Reimagining Software Engineering, What’s next for Engineers for the AI-Powered future?

In my previous post, I shared my perspective on why AI won’t replace human software engineers anytime soon. In this post, I want to share another perspective on the growing role of AI in software engineering and how it’s transforming the way we work.

AI will change the dynamics of how software engineering teams function, shifting the focus from coding to higher-level tasks such as problem-solving, oversight, and decision-making. AI is a powerful tool, but it’s still up to software engineers to guide the process and make critical judgments.

Now, let’s take a closer look at how software engineers should think, work, and adapt to the evolving landscape in the AI era.

The importance of Coding skill

Coding was undeniably an essential skill for software engineers. It was the foundation of building functional software and solving technical problems. However, coding has never been the most important aspect of being a software engineer. The true value of engineers has always been in their ability to solve problems, translate product requirements into technical solutions, ensure the quality and maintainability of the solution, and make decisions that align with broader context. It’s misleading for anyone to think that simply being good at coding is enough for a successful engineering career.

AI can quickly generate basic code structures, scaffold entire features, and even create initial tests and documentation. This allows engineers to move at lightning speed, focusing on the “what” rather than the tedious “how”. With AI doing much of the heavy lifting, it can dramatically accelerate development, and it will make people think that coding itself becomes less important. However, the reality is that even in the AI-powered future, strong coding skills will remain essential for software engineers. AI can generate code quickly, but it still requires oversight from experienced engineers who can maintain a tight feedback loop with the AI to ensure quality.

As AI tools take on more of the routine coding tasks, engineers will need to shift their focus to higher-level responsibilities that AI cannot easily replicate. This includes problem-solving, understanding product requirements, translating them into technical solutions, and ensuring that the solution aligns with the overall context. Engineers will also need to make judgment calls that balance trade-offs, such as scalability versus speed, user experience versus performance, and maintaining flexibility for future updates. In addition, engineers will play a crucial role in overseeing AI-generated code, ensuring it meets quality standards, and making decisions that will drive long-term success. This is where human experience and expertise become indispensable, AI can’t yet navigate the nuanced, strategic aspects of product development. Good engineers of the future will need to be able to:

• Review and refactor AI-generated code: AI is great at generating boilerplate, but engineers will still need to refactor and optimize the code for maintainability, modularity, and scalability. Having a solid understanding of coding will allow engineers to spot inefficiencies and improve the code, ensuring it meets long-term needs.
• Identify and fix issues: Not all AI-generated code is flawless. Engineers will need to use their coding skills to debug and troubleshoot any errors, add missing features, and ensure that the code works seamlessly with the rest of the system.
• Ensure tight feedback loops: As AI generates code, engineers must provide constant feedback, reviewing every line of generated code. This feedback loop ensures that the AI continues to improve, generating better code with each iteration. Strong coding knowledge is essential for engineers to effectively guide AI in producing high-quality work.
• Maintain high standards: AI can handle repetitive tasks, but it cannot match the judgment and intuition of an experienced engineer when it comes to ensuring quality, adding error handling, or making decisions about architecture and design. Engineers will still need to uphold engineering standards and make decisions that align with the long-term.

Reimaging Software Engineering

With the rise of AI, the structure and capacity distribution of teams will have a significant transformation. In the past, a team might have needed 2 senior engineers and 4 junior engineers to get a certain amount of work done. But in the future, that same team might only need 2 senior engineers who are experts at using AI tools to guide the development process, plus 2 junior engineers to help with specific tasks. The rest of the work like generating code, handling repetitive tasks, or managing routine tasks can be offloaded to AI, allowing the team to do the same amount of work, or even more, with fewer people.

This setup doesn’t just reduce the number of people needed for routine tasks, it also frees up the senior engineers to focus on coaching and mentoring the junior engineers, helping them grow into the next generation of skilled engineers. Instead of spending time on repetitive coding tasks, senior engineers can help junior engineers learn, improve, and eventually take on more complex challenges themselves.

The way engineers work with their product counterparts will also have a significant shift. In the past, the baseline for many engineers was focused on implementing features based on product requirements, taking the requirements, and turning them into code. However, in the future, this baseline will no longer be enough. Engineers will need to work much more closely with product managers to solve problems collaboratively. Instead of simply receiving a list of features to build, engineers will actively engage with product managers to understand the core problems and the desired outcomes. This partnership will allow engineers to better translate product goals into clear technical tasks and create solutions that align with the product vision and this deeper level of collaboration will be the new standard.

Engineers who cannot adapt to this change, who cannot shift from just executing features to solving problems alongside product teams, may find it difficult to remain relevant in the market. The demand will no longer be for engineers who merely take orders, the future will require engineers who can contribute to the product strategy and work hand-in-hand with product managers to translate ideas into well-designed, technically sound solutions. If you can’t keep up with this shift, the market simply won’t accept you.

To make this collaboration even smoother, all product requirements, decisions, and technical specifications will be documented in the same code repository. This will help ensure that all context is centralized and easily accessible to both engineers and the AI tools they use. With everything in one place, AI can draw from this rich context when generating code, ensuring the solutions are aligned with the context and goals. This centralization will create a single source of truth that everyone, including engineers, product managers, and AI can reference, which will be important for maintaining alignment throughout the development process.

In this new setup, you might even see product managers or other stakeholders directly making merge requests to your repository. Instead of just sharing documents or requirements in a separate space, they will update the code, documentation, or specifications directly within the repository. This change fosters a more integrated and seamless workflow, where everyone, regardless of their role, is actively contributing to the codebase and the overall product development process. Engineers will no longer be isolated from product teams but will be working side-by-side with them, with everyone having direct visibility and input into the product and code.

However, even with this more collaborative and integrated approach, engineers will still be responsible for the quality of the codebase, the solution, and the system. While product managers and other stakeholders may contribute directly to the repository, engineers will be the ones ensuring that all changes align with technical standards, maintainability, and the long-term vision of the product. Engineers will need to constantly review the code, manage technical debt, and make sure that all decisions, whether from product or other teams, don’t compromise the stability, scalability, or quality of the system. Ultimately, engineers will remain the guardians of the codebase, always staying on top of the process to ensure the product evolves in a way that meets both technical and business goals.

How to stay relevant in the AI-powered era?

As AI continues to transform the landscape of software engineering, the way teams are structured and the tasks they handle will change drastically. We’ve already discussed how teams may no longer need as many junior engineers to handle repetitive tasks, with more of the execution offloaded to AI. But despite these changes, this is not the end of junior software engineers.

In fact, the demand for junior engineers will shift towards tasks that require creativity, learning, and personal growth. They will still be responsible for contributing to the codebase, working alongside senior engineers, and continuously developing their skills. As AI takes on more of the routine work, junior engineers will have the opportunity to focus on learning from their more experienced counterparts, contributing in ways that add value to the overall development process.

The AI era will actually create a shift in how junior engineers develop and grow, enabling them to focus on becoming the next generation of engineers who will guide the process and make high-level decisions. To remain relevant in this new AI-powered era, there are several key skills that we as engineers must continue to develop:

• Master the Basics: While AI can generate code, engineers need a solid understanding of coding fundamentals to review, refactor, and improve AI-generated work. Mastering the basics ensures that you can guide AI tools and ensure the generated code aligns with your needs.
• Know how to Debug: Not all AI-generated code will be perfect. Engineers must be able to thoroughly review and debug code, ensuring that everything works as intended. This skill is more important than ever, as AI might miss edge cases or introduce errors that need fixing.
• Problem-Solving: The role of an engineer is no longer just to code exactly what is given. Engineers must understand the problems at hand, work closely with product managers, and translate those problems into technical solutions. This requires critical thinking and an understanding of both short-term needs and long-term goals.
• Human Judgment: AI can generate code, but it’s up to you to determine the best solution. Human judgment will be necessary to assess the effectiveness of AI-generated code, ensure it meets design standards, and choose the most effective approach for the product. Engineers must still make decisions about code structure, performance, and maintainability, ensuring that the AI’s output aligns with the context and standards.

Conclusion

As we move further into the AI-powered era, the role of the software engineer will evolve, but it will not become obsolete. AI tools will handle routine coding tasks, allowing engineers to focus on higher-level responsibilities like problem-solving, collaboration, and ensuring the quality of the product. Software engineers will still be crucial in guiding AI, making key decisions, and maintaining a focus on long-term quality and scalability.

The demand for junior software engineers will shift, but it won’t disappear. Junior engineers will have more opportunities to learn, grow, and contribute meaningfully to the development process. The future will require engineers to master not only the basics but also skills such as debugging, collaborating closely with product teams, and applying human judgment to create elegant solutions.

By evolving alongside AI, we can ensure that software engineers remain at the forefront of innovation. The software engineering profession will continue to grow, the future of software engineering is bright, but only for those who are ready to adapt and grow with it.