New releases

How not to lose your job to AI

AI guide by · Published June 2025

About half of people are worried they’ll lose their job to AI. And they’re right to be concerned: AI can now complete real-world coding tasks on GitHub, generate photorealistic video, drive a taxi more safely than humans, and do accurate medical diagnosis. And over the next five years, it’s set to continue to improve rapidly. Eventually, mass automation and falling wages are a real possibility.

But what’s less appreciated is that while AI drives down the value of skills it can do, it drives up the value of skills it can’t. Wages (on average) will increase before they fall, as automation generates a huge amount of wealth, and the remaining tasks become the bottlenecks to further growth. As I’ll explain, ATMs actually increased employment of bank clerks — until online banking automated the job much more.

Your best strategy is to learn the skills that AI will make more valuable, trying to ride the wave of automation. So what are those skills? Here’s a preview:

Skills most likely to increase in value as AI progresses

These will be especially valuable when combined with knowledge of fields needed for AI including machine learning, cyber & information security, data centre & power plant construction, robotics development and maintenance, and (lesso) fields that could expand a lot given economic growth.

In contrast, the future for these skills seems a lot more uncertain:

  • Coding,

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#218 – Hugh White on why Trump is abandoning US hegemony – and that’s probably good

Podcast by · Published June 12, 2025

For decades, US allies have slept soundly under the protection of America’s overwhelming military might. Donald Trump — with his threats to ditch NATO, seize Greenland, and abandon Taiwan — seems hell-bent on shattering that comfort.

But according to Hugh White — one of the world’s leading strategic thinkers, emeritus professor at the Australian National University, and author of Hard New World: Our Post American Future — Trump isn’t destroying American hegemony. He’s simply revealing that it’s already gone.

“Trump has very little trouble accepting other great powers as co-equals,” Hugh explains. And that happens to align perfectly with a strategic reality the foreign policy establishment desperately wants to ignore: fundamental shifts in global power have made the costs of maintaining a US-led hegemony prohibitively high.

Even under Biden, when Russia invaded Ukraine, the US sent weapons but explicitly ruled out direct involvement. Ukraine matters far more to Russia than America, and this “asymmetry of resolve” makes Putin’s nuclear threats credible where America’s counterthreats simply aren’t.

Hugh’s gloomy prediction: “Europeans will end up conceding to Russia whatever they can’t convince the Russians they’re willing to fight a nuclear war to deny them.”

The Pacific tells the same story. Despite Obama’s “pivot to Asia” and Biden’s tough talk about “winning the competition for the 21st century,” actual US military capabilities there have barely budged while China’s have soared, along with its economy — which is now bigger than the US’s, as measured in purchasing power. Containing China and defending Taiwan would require America to spend 8% of GDP on defence (versus 3.5% today) — and convince Beijing it’s willing to accept Los Angeles being vaporised. Unlike during the Cold War, no president — Trump or otherwise — can make that case to voters.

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3 reasons AGI might still be decades away

Blog post by · Published June 6, 2025

We recently argued that AGI could be here by 2030.

And we’re not the only ones — CEOs of leading AI labs and many AI researchers are saying similar things.

But many people disagree, and there’s a good chance that AGI won’t be here by 2030. Some think it could still be decades away.

So what are the reasons to expect a longer path to AGI?

Reason 1: The path to AGI isn’t obvious

Today’s AI systems can write excellent code, produce research reports, and do Nobel Prize-worthy work in protein folding.

But frontier systems still can’t:

  • 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.

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#217 – Beth Barnes on the most important graph in AI right now — and the 7-month rule that governs its progress

Podcast by · Published June 2, 2025

Beth Barnes on the most important graph in AI right now — and the 7-month rule that governs its progress

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.

That’s both an exciting and troubling development, because it could radically speed up advances in AI capabilities, accomplishing what would have taken years or decades in just months. That itself could be highly destabilising, as we explored in a previous episode: Will MacAskill on AI causing a “century in a decade” — and how we’re completely unprepared.

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.

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80,000 Hours completes spin-out from Effective Ventures

Blog post by · Published May 28, 2025

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:

  1. 80,000 Hours Limited — this is a nonprofit entity that houses our website, podcast, job board, one-on-one service, and our operations.
  2. 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
  • Joshua Rosenberg — CEO of the Forecasting Research Institute
  • Emma Abele — former CEO of METR

Board of Trustees (80,000 Hours Foundation):

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Beyond human minds: The bewildering frontier of consciousness in insects, AI, and more

Podcast by · Published May 23, 2025

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)

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Don’t believe OpenAI’s “nonprofit” spin (emergency pod with Tyler Whitmer)

Podcast by · Published May 15, 2025

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.

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Emergency pod: Did OpenAI give up, or is this just a new trap? (with Rose Chan Loui)

Podcast by · Published May 8, 2025

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.

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#216 – Ian Dunt on why governments in Britain and elsewhere can’t get anything done – and how to fix it

Podcast by · Published May 2, 2025

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:

  1. 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.
  2. 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.
  3. 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.
  4. 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.
  5. 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.
  6. 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.
  7. 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.

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Serendipity, weird bets, & cold emails that actually work: Career advice from 16 former guests

Podcast by · Published April 24, 2025

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)
  • Alex Lawsen on getting good mentorship (80k After Hours)
  • 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)

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[Closed] Open position: Engagement Specialist

Blog post by · Published April 23, 2025

Why this role?

80,000 Hours provides free research and support to help people find careers tackling the world’s most pressing problems, especially mitigating risks from advanced artificial intelligence.

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,

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#215 – Tom Davidson on how AI-enabled coups could allow a tiny group to seize power

Podcast by · Published April 16, 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:

  1. 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.
  2. Systems could contain “secret loyalties” inserted during development that activate at critical moments, as demonstrated in Anthropic’s recent paper on “sleeper agents”.
  3. Superior cyber capabilities could enable small groups to hack into and take full control of AI-operated military infrastructure.

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Expression of interest: Shortform Video Editing Contractor

Blog post by · Published April 15, 2025

Video Cartoon

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,

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Which industry has the highest-paying jobs?

Article by · Last updated April 2025 · First published May 2017

Which industry has the highest paying jobs?

Simple question. Hard to answer.

Until recently, good data wasn’t available: tax returns weren’t consistently categorised by job type until 2005.

Many rankings of the highest-paying jobs are based on job seeker surveys from GlassDoor, and this almost entirely misses the highest-earning people.

Other rankings, such as this one by US News, use median earnings rather than the mean, making them uninformative for industries with a wide spread of earnings.

The government’s Bureau of Labor Statistics (BLS) is much better, but it still doesn’t properly sample the highest-earning people. It also uses overly narrow categories –- “CEO” is one of the highest-earning jobs even thought CEO isn’t a career path you can enter.

But we’ve got hold of a forthcoming paper by three economists, Benjamin Lockwood (UPenn), Charles Nathanson (Northwestern University) and Glen Weyl (Yale) that combines the BLS figures with new tax data to accurately estimate the whole income distribution of the 11 highest-earning professions. This is the best ranking we’re currently aware of.

In this article, we explore this data, and how it could be used to estimate the expected lifetime income of different professions.

At 80,000 Hours, we help people we find fulfilling careers with positive impact. Earning more money only makes you a little happier, but it can be used to have a big positive impact.

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The case for prioritising AI risks

Blog post by · Published April 11, 2025

Within 5 years, there’s a real chance that AI systems will be created that cause explosive technological and economic change. This would increase the risk of disasters like war between US and China, concentration of power in a small minority, or even total loss of human control over the future.

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.

Graph of lengths of tasks AIs updated in April 2025 for o3 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,

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Guilt, imposter syndrome & doing good: 16 past guests share their mental health journeys

Podcast by · Published April 11, 2025

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)
  • Luisa Rodriguez on grieving the gap between who you are and who you wish you were (from our 80k After Hours feed)
  • Cameron Meyer Shorb on how guilt and shame became his biggest source of suffering — and what helped (episode #210)
  • Tim LeBon on the trap of moral perfectionism, and why we should strive for excellence instead (episode #149)
  • Cal Newport on why we need to make time to be alone with our thoughts (episode #106)
  • Michelle Hutchinson and Habiba Islam on when to prioritise wellbeing over impact (episode #122)
  • Sarah Eustis-Guthrie on the emotional weight of founding a charity (episode #207)
  • Hannah Ritchie on feeling like an imposter, even after writing a book and giving a TED Talk (episode #160)
  • Will MacAskill on why he’s five to 10 times happier than he used to be after learning to work in a way that’s genuinely sustainable (episode #130)
  • Ajeya Cotra on handling the pressure of high-stakes research (episode #90)
  • Christian Ruhl on pursuing a high-impact career while managing a stutter (from our 80k After Hours feed)
  • Leah Garcés on insisting on self-care when witnessing trauma regularly (episode #99)
  • Kelsey Piper on recognising that you’re not alone in your struggles (episode #53)

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#214 – Buck Shlegeris on controlling AI that wants to take over – so we can use it anyway

Podcast by · Published April 4, 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.

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To understand AI, you should use it. Here’s how to get started.

Blog post by · Published April 4, 2025

To truly understand what AI can do — and what is coming soon — you should make regular use of the latest AI services.

This article will help you learn how to do this.

1. You have a team of experts

Think of ChatGPT as a team of experts, ready to assist you.

Whenever you have a question, task, or problem, consider who you’d want in the room if you could talk to — or hire — anyone in the world.

Illustration of the ChatGPT user interface

Here are some of the experts I work with on a regular basis:

  • Programmer: write, debug, and explain code. I’m an experienced software developer, yet more than 95% of the code I ship is now written by AI.
  • Manager: plan and debug my days, weeks, and months. There’s an example transcript in section four below.
  • Product designer: ideate, create prototypes, design and critique user interfaces, and analyse user interview transcripts.
  • Writer and editor: give feedback on my writing, rewrite things, write entire drafts based on a rough dictation, proofread, and fix Markdown.
  • Data analyst: write SQL queries, analyse spreadsheets, create graphs, and infographics.
  • Language tutor: do written exercises and have spoken conversations in French and Icelandic.
  • Intern: extract data from text,

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Gradual disempowerment

Problem profile by · Published April 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.

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We’re shifting our strategic approach to focus more on AGI

Blog post by and · Published April 4, 2025

Why we’re updating our strategic direction

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 could plausibly arrive within the next decade — 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,

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