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Curated and popular this week
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In recent months, the CEOs of leading AI companies have grown increasingly confident about rapid progress:
* OpenAI's Sam Altman: Shifted from saying in November "the rate of progress continues" to declaring in January "we are now confident we know how to build AGI"
* Anthropic's Dario Amodei: Stated in January "I'm more confident than I've ever been that we're close to powerful capabilities... in the next 2-3 years"
* Google DeepMind's Demis Hassabis: Changed from "as soon as 10 years" in autumn to "probably three to five years away" by January.
What explains the shift? Is it just hype? Or could we really have Artificial General Intelligence (AGI)[1] 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.
We don't know how capable AGI will become, but extrapolating the recent rate of progress suggests that, by 2028, we could reach AI models with beyond-human reasoning abilities, expert-level knowledge in every domain, and that can autonomously complete multi-week projects, and progress would likely continue from there.
On this set of software engineering & computer use tasks, in 2020 AI was only able to do tasks that would typically take a human expert a couple of seconds. By 2024, that had risen to almost an hour. If the trend continues, by 2028 it'll reach several weeks.
No longer mere chatbots, these 'agent' models might soon satisfy many people's definitions of AGI — roughly, AI systems that match human performance at most knowledge work (see definition in footnote).
This means that, while the compa
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SUMMARY:
ALLFED is launching an emergency appeal on the EA Forum due to a serious funding shortfall. Without new support, ALLFED will be forced to cut half our budget in the coming months, drastically reducing our capacity to help build global food system resilience for catastrophic scenarios like nuclear winter, a severe pandemic, or infrastructure breakdown. ALLFED is seeking $800,000 over the course of 2025 to sustain its team, continue policy-relevant research, and move forward with pilot projects that could save lives in a catastrophe. As funding priorities shift toward AI safety, we believe resilient food solutions remain a highly cost-effective way to protect the future. If you’re able to support or share this appeal, please visit allfed.info/donate.
Donate to ALLFED
FULL ARTICLE:
I (David Denkenberger) am writing alongside two of my team-mates, as ALLFED’s co-founder, to ask for your support. This is the first time in Alliance to Feed the Earth in Disaster’s (ALLFED’s) 8 year existence that we have reached out on the EA Forum with a direct funding appeal outside of Marginal Funding Week/our annual updates. I am doing so because ALLFED’s funding situation is serious, and because so much of ALLFED’s progress to date has been made possible through the support, feedback, and collaboration of the EA community.
Read our funding appeal
At ALLFED, we are deeply grateful to all our supporters, including the Survival and Flourishing Fund, which has provided the majority of our funding for years. At the end of 2024, we learned we would be receiving far less support than expected due to a shift in SFF’s strategic priorities toward AI safety.
Without additional funding, ALLFED will need to shrink. I believe the marginal cost effectiveness for improving the future and saving lives of resilience is competitive with AI Safety, even if timelines are short, because of potential AI-induced catastrophes. That is why we are asking people to donate to this emergency appeal
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We’ve written a new report on the threat of AI-enabled coups.
I think this is a very serious risk – comparable in importance to AI takeover but much more neglected.
In fact, AI-enabled coups and AI takeover have pretty similar threat models. To see this, here’s a very basic threat model for AI takeover:
1. Humanity develops superhuman AI
2. Superhuman AI is misaligned and power-seeking
3. Superhuman AI seizes power for itself
And now here’s a closely analogous threat model for AI-enabled coups:
1. Humanity develops superhuman AI
2. Superhuman AI is controlled by a small group
3. Superhuman AI seizes power for the small group
While the report focuses on the risk that someone seizes power over a country, I think that similar dynamics could allow someone to take over the world. In fact, if someone wanted to take over the world, their best strategy might well be to first stage an AI-enabled coup in the United States (or whichever country leads on superhuman AI), and then go from there to world domination. A single person taking over the world would be really bad. I’ve previously argued that it might even be worse than AI takeover. [1]
The concrete threat models for AI-enabled coups that we discuss largely translate like-for-like over to the risk of AI takeover.[2] Similarly, there’s a lot of overlap in the mitigations that help with AI-enabled coups and AI takeover risk — e.g. alignment audits to ensure no human has made AI secretly loyal to them, transparency about AI capabilities, monitoring AI activities for suspicious behaviour, and infosecurity to prevent insiders from tampering with training.
If the world won't slow down AI development based on AI takeover risk (e.g. because there’s isn’t strong evidence for misalignment), then advocating for a slow down based on the risk of AI-enabled coups might be more convincing and achieve many of the same goals.
I really want to encourage readers — especially those at labs or governments — to do something
How to upskill in AI Safety after AGISF, and how to help others upskill
For people who want to start working on AI Safety, it feels like the AGISF program is widely accepted as the start of the pipeline. It also feels like there are some things we want to see work on within AI Safety: conceptual work, engineering work, empirical work, etc.
However, I think there’s an upskilling gap that isn’t currently clearly signposted by the community: how to go from AGISF to being able to contribute! The main programmes I’m aware of for taking someone who knows a bit about alignment to being able to contribute to AI Safety are SERI MATS (trying to produce conceptual researchers), MLAB (trying to produce engineers), and internships at orgs like Conjecture, CHAI etc.
Without having done these, my impression is that they seem like the best options for upskilling past AGISF, but these are bottlenecked pretty hard by mentor time. So, I think there are plenty of people who could be doing useful work with sufficient upskilling, but who will have to do upskilling more independently. For these people, useful resources like How to pursue a career in technical AI Alignment by Charlie Rogers-Smith exist, but I think some clear signposting for people post-AGISF, pre-job would be useful.
So, after AGISF, what should an excited safety person be doing to upskill? Here are a few ideas
Engineering:
Conceptual work:
How to use these resources?
I think for almost all of these resources, the order of how good I would expect them to be is:
In person with mentorship > Online with mentorship > In person with no mentorship > Online with no mentorship >> solo. A caveat is that I’m not super sure about how high to rate mentorship compared with simply working through these resources with other enthusiastic people. It seems less important for some areas of engineering work than it does for conceptual work (like getting research taste), but I’m not sure about this.
Implications for AI Safety field-builders.
I think the gap between someone doing AGISF and doing impactful work has two implications for field builders in AI safety:
Firstly, I think there would be a lot of value in creating online versions of programmes which essentially go through some of these resources, similarly to how AGISF currently works. I think the biggest bottleneck for these would be mentor time, but I think lots of these could be used successfully without mentors, if there are others who are excited to work through the resources who also have some background in the area.
Secondly, I think that field builders working in local groups with enough members and resources (Oxford, Berkeley, London) could try to run versions of these programmes in person. If online versions of these programmes exist, this becomes even easier: essentially all it requires is scheduling when these groups need to meet!
Conclusion
If you have done AGISF and want to start upskilling, hopefully some of these resources will be useful!
If you want to help others upskill, I think running programmes centred around some of these resources would be a good idea!