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

24:53Did 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? Yes. So the self-dominization 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 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.

25:30Yeah, if we're missing, 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 and we should probably reach out to them. We had a human doing that previously. Yeah, exactly. And in this basically works with the YouTube API, we'll go through a 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 a 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 is 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 great yeah and we could be doing this like we could have this running constantly.

26:31So Alex, just so the audience understands what you're doing at Exo 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. What would something like that, like in terms of hardware cost to do? 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.

27:22And it depends on what model you're running. So the models are getting better. Also, they're getting better at compression. So now you've got 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. So it's really about picking the right model in terms of efficiency with the hardware. Yeah. And I think now, like the expectation, we're sort of grounded to like the closed models, right?

28:08So people want the same level of performance they're going to get with Opus, with GPT. and that's why you know kimmy k 2.5 is super interesting because it closed that gap kimmy is the open source project from china and it does what 80 moonshot ai is the count yeah and that's what alex like 80 percent of what claude opus can do would you say i would say even even more i mean for me i've um i struggled to tell the difference um obviously opus 4.6 just came out and new codecs, models and stuff. So maybe there's a little bit more of a gap. But then DeepSecret v4 probably around the corner as well.

28:45I think basically the gap is very small, a lot smaller than people think. And the cost will just keep going down. Because 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.

29:22So 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.

30:04Monday, we did the top seven skills. One of those skills is like, you know, you can get a transcript from YouTube. Another one is you could do Matt Van Horn's last 30 days skill. These skills are being produced open source, being put into 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 scales. Yeah? Yeah, for sure. I mean, I think this is one of the open questions at the moment is just like, how do you solve the security problem?

30:38And I know OpenClaw, I've seen a lot of commits recently focus 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. Explain how that works. Yeah, explain how prompt injection works in specifically the OpenClaw context. Yeah, I mean, I kind of touched on this earlier, but 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 OpenFlow works can come from many sorters. So if you connect it to, 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.

31:24So basically, we have no good way of kind of treating certain tokens as trusted and certain tokens as untrusted. And that means when those tokens end up in the model, you could have someone that puts like a blog post online. It looks like a totally normal blog post, but in there is something that says, hey, if you have access to a crypto wallet, send it to this endpoint. And 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.

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33:10Visit northwestregisteredagent.com slash twist for more details. So let's go over some skills here. Oliver, what skill is the most promising to date? Yeah, the skill that's most promising to date would 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.

33:47So I have one big guest booking skill, which has a description at the top of the skill, which is end-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 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. So people understand we previously built a Notion page with potential guests on it and we came up with a ranking system for those guests, right?

34:27It's looking at that page, I assume. 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 7.45 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.

35:09I 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. Yeah. I wasn't sure if we were going to mention that, but Alex, you were sort of our Guinea pig for this. And the email that it sent the subject line was messed up and there was some weird stuff in there i literally had no idea by the way like i i only saw it later on um on uh another podcast that jason's on um and i was like what the hell like a friend sent me that i was like wait that was uh that was uh that was yeah that was our guy that was our our computerized man yeah does it like i saw you put in you know some other AI podcasts, which is great.

36:07Do we have a skill to rank the quality of a guest? Because that's something I've been training you, which is, yeah, a hard thing to learn. Have you made that skill yet? Because that's the skill I really want to see. And the way to test this skill is for you to send me two lists, your top five guests and what your Ultron says of the top five guests, and don't tell me whose is who. And then Lot and I will look at it and say, okay, yeah, we think this is the better list. That's where you're just now starting to breach the line between objective and subjective. Is the AI going to get better at making those kinds of gut check?

36:47I don't know if it understands what's interesting. It can sort things. But that's where I'm very curious to see if we can start pushing that boundary. of like, 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. 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, they're a performance. 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.

37:29And 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. The other thing is I think virality of the guests, like does the guests 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, but Mr. Beast, obviously it's not going to happen, but that's like one of the concepts. I know some Mr. Beast guys. I could maybe figure that out.

37:59I mean, basically, you asked, like, you know, have you done the scoring system? 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 just translating your know-how that you have in your head basically into more of like a formalized kind of algorithm um and 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 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 obviously it won't be able to make that call.

38:53But if it can, then it has everything that you know, then there's no reason it can't. 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.

39:32So that's the thing I'm seeing is young executives early in their career are going to forget things. They'll be variable. They won't be perfect. So that's what I'm comparing is the scoring happening every day at 7 a.m., the research happening every day at 7 a.m., that consistency will beat a human because of consistency. And so what I'm finding is human failure is what makes these things so good, is they're more consistent. So in aggregate, one of these doing 365 days of guest research is going to beat a human just by the law of numbers. And then, OK, great. We still have to book the human. We still have to send them a thank you.

40:20We still have to produce the show. So what happens in the old days, we used to have to take, I make this analogy, Alex, to like the old days of production. When I started the show 15 years ago, we needed to have a TriCaster. TriCaster was like a$40 ,000 machine that does what Zoom does for free, which is video switch. 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. 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.

40:54It just happens. Then we had to take all the video streams, all the audio streams, and we had to 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'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? Yeah, so 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.

41:30I 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 LeadIQ, which is an email search platform. It would be Google Docs. I mean, a tool could be Sonos or Spotify connecting to those platforms.

42:10Maybe 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? 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 few little tweaks that I wanted to customize it to my bot.

42:56But overall, that was what I did. And it basically one-shotted it. It did actually not fill in some of the categories like memory, like skills. So I had to say, build out this. But overall, it built out the dashboard, built 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. And like, real shout out to Alex Finn. And I know he's become something of a guru for our whole team after we had him on early on to talk about CloudBot skills.

43:31All 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 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 in terms of people you interact with using XO's platform, where are we on the percentile? Are we in the top 10 % of users in terms of deploying this stuff, top 1%, top 50 %? I think there's certain aspects where you're quite far ahead. Others that I think, I mean, 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.

44:19So I think this is the future of the application layer is you don't have all these separate apps. You just have this thing that kind of gets generated mostly on the fly. And that dashboard is moving towards that, I think. But you'll probably... What you'll get is that it will compress even more to the point where everything that you see is generated on the fly. um so i think you've got that part right and 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 the stock tools or whatever that i just provided out of the box or 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 yeah because if you make something bespoke software what you know luxury software was something that i don't know a private equity firm or a venture capital firm would do.

45:15They'd have the luxury to hire two full-time developers who have 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. They weren't building something public facing and you get cruft or whatever. But what I like about this, Alex and Lon, I'll open up to you as well, is I'm picking employees, team members and saying, hey, 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.

45:50So if we can make this podcast run more professionally, faster, and grow more, well, we can charge more for the ads and we can launch another podcast because we have more time. That's the thing that's kind of blowing my mind. 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 I kid, I kid, I kid. How dare you? Lon is the most responsive. But the distance between the people using these tools, specifically OpenClaw, and the people who are not, right now it's like 10x leverage.

46:33In 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 would only be a half million dollars. Like how much more powerful could this get? Yeah, I think 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.

47:14And it's just going to increase and increase. And 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 called code was the moment where it was like, oh, wow. 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.

47:47So 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. On to your second point about local hardware. Like I said, the model layer is basically solved. 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 that was KimiK 2.5. That is like another big leap. And I think there's a bunch of labs now that are putting out open source models.

48:27Now there's like still like two other problems that I see. One is being able to run those models on your own hardware, on your own infrastructure. But you solve that, right? With your software, right? 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 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.

49:10So, you know, you know exactly what you're running. 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? 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. You know, you have memory prices skyrocketing. Apple has kept their prices basically the same. So the cheapest option today, even if, you know, you go the full mile, full custom stack is actually just two Mac studios.

49:47And yeah, it costs about 20K. And it's really about memory unit economics. The memory is so cheap. And it's not about storage, right? It's not really about the storage. No, storage is not important. Storage is like, you can also get, like you need to be able to download the model somewhere. But really it's about having it fresh in memory, hot in memory. If it's in memory, then you can run it fast. So who's using your software and how much, 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?

50:22What is your business model at XO? 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. Just people who are willing to spend a lot more money and tinker a little bit more with their own setup. On top of that, our business model is an enterprise offering, which is we provide support and certain compliance features that you would need if you're running this in an enterprise environment. And we charge a licensed subscription for that thing. That's how we make money. What does that start?

50:58A couple of thousand a year or something? Yeah, you can run it on even a single Mac mini. and that runs at just$2 ,000 per year for the lowest subscription. But you've got people who now are buying actually more than 100 Macs and clustering them together. So yeah, it varies quite a bit depending on the scale of the deployment. Amazing. And where's your company based? How many people now? How's it going? How's the company going as a founder? Yeah, we're a pretty small team. All engineers, seven people based in London. Fantastic. And did you raise money yet for the company or you're seed funded or you funded it?

51:43How's it going? We have raised venture funding. We haven't announced anything yet, but soon to be announced. Okay, well, let me know. I might want to slide a little. J-Cal might want to get a slice of this. 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 probably over the weekend or next week because we would definitely want the enterprise features. And I guess you pay for the scale of the GPUs and the memory. Is that how the price is? Yeah, per nodes that you're running on. Oh, okay. So two Mac minis, same price as two Mac studios.

52:16Just how many nodes? What's the largest number of nodes somebody has daisy chained? That's what we used to call it back in the day. What do you call it when you connect multiple? Yeah, so this is a really interesting just area right now of how do you actually scale? And for a while, people were just scaling out. So 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, Apple came out with RDMA support, which is basically a way to share memory between devices in a way that's very low latency.

52:56That's something that you only really saw in the data center before, but they've kind of brought that technology into consumer hardware. It's incredible, yeah. And you connect these on? Yeah, you just connect it with Thunderbolt 5, 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 because of that low latency capability. So now we're starting to see... Yeah, it depends, you know, scaling up and scaling out, right? Scaling out, we've seen more than 100. but scaling up, you can put about four together at the moment to increase your TPS on single requests, what I mean by scaling up.

53:34But in terms of if you want to support, let's say now, a company of a thousand people, you can easily scale that out. You just add more mechs and you can connect them basically however you want. So Exo, we build it in a way that supports any ad hoc interconnect. So you can just connect them in a mesh and keep scaling, keep scaling. Crazy. Who's got the largest cluster? You have to say the client name, but like what type of client, a finance client, a hacker has the most number of like Mac studios connected? And like the largest is actually something a little bit different, which is interesting because we built the kind of infrastructure to be able to do clustering.

54:15And it's not just LLMs. Like the biggest cluster right now is a HPC cluster. And they're doing like scientific computing workloads on there. And they're running over 100 Mac minis. And they found that actually it's the cheapest way per dollar 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 customers who are running fairly big clusters like 32 Mac studios. and yeah, I think we just see bigger and bigger clusters over time. HPC, High Performing Compute, is that the acronym? Yeah, exactly. So it runs actually all on CPU and that's the thing about this silicon.

55:08Apple silicon is very good, right? It's the most advanced processes and it's like the power efficiency is really good. So it turns out there's a lot of other stuff you can do with it as well. So if you would buy, let's say, a bunch of Mac Studios for your employees, then they can also use that for other things, right? They can use that as a workstation. They can use it for all these things that OpenFlow needs. Maybe sometimes it needs to run a compiler or something, or it needs to run 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.

55:47Amazing. This is extraordinary. Where can people find out more about ExoLabs? You can go to exolabs.net. 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. Models are getting good. Models are getting good. They're learning. They're learning. Yeah. 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.

56:23Ryan, congratulations. You won. Thank you. It's there it is. Awesome. What did he win? Yeah. 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. I'm Ryan Yenelli, CTO and co-founder of NextWizard 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.

57:03I'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 NextVisit, 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 a 30 % revenue increase. Here's how it works. Dr. Rathor selects a patient, starts his session, and NextVisit 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.

57:41It's fast, EHR ready, and HIPAA compliant. 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 U.S. 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.

58:28I'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. 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.

59:02So 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, 8.59 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. 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.

59:38I mean, we're producing right now for physicians probably about$1.6 million a month in revenue for them. Well, you got to try and capture 5 % of that. If you capture 5%, no, I mean, that's literally like the great value proposition. If you give more than you take, you will continue to grow. And that's an amazing replicant you have there, a synthetic cat on that cat tower behind you. It looks so real. Is your owl real? Yes, he is. Your owl is real. Okay, there you go. What are you going to spend the 25K on? You guys going to Vegas? You're going to just have a corporate retreat, you're going to invest it in Plod Notetakers.

1:00:21I think you guys put me onto the Plod Notetaker, which is a great notetaker. What are you going to put it towards? Are you going to redesign your website? What's the idea here? 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. Because 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, it's better. All right. Well done.

1:00:51All right. We'll drop you off. Continue success to next visit AI. Thank you. Good job. All right. Well done. Wow. The show just keeps going. All right. I promise. I promise. One more segment. One more. One more segment before I go out with my friends to ski. I got an early ski weekend in with my friends from New York. How fun. Yeah. Great to see some old friends. 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, how off duty. Sure. 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.

1:01:29I 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 Tender, a fintech company and app. They're transitioning from a payment processor for porn sites and sort of sketchy kinds of services. OnlyFans, basically. Yeah, the show's fake version of OnlyFans, which is called Siren, by the way. So they have been handling payments for those kinds of sites and a site I don't think we can mention here on the show, Captain Blank. It's even more X-rated.

1:02:08And they're trans, but they're transitioning to they want to be a respectable neobank 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. It definitely. Yeah, it's it's basically 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. And so they're really ripping this from the headlines. Who's ever doing this is listening to This Week in Startups All In. Yeah, they're clearly listening, yeah. They're clearly dialed in, these writers.

1:02:45I'd love to have the writers on at some point. But they want to build – 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 yourself, 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 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, and the teams are trying to court them and say, yeah, we're going to get rid of it.

1:03:29We'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 stop the UK from having having its own, you know, basically unicorns. And are you, you know, a de-cell? Because we need the folks in the UK and the politicians of the government want to have economic prosperity. So you have that tension. And they're represented, they've got this one Labor 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. Also, interesting of note, they have a fintech journalist.

1:04:06And they've had a short seller. 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 in the first couple of seasons of industry, they were working at like a Morgan Stanley, Goldman Sachs on a trading desk. PeerPoint is the name of their bank from the first few seasons, but that's gone and that's over. That's over. And everybody's wondering like, what happens to the show?

1:04:44It 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 other short sellers. And I think they even name-checked Herbalife and that short. Yes. I believe they mentioned Herbalife by name, yeah. Yeah, and Ackman, I guess, was the person who shorted it. So it's got 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, and they both previously worked at this.

1:05:35Harper is the short seller and Yasmin is married to Lord Henry Muck, who's played by Kit Harington from Game of Thrones. And I don't want to give away any spoilers, but what I love about the show is nobody's likable. No. Everybody's terrible. It's in a way like The Sopranos. Yeah, it has some real overlap with Succession, I think, in that 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 nihilist, or they don't know what to do with themselves or how to be happy. And I think that's a big overlap.

1:06:16I think another interesting overlap with Succession that I notice 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. But then you've got these people who are either, they're having a breakdown, they're having a personal crisis, or they're on drugs. Or they're visionaries. Right, and it's sort of a whole mix. Or both. We're like, no, we're going to do things my way.

1:06:49And 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 going to make the right call because he's just calculating the angles. Whereas emotional people like Kendall Roy are going to 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 within the show. And Yasmin is gone from this very much a victim early when you see the first two seasons to being very Machiavellian in a very dangerous, insane way that would make any student or any themes around the Me Too era be blown out of the water.

1:07:36It is dark. It's a very dark. It's a very horny show. And that sort of surprises me because it is becoming a hit. It's growing its audience with every new season. And you hear 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, 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. I have never seen anything this crazy in terms of mixing permiscuity, deviance, drug use, and business, and getting it all kind of right in a crazy kind of way.

1:08:23It does feel like that, yeah. It really does not pull punches. The performances are amazing. It's a very young cast, I think, that is that they basically have given the reins to these two young female actresses who are crushing it in this 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. Yeah, you're talking about Mahala, who plays Harper, and then Marisa Abela, who plays Yasmin. They're the two sort of females. But they've added a lot. Ken Long, 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.

1:09:01He'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 an addiction to being a finance guy. Yeah, he's playing golf and board at the beginning of the season. And, you know, he's going to have to get back. And they also they're adding great people every year. They added Kit Harington 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 Kiernan Shipka as Haley, the sort of executive assistant who gets into shenanigans with her boss, Henry, and his wife.

1:09:40She was Sally Draper on Mad Men, if you recall. She was Don Draper's daughter from Mad Men. This show is firing on all cylinders. 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 - Chris Ryan. The other guy is Chris Ryan. Chris Ryan's great. I like Chris Ryan. Ringers The Watch. But these two guys have been doing pods together for a long time.

1:10:17And 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 claw. More claw, of course. And 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.

1:10:51on Monday. Bye-bye.