Why AI Won’t Replace Software Engineers

AI’s ability to do what humans can do has drastically expanded over the last few years. As AI becomes more powerful, there are more and more questions about whether AI is able to replace workers and to what extent. For software engineers, generative AI can already write code and documentation, generate test cases and automate tests, and optimize and refactor code. These are core tasks that software engineers do every day, so given that, can AI replace software engineers? 

We don’t believe so, and there are several reasons why.

AI Isn’t Capable of Complex Problem-Solving

AI is good at producing repetitive, standardized code and “catching formatting inconsistencies, wonky coding lines, and minor bugs.” For example, a user of Github Copilot, a popular AI developer tool, said that Copilot was very effective for generating boilerplate code. Developers who used OpenAI Codex, a model that turns natural language into code, report only a 36% accuracy rate when producing coding solutions, showing how AI that specializes in coding aren’t sophisticated enough to solve complex coding problems. 

Instead of trying to use AI to develop complex code and solutions, it’s better to lean into what AI has proven to excel at. By using it to automate simple code creation and routine tasks, companies can free up software engineers to spend more time tackling difficult challenges. 

AI Merely Replicates Creativity

While AI is able to come up with ideas and code, it’s essentially mimicking creativity by remixing and repurposing data and patterns. AI doesn’t have the same creative capabilities as humans do and isn’t able to come up with truly novel ideas or solutions. Instead, the results are “bland and unoriginal” because AI code bots primarily use open-source code to learn. 

Just as it’s better to leave complex problems to human engineers, work that requires creativity is better handled by your employees. However, AI enables creativity by automating straightforward, simple tasks and giving engineers more time to innovate and come up with interesting ideas. 

AI Requires Human Oversight

AI isn’t capable of operating entirely autonomously. It requires a human to provide direction through a prompt, such as instructing the AI what code to write. Additionally, one of the biggest challenges with using AI for code development is accuracy and reliability. Large language models (LLMs) can “produce syntactically correct code, [but] they may not always generate functionally correct or optimized solutions.” Although AI will become more accurate and reliable over time, AI outputs currently need to be reviewed by a human or you’ll risk introducing bugs, security vulnerabilities, and performance issues. 

IT services provider BDO Digital quickly learned that AI could be used to augment engineers but not replace them. “The need for human oversight to ensure the quality and functionality of AI-generated code quickly became apparent,” said Kirstie Tiernan, a principal at BDO Digital. Similarly, the healthcare technology company Lyric realized early on that a lot of work was needed to get the correct results from large language models. “Out of the box they are somewhat generalized, miss the mark, and hallucinate,” said Akshay Sharma, Chief AI Officer at Lyric.

AI Isn’t Capable of Ethical Decision-Making

The use of AI for software engineering raises ethical concerns, particularly around bias. AI isn’t able to make ethical decisions and consider the implications of a decision. AI is also only as good as the data it’s trained on and the algorithms it uses. This means that if the input data or the way the algorithm is designed is biased, it’ll result in biased results. Without the ability to consider whether code will produce biased results, AI needs humans to review the code and evaluate whether it might lead to discriminatory or harmful outcomes.  

How to Adapt Your Engineering Talent

Although AI won’t replace software engineers, it is changing how engineers work. AI will increasingly be integrated into their work, so engineers need to know how to work with AI tools and be comfortable with AI. While 83% of senior leaders are trying to attract workers who are knowledgeable about AI, only 37% said that their organizations are upskilling employees on AI. This represents a gap that companies should prioritize closing, especially since there’s high demand for and a shortage of engineers who already know how to work with AI. 

As you continue hiring for software engineers, Karat can help you reach your hiring goals faster while relieving your team of countless hours spent interviewing candidates. Our automated top-of-the-funnel assessment, 24/7 interviews, and data-backed insights have been used by top tech companies to build world-class engineering teams. Contact us to learn more.