Venture with Grace
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.
00:20:10 · Grace Gong, Jerry Tworek
[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. [00:20:47] S2: I do believe we are heading towards what I call post labor society. [00:20:52] S1: Okay. There [00:20:52] S2: is a future where we don't necessarily So the wolves [00:20:55] S1: be all in, like, [00:20:56] S2: Yeah. The worst For example, I sometimes imagine life like like high school, where high school is a moment in life where no one has to really work. You don't have to generate value. You mostly spend your time learning and hanging out with friends. And if I if I imagine, like, how could a life look like if I was completely free of any obligations, [00:21:16] S1: You have I would much better high school experience than me. I was doing homework. [00:21:22] S2: You can still do homework if you would like that. Okay. Was definitely somewhat of an anti homework person. [00:21:30] S1: You're an anti homework person? [00:21:32] S2: I I didn't like homework that much. [00:21:34] S1: Yeah. You look like you will finish your homework within two minutes, and then people will copy your homework.
00:21:34 · Grace Gong, Jerry Tworek
[00:21:34] S1: Yeah. You look like you will finish your homework within two minutes, and then people will copy your homework. [00:21:38] S2: I would... Would usually copy it in the morning, but but... I I didn't like spending my my time at home on it. [00:21:46] S1: So what do people... Like, so what... Like, what kind of job will be replaced the fastest? [00:21:51] S2: It's hard to say, really. I think I think it will more be, like, various types of job will be replaced gradually. When we look at the job market today, was the job of programmers really replaced? I don't think so, but it was already radically transformed. Every single engineer can write code much faster and much differently than before. There are whole groups of people that stopped reading code completely, almost in many ways. So the profession has changed. The dynamics of it has changed. And I think that also affects the job market. But I don't think we can say anything got replaced. I think the replacement will happen, if anything, all at the same time, rather than one job faster than the other. Once you have partial automation, you still usually need a human in the job and you still need to be able to have this level of oversight and the level of context that only humans can. Once we achieve technology that can do the work for you, I think it will happen everywhere at once. And I think that's kind of like, in a way, slightly better for the society. Because we are not in this weird world where all, like, one job got automated, the other one didn't. And how do we really, like, navigate that?
00:21:51 · Jerry Tworek, Grace Gong
[00:21:51] S2: It's hard to say, really. I think I think it will more be, like, various types of job will be replaced gradually. When we look at the job market today, was the job of programmers really replaced? I don't think so, but it was already radically transformed. Every single engineer can write code much faster and much differently than before. There are whole groups of people that stopped reading code completely, almost in many ways. So the profession has changed. The dynamics of it has changed. And I think that also affects the job market. But I don't think we can say anything got replaced. I think the replacement will happen, if anything, all at the same time, rather than one job faster than the other. Once you have partial automation, you still usually need a human in the job and you still need to be able to have this level of oversight and the level of context that only humans can. Once we achieve technology that can do the work for you, I think it will happen everywhere at once. And I think that's kind of like, in a way, slightly better for the society. Because we are not in this weird world where all, like, one job got automated, the other one didn't. And how do we really, like, navigate that? [00:23:10] S1: All the jobs will be automated at the same time. Basically, that's kind of the conclusion. Not automated, but somewhat.
00:23:10 · Grace Gong, Jerry Tworek
[00:23:10] S1: All the jobs will be automated at the same time. Basically, that's kind of the conclusion. Not automated, but somewhat. [00:23:15] S2: Yeah, the physical aspect. That's one kind of having the big divide is like jobs that can be done at the computer and jobs where you actually need your muscles and move stuff around. That's probably one big divide I am imagining. And everyone who works with hardware knows robots will take a while longer for it, but at the same time, there is so much investment, so much work done in this space that a lot will happen eventually. [00:23:41] S1: When guess, when you're thinking about the business side of things, do you find so nowadays, I feel like whenever I chat with a founder, typically people's opinion are like, you have to chat with the customer first, hearing about what the customer are like. And who are your customers? Are they, I guess, enterprise or are they other researchers? And how do you think about I guess, creating, generating revenue side of things? Because obviously, you guys are lab, and it probably takes long time to actually generate revenue. Yeah. What's your kind of thought process on building the company? What are the momentum that you're chasing to maybe raise the next round or do stuff?