A sector-level blueprint and AI-native rating framework for future-fit organizations
The AI Future-Fit Index™ (AFFI™) is a proposed rating framework that evaluates how structurally prepared an organization is to thrive in a human-led, AI-powered market. Algorithmic implementation, portfolio construction, and validation are addressed in portfoliospecific documents such as the Human Frontier 60™ Portfolio (see Appendix D therein).
Based only on shared Idea Explorer topics and areas.
This collection contains 355 selected essays, research papers, proposals, and supporting documents—with or without podcasts. Introductions draw closely from the PDFs’ preambles, executive summaries, openings, and conclusions. Passages may be shortened and joined; they are close adaptations, not necessarily continuous quotations. They express the source authors’ positions, not independent verification of every claim. Contributed material is attributed separately where identifiable.
Search words in titles, summaries, topics, authors, and podcast titles. Turn on Search inside PDFs to include extractable document text. All search words must appear somewhere in a record or its PDF; use quotation marks for a phrase. A PDF result shows the first relevant file page. Text embedded in images or affected by extraction defects may not be searchable.
Dates are separate: “By Date” sorts by the original PDF file’s last modification, latest first—not its upload or copy date. “By Name” sorts A–Z. “Essay” is a document date where reliably identifiable; “Podcast” is the YouTube publication date. Missing dates are marked “Not stated” and never inferred from the recording. Year-only or month-only dates retain that precision. Newest sorts place undated items last.
308 entries have matched podcasts, representing 284 distinct recordings. A recording may discuss more than one source. 45 entries are essay-only; 2 more remain available with uncertain primary podcast links withheld. Linked podcasts stay on YouTube. PDFs here are unchanged copies, not live links to Dropbox. Retained companion papers and alternate editions remain separate.
Research concepts and proposed medical or financial methods are presented for reading and discussion, not as established treatments or individualized professional advice. A proposal’s presence here does not imply clinical validation, regulatory approval, or an investment recommendation.
Readers can browse freely. Only the signed-in author can exclude or restore essays, assign them to one or more categories, or add categories. Category counts may overlap; the overall total counts each essay once. Confirmed changes are saved in the site database; the original source inventory and PDFs remain unchanged. The PDF reader’s return bar restores your previous view. Search filters remain in the address bar. Copyright and authorship remain with the respective rights holders.
Visit statistics: This Catalog counts page views, PDF reader opens, download clicks, and YouTube link clicks for the author. It uses a temporary, random browser-tab token, not tracking cookies, and stores no names, email addresses, IP addresses, search terms, or individual browsing histories. Duplicate-prevention tokens expire after 48 hours and are removed as new events arrive. Daily totals are retained. Do Not Track and Global Privacy Control signals are respected. YouTube has its own privacy practices.
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