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“Software engineers have a new tool up their sleeves to help them be more productive,” says Samuel Bechara, PhD, an associate professor of practice in mechanical engineering at the Walter Scott, Jr. College of Engineering at Colorado State University. “It is already impacting the industry.”
At their most basic, AI-powered coding tools act as easily searchable repositories of code. As they get integrated further into the coding process, they begin to resemble auto-complete. But this isn’t as radical a departure from contemporary coding as it might first appear.
“The reality is that even before AI tools, a lot of code that software developers create is ‘borrowed’ from other projects or online resources such as Stack Exchange,” Dr. Bechara says. “But AI tools such as GPT-3 and Stack Exchange are revolutionizing the way software engineers work, providing new tools to increase productivity and tackle complex coding problems. As AI tools continue to advance, I believe that they will increasingly be used to automate repetitive coding tasks and assist in the development of more sophisticated software systems.”
What is radical is the speed at which AI can now perform certain coding tasks, shortcutting the most laborious aspects of the process. Early research from MIT, Github, and Microsoft found that developers using AI can be 50 percent more efficient; tools like AlphaCode are performing better than nearly half of all human entrants in coding competitions.
But AI-powered coding is far from infallible. While AI is good at automating basic processes and fixing simple errors, it still can’t solve complex, nuanced problems. When AI does generate novel code ideas, they can be at risk for bias and security threats unless thoroughly vetted by human operators.
“The biggest opportunity for AI in coding is in increasing productivity by automating routine coding tasks, allowing developers to focus on more complex and innovative work,” Dr. Bechara says. “However, the limitations of current AI tools in generating code mean that human intervention is still necessary to ensure the code is of high quality and easily understandable.”
Coding has always been a dynamic space, both professionally and academically. Curricula and job descriptions have to update nearly as often as smartphone software does. AI-powered coding tools represent one of the biggest updates yet, ushering coding education into completely new territory.
“In my Numerical Methods class, I used to assign a problem where students had to write an algorithm to determine all of the prime numbers between 0 and some input number, n,” Dr. Bechara says. “The problem is that AI can do this easily. I have had to rethink the problems that I assign to make sure they are sufficiently complex that an AI tool might not arrive at the correct solution.”
Some experts have offered up an analogy in the impact of high-powered calculators on math education: their introduction made it less important for humans to be able to calculate square roots and logarithms independently, but more important to be able to use the answers effectively to solve bigger problems. Similarly, AI-powered tools may make some aspects of coding knowledge, like memorizing syntax and library APIs, less relevant; but others will be as crucial as ever: testing and debugging code, for example, or understanding the strengths and limitations of certain algorithms.
“There needs to be an increasing emphasis on teaching students the skills required to understand and work alongside AI, such as data analysis and machine learning,” Dr. Bechara says. “To prepare the coding workforce of the future, I would want to instill skills such as critical thinking, problem-solving, and communication, as well as expertise in AI tools and technologies.
AI-powered tools are changing how coders work, but they aren’t stopping coders from working or reducing demand for coders, either. The US Bureau of Labor Statistics projects the need for software developers, quality assurance analysts, and testers to grow 25 percent between 2021 and 2031—a rate that far outpaces the national average for all professions—adding nearly half a million jobs in the process (BLS 2022).
AI isn’t taking coders’ jobs, it’s promoting them to more managerial positions. Even as AI-powered tools grow in capability, more human minds will be needed to plug their holes: adjusting for bias, accounting for nuance, and finding new routes toward creativity.
“While AI is likely to automate some coding tasks in the future, I do not believe it will lead to significant job displacement in the software engineering industry,” Dr. Bechara says. “Rather, I believe it will create new opportunities and skill requirements, with a greater need for engineers to work alongside AI tools and develop more sophisticated software systems. As such, the future of software engineering is likely to be dynamic and require ongoing adaptation to new technologies.”
AI will continue to impact coding professions going into the future, and more engineers will be needed to help assess its risks and benefits. Ideally, the next generation of coders will be empowered by AI to tackle new areas of heightened creativity and promise. Entirely new programming languages and tools could develop. While the future of AI and coding remains uncertain, it’s a future that coders, and AI, will write together.
“I believe the future of coding professions is bright, with AI playing a significant role in driving innovation and productivity,” Dr. Bechara says. “However, as AI tools continue to develop, there will be new challenges and opportunities that arise, requiring engineers to constantly adapt and develop new skills. The future of coding will be dynamic, with a greater emphasis on working alongside AI and leveraging its capabilities to create innovative and impactful software systems.”
Matt Zbrog is a writer and researcher from Southern California. Since 2018, he’s written extensively about a wide range of engineering disciplines, with a particular focus on the potential impacts of major technological breakthroughs. His work largely centers around conversations with engineering faculty at top universities, highlighting their research in areas such as nuclear power, artificial intelligence, soft robotics, and semiconductor design. He’s also worked with leaders and subject matter experts at the Institute for Electrical and Electronics Engineers (IEEE), the California Department of Water Resources (DWR), the National Society of Professional Engineers (NSPE), and the Society of Women Engineers (SWE).
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