Leopold Aschenbrenner

"It is strikingly plausible that by 2027, models will be able to do the work of an AI researcher/engineer That doesn’t require believing in sci-fi; it just requires believing in straight lines on a graph"

"Imagine if when asked to solve a hard math problem, you had to instantly answer with the very first thing that came to mind,” he writes. “It seems obvious that you would have a hard time, except for the simplest problems. But until recently, that’s how we had LLMs solve math problems. Instead, most of us work through the problem step-by-step on a scratchpad, and are able to solve much more difficult problems that way. “Chain-of-thought” prompting unlocked that for LLMs. Despite excellent raw capabilities, they were much worse at math than they could be because they were hobbled in an obvious way, and it took a small algorithmic tweak to unlock much greater capabilities."

"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 any relevant context, hasn’t read the company docs or Slack history or had conversations with members of the team, or spent any time understanding the company-internal codebase. A smart new hire isn’t that useful 5 minutes after arriving—but they are quite useful a month in! It seems like it should be possible, for example via very-long-context, to ‘onboard’ models like we would a new human coworker. This alone would be a huge unlock."

"Look. The models, they just want to learn. You have to understand this. The models, they just want to learn…"