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AI Efficiency

132 summarised stories about AI Efficiency, each linking back to the original source. Browse all topics →

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Thursday, 20 August 2026

Ok, can we actually cool data centers with our pee?

TechCrunch 1 month ago 42

Liquid Death and Jason Kelce ran a joke campaign suggesting people’s urine could be used to cool AI data centers, while experts noted that data centers can already rely on non-potable sources like recycled water. Meta will invest at least $270 million in wastewater infrastructure near its data centers. The focus shifts from a prank to scaling recycled-water infrastructure, including possible government support such as a 30% tax credit to expand treatment capacity for data center cooling.

Stop the token bleed: building token-efficient multi-agent systems

The New Stack 1 month ago 42

Token-efficient multi-agent systems article argues that production costs come less from the model itself and more from workflow inefficiencies like repeated retrievals, duplicate prompts, oversized contexts, and redundant model calls. It proposes a context budget capped at 2500 tokens when building the context for the final LLM call. The recommended architecture changes by adding routing, semantic caching, single retrieval shared across agents, context budgeting, token estimation telemetry, and model selection so the LLM is used only as a final, most-expensive step.

The Sequence Opinion- Issue 918: The Energy Scaling Laws of AI

TheSequence 1 month ago 20

The Sequence publishes an opinion on how future AI scaling is governed by real-world energy systems instead of just model parameters. Issue 918 focuses on energy conversion efficiency from photons and atoms into intelligence. It reframes AI “scaling laws” to include how power, cooling, and network losses limit what the next wave can achieve.

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