Philanthropy as Risk Capital: Shaping Trust and Learning at the Speed of AI
Bibliographic record
Abstract
As artificial intelligence rapidly reshapes society, philanthropy is increasingly called to act as "risk capital" for the public good. But risk alone is not enough. Without rigorous, field-wide learning, philanthropy's bold bets may remain isolated and short-sighted, failing to catalyze the systemic change this moment demands. This article draws on three sources of learning at Omidyar Network—an evaluation of The Tech We Want initiative, external strategy consultations with 29 stakeholders, and early learnings from a generative AI portfolio—to identify how philanthropy can uniquely "de-risk" AI innovation for collective benefit. Through trust-based partnerships, ecosystem infrastructure support, and narrative change, these learning opportunities revealed that philanthropy must show up beyond funding, signaling what works, sharing lessons openly, and creating enabling conditions for others to act. This article identifies four critical insights for how philanthropy can use learning to unlock collaboration, shift public narratives, and equip the field to act with both urgency and wisdom in shaping AI's trajectory toward shared power, prosperity, and possibility.
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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 | 0.013 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.016 |
| Scholarly communication | 0.011 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".