Unsupervised Learning with Jacob Effron
00:08:39 · Diogo
[00:08:39] S1: I would say it's almost none real world feedback. I think people should be complaining to us so much more. I think that, like, this... The field is still in a Stockholm syndrome of, like, oh, yeah. The model providers just do stuff in their one track path, and we happen to be, like, the victim is the wrong word. But, like, you know, like, in, what's that movie? Wall e. You know, like, they're, like... They're just like the people in the chairs that are just getting fed the AI. And I think this is, like, the state of things right now where, like, people plan around AI changing, but they don't have a sense of agency of where to take the field. And I think that this is not the way it should go. That's actually really disempowering and kind of lame in my opinion. And, also, I think that there's so much more we could do collaboratively as a field. And I think that the field could, like, push us a lot more to say, like, hey. We want to be doing these things. We think it is within the realm of capabilities. Let's figure out how to do it together. But not just putting stuff into the model, but figure out how to prompt them, figure out how to, unearth capabilities. Let's work together for these sci fi use cases because I think that is the way the future is gonna go. And I would want so much more of that. My my joke internally is that the number one way to get me to respond to something is to talk about model jaggedness because you have, like, very high up people say, like, hey. Can we meet with Diogo? And I'm just like, no. Can you meet with my CTO or GTM head instead? But if they complain about model intelligence, I am, like, on that and, like, debugging that myself for better or for worse because that, I think, is the thing that will really move the needle for us long term. So right now, we mostly have to go with our old road map because we we are not... I I think people are still in that mode of, like, yes. Let's just do whatever the model providers let us do instead of, like, let's collaborate on this, like, insane future together. And, actually, there's another aspect of that. I think people have a deep distrust of model providers too because they train on their data. And, you know, like, this whole, like, discussion of, like, can GPT wrappers even exist, I think, is very disempowering too because the whole application ends up becoming a chatbot.
00:10:45 · Diogo, Jacob Effron
So the moat ends up becoming the equivalent of a system message that you send every time versus, like, the moat and the intelligence and the behavior and the specialization actually existing in software where we just make little micro decisions for you, and you do all of the sick logic. Yeah. So it... There's so much to do. There's so much to do. [00:11:04] S0: No. I think your point's really interesting because I feel like when Jev came out, there became maybe, like, a common set of patterns or rules that folks were like, oh, yeah. Like, this is a really good use case for for Jev, you should, you know, try it out in this thing or or or that thing. And everyone tried it out and, you know, kind of got that sense of patterns. You know, I almost feel like people are trying to get their own mental models of like, okay. I use, like, you know, Opus five five for, like, this kind of thing, and I use Jev for, like, this kind of thing. And I feel like your point's well... You know, that your point is like this is a dynamic evolving thing that you could... There's not strict boundaries, I guess, between those two types of model categories. But, like, what's your
00:11:04 · Jacob Effron, Diogo
[00:11:04] S0: No. I think your point's really interesting because I feel like when Jev came out, there became maybe, like, a common set of patterns or rules that folks were like, oh, yeah. Like, this is a really good use case for for Jev, you should, you know, try it out in this thing or or or that thing. And everyone tried it out and, you know, kind of got that sense of patterns. You know, I almost feel like people are trying to get their own mental models of like, okay. I use, like, you know, Opus five five for, like, this kind of thing, and I use Jev for, like, this kind of thing. And I feel like your point's well... You know, that your point is like this is a dynamic evolving thing that you could... There's not strict boundaries, I guess, between those two types of model categories. But, like, what's your [00:11:39] S1: Ish. Ish, I I actually think that what you're describe... Describing is a little bit sad to me, and I, you know, I understand that, like, we are playing a role in this. I do not think that that is the most developer centric worldview. Like, for people to start building, like, theories of mind of the submodels they call and have their systems locked into that is, in my opinion, a really, really bad thing. I think the North star for intelligence is gonna be a lot like SQL. Like, we wanna be so reliable that it's actually provider agnostic. We obviously want the best models, but, like, we don't want it to be locked into, like, things that only work with Jev.
00:11:39 · Diogo, Jacob Effron
[00:11:39] S1: Ish. Ish, I I actually think that what you're describe... Describing is a little bit sad to me, and I, you know, I understand that, like, we are playing a role in this. I do not think that that is the most developer centric worldview. Like, for people to start building, like, theories of mind of the submodels they call and have their systems locked into that is, in my opinion, a really, really bad thing. I think the North star for intelligence is gonna be a lot like SQL. Like, we wanna be so reliable that it's actually provider agnostic. We obviously want the best models, but, like, we don't want it to be locked into, like, things that only work with Jev. [00:12:14] S0: So what is your, you know, mental model for how this all evolves in terms of, you know, this, like, thing people are doing today of, like, you know, hey. You know, Jev works really well for these things. Maybe, like, there's other sets of models that have been not... Like, to your point, like, RLVR works really well for code and math, other... You know, we'll see what else it it ends up working really well for. Like, does... Well, I I have to interrogate that gesture. What what what was that about? [00:12:36] S1: No. You wanna you wanna interrogate it right now? [00:12:39] S0: Yeah.
00:12:39 · Jacob Effron, Diogo
[00:12:39] S0: Yeah. [00:12:39] S1: Yeah. Well, really well is quite relative. I... As far as I can tell, even the best companies still require humans in the loop for all of this stuff despite superhuman results in some axes. And that still... Like like, like, people should have an answer to why that is the case. Like, if they are so unbelievably good at software, like, why do normal software engineers have anything to do? Not that I want them automated, but, like, they clearly aren't, and there's clearly something missing there. And I think thinking of, like, intelligence as this single axis that is measured by whatever the benchmark of the day is is, like, totally wrong. And there's, like, obviously nuance missing there because the people who go all in on not understanding that nuance get burned super hard. So I think that RLVR is incredible, but it is incredible at human in the loop stuff still. And I think that wisdom comes from understanding reality where it's at, knowing its weaknesses, and I... This is we are obviously use agents a lot, but we really value understanding its limitations. You know, I just wouldn't say that it's like, oh, super great, and it just solves everything, personally. May... Maybe I'm using the wrong models.
00:13:48 · Jacob Effron
[00:13:48] S0: I think a lot of our listeners are trying to figure out, you know, how... Like, a simple mental model maybe of of Jev, which I know you disagree with is is like, you know, hey. There's some set of tasks or some set of things I'm trying to do that will make sense for this. There'll be some set of things that make sense for for other things. And I'm sure 95% of our listeners right now are trying to just, adjust their own mental model of, like, okay. What am I, like, Jev use cases? When do I use Jev?