The Rise of Generative AI: More Code, More Problems, More Opportunity

Generative AI is enabling developers to generate code on demand in various languages, increasing productivity. However, the generated code often has security vulnerabilities and may not meet enterprise standards. Additional work is needed to analyze, fix issues, and manage the generated code.

The increased use of generative AI for coding is contributing to "code sprawl" and greater technical debt as codebases expand rapidly. This makes understanding existing code and managing code more difficult.

Generative AI is enabling greater personalization in applications to meet individual user needs. But human developers are still needed to curate training data, examine discrepancies, and ensure quality and business alignment.

Technology professionals will need to take on more responsibilities, including focusing on design thinking, creating AI strategic roadmaps, identifying data anomalies, and coaching others in developing AI applications. Skills in areas like natural language processing, analytics, and critical thinking will become more important.

For some technology roles like IT support and maintenance, generative AI may actually increase productivity by helping to automate responses to common issues and personalize service. But for developers, the rise of generative AI makes their jobs more complicated.

To adapt to the rise of generative AI, developers and technology professionals should focus on honing skills in design thinking, analytics, and natural language processing. They should also aim to create AI roadmaps, identify data issues, and coach others in building AI apps. By taking on these new responsibilities, technology professionals can thrive in an age of increased automation and ensure that AI-generated code meets business needs. The future is here - are you ready to embrace it?