Crypto Philanthropy: How to Donate to Make Real-World Impact - Ep.296
Some of the biggest names in the crypto industry discuss their personal giving philosophies, how blockchain technology could change the dynamics of giving to charity, and which crypto projects are making a lasting real-world impact. Guests include Haseeb Qureshi, managing partner at Dragonfly Capital; Caroline Ellison, co-CEO of Alameda Research; and Arthur Breitman, co-founder of Tezos. Show topics:
- the definition of effective altruism
- why Haseeb thinks crypto is the best industry for effective altruism
- how Haseeb and Arthur differ in their philosophy of doing good
- why blockchain technology is less important than liquidity when donating money across borders
- whether Axie Infinity is making a real-world impact
- why Arthur thinks Axie’s impact will be short-term and not sustainable
- whether people should donate now versus later
- Haseeb’s and Arthur’s thoughts on how giving changes people
- what people working in crypto can do to give back
Thank you to our sponsors!
Crypto.com: https://crypto.onelink.me/J9Lg/unconfirmedcardearnfeb2021
Nodle: https://bit.ly/3AXGydJ
Brave: http://brave.com/Unchained
Episode Links
GiveDirectly
- https://www.givedirectly.org/unchained — all donations made here will be matched! There are $50,000 in matching funds available!
- https://www.givedirectly.org/crypto-for-good-2021/
Guests
- Caroline Ellison
- Haseeb Qureshi
- Arthur Breitman
Miscellaneous
- Effective Altruism
- Code to Inspire
- Pineapple Fund
- BitGive
Learn more about your ad choices. Visit megaphone.fm/adchoices
Available Results
Generated results are saved to your library for reuse and search.
Choose Template
Pick the result you want. You can review provider and model before generating.
A concise first-pass summary for understanding the episode quickly.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Repeatable methods, frameworks, mental models, processes, and systems.
A dedicated inventory of concrete resources named in the episode.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Explicit actions, next steps, habits, recommendations, and things to avoid.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.
A concise first-pass summary for understanding the episode quickly.
A detailed readable summary organized by chapter or topic.
A navigable map of subjects, topic flow, and suggested chapters.
A comprehensive, source-grounded extraction of the reusable knowledge in an episode.
Reusable atomic knowledge units extracted from the episode.
A comprehensive extraction focused on health practices, protocols, claims, and safety caveats.
A comprehensive extraction focused on opportunities, strategy, markets, and company building.
Scientific findings, mechanisms, studies, hypotheses, and the limits of the evidence discussed.
Technologies, AI models, technical methods, capabilities, limitations, and adoption implications.
Investment theses, assets, catalysts, valuation reasoning, time horizons, and risks.
Chronologies, actors, causes, consequences, turning points, and competing historical interpretations.
Policies, proposals, stakeholders, arguments, implementation constraints, and predicted effects.
Career paths, skills, hiring signals, workplace decisions, transitions, and limitations of the advice.
Behavioral mechanisms, biases, motivation, habits, emotions, interventions, and evidence limitations.
Economic mechanisms, incentives, indicators, market structure, forecasts, and uncertainty.
Leadership principles, team systems, organizational design, culture, feedback, and failure modes.
Audience, positioning, messaging, acquisition, retention, experiments, metrics, and failed approaches.
Teaching methods, learning strategies, practice, feedback, assessment, and effectiveness evidence.
Theses, premises, arguments, objections, values, thought experiments, and unresolved questions.
Communication patterns, conflict, boundaries, expectations, repair methods, and contextual limitations.
Books, papers, authors, courses, and other learning resources mentioned in the episode.
Repeatable methods, frameworks, mental models, processes, and systems.
Explicit actions, next steps, habits, recommendations, and things to avoid.
Memorable statements and important claims with attribution and source context.
A dedicated inventory of concrete resources named in the episode.
A dedicated analysis of warnings, limitations, trade-offs, weak evidence, and uncertainty.