Hide table of contents

Being more ambitious has become a very popular meme within the EA community and “a culture of ambition” was the unofficial motto of the recent EAG London conference.

I understand the argument that we should strive for opportunities which have a small chance of success but huge payoff in impact if they work out. If most of us were to act this way, then the aggregate impact of the whole community will be bigger and what we care about is more overall good in the world, disregarding who brought it about. 

Most of the people taking this attitude will fail at what they are trying to achieve, because the things they are working on are very hard. That is an intrinsic property of this “high risk, high reward” style of doing good, but it feels bad to fail

Here is the idea in the words of SBF on the recent 80k podcast

“So I think there are really compelling reasons to think that the “optimal strategy” to follow is one that probably fails — but if it doesn’t fail, it’s great. But as a community, what that would imply is this weird thing where you almost celebrate cases where someone completely craps out — where things end up nowhere close to what they could have been — because that’s what the majority of well-played strategies should end with. I don’t think that we recognize that enough as a community, and I think there are lots of specific instances as well where we don’t incentivize that. ” (emphasis mine)

Supporting the argument intellectually is one thing, but coping with failure is another. How can we make it feel more rewarding for everyone who did the right thing and tried the ambitious project, but ultimately didn’t succeed?

Anecdotally, I recently found myself in a career situation where I was trying to build up skills which me and others consider valuable to have more of in the EA community. Unfortunately I started noticing that the situation wasn’t ideal for me and my mental health was getting worse. While making the decision to stop pursuing this particular path, I had thoughts like “Oh man, I really don’t enjoy doing this, but it would sure be useful for me to build up skill X so that in five years I can realize this really ambitious project Y. Therefore maybe I should continue anyways”. 

Surely others have been in a similar place. A few data points of people mentioning the bad feeling of failure to the point of burnout (!) are in this recent thread of EAs failing in high risk, high reward projects.

Some people will thrive in an environment with a high risk of failure and the thought of potentially achieving something incredible will be enough motivation for them. Many others will find such an environment difficult and I worry that they will be put off from EA. 

In short: How do we create a culture of ambition without deteriorating the community’s mental health?


 

New Answer
New Comment


8 Answers sorted by

I think I read this somewhere, but can't remember who to attribute the idea to: Maybe we need something like an EA safety net, similar to an insurance. Knowing that you will have enough money to take care of your family even if you fail at your ambitious project would at least take away some of the anxiety of not succeeding. This would also be helpful in case you suffer burnout (which we should prevent in the first place!). 

This would be a good thing to link to grants for ambitious projects

Thank you so much Max for writing this! I started a draft forum post for a proposal just yesterday. My idea was to have groups of EAs that aim high and fail often and that support each other. Knowing that others are in similar situations and having a smallish group to discuss the strain and celebrate trying might make things easier. I at least would like it. I was planning to the share the draft with you anyway and would love to get your take on it.

Strongly agree, looking forward to your post :)

Perhaps normalizing and encouraging more failure discourse could help?

Something I want to start in EA Austin is a group failure spreadsheet as discussed on the forum here and in greater detail here. I think this is something that EA slack workspaces and other groups could really benefit from! I think community organizers could spearhead promoting this to their cohort!

P.S. I realize this comment is late. Just found this post while skimming the nonlinear library and felt I had something actionable to contribute.

A meta-level issue is ensuring consistency in this "high risk, high reward" approach. 

For example, some grantmakers in EA indicate they take this approach and will support relevant projects. Which is great! 

But if they then decide against funding a project merely because they think it's unlikely to succeed, this implies they actually aren't taking such an approach. Ideally they would provide feedback such as "well you think this project has a 10% chance of succeeding, but we think it's actually more like 1% because you haven't considered X, Y, Z, and this now means the expected value is below other projects we have chosen to fund instead". 

If grantmakers fail to do this, they are failing to even give people the chance to fail. This obviously doesn't have the same consequences as a project failing, but does require coping with rejection that is perceived to be unjustified and inconsistent with the purported approach, and could discourage ambition.

I think positive affirmation of people who did a positive EV thing is great! A friend of mine lost a ton of money on a positive EV investing situation and I think basically got approval for his decisions.

Anyone in EA who feels like a coach or therapist would be helpful in talking through their relationship with “failure” (however they are defining it for themselves) should absolutely schedule a session. Nearly everyone in the EA coaching/therapy space offer the first session for free to EAs. Full disclosure: I am a professional coach and listed on this page:
https://www.eamentalhealthnavigator.com/therapistsandcoaches

Really glad that you brought up this topic Dedicating one's career (or an appreciable fraction of time or happiness) to a project that will likely fail is a huge deal for someone's personal narrative, and we're hoping that swathes of people will be committed enough to do this. I don't have any answers that aren't mere applause lights, but hope this remains a prevalent discussion.

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
 ·  · 6m read
 · 
This post summarizes a new meta-analysis from the Humane and Sustainable Food Lab. We analyze the most rigorous randomized controlled trials (RCTs) that aim to reduce consumption of meat and animal products (MAP). We conclude that no theoretical approach, delivery mechanism, or persuasive message should be considered a well-validated means of reducing MAP consumption. By contrast, reducing consumption of red and processed meat (RPM) appears to be an easier target. However, if RPM reductions lead to more consumption of chicken and fish, this is likely bad for animal welfare and doesn’t ameliorate zoonotic outbreak or land and water pollution. We also find that many promising approaches await rigorous evaluation. This post updates a post from a year ago. We first summarize the current paper, and then describe how the project and its findings have evolved. What is a rigorous RCT? We operationalize “rigorous RCT” as any study that: * Randomly assigns participants to a treatment and control group * Measures consumption directly -- rather than (or in addition to) attitudes, intentions, or hypothetical choices -- at least a single day after treatment begins * Has at least 25 subjects in both treatment and control, or, in the case of cluster-assigned studies (e.g. university classes that all attend a lecture together or not), at least 10 clusters in total. Additionally, studies needed to intend to reduce MAP consumption, rather than (e.g.) encouraging people to switch from beef to chicken, and be publicly available by December 2023. We found 35 papers, comprising 41 studies and 112 interventions, that met these criteria. 18 of 35 papers have been published since 2020. The main theoretical approaches: Broadly speaking, studies used Persuasion, Choice Architecture, Psychology, and a combination of Persuasion and Psychology to try to change eating behavior. Persuasion studies typically provide arguments about animal welfare, health, and environmental welfare reason
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 99% automation of fully-remote jobs 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 dataset (thou