I would like if LessWrong provided an optional newsletter like the EA forum digest, for people who want the chance to catch non-curated posts without having to open LessWrong and sift through it directly every few days.
Here’s what the EA Forum digest looks like : a list of titles + author + time to read.
I don’t know exactly how much manual curation goes into it and I’m not asking for that. I’d find a simple karma threshold and this format valuable.
I am also writing up this quick take notably because I’ve had discussions with other people who’d like this, and because recently costs of development and maintenance of software like this have gone down.
I don’t find the existing RSS feed a preferable alternative. - I have never setup an RSS feed reader or similar process, I don’t think I want to and guess most LW readers are similar. - It seems it would on top of that would take extra work to get the format I want out of it
This Feb 2026 survey of some AI safety leaders found median timelines of 2033 for the following definition of AGI
An AI system (or collection of systems) that can fully automate the vast majority (>90%) of roles in the 2025 economy. A job is fully automatable when machines could be built to carry out the job better and more cheaply than human workers. Think feasibility, not adoption.
It featured the following comment
“I think >10% of roles in the 2025 economy are either manual or otherwise require human-like bodies: construction, barbers, restaurant server, etc. If we restrict to knowledge workers (roughly, jobs that can be done on a laptop), these dates move even closer.”
On the current paradigm, AI capabilities progress on niche tasks and diffusion will be linked[1]and diffusion can go rather slowly even when tools are incredibly productivity enhancing, thus there could be an intuitively surprisingly large gap between automation of 50% human tasks[2] and 90% and 99%, true even if we restricted the prediction to computer work tasks.[3]
I’m 80%+ confident we get automated expert+ level coding and ml research by 2030, and that there will be a significant amount of low hanging fruit in software/algorithmic space to allow fast progress on all tasks for which we have data, but I believe generalisation will stay somewhat limited (very very far from “figure out gravity from a picture of a bent blade of grass, more like “when speaking to a human expert in a niche field, knows how to interview them over 10 to 100 hours to extract most important info and then be mostly autonomous on known tasks, but still needs feedback from reality to learn more”), aka ~human level generalisation at best up to 2031.
The combination of “need feedback from reality” and slow diffusion makes slower timelines to “superintelligence” (eg. better than all humans at 99.99%+ of 2026 tasks) surprisingly plausible (eg. 5 to 10 years between AGI and ASI, thus ASI by 2040). I guess without a pause/significant politically influenced slowdown, we’d 80%+ have ASI by 2040. I’d set my 50% for ASI around 2036.[4]
I think technical alignement for human level AGI is solvable and not even off track, thus the world will look fine/good in 2030 (few to zero severe power seeking and deceptive misalignment problems in deployment from Anthropic AI systems) but have high uncertainty about the “use ai to do ai safety work” plan allowing us to successfully know how to train aligned ASI within five years of that. Overall I place myself at 10% or less p(doom) from sharp left turn risks, but around 40% all things considered p(doom) by including gradual disempowerment/value drift and societal response.
We need people to be deploying the technology to gather the relevant data to train/learn from, because generalisation is limited and because lots of expert knowledge only exists in human minds and structures of human relationships right now.
Note I’m weighing by “meaningfully different task” rather than “frequency of task”. Given power law distributions most tasks might be “read email/slack, respond”, which computer use will know how to operate, but not be able to respond to intricacies of different work situations.
I haven’t researched robotics enough to know how fast we could produce and deploy 100 million humanoid robots worldwide which seems like an appropriate level of effort required to gather the required data.
I would like if LessWrong provided an optional newsletter like the EA forum digest, for people who want the chance to catch non-curated posts without having to open LessWrong and sift through it directly every few days.
Here’s what the EA Forum digest looks like : a list of titles + author + time to read.
I don’t know exactly how much manual curation goes into it and I’m not asking for that. I’d find a simple karma threshold and this format valuable.
I am also writing up this quick take notably because I’ve had discussions with other people who’d like this, and because recently costs of development and maintenance of software like this have gone down.
I don’t find the existing RSS feed a preferable alternative.
- I have never setup an RSS feed reader or similar process, I don’t think I want to and guess most LW readers are similar.
- It seems it would on top of that would take extra work to get the format I want out of it
This Feb 2026 survey of some AI safety leaders found median timelines of 2033 for the following definition of AGI
It featured the following comment
On the current paradigm, AI capabilities progress on niche tasks and diffusion will be linked[1] and diffusion can go rather slowly even when tools are incredibly productivity enhancing, thus there could be an intuitively surprisingly large gap between automation of 50% human tasks[2] and 90% and 99%, true even if we restricted the prediction to computer work tasks.[3]
I’m 80%+ confident we get automated expert+ level coding and ml research by 2030, and that there will be a significant amount of low hanging fruit in software/algorithmic space to allow fast progress on all tasks for which we have data, but I believe generalisation will stay somewhat limited (very very far from “figure out gravity from a picture of a bent blade of grass, more like “when speaking to a human expert in a niche field, knows how to interview them over 10 to 100 hours to extract most important info and then be mostly autonomous on known tasks, but still needs feedback from reality to learn more”), aka ~human level generalisation at best up to 2031.
The combination of “need feedback from reality” and slow diffusion makes slower timelines to “superintelligence” (eg. better than all humans at 99.99%+ of 2026 tasks) surprisingly plausible (eg. 5 to 10 years between AGI and ASI, thus ASI by 2040). I guess without a pause/significant politically influenced slowdown, we’d 80%+ have ASI by 2040. I’d set my 50% for ASI around 2036.[4]
I think technical alignement for human level AGI is solvable and not even off track, thus the world will look fine/good in 2030 (few to zero severe power seeking and deceptive misalignment problems in deployment from Anthropic AI systems) but have high uncertainty about the “use ai to do ai safety work” plan allowing us to successfully know how to train aligned ASI within five years of that. Overall I place myself at 10% or less p(doom) from sharp left turn risks, but around 40% all things considered p(doom) by including gradual disempowerment/value drift and societal response.
We need people to be deploying the technology to gather the relevant data to train/learn from, because generalisation is limited and because lots of expert knowledge only exists in human minds and structures of human relationships right now.
Note I’m weighing by “meaningfully different task” rather than “frequency of task”. Given power law distributions most tasks might be “read email/slack, respond”, which computer use will know how to operate, but not be able to respond to intricacies of different work situations.
Because computer work often involves using domain expert knowledge to do the right things on the computer.
I haven’t researched robotics enough to know how fast we could produce and deploy 100 million humanoid robots worldwide which seems like an appropriate level of effort required to gather the required data.