I. From GPT-4 to AGI: Counting the OOMs

Trust the trendline.

From chatbot to agent-coworker

What could ambitious unhobbling over the coming years look like? The way I think about it, there are three key ingredients:

1. Solving the “onboarding problem”

GPT-4 has the raw smarts to do a decent chunk of many people’s jobs, but it’s sort of like a smart new hire that just showed up 5 minutes ago: it doesn’t have a... See more

There is a potentially important source of variance for all of this: we’re running out of internet data. That could mean that, very soon, the naive approach to pretraining larger language models on more scraped data could start hitting serious bottlenecks.

We have machines now that we can basically talk to like humans. It’s a remarkable testament to the human capacity to adjust that this seems normal, that we’ve become inured to the pace of progress.

• What a modern LLM does during training is, essentially, very very quickly skim the textbook, the words just flying by , not spending much brain power on it.


• Rather, when you or I read that math textbook, we read a couple pages slowly; then have an internal monologue about the material in our heads and talk about it with a few study-buddies; read an

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