US policy master’s degrees
New releases
Why you might consider switching careers — and what it takes to do it
The idea this week: switching careers can be terrifying — but it can also be the key to finding more satisfying and impactful work.
Trust me — I’ve tested my fit for at least four different career paths before landing where I am now. After a first job in teaching, I explored:
- Academic philosophy
- Journalism
- Nonprofit research and communications
When I graduated from university with a degree in philosophy, I didn’t know what to do next, but I knew I wanted to find a job that helped others and wasn’t harmful. I looked for roles at nonprofits nearby and ended up getting hired at a special education school.
I loved many parts of the job and the students I worked with, but when the opportunity arose to get my master’s in special education, I realised I didn’t envision spending my whole career in the field. I had gotten involved with local vegan advocacy and an effective altruism group, and I was curious if there were even more impactful opportunities I could pursue with my career.
I once thought that most of my impact would come through donating — but a lot of the people I was talking to were discussing the idea that career choice could be even more impactful than charitable giving (especially since teaching wasn’t particularly lucrative in my case).
#161 – Michael Webb on whether AI will soon cause job loss, lower incomes, and higher inequality — or the opposite
In today’s episode, host Luisa Rodriguez interviews economist Michael Webb of DeepMind, the British Government, and Stanford about how AI progress is going to affect people’s jobs and the labour market.
They cover:
- The jobs most and least exposed to AI
- Whether we’ll we see mass unemployment in the short term
- How long it took other technologies like electricity and computers to have economy-wide effects
- Whether AI will increase or decrease inequality
- Whether AI will lead to explosive economic growth
- What we can we learn from history, and reasons to think this time is different
- Career advice for a world of LLMs
- Why Michael is starting a new org to relieve talent bottlenecks through accelerated learning, and how you can get involved
- Michael’s take as a musician on AI-generated music
- And plenty more
If you’d like to work with Michael on his new org to radically accelerate how quickly people acquire expertise in critical cause areas, he’s now hiring! Check out Quantum Leap’s website.
#160 – Hannah Ritchie on why it makes sense to be optimistic about the environment
In today’s episode, host Luisa Rodriguez interviews the head of research at Our World in Data — Hannah Ritchie — on the case for environmental optimism.
They cover:
- Why agricultural productivity in sub-Saharan Africa could be so important, and how much better things could get
- Her new book about how we could be the first generation to build a sustainable planet
- Whether climate change is the most worrying environmental issue
- How we reduced outdoor air pollution
- Why Hannah is worried about the state of biodiversity
- Solutions that address multiple environmental issues at once
- How the world coordinated to address the hole in the ozone layer
- Surprises from Our World in Data’s research
- Psychological challenges that come up in Hannah’s work
- And plenty more
Operations management: how I found the right career path for me
The idea this week: how I learned a lot about my skills by testing my fit for operations work.
Like a lot of students, I spent much of my final year at university unsure what to do next. Should I pursue further studies, start out on a career path, or something else?
I was excited about having an impact with my career, and I thought I might be a good fit for policy work — which seemed like a way I could contribute to solving pressing world problems. I figured this would involve further studies, so I looked into applying for graduate school.
- Check out our career reviews page, where we discuss key categories of job skills, the priority paths we think might have the most impact over the long term, and promising avenues for pursuing career capital.
But I was probably deferring too much to my sense of what others thought would be high-impact work, rather than figuring out how I could best contribute over the course of my career. I ended up doing the 80,000 Hours career planning worksheet — and it helped me to generate a longer list of options and questions.
It pointed me toward something I hadn’t considered: doing something that would help me test my fit for lots of different kinds of work.
#159 – Jan Leike on OpenAI’s massive push to make superintelligence safe in 4 years or less
In July, OpenAI announced a new team and project: Superalignment. The goal is to figure out how to make superintelligent AI systems aligned and safe to use within four years, and the lab is putting a massive 20% of its computational resources behind the effort.
Today’s guest, Jan Leike, is Head of Alignment at OpenAI and will be co-leading the project. As OpenAI puts it, “…the vast power of superintelligence could be very dangerous, and lead to the disempowerment of humanity or even human extinction. … Currently, we don’t have a solution for steering or controlling a potentially superintelligent AI, and preventing it from going rogue.”
Given that OpenAI is in the business of developing superintelligent AI, it sees that as a scary problem that urgently has to be fixed. So it’s not just throwing compute at the problem — it’s also hiring dozens of scientists and engineers to build out the Superalignment team.
Plenty of people are pessimistic that this can be done at all, let alone in four years. But Jan is guardedly optimistic. As he explains:
Honestly, it really feels like we have a real angle of attack on the problem that we can actually iterate on… and I think it’s pretty likely going to work, actually. And that’s really, really wild, and it’s really exciting. It’s like we have this hard problem that we’ve been talking about for years and years and years, and now we have a real shot at actually solving it. And that’d be so good if we did.
What recent events mean for AI governance career paths
The idea this week: AI governance careers present some of the best opportunities to change the world for the better that we’ve found.
Last week, US Senator Richard Blumenthal gave a stark warning during a subcommittee hearing on artificial intelligence.
He’s become deeply concerned about the potential for an “intelligence device out of control, autonomous, self-replicating, potentially creating diseases, pandemic-grade viruses, or other kinds of evils — purposely engineered by people, or simply the result of mistakes, no malign intention.”
- Read our review of AI governance and coordination career paths to see how you could get involved.
We’ve written about these kinds of dangers — potentially rising to the extreme of an extinction-level event — in our problem profile on preventing an AI-related catastrophe.
“These fears need to be addressed, and I think can be addressed,” the senator continued. “I’ve come to the conclusion that we need some kind of regulatory agency.”
And the senator from Connecticut isn’t the only one:
- The White House has led a coalition of the top AI companies to coordinate on risk-reducing measures, and they recently announced a joint voluntary commitment to some key safety principles. President Joe Biden and Vice President Kamala Harris have been directly involved in these efforts, with the president himself saying the technology will require “new laws, regulation, and oversight.”
- Four top companies developing advanced AI systems — Anthropic,
#158 – Holden Karnofsky on how AIs might take over even if they’re no smarter than humans, and his 4-part playbook for AI risk
Back in 2007, Holden Karnofsky cofounded GiveWell, where he sought out the charities that most cost-effectively helped save lives. He then cofounded Open Philanthropy, where he oversaw a team making billions of dollars’ worth of grants across a range of areas: pandemic control, criminal justice reform, farmed animal welfare, and making AI safe, among others. This year, having learned about AI for years and observed recent events, he’s narrowing his focus once again, this time on making the transition to advanced AI go well.
In today’s conversation, Holden returns to the show to share his overall understanding of the promise and the risks posed by machine intelligence, and what to do about it. That understanding has accumulated over around 14 years, during which he went from being sceptical that AI was important or risky, to making AI risks the focus of his work.
(As Holden reminds us, his wife is also the president of one of the world’s top AI labs, Anthropic, giving him both conflicts of interest and a front-row seat to recent events. For our part, Open Philanthropy is 80,000 Hours’ largest financial supporter.)
One point he makes is that people are too narrowly focused on AI becoming ‘superintelligent.’ While that could happen and would be important, it’s not necessary for AI to be transformative or perilous. Rather, machines with human levels of intelligence could end up being enormously influential simply if the amount of computer hardware globally were able to operate tens or hundreds of billions of them, in a sense making machine intelligences a majority of the global population, or at least a majority of global thought.
Why many people underrate investigating the problem they work on
The idea this week: thinking about which world problem is most pressing may matter more than you realise.
I’m an advisor for 80,000 Hours, which means I talk to a lot of thoughtful people who genuinely want to have a positive impact with their careers. One piece of advice I consistently find myself giving is to consider working on pressing world problems you might not have explored yet.
- Check out our list of the world’s most pressing problems — or read our advice on how to think about the question for yourself.
Should you work on climate change or AI risk? Mitigating antibiotic resistance or preventing bioterrorism? Preventing disease in low-income countries or reducing the harms of factory farming?
Your choice of problem area can matter a lot. But I think a lot of people under-invest in building a view of which problems they think are most pressing.
I think there are three main reasons for this:
1. They think they can’t get a job working on a certain problem, so the argument that it’s important doesn’t seem relevant.
I see this most frequently with AI. People think that they don’t have aptitude or interest in machine learning, so they wouldn’t be able to contribute to mitigating catastrophic risks from AI.
#157 – Ezra Klein on existential risk from AI and what DC could do about it
In Oppenheimer, scientists detonate a nuclear weapon despite thinking there’s some ‘near zero’ chance it would ignite the atmosphere, putting an end to life on Earth. Today, scientists working on AI think the chance their work puts an end to humanity is vastly higher than that.
In response, some have suggested we launch a Manhattan Project to make AI safe via enormous investment in relevant R&D. Others have suggested that we need international organisations modelled on those that slowed the proliferation of nuclear weapons. Others still seek a research slowdown by labs while an auditing and licencing scheme is created.
Today’s guest — journalist Ezra Klein of The New York Times — has watched policy discussions and legislative battles play out in DC for 20 years. Like many people he has also taken a big interest in AI this year, writing articles such as “This changes everything.” In his first interview on the show in 2021, he flagged AI as one topic that DC would regret not having paid more attention to.
So we invited him on to get his take on which regulatory proposals have promise, and which seem either unhelpful or politically unviable.
Out of the ideas on the table right now, Ezra favours a focus on direct government funding — both for AI safety research and to develop AI models designed to solve problems other than making money for their operators. He is sympathetic to legislation that would require AI models to be legible in a way that none currently are — and embraces the fact that that will slow down the release of models while businesses figure out how their products actually work.
How many lives does a doctor save? (Part 3)
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,
How many lives does a doctor save? (Part 2)
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),
How many lives does a doctor save? (Part 1)
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.
Hannah Boettcher on the mental health challenges that come with trying to have a big impact
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
#156 – Markus Anderljung on how to regulate cutting-edge AI models
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.
Working in US AI policy
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,
What the war in Ukraine shows us about catastrophic risks
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.
#155 – Lennart Heim on the compute governance era and what has to come after
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.
Global priorities research
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.
How to cope with rejection in your career
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.