AI has made it possible to go from an idea to working software at a speed that would have seemed unrealistic only a few years ago.
Describe the feature. Generate the implementation. Fix an error. Add authentication. Change the database schema. Refactor the component. Keep going.
The feedback loop is incredibly fast.
But there is a subtle problem hiding inside that workflow.
The same AI helping you build the application often becomes the AI you ask to tell you whether the application is good.
You generate a feature and ask:
Does this look right?
You encounter a problem and ask:
Did we fix everything?
You finish a workflow and ask:
Is this ready for production?
The model reviews the system through much of the same context, assumptions, and implementation history that produced it.
That can be useful.
It should not be your only form of validation.
Building and validating are different jobs
Traditional software teams separate these responsibilities for a reason.
Developers build features.
Code reviewers challenge implementation decisions.
Automated tests check expected behavior.
QA teams try workflows developers may not have considered.
Security teams examine different failure modes.
Users eventually interact with the application with none of the context the development team had while creating it.
Each perspective sees something different.
AI-assisted development does not make those perspectives less important.
If anything, faster development makes them easier to skip.
When one person can produce features at the speed that once required an entire team, some of the checks previously provided by that team can disappear with it.
That is one reason vibe coding can enter a death spiral. The application keeps growing while confidence in what is underneath it fails to grow at the same rate.
The builder knows too much
Knowing how something works can make it surprisingly difficult to experience it like someone who does not.
A developer knows that clicking a particular button should open a modal.
They know which account state the feature expects.
They know what happens after a successful API call.
They know which route the user is supposed to take.
They know what they meant.
The user knows none of that.
They click the button.
They close the modal unexpectedly.
They refresh halfway through a process.
They arrive through a different route.
They have an older account.
They use the feature in an order nobody anticipated.
The difference between those two perspectives matters.
An application can be logically correct from inside the implementation and still behave incorrectly from the user's side of the screen.
AI inherits the builder's context
This becomes particularly interesting when AI participates heavily in development.
An AI coding assistant may know:
- the feature request
- the code it generated
- the files it modified
- the errors encountered during implementation
- the fixes it attempted
- the assumptions discussed during the session
That context is extremely valuable while building.
But it is not the same context a user has.
If you ask the same development environment whether the feature works, you may be asking a system with extensive knowledge of what the application is supposed to do.
Independent validation starts from a different question:
What does the application actually do?
Not:
Does the implementation appear consistent with what we intended?
Those are related questions, but they are not identical.
A passing code review does not mean a passing experience
Imagine an AI-built marketplace.
The code for a product modal looks correct.
The close handler works.
Navigation is valid.
The component compiles.
The tests included with the feature pass.
From the implementation side, there may be no obvious problem.
But when a real user opens a product from the marketplace and closes the modal, they are unexpectedly returned to the home page instead of the marketplace.
Nothing necessarily looks catastrophic in the code.
The problem exists in the experience.
This is where validating a running application becomes fundamentally different from analyzing its source.
Source code tells you how the application was constructed.
The running application tells you what the user actually encounters.
You need both perspectives.
This is not an argument against AI coding tools
The lesson is not that AI-generated software is inherently unreliable.
AI coding tools are becoming extraordinarily capable.
They can help developers and non-developers create software faster, understand unfamiliar codebases, debug problems, write tests, refactor systems, and explore implementation options.
The mistake is expecting one tool or one perspective to perform every role in the development process.
A strong builder can still benefit from a reviewer.
A comprehensive test suite can still benefit from exploratory testing.
A secure architecture can still contain a broken user flow.
And an AI system capable of producing excellent code can still benefit from an independent system evaluating what it produced.
The better AI development becomes, the more important this distinction may become.
Speed changes the bottleneck
For decades, generating software was expensive.
A significant part of the challenge was simply translating requirements into functioning code.
AI is rapidly reducing that constraint.
You can now create more software, more features, and more iterations with fewer people and in less time.
But increased output creates another challenge:
How do you maintain confidence in everything you are producing?
The bottleneck begins moving.
From:
Can we build this?
Toward:
Do we understand what we've built?
Does it behave correctly?
What assumptions are hiding inside it?
What deserves our attention next?
This is not purely technical debt.
It is increasingly a form of comprehension debt.
The software can grow faster than the builder's understanding of the software.
Independent validation is a separation-of-responsibilities problem
There is a useful principle here that software engineering shares with many other disciplines:
Creation and evaluation benefit from separation.
Writers use editors.
Researchers use peer review.
Financial systems separate transactions from auditing.
Security teams use red teams.
Software teams use independent reviewers and QA.
None of those systems assume the creator is incompetent.
They recognize that different roles produce different perspectives.
AI-assisted software development deserves the same separation.
The system helping create the application can remain exceptionally good at creating it.
Another system can approach the application with a different responsibility:
Find out what is actually there.
Validate from the user's side of the screen
This is an important distinction for Gimbl.dev.
Gimbl.dev does not exist to compete with the AI that builds your software.
It occupies a different position in the workflow.
Your coding assistant helps create the application.
Gimbl.dev independently examines the application that was created.
That means looking beyond whether the code appears reasonable and toward questions such as:
- What happens when someone actually uses this workflow?
- Which paths behave differently than expected?
- What interactions break?
- What assumptions are visible in the running product?
- What problems deserve attention first?
The objective is not to replace developers, coding assistants, tests, or existing engineering tools.
It is to provide another perspective.
FAQ
Why shouldn't the AI that wrote the code be the one validating it?
The model reviews the system through much of the same context, assumptions, and implementation history that produced it. That can be useful. It should not be the only form of validation.
What is independent validation?
Independent validation starts from a different question: what does the application actually do? That is not the same as asking whether the implementation appears consistent with what was intended.
Does this mean AI-generated code is unreliable?
No. The lesson is not that AI-generated software is inherently unreliable. AI coding tools are becoming extraordinarily capable. The mistake is expecting one tool or one perspective to perform every role in the development process.
Can a passing code review or test suite replace that check?
No. The code can look correct, the component can compile, and the included tests can pass, while a real user path still behaves incorrectly. Source code tells you how the application was constructed. The running application tells you what the user actually encounters. You need both perspectives.
What is comprehension debt?
Comprehension debt is what appears when software grows faster than the builder's understanding of it. The bottleneck moves from whether something can be built toward whether you understand what you've built, whether it behaves correctly, and what assumptions are hiding inside it.
How is Gimbl.dev different from an AI coding assistant?
A coding assistant helps create the application. Gimbl.dev independently examines the application that was created. The objective is not to replace developers, coding assistants, tests, or existing engineering tools. It is to provide another perspective.
Closing Insight
AI has changed the economics of software creation.
That is unlikely to reverse.
More people will build software.
Smaller teams will build larger products.
Applications will evolve faster.
Coding agents will become more autonomous.
The logical response is not to slow that process down.
It is to create better systems around it.
Use AI to build.
Use AI to iterate.
Use AI to solve problems faster than was previously possible.
But do not confuse the ability to generate software with independent evidence that the software works the way you believe it does.
The AI that helped build your application can tell you a great deal about it.
It just should not be the only perspective you trust.
Build with AI. Validate independently.