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AI’s intellectual-property reckoning will not end with training data. As AI products evolve from passive models into persistent agents that remember, schedule, coordinate, decide and act...
...they increasingly enter territory occupied by decades of patents covering computing architecture, automation, control systems and machine intelligence.
Whose Knowledge Is It? The Legality, Consequences & Governance of Employer and Government Capture of Worker Expertise via AI. The new practice of recording how experts work & using it to train AI...
...that could replace them sits in a contested legal grey zone touching data-protection law, the EU AI Act, and human-rights instruments—none of which clearly prohibits it. The threat is real.
Granillo v. State and America’s Constitutional Crisis: Justice Todd Eddins accused the US Supreme Court of systematically weakening voting rights, civil liberties, democratic accountability and...
...protections for less powerful Americans.The opinion suggests an increasingly fragmented America in which state constitutions become essential safeguards.
Companies are purchasing physical books because books published before the widespread adoption of generative AI offer something...
...the contemporary web can no longer guarantee: text written by humans rather than by earlier AI systems. AI development is becoming ecologically, economically and intellectually unsustainable.
When the Machine Knows More: The epistemic, social and relational frictions of AI as the most knowledgeable participant in the room.
Expertise loses its value as an information monopoly and the human helper—teacher, expert, elder—is quietly bypassed. Individual gains come at the cost of homogenized, converging ideas.
The central problem is not simply that GDID exists. It is that Microsoft appears to have created an unusually consequential identity layer without explaining its scope,...
...limiting its uses or giving ordinary users meaningful control over it. Microsoft can reconstruct a remarkably detailed history by joining records around a stable identifier.
What Current Systems Cannot Know — and Why the Field Is Racing to Build Machines That Understand Reality
A research briefing on the motivations behind world models, the limitations of today's AI, and the specific things current systems cannot understand without a grounded model of the physical world.
Beyond Jobs and Investment. Hidden and Under-Discussed Drivers of the Global Data-Center and AI-Compute Boom.
Higher electricity and water costs shifted onto locals, secrecy via NDAs and shell companies, and permitting fast-tracks — is real and well-documented. An AI capex race many call a possible bubble.
6th International Wicked Symposium: AI, climate change and geopolitical disruption are interconnected “wicked problems” requiring collaboration between corporations, governments, investors and...
...academia. Advantage will come from trusted sector expertise, proprietary data and industrial applications of AI, while organisational readiness, infrastructure & geopolitics will determine success.
Hacking of Suno matters because it appears to expose something that generative-AI companies have usually kept hidden: the operational machinery used to assemble a commercial training corpus.
It may give rights owners a highly valuable roadmap for discovery and appears to corroborate allegations that were already before the courts.