At this point, it’s an understatement to say that AI is changing the way every industry operates. Its impact has been so granular that we would have to examine every individual role in every sector to truly gauge the shift it’s created. Software engineers, evidently, are in this mix. According to GitHub, 92% of US-based developers are using AI coding tools at work — what we now call copilots. Everything changed the moment ChatGPT’s first publicly available version hit the shelves, creating a ripple effect in how LLMs work and how AI is applied and developed for different use cases. The technology has evolved to the point that it can understand more than just the syntax of code — it grasps context and intent — and this has been a game-changer in the software engineering realm. Copilots began gaining more ground as a result of the fast evolution of ML, the rise of AI, and enhanced computational power. Plus, the vast amount of code available for training has allowed these models to become highly effective. Now, copilots are helping developers code faster and more accurately and relieving them of repetitive procedural tasks so they can focus on the critical-thinking part. What these AI tools are bringing to the table is the potential to amplify an engineer’s productivity and impact, which will only continue to increase as the tech is refined for better performance. Leading a software development company has given me heightened visibility of these quick changes, making me realize that we’re just getting started when it comes to copilots. They’re opening up new possibilities for innovation, efficiency, and creativity in the field, which is nothing short of exciting. So, what will the future of software engineering look like with AI assistants? Let’s examine the good, the not-so-good, and what engineers should keep in mind to evolve alongside copilots. One Engineer Equals One Team To set the stage for what the future with copilots should look like, let’s picture a day in the life of an engineer. What they currently do manually will drastically change — engineers love optimization and automation, so copilots fit right in. Copilots will do just that for them, providing real-time code suggestions and taking over repetitive activities like testing code and basic debugging. They’re also quick to learn new coding languages and apply frameworks, meaning engineers will spare this step if they ever need to deal with a language they’re unfamiliar with or adapt to frameworks. Instead, they’ll be able to use that time to focus on more strategic and creative work. As a result, humans will become more productive and create a broader impact, which could potentially equal that of an entire team. But this isn’t news — we’re already seeing this impact today. AI-based code review tools are becoming widespread, helping engineers and teams ensure their code is up to standard with less manual effort. Other specialized AI tools are also helping managers quickly assess their team’s progress and quality without overseeing every detail manually. Ultimately, copilots aren't just speeding up the process; they're encouraging a more thoughtful, senior-level approach to engineering. Shifting Engineering Skills Over half of today’s public sector IT workers report the lack of AI skills as their top challenge. It wouldn’t be surprising if this number is even higher in the private sector. As AI becomes a crucial tool in IT, and more importantly, in software development, workers will need to evolve their skills with the market’s shifting demands. But beyond knowing how to implement AI, what’s true is copilots are leading developer skills to change to more senior-level activities. In this sense, engineers will need to start thinking like managers because tasks like manual code review will become less central. AI oversight, managing and directing copilots, and adopting a strategic decision-making mindset will be required skills instead. Many more tasks will be automated, so engineers will need to understand how to use AI tools to their advantage, maximizing productivity, minimizing errors, and maintaining high-quality outputs. For example, prompt training will be one of those essential aptitudes to master so that engineers can see positive results in using copilots. Future engineers will need a blend of technical skills and AI management. These AI tools will likely lead to new roles — not just for humans but for AI to review and manage tasks completed by copilots. These roles could be AI overseers, who ensure copilots are working effectively, and AI-augmented project managers, who integrate AI tools into the broader development process. This will also play into engineers’ career trajectories. While many think this will stall them or reduce their work, the truth is career paths will move faster for those who know how to implement copilots effectively. Think of it this way: If a one-man show has the impact of an entire team, copilots could lead to faster career progression and opportunities for more significant projects. Roadblocks of Heightened Copilot Adoption These positives don’t come without their downsides. From overuse to not capitalizing on copilots enough, successful software engineering will become a balancing act. At first, engineers might struggle with the steep learning curve of responsibly adopting copilots, which could lead to becoming overly reliant on them. This will be especially true when they’re expected to work faster while delivering quality results — there must be a healthy middle. Otherwise, overusing AI tools could translate into a decline in hands-on coding skills. Soon enough, AI will also write so much code that engineers won’t be able to review it all themselves, meaning engineers will need to use copilots wisely to provide oversight into good code. At the end of the day, the workforce will need intensive training that highlights the role of AI as an augmentation rather than a full replacement. The future I’m talking about might come sooner than we expect. Today’s engineers must start honing their AI management skills to thrive in this copilot era, offering exceptional output by leaning on these tools to achieve it. It’s certainly a bright future, but in order for AI adoption to bring positive results, the industry must strike a usage balance and more learning opportunities for engineers to keep up and deliver top products. At this point, it’s an understatement to say that AI is changing the way every industry operates. Its impact has been so granular that we would have to examine every individual role in every sector to truly gauge the shift it’s created. Software engineers, evidently, are in this mix. According to GitHub, 92% of US-based developers are using AI coding tools at work — what we now call copilots. 92% 92% Everything changed the moment ChatGPT’s first publicly available version hit the shelves, creating a ripple effect in how LLMs work and how AI is applied and developed for different use cases. The technology has evolved to the point that it can understand more than just the syntax of code — it grasps context and intent — and this has been a game-changer in the software engineering realm. Copilots began gaining more ground as a result of the fast evolution of ML, the rise of AI, and enhanced computational power. Plus, the vast amount of code available for training has allowed these models to become highly effective. Now, copilots are helping developers code faster and more accurately and relieving them of repetitive procedural tasks so they can focus on the critical-thinking part. What these AI tools are bringing to the table is the potential to amplify an engineer’s productivity and impact, which will only continue to increase as the tech is refined for better performance. Leading a software development company has given me heightened visibility of these quick changes, making me realize that we’re just getting started when it comes to copilots. They’re opening up new possibilities for innovation, efficiency, and creativity in the field, which is nothing short of exciting. So, what will the future of software engineering look like with AI assistants? Let’s examine the good, the not-so-good, and what engineers should keep in mind to evolve alongside copilots. One Engineer Equals One Team To set the stage for what the future with copilots should look like, let’s picture a day in the life of an engineer. What they currently do manually will drastically change — engineers love optimization and automation, so copilots fit right in. Copilots will do just that for them, providing real-time code suggestions and taking over repetitive activities like testing code and basic debugging. They’re also quick to learn new coding languages and apply frameworks, meaning engineers will spare this step if they ever need to deal with a language they’re unfamiliar with or adapt to frameworks. Instead, they’ll be able to use that time to focus on more strategic and creative work. As a result, humans will become more productive and create a broader impact, which could potentially equal that of an entire team. But this isn’t news — we’re already seeing this impact today. AI-based code review tools are becoming widespread, helping engineers and teams ensure their code is up to standard with less manual effort. Other specialized AI tools are also helping managers quickly assess their team’s progress and quality without overseeing every detail manually. Ultimately, copilots aren't just speeding up the process; they're encouraging a more thoughtful, senior-level approach to engineering. Shifting Engineering Skills Over half of today’s public sector IT workers report the lack of AI skills as their top challenge. It wouldn’t be surprising if this number is even higher in the private sector. As AI becomes a crucial tool in IT, and more importantly, in software development, workers will need to evolve their skills with the market’s shifting demands. lack of AI skills lack of AI skills But beyond knowing how to implement AI, what’s true is copilots are leading developer skills to change to more senior-level activities. In this sense, engineers will need to start thinking like managers because tasks like manual code review will become less central. AI oversight, managing and directing copilots, and adopting a strategic decision-making mindset will be required skills instead. Many more tasks will be automated, so engineers will need to understand how to use AI tools to their advantage, maximizing productivity, minimizing errors, and maintaining high-quality outputs. For example, prompt training will be one of those essential aptitudes to master so that engineers can see positive results in using copilots. Future engineers will need a blend of technical skills and AI management. These AI tools will likely lead to new roles — not just for humans but for AI to review and manage tasks completed by copilots. These roles could be AI overseers, who ensure copilots are working effectively, and AI-augmented project managers, who integrate AI tools into the broader development process. This will also play into engineers’ career trajectories. While many think this will stall them or reduce their work, the truth is career paths will move faster for those who know how to implement copilots effectively. Think of it this way: If a one-man show has the impact of an entire team, copilots could lead to faster career progression and opportunities for more significant projects. Roadblocks of Heightened Copilot Adoption These positives don’t come without their downsides. From overuse to not capitalizing on copilots enough, successful software engineering will become a balancing act. At first, engineers might struggle with the steep learning curve of responsibly adopting copilots, which could lead to becoming overly reliant on them. This will be especially true when they’re expected to work faster while delivering quality results — there must be a healthy middle. Otherwise, overusing AI tools could translate into a decline in hands-on coding skills. Soon enough, AI will also write so much code that engineers won’t be able to review it all themselves, meaning engineers will need to use copilots wisely to provide oversight into good code. At the end of the day, the workforce will need intensive training that highlights the role of AI as an augmentation rather than a full replacement. The future I’m talking about might come sooner than we expect. Today’s engineers must start honing their AI management skills to thrive in this copilot era, offering exceptional output by leaning on these tools to achieve it. It’s certainly a bright future, but in order for AI adoption to bring positive results, the industry must strike a usage balance and more learning opportunities for engineers to keep up and deliver top products.

This story contains AI-generated text. The author has used AI either for research, to generate outlines, or write the text itself. 

The is an opinion piece based on the author’s POV and does not necessarily reflect the views of HackerNoon.

A Glimpse Into The Future of Software Engineering with Copilots

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