Name: The Future Makers
Uploaded: 2025-12-10
Duration: 19 min 6 s

A generation of Gen Z founders is redefining how intelligence shows up in the world. They’re advancing the science behind how machines perceive reality while reimagining how humans connect, create, and share meaning. By blending research, storytelling, and design, these emerging leaders are shaping how artificial intelligence moves through everyday life. As AI pierces the cultural zeitgeist, they share how they’re helping to shape the future of technology, creativity, and human connection.

The Future Makers

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Welcome to Fortune, Tiara. Thank you so much for having me. I am so excited for this conversation. When we were putting this agenda together, someone on the team suggested that we should have the Gen Z perspective, and I was like, yes, I want to be the one to moderate this conversation. So, um, I am a big believer in including youth voices in, um, shaping the future of AI, and you are right there making it happen. So I I'm guessing a lot of the audience are not familiar with who you are, so I would love to give you an opportunity to share a bit of your story. But you are so impressive, you actually, when I was prepping for this, you all, I was like, oh my God, I feel like a loser. You've been named to the Time's most influential teens, Forbes 30 under 30. You're a Peter Thiel fellow, a YC Combinator, uh YC alum, Y Combinator alum, and you're the founder, co-founder and CTO of Chima. So just give us a little bit about your story and how that all culminated into you starting Chima. So I did computer science at Stanford and before that I actually have always been very passionate about research and algorithmic development. So pre um college, I won the Google Grand Prize for seminal research in AI and applying that to climate change. You, you were like 16. I was 1617, OK. And I, I've always been really fascinated about the advancements that we can make with AI models, but also applying them to real world use cases, very much with what we're seeing right now with large language models and generative AI being applied to real world use cases. So I've always been really passionate about this application layer from sort of the model layer to real world use cases and Stanford, a lot of my time was spent at the human-centered AI labs where we actually saw this rich amount of data being applied in different use cases and that really was some of the founding stories of how we started and we operate as an applied AI lab and we are backed by YC, General Catalyst, incredible angel investors, and really Focus on what we call Sam Altman as an investor. So, so what we really do with an incredible number of supporters is, um, focus on how we take these incredible models from different, uh, sort of AI labs but really unlock it in application for different enterprises. Yeah, I would love for us to just spend a few moments, um, double clicking on that. What, who's your target customer and Bring this all to life. Give us an example of what you would do for one of your customers. And so it's really interesting because what we call our target customer can range from medium to a large scale enterprise because what we really see is that as AI models right now from let's say Opus 4.5 to a GPT 5.1 get really smarter, the amount of use cases they unlock exponential. increase, but even if the models stopped increasing in efficiency and improving in the benchmarks right now, the amount of economic unlock and efficiency unlock that they provide is still incredible. And so there's still so many ways that they can be applied that they haven't been applied that even if they infiltrate every part of the enterprise is really incredible in terms of what we really do. I really understand from a very clear use case how a certain function or operation occurs in the enterprise. Think about it as a full back office process and we take it all the way from actually automating that full back office process to end to end redoing it with AI from the ground up. Interesting. So you would come into a potential enterprise and you would like inspect. Workflow and then reimagine that with AI and and it really ranges from being able to apply not just basic large language models through coding agents to also computer use agents. So everything from how do you take the best and most capable computer use agents that can actually automate RPA typical work to actually being able to go fully multimodal. So if you think about this as a Full go to market function being able to understand from let's say the research perspective having deep research agents unlock and fulfill that full end to end function to actually going fully fully multimodal. So what does that mean for uh an enterprises' full campaign as they expand it and enter an entire new market? Yeah, very cool. So you are building in an incredibly fast moving AI landscape and you're doing it super early in your career, um. Without decades of legacy kind of patterns to unlearn, so what advantage does your youth, um, give you as a co-founder? Is it your mindset, your intuition, your hustle, something else? I love that question because I think specifically being part of Gen Z and the sort of younger generation being builders is what we're actually seeing is that most young people that are building these models have an inherent fluency with how they interact with them. We're not. The truth is the younger generation. Isn't adopting AI. We're growing up fluent in AI, so we aren't thinking about coding from scratch. We're thinking about coding with a coding agent side by side, and that fundamentally changes how you write, how you take tests, how you apply to jobs or different applications, because it's not from the ground up. It's actually being able to do that. different models or agents side by side and I think what that really means is that this broad level of use cases and applications we're seeing is really being pioneered by the younger generation. So we didn't have the mobile era as as being sort of builder first, but we do have the AI era which is going to be, if not bigger than than the previous one. I'm gonna go to questions next, um. But I want to talk about the future of work. So yesterday we had a really robust conversation around is AI killing entry level jobs, and it sparked a lot of debate, and I actually want to make it personal for a second. So I have two Gen Z kids. My daughter is 22 and she just graduated from college. He's a food anthropologist. It's a thing. And my son is almost 17. He's a junior in high school. He's my AI guy. He's AI forward and, but he's trying to figure out what to, you know, what to study. Um, so what advice would you have for both of them? Well, I think your daughter is incredible and probably it's not going to take her job because I don't know, but I also think that a lot of the conversation and the anxiety around AI is because often we haven't sort of embraced that it is coming, right? The actual sort of nature. of the future of their work is going to change and what that really means is things like customer support or typical back office jobs are going to change whether they change now or whether they change in a couple of months or years. What that means with the new types of jobs that they take on is something that I think The best positioned to think about very deeply now um but with that being said, I also think that them being able to build in the space is incredible, right? I think they're well positioned to not just join other companies but also contribute to the space because. Never before has there been so many unexplored ideas and use cases with AI that they obviously we've all heard about the one person billion dollar company, um, but what that really means is they can be that, right? And so being a builder or being somebody that's excited to contribute is incredibly exciting right now with AI. Yeah, amazing. Um, do we have any questions? I can't see. Is there a question here? Yes. Charlotte. Hello, my name is Charlotte. I am with Fortune. Um, Kara, I have a question for you that I want your perspective on. We hear a lot in the media world from a lot of CEOs. Like I feel like one of the main talking points is AI is not gonna take your job, but the person who knows how to use AI does. Um, my question for you from your POV is like, where, where does that statement sort of stop and end? Like what is the meaning from your perspective behind that statement and how do you see that like in the future? I love that question because I think. Coming from some of my earliest research has been in climate change, and I think a lot of people are very familiar with the term climate anxiety, which is this idea that there's this movement of climate change coming up and we don't really know what to do, but we know it's coming and nobody is moving as fast to solve the problem. There's a similar thing with AI which is AI anxiety, which is there's literally just In the past couple of weeks been two model releases that have engulfed the benchmarks in such an enormous way that pretty much everything you've ever used AI for can now just be 10x right before Gemini 3 we didn't know what was possible. Now there's a ton of other use cases. So there is a sense of AI anxiety because if you're not consistently ahead of it or on top of it, you're kind of left behind. And the truth is that If that is the case, how can we all stay informed or use these tools every single day so that we do feel as up to date as possible. Now what that means is I'm not saying everybody has to sort of keep up to date with every model release, etc. but that does mean sort of arming yourself with a couple of different tools, whether that be I I'm, I'm not gonna. Sort of name specific ones, but if it's sort of Chat GBT or Gemini, because ultimately there are sort of some main model players are coming out and being able to use them as co-pilots and tools in your everyday life, right, because when Gemini 4 comes out, it's still going to be sort of in the Google interface, right, when the next open AI model comes out, it's still going to be in chat GBT being able. To understand and use these different tools as they expand are really key to staying ahead, right, because being able to use them in different interfaces allows us to become really familiar with them, become really comfortable with them, but also use them as they continuously push different interfaces out, um, but also not having that sense of anxiety. About it because you're sort of comfortable with them. Kiara, um, one of my investment thesis is human centric AI, so AI that creates massive economic opportunity but also unlocks human potential. You did your masters at the human centered AI lab at Stanford. What are some of the principles or values that you, um, kind of explored at Stanford that you've brought with you to the company? I think what's really interesting about this concept of human-centered AI is that pretty much every Interaction with a model, whether that be an image model like nano banana or um a sort of large language model is really rooted in human centered AI. And so we see incredible benchmarks, we see um sort of the next model improving on the on the SWI benchmark and coding agents, but really if you take a step back, what makes specific models feel different than the other ones you see this. Very clearly in image models, right? People see and and work with nano banana and multi-turn edits, and it really is because they see something in the image quality that's very different to a seed dream, right? Um, it's really diff difficult to measure the quality differences between these models and, and why that's the case is that as models and use cases and efficiencies change, the the the key differentiator. is taste. Gary Tan from which is not captured in the benchmarks around accuracy, right, right, and we see this with design. If you've ever sort of coded with a coding agent, you know it loves a couple things. Sparkle emojis is one. It puts it all over front end UI. It loves sort of shades the end sort of components in a way that um you, you know something is vibe coded if you've ever sort of worked with a coding agent and the. The the key sort of differentiator is what makes it taste and feel different, and that is human centered, which is what do people actually want to see in an AI application or an AI use case. Yeah, I would love to see more benchmarks that kind of capture that aspect of these models like the design principles and kind of the the human aspects of it and I'm involved with this MIT group. It's called Advancing Human. With AI and they are actually trying to collaborate with some of the big kind of the open AIs of the world to build these benchmarks. So it'll be exciting to see, yeah, I, I think as models code more on the front end and, and less on the back end, that's, that's going to be so important. Do we have more questions in the audience? OK. What is, um, the biggest misconception about the Gen Z, um, usage of AI? Like how do you guys use AI? I think that or think about AI. I think that the biggest misconception is that young people are using AI to not think things through. I think that like as a shortcut, yeah, I, I think that really intelligent Gen Z, um, individuals are using it to think even deeper because you can run. A deep research report on let's say parallel AI that goes on for 34 hours and what are you doing when you actually have that report on your fingertips? What does that unlock for you if you're looking at the financial market, if you've done a deep dive on your favorite sort of article or topic, what that unlocks for you and I. I think people think that instead of you doing all that work, you've now sort of handed off the cognitive load to an AI model, but really what does that allow you to think differently about and think even deeper on and I think that's the biggest misconception that people have about young people and their use of AI. I, I know, so impressive, right? So cool. So cool, um. I have to ask you this. You're a Peter Thiel fellow. Did you consider dropping out of school to start your company? Please say no because my son may listen to this conversation and. And, and I do want him to, no judging, but yeah, but I, how did you think about this? I, I think what I would say to, to, to that point is that the community that, that brings people together in places like Stanford is incredible, right? Just because, um, as we're seeing sort of model or AI applications proliferate, where it really comes back to our previous point is how people come together and build really unique things. And, and I think that was key to what is a sort of Stanford college experience. And definitely through my experience I think for his use case or his application, I think that's really dependent on on sort of what how he sort of sees himself contributing to the space building or even working with different AI models. I, I think this is such an important point because in this age of AI, I think human connections are going to be even more important and, and, and experiences and spaces that allow you to build these authentic human connections are going to be so important. OK, so you have a lot of older people in the audience today. Um, what's your advice to all of us? I would love to say that the sort of you can be honest I'm OK when we sort of have a conversation about younger people using AI models and and being really comfortable with them, it's just as important as the older generation using it because AI fluency is just as important for people that are already in the workforce. not going to teach it to younger people to use AI. And the truth is, if, if there's this sort of anxiety about it, we are just at the beginning. The models right now are as dumb as they are ever going to be. What that really means is that when you look at a couple of months down the line, we are going to be in a very Different landscape and so being able to be really comfortable with that and having your core tools that you use um and and and get comfortable with that is really important because we're sort of at the beginning. It is only going to get faster, more advanced and more intelligent each and every model from from here on out. So being. Really comfortable with that is, is no AI anxiety, you guys. Kira, thank you so much for this wonderful conversation. It was great. Thank you.