New releases

How many lives does a doctor save? (Part 3)

Article by · Published July 2023

This is Part 3 of an updated version of a classic three-part series of 80,000 Hours blog posts. You can also read updated versions of Part 1 and Part 2. You can still read the original version of the series published in 2012.

It’s fair to say working as a doctor does not look that great so far. In general, the day-to-day work of medicine has had a relatively minor role in why people are living longer and healthier now than they did historically. When we try and quantify the benefit of someone becoming a doctor, the figure gets lower the better the method of estimation and already is low enough such that a 40-year medical career somewhere like the UK would be on a rough par with giving $20,000 dollars to a GiveWell top charity in terms of saving lives.

Yet there is more to say. The tools we have used to arrive at estimates are general, so they are estimating something like the impact of the modal, median, or typical medical career. There are doctors who have plainly done much more good than my estimates of the impact of a typical doctor.

So, what could a doctor do to really save a lot of lives?

Doing doctoring better

What about just being really, really good? Even if the typical doctor’s work makes a worthwhile — but modest and fairly replaceable — contribution,

Continue reading →

How many lives does a doctor save? (Part 2)

Article by · Published July 2023

This is Part 2 of an updated version of a classic three-part series of 80,000 Hours blog posts. You can also read updated versions of Part 1 and Part 3. You can still read the original version of the series published in 2012.

In the last post, we saw that although the reasons people live longer and healthier now have more to do with higher living standards than more medical care, medicine still plays a part. If you try and quantify how much medicine contributes to our increased longevity and health, then divide that amount by the number of doctors providing it, you get an estimate that a UK doctor saves ~70 lives over the course of their career.

Yet this won’t be a good model of how much good you would actually do if you became a doctor in the UK.

For one thing, the relationship between more doctors and better health is non-linear. Here’s a scatterplot for each country with doctors per capita on the x-axis and DALYs per capita on the y-axis (since you ‘gain’ DALYs for dying young or being sick, less is better):

The association shows an initial steep decline between 0–50 doctors per 100,000 people, then levels off abruptly and is basically flat when you get to physician densities in richer countries (e.g. the UK has 300 doctors per 100,000 people). Assuming this is causation rather than correlation (more on that later),

Continue reading →

How many lives does a doctor save? (Part 1)

Article by · Published July 2023

This is Part 1 of an updated version of a classic three-part series of 80,000 Hours blog posts. You can also read updated versions of Part 2 and Part 3. You can still read the original version of the series published in 2012.

Doctors have a reputation as do-gooders. So when I was a 17-year-old kid wanting to make a difference, it seemed like a natural career path. I wrote this on my medical school application:

I want to study medicine because of a desire I have to help others, and so the chance of spending a career doing something worthwhile I can’t resist. Of course, Doctors [sic] don’t have a monopoly on altruism, but I believe the attributes I have lend themselves best to medicine, as opposed to all the other work I could do instead.

They still let me in.

When I show this to others in medicine, I get a mix of laughs and groans of recognition. Most of them wrote something similar. The impression I get from senior doctors who have to read this stuff is they see it a bit like a toddler zooming around on their new tricycle: a mostly endearing (if occasionally annoying) work in progress. Season them enough with the blood, sweat, and tears of clinical practice, and they’ll generally turn out as wiser, perhaps more cantankerous, but ultimately humane doctors.

Yet more important than me being earnest — and even me being trite — was that I was wrong.

Continue reading →

Hannah Boettcher on the mental health challenges that come with trying to have a big impact

Podcast (80k After Hours) by , and · Published July 2023

In this episode of 80k After Hours, Luisa Rodriguez and Hannah Boettcher discuss various approaches to therapy, and how to use them in practice — focusing specifically on people trying to have a big impact.

They cover:

  • The effectiveness of therapy, and tips for finding a therapist
  • Moral demandingness
  • Internal family systems-style therapy
  • Motivation and burnout
  • Exposure therapy
  • Grappling with world problems and x-risk
  • Perfectionism and imposter syndrome
  • And the risk of over-intellectualising

Who this episode is for:

  • High-impact focused people who struggle with moral demandingness, perfectionism, or imposter syndrome
  • People who feel anxious thinking about the end of the world
  • 80,000 Hours Podcast hosts with the initials LR

Who this episode isn’t for:

  • People who aren’t focused on having a big impact
  • People who don’t struggle with any mental health issues
  • Founders of Scientology with the initials LRH

Continue reading →

#156 – Markus Anderljung on how to regulate cutting-edge AI models

Podcast by , and · Published July 10, 2023

In today’s episode, host Luisa Rodriguez interviews the Head of Policy at the Centre for the Governance of AI — Markus Anderljung — about all aspects of policy and governance of superhuman AI systems.

They cover:

  • The need for AI governance, including self-replicating models and ChaosGPT
  • Whether or not AI companies will willingly accept regulation
  • The key regulatory strategies including licencing, risk assessment, auditing, and post-deployment monitoring
  • Whether we can be confident that people won’t train models covertly and ignore the licencing system
  • The progress we’ve made so far in AI governance
  • The key weaknesses of these approaches
  • The need for external scrutiny of powerful models
  • The emergent capabilities problem
  • Why it really matters where regulation happens
  • Advice for people wanting to pursue a career in this field
  • And much more.

Continue reading →

Working in US AI policy

Article by · Last updated July 2023 · First published January 2019

This article was originally published in 2019. It was revised in July 2023 to reflect more recent developments and thinking.

At 80,000 Hours, we think a significant number of people should build expertise to work on United States policy relevant to the long-term effects and potentially catastrophic risks of the development and use of artificial intelligence.

In this article we go into more detail on this claim as well as discuss arguments in favour and against working in this area. We also briefly outline which specific career paths to aim for and discuss which sorts of people we think might suit these roles best.

We have separate articles that cover a broader set of potential career paths in the AI governance and coordination space and in AI safety technical research.

— President Joe Biden, remarks on artificial intelligence from the White House on July 21, 2023

The aims of AI policy

In our career review on AI governance and coordination, we outline a number of potential aims for impactful policy in this area, while noting that there a lot of remaining uncertainties about the best approach going forward:

  • Preventing the deployment of any AI systems that pose a significant and direct threat of catastrophe
  • Mitigating the negative impact of AI technology on other catastrophic risks,

Continue reading →

What the war in Ukraine shows us about catastrophic risks

Blog post by · Published June 30, 2023

A new great power war could be catastrophic for humanity — but there are meaningful ways to reduce the risk.

We’re now in the 17th month of the war in Ukraine. But at the start, it was hard to foresee it would last this long. Many expected Russian troops to take Ukraine’s capital, Kyiv, in weeks. Already, more than 100,000 people, including civilians, have been killed and over 300,000 more injured. Many more will die before the war ends.

The sad and surprising escalation of the war shows why international conflict remains a major global risk. I explain why working to lower the danger is a potentially high-impact career choice in a new problem profile on great power war.

As Russia’s disastrous invasion demonstrates, it’s hard to predict how much a conflict will escalate. Most wars remain relatively small, but a few will become terrifyingly large. US officials estimate about 70,000 Russian and Ukrainian soldiers have died in battle so far. That means this war is already worse than 80% of all the wars humanity has experienced in the last 200 years.

But the worst wars humanity has fought are hundreds of times larger than the war in Ukraine currently is. World War II killed 66 million people, for example — perhaps the single deadliest event in human history.


Author’s figure. See the data here.

Continue reading →

#155 – Lennart Heim on the compute governance era and what has to come after

Podcast by and · Published June 22, 2023

As AI advances ever more quickly, concerns about potential misuse of highly capable models are growing. From hostile foreign governments and terrorists to reckless entrepreneurs, the threat of AI falling into the wrong hands is top of mind for the national security community.

With growing concerns about the use of AI in military applications, the US has banned the export of certain types of chips to China.

But unlike the uranium required to make nuclear weapons, or the material inputs to a bioweapons programme, computer chips and machine learning models are absolutely everywhere. So is it actually possible to keep dangerous capabilities out of the wrong hands?

In today’s interview, Lennart Heim — who researches compute governance at the Centre for the Governance of AI — explains why limiting access to supercomputers may represent our best shot.

As Lennart explains, an AI research project requires many inputs, including the classic triad of compute, algorithms, and data.

If we want to limit access to the most advanced AI models, focusing on access to supercomputing resources — usually called ‘compute’ — might be the way to go. Both algorithms and data are hard to control because they live on hard drives and can be easily copied. By contrast, advanced chips are physical items that can’t be used by multiple people at once and come from a small number of sources.

According to Lennart, the hope would be to enforce AI safety regulations by controlling access to the most advanced chips specialised for AI applications. For instance, projects training ‘frontier’ AI models — the newest and most capable models — might only gain access to the supercomputers they need if they obtain a licence and follow industry best practices.

Continue reading →

Global priorities research

Problem profile by · Last updated June 2023 · First published April 2016

Governments, foundations, and individuals spend large amounts of effort and money to improve the world. However, a lot more research could be done to help figure out how to use it best.

Global priorities research can take many forms, using techniques from economics, philosophy, maths, and social science to help people and organisations choose which global problems they should spend their limited resources on, in order to improve the world as much as possible.

Continue reading →

How to cope with rejection in your career

Blog post by · Published June 18, 2023

The idea this week: getting rejected from jobs can be crushing — but learning how to deal with rejection productively is an incredibly valuable skill.

I’ve been rejected many, many times. In 2015, I applied to ten PhD programs and was rejected from nine. After doing a summer internship with GiveWell in 2016, I wasn’t offered a full-time role. In 2017, I was rejected by J-PAL, IDinsight, and Founders Pledge (among others). Around the same time, I was so afraid of being rejected by Open Philanthropy, I dropped out of their hiring round.

I now have what I consider a dream job at 80,000 Hours: I get to host a podcast about the world’s most pressing problems and how to solve them. But before getting a job offer from 80,000 Hours in 2020, I got rejected by them for a role in 2018. That rejection hurt the most.

I still remember compulsively checking my phone after my work trial to see if 80,000 Hours had made me an offer. And I still remember waking up at 5:00 AM, checking my email, and finding the kind and well-written — but devastating — rejection: “Unfortunately we don’t think the role is the right fit right now.”

And I remember being so sad that I took a five-hour bus ride to stay with a friend so I wouldn’t have to be alone.

Continue reading →

#154 – Rohin Shah on DeepMind and trying to fairly hear out both AI doomers and doubters

Podcast by and · Published June 9, 2023

Can there be a more exciting and strange place to work today than a leading AI lab? Your CEO has said they’re worried your research could cause human extinction. The government is setting up meetings to discuss how this outcome can be avoided. Some of your colleagues think this is all overblown; others are more anxious still.

Today’s guest — machine learning researcher Rohin Shah — goes into the Google DeepMind offices each day with that peculiar backdrop to his work.

He’s on the team dedicated to maintaining ‘technical AI safety’ as these models approach and exceed human capabilities: basically that the models help humanity accomplish its goals without flipping out in some dangerous way. This work has never seemed more important.

In the short-term it could be the key bottleneck to deploying ML models in high-stakes real-life situations. In the long-term, it could be the difference between humanity thriving and disappearing entirely.

For years Rohin has been on a mission to fairly hear out people across the full spectrum of opinion about risks from artificial intelligence — from doomers to doubters — and properly understand their point of view. That makes him unusually well placed to give an overview of what we do and don’t understand. He has landed somewhere in the middle — troubled by ways things could go wrong, but not convinced there are very strong reasons to expect a terrible outcome.

Today’s conversation is wide-ranging and Rohin lays out many of his personal opinions to host Rob Wiblin, including:

  • What he sees as the strongest case both for and against slowing down the rate of progress in AI research.
  • Why he disagrees with most other ML researchers that training a model on a sensible ‘reward function’ is enough to get a good outcome.
  • Why he disagrees with many on LessWrong that the bar for whether a safety technique is helpful is “could this contain a superintelligence.”
  • That he thinks nobody has very compelling arguments that AI created via machine learning will be dangerous by default, or that it will be safe by default. He believes we just don’t know.
  • That he understands that analogies and visualisations are necessary for public communication, but is sceptical that they really help us understand what’s going on with ML models, because they’re different in important ways from every other case we might compare them to.
  • Why he’s optimistic about DeepMind’s work on scalable oversight, mechanistic interpretability, and dangerous capabilities evaluations, and what each of those projects involves.
  • Why he isn’t inherently worried about a future where we’re surrounded by beings far more capable than us, so long as they share our goals to a reasonable degree.
  • Why it’s not enough for humanity to know how to align AI models — it’s essential that management at AI labs correctly pick which methods they’re going to use and have the practical know-how to apply them properly.
  • Three observations that make him a little more optimistic: humans are a bit muddle-headed and not super goal-orientated; planes don’t crash; and universities have specific majors in particular subjects.
  • Plenty more besides.

Continue reading →

Practical steps to take now that AI risk is mainstream

Blog post by · Published June 6, 2023

AI risk has gone mainstream. So what’s next?

Last Tuesday’s statement on AI risk has hit headlines across the world. Hundreds of leading AI scientists and other prominent figures — including the CEOs of OpenAI, Anthropic and Google DeepMind — signed the one-sentence statement:

Mitigating the risk of extinction from AI should be a global priority alongside other societal-scale risks such as pandemics and nuclear war.

This mainstreaming of concerns about the risk of extinction from AI represents a substantial shift to the strategic landscape — and should, as a result, have implications on how best to reduce the risk.

How has the landscape shifted?

Pictures from the White House Press Briefing. Meme from @kristjanmoore. The relevant video is here.

So far, I think the most significant effect of the changes in the way these risks are viewed can be seen in changes in political activity.

World leaders — including Joe Biden, Rishi Sunak, Emmanuel Macron — have all met leaders in AI in the last few months. AI regulation was a key topic of discussion at the G7. And now it’s been announced that Biden and Sunak will discuss extinction risks from AI as part of talks in DC next week.

At the moment,

Continue reading →

#153 – Elie Hassenfeld on two big-picture critiques of GiveWell’s approach, and six lessons from their recent work

Podcast by and · Published June 2, 2023

GiveWell is one of the world’s best-known charity evaluators, with the goal of “searching for the charities that save or improve lives the most per dollar.” It mostly recommends projects that help the world’s poorest people avoid easily prevented diseases, like intestinal worms or vitamin A deficiency.

But should GiveWell, as some critics argue, take a totally different approach to its search, focusing instead on directly increasing subjective wellbeing, or alternatively, raising economic growth?

Today’s guest — cofounder and CEO of GiveWell, Elie Hassenfeld — is proud of how much GiveWell has grown in the last five years. Its ‘money moved’ has quadrupled to around $600 million a year.

Its research team has also more than doubled, enabling them to investigate a far broader range of interventions that could plausibly help people an enormous amount for each dollar spent. That work has led GiveWell to support dozens of new organisations, such as Kangaroo Mother Care, MiracleFeet, and Dispensers for Safe Water.

But some other researchers focused on figuring out the best ways to help the world’s poorest people say GiveWell shouldn’t just do more of the same thing, but rather ought to look at the problem differently.

Currently, GiveWell uses a range of metrics to track the impact of the organisations it considers recommending — such as ‘lives saved,’ ‘household incomes doubled,’ and for health improvements, the ‘quality-adjusted life year.’ To compare across opportunities, it then needs some way of weighing these different types of benefits up against one another. This requires estimating so-called “moral weights,” which Elie agrees is far from the most mature part of the project.

Continue reading →

The public is more concerned about AI causing extinction than we thought

Blog post by · Published May 26, 2023

What does the public think about risks of human extinction?

We care a lot about reducing extinction risks and think doing so is one of the best ways you can have a positive impact with your career. But even before considering career impact, it can be natural to worry about these risks — and as it turns out, many people do!

In April 2023, the US firm YouGov polled 1,000 American adults on how worried they were about nine different potential extinction threats. It found the following percentages of respondents were either “concerned” or “very concerned” about extinction from each threat:

We’re particularly interested in this poll now because we have recently updated our page on the world’s most pressing problems, which includes several of these extinction risks at the top.

Knowing how the public feels about these kinds of threats can impact how we communicate about them.

For example, if we take the results at face value, 46% of the poll’s respondents are concerned about human extinction caused by artificial intelligence. Maybe this surprisingly high figure means we don’t need to worry as much as we have over the last 10 years about sounding like ‘sci fi’ when we talk about existential risks from AI, since it’s quickly becoming a common concern!

How does our view of the world’s most pressing problems compare?

Continue reading →

Give feedback on the new 80,000 Hours career guide

Blog post by · Published May 25, 2023

We’ve spent the last few months updating 80,000 Hours’ career guide (which we previously released in 2017 and which you’ve been able to get as a physical book). This week, we’ve put our new career guide live on our website. Before we formally launch and promote the guide — and republish the book — we’d like to gather feedback from our readers!

How can you help?

First, take a look at the new career guide.

Note that our target audience for this career guide is approximately the ~100k young adults most likely to have high-impact careers, in the English-speaking world. Many of them may not yet be familiar with many of the ideas that are widely discussed in the effective altruism community. Also, this guide is primarily aimed at people aged 18–24.

When you’re ready, there’s a simple form to fill in:

Give feedback

Thank you so much!

Extra context: why are we making this change?

In 2018, we deprioritised 80,000 Hours’ career guide in favour of our key ideas series.

Our key ideas series had a more serious tone, and was more focused on impact. It represented our best and most up-to-date advice. We expected that this switch would reduce engagement time on our site, but that the key ideas series would better appeal to people more likely to change their careers to do good.

Continue reading →

#152 – Joe Carlsmith on navigating serious philosophical confusion

Podcast by , and · Published May 19, 2023

What is the nature of the universe? How do we make decisions correctly? What differentiates right actions from wrong ones?

Such fundamental questions have been the subject of philosophical and theological debates for millennia. But, as we all know, and surveys of expert opinion make clear, we are very far from agreement. So… with these most basic questions unresolved, what’s a species to do?

In today’s episode, philosopher Joe Carlsmith — Senior Research Analyst at Open Philanthropy — makes the case that many current debates in philosophy ought to leave us confused and humbled. These are themes he discusses in his PhD thesis, A stranger priority? Topics at the outer reaches of effective altruism.

To help transmit the disorientation he thinks is appropriate, Joe presents three disconcerting theories — originating from him and his peers — that challenge humanity’s self-assured understanding of the world.

The first idea is that we might be living in a computer simulation, because, in the classic formulation, if most civilisations go on to run many computer simulations of their past history, then most beings who perceive themselves as living in such a history must themselves be in computer simulations. Joe prefers a somewhat different way of making the point, but, having looked into it, he hasn’t identified any particular rebuttal to this ‘simulation argument.’

If true, it could revolutionise our comprehension of the universe and the way we ought to live.

The second is the idea that “you can ‘control’ events you have no causal interaction with, including events in the past.” The thought experiment that most persuades him of this is the following:

Perfect deterministic twin prisoner’s dilemma: You’re a deterministic AI system, who only wants money for yourself (you don’t care about copies of yourself). The authorities make a perfect copy of you, separate you and your copy by a large distance, and then expose you both, in simulation, to exactly identical inputs (let’s say, a room, a whiteboard, some markers, etc.). You both face the following choice: either (a) send a million dollars to the other (“cooperate”), or (b) take a thousand dollars for yourself (“defect”).

Continue reading →

#151 – Ajeya Cotra on accidentally teaching AI models to deceive us

Podcast by , and · Published May 12, 2023

Ajeya Cotra on accidentally teaching AI models to deceive us

Imagine you are an orphaned eight-year-old whose parents left you a $1 trillion company, and no trusted adult to serve as your guide to the world. You have to hire a smart adult to run that company, guide your life the way that a parent would, and administer your vast wealth. You have to hire that adult based on a work trial or interview you come up with. You don’t get to see any resumes or do reference checks. And because you’re so rich, tonnes of people apply for the job — for all sorts of reasons.

Today’s guest Ajeya Cotra — senior research analyst at Open Philanthropy — argues that this peculiar setup resembles the situation humanity finds itself in when training very general and very capable AI models using current deep learning methods.

As she explains, such an eight-year-old faces a challenging problem. In the candidate pool there are likely some truly nice people, who sincerely want to help and make decisions that are in your interest. But there are probably other characters too — like people who will pretend to care about you while you’re monitoring them, but intend to use the job to enrich themselves as soon as they think they can get away with it.

Like a child trying to judge adults, at some point humans will be required to judge the trustworthiness and reliability of machine learning models that are as goal-oriented as people, and greatly outclass them in knowledge, experience, breadth, and speed. Tricky!

Continue reading →

How 80,000 Hours has changed some of our advice after the collapse of FTX

Blog post by · Published May 12, 2023

Following the bankruptcy of FTX and the federal indictment of Sam Bankman-Fried, many members of the team at 80,000 Hours were deeply shaken. As we have said, we had previously featured Sam on our site as a positive example of earning to give, a mistake we now regret. We felt appalled by his conduct and at the harm done to the people who had relied on FTX.

These events were emotionally difficult for many of us on the team, and we were troubled by the implications it might have for our attempts to do good in the world. We had linked our reputation with his, and his conduct left us with serious questions about effective altruism and our approach to impactful careers.

We reflected a lot, had many difficult conversations, and worked through a lot of complicated questions. There’s still a lot we don’t know about what happened, there’s a diversity of views within the 80,000 Hours team, and we expect the learning process to be ongoing.

Ultimately, we still believe strongly in the principles that drive our work, and we stand by the vast majority of our advice. But we did make some significant updates in our thinking, and we’ve changed many parts of the site to reflect them. We wrote this post to summarise the site updates we’ve made and to explain the motivations behind them, for transparency purposes and to further highlight the themes that unify the changes.

Continue reading →

#150 – Tom Davidson on how quickly AI could transform the world

Podcast by , and · Published May 5, 2023

It’s easy to dismiss alarming AI-related predictions when you don’t know where the numbers came from.

For example: what if we told you that within 15 years, it’s likely that we’ll see a 1,000x improvement in AI capabilities in a single year? And what if we then told you that those improvements would lead to explosive economic growth unlike anything humanity has seen before?

You might think, “Congratulations, you said a big number — but this kind of stuff seems crazy, so I’m going to keep scrolling through Twitter.”

But this 1,000x yearly improvement is a prediction based on real economic models created by today’s guest Tom Davidson, Senior Research Analyst at Open Philanthropy. By the end of the episode, you’ll either be able to point out specific flaws in his step-by-step reasoning, or have to at least consider the idea that the world is about to get — at a minimum — incredibly weird.

As a teaser, consider the following:

Developing artificial general intelligence (AGI) — AI that can do 100% of cognitive tasks at least as well as the best humans can — could very easily lead us to an unrecognisable world.

You might think having to train AI systems individually to do every conceivable cognitive task — one for diagnosing diseases, one for doing your taxes, one for teaching your kids, etc. — sounds implausible, or at least like it’ll take decades.

But Tom thinks we might not need to train AI to do every single job — we might just need to train it to do one: AI research.

Continue reading →

Some thoughts on moderation in doing good

Blog post by · Published May 5, 2023

Here’s one of the deepest tensions in doing good:

How much should you do what seems right to you, even if it seems extreme or controversial, vs how much should you moderate your views and actions based on other perspectives?

If you moderate too much, you won’t be doing anything novel or ambitious, which really reduces how much impact you might have. The people who have had the biggest impact historically often spoke out about entrenched views and were met with hostility — think of the civil rights movement or Galileo.

Moreover, simply following ethical ‘common sense’ has a horrible track record. It used to be common sense to think that homosexuality was evil, slavery was the natural order, and that the environment was there for us to exploit.

And there is still so much wrong with the world. Millions of people die of easily preventable diseases, society is deeply unfair, billions of animals are tortured in factory farms, and we’re gambling our entire future by failing to mitigate threats like climate change. These huge problems deserve radical action — while conventional wisdom appears to accept doing little about them.

On a very basic level, doing more good is better than doing less. But this is a potentially endless and demanding principle, and most people don’t give it much attention or pursue it very systematically. So it wouldn’t be surprising if a concern for doing good led you to positions that seem radical or unusual to the rest of society.

Continue reading →