I was born in the 1970s and grew up during an era when industrial growth often came before environmental responsibility. 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.
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.
I still remember being in Busan, Korea in 1998. After being exposed to heavy rain during a period of severe pollution, I became seriously ill for long.
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.
This is why I am increasingly concerned about the way Artificial 智能 is being deployed. 大型模型s are often used 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 discipline. We spent decades cleaning up the consequences of uncontrolled industrialisation. We should not repeat the same mistake with Artificial 智能. At NeuralOps, our principle 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. 构建 more intelligence. Use less unnecessary compute. 设计 responsibly from the beginning.
