Latent Space / episode intelligence
Speakers in the public record
Claim mix
Evidence policy
Every row below preserves an exact excerpt. Identified speakers are linked; unresolved voices are labeled and excluded from people counts.
Claim ledger
16 published records
01 / belief
“I think Sun is also too polite to point out that, both like Google’s genie, demos as well as world Labs is marble, do not have interactive worlds.”
02 / uncertainty
“Like, which it’s hard to tell because you put out the end results, but we don’t know the inputs that go into it.”
03 / belief
“I think for your standards it is, for me it feels like vision is, is. I’ll leave that to the big labs graphics.”
04 / belief
“I dunno if Chris, you wanna say it, but I think, I think generally I feel like physics is better with symbol P priors.”
05 / belief
“I honestly, do think that if people deeply understand everything we just covered, they will see what’s coming. I think you guys have, made some, a really significant contribution here.”
06 / belief
“There, there’s, there’s a versions of that that are done. But I, I think we are aligning audio, text, language and video.”
07 / commitment
“On the model side we are actually striving towards basically a self-improving system. But what that means is that we need people to set up the self-improving system.”
08 / prediction
“We’re not saying that it’s, it’s like, not a great world simulator, but actually in the blog that we, we, we, we wrote, the bet is more so that there are gonna be disproportionately large share of value of real world tasks or, and virtual tasks where high resolution pixel fidelity is not needed.”
09 / observation
“When people are choosing to use, GPT five or Gemini or clawed, individuals are trying out these different models and deciding, oh, I like the kind of answers that GT five gives me, or no, I feel like I get more accurate detail from Claude, right?”
10 / preference
“You could have just said no audio And audio in my mind has a lot of recursion, whereas in video you can just do recasting and that’s much computationally much simpler.”
11 / evaluation
“Like I think like maybe what you’re trying to get at is like, okay, people are saying pixel prior, everything. But what we’re saying is, okay, there’s a boundary that we draw where this is where we think provides the most economical value for the domains and things that we care about today.”
12 / preference
“I will also say, one, my favorite story, books or biographies ever is, creativity Inc.”
13 / commitment
“Apples, I’m gonna, my weapon of choice, my bullet’s gonna turn into apples. And that’s, that’s possible because we can say, we can basically dynamically have certain game state trigger the, the preconditions to the render such that the rendering is now part of the game loop too.”
14 / preference
“Because it’s like we care about creativity and using that as a funnel to solve a GI.”
15 / prediction
“Other folks to manually curate these types of data. It was very clear to us that, okay, on our way to, let’s call it embody general intelligence models need to learn the consequences behind their actions, which means that they need interactive data and the demand for those types of data are growing exponentially.”
16 / evaluation
“That you guys do. But, I, I could see, I could see that I think the, the human intent is something that people are not even used to because we’re so used to static worlds or, worlds that just don’t react, or, I don’t know.”