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Reid Hoffman (0:00)

You want to combine the superpowers of these AI agents with our superpowers. The likelihood that we actually, in fact, are much better in collaboration is high.

Parth Patil (0:09)

Arguably, probably the most important agent is that personal agent, the one that is looking out for you.

Reid Hoffman (0:14)

How do we orchestrate to something that's really new, that is improbable? I think that there is a much more enduring role for us. You know, one of the things we've been doing this summer has been this great Tokens to the Future program. And Parth has been the primary architect of this because it intersects his personal mission to try to get everyone understanding how to get on their surfboards and kite boards and efoils and everything else, you know, windsurfers and get into AI. And undoubtedly, it's a bunch of the Token grantees, a bunch of people Parth's met and are doing creative things.

So, Parth, why don't you kick us off on our summer kind of wrap up?

Parth Patil (1:05)

This year, this summer, we did something very experimental, something new and interesting. We ran this Token grantee program. And we picked seven grantees this season. We picked people from a wide range of industries, everything from creative to media to security, coding, game development, robotics, really picked a pretty wide group of people. And we basically deployed $1,000 a week in each person's hands to deploy AI, to experiment with technology, see what's possible, see what new capabilities are coming online, explore the frontier. And I mean, I had my expectation going in like, oh, this will be fun, you know? I genuinely think you have to burn tokens to learn tokens. You have to like, you have to, it's a 10,000 prompts, right? You have to put 10,000 prompts in to understand even a fraction of what's coming out of these models, especially with how good they're getting. And I can't do that alone. So I had to pick people I thought that would help me map this out. People that had their own, you know, unique superpowers and interests and passions. People that could see the many frontiers of this kind of like moment in AI.

And so we created this program. And I think it's been, you know, it's been very eye-opening. It's surprised me in so many more ways than I expected.

And I guess, like, I'm really excited to have, you know, I'm really excited to talk about it and, you know, recap this and hopefully inspire others to run similar programs, you know, others to also pick up the tools and experiment and see where the frontier is headed.

Reid Hoffman (2:38)

So, you know, you handpicked this group of grantees, which I think was an exact right way to start.

And, you know, again, as it was mentioned, it's kind of like it's different zones, but in depth and with a willingness to be, you know, bold pioneers across this kind of AI landscape. So looking back at the season, you know, what patterns show up for you across them and especially those that you didn't design for because you had a going-in theory, like what emerged?

Parth Patil (3:16)

One thing that surprised me, okay, maybe I was biased towards the tools that I had already used and I was like, oh, you know, people, someone's going to want to use this model. And then I realized, like, well, actually, like the different, the best model for the task might be different or, you know, the best model for the workflow might be different. And so I was like, and just watching where people like, oh, I'm going to spend, you know, I'm going to spend the tokens on this set of video models, this set of image models because it unlocks this new format in storytelling. Or I like, these are the coding agents that I like. So it's very interesting getting to see like, oh, you know, why do you like that agent? What's, what's special about that agent? You know, factory AI, like the factory approach to software. Why do you like this versus like a quad code or a codex? So seeing a wider range of the, because I can only see as far as the tools that I use, but then seeing all the other tools that I'm missing, it shows that there's a pretty healthy ecosystem of options out there. But I think one of the interesting patterns, I think every single grantee demonstrated was that independent of where AI is right now, they're kind of projecting out the capabilities over a six month, two year, three, four year timeline, right? So AI is like good at X, not quite good at Y. But they're all aware of those limits in the current form, and none of them are like, well, it'll never be better at that thing. They're actually, there's this common mindset of like, once it can do this, then these are the seven things that we're gonna wanna do with it. And so they're kind of like drawing that exponential out a little bit and helping us paint a picture of like, well, when you can, if you can use a video model and two people can tell a short story, well, how far is it from, what kind of short story? Can you do science fiction?