants.
Instead, teams need to make sure their review and testing processes can keep up with the faster pace of development.
Give Every Review Clear Ownership
Every code change should have an identified reviewer or an established review process.
Without clear ownership, pull requests can remain untouched because everyone assumes someone else will take care of them.
Prioritise Reviews Based on Risk
Not every code change needs the same level of scrutiny.
A small documentation update doesn't carry the same risk as a change involving authentication, payment processing or database permissions.
Reviewing changes according to their potential impact helps teams use their available time more effectively.
Define Acceptance Criteria Before Writing Code
Developers and testers should have a clear understanding of what a feature is expected to do before implementation begins.
When acceptance criteria are properly defined, it's easier to identify missing functionality, understand expected behaviour and prepare meaningful tests.
Treat Reviews and Testing as Actual Project Work
Code reviews, QA validation and bug fixing shouldn't be treated as activities that happen after the real development work is finished.
They're part of the development process.
Just like implementation tasks, they need clear owners, priorities and deadlines.
Make Blocked Work Visible
If a task is waiting for approval, a review or a bug fix, the dependency should be visible to the people involved.
A task might appear almost complete, but it can still delay several other activities if nobody knows what's holding it up.
Keeping these dependencies visible makes it easier for teams to identify where attention is needed.
5. AI Should Improve the Workflow Around Code Too
Most conversations about AI in software development focus on generating code, debugging errors and creating test cases.
But there's another area worth considering: how teams organise and coordinate everything surrounding development.
An AI-powered project management assistant can help teams understand what's happening across their projects and keep everyday activities organised.
For example, it can help with:
- Identifying tasks that are pending or overdue.
- Assigning bugs to the appropriate developers or QA testers.
- Keeping track of deadlines and task ownership.
- Reviewing project health and potential risks.
- Understanding which activities are waiting for attention.
Of course, this doesn't replace proper code reviews, automated testing or engineering judgement.
It helps teams manage the work surrounding those activities.
This is where ProjTL brings project and task management together with Atlas, its AI assistant.
Teams can use ProjTL to organise tasks, assign bugs and track ongoing work. Atlas also supports project health assessments, workload checks, risk reviews and status updates.
Through its WhatsApp integration, users can add and assign tasks and check pending or overdue work without constantly switching between applications.
The distinction is important: generating code and managing everything required to deliver that code are two separate challenges.
Engineering teams need to pay attention to both.
6. Measure Verified Progress, Not Just Generated Code
AI has made producing code easier and faster. That's valuable, but the amount of code generated isn't a reliable measure of successful software delivery.
Teams need to look at what happens throughout the entire development process.
- Activity What teams should measure
- Implementation Work completed against requirements
- Code review Review waiting time and rework
- Testing Defects identified and quality of test coverage
- Bug management Open bugs, ownership and resolution time
- Delivery Work successfully reviewed, tested and completed
These measurements can help managers identify where development is actually slowing down.
They also prevent teams from making a common mistake: assuming that generating more code automatically means delivering software faster.
Final Thoughts
AI is changing how quickly developers can build software, but it hasn't removed the need to make sure that software works reliably.
If code generation becomes faster while reviewing, testing and coordination remain unchanged, teams could end up with larger queues instead of faster releases.
The next challenge for engineering teams isn't simply finding ways to generate more code.
It's making sure that every change gets the attention it needs, passes the necessary checks and reaches delivery without creating another pile of unfinished work.
Because development speed shouldn't be measured by how quickly code is written. It should be measured by how confidently a team can ship it.
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