Digital Strategy, Content, and AI Since 2009
President Trump renamed artificial intelligence after a mind no one has built.
Executive Order 14434 gives today’s ordinary AI the name researchers reserved for an intellect beyond ours, and the minds who defined that word were not in the room. Basil C. Puglisi traces superintelligence from Turing and Good to Bostrom and shows how the order skips the AGI rung. The rename is a bad joke if superintelligence is far off and dangerous if the fears are right.
Google’s own Search guidance undercuts several paid GEO tactics.
Google says foundational SEO and unique, non-commodity content still matter for AI Overviews and AI Mode, while llms.txt, forced chunking, AI-specific rewrites, inauthentic mentions, and special schema are unnecessary for Google Search. The article separates Google-specific guidance from broader GEO research and shows what Search Console can actually measure.
Human oversight of automated systems is older than AI, and current AI practice still falls short of it.
This paper traces human oversight and accountability from the cybernetics literature of 1948 to Article 14 of the EU AI Act, then separates Responsible AI from AI Governance. It sets out Checkpoint-Based Governance, where a named human holds binding authority to accept, modify, or reject an AI output and leaves a record, and it proposes the studies that would test it. It closes on the Growth OS, a future of work in which AI amplifies people instead of replacing them.
The machine is never the one who answers for what it does.
A September 2025 essay argued that AGI talk is usually about money and not reality. This September, Andrew Ng, Timnit Gebru, and Emily Bender reached the same point about responsibility from opposite sides of the AI debate. The piece separates real capability from the claims stacked on it, argues that superintelligence is further off than sold and will not end us, and moves from a human chain of responsibility to a real checkpoint.
If AI is a disability tool, are the rules restricting it unethical at best and illegal at worst?
This companion working paper maps which United States laws reach which institutions and classifies six types of AI policy risk, from outright bans to detection tools. It works a pending smart-glasses case, compares the EU AI Act, and closes with a model accommodation clause any institution can adopt.
A blind professional asks AI to describe a chart. The paper asks whether that use is assistive technology.
This working paper names the function Artificially Intelligent Disability Assistance (AIDA) and gives a three-prong test for when a use of general-purpose AI qualifies. It runs seven cases, from blindness and speech loss to post-traumatic stress, and it names the case the test excludes and the dissent it has to answer.
Verified AI is not a settled category, and its history explains why.
This working paper traces the term from the verification of knowledge-based systems in 1988 through the 2016 formal definition, shows how that concept shaped the HAIA Ecosystem, and sets out a practitioner definition built on records a human can review and accept. It closes with the 2026 trend and how the Proof-of-Control standard contrasts with it.
A standards body just wrote down that AI governance can exist without a human in control.
In September 2026 the U.S. TAG to ISO/TC 258 answered a public comment on the draft AI standard for project management. It accepted two editorial corrections and rejected the one that would have put binding human authority and attributable accountability inside the definition. This is the filing, the four reasons for the rejection, and why a definition without a governor fails the moral test the public is already applying.
AI products can be purchased. AI transformation has to be built.
This paper reframes fifteen commonly cited requirements for AI transformation as operational management requirements, from data provenance and purpose to decision traceability and value realization. It shows why automation and observation cannot serve as the sole control on consequential work, and how named human checkpoints close that gap.
Six positions in nine days, and every one of them fits the speaker’s balance sheet.
In September 2026 four frontier labs called for a slowdown, the President called the whole thing a hoax, and two of the largest companies in AI said the market already handles it. This piece argues the slowdown is not evidence that governance is working. It is evidence that economic incentives are still in charge, with the sign flipped. Readers get the dated exposure curve behind the calls, the reason liability cannot currently steer, and a narrower proposal than pacing the frontier.
