The article critically examines ten major problems of modern AI, from excessive energy and water consumption and dubious employment promises to pervasive surveillance, militarization, ethical abuses, copyright theft, mental‑health risks, societal erosion, job displacement, and the looming AI hype bubble.
The article examines the soaring electricity demand of data centers worldwide, especially in China, highlights regional distribution and PUE improvements, explores AI's massive power consumption, and outlines green computing strategies such as efficiency upgrades, waste‑heat reuse, and renewable energy integration.
This article provides a concise technical overview of AI chip key metrics—accuracy, throughput, latency, and energy consumption—explains their impact on hardware design, discusses critical design points such as MAC reduction and processing element optimization, and summarizes practical takeaways for evaluating AI accelerator solutions.
Sam Altman revealed that excessive politeness toward ChatGPT, such as saying “please” and “thank you,” can drive up server workload and electricity use, potentially costing OpenAI tens of millions of dollars, while highlighting broader concerns about AI energy consumption and user behavior.
A recent study benchmarked 27 programming languages using the Computer Language Benchmarks Game and Rosetta Code, measuring execution time, energy consumption via Intel RAPL, and memory usage, revealing that compiled languages like C and Rust dominate performance while interpreted languages lag behind.
