he literally pays podcasters to appear on their podcasts
I thought he was 180 and 100% genuine at first, too, but I think he's just a very smooth grifter. Remember, his first startup was literally bankrolled by Sam Bankman-Fried. He also used to work for Anthropic. He's in all of the same EA circles. He's extremely clever, that's for sure.
I'll also point out that Connor does *not* understand LLMs. I just heard him speak on another podcast and he made two completely false statements: First, he claimed that more compute is spent on "reinforcement learning" than anything else. Second, he claimed that LLMs aren't suffering a training data shortage because they're making their own data.
I'll start with the first claim, since it's the easiest to debunk. Reinforcement learning has been around forever; it's just a way to update a model by maximizing the cumulative reward over a sequence of decisions. It's similar to online learning, with the exception that past predictions now actively change the distribution of future data. Connor is probably referring to "reasoning", which has nothing to do with reinforcement learning. "Reasoning" is the chore of breaking a complex, human-supplied task into its individual steps based on chain-of-thought reasoning and external tool calls. It's actually called retrieval augmented generation (RAG), which can be a step in an RL pipeline. Connor is very articulate, so there's no excuse for this fundamental mistake.
Debunking his second claim is also pretty easy, but it requires some work. Model collapse is well-studied (e.g., https://arxiv.org/abs/2305.17493 ) and pretty much guaranteed if you incestually train your models, e.g., use output from gpt-6.1 to train gpt-6.2. It's the machine learning version of mad cow disease. Connor actually debunks the claim himself by admitting that much of the recent gains are because AI companies are literally paying PhD-level researchers to interact with their LLMs. They don't really need to, because they already get most of that data for free because professors and grad students already interact with the models. Additionally, they're throwing even more compute at the models, so the quality continues to increase exponentially. But all exponential growth eventually stops. I think model collapse is perhaps the largest hurdle for AI to overcome.