For several years, I was one of Power Apps’ strongest advocates. I collected every single Power Platform certification Microsoft offered, from the fundamental badges all the way through to the architect-level credentials. I helped teams build internal tools and championed the idea that low-code was the fastest way to deliver applications in environments where IT teams were stretched thin. And at the time, that made perfect sense. Low-code gave domain experts the ability to build without needing deep technical skills. It filled a genuine gap.
A lot of that belief came from my own background. I was always comfortable with the back-end. Databases, APIs, integration layers. But front-end development was never something I felt confident in or had the capacity to learn deeply. Frameworks evolved too quickly. UI design was its own discipline. Power Apps solved that problem for me. It let me focus on the logic without getting lost in the visual side.
But the ecosystem shifted. AI coding assistants changed the equation completely. The early tools were helpful but inconsistent. Then I tried Claude, and everything moved forward dramatically. I could generate front-end components just by describing them. I could request layout refinements, responsive behaviour, state logic or API integrations, and Claude produced clean, working React or TypeScript code. Then magicpatterns took that a step further by letting me describe entire screens and workflows, and it produced full wireframes with working mock data that I could export and use immediately.
And then came Kiro, the agentic IDE from AWS. Kiro is not a code generator. It is closer to a staff-level engineer who reviews everything you build. It refactors your project, restructures folders, enforces patterns, catches logical inconsistencies and helps clean up technical debt before it appears. Instead of simply producing code, it maintains code quality as the project evolves.
This combination changed how I build forever. Earlier this year, I built a complete public-facing application in less than two days. The front end ran on Azure Static Web Apps. The API layer was an Azure Function. The back end was a fully indexed and relational Azure SQL database with stored procedures and proper OLTP design. ChatGPT helped me think through the architecture and generate detailed prompts, often through voice chat while I was walking between meetings or driving home. It felt less like using a tool and more like calling a close friend who happens to be a software guru, talking through ideas in real time and shaping the design as we went. Magicpatterns generated the working UI wireframe and components. Claude code wrote the API layer. Claude and Gemini produced the SQL schema and stored procedures. And Kiro improved the entire repository after the fact, so the project looked like something built by a team rather than a single developer working at speed.
The speed difference is significant. Tasks that took days inside Power Apps now take a fraction of the time with AI-assisted full-stack development. Instead of being confined to a visual editor, I can shape the application exactly the way I want. I can version the code. I can scale it without friction. I can reuse components and manage it like any modern engineering project. And when I hit roadblocks, the AI steps in. It explains. It fixes. It reviews. It troubleshoots. The experience moves from working around the tool to collaborating with an intelligent partner.
The benefits extend beyond speed. AI-assisted full-stack development opens capabilities that low-code platforms simply cannot match.
1. Full control
There are no restrictions. I can change the UI, restructure the logic, rewrite the API layer, redesign the data model, or implement security exactly how I want it. Nothing is locked away behind a formula bar or a pre-defined widget.
2. The entire internet becomes your knowledge base
Most Power Apps resources are focused on narrow patterns or isolated formulas. Full-stack has an entire decade of open-source material, tutorials and community knowledge. React components, SQL patterns, .NET snippets, Python workflows. The answers are everywhere.
3. Code is portable
React, Node, .NET and Python can run anywhere. Azure, AWS, GitHub Pages, containers, mobile apps or serverless. Low-code apps are virtually locked inside the ecosystem that created them.
4. Versioning and DevOps are natural
Git is built for code. Pull requests, testing, environments and collaboration. Low-code tools try to retrofit this, but it never feels native.
5. AI fills the skill gaps
You do not need to be a senior engineer. You simply need to describe what you want. AI handles the rest. And that was always the promise of low-code in the first place.
Of course, giving full-stack power to non-developers introduces risk. But it is important to remember that low-code was never an ungoverned playground. Power Platform admins set boundaries, applied security policies and controlled environments. Nothing stopped a poorly designed low-code app from breaking something without that governance. Full-stack is no different. You can still have senior developers reviewing pull requests. You can still have cloud engineers controlling permissions and resource creation to avoid misconfigurations or exposure. The difference is that you are giving the development power back to the people who understand the business context and can build with that in mind.
Will Low-Code Disappear?
Not immediately. It remains valuable for simple forms, quick workflows, internal processes and organisations already deeply invested in the ecosystem. But the strategic picture is shifting. Low-code was built on the assumption that coding is hard. AI is erasing that assumption.
The Likely Future: Low-Code Becomes the “Excel of App Building”
Like Excel, low-code will always have a place. It will remain the convenience layer. The rapid prototyping tool. The place where ideas begin. But full-stack development, accelerated and guided by AI, will become the default for anything that needs scale, flexibility or long-term maintainability. AI has made full-stack accessible, fast and intuitive. And once organisations experience that shift, low-code’s limitations become much harder to justify.
About the Author:
Maha Kepakisan, CPA, is a former accountant turned data engineering leader with over a decade of experience delivering enterprise-scale data solutions across industries including finance, telecom, insurance, and marketing technology. Currently Analytics and Engineering Manager at Ritchies Transport, Maha has led large cloud migrations, built scalable architectures, and developed analytics platforms used by some of New Zealand’s most recognisable organisations. He holds multiple Microsoft certifications and is passionate about helping finance and business teams modernise their data workflows through automation, strong governance, and a citizen-led approach.
