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The AI infrastructure spending race shows no signs of slowing down. Nscale and Anthropic, who announced a forty-five billion dollar compute deal just months ago, are reportedly in talks to double down with another agreement of identical scale. That is ninety billion dollars in compute commitments between two companies, signaling just how seriously the industry is betting on insatiable demand for AI processing power.
That appetite for AI is worth examining alongside some uncomfortable findings from the workplace. New research suggests that workers using AI tools are spending more time correcting the output than they would have spent simply doing the work themselves. Nearly half reported making substantial corrections to AI-generated material, and over a third said the review process took longer than the original task. The productivity promise, it turns out, comes with a significant asterisk.
On a more technical note, AWS has released TorchNeuron, a library designed to let developers run PyTorch workloads on Amazon's own Trainium chips with minimal changes to existing code. It is a quiet but meaningful move in the cloud provider's push to reduce dependence on third-party silicon and give developers a smoother path onto its own hardware.
That is where things stand today across the industry. Keep surfing. Tech Beat out.
