Independently carry out complicated tasks for hours, days, or weeks
Interact with the physical world in complex and adaptive ways
Consistently learn from past interactions to improve performance over time
In other words, we don’t yet have AGI — AI systems with general intelligence that can reliably replace humans on a wide range of tasks.
In 2024, OpenAI’s Sam Altman declared, “we are now confident we know how to build AGI.” But how will we get there?
Some people think that we can build AGI by scaling up existing models. But others argue that scaling has only seriously improved AI performance in areas like software engineering, where the tasks are clearly defined and often quickly verifiable.
Blog post by Matt Beard · Published June 4th, 2025
Sometimes, our advising team speaks to people who have enthusiasm for technical AI safety and a related skill set but need concrete ideas for how to enter the field. This list was developed in consultation with our advisors to find the resources they commonly share, including articles, courses, organisations, and fellowships.
While we recommend applying to speak to an advisor for 1-1 tailored guidance, this page gives a practical, non-comprehensive snapshot of how you might move from ‘interested in technical AI safety’ to ‘starting to work on technical AI safety.’
AI models today have a 50% chance of successfully completing a task that would take an expert human one hour. Seven months ago, that number was roughly 30 minutes — and seven months before that, 15 minutes.
These are substantial, multi-step tasks requiring sustained focus: building web applications, conducting machine learning research, or solving complex programming challenges.
Today’s guest, Beth Barnes, is CEO of METR (Model Evaluation & Threat Research) — the leading organisation measuring these capabilities.
Beth’s team has been timing how long it takes skilled humans to complete projects of varying length, then seeing how AI models perform on the same work.
The resulting paper from METR,”Measuring AI ability to complete long tasks“, made waves by revealing that the planning horizon of AI models was doubling roughly every seven months. It’s regarded by many as the most useful AI forecasting work in years.
The companies building these systems aren’t just aware of this trend — they want to harness it as much as possible, and are aggressively pursuing automation of their own research.
And having AI models rapidly build their successors with limited human oversight naturally raises the risk that things will go off the rails if the models at the end of the process lack the goals and constraints we hoped for.
Beth thinks models can already do “meaningful work” on improving themselves, and she wouldn’t be surprised if AI models were able to autonomously self-improve in as little as two years from now — in fact, she says, “It seems hard to rule out even shorter [timelines]. Is there 1% chance of this happening in six, nine months? Yeah, that seems pretty plausible.”
While Silicon Valley is abuzz with these numbers, policymakers remain largely unaware of what’s barrelling toward us — and given the current lack of regulation of AI companies, they’re not even able to access the critical information that would help them decide whether to intervene. Beth adds:
The sense I really want to dispel is, “But the experts must be on top of this. The experts would be telling us if it really was time to freak out.” The experts are not on top of this. Inasmuch as there are experts, they are saying that this is concerning. … And to the extent that I am an expert, I am an expert telling you you should freak out. And there’s not especially anyone else who isn’t saying this.
Beth and Rob discuss all that, plus:
How Beth now thinks that open-weight models are a good thing for AI safety, and what changed her mind
How our poor information security means there’s no such thing as a “closed-weight” model anyway
Whether we can see if an AI is scheming in its chain-of-thought reasoning, and the latest research on “alignment faking”
Why just before deployment is the worst time to evaluate model safety
Why Beth thinks AIs could end up being really good at creative and novel research — something humans tend to think is beyond their reach
Why Beth thinks safety-focused people should stay out of the frontier AI companies — and the advantages smaller organisations have
Areas of AI safety research that Beth thinks is overrated and underrated
Whether it’s feasible to have a science that translates AI models’ increasing use of nonhuman language or ‘neuralese’
How AI is both similar to and different from nuclear arms racing and bioweapons
And much more besides!
This episode was originally recorded on February 17, 2025.
Video editing: Luke Monsour and Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Transcriptions and web: Katy Moore
We’re excited to announce that 80,000 Hours has completed its spin-out from Effective Ventures (EV) and is now operating as an independent organisation. We announced this decision here in December 2023 and we’ve now concluded spinning out from our parent organisation. We’re deeply grateful to the Effective Ventures leadership and team for their support, especially during the complex transition process over the past year.
Our new structure
We’ve established two new UK entities, each with their own board:
80,000 Hours Limited — this is a nonprofit entity that houses our website, podcast, job board, one-on-one service, and our operations.
80,000 Hours Foundation — this is a registered charity that will facilitate donations and own the 80k intellectual property.
Our new boards
Board of Directors (80,000 Hours Limited):
Konstantin Sietzy — Deputy Director of Talent and Operations at UK AISI
Alex Lawsen — Senior Program Associate at Open Philanthropy and former 80,000 Hours Advising Manager
Anna Weldon — COO at the Centre for Effective Altruism (CEA) and former EV board member
What if there’s something it’s like to be a shrimp — or a chatbot?
For centuries, humans have debated the nature of consciousness, often placing ourselves at the very top. But what about the minds of others — both the animals we share this planet with and the artificial intelligences we’re creating?
We’ve pulled together clips from past conversations with researchers and philosophers who’ve spent years trying to make sense of animal consciousness, artificial sentience, and moral consideration under deep uncertainty.
You’ll hear from:
Robert Long on how we might accidentally create artificial sentience (from episode #146)
Jeff Sebo on when we should extend extend moral consideration to digital beings — and what that would even look like (#173)
Jonathan Birch on what we should learn from the cautionary tale of newborn pain, and other “edge cases” of sentience (#196)
Andrés Jiménez Zorrilla on what it’s like to be a shrimp (80k After Hours)
Meghan Barrett on challenging our assumptions about insects’ experiences (#198)
David Chalmers on why artificial consciousness is entirely possible (#67)
Holden Karnofsky on how we’ll see digital people as… people (#109)
Sébastien Moro on the surprising sophistication of fish cognition and behaviour (#205)
Bob Fischer on how to compare the moral weight of a chicken to that of a human (#182)
Cameron Meyer Shorb on the vast scale of potential wild animal suffering (#210)
Lewis Bollard on how animal advocacy has evolved in response to sentience research (#185)
Anil Seth on the neuroscientific theories of consciousness (#206)
Peter Godfrey-Smith on whether we could upload ourselves to machines (#203)
Buck Shlegeris on whether AI control strategies make humans the bad guys (#214)
Stuart Russell on the moral rights of AI systems (#80)
Will MacAskill on how to integrate digital beings into society (#213)
Carl Shulman on collaboratively sharing the world with digital minds (#191)
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Additional content editing: Katy Moore and Milo McGuire Transcriptions and web: Katy Moore
OpenAI’s recent announcement that its nonprofit would “retain control” of its for-profit business sounds reassuring. But this seemingly major concession, celebrated by so many, is in itself largely meaningless.
Litigator Tyler Whitmer is a coauthor of a newly published letter that describes this attempted sleight of hand and directs regulators on how to stop it.
As Tyler explains, the plan both before and after this announcement has been to convert OpenAI into a Delaware public benefit corporation (PBC) — and this alone will dramatically weaken the nonprofit’s ability to direct the business in pursuit of its charitable purpose: ensuring AGI is safe and “benefits all of humanity.”
Right now, the nonprofit directly controls the business. But were OpenAI to become a PBC, the nonprofit, rather than having its “hand on the lever,” would merely contribute to the decision of who does.
Why does this matter? Today, if OpenAI’s commercial arm were about to release an unhinged AI model that might make money but be bad for humanity, the nonprofit could directly intervene to stop it. In the proposed new structure, it likely couldn’t do much at all.
But it’s even worse than that: even if the nonprofit could select the PBC’s directors, those directors would have fundamentally different legal obligations from those of the nonprofit. A PBC director must balance public benefit with the interests of profit-driven shareholders — by default, they cannot legally prioritise public interest over profits, even if they and the controlling shareholder that appointed them want to do so.
As Tyler points out, there isn’t a single reported case of a shareholder successfully suing to enforce a PBC’s public benefit mission in the 10+ years since the Delaware PBC statute was enacted.
This extra step from the nonprofit to the PBC would also mean that the attorneys general of California and Delaware — who today are empowered to ensure the nonprofit pursues its mission — would find themselves powerless to act. These are probably not side effects but rather a Trojan horse for-profit investors are trying to slip past regulators.
Fortunately this can all be addressed — but it requires either the nonprofit board or the attorneys general of California and Delaware to promptly put their foot down and insist on watertight legal agreements that preserve OpenAI’s current governance safeguards and enforcement mechanisms.
As Tyler explains, the same arrangements that currently bind the OpenAI business have to be written into a new PBC’s certificate of incorporation — something that won’t happen by default and that powerful investors have every incentive to resist.
Without these protections, OpenAI’s new suggested structure wouldn’t “fix” anything. They would be a ruse that preserved the appearance of nonprofit control while gutting its substance.
Listen to our conversation with Tyler Whitmer to understand what’s at stake, and what the AGs and board members must do to ensure OpenAI remains committed to developing artificial general intelligence that benefits humanity rather than just investors.
This episode was originally recorded on May 13, 2025.
Video editing: Simon Monsour and Luke Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Transcriptions and web: Katy Moore
When attorneys general intervene in corporate affairs, it usually means something has gone seriously wrong. In OpenAI’s case, it appears to have forced a dramatic reversal of the company’s plans to sideline its nonprofit foundation, announced in a blog post that made headlines worldwide.
The company’s sudden announcement that its nonprofit will “retain control” credits “constructive dialogue” with the attorneys general of California and Delaware — corporate-speak for what was likely a far more consequential confrontation behind closed doors. A confrontation perhaps driven by public pressure from Nobel Prize winners, past OpenAI staff, and community organisations.
But whether this change will help depends entirely on the details of implementation — details that remain worryingly vague in the company’s announcement.
Return guest Rose Chan Loui, nonprofit law expert at UCLA, sees potential in OpenAI’s new proposal, but emphasises that “control” must be carefully defined and enforced: “The words are great, but what’s going to back that up?” Without explicitly defining the nonprofit’s authority over safety decisions, the shift could be largely cosmetic.
Why have state officials taken such an interest so far? Host Rob Wiblin notes, “OpenAI was proposing that the AGs would no longer have any say over what this super momentous company might end up doing. … It was just crazy how they were suggesting that they would take all of the existing money and then pursue a completely different purpose.”
Now that they’re in the picture, the AGs have leverage to ensure the nonprofit maintains genuine control over issues of public safety as OpenAI develops increasingly powerful AI.
Rob and Rose explain three key areas where the AGs can make a huge difference to whether this plays out in the public’s best interest:
Ensuring that the contractual agreements giving the nonprofit control over the new Delaware public benefit corporation are watertight, and don’t accidentally shut the AGs out of the picture.
Insisting that a majority of board members are truly independent by prohibiting indirect as well as direct financial stakes in the business.
Insisting that the board is empowered with the money, independent staffing, and access to information which they need to do their jobs.
This episode was originally recorded on May 6, 2025.
Video editing: Simon Monsour and Luke Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Transcriptions and web: Katy Moore
Want to get up to speed on the state of AI development and the risks it poses? Our site provides an overview of key topics in this area, but obviously there’s a lot more to learn.
We recommend starting with the following blog posts and research papers. (Note: we don’t necessarily agree with all the claims the authors make, but still think they’re great resources.)
The article concisely explains how AI has gotten better in recent years primarily by scaling up existing systems rather than by making more fundamental scientific advances.
Read this to understand why it’s plausible that AI systems could pose a threat to humanity, if they were powerful enough and it would further their goals.
It’s important to understand why there’s enthusiasm for building powerful AI systems, despite the risks. This post from an AI company CEO paints a positive vision for powerful AI.
When you have a system where ministers almost never understand their portfolios, civil servants change jobs every few months, and MPs don’t grasp parliamentary procedure even after decades in office — is the problem the people, or the structure they work in?
Today’s guest, political journalist Ian Dunt, studies the systemic reasons governments succeed and fail.
And in his book How Westminster Works …and Why It Doesn’t, he argues that Britain’s government dysfunction and multi-decade failure to solve its key problems stems primarily from bad incentives and bad processes. Even brilliant, well-intentioned people are set up to fail by a long list of institutional absurdities.
For instance:
Ministerial appointments in complex areas like health or defence typically go to whoever can best shore up the prime minister’s support within their own party and prevent a leadership challenge, rather than people who have any experience at all with the area.
On average, ministers are removed after just two years, so the few who manage to learn their brief are typically gone just as they’re becoming effective. In the middle of a housing crisis, Britain went through 25 housing ministers in 25 years.
Ministers are expected to make some of their most difficult decisions by reading paper memos out of a ‘red box’ while exhausted, at home, after dinner.
Tradition demands that the country be run from a cramped Georgian townhouse: 10 Downing Street. Few staff fit and teams are split across multiple floors. Meanwhile, the country’s most powerful leaders vie to control the flow of information to and from the prime minister via ‘professionalised loitering’ outside their office.
Civil servants are paid too little to retain those with technical skills, who can earn several times as much in the private sector. For those who do want to stay, the only way to get promoted is to move departments — abandoning any area-specific knowledge they’ve accumulated.
As a result, senior civil servants handling complex policy areas have a median time in role as low as 11 months. Turnover in the Treasury has regularly been 25% annually — comparable to a McDonald’s restaurant.
MPs are chosen by local party members overwhelmingly on the basis of being ‘loyal party people,’ while the question of whether they are good at understanding or scrutinising legislation (their supposed constitutional role) simply never comes up.
The end result is that very few of the most powerful people in British politics have much idea what they’re actually doing. As Ian puts it, the country is at best run by a cadre of “amateur generalists.”
While some of these are unique British failings, many others are recurring features of governments around the world, and similar dynamics can arise in large corporations as well.
But as Ian also lays out, most of these absurdities have natural solutions, and in every case some countries have found structural solutions that help ensure decisions are made by the right people, with the information they need, and that success is rewarded.
This episode was originally recorded on January 30, 2025.
Video editing: Simon Monsour Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Music: Ben Cordell Camera operator: Jeremy Chevillotte Transcriptions and web: Katy Moore
How do you navigate a career path when the future of work is uncertain? How important is mentorship versus immediate impact? Is it better to focus on your strengths or on the world’s most pressing problems? Should you specialise deeply or develop a unique combination of skills?
From embracing failure to finding unlikely allies, we bring you 16 diverse perspectives from past guests who’ve found unconventional paths to impact and helped others do the same.
You’ll hear from:
Michael Webb on using AI as a career advisor and the human skills AI can’t replace (from episode #161)
Holden Karnofsky on kicking ass in whatever you do, and which weird ideas are worth betting on (#109, #110, and #158)
Chris Olah on how intersections of particular skills can be a wildly valuable niche (#108)
Michelle Hutchinson on understanding what truly motivates you (#75)
Benjamin Todd on how to make tough career decisions and deal with rejection (#71 and 80k After Hours)
Jeff Sebo on what improv comedy teaches us about doing good in the world (#173)
Spencer Greenberg on recognising toxic people who could derail your career (#183)
Dean Spears on embracing randomness and serendipity (#186)
Karen Levy on finding yourself through travel (#124)
Leah Garcés on finding common ground with unlikely allies (#99)
Hannah Ritchie on being selective about whose advice you follow (#160)
Pardis Sabeti on prioritising physical health (#104)
Sarah Eustis-Guthrie on knowing when to pivot from your current path (#207)
Danny Hernandez on setting triggers for career decisions (#78)
Varsha Venugopal on embracing uncomfortable situations (#113)
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Content editing: Katy Moore and Milo McGuire Transcriptions and web: Katy Moore
Problem profile by Cody Fenwick · Published April 24th, 2025
Advanced AI technology may enable its creators, or others who control it, to attempt and achieve unprecedented societal power grabs. Under certain circumstances, they could use these systems to take control of whole economies, militaries, and governments.
This kind of power grab from a single person or small group would pose a major threat to the rest of humanity.
Since we started investing much more in growth in 2022, we’ve increased the hours that people spend engaging with our content by 6.5x, reached millions of new users across different platforms, and now have over 500,000 newsletter subscribers. We’re also the largest single source of people getting involved in the effective altruism community, according to the most recent EA Survey.
Even so, it seems like there’s considerable room to reach more people — and there are many exciting growth projects we’re unable to take on because of low capacity on our team. So, we’re looking for a new Engagement Specialist to help us ambitiously increase the amount of engagement with our advice and our impact.
We anticipate that the right person in this role could help us massively increase our readership, and lead to hundreds or thousands of additional people pursuing high-impact careers.
As some indication of what success in the role might look like, over the next couple of years you might have:
Cost-effectively deployed $5 million reaching people from our target audience.
Reached hundreds of millions of people on social media with key messages.
Partnered with some of the largest and most well-regarded YouTube channels (for instance, we have run sponsorships with Veritasium,
Podcast by Robert Wiblin · Published April 16th, 2025
Throughout history, technological revolutions have fundamentally shifted the balance of power in society. The Industrial Revolution created conditions where democracies could dominate for the first time — as nations needed educated, informed, and empowered citizens to deploy advanced technologies and remain competitive.
Unfortunately there’s every reason to think artificial general intelligence (AGI) will reverse that trend.
In a new paper published today, Tom Davidson — senior research fellow at the Forethought Centre for AI Strategy — argues that advanced AI systems will enable unprecedented power grabs by tiny groups of people, primarily by removing the need for other human beings to participate.
When a country’s leaders no longer need citizens for economic production, or to serve in the military, there’s much less need to share power with them. “Over the broad span of history, democracy is more the exception than the rule,” Tom points out. “With AI, it will no longer be important to a country’s competitiveness to have an empowered and healthy citizenship.”
Citizens in established democracies are not typically that concerned about coups. We doubt anyone will try, and if they do, we expect human soldiers to refuse to join in. Unfortunately, the AI-controlled military systems of the future will lack those inhibitions. As Tom lays out, “Human armies today are very reluctant to fire on their civilians. If we get instruction-following AIs, then those military systems will just fire.”
Why would AI systems follow the instructions of a would-be tyrant? One answer is that, as militaries worldwide race to incorporate AI to remain competitive, they risk leaving the door open for exploitation by malicious actors in a few ways:
AI systems could be programmed to simply follow orders from the top of the chain of command, without any checks on that power — potentially handing total power indefinitely to any leader willing to abuse that authority.
Superior cyber capabilities could enable small groups to hack into and take full control of AI-operated military infrastructure.
It’s also possible that the companies with the most advanced AI, if it conveyed a significant enough advantage over competitors, could quickly develop armed forces sufficient to overthrow an incumbent regime. History suggests that as few as 10,000 obedient military drones could be sufficient to kill competitors, take control of key centres of power, and make your success fait accompli.
Without active effort spent mitigating risks like these, it’s reasonable to fear that AI systems will destabilise the current equilibrium that enables the broad distribution of power we see in democratic nations.
In this episode, host Rob Wiblin and Tom discuss new research on the question of whether AI-enabled coups are likely, and what we can do about it if they are, as well as:
Whether preventing coups and preventing ‘rogue AI’ require opposite interventions, leaving us in a bind
Whether open sourcing AI weights could be helpful, rather than harmful, for advancing AI safely
Why risks of AI-enabled coups have been relatively neglected in AI safety discussions
How persuasive AGI will really be
How many years we have before these risks become acute
The minimum number of military robots needed to stage a coup
This episode was originally recorded on January 20, 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
Help make spectacular videos that reach a huge audience.
We’re looking for someone to contract as a video editor, who can quickly learn our style and make our videos successful on shortform video platforms. We want these videos to start changing and informing the conversation about transformative AI and AGI.
Why this role?
In 2025, 80,000 Hours is 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. Right now not nearly enough people are talking about these ideas and their implications.
A great video program could change this. Time spent on the internet is increasingly spent watching video, and for many people in our target audience, video is the main way that they both find entertainment and learn about topics that matter to them.
To get our video program off the ground, we need great editors who understand our style and vision and can work quickly and to a high standard.
Responsibilities
Be able to work at least 10 hours a week
Be able to turn around drafts of edited shortform videos in 24-48 hours
Take feedback well
Learn our style and adapt to it quickly
About you
We’re looking for someone who ideally has:
Experience making shortform videos
Experience with Capcut, Descript or similar
Good taste in shortform video
Knowledge of the current trends and what succeeds in shortform video
The ability to work quickly and take feedback well
Familiarity with AI Safety
If you don’t have experience here but think you’d be a great fit,
Many people — with a diverse range of skills and experience — are urgently needed to help mitigate these risks.
I think you should consider making this the focus of your career.
This article explains why.
1) World-changing AI systems could come much sooner than people expect
In an earlier article I explained why there’s a significant chance that AI could contribute to scientific research or automate many jobs by 2030. Current systems can already do a lot, there are clear ways to continue to improve them in the coming years. Forecasters and experts widely agree that the probability of widespread disruption is much higher than it was even just a couple of years ago.
AI systems are rapidly becoming more autonomous, as measured by the METR time horizon benchmark. The most recent models, such as o3, seem to be on an even faster trend that started in 2024.
2) The impact on society could be explosive
People say AI will be transformative, but few really get just how wild it could be.
What happens when your desire to do good starts to undermine your own wellbeing?
Over the years, we’ve heard from therapists, charity directors, researchers, psychologists, and career advisors — all wrestling with how to do good without falling apart. Today’s episode brings together insights from 16 past guests on the emotional and psychological costs of pursuing a high-impact career to improve the world — and how to best navigate the all-too-common guilt, burnout, perfectionism, and imposter syndrome along the way.
You’ll hear from:
80,000 Hours’ former CEO on managing anxiety, self-doubt, and a chronic sense of falling short (from episode #100)
Randy Nesse on why we evolved to be anxious and depressed (episode #179)
Hannah Boettcher on how ‘optimisation framing’ can quietly distort our sense of self-worth (from our 80k After Hours feed)
Mental Health Navigator is a service that simplifies finding and accessing mental health information and resources all over the world — built specifically for the effective altruism community
Audio engineering: Ben Cordell, Milo McGuire, Simon Monsour, and Dominic Armstrong Content editing: Katy Moore and Milo McGuire Transcriptions and web: Katy Moore
Podcast by Robert Wiblin · Published April 4th, 2025
Most AI safety conversations centre on alignment: ensuring AI systems share our values and goals. But despite progress, we’re unlikely to know we’ve solved the problem before the arrival of human-level and superhuman systems in as little as three years.
So some are developing a backup plan to safely deploy models we fear are actively scheming to harm us — so-called “AI control.” While this may sound mad, given the reluctance of AI companies to delay deploying anything they train, not developing such techniques is probably even crazier.
Today’s guest — Buck Shlegeris, CEO of Redwood Research — has spent the last few years developing control mechanisms, and for human-level systems they’re more plausible than you might think. He argues that given companies’ unwillingness to incur large costs for security, accepting the possibility of misalignment and designing robust safeguards might be one of our best remaining options.
Buck asks us to picture a scenario where, in the relatively near future, AI companies are employing 100,000 AI systems running 16 times faster than humans to automate AI research itself. These systems would need dangerous permissions: the ability to run experiments, access model weights, and push code changes. As a result, a misaligned system could attempt to hack the data centre, exfiltrate weights, or sabotage research. In such a world, misalignment among these AIs could be very dangerous.
But in the absence of a method for reliably aligning frontier AIs, Buck argues for implementing practical safeguards to prevent catastrophic outcomes. His team has been developing and testing a range of straightforward, cheap techniques to detect and prevent risky behaviour by AIs — such as auditing AI actions with dumber but trusted models, replacing suspicious actions, and asking questions repeatedly to catch randomised attempts at deception.
Most importantly, these methods are designed to be cheap and shovel-ready. AI control focuses on harm reduction using practical techniques — techniques that don’t require new, fundamental breakthroughs before companies could reasonably implement them, and that don’t ask us to forgo the benefits of deploying AI.
As Buck puts it:
Five years ago I thought of misalignment risk from AIs as a really hard problem that you’d need some really galaxy-brained fundamental insights to resolve. Whereas now, to me the situation feels a lot more like we just really know a list of 40 things where, if you did them — none of which seem that hard — you’d probably be able to not have very much of your problem.
Of course, even if Buck is right, we still need to do those 40 things — which he points out we’re not on track for. And AI control agendas have their limitations: they aren’t likely to work once AI systems are much more capable than humans, since greatly superhuman AIs can probably work around whatever limitations we impose.
Still, AI control agendas seem to be gaining traction within AI safety. Buck and host Rob Wiblin discuss all of the above, plus:
Why he’s more worried about AI hacking its own data centre than escaping
What to do about “chronic harm,” where AI systems subtly underperform or sabotage important work like alignment research
Why he might want to use a model he thought could be conspiring against him
Why he would feel safer if he caught an AI attempting to escape
Why many control techniques would be relatively inexpensive
How to use an untrusted model to monitor another untrusted model
What the minimum viable intervention in a “lazy” AI company might look like
How even small teams of safety-focused staff within AI labs could matter
The moral considerations around controlling potentially conscious AI systems, and whether it’s justified
This episode was originally recorded on February 21, 2025.
Video: Simon Monsour and Luke Monsour Audio engineering: Ben Cordell, Milo McGuire, and Dominic Armstrong Transcriptions and web: Katy Moore
Problem profile by Cody Fenwick · Published April 4th, 2025
The proliferation of advanced AI systems may lead to the gradual disempowerment of humanity, even if efforts to prevent them from becoming power-seeking or scheming are successful. Humanity may be incentivised to hand over increasing amounts of control to AIs, giving them power over the economy, politics, culture, and more. Over time, humanity’s interests may be sidelined and our control over the future undermined, potentially constituting an existential catastrophe.
There’s disagreement over how serious a problem this is and how it relates to other concerns about AI alignment. It’s also unclear, if this is a genuine risk, what we could do about it. But we think it’s potentially very important, and more people should work on clarifying the issue and perhaps figuring out how to address it.
Since 2016, we’ve ranked ‘risks from artificial intelligence’ as our top pressing problem. Whilst we’ve provided research and support on how to work on reducing AI risks since that point (and before!), we’ve put in varying amounts of investment over time and between programmes.
We think we should consolidate our effort and focus because:
We think that AGI by 2030 is plausible — and this is much sooner than most of us would have predicted five years ago. This is far from guaranteed, but we think the view is compelling based on analysis of the current flow of inputs into AI development and the speed of recent AI progress. You can read about this argument in far more detail in this new article.
We are in a window of opportunity to influence AGI, before laws and norms are set in place.
80,000 Hours has an opportunity to help more people take advantage of this window. We want our strategy to be responsive to changing events in the world, and we think that prioritising reducing risks from AI is probably the best way to achieve our high-level, cause-impartial goal of doing the most good for others over the long term by helping people have high-impact careers. We expect the landscape to move faster in the coming years, so we’ll need a faster moving culture to keep up.
