Urgent.News

What's breaking now, across thousands of outlets.

AI

BC Greens say public must have a say whether AI data centres get built

On October 11, 2026, BC Greens Leader Emily Lowan expressed her disappointment with NDP policies regarding AI data centres in British Columbia, stating that the current measures fall short of her party's expectations. Lowan believes that the public should have a say in the approval process of these data centres.

The NDP, led by David Eby, has proposed several policies for AI data centres, such as water-efficiency standards, mandatory reporting of water consumption, and community engagement on conservation and drought planning. However, Lowan has questioned whether the NDP would grant communities a veto on construction projects.

The Greens have previously pledged to halt the construction and operation of AI data centres until a comprehensive public-interest audit is completed and a regulatory regime is implemented. They have also expressed interest in collaborating with the federal government to establish an independent AI oversight commission.

In May, Telus and federal AI Minister Evan Solomon announced three AI data centres in B.C., with Telus estimating that they would consume 150 megawatts of power by 2032. This power equivalent to 12-14% of the Site C dam's output, which was built in northeast B.C.

Written by urgent.news from Winnipeg Free Press's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.

Read the original at winnipegfreepress.com →

More in AI

WildTrace — Every Small Habitat Has a Story

This is a submission for the Hacktoberfest Open-Source AI Challenge — Week 1: Touch Grass. What I Built I spend a lot of time in front of a computer building things with AI.

  • WildTrace is an AI-powered micro-habitat observation platform
  • Users document small natural environments and compare photos over time
  • Image-analysis techniques assess changes in habitats

We benchmarked 4 web scraping APIs for AI agents against DataDome, Cloudflare, and Akamai

This article was originally published on the Zenrows blog . This post compares five web scraping APIs for AI agents using 800 benchmark requests against DataDome, Cloudflare, and Akamai.

  • Zenrows achieved 100% success rate for all targets with an average response time of 5,215 ms.
  • Jina AI Reader had a 99.33% success rate and an average response time of 1,257 ms.
  • CrawlForge failed all requests with a 0% success rate and an average response time of 867 ms.

More from Sunday 11 October →