The new rules for hiring, building, and betting on AI | This Week in AI E002

17:32Tanay Kothari:And if you have four extra hours a week and you can make$100, bucks and then pay down your credit card or, I don't know, buy some extra comic books, whatever you use your surplus cash for, people were drawn to that. And this is where I think this Doom post, this Dr. Doom post kind of misses things, specifically kind of shows some real lack of expertise in marketplaces. So that was the part where I was like, this doesn't make much sense. I'm not sure who wrote this or what their background is, but quote, the DoorDash moat was literally, you're hungry, you're lazy, this is the app on your home screen.

18:10Tanay Kothari:An agent doesn't have a home screen. This is the first time in history the most productive asset in the economy has produced fewer, not more jobs. I thought this was completely naive, and I'll open it up to both of you, because what's unique about DoorDash isn't exactly the software. It lets a beautiful interface, and you know, they match jobs and, you know, it's very fast and great, awesome. And it's global now, but the relationship with the restaurant and the trust built with the user, that's actually what makes the network effect work. So your thoughts on the DoorDash section, either of you?

18:44Jason Calacanis:I think at a high level, DoorDash is a great example of the kinds of apps that you cannot just vibe code. You know, a lot of people say, oh, software is all going to go to hell and everything is going to vibe code. People are not going to vibe code things where permissions are mission critical so far, where you need a two-sided marketplace, where you need these complex network effects. Those things are very hard. Sure, you can get a quick app for Instagram, like an Instagram clone, right? But if you don't have millions of users actually using Instagram, then it's not going to be a very interesting app to use.

19:16Jason Calacanis:And so I think DoorDash is a perfect example of that as well.

19:19Tanay Kothari:How does it affect how you're running your company today? And any thoughts on the DoorDash one. I didn't get to get your take on marketplaces. But also running a software company, people in this doomerism would say, oh, I can just create Whisper. I can just create any software. I can create Notion. I can create Slack. Your thoughts on this sort of everything can just be vibe coded.

19:38Richard Socher:Yes, you can create a V0 of it. Like honestly, if you today wanted to go make a Notion clone, a Whisper flow clone, it's going to take you two hours and you can just do it. But hey, here's the real thing. Once you do that, you will actually realize the key pieces that make these things really hard, that make these businesses really hard. Same for DoorDash, right? You make this thing and then you realize like, hey, I actually got to go talk to all of these restaurant owners and I got to build a brand that people trust and I got to figure all of the other things out. DoorDash is actually a three-sided marketplace, which is even harder to build than most other marketplaces that you have to.

20:13Richard Socher:And so that's, I think, one of the things that is all the grunt work that goes behind the scenes in building these businesses. That is really hard. Voice for Flow, for example, right? The hard thing isn't building this voice to text that works everywhere. The hard thing is getting about all the different edge cases. It's about how do you make it so seamless that my dad or my grandfather can easily use this product? It's about, hey, how do we make sure that a person doesn't get the feeling of loss? A person feels that delight. Those things are really hard. Same with Open Claw, right? Open Claw is out.

20:51Richard Socher:And it caused a lot of people are super excited about it. But hey, guess what? 99 % of the human population is never going to set it up because they're not used to setting up systems like this. They're not used to installing the skill. Like I know a lot of people in my family, like my dad, my grandparents, right? They're not going to do that. And so the really hard thing with OpenFlow actually, and we get hundreds of requests a week. It's like, Tanya, can you integrate with OpenFlow and build like a voice open claw thing. And I was like, yes, but that's not the hard problem. The hard problem is how do you actually make that something that is seamless to use by everybody?

21:32Richard Socher:So it's just one tap and it works and you remove all of the technical complexity that comes over it.

21:40Tanay Kothari: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.

22:19Tanay Kothari: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 in correctly for each segment.

22:56Tanay Kothari: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 three, four, five times more powerful for the same price. Try Notion with Notion agent at notion.com slash twist. all lowercase letters, notion.com slash twist. It's in the show notes. Try your new AI teammate, Notion agent today.

23:23Richard Socher:And so the great thing about white coding that I love is what you thought of previously as the boilerplate, like setting up login, setting up a database, making sure things are syncing well together and all of that. That work can be taken care of. So you as engineers, or like problem solvers in general, you get to solve the really hard problems that matter that it would take years for you to get to otherwise because you're just spending time in boilerplate.

23:58Tanay Kothari:Yeah, this is, I think, one of the things people are not recognizing, which is smart people, entrepreneurs. They will, when they get through their chores, the easy stuff like, oh, I need to have a help page. I need to have, you know, I need to write job descriptions for my next hire. I need to do accounting. I need to do my cap table. As this stuff gets easier and more abstracted away in terms of running a company, you release extra time. Again, cognitive surplus. So what do you do with that? Well, you refine the product in ways that are uniquely human. The design of it. Oh, well, the AI is going to make better design.

24:32Tanay Kothari:Great. Give me your best designs, and then I'll take them 20 % better from there. And there'll be more features to add. And there'll be ways to make it more reliable. So this concept that SaaS software is going to be static and it's not going to improve and therefore people are going to rip it out and make their own. Well, if the cost of the SaaS software is low enough, I do think people will be like, well, why bother? Now, if the software is too expensive, what I do think is going to happen. So this is where I think the market is getting some of the reaction correct. The cost per seat and your renewal is going to be very different.

25:11When you talk to, I don't know, I'll just take Slack as an instance.

25:15Tanay Kothari:When we go back to Slack to do our yearly or two-year contract, we're going to say, well, you know, our OpenClaw AI replicants set up Matterpost for us, an open source version of Slack. And it works pretty well. And you're charging us$30 ,000 a year. We can set this up and maintain it for$1 ,000. Can we meet somewhere in the middle here in terms of cost? So we'll have some negotiating position that we'll be able to use to compress the cost. But that creates efficiency across the whole economy. And it forces Slack and Salesforce to make a better product that then counters us saying, well, we don't need all your features.

25:53Tanay Kothari:Now, I could see us getting rid of a third of our SaaS spend if they don't change and they don't evolve. But I don't see a reason to rip out Slack right now unless the new version that we went to, the open source version, let's say, or the one we vibe coded at our 20 person firm was materially better. Because the cost of this stuff, Richard, is so low already. right yeah so a few thoughts uh i think defaults are really really powerful here also 80 percent of

26:21Jason Calacanis:iphone users heard this crazy statistic when i'm still working in consumer land 80 percent of iphone users never change any setting on their phone it means whatever comes as a default it's just going to be like the default search engine and so on now in terms of you know vibe coding like you cannot just vibe code a search engine either you know like to actually implement an index and crawl the whole internet that costs like tens of millions of dollars or more. So that's not going to happen. So we're seeing this reinvention of infrastructure that AI can use, like GPUs first were used for gaming, now for AI.

26:54Jason Calacanis:Browsers, search engines, they can all be reinvented for AI as the main user. Now, when you talk about cognitive surplus, I think the biggest thing that people don't often have is agency. They're so busy with their lives and just doing the robotically sort of the things that once they free up, there's going to be a new skill to actually get people excited about, hey, now with this all, what are you going to do with all this new free time? That kind of mindset will require a lot more agency from people. And then in terms of what you mentioned about the markets and what they're getting right with SaaS, it is true, for instance, that when you look at China, China has much less of a developed SaaS ecosystem and economy because labor was so cheap that a lot of times companies would just build their own tools instead of buying them.

27:44Jason Calacanis:And now with AI and vibe coding, some of those tools will get cheaper too. But the more complex they get, the more mission critical they are, the less you're going to vibe code it and the more repetitive the processes are. And then also in some cases in SaaS, you want your employees to follow a certain step-by-step procedure, right? Because you're in a regulated industry that requires you to go through those steps. You cannot just let them vibe code it and then automate some steps away.

28:10Tanay Kothari:Yeah, this is a key piece. If you vibe code it and information leaks or gets hacked, now you've got a whole other level of problem. There was an interesting quote in here I want you guys to react to. And this, I think, got a little bit panned. The marginal cost of an AI coding agent had collapsed to, this is in their future world in 2028, had collapsed to essentially the cost of electricity. Where do you think you fall on this one? Either of you, yeah.

28:38Jason Calacanis:High level, I'm very excited to bring electricity costs down. I think electricity and energy are at the very core of so many things. A lot of people are like, oh, we have a shortage of water. We don't have a shortage of water. If you had infinite or very, very cheap electricity, you could just desalinate ocean water, right? So there are a lot of these things that are ultimately energy problems. And I'm personally excited to get the marginal cost of intelligence to be closer and closer to that of electricity. And I think that will then show that the bottleneck will be the agency and creativity of the people and less sort of the ability to execute on good ideas.

29:19Richard Socher:Coding agents, very similar to LLMs, are going to be one of the things that a large fraction of people will start getting on and start using as going to be part of their day-to-day life. When something like that happens, when you have so much demand, there's going to be a race to the bottom with everybody trying to win on cost. Because right now it's very clearly one of the big blockers, honestly, to building tools. You see a lot of white coding companies running on basically negative or very minimal margin. And so there's other people innovating on reducing the cost down. And so I think historically also So you've seen that whenever you have extreme demand for certain commodities, you just basically go down to single digit margin that you're making on top of your base cost, which in this case would be electricity.

30:17Richard Socher:So I wouldn't be surprised. I pretty much expect that to happen for just if you're just talking about the pure model there going forward.

30:27Tanay Kothari:It does feel like free or close to free is what tokens will cost. And then the question is, how complex is your actual work? And we this week stood up Kimmy, and it's obviously nowhere near as good as Opus 4.6. It's not comparable head to head. But for simple tasks, hey, summarize this or sort this or tag this. It does it just as well. So we are now in that process of, hey, what should we send to Claude and pay for? And then what should we just on our own Kimmy 2.5 for 10, 20 bucks a month? What should we send to that job? So that opens up the big open source discussion. Where do we see open source playing into this or accelerating it even?

31:20Tanay Kothari:Because it does feel, to me at least, when you start using agents, you start seeing your Claude bill or OpenAI bill or whatever you're using, Gemini, go past$100 a day for the agent or$200 a day. You're like, wait a second, that's$70 ,000 a year. That's like an entry level salary. Is that doing the work of another human or should I get another human? Or it's at$150K. It's the same price as the developer. And then you start looking for other opportunities. How do we think open source plays into all this?

31:52Jason Calacanis:I mean, this week in AI, an interesting thing that also happened was Anthropik complaining about how some other labs were distilling its models by sourcing them, talking to them, and sucking out information, which then, of course, a lot of people on X pointed out the irony because they also have done that to the original human knowledge. And I think the ability to distill knowledge out of closed models A, reduces some of their moat, and it shows that open source will be better and better and catch up faster and faster, as long as it has access in some way to these closed models as well. and overall open source here massively proliferates this technology and enables everyone to play at it.

32:42Jason Calacanis:And just like this idea that you can predict the future based on the goods and services that wealthy people have access to, if you're wealthy enough, you can hire 10 programmers and do something for yourself, implement them an app idea that you have. Now we're going to give everyone those capabilities just like a personal tutor and so on. So I'm all for it. I think open source is great. You just have to be careful about how you use it and exactly like you said, know when to use it. And that is, I think, another sign of getting this new skill that is the management of AI agents up in people's hierarchy of sort of required skills to do your job.

33:19Jason Calacanis:I think in the next 10 years, being able to say, oh, I'm not so good with this agent thing is like in the workplace saying now I'm not so good with this computer thing or this internet thing. We just don't have people anymore that are in that.

33:32Tanay Kothari:When PCs came out today, this is probably a little before your time, I was an IT engineer and we were putting them on people's desks and they were getting them for the first time. And like some of the lawyers, I was putting it on their desk. I was working for a firm that installed them for lawyers. The lawyer would be like, I don't want to use this. And I'd be like, I have to put it in your office. They're like, put it on that side desk over there. And they had a secretary, you know, now we call them executive assistants, but they were called secretaries and they would type and they would take dictation.

33:58Tanay Kothari:They would write a memo. They would come in with a legal pad. And those folks who didn't make the paradigm shift, they just retired, basically. But Tanaya, I'm wondering how you think about, and we'll circle back to the agent issue, but before we get there, how do you think about open source? What models is Whisper built on? You must have tried all of them. And so how do you think about your costs? Because you've got millions of users. Grammarly has got tens of millions of users. You have all these services at scale. the token usage is a big part of their expense so how do you think about open source versus you know open ai claude and all the other paid proprietary llm uh so i'll show you about three

34:39Richard Socher:three different points quick so one is one of my favorite things that happened last year was when deep sea came out it forced basically every single lab to have a deep research mode that they built to release that extremely fast and to release that kind of cheap because they knew that other people had that alternative. The same thing that you were talking about, Jason, about kind of the Slack alternative that you can just build in-house. It gave people a reason to demand lower prices. And so I think that actually drove a lot of innovation. And we've always seen open source over the last 20, 25 years do that.

35:17Richard Socher:It's even more accelerated today. And so I'm actually very excited for everybody who's building an open source, because you're actually having a major impact in all of these companies that are driven by hundreds of billions of dollars to provide things to people for cheaper and make it much, much harder to create basically an oligopoly there.

35:37Tanay Kothari:it is great downward pressure right like it keeps them honest they can't hold you hostage because they have the best model because people go yeah it's it's a great model it's 20 better but i don't need 20 better i'm good with what i got um and that is materially i think keeping the prices going down whatever it is five or ten x a year per token yeah exactly whisper and history Whisperflow was initially built on open source models, and that actually accelerated our

36:07Richard Socher:development early on. The thing we realized, though, is every voice model today was solving a different problem. They're all transcription models, fantastic for getting subtitles of movies or getting summaries of YouTube videos, but actually terrible for human input. And so we spent basically the first six to nine months putting a lot of Band-Aids on these open source models to make them not hallucinate. If you speak English in a Russian accent, it would actually transcribe Russian. It's a problem. So we had to fix all of these things. And then eventually we learned so much about what we need to do with these models that we had the ability to go and build our own.

36:44Richard Socher:And so Whisper today runs on our own models. As a business, it's fantastic. It's about 90 % gross margin because you don't have to pay OpenAI, Cloud or anybody else for that. and we get to innovate, ship new things to people. We're not beholden to anybody waiting for somebody else to do innovation so our product can be better. And generally, the way I view the world is, hey, here's where we are. We want to get to the point where we actually have Jarvis in the hands of people, a system that just gets you, understands you. It's with you 24-7. And there's a lot of problems that we need to solve at that point.

37:21Richard Socher:If somebody else solves them well, fantastic. We'll integrate with that, right? I think OpenClaw is a fantastic resource and we'll probably have something built with that into Whisper that is accessible. But hey, there's a lot of other problems that still need solving. And so we spend all of our R &D energy at the company working on those instead.

37:42Tanay Kothari:All right. Let's shift to building an AI first team. Richard, you mentioned, hey, if you don't know how to use agents or manage agents or train agents, what value are you going to be? You said 10 years. I'd say like 10 weeks or 10 months. Like how could you possibly operate at work if you don't know how to manage an agent? Because that's the new interface. So much so I had everybody come into the company on Sunday. We did an optional course. It was optionally mandatory for folks. It was optional, but I mean, It's kind of mandatory if you want to survive in the workforce in the future. And everybody started using OpenClaw and learning how it works, et cetera.

38:21Tanay Kothari:I spent my whole morning just educating my OpenClaw agent on who I introduced them to each person in the company. I said, study what they do, study their emails, study their notion, study their calendar, and study their activity on Slack. And then on the weekend, I want you to act as an executive coach. and I want you to send a DM to their manager. So if it was a salesperson, Ricky's their manager, if it's on the investment team, Jackie, whoever it happens to be, and myself. So you have the employee, the team member, their manager, myself, the CEO, essentially, and then the replicant. And I said, recap their week and then coach them on what they could have done better and what they got right and then ask them some probing questions about how they could do better at their job.

39:12Tanay Kothari:And that will happen this weekend. But I gave them access. And this was like a major breakthrough for me. I said, hey, let's give my agent system-level access to Gmail so they can see all the employees' Gmails in and out. They can see all the Notion edits in and out. They can see all the Zoom meetings and their calendar, and they can see all their Slack messages. Now they'll have like a whole understanding of that person and what they did this week. and then for the first time the manager doesn't have to ask the team member what did you do this week and then have the team member selectively try to remember what they actually did it will just be like here's what you did here's the trends i'm seeing in it now we start the discussion which i think if you're a young person that's what we all wanted we always wanted at least for me as a high performer i wanted you to know what i did and just tell me how do i get better how do i get better so how are you guys building ai first teams and how do you think about that it's obvious with developers ship more code go faster have less bugs whatever but for the rest of the team how do you think about that richard and then we'll go to you today yeah you're right like of course the majority

40:21Jason Calacanis:in all my organizations are developers so uh that is the biggest shift uh there what we're seeing is just multitasking keeping lots of things in your head and being able to delegate these tasks better and better. I think for everyone else, this management sounds kind of boring, but it is one of the most important skills that people have to have is the delegation, knowing when and how to trust what context those people, but also those agents have access to, so you ask them the right things. That is something that we're training. And when we're rolling out actually agents with our customers inside like for you.com too, we're seeing basically that you have to do certification you have to do training and then you can actually do with ai also role specific training or it's like okay you're a marketing manager here's a like help us we'll help you now create a marketing agent so you can think about like how to write a blog post for your company and you can personalize that those certification programs too and we've seen a massively more usage of our agent creation platform on u.com when we required or when our customers ceos require those certification programs for their people.

41:33Jason Calacanis:So I think that is a big part of that change management in people's heads, not just the technology question.

41:39Tanay Kothari:Tanay, how do you think about building an AI first team, customer support, sales, marketing, hiring, operations, accounting? How are you thinking about that? And getting the developers, you don't really need to convince them that a co-pilot works because they've been using them for years or watching other people use them. But this knowledge workers outside that group are going to resist in some cases and some are going to embrace. How do you think about it?

42:06Richard Socher:The way Whisper runs today is so different from how we ran as a company four months ago. The first thing is, by the way, we didn't scale down hiring at all. We're still growing our team at about 50 % every quarter. And the kinds of people we hired today are very different. And so on the engineering team, we basically are now hiring senior and staff level engineers because every person is running five to 10 plot agents in parallel to get work done. Which means like we just shipped, for example, our Android app yesterday. It is right now the number one voice app. It works across every single platform and all you have.

42:45Richard Socher:It was all coded by one guy running 10 plot agents simultaneously. Now, he did work 20 hours a day for the last three months to get it out there. But man, you don't need junior engineers anymore, was the first big realization. So every engineer at the company is essentially an engineering manager now. Every engineering manager is basically a director of engineering who's managing this massive team because your output velocity is just so high, which means we can do so much more. But then that expands to all other areas of the company as well. So what we did was we actually hired this one guy. He built a$300 million GMV restaurant OS business before.

43:29And his main job is to go team by team, sit beside them, see what their day looks like, create an automation plan, and then go and automate the entire system one by one and then ship

43:44Richard Socher:those internal features. For example, let's take customer support. Now, most people who think about AI and customer support, they think it responds to tickets. Yes, that is one of the 15 things a customer support team does. And so we basically broke down every single loop that happens inside customer support, creating automations for that. So, hey, first one is, you know, customer complains, you write them a response back, great. Then you realize, hey, we actually don't know what to say to this person. And so what we built another loop for is, hey, for questions we don't have answers to, get the answer from a human, make a doc, so we have that for the future.

44:21Richard Socher:And the next thing is, hey, if this is an engineering issue that's happening, let's actually get access to our code base, the logs, what the user complained about. Let's actually compile that into a specific ticket for the engineer. And hey, you know what? Let's also pull up a PR for it. So we actually take a first stab at fixing that problem. Then there's another loop. where people are like, hey, how do I do this thing? And we realize it's something they're confused about. So that usually goes to the product and product marketing teams because it's our fault that we haven't educated the users.

44:55Richard Socher:And so that's a problem to fix. So we have all of these different loops built in and they're all just running now autonomously within the company. And so what you have is a company that has millions of consumers and we want to give a seven-star experience to all of them. is you need a customer support team of 200 people to just manage that, to manage just the tickets coming in. And we have four humans in our customer support team. We have enough now where we can even get two of them to just give white glove service to our enterprise customers. And the rest of it is just managed by these two people who are taking care of tens of thousands of tickets every single week.

45:41Tanay Kothari:This is a concept, I think, Tanae, you've talked about before, which is what is your expertise as a human? And then how quickly before what's in your skill stack? Shout out Scott Adams, creator of Dilbert. He always talked about a skill stack. You spend a career adding things to your skill stack. One of them is identifying bugs in software. One of them is identifying when you have a confusing UX and, you know, or confusing tool tips and just product is not educating the consumer properly or is in design properly. And then, of course, you just have regular tickets. Hey, I lost my password or I don't understand it.

46:17Tanay Kothari:You know, just could be dumb customers. Sometimes customers act dumb and sometimes customers make mistakes, right? Right. You, that used to be people's jobs to navigate all that. And now it's abstracted away. So you went from what you think would have been 200 to four. That's a 98 % reduction. How does a human keep, how does a human process the fact that what they did for the past 10 years is now automated and it can be done better by an agent. And I need to learn something new. You must have had this happen. You must have some anecdotes, both of you, of somebody going, what am I supposed to do now?

47:01Jason Calacanis:Yeah, so I think the most interesting thing here that actually happened in a lot of people's head with Claudebott is that they realized that when you're an entrepreneur or when you're someone who cares about the outputs of an industry or an organization, you love AI. When you're getting paid by the hour, you hate AI. And in this particular case, imagine you're an entrepreneur and you have a cloud bot or some kind of bot that looks at all the things you do. And then as soon as it gets enough examples, it starts doing it for you. You'd love it, right? Because you own it. But if you're getting paid by the hour and you're doing that work, then your company owns it.

47:41Jason Calacanis:And then they might just let you go in the end. And so that will, I think, create a massive positive long-term incentive to become an entrepreneur and not an hourly employee.

47:53Tanay Kothari:You will need to be entrepreneurial to make it through this because let's face it, Tanae, a lot of people, you know, they live, they work to live maybe. They're not cut out of the same cloth as, say, entrepreneurs who are like, I need to solve this mission. It's important to me. And I'm going to burn the midnight oil until I do. So have you started to have this experience internally? And how have you managed the human response to, wow, what is my job now?

48:24Richard Socher:I know this creates a lot of stress for people, right? Even the smartest people as well. Because, yeah, a lot of them have imposter syndrome. So the way we've been dealing with this, you know, separate from hiding just inside the company is not just telling people like, hey, you need to figure out how to do better, use AI or like your job will be cut. But more so just like, hey, let's figure it out together how we build the org, how we work on this. And whenever somebody in the team learns something new, let's have a session where we teach that to everybody else and everybody's up leveling themselves.

49:03Richard Socher:because you know interestingly i felt this personally so i've been i i prided myself on being a great software engineer and i've been writing code since i was nine years old to today where now whenever i build things i read code yes that thought code produces but i've

49:19Tanay Kothari:barely written a single line of code in the last one year which is ridiculous to me i'm sad because I spent 17 years wanting my craft and now it's all kind of useless.

49:32Richard Socher:And I felt that pain for a couple of months. But honestly, at this point now, I love it. Because the thing I realized was it wasn't writing code and the syntax that I learned. What I learned was how do you think? How do you take an extremely complex problem and break it down into small pieces and actually go solve it? How do you have taste for what looks good, what feels good? And so now with Cloud Code, I was like, wow, I can just go way faster and I can 5x myself and produce things in a day that would have taken me a week otherwise. And so I'm getting the thrill of something different now. And I want to create this feeling and this realization for people, but also having the empathy that it's going to be hard to get used to this change.

50:25Richard Socher:so just recognizing that humanity and people too yeah so speaking of humanity here's producer

50:33Tanay Kothari:oliver works for me producing podcasts and working on the investment team and when he was on the podcast team i said let's take some of these repetitive tasks and here we are ao 30 it's the 30 days since we started covering uh open claw so after open claw and uh we started to really refined skills and tasks. So I'll have Oliver come on and show just three of the tasks. Here is producer Oliver, who has been doing all these amazing demos. Producer Oliver, I asked you to take the work you've been doing over the last two or three months. And over the last two or three weeks, we've been trying to get OpenClaw to do them.

51:12Tanay Kothari:These are tasks that would take hours a day, I think typically, which means they would take, you know, dozens of hours a month. Let's walk through each of the skills. And then I want you to tell me like just broadly what it's been like to be able to offload this kind of work, what I'll call chores. So let's see the first skill that we gave that Oliver created. Tell us about this first skill. And Tanay and Richard, you can feel free to grill producer Oliver, ask hard questions, et cetera. And you guys can ask each other questions as well, obviously on the pod. This is one of the first skills that I actually automated using OpenClaw.

51:51Tanay Kothari:And this is an attendance check. Every morning, everyone that works at launch and this week in startups will explain what they're going to do that day. This is kind of, you know, housekeeping, make sure everyone stays on task, but also it's a way for everyone to know what everyone else at the company is doing. And this was accountability. And, you know, we created this SOD, EOD program during COVID. because we weren't in the same room. Nobody knew what anybody was doing when it was just confusing. And so we just said, hey, five minutes in the morning when you're having your coffee, 10 minutes at the end of the day when you're packing your bag.

52:26Tanay Kothari:Just what did you what are you trying to accomplish today? And what did you get done? I had about three, four, I had about four people in the company who resisted me doing this. Three of them are no longer at the company. One of them still at the company is a super high performer. And he actually does it now because we felt left out. But we had a challenge, which people would forget them. So I had an Athena assistant actually doing this. Now I have the Athena assistant on better work. So explain what this does and how it's changed things for you. Yeah. So every day, everyone goes into this specific Slack channel, tells us what they're going to do that day.

52:58Tanay Kothari:And as you mentioned, this was a tedious task that we had a human doing. And now this is done autonomously, tags people who hasn't done it yet, flags Jason, says this person hasn't sent in their SOD, as their start of day. And basically this skill just automates that task, saves the Athena assistant 30 minutes for the Athena assistant to go do other work. But this task specifically, it looks at everyone's messages in that Slack channel and then just basically reports back. So we created the attendance check skill. This happens every day at 12 for specifically the start of day. In the skill, it has the format that it wants it to send, including tagging you, tagging the people who haven't sent it yet.

53:37Tanay Kothari:So this is just one of those tasks that we have. Now you wrote the scale or the agent wrote the scale and you would give it instructions on how to change it? I rarely look at the markdown format, which is how the scale is made. But when it does make mistakes, I will sometimes go in there and see what exactly it wrote. Because sometimes that's what we're showing now. This is the markdown. And sometimes this does it a little crowded. Sometimes it writes things it doesn't need. Sometimes your agent will actually write the task on the cron job and the skill, which basically will cloud the context.

54:11Tanay Kothari:So you want to make sure that the way that you structure a skill or a task is very organized. And I think that's something we've been learning as we're trying to create a ton of new tasks. You want to create a task one at a time. I think that's the right way to do it for OpenClaw. And that's kind of what we're going to lean into for the rest of our team as I onboarded some of our team over the weekend with their own OpenClaw agent. And as we continue to onboard them, the way we're going to do it, we're going to ask them, what is something that you believe an OpenClaw agent can help you with? They've learned through us, through watching the podcast, what it can do, but they're going to give us five to 10 things that they think could be automated.

54:50Tanay Kothari:I'm going to talk through it with them, how this would potentially work. And then we're going to do it one at a time. Because if you don't, your open call contacts can get very clouded, your skills are going to be messy. And that's something that we've learned. We're doing that one at a time, you got to take it slow, while you're taking everything else pretty fast. Yeah, and we've now given this access to all the zoom meetings. And so instead of people having to say what zoom meetings are doing, or having people linked to notion pages for the related tasks they're doing that day, for example, if I said, I'm meeting with this startup, and here's my notes from the call, that could automatically be included.

55:27Tanay Kothari:Or our replicants could understand the context of that and take it on a go-for-it basis. Any thoughts on this, Richard or Tanay?

55:35Jason Calacanis:I love it. This is exactly what I meant with you have to learn how to delegate this, chop down the task into concrete chunks that you can verify that the AI doesn't get confused with too large of a context window for now, and all of those things. Thanks for sharing.

55:54Richard Socher:Mm-hmm. One thing I'm curious about is, have you thought about, experimented with doing the EO Day report automatically? I know, Jason, you briefly mentioned, like, you know, looking at their Zoom meetings, Notion Docs and all. Or is there still a benefit that you still see from people doing it themselves?

56:11Tanay Kothari:That is a great question. Today, I started asking my, you know, essentially Ultron I've talked about as a concept, the God that understands the oracle of the entire organization. I've asked it to reply to people and do that weekend coaching based on not only what they self-reported, but that it sees in the data. And so we do think this could ultimately be done just by the replicant, but there's something for the human to set their own priorities in the morning. So maybe the end of the day could be done by the replicant, or it could make a first pass at it and say, hey, it's 7am. Here's what I think you should be working on.

56:52Tanay Kothari:You reply to it and say, yeah, those three things are low priority. I'll do those next week. And you missed this thing that Jason called me on the phone about last night from his car. That's actually top priority now. Now the Oracle is, you know, kind of like your coach. So I've been trying to frame it with the team that everybody gets an executive coach. Richard, I think you said at the beginning of the show, one way to think about it is, what are things rich people have that everybody could have? Rich people had chauffeurs. Then you got Uber. Rich people had summer homes or ski houses. And now they have Airbnb.

57:32Tanay Kothari:Rich people had executive assistants. Now they have agents. Rich people had executive coaches. but executive coaches cost five to$25 ,000 a month. Boards are happy to pay for it when a company raises a series A. I'm sure you've seen this or you gentlemen have had a board member say, you need an executive coach and you get too many complaints from people saying you're hardcore and you're not really communicating well with your team. Your ratings are low. You need an executive coach to help you. Now imagine everybody has an executive coach. To me, that's like a super inspiring thing for a person in their first job, like Oliver to, with his first full-time job at a school, to have an executive coach?

58:11Tanay Kothari:That doesn't exist in the world, an executive coach for a 23-year-old. It's not a thing. So that's how I look at it is executive coaching. It's a great question. All right, Oliver, let's go to your next skill. Oliver's three skills, and he's got many more, but we're only in month one of this. Yeah. I mean, these are just some that I'm excited about that have been working well. and I think these skills particularly the open claw agent you know using Opus using Kimmy have specifically you know done a lot of the heavy lifting so this is one that I'm really excited about I talked about this I believe last week it's the twist archivist which basically takes the job away from you know two hours we I think we discussed one hour last week of you know finding a good clip downloading the clip and editing the clip it can do all of this for you so we're really excited about this.

59:03Tanay Kothari:This is crazy. So I was able to make an agent that basically will go look through 15 years of this. We can start up clips, find a clip. Maybe there's a fun Travis clip. Maybe there's a clip of Jason talking about what he thinks the future of robotics will be. These are really fun clips. We've seen Jason's tweet about everyone in crypto should move to AI. Those are fun. So things like that, people like bringing in the past. So this was a skill, twist archivist, really excited about that. I actually did see today someone had their Open Call agent build in Visual Editor. So it can edit and basically it created a dashboard that looks like something like Premiere or CapCut.

59:42Tanay Kothari:So those are really exciting things. And Open Call, what I noticed here is what it can build for itself to be able to complete tasks. So it built the video downloader. It built the editor. It built the ability to put captions on and it built the ability to import things into Google Drive. So it's really cool. This is one of my favorites. this is um tremendous because i was gonna hire somebody uh to be the twist archivist like a full-time position so i was like okay we'll hire somebody for 60 70 80 grand it could be work from home whatever they're obsessed with startups and they just want to watch old episodes and clip it the problem is we could never find somebody who wanted that job it's like finding a librarian like the people who want to be librarians it's like a it's a really like needle in a haystack type job and if we did find somebody they'd be like well i want to work on the new episodes and the new hotness.

1:00:29Tanay Kothari:I don't want to work on that. So it'd be tedious. That's one of the things that I'm noticing is there are some tedious tasks that you would never, a human would never take, that the AI is more than happy to do. And this is one of them. And so 2 ,000 episodes into This Week in Startups, 250 episodes into All In, two episodes into This Week in AI, you start to have this archive and it's sitting there and there's all these nuggets in it, but you need to have judgment and then you need to do seven tests so i love this one um and then i think even having a layer where it tests and it learns and that's going to be the key is can this one because we don't have this one doing any kind of learning yet so i what i'd like you to do is have it learn or maybe it's a second skill where when it posts the clip it says that clip got a lot of engagement why did it get engagement and then it goes and finds more with more you know that have more qualities like the ones that previously worked if that makes sense i love it that pushes

1:01:32Jason Calacanis:it also to just have longer time horizons you know like which is a thing a lot of people are thinking about for their agents and if you can really close that loop now it might eventually get better and better at finding the right agents that will create the most engagement now of course it will maybe do some reward hacking and just find out well if you show certain body parts you get a lot of engagement and things like that so you need to be careful about what you allow it to pull in in terms of content but yeah that would be a really cool this one is just crazy uh

1:02:01Tanay Kothari:okay give us your last one there oliver before i show the last one i'd love to ask richard a little bit more about recursive ai i know that that's your your new project and i'd love to ask you you know you're focused on self-improving you know we're focused on self-improving at a little bit of a lower level, but I'd love to ask you a little bit more about that project and what the future lies there. Yeah.

1:02:23Jason Calacanis:I think overall AI is in this dual state right now where on the one hand, it's electricity-like and the other one, it's still research. And on the electricity, we know what to do to automate certain tasks. We give it some inputs X, we give it some outputs Y, we can train these models. It gets better and better. And we just like with electricity can infuse it into every different industry. But there's still this sort of elusive goal of building superintelligence, doing real research towards that. And what does that enable you to do? Well, you don't have to manually define the context. You don't need to do all this manual onboarding of an agent and just give it very high level complex goals and rewards and inputs.

1:03:04Jason Calacanis:And it will itself onboard into a complex code base. It itself will be able to deal with complex rewards that you give it, and it can eventually automate the scientific method itself, which is basically what allowed humanity to stop mucking around for hundreds of thousands of years and now create all these incredible technologies. And so what we're doing at Recursive is essentially automating the knowledge discovery piece and fully closing the loop on the ideation, implementation, and validation of AI research ideas and eventually taking that whole machinery and applying it to all future digital jobs, but eventually also science.

1:03:47Jason Calacanis:Most people don't say we want more scientists, but most people love good scientific breakthroughs of better batteries, better fusion, like functioning fusion reactors, better drug development and medications and cures for different diseases and eventually for aging. Most people love the outputs of research, And so they're aligned, similar to in healthcare, on caring about outputs. And I think a recursive self-improving superintelligence could eventually be the ultimate Eureka machine for humanity.

1:04:18Tanay Kothari:Tanay, how do you think about recursive learning and making your product whisper better and better for the users? How do you do that today? I'm curious.

1:04:30Richard Socher:So one of the actually hardest and most important problems to solve overall is having the feedback loop back into the system that is relatively human free. Because what that lets you do is what you're saying, right? Like, Oliver, you made this thing where you can, okay, now start to feed in the results of that and have it generate even better and better clips. But you would still need to have some level of discretion there. Like I saw it first posted in a Slack channel. You haven't let it run fully free yet because you don't know yet if it's going to do the right thing or not. But imagine if it starts doing the right thing and imagine if you can scale that up over millions of people, billions of people.

1:05:15Richard Socher:Whenever a system does that, it creates so much delight because that is also one of the skills we respect so much in people. It's like an extremely smart intern versus like a mid intern. Like what you realize the biggest difference is how quickly can you just learn from feedback and grow? Because you're kind of starting from like little to no experience at all. And so with Whisper, you see this one small thing we do where if Whisper makes a mistake and you manually go fix it with your keyboard, Whisper says like, hey, I added that word to your dictionary. Sorry, I made the mistake. Not going to happen again.

1:05:52Richard Socher:and whenever i see whenever people see that there's such a big smile on their face because that's something they've been dying to see from siri for like the last 15 years oh my god siri

1:06:02Tanay Kothari:doesn't know how to spell my fucking last name and we're like i i literally ask it to call my wife and it's like who and i'm like the number one in my favorites the person i communicate with more than anybody on the planet i just want to talk to her on the phone i'm on a ski lift dial her number it's so dumb siri is disgracia to the highest level show us your third skill here you're doing great work oliver oliver is one of my favorites top three right now for me high praise it's high it is high praise it is high praise actually i think once we when we started open claw the task i was trying to automate was the was our athena assistance task because these are pretty low level tasks, repeatable.

1:06:48Tanay Kothari:And, and, you know, we have one of the top Athena assistants, he does a great job, he can spend his time elsewhere. But so this, this task basically looks for podcasts, then looks at who their sponsors are. And this would help our sales team. What this agent does is it will basically go and look at what sponsors these other podcasts had and then for the last five episodes and it cycles through a list of different podcasts and then it will send them into our sales channel so then they can potentially reach out to those potential partners so this is a skill very tedious going through a lot of different youtube channels going through the transcriptions but now the agent can do it in a minute this is just another great example of a single task that's saving you know i would say we do this five times a week it's And we used to do it three times a week, I believe.

1:07:39Tanay Kothari:So now, you know... I was trying to do it five times a day, but just people don't want to do this task, so they do anything but this task because it's tedious.

1:07:47Jason Calacanis:I think this will be such a mainstay for the future. Like people always look back at past jobs and be like, why would you want to work manually with your hands in a field? Why would you want to weave clothes manually? Why would you want to be an SDR, sales development rep,

1:08:01Tanay Kothari:which is what this is?

1:08:01Jason Calacanis:Why would you want to drive a truck for hundreds of miles all by yourself, getting diabetes, being alone, and so on. People will never look back in 100 years and look at the jobs that most people do right now and be like, oh, I wish we had those back.

1:08:16Tanay Kothari:It's incredible. And then the next step it does is it looks in our pipe drive, which is a really cool CRM we use, and then says, are they in the CRM? What's the last date that they were contacted? And who is their account rep at mention them? So this has become really interesting because that was a task I always had, or now that we have a sales manager, Ricky, it was her task, which is, oh, when's the last time we talked to AWS? And, you know, we have Microsoft Azure and we have Gemini. Why don't we have AWS involved in the podcast? And it's like, oh, okay, we never called them. We haven't called them in two years.

1:08:54Tanay Kothari:You know, sometimes that happens. A lead will slip through the fingers or somebody was their sales rep and they left and then it didn't get reassigned. So all those things come out in the wash. three great skills. The Athena assistants are really interesting because you can go to athenawow.com and get a couple of weeks for free. I'm an investor in the company and I love it. They're really good at doing business process outsourcing, BPO. And then a bunch of the BPO companies, you can find this tweet, somebody on the research team, while we're talking about it, They also got caught up in this Citron madness and the dooming because they got caught up in this because they thought, oh, well, all these agents are going to get rid of business process outsourcing and India is going to lose all these jobs where all these intelligent people are.

1:09:47Tanay Kothari:Actually, I think it's the opposite. I think those people are going to be the ones who actually manage the agents and you're going to just find more and more work for them to do down the long tail of jobs. But there was a tweet that came out just talking about business process outsourcing, if you can find it or throw it in post.

1:10:04Jason Calacanis:Jason, what are some skills that you would love your AI agents to have, but they're still tripping over, like access to certain systems or capabilities?

1:10:14Tanay Kothari:So one of them is having editorial judgment. So this is something we are going to figure out with them, which is, can we get the assistant when it picks the title of the show or the best moment from a podcast? Will it actually pick the most interesting moment in the podcast to make a clip out of, as an example. Or if it's looking up potential partners, will it be able to handicap and qualify the lead? This lead is, you know, 90 % to close. They advertise on these four podcasts. Those have the same demographics. If they love those four, they're going to love us. This one's a 10 % chance and actually have that be more accurate than a human's assessment.

1:11:04Tanay Kothari:So a lot of times our salespeople are like, oh yeah, we shouldn't have, you know, this supplement company on the podcast because, you know, the supplement companies are going after pod, you know, they have products that cost$100 a year, not 10 ,000. So it's just better they are on Bill Simmons or, you know, Call Her Daddy than a niche podcast about technology or AI. It's just not aligned. Or this startup company that pitched us, how likely is it to raise a round of funding in the future? So will this clear market with venture capitalists? We get over 10 ,000 people applying for funding. I need to know which ones we should engage because they will pull through in the future.

1:11:49So those are the kind of, I call it judgment, artistic, knowing a joke's funny.

1:11:56Tanay Kothari:and there's a company called, is it Human and the Loot? Oh, Rent-A-Human, who we had on the podcast a couple of weeks ago. And Rent-A-Human is gonna allow your agent, when it gets to one of these moments and say like, I don't know which thumbnail is the best thumbnail for this episode. You could just rent a hundred humans, say here's four YouTube pages, click on of these four thumbnails, which one you think is the most interesting. And just say, click on a, and we wouldn't even tell it which one's most interesting. They just say, click on a thumbnail. Clicks on the thumbnail. And one of the four thumbnails is ours.

1:12:30Tanay Kothari:And we just see if we beat, you know, Mr. Beast and MK, you know, Marquez. Can we beat those, right? In the human test. So that's actually the next level for us. But we have so much more to do. At rentohuman.ai, 560 ,000 humans have made themselves available for rent. It sounds a little bit like Amazon Mechanical Turk.

1:12:50Jason Calacanis:Yeah, exactly. But maybe those guys will go into physical space and catch something for you or something.

1:12:55Tanay Kothari:Yes. What he showed to Nei on our podcast when he was on last week was you could, in Shibuya Station in Japan, you could have 10 humans say, use Whisperflow, now available in Japanese, and hold up a sign for$100 for two hours in Shibuya Station when everybody's walking by. and you could say okay find me 10 shibuya stations around the world and say whisper is out for android in the markets that have the most android users like maybe you know there's a one of the boroughs in brooklyn has the most and maybe queens has the most android user so you put it on the queen's train stations like crazy stuff like that that an ai wants to get done or today another example might be you want to know how to pronounce Calacanis or Tanei and you just have humans say it and read clips for you or something and you need humans to give you a feedback loop.

1:13:55Tanay Kothari:I don't know. That's what I think it's good at.

1:13:57Jason Calacanis:I think the open source models do give China a lot of soft power. A lot of countries that can't afford the high token costs that the closed models have right now are downloading the Chinese models. And so what used to be sort of Hollywood having a lot of soft power in the world and narrative and storytelling now become Chinese open source models.

1:14:21Tanay Kothari:That's an incredible point. Tane, why don't we have like a champion here in the US that's doing an open source model, do you think? So strange. Like, shouldn't we have like five of them by now?

1:14:31Richard Socher:You're talking about a big company trying to do it?

1:14:33Tanay Kothari:Yeah, like I know OpenAI released their like, you know 2.5 is now open source whatever things that don't matter in their mind yeah but you have these champions like half dozen serious ones in china and to richard's point thinking about soft power if you're going into africa south america the frontier markets the emerging markets and you say hey we want you to spend a billion dollars on tokens this year they're gonna be like we don't have a billion dollars spent on tokens and then china comes in and says yeah we can just put up some servers for you it's free for the first million tokens a day this is like really on a soft power basis a way to have and if you focus on their languages and their data sets this could be tremendous or do we have champions and nobody knows them it's just a capitalism thing right

1:15:20Richard Socher:because i think the way the way the economy companies work in in china at least from what i understand uh it tends to be less capitalistic because of how tied to the government you have to be. And in the US, you just are. Of course, OpenAI is not going to put their best models on the market because guess what? They make way more money from people paying$20,$200 to them than they would ever buy an API. And that is true for so many other businesses. And so that is the key thing that if you just do a cost-benefit analysis, you would cannibalize yourself if you try to open up APIs because APIs actually don't make as much money in the long term as owning the customer, having a recurring pay that's coming from them, and not having to just compete on price as a commodity in that whole LLM market.

1:16:21All right.

1:16:22Tanay Kothari:In breaking news, gentlemen, Breaking news, Tim Cook has announced as part of their on-shoring efforts that they are going to make the Mac Mini in Houston, Texas. Just next door to me, here's Tim Cook's tweet. As part of our$600 billion commitment, Mac Mini will be produced in the USA for the first time later this year. we're accelerating our progress even further, producing more AI servers and opening an all-new Apple Advanced Manufacturing Center for hands-on training. In addition to this, they have also said in the past that they're going to purchase 100 million chips from TSMC's Arizona factory, which is opening, I think, later this year.

1:17:06Tanay Kothari:And as part of this, the New York Times ran a story about About the tensions with Taiwan, it turns out Silicon Valley has been warned that the CCP, Chinese Communist Party, plans on taking military action in 2027. And that we should expect massive disruption and that these Silicon Valley companies have largely ignored this warning. I've heard from my sources that this 2027 action by the CCP has been pushed back to 2029 after Trump's out of office because China's a little bit concerned. Quote from the New York Times article, this invasion that could happen in this report could cut the supply of chips from Taiwan.

1:17:59Tanay Kothari:According to this story, cutting the supply of chips from Taiwan would lead to the largest economic crisis since the Great Depression. Richard, your thoughts on the growing tensions around Taiwan and Apple onshoring. I mean, this may be above all of our pay grade here, but it is the topic of the moment.

1:18:21Jason Calacanis:I do think trade is an incredible force for peace. and I'm kind of saddened to see sort of it being used more and more against other countries. I do think when you trade with someone, you're not going to attack them, right? Because you now shoot yourself in your foot, whether you're supplying things to them or you're getting things from them. It usually doesn't make sense to attack deep trading partners. And so I hope that sort of international trade cannot be completely reduced. But I also understand why, in terms of matters of national security, you do need to have certain crucial sectors to be more within your own borders.

1:19:13Tanay Kothari:Tane, any thoughts on the global chessboard and how this affects our business? Again, understand if it's something above all of our pay grades, but I think Richard makes a great point. Companies that trade together, companies that are in business together, they generally don't go to war. Companies that are not in business together and don't have that fabric, don't have that shared prosperity, they're more likely to go to war. So your take, Tanay, on Mac Minis, Taiwan, Fabs, all of it.

1:19:45Richard Socher:You know, if you look back over the last 200, 500 years or so, there's kind of this shift that keeps happening back and forth where you get more globalized and then you realize, oh, wait, there's a lot of risks. Then you try to be a lot more independent as a country and try to build a lot of things in-house. And then the world changes. You kind of forget the scars of the past and you start to get more globalized and more dependent on others. And I think that is what people are realizing today. because so much of the US GDP now is built on top of technology. So much of the technology is built on top of chips and those chips are in risk.

1:20:24Richard Socher:So even if that is a 5 % chance or less of that happening, that is a massive chance for a black swan event. And so I think just that threat itself presenting up and the fact that, okay, this is one of the biggest bottlenecks for technology all over the world, right? United States, it's big for India. It's even going to hurt China. And so you're seeing all of these countries trying to get more of these raw materials, more of the base on which everything else is built on top of just in-house. And as much as we would all love to live in a world that's peaceful and no countries attack each other and we all happily trade with each other forever after, that's just not the world we live in unfortunately so um my reaction to this is not a surprise that this is happening it's going to be incredibly sad if it does happen because there's such incredible talent in taiwan that's you know tsmc and in a number of regions of China as well.

1:21:33Richard Socher:Like China and US trade so much among each other. And there's going to be kind of hiccups that happen here because of this ongoing global power domination battle that all these countries are into. And you're just going to have a lot of casualties in different ways than previous wars.

1:21:55Tanay Kothari:Here's what's going to happen. I'll give you my prediction. Taiwan and China have a long history. China obviously wants to be respected as the owner in their mind of Taiwan. That's what they believe. They believe it's one country. Taiwan believes it's its own country. And the United States has had this amazing ambiguity, strategic ambiguity, saying, yeah, Taiwan's an amazing country, great leaders. and yeah, Taiwan and China have this amazing relationship that we cherish and we respect. And you have had this great ambiguity. But China, I think, wants to enforce its sovereignty and test if that is actually true and if the West will fight for Taiwan.

1:22:41Tanay Kothari:I can tell you that Taiwanese people are very proud and the rest of the world that's dependent on this is gonna take two or three steps over the coming years to avoid China getting control of those fabs. The first is they're building fabs here in the United States, as we talked about, Arizona, et cetera. That's going to take some time, but it's well underway. It should be moving faster. There should be a greater sense of urgency. The second is we're going to airlift every one of those engineers and everybody who works at those factories and give them United States citizenship or move them to another area outside, far enough outside of China's purview that they wouldn't invade.

1:23:19Tanay Kothari:What countries would China not want to invade? They wouldn't want to invade India, Australia, Korea, Japan. So those talented individuals could be given citizenship and be underwritten to go to those four or five nations very quickly. Just be given pure amnesty for them and their families to get the heck out of Dodge. And then there's the burn it down scenario. And I wouldn't be surprised after we airlift all of those fabs and dismantle them and send all the talent to the four corners of the earth to keep it away from China, if the Taiwanese people don't torture them. This sounds crazy and ludicrous, but if you were Taiwan and they said, we're coming there because of those fabs, not just because of sovereignty, not just because we want to exert our power and we believe Taiwan's ours.

1:24:08Tanay Kothari:If China's saying we want Taiwan for those fabs, what would the Taiwanese people do if they were actually facing innovation, if the ships were actually coming around they'd burn them to the ground before they would give them to the Chinese. This is the scenario. I know I sound like a loon perhaps, but I think that these scenarios are being planned. In that 5%, there's a 1 % chance in each of those five. There's probably five different plans. The first is to defend Taiwan. The first is to make peace. The second is to defend Taiwan. The third is to replicate Taiwan. The fourth is to get everything moved out of there.

1:24:43Tanay Kothari:And the fifth is burn it to the ground. That's like the crazy-o scenarios. You know the CIA, the leadership of Taiwan, and all the countries around it, Singapore, et cetera. And Bloomberg predicts a$10 trillion economic cost if the country, the sovereign country of Taiwan, gets invaded by China. This has been an amazing second episode of This Week in AI. Thank you to my guests. Where can people find out more about your companies today? And who are you hiring for? If you're hiring at all, maybe you're just firing people and you're going to be a solo shop of four people or something. I assume you're hiring.

1:25:28Tanay Kothari:Who are you hiring for? We're hiring a lot of people.

1:25:32Richard Socher:I think we went from 20 to 45 in the last three months. Wow, that's big. You raised some money, yeah. Yeah, and we're hiring for talent across every single software engineering role. Great. Building our sales team, everybody in growth, customer support, marketing, and all the roles are up on our website, which is whisperflow.ai. That's W-I-S-P-R, F-L-O-W. And you can find us there and follow me on LinkedIn or Twitter.

1:26:08Tanay Kothari:Perfect. Richard, people want to get your money as an investor. How do they reach you? And then tell us what you.com is offering.

1:26:18Jason Calacanis:So yeah, AIX Ventures. Yeah, you can just follow me on Twitter and X, Richard Socher, ping me there for your company ideas. And then for you.com, we're hiring a ton of folks in sales, businesses ramping.

1:26:35Tanay Kothari:Explain to people the business of you.com.

1:26:38Jason Calacanis:Happy to, yeah. We build AI search infrastructure. So whenever you want your LLM to be up-to-date, accurate, and have citations, we are essentially the Google for LLMs, the search for your large language model. We have customers like ThoughtSpot, OpenAI, Databricks, Salesforce, Winsurf, Accenture. So they can't get this through an API from Google.

1:27:00Tanay Kothari:There's no search API anymore for Google. Yahoo got rid of Search Monkey back in the day. It's really just you and Brave are the only two people who offer anything like this, yeah? That's right.

1:27:10Jason Calacanis:And we offer all kinds of other complex content APIs. Because essentially the way search is very different to how people search. LLMs can read a ton more content before they summarize you these kinds of links that you would have gotten from Google, very short snippets. So we can do both very fast, very short, very accurate, slower, but cheaper, and just like around the Pareto Optimal Frontier. So we're hiring search engineers, salespeople, marketing folks. Marketing is something that I think is actually a big role where ultimately a lot of things aren't zero-sum that people think are zero-sum, but human attention is zero-sum.

1:27:52Jason Calacanis:And so we have to get better and better at marketing. And so I think that's something that we're looking forward to hiring too.

1:27:58Tanay Kothari:All right, everybody. We will see you next time on This Week in AI coming out every Wednesday or Thursday. We're figuring it out thisweekina.ai.