According to many experts, large language models and artificial intelligence are dramatically simplifying the process of creating quality software, and this is a perspective that is being touted a lot. It has even been predicted that this trend could lead to software engineers becoming redundant in the future and simplified abstractions, including no-code solutions, handling all of our business problems. There is an overwhelming belief that artificial intelligence will destroy developers' jobs, but this is a fundamental misconception about the profession.
It is generally thought that software developers are the ones who take specifications and turn them into code. That is true in a way, but the true purpose of a software developer lies far deeper than that. Many businesses are keen on integrating new applications for artificial intelligence into their products and services, regardless of their size. An employment survey conducted by Hired in March 2024 found that 56% of employers plan to incorporate or launch products using AI tools by the end of 2024. It is not surprising that AI provokes excitement, confusion, and fear in the same way that every technological advancement does.
Software engineers is in a unique position as a result of the fact that they can create AI rather than just use it—they can build it. As the future of software engineering is set to begin, artificial intelligence is poised to change the way software engineers perform their duties, the skills required to excel as engineers will change, and what success in the technology industry will mean. The Hired CTO Dave Walters stated that Artificial Intelligence will be one of the biggest disruptors of software engineering in the future.
By using AI to handle routine tasks and accelerate development, engineers will be able to focus on innovation while AI takes care of routine tasks. Even among those who warn about AI’s drawbacks, there is a consensus among the engineering community that AI is an invaluable tool that engineers can utilize. It is important to realize that artificial intelligence (AI) has the potential to boost productivity and efficiency in teams by streamlining workflows, speeding up prototyping, automating repetitive tasks, and even writing code.
According to Walters, early adopters are using AI to generate standard code blocks that they optimize for their companies and organizations.
Additionally, AI is an effective tool for creating documentation with less effort and interpreting data. A tool such as GitHub CoPilot, for instance, can offer developers suggestions in real time when they are working with boilerplate code blocks, which can be particularly helpful for those who work with them.
Besides debugging AI tools, many tools can be used to assist with testing and debugging such as spotting common or recurrent issues in a code base, allowing software engineers to focus on the trickier areas of the codebase rather than on the mundane ones. In addition to the practical uses of AI, the technology can also generate documents and sum up meetings in a simple and manageable way. In Peter Bell's view, and as he explains in the following video, AI has many applications that can be applied to a variety of industries and situations.
“It is not just about code generation (which is still of variable quality), but also helps you with thinking through your business challenges, creating requirements documentation, working with your team more efficiently, and creating documentation.” To clear the way for developers to perform more meaningful work, artificial intelligence can be used to remove key blocks to deeper and deeper understanding. The company TrueNorthCTO, founded by Bohdan Zabawskyj, has developed a tool that will allow the company's founder to take care of routine tasks more efficiently using AI tools such as Copilot, which has been developed by TrueNorthCTO.
By doing this, they will be able to devote more time to solving larger problems and problems that are more complicated. There has been a trend in recent years for companies to develop more AI-based tools that will help their developers make their work even more effective, so they can make a bigger impact with their work." Naturally, every new technology comes with its risks, and AI is no different.” There has been a lot of talk about biases in artificial intelligence models trained on skewed data sets, and security and privacy concerns have been raised based on the processing and storage of information.
The risks of artificial intelligence are acutely aware among even those who are optimistic about the technology. Taking special care to address and mitigate issues related to privacy and data security is essential for software engineers when approaching AI models and tools," indicates Zabawskyj, "and they must approach these issues with an understanding of their limitations.". The Role of AI in Transforming Software Engineering: Opportunities and Challenges
As AI technologies advance, their ability to process personal and sensitive data necessitates robust measures to safeguard this information and ensure adherence to relevant data protection laws and ethical guidelines.
AI also raises important questions regarding ownership and accountability: if the code is written by AI, who owns it? Additionally, there are concerns about the potential for AI to generate false information. Zabawskyj explains, “AI systems, while sophisticated, can sometimes generate misleading or entirely fabricated information — a phenomenon known as ‘hallucinations.’ These inaccuracies can arise from biases in the training data or the model’s inability to understand context deeply.”
For individual software engineers, the imperative for proactive, continuous learning is as crucial as ever. Even those not currently utilizing AI in their organizations can benefit from seeking opportunities to familiarize themselves with AI technologies. Creating internal channels for knowledge-sharing, where developers can exchange insights and learnings, is also valuable.
There is no substitute for personal, hands-on experience. Bell encourages engineers to “try different models, learn about the strengths and weaknesses of generated code, and use large language models (LLMs) to become proficient in new languages and open-source software code bases.” Experimenting with AI in personal activities, such as cooking, exercising, playing guitar, or dating, can also help individuals learn how to prompt better and maximize the utility of AI models.
To thrive in the AI-driven landscape, software engineers should not only focus on technical skills but also enhance their soft skills. Communication, problem-solving, and emotional intelligence will become increasingly critical.
AI serves as a valuable tool for engineering teams, streamlining workflows, automating tasks, and assisting with debugging, ultimately enhancing efficiency and productivity.
However, there are risks associated with biased models, privacy concerns, and the generation of false information, emphasizing the need for understanding AI's limitations and implementing robust safeguards. AI will reshape the role of software engineers, allowing more time for complex projects and shifting the focus towards model development and data analysis, with an increased emphasis on architectural knowledge and soft skills. Junior engineers will have opportunities to accelerate their learning and productivity, while senior engineers will focus on guiding AI integration and making complex decisions, leading to a transformation in job responsibilities.
The impact of AI varies depending on business size, with smaller companies benefiting from increased efficiency and larger enterprises facing challenges integrating AI into existing systems and processes. As AI becomes more prevalent, software engineering specializations and skills are evolving, with a growing demand for roles like Machine Learning Engineers and an emphasis on soft skills such as critical thinking and communication.
To build teams prepared for the AI age, tech leaders should seek candidates with emerging coding and soft skills, encourage experimentation with AI tools, and foster a culture of continuous learning and improvement. Employers should embed AI into company culture, provide AI tools and education access, and promote knowledge-sharing and experimentation among team members to facilitate upskilling.
Individual software engineers should engage in continuous learning, gain hands-on experience with AI, and develop soft skills like communication and problem-solving to succeed in the age of AI. To thrive in this new era, both software engineers and engineering organizations must embrace continuous learning, adaptability, and evolution, focusing on problem-solving, design, and soft skills alongside technical expertise.
