World Models & General Intuition: Khosla's largest bet since LLMs & OpenAI
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What makes Medal’s 3.8 B game‑clip dataset a goldmine for training world models?
Hi listeners. As you may know, I recently wrapped up the AIE Code Conference in New York, and while I'm traveling, I do like to visit top AI startups in person to bring you interviews that you don't find on any other podcasts that just does a Zoom call. General Intuition, or GI for short, is a spin-out of a 10-year-old game clipping company called Metal, which has 12 million users, but in comparison, Twitch only has 7 million monthly active streamers. Metal collects this data by building the best retro. Clipping software in the world. In other words, you don't need to be consciously recording. You actually just have metal on in the background while you're playing, and you hit a button to clip the last 30 seconds after something interesting happens.
It's very similar to how Tesla and self-driving does bug reporting if you ever ever done a self-driving bug report in Tesla's. The result is that Metal has accumulated 3.8 billion clips of the best moments and actions in games, resulting in one of the most unique. Unique and diverse datasets of peak human behavior, actively mining for the interesting moments. They were also very prescient in navigating privacy and data collection concerns by mapping actions to these visual inputs and game outcomes. As you saw on our Feifei Li and Justin Johnson episode with World Labs, and with the recent departure of Yan Lakoon from Meta, there's a lot of interest in world models as the next frontier after LLMs to improve on spatial intelligence.
And to work on embodied robotics use cases. DeepMind has been working on this with Genie12 and 3 and SEMA 1 and 2. And this year, Oaken AI seem to finally agree because they have been petting on LLMs a lot, and they made the news by offering $500 million for Metal's video game clip data. Our guest today, Pim, turned down that money and instead chose to build an independent world model lab instead. Coastal Ventures led the 134 million. Dollar seed round, which is Vinod Kosla's largest single seed bed since OpenAI. We were able to get an exclusive preview of GI's models, which unfortunately we cannot show you directly, but I can confirm they were incredibly human-like and we chose to include the first 11 minutes of the demo discussion, even though I couldn't show it to you.
It may be hard to follow, but I tried to call out what was noteworthy for you to know as your likely reaction if you were watching along with us. Now enjoyed the world's first look at my first look at Genu Intuition.
So what I'm about to show you is a completely vision-based agent that's just seeing pixels and predicting actions the exact same way a human would. Um and so yeah, what I'll show you here is what this looks like uh four months ago. So this was uh so again, this is just an agent that's seeing that's receiving frames um and it's just predicting action. So you can see it has like a decent sense of um uh of of being able to you know navigate uh around. Um it Tab the scoreboard, just like gamers always tab the scoreboard. So these are purely these are pure imitation learning.
So the LC is slicing the knife.
Yeah, exactly. So it's doing everything that like humans would. In this case, here's here was the first interesting part that we saw. Like it gets stuck and then it has they have memory as well. So you see it can get unstuck. Um how long is the memory? Uh four seconds. Yeah, uh four seconds from straight and cut. Okay, so this was four months ago. This was maybe a few weeks after that. So you can you can see there's like it's still doing the scoreboard thing, but it's there's still there's still uh uh quite like the you you and these are bots too, so you can see that. It's very human, let's just say that. Yeah. Uh and then um Uh right, so this was really like the early days of research where you can see right it does one thing and then goes for another.
Um and then we've been scaling right um uh on on data and compute and also we've just been making the models better. Um and this is where we are now. So what you're seeing is
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Chapters
8 chapters
1
What makes Medal’s 3.8 B game‑clip dataset a goldmine for training world models?
0:00–7:45
2
How do fully vision‑based agents learn to play games just by seeing frames and predicting actions?
7:45–15:00
3
How are the game‑clip models transferred from arcade games to realistic games and then to real‑world video?
15:00–23:25
4
What’s the difference between world‑model video generation and traditional video prediction?
23:25–31:38
5
Why did Khosla Ventures back General Intuition with a $134 M seed—the largest since OpenAI?
31:38–39:36
6
How does General Intuition’s API replace brittle behavior trees for games, engines, and robots?
39:36–48:14
7
How does the team move from imitation learning to reinforcement learning using episodic memory clips?
48:14–55:40
8
What’s the 2030 vision for spatial‑temporal foundation models and their impact on atoms‑to‑atoms interactions?
55:40–1:04:06
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