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**Cadi Zhang** (0:00)
We really haven't seen robots operate in the real world yet. Getting their own data and then training on that. I think a very small subset of robots that have been able to successfully go out in spaces. Because no one really wants 100 pound robot in their house, and like you said, fall on their cat. That would just be catastrophic, right? I was building out PokéTax was just my spin on filing a tax form in Pokémon games. And it requires, I feel like, some level of thought and guidance to the AI. Like, what if you never filed any of your taxes in Pokémon? Because you're always earning money as you battle trainers. And so it's like, you really have to provide some level of guidance.

**Parth Patil** (0:36)
The Token Grantee program gives $1,000 a week in tokens to high potential creators already deep in AI, across film, gaming, comics, print, and digital art. There are no tool restrictions. The freedom to choose is the point. Grantees also get access to my own custom fleet of agents, and an ongoing collaboration with me.
The goal, to close the gap between an idea and the world.

**Reid Hoffman** (1:03)
Welcome to another episode of Reid Rifts. We've got our real Parth, not just Parth AI. It's also the real Reid, not just Reid AI. And we have an excellent guest, Cadi. Thank you for joining us.

**Cadi Zhang** (1:18)
Yeah, I'm super excited to be on. Thank you guys so much for having me.

**Reid Hoffman** (1:21)
It's our pleasure and honor. And why don't we start with, okay, so Parth, how did you select Cadi?

**Parth Patil** (1:27)
Yeah, I'm super excited to have Cadi on.
And Cadi, I think we've known each other now for maybe like three or four years. When I met Cadi, she was working on a hackathon, trying to build a kind of co-pilot for game design and game development. And me being obsessed with video games, I was like, oh my God, I want this. Like, this would be awesome. Like I saw what Cursor did for me for programming. But imagine if we had something similar for making games. And so that's when I met Cadi. But over the last couple of years, I've gotten to know her as she's exploring AI, but also getting to know her like wide ranging career arc. So she has experience in AI for accounting, some crypto transaction analysis, as well as like now she works in robotics as a product operation specialist. So she has this incredible like exploratory career arc, which has been really exciting to follow. And seeing how she connects the dots across all of these different problem spaces and using these like the AIs to fill in our gaps has been super inspiring. So I figured, I mean, super, it would be awesome to like accelerate your own creative projects and see what you can do and bring you onto the show and see what you've been learning over the last few years.
So welcome Cadi. Tell me more about how you got into AI and what your initial goals were and where you kind of ended up. And talk about your journey to this point over the last few years.

**Cadi Zhang** (2:45)
So I think overall I had a pretty unconventional path into AI in general.
I was working on basically creating games in Unity. I was just super obsessed, really just wanted to create something and was working on 2D platformers and such and kind of like entered in and was really lucky at the time with robotics just being there and jumped on because we were basically programming VR interfaces with robotic dogs and it was kind of my foray into like actually starting to program and learn. And I think at the time it was we just started getting introduced to like GPT and just having that as like the magical thing where you're copying and pasting code snippets literally in at the time. So it's like hilarious to see kind of where we are at now. But that kind of really spiraled into me doing like haptic suits coding more VR stuff and Unity to now working in AI for a bit for AI accounting. And then now moving over to more of like the data collection and robotics portion of how can we really gather the data and what is the data that is needed for the next generation of like embodied intelligence or robotics in general.

**Reid Hoffman** (3:59)
Very cool.
So go a little bit into kind of how building game worlds and intelligence for the physical world, what do they have in common? What do the fields have to learn from each other? What's the bridge, as it were, from bits to atoms as you're moving from game worlds to intelligence for the physical world?