Principles for AI, platforms, and scaling engineering teams.
There’s a fine line between a revolution and a gold rush. Right now, GenAI is toeing it. It’s the hottest thing since the Internet, the cloud, and crypto—rolled into one ChatGPT-branded hype cycle. But peel back the buzz, and a question emerges: is this momentum or mania? Let’s look past the noise.
Software is eating the world, but big companies are choking on the meal. Every Fortune 50 company knows this secret: the bigger they get, the slower they ship. It's not a bug—it's a feature of scale itself. While a ten-person startup pushes to production five times a day, Enterprise Inc. celebrates quarterly release like it's the moon landing. This isn't incompetence. It's economics.
AI won’t close the door on junior developers — it’s widening the hallway for all of us. Beginners will onboard faster. Seniors will mentor with sharper tools. Managers will scale teams with less friction. Executives will rethink how value gets created. Far from ending careers, AI is redrawing the developer journey into something bigger, faster, and more connected. From first jobs to executive seats, AI is shifting the developer journey, not ending it.
The tech industry has a knack for turning simple concepts into incomprehensible jargon. AI is no exception. Strip away the buzzwords, and you'll find something remarkably straightforward—and more useful than the hype suggests. Let's walk through what AI actually is and where we are on the curve and journey to creating useful tools.
Most engineering leaders fail because they mistake motion for progress. Big Tech glorifies complexity, Google-scale infra for a team of five, ML pipelines for a product with no users, microservices because “monoliths don’t scale.” This isn’t engineering. This is cosplay. The first principle: define the real problem (outcome > output). Shipping 10,000 lines of Kubernetes YAML that no customer notices is not progress. A two-line script that saves a customer $10M is.