AI-Automated Film Making

An recent article in Variety was titled: "Sylvester Stallone-Backed Largo.ai Teams With Brilliant Pictures for ‘World’s First Fully AI-Automated Film Company’". Obviously this caught our attention! We sit down with Sami Arpa, CEO of Largo.ai, to unpack how films are developed, funded, and brought to life using AI. We discover how tools like script analysis, financial forecasting, and digital twins are helping creators and studios make smarter decisions. Featuring: Sami Arpa – LinkedIn https://www.linkedin.com/in/samiarpa/Daniel Whitenack – Website https://www.datadan.io/, GitHub https://github.com/dwhitena, X https://x.com/dwhitenaLinks: Largo AI https://home.largo.ai/Sylvester Stallone-Backed Largo.ai Teams With Brilliant Pictures for ‘World’s First Fully AI-Automated Film Company’ https://variety.com/2025/film/global/largo-ai-brilliant-pictures-ai-film-company-1236398707/Sponsors: Outshift by Cisco https://agntcy.org/: AGNTCY is an open source collective building the Internet of Agents. It's a collaboration layer where AI agents can communicate, discover each other, and work across frameworks. For developers, this means standardized agent discovery tools, seamless protocols for inter-agent communication, and modular components to compose and scale multi-agent workflows.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:03Speaker 00

  Welcome to Practical AI, the podcast that makes artificial intelligence practical, productive, and accessible to all. If you like this show, you will love The Change Log. It's news on Mondays, deep, technical interviews on Wednesdays, and on Fridays, an awesome talk show for your weekend enjoyment. Find us by searching for The Change Log wherever you get your podcasts. Thanks to our partners at fly.io. Launch your AI apps in five minutes or less. Learn how at fly.io.

• 0:44Daniel Whitenack?

  Welcome to another episode of the Practical AI Podcast. This is Daniel Whitenack. I am CEO at Prediction Guard. And today, we're really excited to talk about AI in filmmaking and content production, as we have with us Sami Arpa, who is CEO and co-founder at Largo AI. Welcome, Sami.

• 1:10Speaker 02

  Thank you. Thank you for having me.

• 1:11Daniel Whitenack?

  Yeah, yeah, it's great to it's great to have you here. I remember specifically, you know, of course, we're always looking for for interesting folks to join us on the show and talk about, you know, how AI is being used in various verticals and industries. And I remember seeing a Variety article about this sort of Sylvester Stallone-backed team at Largo AI. And, you know, it talked about the world's first fully AI-automated film company, which was was very intriguing to me. I'm sure we'll get into a lot of those details. But maybe before we hop into the specifics

• 1:53Daniel Whitenack?

  about Largo AI, I know that you all have have been in the industry for some time and been doing this work. Could you just give us a maybe a high level picture of how AI and kind of advanced technology has been?

• 2:10Daniel Whitenack?

  Evolving in recent times in the film industry, of course, for a long time, maybe many people know about CGI and certain technology that's actually fairly advanced, that's been used in filmmaking for some time. But maybe give us your kind of state of AI in filmmaking and how that's evolved in recent years.

• 2:32Speaker 02

  Yeah, absolutely. So film industry has always been advanced with the technology. But yeah, the progress, the adoption with AI has not been easy. I can tell that with our journey past, during past six, seven years. So I mean, we can think the primary adopters of AI in the industry as Netflix and Amazon, because they started to use AI for recommendation systems. That was already 20 years ago. And then they started to create their own content with original content. And that also provide, that created another step for AI,

• 3:16Speaker 02

  because they could analyze the content and select, order the type of content that they would, they know already that that will work. That was also with AI, because specifically for Netflix, they were having the system of microgenres, which they still have that, knowing the audience behavior and also understanding content at earlier stage by using AI tools. You could order the right content right from the start. So this way, they could, they could get larger audience with narrower catalogs. And so then if you put this as two phases, then we can also name a third phase. This is the phase that we see starting with ChatGPT.

• 4:03Speaker 02

  Like every industry, in the film industry as well, that we saw more of applications. And also that has changed the adoption of the industry. We measure adoption of the industry for usage of AI during the past six, seven years. It was one, two percent when we started Larbo AI. Now it is around thirty percent. And that is, that is a great progress. And it is a progress despite the things like strikes. We had two big strikes also in Hollywood and partly that was also against AI.

• 4:37Daniel Whitenack?

  Yeah, and you know, maybe just to dig in a little bit there. What some people might think of as kind of the, the first or, or the first thing that pops into their mind with AI and film is maybe what they've seen around the actual video generation side or changing the visual effects. But it sounds like you're talking a little bit more kind of wide reaching and operationally across the, the film industry. So could you give us a little bit of a picture of, maybe the question is how kind of an overall categorization of how AI might be used

• 5:20Daniel Whitenack?

  in different parts of the, of the AI industry? I know you're digging into certain parts of that, but maybe you could help us understand kind of more generally the different categories or ways that it, that it could be used.

• 5:33Speaker 02

  Yeah, I think for that it's important to understand the chain of development of a film. A film is a very big project and it takes many years. As audience, we just see the end results on the screen. But any film has four main states of development. The first part is development of overall story, then pre-production, the stage that, that we attach also people to the story and also raising the budgets. Then we have production and then post-production and distribution stages. So at any stage, there are many people who are involved and there's a lot of work.

• 6:15Speaker 02

  And and many projects cannot finish all this process. And actually, so for example, going to going to development to pre-production, already ninety percent of the films are eliminated. Or even coming to development, that there's a big amount of projects are eliminated, like there are scriptwriters, artists, screenplay states that that are never picked by the producers. But at at each of these steps, there are a lot of works. And eventually, obviously, the goal is to bring that to the screen, but only few projects are coming to that stage.

• 6:54Speaker 02

  And so here, so of course, the sexy part is text to video, the visual part. But for all other parts, that there is applications of AI. And actually, there is a big benefit of usage of AI. That's something we have focused as well. We have focused more on earlier stages for the usage of AI, understanding the content, character, casting, and predicting financial results for for for helping to to raise the budget for the project. And of course, there is strong applications for post-production, production, with new text to text to image tools, that we will see more more promising applications of that as well,

• 7:37Speaker 02

  that that we have seen recently. Google V O three has been released, which which is which is amazing. Actually, the results look amazing. So that will change still significantly post-production and production parts as well. So we will still observe that there is not enough strong applications at that stage for especially live action movies. But the the point here to to summarize for every step. There is important efforts, some parts are not visible to the audience. And for every step, there is there is type of AI tools that that we can apply.

• 8:17Daniel Whitenack?

  Yeah, and just for for, of course, probably most of the audience that, you know, has not been directly involved in in any of these stages of film production. It maybe except, you know, watching it on Netflix or or wherever the venue might be. But could you give us a sense of the kind of investment and and kind of how much effort is put in kind of proportionally in each of these stages leading up to the the distribution investment in both kind of time and and people and and scale?

• 8:57Speaker 02

  Yeah, biggest investment is in terms of money. It is for production and distribution stages at production, just to produce the content on the set. And and then I include post-production budget in that as well. And then distribution is the parts for spanning marketing budgets. So these are investment wise, these are the biggest parts. But for time wise, for most of films, they are not actually. Because in for many projects, the development and pre-production states are taking much, much more time. There are projects even like having like a six, seven years of development or pre-production.

• 9:40Speaker 02

  And the biggest challenge over here to convince many people to bring around a project. And while doing that, you need to make a lot of iteration. The producer, screenwriter, even director might be involved at that stage. So you create a story and that's if you engage people around that story. And you need to find people to to put money in that project. That might be studios, investors, etc. That's actually normally biggest, most difficult step for for for for most of projects. And in general, most of projects are not good, actually.

• 10:20Speaker 02

  That that's also a reality, which we can see in our AI system as well, that that we have producers. They put their projects to get financial results. And in most of cases, it says that project will fail. Because yeah, I mean, finding good project is not an easy task. And it requires a lot of work, a lot of study. That's why AI tools at that stage can be very, very helpful, very critical. As and and for a producer filmmaker, the biggest hurdle for future is to fail at the current project.

• 11:03Speaker 02

  Because that's also a way to open the door for next projects or not. That's why, that's why like making sure that that project will be successful is is is very critical.

• 11:17Daniel Whitenack?

  Yeah, and that's very interesting to me that you're sort of focused in this. I guess what I what I'm hearing is there's sort of noisy early phases of these projects where you've got a lot of kind of maybe good projects mixed with a lot of noise. There's difficulty in kind of parsing through that also for the for those that, you know, maybe have written this story or are promoting the the production of a of a project. It's hard maybe to to stand out. How is, from your perspective, is this sort of technology? And we'll get into, you know, exactly what you all are doing. But generally, in digging, you know, bringing technology to these early stages,

• 12:00Daniel Whitenack?

  does that change the dynamics of, you know, smaller, you know, smaller studios or script writers or or maybe lesser known folks that could maybe use technology to help them play on maybe more of a level playing field with kind of the the the big studios or or well-known known folks? How did, how is that dynamic sort of shifting or or is it?

• 12:25Speaker 02

  Absolutely, yeah. I mean, the AI and new technology create much more opportunity for smaller production companies, newcomers. I mean, if you think from studio perspective, they have, especially at early stage, they have a lot of resources to understand if a content can be successful or not. They have experience and but they can get, they can get many research done, including focus groups at very early stages. So those kind of things are not available for early stage, for newcomers or small capacity production companies.

• 13:08Speaker 02

  At development stage, you don't have your movie budget, you have not raised yet. So if you are lucky still, you can find some people or some institutions are investing at development stage. If not, they use their own resources to understand if a content will be successful or not. That's why using AI tools at that stage is a very cost and time effective way to understand the potential success of content. That's also for later stages, it will be the same as well, by the way. Because we will see the cost of production, post-production will reduce significantly with the AI tools.

• 13:48Speaker 02

  If a hundred million dollar budgets can be produced for one million dollar budgets, that will change whole ecosystem, right? Because one to ten million dollar budget films can be produced by independent producers, but not hundred million dollars. That's, we will see also all those changes in next years.

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• 15:15Daniel Whitenack?

  Well, Sami, we've kind of talked or referenced some of Largo AI and you know, the way that you're digging into these early stages. Could you give us a little bit now, kind of digging in specifically to what you all are doing? Could you give us a little bit of the backstory of Largo, kind of how it came about, what those initial ideas were? Obviously, you mentioned it's existed for, I think you said, six years. So this is, you know, before the latest kind of boom of AI, at least as far as the general public has perceived it. So yeah, give us a little bit of the backstory. I'd love to understand how all this came about.

• 15:55Speaker 02

  Yeah, sure. I will connect that with my personal background because that's how Largo started, the story of Largo started. I did my PhD at EPFL, which is a university in Lausanne, Switzerland, in the field of computational aesthetics, understanding art and generating art by using computers. And film has been also one of the subfields over there. So that's the technical part, technical scientific part. But parallel to that, I've been also director-producer on some small projects. So I've been on the creative side as well,

• 16:35Speaker 02

  like just working hands-on. While working on my own projects with a bit of head of engineer as well, I was always curious why we don't have a way of representing films similar to music. Because music, we can represent with musical sheets with the partitions, which gives a way of understanding of the certain formats, the rhythms, and the type of structures for different genres. It doesn't, that doesn't make music less creative. In reverse, actually,

• 17:18Speaker 02

  it makes more creative because you can focus on like a specific structure and go deeper on that.

• 17:22Daniel Whitenack?

  And when you, when you say that sort of partitions or structures, you would mean, you know, like a, like a pop song or something like that has a verse, chorus, verse, bridge or something, or even in the musically, there's bars and other things like that?

• 17:38Speaker 02

  Exactly, all those things.

• 17:40Daniel Whitenack?

  Yeah.

• 17:40Speaker 02

  In film, there is a bit of structures, but these are like too much formulated. So it's not like a same type like these low level structures. That was a bit of my curiosity also, like that's that's something I shared with my PhD advisor at that time as well. So we work a bit on that. We we were thinking how we can create like similar type of structures for film as well. That can be useful both for people working on that creatively, but also for machine. Because understanding and learning from films is also very difficult for for machines. Because yeah, like film is like a two hours of content, all the frames, millions of pixels.

• 18:22Speaker 02

  Or like a screenplay is like hundreds of pages of content and you need to associate this with all metadata. But there is not even enough sample of data to learn confidently on those. So if we can represent any film in a more structured way, in a smaller space, a smaller vector space, that's even easier for machine to learn from that. But that was our starting point and it was with that starting point, we created this, we call that genre recipes, emotion recipes. So this is like we put any film in nine dimensional space of genres. And then, for example, let's say drama, drama is like one of these pattern. We find how drama is evolving over the story from start till the end.

• 19:06Speaker 02

  Same thing for comedy, for romance, for thriller, horror. So this way we get like on a on a timeline, we get a map of of of a film or of a TV series or for for any content. That can be from screenplay or or from from a direct video material of a content. So this is like a baseline representation of a film for us. That was our starting point and with that, of course, then we engage many other points, metadata like the actors, the budget, all the other content. That becomes a representation for a film. So this is, this, this we start to use those as a base boats to provide as a feedback to,

• 19:51Speaker 02

  to creatives, so they can see the really structure of the content, but also to machine. Once machine is learning from that type of data, it becomes much easier to learn. For example, easier to learn financial predictions, box office predictions, streaming predictions.

• 20:05Daniel Whitenack?

  Yeah, yeah, that, that makes sense. I can, I can sort of imagine this graph of, you know, drama or comedy going up or down on the, on the arc of, uh, on the, on the arc of a movie. So that gives kind of a understanding level, I guess, of the, of the movie. How then does that connect to more concretely? How, how can that connect to concrete kind of value for those involved in promoting or, or writing a story or producing, uh, producing a movie?

• 20:37Speaker 02

  Yeah, I mean, for producing movie, there is three important elements. The first one is content itself. The second part, the people are involved, primarily the cast. And the third part, the financial part, the budget and expected return for that. And our forecast insights are also in these three main categories. So the content analysis, it provides insights related to weeks from points of the content and audience emotional reaction to that content. Character casting part is about understanding the characters and then making casting propositions.

• 21:20Speaker 02

  So that the AI is making propositions for the cast. It asks for this character, that, that actor will be best fit, for example. And then the, the third part is the financial part. So here for that part, it makes the predictions directly, how much money the film will make with given content and then all invested money and the other metadata like attached cast director, et cetera. And that part, we, of course, there is many sub-part of that because that part is also relevant to the, to understand the audience. Because how much money you make is relevant to understanding audience, writing the rights, marketing, all these things.

• 22:03Speaker 02

  So it goes deeper to get predictive demographics of the audience for specific countries. And along with that, we have also the simulated focus groups, which is like one of our most exciting tool. There you can really get quantitative and qualitative feedback from the audience.

• 22:22Daniel Whitenack?

  Yeah, that's, that's great. Uh, I, I'm wondering, you know, cause in the earlier stages, like you talked about of, you know, determining what, what type of content to make, you know, casting all of that. I'm assuming the assets, if, if you, I'm, I'm thinking more from the, I guess from the technical side now. The assets that you have to work off of, I guess, are the, the script and maybe some, some other things. Could you talk a little bit about kind of the inputs to this? Like what's required to really get good results out of a system like this as a, as a starting point?

• 23:01Speaker 02

  Yeah, it a bit depends on the stage, but if starting very early stages, the system would need at least a treatment. That treatment is a very short version, early version of story. Typically it can be even like a two or three pages. And a bit later stages, it will be a screenplay. That's like a full storyline of, of, of a, of a. Coming to screenplay stage, together with that, the system would ask also basic packaging information. Because a screenplay, if you look at, if you think about financial forecasts, a screenplay can make any money, bad or good. It can make any money because it's how much it makes is also very relevant.

• 23:46Speaker 02

  The attached people to that project and also the, the budgets, how much budgets that has been put.

• 23:54Speaker 02

  Like with the current standards, let's say, if you try to make a perfect sci-fi screenplay with one million dollar budgets, we can tell that the results will be very, very bad, right? So it's not difficult to tell even for for for regular people, but yeah, I mean like that AI will give the same warning as well. So actually, in that manner, that's like we always say, content is the king. It is very important, but once we look at, once we put all the features of making a film and look at AI learning, we see the parameter that is impacting most the financial results is budget.

• 24:38Speaker 02

  And it doesn't mean having a high budget, it means the right, right budget, especially for for a good return on investment. Sometimes some films are having too much of budgets than what they need, then it becomes very difficult to make it, to make it profitable for people who make the project.

• 24:57Daniel Whitenack?

  Yeah, and and just practically for maybe some of the, some of the practitioners out there that are working, maybe not in the film industry, but they might be working on, you know, simulating other things in other verticals or different types of production processes or whatever that might be, maybe unrelated to film. But just for their benefit, it it sounds like the system that you've kind of built with Largo, you know, works on various types of projections, there's various stages to it. I'm assuming, you know, that sometimes there's this misconception now, I think exacerbated by Gen AI, that you have kind of one model, you put one thing in and then you get everything out.

• 25:42Daniel Whitenack?

  I'm assuming that your system, which has been developed over years, kind of involves multiple models that maybe do different things, like you mentioned the one around kind of detecting or or mapping this, this, these genre distributions or or semantics across a film. I'm assuming there's different stages of these things with different models involved. Maybe, you know, the financial forecasting model would be different than, you know, the model that's producing the the genre results. Could you give us just kind of a, at a high level, an understanding of of how this kind of all fits together as a, as a system?

• 26:23Speaker 02

  Yeah, absolutely. Uh you are right that we we we have a lot of models. Uh so using the models that we use for genre prediction for financial forecasts. It wouldn't work so because like yeah that it especially a financial forecast is typically is very, very different models. Or shallow models compared to content understanding models which are much deeper uh models. So tha- that's also like the important thing with the current A I wave that that yeah L L M's are really great.

• 27:03Speaker 02

  That two two answer for many things, but even like if you go to L L M's even chat G P T, we see that they have many models, actually each model is. Better at different type of solution. The same thing, of course, for us as well. So we have like a. For each type of task, we have different models. And also, we have two main categories. One category. Is for the models that are learning from past data. And and it uses this learning to to predict uh to make the predictions for for new content. That's one way of learning. The second learning is learning uh audience. There what we do is basically we are creating digital twins of real people.

• 27:48Speaker 02

  So we don't learn anything content related, we learn people themselves. And then we show the content to these digital twins of real people. And and actually, the second one is having advantage of uh. Not missing outliers almost. Because like the one big danger for like just learning from past data is yeah outliers in film industry that we can often have, okay. So for general content, we can predict successfully, but you can always have something completely new that we don't know well. The type audience behavior for that type of content, the model will miss that. But with the second approach, with this digital twin approach,

• 28:31Speaker 02

  we can even capture outliers because you are much closer to humans. You are already creating their digital twins. And that digital twins are having very short lifetime, like one year. So you are very close to the current behavior of people. And it is very successful also to capture new approaches.

• 28:49Daniel Whitenack?

  Well, I'm really intrigued by kind of the way that you've built up this system of tools that kind of helps in various ways throughout the film creation, film production process. I'm wondering, in terms of, and this is probably something on a lot of people's mind in relation to AI models and content, especially, you know, art or movies or images, that sort of thing. Obviously, you need some sort of reference data with which to train models and kind of help them produce results. You know, maybe for financial projections or something,

• 29:32Daniel Whitenack?

  you know how much a movie has brought in or something, and that's public information. I'm not sure actually how much of that is public information.

• 29:40Speaker 02

  Not fully, but yeah.

• 29:42Daniel Whitenack?

  Yeah, so basically, my question is, how do you go about kind of creating the data sets you need in an industry where, of course, there's a lot of, you know, there's proprietary or copyrighted content, that sort of thing. What does that look like for you as a company?

• 30:01Speaker 02

  Yeah, I mean, there are open data that we can learn, like, for example, movie summaries. That's like a pretty open or movie metadata, like who has been engaged with which films. So there is already a lot of open data or box office data, like how much they have done, which is, for most of films, is publicly announced. But there are also type of data that is not publicly available. And one of the most important of them is streaming data. Streaming platforms do not provide data. Netflix has started to publish some data recently in terms of like a viewership, but it's it is still very limited.

• 30:46Speaker 02

  And also, yeah, like not having that type of data is shaping the industry, not just going outside of like A I perspective. Because we know many producers are complaining not to have that data. Because like the value of a film is very much related to the size of audience.

• 31:08Speaker 02

  And that relationship is very clear in the box office. Because you are just putting the film in the box office and get the money as much as the tickets have been sold. But that relationship, at least from the producer's side, is not clear uh on the streaming platforms. Of course, platforms themselves, they know they can make a value on their sides, but it becomes a bit one-sided. That's that has been a bit of problem. We do streaming forecasts as well. And and the way we approach to that is is analyzing uh uh social noise in the past. And we created the models to correlate social noise with the households uh viewership.

• 31:49Speaker 02

  And from that, we even started to create like a fair value calculations. So basically, if streaming platforms were paying

• 32:00Speaker 02

  according to household's uh viewership share, how much they should have been paying, considering also their subscription revenues. We also make this kind of fair value calculations. Of course, it's it is not relevant with what they are paying, because they are paying according to their own calculations. That's that's the way the way we calculate. We say if if it was open. Like box office, that will be the shape of the film. So yeah, I mean, the data part i- i- i- is like that. Obviously, there's like a different model requires different type of data content models. We look more content data, financial models looks content metadata and financial results.

• 32:44Speaker 02

  And then again, yeah, here our data dependency is a bit reducing with our simulated focus group. This digital twins approach, because there you don't need anyway the past film's data. Because we just get people that that people people's digital twins. So their reaction becomes our data and and it's like a it already tells us like how film will perform.

• 33:08Daniel Whitenack?

  And one of the things that has been going through my mind as you've talked about this platform that you've built, uh, which is fascinating is, you you know, what what was occurring to my mind is, well, why don't we just make this thing a loop? If we have this whole, uh, if we have this whole process, which can give us these projections and put the right casting together and and all of those things. There's one thing to say, well, we can take in a script or a screenplay into the input of this process and then create all of those projections and help them plan. What's preventing us, or maybe there's nothing preventing us from just looping that feedback back

• 33:47Daniel Whitenack?

  and modifying the screenplay or script to kind of update the projections in a in a sort of more favorable way. Um, has that has that been discussed or or part of the conversation?

• 34:02Speaker 02

  Yeah, I mean, it's not that easy for for several reasons. Of course, yeah, with machine, with with the models you can put into loop and make continuous improvement even automatically. But even that, I mean, it's like a reaching a point of perfection is not easy. Because you know, even like the at current stage, like our financial forecast models are having like eighty percent accuracy, which is like a for it might be look looking low for if you think like a many machine learning models is coming like ninety five, ninety seven, ninety nine percent accuracies. It's difficult to go over eighty percent because there is many elements.

• 34:50Speaker 02

  That you cannot control. Because a film's success becomes a success together with audience behavior. And audience behavior might change even very quickly in short term. Like a big natural disaster happens that change all the ambience. Or like some political situation change overall behavior. Like a heat wave arrives. For example, for a box office movie, they were not calculating that. And then it people goes to the beach instead of movie theaters. So there's a lot of factors that you cannot still fully

• 35:23Speaker 02

  fully determine because still is relevant to to audience behavior with many factors. That's one element. The the second thing is the dynamic of creatives. Because like films are done with many people. Many people are contributing for certain decisions. It's not like somebody can tell, hey, let me make this script better and people better, this etc. And let's go to next stage. No, because you have many companies are involved. So still you need a lot of agreements to be done among many people. So I think that's also like some blocking point. Even if even if we have like a machine looping, making making better that that wouldn't be

• 36:08Speaker 02

  that wouldn't be easily the case. Maybe more in the future, but yeah. But then there if the machine is all the time looping without human touch, that might be also creates too much like movies as well, of course, that kind of dangerous as well.

• 36:24Daniel Whitenack?

  Yeah, yeah, maybe on that point specifically. The the other question I had, which you actually already just mentioned in passing was outliers. I think, you know, there would be a lot of maybe there are some people out there that are that might think, well, I've seen what sort of AI does to content. Let's say on LinkedIn, I I go on LinkedIn and there's just like a feed of AI generated posts that are sort of all similar, right? They they just sort of look look the same, right? And I I think, you know, here we're talking more about forecasting, maybe simul. Focusing groups, that sort of thing, but you know, there might be people that would say, well, that's really good.

• 37:06Daniel Whitenack?

  You know, you can hone that in and you know, obviously help these, you know, there's a really beneficial part to that as we talked about to helping bring up smaller studios, give them tools, augment them with technology. That's really amazing. But then there might be other people that say, well, if we start doing that sort of projection, everyone will be kind of shooting for the same thing or trying to hit the same metrics. So there, what about kind of the, the artistic piece of it? And I'm sure even hearing your background, that is likely a very important piece of, of why you love this sort of art and, and, uh, and content. Right? So yeah, I would love to hear your perspective on that.

• 37:49Speaker 02

  Yeah, I think that's very important point. And firstly, in our product, we don't do the reverse process for that reason. So it's always forward process. We, that's what I mean. So we always get human content as an input. And we provide the all AI insights and then they take a decision and then they again go forward. So we don't tell them, hey, you should do this, this, this type of content, write this kind of story. That's the reverse side. So we don't, we don't do this reverse side formulation. I think one reason for that is exactly that, that danger because we think if you do forward process, AI will augment creativity.

• 38:33Speaker 02

  Reverse side, it might create too much allied content. That, that's, uh, that is definitely one thing. And, and we, we, we can see as well in, in the results that we are looking in forward process that the variations of the content and improvements are really, really great because then with the AI insights, again, human are improvising over that. It gives them to inspiration to, to do something different. That, that's amazing, amazing to see. That's why I'm telling you, I mean, like, I think, I don't think we should put in a basket. AI will just make all content same or like it will augment creativity. I think it really depends how you use it.

• 39:17Speaker 02

  Yeah. And this is also one thing related to fear because we see that there's a lot of people are having fear of that, especially in film industry. The part of strikes were relevant to that as well, the strikes that happened in Hollywood. So, I mean, in our view, it's still very difficult to beat a human, like a very good scriptwriter. Film maker is very difficult to beat their version of using A I. So that's like a that that's what we see because a regular person, they can go and write a screenplay as well now using chat G P T. But that's always very average.

• 39:57Speaker 02

  If a very good scriptwriter is also using A I and writing screenplay, it's difficult to to reach that level. So that's we will see that bar will get higher and higher. But again, to go above that bar, we need really skilled people in that field.

• 40:12Daniel Whitenack?

  Yeah, well, you you already started going there, but as we as we kind of draw to a close here, I'd love to hear your perspective on what you're really excited about as this technology gets adopted more and more in this industry. You know, what what excites you kind of looking to the next year or two? What what do you expect to see? What are you excited to see?

• 40:34Speaker 02

  Well, what I am excited is first the production budgets. I think the production budgets will go down. That means we will see more films to be done. It we will have some quantum inflation. But because of that, I think there will be also more competition. We will augment the creativity over there. I think we will see like a much better films. It doesn't mean we didn't have good films. We definitely have a lot of great films from great directors. But we will see much more of those. So that's great news for for for the audience. But of course, that creates problem a bit with with the industry itself, because the way that they will work will change.

• 41:20Speaker 02

  I think it will be more of frequency game. So a good filmmaker, let's say they were making one film per year, maybe now they will do two, three of them.

• 41:30Daniel Whitenack?

  Yeah. Awesome. Yeah, well, I certainly, I certainly look forward to consuming some of that, that great content that you're, that you're helping produce. So yeah, thank you for your work. Thank you for digging into this over, over years and kind of really innovating in, in this industry in a way also that I think is, is responsible and promoting kind of the human augmentation of, of the process with the, with the kind of human as, as pilot. So really appreciate your perspective there. Thank you for joining Sammy and hope to have you on the show again. Yeah, thank you very much. I really enjoyed the conversation. Thank you.

• 42:14Speaker 00

  All right. That is our show for this week. If you haven't checked out our change log newsletter, head to changelog.com/news. There you'll find 29 reasons. Yes. 29 reasons why you should subscribe. I'll tell you reason number 17, you might actually start looking forward to Mondays.

• 42:35Speaker 04

  Sounds like somebody's got a case of the Mondays.

• 42:38Speaker 00

  28 more reasons are waiting for you at changelog.com/news. Thanks again to our partners at fly.io, to Breakmaster Cylinder for the Beats, and to you for listening. That is all for now, but we'll talk to you again next time.