00:19:47 · Finn Meeks, Amit Jain
[00:19:47] S1: So you might see kind of just systems of models, with a unified model guiding guiding them tailored to a specific application. [00:19:54] S0: Correct. The unified model will replace LLMs as the core, but you might do a tool call to an LLM and ask it like, you know, hey, can you just code this out because you're more efficient than me? [00:20:03] S1: Tell us a little bit more about Luma Agents. This is a recent release of yours, and I I think it kinda goes back to to what you were saying earlier is that there's a lot of value too in just the the orchestration and the ability to actually, like, solve, you know, that end to end problem for our customer beyond just having a good model. And there's more than just a model that goes into it. Can you tell us a little bit more about Luma Agents and just how you actually think about making these useful for customers?
00:20:27 · Amit Jain
[00:20:27] S0: Right. So Luma now has a very fast growing and sprawling business in entertainment, media, advertising. Basically the use cases for visual models when the accuracy is good enough, where humans think it looks real, but not good enough yet where it's metrically accurate. So for these kind of works, it's actually really useful. Just to give you a sense of that market, by the way, on average, companies spend about 10% of their revenue, nonprofits, revenue on telling their own stories. Every company, if you're a consumer, you're going spend about 15% to 20% of your revenue on telling the world about it. If you are a services company, you're going be somewhere in the 10% to 12% range. And if you are a government or defense or those kind of companies, you're going to spend less, not zero, less. How many of you know about Palantir, right? Like how many of know about Northrop Grumman? You have no business knowing about Northrop Grumman because none of us are going buying jets like on Sundays at IKEA. But we know about Northrop Grumman because well, at the end of the day, the dollars come from our funds and then from our like tax, right? So every company needs to be known in the world. So that's a very big business. So if you have a stock market that is trading at 30,000,000,000,000, right? About 3,000,000,000,000 of that value is actually going towards telling that story, media advertising. But the thing is like, and this doesn't apply just to our business, it applies to everything. Promise of AI is not doing a little bit of spot work, all right? Hey, I can make copy. It's like, all right, good for you, man. Can you actually make the book for me fully? Can you actually just go talk to the customer? Hey, I can make a little bit of image for you or video for you. It's like, who cares? Right? Can you actually do the full end to end movie or can you do a campaign? In 2024, 2025, we struggled with this a lot because agents weren't really there and the lens weren't really good enough for that. Our models were also really not good enough to do long horizon tasks. But we just actually announced a full production on Prime Video with Sir Ben Kingsley. It's a TV show where the production budget is $4,000,000 an episode and it has Sir Ben Kingsley, it's called Moses, right? And now we are at a place where it is about like 90% produced with Luma Agents.
00:22:36 · Amit Jain
And it was never possible to do before that. So what is that? So it's a product that allows you to do large scale end to end production in visual world. So if you are a company that is selling a product online, it can actually help you produce very large scale assets in every language, for every geography, for every location, and it can interpret a huge amount of things. We use LLMs as well and those kind of things. But the big gap was in order to make agents, you need two things. The generation part, the thing that does the task and the understanding part. Coding agents didn't get good enough until models got good enough to understand the code they were writing at reading large volumes of code and running it. Until you do that, you don't know you made a mistake and you can't fix it. And then the person who is at the other end is now left to hold the bag and understand like what you did and where you made a mistake. So we had to build really elaborate systems to be able to understand the work that these models are doing. Right? So understanding and then generation. So Luma Agents now is like the flagship product for Luma. And it's also actually really, really interesting for other reasons. So first of all, it's really interesting because it's really valuable to our customers and to a large population in the world. There's 120,000,000, by the way, you just... If you don't know, visual artists in the world, people who are doing creative work. That is about two times to three times the number of coders in the world, massive part of the economy. Second, it's really valuable because now we have a end to end optimizable loop. Every interaction and task that someone is doing in Luma Agents is then used to train the agents, is then used to train the models. This is the only way. So we can bootstrap on the internet, but that's the best we can do, right? We can't actually do the final end to end problem because nothing on the internet has the data for how you got to that artifact. You can see the movie, but you don't know how the movie was made. And here we come into the same problems as robotics, same problems as biology, same problems as literally everything in coding and whatever have you, we're like, how did you get to it? And if you want to have agents that can do end to end work, they need to know how you get to What actions do you do? How do you iterate on it?
00:24:46 · Amit Jain, Finn Meeks
All these kind of problems. And now there's this really incredible flywheel where we get to see some of the best creatives in the world using them and making like really, really great things, and then that improves our models, That improves the agents further. Now the people take it even further, that gives us even more intelligent data. And then that's how you get to a system that is like Claude Code, where just every iteration is able to do many, many, many, many more things. And the goal of Luma Agents is end to end multimodal or creative work. [00:25:17] S1: I think maybe a week or two ago we hosted a demo night at SPC and one of your forward deployed creatives came by and gave a demo here. I'm curious, tell us a little bit more about that program and why you spun it up and maybe how that also plays back into a similar loop of creating real end customer value and making sure these models are actually solving their problems.
00:25:38 · Amit Jain
[00:25:38] S0: Again, Luma is a deeply research focused company and the prime directive is end to end optimization or this optimization loop. FDCs, FDEs, whatever you wanna call it, like FDCs is a term we invented for creative people because they're not really engineers. So FDCs, one, help our customers actually deploy these really, really powerful systems into their organizations, which is not a given because organizations are very complex organisms. Especially when you work in enterprise, you're always amazed by the level of complexity that is in any workflow really, especially if that workflow is critical to their business. So if you think about a customer Coke for a second, you might think like, okay, it's Coca Cola. Well, one, the sugar water is actually really special and incredible that we can't put it down. But two, Coke is like, their business is actually two things, right? Like selling Coke, but to the brand of Coke. Coke doesn't make new Cokes every year because that's not a good thing. We learn from New Coke, if anybody know what I mean. But Coke is a massive brand in the world, right? They came to the second largest only after Apple. And they spend about $2,500,000,000 a year on content, right, on producing every image that you and I see and subconsciously make us think about the drink and then we go drink it. It's such an incredible operation. It's driven by weather, right? It's driven by where you are, all of these kinds of things. And once you start to learn the details of it, like every organization is very, very complex, especially if they're successful because they didn't get to be successful by doing like baseline stuff. They got to be successful because they just really goddamn honed in on that. And so when you're trying to sell them a product or when you're trying to solve a problem for them, you need to mask that complexity. And as a technology company, how do you do that? How do you do for every single customer you have? You have this forward deployed motion that obviously Palantir pioneered because they had to go in government institutions, which are the epitome of complexity if I have ever seen anything. So these forward deployed creatives help our customers by understanding what they're trying to achieve, and then one, helping them with our products, and then second, tailoring the product to what they need it to be.
00:27:56 · Amit Jain, Finn Meeks
And two, they help our research teams understand these things and create these optimization loops that literally actually go directly to our model in the training process, in the data, in deciding what features the next model will have, in deciding if you need to really scale the size of the model, whatever have you. So extremely valuable. [00:28:15] S1: I think everyone here who's, you know, probably with ChatGPT or, you know, with coding agents has felt that magic moment. What do those magic moments look like for for your customers in the in the visual and creative domains?