Venture with Grace
00:12:55 · Jerry Tworek, Grace Gong
[00:12:55] S2: But most importantly, it's not something you really want humans to be spending a lot of time on. [00:13:04] S1: What about, like, the business model? So, like, I feel like, you know, OpenAI and... I guess, like, there's two things. One is, like, one is, like, the strategy of the company, and then the other part is, like Yep. The business model side of things. I feel like nowadays, like... Oh god. So sorry. Apparently, whenever we're recording, people start making coffee. It's very important for people. [00:13:27] S2: A lot of people care about coffee. [00:13:30] S1: I guess, like, when you're thinking about the business model or, like, you know, the past, like the OpenAI Anthropic Solutions. So I guess OpenAI started with consumer driven product. I mean, from the news, it seems like it was not supposed to be as famous or as popular as it It wasn't flawed. But it became a very popular consumer product. And then on the other hand, Anthropic went to building a very strong work tool. And I'm just curious, from the business perspective, like, how do you think about positioning yourself, like, in the business arena? And then since you guys are... I guess, like, you guys are... I was, like, chatting with a lot of executives from, like, different AI companies. People were saying this is the last batch of foundational model getting funding. What's your opinion on that? Is there more AI labs gonna come around and why or why not?
00:14:29 · Jerry Tworek, Grace Gong
[00:14:29] S2: Yeah. I I do think we may be one of the last batch of companies getting founded mostly because of the runaway effects of AI and models being so good that it will be hard for for the startups to be able catch up and enter the ring. I do think that a lot of reasons why startups exist and can win is because larger companies are inefficient and larger companies are moving too slow and adapt in a place where a nimble player can in a rapidly changing landscape. What AI will do in the next few years is that the big companies will be able to move very quickly because they will not have to have all the political issues around them. They will they will just be able to move AI to direct resources. So I I do think that will I [00:15:23] S1: think we can keep going. So sorry. [00:15:26] S2: Yeah. I do. [00:15:27] S1: Whenever... Yeah. Like, this... Okay. So we're talking about, like, there... It needs a lot of resources for... Keep going. Sorry.
00:15:27 · Grace Gong, Jerry Tworek
[00:15:27] S1: Whenever... Yeah. Like, this... Okay. So we're talking about, like, there... It needs a lot of resources for... Keep going. Sorry. [00:15:39] S2: Yeah. So so the the story was, I think, the AI driven efficiency will likely make big companies be able to, like, compete much, much faster. And in some way, if there is a future without work and without labor, it's hard to imagine how starting up companies exactly will look like. There's probably some amount of risk taking that may take place. But I think it will be different. It's hard exactly to predict how the future will look like there. But yeah, what are largely focusing on for our models and what we are building is models that do work for you. Core Automation is not interested in building your friends, not your therapist. It's like the model that is meant to... You tell it what you want it to do and gets it done for you, generating value on the tasks that you care about and all the work you want to be done. And we don't even want to build the models that have that ability off the shelf that know everything you want. You teach them. You're like onboarding an employee. You take that model and show them, this is how my house looks like. This is how my computer looks like. This is how my data looks like. These are the things I want you to do. I will show you a few times, and then I expect you to be doing them for me. [00:16:53] S1: Would you say Core Automation is gonna become like an agent? That's like an agent replacement.
00:16:53 · Grace Gong, Jerry Tworek
[00:16:53] S1: Would you say Core Automation is gonna become like an agent? That's like an agent replacement. [00:16:58] S2: It is meant to be an agent, but it's... I think it will be a slightly different experience with an agent. I think it will be a bit more personal if the agent meaningfully adapts to you over time. [00:17:07] S1: Do you like my email that I'm sent by Instinct? No? Was it personal enough or no? [00:17:15] S2: I haven't paid attention. [00:17:17] S1: Okay. So I'm just curious, like, what is the difference between the current agent landscape of... Nowadays, everyone has five agents, whether it's RockBat or name your favorite agent. What's the core difference between them? And then Core Automation.
00:17:34 · Jerry Tworek
[00:17:34] S2: Yeah. I think one thing, how all of the personalization of the current agents happens in context. So, we usually have a system message, which you can specify a little bit. And then, you can put in some memory files that the model sometimes can use. And this is great. This is how today's systems work. But one thing is that agents don't relearn any new skills in any meaningful way. Like, if there is something that the agent has a hard time doing now, it will not improve itself until Anthropic or OpenAI releases a new model for you or for the sake of completeness. And it has a hard time getting things that are not very easy to verbalize. Everyone who has ever prompted a model knows and understands that not everything is very easy to describe. The system messages, you can try to steer them all in a particular way, but it's much more often frustrating than not. Sometimes you think, oh, maybe I could show them all. Maybe I will tell them, hey, a little bit less of this and a little bit more of that. Such an update should be happening. And again, in model that learns from you, it will just happen. The model will work with you and then within the next day, we'll get what you need in your context slightly more and slightly better and slightly understand what you are, what you are asking, even if you are vague and incomplete, better than the current models. Like, they just... They have their limit ability to update themselves.
00:19:04 · Grace Gong, Jerry Tworek
[00:19:04] S1: So when the model is updating right now, it's based off, like, the data feeds into it. And nowadays, like, you know, each company is feeding their own data into the model. So I guess, what is the format gonna be significantly changed in the core automation agent era? And is there any particular use case that you're imagining this gonna create significant differences. The [00:19:32] S2: thing what I think about the most is thinking about how companies operate. How do we take companies to a place where humans come in and do their work, maybe with a little bit of help of agents here and there? But you are hearing more and more how a lot of companies are having even a hard time finding the places where the agents are meaningfully useful and how to turn those into productivity, into places that are largely driven by AI work and agentic workforce, and places where we kind of think of it less as a place of labor and more like places of resource allocation. [00:20:10] S1: I guess when it comes to charging, so nowadays, people are doing usage based charging things. And then like, some people are kind of like replacing the human of like different workforce. Do you feel like that aspect will change? And for for people like, you know, listening to our show, or like, you know, for a lot of our LinkedIn audience, sometimes people are looking for jobs in the AI industry. What does that meant for them? Are people going to be doing something significantly different? Or obviously, could be cooking Yeah, or exactly.