The Twenty Minute VC (20VC): Venture Capital | Startup Funding | The Pitch
00:07:15 · Harrison Chase
[00:07:15] S0: There's no easy answer. On one hand, what are you going to do? Not build. Like, that's what I like to say to founders who ask this. Like, what do I do? Like, stuff is changing. You know, the models are changing. If I build something now, it might not be relevant in six months. What's the alternative? You just don't build anything. And so I think you have to make kind of like educated guesses as to what parts are going to be around and what parts aren't going to be around in the future. So for example, early on, the way that we would do tool calling in Linkchain was by having the model generate JSON, DSL, and then parsing it. And that was something that I think we all thought or knew was like a little bit of a hack at at at the time, but we had to do it because that was the only way to get it to to actually steer things because you have to get it to do this type of tool calling. And then a few months later, OpenAI came out with tool calling. And I think we recognized pretty early... And this is like a more native version of tool calling that's directly in in in in the model and in the APIs. And I think we recognized pretty early on that that was the future, and so we invested a lot of efforts and just completely switched from JSON parsing to this tool calling. And so I think you have to basically recognize what things are short term hacks and what things are long term kind of like will always be there. The models will never know your proprietary data. They will never be able to kind of like actually get the output of APIs. You have to take their, like, API call, pass it to the API, get the output, and then continue in the loop. And so I think the system around the model connecting it to other places will always be there. But a lot the hacks we do around chain of thought or planning or tool calling, things that can just be much more closely integrated into the model or into the model API will go away, and I think you just have to be quick to recognize that and go on.
00:08:48 · Harry Stebbings
[00:08:48] S1: Why do we need neutral harnesses? We see, you know, bluntly, very brilliant agent builders from the models themselves. Why does your layer not just get eaten by them?
00:08:58 · Harrison Chase
[00:08:58] S0: I think the reason we need neutral harnesses is that there is a lot of state that you build up either before you build the agent or as part of the agent running. And the state can include things like memory, whether that's short term memory or long term memory. Memory also comes in different form factors. It can be from interacting with the agent versus from the agent learning from the environment. And the reason that this state gets built up is the agent interacts. It goes out. It learns. It chats, and and it learns things to make it better. I think I think one of the most interesting things right now is how to make these agents do better over time and learn. And some of that learning and some of the doing better over time will be in the model weights, but a lot of it is outside the model, and a lot of it is in this in this context. When you build up this context, you really don't want it to be linked to a single kind of, like, model provider. The reason for that is that then it becomes linked there. You can't go to other models. Being able to change models is very offensive and defensive. So it's offensive in the fact that you wanna be able to take advantage of the best model when it comes out no matter who it's coming from. It's defensive in the fact that you don't wanna get locked in, so can't raise rates, things like that. So you wanna be able to switch models. The harness can be very tied to the model. It can also be neutral. The context right now is really intricately linked to the harness. So the harness's job is to basically take the model and interact with all the other things. All the other things includes data and APIs, but also includes memory. And so if you build this memory in a way that is linked to the harness and the harness is linked to the model, then you're kind of stuck.
00:10:23 · Harry Stebbings
[00:10:23] S1: So the true value of the harness structure is the portability of data that it allows the user?
00:10:29 · Harrison Chase
[00:10:29] S0: Yeah. You wanna basically not be locked in to models, and if memory, if context starts building up in a way that is specific to how one model wants to interact with memory or wants to interact with context, that's very bad for portability, and then that puts you at a disadvantage building your your business going forward. We talk with a lot of people about owning their intelligence and what that means, and there's a lot of different versions of that. The version that that that we think, like, owning your intelligence means, we we think there's a few different components of that. But one key component is having kind of, like, this open and controllable agent. Like, you wanna be able to adjust all the different pieces. You wanna be able to adjust the model. You wanna be able to adjust the harness as well because sometimes these model labs are training harnesses to be really good at the things that are in distribution for them, which oftentimes is coding, and coding is great, and there's a ton of use cases around coding, but there are a ton of use cases that aren't coding. And maybe they'll write code as part of it, but they're using that as as a way to do sales, as a way to do research, as a way to do these... The law, as a way to do these other vertical specific things. And so when you get out of distribution of what the the model labs are training on, you wanna have control over, like, what the harness does. And then you also want control over the context as well. So model, the harness itself, and the context make up kind of like this open and controllable agent architecture, and we think that's a big part of what it means to own one intelligence.
00:11:42 · Harry Stebbings, Harrison Chase
[00:11:42] S1: I just wanna back up a little bit. When we think about, like, agent value, so to speak, some people say that the value is actually in the verification, like whether it did well or not, because that unlocks the next step, which [00:11:55] S0: is [00:11:55] S1: automation. If value is derived through verification, who wins? The harness?