The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:28:33 · Harrison Chase, Harry Stebbings
[00:28:33] S0: Probably. I mean, seems like they'll get better at everything. [00:28:35] S1: Do you think the speed of model improvement will continue in the same manner of acceleration that it has done, or do you think we are approaching some form of asymptotic plateauing? [00:28:45] S0: I think the speed of model improvement is something that's probably really tough to say without being inside of a lab. I would say I think there are a lot of tasks for which the model improvements will flatline in the sense that, like, it's already good enough. Like, I think there are a bunch of tasks where the models are just good enough. [00:28:59] S1: Which would be like coding? [00:29:00] S0: Yeah. Coding's getting there, like summarization, routing tasks. There are probably more difficult tasks or tasks that we don't even know to think about, know to throw a model at. I would probably say, like, this type of, like, meta learning is one of those tasks that the models aren't good at now. I would imagine is very important to the labs in terms of kind of like cracking RSI or or things like that. And so I do think the models will get better at that over time, and it's possible that that progress actually looks much faster than than previous. Think I it's really tough to have an educated opinion without being inside of a lab. There are some tasks where it's saturated. There are other tasks where I think it will probably just increase faster than before. [00:29:40] S1: What about agents today? Sounds crazy that you think will be incredibly common in two to three years.
00:29:40 · Harry Stebbings, Harrison Chase
[00:29:40] S1: What about agents today? Sounds crazy that you think will be incredibly common in two to three years. [00:29:47] S0: I mean, I think this memory layer for agents is still massively unsolved. I think one thing about agents that is rare today but will be incredibly common in the next few years is is this memory layer. So what I mean by that is if you look at, like, Instinct or Muse, I think one of the things that they've nailed is this kind of, like, personalization and learning and seeming like it's something for you, whether it's because it's speaking in the tone or because it's just following up, like that proactiveness. And so I think memory is super, super early. We've been talking with folks about memory for like two years and trying to do things in the open source. We we launched an alpha and then... And it killed a commercial solution for memory. It's just so early on in memory, and it's so domain specific. What memory looks like for Instinct or Muse is actually pretty different than what it probably looks like for a company or org harness, and it's pretty different than what it looks like for legal or finance or things like that. So I think memory is one thing that really makes a difference. You see this make a different... I think Instinct and Muse have done a great job at this, and so you see how it makes a difference there. But I think it's still very, very early on, and I would expect all products in a few years to have some aspect of memory [00:30:46] S1: in With respect, is that not quite obvious?
00:30:46 · Harry Stebbings, Harrison Chase
[00:30:46] S1: in With respect, is that not quite obvious? [00:30:48] S0: I think the fact that memory will have an impact is quite obvious, and that's why you see so many people interested in it and have... Having it be interested in it for for the past few years. I still think it's non obvious what the answer is. I still think it's incredibly non obvious what the answer is. I don't think anyone knows what the answer for memory is. [00:31:03] S1: Why did you kill the commercial solution for it, given the obviousness of it and the clear need for it in in almost everything? [00:31:11] S0: We killed it because we didn't find product market fit. Why didn't we find product market fit? I think we actually tried to be too general. I think memory so early that I think a lot of the solutions that you wanna build are actually very, like, domain or agent specific. [00:31:23] S1: And so you would build vertically specific memory solutions? What would you do differently?
00:31:23 · Harry Stebbings, Harrison Chase
[00:31:23] S1: And so you would build vertically specific memory solutions? What would you do differently? [00:31:28] S0: So, again, going back to what the hard part of memory is, I think the hard part of memory is that process for learning from facts about what to remember and that agent that that that does that remembering. As I said, I'm I'm massively bullish in kind of, like, vertical agents. I think having those agents be good at learning things specific to their domain is is a key part of of that. And so I do think these kind of like learning agents, at least to start, probably have to be domain specific. And I don't know if there's like a general... Like, what what does the what does the harness for these learning agents look like? It probably looks pretty similar to a lot of the harnesses we have now. It probably looks pretty... We build deep agents. Deep agents is an agent harness. Probably looks similar to that, and, oh, I'm running the loop. But it's got specific instructions and specific tools, and maybe there's some specific sub agents that are really good at navigating existing memory structures or or distilling kind of like facts. And so I think you'll have you'll have agents for memory things. I just think they'll be domain specific. [00:32:20] S1: Do you think you will have vertical agents for consumer applications? I understand, and I agree with you when you look at finance, accounting, sales, marketing, legal. You mentioned muse and instinct. They would be horizontal agents. Do you think we'll have vertical consumer agents?
00:32:20 · Harry Stebbings, Harrison Chase
[00:32:20] S1: Do you think you will have vertical agents for consumer applications? I understand, and I agree with you when you look at finance, accounting, sales, marketing, legal. You mentioned muse and instinct. They would be horizontal agents. Do you think we'll have vertical consumer agents? [00:32:35] S0: I think my take on the consumer space is that you'll both. You'll have both horizontal and vertical agents, and the horizontal agents will interact with the vertical agents. [00:32:43] S1: Where does it make sense to have a vertical agent versus not?
00:32:43 · Harry Stebbings, Harrison Chase
[00:32:43] S1: Where does it make sense to have a vertical agent versus not? [00:32:46] S0: I think for most apps, it probably makes sense to have a vertical agent. And I think the reason that having vertical agents makes sense at all is is probably twofold, maybe more, but two that jump to mind. One, a lot of these apps have knowledge about you and can learn things about how you interact with that app better than a general horizontal agent could. So back to the point about domain specificity, how how you interact with music for, say, like Spotify is very different than how you might interact with Strava. And so those apps are actually probably best at learning things about you. And I think the best way to expose those learnings is probably through an agent. The other thing is a lot of these apps have a ton of APIs. And so you could say, hey, Spotify, don't build an agent. Just expose a bunch of APIs. I actually think it's better for... It's a better experience if there is an agent that kind of like wraps those APIs and exposes a single or maybe multiple, but like probably a single, like, natural language interface for a horizontal agent to interact with that vertical agent.