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.
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.
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.
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.
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.
The AI jobs numbers look calm. Cut them by age, and the calm disappears. The dashboard that found the early-career decline can diagnose the trend but cannot see whether a single worker is being grown or replaced. Here is what it shows, what it cannot, and the measurement the moment needs.
A new OECD and European Commission framework will shape how a generation learns to use AI, and it feeds the PISA 2029 assessment. It names the method that produces learning, and then it makes every step toward that method optional. Read why that choice matters, and what a checkpoint that actually binds a decision requires.
A German court just told Google it answers for what its AI publishes. The Munich ruling treats AI Overviews as Google’s own statements, not safe search results, and says the disclaimer does not transfer the duty. Read what the decision means for AI accountability and why it mirrors New York’s Part 161 from the other end.
New York let its lawyers use AI in court and skipped the disclosure form everyone expected. That is not the relief it looks like. With nothing to disclose, the whole duty lands on the signature. Here is what Part 161 changes, the two cases that show the stakes, and why accountability outlasts disclosure.
