Effective Altruism’s metrics-driven model of philanthropy is elitist, condescending and — most damning of all — extremely ineffective.
Effective Altruism’s Bait-and-Switch: From Global Poverty To AI Doomerism
The Effective Altruism movement Effective Altruism (EA) is typically explained as a philosophy that encourages individuals to do the “most good” with their resources (money, skills). Its “effective…
Effective Altruism’s Problems Go Beyond Sam Bankman-Fried - Bloomberg
archived 12 Mar 2026 21:01:45 UTC
EA is worse than traditional philanthropy in the way it excludes we the poor.
Effective altruism is worse than traditional philanthropy in the way it excludes the extreme poor in the global south. By Anthony Kalulu @KaluluAnthony | December 3, 2022: —- I have spent the vast portion of my life in ultra poverty. My region Busoga, is also Uganda’s most impoverished, yet Uganda itself is among the poorest countries … Continue reading ""
The sad decline of effective altruism
A once wholesome and inspiring movement has become entangled with cults and has lost years chasing dead-end ideas
Effective Altruism Funded the “AI Existential Risk” Ecosystem with Half a Billion Dollars
The “AI Existential Safety” field did not arise organically. Effective Altruism invested $500 million in its growth and expansion.
Effective Altruism Is a Dangerous Cult — Here's Why
Understanding the "Scientology of Silicon Valley." (7,700 words)
Effective Altruism is self-recommending
Recently we bought my 3-year-old daughter a "behavior chart," in which she can earn stickers for achievements like not throwing tantrums, eating fruits and vegetables, and going to sleep on time. We successfully impressed on her that a major goal each day was to earn as many stickers as possible.
Framing charitable donations as exceptional expenses increases giving.
Effective Altruism Has a Hostile Culture for Women, Critics Say
Seven women connected to effective altruism tell TIME they experienced harassment and worse within the community
Altruism | The Paper Pilot
The Paper Pilot's digital garden of thoughts on philosophy, politics, and sociology
Integrative experiments identify how punishment affects welfare in public goods games
Despite decades of research, the conditions under which punishment promotes cooperation remain unclear. Through an integrative experiment varying 14 design parameters of public goods games across 360 experimental conditions (147,618 decisions from 7100 participants), we reveal substantial heterogeneity in punishment effectiveness: Its impact on welfare ranges from 43% improvement to 44% reduction depending on the game parameters. To characterize these patterns, we developed models that outperformed human forecasters in predicting punishment effectiveness in new experiments. Communication emerges as the most important factor, followed by contribution framing (opt out versus opt in), contribution type (variable versus all-or-nothing), game length, and outcome visibility, though these factors often interact. The results reframe the debate from whether punishment works to when it does, demonstrating how integrative experiments enable discovery of generalizable patterns in social phenomena. , Editor’s summary People face conflicts between maximizing personal gain versus supporting collective interests. If we cooperatively recycle or donate to charities, it benefits society, but it also costs us time and resources that could be selfishly preserved for ourselves. We impose penalties to deter those undesirable or selfish behaviors, but under what conditions do punishments or penalties effectively modify behavior to benefit group welfare? Alsobay et al . systematically and simultaneously varied 14 factors together instead of in isolation. Punishment was unequivocally most effective when paired with consistent communication, particularly over time. Another effective factor was “opting out” or withdrawing some, but not all, endowments already in the public fund. These methodological advances revealed when, rather than whether, punishment works. —Ekeoma Uzogara , INTRODUCTION Human societies face many situations where individual and collective interests conflict, often referred to as social dilemmas. Costly peer punishment has been studied for more than 25 years in public goods games (stylized behavioral experiments in which individuals decide how much to contribute to a shared pool that benefits everyone) as a mechanism to promote cooperation. Prior research has identified many contextual factors that moderate punishment’s effectiveness, including game length, communication, group size, punishment cost, and so on. However, the specific conditions under which punishment improves group welfare remain unclear. RATIONALE We argue that this lack of clarity derives from the dominant experimental paradigm, in which any given study manipulates only one or a few theoretically informed factors. Because such studies differ in many ways (different experimental procedures, populations), their results are often difficult to compare or integrate. Consequently, one can list many factors that have some effect, but cannot say how much each matters relative to the others, or how they work together, and as a result, cannot predict when punishment will help or harm welfare in new settings. To address this fundamental knowledge gap, we use an integrative experimental design and systematically vary 14 parameters across 360 conditions (147,618 decisions from 7100 participants) to elucidate when punishment improves versus undermines welfare in public goods games, which factors matter most, and how they interact. RESULTS The effect of punishment on welfare ranged from 43% improvement to 44% reduction depending on the specific combination of game parameters. To characterize this heterogeneity, we trained a model that outperformed all 553 human forecasters (laypeople and experts) in predicting whether punishment would help or harm welfare in new experiments. Communication emerged as roughly three times more important than any other factor, followed by contribution framing (opt in versus opt out), contribution type (variable versus all-or-nothing), game length, and peer outcome visibility (whether participants can see others’ earnings). These factors often interact. For example, longer games enhance punishment’s effectiveness only when communication is available, and contribution framing effects depend on both contribution type and outcome visibility. CONCLUSION Many phenomena in social science are shaped by many factors whose interactions are consequential, yet the dominant experimental paradigm often limits its inquiry to “does a given effect exist?” and examines hypothesized factors in isolation. As a result, research programs can accumulate many partial explanations without a clear picture of how they combine to determine outcomes across settings. Knowing that factors matter individually is fundamentally different from knowing how much each matters and how they interact. The integrative approach implemented here offers one way forward. It varies many factors simultaneously within a shared design space, evaluates models by their predictive accuracy on new experiments, and probes those models to constrain and develop theory. Our hope is that integrative experiment designs, combined with models that integrate prediction and explanation, represent a path toward more cumulative social science. Integrative experiment reveals when punishment helps versus harms. We systematically varied 14 design parameters across 360 experimental conditions. The effect of punishment on cooperation efficiency ranged from −44% to +43% depending on the specific game parameters. Communication emerged as three times more important than any other factor, followed by contribution framing, contribution type, and game length.
The Effective-Altruism Comeback
Out of the spotlight, the movement has been preparing for the soon-to-be AI rich to donate billions.
Anonymous EA comments
After seeing some of the debate last month about effective altruism's information-sharing / honesty / criticism norms (see Sarah Constantin's follow-…
