This rings true to me. In my experience, the real divide is increasingly not between companies that have access to AI and those that don’t. It’s between teams that have learned how to integrate AI into repeatable work and teams that are still treating it as a promising but dangerous sideshow, as I’ve written.
This is also why I think the distinction of task versus job matters. Writing a chunk of boilerplate code is a task. Engineering is a job. Jobs bundle judgment, trade-offs, accountability, architecture, security, integration, testing, and the ugly reality of operating systems in the real world. AI can automate more tasks, but it hasn’t eliminated the need for jobs, especially in environments where bad software decisions carry real operational or regulatory consequences. In fact, McKinsey’s broader AI survey found that most organizations are still navigating the transition from experimentation to scaled deployment, and that high performers stand out precisely because they redesign workflows and treat AI as a catalyst for innovation and growth, not just efficiency. That is a very different thing from saying, “We gave everyone a chatbot and now we need fewer people.” (By the way, that would be a very naive statement.)
So no, AI isn’t plodding (or rocketing) toward one uniform enterprise future in which software engineers quietly fade away. Instead AI is splitting enterprises into fast-learning and slow-learning teams and is rewarding organizations that redesign work, govern risk, and turn lower software costs into more software, not less. The code may be getting cheaper, but the ability to decide what should be built, how it should fit together, and how to keep it from breaking the business keeps increasing in value.
