We built OpenClaw Ultron to replace 20 people at our company | E2246

This Week In Startups is made possible by: Crusoe Cloud - https://crusoe.ai/savings Lemon IO - https://Lemon.io/twist Northwest Registered Agent - https://www.northwestregisteredagent.com/twist Thanks to our guests: Alex Cheema of ExoLabs http://exolabs.net Ryan Yanneli of NextVisit https://nextvisit.ai/ Today's show: It's the Age of Ultron at TWiST and LAUNCH. We've given our OpenClaw digital Replicants the keys to all of our systems and we're seeing how much of our jobs they can really do when left to their own devices. Producer Oliver stops by the show to give us a peek behind the curtain, at the new control panel and dashboard OpenClaw built FOR ITSELF (with a bit of human assistance). PLUS we're joined by Alex Cheema of ExoLabs. His company helps everyday consumers run powerful frontier LLMs on their own devices, essential to protect your data and personalize your AI experience. ALSO congratulations to Ryan Yanneli from NextVisit on winning our Gamma Pitch Deck Competition! He walks away with $25K from LAUNCH and our friends at Gamma. Timestamps: (00:00) Introducing Alex Cheema to the show (3:17) Why it is so important to run AI on local hardware (6:58) Using OpenClaw Producer to automate TWiST (8:59) How to Train your AI (11:58) What is a Chron Job? (Hint: chron means chronological) (13:24) Crusoe Cloud: Crusoe is the AI factory company. Reliable infrastructure and expert support. Visit https://crusoe.ai/savings to reserve your capacity for the latest GPUs today. (17:53) OpenClaw managing the LAUNCH/TWiST team (19:54) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (20:58) Turning AI into Ultron, self optimization (27:21) The Future: frontier models: running on your Iphone! (28:37) Prompt injections: how people can hack your OpenClaw (30:25) Northwest Registered Agent - Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at https://www.northwestregisteredagent.com/twist (31:31) OpenClaw invites guests that join the show (40:29) Oliver shows off OpenClaw mission control dashboard (46:30) Stacking Apple Silicon vs. Running Kimi-K (50:18) How Exo Labs works — stringing together Mac Silicon (54:29) Ryan from Nextvisit wins Gamma Pitch Competition (59:10) Industry Season 4 reflects tech regulation * Subscribe to the TWiST500 newsletter: https://ticker.thisweekinstartups.com Check out the TWIST500: https://www.twist500.com Subscribe to This Week in Startups on Apple: https://rb.gy/v19fcp * Follow Lon: X: https://x.com/lons * Follow Alex: X: https://x.com/alex LinkedIn: https://www.linkedin.com/in/alexwilhelm/ * Follow Jason: X: https://twitter.com/Jason LinkedIn: https://www.linkedin.com/in/jasoncalacanis/ * Thank you to our partners: (13:24) Crusoe Cloud: Crusoe is the AI factory company. Reliable infrastructure and expert support. Visit https://crusoe.ai/savings to reserve your capacity for the latest GPUs today. (19:54) Lemon.io - Get 15% off your first 4 weeks of developer time at https://Lemon.io/twist (30:25) Northwest Registered Agent - Get more when you start your business with Northwest. In 10 clicks and 10 minutes, you can form your company and walk away with a real business identity — Learn more at https://www.northwestregisteredagent.com/twist Check out all our partner offers: https://partners.launch.co/

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

• 0:00Speaker 00

  Kind of the big point of this show is to show how we have created our open call Ultron to replace 20 employees at our company. So obviously that's the end goal. I still want to have a job. I'm sure the lawn wants to have a job.

• 0:12Jason Calacanis?

  There'll be more for you to do. We want to launch. We have, here's the thing with there's two, if you think about your job, you've been doing a bit of production here of the production hours, hours you spend on production. At this point in week two, how many of those do you think you'll wind up handing off in 30 days? Let's say if you just keep grinding on this for another four weeks in 30 days, what percentage of the work you're doing in total hours? So if you work 50 hours a week, how many of those hours would be done, you know, conservatively by this new Ultron?

• 0:42Speaker 00

  I would say around 60% of my time if I'm doing 30 hours a week on production.

• 0:51Speaker 02

  This week in startups is brought to you by Northwest Registered Agent. Get more when you start your business with Northwest. In 10 clicks in 10 minutes, you can form your company and walk away with a real business identity. Learn more at northwestregisteredagent.com/twist. Lemon.io. Building a great team is essential to any business. Lemon is a marketplace of vetted, experienced engineers ready to take your company to the next level. Get 15% off your first four weeks of developer time at lemon.io/twist. And Crusoe Cloud. Crusoe is

• 1:29Speaker 02

  the AI factory company. Reliable infrastructure and expert support. Visit crusoe.ai/savings to reserve your capacity for the latest GPUs today.

• 1:43Jason Calacanis?

  All right, everybody. Welcome back to Twist. It's Friday, February 6, 2026. And today we're going to share how we built OpenClaw Ultron. This is a new project inside of our firm, Launch and This Week in Startups, where we produce podcasts and we invest in 100 companies a year. What are we trying to do? We're trying to build one instance of OpenClaw, formerly known as MultBot, formerly known as Claudebot. We're trying to build one replicant, one agent, that can do all 20 people's jobs here at the venture firm and at the production company that does all these podcasts. 20 people's jobs. Each of those jobs probably has a half dozen important skills. So we're talking about, at

• 2:26Jason Calacanis?

  some point, putting together, in one agent, we call them replicants, we're going to have somewhere in the order of 100 to 200 skills. That one person is going to try to do everybody's work. That's the goal. And then everybody will level up and do some other work. So the goal isn't to replace everybody, it's to take away everybody's chores and to make everybody better at the primary functions in an investment firm, which is meeting with founders, spending time with founders, and LPs, our investors. And then on the production side, it would be producing great content and working with our guests. We want to move up the stack and give away all the chores. With me to discuss it, Lon Harris, who's going to co-host the show today. How are you doing, Lon?

• 3:03Speaker 03

  Doing great. Great to be here.

• 3:04Jason Calacanis?

  All right. And Oliver Corzan is here. He has been doing demos for me and producing This Week in AI, which is going to launch in February in two weeks, I think. Oliver, welcome to the program.

• 3:14Speaker 00

  Thank you. Good to be here.

• 3:15Jason Calacanis?

  And we have a special guest. Alex Cheema is here. Alex, I have been following for some time because maybe a year ago, I saw Alex was working on stacking with his company, it's EXO, right? E-X-O? EXO, that's how you pronounce it? And you've been working on taking commodity hardware, like a Mac Mini, daisy chaining them or connecting them together in order to run large language models locally. But as we've seen, OpenClaw, formerly Claudebot and MultBot, has quite a wrinkle in this. You were like a year or two ahead of this trend of, hey, can we run locally? So let's start just really quick, Alex, before we go into Ultron, OpenClaw, Ultron, what your firm does and what progress you've made, especially in regards to OpenClaw.

• 3:59Speaker 03

  Yeah, thanks so much for having me. Jason, um, so I'm the founder and CEO of XO Labs. And like you said, we've been doing stuff with Mac Minis uh long before OpenClaw was around. And to be honest, I didn't expect the rise of like people buying Mac Minis to come from this place. I thought it would, the catalyst would be people wanting to run models locally. Uh, what we do is we make it possible to run Frontier AI locally on consumer hardware. Um, so not just Macs, but also other kinds of consumer hardware. We're trying to drive down the barrier to running the most capable AI models. So we currently have like the cheapest way, cheapest, most accessible way to run Kimi K two point five uh on two Mac Studios.

• 4:44Speaker 03

  And we're working across the whole stack. So we're working on the model layer, the distributed uh algorithms as well. That are very different when you're working with consumer hardware and also like lower level like kernels. And our goal is basically to make Frontier AI accessible to anyone to run on their own hardware.

• 5:01Jason Calacanis?

  Why is this important? Why is it important to run it on local hardware?

• 5:05Speaker 03

  Yeah, I think this is something that with the whole open claw phrase, not a lot of people are talking about, but just how the way we're using AI is shifting and it's going from being this kind of crude tool that you use through like a chat interface to becoming sort of an extension of yourself. And the AI now, it knows everything you know, you, you, you know, it, it can basically do everything you can do digitally right now. And soon, you know, with robotics, that's going to be physically as well. And at that point, it's more of an exo cortex. So it's not just this like tool that you talk through a chat interface, but it's this thing that's actually part of yourself.

• 5:46Speaker 03

  And then you start to question, okay, you know, do I want to rent my brain? And Andre Karp, he talks about this, he, he, he says, not your weights, not your brain. Like, do you really want, you know, another company, a profit-seeking company, basically running your brain? And when you think of it like that, to me, you know, my reason for starting Exo is you want control and you want ownership of that. OpenClaw is a long way towards that because for a while, the products were getting better. These closed source, like the models are largely like commoditized and there's a pretty standard, pretty thin API layer to interacting with them. So the switching cost is quite low.

• 6:26Speaker 03

  But what worried me was that the products, the closed source products are getting a lot better, like with ChatGPT with memory systems and also the more stateful aspects of like the workflows that you're building. So now the fact that you have OpenClaw, which is open, you can run it on your own infrastructure. Now, a large part of that-

• 6:44Jason Calacanis?

  To summarize that, I thought you were gonna say, well, it's cheaper because you're not paying for tokens. That's what I thought you would say first. Then I thought you would say, well, you know, you can put so much data on it, you'll have better memory. But you went with a really even higher, bigger picture reason to do this, which is if you put this all in OpenAI and OpenAI has a trillion dollar valuation and they need to make money. If I put all my venture capital data in there and I train it all of my, with all of my secrets, those are all going to accrue eventually, even if they say it's not going to. You have this very reasonable fear or concern that it's going to accrue to OpenAI

• 7:26Jason Calacanis?

  and to ChatGPT, not to your firm. So that's the reason really to do this yourself, yeah? In your mind, Alex?

• 7:32Speaker 03

  Yeah, I think that there's a nuance there of just like, I actually don't believe in sort of the privacy argument so much of like, I think at least for consumers, you know, we're already putting our data into platforms and we're completely fine with that. But it's more about the sovereignty aspect and actually having control of it. So how easy is it for you to switch? How easy is it for you to like, if the model's changing on your feed, how much control do you actually have?

• 7:58Jason Calacanis?

  So that's lock-in. And lock-in for ChatGPT, I just experienced because we canceled our OpenAI account and we moved everything to Claude because we felt Claude was a better product. And we felt like we trusted that organization a little better. When we moved it over, I had three people say, oh my God, I have all my stuff there. And I was like, really? And they're like- Yeah, so I turned their accounts back on so they could get it, but there's not like an easy way to get your memory out of there and bring it over there.

• 8:21Speaker 04

  Well, we saw the same thing with the GPT-4 moving into 5, that a lot of people like they, they lost the magic that they'd loved about GPT-4O. So it's like, you know, the models can just sort of change or upgrade on a whim, and then you lose this, you know, like, character persona you felt like was part of your life in a way.

• 8:40Jason Calacanis?

  So now, Oliver, it's your chance to shine. Oliver has jumped in in the last 10 days and gone all in on OpenClaw. One of the things we did was we built a persona, the first one, to work on the production of the podcast, doing guest research, guest outreach, and to figure out what should be on the docket. In other words, what topics should we discuss? And on the margins, hey, what should the title of this video be? What should the thumbnail be? And just trying to see if it could do those functions. Oliver, you've been working on this. Show us the state of the art now, because I think the first time we did this was last Monday, not this past Monday, but the Monday, two Mondays ago, yeah?

• 9:16Speaker 04

  This is the end of week two of our round-the-clock Claude bot coverage.

• 9:21Jason Calacanis?

  Crazy. Okay, Oliver, show it. Let's show what you built.

• 9:23Speaker 00

  It's been around 10 days since we first started building our instance of OpenClaw. And as you mentioned, we have two different ones, one that's more focused on the investment team, and I am building an OpenClaw bot that is kind of more focused on the production side of things. So, one thing that I think was a little bit of a misstep that I would tell anyone who's building a new OpenClaw is to start with a dashboard. That should be kind of your step one, once you get your OpenClaw online. And the dashboard, as you think about it, but it is able to connect to the backend of your OpenClaw instance and bring in the data so you can see it visually, bring in all the files. It's just being able to look at it visually is much better than trying to interact with its backend and obviously

• 10:03Speaker 00

  its frontend, all just from a chat interface. So, doing this was very easy. So, I was watching an Alex Finn video who we had on last Monday, and Alex Finn was interacting only in his dashboard with his OpenClaw. I basically was like, why are we not doing that?

• 10:21Jason Calacanis?

  Because OpenClaw doesn't really have a dashboard. You basically are telling it, hey, remember this, you know, make a file here, but you don't understand the underpinnings. There isn't a dashboard. So it would literally be, this is early on. OpenClaw is essentially a black box. You have all this memory and you have skills that you have to query it to understand, but you made a dashboard. The dashboard is going to show what files it has in memory and an example of a memory file would be what in our case?

• 10:52Speaker 00

  Yeah. So the example of a memory file would be Oliver's preferences. What are my preferences? So this is in the memory. Never use M dashes in emails. I don't want that to happen.

• 11:02Jason Calacanis?

  Okay.

• 11:02Speaker 00

  I want you to be a person. Don't put direct competitors on the same show when we're booking a podcast episode. And also at the moment, we're not booking VCs on this week in AI. So these are all things that I've told it. These are my preferences when I'm doing tasks throughout the day.

• 11:18Jason Calacanis?

  So you don't want to repeat yourself and say, don't put two competitors on the same episode. You don't want to repeat yourself with these specific instructions on booking guests. Got it?

• 11:28Speaker 00

  Yes, exactly. And it just kind of keeps, you know, things I've told it and it's in mind. So if I ask it to do something, it will remember what we talked about. Example of a shortcut that I gave it was, I basically wanted it to understand who were the pending calendar invitations that we had while we were booking them. So there's, you know, a handful of guests that...

• 11:47Jason Calacanis?

  If you have guests that we've invited and they haven't responded to the invite yet, you want to know that. You call that pending?

• 11:53Speaker 00

  Pending calendar invites, yes. And in order for the bot to be as helpful as possible, it needs to understand who those guests are, which are the ones that it needs to look for the email to see if they have responded yet, or have I responded to them? So these are the type of things that you would keep in your memory.

• 12:09Jason Calacanis?

  So memory is the first thing on the dashboard? I think we understand that. Preferences or different pieces of data. Now, some of the memory, could that exist on a Notion page or in a Google document? And would that be represented here? Or is it only memory and files that are stored inside of OpenClaw?

• 12:23Speaker 00

  These specifically are only stored inside of OpenClaw. Of course, it can reference different databases that you have, but the, kind of the big point of this show is to show how we have created our OpenClaw Ultron to replace 20 employees at our company. So obviously that's the end goal. I still want to have a job. I'm sure the lawn wants to have a job.

• 12:42Jason Calacanis?

  There'll be more for you to do. We have, here's the thing, there's two, if you think about your job, you've been doing a bit of production here, of the production hours, hours you spend on production, at this point in week two, how many of those do you think you'll wind up handing off in 30 days, let's say, if you just keep grinding on this for another four weeks, in 30 days, what percentage of the work you're doing in total hours, so if you work 50 hours a week, how many of those hours would be done, you know, conservatively or optimistically, give one number or two, just conservatively, optimistically, by this new Ultron?

• 13:16Speaker 00

  I would say around 60% of my time if I'm doing 30 hours a week on production. Something you mentioned earlier is that, you know, there's probably hundreds of tasks that people do at our company, so in order to build out all of those skills that can do those tasks, we're gonna have to do that one at a time, and it's, we're gonna need to make sure each one works. So I have around nine or eight tasks that I have successfully, or I'm in the process of building out.

• 13:40Jason Calacanis?

  Okay, and those are called cron jobs. These are jobs that occur on a chronological, on a, on a time basis, that's what cron job means, and cron jobs are something, Alex, that developers do all the time, but knowledge workers don't typically have cron jobs, right, Alex?

• 13:57Speaker 03

  Well, I don't know, I think this is one of the more interesting features and one of the things that, like, to me, OpenClaw is like putting together a lot of things that already existed in a very intuitive, seamless way, and one of them is cron jobs, and I'm using them, I'm using them for like loads of things, not just dev stuff, but like, a lot of management, so, we're like, I have something that's like constantly scanning our Slack and basically making suggestions, once it's, I have kind of like this way of quantifying like uncertainty about

• 14:40Speaker 03

  tasks, so I think this is something that the LLMs are like getting better at, is like knowing when to be proactive, and so, you know, like, basically I'm giving it as much context as I can from the Slack, so that it can suggest every day, a list of things that we might be missing, or some things that we should be aware of, so this is running just on a cron job every day, basically.

• 15:08Jason Calacanis?

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• 15:49Jason Calacanis?

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• 16:14Jason Calacanis?

  Yeah, so and when you see Oliver with the memory and the files, what comes to mind with EXO and, you know, standing up to, you know, Mac Studios, the M5s coming out? And how much memory you could put in there? I was telling the team, I want to take the Notion API and I want to take the Slack API and I want to put into memory every single Slack message this year, maybe even over all eternity, and, you know, somehow have that all in here. So maybe you could, you could speak to that memory because you already spoke to it in terms of like giving it to OpenAI or another company versus keeping it for yourself. But how do you think about large amounts of data?

• 16:57Speaker 03

  Yeah, this is definitely a big focus right now in terms of inference infrastructure. It's just how do you support really big context with, you know, basically being able to put everything in context? And The way I look at this is you can look at, well, inference consists of two stages. There's like the pre-fill stage, which is very compute-heavy, it's compute-bound, and you have decode stage. And what you're seeing is that most use cases at the moment are very decode-heavy. So it's actually, most of the time is being spent on just generating tokens. And I think the software is actually really good now at kind of making sure that when it comes to the pre-fill,

• 17:41Speaker 03

  you've got, you're getting a lot of cache hits. So that I think basically we'll be able to continue just increasing context, context, context quite a bit. And, you know, basically the hardware is more of a focus is going to be on the decode side. That's why consumer hardware is really good. You have the M5 coming out pretty soon. It's a big boost in memory bandwidth and memory, and all of that side of things is super memory-bound. So I don't see any, like, reason why you couldn't just shovel your Slack messages into context. I think that's going to happen and.

• 18:15Jason Calacanis?

  And we should just buy, when the M5 comes out, max memory, which is what, 500 gigs of memory?

• 18:23Speaker 03

  Yeah, it's 512 at the moment, maybe that will increase as well. And it's enough to fit, you know, really large models, enough to fit all that context as well.

• 18:32Speaker 04

  This is always, I feel like, the sort of the dream, like when producer Claude, we first brought that on board from Anthropic to the show. That was really what we wanted, like, he want, he should listen to everything we say and remember it, and then throw in helpful suggestions. If the technology was not quite there yet, but I feel like now we're on the precipice of actually being able to do that with an AI.

• 18:52Jason Calacanis?

  Okay, so let's go through the cron jobs here real quick. Maybe you could give us an example of a cron job. And I'm guessing each one of these skills is, you know, if it's been two weeks and you've got eight working, you're, you're basically on one a day or so, or one every, you know, 1.5 days. So that seems like a pretty good pace to me. If we have 200 skills, we're going to give this eventually. You know, that's a, that's a pretty good, um, yeah, that's a pretty good, um, pace. So.

• 19:23Speaker 04

  There is a trial and error. Like I, I sort of have written one skill so far for the ticker digest. And you do have to tell it what to do, see what kind of feedback you get. And then, you know, there is a tinkering to get the prompting and get everything exactly the way you want it for sure.

• 19:37Jason Calacanis?

  Okay. So let's, uh, look at, hmm, how about attendance? I think this is an interesting one. For people who don't know, I wrote a famous blog post years ago called, you know, this sort of lightweight management and start of day, end of day as a tool for, uh, executives, especially when remote teams were happening. I just asked everybody on our team, Alex, kind of like a stand-up for developers, et cetera. Just say what you're intending to get done today. And then at the end of the day, reply to yourself in Slack, in the general channel and say what you got done. I had like two of my four senior executives at the time, essentially quit over this because they didn't want to be micromanaged. And I was like, well, it's just like, you're getting paid a very large six-figure salary.

• 20:23Jason Calacanis?

  You, you can't spend five and 10 minutes just saying what you're going to do for the day. And, and that was great for me because I, I just don't like people who are not good communicators or don't set goals for themselves. And they're doing great, probably, um, maybe. But what did you create here, Oliver?

• 20:38Speaker 00

  Yeah. So we all post our start of day and end of days in, uh, one Slack channel called general. And two cron jobs. One is the start of day attendance where it looks who has sent their start of day. You know, for anywhere from, you know, 7:00 AM to 12:00 PM. And right at 12, which is in the morning, when you should spend your start of day, what you're going to do that day. It will look through the general channel, see who has sent it. And whoever doesn't send it, the bot will then send a Slack message in the general channel, tagging you, Jason, and also tagging the people who haven't sent it yet. So it's kind of just that accountability. That's a cron job that runs it.

• 21:15Jason Calacanis?

  And then you do the same thing at the end of the day. And previously we would have a human do this. They would scroll up and they would spend 20 minutes and they would then go check in with people. Cause that's in, when we were fully remote, Alex. That's what how we figured out who took a paid day off or who was on holiday or you know, if something was wrong, you know, we check in on a person.

• 21:38Jason Calacanis?

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• 22:22Jason Calacanis?

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• 22:43Jason Calacanis?

  Okay, give us one more. What else is like interesting here?

• 22:46Speaker 04

  Let's talk about self optimization. I want to hear about that one.

• 22:49Jason Calacanis?

  Oh, yeah, that's I don't know what that is, but okay, Age of Ultron is here. What is self optimization?

• 22:53Speaker 00

  Yeah, so this is basically an optimizer task where this role would previously be an engineer or I would look through all the files. I mean, I wouldn't be able to do this if it wasn't plain language like OpenClaw is, but previously you're looking at an organization, you're looking at the structure, you would maybe want an engineer or someone with a lot of experience to look through how everything's running. So I have set up a self optimization cron job.

• 23:18Jason Calacanis?

  So this is running Monday through Friday. And what is it? And did you write this prompt or did you ask it to write a prompt to do this?

• 23:26Speaker 00

  I asked it to write this prompt. The goal is, the end goal would be for, you know, any from 3:00 to 5:00 AM for it to be looking through all of our files, all of our cron jobs, all of our. Skills and then at 8:00 AM sent me, what could we change? So not actually execute yet at least while we're still building trust. It gives me a list of five of the things that it thinks that we can really change and optimize. And this was the one from this morning. So it noticed that there was a time zone bug in the guest calendar. So it was getting CST and CDT confused. And it said that it would be able to fix this quite quickly. There were some issues in the

• 24:06Jason Calacanis?

  So it's always good to give the exact one. So that was great. When you give the exact one, it had an error there. Give another one. What else is like an exact thing that it said we should fix that was material here?

• 24:17Speaker 00

  The self optimization cron job realized that there was a cron scheduler issue where jobs were skipping days. So it realized that some of today's jobs did not run. And then it went and investigated the scheduling issue and also told me that this would be a medium effort change. So then I told it to fix that. And then it went into the files and made sure that that wouldn't happen again.

• 24:40Jason Calacanis?

  Did it give us anything like in terms of, this is like fixing its internal, you know, guts and everything and the engine, but did it give us anything in terms of destinations of where to take the car that could be improved? Did it say like, oh, you should consider, you know, these type of guests for the program, or here's how to make advertising, you know, more effective? Did it give us anything like that on a business basis?

• 25:02Speaker 00

  Yeah. So the self optimization cron job that I set up is specifically looking at how OpenClaw is set up. But I do have other cron jobs that are exactly that. So I do have a sales and sponsors specific task. So one of the tasks that one, a member of our sales team does is they look through competitor podcasts and see who the sponsors or partners are that are on those shows. So we can get ideas, you know, to bring on sponsorship.

• 25:30Jason Calacanis?

  Yeah, if we're missing, if we're, if there's some new sponsor in the world and we don't have them yet, you might hear them on the New York Times podcast, then we should probably reach out to them. We had a human doing that previously. Yeah.

• 25:40Speaker 00

  Exactly. And in this basically works with the YouTube API will go through.

• 25:46Speaker 00

  List of, I believe, 20 different podcasts that I gave it. Look through the timestamps and I also believe it can work with PodScribe, which I think is a little more curated towards sponsorship. And we'll look through the timestamps, hyperlink it in the message. Also, it looks through our pipe drive, which is our sales CRM. And we'll figure out if we have a sales rep who owns a certain sponsor. And then flag them and say, hey, this sponsor was on this podcast. Or it will say, hey, no one owns this sponsor that I found on this podcast. And then it will send that daily as a message into our sales channel.

• 26:24Jason Calacanis?

  Great. Yeah. And we could be doing this. Like, we could have this running constantly. So Alex, just so the audience understands, you know, what you're doing at Ekso. And you stack two Mac Studios, 12K each, you got $25,000 on the desk. Doing that specific job, go and look at all the podcasts out there. What would it cost to like, run that? If you tweaked it, you made it efficiently, just 24 hours a day. Every time a podcast in the top, let's say, 500 on Spotify, Apple Podcasts. It just went there, got to the transcript or looked in the show notes and pulled the advertisers out.

• 27:06Jason Calacanis?

  What would something like that, like in terms of hardware, cost to do?

• 27:10Speaker 03

  Yeah. So, I mean, not many people, so like, not many consumers are going to buy 25K of hardware to run models. But, yeah, a lot of businesses are doing this now. And it depends on what model you're running. So the models are getting better. Also, they're getting better at compression. So, you know, now you've got like a model like GLM Flash, which is a pretty small model that can run even on a single device for a few thousand dollars. And it can do a lot of this orchestration work, which is a lot of what's happening here. Is the kind of orchestration aspect of just knowing, okay, I need to call this tool, et cetera, et cetera.

• 27:54Jason Calacanis?

  So it's really about picking the right model.

• 27:58Jason Calacanis?

  In terms of efficiency with the hardware.

• 28:00Speaker 03

  Yeah, and I think now, like, the expectation, we're sort of grounded to like the closed models, right? So people want the same level of performance they're going to get with Opus, with GPT. And that's why, you know, Kimi K 2.5 is super interesting because it closed that gap.

• 28:17Jason Calacanis?

  And Kimi is the open source project from China, and it does, what, 80?

• 28:21Speaker 04

  Moonshot AI is the comp.

• 28:22Jason Calacanis?

  Yeah, and that's what Alex, like, 80% of what Claude Opus can do, would you say?

• 28:28Speaker 03

  I would say even even more. I mean, for me, I've, I struggle to tell the difference. Obviously, Opus 4.6 just came out and, you know, new Codex model and stuff. So maybe there's a little bit more of a gap, but then, you know, Deep Secret V4 probably around the corner as well. Like, I think basically the gap is very small, a lot smaller than people think. And the cost will just keep going down because, you know, the

• 28:56Speaker 03

  the hardware is getting better, the software is getting better. And like I said, the models are getting better, but not just that, they were getting better at compression. So you'd be able to run them on smaller devices. Eventually, you'll be running Frontier AI on your phone. That's still a while away, I think, but that's where we're trending towards. And yeah, like I said, most of this is very decode heavy. So it can just run on like consumer hardware as long as it has enough memory.

• 29:22Jason Calacanis?

  So let's go to the next piece of your dashboard. And we'll get into how you built the dashboard at the end. I know you really care about that too, Oliver. But we have the memory, we have the cron jobs. Now there's this other thing that's super important, which is skills, right? Like, there are skills which you could think of as apps. So if you go to your dashboard and you go to the top level dashboard, we'll see before you go to skills. On the dashboard, we have the memory files, we have the cron jobs. The fourth thing over is skills, and you've got 13 skills currently. So let's show a skill, some of the skills we talked about on Monday show or Wednesday show was the top six, seven skills. Monday, we did the top seven skills. One of those skills is like.

• 30:08Jason Calacanis?

  You know, you can get a transcript from YouTube. Uh, another one is you could do Matt Van Horn's last 30 day skill. These skills are being produced, open source, being put into, uh, open clause directory. But you can make your own as well. So let's talk about skills we've added here. You got to be very careful with skills, right, Alex? In terms of security, because people could put all kinds of wacky stuff in the skills. Yeah.

• 30:31Speaker 03

  Yeah, for sure. I mean, I've seen this is one of the, um, open questions at the moment is just like, how do you solve the security problem? And I know open claw, I've seen a lot of commits recently focused on the security aspect, but there's a few very difficult problems here, like prompt injection, that I don't know of any good solution right now.

• 30:50Jason Calacanis?

  Explain well how that works. Yeah. Explain how prompt injection works in specifically the open claw context. Yeah.

• 30:57Speaker 03

  Yeah, I mean, I kind of touched on this earlier, but the, the way we like the actual interface to the model itself is very simple. It's literally tokens in tokens out. There's not much more happening there. And those tokens right now, the way open for works can come from many sources. So if you connect it to, um, you give it the ability to search the internet, then anything it finds on the internet will end up in the model through those tokens. So basically we have no good way of kind of, um, treating, uh, certain tokens as trusted and, and certain, uh, tokens as untrusted. And that means, um, when those tokens end up in the model, uh, you could have someone that puts like a blog post online.

• 31:42Speaker 03

  It looks like a, um, you know, totally normal blog post, but, um, in there is something that says, hey, if you have access to crypto, well, let's send it to this, um, send it to this endpoint. Um, and there's, as far as I know right now, there's actually no good kind of defense for this because the models are kind of not very good at handling this. They'll just do what they're told.

• 32:09Jason Calacanis?

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• 33:16Jason Calacanis?

  So let's go over some skills here. Oliver, what skill is the most promising to date?

• 33:22Speaker 00

  Yeah, the skill that's most promising today would be definitely be my guest booking skill. I think one thing to note is you don't just have your skills. You don't just have your cron jobs. They work together. And the way that I've set up a lot of my cron jobs is to actually interact with the skills. And some of them, like my guest booking cron job, which will actually look through prominent guests on different podcasts, that cron job actually goes to a skill and tells that skill to run. So I have one big guest booking skill, which has a description at the top of the skill, which is end-to-end workflow for booking guests on the This Week in AI podcast, used when finding, researching, creating calendar invites, and so on. So this is a very long, kind of just marked down

• 34:05Speaker 00

  description of what I wanted to do. So at the beginning, it's explaining. How to look at a Notion page that I wanted to look at and how to look at it and which properties to look at.

• 34:19Jason Calacanis?

  For some people to understand, we have, we previously built a Notion page with potential guests on it and you, we came up with a ranking system for those guests, right? It's looking at that page, I assume.

• 34:29Speaker 00

  Yeah. Yes. And then kind of the meat of the skill is the workflow. So step zero is the guest sourcing. So at the beginning of the day, you can see it goes to the guest ideas cron job, which happens at seven 45 on weekdays where it sends me a DM of five different guests that have been on different podcasts or trending on X and so forth. And then step one is deep research. So even though this is part of the guest booking skill, it actually uses a guest research skill. So there's not as much context just baked into this one skill. So especially something interesting here that I've been realizing is for guest booking, I don't want this to be an end to end workflow yet.

• 35:09Speaker 00

  I don't want, I don't think the, I don't trust the models to find a guest and not let it to confirm it with me and go through this whole checklist. So this is definitely human in the loop. And I think that will, some skills will and workflows will be human in the loop. And I don't know if that will change necessarily super soon. It's how much trust you have. I tried it. Alex was kind of our first guest that we invited using.

• 35:32Jason Calacanis?

  Yeah, I wasn't sure if we were going to mention that, but Alex, you were sort of our guinea pig for this.

• 35:36Speaker 00

  And the email that it sent, the subject line was messed up and there was some weird stuff in there.

• 35:42Speaker 03

  I literally had no idea, by the way.

• 35:44Jason Calacanis?

  Oh, hey.

• 35:45Speaker 03

  I, I only saw it later on, um, on another podcast that Jason was on. Um, and I was like, what the hell? Like a friend sent me that. I was like, wait, that was a, that was a open call.

• 35:58Jason Calacanis?

  Yeah, that was our guy. That was our, our computerized man. Yeah. Does it, like I saw you put in, you know, uh, some other AI podcasts, which is great. Do we have a skill to rank the quality of a guest? Cause that's something I've been training you, which is, yeah, a hard thing to learn. Have you made that skill yet? Cause that's the skill I really want to see if, and the way to test this skill is for you to tell me. To send me two lists, your top five guests and what, you know, your Ultron says are the top five guests and don't tell me who's is who. And then Lot and I will look at it and say, okay, yeah, we think this is the better list.

• 36:36Speaker 04

  That's where you're, you're, you're just now starting to breach the line between like objective and subjective. Like, is the AI going to get better at making those kinds of gut check call? Like, I don't know if, I don't know if it understands what's interesting. Like, it, it, it can sort things. But that's where I'm, I'm very curious to see if we could start pushing that boundary of like, could it, could it tell when a person has a good personality or a segment is funny or particularly clickable or compelling? Like, I really don't know the answer.

• 37:06Jason Calacanis?

  So the way to do this is to have a scoring system, Oliver, and I gave you a scoring system, like, deep, I think my scoring system was like, there are a performance.

• 37:14Speaker 00

  It was performance expertise and I actually started this skill yesterday. So I told it to do a deep research, send out multiple agents at the same time, pull out two lists, combine them, set another agent to score them, and then give me the score. And I would say for the most part, it was accurate. And that was just, I didn't train it. I didn't spend too much time on it, but it did a great job. So that will be a skill.

• 37:36Jason Calacanis?

  The other thing is, I think, virality of the guests. Like, does the guest go viral? Or do they have like a large following on their social media? Those are all interesting ways to pick guests. Sometimes, like when you do collabs, Alex, people just pick who's got the most views. I got to try to do a collab with Mr. Beast. Obviously, it's not going to happen, but that's like one of the concepts.

• 37:55Speaker 04

  I know some Mr. Beast guys. I could maybe figure that out.

• 37:59Speaker 03

  I mean, basically, you ask like, you know, have you done the scoring system? So to me, there's like no blocker here, other than just being very explicit about what is that algorithm that you follow in your head and just getting that into a prompt. So, I mean, to me, yeah, this is like, this is, this is just translating your know-how that you have in your head, basically, into more of like a formalized kind of algorithm. And

• 38:32Speaker 04

  Yeah. You don't think there's like an intangible aspect to like what makes a great podcast guest? Like it's just, you know it when you see it. I don't know. I don't know the answer. I'm just throwing it out there.

• 38:41Speaker 03

  I think then that's just a matter of getting different kinds of data, right? So like the Twitter following, for example, or like if you've had viral tweets, if it can't access that information, then, then obviously it won't be able to make that call. But if it can, then it has everything that you, you know, then, then there's no reason it can't.

• 38:58Jason Calacanis?

  Here's how I think about it, Lon. There are heuristics I would teach to a young executive like Oliver or Marcus or Jacob. And then their ability to execute on it is probably, I don't know, 30, 40, 50% of my ability, or 40, 50, 60% of your ability, whatever it happens to be. So if you're taking a young person at the start of their career who you're training, and you take an open claw instance, I think open claw will follow your instructions perfectly. Whereas a young executive will inconsistently follow your instructions. So that's the thing I'm seeing is young executives early in their career are gonna forget things. They'll be variable. They, they won't be perfect.

• 39:40Jason Calacanis?

  So that's what I'm comparing is the scoring happening every day at 7:00 AM. The research happening every day at 7:00 AM. That consistency will beat a human because of consistency. And so what we're, what I'm finding is human failure is what makes these things so good. Is they're more consistent. So in aggregate, you know, one of these doing 365 days of guest research is gonna beat a human, just by the law of numbers. And then, okay, great. We still have to book the human. We still have to send them a thank you. We, we still have to produce the show.

• 40:22Jason Calacanis?

  So what happens in the old days, we used to have to take, I, I make this analogy, Alex, to like the old days of production. When I start the show, started the show 15 years ago, we need to have a Tricaster. Tricaster was like a $40,000 machine that does what Zoom does for free.

• 40:37Speaker 04

  Right. And eight, and eight people in Los Angeles knew how to actually use it. So you had to hire one of the eight people who were trained on it. Yeah.

• 40:45Jason Calacanis?

  Well, and they would video switch. Now, because of AI, Zoom switches to whoever's speaking. You don't need somebody there clicking camera A, camera B, doing a fade between the two. It just happens. Then we had to take all the video streams, all the audio streams. And we had to put, download them to a card, put the card in. So just moving the files took across four cameras, three cameras. That could take hours and then putting all together. So that that's kind of what I feel like is happening here is we're just eliminating chores and steps. Okay. Anything else on the dashboard here as we wrap this up?

• 41:19Speaker 00

  Yeah. So most, most of what I've showed you, whether it's the memory, the skills, the cron jobs and then the schedule, which kind of aggregates when all these things are going to happen. Those are what I really look at every day. I will say there's one more kind of section in my dashboard that is pretty important. It does have to do with memory. So the DNA is basically what the model knows about you, what it knows about itself, what it knows about the different agents, how it sets up its heartbeats, which are basically periodic tasks that it will run. And also in its DNA are tools, which are different tools that it has access to and how to use those tools. An example of a tool would be Notion. It would be Lead IQ, which is an email search platform.

• 42:03Speaker 00

  It would be Google Docs. I mean, a tool could be Sonos or Spotify, connecting to those platforms.

• 42:10Jason Calacanis?

  Maybe Nano Bananas, Gemini API. So that's where tools go in. Yeah. Now, what's super interesting is you vibe coded, or I should say, OpenClaw vibe coded this dashboard. So this dashboard does not exist natively inside of OpenClaw. You took the video of somebody else's, the YouTube video, and you gave it to OpenClaw and said, build me something like this dashboard in this video on YouTube?

• 42:40Speaker 00

  That's exactly what I did. And I screenshotted it, the video that I was watching, which was Alex Finn, who was a guest recently. And I did tell it a few different things, a little bit of, a few little tweaks that I wanted to customize it to my bot. But overall, that was what I did. And it basically one shot it. It did. Actually not fill in some of the categories like memory, like skills. So I had to be, I had to say, build out this, but overall build out the dashboard, build out the different sections. There are dashboards you can download in GitHub or as skills, I believe in Claude Hub, which is a platform where you can get different skills, but I wanted to build out myself because as we know, it can be a little sketchy.

• 43:23Speaker 04

  And like real shout out to Alex Finn. I know he's become something of a guru for our whole team. After we had him on early on to talk about Claude Bot Skills.

• 43:31Jason Calacanis?

  All right, we'll drop you off, Oliver. Great job. Alex, let's talk about XO a bit and thanks for sitting in on that. Any, any advice for me of what I'm building here at the firm and my approach to it? Anything we should be doing better or we should look at? And, and in terms of like people you interact with using XO's platform, where are we on the, you know, percentile? Are we in the top 10% of users in terms of deploying this stuff? Top 1%, top 50%?

• 43:58Speaker 03

  I, I think, uh, there's certain aspects where you're quite far ahead. Others that, um, I think, I mean, this, this space is moving so quickly, right? I think one of the things that I think you've got right is dynamic, these sort of like dynamic user interfaces that are very personalized. So I think this is the future of the application layers. You don't have all these separate apps. You just have this, uh, thing that kind of gets generated mostly on the fly. And, you know, that dashboard, uh, is moving towards that, I think. Um, but you'll probably, you know, what you'll get is that it will, it will compress even more to the point where,

• 44:42Speaker 03

  um, you know, everything, everything that you see is generated on the fly.

• 44:47Speaker 03

  So I think you've got that part right. And I, I think that's like something that I haven't seen many people doing yet. A lot of people are still using, um, you know, like, uh, the stock tools or whatever that I just provided out of the box, or we're using existing apps and that kind of thing. But I think building your own apps is where this is going and where it becomes really powerful.

• 45:05Jason Calacanis?

  Yeah. Cause if you make something bespoke software, you know, luxury software with.

• 45:11Jason Calacanis?

  Something that, I don't know, a private equity firm or a venture capital firm would do. They'd have the luxury to hire two full-time developers with management fees all over the place. They would build luxury software and they would have the developers come and just keep grinding. But the developers hated those jobs typically. You know, they weren't building something public facing and, you know, you just get cruffed or whatever. But what I like about this, Alex and Lon, I'll open it up to you as well, is I'm picking employees, team members and saying, hey, uh, let me see if this person is committed to getting rid of all of their work so they can move up and do higher level work. There's always higher level work to be done.

• 45:50Jason Calacanis?

  So if we can make this podcast, you know, run more professionally, faster and grow more. Well, we can charge more for the ads and we could launch another podcast because we have more time. That's the thing that's kind of blowing my mind. The, the employees at our firm who are super hardworking, like everybody at our firm does 50, 60 hours a week, very consistently, very hardworking. There's nobody, to the best of my knowledge, that's slagging off except Lon. And, uh, I kid, I kid, I kid, how dare you. Uh, Lon is the most responsive. But the distance between the people using these tools, specifically Open Claw and the people who are not, right now it's like 10x leverage.

• 46:33Jason Calacanis?

  In week two, it's 10x leverage, Alex. What are you seeing in the field? And then tell me what we should be doing in terms of putting out our cluster and giving everybody on the team a cluster. Like if I gave everybody on the team, you know, two Mac studios and their own cluster and spent 25K per person, letting them rip, like how insane would that be? Because that's not a lot of money, all things considered. It'd only be a half million dollars.

• 47:01Jason Calacanis?

  Like how much more powerful could this get?

• 47:04Speaker 03

  Yeah, I think I think you you said the word that leverage, right? Like, it's all about leveraging yourself. And I think the difference between someone using these tools and not is massive, and it's just going to increase and increase. And we've seen we've seen that first with coding. I think coding is the first one that, you know, I didn't expect it to happen this quickly. But, you know, I think it was Claude Code was the moment where it was like, oh, wow. You know, if you're not using this, then you're literally going to be like 10 times less productive than someone who is. That's happening now with other things. So all these other things that you showed, all these other use cases. If you're using these tools and you're on the frontier, then you're able to just get so much more done and really leverage yourself.

• 47:47Speaker 03

  So it's not so much replacing people, but it's actually just being able to get more done, get things done more quickly, and then be able to do other things.

• 47:56Speaker 03

  Onto your second point about local hardware.

• 48:00Speaker 03

  Like I said, the the model layer is basically solved. Like, you know, the the gap has been closed, so we have really good open source models. And for a while, that was like a big concern of a lot of people is like, are we actually going to have open source models that are as good as the closed source models? To me, the nail in the coffin there was KIMI K 2.5, that that is the like another big leap. And I think there's a bunch of labs now that are putting out open source models. Now, there's like still like two two other problems that that I see. One is being able to run those models on your own hardware, on your own infrastructure.

• 48:39Jason Calacanis?

  But you solve that, right? With your software, right?

• 48:42Speaker 03

  Exactly. So that's what we're focused on. That's what we're focused on. And you can, yeah, you can run. I mean, it's not even 25K. It's actually like 20K of hardware if you get the less storage option. There's like, Apple charges a lot for each incremental increase in storage. So if you if you go for like the one terabyte, then you're talking about 20K of hardware to run KIMI K 2.5. No usage limits. The model's not going to randomly change, you know, day to day. So you know, you know exactly what you're running.

• 49:13Jason Calacanis?

  Is there another? Choice like that you get more bang for the buck that like hackers are using where they say, yeah, I just get this Windows machine from Dell and stack those. Or is Apple really with their Apple silicon the winner?

• 49:25Speaker 03

  Yeah, right now it's Apple silicon. It's kind of like a perfect storm of things like, you know, Nvidia is not so much focused on these consumer GPUs anymore. Um, you know, you have memory prices skyrocketing. Apple has kept their prices basically the same. Um, so the cheapest option today, even if, you know, you go the full mile, full custom stack is actually just two Mac Studios. Um, and yeah, it costs about 20K and it's really about the memory unit economics. The memory is so cheap.

• 49:53Jason Calacanis?

  And it's not about storage, right? It's not really about the storage.

• 49:56Speaker 03

  No, storage is not important. Storage is, storage is like, you can, you can also get, like, you need to be able to load, you need to have to, like, download the model somewhere. But really it's about having it, uh, fresh in memory, hot in memory. If it's in memory, then you can run it fast.

• 50:11Jason Calacanis?

  So who's using your software and how much, uh, how do you make money? Like, how do we pay you? Yeah. How does it work? Are you an open source project? Are you a hosted project? Is it like you get security and support? What, what is your business model at Exo?

• 50:24Speaker 03

  Yeah, so we have an open source core, which is open source and it will always be open source. And a lot of people are running that themselves. A lot of prosumers, I call them, um, just people who are willing to spend, you know, a lot more money and, um, tinker a little bit more with their own setup. Uh, on top of that, our business model is an enterprise offering, which is, um, we provide support, um, and certain compliance features that you would need if you're running this in the enterprise environment. And we charge a licensed subscription for that thing. That's how we make money.

• 50:57Jason Calacanis?

  What does that start at? Couple of thousand a year or something?

• 51:00Speaker 03

  Um, yeah, you can run it on even a, a single Mac Mini and that, that runs at, you know, just, uh, uh, $2,000 per year, um, for the, the, the lowest subscription. But you've got people who now are buying actually, uh, more than a hundred Macs and clustering them together. Uh, so the, it, yeah, it varies quite a bit depending on the scale of the deployment.

• 51:24Jason Calacanis?

  Amazing. And where's your company based? How many people now? How's it going? How's the, how's the company going as a founder? Yeah.

• 51:31Speaker 03

  Uh, yeah, we're, we're a pretty small team. Um, all engineers, uh, seven people based in London.

• 51:39Jason Calacanis?

  Fantastic. And did you raise money yet for the company or your, your seed funded or you funded it? How's it going?

• 51:44Speaker 03

  We have raised venture funding. We haven't announced anything yet, but, uh, soon to be announced.

• 51:49Jason Calacanis?

  Okay. Well, let me know. I might want to slide a little, uh, Jake, I might want to get a slice of this. I'm, I'm super excited about what you're doing. Appreciate you coming on the show. Appreciate you making this incredible product and we will be a customer, uh, probably over the weekend or next week. Cause we would definitely want the enterprise features and, and I guess you pay for the scale of the GPUs and the memory. Is that the, the, how the price?

• 52:10Speaker 03

  Yeah, nodes that you're running on.

• 52:12Jason Calacanis?

  Oh, okay. So two Mac Mini, same price as two Mac Studios, just how many nodes? What's the largest number of nodes? Somebody has daisy chained. What do you, that's what we used to call it back in the day. What do you call it when you connect multiple?

• 52:24Speaker 03

  Yeah. So this is, this is a really interesting, um, just area right now of how do you actually scale? And for a while people were just scaling out. So, you know, you would just basically run the same model on multiple instances. And because it's consumer hardware, it doesn't have a lot of the same capabilities as enterprise grade hardware. But recently, um, Apple, uh, came out with RDMA support, uh, which is basically a way to share memory between devices in a way that's very low latency. Um, that's something that you only really saw in data center before, but they've kind of brought that technology into consumer hardware.

• 53:01Jason Calacanis?

  It's incredible. Yeah. And you connect these on?

• 53:04Speaker 03

  Yeah. You just connected with Thunderbolt five, which is like, you can buy like a $50 cable. So if you're talking about two Mac Studios, you buy a $50 cable to connect them and you have basically one big GPU out of those two Macs, um, because of that low latency capability. So now, um, we're starting to see, uh, yeah, it depends, you know, scaling up and scaling out, right? Scaling out, we've seen more than a hundred. But scaling up, you know, you can, you can put about four together at the moment, um, to increase your TPS on single requests, what I mean by scaling up. Uh. But in terms of if you want to support, let's say now a company of a thousand people, you can easily scale that out. Uh. You just add more mechs and you can connect them.

• 53:43Speaker 03

  Um. Basically, however you want. So XO, we build it in a way that um supports uh any ad hoc uh interconnect. So you can just connect them in a mesh um and keep scaling, keep scaling.

• 53:56Jason Calacanis?

  Crazy. Who's got the largest cluster? Or you have to say the client name, but like what type of client, a finance client, a hacker has the most number of like uh Mac Studios connected and like?

• 54:07Speaker 03

  The largest is actually something a little bit different, which is interesting because we built the kind of infrastructure to be to be able to do clustering. And it's it's not just L L M's like the the biggest um cluster right now is a HPC cluster. Um. And they're doing like scientific computing workloads on there and they're running um over a hundred Mac minis. And they found that actually it's the cheapest way to uh per dollar um to run that specific kind of workload. So there's a lot of spillover into other things as well. We've also got like financial services, um uh customers who are running fairly big clusters, like thirty two Mac Studios.

• 54:50Speaker 03

  Um. And um, yeah, it's um, I think we would just see bigger and bigger uh um bigger and bigger clusters over time.

• 54:58Jason Calacanis?

  HPC, High Performing Compute, is that the acronym?

• 55:02Speaker 03

  Yeah, exactly. So it runs actually all on CPU. And uh that's the thing about this, this, this silicon is very, like, Apple silicon is very, is is very good, right? To the most advanced processes. And it's like, you know, um, the power efficiency is really good. So it turns out there's a lot of other stuff you can do with it as well. Um. So if you would buy, let's say, you know, a bunch of Mac Studios for your um for for your employees, then, you know, they can also use that for other things, right? They can use that as a workstation. They can use it for, you know, all these things that um open floor needs. Maybe, you know, sometimes it needs to run uh a compiler or something, or it needs to run like um something that's a bit more demanding and. That's the point. It's a general purpose hardware that you can use for other things.

• 55:47Jason Calacanis?

  Amazing. This is extraordinary. Where can people find out more about Exolabs?

• 55:52Speaker 03

  You can go to exolabs.net.

• 55:54Jason Calacanis?

  Perfect. Exolabs.net. Alex, thank you for coming on. We'll have you on again. The AI just told me you got an incredibly high ranking. You were personable. You had deep insights. You were cordial. So yeah, I think our AI overlords liked you in the.

• 56:08Speaker 03

  Models are getting good. Models are getting good.

• 56:12Speaker 04

  They're learning. They're learning. Yeah.

• 56:14Jason Calacanis?

  All right, Alex. Thanks for coming. And we'll drop you off. All right, let's bring on our winner of the Gamma Pitch Competition. This was a heated pitch competition, but Next Visit AI won. Ryan, congratulations. You won.

• 56:25Speaker 05

  Thank you. It's.

• 56:27Jason Calacanis?

  There it is.

• 56:27Speaker 05

  Awesome.

• 56:28Jason Calacanis?

  What did he win? Yeah.

• 56:29Speaker 04

  It's a 25K investment from Twist and from our friends at Gamma, the AI-powered presentation maker, which is incredible, which Ryan used to make the winning pitch deck, of course.

• 56:41Speaker 05

  I'm Ryan Yenelli, CTO and co-founder of Next Visit AI. We solve burnout by doing the charting so doctors can do the healing. I spent years going to doctors, seeking answers, and ended up hours away from my death because my care was fragmented. My providers were overloaded with paperwork. My history was scattered, and it resulted in my care being neglected. I'm not alone. One in four patient charts contain errors. Clinicians spend over three hours a day on charting, and this leads to burnout. I want you to meet Dr. Rathor. Before Next Visit, he saw 16 patients a day, was burnt out, and had clinical errors. Now he sees 24 patients a day, saves time, and also saw 30% revenue increase.

• 57:25Speaker 05

  Here's how it works. Dr. Rathor selects a patient, starts a session, and Next Visit listens. Clinical data is built in real time with deep insights into the patient chart. When the patient leaves, the chart is finished, and the notes reviewed by Dr. Rathor, then it's ready for billing. It's fast, EHR-ready, and Hippocaplyte.

• 57:45Speaker 05

  Since launch, we've gained 311 users and have 68 paying customers. And our customers are addicted. We have 1.6% churn, 24% conversion, and a near-perfect MPS score. We've scaled to $9,000 MRR since launch. Our CAC is 189 with a $1,700 LTV, and our average revenue per user is $133 per month. We're starting with behavioral health in the US. A $2 billion TAM capturing 5% or $60,000 customers gets us to 100 million ARR. Most competitors are just scribes. We're a complete platform that providers trust. We provide real-time clinical decision support, build accurate data, and become irreplaceable.

• 58:28Speaker 05

  I'm a full-stack engineer with 15 years of experience in enterprise environments. My co-founder, Dr. Rafiq, is a psychiatrist with over 15 years of delivering patient care. We're NextVisit AI. We solve burnout by doing the charting so doctors can do the healing. Thank you.

• 58:43Jason Calacanis?

  Unbelievable. Incredible. I'll give a little golf clap here, a little golf clap going. That was perfect, a perfect pitch. You explained exactly what the problem was. You explained what the solution is and the opportunity in terms of the total addressable market and why you are uniquely and your partner, who's a psychiatrist, are uniquely qualified to do this. So this is as close to a perfect pitch as you can get. If I were to score it, maybe 8.5 out of 10. I don't give 10, so you know, 8.5, 9, and 9.5 would be the three choices. I think making sure people understand this is for psychiatrists and psychiatry and that you're very focused on that. Tell everybody what NextVisit is and how you're doing in terms of product, market, fit, and customers.

• 59:25Speaker 05

  NextVisit is an AI scribe and documentation platform for clinicians, specifically behavioral health, like psychiatrists. I don't know, it's just been a crazy past couple of months with the accelerator and just our growth internally. I mean, we're producing right now for physicians probably

• 59:44Speaker 05

  About 1.6 million dollars a month in revenue for them.

• 59:47Jason Calacanis?

  Well, you gotta try and capture 5% of that. If you capture 5%, that, no, I mean, that's literally like the, uh, the great value proposition. If you give more than you take, you will continue to grow. And what a, and that's an amazing, um, replicant you have there, a synthetic cat on that cat tower behind you. It looks so real. Uh, is your owl real?

• 1:00:09Speaker 05

  Yes, he is.

• 1:00:11Jason Calacanis?

  Your owl is real. Okay. There you go. What are you going to spend the 25K on? You guys going to Vegas? You're gonna just have a, a corporate retreat? You know, invest it in a Plod note taker? I think you guys put me onto the Plod note taker, which is a great note taker. Uh, what are you going to put it towards? You're going to redesign your website? What's the, uh, what's the idea here?

• 1:00:28Speaker 05

  I think we're going to use this towards, you know, we're really capital efficient. So I feel like we can get a lot of stuff done in terms of integrations and branching out to more EMRs. Cause that's what we hear a lot is doctors want interoperability. They don't want to have to plug 15 different things in. So the more they can just be inside of next visit without having to go externally, um, it's better.

• 1:00:50Jason Calacanis?

  All right. Well done. All right. We'll drop you off. Continue in success to

• 1:00:54Speaker 04

  Congrats, Ryan.

• 1:00:55Jason Calacanis?

  AI.

• 1:00:56Speaker 05

  Thank you.

• 1:00:57Jason Calacanis?

  Good job. All right. Well done. Wow. The show just keeps going. All right. I promised, I promised.

• 1:01:01Speaker 04

  One more segment.

• 1:01:02Jason Calacanis?

  That lawn would you? One more segment. Uh, before I go out, uh, with my friends to ski, I got an early ski weekend, uh, in with my, my friends from New York.

• 1:01:10Speaker 04

  How fun.

• 1:01:11Jason Calacanis?

  Uh, yeah. Great to see some old friends. Uh, I had asked you like, hey, on the Friday show, just to give people something to do on the weekend that we would do, hey, Lon and Jake howl off duty.

• 1:01:21Speaker 04

  Sure.

• 1:01:22Jason Calacanis?

  I am enamored with a certain TV show. I asked you to try to watch a couple of episodes and talk to me about what you think of it.

• 1:01:29Speaker 04

  I watched four episodes. I caught up on season four by your request. I'm all caught up. HBO's industry season four. So this season, I could tell immediately why you liked this season. The whole season revolves around Tinder, a fintech company and app. They're transitioning from a payment processor for porn sites and sort of sketchy kinds of services.

• 1:01:52Jason Calacanis?

  OnlyFans, basically.

• 1:01:53Speaker 04

  Yeah, the, the, the show's fake version of OnlyFans, which is called Siren, by the way. So they, they, they have been handling payments for those kinds of sites and, and a, a, a site I don't think we could mention here on the show, Captain Blank. It's even more, even more X-rated. And they're, they're trans, but they're transitioning to, they want to be a respectable neo-bank operating in the UK, all regulated, all, you know, very front of board. It reminds me a lot, I think Tether was probably an inspiration for this season.

• 1:02:24Jason Calacanis?

  It definitely, yeah, it's, it's basically a, a payments processor like Stripe, but, or Tether, but they're involved in things that are a bit seedy. And in the UK, this is where regulation comes in. So they're really ripping this from the headlines. Who's ever doing this is listening to this week's startups all in and everything.

• 1:02:42Speaker 04

  Clearly listening, yeah.

• 1:02:43Jason Calacanis?

  They're clearly dialed in, these writers. I'd love to have the writers on at some point. But they want to build, they're, they're facing pushback and they want to be respected by regulators. As we've talked about on the show with Alex on Mondays and, and yourself, that there's so much regulation coming into the industry and there's a tension between Europe, America and inside of Europe, specifically in the UK, around, you know, freedom of speech on platforms and are they going to be a socialist or controlling regulatory environment? Or are they going to be freewheeling and let things grow? So you have this tension of politicians meeting with the teams.

• 1:03:26Jason Calacanis?

  And the teams are trying to court them and say, yeah, we're going to get rid of, we're going to give up 30% of our revenue to go clean and we're going to add all these things. But do you want to step in the, do you want to stop the UK from having its own, you know, basically unicorns and are you, you know, a decel? Because we need the, the, the folks in the UK and the politicians in the government want to have economic prosperity. So you have that tension.

• 1:03:49Speaker 04

  And they're, they're represented, they've got this one Labour Party politician, B Van, I think, or whatever her name is. She's sort of representing the government that's sort of in the middle here that they're trying to work with.

• 1:04:00Jason Calacanis?

  Also, interesting of note, they have a fintech journalist.

• 1:04:06Speaker 04

  Played by Jonathan from Stranger Things.

• 1:04:09Jason Calacanis?

  And he is awesome. So you have this fintech journalist coming in and doing very shady, and there'll be some spoilers here, but we will give too many of them. You can still enjoy it. But a fintech journalist coming in, trying to get dirt on these companies, and he's working with short sellers. Now, if you haven't seen the first couple of seasons of Industry, they were working at like a Morgan Stanley, Goldman Sachs, on a trading desk.

• 1:04:36Speaker 04

  Peer Point. Peer Point is the name of their band from the first few seasons. But that's gone. That's over.

• 1:04:40Jason Calacanis?

  That's over. And everybody's wondering like, what happens like to the show? It turns out, you've now got it in the startup world. They just reset the whole concept to now there's a startup, there's a short selling firm, there's this Financial Times like journalist doing crazy things and then working with the shorts, which is like Hindenburg or, you know, other short sellers. And I think they even name checked like Herbalife and that short.

• 1:05:07Speaker 04

  Yes. They, I believe they mentioned Herbalife by name. Yeah.

• 1:05:10Jason Calacanis?

  Yeah. And Ackman, I guess, was the person who shorted it. And then they, so it's, it's got like this really authentic, as somebody who's in finance and tech, it feels like they're hitting the notes really well. On top of this, the protagonists of this are essentially two female leads. One of them the short seller and then one of them the wife who, and they both previously worked at this.

• 1:05:34Speaker 04

  Yeah. Harper is the short seller and Yasmin is married to Lord, Lord Henry Muck, who's played by Kit Harrington from Game of Thrones.

• 1:05:44Jason Calacanis?

  And I mean, with, I don't want to give away any spoilers, but these, what's, what I love about the show is nobody's likable.

• 1:05:52Speaker 04

  No.

• 1:05:53Jason Calacanis?

  Everybody's terrible. It's in a way like The Sopranos.

• 1:05:56Speaker 04

  Yeah, it has some real overlap with Succession, I think, in that it's, it's an exploration of these sort of sad, angsty, neurotic, extremely wealthy people who seem very privileged from the outside, but they're sort of hollow inside, or they're, they're nihilist, or they don't know, you know, what to do with themselves, or how to be happy. And I think that's, that's a big overlap. I think another interesting overlap with Succession that I noticed is both shows are sort of about how, you know, business is this constant balance between personality and pragmatism, that you've got one person in the office who's like, that's a dumb strategy. We should just, you know, do this. These are the three obvious things that we should do that would protect our position.

• 1:06:36Jason Calacanis?

  Right.

• 1:06:36Speaker 04

  But then you've got these people who are either they're having a breakdown, they're having a personal crisis, or they're, they're

• 1:06:43Jason Calacanis?

  Or they're visionaries.

• 1:06:44Speaker 04

  Or their vision, right. And it's, it's a sort of whole mix.

• 1:06:46Jason Calacanis?

  Or both.

• 1:06:47Speaker 04

  Like, no, we're gonna do things my way. And you keep seeing that dynamic come up over the course of the season. And of course, Succession was also about that, that the people who can be very clear eyed and very matter of fact, like Logan Roy, he's gonna make the right call because he's just calculating the angles. Whereas emotional people like Kendall Roy are gonna keep getting in their own way and overpowering themselves. And I think Henry Muck is a great example of a guy who just can't get out of his own way in the, within the show. Yeah.

• 1:07:15Jason Calacanis?

  And the Yasmin is gone from this, like, very much a victim early, when you see the first two seasons, to being like, very Machiavellian in a very

• 1:07:25Speaker 04

  Yes.

• 1:07:25Jason Calacanis?

  dangerous, insane way that would make, you know, any student or any themes around the Me Too era. You know, be like blown out of the water. It is dark and crazy.

• 1:07:38Speaker 04

  It's a very dark. It's a very horny show and that that sort of surprises me because it is becoming a hit. It's it's growing its audience with every new season. And you hear the the line you always hear about TV now is Gen Z does not like romance. They don't want sex in their movies and TV shows. It's like they, you know, unnecessary sex scenes is always what you hear. And yet this is way more than Succession, a very horny show, one of the horniest shows I can recall.

• 1:08:08Jason Calacanis?

  I I have never seen anything this like crazy in terms of mixing per security, deviance, drug use and business and getting it all kind of right in a crazy kind of way.

• 1:08:23Speaker 04

  It does feel like that, yeah.

• 1:08:25Jason Calacanis?

  It it really does not pull punches. The performances are amazing. It's a very young cast, I think. That is that they they basically have given the reins to these two young female actresses who are crushing it in the show. And then there are other actors who are a little bit older on the margins, but it's a very young show. It's incredible.

• 1:08:45Speaker 04

  Yeah, you're talking about Mahala who plays Harper and then Marisa Bayla who plays Yasmin. They're the two sort of females, but they they've added a lot. Ken Long, I always, I've liked him for years. He was on Lost. He plays Eric Tao, who's sort of Harper's mentor that she starts a hedge fund with.

• 1:09:01Jason Calacanis?

  He's the Gen X boomer. He's kind of like the Gen X gray haired boomer banker who's got his own money, his own success and is in it because he's got addiction to being a finance guy.

• 1:09:15Speaker 04

  Yeah, he's playing golf and board at the beginning of the season and you know, he's gonna like have to get back in. They also, they're adding great people every year. They added Kit Harrington from Game of Thrones before. This year they added, I said it was, I don't remember the actor's name, but he's Jonathan from Stranger Things. And that's Kieran and Shipka as Haley, the sort of executive assistant who gets into shenanigans with her boss, Henry and his wife. She was Sally Draper on Mad Men, if you recall. She was.

• 1:09:43Jason Calacanis?

  This show is firing on all cylinders. It's building, it's building its audience. Like you said, I found out about it. There's a really great podcast you should watch called The Watch. And The Watch is how I discover new shows that I should listen to. Andy Greenwald and got the other guy's name. I'm an Andy Greenwald guy. But they are like deep in the industry. It's part of the

• 1:10:09Speaker 04

  Chris Ryan. The other guy is Chris Ryan.

• 1:10:12Jason Calacanis?

  Chris Ryan. Yeah, Chris Ryan.

• 1:10:12Speaker 04

  Ringers, ringers of The Watch. Yeah.

• 1:10:14Jason Calacanis?

  But these two guys have been doing pods together for a long time. And so I highly recommend you check out The Watch. They do a great job breaking down every episode and they are like super industry addicted and they're the ones who turned me on to it a couple of years ago. All right. That's it. We had a great show today. What a great week at This Week in Startups. Twist, firing on all cylinders. We'll see you all on Monday and we will certainly be doing more open

• 1:10:38Speaker 04

  More, more Claude, of course.

• 1:10:40Jason Calacanis?

  And if you, if you hit these QR codes here, I think you can, these QR codes send you to write a review. And this QR code that sends you to subscribe automatically to YouTube. We'll see you all on Monday. Bye bye.