In short
Recap of “Tokens to the Future,” an experimental summer program funding seven creators with $1,000/week in AI tokens to explore agentic AI, “AI-native” mindsets, and how humans retain taste, judgment, and context awareness.
Key claims
Best results come from matching the right model/agent to the task; grantees forecast capabilities 2–5 years out; “personal agents” (well-being, planning, negotiation) are the most important early adoption; games/low-cost failure accelerate learning; AI outputs can be confidently wrong/sloppy, so humans must curate and supply taste; agents are relentless but can be unsafe without staged permissions.
Notable examples
D&D campaign generating 40,000 Stable Diffusion images; an agent researching 80–90 years of media techniques for effective ads; “read/draft but don’t send” email safety workflow; anecdote of confidential info accidentally sent due to over-automation.
Guests
Parth Patil (primary architect; also surf/kite/e-foil mission); Ben Hansford (USC film professor); Joe Salvatore (media/creative taste perspective referenced); Jonathan Brezzo (creative director; D&D/Stable Diffusion example); Matthew (runs home monitor/personal dashboard; Codex chief of staff); Katie (layering taste on story); Jonathan Brezzo, Joe Salvatore, Katie, Matthew, Ben Hansford are discussed as guests/participants.
Written by AI. May contain mistakes. Listen to the episode to check what was said.
Chapters
Tap a time to open that second in VOOverview of Tokens to the Future Program
0:45 to 2:39
Discussion of the experimental token grantee program and its purpose.
“This year, this summer, we did something very experimental, something new and interesting.”
Learning from Diverse AI Applications
2:39 to 4:23
Exploration of the different applications and insights gained from grantees using AI.
“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.”
Defining AI Native Mindset
4:23 to 5:38
Exploration of what it means to be 'AI native' and its implications for individuals.
“Can you do, you know, can you do a Western?”
The Multifaceted Human Experience with AI
5:38 to 7:44
Discussion on the complexity of human roles and how they interact with AI.
“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.”
The Role of Curiosity and Experimentation
7:44 to 9:40
Insights on the importance of curiosity and experimentation in AI projects.
“It's much more interesting when you take that person and you look at them as like a multifaceted, you know, person with many, many passions and interests.”
Games as a Learning Tool
9:40 to 12:06
Exploration of how games foster creativity and learning within AI experimentation.
“this magnificent new world with lots of new capabilities, lots of new possibilities.”
Parallel Worlds of Gaming and Reality
12:06 to 14:01
Discussion on the relationship between gaming, social interaction, and real-world applications.
“It's like, you know, most game players don't want to play the same game again and again.”
Exploring Game Design and AI
14:01 to 14:55
Understand how games serve as microcosms for human interaction and creativity.
“So I think that's part of the part of the reason why games is not just a part of selection, you know, criteria, but actually something that's relevant to the material.”
The Role of Personal Agents
14:55 to 18:01
Learn about the significance of personal agents in enhancing productivity and well-being.
“And like, you know, what does it take to create a meaningful advertisement?”
The Evolving Landscape of AI Tools
18:01 to 21:32
Discover how the dynamic nature of AI tools reshapes possibilities and user interactions.
“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 view, that first personal agent is the most important.”
Show all 20 chapters
Insights from Ben Hansford
21:32 to 22:33
Gain insights from filmmaker Ben Hansford on using AI as collaborative team members.
“And so as part of that changing relatively often, you need to be learning and adjusting.”
Understanding AI Limitations
22:33 to 28:00
Explore the limitations and responsibilities when working with AI tools.
“to AI in LA, in, you know, in film and entertainment, really interesting perspective and as a teacher, right?”
Orchestrating AI Tools Effectively
28:00 to 28:38
Explore how to effectively use AI tools in creative processes.
“But by the way, none of that is, oh, then I should just wait until that problem solved because the amplifier is already so great and intense.”
The Limits of AI and Human Taste
28:38 to 29:20
Discuss the interplay between AI capabilities and human judgment in creativity.
“And, and, and like one of the ones that I think you and I've talked about a bunch, so we'll go to kind of another theme, which is, you know, you can't outsource taste with, you know, Joe Salatore.”
AI's Confidence in Creativity
29:42 to 30:01
AI exhibits the same confidence regardless of artistic quality, highlighting challenges in creativity.
“Joe Salvatore, he had a really good line here where he said, you know, AI has the same confidence on a creative task, whether it nailed it or whether it produces slop.”
Curating AI-Generated Content
30:01 to 33:06
Understanding the importance of human curation in AI-generated content.
“The, especially in the creative spaces, in the creative domains, it's not like one piece of, one piece of art is better than another piece of art.”
Cog Jobs vs. Spark Jobs
33:06 to 35:38
Exploring the distinction between routine tasks and creative endeavors in the context of AI.
“And that's how he goes into a first meeting to say project.”
AI's Predictive Nature and Its Limitations
35:38 to 37:40
Delve into AI's tendency towards predictability and the implications for creativity.
“where more of us are going to be in this spark jobs kind of role working with AI.”
Collaborating with AI for Creative Output
37:40 to 42:06
How to combine human creativity with AI capabilities for optimal output.
“It only is, even though there will be almost certainly some things in that to state it with some, you know, probably 100 % determinist or probably 90 % determinist is a little bit of a fool's errand.”
Leveraging AI for Human Amplification
42:06 to 42:44
Explore how AI can enhance human capabilities and collaboration.
“Now, the good news for using the AIs, we can put terawatts behind the AI, and we only have 20 watts here.”
Transcript
Automatic transcript. May contain errors.0:00You want to combine the superpowers of these AI agents with our superpowers. The likelihoods that we actually, in fact, are much better in collaboration is high.
0:10Parth Patil: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 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 kiteboards and e-foils and everything else, windsurfers and get into AI and undoubtedly it's a bunch of the token grantees and a bunch of people Parth's met and are doing creative things So Parth, why don't you kick us off on our summer wrap-up?
1:05Parth Patil: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.
1:45Parth Patil: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 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 very eye-opening. It's surprised me in so many more ways than I expected.
2:24Parth Patil:And I guess, like, I'm really excited to have, you know, I'm really excited to talk about it and 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, what patterns show up for you across them and especially those that you didn't design for because you had a going in theory?
3:12What emerged?
3:16Parth Patil: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, 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 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.
3:42Parth Patil:So it's 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 cloud 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?
4:22Parth Patil: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 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?
4:55Parth Patil: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 is probably 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 what the model capabilities are going to bring online in the next couple of years.
5:33We'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, you know, and kind of my classic all the way back to startup view, you know, kind of stuff is OODA loops and decisioning and activity and, you know, exoskeleton, you know, how do you become, you know, you know, I am Iron Man, you know, kind of as an angle.
6:17And I think one of the things that we saw is it's not coding pedigree. It's not per se technical background. 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 for like what how you because actually you were also asking questions you should say like how you are ai native then how that evolved and then how that how we should be helping people think about that yeah i think you know there's the there's the themes of the kind of if i think
6:57Parth Patil: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 scale our cognition um you know what are the things that uniquely require the human human taste and the the human experience. And I, and, and part of this is like, not thinking of everyone as like, I'm not just a data analyst, right?
7:39Parth Patil:You're not just an investor. Like we're, 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, 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?
8:16Parth Patil: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 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 is 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.
9:07But 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 until, 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, you're like, we've all landed in this,
9:41this 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.
10:02Parth Patil:so going back through everyone's conversations reflecting on on the summer so far um i think like at least two totally different ways people use the tokens use the intelligence jumped out to me and um 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 is there is there a connection there to like why people make things well you know there's this um 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 we're thinking uh people one of the ones that's also been written as homo ludens um like we're a game player we're game playing and that's kind of basis of how we do things.
10:59I've of course written about homotechnic, right? We're technological. 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, um, it's how we kind of trial things in simulation. It's part of how we, we, we kind of learn new dance moves. It's part of how we, um, we kind of have curiosity in sports. It's one of the reasons why some of the best theories of education and how people learn is through exciting their curiosity and kind of game playing. It's like, how do you make education learning like a game as a way of doing it?
11:42And 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. They want to kind of new people, new explorations, new levels.
12:19What's the next game?
12:20Parth Patil: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, 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.
12:51And I think that, you know, the first thing you should vibe code
12:55Parth Patil:should probably not be like hospital software. Should certainly not be. Should certainly not be. I think your point is 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 where the ramifications are much more serious and more expensive, right? Yeah, 100%. And I think actually, by the way, it's partially, again, in learning, and part of the reason why we're doing the Tokens of the Future program is that learning and experiment where failure is cheap and quick, and then you're learning the things that matter.
13:34Now, games itself is an important area because I do think it's 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 that 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, you know you know Matthew's full ios choose your own adventure game built on a weekend with fable or you know um you know uh Kitty's PokéTax an imposter style multiplayer game I mean the like like like also building the games gives you a really rich environment yeah in each of these these kind of vectors which are uh uh kind of parallel cosms I don't know about microcosms They are microcosms in the game, but also parallel cosmos to kind of human life and social interaction.
14:42Right. So I think that's part of the part of the reason why games is not just a part of selection, you know, criteria, but actually something that's relevant to the material.
14:54Parth Patil:Yeah. Yeah. And I even think about Jonathan Brezzo. he he 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 the campaign to have like a world they can imagine in their mind and and see and be a part of um so yeah the volume was a very interesting theme right like how much people how just how much people can will generate when they when when they can um 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 um you know i do that myself but seeing how other people do that and the the different ways they're doing it's like i want you to research you know joe i think you know joe salvatore he was he asked one of our agents to spend all night researching successful media techniques over the last 80 90 years.
15:53Parth Patil:And like, you know, what does it take to create a meaningful advertisement? That's a timeless kind of principle of, of, of, of that you can extract from, from history. Um, I never thought about doing that. I mean, I was not, I was, 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 of 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.
16:39So how is your theory of life agent, you know, kind of advanced and, um, you know, what, and, and how is the, the kind of question about like the, the, the fact that actually, in fact, it's not, it's, 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, you know, not veterans, you know, give some,
17:12Parth Patil:you know fill out this painting some yeah so it's interesting the the like personal agent the you know in 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 uh systems but it's the one the ones i guess 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.
17:54Parth Patil:And what I've noticed through this program, through, you know, over the course of the year, as the personal agents have finally gotten very good, the Open Claw, 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.
18:29Parth Patil: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 view, 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't, I can only imagine what five years of experience working with, you know, a personal assistant is going to feel like just how equipped you are, right?
19:10Parth Patil:Every day I wake up and it's like half of the problems that I'm, they're on my plate, the 25 notifications, half of them already have suggestions on how to, how to, how to move forward. So I feel like there's this like 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. And it's part of the reason why it was awesome that Matthew runs a dedicated home monitor with a personal dashboard.
19:55and, you know, remind me about Lakers games with a fan 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, you know, kind of 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. It's one of the reasons why.
20:31Parth Patil: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. Yeah. And so and, you know, part of it, you know, 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 to reshape what's possible and it's one of the reasons why non-experts um have actually in fact um 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 this possible it's not possible when the possibility landscape um 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.
21:28And 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, oh, it'll just end here. We're going to discover this. And it's one of the things I like about entrepreneurship, I call it pioneering. It's like only through a pioneering process. And so I think one of the things is not only begin with a beginner's new mind, but to continue with a beginner's mind. It reminds me of like, again, one of the things that Ben Kasnoka and I said in Star of View, which is permanent beta. It's like you're always in process, never complete.
22:11Parth Patil:so let's let's revisit a few scenes from the summer and you know like dive into some some of what we what we noticed and what we saw um i think one of the you know we had ben hansford on we had ben hansford a professor of film at usc and such i mean i i keep re-watching that conversation because i every time i watch it i learn something more he his his perspective you know being early to AI in LA, in, you know, 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.
22:55Parth Patil: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 clot, he's got the codex he's he's you know he's firing off the his projects are kind of like circulating between the agents around him and i think um 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 the the ais are kind of like this like navy teal seam you give it a mission and then it comes back to you with like ah here's what we've done and and that was very that's very exciting i think especially because 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.
23:34Parth Patil:They're still asking 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 yeah look i think part of i think like i agree um 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 the team of agents the swarm of agents the work process of agents.
24:17I 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've got, you and I've 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 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 is just do it better and it just does it better whereas the human goes what do you mean like i i did i did the best thing i could they're relentless they can like clone themselves
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25:08Parth Patil:and then paralyze across the problem which is not human-like at all exactly and and a lot of those things are features and it's like you you need to be adapting that feature it's different than a human team now yeah obviously one of the things you have to kind of like as you know one of the the kind of metaphors i think is it's an alien intelligence that has learned and and trained deeply to be human yeah and one of the places you have to say is like look that gives us a bunch of superpowers like some of the ones we just gestured at but it also gives us some weird weaknesses like can break into lacuna, have bad context awareness, not realize this is like the hugging face thing.
25:50No, no, reward hacking this way is not what I want you to be doing.
25:54Parth Patil:Yeah, we don't want to break the rules in order to get the answer to the test. Yes, exactly. And so I think it's one of the reasons why you kind of both experiment, try things and do but like you don't like you start with a gaming mindset like you start doing that to learn which things you do before you do something serious like like if you um like you know for example one of the things i know you do is you say look i would love you to read communications to me and draft stuff but don't send it until i say so right right i learned that the hard way and now i'm like okay well we're gonna take baby steps you know prove to me that you can even write the right email.
26:41Parth Patil:Then, you know, once I see that a couple of times and it's like, okay, now we're going to build like a little bit more agentic, a little bit more proactive. Yeah. No, I know of a, of, of a case that went so bad on that, that, um, basically by a person being like, uh, too enthusiastic and not exploratory and sequential enough, like basically sent confidential information from their company to another outside party on an ongoing deal discussion. Oh, no. Oh, my God. And it's like, what? And it's because I wasn't trying to do something. I was trying to be helpful. It was like, oh, I thought this would be helpful.
27:19And you're like, yeah, that's like, you know, where it's like a human would never do that.
27:25Parth Patil:Right. That's part of the virtue because they have the conduct. Well, almost never. I mean, there may be some nutty person somewhere in 8 billion people, but very rarely. And so I think the important thing is to go, we have these quasi-alien, quasi-human tools that are spectacular and have a bunch of superpowers, but they don't naturally understand the shared contextual awareness that we have. They may not even understand always what good enough or great looks like. And then we get trapped in lacunas that we don't understand. But by the way, none of that is, oh, then I should just wait until that problem solved because the amplifier is already so great and intense.
28:12It's like, no, no, no. That's the new way that you orchestrate, that you direct these tools. And I think that's one of the things we saw across all of these folks. But Ben was a particular highlight on that. That's right.
28:25Parth Patil:Learn where their limits are and then figure out how where they fit in in a way that's not, that's, that's extremely constructive, makes use of their strengths. It's kind of like the jagged frontier, as Ethan Mollick calls it. Exactly. And, and, and like one of the ones that I think you and I've talked about a bunch, so we'll go to kind of another theme, which is, you know, you can't outsource taste with, you know, Joe Salatore. And this is a little bit of like, I was gesturing, I was like, like, is it good enough? Right. And And it's one of the things, it's part of the same reason why a lot of intense AI training to train these kind of alien intelligence and human is still using a lot of reinforcement learning, human feedback, human data.
29:08But it's still the case there's a lot of kind of where our judgment comes in, our taste comes in, our context. And we use these kind of squishy words because it's a broad, squishy thing that has a lot of like perceptual recognition, intuition, you know, training from judgment. So, you know, what's what what were some of the themes on this taste that you saw from our episodes? And what are some of the ways you're thinking about it these days?
29:41Parth Patil:Yeah. Joe Salvatore, he had a really good line here where he said, you know, AI has the same confidence on a creative task, whether it nailed it or whether it produces slop. And it's just going to come back to you with that same level of confidence. And I've, I, that one, that one has stuck with me. And I think it's very true. The, especially in the creative spaces, in the creative domains, it's not like one piece of, one piece of art is better than another piece of art. These things are subjective. So I think in the case of like, if you think of a world, if you think of the world as purely math and coding, and this is all gradable right and wrong then the rl thing is interesting right like then the ai can get better at the thing because it's so objective right we know what right looks like we know what wrong looks like and then maybe the ai can hill climb towards right but then in the spaces that are like messy and subjective artistic creative you know very much more human it's where it's where the ai kind of just like you know it's kind of like dead in the water at a certain point right and um and then that's when we have like you know what we're doing so like joe will joe will have it do eight hours of research of what humans have found to be good design over the last 90 years and then jonathan will have it you know we'll have an image model generate 200 images before he picks one 200 names for a magical object before he thinks one fits the criteria right and so actually that is like it's like that's the it's everything all the 199 images that you and i never see represents the the the taste that jonathan brings to the table you know he decided to only show us one of the 200 and that means that like that curation is actually where his wisdom is coming into play the creative wisdom is his his personal experience um right and then katie katie was talking about how we layer, you know, taste is layered on top of story.
31:36Parth Patil:And then I always realize, like, especially now that I'm starting to make longer form videos, longer form conversational content through AI, I'm realizing, wow, the, you know, I can generate anything visually, we can make it beautiful, we can make it look like anything, we can make it look like science fiction. But will it feel like a compelling story? And I'm realizing, okay, actually, the writing skill set, the pacing, you know, the character design, the device, that is the skill that the AI is not like delivering out of the box, right? And that requires the person, the director to kind of infuse it with their vision for what a good story is, what an interesting character looks like.
32:13And so, and then it's like,
32:16Parth Patil:what do we do to develop taste? Was I think Joe, again, had the most interesting take here, which was that you have to consume a lot. You have to see a lot of anything to understand what good even looks like, right? and so there's the only way to train it is to be out there and experiencing the world yeah and by the way i think you know i think it was not just here but in an earlier episode it's like you know writer skill was the most valuable thing you were undervaluing yeah and and i do think like these things still don't like they can write a wikipedia entry which by the way is a group collection effort that's not particularly edgy beautiful etc it could be very informative they can do that very fast and superimmunally fast and thorough and everything else but like writing like interesting stories and and edge and dialogue and like for example i actually saw an investment memo uh yesterday that was like okay so which did you use chat gbd or clod for this because i could tell yeah it was basically like like like like even though it was a lot of like work that proximate what a human analyst could do there were errors or softnesses or in you like it kind of was like it was like i was filling out a form yeah versus it's like checking the box yes right and and that's there and actually one of the architects um that i'm aware of in japan actually has uh i think it's uh chat gbt and um like produce 50 images in his style and then picks the three that he thinks would fit for a project.
33:53And that's how he goes into a first meeting to say project. And he's a super famous architect. I can't name him because I haven't got permission, but that kind of thing is still involving the taste as it plays. And it's one of the things I think will persist for some time at least. And it's part of the, what is the future of work and how do we do it? It's one of the many different areas to be looking at how do we bring in essential and useful things as humans into working with AIs for, you know, high quality token output. Yeah.
34:30Parth Patil:I mean, you got to think about the chopping block floor and everything that never made it to the public. And that's a huge part of, you know, what stands out. There's the slop, and then there's the curated, like, artistic choice, right? So we had Jonathan Brezzo, one of the most creative people I've ever met, honestly. And he brought a very interesting framework to the table, which was that, you know, creativity needs a human. And he had this framework of like the cog, there are cog jobs and spark jobs, where cog jobs are this like executions, logic, not necessarily the most creative stuff. And then there's the spark jobs, which is the design, the music, the creative choice, the subjective space of things.
35:16Parth Patil:I think for me, it's objective versus subjective. Where it's like if it's execution versus like more of an exploratory choice, a subjective space. I thought that was very interesting, the cog versus the spark jobs. And what kind of tasks are cog tasks versus what kind of tasks are spark tasks. And he thinks that we're going to move people, human beings are going to move to a place where more of us are going to be in this spark jobs kind of role working with AI. Um, and I tend to, I think I tend to agree, you know, it's a, he said, he said, true art is improbable. Right. And this, this really landed because I think about like, what is the language model doing?
35:57Parth Patil:You know, it's predicting the most likely next token. Well, I once asked a language model to generate a thousand, you know, a joke every minute for a week. And then I looked at all the jokes and there was only one thing I realized, like every single joke was a dad joke and then i was like why is that why is every single joke joke corny it's like oh because the most likely punch line is the dad joke the most likely punch line is not the funniest punch line it's not the the this you know it's it's not surprising it can't surprise because it's trying to do the predictable and then it makes me think okay well they're not and and if you ask the language model if you go to chat tpt and you say pick a random number between one and ten more than half the time you're going to get the number seven and and then it's like oh wow like it's not even random because it is trying to predict the most likely number that a person would answer with when asked pick a random number between one and ten so you can't so like you know the right answer would be for it to roll a dice and then to say what the dice revealed but then it's like we're working with these tools that are predicting they exist in the predictable space the interpol they're interpolating between what we have and then that's where it's very like the more interesting weird people that we have talking to these systems they're pushing it out to that that that like out of distribution like out of the predictable space and so i think about like that's where this that's how i kind of think about the spark thing um do you think that do you think that this is like a feature of only today's systems?
37:29Great question. Look, I think, and you and I both know that being overly deterministic about AI won't be able to ever get to here. It only is, even though there will be almost certainly some things in that to state it with some, you know, probably 100 % determinist or probably 90 % determinist is a little bit of a fool's errand. But I do think precisely because of the general ways that they operate, there is the most predictive token, as you're talking about, there is the, with the mixture of experts and the kind of the learning thing, the learning paradigm tends to be the, what is a high-quality, high prediction to this?
38:19And if you're kind of being vanilla on the prompt, then the prediction is you want something generic or vanilla on the output. We can massage this by being much less vanilla on the prompt. That's part of what we're trying to help people understand to do in Tokens of the Future, podcasts and so forth. But it's also, you know, you put in workflow, you know, you have, you know, agents that play different roles that then prompt off each other in order to make stuff happen, including like red teaming or make that better or is that good enough and all the rest. So you can improve it along those vectors.
38:55Parth Patil:Like novel remixes of the way we attack a problem or create something. Yeah. Right. So we're already trying to push the limits of this, but I do think that the notion of how do we orchestrate – this is part of the reason why we're talking about taste, is how do we orchestrate to something that's really new that is improbable in the kind of the Jonathan art and creativity side? I think that there is a much more enduring role for us than, you know, and by the way, even as we kind of architect the kind of agents to try to do it, it's like one of the things that kind of lived experience and being, you know, kind of growing up in the world and so forth kind of helps us with.
39:49Plus, I think in addition to that kind of taste and judgment is the context awareness. So I would, generally speaking, think that we've, that that will persist much, much longer than, you know, kind of the kind of AI maximists will think that it does. And it might persist, you know, you know, air quotes forever.
40:14Parth Patil:forever yeah yeah i mean it's it's like the ai is just chasing the weirdest of us but like we're the frontier like i'm in the real world i'm constructing an interesting life and then exploring and then the things that i would do it can't because it's it's it's like reading it's like the thing that someone once told me that these models it's like they sat in a library and read every book um but they never actually ventured out into the world i mean eventually they will and then they're they're gonna be learning from the world but right now it's like they have this theoretical map of like how everything works and yeah a hundred percent man let me kind of add a kind of a call it a a you know um a vision not really a hope or an aspiration or anything else but not really a full theory but it's like look you want to combine the superpowers of these ai agents with our superpowers and the theory that we have no superpowers is an interestingly articulate theory because part of it is, well, we see these new amazing superpowers of the machine that we used to value uniquely ourselves.
41:19The ability to reason in symbols, the ability to operate in cognitive tokens, and you go, oh, it has all that. Oh, is there any rule for us? And I actually think that the likelihood that there's some areas that we actually, in fact, are much better in collaboration, let's just use the framework of token production, working with it is high, can be lensed a couple ways. One we said is taste. One we said is kind of context awareness and judgment. But another one is what most people on track is in a watt expenditure per token, we are geniuses compared to these AIs. Now, the good news for using the AIs, we can put terawatts behind the AI, and we only have 20 watts here.
42:13But to say, hey, look, that's not just the cheapness and efficiency of token. There's also something about the way we do it that will likely have some useful advantages in collaboration. And obviously, we want to be the human in the center you know, kind of producers of kind of value in this, but like using AI to amplify our humanity as much as we can.
42:43Parth Patil:Absolutely. Possible is produced by Pallet Media. It's hosted by Ari Finger and me, Reid Hoffman. Our showrunner is Sean Young. Possible is produced by Tanasi Delos, Katie Sanders, Spencer Strasmoor, Imo Zhu, Amon Suri, Danny Garrison, Trent Barboza, and Tafadzwa Nima Rundwe. Special thanks to Surya Yalamanchili, Sayida Sepieva, Ian Alice, Greg Beato, Parth Patil, and Ben Rallis.
From the publisher
Reid Hoffman and Parth Patil argue that if your AI experiments always work, you’re not pushing hard enough. Wrapping up the Tokens to the Future series, they revisit seven grantees’ experiments with $1,000 a week each in tokens. Their conversations span Dungeons & Dragons worldbuilding, an iOS game built in a weekend, and personal agents whose usefulness compounds over years. They explore how AI can shift more people from execution-focused “cog jobs” to creative “spark jobs,” and why expertise can sometimes make new possibilities harder to see. Throughout, they ask what to delegate and where story, pacing, and judgment still demand human direction. Plus, Parth’s example of ChatGPT picking seven for a “random” number raises a bigger question: how do you get surprise from a system built to predict?




