Will AI replace developers?
The debate has been heating up over the past couple of years. The software development industry, which has long relied on skilled developers, is presented with the option of Artificial Intelligence entering the workforce.
There is panic among developers, confusion among organizations and general uncertainty in the industry.
As of today, organizations in need of software development hire developers across expertise levels or outsource the entire project or parts of it to external teams. The developers build an MVP, enhance the product and keep on iterating until product market fit is achieved.
AI is poised to enter this pipeline and enhance developer productivity, rather than taking the entire job away from them. Instead of a battle between humans and machines, AI has started becoming a powerful collaborator, transforming developers into ‘Super Developers.’
AI vs Human Developers – The Battle is on?
Ever since the LLMs came into the forefront of the tech space, folks have started comparing human capabilities with that of AI in different roles. Software development is also a hot topic, gaining even more attention in the past few months with the announcement of proposed 'AI Software Engineer' claims.
If AI were to enter the software development industry, it is not by replacing humans. The current LLMs are not capable of developing software applications that could rival the perfection of a human engineering team.
AI could make mistakes; it could hallucinate; and the level of randomness in each output is something that makes it unreliable. Today's AI is best to be placed in an exhibition, as a depiction of the future. But it falls behind in the professional market.
So, let's for the love of God, stop comparing AI with human software engineers.
AI’s Practical Role in Your Software Development Workflow
AI can make things fast. It can save a lot of time on different aspects of development. But you would need to curate it to run that way.
The problem is with the expectations. When people think of AI doing a job, they expect it to complete everything on its own.
Startups are trying to build AI platforms that are not completely rooted in reality. They are not looking to build a product that could make an impact on an existing market.
If you are dreaming of an AI that would completely develop full scale apps with just prompts, then you are weighing more to the fantasy side than productivity. AI will surely be able to do it one day, but not anytime soon.
How can AI change the way we develop software?
Today, even with a good set of developers, it would take months to even develop an MVP. The primary reason being the huge number of repetitive tasks in each project. These tasks can eat up to 80% of development time, leaving less time for developers to focus on the core features and functionalities of the application.
This is where AI steps in. AI can be trained to automate these repetitive tasks, freeing developers from drudgery and allowing them to focus on what they do best – innovation.
1) AI to automate repetitive tasks.
Instead of expecting AI to complete everything on its own, AI can handle the 80% repetitive part that most developers find mind-numbing. This massive chunk involves work that is less dynamic. So, the randomness of the AI can be significantly reduced.
2) AI to reduce the number of developers required for a project.
Since 80% of the project will be handled by AI, there is no requirement for too many developers to be present in a team. A single developer can build a project over a week's time, leaving room for other developers to work on different projects.
3) Developer can focus on the core features; AI handles the boring part.
Repetitive tasks like user login, dashboards, and UI/UX will be handled to a great extent by AI, so that developers can focus on the main features of the app.
The Rise of the Super Developer
Capitalizing the capabilities of AI can increase the productivity of the development, reduce cost and build projects that are market ready, faster.
Developers were intimidated with the arrival of AI, but the reality is AI turns human developers into "Super Developers.” A developer with an ideal AI advantage can:
Focus completely on the core features of a project without worrying about anything else.
Can deliver projects 10 times faster i.e., a single developer can deliver multiple projects a month.
Can ensure code quality and standardization.
Can reduce the overall development cost.
MERN.AI: An IDE that Makes You a "Super Developer"
One big example of an ideal AI development tool is MERN.AI. Instead of building an AI platform for all languages, our team at Skyslit built MERN.AI specifically to develop web applications on MERN (Mongo-Express-React-Node) stack. This way AI can be trained particularly for MERN stack and how to build better on it.
MERN.AI is an IDE where developers can utilize AI and automations to build web apps faster than ever before. It allows developers to turn a blank project into a market-ready web app within hours.
Here is how MERN.AI infused AI in the normal development pipeline & automates 80% of the development:
Pre-built templates & AI: The IDE has a growing list of pre-built templates for repetitive features, UI and APIs. The AI will study the project and use templates wherever necessary.
AI as a guide: For the remaining custom development, AI will generate a series of steps and instructions that help developers to follow best practices while building a project.
Code generation & assistance: AI assists by generating compatible code at each step. AI can also answer questions based on the mern.ai documentation right from the IDE.
Conclusion
The intersection of AI and software development is not about replacing human developers but enhancing their capabilities. While AI may not yet possess the effectiveness of a seasoned engineering team, it excels at streamlining repetitive tasks and augmenting human intelligence.
Rather than fantasizing about AI autonomously crafting entire applications from prompts, the focus should be on leveraging AI to rid developers of mundane tasks, allowing them to concentrate on the core features and complexities of a project.
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