In the ever-evolving landscape of software development we are starting to get back to the convergence of the Product Manager (PM) and Engineer roles. It's a return to the roots of product management. Historically, many of the earliest PMs came directly from engineering backgrounds. Organizations often held, and many still do, the belief that PMs need to be technical. With the advent of AI-powered tools like GitHub Copilot and large language models (LLMs), we are going to see a future where the engineer and the PM are going to converge again. Or at least get closer again. We might not see much difference between the two.
In the early days of software development, the roles of PMs and Engineers were often intertwined. The individuals who understood the problem deeply were the same people who built the solutions. This close connection ensured that the products were tightly aligned with user needs and technical possibilities. Startups exemplify this model even today, where founders often wear multiple hats—as the product visionary, salesperson, and chief technical officer (CTO). In many cases, the founder codes the first iterations of the product, seamlessly merging product management and engineering.
Over time, as software projects grew in complexity and scale, specialization became necessary. The separation between PMs and Engineers was rooted in the need for focused expertise. Engineers were tasked with the intricate job of building software—writing code, debugging, and ensuring the technical feasibility of a product. Their focus was on the "how" of product development.
Conversely, PMs concentrated on the "why" and the "what." They delved into understanding user needs, market trends, and business objectives. PMs crafted the vision and strategy, ensuring that the product not only solved the right problems but also aligned with business goals. This division allowed for depth in both areas but introduced new challenges.
This divide made a lot a sense as a competitive advantage as companies wanted to move faster.
This functional separation has had its drawbacks.
Communication Gaps: PMs often envision innovative solutions only to face technical constraints during implementation. Engineers, immersed in code, might lack deep insights into user pain points, leading to misaligned priorities.
Inefficiencies: The back-and-forth between ideation and execution could slow down the development process. Misunderstandings and rework were common, consuming valuable time and resources.
Limited Perspective: PMs without technical backgrounds might propose solutions that were impractical, while engineers might not fully grasp the user experience implications of their technical decisions.
Could we still get the competitive advantage of specialization, while also removing all the disadvatanges? Yes, and it going to be the competitive edge. The resurgence of the hybrid PM-Engineer role is being fueled by AI-powered development tools. Platforms like GitHub Copilot have revolutionized how code is written. Recent studies indicate that developers using Copilot can code up to 55% faster on repetitive tasks. These tools leverage machine learning to suggest code snippets, automate routine coding tasks, and even generate entire functions based on simple descriptions.
For non-technical professionals, low-code and no-code platforms augmented with AI are making software development more accessible than ever. A PM without a deep technical background can now build prototypes or even full-fledged applications using intuitive interfaces and AI assistance.
Imagine a scenario where the person closest to the problem can easily dictate or even build the product themselves. This nirvana isn't just a dream, it's increasingly becoming reality. In startups, this model thrives. The product person, salesperson, and CTO roles often overlap, with founders coding the initial versions of their products. This direct involvement ensures a deep understanding of both the problem and the solution.
With AI tools reducing the technical barriers, even larger organizations can adopt this approach.
Engineers Gaining Product Insight: As AI handles routine coding tasks, engineers have more time to engage with users, understand their needs, and contribute to product strategy.
PMs Becoming Builders: Empowered by AI, PMs can translate their visions directly into prototypes or products, reducing reliance on engineering teams for initial development.
Enhanced Collaboration: When PMs and Engineers share responsibilities, collaboration becomes more fluid. Both parties have a holistic understanding of the product, reducing miscommunication and aligning objectives.
Increased Efficiency: Smaller, cross-functional teams in the future will achieve what previously required larger, specialized groups. Reducing a team of 7-10 specialists to a lean team of 2-3 hybrid professionals accelerates development and reduces costs.
Better Products: Those closest to the problem are crafting the solutions, leading to products that are more user-centric and effective. The feedback loop is shorter, and iterations are more informed.
The notion of a professional who excels in both product management and engineering has long been considered rare and highly sought after. However, as AI tools lower the barriers to technical proficiency, this hybrid model is becoming more attainable. The mystical unicorn is becoming a tangible asset within reach for many organizations. And those who start to build these team will start to see a highly competitive edge.
The ultimate goal is to envision a product and have it materialize with minimal friction—a process where imagination leads directly to creation. AI is propelling us toward this future. As these tools continue to evolve, the convergence of PM and Engineer roles will likely accelerate.
From a business perspective, this shift is monumental. The potential to reduce team sizes, cut costs, and speed up development cycles offers a competitive edge. Moreover, products are likely to be better aligned with user needs, enhancing customer satisfaction and driving business growth.
We are witnessing a return to the roots of product development, where the lines between product management and engineering blur. The person closest to the problem can now build the solution, thanks to AI-powered tools that democratize software development. This convergence is reshaping how we build products and who builds them.
The traditional barriers are dissolving, paving the way for more integrated, efficient, and innovative teams. The future where we can imagine a solution and bring it to life swiftly is not a distant dream!
