transcript

Speaker 1:
[00:00] Hey, can we just have a better CEO than Jason? Can we have a better board of directors than your board of directors because they have all information? You're saying now you're building that, Josh.

Speaker 2:
[00:11] Yeah, exactly. What we're calling this is a company world model to make that vision true, an algorithm that can actually have the right reward functions.

Speaker 3:
[00:20] We can get up to 20 parcels in the drone.

Speaker 1:
[00:23] 40 plus pounds.

Speaker 4:
[00:25] Holy cow. If you're already doing logistics, you could just add drones to your fleet.

Speaker 1:
[00:29] This is kind of like going from level 2 autonomy to level 4.

Speaker 3:
[00:33] Yes, exactly.

Speaker 1:
[00:34] It's happening, folks.

Speaker 5:
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Speaker 1:
[01:18] Hey everybody, it is 420. Lon Harris is here. He is ready to go. Oh yeah. And it's Twist. And we got a full docket. Monday, Wednesday, Friday. I do Monday and Friday. I do some Wednesdays.

Speaker 4:
[01:31] He is here on Wednesday.

Speaker 1:
[01:32] And the This Week in Empire is growing. This Week in AI drops on Wednesdays. We also put half the show into the This Week in Startups feed just to tease you a little bit, give you a taste, and get your first 30, 40 minutes. And then we remind you, hey, go over to the This Week in AI show and subscribe there.

Speaker 4:
[01:50] Like a dealer. The first half is free and that's how we get you.

Speaker 1:
[01:54] Sure. A 420 perfect analogy. And we got a lot of important things going on. There's a lot of notes you're going to need to take. This Week in Startups, our core pillars are tactical and practical and experts only. Those are the first two pillars. And when you have tactical, practical information and experts only on the show, man, this means you're going to get a lot out of it as a founder. So I want to just take a minute for Plaud. I'm wearing it on my wrist today. I've been enjoying my wrist. And I just pressed this little button, Lon. And oh, the red light's on. And now if I think of something, oh, the next This Week in Show that we're going to launch, soft launch in a couple of weeks, make sure that blah, blah, blah, blah, blah.

Speaker 4:
[02:39] Oh, we're not giving it away.

Speaker 1:
[02:40] And that we blah, blah, blah, blah, blah, which is what I sent in text today. But I will do this frequently to myself. And I did it when I was on the road. I was on my way to the Breakthrough Prize. I'll talk about that in Off Duty. I always do Off Duty at the end of the show. And I'm wearing this thing. And just I'm going through airports. I'm on a plane. I'm getting my Starbucks. Bing, dah, dah, dah, dah, dah. I get my go-to. I get my action items. Then when it goes into the tray and I charge it, which you only need to charge this thing, what are you charging? Like every 10 days or something?

Speaker 4:
[03:10] That's one of the standout things. Once a week, I remember to drop it on that charger. And that's all. Otherwise, it stays just on my suit jacket right here.

Speaker 1:
[03:18] And if you want to get applaud, we have a deal for you.

Speaker 4:
[03:20] Yeah, if your work relies on conversations, we want you to get a Plaud NotePin S. You can check it out yourself at plaud.ai/twist, and just use the code TWIST and you'll get 10% off on your Plaud Pin.

Speaker 1:
[03:32] Let's get to work. Let's get our first guest on. We'll do Off Duty. I'll talk a little bit about the venture training program I've launched in Austin. And I'm going to talk about the venture training program.

Speaker 4:
[03:45] I got so much news today, too. Palantir had their big statement. Moonshot launched Kimi K26. So much to talk about.

Speaker 1:
[03:52] There's a lot to talk about. But when we get to Off Duty, two things. I'm going to talk about the Breakthrough Prize, the Science Prize with unlimited celebrities, the most powerful investors, founders in the world, all in one room. Somehow I made it in. I'll explain that. Three or four besties were there. And I'll also talk about our venture training program. I've now made it six seats because there's so much demand. We have a couple of hundred people applied already. So I'm going to get 600 applicants. I'm going to pick the top 1 percent. And we'll talk about that at the end of the show. But let's get to our first topic and our guest.

Speaker 4:
[04:27] It's our guest. We're bringing on Josh Sirota. He's the CEO of Eragon, Jason. There are sort of an enterprise version of OpenClaw, maybe an AI operating system for enterprises. Their big concept is they want to help companies own their intelligence. So they're connecting a company's existing tools and data to custom models that have been trained on their data and purpose-built autonomous agents. So Josh, thanks for being here.

Speaker 1:
[04:52] Yeah. Great to see you. So I always like for the founder to tell us why they're creating this, how much it costs, who are the customers and do a demo. So let's get to work. Why did you build it? Who are the customers? What does it cost? Let's demo it. Demo or die here on Twist.

Speaker 2:
[05:10] Amazing. Let's do it. So as far as why we built Eragon, basically our view on what's happening right now is as follows. So you are taking a frontier model, and as we saw with OpenClaw or Hermes or different other innovations, there's been a lot of research and a lot of work that has went across these harnesses around these frontier models. Basically, for us to be able to execute all of these tasks inside of companies, what we're doing is collectively creating these memory systems with these.mds, and trying to basically pull all of this into the context of a model. So what you see is if you look at the number of tokens in the world, it's like 1000X input tokens versus output tokens, and lots of workflows, like I don't know if you guys saw Uber CTO just said that they blew past their whole budget for Play Thix already in April. So it turns out that even with all of these innovations and harnesses, it's actually quite expensive depending on the workflows that you want to do to try to pull into context everything that you need into the model. So what our view is and what we're researching is basically, an RL algorithm that can actually take a lot of the things about your company and have those things be in the actual weights of a model that you own, that's specific to you and your company, that can update itself overnight. Therefore, it actually begs the question of, what do you need to use the Frontier Close Source Models for, versus what can you actually own on top of a lot of your proprietary data sets? That's why we started this, which is we just think there is a huge amount of inefficiency as far as how we're executing tasks, and we don't necessarily think you have to use these Frontier Close Source Models for everything. What we do is basically have this RL piece that can actually take certain parts of your company and put it into the weights of a model that you own, and therefore our whole slogan is actually own your intelligence. This should be your own asset.

Speaker 1:
[07:34] I think that this is exactly where things are going. I recently tweeted, like, hey, we're on a bit of a collision course, and we were talking about SLMs just last week with Rob. Small language models can be produced and run on local hardware, and if they have all your data in it, well, what becomes the difference between an open claw and all these skills being made that then go have a context window, have memory, or it all being dumped directly into an SML you own? So you take your entire sales database, you take your entire Slack, you take every email, and you put it into this SML. And this is the vision of Ultron we had 80 days ago. We're sitting here in AO 84, I think, Lon. And we were like, hey, can we just have a better CEO than Jason? Can we have a better board of directors than your board of directors because they have all information? And you're saying now you're building that, Josh.

Speaker 2:
[08:38] Yeah, exactly. So I think like a couple of things to add there is like, what we're calling this is a company world model. Most of my team is X deep mind and PhDs from Berkeley AI Research Lab. And a big thing on the frontier side and research is really RL, reinforcement learning. And what doesn't exist yet is like to make that vision true, is an algorithm that can actually have the right reward functions and be able to update the weights of the model in a frequency that actually makes sense for a business. So instead of updating this model once every three months and it costs a lot of money, can this model actually update itself overnight based on the interactions that it has with people?

Speaker 1:
[09:28] Where do you run the model? What models are you building this on? Kimi, DeepSeek, what do you use as your foundational model to then merge? What's the term when you put the data in for my company every night? What do you call that? Is it a build? Is it post-training, pre-training? How would you explain this? Details matter. Building a great company is all about paying attention to details and they add up along the way. That's why today's episode is brought to you by Qo, Q-U-O, the smarter way to run your business communications. And I use this product. We love it at launch. Why? Well, I don't want my sales team using their personal phones. I want them to have their own phone number. For some phone numbers, we want there to be one phone number that we can put at the bottom of an email, on the bottom of our website, so that no calls get missed. And it's so much easier to service an existing customer than it is to find a new one. And Qo is the way to do that. They're being used by over 90,000 businesses. Everything from solo operators to at-scale teams love Qo, and it works wherever you are. Android, iOS, desktop, it's going to work everywhere. So sign up for Qo's smart system and make sure no more opportunities slip through your fingers. Try Qo for free and get 20% off your first six months when you go to quo.com/twist. That's quo.com/twist. Quo, no missed calls, no missed customers.

Speaker 2:
[10:52] So this is all called like post-training. It all falls into the category of post-training. And then the method is called RL, reinforcement learning. And as far as the model that we've found that has like very good kind of base is Kimi. So we're also very excited for all of the announcements that they're going to be making. And as far as the concept, right, it actually doesn't yet exist to do the updating. This is what's called continuous learning.

Speaker 1:
[11:21] So demo or die time, Josh, let's do it.

Speaker 2:
[11:23] Okay, let's do it guys. All right, so I think first thing that we can go through is you guys had just brought up not checking email. So what workflow, that's a pretty straightforward workflow. I receive I think probably 200 or 250 emails a day. I find my email to be quite unusable. Most of this is things that actually are not relevant to my day-to-day. So if we just go in and say like, hey, what's the latest from email that I need to get back to? Right? We can do this. Simultaneously, we can do concurrent tasks at once. So if I want to also go ahead and, can you see from the Alexander Chapman Slack, can you read all the candidates that they have sent us and make me a dashboard of our candidates that we are speaking to?

Speaker 1:
[12:29] Great. So this is a strong demo here. You are exporting the entire Slack corpus every night, every week, or using the API, or you have an agent in that room. How do you get the data out of there into the model?

Speaker 2:
[12:43] I'm curious. Basically, what we do is we run like a cron job at a frequency of about every 15 minutes. So every 15 minutes, we'll have Kimi, for example, go and read everything that I get in Slack across all of the different channels. And then it will go ahead and take all of that information. And then we create, you know, we use Chroma, we use Ollama, and it'll put that inside of this ChromaDB, right? And now it'll almost have like a recreated knowledge base, if you will, of our Slack. So we'll do things like this.

Speaker 1:
[13:13] Got it. And is that more stable and a better way to do it than like OpenClaw? With OpenClaw, with Perplexity Computer, with Cowork, we find it's not comprehensive. It gets confused at times. And in one conversation I had with my OpenClaw, I was like, you should just export the entire database every Saturday night and upload it or something. And that doesn't seem like a really great strategy. So how do you know it's comprehensive and not so brittle?

Speaker 2:
[13:45] So this is kind of like the wow moment, like why companies like Deal, why Slash, why Corgi, basically are running almost a ton of different workflows actually on Aragon. It's because you don't have to, for example, specify. Like I was just, we had to fly to New York, we're doing a really exciting partnership with a very exciting ERP company. And they said, for example, can you make an MSA and can you send it to Josh? And they realized, they're like, oh, wait, I didn't say Josh from Aragon, right? But Aragon actually knew, it sent it to me, right? And they're like, whoa, that's really, really cool. It's like, actually, it's that good. Where I can even on first time, I can say, hey, just send it to Josh, right? And it by itself, the way we've architected it, it'll go look at calendar, it'll go look at recent, right? And it will basically say, okay, this Josh at Eragon is who we're talking about here versus like almost anywhere else, it will ask you and say, oh, which Josh? Here are five different Joshs, for example.

Speaker 1:
[14:48] So all of this then gets rolled up into the model on some cadence. And then because this reminds me of a lot of things I do with open claw or perplexity. But when it's in the model itself, do you have an example where it's in the model and that provides a massive lift for the company? Like what's the most compelling example of that?

Speaker 2:
[15:12] For example, okay, two things. One is like, okay, I just went through probably, in this case, like, I don't know, maybe 50 emails or so. So we have to act now. So Vinod Kozla wants to meet, Nicky's is EA, we need to respond back with a different availability, et cetera. Like, that's a pretty important one, right? So great, we should probably tell Vinod when we're available to chat about Eragon. But here, for example, Milton is our cleaning service for the office. Can you go ahead and pay the Milton invoice for me, please? And I can have Eragon go and actually pay that invoice as well.

Speaker 1:
[15:56] So that speaks to the agentic nature of this and the hooks. You have it hooked into a bunch of different services, I take it. So it's your QuickBooks or your PayPal or your Venmo.

Speaker 4:
[16:05] Yeah, it also has a financial infrastructure too, you partner with Slash to allow enterprises to give their agents bank accounts. So this is one of the cases where the agent has a little bit of financial agency.

Speaker 2:
[16:18] Yeah, exactly. And so then the thing that we are working on with our partners is also building out this ecosystem inside of Aragon. So for example, coming very soon, you'll be able to do a backslash, right, Slash, and then they provide credit cards, they provide bank accounts, they're like a Brex competitor, if you will, right? And how do you actually issue credit cards for these agents? How do you actually set different policies, different limits for the cards that you issue to these agents? You want to do it in a secure, kind of centralized...

Speaker 1:
[16:52] Human-in-the-loop, maybe. Maybe only pay certain known accounts. So this is all part of the blocking and tackling 101 that these agents need to catch up on. So you're really competing directly, heads up, with OpenClaw, Perplexity Computer, and QuadCore Work on the interface for agentic computing. But then on the back end, I keep getting back to the SLM, the small language model you're building, because that, to me, seems like there would be some massive lift. So show me something there, or unlock that for me, because that's what I haven't seen in the demo yet, is that unlock. And by the way, man, this is just another startup I should have invested in. Oh, wait, I'm on the capture table of this company. You started, while you're getting cooking there, if I remember correctly, just Agents for Sales, and you were so far ahead of everybody, right? This is two years ago or something. Agents for Sales was kind of the first ICP?

Speaker 2:
[17:51] Yeah, so I mean, exactly. So we basically kind of started there, and then pretty much we did our pre-seed with A16 and a few others. And what we found when we started showing companies was like, everyone wanted to ask for different, I would say like sources to be able to come into Aragon outside of maybe just sales. And it became pretty clear to me, like actually, you know, from like a venture model, everyone wanted to verticalize. But like the power of these like frontier models, you know, just kept getting better and better and better and better. And it begs the question of like, are we going to have such a thing as like a vertical kind of like sales agent or are these going to be so powerful that you can just create, right, like a sales agent just from inside of Eragon and have like Eragon be like your source for all sorts of different, you know, specific agents. And then what we decided to do is actually go to a partnership ecosystem approach where for companies that are going really deep. So I'll shout out, you know, Orange Slice is a YC company. They've done a phenomenal job. They're like a clay alternative. And we have companies like Corgi that basically will do like all of their enrichment natively inside of Eragon. And then that's powered behind the scenes by Orange Slice, right? Where they went really deep there. And so then that's kind of the approach that we've been taking as well. So while this, while this cooks, just as far as, you know, the point on this RL piece and updating the weights behind the scenes. So basically what it's going to do is like, for example, for all of your sales, right? If you look at, or all of the guests that have been on This Week in Startups, let's just say. Sure. So these are all facts. This is all historical. This is all already happened, right? And so like we just did this pretty fast. I would argue like it would be, you know, an order of magnitude faster than anywhere else. And then also on the cost side, right? It'd be much cheaper to do inside of here as well, right? And so here is like for our recruiting, right? Now I have my dashboard here for all of the candidates that we're speaking with, right? Awaiting response, you know, follow up, source, so we're sourcing, you know, minks from Stanford, Srinath from AnyScale, et cetera. This is actually all real. This is all production. We run Aragon inside of Aragon.

Speaker 1:
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Speaker 2:
[21:15] And because it has in the weights, because it's updated itself, it doesn't need to go for that, like basically workflow and read all of my email, read all of the Slack, right?

Speaker 1:
[21:28] So it should be lightning fast.

Speaker 2:
[21:31] Exactly.

Speaker 1:
[21:32] It should be incorporated into its logic at more base level. Explain to the audience, you know, if they've heard the word weights over and over and over year, but explain to them, given your product, what that means for, let's call it like if Uber had Eragon, they had an SLM, they had the entire history of the company, every quarterly report, every employee had ever been there, every single Slack message for over 10 years, every, or they made an S, or maybe they make multiple SLMs. There's one SLM just for complaints and customer support, one just for code, et cetera, because I would assume that at some point, you might break it up and have 10 SLMs by vertical so that they were even more tweaked. Am I correct in that concept?

Speaker 2:
[22:25] Absolutely correct, yeah. So you'll actually have multiple. And so what it means for a company like Uber is basically two things, cost and performance. So for the first thing, un-cost, right? Let's say you have some sort of workflow that requires knowledge of all of your customer success documentation, for example. So instead of basically a harness, trying to pull all of this context into a frontier model every single time, right? That info will just be in the weights. And what that means is much less tokens, like 100 times less tokens will actually have to do that. So therefore, it's way more cost effective. On the second piece, which is like the speed and performance, the workflow will be done much faster, right? Like 10 times faster as well. And so the best way the audience can kind of think about this is if you go to the Frontier Model and you ask it, you know, who is Abraham Lincoln, for example? Imagine if in order to answer the query, a model would have to open up a browser use, go search Google, go pull all of the info.

Speaker 1:
[23:41] Yeah, we don't have to imagine it because sometimes it does that, right? It's so sometimes there's some breaking news story. You're like, tell me about Coachella Weekend 2 and Madonna's performance. And it's like, okay, searching web. And you know, that's going to take 60 seconds. That's why you turn notifications on.

Speaker 2:
[23:56] But actually, actually, so for that, that's that's a little different, right? So like, for example, for like the Abraham Lincoln, right? I'm saying it doesn't have to go and do that, right? And the reason why is because it was trained on who Abraham Lincoln.

Speaker 1:
[24:09] Correct.

Speaker 2:
[24:10] And so it's in the weight, right? So for a company, right? If you want to find out, for example, you know, like how did we grow over 2019 to 2024, something like this? All of that's historical, it's already happened, right? Instead of right now, you have to go and you have to read every single record, every earnings, every quarterly statement from those 2019 to 2024. And then that might take actually two hours, right? For the model to do. And that might actually cost you like 1500 bucks, right? All of that info should just be in the weights. It should be instant. The model should be trained on this. It should know that, right? Or things like what you were just mentioning, which is like very recent, for example, like a Coachella. You should still be able to do tool calls and go search the web for info that maybe is not inside of the weights of the model. But those things are like much faster, kind of lower token usage and a little bit more few between, right? So our view is basically in order to actually own your intelligence, to have an asset that the company owns that's actually something that is theirs. The only way to actually do this is to train a model on your proprietary data and then give you the weights, right? So you own it, it's yours. And that's what we're working towards.

Speaker 1:
[25:31] It's genius. What does it cost? How do you charge for it? This is always very important and it's always a great tell for a founder. For me, Lon, when I'm working with a founder, how crisply they can explain the business model. Here we'll give it a shot here with Josh who is a very, I would say very strategic founder in my experience.

Speaker 2:
[25:51] We charge by tokens. We have a $5 cost per million tokens blended. You can comp that with what Anthropic Charges, which is like $15 per million tokens for Opus. It's $5 per million tokens.

Speaker 1:
[26:06] Amazing. How would that compare to using a frontier model?

Speaker 2:
[26:10] It's anywhere from a third to half the price.

Speaker 1:
[26:17] And they pay a service fee as well. Do they have to pay something for this integration? I would think you would need forward deployed engineers type of thing to do this today. So talk to me a little bit about when you have a cutting edge product like this, the implementation, because if you went into Uber, now it's like, okay, you could boil the ocean here. That company is a wash in data. So you probably have to find a department that wants to do it. You probably have to find a data set. You got to clean a data set. You got to create a process for it being updated. Talk to us a little bit about what Eragon does there.

Speaker 2:
[26:52] So what we do is we do have a forward deployed motion, and we do start, like to your point, on identifying workflows and use cases where you can start. So I can give an example of Corgi, which is a very fast growing company. They might have announcements of their own. I don't know if they're coming out about different rounds. So they're a very fast growing company in Silicon Valley, and they do insurance. They're an AI native insurance carrier. And so what we did is we said, okay, hey, what does your inference costs look like right now from a frontier perspective? Shout out to Corgi. And basically they told us how much they were spending on inference basically with frontier. And so we said, okay, look, we can actually do this for probably a third of the price. And they're like, okay, that's very interesting. And so then it was like, all right, let's find a workflow that currently happens. It's very repetitive and manual and let's start there. And so the example of that is we're on their website now. You can actually go and basically request a quote. And what would happen before is when you go request a quote, a startup puts in different information about their company. So like Aragon, our stage, our funding, revenue, etc. And you'll go and finish this workflow. Basically, you would put in all of this information. And afterwards, what would happen is there's a lovely person in operations. Her name is Evelyn. She would take all this information. She would then manually figure out what coverage you need, what policies you need. And then they had an algorithm that they would put that in to give you an actual quote, right? And imagine doing this like 40 times a day. That was her whole day. And so now what happens is all of that goes into Eragon. Eragon is trained on the policies they have. Eragon is trained on the coverage they have. Eragon is trained on kind of how they underwrite, right? So all of that comes in end to end. And then Evelyn gets a WhatsApp saying, hey, Josh from Eragon just requested a quote. Here's the policies you need. Here's the quote. Do you want me to go publish it to his dashboard? Yes or no.

Speaker 1:
[29:09] Wow.

Speaker 2:
[29:10] And all of that is now done.

Speaker 1:
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Speaker 2:
[30:32] I think it was like maybe May or something of last year.

Speaker 1:
[30:35] So it was 34, I think, yeah. And yeah, great pivot in there. Some great new investors you pulled through. About half the companies that come through our programs pull through. We like to take a lot of risk. And I think I always track in our database now where we found somebody. I said, if you got rejected from YC, please email us. We'll take a look at your company. And you emailed us and I'm not dumb. We met you and we're like, this kid's really smart. I don't know. I told the team, because I have my notes here. I don't know if he's going to be successful with this startup, but I know Josh is going to be successful. So let's start the relationship now. Josh, so proud of you. Congratulations. And always let me know when I can be helpful to you. You have my phone number. You got my email. Always let me know if we can be helpful. I'm good for a re-tweet once in a while, or a prescient introduction. But great job, Josh. I really appreciate you. And it's just great to be in business with you.

Speaker 2:
[31:34] Absolutely, Jason. It's been a pleasure. And for anyone in the audience that's listening, I think Jason and team has built an absolute world-class accelerator program. They've been tremendous, especially right in the early stage. You really have an idea. And I think a lot of times for anyone in the audience, we just announced our 12 million at 100 seed, but everyone listening, founders, everyone is just trying to figure things out. And I think that the best thing, Jason and team, they really have an amazing structure and cadence and resources at the earliest stage for how you can think through your business. And that would be helpful for us. And I would encourage everyone listening to absolutely reach out. And maybe soon it will be an agent on Jason's side that goes. Yeah.

Speaker 1:
[32:26] You cut me off at the past, brother. I was going to say, like, we are working on this now because, you know, when you get over 10,000 applications, we have a weighted system, speaking of weights. And, you know, Marcus is a really talented young fellow I have working for me, and he's been building this system. And what we found was, with our AI, our weighted system does essentially the same job a human can do. It can identify that. Now, we still have to decide who do we meet with. And then after we meet with them, we have to make another decision. But what I really want your help with, Josh, this could be a great project for us, is to look back at our meetings and everything, put it into a VSLM, very small language model, on my laptop that I don't train OpenAI and Anthropic or Gemini on how to do what we do here, our secret sauce. But then just be able to say, hey, we miss some things. Because when we miss something, Josh, what I do now, if somebody just told me, Molly Wood, who worked for us for a bit and co-hosted with me for a while, she had found a military tech company. And I was like, oh, we don't do that. It's kind of not what we do, but now we do do it. And two years ago, she found this one and it pulled through and it, I don't know if it's raising $100 million now. And I was like, okay, if we missed it then, but we built a little relationship, what can we learn? Number one. Number two, hey, let's go to the syndicate.com and we'll have a phone call with them, invite them on This Week in Startups and see if there's an opportunity for us to do a late stage investment, which the members of our syndicate love to do. Yeah, there it is. There's the syndicate.com. All right. We'll talk to you soon, Josh. And let me know if you're ever in Austin and we'll go grab some barbecue with Lon. Any excuse for Lon to get some salt lick with a Terry Blacks beef rib? You're not a vegan or something like that, Josh. Are you? Please.

Speaker 2:
[34:19] No, no. Well, we're definitely in for some Terry Blacks.

Speaker 1:
[34:23] I got the Terry Blacks beef rib and brisket, very solid. But if you come out to the Hill Country, I'll take you to the original salt lick.

Speaker 4:
[34:30] You gotta see it. It's very Texas.

Speaker 1:
[34:32] It's pretty, pretty Texas. And they got bison ribs there. They got the bison ribs out of the salt lick. I broke a tooth. You know, I had this like 20-year, I'm an old man now, I had a 20-year-

Speaker 4:
[34:43] Crown.

Speaker 1:
[34:43] What they call that, a brew canal.

Speaker 4:
[34:44] Yeah, a crown. You had a crown. You broke a crown.

Speaker 1:
[34:47] I had a crown. I'm just chewing on this beef rib. Snap. That's how you know it's a good beef rib.

Speaker 4:
[34:51] Yeah.

Speaker 1:
[34:52] When an uncle loses a tooth, it's a good beef rib. All worth it. I'm getting it put back. It's getting put back. All right. Good to see you, brother. I wasn't fishing for the endorsement, but I'll clip the heck out of it.

Speaker 4:
[35:04] We're going to have our agent throw that up on the website right away, already working on it.

Speaker 1:
[35:08] Here's my tweet. Pull up my tweet here. PredictSkills.md and VSLM's.

Speaker 4:
[35:12] Oh, sure. I'm on it.

Speaker 1:
[35:15] I've got a typo there. Are on a collision course, and it's going to be insane. Imagine legal HR marketing, et cetera, SML, god, I got to get that right, SLM paired with an open-claw scale, running relentlessly and recursively, as he mentioned, on your Mac studio, Dell Workstation, madness, free, constantly improving intelligence. It's happening, folks. And I put a picture of this Dell NVIDIA Gb300 desktop, which Michael Dell has not sent me yet. But I've been lobbying for it. I mean, I'm super excited for this future.

Speaker 4:
[35:48] I mean, that's, I think to me, that's the crucial step. We've been trying to have the agents run in a sandbox and in Amazon's rack somewhere. They need to spread their wings. They need to get out there in the world. You got to have your Mac studio or your amazing Dell standalone system.

Speaker 1:
[36:05] That's where your agency can live. We can get these working on a laptop. I don't know the 48 gigs gonna do it, but I think it'll be the 128 gig eventually. I am going to upgrade everybody to a $5,000 laptop from whatever $1,800, $2,000 one. It'll be 3,000. It'll cost me an extra three times 25 people, 75K for the next three years. So it'll cost me an extra 2K a month if it has a three year lifespan. But just think about how wild it would be if everybody's laptop was just cranking. I mean, it's also dangerous and scary.

Speaker 4:
[36:38] It is, but it's that recursive loop. It's that every time your agent does something, you're teaching it how to do that thing and it's refining it over time. That's the crucial thing you miss out on if your agent can't really spread out and make its own way in the world.

Speaker 1:
[36:54] Hey, anything going on in Polymarket? I got a Polymark going for my Knicks tonight.

Speaker 4:
[36:59] So we should talk about this briefly, that Moonshot has launched Kimi K26. This is their most advanced open source model to date. It upgrades for long horizon coding, agent swarms, more proactive agents, clog groups to which you can bring in your own agents and skills from outside. But it did lead me to this Polymarket, which I will share right now. The sharps are debating, when are we going to get K3? Here we go, so you can see, not a lot of volume. Only about 30K in volume so far, because this is so new. But if you look at June 30th, the sharps are betting 50, now it's a 59% chance that we are going to get Kimi K3 by the end of June, which would be very exciting.

Speaker 1:
[37:43] And we're at 2.6 or 7.

Speaker 4:
[37:45] 2.6. We were on 2.5, 2.6 got released this morning, so we're just, everybody's just getting their first crack at checking it out.

Speaker 1:
[37:54] People were kind of speculating here that it's really great at coding. And that's what everybody's focused on right now. Cursor's got a new model coming out. I saw they were raising a $50 million across x.com, but we're talking about that. You got Codex, you got QuadCode, Grok's doing great work there. They'll have some product coming out, I'm sure. It's just over and over and over again. We're going to see everybody get lightning focused. I heard that Sergei, who I saw Saturday night at the Breakthrough Prize and I talked to him for a bit. My understanding is he sent a memo? Is there like a Sergei memo that came out either this weekend or something that got leaked? And these memos are kind of designed to get leaked to them as I've always told everybody.

Speaker 4:
[38:41] According to the information, the Google co-founder said in a memo to DeepMind employees that, and here's the quote, every Gemini engineer must be forced to use internal agents for complex multi-step tasks.

Speaker 1:
[38:54] Right. So, they're dogfooding. They want them to catch up here and developers, developers, developers, once you get recursive developers going, it informs your whole product production. So people have been talking about, wow, it's incredible how much product is getting shipped out of Claude. Well, Claude focused on code, code then let them release more product, and then you get this flywheel going. The other companies are obviously trying to catch up with that. Yeah.

Speaker 4:
[39:26] Exactly. Should we bring on our next guest?

Speaker 1:
[39:28] Absolutely. Let's keep this train of experts only moving here. If you listen to Twist, you're going to get practical and tactical, and you're going to get to peek around the corner with experts only.

Speaker 4:
[39:42] So that's what we're doing. He is the founder and CEO of IONA. They make fully integrated autonomous delivery systems for logistics operators. The idea is like a white glove drone service that if you're already doing logistics, you could just add drones to your fleet. Give it up for Etienne Louvet. Etienne, thanks for joining us.

Speaker 3:
[40:02] Thanks for having me. It's a pleasure.

Speaker 1:
[40:04] All right. So do you want to demo and show us what you've built? You want to talk a little bit about why you built it, and who the customers are, and what it cost?

Speaker 3:
[40:13] Definitely. I think the team might have the videos in the background.

Speaker 4:
[40:15] I got some videos. We can pull up a video right now.

Speaker 1:
[40:17] Here we go then.

Speaker 3:
[40:19] And maybe before it even start, I can explain a bit like the origin story because it explains it all. IONA is an island in Scotland, the company, so I'm French. Unfortunately, I can't hide my accent. But we're located in the UK and Ireland as well as in the US now. I'm actually sponsored today by Gradient, a co-working space in Tulsa because I'm doing some...

Speaker 1:
[40:44] There it is. We got a plug-in for Gradient. 195 francs per desk. Here we go. Use the promo code TWIST to get six francs or a panneau chocolat. Wait, it was in France or Scotland?

Speaker 3:
[41:00] No, it's in Oklahoma actually.

Speaker 1:
[41:02] Yeah, Tulsa. Okay. Shout out to Oklahoma. Yes. Beautiful shot video by the way. It looks like you shot it with a drone.

Speaker 3:
[41:10] We have multiple. No, what IONA is doing is basically, it's coming from a vision. You can be on the Isle of Iona, which is a remote island in Scotland. You can be an IT consultant with a Starlink, a Zoom, Slack and so on. But what you can't get access to is physical goods. So what we're building at IONA is the physical internet. It's also close to my heart because a part of my family is coming from rural places. And it's solving this paradox because at the moment, when you have less than 500 people per square mile, for instance, in a location, that's 99% of the US. That's 99% of the world. And yet, our systems and logistics are optimized the way we were doing logistics 100 years ago with like a bulky delivery van delivering everything. So, what we're doing is we're building the full autonomy stack for logistics operators to take that transition, which is crucial for them because, unfortunately, they realize that the big tech are coming and that autonomy is the biggest opportunity for them, but it's also the biggest threat.

Speaker 1:
[42:16] So this is, you're talking about the FedEx, UPS, DHLs of the world. You're going to provide them with the infrastructure. So in other words, like a white labeled zipline in some ways?

Speaker 3:
[42:27] Yes and no. So yes, in the sense of it's going to be a white label and it's going to be for logistics operators, but we're going to also reduce the threshold. At the moment when a drone is flying in the US without going too much in the jargon, it's flying on the regulatory framework that is made for aerospace. It means that it's extremely complex to manage to... Part 108 is coming. It's a different regulatory framework. We have an expertise in that because one of...

Speaker 1:
[42:52] Part 108 is the number, did you say?

Speaker 3:
[42:55] 108, yeah. And part 108 is coming, and it's a different threshold. And we have an expertise in that because we fly autonomously already in Europe, and it's going to be the same system in the US. And what it enables is also to open the range of customers to also more local logistics operators, hospital, anybody with a delivery need.

Speaker 1:
[43:15] Got it. And unpack part 108 for us. We can get... We can double click on these kind of things here on the show. That's why we're here at Twist. So tell us a little bit about why part 108 is so different and transformative.

Speaker 3:
[43:29] Yes, it's a fundamental milestone. So in Europe in 2021, we had a similar framework. For once, Europe was ahead, not for long. And the reality is it just unifies anyone around the way we think about drone delivery and the way we think about drones in the airspace. So what it does is that it's a risk assessment framework. And one of the co-author of the framework in Europe also inspired some of the rules for the FAA. So what it creates in the world is that we have a common framework, a global consensus on the way drones should be implemented in the airspace, which means that we can finally have Europe, the UK, the US and other part of the world joining around that. And in more details, what Part 108 does is, first, you don't need to be an aerospace manufacturer with what we call Part 135 to operate. So it uses the threshold and it creates a framework that is much more adapt to a delivery with a drone, for instance. Because if you're not flying a 747, you shouldn't be regulated the same way. And what it does...

Speaker 1:
[44:48] My understanding is the BVLOS, I don't know how they pronounce that in the industry, Bivalose, I don't know. Is that how they pronounce it?

Speaker 3:
[44:58] Usually we say BVLOS here. Bivalose.

Speaker 1:
[45:01] Bivalose in French is a much more gracious way to say it. But this is Beyond Lon, visual line of sight. So I think 107, because I've looked at these companies for 10 years, was like, hey, you can fly your drone if you can see it. But once you can't see it, then you get into, I think a tip into 108. Etienne, am I correct? Yeah, that's the distinguishing factor here.

Speaker 3:
[45:26] It's true. So the BVLOS, so it stands for Beyond Vision Line of Sight. So when you can't see the drone, it means that the drone has to be highly automated or autonomous, and the significant change in that is that it means that you can have multiple drones or aircraft supervised by the same team that doesn't have to be on-site, because if you don't have to see the drone, you can be in Tulsa and operate a function somewhere else as well.

Speaker 1:
[45:55] So this is key.

Speaker 3:
[45:56] He is much greater.

Speaker 1:
[45:58] So when we contextualize it, this is kind of like going from level two autonomy to level four. If you're using autopilot and you got to keep your eyes on the road, but when you get to FSD, like level four, Waymo, et cetera, you don't need to be paying attention. 107 Lon, you need a pilot, like a plane, and you need a line of sight. And then 108, you need an operator, which could be a group of people in Manila. You could have a thousand Athena assistants who could be very easily trained in this to know what to do. They could be anywhere. They could be getting paid a dollar an hour. They could be getting paid 30 bucks an hour.

Speaker 4:
[46:35] It's that level up. I mean, you think of drone operators as having that remote and they're looking and it's like, it's got to be above the tree line. I know we lost contact with it and running after it. And it's going from that to like the air traffic control model where you could have a tower that's monitoring all of these drones and it doesn't have to be like a group of people that are actually watching the drone. It's obviously we're going to have to get there before we're going to have drone deliveries happening sort of en masse. And I guess my question would be like, is the goal ultimately just like unsegregated airspace, like drones are out there flying alongside every other kind of aircraft and we're monitoring them in sort of the same ways?

Speaker 3:
[47:13] Yes, exactly. So what you refer to is what we call the piece is called the detect and avoid second piece is called the on-man traffic management. I deeply believe that the way we will basically innovate in drones is going to affect general aviation later on because it's very hard to do on-man traffic management for aviation. At the moment, 98% of the control towers in the US are understaffed. Right. And it's not going to get better, to be honest, because it's a difficult job and because it takes a lot of time to train people. And that's something that we can also do with a drone natively because it's a bit like an autonomous system doesn't need a GPS. Well, it needs a GPS, but it doesn't need like Google Maps basically. And it's a bit the same because we the drones are autonomous. They can talk to each other. And because they can talk to each other, the scale in terms of efficiency is drastic. And this is where we go on the vision. And I like this analogy. When you think about it, roads are a Roman technology from 3,000 years ago. And we should build a physical Internet that doesn't rely on roads. We should build a physical Internet that can transport anything anywhere.

Speaker 1:
[48:25] And you're building these in Galway, is my understanding. That is a great town. I haven't been there in 25 years, but I drove from Dublin to Galway. It's a college town. Is that right? You're building the drones in Galway? Yeah, we have. The Celtic Tiger here?

Speaker 3:
[48:45] Yeah, we have a manufacturer there. And the reason is Ireland in general is an amazing place for drones. They operate with the same regulatory framework than Europe, which is the one that will be common with the US in terms of the way we think about it, so great. But they do it extremely fast. And the fast pace of iteration is especially very important. So we can get the paperwork back sometimes in less than 30 days when it takes up to six months in most places. So it made us drastically more cost efficient.

Speaker 1:
[49:18] Now, do you have any partners in logistics yet that you've announced?

Speaker 3:
[49:21] Yes, we work with a few CMA, CGM in Europe.

Speaker 1:
[49:26] What is that?

Speaker 3:
[49:27] CMA is one of the largest shipping company in the world. They also operate, they have Miami, Singapore, and Marseille. And they also acquired a number of companies. And so they have logistics needs. We're working with Welsh group. We have an amazing neighbor in Cambridge in the UK, Welsh group that is a family owned business. And it's exactly what we're trying to depict as a vision. They operate a number of deliveries in Cambridgeshire and we did a demo with them, a public demo. So we demonstrated the tech end to end how does integrate in their system and so on. And we have a few more exciting news coming very soon, including with high profile partners.

Speaker 1:
[50:12] Yeah, and for people who don't know, there's a great program, Enterprise Ireland. I'll give them a shout out. They used to, god, they've been around forever, but they used to even buy ads and host startups from Ireland back in my Silicon Alley Reporter days. Enterprise Ireland always was a big supporter of Silicon Alley Reporter and I think maybe even the early days of This Week in Startups. And they gave you, I think, Etienne, a couple of million euro investment or grant? How do they do it these days?

Speaker 3:
[50:44] No, it's a grant project that we have for manufacturing. That's the part in Galway. And the reason is because there is a nice story also. We operate drones as well in Galway area. It's the area, so it's quite difficult to access. It's quite remote and there are a number of communities there. So we want to, we're working on the project to actually connect the islands and connect the number of remote places there and remote communities with Galway and with other cities in the...

Speaker 1:
[51:13] Which, what do people most want to order? Like when you talk to your consumers, you know, you got people in Galway, they're on farms, they're in rural areas. What do they most want to deliver? Is it just like, I need coffee and milk?

Speaker 4:
[51:26] This is what's interesting is it's not food, right? You guys purposefully are not doing like food delivery. It's like cargo, right?

Speaker 3:
[51:33] Yes, we're calling that segment the light cargo. So we know very well MANA, for instance, and we know quite well Google Wing and even some people at Zipline. It's another segment. What I usually say is a drone is like a vehicle with wheels between a motorcycle and a 44-tons, same philosophy, but not exactly the same tech. So what we do is significantly different. Like we have to get long range. We go 60 to 120 miles with a significant volume. So we can get up to 20 parcels in the drone and a capacity to independently drop volumes. So we can do delivery one, delivery two, and then go back to base, for instance. So what we really the sweet spot for us, and this is also why we're B2B, is whatever needs to be transported. At the moment, in those places, there is a cruel lack of accessibility for day-to-day items. We're talking about the parcels day-to-day, and it's something that affects actually even the purchasing power of local communities. And then we can actually scale that to medical deliveries, even groceries. It's true that the drone is a bit big, so if you want coffee and milk, it might be a bit of an overshoot, but we could do it.

Speaker 1:
[52:49] What's the poundage here? How many pounds?

Speaker 3:
[52:52] Up to 44 pounds.

Speaker 1:
[52:54] Up to 44. So that's significantly larger than the other folks out there.

Speaker 4:
[52:59] It's 420, Jason. Our Taco Bell order might get up there. I don't know.

Speaker 1:
[53:02] We're close. We're closing in. There's a lot of tostadas coming. 40 plus pounds.

Speaker 3:
[53:10] Yeah, up to 4. The first version that we have is...

Speaker 1:
[53:12] What distance can it do when it's at max load?

Speaker 3:
[53:15] At max load, the XL will need to be the one that you use, and in that case, it's going to do 60 to 80 miles.

Speaker 1:
[53:22] Holy cow. But, wait, 60 to 80 miles, which means 20 miles out, 20 miles back with a buffer?

Speaker 3:
[53:29] No, it's a straight line. So half of that, a bit less than half of that, if you want back and forth.

Speaker 1:
[53:35] So 20, 30 miles, no problem. And then you got to have room to come back. The FAA makes you keep 50% in the tank when you land or something like that?

Speaker 3:
[53:42] Depending on where you operate, but yes, about. And the thing is, it's fully electric. So also what we build is, again, it's the full autonomy stack. So for instance, we also have demonstrated the prototype for loading and unloading the parcels autonomously. So you can have like an autonomous network. So you can do like delivery point number one, you load a few parcels, you unload a few others. Then you go to a backyard delivery somewhere, then you go back to another depot. Like you can really have a data driven autonomous logistics network.

Speaker 1:
[54:12] Yeah. Talk to me about charging. That's my final question there. It would seem to me, we have supercharger stations, right? And you got Zipline in some markets, you've got a bunch of people, Amazon is doing this. Should there be a common standard, a common infrastructure for charging drones? Or are we going to just have a proprietary war now where you all have flying drones everywhere and there's no way to stop and recharge? Because if there were charging stations, they would not, I could put one on the ranch here for y'all. I could give you two acres, cut off a little spot. You come down here, the noise won't be an issue. There's nobody in earshot. And they don't need to be on a highway. They don't need to be on the 405 in LA or 290 here in Austin. You can put these anywhere and you could all be recharging without having to go back to base.

Speaker 3:
[55:04] So I think I would slightly disagree with that because I think it's going in the way of innovation. The drones that you're thinking about, especially the zipline and everything, they're mostly multi-copters. So they have a very short range, a different type of technology. My drones, because they're doing logistics, they're extremely energy efficient. And it's not only because it's sustainable or whatever, it's because it's competitive. The more energy efficient you are, the less expensive you are on a unit cost basis. So I need to have a glide ratio. I need to have a wing, for instance. I need to have a set of things like that. So when you see-

Speaker 1:
[55:41] What do they call, because I know there's quadcopters, I know there's fixed wing, what do you call these ones that are the hybrid? Is there a technical industry term?

Speaker 3:
[55:48] So if it's an hybrid, it's called an hybrid, in that case, like when they have-

Speaker 1:
[55:51] Okay, so technically it's a hybrid, got it.

Speaker 3:
[55:54] Yeah.

Speaker 1:
[55:54] Is the technical name Lon for a hybrid.

Speaker 3:
[55:56] Or it's hybrid.

Speaker 4:
[55:57] Yeah.

Speaker 3:
[55:58] Or it's are like what we call the tilt rotor. And so it takes off vertically and it's transitioned to horizontal flight so that we can have the convenience of the vertical takeoff. But we had-

Speaker 1:
[56:09] And the tilt rotor gets you the glide path. And that's what Joby is doing too for humans, yeah?

Speaker 3:
[56:15] Yeah, it's similar to Joby in some ways. So we have less propellers and it's of course not the same product, but-

Speaker 1:
[56:21] So you're bigger than a zipline, smaller than a Joby is the way to think about it.

Speaker 3:
[56:24] Exactly, light cargo is like when you're in the middle. Joby is like the new helicopter, zipline is the new door dash, we're like the new delivery van in the sky.

Speaker 1:
[56:32] I love it. And what weight do you want to get to? Is there a goal that like, you know, you want to get to 150 pounds, 250 pounds, be able to deliver, you know, something of a little more heft?

Speaker 3:
[56:45] Yeah, you quote the famous philosopher, never say never, but we're not targeting that segment right away because the majority, like, what I like about my segment is that 99% of the streams are actually in that segment. So I don't want to over-optimize for the one time you need a plasma TV because you can probably wait for a delivery van or any other delivery van.

Speaker 1:
[57:09] Yeah, plasma TV, that's a weird footprint, but yeah. If Lon's getting kettlebells, you know, Lon's getting in shape here, so I've had a twist, no diggaming if you want to do some buff AI slop of Lon. But if you wanted to deliver a set of kettlebells, it could be 300 pounds worth of kettlebells and just drop it off on Lon's house.

Speaker 4:
[57:29] But lower it slowly. Don't just drop it, because that's dangerous.

Speaker 3:
[57:33] My story about that, we were supported by a grant and we had to explain why we were buying kettlebells. And I had to promise, no, it's not for personal use. I'm not like just buying a gym with the grant money because we're testing propellers. And if we can't find a way to leave the table on the ground, it's going to take off.

Speaker 1:
[57:52] Yeah, love it. Kettlebells are multi-use. All right, listen, Etienne, continued success and be safe out there, right? Safety first, that's what we always say here at Twist. Or if you're a Burning Man, there's a famous expression, Lon, safety third.

Speaker 4:
[58:09] Oh, no.

Speaker 1:
[58:10] First Burning Man's I'm at, there's like a bunch of signs.

Speaker 4:
[58:13] What's first? If safety's third, what's first at Burning Man?

Speaker 1:
[58:16] Like have a good time, be creative, I don't know.

Speaker 4:
[58:18] But they just, Number one vibes.

Speaker 1:
[58:20] Literally, these guys have this huge tower where they're doing aerial stuff.

Speaker 4:
[58:23] Sure, yeah, yeah.

Speaker 1:
[58:24] And there's a sign, it says safety third.

Speaker 4:
[58:27] Vibes first, vibes above all, yeah. We got so much news, we got Off Duty, there's a million places we could go. Did you want to talk about the Breakthrough Prize?

Speaker 1:
[58:36] Oh, yeah, so a friend of mine, Yuri Milner, and his wife, Julia, do this incredible Breakthrough Prize every year. I'll show some photos here. I've been invited, and this was the first year I've been able to go. What's really nice about this is-

Speaker 4:
[58:57] Look at all these celebrities that were there.

Speaker 3:
[58:59] Salma Hayek was there?

Speaker 1:
[59:01] Salma Hayek was there.

Speaker 4:
[59:01] Naomi Watts was there, Ben Affleck, look at this.

Speaker 1:
[59:04] Yeah, I didn't say hi to any of them, but I had a really nice conversation with Gal Gadot.

Speaker 4:
[59:10] Wow.

Speaker 1:
[59:11] Yeah, and that was very nice. I spoke to Gal Gadot. I met Rob Lowe. I met Rob Lowe. I traded phone numbers with Rob Lowe. Julia Chastain was at the table next to me. Olivia Wilde saw her there. Then he just had like just every- Lionel Richie came up and did a version of- He sang, We Are the World. Sure. I sat next to a woman named Francis, who's at Caltech, who won the Nobel in chemistry. We had a really nice conversation. I also at my table was Darren Aronofsky, the director.

Speaker 4:
[59:44] The great filmmaker.

Speaker 1:
[59:46] Yes. I had a nice conversation with Darren because I went down memory lane with Darren, we're off duty here, so I can chew the fat with Lon. I said, hey Darren, you don't remember this, but we met with Sean, who was in Requiem for a Dream back in the day, and I think he was also in Pi. He was the lead in Pi. I said, and you were doing a film, I had Silicon Island Reporter, I was writing for Paper Magazine, I met you guys and we had a drink, and then you were lamenting you needed to do screenings for Pi, and it cost like two or three grand to host a screening, so I did one for my magazine, and you came and you did drinks after, it's oh my God, it's incredible, and we had this like nice little thing, and we started talking, he's doing a startup. Everybody who's in startups and venture capital wants to do movies, everybody's in movies wants to do it. Of course, of course. And so we got to connect, and then we talked about Ikaru, one of my favorite Kurosawa films, I'm watching a documentary on Ikaru, like a random, I go down into the dark web, into like the crevices of people trading tapes on Reddit and all this craziness, and I look for weird stuff, and I found an Ikaru documentary, I don't know who produced it, it's in Japanese, but somebody took the time to put subtitles on it, and they go through that film, which I love, and so what I'll recommend is Ikaru, this great Kurosawa film.

Speaker 4:
[61:03] It's often overlooked among the great Kurosawas because it's not a samurai movie. People always focus on Yajimbo, Sanjuro, Seven Samurai, they forget about, yeah, Ikaru and High and Low is the other one I really love that is not a samurai.

Speaker 1:
[61:16] Well, yeah, that was during his High and Low, yeah, there it is, see, this is an incredible film about a guy who gets cancer. This will be my off duty for today. And he works in the government for 30 years. He gets cancer. He realized he's never lived. Then he meets a young woman and they kind of, in the second half of the film, he decides, I'm going to live a little. And he goes out and he starts drinking, has to have fun. He's got stomach cancer, but he decides he's going to live a little. And then I watch this documentary about it and they're like talking about the lighting and they're talking about, if you do the office scene, if you just go into Google Images, you can look for the office scene.

Speaker 4:
[61:53] I know the one you're talking about.

Speaker 1:
[61:55] And in Icarus, the office scenes are incredible because they stacked all these books and all these papers. So if you look in the background, it's just nonstop. There's a couple of different images here, like right there behind him, you'll see stacks and stacks of paper right behind him. The photo, yeah, there it is. And they explained how they wanted to build these like towers of papers and he was approving them. They couldn't find them, so they went into Toho Studios archive of all the receipts and they brought them there with the dust on it, because somebody brought them and they took the dust off and cleaned them up. And Kurosawa had a conniption and was like, hey guys, I want them with the dust. I want this to seem like the most oppressive place ever where he's sitting there and people are complaining to him about getting approval. He goes and he decides he wants to do one thing in his life that just counts. One thing to spread joy, I'm gonna leave it there. Great film, so I spent a little time talking to Aronofsky about it and we were like the only two people in California.

Speaker 4:
[62:52] Do you see this one?

Speaker 1:
[62:52] Seamless film, it means to live.

Speaker 4:
[62:54] 2022 Living, you should check it out. It is a British remake of the same story of Icarus. It stars Bill Nighy, but it's a retelling. He's a bureaucrat, he learns he has a fatal disease and he decides to live for one day. It came out in 2022, it was at Sundance.

Speaker 1:
[63:11] Oh wow. So anyway, I've been having my Hollywood moment these days, two weekends in a row, it's in Hollywood.

Speaker 4:
[63:16] Oh wow.

Speaker 1:
[63:16] And out with folks. And so that is my off duty breakthrough prize, just thank you for inviting me. But what I noticed, and I was talking to Francis about this incredible Nobel Prize winner, is the pace of science is moving so fast because of AI. All the stuff we talk about here is extraordinary, but the things they're doing in science and the breakthrough prize they're making, it's essentially we can choose as a society where to point the AI gun. Right now it's at developers, okay fine, okay it's at productivity, okay great. We're going to make new backgrounds in Star Wars. And I was talking to Gal Gadot about this, and she said the movie she's working on, instead of spending 200 or 300 million on it before promotions, P&A, she said we can now do it for 70 million and everything's done with AI except for the performances, except for the writing. And she said, I am great with this because, again, we're talking about CGI, like CGI obviously you play Wonder Woman, et cetera.

Speaker 4:
[64:21] Is this the Bitcoin movie?

Speaker 1:
[64:21] But you could lower the cost dramatically. What's that?

Speaker 4:
[64:24] Was this the Bitcoin movie? She's in that Doug Liman movie that they're talking about a lot about the history of Bitcoin, which they're doing.

Speaker 1:
[64:31] Oh, I don't know.

Speaker 4:
[64:32] Yeah.

Speaker 1:
[64:33] By the way, she's tall. She is. She is Wonder Woman. She is tall.

Speaker 4:
[64:38] Yeah.

Speaker 1:
[64:39] And she's got presence, but she's also a lot of fun. But things are moving fast now.

Speaker 4:
[64:43] The way they're doing it, he's shooting it entirely in rooms on green screen, and then all the backgrounds are going to be filmed in.

Speaker 1:
[64:49] OK, so that's the movie. Pull up a story about this. Yes, I will. Let me see what you're talking about here. And I asked her, is it Ben Affleck's new technology? Because Ben Affleck was building something like this, and it was, no. But she said, I asked her, like, as an actor, what the future was, and this is what she was telling me about. And she said, you know, Jason, you know, Jake, the thing here that is super compelling is because we are, you know, like on this green screen, et cetera, and you strip everything away, she said, I can actually, you can focus on performance. Like, it's all performance. And, yeah.

Speaker 4:
[65:27] Yeah, I'm trying to find a good image. They haven't released a lot of good images yet, but it's in a London sound stage. They're building, they're shooting the entire thing in a London sound stage. And, this has got to be it. It's called...

Speaker 1:
[65:39] Oh, it is, because she said Casey Affleck...

Speaker 4:
[65:41] .Bitcoin Killing Satoshi, a $70 million feature film shot entirely on a custom gray screen sound stage with AI-generated backgrounds and lighting. So, all the backgrounds, all the lighting is all going to be done by AI. The only thing that's real is going to be the actors in front of this gray backdrop. They're shooting this right now. It's very timely.

Speaker 1:
[66:02] I was just reading about this. Do you, Claude, if you do Claude's sidebar or your Perplexity sidebar, you could ask it what technology platform they're using to accomplish this.

Speaker 4:
[66:13] I already found it. It's a company called ACME AI and FX. They're building a new kind of network or background just to power this project.

Speaker 1:
[66:23] Okay, let's book them for the show. Let's get them on the pod.

Speaker 4:
[66:24] It would be really cool to talk to them.

Speaker 1:
[66:26] Get them on for Friday.

Speaker 4:
[66:27] I would love to talk about this.

Speaker 1:
[66:29] So, this is definitely the film she was talking about. Her main point is, we're going to be able, I guess this would be the counter to the end of the movie industry, the end of the film industry. There is going to be many more films made because of this. The whole hand-wringing, okay, I get it, set designers losing their jobs, FX people, maybe. It's going to be a bummer for a group of people, just like it was a bummer when the typing pool went away, the messenger pool went away because of email and word processing. But at the end of the day, tell more stories. Okay, those people are going to have to adapt and do other things, and there will be people who do it with sets, of course. But this does seem to me to be a counter to that. And then what if these things could stay in theaters a little bit longer because there was many more of them?

Speaker 2:
[67:19] Yeah.

Speaker 1:
[67:20] We could see a kind of resurgence where people might get out of their seats and go to the theater because there's more adult fare. Everything can't be Snow White, which she was in.

Speaker 4:
[67:33] I mean, if you think about the variety, I think you're absolutely right. I think it's just like we've seen with so many other fields. There's always going to be humans in the loop. AI can't make movies by itself. It doesn't know how to make a movie that would entertain anybody. You use AI to do certain functions and speed things up and make things cheaper to allow the creative people to do more with the creative side. I mean, I wanted to make a horror movie years ago with my friend, but we felt like on the bare minimum, a practical budget just to get the locations, just to make the costumes, just to do the makeup and stuff, it's like 100K. So imagine if we could just step into a room, we'd still have actors, you'd still have makeup artists, you'd still have the bare bones.

Speaker 1:
[68:12] But you might cut it 50K.

Speaker 4:
[68:14] You wouldn't need to take all the actors to some real location. You wouldn't need to pay all of these people like a production designer. So yeah, on the balance, there's some jobs being taken over by AI, but it also allows for a whole movie to get made that might otherwise not. And a human movie written by people, starring people, made by people. So we're not talking about it. I think that was always what led people astray, was all those tweets where it's like, Hollywood is over, sorry, actors and all that stuff. It's like, I don't think AI is ever going to replace the creative people. It's going to give them more tools to make more and bigger things.

Speaker 1:
[68:49] Correct. And so absolutely great for Hollywood. Got anything else off duty here?

Speaker 4:
[68:55] Oh my God, so much stuff.

Speaker 1:
[68:57] I started watching, by the way, the Love Story.

Speaker 4:
[69:00] Oh right, the Ryan Murphy. Yeah, the Ryan Murphy thing.

Speaker 1:
[69:03] The wife and I are watching that. We're two episodes in. And man, it has got me nostalgic for New York in that period. I mentioned on the show before I got to meet JFK Jr. twice. And it is just like, in one scene, they're going to the Four Seasons restaurant with the pool in the middle. There's a party there. I remember going there many times. In another one, they were at another place that I've been to a bunch of times. I forgot what the scene was, but I was like immediately like, oh, I've been to that restaurant a bunch of times.

Speaker 4:
[69:32] That's always the appeal. That's what Ryan Murphy and that crew do so well. Like, if you remember that OJ Simpson show, the American Crime Story one, it was the same with LA in the 90s. They recreated it so carefully that if you were there, you were like, this is bringing me right back to this time and place and moment. Yes, which is part of the fun, for sure. Part of the fun.

Speaker 1:
[69:53] It is definitely, oh, and they were playing Bjork and stuff like that. I've talked about that here before. But yeah, there are just tons of interesting shots of like his loft, which I remember was on Walker Street. And then there's a bar, Walker's, Bubbies, the Odeon, Tompkins Square Park, the Guggenheim. What else were they at? So, it's just like really Indochine. Like these were all places I went in the early 90s. So, I'm just loving it. The Odeon's still there, Bubbies still there, Walker's still there. So, those three are still there. And he's also, you know, at a gym. I'm not sure what gym he's at when he's there. And they also show him.

Speaker 4:
[70:46] It's all real. They do their research and they get everything sort of just right. And that definitely helps make the show feel more compelling.

Speaker 1:
[70:54] North Moore Street, that's what it... Walker's is on the corner of North Moore and I think Sixth Avenue. And there's a Calvin Klein's office. And I was literally speaking to David Geffen, the famous music producer a week or two ago. And we were talking about the film and he gets a shout out in the first episode that Calvin Klein is, you know, taking a phone call from David Geffen, favorite music producer. It was really cool. What else do you got Off Duty? You must have something to... So anyway, I'm two episodes in and it's great. I think it's great couples watching because it's a love story.

Speaker 4:
[71:27] Yes, there's a movie on HBO Max that just popped up. It's Brian Fuller, who people remember he did that show Pushing Daisies. He did that show Hannibal that I liked really much, the Hannibal Lecter show. He's got his first feature film out. It's called Dust Bunny. It's on HBO Max right now. Nobody paid attention to this in theaters, but it's really interesting. It's really fun. It's really whimsical. It's kind of a comedy, fantasy, horror about this. And it's about this young girl who believes in monsters, and she believes there's a monster living under her bed that has eaten her parents. And she finds Mads Mikkelsen plays her neighbor from down the hall who turns out to be some sort of a man of mystery, a mercenary, a hitman of some kind. And so she hires him to kill this monster that she believes is lurking under her bed. Oh, good premise. And it's the question becomes, is something real happening to this girl? Did her parents abandon her? Were they murdered? That's what Mads Mikkelsen is trying to figure out. But she keeps filling in like, no, I'm telling you, it is a actual monster. And that becomes the drama of the movie. It's a lot of fun, really well designed, very cool.

Speaker 1:
[72:35] Chip, could I watch this with a 16 year old with?

Speaker 4:
[72:37] Yes.

Speaker 1:
[72:37] Or two 10 year olds?

Speaker 4:
[72:39] 10 might be. I mean, if they are mature 10 year olds, it's a little bit of violence. There's a little bit of mayhem. But 1600%, let the 16 year old, I predict is going to really enjoy it.

Speaker 1:
[72:49] All right. Listen, I have been, thank you to my friends over at roe.co/twist. Go there and you get to the front of the line. You get to see if you can get your GLP.

Speaker 4:
[72:58] Your insurance checker. Yeah, your insurance checker.

Speaker 1:
[73:03] So I have been looking for great sources of protein. I need stuff that's quick and easy, right? You can make a protein shake. You got all these things. It takes a lot of work. I do like doing the egg whites once in a while. I have a little breast skid. I have been really trying to focus in on the most bang for the buck in terms of calories to protein. Because when you're on a GLP, you can lose a little bit of muscle mass, right? That's like I know that not everybody, but some. So I started looking for the best bang for the buck. Egg whites are up there, Greek yogurt's up there, but this one turns out tinned fish. So I have bought like five different tinned fish, and this is the one right now that I'm really feeling. It's called Wild Planet. I think they're like amongst the more, you know, healthy ones or great, or sourced really well. And 160 calories for 28 grams of protein.

Speaker 4:
[74:01] Wow.

Speaker 1:
[74:02] You're trying to get that 30 grams of protein in the morning. So sometimes I'll just make myself a piece of toast. I get a little bit of carbs. Okay, whatever. It's from my Happy Jane Bakery. I love so much here in Austin, but these anchovies. I love anchovies. And you put a little lemon on it. You could put some mustard.

Speaker 4:
[74:18] So toast, you're making toast and then you're putting the anchovies on?

Speaker 1:
[74:22] I'll just dump the whole thing with the olive oil on a plate. I eat them up.

Speaker 4:
[74:25] Oh, wow.

Speaker 1:
[74:26] Dip a little bit of my olive sourdough in there. And this is very good for you. I suggest you buy a 10 pack, a tin, Lon. And when you're gonna make a bad decision, not saying you make bad decisions, I'm not saying you don't. Not saying you're overweight, but I'm not saying you couldn't lose 10.

Speaker 4:
[74:43] I could lose.

Speaker 1:
[74:43] I'm not saying any of those things.

Speaker 4:
[74:44] I could lose a little bit of weight.

Speaker 1:
[74:46] I think 10. So this would help you. And then I looked it up, and now my YouTube algorithm is serving me up. A bunch of friends of mine, like Tim Ferriss, was talking about people go on anchovy fasts. So I don't like this fasting. It's like I get dizzy. I got to perform on the show. I get grumpy. But people do anchovy fast now. So they'll do like three cans of this a day. They reduce themselves to 600, 800 calories, whatever it is, instead of fasting, full on fasting. And this puts you in that ketosis thing. So anyway, listen, I'm no expert on this stuff, but I have lost the 40 pounds. I do feel great. And the shape I've been since my twin.

Speaker 4:
[75:26] There is something about tinned fish, I think that is sort of unappealing to me. But then I think back, like as a kid, tuna fish out of the can was one of my favorite snacks. I would just eat the tuna fish right out of the can. So I've developed this over time. I didn't used to have a thing about tinned fish.

Speaker 1:
[75:41] I showed you mackerel. The other one that's really good is sardines. So you can get sardine fillets and they're incredible as well. Also same situation, you're going to get into keto. You can also get, and there's people who really love this stuff. You can make a little plate of them. So sardines are probably my, I like mackerel above sardines, but it's just right behind it. So wild sardines, mackerel, and then, I think I tried one other fish. Yeah, no, I think those are the two I've tried. Anyway, people make plates of these. So you make like a charcuterie, but you include some of this. Oh, you know what? White anchovies are the other really good ones. The bocorinos from Spain.

Speaker 4:
[76:27] And then herring, of course, the classic elderly Jew snack of tin-tin herring.

Speaker 1:
[76:31] Well, I want to get... There's a herring from Sweden where they mix it with a mustard cream sauce.

Speaker 4:
[76:37] Yeah, for sure I know about this.

Speaker 1:
[76:38] And I had it there a couple of times when I was in Stockholm with Tyler, and when he was living there. And man, that is... I'm going to find that recipe where they do like... I think it's like egg whites and mustard, and it's a very creamy sauce, and they dump the herring in it, or they'll put cod in it. It's unbelievable. So there's your tip, folks. I hope you stay healthy, and we'll see you on Wednesday for Twist.