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
19 published records
01 / belief
“I think there's a lot of inference insights that we can get from that, as well as human psychology insights, kind of a weird blend of the two.”
02 / belief
“Yep, so I was simultaneously running an algorithmic trading company, but I fortunately was able to kind of exit from that, I think just in Q3 last year.”
03 / belief
“I think one problem I have, and this is a broader products question maybe, is that the ELOs apply to the whole user population.”
04 / belief
“Exactly. So I think if you want to talk about what would intelligence look like, it looks much more like tree search.”
05 / evaluation
“The surprising thing, like the most surprising thing, what doesn't work is almost everything doesn't work.”
06 / belief
“Before we go into the inference, some of the deeper stuff, can you give people an overview of like some of the numbers? So I think last I checked, you have like 1.”
07 / uncertainty
“Oh, okay. Yeah, that one I don't know. I'm curious, like, you know, it's kind of like similar content, but different platform.”
08 / belief
“Lots of cool stuff. So really quickly, I think inference is very, very important.”
09 / uncertainty
“I don't know that they do tree search. They never said they do. It's implied. Yes.”
10 / commitment
“And so I just kept creating bots. And so every single night after work, I'd be like, okay, I like, we have AI, we have this platform.”
11 / preference
“I think Elo is a fantastic north star and the reason for it, or like it's the main one we want to see go up because it's this human feedback.”
12 / evaluation
“That's what we're excited to build. And getting too caught up in the tech, I think is a fool's errand.”
13 / evaluation
“I achieved nothing. But with AI, because you're interacting, you feel like you're, it's not like work, but you feel like you're participating and contributing to the thing.”
14 / evaluation
“This is like the Silicon Valley style, um, hyper scale business. And so, yeah, we moved to Silicon Valley and, uh, got some funding and iterated and built the flywheels.”
15 / evaluation
“So, yeah. So finance is a machine learning ecosystem because all of these quant trading firms are running machine learning algorithms, but they're running it on a centralized platform like a marketplace.”
16 / preference
“I think we use at the top, spreading out to different experts and then at the bottom with rejection sampling, choosing from different paths.”
17 / preference
“Exactly, exactly. So I think if you want to make audio work, it has to be a unique, compelling, exciting experience that they can't have anywhere else.”
18 / preference
“It's the thing that gets me excited. It's, it's why I think, you know, I really love working at Chai is because it's a place of talent.”
19 / evaluation
“I think the issue that people, you know, some people may think this is an obvious fact, but running a business can be very competitive, right?”