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Engineering

AI didn't replace engineering. We just stopped talking about it.

Artificial Intelligence has become impossible to ignore. Open Gartner. AI. Read CIO.com. AI. Attend Microsoft Build, Google I/O or AWS Summit. AI. Scroll through LinkedIn for five minutes and you'll quickly get the impression that every meaningful conversation in technology now begins and ends with large language models, autonomous agents and AI-assisted development. I understand the excitement. AI is a remarkable technological breakthrough, and its impact will be difficult to overstate. But I've started wondering about something else. Not what we're talking about. What we've stopped talking about. The conversations that quietly disappeared A few years ago, our industry spent enormous amounts of time discussing operating models, governance, architecture, automation, platform engineering and cloud operating practices. Those conversations weren't glamorous. They rarely filled conference halls. They certainly didn't dominate social media. But they mattered. Because they determined whether technology actually worked once the keynote was over. Today those disciplines seem strangely absent from the conversation, as though AI somehow made them less relevant. It didn't. If anything, it made them significantly more important. Engineering never disappeared One of the more curious assumptions behind today's AI enthusiasm is that intelligence somehow compensates for engineering. That if an AI model can generate code, architecture becomes less important. That governance becomes something you can add later. That operational excellence is simply another problem AI will eventually solve. I'm not convinced. Software has never failed because people lacked ideas. It usually fails because complexity quietly grows beyond anyone's ability to understand or control it. AI doesn't remove that complexity. It introduces an entirely new category of it. Unlike traditional software, these systems are probabilistic. They don't always behave the same way twice. They require validation instead of assumption, observation instead of certainty. That doesn't reduce the need for engineering discipline. It raises the standard. Demonstrations have an unfair advantage One reason the current conversation feels so optimistic is that most of what we see are demonstrations. Someone builds an agent in twenty minutes. Another team generates an application from a prompt. A startup orchestrates half a dozen AI services into something that looks almost magical. And genuinely—it often is impressive. But demonstrations have an unfair advantage. They don't have to survive production. They don't have to operate for three years. They don't have to pass security reviews. They don't have to explain themselves during an audit. They don't wake someone up at three o'clock in the morning because an automated decision suddenly affected thousands of customers. Production has always been where technology stops being exciting and starts becoming accountable. That hasn't changed. Abstraction is a wonderful servant The cloud taught us an important lesson: Abstraction is incredibly powerful. We no longer think about physical servers before deploying an application. Kubernetes allows developers to focus on workloads instead of individual machines. Managed services remove enormous amounts of operational burden. Those are extraordinary achievements. But abstraction has always come with an implicit agreement. Someone still needs to understand what happens underneath. Every abstraction layer increases productivity for thousands of people while simultaneously reducing the number of people who understand the foundation beneath it. That trade-off is acceptable. Until the abstraction breaks. Then expertise suddenly becomes scarce. I wonder what we're teaching the next generation When I speak to younger engineers, I'm often impressed by how quickly they adopt new technologies. Many can build sophisticated cloud-native applications long before they have ever managed a physical server. Increasingly, many can also build AI-powered applications before they've fully understood distributed systems, identity, networking or storage. None of that is their fault. We teach what the industry rewards. And right now, the industry rewards speed of adoption far more visibly than depth of understanding. I sometimes wonder what happens twenty years from now. Not when AI becomes more capable. But when the people responsible for critical systems have never needed to understand the layers beneath the abstractions they inherited. The question that interests me most Perhaps this isn't really an article about Artificial Intelligence. Perhaps it's about attention. Technology has always moved in waves. Every few years we collectively decide what deserves our attention, and everything else quietly disappears into the background. Today, AI occupies almost all of that space. Meanwhile, architecture, governance, operational excellence and systems thinking continue doing what they have always done. Quietly determining whether ambitious ideas become reliable systems. Or expensive experiments. Final reflection I have no doubt that Artificial Intelligence will transform our industry. I also have no doubt that most organizations are underestimating what it takes to operationalize it responsibly. Because intelligence alone has never been enough. Not in software. Not in leadership. Not in engineering. Perhaps that is what concerns me most. We celebrate every new abstraction as progress, while paying remarkably little attention to the knowledge it slowly replaces. Every generation of technology asks us to understand a little less of what happens underneath. AI simply accelerates that trend. Maybe that is inevitable. But history has rarely been kind to civilizations that confuse convenience with understanding. The industry is celebrating intelligence while quietly abandoning wisdom. And history has never been particularly kind to civilizations that confused the two.