Can AI in product development truly accelerate coding without sacrificing quality? Read the comparison revealing the truth about AI software development.
Can AI software development tools make developers and the whole development process faster without sacrificing product quality? It’s the million-dollar question that’s been polarizing the tech community. We’ve all heard the hype; AI is the future, AI is hype, AI will replace developers, AI can’t be creative. The noise is endless, but at Dash Technologies, we decided to cut through the chatter and put these claims to the test the impact of AI on software development.
We designed a controlled experiment that would give us real, measurable data. Two developers, one task, two completely different approaches: Traditional manual coding versus AI software development using Cursor, an AI-powered code editor designed with developer context in mind. Why? To once and for all settle the question of whether AI product development actually speeds up the development process without compromising the quality our clients rely on.
The outcome? You might say they were really eye-opening. In this blog, we walk you through the process of our experiment and reveal preliminary results that might surprise you. Stay tuned for Part 2, as we take a deeper look into how AI software development is changing the future of coding; and what it means for your next project.
Here’s where things start to get interesting. We didn’t just take two of our best Devs and throw them into the ring. We carefully vetted and selected two professional software developers who would actually make sense to conduct this test on, which will help reveal to us great insights into AI product development.
Participant Profiles:
Developer A: Traditional Methodology Baseline
– Java expert with 5-6+ years of battle-tested experience
– Senior full-stack developer who’s seen it all
– Proven track record in complex system architecture & implementation
– Approaches coding the old-school way: manual writing, debugging, & testing
Developer B: AI-Assisted Development
– Python specialist with zero Java experience (yes, you read that right!)
– Junior resource with fresh perspectives
– Already familiar with Cursor & AI Code Assistants
– Leveraged AI Code Assistant for code generation, debugging, and optimization
Now here’s where we put the wheat from the chaff. We didn’t assign our developers some superficial “Hello World” project that you’d see in a coding bootcamp. No. We dropped them in the deep end with a problem that would make even the most experienced developers break a sweat.
The Mission: Build a full-stack note-taking and task management application that could actually compete with real-world products. This wasn’t just about testing AI software development capabilities; this was about seeing if AI in product development could handle the complexity that defines modern software engineering.
The developers had to construct a full set of capabilities intended to analyze all aspects of AI product development:
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