The Future Of App Development: How AI Is Streamlining The MVP Race

• There is a tectonic shift happening in the DevOps space, which has a significant impact when it comes to developing something new — whether it’s an app, a new product feature, or a new service.
• I strongly believe we’ll see the emergence of tools that significantly accelerate MVP development for innovative apps, product feature releases, and services, reducing timelines from months to mere weeks, or even just days.
• GenAI can accelerate your workflow and expedite delivery; there will soon be two types of people: those who leverage AI, and those left behind.

I recently spent time at the Raleigh Durham Startup Week hearing exceptional content from many speakers, and meeting a ton of founders with incredible ideas. 

As I listened to various stories, I realized I was seeing a tectonic shift in the DevOps space, which has a significant impact on the future of work when it comes to developing something new — whether it’s an app, a new product feature, or a new service.

There were many examples, but one that sticks out to me is one of the speakers, Josh, who presented his AI-powered app designed to streamline business networking follow-up. 

While the app’s AI functionalities are undeniably useful, the timeline it took him to build a minimum viable product (MVP) sparked my curiosity about the future for developers across the board.

To expedite his time to market, Josh strategically used a “no-code” app-building tool called Glideapps. This wasn’t because he lacked coding expertise, but rather as a way to significantly reduce development costs from tens of thousands of dollars to just a few hundred, while also drastically cutting down development time. 

AI is already making a substantial difference for him, not only in the features his product offers to the public but also in his method of getting a working concept into users’ hands to gather crucial feedback that fuels continuous improvement cycles for his product’s evolution.

Recently, I collaborated with a client using a similar approach, yet with a distinct twist. I was building an app using Flutter and employed a GPT called Grimoire to handle the actual coding. This was a truly eye-opening experience. 

Despite having no prior experience with Flutter (although my knowledge of several programming languages makes learning new ones a less daunting task), I was incredibly impressed by how quickly I was able to achieve a functional prototype homepage. 

While there were still months of work ahead to complete all the functionalities, interactions, automations, and back-end development, the initial progress was undeniably impressive.

The Future is Now, But What About Tomorrow?

Considering the rapid pace of innovation in this space, I strongly believe we’ll see the emergence of tools that significantly accelerate MVP development for innovative apps, product feature releases, and services, reducing timelines from months to mere weeks, or even just days.

However, I don’t envision AI completely replacing developers. 

Similar to how using GenAI verbatim without vetting it first leads to errors, the same will hold true for AI-generated development work. It remains critical to review the code and architecture being generated, to validate and confirm the logic, inspecting for potential issues, conducting thorough QA testing, and exploring edge cases to ensure the intended functionality.

While GenAI will undoubtedly improve in these areas, a human element will always be necessary for intelligent review to confirm deliverables meet expectations.

While GenAI will undoubtedly improve in these areas, a human element will always be necessary for intelligent review to confirm deliverables meet expectations.

That being said, in the future, development will likely occur at a higher level, requiring fewer intermediaries. 

Currently, a robust Software Development Life Cycle (SDLC) process involves developers, project managers, program managers, architects, QA testers, product release teams, and others to deliver high-quality products with minimal defects on schedule. 

When we look at where this will be for the future of work, the teams will be smaller, and the work will be shifted higher in the ranks. Architects and technical project or program managers won’t necessarily need developers to facilitate the stakeholder’s vision. 

There will be a merging of roles with developers transitioning from needing specific language expertise, to possessing a strong understanding of development principles and project management. 

They’ll leverage GenAI to generate deliverables and execute automated QA processes, essentially functioning more in the role of a hybrid project manager/developer, where the roles of each have merged while also working collaboratively with GenAI to achieve goals.

Lessons from the Past for a Glimpse into the Future

The auto industry offers a valuable historical parallel. Look at automation in the auto industry over the last 50 years. It used to be that human beings did every step in the process. From pulling the levers and pushing the buttons on the presses that cut and formed the steel, to actually holding the welding tools which stitched all those parts together into a car. 

Today that has shifted to the point where robots do all of that work now, and humans on the line are working at a much higher level, ensuring that the robots are functioning as expected, keeping the systems moving from one step to the next, and reviewing the deliverables to ensure they meet expectations and quality guidelines. 

This revolutionized the workforce, the number of people needed, and the speed of delivering an automobile off the line. The same thing will happen for software development as GenAI grows, and by 2026 if not sooner, you’ll see this same transformation. 

So, what should you do?

As someone actively involved in this field, I’m taking a page from the playbook of those auto workers. I’m actively using the tools, understanding their strengths, weaknesses, and potential pitfalls. 

It’s crucial to stay ahead of trends. In those factories, workers who reskilled in robotics and control systems became the new line managers and leaders. Companies valued their product knowledge and interest in the new manufacturing methods. 

Similarly, explore how GenAI can accelerate your workflow and expedite delivery. As a friend said, there will soon be two types of people: those who leverage AI, and those left behind.