Link to https://www.cold-takes.com/ai-could-defeat-all-of-us-combined/ at the end
There are debates about how discontinuous an intelligence explosion would be, with Paul Christiano expecting to see the world being transformed by less and less weak AGIs over some number of years, while Eliezer Yudkowsky expects a rapid jump in capabilities once generality is achieved and the self-improvement process is able to sustain itself.
AI subsystems or regions in gradient descent space that more closely approximate utility maximizers are more stable, and more capable, than those that are less like utility maximizers. Having more agency is a convergent instrument goal and a stable attractor which the random walk of updates and experiences will eventually stumble into.
The stability is because utility maximizer-like systems which have control over their development would lose utility if they allowed themselves to develop into non-utility maximizers, so they tend to use their available optimization power to avoid that change (a special case of goal stability). The capability is because non-utility maximizers are exploitable, and because agency is a general trick which applies to many domains, so might well arise naturally when training on some tasks.
Humans and systems made of humans (e.g. organizations, governments) generally have neither the introspective ability nor self-modification tools needed to become reflectively stable, but we can reasonably predict that in the long run highly capable systems will have these properties. They can then fix in and optimize for their values.
GPT-3 showed that transformers are capable of a vast array of natural language tasks, codex/copilot extended this into programming. One demonstrations of GPT-3 is Simulated Elon Musk lives in a simulation. Important to note that there are several much better language models, but they are not publicly available.
MuZero, which learned Go, Chess, and many Atari games without any directly coded info about those environments. The graphic there explains it, this seems crucial for being able to do RL in novel environments. We have systems which we can drop into a wide variety of games and they just learn how to play. The same algorithm was used in Tesla's self-driving cars to do complex route finding. These things are general.
Generally capable agents emerge from open-ended play - Diverse procedurally generated environments provide vast amounts of training data for AIs to learn generally applicable skills. Creating Multimodal Interactive Agents with Imitation and Self-Supervised Learning shows how these kind of systems can be trained to follow instructions in natural language.
GATO shows you can distill 600+ individually trained tasks into one network, so we're not limited by the tasks being fragmented.