The biggest names in AI are backing a new way to opt out of scraping

Major model developers adopt unified policy token to streamline publisher training opt-outs

By GPU Data Hub Desk · GPU Data Hub·Editorial standards·30 Oct 2026, 20:26

Major artificial intelligence vendors, including OpenAI, Google, Microsoft, and Anthropic, have agreed to adopt a shared standard permitting digital publishers to opt out of automated content scraping. The initiative employs a unified policy token that allows content owners to broadcast training exclusions across multiple model developers at once.

Original source

This summary was written by the GPU Data Hub desk from reporting published by The Verge on 30 Oct 2026, 20:26. Read the full article at the original publisher.

Read Original Source →

Companies

OpenAI Google Microsoft Anthropic

Topics

AI training web scraping data governance AI policy

LinkedIn X

More from GPU Data Hub

Latest →

The Race We’re Actually Running, Part 2: Good Enough at a Fraction of the Price

An examination of the strategic rivalry between the United States and China contends that model efficiency and unit economics will dictate long-term market dominance. The analysis suggests that broadly competent architectures delivered at low cost may ultimately prove more impactful than premium flagship systems.

AI Infrastructure·9h ago·HPCwire

OpenAI CEO says it's 'quite terrible' for your PC to have RAM — Sam Altman says data centers offer better utilization

OpenAI chief executive Sam Altman has argued that allocating system memory to individual personal computers represents an inefficient use of resources compared to centralised AI facilities. Speaking during a podcast, Altman suggested that high-performance memory would deliver superior utilisation rates if deployed within data centres rather than consumer hardware.

AI Infrastructure·12h ago·Tom's Hardware

Designing infrastructure for enterprise AI

A strategic advisory framework has examined the process of pairing corporate artificial intelligence workloads with appropriate compute infrastructure. The publication serves as a decision-making reference for selecting suitable hosting and architectural models. It addresses how organisations can structure technical environments to match specific computational needs.

AI Infrastructure·13h ago·Data Center Dynamics

Things That Go Beep in the Night: Agentic AI Joins the Search for Extraterrestrial Signals

Nvidia has detailed the expanding role of agentic artificial intelligence architectures within scientific projects searching for extraterrestrial intelligence. Automated software agents are increasingly tasked with listening for and parsing potential signals across high-volume astronomical datasets. The initiative underscores the broadening application of autonomous machine-learning workloads in computational research.

AI Infrastructure·14h ago·NVIDIA Newsroom

A primer on key AI LLM concepts — KV cache, weights, quantization, and how inference actually works

An introductory guide details foundational concepts governing large language model inference and execution. The analysis explores technical components including key-value caching, neural network weights, and quantization techniques to explain operational mechanics.

AI Infrastructure·15h ago·Tom's Hardware

AI projects not paying off? Keep at it, says Uni Productivity Institute

Research from the Productivity Institute suggests enterprises should exercise patience with artificial intelligence deployments that appear slow to generate financial returns. The study highlights that capital expenditure requires substantial time to yield measurable results, cautioning that tactical efficiency gains do not immediately translate into wider business productivity. The findings come amid growing scrutiny from corporate boards over the pace of return on AI spending.

AI Infrastructure·16h ago·The Register