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.
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.
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.
A woman applies for a car loan on a Tuesday morning. Before a human being reads her name, a model has scored her, a ranking system has chosen what she sees, and a classifier has flagged her scan.
She met artificial intelligence roughly a dozen times before lunch, and none of it said a word to her. This is the story of what AI actually is, told in the order it happened, from the era when humans wrote every rule through machine learning, deep learning, the transformer, language models, agents, and embodiment. It ends where the sequence was never resolved: nobody settled who holds authority over the decisions.
The major AI literacy frameworks name governing and managing AI as a competence and stop short of measuring it. This working paper introduces the Human Enhancement Quotient, a behavior-anchored instrument that scores the governance relationship between a human authority and a machine capability inside a working decision process, with a universal floor keyed to irreversible consequence and an audit record reviewable on appeal.
Nine judges carry two votes’ worth of information. Run the same task across many AI platforms and the agreement that comes back may be a shared blind spot rather than a finding. HAIA-CAIPR is the governance framework for orchestrating that comparison so it produces evidence instead of confidence. It sets nine invariants, five configuration variables that each name what they cost, and a named human who reads the raw returns before anyone decides.
Europe withdrew its AI liability directive, and many executives read relief into the news. The surviving Product Liability Directive tells a different story, reaching software and AI with strict liability and presumptions that reward the documented company. This analysis maps what changed, who carries the exposure across providers and deployers, and the record that answers a claim.
