Software is rarely finished when it launches. There are bugs to fix, screens to adjust, integrations to update and small requests that keep appearing after people start using the product. Eventually, even a well-built application accumulates changes that nobody could have predicted when the original version was created.
For businesses, maintenance can become one of the less visible costs of owning software.
AI-assisted development is changing that part of the process too. The interesting shift isn’t simply that AI can create new applications. It can also make some changes to existing ones considerably easier to attempt.
Most software changes are smaller than new features
When people talk about software development, they often imagine large additions.
In practice, maintenance includes plenty of less dramatic work.
A company changes its approval process. A form needs another field. A report needs a different filter. An administrator wants to export information in another format. A notification needs to be sent at a different stage of the workflow.
None of these sounds particularly difficult.
But in a traditional codebase, even a small change can touch several parts of the application. Developers have to understand how those pieces connect before making the adjustment, then test that the change hasn’t created problems elsewhere.
The work is rarely just the number of lines of code involved.
Old software can become expensive to understand
There is another problem that appears as applications age.
The people who built the original system may no longer be working on it. Documentation may be incomplete. Certain decisions may have been made years ago and never written down.
A seemingly simple request can therefore begin with a period of investigation.
Where is this behaviour controlled?
Which database table stores this information?
What else depends on this component?
Is this old function still being used?
That investigative work can take longer than the actual modification.
AI coding systems can help developers navigate parts of this process by working with existing code and generating suggested changes. The developer still needs to understand the system and review what has been changed, but the starting point can be different from opening a large unfamiliar codebase and searching through it manually.
A change can be described in terms of the outcome
This is one of the more interesting developments in AI-assisted software work.
A developer or product manager can describe what should happen rather than immediately explaining how every part should be implemented.
For example, a business might need its support team to see a customer’s previous requests whenever a new ticket is opened. The important requirement is the behaviour.
An AI development system can help translate that description into changes across the application.
The technical implementation still matters, particularly when the application is complex. But the first interaction with the change can happen at a higher level.
Emergent uses this natural-language approach to generate and modify full-stack web and mobile applications. That makes it relevant not only when a new product is being created, but also when teams are iterating on an application and need to turn new requirements into working changes.
Faster changes can create a new problem
There is a catch.
If software becomes easier to modify, people may become tempted to modify it constantly.
That isn’t necessarily healthy.
A product can become cluttered with features simply because adding them is easy. Small changes can interact in unexpected ways. An application that was once straightforward can accumulate layers of logic that nobody fully understands.
So faster maintenance does not mean every request should be implemented immediately.
It may actually make product discipline more important.
Teams still need to decide whether a change solves a genuine problem, whether it belongs in the product and whether the resulting complexity is worth carrying forward.
Testing has to keep pace with the edits
The faster an application changes, the easier it is for regressions to slip through.
A modification to one workflow can affect another. Changing how information is stored can break an existing report. Updating authentication can affect integrations that nobody remembered were connected to it.
That makes automated testing particularly valuable.
AI can help produce code, but businesses still need mechanisms for checking whether the application behaves correctly after those changes. For important systems, human review remains necessary as well.
The convenience of changing software quickly only works if the team can tell when a change has caused damage.
The economics of frequent iteration
Emergent’s pricing is structured around monthly credits, giving teams different levels of capacity for building and modifying applications.
The Free plan includes 10 monthly credits, together with core web and mobile app development, one-click LLM integration and access to the latest AI models.
The Standard plan is $20 per month or $204 per year and includes 100 monthly credits, private project hosting, GitHub integration, task forking and the option to purchase additional credits.
For heavier usage, Pro costs $200 per month or $2,004 per year. It provides 750 monthly credits and adds a 1-million-token context window, high-performance computing, custom AI agents, advanced reasoning capabilities and priority customer support.
For businesses, that model can be useful when software needs to evolve continuously rather than being built once and left largely untouched.
Maintenance could become less of a separate phase
The traditional idea of software maintenance makes it sound like something that happens after development.
AI-assisted development blurs that distinction.
If creating a new feature, fixing an issue and changing an existing workflow all happen through similar development interactions, there is less reason to think of maintenance as a completely separate stage of a product’s life.
The application becomes something that is continually shaped by the business using it.
That doesn’t make maintenance free or effortless. Production systems still require engineering oversight, testing, security reviews and careful change management.
But it may reduce the friction involved in making sensible changes.
And that could be one of the more practical consequences of AI in software development: not that businesses can build an application once and forget about it, but that they may have an easier time keeping the application aligned with the business after the original requirements have inevitably changed.
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