我们花了数十年清理工业化的后遗症。不要让 AI 重蹈覆辙。✎ Edit

I've spent my career building and deploying AI, IoT and neural 系统 in production environments, and the 设计 principle that guides my work didn't come from a whiteboard. It came from watching industrial 系统 scale faster than the controls meant to contain them. I grew up in the 1970s, an era when that gap was everywhere. One of the cities I lived in for decades was once known for being heavily polluted, and the river there was, at times, very toxic.

马来西亚 was not alone. Across the world, factories expanded rapidly, agriculture intensified, livestock production increased, and waste controls were still weak. The infrastructure went in first; the monitoring, regulation and remediation 层 were added later.

By the 1980s and 1990s, environmental awareness campaigns had become more visible. Cleaning rivers, improving air quality and changing industrial practices required regulation, infrastructure, technology and, most importantly, time. Those were 系统-integration problems as much as policy problems.

I still remember being in Busan, Korea in 1998. After being caught in heavy rain during a period of severe pollution, I became seriously ill for a long time. It was a stark reminder that when environmental controls 失败, the health impact is immediate and the recovery is slow.

从 the 2000s onward, the world started making significant progress. 环境 standards improved, wastewater treatment expanded, renewable energy grew, cleaner transportation became mainstream and EV adoption accelerated. China, Korea, 马来西亚 and many other countries invested heavily in improving the conditions created during earlier decades of uncontrolled industrialisation.

今天, many cities are dramatically cleaner than they were thirty years ago.

That progress should remind us of something important: environmental damage is not inevitable, but preventing it early is far easier than repairing it later. The same rule should govern how we 设计 AI 系统.

This is why I am increasingly concerned about the way 人工智能 is being deployed in the field. 大型模型s are often called for simple tasks, inference is repeated unnecessarily, and 系统 are built without proper routing, segmentation or deterministic processing. 背后 every AI request are GPUs, servers, electricity, cooling 系统, water and physical infrastructure. AI may look digital, but its environmental footprint is very real.

问题 is not AI. 问题 is using AI without engineering discipline. We spent decades cleaning up the consequences of uncontrolled industrialisation. We should not repeat the same mistake with 人工智能. At NeuralOps, the principle we apply in production is simple: use advanced AI only when advanced intelligence is genuinely required. 智能路由, specialised models, detached 系统 and deterministic processing can reduce unnecessary compute while preserving capability where it matters. 构建 more intelligence. Use less unnecessary compute. 设计 responsibly from the beginning.