Look around any modern company today. We’ve automated payroll processing, customer support with chatbots, marketing campaigns, even coffee machines that remember how you like your latte. But when it comes to creating the very software that enables all this automation, we’re still largely stuck in the same patterns we’ve used for decades. Isn’t that strange?
I was reviewing some of the principles from The Qgenius Golden Rules of Product Development recently, particularly the one about 「new technologies needing to find their cognitive path.」 It struck me that we’ve been trying to automate software creation since the 1980s with CASE tools, model-driven development, and now with AI assistants. Yet the fundamental process remains stubbornly human-intensive.
The irony is palpable. We’re using software to automate everything except making software itself. Think about it: manufacturing plants have robots building cars, financial institutions have algorithms trading stocks, but software teams still gather in rooms to debate requirements and write code line by line. Why does this particular activity resist automation so fiercely?
Part of the answer lies in what makes software creation unique. Unlike manufacturing or routine business processes, software development isn’t just about following predefined steps. It’s fundamentally a creative problem-solving activity that requires understanding complex, often ambiguous human needs and translating them into precise machine instructions.
Remember the dot-com bubble? Companies poured millions into automated website builders and 「push-button」 e-commerce solutions. Most failed spectacularly because they assumed building software was like assembling furniture from IKEA instructions. The reality is that good software requires deep understanding of user psychology, business context, and technical constraints – areas where human judgment still outperforms automation.
I’ve noticed something interesting in successful teams though. They don’t try to automate the creative core of software development. Instead, they automate everything around it: testing, deployment, monitoring, and infrastructure management. This aligns beautifully with the Qgenius principle about 「reducing cognitive load」 – by automating the routine aspects, we free up human creativity for where it matters most.
The current wave of AI coding assistants like GitHub Copilot represents progress, but they’re still assistants rather than replacements. They can suggest code snippets, find bugs, or generate boilerplate, but they struggle with the holistic understanding required for architectural decisions or novel problem-solving.
Here’s what I’ve observed: the most effective teams treat software creation as a craft that combines human creativity with strategic automation. They automate the predictable parts while preserving space for human judgment in areas requiring innovation and adaptation. This isn’t about resisting progress – it’s about recognizing that some activities benefit more from augmentation than full automation.
So the next time someone promises to automate software development entirely, ask them: Can you automate understanding what users really need? Can you automate the creative leap that turns a business problem into an elegant solution? Until we can answer yes to these questions, maybe software creation should remain the last frontier of automation.