What is the most “EA” activity? The example that comes to mind is Givewell doing cost-benefit analyses on which interventions produce the best return on investment, in terms of lives saved per dollar spent. When this was first conceived, it made a lot of sense: there were all kinds of charities running out trying random interventions, and what was missing was an attempt to quantify how these stacked up against one another.
But this analytical origin has permeated through the EA culture, such that the default go-to methodology for tackling a question is “think real hard, then write ten thousand words”. It feels like most EA work happens through long, philosophical, deeply researched tracts with copious footnotes posted on the EA Forum, rather than through trying things out and seeing what happens. Many topics discussed have little experimental grounding, and there’s little appetite for experimentally validating things.
Contrast this to building a software product. At many different levels, you have iterative feedback loops telling you whether the thing you did was good:
These feedback loops rapidly ground a developer’s understanding of what actually happens in the world. What are the feedback loops inside the EA ecosystem?
Another common startup adage is that “ideas are worthless, execution is everything”. But in the EA world, most of what gets evaluated are ideas. Karma, prestige, attention and funding accrue to those who speak prettily.
An analogy: there are two ways to aim a gun. You can sit there with a scope, chart of bullet velocities, wind map, and calculator, trying to estimate exactly where the bullet will land before taking the shot. Or: you can fire a tracer bullet, leaving a streak behind the flight path of the bullet, and then see how different angles
There are times when you want to do the first! If you’re firing a sniper rifle, and only have one chance at getting things right, then you do want to step back and run your calculations carefully; to measure twice before firing once. But it’d be a very costly mistake to use that same process to eg shoot down enemy aircraft; there, you should be firing hundreds of bullets a minute, using tracer rounds to identify where your shots are actually going.
Ben Kuhn describes the virtue of impatience:
Being impatient is the best way to get faster at things. And across a surprising number of domains, being really fast correlates strongly with being effective.
There’s an obvious way in which moving faster is important: if you’re 10% more productive, you will finish your work in 10% less time, so you can do 10% more work total. But I don’t think that’s the main reason that speed is important…
The faster you process information, the faster you can incorporate the result into what you do next. In other words, the main benefit of being fast is that you end up doing different things. Nelson Elhage’s point—“having faster tools changes how users use a tool”—applies across nearly every domain:
- If you respond to your emails quickly instead of slowly, you’ll get access to more new opportunities, and end up prioritizing them over whatever you would have done instead.
- If you make it 10x faster to test your code, you don’t just save time waiting on tests—you can start doing test-driven development, discover your mistakes earlier, and save yourself from going down bad paths.
- If you deploy your new app now instead of next week, you’ll learn how users like the new features one week earlier, and you’ll be able to feed that knowledge back into future product decisions.
That means that moving quickly is an advantage that compounds. Being twice as fast doesn’t just double your output; it doubles the growth rate of your output. Over time, that makes an enormous difference.
With few exceptions, I have a hard time associating “impatient” with EA. The most impatient fastest-moving altruists I have heard of like Tara MacAulay and Sam Bankman Fried took leadership roles in CEA… then left to make a bunch of money instead. I speculate about an evaporative cooling effect, where capital EA “Effective Altruism” orgs lose out on fast movers because fast movers are allergic to staying in slow orgs.
Example: There’s a huge difference in payment speed from fundraising in Silicon Valley, vs receiving grants from FTX/SFF/LTFF. When Manifold went to raise our seed round, angel investors would usually sign after a single video call; median time between getting into contact with the funder to having the check hit our bank account was about two weeks. In contrast, with charitable funding from EA orgs, I can’t recall a single application process that got back to us in under a month; wiring the funds would usually take another 2+ weeks.