What is Holding OpenClaw Back?! | This Week in AI Episode 1

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21:39Alex Elias:So now how I'm using it personally, you know, when 1000 people applied for the job and I like okay the old way of doing it is like i have to review each resume one minute each um 50 people i have to do 30 minutes chat with them so that comes out to be 2500 minutes together um right that is so it's a week of work yeah 41 hours right and i don't have that my cto does not have that time my tech leads don't have that time so um i automated the review uh with open cloud right it it went through each resume it had the requirement it staged them to rejection if they did not fulfill the requirement uh but we realized that there was a lot of overqualified candidate it created its own pipeline to put those candidate into that pipeline so i was actually thinking about just last week to hiring a technical recruiter to help me out with sort of all of these 1 ,000 application.

22:42Alex Elias:I don't need a technical recruiter today. I was able to automate and save that 40 hours. So this is another, yeah.

22:50Jason Calacanis:When you looked at the top 20 selections and you looked at the ones who were, I'm sure you spot checked the ones that were overqualified. I'm sure you spot checked the ones that were passed on. How accurate was it when compared to if you had put one of your, you know, average people at your company or a technical recruiter at 100 bucks an hour or 200 bucks an hour on this task. How accurate was it in your mind?

23:12Alex Elias:Yeah, I was very on top of its work. I was obsessively looking at it as like, I didn't say go, you know, do sort out these 1000 candidates, like do the first 20. Then we, you know, had a little bit of a back and forth, do the first 50. So there was a little bit of back and forth, but within two hours, it was doing it flawlessly. You can compare it to a technical recruiter of $100 per hour,$150 per hour. And it took it like eight to nine, 10 hours to complete this task, but we had a very, you know, I'm happy with the results. Yeah.

23:49Jason Calacanis:See, this is, I wrote a blog post, which I'll pull up on the screen in a moment, but I wrote a blog post about Open Claw, the end of chores and the great hiring hiatus. And what I hear consistently with folks is, hey, I think, let me give this a shot with my Open Claw agent. Let me give this a shot with Cowork, whatever platform people are using. Usually it's one of those two right now. And maybe I can delay hiring or let me put one of my people on that and then let's check the result. and nine times out of 10, it does seem that it comes out great. Maybe, Oliver, you can come on the pod for a second.

24:29Jason Calacanis:I know you built something new, and I want you to have your chance to show it to three actual CEOs, and myself, I'm a CEO too, of our firm and our media company. And if you could just pull up my blog post, I want to talk a little bit about how this impacts hiring, Mitesh. when you start thinking, hey, how many people do you have at the firm now? We have 51 people at the company.

24:54Mitesh Agrawal:And yeah, like, I mean, we're a silicon company. So basically 47 of us are doing engineering work. There is one general counsel, myself, one salesperson, and one customer engineer. That's basically the company in itself. And I don't know where the question is going, But I really want to say, you know, to Kashi's point, I think it really is around when you can automate some of the workloads. It's either about either delaying some of the hires, and that's maybe what the hiatus, and I haven't read that, so I'll be curious to see that come up on the screen. But also, or it's more around like, okay, you know, instead of having multiple technical recruiters, you kind of just have one to feed the pipeline or source the pipeline sort of thing.

25:39Mitesh Agrawal:So very, very interesting in terms of how it definitely changes the hiring mindset.

25:46Jason Calacanis:Alex, for you, have you thought about hiring and professional development in lieu of, let's call it the bottom one third of what we do every day, clearly being in the kill zone in 2026 of OpenClaw of co-work? And how do you think about inspiring your team to be 100 % working with an agentic partner?

26:11Kash Ali:Yeah, so I think in some respects, the addressable market has expanded so dramatically for what we do. I mean, previously it was presentational personalization. Now it's entire agentic workflows and Fortune 50s thinking about those. So we actually, to some extent, it's put pressure on hiring kind of counterintuitively because we need more integration engineers. We need people, at least in the short to medium term, to help bridge the gap between kind of that integration case and leveraging our taste middleware, if you want to call it that. Because in most cases, we're addressing actual infrastructure.

26:54Kash Ali:That's also created more compliance burdens. So we've hired, you know, we have a GC who's brilliant. We're kind of onboarding more legal support. Where I think there has been substitution is kind of in more rote service providers. So are we kind of bringing on someone to help with lead gen or help with content marketing or help with, you know, PR, pure PR outreach? That kind of stuff has definitely, you know, been put on the back burner, I think, with a lot of, you know, a lot of the tools that are now available. But on the hiring point, generally, there's one anecdote that I think is kind of, because there has been, obviously, there's this kind of catastrophizing and a draconian view that we're going to be.

27:38Kash Ali:But there's a lot of kind of bright spots. And I was talking recently with a filmmaker who's fairly prominent, and he essentially had a project that got about$15 million in financing, committed from a very large studio. but when they budgeted the film it was kind of a 30 40 million dollar film it's an independent kind of passion project to his so it was originally going to be killed but it turns out this particular studio has this new kind of ai division and the reason it was so expensive and i don't want to mention any names and so but the reason the reason it budgeted so high was because there was these large dramatic scenes and crowds and nightclubs and so on where yeah typically you'd need a ton of extras, right?

28:22Kash Ali:And so that's a scary thought that, you know, AI is potentially going to supplant that. But it actually was the case that because of this new division, they were able to kind of represent a lot of those scenes in a way that was convincing enough. And it actually saved the entire project. So it was kind of a binary of does this film get made or not. So even though we're on the margins, kind of, you know, there's not, those extras are not employed and those scenes and the, you know, so on, it actually is saving the, the, the kind of, you know, the, the actual product. And a lot of people are getting involved.

28:56Kash Ali:Here's the way to think about it. Yeah.

28:57Jason Calacanis:Alex, pretty simple way to think about it. The entire concept of cinema and personal cinema, Quentin Tarantino, my, my guy, QT, said, Hey, this is basically dead. It's over since, you know, the nineties Sundance, it's basically over. Right. It's just, people won't fund it. So you're faced with either this beautiful art form goes away or it gets more efficient and it gets massively more efficient. And in order to do that, yes, some people are going to lose their jobs. But you have to ask yourself in the binary question, would you rather that personal intimate film get made for 15 or not get made?

29:32Jason Calacanis:And then if you think about extras, here's the message to those extras. If they can get a 35 million film to 15, you can get a 15 down to five and you get a five down to 500, which means those extras could make their own personal short film for under 100K, 200K. And that's what the Sundance Film Festival was all about. Just make a short film on a digital video camera and just leave out the scene with the extras. Now you can leave the scene in with the extras. You can have them go to a nightclub and use these tools. They're available to everybody. And then maybe you don't get the, $600 a day extra package, you get to actually make your own where you're the lead in the short or in an hour long film.

30:15Jason Calacanis:Like, so take the win is what I would say is my interpretation of it. The positive interpretation is take the win. Right. Cash, how are you thinking about hiring and then inspiring the team? This is top of mind for me. I've got four people on the team who are all in on this, 20 % of my staff. I'm going to call Code Red this weekend, and I'm just having everybody work Saturday or Sunday. I'm going to create two four-hour slots telling everybody to get online and set up their agent, and I'm going to put my four people, two of them on Saturday, two on Sunday, to just say professional development, sign up for your personal open claw, show it, and let's just get everybody building on it outside of the company just so nobody gets left behind because I'm looking at a cache and I'm always very candid about this.

31:04Jason Calacanis:The two or three people in the organization who have built replicants now are a full 10x more efficient than the bottom folks. That's not going to be sustainable for long. If you're at the bottom and you don't know how to use this technology, it's literally like I have people who have laptops and computers in the internet and then I have people who have old school PCs with floppy disks. That's the distance between these two modalities? How are you thinking about professional development in the firm and all these chores and how to get everybody embracing it?

31:35Alex Elias:I already called the code red with our all hands last week. We are, everyone in the team is really, really excited. And, you know, like if you are working for an AI company that's a bleeding edge of taxes and AI, right? And you're not adopting AI, there's a problem, right? so but my team is extremely excited they are able to do more one and this is not only engineering by the way you know engineering is like we are hiring more the way that we are expanding is our product vision has opened up we have the distribution all of a sudden we can serve our customers better and I can comment on how the accounting and tax industry is going to change with this um but in the marketing in the sales like all of a sudden people are pulling the data from a lot of different system and they are being able to do more consultative sales because they have the 360 view of this customer that is all of a sudden very powerful and customers are leaving with a very good reviews with a very uh good feeling that this company knows us this company cares about us and they know about our needs so people are uh we are using it to personalize more of our you know demo experiences we are using it to in the marketing uh you know pulling up the resources in the product um you know doing the surveys and making sure what to build next what is the uh the reception of the things that we are launching is what is the adoption usability net retention rate.

33:17Alex Elias:So it is helping us out a lot from that perspective. In our industry that we are serving, accounting, there is less than, there is 340 ,000 more accountants that U.S. need, right?

33:32Jason Calacanis:Wait, wait, there are more, we need more accountants or there's an oversupply?

33:36Alex Elias:No, there is a shortage of accountants. U.S. need more than 340 ,000 more accountants thus uh the new accountants coming into industry in the last six seven years less and less cpas are coming and taking that exam right so this industry is in crisis um it has uh reflected in a few years ago lyft made a 50 million dollar mistake in their quarterly earning and they got bashed in the public market for that there are counties in the country where they there are no accountants that can issue the funds to repair a road so this industry is in shortage extreme talent crisis the way and what people do is you know um accounting firms do and here's another interesting fact about accounting industry 85 of the industry is small to medium Wow.

34:37Jason Calacanis:So it's all mom and pop. They're all boomers and Gen Xers. I understand like 75 % are nearing retirement. And the reason they're nearing retirement is it's well paying and they're burnt out. It's a high stress job. So then that begs the question, if all these kids are going to school for these weird degrees, why don't they just get a CPA? And can't these tools, since people are not taking the CPA test, couldn't you just, you know, I know you have to get accreditation and all this stuff, but just dollars to donuts here. if you could just create an online test or just be candid, Cash, if you created an online test and people spent six hours a day studying and doing quizzes, how many days would it take an average college graduate or average high school graduate to learn to be a CPA, do you think?

35:34Jason Calacanis:In order to be - Adaptive education, LLM, teaching them.

35:39Alex Elias:there are a lot of credentials that exist irs issue credential is called enrolled agent the cpa has its own qualification but yes but put them aside like if imagine they did accreditation as a concept didn't exist but to be in the top half just just to be a competent

35:56Jason Calacanis:accountant how many days of online training with an agent and adaptive learning partner could somebody become a CPA? A thousand days, 500 days? I think one year will be a very good timeline,

36:14Alex Elias:365 days, 12 months, because there is a lot of hands-on experience. And, you know, the credentialing not only need to be done by what you know, but also with the experience, right? So the AI agents can also upscale you like a lot of our partner firms are using it They are hiring junior associate and asking tax repeat to use upscale They're utterly hires to go go them to the next level Technically speaking like California only requires 60 hours of training in order to be a certified tax preparer Right 60 hours so that that can be done in a month even two weeks Right. So coming back to, you know, the problem we are we are solving the talent shortage crisis.

37:03Alex Elias:We believe there will be one person, one million dollar accounting practice in very near future. And we are building the tools where one person can command the army of agents doing the task and very focused on the customer relationship. right now one resource in an accounting firm is written 3x the investment so if you are getting paid 70 ,000 as a tax preparer an accounting firm will be lucky to make 140 150 ,000 back but if your accounting firm use ai those accounting firms are making 200 ,000 per head back right so our goal is to create these tools that one person one million dollar accounting practice will be possible we already have Few folks that with two three two people they are running seven hundred eight hundred thousand Dollar practice.

37:57Alex Elias:So this opens up the opportunity and industry that and firms will be able to offer Full stack of services. They are not only preparing they are doing advising they are doing bookkeeping They are becoming the CFO for the small businesses payroll sales tax. So what is big four has or what is big hundred accounting firms has these small and mom and pop shop and medium size accounting firm will be more full stack they will be offering more full stack services because the bottleneck of um of talent is removed now you have agents and skills to deploy and um so that that is the vision that we are seeing and um i'll double click on alex saying that it incredibly expanded their TAM.

38:48Alex Elias:It's the same the case for us. It's incredibly expanded our TAM. And when we see that we are building an AI agent for everyone, AI tax assistant for everyone, starting from accountants, we also include other agents as our TAM too. So we can add the intelligence layer into all the other places as a skill to serve them wherever the agents are working.

39:14Jason Calacanis:Okay, I want to bring Oliver on. Oliver is one of those top three people in the organization here at launch. It's my venture firm that does 100 investments a year or so. And our This Week in Media division that does This Week in AI, the podcast you're soaking in, and This Week in Startups. And previously, we're the producers of All In, although that's spun into its own company, but we still do part of the production there. So let's talk about, in terms of producing the docket, let's bring Oliver on, and then he'll get to have candid feedback from four CEOs. Mitesh, Alex, I'm instructing you, Cash, to be brutal, as if he was one of your employees, and you give him brutal feedback and card questions.

39:54Oliver, your turn to shine.

39:57Jason Calacanis:Come on the air. Producer Oliver. He's aiming to be an associate at the firm. We have a three-year program. Researcher, analyst, associate. You got to put in three hard years. And yeah, I think one or two people have made it to associate already since we started this program three or four years ago. All right, Oliver, how much have you learned in a year versus your four years at UT?

40:22Mitesh Agrawal:I would say my experience at launch has so far matched my four years at UT Austin. I think one thing that's so great about what we do here at launch and what Jason and the program he's running here is that everyone on the team has so much responsibility. And Jason expects you to execute even when there's a lot going on. So really – and I think obviously that's the best way to learn is through experience. So yeah, we're learning every day. Why hasn't anybody tapped out?

40:55Jason Calacanis:Why hasn't anybody quit? I don't understand. I made you guys come in two of the last six weekends to do four hours of work. I can't get anybody to quit, Oliver, and everybody's putting in 50, 60 hours a week. What's going on? I thought your generation was supposed to be a bunch of fuck-ups. Did I just pick well? What's going on?

41:13Mitesh Agrawal:I think that's really interesting that you say that because I've actually been thinking about that a little recently. And I do think it's because people have so much ability to make an impact. And when you look at someone like Maddie, who you gave full responsibility for our syndicate program and she's doing an amazing job. She loves running that program and she has the ability to make a real major impact. And for someone who you know is at the beginning of their career and has learned a lot and is doing a great job, that's such an amazing experience for them. And for me running this week in AI, coming on live on the air, it's just a great experience and it can be a little intense, but you know.

41:53Mitesh Agrawal:But nobody will quit.

41:55Jason Calacanis:Alex, you're a big Nick fan, a big basketball fan and how have the knicks built this roster how have the knicks built the roster

42:03Kash Ali:josh hart mitch you you tell me you're the you're the veteran it's all late first round picks or

42:10Jason Calacanis:early second round picks in other words motivated highly motivated dogs great and having a bench where 15 out of 15, or I should say 13 out of 15, are just blue-collar, hardworking. And then you have like Cat, who was obviously number one draft pick, Carl Anthony Towns. And you got Brunson, I think it was late first round or early second. Anyway, these players are all grit. And all of them want minutes. And all of them play hard. And they play for each other. And they play together. There's no like all-star prima donnas who came from Harvard or Stanford. and you just get grinders like Oliver and none of them will quit.

42:51Jason Calacanis:I'm now moving to a new strategy. Instead of hiring people, I'm just going to charge their parents $50 ,000 to go to JCal Academy per year to work for me. You just pay me 50 grand and in two years for 100 grand, I'll make your child a venture capitalist.

43:09Kash Ali:Instead of putting five,

43:10Jason Calacanis:it would work actually if I just charged her.

43:13Kash Ali:Let us know if there's a scholarship fund. That would be awesome.

43:16Jason Calacanis:Or whatever. I think I literally could charge. I know if there was an opportunity to go to a school that taught you venture capital and one of my daughters wanted to go, I would pay 50K a year for that over UT or whatever. I mean, why not? All right. Yeah. Oliver, it's your time to shine. Give us the full context here. You've been on Open Claw running your agent. You're one of my two all-stars at the firm with Lucas who are doing this basically 50, 60 hours a week. You've been doing it for 15 days maybe. or so? Explain where you're at.

43:50Mitesh Agrawal:Yeah, it's been around 15 days. And Jason, you've talked about this a lot on the show. We're trying to automate around 10 % of our tasks per week. So, you know, some weeks there'll be more, some weeks, some tasks will be more impactful. But we're really just kind of chipping away. And I think that that's something that a lot of people don't understand about OpenClaw is you're really doing it one task at a time. And over time, you'll build those tasks, you'll stack the skills, as they call it, and you'll continue to build those out. So one of the tasks that I have created is an autonomous content clipper at a lot of different teams and at Twist and This Week in AI.

44:30Mitesh Agrawal:We make a ton of different content and we do five shows a week. And the way to get that content out there is to make a lot of clips, post them on X, post them on TikTok, post them on YouTube. So I made an agent that basically gives me ready-made clips for me to post on different platforms. So I will show you a little bit more about how that works. So I built three different skills. And right here, I'm showing a little dashboard I made actually using Claude.ai, not Claudebot, just by feeding in exactly what I'm doing and it kind of visualized the process that my agent goes through. So I built three different skills.

45:11Mitesh Agrawal:One is specifically for X, one is for YouTube, and one is for Twist Archives, which basically means going through This Week in Startups, which has been going on for over 15 years and finding an episode from that date in the past. So yesterday we did a post on, which was on this day in twist history a couple days ago from february 13th and it found an amazing clip of jason and molly wood talking about and jason actually compared being a vc to playing basketball without knowing the score because and this is an amazing clip that open claw found fully by itself this is the first clip that it found using this skill and i was really impressed.

45:56Mitesh Agrawal:So this is one of the skills that I made and I can kind of walk you through the process.

46:00Alex Elias:Oliver, quick question. What is the inherently skill difference between the X Clippy and YouTube Clippy?

46:08Mitesh Agrawal:It all lives in like in the way that the agent goes and finds the clips. So, you know, some of the process is very similar and some of it is different. So in terms of, you know, how the skill works, the cron fires, and then the agent will read the skill file. And then for X, it'll go and hit the X API. For YouTube, I had to set up a proxy so it doesn't get blocked by YouTube when it's scanning different accounts trying to find clips. And for X, I gave it certain channels to look for. And on YouTube, I gave it different channels to look for. On YouTube, I told it to do long form video only, look for long form videos to make clips out of.

46:49Mitesh Agrawal:But on X, you know, there's a lot of great 16 by 9 clips that it can find on its own. So that was the process there and why they're different.

46:56Alex Elias:Can you modify the scale to understand like based on the virality of the clip? Obviously, the content is the key here, but the way X or YouTube or TikTok or Instagram, like things go viral there has slightly different algorithmic treatment to that. Is that that knowledge is in this skill that it knows what to do with this? Exactly.

47:25Mitesh Agrawal:So for the X field specifically, I set it up where it actually does a ratio of followers to interaction. So whether that's likes or comments and the higher the ratio, the more viral the content is. So that's just one tool that I made to be able to look for that virality. but obviously it's looking at likes on YouTube and on X, it's looking at when the clip was published. So I'm pretty sure I'm looking for all within 48 hours to kind of make sure it's up to date. So yeah, that's a little tool that I've made to check the virality. Oliver, that's the, that's the interesting part. You made that tool, right?

48:02Mitesh Agrawal:So you're not using a tool like an in-video or an existing Clippy tool. I'm just saying, Hey agent, use the Clippy tool to like put the video in and do it. you're actually describing what the tool you wanted made based on what parameters you like. That's the difference I want to highlight again with OpenCloud versus just having an agentic workflow of calling some application out there. So I just wanted to double click on that. So you made those description tools of what do you like in a YouTube video or X video or things like those, right? Yeah, exactly. And one thing important too is kind of post-training your agent where it found some videos and not all of them are great.

48:41Mitesh Agrawal:And that's when you have to go back in and tell your agent what you liked, what video you didn't like, and then it will save that in its memory for future when it's looking through clips.

48:51Kash Ali:This is awesome. Did you give it some sense of discernment as to like what you find compelling or how did you brief it to have that kind of discernment just in terms of the picking the best moment workflow? Wow.

49:07Jason Calacanis:That feels like number four here, right? AI analysis. So you first, you do the cron job, you understand the skills. Second, you find the best candidate, candidate being clipped. Three, you download the MP3, transcribe it. And then four, that's the AI analysis to pick the best moment from it. Yeah. Alex, this is a great question. What happens in step four? Exactly. What instructions did you give your agent in step four?

49:34Mitesh Agrawal:yeah so this is when the agent goes through and looks through the transcript so in going back to step three really briefly what was happening originally when i first set up the skill was it was downloading the full mp4 and that was taking a long time and you know a lot of memory it then and it was putting it through deep gram which is a speech to text platform that the ai would then analyze so that process was pretty clunky so i just had to take the mp3 put it through deep gram, get the transcript, and then put it into Opus 4.6, which is the AI brain here that's picking the best clip. So here, it's actually just doing this all by itself.

50:12Mitesh Agrawal:There wasn't a lot of training here. I basically was just like, find the best moment from these clips. And for the most part, it worked really well. Once it finds the clip, it'll then go and use a built-in tool called FFMPEG, FFMPEG, and it will clip that segment based on the selection of the transcript that it made. Okay, so then it downloads it, it trims it, and it puts captions in it automatically? Yeah, so it's able to build in the captions and edit it all within OpenClaw. This is actually a tool that's built into FFM PEG, and this is all in OpenClaw. It doesn't do an API call to some editing platform.

50:51It built all of this all in OpenClaw,

50:55Mitesh Agrawal:which I just think is insane. It built your software.

50:57Jason Calacanis:You didn't need to hire a third-party piece of software to clip it. Mitesh, when you see this, what are you thinking? I see you nodding and you've, like any great CEO, you're beaming. You're beaming when you see efficiency, yeah? Yeah.

51:12Mitesh Agrawal:I mean, the part that I just, you know, what I already really magnified is like, and Oliver, without knowing any of your background, like, let's not even talk about it. Like if I was building like, you know, literally like 21 days ago or even before that, I'd be just like, okay, what is like, my first search will be to an AI tool. What is the best clipping agent that does it with more context? And then it's like, okay, how do I make it such that, you know, it goes and automatically searches it and it'll be a constant back and forth of doing that. And it will be integrating that software tool into that piece of system.

51:48Mitesh Agrawal:Whereas here, it really is around like you customate your own toolkit, you know, and literally it could be like Oliver's, you know, smart AI tool, like AI clipping toolkit that you can advertise out in the market if someone wants to use it and then build a software piece. saying that is not only just efficiency there, like that is like just a level of like new kind of thought process that can result into that. Like I know, Oliver, that you explain how you do the X kind of algorithm in terms of how you want to do it. Maybe, you know, you have some content kind of genius in you that you like, nope, this is the way that I always want to pick my clicks.

52:24Mitesh Agrawal:No one else does it this way. And that becomes like actually a true metric. And you can now share that with the rest of the world as a full software toolkit, not just saying that this is what you need to look into. And that is really, really cool. I think driving those new, when we talk about new use cases, that's kind of what it is that OpenClaw drives. Yeah, and when we talk about, you know, we don't need to buy, we don't even need Capca anymore for clips. We don't need certain software. This is wild. Previously, you know, I would have been so happy if there was a way that I could have connected the OpenClaw to, you know, CapCut, had it worked some magic and then send it back.

53:01Mitesh Agrawal:I would have been thrilled to pay, you know, even a hundred dollars a month for that. But now we don't need to do that anymore. And you can see just on This Week in AI, we've been posting some of these clips and they've done, you know, relatively well, 300 likes, you know, 40 ,000 views. Wow.

53:16Jason Calacanis:Proofs in the pudding. So how much faster does this make you? Just like if you were to do one of these clips, like that Molly clip, how long would it have taken you to do the six-step process here, to do that one clip, you think?

53:31Mitesh Agrawal:Well, so the process starts with me going on X most of the time and looking through different channels, different viral clips. Sometimes that can take two minutes for me to find a clip. Sometimes I can take even up to 30 just because I want to find - Okay, so we'll put it at 15 minutes, 15 minutes to curate a great clip. Okay. And then once, then I have to download it, put it in CapCut, burn in the captions. That's about, you know, 10 minutes, 15 minutes at the max. Okay, so now you're at 30 minutes.

53:58Jason Calacanis:Okay.

53:59Mitesh Agrawal:And then publishing takes around 15 to 30 minutes. So that's a 45 minute to an hour process.

54:05Jason Calacanis:Okay, let's go with 45 if you did it fast. What is it now? Now it's as fast as five minutes. Now, it doesn't allow you to post to social media though, right?

54:18Mitesh Agrawal:I was going to say, just the publishing that you do manually now and that everything else is all.

54:24Jason Calacanis:Because I tried to I tried to post and retweet stuff like all my founders send me their links on a pretty regular basis, asking, begging for tweets, begging for replies. I'm not just talking about you, Alex, and you, Cash. I'm talking about the other founders begging for tweets from their million follower.

54:42Jason Calacanis:But it wouldn't allow me to do that. It was like, you can't do that. It has to be authentic behavior. So have you figured out a hack to maybe put it in drafts or something?

54:53Mitesh Agrawal:Yeah, I haven't gone through the publishing just yet, but I know that you can have your OpenClaw take control of your whole computer, your browser. It can access all your files.

55:04Jason Calacanis:But it stops you, though. It stops you right now. Where is the limitation of OpenClaw? It reads the terms of service of Reddit X and says you can't do that. How do you unleash OpenClaw to do things it's not supposed to do ethically?

55:17Mitesh Agrawal:You can actually prompt OpenClaw and ask it to get around certain things. So I haven't actually gone and really grinded. That's your next mission. That's the next mission.

55:26Jason Calacanis:But I will say.

55:27Kash Ali:Or mind you, you're live on air, Oliver. No, I'm not.

55:30Jason Calacanis:I mean, listen, Oliver, I'm not telling you to break the rules. I'm asking you to bend them.

55:34Kash Ali:You know, Oliver, I'd love to see this interview rag through that. You know, I'd love to see what the outputs are from this conversation. Yeah.

55:44Alex Elias:I can add a 10-second. One part of my life, I was the news producer, researcher, producing one of the top three news shows in Pakistan. And then another part of my life, I worked for Adobe for three years. So this is what it is getting billed, the software getting commoditized and the archiving and finding the content. I can see so many of these applications. And the craziest part is it is less than being just 30 days. So very well done, Oliver. Thank you for showing this.

56:21Mitesh Agrawal:Yeah, thank you. Yeah, thanks for having me.

56:23Jason Calacanis:I want to show, and so how do you make it memory or a skill in OpenClaw? Like how does, like when it learns something, how do you get it to recurse it into the, you know, skill you have there? This is one of the things I do with my replicant is I have them run every weekend a cron job on Saturday and Sunday on how to get better at thumbnails and titles on YouTube specifically. And I just added one, how to do get better at X slash Twitter trending post and then how to get better at Instagram trend research. So you can see here, Deckard has these two cron jobs, and it runs every Saturday and Sunday, YouTube thumbnails research, and YouTube title research.

57:18Jason Calacanis:And it runs it every weekend when the machines are not busy with other jobs, and then it puts it into a Google Doc. I asked it to get this into memory, to get this into its short-term memory, so the context window or something. I just had to create two more cron jobs every Saturday, 1 p.m. Research, viral, tweet, patterns, hook strategies, thread structures, engagement tactics, optimal posting times. It added what it does there, by the way. I gave it a much more generic thing, and I had to do one for there. And it said, the self-improvement part, each run reads a cumulative knowledge base, memory slash Instagram trends, research.md, before researching.

57:56Jason Calacanis:So it never repeats the same insights, builds on what's learned before, tracks which patterns are confirmed over multiple weeks versus just new. Notes what stopped working. Get smarter every week. So your weekend research lineup is now these four things. So.md files is how you save this stuff into memory. All right, gentlemen. So the way this works is OpenClaw has something called MD files. These are markdown files. They serve as the AI agent's memory system. And there's a bunch of primary ones. One's agents.md. That's like how it prioritized and does workflow. That's built into it. Soul.md, that's the behavioral one, how it's voice, it's temperament, it's value, it's values.

58:38Jason Calacanis:And then you can add to it, right? And so that's what we're doing here is adding a memory and a skill. So as you can see here, this is an interface that Oliver created previously, or I should say his agent created. We didn't buy this from a third party. It just made it. And it has all the different memories in there. And he has his cron jobs, et cetera. So my hope is that over time, our agents, Alex, are going out there every week studying the latest and greatest and cumulatively building a knowledge base. You know, I want humans to do this, Cash and Mitesh, but when you ask a human to get better at a skill, I think like 5 % of people have the discipline to do that, whereas an agent just does it.

59:22Jason Calacanis:You don't have to beg it to do it. Do you have something like this going where you're trying to make Leon better every day?

59:30Mitesh Agrawal:Yeah, I do already have something like this going, which is the optimization research and the optimization debrief. So how all the systems are looking and what I can implement to make that better. So it looks through all of my skill files, all of my memory files, sees if there's any overlap within skills or memory. Because you don't need the same information in two places. You just need to make sure that your agent knows where to look. So the optimization agent goes and looks through this. And then the optimization debrief lets me know what we can improve on and then gives me actionable items that it can go do by itself.

1:00:07Mitesh Agrawal:So this isn't 100 % autonomous, but it does go through and give me the ability to make major improvements. And I could give it full ability to make whatever changes it wants. But at this point, this just takes one minute.

1:00:21Jason Calacanis:All right. Cash, Alex, Matish, any other questions for Oliver about this? Or, you know, challenges for him if you were his CEO. Things for him to work on.

1:00:35Kash Ali:Amazing work, Oliver. I guess, do you have any deep concerns?

1:00:37Jason Calacanis:You can give him a little praise, just a little praise, not too much.

1:00:41Kash Ali:Definitely, definitely worthy of that praise. This is amazing. And, you know, it's ragged this up in such a short period of time. Do you have, kind of having been in the weeds of this, Do you have any deep-seated concerns or any anxieties about sort of the agent beginning to, you know, deliver value to the overall org? Or is there anything that keeps you up at night while the agent's up at night?

1:01:06Mitesh Agrawal:I want to make sure that all the tasks are working correctly. And making sure that that's happening has been a little annoying because there's so many nuances to certain tasks that you're giving it. You know, it's working with it's, you know, analyzing a Slack channel with a bunch of humans. Not everything is, you know, zeros and ones. It has to make, you know, decisions that it might not be capable of making. And, you know, while I was doing some of the prep for this episode, Alex, you mentioned something about, you know, agents are great at tasks, but when they're trying to get some human nuance, they're not as good as that yet.

1:01:39Mitesh Agrawal:And I do think that the tasks that will start to make the most impact will be those repeatable tasks, but not yet the, you know, human-led, human-type decisions just yet. And I think that that will come, you know, as Vitesh starts to, you know, make better memory in his chips. And it's all going to get very exciting. And we see, you know, Opus 4.6, larger context windows. It's going to keep getting better.

1:02:04Kash Ali:I have a great anecdote about that. Someone in my company tried to brief. It was an agentic browser, so it was just prior to this whole world, but briefed it to get his sister a whimsical gift, kind of a funny whimsical gift, and it went all the way through the Amazon workflow and selected this horrifically inappropriate garden gnome that I will not describe in any detail, but it went all the way to add that to cart and it was just kind of a hilarious example of discernment got wrong. But yeah, all fixable problems in the medium term.

1:02:44Mitesh Agrawal:Yeah, and that's still human. I think you still have to have a little bit of human in the loop, but as we move forward, we'll be more comfortable with less and it'll make more correct decisions. So it's all very exciting.

1:02:56Alex Elias:And especially when human has the liability and responsible for making financial decisions or any life and death decisions, humans definitely will have way more productivity, way more knowledge, and making the executive decisions that need to be made. Oliver, very well done. Appreciate it. That was super cool.

1:03:22Jason Calacanis:Oliver, you're going to do a Saturday session. Pick, I think you can handle like seven team members, seven slots first come first serve give everybody can put on their corporate card at like an instance to set up and then i want you to train seven of our people seven slots are open for saturday pick a time window you can do it in person if you want to buy lunch for everybody or you can do a virtual whatever you guys want to do and i want to report back on all seven people who came and how great they did only open to seven people on the team great very exclusive and oliver you're very exclusive.

1:03:55Jason Calacanis:Maybe five. Maybe we should just do five. Would it be better to do just five at a time? What do you think you can handle like them showing their work going back and forth and making it better?

1:04:05Mitesh Agrawal:You know, I think what's great about OpenClaw is once you get it set up, you can basically, you know, do it yourself. And so once we get it set up, there's actually a video on This Week in AI YouTube channel on how to set up your own OpenClaw on AWS CC2 server. So it's not the most secure, but it is the fastest and one of the cheapest ways to get your open claw. Okay, do five.

1:04:26Jason Calacanis:Do five people because I want to see five people get good at it. Do five. Five slots open to my team members. First come, first serve. Sign up now. DM Oliver on the team. Oliver, what are the limitations right now of open claw and how should we as CEOs be looking at that? We know there's inference issues. We know there's memory issues. There's token issues. What are the top blockers? We said at the top of the show we were going to talk about blockers right now for AI? What are the blockers?

1:04:51Mitesh Agrawal:I mean, clearly price is up there. And, you know, what's interesting about the Mac mini Mac studio situation is, you know, you can get great models. You can get good models locally, but they're not as good as the frontier models like Opus, like Gemini, like GPT 5.2. So you can run them. They're good at certain tasks. What's great about the local models is they can consistently run, you know, look to X, flag certain things, but they're not going to make the right decisions like an Opus would. So I think when you're using those frontier models, using an API, they get very expensive. So that's price.

1:05:29Mitesh Agrawal:And that's also the limitations of the local models. So I think those are two blockers. Once we see the local models improve a little bit more, maybe the Mac M5, I believe, is a new one. Maybe that makes a huge impact. I'd love to hear what Mitesh thinks about this as he's kind of building out these chips. Yeah. I think that's kind of the right way to think about it, Oliver, is like, you know, the price kind of exchange that you're doing for the level of knowledge. And look, I think, first of all, Mac 5, yeah, like the Mac Mini with M5 will be like really awesome because more unified memory. So it'll be able to run some of the, especially the open source models that got launched last week, you know, some of them, you know, combining them together maybe.

1:06:11Mitesh Agrawal:But the point is the frontier keeps on moving forward. And I like to equate it. I think I mentioned it before, but I like to incorporate kind of like a small brain, big brain. Like you'll always have small brain getting better at kind of like the Mac mini levels and then it will get better and better. But then your requirements for the task will also keep on improving. And then that's where you have to go to the cloud. So, you know, when we were doing Lambda, we thought that on-prem will get massive with AI. And it is growing. Like the thing is the overall industry is growing, but the cloud capex is so far outspending the on-prem side of things that it clearly is that the frontier use cases are much massive.

1:06:47Mitesh Agrawal:I think the biggest improvement though, I saw when you were scrolling, kind of the clips it picked up, it picked up Dario's clips around. And if you hear and basically summarize the entire Dario podcast, you'll be like, all right, how can I get more context? Just basically he thinks everything, all the learning improvements can happen with massive context. And whether that's self-improvement, whether that's just models getting better, whether that's everything. So it's all about that context and more context means more memory. You know, also attention scales quadratically up. So like, it just means that it's a, we're not going to be out of this memory cycle for a long, long time.

1:07:25Mitesh Agrawal:But overall, I think all of us, you know, including Positron, but including just, you know, if you look at what NBDI is focused on, what all the other silicon companies are focused on, it's to figure out ways to improve the context without degradation. You know, right now, you know, you can go 200K, 400K, maybe you can go a million on Gemini, but you start seeing degradation, you know, about that. And it's like, how do you do that? Like when we are running cloud agents to do our internal engineering workloads, every, you know, depending on the task, every few minutes, every few hours, you're to constantly clear out and then summarize the memory and then use a summarized memory as part of it.

1:08:01Mitesh Agrawal:I'd much rather than have the full context. And I think that's a big improvement that can come there. Yeah, I actually have a question for you, Mitesh. Do you think it's better to build out a bunch of microagents that have maybe just one or two memory files and three or four skills versus building out like a mega open call agent? Ultron. Yes. What do you think makes most sense? I don't know the exact... It depends on the task. For a lot of the engineering, at least in code development and things like those, I think some of the big inner agent, the ultra-agent is always better in terms of like if it can hold the context and if it fits within that context, then it gives a much better output.

1:08:45Mitesh Agrawal:But I think for smaller tasks, I think the macro-agent way is preferred. It's faster. Also, you touched upon the price. It's probably a cheaper way to do things there. But I personally prefer one thing to rule them all kind of set up personally, just from the ease of growing that for the ease of having everything. It's just not, you know, today, It's not, we're not there yet to have that, to rule, you know, years and years of context and everything like that. So that's, that's the thing. But personally, that's my preference.

1:09:14Jason Calacanis:All right. We'll drop, let's drop Oliver off. Well done. I, it's, yeah, it's, I think we're going to go back and forth on this, Oliver, between, you know, micro instances. And then as we get more memory, we're like, oh, let's try to get them, you know, to be one. I like the idea of individual agents just keep refining them, making them better and better at it. Just like I would prefer that with a human metaphor. I would love for one person on the team just to be so awesome at social media, so awesome at editing clips, so awesome at sorting applications for the accelerator that, you know, until those skills and agents and memory gets perfect, we should just have one doing it.

1:09:54Jason Calacanis:If it does make it perfect eventually and there's no gains left, well, of course, yeah, then it could just become folded into Ultron. All right, well done. This has been another great episode of This Week in AI. Thank you, gentlemen, for doing the new podcast. Cash, hiring for some positions, trying to get more accountants to learn how to use the product. It is taxgpt.com or.ai?

1:10:17Alex Elias:Dot com.

1:10:18Jason Calacanis:Dot com. If you are an accounting firm, I want you to email Cash at?

1:10:24Alex Elias:Taxgpt.com.

1:10:26Jason Calacanis:Taxgpt.com. And just do a trial and give them some feedback. What startups need is really motivated customers who are willing to give great feedback, right, Cash? Yeah. That's the number one thing you need or you need some frontline engineers too?

1:10:41Alex Elias:We're looking for far deployed engineers. Around 2 % of accounting firms in the country are using TaxShipity. So we have... Let's add a zero. Yeah. Yeah. That's the goal in the next 12 months.

1:10:56Jason Calacanis:I love this game. Woo! So, Alex, how can developers, I know you need developers to play with the API and give feedback. You're Alex at clueqlo.com. Is that your number one need right now is people to use the API and give you feedback? I assume customers are never a better way.

1:11:13Kash Ali:Totally, yeah. We love supporting new use cases. We have, yeah, a pretty amazing expanded set. And please do reach out. And we're always, if you're at a large company, a small company who wants to kind of bake in that level of judgment and inference and get these agents on the proper rails, reach out. We'd love to support.

1:11:33Jason Calacanis:Mitesh, what do you need? Aside from a shovel to snow out of this blizzard, we're both trapped in here in Lake Tahoe. My lord, look at behind me. My entire window is going to be covered in a minute. It's literally a foot.

1:11:45Mitesh Agrawal:I think the biggest thing is, I mean, people are, everyone's retweeting and quoting how many tokens that they need. So first and foremost, we are building chips that are going to make these tokens cheaper and faster and more. And we have massive deployment. So anyone who needs a token, please reach out to me. And the second thing I would say is anyone wants to work on amazing silicon architecture to really drive, be the fundamental pillar of this technology for the coming years. I think that's another thing. So ASIC engineers, software programmers for Silicon, please, please reach out. Mitesh at positron.ai.

1:12:21Jason Calacanis:All right, everybody. This Week in Startups, This Week in AI, all in your three favorite podcasts. I'm Jason, Alex, Cash, Mitesh. Great job, and we'll see you next time. Bye-bye.