Examines financial risks associated with AI infrastructure investment, borrowing and interconnected financial exposures, and how a downturn could affect households and the wider economy. Proposes transparency, regulatory and financial-resilience measures without presenting a crisis as inevitable.
A blog from Tinfoil covering confidential computing and privacy-preserving AI infrastructure, with posts on how the company secures AI workloads and related technical developments.
Technical report examining whether off-grid solar microgrids in the United States could meet near-term AI datacenter power needs as a faster, lower-emission alternative to building new natural gas plants, based on 20-year powerflow modeling across thousands of site configurations.
Bain & Company's 2026 technology report on the financing gap behind AI's projected $6 trillion infrastructure buildout. Described from the title — the publisher's page would not let our reader in.
An open-source engine for serving large language models with high throughput and efficient memory use. Its PagedAttention approach helps reduce wasted GPU memory during inference.
A long explainer on what an "agent harness" is — the loop, tools, memory, sandbox, permissions and budget caps wrapped around an AI model — its six core components, and why the wrapper now matters more than which model you pick. Opens with an agent that burned $400 in API calls overnight in a retry loop.
Why I recommend it: Free to read, no sign-up, and the best single walkthrough of the six pieces if you are trying to understand why agents that demo well fall over in real use. Two honest caveats: it is published by a consultancy that sells help with exactly this, so the framing favors building a harness; and the byline on it is not a person I can verify anywhere independently, which on this site means treat the article as a useful explainer rather than a sourced authority. For neutral definitions, the Wikipedia entry on agent harness and Addy Osmani's write-up cover the same ground.
Pew Research Center survey of 10,548 U.S. adults finds growing concern about data centers' environmental impact, home energy costs and quality of life, with majorities now saying they are mostly bad for the environment.
Why I recommend it: Pew Research Center short read published 22 September 2026, survey fielded 20 July–9 August 2026. 54% of Americans say data centers are mostly bad for the environment (up from 39% in January), 50% say mostly bad for home energy costs, 49% say mostly bad for nearby quality of life. Views on local tax revenue and jobs are mixed. 12% had not heard of data centers at all.
#AI infrastructure#data centers#energy#environment#Pew Research Center#public opinion#research#survey
Company building thermodynamic computing hardware it claims is far more energy efficient than GPUs.
From the site: Building thermodynamic computing hardware that is radically more energy efficient than GPUs.
Why I recommend it: The energy cost of AI is becoming the constraint, and this is one bet on solving it in hardware. The efficiency claims are the company's own and unproven at scale.
