Today, we’re thrilled to sit down with Anand Naidu, our resident development expert with a wealth of experience in both frontend and backend technologies. Anand brings a unique perspective on the evolving role of AI in software engineering, offering deep insights into various coding languages and the challenges of integrating cutting-edge tools into development workflows. In this conversation, we dive into the productivity paradox of AI-assisted coding, exploring how it impacts code generation, team dynamics, and project timelines. We’ll also discuss the importance of human oversight, the risks of burnout, and strategies for adapting processes to harness AI’s potential without compromising quality or sustainability.
How would you describe the “productivity paradox” when it comes to AI-assisted coding?
The productivity paradox in AI-assisted coding is this fascinating contradiction where AI tools are speeding up how fast developers can write code, but they’re not necessarily making entire projects finish quicker. On one hand, developers are churning out lines of code at an incredible rate thanks to generative AI. On the other, we’re seeing bottlenecks pop up in areas like code review, integration, and testing. It’s like upgrading one part of a machine without touching the rest—you end up with a pile-up instead of a smoother process. The speed in code generation exposes weaknesses elsewhere in the development lifecycle, and that’s where the paradox lies: more output doesn’t always mean faster delivery.
What do you think is behind the fact that faster code writing isn’t cutting down overall project timelines?
It really comes down to the downstream effects. When developers write code faster with AI, they often produce larger volumes or more complex changes in a single go. That sounds great until you realize the review process, integration steps, and testing phases haven’t scaled to match that output. Reviewers get overwhelmed with massive pull requests, and testing can’t keep up with the sheer amount of code to validate. Plus, AI-generated code often needs significant edits or debugging, which eats up time. So, while one part of the process is accelerated, the rest of the system is still operating at a human pace, creating a mismatch that drags out timelines.
How have you observed this paradox playing out in your own projects or with your team?
In my team, we’ve definitely felt it. There was a project where we adopted AI tools to speed up feature development, and initially, it was amazing—developers were pushing out code like never before. But soon, our code review queue started piling up. Pull requests were getting bigger, and reviewers couldn’t keep up without sacrificing depth. We also noticed more bugs slipping through because we were rushed. It became clear that while AI helped us write faster, the rest of our workflow—reviews, testing, deployment—wasn’t ready for that volume. We had to step back and rethink how we balance speed with thoroughness.
In your experience, how has AI influenced the volume of code developers are producing?
AI has been a game-changer in terms of volume. Developers are generating way more code in less time, especially for repetitive tasks like boilerplate or unit tests. I’ve seen team members tackle complex features in half the time they used to, simply because AI can draft a solid starting point. But it’s a double-edged sword—more code often means more to sift through later. It’s not just about writing; it’s about managing that increased output without drowning in it. The sheer quantity can be overwhelming if you don’t have processes in place to handle it.
What challenges have emerged in code review since AI started generating more code?
The biggest challenge is the sheer scale. AI often produces large chunks of code or massive pull requests that are tough to digest. Reviewers have to spend extra time understanding the context and intent behind these changes, which can be mentally taxing. There’s also the issue of trust—AI code might look correct syntactically, but it can hide subtle issues like inefficiencies or security flaws. So, reviewers need to be extra vigilant, which slows things down. Without enough reviewers or time, you either rush and miss problems, or you create a bottleneck where developers are waiting for feedback.
Why do you believe human review remains critical, even when AI-generated code seems fine at first glance?
Human review is non-negotiable because AI doesn’t truly understand context or intent. Sure, it can spit out code that runs and looks polished, but it might not align with the project’s architecture, security standards, or long-term maintainability. I’ve seen AI suggest deprecated libraries or inefficient solutions because it’s pulling from a broad dataset, not our specific guidelines. Humans bring judgment and experience to the table—spotting edge cases, ensuring compliance, and thinking about how code fits into the bigger picture. Without that oversight, you’re rolling the dice on quality and future headaches.
The concept of different developer workflows—legacy, augmented, and autonomous—has come up. Which of these do you see most often in your team, and why?
In my team, I’d say the augmented workflow is the most common right now. Many developers are embracing AI as a partner for specific tasks like debugging, generating tests, or speeding through routine code. They’re not fully offloading to AI, but they’re using it to boost efficiency on well-defined problems. I think it’s because there’s still a healthy skepticism about AI’s reliability for bigger, more creative tasks, combined with a desire to stay hands-on with the craft. It’s a middle ground that feels safe and productive for most developers I work with.
How do these varying workflows affect collaboration within your team?
They can create some friction, honestly. When you’ve got developers working in different modes—some relying heavily on AI, others sticking to traditional methods—communication and expectations can get messy. For instance, someone in an autonomous workflow might submit a huge pull request full of AI-generated code, while a legacy developer reviewing it might push back hard because they don’t trust the output or find it hard to parse. It impacts pacing too; augmented or autonomous developers might move faster, leaving others feeling pressured or out of sync. We’ve had to work on setting clear norms around code submissions and reviews to keep everyone aligned.
What bottlenecks have you run into in your development process since incorporating AI tools?
The biggest bottleneck for us has been code review, hands down. With AI, developers are submitting more and larger pull requests, but we haven’t increased the number of reviewers or hours in the day. That creates a logjam where developers are waiting for feedback, slowing down the whole pipeline. Testing is another pain point—AI can generate tests, but validating their quality or fixing gaps takes time. Integration also gets tricky when you’ve got tons of new code that needs to mesh with existing systems. These stages haven’t scaled with the speed of code generation, and that’s where things get stuck.
How do you balance the speed of AI-driven coding with ensuring the work remains sustainable for your team?
It’s all about setting boundaries and priorities. We’ve had to be deliberate about not chasing raw speed as the ultimate goal. Instead, we focus on sustainable throughput—delivering quality code at a pace that doesn’t burn people out. That means enforcing standards for pull request size, so they’re manageable for reviewers, and carving out time for thorough testing. We also encourage developers to pause and reflect rather than just accepting AI output at face value. Regular check-ins help us spot signs of stress or overload early. It’s a constant juggling act, but the key is keeping the human element at the center, not just the tech.
Looking ahead, what’s your forecast for the role of AI in software development over the next few years?
I think AI will become even more deeply embedded in software development, but the focus will shift from just generating code to creating smarter, more context-aware systems. We’ll see better frameworks for guiding AI—think tailored prompts and guardrails that align output with company standards right from the start. I also expect automation to take on more of the grunt work in testing and integration, though human oversight will always be critical. The real leap will be in collaboration—AI could evolve into a true teammate, helping bridge workflow differences and reducing bottlenecks. But it’ll only happen if we prioritize building systems that balance speed with quality, and that’s the challenge I see dominating the next few years.
