In today’s episode, host Rob Wiblin speaks with repeat guest Ian Morris about what big-picture history says about the likely impact of machine intelligence.
They cover:
Some crazy anomalies in the historical record of civilisational progress
Whether we should think about today’s technology from an evolutionary perspective
Whether war will make a resurgence
Why we can’t end up living like The Jetsons
Whether stagnation or cyclical futures are realistic
What it means that over the very long term the rate of economic growth has increased
Whether violence between humans and powerful AI systems is likely
The most likely reasons for Rob and Ian to be really wrong about all of this
How professional historians react to this sort of talk
The future of Ian’s work
Plenty more
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Milo McGuire Transcriptions: Katy Moore
In today’s episode, host Luisa Rodriguez interviews Seren Kell — Senior Science and Technology Manager at the Good Food Institute Europe — about making alternative proteins as tasty, cheap, and convenient as traditional meat, dairy, and egg products.
They cover:
The basic case for alternative proteins, and why they’re so hard to make
Why fermentation is a surprisingly promising technology for creating delicious alternative proteins
The main scientific challenges that need to be solved to make fermentation even more useful
The progress that’s been made on the cultivated meat front, and what it will take to make cultivated meat affordable
How GFI Europe is helping with some of these challenges
How people can use their careers to contribute to replacing factory farming with alternative proteins
The best part of Seren’s job
Plenty more
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Dominic Armstrong and Milo McGuire Additional content editing: Luisa Rodriguez and Katy Moore Transcriptions: Katy Moore
In today’s episode, host Rob Wiblin gets the rare chance to interview someone with insider AI policy experience at the White House and DeepMind who’s willing to speak openly — Tantum Collins.
They cover:
How AI could strengthen government capacity, and how that’s a double-edged sword
How new technologies force us to confront tradeoffs in political philosophy that we were previously able to pretend weren’t there
To what extent policymakers take different threats from AI seriously
Whether the US and China are in an AI arms race or not
Whether it’s OK to transform the world without much of the world agreeing to it
The tyranny of small differences in AI policy
Disagreements between different schools of thought in AI policy, and proposals that could unite them
How the US AI Bill of Rights could be improved
Whether AI will transform the labour market, and whether it will become a partisan political issue
The tensions between the cultures of San Francisco and DC, and how to bridge the divide between them
What listeners might be able to do to help with this whole mess
Panpsychism
Plenty more
Producer and editor: Keiran Harris Audio engineering lead: Ben Cordell Technical editing: Simon Monsour and Milo McGuire Transcriptions: Katy Moore
In today’s episode, host Rob Wiblin speaks with repeat guest and audience favourite Anders Sandberg about the most impressive things that could be achieved in our universe given the laws of physics.
They cover:
The epic new book Anders is working on, and whether he’ll ever finish it
Whether there’s a best possible world or we can just keep improving forever
What wars might look like if the galaxy is mostly settled
The impediments to AI or humans making it to other stars
How the universe will end a million trillion years in the future
Whether it’s useful to wonder about whether we’re living in a simulation
The grabby aliens theory
Whether civilizations get more likely to fail the older they get
The best way to generate energy that could ever exist
Black hole bombs
Whether superintelligence is necessary to get a lot of value
The likelihood that life from elsewhere has already visited Earth
And plenty more.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Simon Monsour and Milo McGuire Transcriptions: Katy Moore
A mistake I have frequently made, and still sometimes do, is not asking for help with applications. I usually feel awkward about others reading my letters or essays or practicing interview questions, and I also don’t want to waste my friends’ time.
But whenever I end up asking for help, it improves my applications significantly, and people are usually happy to help. (I enjoy giving feedback on applications as well!)
–Anemone Franz, advisor
2. Ruling out an option too quickly
I first became concerned about risks from artificial intelligence in 2014, when I read Superintelligence. The book convinced me these risks were serious. And more importantly, I couldn’t find persuasive counterarguments at the time.
But because I didn’t have a background in technical fields — I thought of myself as a writer — I concluded there was little I could contribute to the field and mostly worked on other problems.
In today’s episode, host Luisa Rodriguez interviews Kevin Esvelt — a biologist at the MIT Media Lab and the inventor of CRISPR-based gene drive — about the threat posed by engineered bioweapons.
They cover:
Why it makes sense to focus on deliberately released pandemics
Case studies of people who actually wanted to kill billions of humans
How many people have the technical ability to produce dangerous viruses
The different threats of stealth and wildfire pandemics that could crash civilisation
The potential for AI models to increase access to dangerous pathogens
Why scientists try to identify new pandemic-capable pathogens, and the case against that research
Technological solutions, including UV lights and advanced PPE
Using CRISPR-based gene drive to fight diseases and reduce animal suffering
And plenty more.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Simon Monsour Additional content editing: Katy Moore and Luisa Rodriguez Transcriptions: Katy Moore
Blog post by Lauren Kuhns · Published September 29th, 2023
The idea this week: building career capital is a key part of having an impactful career over the long term — and we have new content about some specific paths you might take.
If you want to do good with your career, we usually don’t recommend trying to have an impact right away. We think most people should spend their early career getting good at something useful.
Working in policy can be an excellent way to have a positive impact on many top problems, including AI, biosecurity, great power conflict, animal welfare, global health, and more.
The first part details the value of policy master’s degrees with a focus on the US — though some of the information is likely to apply more broadly. We think this is one of the best ways to get career capital for a career in US policy.
The second part covers specifics about how to choose which program to apply to based on reputation and personal fit, advice for preparing your application, and information on how to fund your degree.
Career review by Benjamin Todd · Last updated September 2023 · First published November 2021
In 2010, a group of founders with experience in business, practical medicine, and biotechnology launched a new project: Moderna, Inc.
After witnessing recent groundbreaking research into RNA, they realised there was an opportunity to use this technology to rapidly create new vaccines for a wide range of diseases. But few existing companies were focused on that application.
They decided to found a company. And 10 years later, they were perfectly situated to develop a highly effective vaccine against COVID-19 — in a matter of weeks. This vaccine played a huge role in curbing the pandemic and has likely saved millions of lives.
This illustrates that if you can find an important gap in a pressing problem area and found an organisation that fills this gap, that can be one of the highest-impact things you can do — especially if that organisation can persist and keep growing without you.
Why might founding a new project be high impact?
If you can find an important gap in what’s needed to tackle a pressing problem, and create an organisation to fill that gap, that’s a highly promising route to having a huge impact.
But here are some more reasons it seems like an especially attractive path to us, provided you have a compelling idea and the right personal fit — which we cover in the next section.
Blog post by Benjamin Hilton · Published September 19th, 2023
The question this week: what are the biggest changes to our career guide since 2017?
Read the new and updated career guide here, by our founder Benjamin Todd and the 80,000 Hours team.
Our 2023 career guide isn’t just a fancy new design — here’s a rundown of how the content has been updated:
1. Career capital: get good at something useful
In our previous career guide, we argued that your primary focus should be on building very broadly applicable skills, credentials, and connections — what we called transferable career capital.
We also highlighted jobs like consulting as a way to get this.
However, since launching the 2017 version of the career guide, we came to think a focus on transferable career capital might lead you to neglect experience that can be very useful to enter the most impactful jobs — for example, experience working in an AI lab or studying synthetic biology.
OK, so how should you figure out the best career capital option for you?
Our new advice: get good at something useful.
In more depth — choose some valuable skills to learn, and that are a good fit for you, and then find opportunities that let you practise those skills. And then have concrete back-up plans and plan Bs in mind, rather than relying on general ‘transferability.’
Effective altruism is associated with the slogan “do the most good.” On one level, this has to be unobjectionable: What could be bad about helping people more and more?
But in today’s interview, Toby Ord — moral philosopher at the University of Oxford and one of the founding figures of effective altruism — lays out three reasons to be cautious about the idea of maximising the good that you do. He suggests that rather than “doing the most good that we can,” perhaps we should be happy with a more modest and manageable goal: “doing most of the good that we can.”
Toby was inspired to revisit these ideas by the possibility that Sam Bankman-Fried, who stands accused of committing severe fraud as CEO of the cryptocurrency exchange FTX, was motivated to break the law by a desire to give away as much money as possible to worthy causes.
Toby’s top reason not to fully maximise is the following: if the goal you’re aiming at is subtly wrong or incomplete, then going all the way towards maximising it will usually cause you to start doing some very harmful things.
This result can be shown mathematically, but can also be made intuitive, and may explain why we feel instinctively wary of going “all-in” on any idea, or goal, or way of living — even something as benign as helping other people as much as possible.
Toby gives the example of someone pursuing a career as a professional swimmer. Initially, as our swimmer takes their training and performance more seriously, they adjust their diet, hire a better trainer, and pay more attention to their technique. While swimming is the main focus of their life, they feel fit and healthy and also enjoy other aspects of their life as well — family, friends, and personal projects.
But if they decide to increase their commitment further and really go all-in on their swimming career, holding back nothing back, then this picture can radically change. Their effort was already substantial, so how can they shave those final few seconds off their racing time? The only remaining options are those which were so costly they were loath to consider them before.
To eke out those final gains — and go from 80% effort to 100% — our swimmer must sacrifice other hobbies, deprioritise their relationships, neglect their career, ignore food preferences, accept a higher risk of injury, and maybe even consider using steroids.
Now, if maximising one’s speed at swimming really were the only goal they ought to be pursuing, there’d be no problem with this. But if it’s the wrong goal, or only one of many things they should be aiming for, then the outcome is disastrous. In going from 80% to 100% effort, their swimming speed was only increased by a tiny amount, while everything else they were accomplishing dropped off a cliff.
The bottom line is simple: a dash of moderation makes you much more robust to uncertainty and error.
As Toby notes, this is similar to the observation that a sufficiently capable superintelligent AI, given any one goal, would ruin the world if it maximised it to the exclusion of everything else. And it follows a similar pattern to performance falling off a cliff when a statistical model is ‘overfit’ to its data.
In the full interview, Toby also explains the “moral trade” argument against pursuing narrow goals at the expense of everything else, and how consequentialism changes if you judge not just outcomes or acts, but everything according to its impacts on the world.
Toby and Rob also discuss:
The rise and fall of FTX and some of its impacts
What Toby hoped effective altruism would and wouldn’t become when he helped to get it off the ground
What utilitarianism has going for it, and what’s wrong with it in Toby’s view
How to mathematically model the importance of personal integrity
Which AI labs Toby thinks have been acting more responsibly than others
How having a young child affects Toby’s feelings about AI risk
Whether infinities present a fundamental problem for any theory of ethics that aspire to be fully impartial
How Toby ended up being the source of the highest quality images of the Earth from space
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Simon Monsour Transcriptions: Katy Moore
We’re looking for candidates to join our 1on1 team.
The 1on1 team at 80,000 Hours talks to people who want to have a positive impact in their work and helps them find career paths tackling the world’s most pressing problems. We’re keen to expand our team by hiring people who can help with at least one (and hopefully more!) of the following responsibilities:
Advising: talking one-on-one to talented and altruistic applicants in order to help them find high-impact careers.
Running our headhunting product: working with hiring managers at the most effective organisations to help them find exceptional employees.
If you think you’d be interested in taking on more than one of these duties, and enjoy wearing multiple hats in your job, we strongly encourage you to apply. The start dates of these roles are flexible, although we’re likely to prioritise candidates who can start sooner, all else equal.
These roles have starting salaries from £50,000 to £85,000 (depending on skills and experience) and are ideally London-based. We’re able to sponsor visa applications.
About 80,000 Hours
Our mission is to get talented people working on the world’s most pressing problems by providing them with excellent support, advice, and resources on how to do so. We’re also one of the largest sources introducing people to the effective altruism community,
In this episode of 80k After Hours, Luisa Rodriguez and Alex Lawsen discuss common mistakes people make when trying to do good with their careers, and advice on how to avoid them.
They cover:
Taking 80,000 Hours’ rankings too seriously
Not trying hard enough to fail
Feeling like you need to optimise for having the most impact now
Feeling like you need to work directly on AI immediately
Not taking a role because you think you’ll be replaceable
Constantly considering other career options
Overthinking or over-optimising career choices
Being unwilling to think things through for yourself
Ignoring conventional career wisdom
Doing community work even if you’re not suited to it
Who this episode is for:
People who want to pursue a high-impact career
People wondering how much AI progress should change their plans
People who take 80,000 Hours’ career advice seriously
Who this episode isn’t for:
People not taking 80k’s career advice seriously enough
People who’ve never made any career mistakes
People who don’t want to hear Alex say “I said a bunch of stuff, maybe some of it’s true” every time he’s on the podcast
Get this episode by subscribing to our more experimental podcast on the world’s most pressing problems and how to solve them: type ’80k After Hours’ into your podcasting app. Or read the transcript below.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Milo McGuire and Dominic Armstrong Additional content editing: Luisa Rodriguez and Katy Moore Transcriptions: Katy Moore
Blog post by Benjamin Hilton · Published September 4th, 2023
From 2016 to 2019, 80,000 Hours’ core content was contained in our persistently popular career guide. (You may also remember it as the 80,000 Hours book: 80,000 Hours — Find a fulfilling career that does good).
Today, we’re re-launching that guide. Among many other changes, in the new version:
We focus more on avoiding harm (in line with our updates following the collapse of FTX), and explicitly discuss Sam Bankman-Fried when talking about earning to give.
We are more upfront about 80,000 Hours’ focus on existential risk in particular (while also discussing a wide variety of cause areas, including global health, animal welfare, existential risk and meta-causes).
We’ve updated the more empirical sections of the guide using more up-to-date papers and data.
Mustafa Suleyman was part of the trio that founded DeepMind, and his new AI project is building one of the world’s largest supercomputers to train a large language model on 10–100x the compute used to train ChatGPT.
But far from the stereotype of the incorrigibly optimistic tech founder, Mustafa is deeply worried about the future, for reasons he lays out in his new book The Coming Wave: Technology, Power, and the 21st Century’s Greatest Dilemma (coauthored with Michael Bhaskar). The future could be really good, but only if we grab the bull by the horns and solve the new problems technology is throwing at us.
On Mustafa’s telling, AI and biotechnology will soon be a huge aid to criminals and terrorists, empowering small groups to cause harm on previously unimaginable scales. Democratic countries have learned to walk a ‘narrow path’ between chaos on the one hand and authoritarianism on the other, avoiding the downsides that come from both extreme openness and extreme closure. AI could easily destabilise that present equilibrium, throwing us off dangerously in either direction. And ultimately, within our lifetimes humans may not need to work to live any more — or indeed, even have the option to do so.
And those are just three of the challenges confronting us. In Mustafa’s view, ‘misaligned’ AI that goes rogue and pursues its own agenda won’t be an issue for the next few years, and it isn’t a problem for the current style of large language models. But he thinks that at some point — in eight, ten, or twelve years — it will become an entirely legitimate concern, and says that we need to be planning ahead.
In The Coming Wave, Mustafa lays out a 10-part agenda for ‘containment’ — that is to say, for limiting the negative and unforeseen consequences of emerging technologies:
Developing an Apollo programme for technical AI safety
Instituting capability audits for AI models
Buying time by exploiting hardware choke points
Getting critics involved in directly engineering AI models
Getting AI labs to be guided by motives other than profit
Radically increasing governments’ understanding of AI and their capabilities to sensibly regulate it
Creating international treaties to prevent proliferation of the most dangerous AI capabilities
Building a self-critical culture in AI labs of openly accepting when the status quo isn’t working
Creating a mass public movement that understands AI and can demand the necessary controls
Not relying too much on delay, but instead seeking to move into a new somewhat-stable equilibria
As Mustafa put it, “AI is a technology with almost every use case imaginable” and that will demand that, in time, we rethink everything.
Rob and Mustafa discuss the above, as well as:
Whether we should be open sourcing AI models
Whether Mustafa’s policy views are consistent with his timelines for transformative AI
How people with very different views on these issues get along at AI labs
The failed efforts (so far) to get a wider range of people involved in these decisions
Whether it’s dangerous for Mustafa’s new company to be training far larger models than GPT-4
Whether we’ll be blown away by AI progress over the next year
What mandatory regulations government should be imposing on AI labs right now
Appropriate priorities for the UK’s upcoming AI safety summit
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Milo McGuire Transcriptions: Katy Moore
Blog post by Luisa Rodriguez · Published September 1st, 2023
The idea this week: AI may be progressing fast — but that doesn’t mean it will rapidly transform the economy in the near term.
Large language models like ChatGPT are becoming more and more powerful and capable. We’ve already started to see a few examples where human workers are plausibly being replaced by AI.
As these models get even more skilled, will they substantially replace human workers? What will happen to the labour market?
Michael argues that new technologies typically take many decades to fully replace specific jobs.
For example, if you look at two of the biggest general-purpose technologies of the last 150 years — robots and software — it took 30 years from the invention of each technology to get to 50% adoption.
It took 90 years for the last manual telephone operator to lose their job from automation.
So if we look to the past as a guide, it may suggest that if AI systems do replace human workers, it will take many decades. But why does it take so long for very obviously useful technologies to be widely adopted? Here are three reasons:
Adopting new innovative technologies can take lots of money and time.
Working in policy is among the most effective ways to have a positive impact in areas like AI, biosecurity, animal welfare, or global health. Getting a policy master’s degree (e.g. in security studies or public policy) can help you pivot into or accelerate your policy career in the US.
This two-part overview explains why, when, where, and how to get a policy master’s degree, with a focus on people who want to work in the US federal government. The first half focuses on the “why” and the “when” and alternatives to policy master’s. The second half considers criteria for choosing where to apply, specific degrees we recommend, how to apply, and how to secure funding. We also recommend this US policy master’s database if you want to compare program options (see also this list of European programs maintained through our job board).
This information is based on the personal experience of people working on policy in DC for several years, background reading, and conversations with more than two dozen policy professionals.
Part 1: Why do a master’s if you want to work in policy?
There are several reasons why you might want to do a master’s if your goal is to work in policy.
First, completing a master’s is often (but not always) necessary for advancing in a policy career, depending on the specific institution and role.
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).
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.
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Simon Monsour and Milo McGuire Additional content editing: Katy Moore and Luisa Rodriguez Transcriptions: Katy Moore
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
Get this episode by subscribing to our podcast on the world’s most pressing problems and how to solve them: type ‘80,000 Hours’ into your podcasting app. Or read the transcript.
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Milo McGuire and Dominic Armstrong Additional content editing: Katy Moore and Luisa Rodriguez Transcriptions: Katy Moore
Blog post by Jess Binksmith · Published August 11th, 2023
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.
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.
