I’m thrilled to sit down with Anand Naidu, a seasoned development expert whose mastery of both frontend and backend technologies offers a unique perspective on the rapidly evolving world of software creation. With a deep understanding of various coding languages, Anand has been at the forefront of integrating AI into coding practices, witnessing firsthand how these tools are transforming the industry. In this conversation, we dive into the concept of ‘vibe coding,’ the impact of AI on debugging and software production, and the future role of human developers in an AI-driven landscape. We also explore the potential for boutique software, the economic implications for small businesses, and the broader societal shifts that AI might bring to the field.
How did you come up with the term ‘vibe coding,’ and what does it mean to you in the context of working with AI tools?
I coined ‘vibe coding’ to describe this almost intuitive, fluid way of coding with AI assistance. It’s like jamming with a band—you’re not overthinking every note; you’re just in the groove, letting the AI handle the heavy lifting while you guide the direction. It came from my experience of working on projects where I’d throw ideas at an AI agent, and it would riff off them, almost like a creative partner. It’s about feeling the flow of the project rather than getting bogged down in every line of code. Honestly, it’s made coding more enjoyable and less of a grind.
Can you share your experience revisiting a project from six months ago using AI tools now? What stood out as a major improvement?
Absolutely, it was night and day. Six months ago, I worked on a web app, and I had to babysit the AI tool constantly to keep it from veering off track—fixing logic errors, tweaking outputs, you name it. When I picked up the same project recently, the AI was so much sharper. It nailed almost everything on the first try, from structure to implementation. One specific thing that blew me away was how it handled a complex UI component. Last time, I spent hours debugging alignment issues; this time, it just worked. I’m still wrapping my head around how fast these tools are evolving.
When it comes to debugging, how much do you lean on AI, and have there been times when it didn’t quite get it right?
I rely on AI for debugging a ton now, probably 80% of the time. It’s incredibly fast at decoding those cryptic error messages that used to take me forever to figure out. I just paste the error, and boom, it pinpoints the issue in seconds. That said, it’s not infallible. There was a time when it misdiagnosed a memory leak in a backend script, suggesting a fix that actually made things worse. I had to step in and manually trace the problem. So, while it’s a lifesaver, I still keep a critical eye on its suggestions.
Could you walk us through a specific instance where AI tackled a complex issue like package dependency problems?
Sure, I had a project where I was stuck in what I call ‘dependency hell.’ I was integrating multiple libraries, and the versions just wouldn’t play nice—conflicts everywhere, error messages a mile long. Normally, I’d spend hours, maybe days, unraveling the chain of dependencies. But I fed the error logs to the AI, and within a minute, it identified the exact conflicting versions and suggested compatible alternatives. It even wrote the updated configuration file for me. What could’ve been a full day of frustration turned into a quick fix, letting me focus on the actual app development.
You’ve mentioned ’boutique software’ and ‘short form software.’ What kinds of niche apps or websites do you envision popping up with AI lowering the barriers to creation?
I think we’re going to see a wave of hyper-specific, small-scale software thanks to AI. Think of apps tailored to tiny communities or very particular needs—like a mobile tool for local book clubs to track reading lists and meetups, or a website for a specific hobby, like vintage camera collectors, to trade parts. These ideas often got shelved in the past because the development cost wasn’t worth the small audience. With AI slashing those costs, we’ll see more of these bite-sized, creative projects come to life, kind of like how short-form content exploded on platforms like TikTok.
How do you see the reduced cost of software development with AI impacting small businesses or independent developers?
It’s a game-changer for small businesses and solo developers. In the past, hiring a dev team or spending months coding something yourself was a huge barrier. Now, with AI, a small business owner can prototype an app for their shop—say, a custom booking system—in days instead of months, and for a fraction of the cost. For indie developers, it levels the playing field; you don’t need a big budget to compete with larger firms. I think we’ll see more innovation from the little guys, but it might also flood the market with apps, making it harder to stand out.
Since AI excels at coding due to its text-based nature, are there other industries where you think it could have a similar transformative effect?
Definitely. Any field that’s heavily text-driven is ripe for AI disruption. Legal work, for instance, involves drafting contracts and analyzing documents, which is all about processing and generating text. AI could automate a lot of that, just like it does with code. Technical writing is another area—think user manuals or documentation. It’s not as nuanced as creative writing, so AI can churn out solid drafts fast. The key is whether the output can be standardized and less dependent on human judgment. Where it gets tricky is in fields requiring emotional intelligence or physical skills—AI isn’t there yet.
You’ve raised concerns about AI training on its own code if humans write less in the future. Could this lead to issues like propagating bugs or bad practices?
That’s a real worry. If AI starts training predominantly on code it generates, there’s a risk of a feedback loop where errors or suboptimal patterns get amplified. Think of it like a photocopy of a photocopy—each iteration can degrade quality if there’s no original to reference. I’ve seen AI suggest inefficient solutions based on outdated practices it likely picked up from somewhere. Without humans writing fresh, high-quality code to balance the dataset, we might end up with a codebase full of quirks or even systemic flaws. It’s something we’ll need to monitor closely.
As AI takes on more coding tasks, what do you think the role of human developers will evolve into?
I believe humans will shift more toward the conceptual and strategic side of software development. We’ll be the ones defining the vision—coming up with what the software should do and why it matters. AI can handle the grunt work of writing code, but it still needs direction on the big picture. I also think we’ll stay involved in crafting core components or frameworks, ensuring there’s a solid foundation for AI to build on. Plus, there’s the human touch—understanding user needs or cultural nuances—that AI can’t fully replicate yet. We’ll be more like architects than bricklayers.
You’ve drawn parallels between software developers today and historical roles like farmers or autoworkers, whose numbers dwindled over time. Do you think the developer workforce will shrink similarly due to AI?
I think it’s likely, though not overnight. Just like automation reduced the need for farmers and factory workers while still keeping some around for specialized tasks, AI could thin out the number of developers needed for routine coding. We might not need as many people writing boilerplate code or maintaining basic apps when AI can do it faster. However, there’ll always be a demand for high-level thinkers who can innovate or solve problems AI can’t. The question is balance—how many of us will shift to those creative roles versus how many jobs might just disappear.
Can you elaborate on your comparison of today’s developers to the ‘human computers’ from Hidden Figures? What tasks do you see AI fully automating?
The analogy to the ‘human computers’ in Hidden Figures is about how roles that once required intense human effort can be overtaken by technology. Those women did incredible, precise calculations by hand for the space program, but now a simple spreadsheet can do it instantly. Similarly, I see AI taking over repetitive, rule-based tasks in coding—like generating standard algorithms, writing basic scripts, or even optimizing code for performance. These are things that don’t require much creativity, just accuracy and time, which AI excels at. It’s already happening with things like automated testing and refactoring.
Given the uncertainty around where software development is headed with AI, what’s one development you’re most excited to witness in the next couple of years?
I’m really pumped to see how AI might enable fully conversational software creation. Imagine describing an app idea in plain English—like, ‘I want a fitness tracker that gamifies workouts with friends’—and the AI not only builds it but iterates based on your feedback in real-time. We’re close to that now, but in a year or two, I think it could become seamless. It would democratize coding so much, letting anyone with an idea bring it to life without touching a keyboard. That kind of accessibility is thrilling to think about.
Looking ahead, what’s your forecast for the role of AI in software development over the next decade?
I think AI will become the backbone of software development, handling probably 90% of the actual coding tasks within the next decade. We’ll see it integrated into every step—from ideation to deployment—making the process faster and cheaper. Human developers will likely focus on high-level design, ethics, and ensuring AI doesn’t go off the rails with biased or flawed outputs. My hope is that this shift frees us up to tackle bigger, more complex problems, but I also foresee challenges in maintaining quality and originality if we lean too heavily on AI. It’s going to be a wild ride figuring out that balance.
