By early transformative AIs I mean the first publicly announced AI system which can speed up AI capabilities progress by 30x: if cognitive labor done at major AI labs (including hardware manufacturers) was only done by humans assisted with 2020-level-AI, and resources were rearranged to maximize productivity (within AI labs, allowing new hires but within the same financial constraints), then it would take 300 days for AI labs to produce “stronger AIs” it actually created in 10 days, as judged by me.
By primarily using text, I mean the conjunction of the following:

• Text bottlenecks: there isn’t a path of more than 100k serial operations during the generation of an answer where information doesn’t go through a categorical format where most categories correspond to words or pieces of words, in a way which makes sense to at least some human speakers when read (but it doesn’t have to be faithful). Some additional bits are allowed as long as they fit in a human-understandable color-coding scheme. Only matrix multiplications, convolutions, and similarly “heavy” operations count as serial operations. There are 2 serial operations per classic attention block), and 2 serial operations per classic MLP block, so a forward pass of GPT-3 is around 400 serial operations, but GPT-3 generating 1000 tokens is around 400k operations, so GPT-3 generating 100 tokens would count as having text bottlenecks, but not a 100-layer image diffusion model doing 1000 steps of diffusion.

• Long thoughts: The AI generates its answer using at least 1M serial operations. GPT-3 generating 1000 tokens wouldn’t count as “long thoughts”, but GPT-3 generating 10k tokens (within some scaffold) would count.

I will use speculations about AIs architectures to resolve this question. For example, GPT-4 generating 10k tokens would qualify as primarily using text.

(For reference, if it takes 1ms for a human neuron to process the incoming signal and fire, then the human brain can do 100k serial operations in 1’40’’, and 1M serial operations in 16’40’’.)