Hundreds of workers are building new data centres in rural Inner Mongolia to supply the computing power behind China’s AI platforms. The article says China is building “hundreds” of data centres across provinces including Ulanqab and aims to embed AI across 90% of industries and society by 2030. As a result, infrastructure and power generation plans expand while some rural communities report village relocations and limited local benefits, even as others see job opportunities and faster AI innovation.
OpenAI is consulting an independent panel of mathematicians after a reputational crisis tied to how its work handled mathematical results. The company announced the panel on Monday. This change adds external advisory oversight for how OpenAI and other AI companies interact with math research and publish new results.
Scott Bessent argued that the US government should not serve as a liability shield for AI hyperscalers, asserting that AI labs must take responsibility for safety risks. He cited a 10% maximum chance that AI could wipe out humanity within the next decade. The debate shifts toward government guardrails backed by limits on self-regulation, rather than shifting liability to regulators.
KPMG reorganized its AI, innovation, and ecosystem functions into a new Client Technology & Innovation (CT&I) group led by Todd Lohr. CT&I was announced on Sept. 22 and takes effect on Oct. 1, with Lohr serving as vice chair reporting directly to CEO Tim Walsh. The change elevates AI and incubation into a single CEO-led unit that will pursue AI-native product and venture development, including using existing partnerships and expanding focus on commercializing future offerings.
Teradata expanded its Tera AI assistant with a context engine and execution system aimed at helping governed data agents carry out enterprise tasks. In company testing on SWE-bench Pro with the same Opus 5 model, Tera used 73% fewer tokens and finished tasks 42% faster than Claude Code. The update adds traced, policy-aware context for agent requests, tool execution with human approval controls, reusable agent skills, and new capabilities scheduled for availability in Q4 2026.
Baselayer, a business identity and risk infrastructure startup, shifted its focus to AI agents by launching its Agentic Identity Suite after raising early-stage funding. The company raised $35 million and said it has already reviewed more than 10 million applications and blocked over $1 billion in fraudulent transactions. The move retrofits its identity and trust tooling for “agentic commerce,” and adds cryptographic traceability plus supporting tools like a Model Context Protocol server and Agentic Search to ground agent decisions in verified business data.
Data & Society’s report says opposition to new data center construction in Pennsylvania is driven by how residents experience industry messaging and local impacts rather than by a single political cause. It is based on 18 months of ethnographic fieldwork with 44 Pennsylvanians conducted between 2024 and 2026. The findings shift the debate from measuring construction effects to addressing trust, lobbying and local concerns that the AI industry struggles to reconcile with demand.
Zvi (Don't Worry About the Vase)·3 weeks ago·
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The article reports a shift in US AI policy rhetoric after Donald Trump reframed AI existential-risk concerns as “hoaxes” and pushed an “AI Force” plus an “AI czar” plan while White House staff tried to respond with more cautious risk-management ideas. On September 19, around 10:12am, Trump posted a Truth Social message claiming “decimation” or destruction of AI and announcing the AI “czar” and AI “Force.” The practical result described is a more conflicted political stance, with plans for safety standards and model reviews moving slowly and Trump’s approach increasingly undercutting the safeguards that AI executives and safety advocates want.
Donald Trump told the UN General Assembly that the US is now officially renaming artificial intelligence to “super intelligence.” He made the claim in a Tuesday morning speech. This changes the public label used for AI, with Trump arguing that dropping “artificial” makes the term sound more like real intelligence.
Ray Dalio says AI will boost productivity while also creating bubble dynamics that worsen wealth inequality and could devastate many people. He points to the dotcom era, noting the Nasdaq rose 86% in 1999 before falling 77% from its peak by October 2002. The debate shifts from whether AI is beneficial to how societies manage investment overreach and distribute gains from AI-driven capital growth.
AI executives and media coverage have amplified a series of claims—from security “hacking” disclosures to alleged mathematical breakthroughs—while the article argues that experts later found the details less compelling than portrayed. The piece points to OpenAI’s Astra announcement, saying its main result had seen no progress for at least a decade. Policymakers are urged to slow down and rely on independent expert review rather than press releases, especially because the “rogue model” framing deflects accountability from companies and misdirects attention from real-world harms like data-center impacts.
Accenture Federal Services’ Chief AI Officer Garrett Berntsen argues that adopting AI in U.S. national security depends less on model access and more on redesigning institutions, workflows, and decision chains like those built around the U-2 program. He points to the U-2 still being in service after its Cuba overflight success during the 1962 Cuban Missile Crisis as evidence that procurement and analysis systems matter as much as the technology itself. The focus shifts toward experimentation plus “forcing functions” in budgeting and incentives, and toward fixing core business data and update policies so AI can operate at operational tempo rather than only in isolated missions.
The article argues that the U.S. is framing the AI “race” against China around model performance while Americans increasingly distrust AI and policymakers focus less on consumer protection. It cites that 87% of Chinese citizens trust AI versus 23% in the U.S. It concludes that AI adoption and “winning” will depend more on how AI is deployed with protections and transparency than on benchmark performance.
OpenAI outlined priorities and principles for rigorous, secure, independent third-party assessments of frontier AI models and safety safeguards. The article calls for these assessments to cover both frontier models and the safeguards themselves. This sets expectations for how external reviewers should evaluate AI safety going forward.
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