SPEAKER_00: All right, everybody. Welcome back to Twist Monday, March 9th, 2026. Lots going on. Alex,

SPEAKER_02: how are you doing? I'm fantastic. The snow's melting. I'm finally coming out of winter. Tank top season's around the corner. So, Jason, I'm happy. What's the top story in our world? Startups, venture capital, technology.

SPEAKER_01: You know, I think it has to be the Andre Carpathy auto research story. We've talked so much as an industry about the future of AI models eventually being able to improve themselves, getting that loop going. And then at that point, we have real takeoff towards super intelligence. But in this case, what Andre has done, and if you don't know him, he's a former AI head over at Tesla. Jason, he's just one of the best and most followed developers, I would say, in the world. Fair?

SPEAKER_06: Yeah, for AI specifically, obviously worked for Elon for a long time. So yeah, that he would be,

SPEAKER_02: you know, top 20 recognizable names in the space.

SPEAKER_01: Yeah. So when I see his stuff, I immediately take a look at it. And in this case, he has put a tool called auto research over on GitHub. And what this is, it's a really stripped down LLM training loop, and it runs in five minute increments. So you bring your own AI model to be an agent essentially, and then you give it a prompt. And then what the system does is try to improve its own code over a five minute training period. Then it retests itself. And then if the code is improved, if the result is improved, it keeps the changes and then continues to iterate. So it's a very simple loop of AI actually improving itself across certain tasks that you give it. So it's not the full meal deal, Jason. We haven't solved AI recursive self-improvement, but we have shown that it's simple and possible in some contexts. Very cool.

SPEAKER_00: Yeah. And he worked at OpenAI, I think twice. He was like in between stints at Tesla, or maybe Tesla

SPEAKER_09: was between the two OpenAI stints. So he knows what he's talking about. I think he started, he was one of the founders over at OpenAI in 2015 timeframe. So I think, you know, without being an AI researcher myself, what we're seeing here is, as the models get smaller, as people get more comfortable with running a local model, this isn't like an abstraction. There's tons of open source models. And then people are building scales, people are running tests. And people are interested in playing with this technology as they become interested in playing with technology. You'll have somebody like Toby from Shopify, who on the weekend is like, I'll try. And the next person tries. And this is all claw-pilling and just having folks, you know, get excited and tinkering with this technology. So it's kind of like the horse has left the barn in my mind. If Toby Lutke, the CEO of Shopify is like, you know what, I'll try using this. I have no experience in this. Let me give it a shot. And as you can see

SPEAKER_01: here from the tweet, he had some success. Yeah. So he went ahead and gave it a try over the weekend. By the way, all CEOs are dangerous now because they have clawed code and they can tinker. So if you're a developer who liked to have the CEO far from the code base, it's going to be a tough couple of years. Anyways, he ran some tests with auto research, the tool, and he says, I'll just quote here, woke up to a plus 19% score on a 0.8 B model. So 800 million parameters, Jason, very small, higher than the previous 1.6 billion parameter models result after eight hours and 37 experiments. So essentially his auto research setup ran 37 different tries of five minutes a piece and over eight hours managed to find nearly a one fifth improvement in results. He says, look, I'm not an ML researcher. I presume people are doing this in the labs at a much more sophisticated level. But he learned so much from doing this because you can watch how the AI thinks as it goes through the process. The other thing to keep in mind is the gains that we see. Here is an image from Andre himself showing progress. This is 83 experiments of which 15 had improvements. The gains, Jason, as measured by declines in this particular axis, do seem to get smaller over time. So there is a diminishing level of return here, but it works. It works.

SPEAKER_11: I think people are going to keep experimenting with us with this and the idea that there's only 3,000 or

SPEAKER_09: so PhDs in AI who are fought over for $10 million a year, a million dollars a year, a billion dollar package, whatever it happens to be. We've seen these crazy numbers from meta trying to catch up and people poaching each other back and forth. That 3,000 will turn into 300,000 people who understand how LLMs work and who can make meaningful progress on them. And that's great. We used to live in a world where only a small cohort of people could make iPhone apps. Only a small cohort of people understood how to use at scale databases. So it's an interesting time to be in. And if you are an AI researcher, getting in there and playing with it, just like you have knowledge workers like you and I, Alex, who maybe weren't developers or maybe had toiled a little bit, being able to vibe code, being able to use Claude Cowork to make a reoccurring job, being able to install OpenClaw, being able to write a skill, maybe publish it to GitHub. This is the damn cracking from the developers owning the world to everybody building the future. And I'm here for it. It's exciting.

SPEAKER_01: It's very exciting. It is democratizing. And I hope also that what we're seeing here on the edges of public AI work is happening inside AI labs at like twice the speed. Because if this simple setup can show, you know, AI driven improvement of AI outputs, then we must be cooking really, really fast in XAI, OpenAI, Anthropic, et cetera. So to me, this shows that yes, we can tinker. Yes, it is democratizing, but also the pace of improvement in AI this year should be insane. And that's very bullish for startups, for VCs, for everyone who's put money behind a data center. So I think this is one of the most bullish stories that I've seen in weeks, if not months.

SPEAKER_15: Yeah, since OpenClaw, I guess.

SPEAKER_01: Really quickly, I want to talk about the US public, AI, and then China and AI. There was a big boom in OpenClaw over in China. I thought this was just a thing that I saw over on Reddit, people posting pictures of GitHub, sorry, OpenClaw meetups. Here's an image, Jason, of one of those meetups, in case you were curious what that looks like. Here are people outside in Shenzhen teaching each other how to set up OpenClaw. You can even see OpenClaw right over there in English. I thought it was kind of crazy. You know, this is so cool. People are really getting into it. It turns out also there are certain governments inside of China that are setting up incentive structures to get more people to use OpenClaw. All of that to one side. Over here in the US, there's a new poll that came out that showed that AI here in the States is incredibly unpopular. So we get kind of a split screen here between people in China, even older folks really diving into OpenClaw. And here in the States, poll numbers that are pretty terrible. According to the NBC poll, Jason, 26% of people in the US are pro AI and 46% are opposed to it for a negative 20% differential. I was surprised by this. All right. I'm going to give my opinion on

SPEAKER_09: both of these stories after we talk about Plod.

SPEAKER_01: Yes. Jason, we've been talking about the Plod note pen tools, essentially a great way to get all your audio from your ambient area into your device or into your personal cloud. You are a big note taking guy. You absolutely love a checklist. Tell people why Plod has a place in their life.

SPEAKER_22: Yeah, it's pretty straight forward. I have the Plod pin here. It records what I'm doing. Obviously,

SPEAKER_09: you want to do this with disclosing to people you're doing it. And when you're in a meeting, instead of like fumbling with your phone and trying to find a recording app, you put this on the back. See this, this is the pro. And when you press the button, hold it down, it gives you a nice haptic, tells you how many, how much battery life has got a little LED on how much battery life is available and you hold it. And now it does a little recording stream. I don't know if you can see that audio stream going there just to let you know it's working. And that means one click you're recording. Then it syncs it with your device, whether it's the pin, whether it's the pro, and you're all set, you're ready to then transcribe and put it in the cloud. And it will do a straight up summary. It will do a mind map. There's a whole library of different ways for you to use your Plod. And it's a game changer for me because I frequently forget things or I get inspired in a flurry because I'm hiking or doing some ranch work or, you know, the kids are getting dropped off at school. And then I'm like, oh, I remember, I have an idea for the show. Boom. I just say action item. Boom. I say, hey, book this person for the show. So go to plod.ai.twist. It'll give you 10% off.

SPEAKER_01: We do a lot of notes here at Twist. So we live in denial of our ability to keep our information straight. So shout out to Plod for sponsoring the show. Now, Jason. Yes. Over in China, Open Claw. Yes.

SPEAKER_09: Yes. Here in the US, AI hatred. Gusto is a must-o. You've heard me say it a million times. It's true.

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SPEAKER_32: over America. And it's open source. And what's great about open source is when something trends,

SPEAKER_27: everybody embraces it. We've never seen a project get embraced as quickly, as violently, as lovingly, as OpenClaw. It's number one on GitHub. It has the largest number of stars. Why is that happening? I would say it's two reasons. First and foremost, people are getting value from it. Second, it's inspiring for people because if they're starting a company or running a business or they want to stand out at work, they can use this tool to be a better performer. So high performers are drawn to this. And there are tons of high performers around the world. Obviously, in China, you've got a lot of aspirational people. If you're aspirational, you're pulled to this. Now, there are normies, there are citizens, there are human beings who might not be looking to make a career and they might not live to work. They may work to have a great life and there's nothing wrong with that. So

SPEAKER_35: you're not seeing like these OpenClaw things happening in France. I don't think like people are like, you know what? Instead of going to the bistro tonight, I'm going to go do a Claude

SPEAKER_37: meetup. Maybe they are. Who knows? I don't need to give a hard time to the French.

SPEAKER_38: March 19th, OpenClaw meetup Paris. It's at 5B Rue de Boos, which is French, apparently.

SPEAKER_42: We'll see if like six people show up or whatever. But anyway, the second piece to this does dovetail

SPEAKER_09: with it nicely, which is people in China feel like AI is going to make their lives better. They've recently seen their lives get better. They have an immediate history that they can look back on and say, hey, for 20 or 30 years, we've watched people go from living on a farm without running water or sharing a cold spigot between three or four people. This is literally reality. You know, in China, no electricity or limited electricity in the village to working in a factory, living in a dormitory. The dormitory has, you know, air conditioning or heat. They have some spending money. They can go to a bar. They can go, you know, have lunch somewhere. They can start to build a life for themselves, eventually get an apartment. So they've seen this. So they're excited about the

SPEAKER_34: future. Now you go compare that to America. People have for the last two generations said, I've gotten 400K in debt, 200K in debt with my degree, et cetera. And since I'm in debt,

SPEAKER_09: then I can't buy a home. And if I can't buy a home and my job out of school is paying me 50 grand a year and I'm 250K in debt, I'll never get out from under. So there is like a resentment that's brewed over 20 or 30 years. And if you pull up the chart of what people don't trust in America, you know, it has gotten really acute for a couple of groups of people. Obviously the government, obviously journalism and media, both parties seem to have had their reputations just become completely untrustworthy and obviously AI. So Jason, here is the polling data that we're

SPEAKER_01: discussing. This is from NBC and I've gone ahead and highlighted a couple of things so people can read it. Essentially here in the lower red box is AI. As you can tell, based on this poll in March of 2026, 5% of people are very positive in the U S about AI, 21% somewhat positive, neutral, 27%, more people than I thought were neutral, but on the somewhat negative and very negative, it's 24 and 22%. So many more people on the negative side. And people are pointing out on Twitter and other places that, uh, ice immigrations and customs enforcement, a very controversial, I think it's fair to say agency of the government has a 38% return of people saying very or somewhat positive sentiments about it. So AI in the U S is somehow less popular than ice. Well, if you, there are some

SPEAKER_09: extremes going on here. So let's look at the extremes here. If we put the two numbers together, 24 and 22, somewhat negative, very negative, you get 46% of people are very negative or somewhat negative. They're negative about AI here in the United States. Now, if you look at the ice ratings at their peak, um, cause you know, you have five dates there, but March, 20, 26, when we go back to this data here and you look at March, 26, 47%, very negative, 9%, somewhat negative. You put those two numbers together, you get to 56. So, you know, the ice is still in terms of total negativity, eking out AI, but it should be concerning to people. Uh, and then if you look at, um, I'm assuming when they say Iran, they're looking at it negatively. They're talking about either the war or I'm going to guess

SPEAKER_08: it's more of the government of Iran. I'm not sure. I think it's the government. I forget the exact

SPEAKER_51: phrasing of the question, but it was, what is your opinion about, uh, item? And then it's somewhat

SPEAKER_52: positive, very positive, negative, 61% are negative or somewhat negative about Iran. So why is AI as

SPEAKER_09: unpopular as a brutal dictatorship that murdered tens of thousands of peaceful protesters, uh, and ICE agents, uh, an agency that has, you know, uh, had two deaths, whether you consider them murder or not, I'll leave that to the investigations. I have a theory of why AI is so unpopular in this country. And it's that the industry and has done itself no favors in terms of explaining itself. The second is we have seen a social contract in America be broken in big tech and in large corporations in America. It's just not limited to big tech. And some big tech companies are not just tech companies like Amazon is a e-commerce company, right? Sure. There was an explicit agreement that if profits were going up, compensation bonuses and the head count would go up. These two things were tied to each other for the history of modernity. The company's doing well raises the

SPEAKER_34: company's not doing well. Okay. We're going to freeze raises. Oh, the company did well this year. You get a bonus. Oh, the company lost money this year. Hey, the bonus program's on pause. And everybody was like, Hey, that's reasonable, right? We're all working together, swimming in the right direction. And they said, Hey, you know, if things go well, yeah, your group, oh, you're in the sales group. You're in the, uh, customer support group. Yeah. We're going to, we're going to get you a couple

SPEAKER_09: of extra head count. You'll, uh, have, um, you, you won't have to work the weekends as much or burn them in that oil. We understand you need more head count to hit the goals and, uh, Hey, why not? The company's growing for the first time in the history of America. We're seeing that flipped and it used to be if profits are surging, if profits are surging, bonuses and head count will increase. Now profits are surging and now profits are surging because we're lowering comp and moving jobs offshore, or we're cutting head count. This is apparent to Americans. Now they see it themselves. And they also, the gig economy is a major contributor to this. Everybody in their family knows somebody who maybe they didn't fit in in corporate America. Maybe they weren't able to get up in time and do the nine to five thing. And this magical little thing, press a button, get a job came out. It got criticized. Oh my God. You know, you don't get benefits, whatever, but let's call it what it is, Alex. We all knew somebody. I don't want to call them an F up, but they just didn't fit into the nine to five gig. And when they were able to,

SPEAKER_22: or maybe they were a stay at home parent and only had four hours and they wanted to spend time with

SPEAKER_09: their kids, which is sure that's actually laudable, but we have somebody in our life who's like always down on their luck. They get fired, whatever. And all of a sudden they became their own boss. They started doing door dashing. They started doing Uber. They might've done Winolo, all these other services where you get, you can do shift work or make your own hours gig work. They see that going away. So you see two things happening concurrently. Hey, your aunt who worked at Microsoft or Amazon and was in a senior position and was making six figures just got automated. Oh, your cousin who couldn't keep, who couldn't keep that job. It wasn't as aspirational maybe as aunt Susan. Uh, Waymo's in their town and DoorDash has robots that they made themselves zipping around and there's other bots on the street writings on the wall for cousin Sal. So now aunt Susan and cousin Sal are hitting it. And by the way, you know, your uncle Joe, who is the programmer, he's now wondering if he's going to have a job. This social contract's been broken and Americans should not trust AI or the AI industry. Uh, because until that social contract is fixed, they should assume the worst. You can have

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SPEAKER_01: Quo. No missed calls. No missed customers. I do not disagree. And I've always struggled to match my AI enthusiasm, my belief in the progress of technology, lifting boats over the longer term, with what I consider to be the potential for quicker job loss than we can replace with new jobs. And I think the technology industry, if they want to avoid a political crisis in the next four years, maybe a couple of elections, need to start doing more than they are to ameliorate the public's discontent. Because this is not just people saying, no data centers in my backyard. This is people potentially voting people into Congress who want to put relatively strict guards and guard rails around what AI can and cannot do, which could disembowel the industry, which I think you and I both agree would be a net negative for the country over the long term. So I don't know what the solution here is, but there appears to be a political cloud forming in the future.

SPEAKER_22: Oh, it's not forming. It's here. I would argue it's here. You know, there's a group of people who are

SPEAKER_09: denying it. There's a group of people saying, hey, it doesn't matter. We'll create new jobs. And then there's a group of people who are being labeled doomers. And you can always tell when people lose an argument because they want to label you. This happens to me all the time on All In.

SPEAKER_02: I was just about to say, there's a co-host who doesn't like it when you bring up AI job loss.

SPEAKER_22: Yeah. Well, yeah. I mean, it's obviously if you're in charge of that, you probably want to

SPEAKER_09: try to create jobs. Now, I'm not saying Sachs is wrong or I'm right. I just think the American people should have their guard up. They should assume the worst. Why? Because they got to hit rent, because they got to, you know, get groceries, because they have bills to pay. Assume the worst and then be delighted if your job playing the violin, you know, at a cafe, because everything, because there's no jobs becomes a reality. Let's hope that this becomes the Star Trek version. It could. That's a, there's a third of a chance of that, a third of chance of massive job loss and a third of something in between. But a lot of good questions coming in. I think this is a very emotional issue. And our Noti gang, these are the people who have notifications turned on, on YouTube. They get an alert on their phone. Hey, Jake, Alan, Alex are talking. Here's the topic. And then they click the link and they come into the chat room. They watch us live Monday, Wednesday,

SPEAKER_35: Fridays, about 12, uh, Texas time, 12 PM, Texas time, 10 AM Pacific time, 1 PM East coast time.

SPEAKER_14: Uh, what questions do our Noti's have for us? So Nael Roche says, why so negative on open

SPEAKER_01: claw doctrine adoption in Europe? Don't forget open claw was started by a European fair point, Jason,

SPEAKER_14: you now must defend your anti-French agitation. I'm just talking about how delightful the lifestyle

SPEAKER_35: is in Europe and how it's turned into a retirement community slash Epcot center. Um, you know, anybody who is in Europe and who wants to build a great company is probably looking at going to another region because you need to have a bunch of people working for your hardcore doesn't mean it can't be done. Obviously the Scandinavians in Berlin stand out as places where interesting things are happening, but I think we all can call it what it is. Europe's a retirement community,

SPEAKER_02: full stop. Um, I, I, I wonder about that because we're seeing you and I have had this conversation

SPEAKER_01: every three months for several years now, but I keep my eyes open and, uh, in scale, the, uh, UK based Neo cloud just raised $2 billion. The UK government just rolled out. They rolled out a 50 million pound fund, which I made fun of. Then they rolled out a 500 million pound fund. Okay. A little

SPEAKER_70: bit of progress. Yeah. The fact that the government's doing that tells you everything.

SPEAKER_01: Well, they have less large companies. So I think using actually Jason, I think using public funds

SPEAKER_09: to build a sovereign compute is a good idea. You know, most people would say that's socialism or it's putting your thumb on the free market and you really want more entrepreneurs battling it out and the government having nothing to do with that. Typically when the government intervenes like that, it's because the private sector is not getting it done, but we'll put that aside. Uh, you know, the, the state of Europe and, you know, having a couple of, you know, unicorns here or there. Yeah, sure. It just feels to me like with the education level and the resources, they greatly underperform where they should be at. They should be producing much more and they have, they have work to do, uh, in terms of, you know, the taxation there on startups, on investors. If you put up too many roadblocks for founders in terms of hiring and, you know, at will employment and you put up too much, too, too high taxes, then people will invest less and they'll move to other places. Europeans have a very unique ability to live in Dubai insert. Yeah. Okay. Bombs are going off, but they can move to Singapore, Dubai and other places and pay no taxes or pay under 10% taxes. Any logical investor in Europe faced with 50, 60, 70% taxes will get out of Dodge. And that's what they've done. Uh, we had another

SPEAKER_76: couple of questions about AI, um, driven job loss. Jason, before we jump into the, uh, Jason

SPEAKER_01: productivity hack, I'm just curious what your advice is for people out there who are concerned. We talked about, you know, AI won't take your job. People using AI will take your job. I think the agentic world has shown that to be different. So if you're a normie, what's the best way to have armor on your body to keep your job secure, short version? If you're working in corporate America,

SPEAKER_09: being the person who knows how to manage, being a maestro who manages AI is the key person. If you're in any other field, um, moving up the stack to things that a robot can't do and outrunning the robot is the best piece of advice. So what can a robot do? It can deliver a burrito and it can drive full self-driving it's going to be a decade long deployment of those technologies. So what's the next thing up from there being a carpenter, being a handyman, being a plumber, being an electrician. And if you look at that generation tool belt, if you were cousin, Sal, who is not a nine to fiver and loves his Uber door dash when no low draw, you know, on-demand economy, you might need to buckle down and say, you know what? I'm going to learn a trade. I'm going to take out YouTube. I'm going

SPEAKER_34: to take out chat GPT. I'm going to learn how to be a carpenter. I'm going to learn how to build a fence because the robot ain't building no fence. I can tell you, I just built the fence. It cost me like six grand to build a tiny little fence. It was a two day job. You know, do you know what handymen

SPEAKER_85: who can build a fence get paid? It's like a hundred bucks an hour. I I've done fencing. I, yes,

SPEAKER_34: I paid a lot of Uber driving gets paid 30 bucks an hour, you know, in a city or a door dash or 20 bucks, 30 bucks. Sometimes they hit it, make 40 bucks on the weekend. It's easier than ever to

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SPEAKER_09: And that's a hundred bucks an hour, 75 bucks an hour. So you're going to have to outrun the robot. The robot will build a fence in five years, seven years.

SPEAKER_57: Yeah. Then you're going to need to learn how to make the beautiful desk and carve it that the robot

SPEAKER_42: can't. And then at 10 years, forget it. It's over anyway. You'll have your own robot and then you'll

SPEAKER_01: just be managing robots. I'm going to be on the moon in 10 years. So don't call me. It's not my problem. Jason, one thing that you do is a lot. You do a lot of stuff. You keep us all on our toes here. And I was curious if you could drop some gems on us of knowledge about how you are productive.

SPEAKER_09: Oh, I have a productivity hack. Okay. Um, this is a really easy one. Uh, you know, I work with my Athena assistant on this. I call it super distribution. Okay. There's a ton of platforms out there. When you have a great piece of content for your company, for yourself, what people do is they're like, Oh, I had a great piece of content. What you need to do is take that piece of content and then go super distributed. What does it mean to super distributed? It means you post it two or three times in two or three different versions across all platforms. Yes. So if you

SPEAKER_34: have an EA, like I do from Athena, I will tell them, Hey, take this, put it onto my Twitter, put it onto my, uh, you know, put it in the drafts, maybe put it onto LinkedIn, put it in all these different places. Hey, we have these different accounts and go find me other pieces of content that people in my network, like say in my case, portfolio company founders are doing and give me a

SPEAKER_09: list of that. So I can engage them. This social media 101 work. If you were to hire a social media person would be again, 50 bucks an hour, 75 bucks an hour for a freelancer, but you can do it with an Athena assistant. If you want to get into these hacks, just go to athena.com slash J Cal and you will get $2,000 off your Athena assistant. They're also learning how to use, I don't want to tip anybody's cards here, but they know how to use agents at Athena. So Athena plus agents, plus your agent, put them all in the same, uh, Slack instance, and you will get incredibly productive. You have a human in the loop with your Athena assistant who can make phone calls. You'll have your agent, uh, who is open claw or, you know, your Google agent, your Microsoft launched agents, your notion agent, your Slack agent. There's all kinds of different agents out there that you can use multiple

SPEAKER_35: ones, but having a human in the loop loop is going to make you even more productive.

SPEAKER_99: Can I tell you my, uh, my, my recent Athena brainwave? Oh, go ahead. All right. So right now we have a nanny, which is, uh, given what we, we pay, uh, top of market because we care about our children. Yeah. Uh, the moment, the moment we don't have a nanny, I'm hiring an assistant and I was like, we can just get an Athena assistant because, because it's so much cheaper than a nanny and it's going to be so helpful because we have so many things now going on constantly. I can't wait.

SPEAKER_34: This is my, my weekend juggling children, diapers, chaos. You know, also it's a, it's a luxury item. I'll give you that, but it's also a productivity hack for mom. Here's another one. I like a certain

SPEAKER_35: bakery in, uh, the Austin area. It's, it's called Abby Jane. They make incredible bread. They use the

SPEAKER_09: whole grains, um, that artisanal grains. So it's just, this is literally the best bakery in Texas and one of the 10 best in the country. Problem is they're open Thursday to Sunday and they sell out

SPEAKER_34: and they don't deliver. They don't need to the, the best. Right. Uh, but they do take orders and they

SPEAKER_35: do open at 8 AM. Athena assistant calls at 7 59, 8 AM. There it is. Abby Jane. Oh man.

SPEAKER_110: These are their, their bagels getting ready to go. Oh, I mean, it's just unbelievable. The chocolate croissant is like literally from France. So, you know, I was talking to the woman there and I was

SPEAKER_111: like, Hey, you know, you should open up like other days of the week. Maybe you should get an investor,

SPEAKER_35: you know, hint, hint. She's not interested. She's got her thing. It's dialed in. And I was like, why aren't you on Uber Eats? She's like, I don't want to be on Uber Eats. Oh, I sell out. Okay, great. Fair enough. Uber has a courier service. The courier service is like 10 bucks,

SPEAKER_09: but you have to coordinate. So what do we do? My Athena assistant looks at their menu at 7 AM,

SPEAKER_34: finds out what's new on the menu. Cause they change it every week, sends me it with a number

SPEAKER_27: list. I say four of these, two of these, three of these, I would forget to do this. Then he puts the order in, puts it on the credit card, tells them he's coming to pick it up, tells them the name

SPEAKER_34: of the Uber driver who's coming to pick it up. This is for Jason. The person picking it up is Joe. They pick it up. Then they call the ranch, tell the ranch, Hey, this person's coming. Here's the link to the Uber courier. Boom, all done, all done every week. But you know, there's like 10 steps to this. It's not going to be done by an agent anytime soon, but it is done by my Athena assistant, my girls eat healthy. We stopped ordering schlocky croissant from HEB, schlocky bread. And now they have chef's kiss the best in Texas. We'll hack there for you. There's another product. I got it.

David Friedberg: It's a double productivity hack. People don't know about the courier service. Do you know about the courier service? I read about it and then it had fallen immediately out of the back of my head, just instant forgetting. The best thing that you want to get for your wife or your family,

SPEAKER_42: it's not available on DoorDash Uber Eats, but they are available for pickup. So there you have it,

SPEAKER_01: folks. All right, let's do some demos. Let's see some stuff in action. All right. So first up, I'm going to bring up Suresh Ramamurthy. He's from NetX. Hey, man. Suresh is going to show us moving money and interacting with bank accounts, securely using OpenClaw and a skill from his company, NetX. So Suresh, talk us through it and let us enjoy. Hi, Alex. Hi, Jason. I hope you guys can hear

SPEAKER_124: me. Yes, sir. So we have a full stack for banks and we decided that we will connect it to OpenClaw. And we built a cryptographic security for that so that obviously I can't let OpenClaw run away with my money. So we integrated right into a bank mobile app. So whatever OpenClaw does, finally,

SPEAKER_09: I approve it from my bank app and my bank runs on a blockchain. You were one of the lunatics brave enough to give OpenClaw access to your bank. Yes. Which means you gave it your password in two factor

SPEAKER_22: no and authenticated it or you used a password manager. Explain that step in plain English.

SPEAKER_124: I built a banking platform myself. I have 20 plus banks using my platform and I built a special OpenBanking API so agents can access read-only as well as set up transactions that I, as a user, can use my secure banking app, which has my private keys,

SPEAKER_34: ECDSA keys to approve it. Got it. So what's the name of your private company that does this,

SPEAKER_32: the platform? NetXT, N-E-T-X-T. This existed pre-OpenClaw because today is open, today is AO44. It's been 44 days since we started talking about OpenClaw here on the program. Of course. Yes. And so

SPEAKER_124: I built it to handle 100 billion machine IDs about six years ago. I'm ex-Google. I've always been thinking about machine to machine commerce. So when I built this ledger, I planned for a hundred

SPEAKER_130: billion machine IDs, which seems kind of small right now. Okay. So NetXD existed before OpenClaw and it's a banking ledger. Now OpenClaw users can open an account on NextXD. No, on a bank that supports

SPEAKER_27: NetXD platform. Ah, got it. Then the queries go from your OpenClaw to XD, correct? Yes. Got it, got it. So that will only allow it read-only access. But if I want to pay somebody, like, you know, I often will get, you know, here's the gardener, here's the pool cleaner, you know, here's the garbage service, whatever it happens to be. Here's the, you know, septic tank.

SPEAKER_34: You know, you got a ranch, you probably have 20 different vendors to maintain a ranch. I could have,

SPEAKER_35: when their email comes in with their bill, sometimes they just send an email with a document

SPEAKER_34: assigned to it. I could have my OpenClaw file that, queue up the payment through Zelle or whatever,

SPEAKER_09: and then not let my OpenClaw pay it, but queue it up for me to then approve it?

SPEAKER_124: Yes. It'll pop up in your bank mobile app for approval and your bank mobile app as a, you know, secret element with a private key which approves it. What the bank sees is a signed transaction from you, Jason, to the bank, which is the only thing they would approve, which is the only thing they would accept.

SPEAKER_09: I mean, this is, Alex, the productivity we need. This is the power of an open source. You would not have been able to do this if this wasn't open source, right? You would have had to do all this manually. And the fact that there's an open source agent makes it so easy,

SPEAKER_124: right? We have to build so many other plugins. We first did it on Cloud a year and a half ago. We did it. We tried to build all the intention discovery ourselves. And we stopped and said, I know where the elements are going. Instead, we built agentic memory, because you may want to pay your bills in a certain way. And you want the memory to remember, hey, I want to pay this only on the last day of the due date. This one, I want to pay early so that he doesn't stop me from, doesn't supply, you know. So you can have your own long-term memory on how to do things. And we have a memory that goes along with this as well. Suresh, can you show us how it works? Sure. I just open class got a little jealous of bricks and rhymes. Every time I ask you to list capabilities, it says how much better it is. So I'm going to ask three questions at the same time. I'm going to say, check my bank balances, check my memory for any rules I have to optimize my savings in checking, and check, have I paid the light edge invoice, which is what Jason was asking, kind of. So now that I've sent that message through Telegram to OpenClaw, I'm also keeping my mobile app handy. As you can see, I have two controls, Jason, one to control OpenClaw anyway. You can see the request

SPEAKER_149: coming. Okay. So we see that happening on the right side. You've got 3,700 bucks and you're

SPEAKER_151: checking, you got almost 10K in your savings. You're doing okay, kid. You've, oh no, wait,

SPEAKER_153: that's 9 million. You've almost got 10 million in the bank. It's a startup that just got a series A.

SPEAKER_124: Let's think of it that way. It's a hypothetical startup. Okay. So now it says, hey, you're off by $1,300 on your checking because you plan to keep 5,000 there for optimization. So I'm going to say, go ahead, do it, optimize it, move the money. This is something everybody's got to deal with

SPEAKER_09: because you might, if you dip below a certain amount, the banks, the scumbags that the banks may give you a fee for going under 5,000. Yes. I mean, why do they do that? Like,

SPEAKER_124: this should be something they do automatically. You can see that it's moving $1,300 to optimize. I choose it. I approve it with my private key, which is linked to my biometrics. And only then the bank

SPEAKER_57: knows it will process the transaction. You have given it optimization rules in memory.

SPEAKER_09: Keep a 5,000 buffer in checking, sweep excess into savings. We all want that. Weekly auto transfer, $100 to savings. Let's say if you were on the savings tip, windfall capture, 50% of large inflows, savings buckets, emergency 50, investments 30, yada, yada. Monthly goal, 20% income save triggers, paycheck, large inflows, end of the month. Boom. So this is something everybody has. You might say, hey, every month I want to move

SPEAKER_34: anything above this amount in savings into a 529 for my kids into my 401k. But my 401k could only accept, Alex, this amount. So this is where these things can get super powerful. Then, you know what I really want? I wanted to go find better deals from other banks for my savings or

SPEAKER_09: for my deposits, right? You put a deposit into Robinhood, you get like some bonus or something.

SPEAKER_34: So I love the idea of it checking my deals with my bank against other best offers. And then I would give it permission to queue up an email in my drafts folders to send to my bankers. Because this is what I have operations people, like Heidi, who does operations for me. Shout out to Heidi. Shout out to Heidi. Like, I'm like, are we getting the best deal here? And like, I realized I had like a million dollars in some accounts, not getting interest. I'm like, why aren't these in interest accounts? Oh, we're supposed to have an interest account. I'm like, what's the interest rate? It's like, oh, four, three percent. I'm like, well, why don't we have five percent? Other places have five percent. Okay, we got it up to 4.25. I'm like, you realize that's like $45,000 a year. Over three years, we've left 150K. Like, why do I have accountants and all this operations people if we can't get this right? It's like, oh, it's tedious. It's a chore. People forget. That's the key to OpenClaw, is humans forget. Humans are fallible, especially when it comes to chores. I forget to do my chores. You think the dishwasher's clean here? I may have forgot to put stuff in the dishwasher. I may have forgot to bring my dry cleaning to the dry cleaner. That's where AI shines, isn't it?

SPEAKER_51: Are you going to take into the underlying NetXD tech that was used and open source it or make it available for other folks to play with and tinker? We're going to make XT control available to

SPEAKER_124: anybody using OpenClaw. That is the one that is right on the right-hand side. That is the XT control app, which you can use to control XT so that it doesn't use to control OpenClaw. The banking platform, we are opening one bank's going to announce in three months they're going to have an open banking API that'll connect to any AI, not only OpenClaw, cloud, whatever it is,

SPEAKER_126: at which point anybody can open an account at that bank and connect all these things on their own.

SPEAKER_01: All right. Well done. NetXD.com. Suresh, thank you so much for your time. Thank you. I really appreciate it. That was freaking awesome. Next up, we're going to talk to the guy behind PhoneClaw. This is Rohan Arun. It's an OpenClaw extension, if you will, that allows Asians to operate smartphones like humans, which is incredibly cool. And he's going to show us a demo of multi-step phone automation, essentially doing some video work and posting it to multiple phones at once. And, Rohan, are you going to show us with the glasses on or the glasses off?

SPEAKER_172: I want to show you with the glasses on because we've been focusing on wearables and mobile devices, so I think that'll be exciting. Yeah.

SPEAKER_01: All right. So we're going to do this from your first-person view through your Android AR glasses, right? Yes. Yeah.

SPEAKER_172: We also have an iOS app that was just launched, but we're focusing on mobile and AR devices. All right. Let's do it.

SPEAKER_175: The window on the left-hand side of the screen is our app. You can go to GetSupers.com to try it right now. You can essentially connect Android devices. You can also connect to Mac and you can connect multiple different agents. And you can see that I've connected three different Android devices here in parallel that I'll automate in a second. On top of it, we have a real-time assistant. Pete Steinberger said that OpenClaw is not intended for non-technical people. So we've been trying to approach it from the angle of mobile devices and wearables. And so our assistant allows you to, basically, you can connect it in maybe 30 seconds. You can start automating entirely with voice.

SPEAKER_31: So what's happening with your hands there? I see when you show your hand in front of

SPEAKER_09: the glasses, it's putting some notes on each finger and it understands what your thumb is and your index finger, et cetera. What's the point of all that? Is that just it?

SPEAKER_178: Yeah, good question. So basically, I suspect the reason Amazon Alexa and other devices, AI devices previously failed, is because voice alone doesn't solve the problem.

SPEAKER_175: Because it doesn't solve discovery and navigation. So what I'm seeing here in my hand is actually all the different options that I have for voice commands. So we see prowers, browsers, we can automate the Android device, we can automate the chat interface. Those are buttons you can press with your hand. These are actually voice commands. So we're going to be automating four different Androids, and they're going to be posting to social, posting to Twitter, we can automate

SPEAKER_180: all your social apps and things like that. Okay, so you're a social media manager,

SPEAKER_09: and you're going to, with these four different phones, post to your Twitter, Instagram, whatever.

SPEAKER_181: Yes, correct.

SPEAKER_09: This is an exciting demo we've got ready to go here. He's going to tell his agent across three virtual phones what to do. So this is particularly important. We have an election coming up in 2026. And Putin and Xi, they need more phones, Alex, more phones and more

SPEAKER_34: social accounts to cause chaos in our elections in 2026. So here we're going to see how Putin's going

SPEAKER_35: to do this next version of election, where Xi's going to do election interference.

SPEAKER_183: That's the worst pitch I've ever heard. Hey, hey, do you want to take down democracy?

SPEAKER_185: You're thinking it. Phone claw. You're thinking it. I'm just saying it out loud.

SPEAKER_187: Hey, Super, can you hear me? Yes, I can hear you loud and clear.

SPEAKER_175: Can you go to device two? And can you open Twitter? And can you post to Twitter about this week's in startups podcast? Okay, so what it's doing is I just it went to

SPEAKER_180: my second Android, it posted and it made a post, check out the latest episode of this week's in startups podcast, always insightful, inspiring. So you basically take an old Android phone,

SPEAKER_09: and then you give your assistant, you know, the keys to that kingdom and let them rock and roll on apps and they can do things. Now, if I had my Mac mini, it does have native iPhone mirroring, so I can mirror my phone. Um, but I wonder if I have mirrored my phone, if my open claw could then

SPEAKER_35: take over my desktop and do things in my phone. So you can click things on your phone. So that's

SPEAKER_175: also one of the things we solved is we're using the moon dream API. We figured out how to solve this kind of computer use problem with really free models. So you can go to get supers.com and connect your phone or a Mac. And you don't have to pay $200 a month, for example. And so there are other tools that allow you to kind of hack your way to this, but we like to go directly to the phone. And the problem with like, uh, automating certain things on the phone, that's that's screen sharing it.

SPEAKER_195: iOS actually prevents you from automating certain things like calls and for security reasons.

SPEAKER_09: Uh, this is absolutely the future being able to use AR. You really have two startups here. One is using AR, which is, you know, years in the future, but then actually giving your, you know, open claw your

SPEAKER_34: assistant, its own phone to do things because of the app ecosystem, kind of interesting, kind of

SPEAKER_35: compelling. Uh, very cool. And I'll look forward to see where you're taking this next. All right,

SPEAKER_14: let's keep moving. Ron, thank you so much. Appreciate it, man. But I was joking about the Putin stuff,

SPEAKER_34: but if you were actually, uh, trying to do research on apps, let's say being able to have open claw or another agenting technology provision, 10 different Android phones, download the apps, install them, play with them, authenticate with them. Like even just for research as a firm to understand the changes happening, competitive intelligence. I mean, I can see a lot of interesting use cases here. I didn't, I never thought what if the agents had unlimited access to app stores and apps.

SPEAKER_01: It's a pretty interesting idea. Go back to the top of the show. Andre Carpathy's, uh, idea of auto research and letting LLMs train themselves. Now take that, build an app, put it on your dummy device, use phone claw to let the agents interact with it, run tests. I mean, you could build a self-reinforcing, like mobile development loop here with just a couple of pieces kind of duct taped together. And I wonder if that's going to do away with certain testing, because then you don't need to

SPEAKER_66: have a human in the loop at all. You can just run it autonomously.

SPEAKER_09: And it'll certainly accelerate tests, right? So if you were calm.com and you want it to test different aspects of the app, you need to have humans testing it, but you might also want to test responsiveness and speed and other things. And yeah, you could create a test flight and just say, go, here's your metric. And that was the key to Andrew's, um, innovation this weekend is you need to have a metric that the recursive loop can key off of. So you'd have to really set a north star. I

SPEAKER_34: want the app to be faster. I want the app to be, gosh, I don't know, uh, less buggy. I want the app to work better. Like if you said, I want it to work better on iPhone 12s and earlier. So make me a light version of the app that detects you're on an iPhone 12 and just downgrades everything to make it as fast as an iPhone 17. Like that kind of benchmarking and instruction would work. But if you don't have

SPEAKER_200: a good benchmark, you don't have a north star, it's not going to work. So I think that's a key

SPEAKER_01: to the innovation. All right. We have one last demo for us. And that is from someone that you know, Jason, we're going to talk to Eugene Stuckless of Air Inc. Air is a founder UX Japan company. You met him in Tokyo and he's going to show us an AI native testing tool that helps agents test. It's a little complicated. We spent a lot of time talking about it before the show. So Eugene, maybe we should just start with, uh, explain this to Jason.

SPEAKER_207: Uh, why don't I show you? Even better, much better.

SPEAKER_206: So Air Inc is an AI native automation platform. Our first product is Air tests. It's using agents to test websites, right? So you provide a URL. And then with this URL, about 15 minutes later, you get a link, uh, uh, in this link is in, or you get an email. And this email is a deep, deep dive into your website, analyzing it from 10 different criteria, SEO, performance, trust,

SPEAKER_209: UX friction, security, right? This happens in about 15, 20 minutes. This is a free report. This is what it looks like. When you go to the site, you see, and you drill down the evidence about what went wrong, how you fall short. This is free, right? So you pay 20 bucks to buy 150 credits, and then you can run this report maybe four or five more times. What's pretty cool about this is that you see there's, it's very, very in-depth. You'll see you drill down into one specific area. Let's say SEO. This particular, uh, uh, founder has been focused on SEO. So he has put a lot of effort in, and you can see that this test report has tracked his changes over time, translate and translates into business impact that he can understand. This is AI testing top to bottom, right? So this generating of a site readiness report is one workflow we have, right? So agents run workflows. So I would like to show you what it looks like to run and train agents running workflows. This is how I deal with the workload that I have around agent testing. I have built a proprietary piece of software that self trains at maybe four or five different levels. And you see here a dashboard that tracks a particular workflow and it's evolution over time. How many tokens it reduced each loop, what kind of change was made to allow for that to happen? You could see as well, a graph that shows me the workflow, uh, distribution of which models got, which tasks within that workflow and how we learned how to feather more expensive tasks to the expensive models and the cheap stuff to the cheap ones. So, and importantly, you'll see here on the governance page, this is my AI asking me for permission to evolve for high risk that are, let's say adding a new tool. It asks me if it can evolve because that's a very important thing, adding a new tool, right? So, and then one more, I think you'll like this one, Jason. So this is the workflow view on this workflow view. You see the phases broken down. My favorite part is that it has a blast radius. So if you have, yeah. So if you consider when you get signal from the outside world that goes into your open claw, that agent is compromised. Prompt injection is impossible to prevent. So instead of focusing on trying to, let's say, secure the whole system or secure on prompt injection, you focus on isolating the blast radius of that bad actor. So you see here in this workflow, it flagged that this phase is potential for bad actors. So it's flagging it to me, say, Hey, maybe they should have less tools. Hey, maybe this agent should have not have the ability to do this thing because it's getting signal from an outside source. So this is what it looks like to build internal tooling, to manage and train agents, to improve efficiency for the workloads that they

SPEAKER_09: provide to the customer. Okay. So just to separate this out and explain to the audience, you are building a startup that helps people optimize their websites. You have to build tools for those website owners and those tools need to get better over time. So you built agents to make each tool get better and be recursive. So if one of those might be SEO advice. So when you run the SEO advice tool, you want to make sure that it doesn't cause damage and that it is actually getting better

SPEAKER_215: and that you're controlling the costs. Right. So there's a couple of cool things

SPEAKER_209: that are feathered in here. The top of the news, the guy said his recursive loop, this is applying this at scale. So that recursive loop is happening across many, many agents that you see in here. Not only agents, the workflows, the tools they have, it's multi-layer, right? So my job is more like helping it learn how to run my business. That's the point at which this is now peak efficiency. I no

SPEAKER_09: longer have a job. Crazy. Yeah. So it's being like a product manager, but it's got to go get intelligence from the open web. So you're using some protocol to get better at SEO. So you have to make sure that it's not garbage in garbage out. So how do you do that step that the LMLM agent you have specifically for SEO or maybe for, I don't know if you have like call to actions on

SPEAKER_206: the website or copy? Really good question. So how do you, how do you ground data in reality? Uh, there's two ways. One, uh, deep research is really, really effective to allow the AI to model its research in a

SPEAKER_209: way that it can understand so that it understands the context better. It's a better data model, easier to talk to the focus that I was going for. The reason why I put all this engineering effort into get this self training is because my mind has been blown since open claw dropped. Like you, I've been listening to jazz nonstop. I can't do anything else because my mind is so expansive. So this, this codex that I invented to allow myself to encode this learning, this is called Coltrane. Okay. Because the only thing I can use to express myself as, as, as he can, because the way the agents learn, the way I can talk to the system, it learns so fast. So the way it models domains, uh, it learns better how to build something in its language rather than relying on me to do it. It's very interesting. Yeah. So buying this infrastructure to, you know, dominate the QA testing market and maybe, uh, something else later on. Oh, I was just thinking about taking the idea of

SPEAKER_01: self-improving agents that have a, a, a stricter kind of permission structure around them. So they're safer to other areas, uh, Eugene. So if you could just throw some ideas out, like what are the top four or five areas where you think this is going to go next? I think all the founders would like to know where agents are going to get better faster. So I think this is, uh, something,

SPEAKER_209: there's two things. One inference efficiency. So LM's magic thing is they can do inference really well. Right. So the well-trained frontier models, they have the ability to reason through deep stuff and come with very good insights. Right. But we are burning 95, maybe 98% of our tokens, uh, on stuff that doesn't require reasoning. Right. So what we'll find is that people will start to encode their knowledge about how to be more efficient with these things. What's cool about my system, the way it's coded is that if someone makes a contribution to an agent on my system, if that agent is invoked elsewhere on another customer's, uh, let's say platform, they get attribution for that because they participated in the encoding of their knowledge into the AI. Right. So it creates attribute, attribution. It allows for you to pay somebody for knowledge work as they transfer it into the AI.

SPEAKER_85: Right. I see. Well, it's, it's, it's super cool, man. And if people want to learn more, uh, air Inc, but it's spelled E I R. Thank you. Correct. E I R dot I N C. All right. We appreciate it.

SPEAKER_09: Thanks for doing it. All right. Well done. And, uh, Alex, as you know, all jobs are being replaced with AI. There'll be no more employment. I've said this over and over again, that being said, we have three open job requests. So take that for what it's worth. We're doing pretty well over here and we're adding headcount. Uh, if you want to be the community manager and you love founders and you love discord and X communities and slack and circle and all these different platforms,

SPEAKER_34: but most of all, you love founders, you love angel investors, you love venture capitalists and you want, and tech enthusiasts, you want to build that community and help us figure out, Hey, of these millions of users who are the top one or 2% who engage with the content the most email us, uh, what you're good at and what you've done community at launch.co community at launch.co just tell us, we don't care about your resume. We don't, we do care about your experience, but tell us what you've done in plain English. Just email us community at launch.co. We're also hiring two researchers, researchers at our venture firm are looking for, they're hunting for great companies to invest in or have on the podcast. That's one job function. Then they're writing coverage of those companies. That's another job function. And then eventually after they're a researcher, they become an analyst and they get to get on the phone and maybe do some calls. So there's two different aspects to this job. Uh, one is hunting for companies and finding them out there. And then two is sorting through the ones we already have writing coverage. And this is the onboarding. This is the year that will tell you if you're qualified to eventually get that seat at a

SPEAKER_09: venture capital, uh, firm. So instead of hiring venture capitalists from the existing pool of them, we said, let's make our own venture capitalists. Let's invest three, four or five years in professional development. Wrong one on the ladder researchers at launch.co the email. Just tell us about yourself, your analytical ability, why you're passionate about this, what you think you bring to the table researchers at launch.co. We're hiring people out of school for that position entry level

SPEAKER_34: 50, 60, 70 hours a week, seven days a week. You're going to try to crush it and fight to get that job in venture capital. Finally, we have a producer role here at this week in startups. The shows are doing so well. We launched this week in AI. We're going to bring three other podcasts into the fold shortly, and we need a producer producer at launch.co producer at launch.co. If you have produced podcasts before and you have experience producer at launch.co, this is not an out of school one, like researchers at launch. This is a, we need you to have experience and bring something to the table. We'll see you all next time on this week in startups. Bye-bye. Bye-bye.