We expect there will be substantial progress in AI in the coming years, potentially even to the point where machines come to outperform humans in many, if not all, tasks. This could have enormous benefits, helping to solve currently intractable global problems, but could also pose severe risks. These risks could arise accidentally (for example, if we don’t find technical solutions to concerns about the safety of AI systems), or deliberately (for example, if AI systems worsen geopolitical conflict). We think more work needs to be done to reduce these risks.
Some of these risks from advanced AI could be existential — meaning they could cause human extinction, or an equally permanent and severe disempowerment of humanity.1 There have not yet been any satisfying answers to concerns — discussed below — about how this rapidly approaching, transformative technology can be safely developed and integrated into our society. Finding answers to these concerns is neglected and may well be tractable. We estimated that there were around 400 people worldwide working directly on this in 2022, though we believe that number has grown.2 As a result, the possibility of AI-related catastrophe may be the world’s most pressing problem — and the best thing to work on for those who are well-placed to contribute.
Promising options for working on this problem include technical research on how to create safe AI systems, strategy research into the particular risks AI might pose, and policy research into ways in which companies and governments could mitigate these risks. As policy approaches continue to be developed and refined, we need people to put them in place and implement them. There are also many opportunities to have a big impact in a variety of complementary roles, such as operations management, journalism, earning to give, and more — some of which we list below.
AI guide by Benjamin Todd · Published March 21st, 2025
In recent months, the CEOs of leading AI companies have grown increasingly confident about rapid progress:
OpenAI’s CEO shifted from saying in November 2024 he expects “the rate of progress we’ve made over the last three years continues” to declaring just one month later “we are now confident we know how to build AGI as we have traditionally understood it.”
Anthropic’s CEO stated in January 2025: “I’m more confident than I’ve ever been that we’re close to powerful capabilities…A country of geniuses in a data centre…that’s what I think we’re quite likely to get in the next 2-3 years.”
Even Google DeepMind’s more cautious CEO shifted from saying “as soon as 10 years” in the autumn, to “I think we’re probably three to five years away” by January.
What explains the shift? Could they be right, or is this just hype? Could we really have Artificial General Intelligence (AGI) by 2028?
In this article, I look at what’s driven recent progress, estimate how far those drivers can continue, and explain why they’re likely to continue for at least four more years.
In particular, while in 2024 progress in LLM chatbots seemed to slow, a new approach started to work: teaching the models to reason using reinforcement learning. In just a year, this let them surpass human PhDs at answering difficult scientific reasoning questions and achieve expert-level performance on one-hour coding tasks.
This is easy to dismiss. This group is obviously selected to be bullish on AI and wants to hype their own work and raise funding.
However, I don’t think their views should be totally discounted. They’re the people with the most visibility into the capabilities of next-generation systems.
And they’ve also been among the most right about recent progress, even if they’ve been too optimistic.
Most likely, progress will be slower than they expect, but maybe only by a few years.
2. AI researchers in general
One way to reduce selection effects is to look at a wider group of AI researchers than those working on AGI directly, including in academia. This is what Katja Grace did with a survey of thousands of recent AI publication authors.
The survey asked for forecasts of “high-level machine intelligence,” defined as when AI can accomplish every task better or more cheaply than humans. The median estimate was a 25% chance in the early 2030s and 50% by 2047 — with some giving answers in the next few years and others hundreds of years in the future.
Problem profile by Stephen Clare · Last updated March 18th, 2025 · First published June 2023
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. International tension could cause leaders to decide they’re better off fighting than negotiating. And rapidly advancing AI may change the way wars are fought and lead to dangerous geopolitical upheaval.
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 directly 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 pressure, and improving international cooperation, you might be helping to reduce other risks, like the chance of future pandemics.
Help shape the most important conversation of our time
We’re looking for someone exceptional to join 80,000 Hours as a Podcast Host, helping us build a media platform that properly covers recursively self-improving AGI and its implications.
Why this matters
Frontier AI systems are developing rapidly, with capabilities progressing much faster than most expected even a few years ago.
If development continues at its current pace we could see an ‘intelligence explosion’ in which AI takes over AI research and rapidly self-improves, starting in as little as 2-7 years.
This is the defining issue of our time and decisions made in the next decade could shape humanity’s long-term future in profound ways.
Yet the public conversation around AGI remains completely inadequate. Mainstream sources neglect the issue entirely for the most part. When AGI is discussed, it’s often covered superficially and fails in one of various ways:
naive optimism and hype
reflexive dismissiveness
focusing only on immediate issues like job displacement or copyright violations
uncritically accepting narratives from actors with strong financial interests in the industry.
The extremely profound implications of developing AI, then AGI, and then superintelligence are basically ignored.
We believe there’s a critical opportunity to fill this gap by scaling up our podcast from occasional in-depth interviews to a more comprehensive media platform with multiple weekly pieces covering AGI-related developments, risks, governance, and alignment efforts in a substantive way.
Help shape the most important conversation of our time
We’re looking for someone exceptional to join 80,000 Hours as Chief of Staff for our podcast team, helping us build a media platform that properly covers recursively self-improving AGI and its implications.
Why this matters
Frontier AI systems are developing rapidly, with capabilities progressing much faster than most expected even a few years ago.
If development continues at its current pace we could see an ‘intelligence explosion’ in which AI takes over AI research and rapidly self-improves, starting in as little as 2-7 years.
This is the defining issue of our time and decisions made in the next decade could shape humanity’s long-term future in profound ways.
Yet the public conversation around AGI remains completely inadequate. Mainstream sources neglect the issue entirely for the most part. When AGI is discussed, it’s often covered superficially and fails in one of various ways:
naive optimism and hype
reflexive dismissiveness
focusing only on immediate issues like job displacement or copyright violations
uncritically accepting narratives from actors with strong financial interests in the industry.
The extremely profound implications of developing AI, then AGI, and then superintelligence are basically ignored.
We believe there’s a critical opportunity to fill this gap by scaling up our podcast from occasional in-depth interviews to a more comprehensive media platform with multiple weekly pieces covering AGI-related developments, risks, governance,
As AI has advanced rapidly and the risks have become more salient, we’ve seen many more jobs available to help mitigate the dangers.
The number of AI-related jobs we posted on our job board rose throughout 2023 and then plateaued in 2024. But in January 2025, we posted the most AI-relevant jobs yet!
In 2023, we posted an average of 63 AI-related roles per month. In 2024, the average rose to 105 — a 67% increase.
Over this time, nonprofit jobs have been the most common, though they were briefly overtaken by both company and government jobs in early 2024.
This trend could reflect our vantage point. As a nonprofit that works closely with other nonprofits, we may be best positioned to find and assess high-impact roles in this sector while potentially missing other great roles in sectors more opaque to us.
AI guide by Benjamin Todd · Published March 14th, 2025
I’m writing a new guide to careers to help artificial general intelligence (AGI) go well. Here’s a summary of the key ideas. If any surprise you, stay tuned to read the full guide with our reasoning and counterarguments, and for updates as our views evolve.
In short:
The chance of an AGI-driven technological explosion before 2030 — creating one of the most pivotal periods in history — is high enough to act on.
If you can help the world navigate that transition, it’s likely the highest-impact thing you can do.
Hundreds of jobs tackling this issue are currently available.
For most people, the best entry path is completing a crash course in AI or self-study, and applying to organisations you’re aligned with. Our job board and one-on-one advice can help you do that.
Alternatively, build a relevant skillset or contribute from your current position by donating and spreading these ideas.
Why AGI could be here by 2030
AI has gone from unable to string sentences together to linguistic fluency in five years. But the models are no longer just chatbots: by the end of 2024, leading models matched human experts at real-world coding, AI research engineering, and PhD-level scientific questions, as long as the task took under two hours.
Podcast by Robert Wiblin · Published March 11th, 2025
The 20th century saw unprecedented change: nuclear weapons, satellites, the rise and fall of communism, third-wave feminism, the internet, postmodernism, game theory, genetic engineering, the Big Bang theory, quantum mechanics, birth control, and more. Now imagine all of it compressed into just 10 years.
That’s the future Will MacAskill — philosopher, founding figure of effective altruism, and now researcher at the Forethought Centre for AI Strategy — argues we need to prepare for in his new paper “Preparing for the intelligence explosion.” Not in the distant future, but probably in three to seven years.
The reason: AI systems are rapidly approaching human-level capability in scientific research and intellectual tasks. Once AI exceeds human abilities in AI research itself, we’ll enter a recursive self-improvement cycle — creating wildly more capable systems. Soon after, by improving algorithms and manufacturing chips, we’ll deploy millions, then billions, then trillions of superhuman AI scientists working 24/7 without human limitations. These systems will collaborate across disciplines, build on each discovery instantly, and conduct experiments at unprecedented scale and speed — compressing a century of scientific progress into mere years.
Will compares the resulting situation to a mediaeval king suddenly needing to upgrade from bows and arrows to nuclear weapons to deal with an ideological threat from a country he’s never heard of, while simultaneously grappling with learning that he descended from monkeys and his god doesn’t exist.
What makes this acceleration perilous is that while technology can speed up almost arbitrarily, human institutions and decision-making are much more fixed.
Consider the case of nuclear weapons: in this compressed timeline, there would have been just a three-month gap between the Manhattan Project’s start and the Hiroshima bombing, and the Cuban Missile Crisis would have lasted just over a day.
Robert Kennedy, Sr., who helped navigate the actual Cuban Missile Crisis, once remarked that if they’d had to make decisions on a much more accelerated timeline — like 24 hours rather than 13 days — they would likely have taken much more aggressive, much riskier actions.
So there’s reason to worry about our own capacity to make wise choices. And in “Preparing for the intelligence explosion,” Will lays out 10 “grand challenges” we’ll need to quickly navigate to successfully avoid things going wrong during this period.
Will’s thinking has evolved a lot since his last appearance on the show. While he was previously sceptical about whether we live at a “hinge of history,” he now believes we’re entering one of the most critical periods for humanity ever — with decisions made in the next few years potentially determining outcomes millions of years into the future.
But Will also sees reasons for optimism. The very AI systems causing this acceleration could be deployed to help us navigate it — if we use them wisely. And while AI safety researchers rightly focus on preventing AI systems from going rogue, Will argues we should equally attend to ensuring the futures we deliberately build are truly worth living in.
In this wide-ranging conversation with host Rob Wiblin, Will maps out the challenges we’d face in this potential “intelligence explosion” future, and what we might do to prepare. They discuss:
Why leading AI safety researchers now think there’s dramatically less time before AI is transformative than they’d previously thought
The three different types of intelligence explosions that occur in order
Will’s list of resulting grand challenges — including destructive technologies, space governance, concentration of power, and digital rights
How to prevent ourselves from accidentally “locking in” mediocre futures for all eternity
Ways AI could radically improve human coordination and decision making
Why we should aim for truly flourishing futures, not just avoiding extinction
This episode was originally recorded on February 7, 2025.
Video editing: Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Camera operator: Jeremy Chevillotte Transcriptions and web: Katy Moore
Podcast by Robert Wiblin · Published March 7th, 2025
When OpenAI announced plans to convert from nonprofit to for-profit control last October, it likely didn’t anticipate the legal labyrinth it now faces. A recent court order in Elon Musk’s lawsuit against the company suggests OpenAI’s restructuring faces serious legal threats, which will complicate its efforts to raise tens of billions in investment.
As nonprofit legal expert Rose Chan Loui explains, the court order set up multiple pathways for OpenAI’s conversion to be challenged. Though Judge Yvonne Gonzalez Rogers denied Musk’s request to block the conversion before a trial, she expedited proceedings to the fall so the case could be heard before it’s likely to go ahead. (See Rob’s brief summary of developments in the case.)
And if Musk’s donations to OpenAI are enough to give him the right to bring a case, Rogers sounded very sympathetic to his objections to the OpenAI foundation selling the company, benefiting the founders who forswore “any intent to use OpenAI as a vehicle to enrich themselves.”
But that’s just one of multiple threats. The attorneys general (AGs) in California and Delaware both have standing to object to the conversion on the grounds that it is contrary to the foundation’s charitable purpose and therefore wrongs the public — which was promised all the charitable assets would be used to develop AI that benefits all of humanity, not to win a commercial race. Some, including Rose, suspect the court order was written as a signal to those AGs to take action.
And, as she explains, if the AGs remain silent, the court itself, seeing that the public interest isn’t being represented, could appoint a “special interest party” to take on the case in their place.
This places the OpenAI foundation board in a bind: proceeding with the restructuring despite this legal cloud could expose them to the risk of being sued for a gross breach of their fiduciary duty to the public. The board is made up of respectable people who didn’t sign up for that.
And of course it would cause chaos for the company if all of OpenAI’s fundraising and governance plans were brought to a screeching halt by a federal court judgment landing at the eleventh hour.
Host Rob Wiblin and Rose Chan Loui discuss all of the above as well as what justification the OpenAI foundation could offer for giving up control of the company despite its charitable purpose, and how the board might adjust their plans to make the for-profit switch more legally palatable.
This episode was originally recorded on March 6, 2025.
Video editing: Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Transcriptions: Katy Moore
Laura: This is a difficult question, but I think it’s important to ask. I certainly don’t have all the answers, because the future is uncertain — perhaps especially now.
That said, I do think there are sensible steps to take to navigate the trajectory of AI and its global impact
First, let’s address the immediate concern about the job market. While AI is already changing the landscape of work, entry-level jobs haven’t disappeared. The 80,000 Hours job board has hundreds of entry-level openings, and other job sites will have many more. I expect there will still be plenty of jobs available when you graduate, even as AI continues to advance quickly.
As technology becomes better at the tasks that human workers perform, there are typically significant delays before it is widely adopted. Still, the economy will likely change as AI gets more powerful, and that may start happening in just a few years. So, my advice is to stay ahead of the adoption curve. Master prompting and evaluation techniques to ensure the quality of outputs. Learn to set up AI task automation systems. Experiment with new tools and applications.
Blog post by Arden Koehler · Published February 19th, 2025
About 80,000 Hours
80,000 Hours’ mission is to get talented people working on the world’s most pressing problems.
In 2025, we are planning to focus especially on helping explain why and how our audience can help society safely navigate a transition to a world with transformative AI.
Over a million people visit our website each year, and thousands of people have told us that they’ve significantly changed their career plans due to our work. Surveys conducted by our primary funder, Open Philanthropy, show that 80,000 Hours is one of the single biggest drivers of talent moving into work related to reducing global catastrophic risks.
Our most popular pieces get over 10,000 unique visitors each month, and are among the most important ways we help people shift their careers towards higher-impact options.
The role
In this role, you would:
Research and write new articles for the 80,000 Hours website, e.g. articles explaining particular issues within AI risk reduction for a popular audience (e.g. an explainer on misuse, or government abuse of AI technology)
Survey and interview experts in the issues we prioritise to inform our career advice
Update or rewrite core articles with new information and resources or to appeal to new audiences
Write substantive newsletters for an audience of over 500,000 people
Continually learn from analytics and user feedback what kinds of framings are most clear and engaging
Manage and edit external expert contributors to write articles
This week, we’re answering our newsletter subscribers’ career questions.
Question one: I’ve been working over 20 years as a software engineer, and participated in a few publications as a research engineer. I’m open to getting deeper into research, in fact I’m starting a PhD in computer science. I’m close to 50, but most of the career advice I see from 80,000 Hours seems focused on younger folk.
I’m wondering about reorienting towards global issues; does it make sense to continue with my current PhD? Drop it for one directly related to (e.g.) AI? Drop the PhD and just apply as a research engineer to appropriate companies with such engineers? Or just leave it to the next generation?
Sudhanshu: I would pretty much never say “leave it to others” — there might not be many others, and experienced folks like you often have a lot to offer. We are always hearing from impactful organisations all the time who want experienced, mid-career people to join them in key roles.
With 20 years as a software engineer, I’d be excited if you could apply that experience directly to a pressing world problem, unless you’ve gotten some evidence to suggest that you need some new skills. I think it makes sense to try your hand at just working directly on the problem you think is most important before moving on to other options.
If you do think working on reducing AI risk is the most important problem,
Podcast by Robert Wiblin · Published February 14th, 2025
Technology doesn’t force us to do anything — it merely opens doors. But military and economic competition pushes us through.
That’s how today’s guest Allan Dafoe — director of frontier safety and governance at Google DeepMind — explains one of the deepest patterns in technological history: once a powerful new capability becomes available, societies that adopt it tend to outcompete those that don’t. Those who resist too much can find themselves taken over or rendered irrelevant.
This dynamic played out dramatically in 1853 when US Commodore Perry sailed into Tokyo Bay with steam-powered warships that seemed magical to the Japanese, who had spent centuries deliberately limiting their technological development. With far greater military power, the US was able to force Japan to open itself to trade. Within 15 years, Japan had undergone the Meiji Restoration and transformed itself in a desperate scramble to catch up.
Today we see hints of similar pressure around artificial intelligence. Even companies, countries, and researchers deeply concerned about where AI could take us feel compelled to push ahead — worried that if they don’t, less careful actors will develop transformative AI capabilities at around the same time anyway.
But Allan argues this technological determinism isn’t absolute. While broad patterns may be inevitable, history shows we do have some ability to steer how technologies are developed, by who, and what they’re used for first.
As part of that approach, Allan has been promoting efforts to make AI more capable of sophisticated cooperation, and improving the tests Google uses to measure how well its models could do things like mislead people, hack and take control of their own servers, or spread autonomously in the wild.
As of mid-2024 they didn’t seem dangerous at all, but we’ve learned that our ability to measure these capabilities is good, but imperfect. If we don’t find the right way to ‘elicit’ an ability we can miss that it’s there.
Subsequent research from Anthropic and Redwood Research suggests there’s even a risk that future models may play dumb to avoid their goals being altered.
That has led DeepMind to a “defence in depth” approach: carefully staged deployment starting with internal testing, then trusted external testers, then limited release, then watching how models are used in the real world. By not releasing model weights, DeepMind is able to back up and add additional safeguards if experience shows they’re necessary.
But with much more powerful and general models on the way, individual company policies won’t be sufficient by themselves. Drawing on his academic research into how societies handle transformative technologies, Allan argues we need coordinated international governance that balances safety with our desire to get the massive potential benefits of AI in areas like healthcare and education as quickly as possible.
Host Rob and Allan also cover:
The most exciting beneficial applications of AI
Whether and how we can influence the development of technology
What DeepMind is doing to evaluate and mitigate risks from frontier AI systems
Why cooperative AI may be as important as aligned AI
The role of democratic input in AI governance
What kinds of experts are most needed in AI safety and governance
And much more
Video editing: Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Camera operator: Jeremy Chevillotte Transcriptions: Katy Moore
Podcast by Robert Wiblin · Published February 12th, 2025
On Monday, February 10, Elon Musk made the OpenAI nonprofit foundation an offer they want to refuse, but might have trouble doing so: $97.4 billion for its stake in the for-profit company, plus the freedom to stick with its current charitable mission.
For a normal company takeover bid, this would already be spicy. But OpenAI’s unique structure — a nonprofit foundation controlling a for-profit corporation — turns the gambit into an audacious attack on the plan OpenAI announced in December to free itself from nonprofit oversight.
As today’s guest Rose Chan Loui — founding executive director of UCLA Law’s Lowell Milken Center for Philanthropy and Nonprofits — explains, OpenAI’s nonprofit board now faces a challenging choice.
The nonprofit has a legal duty to pursue its charitable mission of ensuring that AI benefits all of humanity to the best of its ability. And if Musk’s bid would better accomplish that mission than the for-profit’s proposal — that the nonprofit give up control of the company and change its charitable purpose to the vague and barely related “pursue charitable initiatives in sectors such as health care, education, and science” — then it’s not clear the California or Delaware Attorneys General will, or should, approve the deal.
OpenAI CEO Sam Altman quickly tweeted “no thank you” — but that was probably a legal slipup, as he’s not meant to be involved in such a decision, which has to be made by the nonprofit board ‘at arm’s length’ from the for-profit company Sam himself runs.
The board could raise any number of objections: maybe Musk doesn’t have the money, or the purchase would be blocked on antitrust grounds, seeing as Musk owns another AI company (xAI), or Musk might insist on incompetent board appointments that would interfere with the nonprofit foundation pursuing any goal.
But as Rose and Rob lay out, it’s not clear any of those things is actually true.
In this emergency podcast recorded soon after Elon’s offer, Rose and Rob also cover:
Why OpenAI wants to change its charitable purpose and whether that’s legally permissible
On what basis the attorneys general will decide OpenAI’s fate
The challenges in valuing the nonprofit’s “priceless” position of control
Whether Musk’s offer will force OpenAI to up their own bid, and whether they could raise the money
If other tech giants might now jump in with competing offers
How politics could influence the attorneys general reviewing the deal
What Rose thinks should actually happen to protect the public interest
Video editing: Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Transcriptions: Katy Moore
Will LLMs soon be made into autonomous agents? Will they lead to job losses? Is AI misinformation overblown? Will it prove easy or hard to create AGI? And how likely is it that it will feel like something to be a superhuman AGI?
With AGI back in the headlines, we bring you 15 opinionated highlights from the show addressing those and other questions, intermixed with opinions from hosts Luisa Rodriguez and Rob Wiblin recorded back in 2023.
You can decide whether the views we expressed (and those from guests) then have held up these last two busy years. You’ll hear:
Some of the deadliest events in history have been pandemics. COVID-19 demonstrated that we’re still vulnerable to these events, and future outbreaks could be far more lethal.
In fact, we face the possibility of biological disasters that are worse than ever before due to developments in technology.
The chances of such catastrophic pandemics — bad enough to potentially derail civilisation and threaten humanity’s future — seem uncomfortably high. We believe this risk is one of the world’s most pressing problems.
And there are a number of practical options for reducing global catastrophic biological risks (GCBRs). So we think working to reduce GCBRs is one of the most promising ways to safeguard the future of humanity right now.
Most philanthropic (vs. government or industry) AI safety funding (50%) comes from one source: Good Ventures, via Open Philanthropy. But they’ve recently stopped funding several categories of work (my own categories, not theirs):
In addition, they are currently not funding (or not fully funding):
Many non-US think tanks, who don’t want to appear influenced by an American organisation (there’s now probably more than 20 of these)
They do fund technical safety non-profits like FAR AI, though they’re probably underfunding this area, in part due to difficulty hiring for this area the last few years (though they’ve hired recently)
Blog post by Cody Fenwick · Published January 3rd, 2025
The idea this week: despite claims of stagnation, AI research still advanced rapidly in 2024.
Some people say AI research has plateaued. But a lot of evidence from the last year points in the opposite direction:
New capabilities were developed and emerged
Research indicates existing AI can accelerate science
And at the same time, important findings about AI safety and risk came out (see below).
AI advances might still stall. Some leaders in the field have warned that a lack of good data, for example, may impede further capability growth, though others disagree. Regardless, growth clearly hasn’t stopped yet.
Meanwhile, the aggregate forecast on Metaculus of when we’ll see the first “general” AI system — which would be highly capable across a wide range of tasks — is 2031.
If AI advances fast, this work is not only important but urgent.
Here are some of the key developments in AI from the last year:
New AI models and capabilities
OpenAI announced in late December that its new model o3 achieved a large leap forward in capabilities. It builds on the o1 language model (also released in 2024),
