Anand Naidu is a seasoned veteran in the world of software engineering, serving as a resident expert who bridges the gap between complex backend architecture and intuitive frontend design. With a career built on mastering various coding languages, he has witnessed firsthand the transition from manual syntax to high-level abstraction. In this conversation, we explore the seismic shift toward AI-driven development—a movement often termed “vibe coding”—and how it is dismantling the traditional “build-versus-buy” dilemma that has long plagued major corporations. We delve into the changing economics of custom software, the evolving role of the developer as an orchestrator, and the critical importance of maintaining infrastructure and operational discipline in an era of rapid code generation.
With the rise of AI-driven tools, we are seeing a shift where custom software development is becoming viable for projects that previously failed the cost-benefit test. How is this changing the internal logic for enterprises when they decide whether to build or buy their tools?
We are currently at a significant inflection point where the accessibility and affordability of AI development tools have completely upended the old “build-versus-buy” binary. For years, many IT leaders felt tired of this rigid choice, often forced to buy off-the-shelf software because building custom solutions required an extensive bench of senior engineers and deep pockets that simply weren’t available. Now, the economics have shifted so dramatically that projects which once required a massive investment in time and resources are viable with only a fraction of those requirements. This change allows IT teams to deliver on business needs at a much larger scale, whether they are modernizing dusty legacy infrastructure or pursuing net-new software that would have never cleared the financial bar in the past. It is an emotional win for developers who can finally tackle the “nice-to-have” projects that actually solve core business frustrations.
The concept of “vibe coding” or AI-driven development is often presented as a successor to the low-code movement of the last decade. How does this new evolution actually change the day-to-day manual work of a professional developer?
Low-code platforms paved the way for a decade by offering a user-friendly alternative to traditional coding, but AI development takes that abstraction and pushes it much further. In this new environment, developers describe exactly what they want in plain language, and an AI assistant translates that intent into working code. It’s important to remember that the code hasn’t disappeared; it is still written in the same programming languages we’ve always used and remains available for refinement when the AI gets it 80% of the way there. This shift reduces the heavy lifting of manual syntax entry, allowing the developer to focus on the higher-level logic and architecture of the application. The experience feels less like being a lone craftsman with a chisel and more like being a conductor of a very fast, very efficient digital orchestra.
A recent report noted that two-thirds of enterprises are already using AI development tools, yet adoption doesn’t always translate to actual value. What is the most common pitfall when non-technical users or inexperienced teams try to jump into this style of development?
The reality is that while generating code has become significantly easier, knowing the intricate ins and outs of an application—how it fits into existing infrastructure and where it is likely to fail—is much harder than it looks from the outside. Most first-timers who experiment with these tools quickly realize that developers are still the best-positioned people to produce enterprise-grade software, even if they aren’t writing every line by hand. When the cost of generating an application drops, the constraint moves from engineering capacity to business judgment. The truly scarce skill in this new landscape isn’t the ability to build the tool, but the ability to identify which applications are actually worth building and how they solve an organization’s specific, messy problems. Without that judgment, you end up with a lot of digital noise that doesn’t actually move the needle for the company.
There is a concern that faster code generation might overwhelm the underlying systems. How does the increase in software volume affect the platform and operations functions within a large organization?
This is exactly where most surface-level takes on AI development fall apart, as faster code generation doesn’t magically eliminate the need for servers, hosting, or cloud spend. In fact, it adds immense pressure to the environments, CI/CD pipelines, observability tools, and secrets management because the sheer volume of software to operate is going up. Any organization that wants to be serious about AI development must be equally serious about the platform and operations function underneath it all. That department doesn’t shrink just because the coding is faster; instead, it reshapes and, in most cases, grows to accommodate the increased complexity. It’s a sensory overload for teams who aren’t prepared for the release process and the “always-on” nature of modern enterprise systems.
You’ve mentioned that AI code generation currently lacks “determinism,” which was a problem low-code platforms solved years ago. Why is this such a major hurdle for mission-critical systems, and how should leaders address it?
Determinism is the guarantee that the same inputs will produce the same outputs, which is vital for systems that need to be governed, audited, and upgraded at scale. Currently, AI code generation isn’t quite there yet, as two different prompts can result in two working but entirely incompatible implementations of the same requirement. For a quick internal prototype or a simple tool, that lack of consistency might be acceptable, but for mission-critical enterprise systems, it is a recipe for disaster. Leaders have to implement strict guardrails, platforms, and review processes to compensate for this variability. Pretending this problem doesn’t exist is how you end up with an estate of applications that are impossible to maintain or secure over the long term.
As we look toward the future of the technology function, how do these shifts change the way a company allocates its headcount and structures its engineering teams?
These shifts are fundamentally changing the shape of the organization, moving beyond just changing the developer’s daily tasks to altering the entire team topology. We are seeing a new balance between builders and operators, where the skill mix is shifting toward those who can manage vendor relationships and maintain platform discipline. The organizations that will actually extract real value from these tools are the ones willing to reshape their headcount across engineering, product, and platform teams rather than just tacking on a new tool. It’s a transition that requires a high degree of organizational willingness to adapt, as the traditional roles of “coder” and “manager” begin to blur. It’s not about rejecting traditional code, but about evolving the developer role to meet the speed of the current market.
What is your forecast for the state of enterprise software development by 2026?
By 2026, software leaders will have more tools at their disposal than at any prior point in history, and the teams that win won’t necessarily be the ones with the flashiest gadgets. Instead, the winners will be the ones who have successfully reshaped their entire workflow and operational discipline around AI development and agentic systems. We will see AI development, used with the right guardrails, as the primary way teams keep pace with or even set the pace for their industries. This evolution will solidify the developer’s role as a strategic architect rather than a manual builder, making the technology function a more integrated and proactive part of every business. The industry will move away from the “mixed feelings” regarding vibe coding and embrace it as the standard professional evolution of the craft.
