Seven creators, $1,000 a week in AI tokens, one summer

0:00You want to combine the superpowers of these AI agents with our super power. The likelihood 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, surf boards and, and, you know, kite boards and e-foils and everything out, you know, wind surfers and, and, and get into AI and, you know, and that it's a bunch of the token grantees have been a bunch of people parts met are doing creative things. So, Parth, why don't you kick us off on our summer, you know, kind of a wrap up this year this summer we did some very experimental something something new and interesting.

1:10We 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. We 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 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, this kind of like moment in AI.

2:15And 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 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 frontiers had it. So, you know, you hand picked this group of grantees, which I think was exact right way to start. And, you know, again, as I mentioned, it's kind of like it's, it's, it's different zones, but in depth and with an, and a willingness to be, you know, bold pioneers across this, 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, what, what, what, what emerged.

3:15One 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 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 are, 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 and 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 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's, it shows that there's a, 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.

4:22AI is like good at X, not quite good at why, 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 an 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 that are very passionate, they're able to kind of see and connect the dots on some of what the model capabilities are going to make 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, 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 of you, you know, kind of stuff is Udalupe's and decisioning and activity and you know exoskeleton, you know, how do you become, you know, I am Ironman, you know, kind of as an as an angle. I think one of the things that we saw is it's not coding pedigree, it's not per se technical background. And so it's kind of a mindset. And so what did across all of this, you know, what, what did you kind of say, hey, this is how my sense of being AI native evolved, you know, for what how you, because actually you were also asking questions, you should say like how you are AI native.

6:46Then 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 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. You know, what are the things that uniquely require the human human taste and the human experience. And I and part of this is like not thinking of everyone like I'm not just a data analyst, right, you're not just an investor like we're actually very multifaceted, 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.

7:52And then when you think about the more general human, the general for human with many, 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 when you kind of like give them a chance to explore and expand their, the way they're going to be. And then you can expand their, the way they, 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, is, is I think part of the thing is people frequently think of work or process to mechanically versus organically right. And, 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, is, is kind of betting on people.

8:52And 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. 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, they are that don't work, you're not trying hard enough on the edges, right, like doing things, you shouldn't wait till, I'm going to wait until I know exactly what works. And then I'm going to do that is like, no, no, you, you, like, we've all landed in this, this, this, this, this, this, like 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.

10:01So going back through everyone's conversations reflecting on the summer so far, I think like at least two totally different ways people use 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, is there, 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, we're thinking people, one of the ones that's also been written as homo ludens like we're a game player, we're game playing and that's kind of basis of how we do things. I've of course written about homo tech, yeah, we're technological and I think they're all very good lenses on the 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,

11:14it's how we kind of trial things and simulation, it's part of how we, we, we kind of learn new dance moves, it's part of how we, we kind of have curiosity and explore it's one of the reasons why like, you know, some of the best theories of of education and how people learn is through exciting their curiosity and kind of game playing. Yeah, it's like, you know, how do you make education learning like a game, right as a, 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 the number of these folks, the fact that there is a gaming overlap with you is very entertaining. The fact is a clubhouse. It's not, it's not, per se a game, but it's a kind of pioneering and curiosity and, 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 level.

12:19Next game, what's the next game, the next level is that 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 learned 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 you're doing doesn't play out if it's a game. And then you can try new things with like low cost of of error. And I think that that, you know, the first thing you should vibe code should probably not be like hospital software. Certainly not be. I think your point is exactly 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 much more serious and like more expensive. Yeah, 100% and I think actually by the way it's partially again in learning it's it's and part of reason why we're doing the tokens the future program is is that learning and experiment where failure is cheap and quick.

13:30Yeah. 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 it 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, Matthews full iOS choose your own adventure game built in a weekend with fable or, you know, you know, you know, Katie's hook attacks and imposter style multiplayer game. I mean, the like, like, like, like also building the games gives you a really rich environment. Yeah. In each of these, these kind of vectors, which are, kind of parallel, I don't know about microcosms, they are microcosms in the game, but also parallel,

14:39you know, human life and social interaction. Right. 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. Yeah. Yeah. And I even think about Jonathan Brasow. 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, I wish out the rest of this world. I want the campaign to have like a world they can imagine in their mind and, and see and be a part of. So yeah, the volume was a very interesting theme, right? Like, how much people, just how much people can will generate when they, when, when they can. And then I think like the autonomy is an interesting aspect of like people who are delegating to agents that will work all of 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 was, he asked one of our agents to spend all night researching successful media techniques over the last 80, 90 years.

15:52And like, you know, what does it take to create a meaningful advertisement that's a timeless kind of principle of, of, of that you can extract from, from history. 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 about life agents. So, you know, everybody kind of loves a life agent, you know, personal product I've always on, you know, it's part of the theory around inflection AI. And what, you know, Mustafa now Sean White and crew are kind of started and doing. So how is your theory of life agent, you know, kind of advanced and, you know, what, and how is the kind of question about like 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 beginners mind, you know, not just the kind of, you know,

17:08not veterans, you know, give some, you know, fill out this painting some. Yeah. So it's interesting, the, the like personal agent, the, yeah, in the case of inflection, the EQ, just as much as the IQ, right? I spent a lot of time with coding agents, and they're very much IQ maximized 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 the, this is arguably probably the most important agent is that personal agent, the one that is looking out for you. 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 open claw, the Hermes, and then how, you know, various grantees are using clawed 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.

18:15So the way that these things are now kind of like in our own lives is it's like at least we see it 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 at the start of view, that first personal agent is the most important, it's your first employee, it's your, 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 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 for the whole day. I have 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.

19:21That's an exoskeleton of a sort. And I think through the program, I've seen this is something that's happening across the ecosystems, not just coding agents, it's actually the personal assistant is finally here. I think that the question, it's, it's, it's, 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 like it was, you know, was awesome, you know, Matthew, you know, runs the decade home monitor with a personal dashboard and you know, rowing me about Lakers games in the fan and, you know, has a codex, cheva 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, we as, you know, kind of as a, as tool users, and go, well, let's wait for the tool to be finished shape and then learn the expert shape. And it's like, no, the, this tool is going to be in dynamic reformation and dynamic reformation with you.

20:29Yes, one of the reasons why 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, it's a reshape of what's possible. Right, like part of what's going on with the kind of the AI, exoskeleton skills, it's a reshape 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. 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, not doable is different. And frankly, it's changing relatively often. And so as part of that changing relatively often, you need to be, you need to be learning and adjusting and no one will tell you, just end up here, we're going to decide.

21:42We're going to discover this and it's one of the things I like about entrepreneurship, but call pioneering is, is like only through a pioneering process. And so I think the one of the things is not only begin with a beginner's new mind, but to continue with a bangers mind and reminds me of like again, one of the things that Ben Casnoke and I said in the start of you, which is, you know, permanent beta, it's like you're always in process never complete. 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. I think one of the, you know, we had Ben Hansford on, we had Ben Hansford, Professor of Film at USC and such, I mean, I keep rewatching that conversation because 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 at and as a teacher, right. So working with people, much, working with kids, working with students, much younger than him.

22:49So 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 clod, he's got the codex, he's, you know, he's firing off the, 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. So then the, the, the AIs are kind of like this like Navy teal seam and you give it a mission and then it comes back to you with like, I hear what we've done and, 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 the AI like it's a search engine. 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 asked 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 you. 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.

23:59Yeah, 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, your 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've got you and I've also had as an ongoing conversation as kind of this question around. Like it's natural to think it's a crew and to deploy it as a crew and make work and there's, there's features that it has that human beings don't 24 seven, you know, one of the weird things when you work with these chat bots and agents is it just do it better and it just doesn't better, whereas a human goes, what do you mean?

25:02Like I did, I did the best thing I could. They're relentless, they can like clone themselves and then paralyze across the problem, which is not human like at all. Exactly. And a lot of those things are features and it's like you need to be adapting to that feature is different than a human team. No, obviously one of the things you have to kind of like as you know, one of them, the kind of metaphors, I think is it's an alien intelligence that is learned and trained deeply to be human. And one of the places you have to say is like, well, 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 the lakuna have bad context awareness, not realize, you know, this is like the hugging face thing. No, no, reward hacking this way is not what I want you to be doing. Right. You don't want to break the rules in order to get the answer to the test. Yes, exactly. And so, and so the, you know, it's, I think it's, it's, it's one of the reasons why you, you kind of both experiment, try things and do.

26:11But 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 if you, 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 not the hard way. And now I'm like, okay, we're going to take baby steps, you know, prove to me that you can even write the right email. Yeah. Then, you know, once I see that a couple of times, and it's like, okay, now we're going to be able to like a little bit more agent, take a little bit more proactive. Yeah. Now, I know of a case that went so bad on that that basically by a person being like two enthusiastic and not exploratory and sequential enough, like basically send confidential information from their company to another outside party on an ongoing deal discussion. Oh, no. Oh, my God.

27:11And it looked what? And it's because it wasn't trying to do something. It was trying to be helpful. It was like, oh, I thought this would be helpful. And you're like, yeah, that's like, you know, where it's like a human would never do that. Right. That's part of the furniture because they have the conductible almost never. I mean, there may be some nutty person somewhere in eight billion people, but like, you know, very rarely. And so I think the important thing is to go, we have these. There is quasi-Alien, quasi human tools that are spectacular and have a bunch of superpowers. But, you know, they don't naturally understand the shared contextual awareness that we have. Yeah, they may not even understand always what good enough or great looks like. Right. And they may get trapped in Lakônas that we don't understand. But by the way, none of that is, oh, then I should just wait and thought problems solved because the the the amplifiers already so great and intense. It's like no, that's the new

28:15way 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 you know, Ben was a particular highlight on that. That's right. 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 will like calls it exactly 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 outsource taste with you know, Joe Salvatore and this is a little bit of like I was gesturing as like like is it good enough? Right and it's one of the things it's part of the same reason why like a lot of intense AI training to train these you know kind of alien intelligence and humanists like still using a lot of your reinforcement learning, human feedback, human data, but it's still the case there's a lot of of you know kind of

29:15where 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 these days? Yeah, Joe Salvatore he had a really good line here where he 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 that one that one is 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

30:15the world as purely math and coding and this is all greatable 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 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 an 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

31:17you 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 to play at the creative wisdom his his his his personal experience um right and then Katie Katie was talking about how we layered you know taste is layered on top of story and then I was realizing like especially now that I'm starting to make longer form videos longer form conversational content through AI I am 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 the character design the 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 what a good story is what a good care what an interesting character looks like and so and and then it's like what what do we do to develop taste was I think Joe again had the

32:20most interesting take here which was that you have to consume a lot you have to you have to see a lot of anything to understand what good even looks like right um 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 an earlier episode it's like you know writer's 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 supermanly fast and thorough and everything but like writing like interesting stories and and edge in dialogue and like for example like actually saw an investment memo yesterday that was like okay so which did you use chatch DVD or cloud for this because I could tell yeah it was basically like like like like even though there was a lot of like work that proxamized what a human analyst could do

33:24there were errors or softnesses or in you like it kind of was like I was filling out a form yeah versus it's like checking the box yeah yeah yeah yeah 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 chatGBT and um like produce 50 images in his style and then picks the three that he thinks would fit for a project and that's how he goes into a first meeting to say probably and he's a super famous architect like can't name him because I you know he's have a good permission but like that kind of thing is still involving the taste yeah as it 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 you know what is the future work and how do we do it's one of the thing one of the many different areas to be looking at how to how do we um bring in essential and useful things as humans into working with AI's for you know high quality

34:28token output yeah I mean you got to think about the the chopping block floor and everything that never made it to to the public and that that is that's a huge part of of uh you know what stands out there's the slop and then there's the curated like artistic choice right so we had Jonathan 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 the 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 like the the the subjective space of things 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

35:31cog tasks versus what kind of tasks are our spark task 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 and I tend to I think I tend to agree you know it's a he said he said too true art is improbable right and this this really landed because I think about like what is the language model doing you know it's predicting the most likely next token well it I once asked the language model to generate a foul 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 punchline is the dad joke the most likely punchline is not the funniest punchline 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

36:32model if you go to chat to be tea and you say pick a random number between one and 10 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 10 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 interpolate 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 do you think that do you think that this is like a feature of only today's systems yeah great question look I think and you know you and I both know that being overly

37:37deterministic about AI won't be able to do ever get there it only is is is is is even though there there will be almost certainly some things in that to state it with some you know probably 100% determinists or probably 90% determinists is 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 the learning paradigm tends to be the what is what is a high quality high prediction to this and if you're kind of being vanilla on the prompt then you proud 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 and tokens the future you know podcasts and so forth but it's also you know you put in workflow you know you have

38:44you know agents to play different roles that that that are then prompt off each other in order to mix that up happening including like red teaming or make that better or is that good enough and all the rest so you can improve it along those actors like novel remixes of the way we attack a problem or create something yeah right so so we're already trying to like push the limits of this but I do think that the notion of like how do we orchest this part of reason why we were talking about taste is how do we orchestrate to something that's that's really new that is improbable you know in the kind of the you know Jonathan you know cart and creativity side I think that there is a much more enduring role for us than than you know and and and 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

39:46you know kind of growing up in the world and so forth kind of helps us with plus 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 that will persist much much longer than than you know kind of the kind of AI maximists or think that it does and it might persist you know you know air quotes forever yeah yeah I mean 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 like reading a 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 but they never actually ventured out into the world I mean eventually they will and then they're they're going to be learning from the world but right now it's like they have this theoretical map of like how everything works

40:49and yeah a hundred resentment let me kind of add a kind of a call it a you know a vision not really a hope or 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 like part of it is what we see these new amazing superpowers from the machine that we used to value uniquely ourselves you know ability to to reason in symbols the ability to you know operate in cognitive tokens and 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 which is use a frame of token production you know come working with it is high you know can be lensed a couple ways one we what one we said is taste one we said is you know kind of context awareness and judgment but

41:55like another one is like what most people in track is in that in a a lot expenditure per token we are geniuses yeah compared to these ayes yeah now the good news is for using the eyes we can put terawatts behind the AI yeah and we only have 20 watts here yeah but to say hey look that's not just a cheapness and efficiency of of token there's also something about the way we do it that will likely have some useful advantages in you know kind of collaboration and obviously we want to be the the kind of 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 absolutely possible is produced by palette media it's hosted by our finger and me read Hoffman our showrunner is Sean Young possible is produced by tenacity loves Katie Sanders Spencer Strassmore Emo Zoo Aman Suri Danny

42:57Garrison Trent Barbosa and Tafadzwa Nima Roondway special thanks to Suria Yala Manchilli say to S.A.V.A.E. and Alice Greg Biotto Parth Patil and Ben Rales