What can AI learn
from a reel of film?

134 timestamped passages. Select a time to play that moment; the # beside it links to the passage.

1. Meet Phil and the archive

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   There we go. So joining us today is uh Phil Mayer. He's the founder of Stockfilm, an independent archive in Broomfield, Colorado [snorts] that preserves and licenses 8 millimeter and super eight home movies. Its material is the everyday record, family gatherings,

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   streets, holidays, and road trips rather than footage staged for a production. The archive makes those scenes searchable for filmmakers and other people working with historical images. Mayer came to this work from software engineering. He started stock in 2015 after finding films his grandfather had made around Chicago and on family trips.

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   What began with those reels became a catalog of more than 162,000 searchable clips. He also builds tools and experiments around the collection. Those include software that suggests a clip likely decade experiments that reconstruct blurred faces and image models trained to generate new scenes with the old with the look of old Paris footage. His production archive remains

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   separate from those generated experiments. On October 7th, he published a collection of moving restoration comparisons together with a tutorial that lets others inspect what the software changes.

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   Hey guys, can you hear me? Okay, Phil, welcome to the show. Hey, thanks for having me. Super appreciate it. So, so many things uh to cover here. Maybe how do you want to start and you know what what's kind of the normal stuff?

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   There's also some pretty far out experimental stuff that I'm super keen to get into, too. But, uh, give us the the kind of normal introduction to the business before we follow you out into the more experimental and and, uh, dare I say weird sci-fi stuff you've been exploring recently.

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   Yeah. Yeah. Thanks. I I appreciate that. That's uh, it's a good way to set it up. So yeah, I started this business back in 2015 as the introduction stated. Um I come from a uh software development background. So I out of college was living in San Francisco writing Java code, working 14hour days for startups, all that kind of stuff. Um but back in

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   2015, I really thought, "Wow, this is neat. I want like a realworld asset, something that I can license, something that I can have that's digital online that isn't already online. Um, and uh, that's kind of how I fell into this field. And I found it very fulfilling in the sense of being a steward for these people's lives, being able to save a lot

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    of this footage from getting uh, deteriorated or just destroyed or forgotten. And uh it just sort of snowballed from there and it just kept I just kept going and going and capturing more footage and I re I retain 100% of the physical master copies of all these 8 millimeter and super eight reels as well. Um in the sense of thinking about

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    okay in the future how can we start to capture these even better? How can we start to use other tools? Um, and this was way before AI kind of came along. And then AI made it very apparent that there was going to be some some real opportunities here to make upgrades to some of this footage um in the sense of,

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    you know, how can we keep it original to um really honor what it is without altering it with AI and other images like that, but also be able to upgrade these things and to really kind of make them come alive. um because you know 8 8 millimeter is a very small format. It's a very small frame that you're dealing

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    with. So there's only so much information that's actually in all those frames. Um so there are limitations but um it's been really fascinating to see how quickly things have moved in the AI space to support research um in this space. Uh we've worked with um a lot of different papers um with people wanting

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    uh a bunch of videos that were damaged to be able to train on them about how to fix uh damage and things like that as well. So um it's just a really exciting time I think to kind of merge this old content with new ideas and what technology can help us do with it.

15. Physical film as a source in the AI era

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    So, um, I'm we're colliding. You want to go first, Pash? Go for it. Um, Phil, it you know, the the whole archive sounds like low background steel to me. You know, you know, low background steel is uh is uh steel that was uh originally smelted before uh nuclear weapons uh were first, you know, exploded in the

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    atmosphere. And so they are uh they have much more a different different composition of radiation and different composition of materials because they didn't have that radioactive material in the in the atmosphere at that point in time. And so uh low background steel has been kind of almost a resource that people can use to kind of differentiate between you know different different kinds of materials um you know which

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    have been produced by humanity. And uh it sounds to me like the super eight stuff is um clearly clearly not AI produ produced clearly like prior to uh any AI uh actually producing these things. uh and and as of such it's kind of um this resource that we may not ever have again right it's a it's it's basically uh we

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    have all of this production of AI material going forward but we have you know this stock of human only and guaranteed human only uh produced material even even if it looks like amateurish in some sense what do you think is the value of this going forward do you do you See, do you see like great value in it? What what do you do you see

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    it as this kind of resource that is, you know, is going to increase in value over time? Like what what is your perception and why do you do this? You know, that's a great question. I I really like that analogy about the steel. That's interesting. I hadn't thought about that, but yeah, that makes a lot of sense. Um, I think that, you know, obviously with the AI stuff that's happening with video especially, um,

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    it's getting so good that there's an infinite amount of video that's going to be created at, you know, near zero cost in the future as we start to look out, you know, 5 10 years or even in infinitely in in timelines here. So from a rarity sort of standpoint, um yeah, I think that the human-made stuff that

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    can't be replicated, that can be shown through Providence that this is actually made um by humans, was on films, and having those physical films to show that, hey, look, this frame matches this digital copy we have and all that. um is it's a pretty powerful way to think

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    about um how we preserve our history and how we keep moving forward. Um, I think from a standpoint of if this is going to have a lot of value, uh, I think so. I think it has a lot of value in the sense that it's rare and beyond that it tells our history that other things can't tell

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    and that, you know, it's a real race against time for these videos as well. Um, well, films actually. So, you know, they degrade rather quickly in the sense of any heat and humidity that's applied to these physical films really starts to break them down over time. Um, as I was, you know, going through, you know, hundreds, which turned into thousands of these reels, there was a lot from the

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    1940s, from the 30s that were unc uncapturable. Um, they they start to degrade pretty quickly. They get really brittle. They break a lot. um they start to shrink. So the the spaces in between the little holes that you're dealing with that keeps it um playing starts to get smaller on there and it jams up and you have a lot of real issues in in the capture process

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    where eventually just becomes sort of a a glued together brick of film that just you know you really can't do much with um at least now. So yeah, I think that there is a lot of value in thinking about our history and preserving it and keeping it like that and that this is a a valuable race against time that we

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    should be capturing more of this footage and cataloging it. Um because eventually these films are just going to be you know gone just like a lot of physical media just like the the problems we have with CDs and other physical media here

28. The cost and craft of digitization

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    that doesn't last forever. Ju just to set the stage what what is the cost of archival like how like how much does it take to you know archive like a 100 hours of old super eight uh film?

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    Yeah that's a good question. Um, you know, it it depends. Most of the we brought everything inhouse and I built systems from scratch to be able to do this. When I started doing this, I didn't find anyone that was doing it in full 4K resolution. Um, and I wanted to really sort of explore this and really be, you know, kind of kind of build this

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    myself. So, I was able to build these capture devices to be able to to get the best quality footage that I could from this. And different generations, it got better and better when I was doing that. But, um, I think typically you're looking at sort of a price per foot kind of scenario. So, maybe a dollar or two per foot of

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    footage. And so generally you have, you know, the the 3in reels um that were pretty popular to use back in the the 60s and 70s. Uh those contain, I think roughly around 60 ft of of film on it.

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    Um so it starts to get fairly pricey after a while, especially if you're really trying to capture it at the highest resolution. Um, and then that doesn't include any sort of cleaning things up, any color correction, anything that you do to sort of even just lightly modify it to make it look a little bit better than it was. Because I mean, you you got to realize that 8 mm

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    and Super 8, this was the film format for amateurs. This wasn't pros. These were people that were filming their their family Christmas, their their trip to France, um all their funny moments and stuff like that. So, it's not very wellshot stuff. A lot of times it can be pretty shaky. It can be pretty pretty amateur film kind of made um just

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    because that's sort of the nature of it. And of course, back then too, it was very difficult to make really good um videos. It was really hard to do. I mean, you had to get your exposure right. You had to have a lot of different settings between working in daytime light versus indoor light. Um, and so it wasn't as easy as just pointing your iPhone at something and it

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    comes out almost perfect. Um, it was it was quite challenging. So, I've gone through a lot of reels that were not in the best shape from a cinematography sort of standpoint like that, too. Um, so yeah, so it's it's really kind of finding the right films that tell the right story in the right place at time that are really interesting that people have um, you know, captured time and

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    space that they didn't really think would be interesting at the time. you know, cool uh background shots of technology or storefronts or things like that that are just in the background that weren't really part of the shot that make it more interesting to tell the story and kind of create that texture and that moment that documentary makers that people that license the clips from us want to have that real

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    kind of image there that really tells a story really quickly. and then having some kind of actor narrate to what you're supposed to be feeling in this scene where 5-second clip you can really just feel in the the grittiness of the streets or whatever you're trying to portray.

39. When a guessed face feels personal

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    You mentioned the process of like AI enhancing some of these old assets and I'm really curious as to what you've learned about what constitutes a job well done there. Like what do people really want in an AI enhancement of an old piece of footage,

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    you know? Yeah, that's that's a great question too. I think that, you know, one thing, one assumption we made, I started working on doing a lot of this um AI stuff with some local machines I had with some beefy GPUs um back in 2013 in the 20 or 20 23 in 2024.

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    um a lot of cool open- source projects. Uh G GFP GAN was a popular one that was uh modeled around faces. And so it did a real good job when you had sort of a clear straighton sort of face, not moving too much sort of thing to really bring that face in. And um you know, obviously it's making the best guess

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    that it can for that face. Um, but that I think was really powerful for people to see their their grandparents, their their a lot of their family and stuff like that in that sort of moment. Um, that it was this grainy uh picture that they'd never really seen before to get um this clear shot of

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    their face, even if it wasn't quite exactly their face. Uh, I think the best guess for that is good enough for a lot of people to have that aha moment to be like, "Wow, this is cool. This is this is what we want AI to do is make this stuff come alive." So, that clarity of resolution. Um, but especially I think with the faces, I think it's all about people and it's all about if we can

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    connect with the face that was there in these historical moments, um, it really feels personal then.

46. Why AI buyers ask for raw footage

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    And how about on the AI training side? This is obviously like a pretty unique source of data and I did see in the footer of the website you're, you know, uh, available to license the data set for, um, people who want to train models on it. What do they need? How are you, do they want the raw? Do they want the enhanced? Do they need like a bunch of meta data? Can they create that on their

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    own? Um, and what role is this playing? you know what what what have you learned about like what they want uh based on the fact that they're interested in these kinds of assets in the first place? Yeah, I mean it's uh it's been pretty popular. You know, we've talked to a lot of people um a lot of different AI companies all over the world about licensing this out to them. Um they want

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    the original footage like that. They don't they don't want anything cleaned up. they just want to take it um for their own research um and for training of course too. And you know they when we talk to them they they want to either clean up the footage themselves or or kind of cherrypick through the best kind of parts um to

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    create different styles. Um because you know, you got to understand that we've got footage from the 30s up to the 80s and Super 8 didn't come out until the late60s, which was just a little bit better than regular 8. Um it's not a huge jump in quality by any means, but you know, a lot of it's black and white, a lot of its color, um and you know,

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    just different different vibes all all over the place like that. I mean, the 30s and 40s look alien. It's an alien world we're dealing with here of just how people look and acted and and the the mannerisms. I like to say that the uh the smile wasn't quite invented yet in a lot of the 30s footage. Like when people pointed a camera at them, they were kind of a deer in headlight. They

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    didn't know what to do sort of scenario versus now where everyone gets the gritty smile when you know they know how to smile like that. So it's uh yeah it's it's it's been really interesting having these conversations about training about what we can do a lot of the metadata we have um we were you know having this collection started in 2015 um you know I ended up using a lot of

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    people through um people that I hired to go and do all the metadata tagging by hand. So people would view a 5-second clip. They would write all these different you know attributes of it, check a bunch of boxes um in a in a form to that we wanted to um you know clarify the quality of it um things like that.

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    Uh we used Mechanical Turk back in the day which I heard they just shut down recently. RIP Mechanical Turk. Uh, but that was a great source, a great way to really scale this, to go through hundreds of thousands of of clips like this to get real people's eyes on that to look at that stuff. So, you know, at the same time, we've run a lot of the, you know,

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    the latest models to look at the content we have to see where we didn't get it right on some of the, uh, you know, the metadata, tagging, and stuff that we've done. And uh it's it it's it's incredible. It's incredible to see how well the technology has come. A few years ago, we were using it. It wasn't that great. But it's as you guys know,

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    um AI is just unbelievable how quickly things just keep going in all these different aspects of it.

57. Archive scope, acquisition and rights questions

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    Yeah. Is there a reason that the data set stops at the 80s? Like are my my parents uh VHS tapes of me as a kid not uh of economic value for the AI machine for some reason? Well, I wouldn't say they're not of economic value. It just wasn't our our focus. Our focus was 8 mm super rate.

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    Let's go super deep into that. Um build these, you know, capture devices for this and really just dig into that. And yeah, around 85 or so is when a lot of the the 8 millimeter stuff has really stopped. I mean, that's really when the VHS took over and all the the tape formats that sort of followed sort of in and around that time. Um, although there's been a resurgence into 8

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    millimeter now for uh you know filmmakers and stuff like that that are doing stuff in Super 8 or 16 or you know um the the Christopher Nolans of the world that are using physical film at 70 millimeter format. But uh but yeah, a lot of it just sort of went away at that time. It was just so cheap and easy for dad to have the big thing on his shoulder to record the VHS tapes of

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    everything that Super 8 really faded away from there. So yeah, we go up to about 85 is is sort of the the last uh years that we have. Um let's talk a little bit about um the acquisition process. How do you acquire archives? Are there any licensing or intellectual property issues? Are there

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    issues with provenence where you know someone is having an estate sale and they have a bunch of old film but you know it's like you know grand granddad's film or whatever and they don't know what to do with it. Like how how does this how does this work in terms of IP privacy and like a bunch of these other things? Yeah. So what we end up doing is you know we find people that want to donate film to us um or you know sell us the

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    rights that kind of stuff like that. Um, so that is a great way to do it. Some of the films I started early on were from my my grandfather and then my wife's dad had a lot of great films from the 60s and 70s too that he had made. Um, so we've got model releases for that stuff um of the people inside there. Um, but

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    otherwise, yeah, it's it's a real sticky situation. I mean, you've got, you know, this idea of orphaned films, films that, you know, we don't pe that were donated to us. We don't really know where they came from. We, you know, use our best guess to look at technology, look at the old cars in a video or, you know, a film and be like, "Oh, well, it couldn't be before that because that's a 57 Chevy"

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    kind of thing. And and, you know, to date stuff, to find the locations, to do all that. Um but yeah that is you know it's it's a big question. I think the the AI companies are really dealing with this in a big way too about what can they train on how can they be allowed to scrape the internet to be able to train on this stuff? Who owns the rights to

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    these things? Um so we end up you know having a lot of our content is marked as commercial or editorial. editorial in the industry is sort of things that don't have a model release on it and are, you know, used in a sort of non-commercial way. Um, but yeah, that's, you know, it's it's something

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    our lawyers talk to us about. It's, you know, different for jurisdictions too based on IP. And you know, at the end of the day, we have 100% of our physical master reels. And you know, that's that's a great start to showing that, you know, we have the rights of this. We don't do anything with um with public domain

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    uh films. There's a lot of websites that you can find that have public domain films. They take them off of archive.org or or some of these other places, Library of Congress, and they license them out. They'll charge you some money for these free videos. We don't we don't do anything like that. I don't think that that's interesting at all. Um, so everything that we have is stuff that we've captured that I've personally captured. Um, you know, 100% of it.

69. Could synthetic data change archival demand?

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    Cool. Well, maybe one more quick question on kind of the business side of things and then I'm very interested to get into the timefly experiment. Um, what's your perception of how hungry the AI companies are for more of this data? Like are they as we obviously they're, you know, scaling many things. Is this something where they feel like they just need a little salt into the rest of the mix and they've kind of got it covered

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    now with your help and maybe some other help or is your expectation, you know, have they given you guidance that's like, hey, uh, Phil, if you had like a hundred times as much of this come next year, like we'd be keen to buy it. Uh, such that, you know, then you maybe go out and make investments in expanding your catalog. what kind of signals are you getting in terms of the elasticity

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    of demand from the AI companies? Yeah, I think that um you know there's a lot of AI companies all over the world and you know we focus on the the top tier ones uh obviously the open AI anthropics of the world but you know when you start to look worldwide there's a lot of people competing in these little niches um to do things. So I think there's there's a lot of demand

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    right now. there's a lot of demand that people are, you know, looking to train very specific things. They need specific data sets and that, you know, they're going to get really good models, um, doing things that are are very laser focused in what they want to do. Um, you know, at the same time, um, I was listening to one of your previous

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    podcasts and the idea of synthetic data that you guys were talking about, I thought was super interesting to think about. And you know, it made me think about my business like, well, wait a second, they could just take some of these videos and salt it, if you will, to be able to have all of these uh, you know, permutations, let's call it, of

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    these different videos in there to create this synthetically too and then train on it as well. So, you know, it's it's a wild world. I think that we just gota, you know, stand by what we have and, you know, a lot of our business is coming from documentary makers and people that want to show, you know, truth where they want they can't be

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    showing AI generated footage of something because it's it damages the whole concept of making a documentary that it isn't true. Um, so that's, you know, really the niche I think that's that's interesting for us to play in.

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    But yeah, you're exactly right. I mean, the synthetic data stuff is going to be really interesting to see how they can just build on this, build on this, and then, you know, cut out um content creators and stuff like that in the future.

78. TimeFly: the film, the wiring and the assumptions

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    Yeah, I think that that low uh background steel analogy is pretty interesting. more interesting as I think about it more. Uh let's talk about this timefly thing. You want to share your screen and this is um this is actually the original thing that really caught my attention in your uh first message to me. It was like whoa this is a far out experiment. So if you want to share screen, bring it up and talk us through it. I'm very curious to see

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    what you've uh what you've done here and and also try to understand how I should interpret it. Sure. Sure. So, um yeah, we um let me pull this up here for a second.

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    Yeah. So, you know, I've been a uh I've been a technology guy for forever and ever. And um so when I heard about the timefly thing come up, I thought, "Wow, this is really interesting. This is a a neat way to do this." Can you guys see that now?

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    Yep. Okay, cool. So, the concept was, you know, I wanted to see what we could do with the um, you know, the mapping of the the fruit flies brain. So, anyone that doesn't, you know, understand what's going on with this, this was a project that came out probably about a month ago now, um, where scientists had

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    mapped the brain of a, um, of a fruitly and they open sourced it. And so basically it's a it's a graph that contains I think about 174,000 modeled neurons um which is like 6.1 million connections. And so what we wanted to do was well what you're seeing

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    on the screen is that there's you know there's several things. There's um a couple examples of our footage and then the sample visual input and then we have a simulated neural activity and of the the flies avatar if you will. And so the flashes are events in the simulation,

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    not recordings of an animal's brain. So what we're doing here is that we took the anatomical source um which is the the data set that was published by the this collaboration and um so those researchers did the reconstruction and the contribution I wanted to make was building an

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    interactive experiment around the published data. Um and so what really makes it worth showing is that we can separate the ingredients. Um, we have the recorded film and then we have the scientific wiring map and the assumptions that turn that activity into movement.

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    And so basically what we're doing is trying to say like, okay, what would the fly see if he was looking at this footage right now? What would they actually be able to see? Um, and so what we have is a light source too that you can move around on the image on the

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    video that directs where sort of the the uh the fly would would move towards. So the assumption is is that the the fly moves towards a light source um more than it doesn't. And so the light sources inside the video as it's changing where it's looking at is flying towards that um sort of path like that.

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    So you know it it's an experiment I wanted to do to see how we could start to incorporate um some different ideas into this. We've done a lot of exper experiments with um like I said with training data with AI um training our own stuff to see how we could do that with you know pictures

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    um and so this was another idea I thought would be interesting to do. I think there's more work to do here. I'd like to explore more ideas around what we can really tap into from this. I think this is really early days for a lot of this um sort of neural mapping, especially when it comes to like an animal and then thinking about how this

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    really applies to other animals in the future and what we can really gain from kind of learning the perspective of an animal. I don't know. It's a little uh it's a little out there, but I thought it was an interesting idea to start to explore and hear what other people kind of thought about it and get some feedback on this of how this could be improved as uh and then eventually open

92. Wake the fly: the live demonstration

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    source, you know, more of this out to the community uh once we have something that's even a little bit more interesting. you want to play it like wake the fly and um talk us through what's happening just procedurally as this happens. Yeah. Yeah. My screen here.

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    All right. So, what we do is we uh Can you see my uh cursor and everything? Yep. Okay, cool. Um, so we wake the fly and we can see that the fly is coming over here and this computer right here.

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    Could you set it to full screen perhaps or uh um Yeah. Yeah. Let me let me do that. Oh, that's much better. Yeah. A little zoom in there. There we go.

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    Yeah, there we go. Um, is that better? Yes. Yes. Much better. Okay. So, this is what the fly sees up here is we have this this sort of idea of, you know, kind of looking through um what it's looking at on the the video as it sort starts to fly around. And then this is our our light capture um idea here that we sort of direct the

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    the fly towards this or will it go towards this? Um so it's moving sort of randomly across the screen but um in the in the sense that it's it's trying to find follow the light of where the the actual video is going like this. Um, so that's sort of the the concept here.

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    And so, you know, I start to think about is there is there more here? Is there what would more scientists think about this? How could we start to contribute more to thinking about what's going on actually inside the the brain here? And um but yeah, it's just uh just a fun

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    demo we put together that I think um you know, we we we like to experiment with this kind of stuff.

100. How does neural activity become movement?

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     So when you move the fly, when the fly moves, how is that being read out? Like we're I I understand we've got this wire. This is in a sense sort of an upload fly, right? This is the the fly uh the M the M fly edition fruitfly edition. So we have the wiring established from real world

102. 1:54:27#

     experiments. We can then say okay like we know what the neurons are. So we can sort of simulate light hitting those. I assume there's probably some pretty important questions around are we doing that quite the right way or not. But leaving those aside, we're at least able to put some light in there. Then the thing sort of propagates.

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     Is is this open source project itself saying like it steers left steers right? Is that how how are you getting to that level of abstraction on the other end of the whole fly brain process? Yeah. So that was the assumption that we made. Um at least most people start to think about how the fly moves around and

104. 1:55:12#

     that it does move towards light. Um and you know at the same time I think there's a lot of other research that's shown that the flies move towards sort of a sense of smell. I mean they are fruit flies and so they're attracted towards you know food and stuff like that. So, you know, not having any smells in these

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     videos um makes it uh that we we chose to go after sort of this like um idea. But in terms of like how you know, how do you interpret its response? You you give it a stimulus, it propagates through the fly brain, now it moves on screen. Help me understand that translation layer from

106. 1:56:01#

     activity in the wiring of the brain to now it's moving left, it's moving right. How are you reading that out from the fly brain? Yeah. Yeah. Um Yeah. So that's you know that's the connections that are are are made in the brain are that you know it's tracking towards light inside of these

107. 1:56:27#

     uh demo videos that we have. And so that is kind of the core of what's going on here. Um, so it sounds like that's basically something that the original library or open source project that you're building on top of is kind of is yielding like it is saying given these outputs and you

108. 1:56:51#

     know however many timestamps the output is fly moves right, fly moves forward, left, what have you, right? Yeah. Yeah. Yeah. That's, you know, we're taking what was already open sourced in this and then saying, okay, well, if this is already part of it, how could we start to, you know, have it look at our our clips and then, you

109. Curiosity, animal perspectives and virtual reality

110. 1:57:14#

     know, interact with it in sort of a way that would make sense. Is there a way that this becomes useful for you or is this just pure uh let's say um sort of Victorian gentleman style uh quicksotic experimentation for you?

111. 1:57:33#

     Well, I think that that's what it is. I mean, it's it's fun to experiment with this kind of stuff and see what the limitations are. Um I think that, you know, there's not really a monetization plan from here. There's not much we need on that. Um, I think that these are like a kind of fun experiments that, you know, you just go down these paths and out of curiosity and kind of see what happens. And maybe this will spur other

112. 1:57:59#

     people to think about other ways that they can use this the these open source projects and all this data behind this brain to do some really interesting stuff and to think about um you know like I think about virtual reality and stuff like that and how [snorts] you could start to map other brains or even just the fruitly for becoming that

113. 1:58:25#

     fruitly. and that you are that fruitly then and you get to experience the world like the fruitly. I mean, I'm sure a lot of people would love to do this once we get up to a dog's brain, their their pet's brain and really experience that through a VR set to what does the dog think and smell and see or just, you know, even just the visual cues um that a dog would

114. 1:58:49#

     generally have and kind of what's their what's their world like?

115. Browser compute and reconstructing an input

116. 1:58:58#

     It's far out stuff and it's this all runs in the browser too, right? Which is an interesting uh detail. I mean, we just saw this analysis from Anthropic where they said that they expect in the next few months small models will start to be capable of self-replicating across the sort of internet of you know things, internet of

117. 1:59:21#

     devices. They were talking about how uh you know even like a Google TV might be enough you know to host a small model and that these things will you know kind of find these little free compute niches out there. This is an interesting biological anchor if you will on just like how much compute is kind of hanging out. uh obviously this is you know

118. 1:59:46#

     hundreds of thousands of neurons six million connections you might call I don't I'm not sure exactly what would be the right anal analogy for parameters here what's the difference between a connection and a syninnapse in this analysis do you have clarity on that you know I think u I think there's a lot to learn here I think this is you know this is a real interesting open space and I think that you know there's a lot

119. 2:00:12#

     of the compute that we've used for AI. Um, when you compare it to something like this, it's way less compute to do all this. So, yeah, I'm not sure exactly um some of the stuff with this. I'm, you know, really excited to experiment more with this though and to see what we can really do with this and how we can

120. 2:00:36#

     really push this. So it strikes me that um you start off with kind of film and then you kind of scan the film into bits and then you have basically a representation of that film in bits. So the question for me is on the on the fly side obviously you then translate through the

121. 2:01:02#

     fly brain into uh the flies representation. Uh, c could you translate from the film bits backwards through the original kind of lens on the camera into kind of like the photons that really

122. 2:01:26#

     came into the camera at that historical period of time and then from there could you translate it onto something else. So basically like you know you have some bits but could you use those to kind of project other bits for example um you know in in the color and in the in the speed of film that the original

123. 2:01:50#

     human being would have seen that archival footage in 1963. Yeah that's a great question. I think that that is, you know, a really good idea for thinking about how to mash up some different ideas around inputs and outputs and how we can start to think about what is really interesting here

124. 2:02:14#

     and how can we really leverage this from because this is way different than using um the the LLM models that we've we've built here that we've become sort of accustomed to um and what's the real power here and so yeah, I think that's a great question.

125. Agent commerce: integrations and actual buyers

126. 2:02:40#

     Check that mute button. Let me segue a little bit here. Um you you've also uh created a way for agents to purchase um clips and film from you directly. I think you've set up a X402 uh endpoint. You set up crypto payments.

127. 2:02:58#

     Uh you set up, I think, an MCP server for purchases. What has the take up been? Have you seen a lot of agents purchasing? Have you seen the take up on the X42 endpoint? Um what's what's been the reception? What what's been the, you know, usage been since you introduced it?

128. 2:03:15#

     Yeah. Yeah, that's a great question, too. Um yeah, we haven't seen much on that. Um I wanted to experiment with that a lot, too. I thought that that was really, you know, everyone's been talking about the next web is agent to agent that people aren't going to be involved in the next web and um so we wanted to kind of get ahead on that. Um we've been, you know, we've had a lot of

129. 2:03:39#

     um people that are MCP server aggregates contact us and add us to their directory, add us into their listings and things like that. Um, so that's been pretty exciting to sort of, you know, be featured on on some of these things. We've made a chat GPT extension. We've done, you know, the um recent meta stuff that they rolled out um with their views

130. 2:04:03#

     AI as well to add into there, too. Um, so we're just trying to get ahead of the curve. I don't think the curve's quite there yet. You know, a lot of people that we work with are, you know, this is this is a real uh businessto business company. We're we're dealing with archival producers. We're dealing with researchers. We're dealing with documentary filmmakers,

131. 2:04:26#

     um, editors, producers, stuff like that. So, you know, we spend a lot of time on LinkedIn in, you know, messages between people. We have great relations with a lot of of the people that are are buyers of our content. And, you know, from a technology standpoint, they're not quite there yet. But I think in the future as we think about how people want to make

132. 2:04:51#

     videos, how things like that will be on demand almost like the ondemand radio stations that are happening now or or the the not even just a radio but a a live stream a 247 live stream of someone having that broadcast ability now where they're using maybe AI to create some

133. 2:05:15#

     videos and maybe the audience is putting um questions and and comments in and that's kind of you know dictating where the the video is going like that. Well, at the same time if it had sort of that access where it could automatically pull up an old clip, bring it into the stream and license it within seconds like that

134. 2:05:39#

     in a real time sort of way. I think you've got some interesting use cases there in the future about how we think about having real time content bringing things in like that. Like think about like a live stream like this. Like all of a sudden you could be like, you know, the AI that's listening to this live stream could automatically go to a site like mine, pull a clip up that kind of

135. 2:06:03#

     exemplifies a point that you're making about something. Um, so yeah, in the future I think that this stuff is going to be huge. I think it's going to be absolutely huge of people doing things in these real time um sort of scenarios.

136. 2:06:21#

     Indeed. Fascinating stuff, man. You've got uh incredible range from to the licensing to uh it's good to get a read on just how active the agent economy is or isn't at this point in time. And then obviously this uh fly brand stuff is um just the next

137. Collaborations and preservation beyond one lifetime

138. 2:06:42#

     generation of weird that I'm very uh keen to stay in touch with. So I appreciate you bringing all these things to us today. Um, anything else you want to leave people with or something we should should have asked about but didn't get to? Um, yeah. I mean, it's it's it's just a really, as you guys know, really fun time to be alive. There's a lot of really cool stuff that's happening. It's tough to keep up with all this stuff.

139. 2:07:04#

     Um, and I just try to keep an open mind. Just try to learn as much as possible and experiment and try new things. And um yeah, if anyone thinks that this stuff is cool and wants to reach out about, you know, chatting about this stuff, I'd love to chat more about it. I'd love to hear any ideas um about how we can leverage the content uh in all different kind of ways. Um I'd like to

140. 2:07:30#

     work on research projects too that we're, you know, donating uh content to to help with, you know, fixing content that has been damaged. um and other sort of you know interesting things that we can do with this. Um so you know it's just it's just really fun to just be doing all this stuff. So I appreciate your time with this and uh yeah it's you know just just

141. 2:07:57#

     the beginning for a lot of this stuff. I think I think that there's there's a long road ahead with preserving this content. And if anyone has content that they want preserved, that they want to donate to us, um, we'd love to talk. I mean, I think it's it's a great way to have your family history be out there. I think one of the things that's interesting, too, is we've thought about

142. 2:08:20#

     how can we have this content be preserved beyond us, beyond me, beyond my daughter, beyond other generations. How can we get a hundred years, a thousand years, 10,000 years of this content being preserved without having it, you know, fall by the wayside? I mean, there's a lot of examples of films that have been destroyed that have are

143. 2:08:45#

     never going to be uh looked at again because of of a fire that happened in a theater or whatever kind of scenario went down. So, you know, that I think is really interesting. We I was um you know I lived in San Francisco many years ago and a startup I worked at um a guy by the name of Danny Hillis came in and talked to us about his long now

144. 2:09:09#

     foundation project and I thought this was really interesting as as a a 21-year-old I didn't quite understand it but now as I'm older I understand it more about having to make this uh this 10,000year clock. um anyone that doesn't know what he's talking about is this idea of like how can we take all the information from humanity and save it in

145. 2:09:32#

     a way in case something bad happens in the earth that anyone can read that information down the road. So, a lot of his technology is around printing things super super tiny. Um, because a magnifying glass would be a technology that anyone could eventually make that's a lot easier to make than

146. 2:09:57#

     silicon chips or or whatever we have to to to try to save data and things like that. So, I think that stuff's important. I think that is kind of a core of what we like to think about too is how do we make this stuff really last for generations. Is there blockchain technology we use for this? Is there this kind of microscribing technology on

147. 2:10:20#

     you know surfaces that are made of diamonds or or granite or something that isn't going to dissolve over time to really kind of save information for the long term. Cool. Fascinating stuff. Bill, the company is stockfilm.com. Thanks for joining us on AI in the AM.

148. 2:10:40#

     Thanks, guys. Had a lot of fun. Hope you'll be back soon. Appreciate it. Take care. Sure.