How I learned to stop worrying and love hyperscaler capex
The New Stack 2 months ago 33
The article reviews criticisms of hyperscaler spending on AI infrastructure and tests the claim that AWS, Google Cloud, and Azure are overspending on AI compute.
AI Efficiency
132 summarised stories about AI Efficiency, each linking back to the original source. Browse all topics →
Key players
Tuesday, 11 August 2026
Caveman
Product Hunt 2 months ago 39
“Caveman” is presented as a short discussion link about using fewer tokens to achieve the same effect. No numeric benchmarks, dates, or prices are provided. As a result, there’s not enough information to determine any concrete change or outcome beyond the general idea being discussed.
Why Scaling AI Compute Performance Requires a New Power Architecture
NVIDIA 2 months ago 30
AI infrastructure needs higher-voltage power delivery to reduce conversion overhead and deliver more power efficiently to next-generation GPU racks. 800 VDC is being standardized via Open Compute Project work, with a joint white paper published in March 2026 and a LVDC Solid-State Transformer Specification released in July 2026. Hybrid 800 VDC deployments let existing AC-based facilities keep their building electrical systems while scaling power density, with rack (MGX-compatible), row (up to 2 MW per row), and facility-scale DC options planned for 2026–2027, supported by 80+ vendor products.
Thinking of ACE? We Can Do It with Fewer Tokens
Hugging Face 2 months ago 24
ALTK-Evolve and ACE both turn an agent’s past trajectories into reusable memory lessons for an LLM ReAct agent, but they differ in how much of that playbook gets sent at inference each step. ALTK-Evolve cut tokens/task from 634K with ACE to 263K on DeepSeek-V3.2 while raising TGC from 80.4 to 89.3 and SGC from 73.2 to 80.4. The change is calibrated delivery (selecting a subset per task) instead of injecting the full playbook every step, keeping accuracy while reducing inference cost.