The school of thought known as “Effective Altruism” isn't what it used to be, but it's still alive, especially in the tech sector. Its practitioners originally aimed to make as much money as possible to maximize their donations to charitable causes and non-profits—”earning to give” is the slogan. The idea appeared to have potential in its inchoate stage, but cracks in the ceiling eventually appeared. And then the do-gooder philosophy’s most prominent adherent, crypto-felon Sam Bankman-Fried, ripped the whole ceiling down. The man who was worth $26 billion at 30 is now serving a 25-year sentence in a federal prison in Lompoc, California. It's possible that he ended up behind bars because he thought recklessly risking billions of dollars of his clients’ money was worth it to improve the plight of global humanity. Either that or he was just trying to save his ass.
The core tenet of EA, which could be characterized as “hyper-Utilitarianism,” is that more “good” can be done by taking a high-paying job and then donating heavily than going to work for a charitable organization or a non-profit. Some see logic in this view, while others look at it as a stretch to find a moral justification for selfishness or an ignorance of the dangers of unintended consequences. Others call it “Moneyball for billionaires.” Forget about sleeping in a thatched hut in Nepal with the Peace Corps. EA offered a way to go to work on Wall Street right out of college while still being able to claim it's all about making the world a better place.
The stated goal of EA, which has a global focus, is to use evidence and reasoning to determine how to help others, and then to take action. In 2003, American real estate mogul Zell Kravinsky took some action in deciding to donate a kidney to a young African-American woman he didn’t know who had been on dialysis. Kravinsky calculated that there was a one-in- -4000 chance that he’d die from the procedure, from which he reasoned that not giving her the kidney would mean that he assessed his own life as 4000 times more valuable than hers. This is the sort of quantitative, emotionless calculation that drives EA decision making, although few have taken it to Kravinsky’s extreme. He also gave away most of his $45 million fortune.
The tech sector’s a natural fit for EA because it also has a radical openness to counterintuitive conclusions. It operates on data-driven decisions, scaling solutions, and debugging systems. This is the same engineering approach EA employs to determine moral decision making, rather than the more traditional reliance on intuition or emotional impact to decide what is "good." For example, GiveWell, one of the foundational EA organizations, took an engineering approach to tackling malaria by using a complex series of calculations to determine that, by spending money on bed nets, it costs roughly $3000-$5500 to save one statistical life using this particular malaria-control method. Putting a specific price on potential solutions puts a framework on decision-making that can be more sensible than falling back on “status quo bias”—doing what's always been done—and/or institutional inertia.
In his 2013 essay, “The Moral Imperative Towards Cost-effectiveness in Global Health,” Toby Ord calculated the cost of providing a seeing-eye dog to a blind person in the U.S. at $40,000, and then compared that to the $20 cost of a surgery in Africa to reverse the effects of trachoma. Ord’s conclusion was that, assuming all human beings have equal moral worth, choosing the guide dog over the surgeries means squandering roughly 99.95 percent of the potential value that the resources could have created. That's the kind of moral decision that a computer can come up with.
Traditional morality is often driven by proximity, mutual affiliations, and relationships. So if one's neighbor asks for a couple of bucks to buy a burger, denying his request carries more moral weight than neglecting to send two dollars to Africa, where it could feed many more people. EA rejects such thinking, calling it a form of moral arbitrariness—i.e. parochialism. Being more charitable to a family member than one in Nigeria is, in EA terms, “kin partiality.” There's an echo of Marxism in this collective way of thinking.
There's also a problem with a philosophy that teaches someone that the best way of becoming a good person is to maximize their earnings. There's a moral hazard in the “earning to give” belief in that it can produce the mentality of “the ends justify the means.”
Bankman-Fried’s arrest, which was preceded by plenty of ignored warnings, sapped the energy and credibility from the previously burgeoning EA movement. Suddenly it looked more like billionaire escapism than a genuine plan to improve the planet. Millions in promised grants went up in smoke. EA non-profits and charities were forced to return their tainted funds.
Effective altruism’s been put in its place now. Its hyper-focus on Utilitarianism led to the exposure of its ethical blind spots, and the world has caught on to its technocratic hubris and elitism. When computers are relied on to shape moral codes, human empathy and wisdom accumulated over centuries can be overridden by the cold logic of an algorithm. The mental compulsion to be one of the white knights who think about how to improve the lives of the billions who live thousands of miles away can overload the brain and lead to a kind of derangement. People aren’t wired to think this way.
But cold logic is Silicon Valley's bread and butter, so EA remains a sub-rosa force there. After the FTX fiasco, a concerted rebranding effort allowed it to quietly embed itself behind the scenes rather than a public movement. The rhetoric about global charity has been swapped out for a focus on mitigating the potential dangers of AI. The tech sector doesn't allow certain ideas to die—they just rejigger their software.