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
Are these constraints on doing harm actually standard among non-consequentialists? I suspect they would go primarily for constraints on ex ante/foreseeable effects per person (already or in response to the paralysis argument), so that
The thresholds could also be soft and depend on benefits or act as a penalty to a consequentialist calculus, if you want to allow for much more significant benefits to outweigh lesser harms.
It might get tricky with possible future people, or maybe the constraints only really apply in a person-affecting way. Building off 3 above, you could sum expected harms (already including probabilities of existence which can vary between acts, or taking the difference of conditional expectations and weighting) across all actual and possible people, and use a threshold constraint that depends on the expected number of actual people. Where u represents the individual utilities in the world in which you choose a given action and v represents the utilities for "doing nothing",
This could handle things like contributing too much to climate change (many possible people are ex ante worse off according to transworld identity) and preventing bad lives. With counterparts, extending transworld identity, you might be able to handle the nonidentity problem, too.
Some constraints might also be only on intentional or reckless/negligent acts, although we would be owed a precise definition for reckless/negligent.
One's modus ponens is someone else's modus tollens.
Michael Huemer wrote something very similar In Praise of Passivity ten years ago, but he bit the deontologist bullet: so (unless you are acting inside the space defined by explicit rights and duties) if you are uncertain of the outcomes of your action, you are doing wrong.