The idea this week: it’s incredible how dedicated many of you are to helping others.
One of my favourite parts of working on the one-on-one advising team is getting to see the important work so many people are doing up close. It’s incredibly inspiring to learn about the thoughtful, dedicated steps you’re taking to have an impact. In our conversations, we get to directly express appreciation for each person’s efforts. But we only get to do that for a fraction of readers, and only occasionally.
So I wanted to take this chance to say thank you to all of you working so hard and intentionally to help others. There are countless ways to make a difference — different problems needing solutions and different approaches to tackle them. I can’t speak to nearly all of those here. But I do want to highlight a few examples of work I know many of you are doing that I find deeply admirable.
To those working long hours at a challenging job in order to donate a significant portion of your salary to effective organisations — thank you. It’s hard to stay motivated when the work itself doesn’t feel valuable. It’s hard to make time outside a full-time job to thoughtfully decide where your money can do the most good. And it can be tough being surrounded by people with different values who get to directly enjoy the fruits of their labour rather than using it to reduce suffering.
Rare events can still cause catastrophic accidents. The concern that has been raised by experts going back over time, is that really, the more of these experiments, the more labs, the more opportunities there are for a rare event to occur — that the right pathogen is involved and infects somebody in one of these labs, or is released in some way from these labs.
And what I chronicle in Pandora’s Gamble is that there have been these previous outbreaks that have been associated with various kinds of lab accidents. So this is not a theoretical thing that can happen: it has happened in the past.
Alison Young
In today’s episode, host Luisa Rodriguez interviews award-winning investigative journalist Alison Young on the surprising frequency of lab leaks and what needs to be done to prevent them in the future.
They cover:
The most egregious biosafety mistakes made by the CDC, and how Alison uncovered them through her investigative reporting
The Dugway life science test facility case, where live anthrax was accidentally sent to labs across the US and several other countries over a period of many years
The time the Soviets had a major anthrax leak, and then hid it for over a decade
The 1977 influenza pandemic caused by vaccine trial gone wrong in China
The last death from smallpox, caused not by the virus spreading in the wild, but by a lab leak in the UK
Ways we could get more reliable oversight and accountability for these labs
And the investigative work Alison’s most proud of
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
Take the White House. This week, President Joe Biden announced a sweeping new executive order to respond to the risks potentially posed by advanced AI systems, including risks to national security.
A requirement for AI labs working on the most powerful models to share information about safety tests and training plans
Direction to the National Institute of Standards and Technology to create standards for red teaming and assessing the safety of powerful new AI models
Efforts to reduce the risk of AI-related biological threats and to mitigate cybersecurity vulnerabilities
Provisions on fraud, privacy, equity, civil rights, workers’ rights, and international coordination
Vice President Kamala Harris also announced the creation of the United States AI Safety Institute this week, which will help evaluate and mitigate dangerous capabilities of AI models.
One [outrageous example of air pollution] is municipal waste burning that happens in many cities in the Global South. Basically, this is waste that gets collected from people’s homes, and instead of being transported to a waste management facility or a landfill or something, gets burned at some point, because that’s the fastest way to dispose of it — which really points to poor delivery of public services. But this is ubiquitous in virtually every small- or even medium-sized city. It happens in larger cities too, in this part of the world.
That’s something that truly annoys me, because it feels like the kind of thing that ought to be fairly easily managed, but it happens a lot. It happens because people presumably don’t think that it’s particularly harmful. I don’t think it saves a tonne of money for the municipal corporations and other local government that are meant to manage it. I find it particularly annoying simply because it happens so often; it’s something that you’re able to smell in so many different parts of these cities.
One of our earliest supporters and a dear friend of mine, Mark Lampert, once said to me, “The way I think about it is, imagine that this money were already in the hands of people living in poverty. If I could, would I want to tax it and then use it to finance other projects that I think would benefit them?”
I think that’s an interesting thought experiment — and a good one — to say, “Are there cases in which I think that’s justifiable?”
Paul Niehaus
In today’s episode, host Luisa Rodriguez interviews Paul Niehaus — cofounder of GiveDirectly — on the case for giving unconditional cash to the world’s poorest households.
They cover:
The empirical evidence on whether giving cash directly can drive meaningful economic growth
How the impacts of GiveDirectly compare to USAID employment programmes
GiveDirectly vs GiveWell’s top-recommended charities
How long-term guaranteed income affects people’s risk-taking and investments
Whether recipients prefer getting lump sums or monthly instalments
How GiveDirectly tackles cases of fraud and theft
The case for universal basic income, and GiveDirectly’s UBI studies in Kenya, Malawi, and Liberia
The political viability of UBI
Plenty more
Producer and editor: Keiran Harris Audio Engineering Lead: Ben Cordell Technical editing: Dominic Armstrong and Milo McGuire Additional content editing: Katy Moore and Luisa Rodriguez Transcriptions: Katy Moore
If we carry on looking at these industrialised economies, not thinking about what it is they’re actually doing and what the potential of this is, you can make an argument that, yes, rates of growth are slowing, the rate of innovation is slowing. But it isn’t.
What we’re doing is creating wildly new technologies: basically producing what is nothing less than an evolutionary change in what it means to be a human being. But this has not yet spilled over into the kind of growth that we have accustomed ourselves to in the fossil-fuel industrial era. That is about to hit us in a big way.
Ian Morris
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 technology from an evolutionary perspective
Whether we ought to expect war to make a resurgence or continue dying out
Why we can’t end up living like The Jetsons
Whether stagnation or cyclical recurring futures seem very plausible
What it means that the rate of increase in the economy has been increasing
Whether violence is likely between humans and powerful AI systems
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
There have been literally thousands of years of breeding and living with animals to optimise these kinds of problems. But because we’re just so early on with alternative proteins and there’s so much white space, it’s actually just really exciting to know that we can keep on innovating and being far more efficient than this existing technology — which, fundamentally, is just quite inefficient. You’re feeding animals a bunch of food to then extract a small fraction of their biomass to then eat that.
Animal agriculture takes up 83% of farmland, but produces just 18% of food calories. So the current system just is so wasteful. And the limiting factor is that you’re just growing a bunch of food to then feed a third of the world’s crops directly to animals, where the vast majority of those calories going in are lost to animals existing.
Seren Kell
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
If you and I and 100 other people were on the first ship that was going to go settle Mars, and were going to build a human civilisation, and we have to decide what that government looks like, and we have all of the technology available today, how do we think about choosing a subset of that design space?
That space is huge and it includes absolutely awful things, and mixed-bag things, and maybe some things that almost everyone would agree are really wonderful, or at least an improvement on the way that things work today. But that raises all kinds of tricky questions.
My concern is that if we don’t approach the evolution of collective decision making and government in a deliberate way, we may end up inadvertently backing ourselves into a corner, where we have ended up on some slippery slope — and all of a sudden we have, let’s say, autocracies on the global stage are strengthened relative to democracies.
Tantum Collins
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
Now, the really interesting question is: How much is there an attacker-versus-defender advantage in this kind of advanced future?
Right now, if somebody’s sitting on Mars and you’re going to war against them, it’s very hard to hit them. You don’t have a weapon that can hit them very well. But in theory, if you fire a missile, after a few months, it’s going to arrive and maybe hit them, but they have a few months to move away. Distance actually makes you safer: if you spread out in space, it’s actually very hard to hit you.
So it seems like you get a defence-dominant situation if you spread out sufficiently far. But if you’re in Earth orbit, everything is close, and the lasers and missiles and the debris are a terrible danger, and everything is moving very fast.
So my general conclusion has been that war looks unlikely on some size scales but not on others.
Anders Sandberg
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.
Imagine a fast-spreading respiratory HIV. It sweeps around the world. Almost nobody has symptoms. Nobody notices until years later, when the first people who are infected begin to succumb. They might die, something else debilitating might happen to them, but by that point, just about everyone on the planet would have been infected already.
And then it would be a race. Can we come up with some way of defusing the thing? Can we come up with the equivalent of HIV antiretrovirals before it’s too late?
Kevin Esvelt
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.’
One thing that you can say in general with moral philosophy is that the more extreme theories which are less in keeping with all of our current moral beliefs — are also less likely to encode the prejudices of our times. We say in the philosophy business that they’ve got more “reformative power” … But that comes with the risk that we will end up doing things that are … intuitively bad or wrong — and that they might actually be bad or wrong. So it’s a double-edged sword… and one would have to be very careful when following theories like that.
Toby Ord
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,
I think the key consideration that I end up highlighting to people who I think are trying to do the best thing right now is something like: It might be that setting yourself up well to do more good later looks like not directly having as much of an impact right now.
Because probably learning is pretty good if you want to have an impact later. Probably getting some signalling experience for a lot of careers, maybe doing a prestigious internship, maybe getting paid a lot of money: these things just often pay off later, and often trade off against doing the very most good with the summer internship you’re doing this year, or in the first two years of your job just after you’ve graduated.
Alex Lawsen
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.
So people have this fear, particularly in the US, of pessimistic outlooks. I mean, the number of times people come to me like, “You seem to be quite pessimistic.” No, I just don’t think about things in this simplistic “Are you an optimist or are you a pessimist?” terrible framing. It’s BS. I’m neither.
I’m just observing the facts as I see them, and I’m doing my best to share for critical public scrutiny what I see. If I’m wrong, rip it apart and let’s debate it — but let’s not lean into these biases either way.
Mustafa Suleyman
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.