Decentralized Altruism: How Solo-Founder AI-Driven Nonprofit Mode (SFADNM) With Anarchist Principles is Reshaping Nonprofits in the Digital Age
Bibliographic record
Abstract
The traditional nonprofit sector is often constrained by financial dependencies, bureaucratic inefficiencies, and hierarchical governance structures that limit innovation and social impact. This study introduces the Solo Founder AI-Driven Nonprofit Model (SFADNM), an emerging paradigm that leverages artificial intelligence (AI) to decentralize nonprofit governance, eliminate financial dependencies, and scale social impact without monetary transactions or hierarchical oversight. Grounded in anarchist principles of self-governance, mutual aid, and autonomy, SFADNM challenges the Nonprofit Industrial Complex (NPIC) by demonstrating that a single founder, supported by AI automation, can sustain a nonprofit organization without external funding, paid labor, or institutional support. Employing a qualitative research methodology, this study combines a comprehensive literature review with a theoretical and case study analysis. Using FASSLING, an AI-driven human services product line from Canadian federally registered nonprofit For A Safer Space (FASS), as a case study, this research explores how AI enables decentralized decision-making, enhances operational efficiency, and ensures continuous service delivery without human intervention. The paper also examines the ethical considerations, sustainability challenges, and governance mechanisms required to maintain AI-driven nonprofit operations. Ultimately, SFADNM presents a disruptive alternative to conventional nonprofit models, illustrating how AI-powered, decentralized altruism can redefine the future of philanthropy, social entrepreneurship, and nonprofit sustainability in the digital age.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".