What the past can tell us about how AI will affect jobs

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?

To try to answer these questions, I spoke to labour economist Michael Webb, for the latest episode of The 80,000 Hours Podcast. He’s worked for Google DeepMind, in the UK government, and at Stanford University.

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.

a switchboard

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.

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    US policy master’s degrees

    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.
    • Second, a master’s helps you build your career capital,

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    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:

    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).

    Continue reading →

      #161 – Michael Webb on whether AI will soon cause job loss, lower incomes, and higher inequality — or the opposite

      Do you remember seeing these photographs of generally women sitting in front of these huge panels and connecting calls, plugging different calls between different numbers? The automated version of that was invented in 1892.

      However, the number of human manual operators peaked in 1920 — 30 years after this. At which point, AT&T is the monopoly provider of this, and they are the largest single employer in America, 30 years after they’ve invented the complete automation of this thing that they’re employing people to do. And the last person who is a manual switcher does not lose their job, as it were: that job doesn’t stop existing until I think like 1980.

      So it takes 90 years from the invention of full automation to the full adoption of it in a single company that’s a monopoly provider. It can do what it wants, basically. And so the question perhaps you might have is why?

      Michael Webb

      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

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      #160 – Hannah Ritchie on why it makes sense to be optimistic about the environment

      There’s no money to invest in education elsewhere, so they almost get trapped in the cycle where they don’t get a lot from crop production, but everyone in the family has to work there to just stay afloat. Basically, you get locked in. There’s almost no opportunities externally to go elsewhere.

      So one of my core arguments is that if you’re going to address global poverty, you have to increase agricultural productivity in sub-Saharan Africa. There’s almost no way of avoiding that.

      Hannah Ritchie

      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

      Continue reading →

      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.

      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.

      Continue reading →

        #159 – Jan Leike on OpenAI’s massive push to make superintelligence safe in 4 years or less

        If you’re thinking about how do you align the superintelligence — how do you align the system that’s vastly smarter than humans? — I don’t know. I don’t have an answer. I don’t think anyone really has an answer.

        But it’s also not the problem that we fundamentally need to solve. Maybe this problem isn’t even solvable by humans who live today. But there’s this easier problem, which is how do you align the system that is the next generation? How do you align GPT-N+1? And that is a substantially easier problem.

        Jan Leike

        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

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        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.”

        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,

        Continue reading →

          #158 – Holden Karnofsky on how AIs might take over even if they’re no smarter than humans, and his four-part playbook for AI risk

          I think a lot of the case for planning things out in advance — trying to tell stories of what might happen, trying to figure out what kind of regime we’re going to want and put the pieces in place today, trying to figure out what kind of research challenges are going to be hard and do them today — I think a lot of the case for that stuff being so important does rely on this theory that things could move a lot faster than anyone is expecting.

          Holden Karnofsky

          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

          Continue reading →

          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.

          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.

          But I don’t think this is true.

          Continue reading →

          #157 – Ezra Klein on existential risk from AI and what DC could do about it

          AI is a great thing to spend lots of R&D money on and have a really strong public research infrastructure around. A good amount of that research should be on safety and interpretability. And we should really want this to work, and it should happen.

          And it’s actually not that expensive. I mean, it is expensive for most companies, which is why OpenAI has to be attached to Microsoft and DeepMind had to be part of Google and so on. But from the perspective of a country’s budget, it’s not impossible to have real traction on this.

          Ezra Klein

          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

          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: Milo McGuire
          Transcriptions: Katy Moore

          Continue reading →

          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,

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          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),

          Continue reading →

          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.

          Continue reading →

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

          We’re in a universe where tradeoffs exist, we have finite resources, we have multiple things we care about, and we have incomplete information. So we have to make guesses and take risks — and that hurts. So I think self-compassion and acceptance come in here, like, “Damn, I so am wishing this were not the case, and by golly, it looks like it still is.”

          And then I think that it’s a matter of recognising that we aren’t going to score 100% on any unitary definition of “rightness.” And then recognise that, “Well, I could just look at that and stall out forever, or I could make some moves.” And probably making moves is preferable to stalling out.

          Hannah Boettcher

          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

          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: Dominic Armstrong
          Content editing: Katy Moore, Luisa Rodriguez, and Keiran Harris
          Transcriptions: Katy Moore

          Gershwin – Rhapsody in Blue, original 1924 version” by Jason Weinberger is licensed under creative commons

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

          At the front of the pack we have these frontier AI developers, and we want them to identify particularly dangerous models ahead of time. Once those mines have been discovered, and the frontier developers keep walking down the minefield, there’s going to be all these other people who follow along. And then a really important thing is to make sure that they don’t step on the same mines. So you need to put a flag down — not on the mine, but maybe next to it.

          And so what that looks like in practice is maybe once we find that if you train a model in such-and-such a way, then it can produce maybe biological weapons is a useful example, or maybe it has very offensive cyber capabilities that are difficult to defend against. In that case, we just need the regulation to be such that you can’t develop those kinds of models.

          Markus Anderljung

          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.

          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

          Continue reading →

          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. Data source: Sarkees,

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

          People think [these controls are] about chips which go into tanks or rockets. No, those are not the chips which go on tanks or rockets. These chips in the tanks and rockets are closer to one in your washing machine; it’s not that sophisticated. It’s different if you try to calculate a trajectory of a missile or something: then you do it on supercomputers, and maybe the chips are closer.

          We’re talking about the chips which are used in data centres for AI training.

          Lennart Heim

          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.

          We have similar safety rules for companies that fly planes or manufacture volatile chemicals — so why not for people producing the most powerful and perhaps the most dangerous technology humanity has ever played with?

          But Lennart is quick to note that the approach faces many practical challenges. Currently, AI chips are readily available and untracked. Changing that will require the collaboration of many actors, which might be difficult, especially given that some of them aren’t convinced of the seriousness of the problem.

          Host Rob Wiblin is particularly concerned about a different challenge: the increasing efficiency of AI training algorithms. As these algorithms become more efficient, what once required a specialised AI supercomputer to train might soon be achievable with a home computer.

          By that point, tracking every aggregation of compute that could prove to be very dangerous would be both impractical and invasive.

          With only a decade or two left before that becomes a reality, the window during which compute governance is a viable solution may be a brief one. Top AI labs have already stopped publishing their latest algorithms, which might extend this ‘compute governance era’, but not for very long.

          If compute governance is only a temporary phase between the era of difficult-to-train superhuman AI models and the time when such models are widely accessible, what can we do to prevent misuse of AI systems after that point?

          Lennart and Rob both think the only enduring approach requires taking advantage of the AI capabilities that should be in the hands of police and governments — which will hopefully remain superior to those held by criminals, terrorists, or fools. But as they describe, this means maintaining a peaceful standoff between AI models with conflicting goals that can act and fight with one another on the microsecond timescale. Being far too slow to follow what’s happening — let alone participate — humans would have to be cut out of any defensive decision-making.

          Both agree that while this may be our best option, such a vision of the future is more terrifying than reassuring.

          Lennart and Rob discuss the above as well as:

          • How can we best categorise all the ways AI could go wrong?
          • Why did the US restrict the export of some chips to China and what impact has that had?
          • Is the US in an ‘arms race’ with China or is that more an illusion?
          • What is the deal with chips specialised for AI applications?
          • How is the ‘compute’ industry organised?
          • Downsides of using compute as a target for regulations
          • Could safety mechanisms be built into computer chips themselves?
          • Who would have the legal authority to govern compute if some disaster made it seem necessary?
          • The reasons Rob doubts that any of this stuff will work
          • Could AI be trained to operate as a far more severe computer worm than any we’ve seen before?
          • What does the world look like when sluggish human reaction times leave us completely outclassed?
          • 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 below.

          Producer: Keiran Harris
          Audio mastering: Milo McGuire, Dominic Armstrong, and Ben Cordell
          Transcriptions: Katy Moore

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          Great power war

          Economic growth and technological progress have bolstered the arsenals of the world’s most powerful countries. That means the next war between them could be far worse than World War II, the deadliest conflict humanity has yet experienced.

          Could such a war actually occur? We can’t rule out the possibility. Technical accidents or diplomatic misunderstandings could spark a conflict that quickly escalates. Or international tension could cause leaders to decide they’re better off fighting than negotiating.

          It seems hard to make progress on this problem. It’s also less neglected than some of the problems that we think are most pressing. There are certain issues, like making nuclear weapons or military artificial intelligence systems safer, which seem promising — although it may be more impactful to work on reducing risks from AI, bioweapons or nuclear weapons directly. You might also be able to reduce the chances of misunderstandings and miscalculations by developing expertise in one of the most important bilateral relationships (such as that between the United States and China).

          Finally, by making conflict less likely, reducing competitive pressures on the development of dangerous technology, and improving international cooperation, you might be helping to reduce other risks, like the chance of future pandemics.

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          Should you work at a leading AI lab?

          Why might it be high-impact to work for a leading AI lab?

          We think AI is likely to have transformative effects over the coming decades. We also think that reducing the chances of an AI-related catastrophe is one of the world’s most pressing problems.

          So it’s natural to wonder — if you’re thinking about your career — whether it would be worth working in the labs that are doing the most to build, and shape, these future AI systems.

          Working at a top AI lab, like Google DeepMind, OpenAI, or Anthropic, might be an excellent way to build career capital to work on reducing AI risk in the future. Their work is extremely relevant to solving this problem, which suggests you’ll likely gain directly useful skills, connections, and credentials (more on this later).

          In fact, we suggest working at AI labs in many of our career reviews; it can be a great step in technical AI safety and AI governance and coordination careers. We’ve also looked at working in AI labs in our career reviews on information security, software engineering, data collection for AI alignment, and non-technical roles in AI labs.

          What’s more, the importance of these organisations to the development of AI suggests that they could be huge forces for either good or bad (more below).

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