
As Jensen Huang puts it, AI can help fight climate change - but only if it inflicts "an enormous amount of pain and suffering" first. The Nvidia CEO discussed the future of energy and AI's impact on our planet in the latest episode of The Ezra Klein Show. But his comments boil down to the same accelerationist spin we've seen from a variety of tech leaders as well as President Donald Trump. They promise that AI will be such a gift to humanity that it's worth the damage caused
The Nvidia CEO discussed AI's potential to address climate change while acknowledging it would first cause "an enormous amount of pain and suffering," comparing the process to surgery. His comments reflect an accelerationist argument from tech leaders that AI's benefits justify the environmental damage from energy-intensive data centers currently powered by fossil fuels. This matters because the approach prioritizes technological advancement over the immediate climate impacts already affecting communities through wildfires, flooding, and other weather-related disasters, while the speaker himself has substantial wealth insulating him from these harms. The tension is underscored by the fact that nearly every country agreed to cut greenhouse gas emissions in half by 2030 and reach net zero by 2050 under the Paris agreement, which requires swift transition to renewable and nuclear energy rather than continued fossil fuel dependence.

Under the One Big Beautiful Bill Act, data center projects in rural areas could be eligible for major tax benefits starting next year. Some hyperscalers do not seem eager to take the free cash.

The price of anything with memory is skyrocketing thanks to AI. Aging streaming devices are no exception.
NVIDIA announced a new 64GB configuration of DGX Spark — from Acer, ASUS, Dell, Gigabyte, HP and MSI — its GB10-powered desktop AI system. It gives developers a way to start with one system for local models and agents, then cluster two 64GB units for 128GB of memory across the cluster and more compute when workloads grow. The direct message is: Run open models and always-on agents on your own desk, then cluster DGX Spark system as the work grows – instead of on a metered API. The ti
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