Save Our Developers and Engineers — AI Is Stealing Our Jobs

Peter was a machine learning engineer who had spent two years honing his skills, writing code, and building models. He believed that his expertise was valuable due to the years of learning and experience he had acquired. However, his job was terminated when his company discovered an enterprise AI data scientist designed to execute end-to-end ML pipelines.

Initially, Peter was not alarmed, as he believed that engineering was more than just coding. One day, he returned to his desk to find an envelope containing a termination letter, stating that the machines could now perform his job.

Companies view technological advancements as cost-saving measures, focusing solely on the financial aspect, while employees experience the emotional impact of automation. Creating code is not the same as building a reliable system. Engineering involves understanding data standardization, potential data leakage, selecting appropriate models, evaluating performance, addressing data distribution changes, deployment considerations, failure detection, and responsibility for model errors.

Generating code and engineering a system are fundamentally different processes. AI has begun to infiltrate various aspects of software development, creating snippets, functions, modules, tests, documentation, applications, and infrastructure configurations. Automation is gradually taking over layers of a profession, affecting developers at different levels.

Junior developers, who typically learn through repetitive tasks, are now being replaced by AI, making their work obsolete before they have had the chance to gain experience. Companies demand experienced engineers, but automation reduces opportunities for gaining experience.

Peter, after losing his job, realized that he couldn't compete with AI at its strongest points. Instead, he focused on learning how to work alongside AI. He studied large language models, retrieval-augmented generation, AI agents, cloud infrastructure, model serving, AI security, monitoring, and the engineering systems surrounding AI.

He learned to judge and evaluate the outputs of AI systems, ensuring their appropriateness and reliability. Peter adapted to the changing landscape by focusing on his unique skills in integrating AI into real-world systems, deploying and managing AI systems, evaluating their outputs, managing infrastructure, identifying weaknesses, and taking responsibility for their operation.

In doing so, he found new opportunities in an economy increasingly shaped by AI. Peter's story highlights the importance of adapting to technological advancements rather than resisting them. By learning new capabilities and redirecting existing expertise, engineers can remain valuable and find new roles in a world increasingly influenced by AI.