SPEAKER_00: Open source is going to win it all. Acme litigators and Delta litigators are both using Harvey, let's say. Harvey now is like, not only do we not trust OpenAI with this, we need to have our own model. Harvey was probably spending, if I had to guess, I'm going to say $10 million a month

SPEAKER_03: with OpenAI. 99% of their spend is coming off the frontier models. They don't want to give

SPEAKER_00: their intelligence to somebody who wants to build the final company. All right, everybody,

SPEAKER_06: welcome back to This Week in Startups. Roy J. Cloud here with Lon Harris, episode 20.

SPEAKER_10: How's that even possible? Year 17 of the show. We're in sci-fi year numbers now. We're in sci-fi year numbers. It's getting crazy. We're just literally like, yeah, it feels like this is

SPEAKER_12: Blade Runner or something like that. And it's appropriate, given how great these bots are doing. I just want to start the show off. You started playing with GrokBot. I owe Elon $200

SPEAKER_14: now. I got to pay. He's making me pony up. All right, yeah, do it. You can sign up for a

SPEAKER_00: corporate. I'm going to do it. I'm going to do it. Yeah, give us your explanation and what you're using it for. And then I'll tell you what I'm using it for. To me, it feels like when OpenClaw was

SPEAKER_16: brand new and you had us all sign up and we were all in Slack, it was what the promise of that was.

SPEAKER_18: But OpenClaw in a virtual machine and like some data center somewhere, it couldn't connect to everything it needed to connect to. It was too walled off. So it wasn't really that useful. You kept having to remind it things and log it in this API key that GrokBot is automatic. You just

SPEAKER_16: tell it what you want. It can find whatever. It's got its own computer. Like just today, I asked it, you know, to look at the news and cross-reference it to X, which is a thing I always wanted and gaffed my old OpenClaw assistant to do, but he couldn't ever get into X reliably. He would try to backdoor and it would never work. It was always very time consuming and painful. GrokBot just is like,

SPEAKER_18: hey, here, I'm going to bring up my computer. It actually shows you what it's seeing. It goes, just log into X for me. I logged in through our This Week in AI account and it's just off to the radius. It sees everything I could see. It's logged in. It's pulling tweets. It's referencing things. It's very intuitive. You just need to tell it in plain language what you want. It's very impressive.

SPEAKER_12: Yeah. The interface is really interesting. I'll just share mine. Who cares? Let's see your GrokBot. Show us your GrokBot. Well, here's my GrokBot and what I'm doing. So we have a thing, tactical and practical. If you make it rhyme, people will remember it. So I want to do more tactical and practical talks here on the show. And, you know, I've been just having a conversation with it, looking for you to make talks, startup founders focused on tactical and practical advice for them to scale their businesses, find product market fit, and generally grow, hiring, sales, paid marketing, fundraising, content marketing, all that good stuff. And it just went to work finding these things and linking to them and building it out, building it out. And then I said, hey, well, give me other ideas for topics and then send it to the tactical practical room on Slack every 48 hours. And then ask Ismael on my team, who's in Tokyo right now, representing us for Founder University. And I want him to be part of this motion. And then I said, also, when you find a great speaker, I want you to give me their X account and LinkedIn accounts for the report. And then I said, please always send a Google sheet, yada, yada, yada. Like you said, you easily connect it. And it's just doing this every day for me. And then I asked it like, hey, do me a favor, find me things on Hacker News and Reddit, because those are where founders are actually talking about their problems. Like, for example, it's like, here's AI unit economics, pricing credits, token spend. And people really care about that. I hadn't thought about that as something that founders would be interested in. And this thing is running over and over. Then I made an AI trends one every night for this week in AI, do a scan, tell me what the top stories are. It's nailing it. Then I said, give me some competitive intelligence. Right. And so I said, for our newsletter business, I am so out of it. I want to know what every other organization is doing. Politico, Economist, Forbes, Axios, Semaphore. And it is going out, as you can see.

SPEAKER_00: And then I told her, follow all of the people who are the most important people in these organizations. And then you were talking about 404 Media the other day. I just asked it like, tell me about this publication. And it gave me all the founders. It gave me their LinkedIn, their X, their Blue Sky, their MasterDone. I followed them everywhere. And then I said, just tell me what they're talking about publicly. Then tell me all the event staff, because I want to hire some event staff. So I was like, okay, let's just figure out who's doing

SPEAKER_12: events at other publications. And this thing is just working for me over and over again. And it makes

SPEAKER_00: a report on just what people are doing at Skift, which I'm an investor in, Newcomer, Punchbowl. It found all these for me. I didn't have to tell it to find these. And it really is, it feels less brittle, which is weird to say, than OpenClaw, which was super brittle, broke all the time, can't get on X, can't figure out LinkedIn. This thing is just rock solid. So for me, I am a fan of systems over goals. The goal is to have the best news stories

SPEAKER_12: here. The goal is to understand what other people are doing for events and newsletters in case we want to incorporate it into what our plans are. My goal is to find another co-host here. Alex is working on his projects. And he'll be back for sitting in for me when I'm on vacation from time time. But I need another co-host for all these pods. You're doing great. But we just need to have a roster. So I was like, go find me everybody and then keep finding them. Figure out CNBC hosts who are getting laid off or who are no longer there. Find me people who are working for The Verge, other places, and no longer working there. Find me some talent. And then figure out what their, I literally had it, figure out what their politics are. Figure out their DEI status. Figure out all this stuff, their point of view. Because like literally when you co-host somebody, like now in today's environment, I have Molly Wood on, I lose like a third of the audience because they hate her lib stuff. I gain a third of the audience back because she's, you know, hates Trump and whatever. Same thing with Alex back and forth. You. And I'm just like, just give me this overview. It's so great.

SPEAKER_00: It just gets it done. And then when you get back to your mobile phone, it's the same thing going on. It runs everything in the cloud. So unlike Claude Coorg where you have to have your app open and

SPEAKER_16: whatever, it's just running all the time. Yeah. We've discovered Claude, it can be a little kludgy trying to share things across networks, across people. A lot of times like, well, I can't see that. I can't share that. So far with GrokBot, it is all very, very seamless. And across

SPEAKER_21: multiple apps, co-collating, collecting things, I've been very impressed with it so far. So I'm

SPEAKER_31: excited to spend the 200. So this is my little mini talk here. Systems over goals. What does it

SPEAKER_12: mean? You have a goal. You want to have, you want to find another co-host. You, you want to do more events and you want to hire an events production person. Okay. You want to have the best stories and you want to find a really interesting speakers for, you know, founder university and launch accelerator. That's the goal. Okay. What system can you build? And the system I built is for these to go do the research every day, real time, every other day, present it to somebody on my team, have that team member rank them, put it into a Google sheet. And then we have a whole bunch of other steps we do, which I won't get into because it gets kind of boring. But if we can remove, let's say 10 hours of research a week, five hours of research a week, and then start with research that's better than human. Okay. Then if when we're inviting people, we can have it, find them quicker, find their connections, invite them, book them on a date. That's typically three hours of work per guest. Yeah. So finding the guests, every guest equals five to 10 hours of research. Every guest equals three hours of inviting multiple guests to get them involved. You put all that together. When a guest comes on this program, it represents five to 10 hours, the end. Um, and, and that's what this stuff. And that's why goal defining the goal is great, but then it's fine to give your, your bot that, whatever one you're using, your agent, but you want to build a system that doesn't break and doesn't require a human. Uh, and the system then ensures it happens. Everybody has to become a systems thinker. What I found is creatives like yourself, Lon, less systems thinking, more

SPEAKER_00: systems thinking, less taste. And so now I'm figuring out as a leader, how to get the people with taste to learn systems thinking. And I've got to figure out, I don't have a solution yet. People who have systems to learn taste. And that's going to be the final piece for me is to have that taste algorithm. Yeah. You know, which you and I have by default for many years of experience. Yeah.

SPEAKER_33: At my firm launch, the pace never slows down. I'm doing my best to just try and keep up.

SPEAKER_03: And we've tried all the expected efficiency and productivity tools. And a lot of these apps end up adding friction and they slow you down. There's one tool that I am obsessed with and that every person in my company gets trained on and has to use from day one. It's called Superhuman Go. Superhuman Go is an AI chat. That's always around when you need it. It's context aware. So it knows what you're working on and it works inside the apps and tools and websites that we already use. So no matter where we are in Slack or Notion, it's in our workflow. If you're trying to just quickly scan some notes before a big meeting, check your travel dates before you set a meeting, or hey, you need to get your grammar correct, or you want to have your emails nice and tight. All of that Superhuman Go can jump in and help without needing a ton of catch up or permissions. You know, I was an investor in Superhuman Raul's company from back in the day, Grammarly, Coda. That's all come together to become Superhuman Go. Find out more. You're going to just go to superhuman.com. I love this product and I've loved it for years.

SPEAKER_18: I think it's interesting. It's the same kind of conversation we have a lot of the time with AI founders, especially on the This Week in AI show. It's about where does the human belong

SPEAKER_21: in the loop? We all get that we need a human in the loop. AI can't do it all by itself yet, maybe one day soon. But it's like, that's the strategy. It's like, where does the human need to be there? And where can you just kind of sit back and let the bot go? And I think it changes

SPEAKER_43: all the time. It's RL. It's in the reinforcement learning phase. And so I have an SDR bot that looks for who's sponsoring other podcasts, other events in business, in categories. And then it looks at our CRM system. I think we use Pipedrive right now, although we'll probably move to just

SPEAKER_44: writing our own CRM system. Sorry to Pipedrive, but that's what my team is advising me to do right

SPEAKER_46: now. I'm like, okay. So I'm going to have to have a conversation with Pipedrive. I don't think we're a major customer for them, but they'll be, you know, their Pipedrive is going to be just

SPEAKER_12: fine. I believe in that. No, I want to talk to them about like, Hey, do, is there a way for us to keep using this, you know, and does it have a good enough API and agentic stuff? So putting all that aside, I need to get like the sales team to look at what the agents are doing and give them feedback. And I always get resistance. Why do I have to do this? Is this necessary? I don't like

David Friedberg: to work this way. I have another modality of work. And I said, guys, let me just stop everybody

SPEAKER_12: right now. I have a new idea. This is a new idea, Lon. Pretend I'm the founder and CEO of the company.

David Friedberg: Pretend. And that I get to decide some things. And one of the things in this, you know,

SPEAKER_12: what do they call it in the Marvel universe? Like a different timeline? The multiverse. Yeah. Like in this version of the multiverse, I actually have authority to make a strategic

SPEAKER_59: decision inside of the company I own 100% of. Yeah. We're in, we're in universe 420 or whatever.

SPEAKER_12: Yeah. And in 420, yeah. Wolverine has the yellow costume and I'm the boss and I get to make a

SPEAKER_63: decision once in a while. You're in charge. Yeah. And I'm in charge of my own company.

SPEAKER_16: They're old school. Our sales team, they're old. They're Willie Loman guys. They just want to get out there with the briefcase and the hat and your door. They're literally going to the shoe repair

SPEAKER_65: shop. Yeah. And the guy's like, oh, yeah, give me the shoes. I got your other pair here. Yeah. I put

SPEAKER_13: the steel on them. This time, they're not going to wear out. Yeah. They're dumping the dirt on the carpet and vacuuming it up. Yeah. Putting their foot in. Yeah. Yeah. Might I interest you? Yeah.

SPEAKER_12: All right. We got some news on the docket. There was a Harvey story. And then here we go with the IPOs again. But those are the two I wanted to cover. All right. Yeah. Well, let's start with

SPEAKER_16: OpenAI. They apparently were now told they're going to be a public company by 2027 or even sooner.

SPEAKER_18: And in all hands on Wednesday, CFO Sarah Friar told Stafford's the AI lab plans to go public next year, maybe even this year. Here's I thought her framing was really interesting. She frames it as the IPO. It's not a finish line. It is a milestone. It's another fundraise. She also noticed that the God noted that the company has flexibility as they raised 122 billion in cash back in March. OpenAI confidentially filed their prospectus in June, but they're in a tight race for users and attention with rivals Anthropic. The latest volley, OpenAI now pledges not to retain data, Jason, from businesses that are using its models. Here's the quote. Interesting. We've heard very loud and clear from businesses that this is important. They often have their own commitments that they have made to their customers. If you think about somebody who is serving other enterprises who are trusting them with incredibly sensitive data, that's Aaliyah

SPEAKER_71: Howes, OpenAI's head of product policy. We all know they're growing like weeds. What we

SPEAKER_12: actually need to know is what their churn is. Who's using it less? Who is hitting the brakes and then why? That data is not coming out and it's not even coming out in the IPO. They're never going to share the churn data. When companies become very robust, they kind of get enough institutional holdings that

SPEAKER_74: a company like Netflix or Verizon has to start reporting on churn. I think these guys are going

SPEAKER_12: to be able to, Anthropics of the world's, Grox of the world, OpenAI's, Anthropics. They're not going

SPEAKER_74: to need to give us how many people are unsubscribing or how those trends are going for the major accounts. That's where I want to know the truth. The second place I want to know is how much does

David Friedberg: it cost to serve this stuff up? You know that funny Italian guy who was on the show with the

SPEAKER_13: bad teeth laughing hysterically. He's laughing hysterically. Yeah, the meme. I saw you share

SPEAKER_12: this meme. Yeah. When I share this meme, you can pull it up. It's literally the guy doing the bit and he's laughing about, he's paying, you know, 20 bucks a month or 200, he's paying 200 bucks a month for Anthropics per pro tier, 2,400 a year. And you can just play it in the background here.

SPEAKER_03: Yeah, I'm pulling it up right now. It is, here he is. I run code all day. Agents,

SPEAKER_85: the sub agents, the works. Okay. End of the month, I check the meter. I check the meter. He slams a hand on the thing. $8,000 of computer.

SPEAKER_90: Yeah, the audio is, he's like, he's almost like expiring from laughing so hard.

SPEAKER_92: It's so crazy. $7,800.

SPEAKER_94: I'm sorry. That's how he's screeching. Yeah. He's like, then he goes on and on. Yeah.

SPEAKER_96: I followed the money bottom of rung and tropic. And then he goes on. I spent 71 cents of compute

SPEAKER_97: per dollar, $40 per dollar on me. And then he goes back to like Nvidia. Microsoft and Nvidia.

SPEAKER_98: Yes. All the way up the stack. All of them paying one another. Yeah.

SPEAKER_00: That's when this whole thing is going to have to face the music. Right. We're getting close to the face the music moment. I think because they know they have to face the music and explain profitability, they'll have a reasonable story, but there's a gap here between how much, you know, people are using and how much they're spending. Yeah. And then there's some people token maxing, but I believe

SPEAKER_12: people now are watching the meter. People have started watching the meter. So this is exactly what I saw up close and personal with Uber in the price wars between Lyft and Uber. And then there were other competitors like Sidecar and then DoorDash, Grubhub, Postmates. They were, you know, talking about this incredible growth. They were moving to every city, but then we knew internally, hey, across these companies, we're losing $5 a ride. Right. And there was two ways to understand. Like we're paying drivers a $500 bonus if they hit a hundred rides a week. So if they do a hundred rides, we'll give them five hundred bucks, an extra $5 per ride. But that wasn't in the riding data. It was like a marketing bonus. Sure. But then Lyft was giving $600. And then the drivers are smart. They're like, oh, you guys are idiots. I'm going to, next week I'm doing Lyft. The week after I'm doing Uber, I hit my incentive. I flip to

SPEAKER_21: the next person. Okay. Yeah. YOLO. I'll DoorDash. If it's possible to figure out a system with these gig apps, people are going to work the system for sure. And one person figures it out and it's on Reddit and now

SPEAKER_12: everybody's working the system. Exactly. Bingo. So what happens is over time, there's this famous Deidre Bosa moment and we should play it in a future episode, whoever this week in startups archivist is, where I said to her, Deidre, you asked me the question, let me answer it. And she's great. And she's now doing her own spinoff and going to be a media entrepreneur. So congratulations to her. She is like, but they're money losing their money losing. I said, okay, let's look at the quarter. There was a billion rides. Incredible. They lost 2 billion. That's 1 billion rides divided into 2 billion is $2 per ride. It was like $6 per ride or $7 per ride. Everybody was losing. Yeah. You pay seven bucks for a ride. That was like, this should be $30 in a cab or, you know, $15 in a cab, whatever it was. They were losing that money to kill their competitors, build their base, attract investors, and essentially do marketing. Instead of giving the money to the network TV shows, the concept was, well, why don't we just give the money to the customers in the form of a discount, have them become addicted to this, and then we can slowly raise the price up to what it actually needs to be. We'll lose 20% of the people who will only use this service because of the discount. We'll keep the other 80%. Game over, right? So venture capital funded it. And instead of giving it to radio, TV, cable, magazines, newspaper ads, the internal discussion was, yeah, just pass this, just give it directly to the customer. That's what's happening right now with these enterprise

SPEAKER_16: products. That's going to unwind. Yeah. I mean, when it does. With Uber, that makes a lot of sense.

SPEAKER_18: Like we all did get addicted and got used to like, if you go out night out, you don't want to drive drunk home. Just got, you use Uber. We all, it got drilled into everybody's brains and now it's there permanently. With AI compute though, there are these alternatives, models, there are other things you can do. Highly competitive. So it doesn't feel exactly the same. Like they're not, Anthropics not killing all of its competitors and embedding in like, you got to use Claude. Like I'm perfectly happy with GrokBot if it works better, you know, like I don't think there is.

SPEAKER_12: And you will be perfectly hot, perfectly okay in all likelihood when Zuck comes out with something similar to GrokBot, the easy to use assistant. Sure. That just abstracts everything. Let alone when new competitors come out and you can go on Amazon web services, Google cloud, and they will provision a bot and a harness and there'll be an open source

SPEAKER_74: harness like WordPress. It's just going to all come apart. Exactly. And it's going to drive

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SPEAKER_74: twist. That's what I'm looking for. There was a moment in this story where you were talking about, give me the quote one more time about, we hear loud and clear that people are, and then segue

SPEAKER_16: into the Harvey story with that. Yes. It's from Aaliyah Howes. She's OpenAI's head of product policy.

SPEAKER_18: We've heard very loud and clear from businesses that this is important. They often have their own commitments that they have to make that they have made to their customers if you think about somebody who is serving other enterprises or trusting them with incredibly sensitive data. And so that does segue very neatly into our other big story today, which is Harvey, the legal AI company, released their very first in-house proprietary model. It helps Harvey's software take on more tasks that were typically performed by lawyers. It is, Jason, a post-trained, open-weight model built on top of Kimi K3. So as you requested, we did pull some clips. This is sort of what you've been predicting all along, which is application layer companies, companies that have this incredibly valuable expert data. They don't want to turn that over to the anthropics and open AIs of the world. They're going to use it themselves to build their own models.

SPEAKER_16: Yes. And, uh, did you say you have a clip? I, we have two, you pick your pick. We have a twist clip on this and we have an all in clip on this dealer's choice. All right. I'll take a victory

SPEAKER_13: fap, but let's keep it short here. Play it at like one and a half speed. All right. Let's get moving

SPEAKER_130: through. Let's take a look. Uh, well, yeah, here we'll, we'll pull up the, the, the twist clip here.

SPEAKER_46: I believe first. Thank you for putting it on one five. Need a little bit of a haircut there.

SPEAKER_134: You need to start working on frontier model. You need to get off the frontier models and use open source ones and own your content and not educate them to the extent you can. And I believe that'll be the trend of 2027 is startups are already doing it. If you give Sam, who is a, you know, sharp elbow guy and he's got to figure out how to fill in a $1 trillion market cap, he's going to do exactly what anthropocit or Microsoft or Facebook, which is, he's going to look at the applications coming in all of those Y Combinator companies who give, who take that deal. They're studying every one of their token usage. They're studying what they're doing. And then they will pick the top five in terms of success and incorporate it as free product into their platform. This is your final warning. Don't trust the platforms. When somebody comes to you with free tokens, you know, free anything, there's no free in life. There's no free beer. There's no free pizza. There's always a price.

SPEAKER_16: And then the second one's from all in, let me do a good victory fat there.

SPEAKER_21: Oh yeah. This one was right before July 4th. Actually, this was the July 4th. All in, as you could tell from your casual laid back, uh, Honolulu vibes in the clip here.

SPEAKER_139: Oh yeah. That's when I had my beard and everybody loved this when I was in Hawaii.

SPEAKER_21: I gotta say, I think this is a good look. I'm not just trying to butter you. I think this is, I like this. This is a good J Cal. I think you should go with this one for a while.

SPEAKER_141: What do I like? A Tom Selleck type situation in Hawaii?

SPEAKER_71: Yeah. It's a little bit, it's a little Magnum PI. Yeah.

SPEAKER_142: Okay. Magnum PI.

SPEAKER_134: To the platform. There is no free pizza. There's no free beer. When somebody like Sam Altman comes to you and says, here's some free tokens, your alarm should go up. Zuckerberg did the same thing. He said, Hey, I'm going to give people a bunch of access. I'm going to give them money. Come to the Facebook platform. Nobody who went to bed with Microsoft in the eighties, Facebook in the two thousands or Sam Altman now in the 2020s did not wake up with their throat slit. This is a message to founders. If you partner with any of these people, they will slit your throat and take your business wholesale. There is nothing to discuss here.

SPEAKER_48: Don't trust them. Use your own models. Yeah. We'll link to it. You guys can go check it out.

SPEAKER_52: There's just the two that we could dig up 10 minutes. I mean, this is the, this is the system I want to build next is like,

SPEAKER_12: I need to have an archive of twists and archive of all in with all of my opinions. And then I want our agent to look at how right or wrong I was and give me two lists. Here's the,

SPEAKER_74: here's where you blew it. Here's your pro professor Galloway moments. This is your

SPEAKER_149: professor Galloway. You've got money, Jim Kramer moments. Yeah. This is my cramp. Yeah. My reverse Kramer, my professor cold takes, you totally blew it. And then here's your best Jake.

SPEAKER_12: Like the on fire. No straconis, a no straconis index. Yeah. And a, and a professor Galloway.

SPEAKER_16: Give me those two. I do for this week in AI, I do have a full, like a clawed skill that we've trained on every episode and I can just go in and ask it anything. And it's brilliant. It is a

SPEAKER_12: little time consuming. But I want the bot to go through and say, give, make a summary of Jason's opinions, his hot takes from each episode and then have an index just of the takes, not the full transcript. Right. So then once you have the takes, then take the takes. How many times you said it,

SPEAKER_154: how did his take change over time? It's rate the takes. It's a take rate. Rate the take.

SPEAKER_00: Then I want you to build, how did it change over time? So did my opinion on Apple, Facebook, whatever change over time? What were the key moments? You know, was I hypocritical, you know, then based on the last six months of news or three months of news of what actually happened with Apple's product, did they ever launch a new iPhone? Remember I was very critical of Tim Cook for a

SPEAKER_37: long period of time. Stock went up. He bought back all the stock, bought back half the equity in the company, I understand, like $100 billion, $200 billion in buyback. They never really came out

SPEAKER_12: with a killer product post the Steve Jobs roadmap. So, you know, right about that, no product ever emerged. Wrong about the stock price because it went. So I need that. So for my team, if you can do that, that would be amazing. And we're working on this kind of stuff. So anyway, what you're seeing with the Harvey thing, I said this two years ago, if you can find the two-year clip where I said when I was in Tahoe skiing, and I think you pulled it for a previous all-in, and I said two years ago, there's a chance that open source is going to win the AI race. I said it two effing years ago.

SPEAKER_37: I think what we learned in 2023 was that the language models are starting to hit parity very quickly, and that the real value is going to be in, and it may even become commodities, and open source may win the day. So then I think the winner is folks who have the training data.

SPEAKER_12: Give myself a pat on the back that you heard it live. That clip was like, I was shocked by that. I was like, really? Like in the age of chat GPT 3.5, 2.5, I said on the pod,

SPEAKER_48: filibuster here a bit. I said on the pod, open source is going to win the day. And I think we talked about that four weeks ago on All In or five weeks ago. That was like a very interesting moment

SPEAKER_74: because that's what's happened now. The evidence. So back to my like Jason's takes and just holding

SPEAKER_46: me accountable to my takes. And my Professor Icicle takes Prof G and Macy's is going to beat Amazon

SPEAKER_12: and Uber and Tesla and Robinhood are going to zero and buy foreign stocks, not US stocks, like all those terrible takes. I need to know when I had a bad take and when I had a great take,

SPEAKER_163: like a Nostraconis level take. That was a Nostraconis moment for me. And anyway, we'll

SPEAKER_14: find it for the VOD folks. I don't know if I can pull it up here live. So I had mentioned when I

SPEAKER_37: talked to Lovable and Eleven Labs, they're huge, huge customers of the Frontier Labs. Frontier Labs can get competitive with them, just like Figma and Design Cursor and Claude Code, Figma and Claude

SPEAKER_12: Design. They are, I kind of asked them on those interviews when I was in Paris in July, hey, are you making your own models? Are you doing that kind of stuff? And they're like, yeah,

SPEAKER_00: we're looking at that. Here it is. Harvey, pull up the story. Harvey is making their own model. They forked an open source model. They're doing their own training and they are working on

SPEAKER_13: behalf of their clients. This is the story. Over the past six months, Harvey's research agenda has

SPEAKER_18: focused on two goals. One, building frontier legal intelligence using open weight models and two, creating systems to allow law firms to build their own specialized models and their own intelligence. Today, we're sharing an update on that research effort, including initial results from our first post-train model, which we're calling Harvey Tenet. Harvey Tenet is a Kimmy K3 base that we post-train together with Fireworks Research for long horizon legal work. In addition, it incorporates harness improvements to make training and test execution more effective. Our initial work shows

SPEAKER_21: promising results for both performance and cost efficiency. I'm here with Keith Pires, the founder

SPEAKER_03: of Lightfield. They are an AI native CRM that builds and updates itself in real time from your email, your calendar, your Slack, your meeting notes, all those things that you keep misplacing. Welcome to the program. Keith. Great to be here. Let's talk about Lightfield, your CRM. That is AI first. What can I do with it? Now that I've got it installed, now that it's pulled in all my data, what is it going to do for me? Because no CRM system

SPEAKER_168: ever had AI built in it. It's true. So the first thing we'll do is handle all of your busy work. You know, all of the emails you promised customers, all of the things that you were supposed to get to, it'll draft all of your follow-ups, send them on your behalf. Second, it's going to go and look at your happiest companies, your happiest customers. Learn from your happiest customers through product usage, feedback, sentiment, and generate you lists of new customers. It'll find contacts for you too. And then third, it'll draft communication for you to send to those folks over email, LinkedIn, phone, whatever it is. So it'll sort of keep proactively building out your pipeline. And then last, I would say, this is maybe the most non-CRM thing, it'll keep your entire company informed and what your customers want, need, and are willing to buy. So you always have tighter

SPEAKER_171: alignment across product and engineering. So lightfield.app is going to tell me what I'm

SPEAKER_43: doing right. It's going to tell me which customers I've forgotten about. And then everybody on the team doesn't have to be a slave to the CRM filling in all these fields. Go ahead and check out lightfield.app.

Chamath Palihapitiya: Okay. So now you have to ask yourself, who were the original backers? Who? Oh, I've looked this up. Who are the original backers? I've, I've looked this up.

SPEAKER_177: Of Harvey and who were their original, uh, frontier model providers? Who were,

SPEAKER_16: who were Harvey's original backers? I'm, I'm looking at this on harmonic.ai. Thanks to our friends at harmonic.ai for allowing me to

SPEAKER_18: so quickly pull that up, uh, a $200 million, uh, investment. Oh, that's, that's, that's March. Yeah. March 25th, 2026, 200 million at 11 billion valuation co-led by GIC and Sequoia capital with a 16 Z co to conviction. Go backwards.

SPEAKER_181: Elad Gill, the backseat early rounds. Oh, okay. Who are, who are early rounds is where we want to go

SPEAKER_182: to that. See, this is what harmonics going to help us with. Yes. If you start getting their

SPEAKER_28: earliest backers, original capital and original models, both ran through this company.

SPEAKER_18: Open AI was the day one believer. They led the $5 million seed round and also gave the founders early access to GPT four, which was a massive headstart in building legal specific fine tuning before anyone else had the model. There you go, Jason. Thanks. Thank you. Harmonic. Yes.

SPEAKER_187: Thank you. Harmonic AI. There you go. $5 million. First investment. Yes. One of the open AI startup

Chamath Palihapitiya: funds first for investments. Yeah. They led the seed round. They led the seed round. Now here we are.

SPEAKER_71: It was the open AI startup fund. Uh, other early backers included Jeff Dean, head of Google AI,

SPEAKER_142: by the way. Okay. Now let's just pause here. Put the two stories together. Sarah Fryer,

SPEAKER_12: CFO of open AI says, Hey, we hear you loud and clear. Harvey saying, Hey, by the way, um, just want to let everybody know six months ago, we started using these open source models. We started building our own and we're building our own for our clients so that our clients don't share the intelligence. So if you're a litigation firm, a, and your top competitor litigation firm, B. So Acme litigators

SPEAKER_00: and Delta litigators are both using Harvey. Let's say Harvey now is like, not only do we not trust open AI with this, we need to have our own model. We need to build a fortress. We're going to, and we'll have our castle. It's going to have a big wall run. We're going to build two keeps, one keep for Acme litigators, one keep for Delta litigators. So y'all are going to get your own litigation data, all your internal memos, all your emails, all of your zoom calls about this. We'll feed your local model for you, but it's not going to make it to our castle. You're in your own keep, defend your keep. And the two keeps will not share data. Right. This is what's going to happen to the frontier models. When they go public, you're going to have this headwind of which of

SPEAKER_03: these customers Harvey was probably spending. If I had to guess, I'm going to say 10 million a month

SPEAKER_37: with open AI. Wow. I would say lovable or 11 labs or, you know, cursor, those level of companies also probably were spending 10 or 20 or 30 million dollars a month with the open AIs and anthropics

SPEAKER_03: of the world. Those customers, let me be clear, are going to move their spend. 99% of their spend is coming off the frontier models. And any spend they do have with the frontier models is likely,

SPEAKER_00: even if it's like 5% of their spend to be them doing distillation. It's going to be them doing distillation. In other words, they're going to be spending 5 million a year or 1 million a month in order to pull information out of the frontier models. Open source is going to win it all. Open source is going to win it all. There you go. I'm saying it now definitively. Two and a half years ago, three years ago, I said, you know, open source could win it. Now I'm telling you, open source is going to win it all. What do I mean by win it all? The majority of tokens, the overwhelming majority of tokens in corporate America will not be on the frontier models. It will be inside those enterprises. Why? They don't want to give their intelligence to somebody who wants to build the final company. As Gavin Baker discussed two weeks ago on All In. Right. If, if Anthrop is going to pop off at cocktail parties or internally telling people, and I believe it's true, they say it's not true. I believe Gavin. It sounds like something. If they're going to say

SPEAKER_21: we're the last company. It sounds like something they would say. To me, it sounds like something I can imagine them saying. It's like, well, if we're being honest, there's a good chance we're going to be the last private company on earth. Like, I believe it. I believe it. Sure. Why wouldn't there

SPEAKER_25: be a, I mean, there, there was a final company in Wally. It's a great science fiction premise.

SPEAKER_199: That one company. Yeah. Buy and grow. I don't remember what it's called. Yeah. Buy and grow,

SPEAKER_200: whatever. It's like the Walmart. Thank you to Jacob for that. We got a guest. I want to bring

SPEAKER_18: up our, our first guest. He is, he is the co-founder and CEO of the AI powered free, speaking of free things that are free, the free AI powered voice dictation app, Willow.

SPEAKER_21: Please welcome Alan Guo. Thanks for joining us, Alan. Okay. We, we made, we made Alan wait,

SPEAKER_12: but Alan is here now. Uh, welcome to the program, Alan. This is a whisper competitor. Free is the

SPEAKER_207: model. It's free to use. Well, then how do you make money? Hmm. Yeah. I'm happy to tell you more,

SPEAKER_209: Jason. First of all, I'm excited to be on this podcast because I heard that you're a big voice

SPEAKER_214: user and I heard that you have a pedal. I have a pedal. I'm addicted to a whisper flow. I use

SPEAKER_25: whisper flow constantly. So that's your competitor. That's where you got to displace now. Let's see

SPEAKER_209: what you can do here, Alan. Yeah. In this period of time, I'm going to have to convince you to use

SPEAKER_12: Willow. Well, I mean, the other thing that I will say, Lon, I don't know if you noticed this,

SPEAKER_37: my Grammarly and my notion are all trying to get in on the dictation game. So they also have dictation

SPEAKER_12: offerings and they're continuing to try to intercept this very important function. So Alan, are you going to demo the product here for us and tell us, you know, your innovation? Yeah, of course. All right.

SPEAKER_209: Demo or demo. Here we go. So as a quick intro on what Willow is, we're a voice dictation product. We're a voice first interface for modern work. And similar to whisper, we have a dictation product. You can speak, you can press a hotkey. It works. You can type anywhere. Yes. That's really, really magical. That's been really catching people's eyes, especially high communication teams is a product called Scribe. And Scribe is a step above dictation. You can think of it like a writing assistant. You tell it what you want to say, and then Willow writes it for you using your style and context. So I'm going to share my screen here. Okay. Here's a demo. Someone said, you know, my dictation quality has gone down this week for some reason. And any idea what's

SPEAKER_230: happening with Willow? Okay. And this is a Gmail and you're responding?

SPEAKER_209: This could be like, you know, a customer support request. Got it. With the patient, as you know, you can press a hotkey and you can say, Hey, Sarah, this is a test best Alan. And you can see, you kind of dictate that. Oh, it's very snappy. You know, less than 250 milliseconds, the most accurate dictation tool. But Scribe is, which was, which is what I really want to show you. I don't even have to word for word dictate. I can just say, respond to her with our common

SPEAKER_43: troubleshooting tips. So this is like a snippet. Oh, wow. Look at that. Coming from the corporate

SPEAKER_209: side of the snippet collection. Yeah. Yes. And I press enter and it magically appears. And what I can also do is, you know, let's say that didn't solve a problem. I could just highlight that again and say, can you also add my calendar link just in case those following tips didn't work for her so we can talk about it live. And there you go. And press enter there. And it even knows my calendar link. Very nice. Very nice. Well done. The power of Scribe here is that it has context, personalization, understanding. And this is something we've deployed across enterprises, airlines, customer support teams, sales teams. We're Scribe as an understanding of your knowledge base, of your context. And so teams right now are no longer even typing. They're not even dictating.

SPEAKER_243: They're just saying, respond to this, respond to that. Tell them X, Y, Z. And boom, it's there

SPEAKER_12: for you. Who manages the canonical level of truth? Because I have been having this problem for a long time. I used to use like a text expander, like open source tool. It was like

SPEAKER_244: shareware. And then I put it on group. It was, remember when I had this line, it was like $10 a

SPEAKER_12: person in the company. Absolutely. Yes. So I was spending $120. And I would write, hey, founders, thank you for applying to the launch accelerator. You didn't make the cut. But I want to make sure you understand. You can send us your updates to updates at launch.co. We want to encourage you to do this. You can watch this week in startups. Here's some codes for free credits from some of our partners. You had a whole thing. But I wanted consistency. Then I told Raul, a superhuman,

SPEAKER_00: please build snippets into the product. He eventually did. And then I said, I need it to be team player. It makes messages. So the challenge with what you're doing, I think, is if you have 10 people

SPEAKER_37: working in sales, and you want them all giving people the same language here, right? So how do you manage which one is the canonical perfect version? Yes. So with customer support teams,

SPEAKER_209: they commonly have a number of documents that are the source of truth that we will understand for them. And then we also have another aspect of this, which is auto learning over time. Will it picks a fact about yourself that you can write and then it learns and so you can rewrite it. I also have a perfect example for you, Jason. Yes. Perfect. I met with what you just said that you have to use a snippet for responding with who you are. This person just happened to say, yes, or you might be joining this week in startups. Can you tell me a little bit more about this guy called Jason? Oh, boy. But what I'm going to do here is I'm going to say, yeah, that's great. You heard that. Can you tell

SPEAKER_253: John more about who Jason is? Here we go. Here we go. Wikipedia page written by all my haters and

SPEAKER_254: jaders. Maybe it's just Wikipedia. You don't know. It's a Silicon Valley entrepreneur, angel investor, podcaster, best known as a third investor in Uber.

SPEAKER_43: There you go. Third or fourth. Perfect. Yeah. Previously worked at Weblog, sold it for 25 to 30 million. That's actually a true, that's a true number. Later back, Robin.com, Dumbtack, and Trello. True numbers today. He runs launch hosts this week and started. Okay. Yeah. Perfect. So yeah, this is great. Good job. So you put a level of interpretation and you still have to hit the send button. Great job. Great product. And so free to use, but then if you want to do multiplayer mode,

SPEAKER_209: that's when you turn on pricing, I guess. Yes. So what we've realized is that dictation is a

SPEAKER_263: commoditizing market. Okay. Always difficult place to be as a founder. Exactly. There's no doubt

SPEAKER_209: in my mind that within two, three years, Apple is going to get 70, 80% as good as dictation tools like will avoids and other ones, AI dictation tools. And the reason is because it's going to be better built into your device, but it's only going to be 70, 80% as good because there's a level that we do that's more pro. Like we have auto learning, we have fine tuned models that are smarter, that do corrections, that do personalization. So we're always going to be a bit better, but when it's 70, 80% as good, a lot of people might not pay for this. And so rather than getting eaten by this commoditization, we want to commoditize it first. Okay. We made speech or text that's faster than whisper flow, more accurate than whisper flow and other dictation tools completely. And we actually launched a comparison video, one showing Willow, our free version and one showing other dictation products. And whisper was better at formatting, better at correction, faster, more accurate than all those other ones. And our paid product is scribe. So scribe again is like a writing assistant writes for you, as well as, you know, team plans enterprise plan, as they're saying,

SPEAKER_276: well, this is the playbook, Alan. Uh, is this your first startup?

SPEAKER_43: It is. Yeah. So you learned one of the most important lessons. Number one, fight up. You want to fight with a competitor who's bigger and established. And two, your margin is my opportunity. So, you know, whispers charging for this, we'll make it free and we'll charge for something else, et cetera, et cetera. Great job. Good luck with it. Uh, and I will give it a try and I will find out with my pedal. If you are better than whisper and I'll let you

SPEAKER_52: know if you can displace whisper, that's going to be hard. I'm kind of addicted. You're a big whisper user, heavy whisper user, heavy whisper. All right. Thanks, Alan. Where

SPEAKER_43: can people find out more? It's a willowvoice.com. Am I correct?

SPEAKER_243: Yes. Willowvoice.com is where they can try it out for free.

SPEAKER_43: All right. Well done, Alan. Congratulations on your success here. Thank you, Alan. This is the best thing possible for the ecosystem, Alan. And for entrepreneurs, there's two tactical practical things I'm going to tell you right now. Number one, you fight up. So if you are, um, Apple, you're not talking about whisper flow. If you're whisper flow, you're not talking about willow voice, but you can fight up, right? You can fight up. So if you're

SPEAKER_12: willow, you want to fight with whisper. If you're whisper, you want to fight with apple, right? Right.

SPEAKER_281: Fight up. And then if you're the bigger fish, never engage going down. Yeah. Because you're taking

SPEAKER_18: the bait. You're taking the bait. You're feeding it to it. Same rule in comedy. Don't punch down. You punch up. You go for, you go for targets that are, that are a higher and better established than you.

SPEAKER_21: You don't make fun of people who are already lower status. Yes. A very simple concept here.

SPEAKER_43: Um, and then second, your margin, my opportunity. So you just look at the roadmap. It's very simple to do. If the roadmap for dictation starts with crisp dictation, then it goes to interpretation, which is kind of what he's doing here. And you have this killer feature known as snippets. What should you do? Um, and then you have team player mode, just make everything free. So if whisper is watching this, they should just say, Oh yeah, by the way, we added to our free version, whatever, you know, who snippets and text expander tools. And here's like our robust one. What's really interesting to me is when these are all going to be done locally and built in with like very crisp local models right now, they still go to the cloud. So when I use whisper, man, it is flawless. It is tremendous. When I'm at home, when I'm driving in the back country and hill country in, uh, Texas, the great state of Texas, and I'm going to the, watch the Knicks beat the Spurs in their own arena, win the NBA championship. And I started using whisper

SPEAKER_13: and I had no connectivity. Uh, you know, it's like, Oh, sending it to the cloud, coming back. I really think hill country. There's a lot, your, your connection isn't always perfect out there.

SPEAKER_43: It's not always perfect in rants. Well, you know what I'm going to do now is, um, when I finally find my roadie, I'm getting a roadie to go with me on the road. Cause I have so many road things that I

SPEAKER_21: do. I get my roadie things. I'm on a lot of the trips, but I'm not good with the heavy stuff.

SPEAKER_65: I mean, it's given where you're at with road.co slash twist, maybe, maybe, maybe sometimes.

SPEAKER_37: What I would like to do is I'm going to get the Starlink mini like on the road one. And I'm going to, I want to get this executive van. I love the Alford, uh, which I think I drove with you when we were

SPEAKER_18: in Japan, it's like, yeah, when you take a car, you take an Uber in Japan, they've these little commuter vans that they pick. Yeah. Did Jacob find it? Yeah. I'll, I'll, I'll find one. They're, they're so neat. Like, I feel like I would actually like drive one of these if you could be at the

SPEAKER_28: Toyota Alford. I mean, it is the, the Toyota Alford in the executive mode is like a minivan. There's lawn on his iPad pro. Exactly. And his suit looking good. Look at how big these seats are.

SPEAKER_16: It's unbelievable. It's very comfortable. You feel like you're smooth. You've got a nice and sort of distance from the drive. They're in their world doing their thing. You're in the back doing your thing. It's very comfortable. I felt right at home in the Toyota Alford. Why don't they have

SPEAKER_37: these in America? I literally tried to import one. You can't import them. Um, I think they're getting

SPEAKER_43: there. Uh, Genesis, uh, 90 EV, uh, just dropped literally this just dropped and people are losing their mind over it. Let me show you this. So I have, I was literally going to import one of these and pay like a 50% import tax. You're not allowed to, but this is the Genesis, uh, GV 90 that's coming out. So, um, what this thing is going to do, um, it's an EV goes 300 miles, whatever you see, the doors look interesting, right? Yes. Um, boom, you open the doors, you can have the, you can have the, uh, captain's chairs face each other. See that? Yeah. Yeah. Uh, they spin around. This is like the old G 90, but this one is something slightly different. GV 90, Genesis GV 90 is what you're looking for. People are losing their minds over it. So that's what I want to get is one of these bad boys. Yeah. Uh, and then put a Starlink on it and then I could just

SPEAKER_291: recapture, you know, whatever an hour a day, but look at that. Oh, wow. That's nice. Look at that.

SPEAKER_00: By the way, this, if you were to build a, uh, like a Becker or something automotive builds these for

SPEAKER_43: $400,000, literally four or 500,000, this is going to be like a hundred, 150, you know, to go crazy.

SPEAKER_319: It was like 150 K was the, on the, on the page you were looking. Now this is deceiving. You have like

SPEAKER_43: the chairs facing each other. Those two chairs are the captain's chairs for the driver on the co-pilot. And this is a parked car on the beach. What I want is an extended version of these with the four chairs. Yeah. Um, so then you could have six seater. Um, see, there's the steering wheel back here. Yeah. Anyway, if you're parked and you want to have a conversation, I don't know who's hanging

SPEAKER_326: out in their car. If you're living in your van, that's a perfect, you know, see, they just,

SPEAKER_43: they got it wrong. Um, in terms of like what actually is going to happen, but, uh, it's going to, it's coming next year. So there's your off duty from J Cal. Let's do our final guest and I got to get the heck out of here. Yeah. We got one more guest. I will, I will

SPEAKER_16: pull them up now. Here we go. Uh, we actually, we discussed our next guest, uh, when their video

SPEAKER_18: went viral and we, you, you wanted to book them. So here they are. He's a Stanford computer science student and the designer of the self-driving robo taxi golf cart that you'll recall.

SPEAKER_21: Finally, all the, uh, all the high, uh, priests of AI and tech were riding around because they all hang out on the Stanford campus. Ethan Goodheart. Thanks for being here, Ethan.

SPEAKER_244: All right, Ethan, what's going on? You're, uh, you're an undergrad, you're grad. What are

SPEAKER_333: you doing over there in Stanford? Uh, yeah, technically undergrad. So I'll be a junior next

SPEAKER_43: year. All right. Incredibly hard to get in. You're a sophomore. Incredibly hard to get into Stanford. So you're either brilliant or your parents built a building or you, uh, Photoshopped yourself playing, uh, lacrosse. Uh, well, which one of those three,

SPEAKER_337: you're just a brilliant kid where you're from. I can definitely confirm that it's one of those three.

SPEAKER_43: Um, no building, unfortunately, no building. Okay. And there's it down. So did your parents Photoshop and degenerative AI to put you in a lacrosse video? Uh, I think the models were not, not there yet. Okay. So you earned your way in. Where are you from originally? I'm from Atlanta. Oh, all right. Well, that's good. See, I actually know people who are moving to smaller states. You want to know how crazy these parents are? And Ethan's nodding. Uh, maybe his parents left Manhattan for Atlanta to get him into Stanford. Some of these lunatic parents look at the tables lawn of which states, because they want to have representation from every state, which state has

David Friedberg: the least number of, and they'll literally move to Nebraska, ruin their lives for two years, three

SPEAKER_345: years. George has got to be doing pretty good. I feel like George is competitive inter, like West

SPEAKER_21: Virginia. That's where you'd go. Idaho. Those are yours. It's true though, right?

SPEAKER_339: Those are your Stanford states. Yeah. I mean, that's not the reason I were in Atlanta. No, but, uh, but you have heard this. Yeah. Yeah, I have. Yeah. This, this is the lunacy. Okay. So,

SPEAKER_43: uh, what are you computer science, electro engineering double? What are you doing?

SPEAKER_353: Yeah. Pure computer science. Uh, this is my first like real hardware project. Got it. So, uh, self-driving,

SPEAKER_291: uh, you know, a lot of my position has been 20 different companies of note are going to get there

SPEAKER_37: in the same 24 month period, putting Waymo out of it, just starting this year and next year into 20, 28. My belief Ethan was 20 people figure it out. I think I'm good. Like in the same 24 month period, we'll see 20 people figure it out. And when I saw Ethan's project, it said to me, I might have underestimated it by a magnitude. It might be 2000 people figure it out in the same 36 months,

SPEAKER_00: just to extrapolate a little bit. Tell us what you built, Ethan, how long it took to build

SPEAKER_339: and just the fidelity of it versus the brittleness of it. Yeah, for sure. Uh, probably the best project I've ever worked on in my life. Uh, first hardware project, he got the first prototype up

SPEAKER_353: in like two, three weeks. So we had a golf cart that could drive with some like very simple models. Um, obviously not as advanced as a Waymo or a Tesla, but, uh, yeah, we've kind of been working on it for a couple of months after that. And, uh, this is a standard golf cart and you built some kind of

SPEAKER_214: rig to go on or a harness, but not a, it's a physical harness that's going over the steering column.

SPEAKER_360: Yeah. Yeah, exactly. It's, it's, it's retrofit. Um, we didn't modify any of the like internal

SPEAKER_353: electronics. This is the oldest place on top of the golf cart. Yeah. Um, so it's like three front cameras, three back cameras, and then no light up all cameras, like no lighter. Okay. Yeah. Um,

SPEAKER_281: and so you take a stock one, you put cameras on it. What is the hardware stack? Because, you know,

SPEAKER_43: Elon has been working on this for a long time, uh, and building essentially the compute locally on the car to do exactly what you're doing here. So I see there's kind of a box on the floorboard. So I'm assuming you have an Nvidia spark or something down there doing this.

SPEAKER_337: Yeah, we, we have a Nvidia Jetson Thor that's wired directly to the battery of the golf cart. Got it. Um, so that uses a decent amount of power.

SPEAKER_43: Yeah. Um, and the golf carts are notorious for going what, 50 miles in a day, 30 miles before charge. So how do you deal with that? Do you buy a bunch of anchor batteries and just bolt them in?

SPEAKER_339: So right now it, the, the range is a little bit limited. So it's definitely something we have to figure out, uh, in the future, but it's still very much in like testing phases.

SPEAKER_214: On the top, you got a Starlink I see. Um,

SPEAKER_374: Starlink Mini, that's right. Fantastic. Uh, what is that doing?

SPEAKER_356: Uh, so that allows us to do two things. Uh, it allows us to test out about inference. So we're exploring running some DLAs and world models in the cloud. And then it also allows us to have good connectivity to write code and ship code live to the golf cart while we're driving around. Got it. Um, so like I often work on it outside of my dorm and a lot of all-nighters in spring quarter working on this and I can like ship code live while I'm on the golf cart instead of having

SPEAKER_43: to like go back into a dorm room or classroom somewhere. Okay. I mean, listen, it is autonomous

SPEAKER_37: is on golf courses. Uh, people don't know this, but a lot of the, uh, grass being cut right now, Lon, and even in a snow country, a lot of the snow being moved is being done by autonomous robots. The team will go, um, find a video or two about this, but even here in the great state of Texas, Ethan, I drive around and I see in the planned communities robots cutting grass. Um, and that's in like hill country that's here today. They're doing a lot of this, uh, golf courses. Uh, you made this as a proof of concept, but you know, you're, you're right next to Sand Hill road. There's so much money sloshing around is the idea to like go to golf courses and say, Hey, we literally can help you bring the golf courses back. So here's the snow one. Check this out. Ethan, this thing's incredible. That's awesome. Um, and so, you know, snow blowing is one year when we had a lot of storms up in, uh, Tahoe at my ski house. I got an incremental $12,000 bill because they're paying the people who shovel snow $70 an hour. They literally getting paid $150,000 a year long. And we had so much to know that it was going to collapse the houses. Therefore there was no choice, but to outbid everybody to save our houses. Anyway, these things are becoming, uh, standard operating procedure. Here's one on a golf course. Um, using a Toro TOF pro anonymous, and these things are really easy to set up. People love them. What's your plan? I mean, people asking you to build this for golf courses, because you might have a situation where somebody stops and leaves their golf court around, or they finish. You want to drive it back to get charged, whatever it is. Do you see this as a business?

SPEAKER_353: I think it's uncertain what the future of this was. This was just a fun side project, which I think it's absolutely incredible that something with the scope now can just kind of be a side project. Uh, the future is uncertain, but there's definitely been a lot of interest from people in the golf world, also people just in the transportation world. So like people like Disney and other companies that have a lot of golf carts for transportation. Um, I think there's a lot of use cases for golf cars and yeah. What can you, uh, if you were to iterate on this twice

SPEAKER_43: and get, you know, obviously this is like a custom sensor pack, but let's say you had 10 million in funding. You do, you get to, you know, Oh yeah. Number 500. What do you think it would cost to retrofit these, these golf carts cost 10 to 20 K depending on how fancy dancy they are. What, what do you think you can get the retrofit to, to have autonomous golf carts in the real world?

SPEAKER_336: Yeah. Uh, even the current version is quite cheap. So I won't say the exact number, but it was a

SPEAKER_391: ballpark. Yeah. Where you could see a retrofit version of a kit like this, where you could sell for

SPEAKER_43: under a couple thousand dollars. Wow. Incredible. Yeah. What software did you use? There's been a lot of talk about Nvidia and Uber and, um, this open source stack that they're providing because they want a Uber, obviously strategically wants open source driving. They want a fragmented market. They want every single car to have it. So that Waymo or Uber or, you know, pick your company, Neuro, Pony AI, Wave, AV, Zoox don't run away with it. They want it to be fragmented and Nvidia

SPEAKER_12: wants to sell compute hardware. So it's good for them if the software is abstracted. Are you using

SPEAKER_37: their stack for software or did you write your own or are there other open source ones? Tell us about the self-driving software, the brains. Yeah. The software is by far the hardest part so far. Um,

SPEAKER_356: we've ended up using our own software. And so we found that a lot of the open source models surprisingly were either way too large or didn't generalize well to the environment that we're driving on. Um, cause it's a little bit different than the environment that most cars are driving on, given like the pedestrian density and temperatures, it's very different. Um, so there's also, there's also the question of the models being too big. So most of the models that have been published by Nvidia so far actually like quite large and hard to run on even the like $4,000 GPU though. Uh, and so we ended up using, uh, our own sort of approach and like custom

SPEAKER_43: approach. Love it. Listen, continued success. Uh, if you make it into a company, just save, uh, save a slice for your unk J cow. Uh, you know, I, I, I'm, I'm good for like, you know, if, uh, if I get on the cap table, I'm always good for like a ticket to, I don't know, an all in summit, something. I mean, it might be good for a retweet. This is what I got to do, Lon. I got to set. I'm

David Friedberg: like a, I'm like 55 year old man, unk talking with 20 year olds. Now I'm trying to get a slice on the cap table. What point is that desperate? Is it 65? No, where it feels desperate or

SPEAKER_16: you just graduate to becoming more and more senior, you know, like where you're okay. Like Vinod people want you on the cap table even more because of your incredible reputation and

SPEAKER_401: yeah. Yeah. Right. Okay. All right. All right. So I get to, I'm in my Obi-Wan era right now,

SPEAKER_281: Ethan. That's a star Wars reference. And then I guess, but no, it is in the Yoda kind of time

SPEAKER_43: frame on the timeline. Uh, Ethan, where, is there a place people can find out more? Is there a name to this company or a website or open source project? Uh, yeah, there's the project name right now is

SPEAKER_353: wind. Uh, so kind of like the wind that you feel blowing on your face. Okay. I love great. You

SPEAKER_293: understand branding, uh, wind self-driving golf cart. I'm going to try to find your domain.

SPEAKER_360: Do you have a domain? Uh, there's no domain, but if you go to my Twitter, okay, there should be a link to the test flight. And then if you're in the Stanford area. Okay.

SPEAKER_43: You can come check it out. Yeah. How did you, uh, how did you get, uh, Jensen to take a ride? How did you, uh, finagle that? You had one of your props or your harangu? Well, he kind of,

SPEAKER_353: he was speaking on campus. And so we basically, uh, waited outside the lecture and said, Hey,

SPEAKER_418: we built a self-driving golf cart. That's my guy. That's my guy. Ethan is learning the startup

SPEAKER_421: lesson. All these guys like rock. Ethan punk rock was a style of music in the seventies.

SPEAKER_281: And what punk rock people did is they didn't give an F and they would just go do stuff. The clash. Have you heard of this band? The clash? Be honest, Ethan. I haven't. No. It's killing me. I'm going to blow your mind. Ethan right now. You probably heard London calling

SPEAKER_362: and just doesn't know that's the class. You and this is going to score you a lot of points

SPEAKER_12: when you throw a party and you meet girls at some point, this is going to happen for you at Stanford. You're going to throw a party, meet some girls or guys, whatever you into, they them, no judgments. I know it's a whole thing, but you're going to get an album and vinyl and you're going to get

SPEAKER_426: the clash, London calling. And you just do a Google search or you do your LLM search,

SPEAKER_299: go down the rabbit hole. The kids, they want pink panthrists or something, Jason.

SPEAKER_427: And then I want you to DJ and I want you to just play rock the cas bar, London calling. And then when you meet a chick or a guy, whatever you're into, again, no judgments here. I know it's like 20, 26, whatever you're into. I want you to just say like, yeah, just, I'm in kind of my class area. I'm reading that. I want you to get the London call, put up London calling. And I want you to get the original vinyl, spend like 30 bucks on it. I want you to get a t-shirt. Don't play it. I wanted you to give me the cover of London calling.

SPEAKER_98: We're not allowed. We're going to get, uh, what we can show a little bit, but it's going to get us in trouble.

SPEAKER_427: And I'm telling you, Ethan, you're going to listen to this while you're coding.

SPEAKER_432: It's going to break your brain and you're going to be able to code 40% better listening to the

SPEAKER_16: class. Show the album cover, London calling. Does Jacob not know? Jacob might not know what you're talking about.

SPEAKER_435: Jacob's like timing. I might, I might. There it is. Look at this cover. There's the classic. You see this, Ethan?

SPEAKER_437: You see, you see this smashing?

SPEAKER_43: Yes. You see the, you see the art direction of this and he's smashing it.

SPEAKER_372: That's you and your startup. That's you intercepting Jensen, your punk rock.

SPEAKER_43: Okay. You're willing to break the rules. You don't give an F. You're willing to smash your guitar on stage and just let out all your emotions. The clash. London calling coming at you on a two for Tuesday.

SPEAKER_281: Yeah. All right, Ethan, get back to work.

SPEAKER_444: And yeah, is everybody cheating on there with, uh, AI? Tell me about the cheating, not you, but other people.

SPEAKER_445: I think there needs to be a real working of the educational system.

SPEAKER_356: I think it's evolved to the point where people kind of do the work that they want to do. They kind of pick and choose what parts of education they want to get out of it.

SPEAKER_353: Um, so I think it's a lot different than how it was before.

SPEAKER_281: So if they, if they're not into this course, they just, AI speed run it.

SPEAKER_401: If they love the topic, they're like, I'm going to go acoustic and I'm going to really learn it.

SPEAKER_16: Is that what you're telling me?

SPEAKER_450: Yeah. I think for the most part, that's, that's pretty accurate.

SPEAKER_16: Can I be honest, Jason? I was already like, by college, I was sort of like that too. Like it was pretty easy. Some of these courses you could sort of like microbiology. I wasn't super into it. You could just kind of figure out what was going to be. But you didn't have the tools to get an A in it. You, you, you would kind of cruise to a B. Like a B, a B plus.

SPEAKER_14: Yeah, I can cruise to a B.

SPEAKER_13: These kids can just speed, you know, speed run it either way.

SPEAKER_28: It wasn't like having Claude. It was still like, I had to do some of it myself. Yeah. You know what I take from that, Ethan? The kids will be all right. I think the kids will be okay.

SPEAKER_37: Because here's the thing. Yeah. That's what adults are doing. They're like, this job is completely effing boring. My boss wants me to do a TPS report. That's an office space, a really good movie that you should see. And they're just like, I'll just speed run this. Give it to my boss. Write some sycophantic answer. You know, ask my boss three questions. Tell him it's brilliant. And then I'll actually do the work at work I actually like. Well, just everybody does that. You ride around the stuff you don't like. You don't do your chores. And you pay somebody to do your chores. In this case, AI.

SPEAKER_165: I like it. The kids will be all right, Ethan. Thanks for the honesty.

SPEAKER_339: I think we live in a world where every young person

SPEAKER_356: has access to a superhuman software engineer,

SPEAKER_395: mathematician, expert in medicine, physics, all at the same time. So if that doesn't fire you up as a young person, I don't know what will. Yeah, it should fire you up.

SPEAKER_421: What are you going to do with this $300,000

SPEAKER_43: in debt you're going into? What's your plan there to pay it back? Well, you got some scholarships.

SPEAKER_281: You look smart enough to get a scholarship. How much in debt do you think you'll be when you get out of this?

SPEAKER_445: It depends on how many self-driving golf carts they build, I guess.

SPEAKER_229: I'm going to give you a secret. When you go raise your round, here's what I want you to say. I'm going to give you the script, okay?

SPEAKER_43: Don't tell your Uncle Jason said this. Unc is going to give you some advice. You're closing that $3 million seat around. Here's what I want you to say.

SPEAKER_432: Listen, I want to give it all my energy. I got a little anxiety. This is like thing over here. My tuition is $140K. When we do the $3 million, would it be okay if I just sold $100K in secondary of my common shares to take it from $140K down to $40K so I'm not anxious about that?

SPEAKER_43: I can put my whole effort in. Boom. That becomes the ticket price to get in

SPEAKER_467: to your new company. That's a tactical and practical tip, right? There's a tactical and practical.

SPEAKER_43: That's very tactical and practical. Here's how to manipulate your seed investments. All right, brother. Good luck with everything. And yeah, when I'm next on campus, I'll be on campus in January, possibly.

SPEAKER_471: Apparently, this is where you have to hang out now, Jason, if you want to be. That's not now.

SPEAKER_43: That's always been.

SPEAKER_471: If you want deal flow, you got to just be hanging out on the Stanford campus. If you go to GSB,

SPEAKER_43: if you wind up going GSB route, Professor Pfeiffer does a course called Power. And there's a case study in it about this podcaster, blogger, a kid from Brooklyn who accumulated far too much power than he deserves in Silicon Valley. And it's about me.

SPEAKER_291: And I go every year to talk to the students about accumulating power through media and other things.

SPEAKER_165: So good luck, Ethan. Hopefully, I'll see you on campus.

SPEAKER_71: Thanks, Ethan.

SPEAKER_475: Yeah, likewise. See you guys around. Peace out.

SPEAKER_71: That's like that Harvard class that Bill Gates's daughter was in that teaches you how to manipulate people and cheat. Did you hear about that?

SPEAKER_303: Oh, how to manipulate your stats on that? Yeah.

SPEAKER_18: No, apparently the rumor is there's a Harvard class that's about like how to rule the world through manipulation. It's like a secret, a secret class. And they say that, yeah, that Phoebe Gates was in it and that's where she learned.

SPEAKER_427: Oh, I think she's being railroaded. I'll be honest. Really? Wow. Because I think there might be at first you were.

SPEAKER_12: I think this might be like a convene. I think everybody's kind of trying to manipulate the attribution tag.

SPEAKER_37: Um, and so I think, well, that's undeniably true. I'll be honest. If she wasn't Bill Gates's daughter and she was a dude, I'm going to be honest, like on two levels, nepo baby plus woman. Yeah. Plus let's, uh, she's like a, I think she's kind

SPEAKER_28: of like an Instagram influencer type. So that has a little baggage with it. It makes people. And the cap table. It's Khloe Kardashian. It's Sydney Sweeney. It makes people resent it. Exactly.

SPEAKER_37: People resent her for being a nepo baby, resent her for celebrity connections, resent her for her Instagram stuff. And, um, if it had been a guy, same set of circumstances, I wonder, and I don't have all the details of it because it's a little bit like in, we'll see none of us. I'm guessing somebody might say to the founder, that's a really clever growth hack. Trying to get the attribution tag. Uh, so you get the commission. Wow. It's a good growth hack. Cookie.

SPEAKER_487: You might want to read, they call it.

SPEAKER_37: Well, they might be like, you should probably read the terms of services because you might be in a gray area. That might be how the conversation actually went down in my experience in a board meeting. Because I've seen people go to the gray and have these kinds of situations. And I always say to them, it feels a little gray. Maybe we should read the terms of services and maybe back off this, you know, a little bit. It might be, you know, we could get in trouble for this.

SPEAKER_00: But there might, is this like a criminal legal case?

SPEAKER_16: I don't, I mean, I don't think so. No. No. And they stopped doing it. As soon as the report came out that said they were doing it, they stopped doing it. So I, no, I don't think.

SPEAKER_13: Anyway, I want to adjudicate this case.

SPEAKER_43: I'm going to adjudicate this next week on the show. All right, we'll talk about it. So I'll see you on Monday and I will put on my robe. This is This Week in Startups. This Week in Startups.com at TWI Startups. We'll see you next time. Bye.