Most of the cross-posted article is a straightforward review of the book. But I thought this segment at the end was especially well-put, even compared to other messages of its type. (Emphasis mine.)

In many ways, The Life You Can Save is a straightforward success story. Singer made his case for helping people, and thousands of people listened, changing the global development world and promoting the growth of new charities that are even better at helping people.

But in 2020, that success story should be — at least slightly — qualified. The year-over-year decline in global poverty will reverse this year because the coronavirus has caused economic disruptions worldwide.

Compounding that is the overwhelming feeling of exhaustion that many of us feel after such a trying period. We’ve all made very real sacrifices this year for our own safety, the safety of our families, and the safety of others. It feels almost unfair to ask for anything more. Beyond that, there may be the matter of morale. For those who have been giving regularly, it was encouraging to participate in a movement to drive down global poverty and needless death year after year after year. 2020 has been the opposite of encouraging. Instead of getting better, many things got worse.

But the core message of The Life You Can Save actually matches some of the lessons of 2020. When you can save someone’s life with steps that might not be easy but are not overly burdensome, you should do so. The challenges we face might feel overwhelming, but the steps we can take are quite concrete and simple. If wearing a mask saves lives, you should wear a mask. If donating $50 or $100 or $500 or 10 percent of your income — whatever you can reasonably give — to the poorest people in the world saves lives, you should do that if you can afford it. You don’t need a grand theory of how you’ll solve the whole problem to save a life. You can do it when the whole world is on your side, and when the whole world is ignorant and ignoring you.

Here’s where the stubbornness of The Life You Can Save ceases to feel like a shortcoming of the book, and starts to feel like its greatest virtue. In many ways, the worst thing about 2020 has been the helplessness. And The Life You Can Save is a book that persistently, repeatedly, point by point refutes all our justifications for helplessness. There are problems that seem so vast and confusing that we may want to believe they couldn’t possibly be our problems. But the challenges that the world’s poorest face — infectious disease, malnutrition, extreme poverty — are easy to beat if the organizations fighting them have the resources they need. And we have the power to help in that fight.

The Life You Can Save is intimidating because it argues you should help people. But it is empowering because it argues that you can help people. At the end of a year shaped by forces beyond our control, that epiphany is a gift.

60

0
0

Reactions

0
0

More posts like this

Comments3


Sorted by Click to highlight new comments since:

Thanks Aaron, I wouldn't read this if you hadn't posted it, and I think it contains good lessons on messaging.

Thanks for posting this! This can be pretty helpful for figuring out from which angle to approach broader audiences and people who are more skeptical about our ability to make a change.

Nice, thanks. I use this message a lot during broader outreach outside of the EA world, I think it works!

Curated and popular this week
 ·  · 12m read
 · 
Economic growth is a unique field, because it is relevant to both the global development side of EA and the AI side of EA. Global development policy can be informed by models that offer helpful diagnostics into the drivers of growth, while growth models can also inform us about how AI progress will affect society. My friend asked me to create a growth theory reading list for an average EA who is interested in applying growth theory to EA concerns. This is my list. (It's shorter and more balanced between AI/GHD than this list) I hope it helps anyone who wants to dig into growth questions themselves. These papers require a fair amount of mathematical maturity. If you don't feel confident about your math, I encourage you to start with Jones 2016 to get a really strong grounding in the facts of growth, with some explanations in words for how growth economists think about fitting them into theories. Basics of growth These two papers cover the foundations of growth theory. They aren't strictly essential for understanding the other papers, but they're helpful and likely where you should start if you have no background in growth. Jones 2016 Sociologically, growth theory is all about finding facts that beg to be explained. For half a century, growth theory was almost singularly oriented around explaining the "Kaldor facts" of growth. These facts organize what theories are entertained, even though they cannot actually validate a theory – after all, a totally incorrect theory could arrive at the right answer by chance. In this way, growth theorists are engaged in detective work; they try to piece together the stories that make sense given the facts, making leaps when they have to. This places the facts of growth squarely in the center of theorizing, and Jones 2016 is the most comprehensive treatment of those facts, with accessible descriptions of how growth models try to represent those facts. You will notice that I recommend more than a few papers by Chad Jones in this
LintzA
 ·  · 15m read
 · 
Introduction Several developments over the past few months should cause you to re-evaluate what you are doing. These include: 1. Updates toward short timelines 2. The Trump presidency 3. The o1 (inference-time compute scaling) paradigm 4. Deepseek 5. Stargate/AI datacenter spending 6. Increased internal deployment 7. Absence of AI x-risk/safety considerations in mainstream AI discourse Taken together, these are enough to render many existing AI governance strategies obsolete (and probably some technical safety strategies too). There's a good chance we're entering crunch time and that should absolutely affect your theory of change and what you plan to work on. In this piece I try to give a quick summary of these developments and think through the broader implications these have for AI safety. At the end of the piece I give some quick initial thoughts on how these developments affect what safety-concerned folks should be prioritizing. These are early days and I expect many of my takes will shift, look forward to discussing in the comments!  Implications of recent developments Updates toward short timelines There’s general agreement that timelines are likely to be far shorter than most expected. Both Sam Altman and Dario Amodei have recently said they expect AGI within the next 3 years. Anecdotally, nearly everyone I know or have heard of who was expecting longer timelines has updated significantly toward short timelines (<5 years). E.g. Ajeya’s median estimate is that 99% of fully-remote jobs will be automatable in roughly 6-8 years, 5+ years earlier than her 2023 estimate. On a quick look, prediction markets seem to have shifted to short timelines (e.g. Metaculus[1] & Manifold appear to have roughly 2030 median timelines to AGI, though haven’t moved dramatically in recent months). We’ve consistently seen performance on benchmarks far exceed what most predicted. Most recently, Epoch was surprised to see OpenAI’s o3 model achieve 25% on its Frontier Math
Omnizoid
 ·  · 5m read
 · 
Edit 1/29: Funding is back, baby!  Crossposted from my blog.   (This could end up being the most important thing I’ve ever written. Please like and restack it—if you have a big blog, please write about it). A mother holds her sick baby to her chest. She knows he doesn’t have long to live. She hears him coughing—those body-wracking coughs—that expel mucus and phlegm, leaving him desperately gasping for air. He is just a few months old. And yet that’s how old he will be when he dies. The aforementioned scene is likely to become increasingly common in the coming years. Fortunately, there is still hope. Trump recently signed an executive order shutting off almost all foreign aid. Most terrifyingly, this included shutting off the PEPFAR program—the single most successful foreign aid program in my lifetime. PEPFAR provides treatment and prevention of HIV and AIDS—it has saved about 25 million people since its implementation in 2001, despite only taking less than 0.1% of the federal budget. Every single day that it is operative, PEPFAR supports: > * More than 222,000 people on treatment in the program collecting ARVs to stay healthy; > * More than 224,000 HIV tests, newly diagnosing 4,374 people with HIV – 10% of whom are pregnant women attending antenatal clinic visits; > * Services for 17,695 orphans and vulnerable children impacted by HIV; > * 7,163 cervical cancer screenings, newly diagnosing 363 women with cervical cancer or pre-cancerous lesions, and treating 324 women with positive cervical cancer results; > * Care and support for 3,618 women experiencing gender-based violence, including 779 women who experienced sexual violence. The most important thing PEPFAR does is provide life-saving anti-retroviral treatments to millions of victims of HIV. More than 20 million people living with HIV globally depend on daily anti-retrovirals, including over half a million children. These children, facing a deadly illness in desperately poor countries, are now going
Relevant opportunities