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.
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.
Jan thinks that this work is actually the most scientifically interesting part of machine learning. Rather than just throwing more chips and more data at a training run, this work requires actually understanding how these models work and how they think. The answers are likely to be breakthroughs on the level of solving the mysteries of the human brain.
The plan, in a nutshell, is to get AI to help us solve alignment. That might sound a bit crazy — as one person described it, “like using one fire to put out another fire.”
But Jan’s thinking is this: the core problem is that AI capabilities will keep getting better and the challenge of monitoring cutting-edge models will keep getting harder, while human intelligence stays more or less the same. To have any hope of ensuring safety, we need our ability to monitor, understand, and design ML models to advance at the same pace as the complexity of the models themselves.
And there’s an obvious way to do that: get AI to do most of the work, such that the sophistication of the AIs that need aligning, and the sophistication of the AIs doing the aligning, advance in lockstep.
Jan doesn’t want to produce machine learning models capable of doing ML research. But such models are coming, whether we like it or not. And at that point Jan wants to make sure we turn them towards useful alignment and safety work, as much or more than we use them to advance AI capabilities.
Jan thinks it’s so crazy it just might work. But some critics think it’s simply crazy. They ask a wide range of difficult questions, including:
If you don’t know how to solve alignment, how can you tell that your alignment assistant AIs are actually acting in your interest rather than working against you? Especially as they could just be pretending to care about what you care about.
How do you know that these technical problems can be solved at all, even in principle?
At the point that models are able to help with alignment, won’t they also be so good at improving capabilities that we’re in the middle of an explosion in what AI can do?
In today’s interview host Rob Wiblin puts these doubts to Jan to hear how he responds to each, and they also cover:
OpenAI’s current plans to achieve ‘superalignment’ and the reasoning behind them
Why alignment work is the most fundamental and scientifically interesting research in ML
The kinds of people he’s excited to hire to join his team and maybe save the world
What most readers misunderstood about the OpenAI announcement
The three ways Jan expects AI to help solve alignment: mechanistic interpretability, generalization, and scalable oversight
What the standard should be for confirming whether Jan’s team has succeeded
Whether OpenAI should (or will) commit to stop training more powerful general models if they don’t think the alignment problem has been solved
Whether Jan thinks OpenAI has deployed models too quickly or too slowly
The many other actors who also have to do their jobs really well if we’re going to have a good AI future
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 below.
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
Blog post by Cody Fenwick · Published August 7th, 2023
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.”
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,
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.
As Holden explains, he sees four key parts to the playbook humanity should use to guide the transition to very advanced AI in a positive direction: alignment research, standards and monitoring, creating a successful and careful AI lab, and finally, information security.
In today’s episode, host Rob Wiblin interviews return guest Holden Karnofsky about that playbook, as well as:
Why we can’t rely on just gradually solving those problems as they come up, the way we usually do with new technologies.
What multiple different groups can do to improve our chances of a good outcome — including listeners to this show, governments, computer security experts, and journalists.
Holden’s case against ‘hardcore utilitarianism’ and what actually motivates him to work hard for a better world.
What the ML and AI safety communities get wrong in Holden’s view.
Ways we might succeed with AI just by dumb luck.
The value of laying out imaginable success stories.
Why information security is so important and underrated.
Whether it’s good to work at an AI lab that you think is particularly careful.
The track record of futurists’ predictions.
And much 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 below.
Producer: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Simon Monsour and Milo McGuire Transcriptions: Katy Moore
Blog post by Huon Porteous · Published July 31st, 2023
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.
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.
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.
By contrast, he’s pessimistic that it’s possible to coordinate countries around the world to agree to prevent or delay the deployment of dangerous AI models — at least not unless there’s some spectacular AI-related disaster to create such a consensus. And he fears attempts to require licences to train the most powerful ML models will struggle unless they can find a way to exclude and thereby appease people working on relatively safe consumer technologies rather than cutting-edge research.
From observing how DC works, Ezra expects that even a small community of experts in AI governance can have a large influence on how the the US government responds to AI advances. But in Ezra’s view, that requires those experts to move to DC and spend years building relationships with people in government, rather than clustering elsewhere in academia and AI labs.
In today’s brisk conversation, Ezra and host Rob Wiblin cover the above as well as:
Whether it’s desirable to slow down AI research
The value of engaging with current policy debates even if they don’t seem directly important
Which AI business models seem more or less dangerous
Tensions between people focused on existing vs emergent risks from AI
Two major challenges of being a new parent
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Producer: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Milo McGuire Transcriptions: Katy Moore
