You want to combine the superpowers of these AI agents with our superpower. The likelihoods that we actually in fact are much better in collaboration is high. Arguably probably the most important agent is that personal agent, the one that is looking out for you.
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 you know, Parth has been the primary architect of this because it intersects his, you know, personal mission to try to get everyone understanding how to get on their, you know, surfboards and kiteboards and e-foils and everything else, you know, windsurfers and get into AI and data. It's a bunch of the token grantees and a bunch of people Parth met are doing creative things. So, Parth, why don't you kick us off on our summer wrap-up 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 there I had my expectation going in like, oh, this will be fun. You know, I think I genuinely think you have to burn tokens to learn tokens. 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.
So, you know, you handpicked this group of grantees, which I think was the exact right way to start. And you know, again, as 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?
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 everyone's going to want to use this model and then I realized like well actually like the best model for the task might be different or you know the best model for the workflow might be. And so I was like and just watching where people, 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 was very interesting getting to see like, oh, you know, why do you like that agent? What's special about that agent? You know, Factory AI, like the factory approach to software, why do you like this versus like a Cline 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 going to want to 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, you know, if you can use a video model and two people can tell a short story, well, how far is it from, you know, what kind of short story? Can you do science fiction? Can you do, you know, can you do a western? Can you do a western sci-fi? And then how many people can make like a longer form movie? And so the connecting the dots helping us connect the dots on like where this is going over a three to five year timeline is something that pretty much every single person did in their own way in their own domain right so whether it was robotics whether it was storytelling film game development coding we can kind of see a little bit further into the future because people are in the areas they're very passionate they're able to kind of see and connect the dots on some of the what the model capabilities are going to bring online in the next couple years.
We're going to dive into, I think, some of these specific patterns. But before we get there, I think one of the things that's important about this kind of frontier work is what it means to be AI native. And I think part of the thing to kind of go into kind of what AI native is is what does that mean? How you operate as an individual, you know, kind of my classic all the way back to startup, you know, kind of stuff is OODA loops and decisioning and activity and, you know, exoskeleton, you know, how do you become, you know, I am Iron Man, you know, kind of as an angle. And I think one of the things that we saw is it's not coding pedigree. It's not per se technical background. It's like it's kind of a mindset.
And so what did across all of this, you know, what did you kind of say hey this is how my sense of being AI native evolved, you know, from like how because actually you were also asking questions you should say like how you are AI native then how that evolved and then how we should be helping people think about that.
Yeah, I think you know there's the themes of the kind of if I think about the shape of the superpowers that are coming online there's the superpowers in automation you know being able to automate things using agents but then what should we be automating and then that's a judgment call right so the superpower of being able to use code to you know blitz through cognition is really interesting but then what becomes more important is like well when should we not do that when should we use our judgment you know now that we can scale our cognition. You know, what are the things that uniquely require the human taste and the human experience and part of this is like not thinking of everyone as like I'm not just a data analyst, right? You're not just an investor like we're actually very multifaceted and you know you have an engineer but it's not just an engineer. It's much more interesting when you take that person and you look at them as like a multifaceted you know person with many passions and interests and then when you think about the more general human the general human with many different facets to them the way they use AI will always surprise you because we're not one-dimensional people right and so this is why I thought it was really important that we would bet more on people and not on like specific roles or categories, right? Because people always surprise you when you kind of like give them a chance to explore and expand the way they think.
Let me add a little bit to this. I think because I think part of you know one of the things I can add since you've selected all the people is I think part of the thing is people frequently think of work or process too mechanically versus organically, right? And a little bit of what I like about kind of investing is, you know, one of the major things I like about investing is kind of betting on people. And I think that part of what we saw as we kind of went across all of these, you know, very different fields, you know, film, social, VFX, security, gaming, energy, you know, the same shift showed up in each of them, but that's because the people are being pioneers across them. And I think that part of the thing is getting this kind of shared mindset, this kind of curiosity, exploration, pioneering, willingness to experiment. I mean, like one of the things that, you know, I try to give people advice is if you're not trying to do things with AI that don't work, you're not trying hard enough on the edges, right? And like doing things, you shouldn't wait till, oh, no, I'm going to wait until I know exactly what works and then I'm going to do that. It's like, no, no. We've all landed in this magnificent new world with lots of new capabilities, lots of new possibilities and so it's people doing them and I think that's one of the great things we did with you selected a great group of token grantees and we saw their curiosity and their boldness and their willingness to set off on new terrain and new journeys.
So, going back through everyone's conversations, reflecting on the summer so far, I think like at least two totally different ways people used the tokens, use the intelligence jumped out to me. And a lot of that actually went into building a lot of people were building games and I think you know I like games, you like games. Maybe I pick people that also like games. These are the people we like. But what do you make about that? Like what do you make of that? Like a lot of people were like Dungeons and Dragons inspired in the past and like how that is there a connection there to like why people make things?
Well you know there's this one of the things that comes into kind of talking about human beings humanity is there's these different articulations of theories one is Homo sapiens where the thinking people one of the ones that's also been written as Homo ludens like we are game players we're game playing and that's kind of basis of how we do things. I've of course written about Homo technologicus and I think they're all very good lenses on this stuff. And I think one of the reasons why in this particular thing Ludens plays out early is because part of games is it's like it's how we kind of trial things in simulation. It's part of how we kind of learn new dance moves. It's part of how we kind of have curiosity explore. It's one of the reasons why like you know some of the best theories of education and how people learn is through exciting their curiosity and kind of game playing. It's like you know how do you make education learning like a game right as a way of doing it. And so that doesn't surprise me that these naturally curious and bold people are games. Now, I do think that it's, you know, as with a number of these folks, the fact that there is a gaming overlap with you is very entertaining. The fact there's a Clubhouse overlap with you is very entertaining because, by the way, early people going in a Clubhouse. It's also, it's not per se a game, but it's a kind of pioneering and curiosity and a willingness to try something new. It's like, you know, most game players don't want to play the same game again and again. And they want to kind of new people, new explorations, new levels, what's the next game, what's the next game, the next level.
I think there's another aspect which is that games are more forgiving and it's like failure is not like catastrophic in a game environment and we learn from our failures. So like when you have these like environments or projects where we're just trying to see what the tool can do for us and we create an environment that failure is not going to be catastrophic. It's not going to, you know, you're not going to lose your job if the game doesn't play out, like the thing that you're doing doesn't play out if it's a game. And then you can try new things with like low cost of error. And I think that you know, the first thing you should vibe code should probably not be like hospital software.
Well, should certainly not be. I think your point's exactly right. Right. You know, you want to give yourself an environment where like, okay, we're going to make some mistakes and it's going to be okay because we're learning a bunch of these new things before we move on to things that are more serious and more you know, where the ramifications are much more serious and like more expensive, right?
Yeah, I 100%. And I think actually, by the way, it's partially again in learning it's and part of the reason why we're doing the Tokens to the Future program is that learning and experiment where failure is cheap and quick.
Yep. And then you're learning the things that matter. Now games itself is an important area because I do think it's part of like you know part of how we think is we have like mental models of things and we learn you know kind of how to do them and games are part of the environment that we set up. Games are also, you know, one of the threads I think was going through our discourse was, you know, kind of single player, multiplayer, you know, kind of how does that play into things? I think that the, you know, questions around, you know, how to think about, you know, anything from, you know, kind of like Matthew's full iOS choose your own adventure game built on a weekend with Fable or, you know, Kitty's Poke Attacks, an imposter style multiplayer game. I mean, the like also building the games gives you a really rich environment.
Yeah. In each of these kind of vectors which are kind of parallel cosmos. I don't know about microcosms they are microcosms in the game but also parallel causation right so I think that's part of the reason why games is not just a selection criteria but actually something that's relevant to the material.
Yeah. Yeah. And I even think about Jonathan Brezzo. He's running a Dungeons and Dragons campaign early in the early days of stable diffusion. Generated 40,000 images because he was like, I need to, you know, flesh out the rest of this world and I want the campaign to have like a world they can imagine in their mind and see and be a part of. So yeah, the volume was a very interesting theme, right? Like how much people how just how much people will generate when they can. And then I think like the autonomy is an interesting aspect of like people who are delegating to agents that'll work all over like all night long. So like scaling their effort by delegating to autonomous agents. You know I do that myself but seeing how other people do that and the different ways they're doing it's like I want you to research you know Joe I think you know Joe Salvatore he asked one of our agents to spend all night researching successful media techniques over the last 80 90 years and like you know what does it take to create a meaningful advertisement that's a timeless kind of principle that you can extract from history. I never thought about doing that I mean I wasn't thinking about media in that way, but seeing how the thing that someone else will ask an agent to do always surprises me, especially the more different they are than I am.
Right. So, let's kind of talk a little bit about life agents. So, you know, everybody kind of loves a life agent. You know, personal, proactive, always on, you know, it's part of the theory around Inflection AI. And what you know Mustafa and now Sean White and crew are kind of started and doing. So how is your theory of life agent kind of advanced and you know what and how is the kind of question about like the fact that actually in fact it's very rarely the people who are kind of deep experts in something that are adopting but it's actually more beginner's mind. So, not veterans, you know, give some, you know, fill out this painting some.
Yeah. So, it's interesting the personal agent, the, you know, in the case of Inflection, the EQ just as much as the IQ, right? I spend a lot of time with coding agents and they're very much IQ maximized systems, but it's the one the ones I guess it's like the agent that is the most meaningful to me is the one that thinks about my well-being, thinks about my life, helps me plan you know my commitments, helps me like negotiate for a better deal on something. That's like covering my blind spots, right? And I think that this is arguably probably the most important agent is that personal agent, the one that is looking out for you. And what I've noticed through this program, through you know the over the course of the year as the personal agents have finally gotten very good the OpenClaw, the Hermes and then how you know various grantees are using Claude using remote control you know being able to tell an agent that's on your computer to go do something for you when you're on your phone. So the way that these things are now kind of like in our own lives is it's like at least we see in the token grantee program we're seeing these are some of the earliest adopters of the most powerful personal agents that have ever existed. And I think especially when you're alone or when you have a small team or you're building your own business kind of like the startup of you that first personal agent is the most important. It's your first employee. It's your EA. It's your co-founder that's like on this journey. And I think we're in the very beginning of understanding this because as they accumulate memory over a couple months to a year to multiple years, they start compounding in their usefulness. And I've seen this in just since February with my agent, but I can only imagine what 5 years of experience working with, you know, a personal assistant is going to feel like just how equipped you are, right? Every day I wake up and it's like half of the problems that are on my plate, the 25 notifications, half of them already have suggestions on how to move forward. So I feel like there's this proactive momentum that I can lean on that's an exoskeleton of a sort and I think through the program I've seen this is something that's happening across the ecosystem. It's not just coding agents. It's actually the personal assistant is finally here.
Yeah. No, I think that the question it's people have a tendency to put it in a box and not realize all the different things. I mean, and it's part of the reason why like it was, you know, was awesome that, you know, Matthew, you know, runs a dedicated home monitor with a personal dashboard and, you know, remind me about Lakers games and, you know, has a Codex chief of staff, you know, drafting meeting follow-ups. And I think these are just the beginnings. And as you mentioned, part of the reason why to start on this pattern is you know we as tool users kind of go well let's wait for the tool to be finished shape and then learn the expert shape and it's like no this tool is going to be in dynamic reformation and dynamic reformation with you. Yes. It's one of the reasons why it's like almost every week like it is getting interesting more you know it earns more access to my life in a way that's like useful and compounding.
Yeah. And so and you know part of it and this is again part of the reason why we decided to do this you know as part of the Possible podcast is because it's a reshape of what's possible right like part of what's going on with the kind of the AI exoskeleton skill is a reshape of what's possible and it's one of the reasons why non-experts have actually in fact some advantages here because when you become expert part of your expertise is you learn what's like this is doable, this is not doable, this is possible, this is not possible. When the possibility landscape shifts, you have to rethink that. You have to kind of rethink the wait a minute, what is now possible and not possible, what is now doable and not doable is different. And frankly, it's changing relatively often. And so as part of that changing relatively often, you need to be learning and adjusting and no one will tell you it'll just end here. We're going to discover this. And it's one of the things I like about entrepreneurship but pioneering is like only through a pioneering process. And so I think one of the things is not only begin with a beginner's mind but to continue with a beginner's mind. It reminds me of like again one of the things that Ben Casnocha and I said in the start of LinkedIn which is you know permanent beta. It's like you're always in process never complete.
So let's revisit a few scenes from the summer and you know like dive into some of what we noticed and what we saw. I think one of the, you know, we had Ben Hansford on, we had Ben Hansford, a professor of film at USC and such a I mean I keep re-watching that conversation because every time I watch it I learn something more. His perspective you know being early to AI in LA in film and entertainment really interesting perspective and as a teacher right so working with people much younger working with kids working with students much younger than him. So he has this like he feels his own age sometimes holds him back and then his students surprise him right so but then he thinks about like how he thinks about AI it's like not a tool but that he starts thinking of it more like his team he's got the Claude he's got the Codex he's you know he's firing off his projects are kind of like circulating between the agents around him and I think he had an interesting line what he said he said you know a hammer can't build a bird box while you sleep you know so then the AIs are kind of like this like navy SEAL team and you give it a mission and then it comes back to you with like ah here's what we've done and that was very that's very exciting I think especially because up until now it seems like you know a lot of people most people are interacting with AI like it's a search engine they're still asking it questions about the world but they're you know Ben's already at this place where he's like no no no these things work for me when I ask them to do something they're going to go take a shot at it and then they're going to come back with some completed work output something new and no it's really exciting to see someone outside of software, outside of Silicon Valley using agents in a way that's extremely like starting to think about them as this like personal team, personal infrastructure.
Yeah. And I think look, I think part of I like I agree. Because, you know, a natural way to start is to be thinking that you're, you know, a conductor, you're a director, you're an orchestrator. And like the team of agents, the swarm of agents, the work process of agents. I mean, this is one of the things that you know I started thinking about in our earliest conversations, you and I, you know, as part of starting to work together and I think that the question when it kind of comes down to this is to say that's a very good lens and a very good way to start and like if you don't have anything else, start there. I do think it's interesting like you know part of what you and I have also had as an ongoing conversation is kind of this question around like it's natural to think it's a crew and to deploy it as a crew and make it work and there's features that it has that human beings don't 24/7. You know one of the weird things when you work with these chat bots and agents say just do it better and it just does it better whereas a human goes what do you mean like I did the best thing I could.
They're relentless they can like clone themselves and parallelize across the problem which is not humanlike at all.
Exactly. And a lot of those things are features and it's like you need to be adapting to that feature. It's different than a human team. Now obviously one of the things you have to kind of like as you know one of the kind of metaphors I think is it's an alien intelligence that has learned and trained deeply to be human.
