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A
The two or three in the organization who have built replicants now are a full 10x more efficient than the bottom. That's not going to be sustainable for long. It's literally like I have people who have laptops and computers and the Internet and then I have people who have old school PCs with floppy disks. That's the distance between these two modalities. How do you unleash OpenClaw to do things it's not supposed to do ethically,
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B
or remind you you're live on air?
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A
I'm not telling you to break the rules, I'm asking you to bend them.
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C
This Week in AI is brought to you by Quadratic, bringing the productivity boost of AI into your spreadsheets. Visit Quadratic AI Twist to sign up and use the code Twist to get one month free of their pro tier subscription. And Notion Notion brings all your notes, docs and projects into one connected space that just works. It's seamless, flex, flexible, powerful and fun to use. Try Notion with notion agent@notion.com twist that's notion.com twist to try your new AI teammate Notion agent today.
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A
All right everybody, welcome back to this Week in AI. This is the new show from your host Jason Calacanis Jcal who brought you this Week in Startups the all in podcast. I've started a new podcast, it's called this Week in AI. What is the goal? To have three people building in AI, three founders from AI companies. Chew the fat. Talk about the news from the week. We have three amazing guests this week and we've got a huge topic today to discuss. Plenty of news in the AI space we'll get to, but our topic number one is going to be what's holding Open Claw Back at this point so many people, millions of people have adopted this open source platform to create agents. It's changing everything at work but we're going to talk about today what could be holding it back and we have three amazing guests. Matish Agarwal is the CEO of Positron AI. Positron AI. They are building chips for cheaper, faster and smarter AI Inference. Alex Elias he is the CEO and co founder of Clue Q L O O of which I'm an investor. It's an AI platform for decoding and pretending, predicting global consumer taste preferences. He was into AI back when it was machine learning and most people refer to it as such and Cash. Ali is the co founder and CEO of Tax GPT. He went through Launch and Y Combinator and he's building an AI tax assistant for everyone starting with Accounting and advisory firms. All right, welcome to the program, everybody. Mitesh. Are you obsessed with Open Claw? Yes or no?
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C
Yes, absolutely. Have to be. I think in the current space, very much so. I think the one thing that I'll just say here is agents existed before openclaw, but the ease of use that openclaw made it happen with for like, you know, as simple tasks like just inbox, Inbox, like adjustment, Slack notification, things like this is just like mind blowing and then obviously now taking next steps into it. But yeah, tldr. Yes.
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A
How are you using it? Tell me, how are you using it? What is your current usage look like? And we're sitting here in AO after Openclaw, I believe it's 21 in the year of our Lord. We basically count the number of days since we talked about OpenClaw and the program is 21 days since our first discussion of it here.
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C
Wow.
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A
So what exactly are you doing with it? Feel free to show something on the screen if you want to share your screen or if it's too confidential, just walk us through your stack and how long you've been using it, what exactly you're doing as CEO of a chip company with it.
- [03:43]
C
Yeah, so the first thing that I'll start off with, because we are in chip company, we treat some of the architectural stuff as trade secrets. I have to be really careful in just understanding it and deploying it. So I have it set up as the least privileged information set up. So always human in the loop. But look, initially I just wanted to play with it once I read about it and kind of how it's built and how it just, as I said, it just made it so easy. I used to have like a agentic workflow to do the inbox kind of filtering and it was like a pain in the butt. It wouldn't actually do it super well. But with openclaw, it not only does the filtering very well, like, you know, spam, unsubscribe motions, responses, but it fully drafts it, it's fully ready. So it's like almost like an executive assistant light version of does the same thing now with Slack, where it's not just a Slack summary, which I know Slack AI could do, also do. But now it like actually has like a draft for my Slack responses ready on my phone. So, you know, while I'm on my phone, I don't have to worry about like, you know, typing too much. It has like some basic, you know, I've, I've given it a thing of where, like where the Slack Status it has to be like very quick responses, like a few words but. But it does it super well for, for those kind of things. And then the last thing that I'm just starting to get into, although like, like right now the way I use openclaw is it's. I don't have it on my local machine. It's. It's set up using cloud platform so I have to be. I'm trying to fully learn it without like damaging the whole kind of. Without like getting into the thing where it has insane amount of privileges but basically just our CICD pipeline on the chip architecture. You know, kind of people constantly update within our engineering kind of tools on what they do. And right now the current motion for me is manual. Like I have to go in, I have to see and get it updated. But now it actually just does summary for me. So those are very basic ways. I'm using OpenCloud right now.
- [05:38]
A
But this is going to save you just Inbox management, Slack management. These are the chores that you typically as CEO would hire a chief of staff and executive assistant to do this. You've now used an open claw agent to do those two frontline events for you. And it makes you. How much more efficient would you say? How many minutes per day do you recapture? Ballpark?
- [06:05]
C
I haven't done any kind of calculation but as definitely like you know, my morning 45 minutes to an hour is now gone down to like probably like 15 minutes to half an hour. It's just the biggest thing is like email draft. You know, the Gemini used to do it for me was okay, like it was not. I found OpenCloud to be better at it, but the agent to be better at it. But the bigger one is the slack one where I would only get the slack summaries before. But I think the big one now is just like from the night that I sleep to morning, all the Slack messages, they're ready with the response out. And that is a big one. I don't know how many minutes yet, but that is a big one. I can tell you because I don't have the anxiety anymore of, of getting up and being in my bed, opening on my phone and opening slack to respond up to date.
- [06:50]
A
Yeah. So I mean for a CEO to save but 30 minutes a day, three hours a week, times 50 weeks, 150 hours, that's like getting three more weeks a year, which is 6% of your year back in the most basic implementation. So you know, I always look at these compounding factors. Alex, it's time for Your confession. Father jcal will hear your confession. Now, are you too obsessed with openclaw? Yes or no? Have you started the implementation process or are you on the sideline? Just looking in.
- [07:25]
B
Well, I'm going to just take. Mitesh was very articulate with kind of strong manning it. So I might take a bit of a slightly more contrarian position. But one of the things too is I have an amazing EA who I've had for years. So I've sort of been, you know, I've been privileged to see what the ultimate personification of openclaw is capable of. And so it is. There's clearly immense potential. And you know, there's been a lot of people at our firm tinkering, not in any sensitive ways, more on the personal front. So things like itinerary planning, being able to kind of route across multiple destinations and localities. But yeah, I remember a conversation with an executive at LVMH years ago who was talking about how some of the most successful consumer products in the years to come would be disintermediating how kind of Uber wealthy people live. So Uber is the private, you know, the private driver. You would have chauffeur assistance. You would have. Exactly. And you know, Airbnb to some extent is the second home. And now, you know, the, the promise here is to kind of democratize the amazing ea, pa, whatever you want to call it. And yeah, it's immensely exciting. I think, you know, clu's uniquely situated in terms of a perspective on this because a lot of where we see breakdown is kind of in taste based tasks. I asked people in the office, you let it plan an itinerary, you gave it a spec and instructions and were you ultimately confident letting it click purchase? And I think that's where there's still a little bit of a gap. And with the seasoned ea, there's kind of just an intimate knowledge of not only kind of the proactive preferences, but also things that you may dislike and so on. So it's interesting, there's been a couple.
- [09:28]
A
So interesting what you just described, because between Mitesh and yourself, Alex, you described what memory and context is for Open Claw or for a human. You mentioned preference. Then you mentioned judgment and the polish. This is something that Open Claw will get over time if you train it properly. And so your, the distance between and I love your metaphor, whatever rich people have the luxury of doing, if you can commoditize it and make it for everybody. Airbnb is your second home, you know, your second ski house, your second, you know, Hawaii House without having to actually ever buy it. It is your Uber is your personal chauffeur. Everybody gets a chauffeur jet suite, jsx, whatever that is. JSX is like your private jet, but it's kind of shared, but it gives you that kind of private jet feeling. This, this is an incredible pattern. And now I think Open Claw becomes the manifestation of that. We're done hiring new humans at launch. Okay. Because Notion's new AI agent is like having twice as many. I'm not exaggerating here. It's like doubling your team size because the AI has been integrated into your Notion knowledge base right in your workspace. So things that used to take a researcher or an operations person, you know, 24 hours, 48 hours, 72 hour return time, maybe even a week, gets done in minutes for me. Notion brings all your notes, your docs and projects into one connected space that just works. It's seamless, flexible, powerful and fun to use. With AI built right in, you spend less time switching between tools and more time creating great work. And with Notion Agent, your AI doesn't just help work, it finishes it. For example, we wanted to reorganize the Twist 500 list. These are the top 500 private companies. But to actually improve and refine the list, we had to remove all the companies that had exits. So we just asked Notion AI to do that for us. And it did it. And we build the docket every day for this week in startups, for this week in AI. And my notes for all in in Notion, and Notion's AI agent makes that completely searchable. So my producers or I can talk about guests, ask questions, make sure I have the ad reads incorrectly for each segment. And if I'm just looking for highlights, I can just ask a simple prompt. Notion is our system of record. It's where it all comes together for our organization. And then notion added these AI features that literally have made the product 3, 4, 5 times more powerful for the same price. Try Notion with notion agent@notion.com twist all lowercase letters notion.com twist it's in the show notes. Try your new AI teammate, Notion Agent today. But you did point out a couple of pieces to it that are critically important. Can it remember your preferences and can it make you comfortable in making that decision? With Clue, you help people. Hey, these are the restaurants, music and books I love. I'm from la. Then when you go to New York, it says, hey, here's the private club, the fashion shopping and the activities you might like. Different location, different Verticals, but you've been able to build that with QLO.com if you want to go see that API. But how do you think about your company now, not just running it internally, where obviously everybody's going to become obsessed with this technology, obviously, but then incorporating it into the product. Do you see a time where you put Clue into a skill, into openclaw, and give everybody, you know, some number of API credits to go have judgment as to what they might like and be the curator? Like, I have executive producer Lon here, our editorial director. You give him three things you like, he gives you seven things you'll love. Right?
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B
Yeah, that's spot on. I think that's exactly right. And we would. I mean, we're already seeing it incorporated in so many agentic workflows. You were kind enough to host or judge a hackathon in Q4 last year that had incredible submissions of things ragged together with Clue and essentially imbuing these systems with judgment. So I think that the way I'd summarize it is just that with workflow agents, as you're kind of describing all the kind of rote tasks of everyday work, the instruction is kind of the spec for personal agents, where things are headed and where the kind of personification of the EA comes in, you are the spec. And there needs to be some dimensionality to how the system kind of interprets that. And it's been great because we've been obviously building this tech for over a decade, but the use case now is sort of perfectly caught up with the infrastructure that we build. So, yeah, I think there's tremendous use cases for putting these systems on Rails. And one of the biggest examples. So we obviously work with very large financial services firms where there's a conservative culture, there's a heavily regulated culture. And one of the most profound examples was pretty recently they were launching a very large company that we all have heard of and used, were launching their first Genai product and agent, and they ultimately initially were going to kill the initiative because they essentially were so conservative with the implementation and they put it on such heavy rails that it just rendered it completely uninteresting. And obviously the alternative was untenable, letting it just go off on its own and hallucinate and make crazy decisions. Decisions. And so it's actually, it's an example where kind of having, you know, having some structured taste, inferencing, providing accurate rails and dimensionality actually kind of ironically sort of liberated the product, like it allowed it to actually do more. And yeah, I mean, we're super excited about the future and to some extent maybe a stop clock is right once a decade, but we've kind of created structure and these systems crave that. I mean, ultimately LLMs crave kind of structured inference about and having an entity spine that's actually reliable and deterministic and so on. So, yeah, super excited to see where it goes and my hope is that it becomes an incredible. Obviously there's all this talk of moving beyond the UI and so on and I think if it really is an EA to be trusted, you wouldn't have to prompt it because otherwise that's just enough.
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D
Another.
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B
Another ui. It should be proactive.
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A
Yes.
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B
And kind of be able to glean,
- [16:20]
A
you know, ask me a question. It shouldn't be, you know, type in your query. It shouldn't be set up a cron job. It should be. I've set up a reoccurring task based on what I see. You know, you were doing your emails and your slacks in the morning. Mitesh. But we're going to do it at one o' clock as well and I'm going to keep it short and brief. This way you don't have as much to deal with at the end of the day. Cash. You were part of the first generation of AI companies to do a copilot copilot. Very simply, hey, it's your guide on the side. It's there to help you out. You're doing tax, you're doing taxes and that's what people were ready for. That's what, or when you started, people weren't ready for it. It was a new concept. But I'm assuming people with tax GPT have gotten used to this concept of being, you know, the guide on the side. It's going to be okay. We're going to give you some information. But this agentic stuff and open clause specifically is really inspiring. I would like to have my tax person, you know, in my Slack instance alongside me proactively in the accounting group, giving me ideas, watching the expenses, coming in and saying, hey, deductible, not deductible, you know, or deductible under these circumstances. So, two questions. First, one, let's get out of the way. Are you personally obsessed with this? Have you been staying up till 2 in the morning? Are you getting sleep? And then second, how does a paradigm shift or change what you were thinking as you went from a GPT to a co pilot to now, you know, I'm assuming you're thinking agentic. Agentic.
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E
Agentic, yes. So our product Vision significantly opened up few things that we were thinking that we're going to be able to accomplish by the end of the year. We are launching next week. So this whole agenda operating system because that's how our product vision started the GPT, the copilot, now the whole operating system of agents. Whichever task that you are doing in your accounting firm, from accounting to advisory to preparation and review. So that's number one thing that it did for us and we are extremely obsessed and you know, working through that. Obviously there is a huge, you know, sensitive data that accounting firms and advisory firm deal with. So we're not you know, putting it. We have to be very thoughtful from the security perspective. So our experiment that we are running is some few partner firms what it opened up for tax GPT we needed more engineers to deploy as forward deployed engineers go into these accounting firms with a solution architect who's a subject matter expert and teach people to use this effectively. So in a nutshell like our product vision extremely opened up things that we were planning to launch by the end of the year. We're launching in a week or two. The second thing what it did is how I'm personally using it. As I mentioned, we opened up the job description for Forward Deployed Engineer and some senior engineers and we have like 1000 people apply for that. Wow. For those roles.
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A
So explain to the audience what this forward Engineer is versus a regular one.
- [19:43]
E
Yeah, I mean forward Deployed Engineer is actually kind of your trusted tech person. Especially when you are in an accounting firm, advisory firm, you do not have the developers right to actually build open cloud or something of situation like that, a GPT or trainer GPT. So what you what follow Deployed Engineer does is it goes work through, make sure that your systems are all connected and you are using the product to the maximum of your benefit. Automating the task, you know, and making sure that everything is done correctly. So it's more of a consultative approach of selling and having that.
- [20:29]
A
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- [21:40]
E
So now how I'm using it personally, you know, when 1,000 people applied for the job and I was like, okay, the old way of doing it is like I have to review each resume, one minute each 50 people. I have to do 30 minutes, chat with them. So that comes out to be 2500 minutes together. Right. That is a week.
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A
It's a week of work. Yeah.
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E
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 I automated the review with OpenCloud. Right. It went through each resume. It had the requirement, it staged them to rejection if they did not fulfill the requirement. 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. I don't need a technical recruiter today. I was able to automate and save that 40 hours. So this is another. Yeah.
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A
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?
- [23:13]
E
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 1,000 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.
- [23:49]
A
Yeah, 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 openclaw 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 ten, it does seem that it comes out great. Maybe Oliver, you can come on the pod for a second. 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?
- [24:51]
C
We have 51 people at that company. And yeah, like, I mean, we are Silicon company, so basically 47 of us are doing engineering work. There is one general counsel, myself, one salesperson, and one, you know, customer engineer. That's basically the company in itself. And you know, that I don't know where the question is going, but I really want to say, you know, to Kasha's point, I think it really is around when you can automate of the. 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. To. To feed the pipeline or source the pipeline side of things. So very, very interesting in terms of how it definitely changes the hiring mindset.
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A
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 Cowork. And how do you think about inspiring your team to be 100% working with an agentic partner?
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B
Yeah, so I think in some respects, the addressable market has expanded so dramatically for what we do. I mean, we were previously it was presentational personalization. Now it's entire agentic workflows and Fortune 50s thinking about those. So we actually to some extent have. It's put pressure on hiring kind of counterintuitively because we need more integration engineers. We need people, at least in the short term, 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 that's also created more compliance burdens. So we've hired. 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 pr, pure PR outreach? That kind of stuff has definitely been put on the back burner, I think with 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 in a draconian view that we're going to be. 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 film. It's an independent kind of passion project. 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 on, but the reason it budgeted so high was because there was these large dramatic scenes and crowds and nightclubs and so on, where typically you'd need a ton of extras. Right. 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 in those scenes. And, you know, so on. It actually is saving the Kind of, you know, the actual product and a lot of people.
- [28:55]
A
Here's the way to think about it. Yeah, 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 90s Sundance, it's basically over, right? It 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? And then if you think about extras, here's a message to those extras. If they can get a 35 million film to 15, you can get a 15 down to 5 and you get a 5 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 is all about. Just make a short film for, you know, on a digital video camera and you know, just leave out the scene with the extras. Now you can leave the scene in with the extras. You could 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 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 gonna 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, you know, the company, just so nobody gets left behind. Because I'm looking at a cash. And I'm always very candid about this. 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?
- [31:36]
E
I already called the code red with our all hands last week. Everyone in the team is really, really excited. And you know, like if you are working for an AI company, that's the 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. 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 good feeling that this company knows us. This company cares about us and they know about our needs. So people are, we are using it to personalize more of our demo experiences. We are using it in the marketing, pulling up the resources in the product, doing the surveys and making sure what to build next. What is the reception of the things that we are launching is what is the adoption, usability, net retention rate. So it is helping us out a lot from that perspective. In our industry that we are serving accounting, there are, there is a less than, there is 340,000 more accountants that us need, right?
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A
Wait, wait. There are more, we need more accountants or there's an oversupply.
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E
No, there is a shortage of accountants. US need more, more than 340 more account. 340,000 more accountants. 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. It has 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, accounting firms do. And here's another interesting fact about accounting industry. 85% of the industry is small to medium.
- [34:37]
A
So it's all mom and pop, they're all boomers and Gen Xers. I understand like 75% are nearing retirement.
- [34:45]
E
Yeah.
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A
And the reason they're nearing retirement is it's well paying and they're burnt out. It's just a, 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 CPS 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, you know, college graduate or average high school graduate to learn to be a cpa?
- [35:34]
C
Do you think in order to be
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A
adaptive education LLM teaching them?
- [35:40]
E
There are a lot of credentials that exist. IRIs issue credential, it's called enrolled agent. The CPA has its own qualification but.
- [35:48]
A
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 accountant. How many days of online training with an agent and adaptive learning partner could somebody become a CPA? A thousand days, 500 days?
- [36:10]
E
I think one year will be a very good timeline. 365 days, 12 months. Because there is a lot of hands on experience and you know, the credentially not only need to be done by what do you know but also with the experiment experience. Right. So the AI agents can also upskill you. Like a lot of our partner firms are using it. They're hiring junior associate and asking tax GPT to use upskill their early hires to go 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 can be done in a month, even two weeks. Right. So coming back to the problem, we are solving the talent shortage crisis. We believe there will be one person, $1 million accounting practice in very near future. And we are building the tools where one person can command the army of AI agents doing the task and very focused on the customer relationship. Right now one resource in an accounting firm is return 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 $1,000,000 accounting practice will be possible. We already have few folks that with 2, 32 people they are running 700 $800,000 practice. So this opens up the opportunity and the industry that and firms will be able to offer full stack of services. They're 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 pop shop and medium size accounting firm will be more full stack. They will be offering more full stack services because the bottleneck of of talent is removed. Now you have agents and skills to deploy. And so that that is the vision that we are seeing at. I'll double click on Alex saying that it incredibly expanded their tam. It's 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 are you we can add the intelligence layer into all the other places as a single skill to serve them where wherever the agents are working.
- [39:14]
A
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 where the producers of all in. Although that spot 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. Oliver, your turn to shine. 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?
- [40:23]
D
I would say, Oliver, 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 in 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.
- [40:53]
A
And why hasn't anybody tapped out? 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.
- [41:12]
D
Did I think. Well, that's, 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 the 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 the 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
- [41:52]
A
know, but nobody will learn.
- [41:55]
D
Nobody will.
- [41:55]
A
Alex, you're a big, you're a big Knick fan, big basketball fan. And how have the Knicks built this roster? How have the Knicks built the roster? Josh Hart, Mitch, you, you tell me
- [42:07]
B
you're the, you're the veteran late first
- [42:10]
A
round picks or 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, hard working and then you have like Cat, who was obviously number one draft pick, Carl Anthony Towns, you know, and you got Brunson, I think was late first round or early second. Anyway, these, 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. I. 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 instead of incredible. It would work actually, if I just
- [43:13]
B
let us know if there's a scholarship fund that, that would be awesome or whatever.
- [43:17]
A
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.
- [43:30]
B
Yeah.
- [43:31]
A
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 like basically 50, 60 hours a week. You've been doing it for 15 days maybe or yeah, it's been around Lane where you're at.
- [43:50]
D
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 and is you're really doing it one task at a time. And over time you'll build those tasks, you'll build, you'll stack the skills, as they call it, and, and you'll continue to build those out. So the, one of the tasks that I have created is a autonomous content clipper at a lot of different teams and at Twist and this week in AI, you know, 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, a little dashboard I made actually using Claude AI, not cloudbot just by feeding in exactly what I'm doing. And it kind of visualize the process that my agent goes through. So I built three different skills. 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 OpenClaw found fully by itself. This is the first clip that it found using this skill and I was really impressed. So this is one of the skills that I made and I can kind of walk you through the process.
- [46:00]
E
Oliver, quick question. What is the inherently skill difference between the x Clippy and YouTube Clippy that
- [46:09]
D
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, 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. 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.
- [46:57]
E
Can you modify the skill 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 knowledge is in this skill that it knows what to do with this.
- [47:25]
D
Exactly. So for the xfield 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 made to check the virality Oliver.
- [48:01]
C
That's the, that's the interesting part. You made that tool. Right. So you're not using a tool like an invideo or an existing clippy tool and 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 Open cloud versus just having an agentic workflow of calling some application out there. So I just wanted to double click on that. So you made that kind of those description tools of what do you like in a YouTube video or X video or things like those, right?
- [48:33]
D
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. 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'll save that in its memory for future when it's looking through clips.
- [48:52]
B
This is awesome. Did you give it some sense of discernment as to like what you find compelling or what you. Or how did you brief it to have that kind of. Yeah, that. That kind of discernment just in terms of the picking the best moment workflow.
- [49:08]
A
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 clip 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? Exact. Exactly what instructions did you give your agent in step four?
- [49:35]
D
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 Deepgram, which is a speech to text platform that the AI would then analyze. So that process was pretty clunky. So I just had it take the MP3, put it through Deepgram, 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. 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.
- [50:32]
A
Okay, so then it downloads it, it trims it and it puts captions in it automatically.
- [50:36]
D
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 FFmpeg and this is all in Open Claw. It doesn't, you know, do an API call to some editing platform. It built all of this all in openclaw, which I just think is insane.
- [50:57]
A
It built your software. 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.
- [51:11]
C
Yeah, yeah. I mean like the part that like I just, you know what I like already, like really magnified is like, and Oliver, like without knowing any of your background, like, like, like, like start on top. Like if I was building like, you know, the in literally like 21 days ago or even before that, I'll be just like, okay, what is like my first search will be to an AI tool? It's 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 it goes and automatically searches it? And it'll be a constant back and forth of doing that and it'll be integrating that software tool into that piece of system. Whereas here it really is around like you customate your own token, you know? You know, and literally it could be like all of our, you know, smart AI tool, like AI clipping toolkit that you can advertise out in the market if someone wants to use it and build a software piece. Like that is not only just efficiency there. Like that is like just the level of like new kind of thought process that can result into that. Like I know Oliver, that you explained 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. 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 like saying that this is what you need to look into. And that is like really, really cool. I think driving those new. When we talk about new use cases, that's kind of what it is. That open cloud draws drives.
- [52:42]
D
Yeah. And when we talk about, you know, we don't need to buy, we don't even need Kafka anymore for clips. Yeah, exactly. Software.
- [52:51]
A
This is wild.
- [52:53]
D
You know, I would have been so happy if there was a way that I could have connected the open claw to, you know, Cap cut had it worked some magic and then send it back. I would have been thrilled to pay, you know, even $100 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.
- [53:16]
A
And, and proofs in the pudding. So how much faster this 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?
- [53:32]
D
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, you know, two minutes for me to find a clip. Sometimes I can take 30, even up to 30, just because I want to, I want to.
- [53:46]
A
Okay, so we'll put it at 15 minutes, 15 minutes to curate a great clip. Okay.
- [53:50]
D
And then once, then I have to download it, put it in Cap cut, burn in the captions. That's about, you know, 10 minutes, 15 minutes at the maximum.
- [53:57]
A
So now you're at that 30, you're at 30 minutes. Okay.
- [54:00]
D
And then publishing takes around 15 to 30 minutes. So that's a 45 minute to an hour process.
- [54:06]
A
Okay, let's go with 45. If you did it fast, what is it now?
- [54:11]
D
Now it's as fast as five minutes now.
- [54:15]
A
It doesn't allow you to post to social media though, right?
- [54:19]
C
I was going to say just the publishing that you do manually now and that everything else is all open quiet.
- [54:25]
A
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 retweets from their million follower anything
- [54:40]
C
for a year now, but
- [54:44]
A
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 like maybe put it in drafts or something?
- [54:54]
D
Yeah, this. I haven't gone through the publishing just yet, but I do know, I know that you can have your open cloud take control of your whole computer, your browser, it can access all your files.
- [55:04]
A
But it stops you, though. It stops you right now. Where is the is a limitation of openclaw? It reads the terms of service of Reddit X and says you can't do that. How do you unleash Open Claw to do things it's not supposed to do ethically?
- [55:17]
D
You can actually prompt openclaw and ask it to get around certain things. So I haven't actually gone and really?
- [55:24]
A
That's your next mission?
- [55:25]
D
That's the next mission.
- [55:27]
B
But I will say you're live on air, Oliver. But, but that's.
- [55:30]
A
No, I'm not. I mean, listen, Oliver, I'm not telling you to break the rules. I'm asking you to bend them.
- [55:35]
B
You know, Oliver, I'd love to see. I'd love to see this interview rag through that. You know, I'd love to see what the outputs are from. From this conversation.
- [55:42]
D
Yeah, those would be the ones I
- [55:45]
E
can add 10 seconds. 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. For Adobe for three years. So this is what it is getting built, 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.
- [56:22]
A
Yeah, thank you.
- [56:23]
D
Yeah, thanks for having me.
- [56:24]
A
I want to show and so how do you make it memory or a skill in openclaw?
- [56:35]
D
Like how does.
- [56:36]
A
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. And if 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 1pm research viral tweet patterns, hook strategies, thread structures, engagement tactics, optimal posting times. It added what it does there by 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, Instagram trends research, MD before researching. So it never repeats the same insights builds on what's learned before. Track switch patterns are confirmed over multiple weeks versus just new notes. What stop 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 work is 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 prioritize and does workflow that's built into it. Sold out. MD that's the behavioral one. How it's voice, it's temperament, its value, its values. And then you can add to it.
- [58:40]
D
Right.
- [58:40]
A
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, etc. 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. 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?
- [59:30]
D
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 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. So this isn't 100% autonomous, but it does go through and, you know, 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, just takes one minute.
- [60:21]
A
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, Amazing work.
- [60:37]
B
Do you have any.
- [60:37]
A
You can give him a little praise. Just a little praise, not too much.
- [60:41]
B
Definitely, definitely worthy of that praise. This is amazing. And, you know, rag 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 that the agent beginning to deliver value to the overall org or is there anything that keeps you up at night while the agent's up at night?
- [61:07]
D
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. It's working with a, it's analyzing a slack channel with a bunch of humans. And 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. 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 Mitesh 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.
- [62:04]
B
I have a great anecdote about that. Someone in my, my company tried to brief, it was an agentic browser, so it was just prior to the, you know, 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 card. And it was just kind of a hilarious example of discernment got wrong. But yeah, all fixable problems in the medium term.
- [62:45]
D
Yeah. And that's still human. I think you still have to have a little bit of human in the loop. But you know, as we move forward, we'll be more comfortable with. With less and it'll make more correct decisions. So it's all very exciting.
- [62:57]
E
And especially when human has the liability and responsible for making financial decision or any life and death decisions, human definitely will have way more productivity, way more knowledge and making the executive decisions that need to be made. Oliver, very well done.
- [63:21]
D
Appreciate it.
- [63:22]
C
That's super cool.
- [63:22]
A
Oliver, you're going to do a Saturday session. Get pick. I think you can handle like seven team members, seven slots, first come, first serve. Give every. 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 it 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.
- [63:52]
D
Great. Very exclusive. And Oliver.
- [63:54]
A
Yeah, very exclusive. 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.
- [64:05]
D
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 open claw on AWS EC2 server. So it's not the most secure, but it is the fastest and one of the cheapest ways to get your openclaw.
- [64:26]
A
Okay, do five. Do five people. Because I want to see five people get good at it. Do five. Five slots open to my team members. 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, we know there's inference issues, we know there's memory Issues, there's token issues. What are the top blockers? We, we said at the top of the show we were going to talk about blockers right now for AI. What are the blockers?
- [64:51]
D
I mean clearly price is, 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 through X flag certain things, but they're not going to make the right decisions like an OPUS would. So I think and when you're using those Frontier models using an API, they get very expensive. So that's price 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 Matesh thinks about this as he's kind of building out these chips.
- [65:47]
C
Yeah, I think that's kind of the right way to think about it, Oliver. It's 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, some of them combining them together maybe. 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 equate it like kind of like a small brain, big brain like you'll, you'll always have small brain getting better at kind of like the Mac Mini levels and it'll get better and better but then your requirements for the tasks 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 and that on prem will get massive but 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. I think the biggest improvement though I saw when you're scrolling kind of the clips it picked up, it picked up Daria's clips around. And if you hear and basically summarize the entire Daria 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 having things. So it's all about that context. And more context means more memory, you know, also attention skills quadratically up. So like it just means that it's a. We are not going to be out of this memory cycle for a long, long time. But overall I think all of us, you know, including Positron, but including just, you know what. If you look at what Nvidia is focused on, what all the other silicon companies are focused on, it's to figure out ways to improve the context length without degradation. Right now you can go 200k, 400k, maybe you can go a million on Gemini, but you start seeing degradation about that and it's like how do you do that? Like when we are running cloud agents to, to do our internal engineering workloads every, you know, depending on the task, every few minutes to few hours, you constantly clear out and then summarize the memory and then use a summarized memory as part of it. I'd much rather that I have the full context and I think that's a big improvement that can come there.
- [68:07]
D
Yeah. I actually have a question for you, Matash. Do you think it's better to build out a bunch of micro agents that have, you know, maybe just one or two memory files and three or four skills versus building out like a mega open claw Ultron? Yes. What do you think makes most sense?
- [68:23]
C
I don't know the exact answer. It depends on the task. Like for a lot of the engineering, at least in co development and things like those, I think some of the bigger agent, the ultra agent is always better in terms of if it can hold the context and if it fits within that context, then it gives a much better output. But I think for smaller tasks, I think the macro agent ways prefer 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.
- [69:14]
A
All right, well, 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
- [69:22]
D
know,
- [69:24]
A
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. 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 tax GPT.com or AI.com.com if you are an accounting firm. I want you to email cash@taxgpt.com 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?
- [70:37]
E
Yeah.
- [70:37]
A
That's the number one thing you need or you need some frontline engineers too.
- [70:42]
E
We looking for far deployed engineers. Around 2% of accounting firms in the country are using tax GPT. So we have.
- [70:50]
A
Let's add a zero.
- [70:52]
E
Yeah, yeah. That's the goal in the next 12 months.
- [70:56]
A
I love this game. Alex. How can developers. I know you need developers to play with the API and give feedback. Your alex@clue q l o.com is that your number one need right now is people to use the API and give you feedback. I assume customers are never totally.
- [71:13]
B
Yeah, we love, we love supporting new users. Cases we have. Yeah. Pretty, pretty amazing expanded set. And please do reach out. We're always. If you have. 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, get these agents on the proper rails. Reach out. We'd love to support Mitesh.
- [71:35]
A
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, it's literally a foot I think
- [71:46]
C
that the biggest thing is, I mean, people are everyone's retweeting and quoting that 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 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 off of this technology for the coming years, I think that's another thing. So ASIC engineers, software programmers for, for silicon, please, please reach out. Mitesh Positron AI.
- [72:22]
A
All right, everybody, this week in startups, this weekend, AI all in your three favorite podcasts. I'm Jason. Alex Keshe. Mitesh, great job, and we'll see you next time. Thank you.