SPEAKER_01: Okay, January 1st, 2027, a humanoid robot will be available for purchase.
SPEAKER_03: Yes.
SPEAKER_01: And delivery.
SPEAKER_03: Yes.
SPEAKER_01: You can have it in your home.You can buy it and have it in your home. By January 1st, 2027.2027.Okay.
SPEAKER_03: And you're asking me.And the bet is, no, the bet is.
SPEAKER_01: I take the over or the under is my.Well, yeah, you take over or under.You're setting the line.
SPEAKER_03: Okay.And whoever's right, the other person buys the robot for that person.
SPEAKER_01: Wow.This is the largest bet in the history of This Week in Service.There's 20 grand on the line here.What are we doing here on this podcast?I don't know.I mean, this is getting out of control.This is a big major bet.It's a big bet.
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SPEAKER_01: All right, everybody, welcome back to This Week in Startups.It's Twist, it's Madra Mondays, but we're moving from AI Monday, since we record on Mondays, and we're going to just start publishing AI Tuesdays.So it's going to be AI Tuesdays going forward with me.I like Madra Mondays.I know.We record on Madra Mondays.
SPEAKER_03: Okay.
SPEAKER_01: And that is my partner in crime, Mr. Sunny Sandeep Madra.You can follow him on Twitter at Sandeep.Definitive Intelligence has been merged into Grok. And so I'm now going to refer to you as what?Co-founder of Grok or chief technology officer?
SPEAKER_03: What is your title going to be?I am the general manager leading the Grok cloud and our commercial business.
SPEAKER_01: Got it.GM of Grok Cloud.G-R-O-Q.As opposed to G-R-O-K, which leads to, I think, maybe a good place to start.I saw that Elon, you know, has been in this lawsuit with Closed AI, formerly known as Open AI.He said he dropped the lawsuit.He's got a sense of humor, obviously, if they just changed their name to Closed AI.But they open sourced Grok.How big of a deal is it that he open sourced Grok?And what has the reaction been to G-R-O-K, which is Elon's?
Yeah.Open sourcing.Big deal, not a big deal.
SPEAKER_03: It's a huge deal.It continues to show the constant battle between open source and closed source.And the biggest deal with their model is the size of it.So it's larger than any other model that's been open sourced.And so... It really gives a glimpse to the community of what someone who has at scale resources is doing and opening that up.Because what we see from a lot of companies is the open source models, but sometimes even their smaller ones.This was sort of the primary one that they were creating.So I think from that perspective, it was really interesting.It also shows a different problem where because the size and scale of it, it's not as, I'd say, usable by the community.
Because in order to use a model that size, you definitely need like a really large cluster.So I think what it's going to do, it's going to show folks that it's going to give people the ideas of what's happening with the larger model.Now, they didn't open the data sets that came with it.So in terms of the spectrum of openness, they made the weights available, but they didn't make the data that was available.And that's for obvious reasons. Yeah, because it's their proprietary data and they can't do that.
SPEAKER_01: So other models like Facebook's model, do they release the data it's trained on or they just say, hey, we used OpenCrawl or something?
SPEAKER_03: They show you exactly what it was used to train it on.Yeah.
SPEAKER_01: Now, when an open source project does that in today's climate where we have a big debate, people like Friedberg are like, yeah, you can train on whatever data you want. It's on the open web.It's a ridiculous position he has that nobody has any rights to their content when it comes to training data.I think it's absurd, but this is how a lot of Googlers think.He's a former Googler, and a lot of tech people think is if it's on the open web, I can do whatever I want.
SPEAKER_03: Free use, right?
SPEAKER_01: Yeah.If it's on the open web, it's fair game, which is ridiculous.Yeah. a lot of things are available in the open web that you just can't take and go leverage.But anyway, putting that aside, is now the reason why people are not going to share the data is they don't want to open themselves up to, hey, somebody posted to Facebook, you know, somebody's copyrighted material, then it got ingested into the model.And now you have, let's just take the biggest IP of all time, Disney, Marvel, Star Wars, somebody put up you know, in their G drive or on YouTube and it hasn't been taken down yet, an Avengers film.And now you've trained on the Avengers data, which you don't have the right to train.
SPEAKER_03: Yeah, I think it's like a big smoking gun in some ways.You know, I didn't get a chance to listen to the podcast, the all in podcast, but I saw it in the show notes.Did you guys talk about the little mishap?
SPEAKER_01: Yeah.What was your take when the CTO of OpenAI talked about the training data for Sora?
SPEAKER_00: What data was used to train Sora? We used publicly available data and license data.So videos on YouTube? I'm actually not sure about that.
SPEAKER_01: What did you think of her reply there?Do you think she's lying?She doesn't want to set up herself for a lawsuit?Because she's a CTO.Shouldn't she know the data was trained on?
SPEAKER_03: Well, that's where I was gonna go.You know, if you're maybe talking to someone, even a low level engineer that's not working on the project, they may or may not know they're working on the code for it.But I think Someone in the position that she's in will have a full understanding of all technical aspects of what's happening and then data aspects of what's happening.So and that's why I think when the question came up, you know, she had some very specific answers.And as the interviewer probed, it started to get much more uncomfortable.Yeah.Yeah.And I think it followed what you were just saying, which is, well, if I can go and I think, you know, I don't use it this way, but I think you can get to Instagram via the web now. Like someone can send you a link and you don't have to open it in Instagram.
Yeah.And same with YouTube.Right.And so I think what the argument that was being made, the way I at least read it was, well, if you can just get to it on the internet without a login or anything else, then it may be inside.
SPEAKER_01: It's what she was saying.It's on the open web, by the way, a lot of New York time stories are on the open web.They don't put everything behind a paywall.That is not the, just because something's on the open web, does it mean you have the right to exploit it for commercial benefit? Let's give two letter grades here.What do you grade the CTO's response at OpenAI since we like to give letter grades?
SPEAKER_03: Well, the response in that interview with the Wall Street Journal gets the response was not great.It was it would probably be, you know, I'd say like a C, you know, I think in that moment.
SPEAKER_01: Yeah, I think.
SPEAKER_03: Oh, my God.It's a big fail.
SPEAKER_01: You have to be media trained on that.What are they doing at opening?They should say we don't talk about the training data.We license data and we use data that's openly available, but we don't talk about what data we use.That's a perfectly good response.
SPEAKER_03: That's what I'm saying.But we shouldn't blame her for that.We can maybe blame the organization.You know, I think it's like, she's doing great work.Yeah.So yeah.
SPEAKER_01: I don't want to single her out, but yeah, the organization failed her and she failed the organization.
SPEAKER_03: They need to be prepped for this stuff.You know, the comms organization gets a bad grade.Oh my God.
SPEAKER_01: They get fired.If there is, even if there is a comms group.Yeah.I mean, is there a comms group at open air?Maybe they just don't.It sounded to me like an organization that doesn't have a comms group and doesn't speak. yeah awfully about these things like you can't wing it when you're open ai and in a lawsuit with the new york times yeah like they're in a lawsuit with the new york times it's it's the this is the content i would say this is the content lawsuit of the century of the past hundred years i didn't
SPEAKER_03: You can't blame the CTO, though.Oh, of course you can.
SPEAKER_01: No, I blame her, too.Yeah, she's got to think better on her feet and be thoughtful.Anyway, I give it an F, you give it a C. What do you give the impact of Elon and Twitter and Grok and all this stuff, open sourcing, ex-open sourcing Grok?
SPEAKER_03: I think impact from, you know, what it means to the community and, like, advancing open source uh thinking around ai a a you know yeah it's an a plus yeah exactly a plus we can't give it that because like we'd want to see no data right we'll just give it an agency exactly so you know there's room to be better And I think this goes back to maybe what the core mission of open AI was supposed to be advanced AI for all of humanity.And so by doing something like that, it does, it does advance things for everyone.
SPEAKER_01: Let's get into other news.We have the first two news stories out of the way.We'd love to do demos here on AI Tuesdays.Go to this week and startups.com slash AI to get to our playlist of all the AI demos we're doing.We're well over a hundred now, folks.
SPEAKER_03: Yeah, so this week we have actually two, one that we just can't demo unless we maybe get the robot.We can go see the folks that are doing it.But the first one is Figure AI.And I'm going to just show a couple of things here.Obviously, I don't have access to this robot, but I'd love to go and maybe we can go.They're here in the Bay Area, so we can go and see them.And so these folks have created this robot very much like the Tesla.What's the Tesla one called?Optimus.Yeah.
Yeah. And what they did that was really interesting.And I'm just going to show another video here really quickly.
SPEAKER_01: This is a real world humanoid robot.It is done with two legs, two arms, a head, which why it needs a head makes no sense.Like, what is the purpose of the head except for design?I mean, you could put all that in the chest, I guess.
SPEAKER_03: So I'm going to show you why.So this in this video is just like a very short clip.You're going to see that this gentleman is speaking and you standing nearby with your hand on the table.
SPEAKER_01: Great, can I have something to eat?Sure thing. They asked the robot to describe what was on the table.On the table were some plates, some glasses, a dish rack, but one thing you could possibly eat, an apple.And they did a conversation, so it did voice recognition of him asking, I want something to eat.The robot assessed what's on the table, and I guess assumed the only thing edible on the table is the apple.Am I correct?Correct.So we've described it for people who are not watching.If you want to watch, just type in This Week in Startups on YouTube.
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It seems very basic, yet it is kind of impressive.So why should we be super impressed here?
SPEAKER_03: Let's go in the following order, right?In terms of like creating humanoid like interaction, I think this is where the head comes in, right?You know, of course you could have done the same thing and you could have had a robot without a head there and it would have been 10 times freakier.So one is just creating sort of, you know, comfort, I think from a human perspective.
SPEAKER_01: Making the humans comfortable is why it has a head.
SPEAKER_03: Yeah.But two, here's something.And, you know, Elon says this when it comes to FSD, you know, the FSD with no LIDAR arguments or no ultrasonics arguments is he goes, look, we're a human and, you know, millions of years of our biology is, have given us two eyes that give us stereoscopic vision.And that's what we can use for depth perception and everything else.And so the argument is, if you mimic the human form and you give it two cameras, it has sort of all the features that humans have.And as you enhance its reasoning capabilities, it can do it the same way that millions of years of our biology landed us having a head with two eyes like this and ears at the side.So these are all evolutionary traits that there's millions of years of biology behind. But we can just copy it immediately and say, well, most likely we put the speaker right here.We put the microphones right here.
We put the vision things right here.Biology has already figured out that's the best format.The same way Elon says you can drive a car with your eyes.So a computer should be able to do it just with its eyes.
SPEAKER_01: Yes, it's just a matter of the software, what the gray matter is in our skulls being able to catch up.And that's the language model, obviously.So here we are starting to see the beginning of a language model being put into a robot.Now we have obviously the Boston Dynamics robots doing backflips, getting kicked, doing all kinds of very interesting, what you would call verticalized software, right?Like very vertical software solutions.Here's how to walk.Here's how to pick stuff up. What this is doing is very different.Explain to the audience why this is so different than the Boston Dynamics, incredibly powerful, you know, demos that we've seen in the last decade.
SPEAKER_03: And my guess is like, this is building on the learnings from what Boston Dynamics were doing and all those crazy videos.And what they were figuring out was movement and they were figuring out movement and stability and agility. What now we're bringing into it is what I'd call reasoning.And in that particular video, you saw the human walk up to a counter and you saw the robot have, again, the sensors that we have, and basically to speak, to listen, and to see.And the human said, can you give me something to eat?And the robot be able to reason over what's in front of it and basically decide, this is the thing you should eat.You can do this... and an action of handing to the person, you can do the same thing yourself, right?You could take a picture of a table and there could be five things there and say, you know, tell me the thing I should eat.Exactly.
And it most likely would tell you the right answer.
SPEAKER_01: Yeah, Chachi, people would know that.
SPEAKER_03: Yeah.It would.And so it's emerging.So I'd say Boston Dynamics was much more on the, like, physics movement agility side and you need that exactly and now you're bringing the capabilities of these large language models into place to do speech to text and then reasoning and then you know text back to speech and then tell the human here you go and then combine that with all the agility stuff to hand the apple back to the human to hand the apple to him because he says what should i eat Yeah.
SPEAKER_01: And it hands it to him.So they've additionally programmed that language model to take an action.And that I think is the most fascinating part.There's no action when you take out your chat GPT-4, take a picture of your counter and say, what can I eat?And it says, oh, you can eat the apple.You can't eat the spoon and the fork and the plate, but you could eat the apple.But it said, it took the action of handing it to them.So they have built software into this to know to give the apple to the human, that the human wants the apple.There's some other code here, right?
Yeah.
SPEAKER_03: There is.And so that's a really great question, actually.Super insightful, Jacob.These LLMs now, they have something called function calling capabilities.And so when you're seeing a lot of the more advanced stuff coming out of builders, what they're doing is they're using the LLM to perform reasoning. And then basically in that reasoning task, they basically allow it to have access to a selection of tools and the LLM decides.So all of this is usually happening in the background.So if we go through the whole workflow there, and I don't know this for sure, but like I'd say, this is accurate, probably within 95%.It's initially a speech to text, right?You got to take what the human says, got to turn that.
We've all seen that happen.That's been happening for a long time. Then you got to parse with the human said, what can I eat here?That then triggered a function and say, well, let me take a look at what's in front of me, take a picture of it.And then I can ask the LLM the same question you or I would, is there anything here I should eat?And then when it says, yes, it figures out there's something there.Then there's probably some function calling that happens as, well, based on what else was asked, what should you do next?And if one of its functions is and that thing, and they could have hundreds or thousands of these. And so what's really interesting here is that's most likely what it's done.It says one of its function calls, if someone asks you for something and you have it, we'll give it to that person.
SPEAKER_01: Right.Now, if that had been, if I had said, what on here is the most explosive item? Well, this nitroglycerin is.
SPEAKER_03: Yeah.
SPEAKER_01: Now, does it decide to hand me the nitroglycerin?Does it decide to hand me the grenade?
SPEAKER_03: This is guardrails, right?
SPEAKER_01: Now we're back to guardrails.
SPEAKER_03: We're back to guardrails, right?And, you know, Isomov's rules around robots and not hurting humans, right?
SPEAKER_01: Right.This is back to prime directive.What is the prime directive of these robots? So now, science fiction, right?In the movie iRobot, there are directives.In the knockoff RoboCop, there are directives, right?And you can't hurt humans.So... Are we to believe that this company has in fact given prime directive to these robots because now it's doing interactions in the world?It must have don't hurt a human in there.
SPEAKER_03: So if I was building one of these, and it might not be as simple as that, but imagine the whole thing is driven by a prompt.Yeah, there you go.The number one item would be do not hurt a human no matter whatever you do.And then I'd have guardrails outside of even the LLM. This is really fascinating because where this is intersecting again with Tesla is you've seen this in the evolution of FSD 1 through 11 and then 12.
SPEAKER_02: Yes.
SPEAKER_03: So the big jump from 11 to 12, which is not a jump because it's a completely different way.Exactly.And so it's odd that it kind of continued the numbers, but they just had to do it, right?Software versioning. But what they did was they went from creating code that was full of all kinds of branching, if this, then that, right?Yep.
SPEAKER_01: And there's a stop sign, do this.There's a speed limit, do this.There's a car runs across the road.A child runs across the road, do this.
SPEAKER_03: Yeah.To basically an approach that's built off vision and learning by watching the video over and over again.
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SPEAKER_03: And so, you know, I was just watching the GTC keynote earlier.That's the big NVIDIA conference.And what someone was saying, well, Jensen, sorry, someone was saying in that conference, which I'd never thought about before, about like, how do humans learn?And we think about textbooks and reading on the internet.No, it's mimicking. It's mimicking.But one thing he said is like, you know, a big way we learn is just watching TV.So imagine you give that robot, you just let it watch TV for, you know, an amount of time or consume things.And so the ability to figure out even what those functions are and then how those functions operate can also become an AI task in and of itself.Wow.
SPEAKER_01: So just to pause there for a second, insert the clip here of the fifth element.When Mila Jovovich, the character, wants to learn, she just like touches a TV or something and all of a sudden, the whole entire history of humanity, including the Holocaust and the moon landing, just every single thing, she learns from a television. as well in the movie the matrix it predicts i know kung fu he just watches every kung fu movie ever he watches every martial arts fight scene and now that's in his programming so now we start thinking about science fiction it literally predicted this that you could just watch the history of television all recorded and then learn everything learn wow It's just extraordinary.And then all this work that Waymo did, all this work that Tesla did on full self-driving, Waymo's done, doing conditional statements, 100% of that code is just going to be thrown in the garbage and never used again.It was all learning up until this point.Now that we have these models, we're just figuring out, hey, what has everybody else done at this intersection?And what would a human do in this intersection when a bicycle cuts across it and you'd stop?Right.
SPEAKER_03: Or how you learned to drive or I learned to drive the same way.Right.You know, we looked at some rules.We watched TV for a long time.We knew that you shouldn't do X, Y, and Z. This is crazy.Yeah.
SPEAKER_01: Hopefully it doesn't watch the French connection.No. For people who are under the age of 40.Great scene.Great scene.I mean, it's the greatest car chase scene ever.Just type in French connection car scene.Okay.So this robot is super interesting.Like it is going to take time, but because it's a human factor, what's very interesting about picking the human factor is evolution made humans to the dominant species on the planet.
So whatever it is, you might look at tigers and say that's more efficient or sharks in the water are more efficient.For some reason, our frail little bodies have done pretty well with the giant brain.Okay, fine.Now you start to think about, well, we also built the modern world.So we built the world, the door behind me, windows, chairs, office spaces, cities.Computers. We built these for our form factor, cars, et cetera.So by building, even if the robot would be better as a little tank with four wheels or taller, smaller, whatever, by building it to be six foot tall or 5'10 or whatever the average human is, 5'8 probably, well, it now can operate in the real world.A factory, a kitchen, a car, whatever. you know, walking through a city or a town.
So it's kind of brilliant that we're making them to look like us.
SPEAKER_03: Yeah.And I think about, you know, one of your great investments, you know, I saw yesterday I was in the airport, the Cafe X. Cafe X, yes.And think about the evolution for Cafe X. right could it be that you can and i know you guys have done a lot of work with the robot they're using the standard machines how does it fundamentally change if you could use the figure robots and think about sort of all the software that would just fundamentally go away where it's you walk up and say i'd love a double espresso you know
SPEAKER_01: It is obviously the future of it.Now, I think it would be slower than a purpose built.So we're now talking about narrow AI, verticalized applications versus wide. What Optimus and this figure, it's called Figure of this company?Figure, yeah, Figure AI.What they're doing is they're going for that, like, 10 years from now, this thing you could general purpose robot, just like, you know, ChatGPT is general purpose, or Gemini is general purpose.That means it's not going to do coffee very well.It's going to be slow and kludgy.It's not going to move... wicked fast in a contained space with walls around it that customers can't get in between it so you're going to still use vertical ai to win a chess or to win at making coffee but if you were doing a restaurant man this this robot would be pretty great to work at a holiday inn like literally you know when there's a holiday inn and they don't have overnight food like this robot would be pretty dope do that but the ramen machine would be better at making just perfect ramen like a verticalized ramen
SPEAKER_03: but it also could be like an expansion for cafe X. Like they get it right.And you sell them into Starbucks, right?They want to keep their storefront form factor.And you maybe want to talk to a human one of these.And because, you know, it just increases their throughput.
SPEAKER_01: So, I mean, it's a brave new world right now, and this is moving at a crazy pace.Um, I mean, looking at this, I think it's like a B. I mean, it's not super impressive to me.They've glued together the LLM and the robot.Congratulations.I'm not blown away yet.But I am impressed that somebody has finally put a connection together. But it's pretty basic.I mean, if it put a bunch of vegetables and fruit on the table and said, make me a salad, and then it asked me a couple of questions, like, would you like onions or not?
SPEAKER_03: Maybe that's a challenge to the team.That could be a cool thing.
SPEAKER_01: Yeah, here's my challenge.We can get one.Actually, I'm taking it back.I give this a C+.I'm giving this a C+.Yes.I'm not impressed. with the robot, because the robots not as good as Boston Dynamics.And I'm not impressed with the AI implementation here, because it's base, basic, you get a C plus for plugging the two things together, I want to challenge the team, put a bunch of stuff on the table and in the refrigerator, give it a night and say, make me a salad, you make me a salad to my specification, I'll be impressed.
SPEAKER_03: Wow, that's my plus.
SPEAKER_01: So there's your that's it.That's your test.
SPEAKER_03: You're a tough customer.
SPEAKER_01: That's my Turing test.What do you give it?
SPEAKER_03: I give it an A because I know how hard it is to put the robots together.I know how hard it is to make all these things work because I do it day to day.But there's a lot of, you know, I'd love to try one.So you know what?If they can reach out or come on your pod or something.
SPEAKER_01: You want to see if the apple's any good when he hands you the apple?
SPEAKER_03: No, I want to see more experiments, right?That's just like a two-minute video.But I see a lot of potential there.I see a lot of areas where there can be some improvement.Like the voice reaction was a little bit slow.Maybe that could go faster. But they're overall, I really like where this goes for.And I do believe, I think Ilana said something is like, there's gonna be something like 10 billion robots on the planet within the next 30 years of these humanoid robots.
SPEAKER_01: Yeah, one for one.Everybody will have their own robot.It'll be like C-3PO.Why wouldn't you have a robot? i mean if it costs i mean the idea that everybody would own a car was farcical like for a long period of time like why would anybody need that and now everybody has a car like i think there's more cars in the united states than people i don't know if that's that's true or not but so yeah why wouldn't there be i mean if they can get this thing i think they can get these down to 10 20 grand and then it's like buying a used car what would you rather have let me ask it this way Would you rather have a robot that could do anything a human can do, or would you rather own a Prius?And you have to take public transportation and have your own robot, or you can have a Prius and not... Ooh.
SPEAKER_03: Which would you rather do?I'd take the robot.100%.
SPEAKER_01: Everybody takes the robot.I'd take the robot.Everybody takes the bus and the robot.Yeah.Because you could have the robot making money for you.You'd be like, hey, you know what?Go find me... You know, go pick strawberries, go pick, go forage for mushrooms in the, you know, in Portland and come back.
SPEAKER_03: Go shovel the snow in Tahoe.In Tahoe, go shovel the snow.I mean, robotics has been waiting for this, hasn't it? Yeah.
SPEAKER_01: Robotics has been waiting for this.This is going to be bigger than AI itself.
SPEAKER_03: I think when you think about it, like... It's like a related though, right?
SPEAKER_01: I know, but AI without the robot means like the problem set is like, you know, 10% of the problems in the world.Yeah. plus robot equals 90 of the problems in the world like i think it is almost everything what what couldn't the robot do like maybe fly or go underwater or maybe there's some problems they can't solve but you know this feels like whoa this is like whoa i'm kind of in the same spot as you i think
SPEAKER_03: seeing multiple companies do this um you know between boston you know boston dynamics the tesla with optimus and then these folks i feel like we're closer than ever and i'd be willing to say definitely within within three years people are going to have these at home wait wait how many years within three years someone there's a bet hold on a second yeah
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Let's make this a proper bet.We got a new bet alert.This week in startups.com slash bets is where all the bets on the show are being put.We got to go back to the... Now that we have AI indexing the whole site, we need to go back and find Alexis Ohanian's original bet.But look at this.This was this company, Root AI, that got bought.Yeah, yeah, yeah.Okay.Unfortunately, it was with a SPAC company, and I think it went to zero.
SPEAKER_03: Oh, okay.
SPEAKER_01: Yeah, which kind of sucks.But look at this robot. Yes.Zip, zip, zipping.This is three years ago, picking tomatoes.And the hand is just perfectly designed for this, and it's using computer vision.And this is before LLMs were in the mix.This is a verticalized application.But look at how precisely it can go pick cherry tomatoes.Or it can do raspberries and strawberries.
Here it's doing bell peppers as well.And so this idea... that you would actually pick vegetables is going to be over soon.Now, this thing only worked in vertical forms.So you see this is an indoor.It's not out in the field.And it's not the dimensions of a human because you need a different type of hand.But if you had the Optimus or this future one, it could take its own hand off.
SPEAKER_03: Of course.Yeah, it will.
SPEAKER_01: And put the regular hand on, right?So what's your bet? Frame a bet here.Come on.I want to get a little active.
SPEAKER_03: Well, I just want to comment on that thing.So what's going to happen is the same thing that's happened with LLX, right?Where it's become a superpower.And what people are going to do is the robot's going to be the blank canvas.And then people are going to program them and prompt them.There'll be a lot of different ways that they'll operate with them.And so the amount of energy it took to create that robot that you just showed us was immense, right? an amount of engineering and testing, and then you had to adapt it to a certain form factor for it to work.But going back to where we started this conversation, if you have a robot in a form factor of a human, which means a human should be able to walk there and go and pick cherry tomatoes, and you give it the slight difference in the hand, like a tool, I think we're going to see that very, very quickly.So coming back to the bet, I think what we'll see is...
Okay, here's the bet.This is going to be their biggest one yet.Okay. Within three years.Three years?2027.End of 24, 25, 26, beginning of 2027, you will be able to get one of these for yourself.
SPEAKER_01: Okay, January 1st, 2027.Seven, yep.A humanoid robot will be available for purchase.
SPEAKER_03: Yes.
SPEAKER_01: And delivery.
SPEAKER_03: Yes.
SPEAKER_01: You can have it in your home.You can buy it and have it in your home by January 1st, 2027. 2027.
SPEAKER_03: okay and you're asking me that is no the bet is i take the over the under is my well yeah you take over under okay and whoever's right the other person buys the robot for that person up to a cap of ten thousand dollars for the robot let's say like 25 i think they're gonna be wow yeah it's a big major it's a big bet yeah it's a big bet but you're setting the line so i picked
SPEAKER_01: Yes.So do we have to put the dollar amount of the robot in there?That's just a cap.You can buy them, but they're 200 grand.Because I would think if you went and you offered 100,000, it would be... Because I'm... Yeah. You're saying January 1st, 2027, in America.Around the price of a Prius.
SPEAKER_03: Around the price of a Prius.
SPEAKER_01: For the price of a Prius, an entry-level Prius.Okay, so now we've got something to pin it off of.For the price of the entry model, the cheapest Prius.Yes.So we could have two bets here.The over-under on the price and the date.So we could do those.So I'll go first.I'll pick the date.Okay.
You pick the over-under on the price of the item, which is a Prius, which I think right now at entry-level Prius, somebody can look it up, but I think that's probably like $40,000.
SPEAKER_03: Yeah, I was going to say $35,000, $40,000.
SPEAKER_01: It might be $45,000, I don't know.Anyway, we know kind of where it is.All right, so I'm going to take the under. on january 1st 2027 for 10k and then you take 10k of the bet for over under on the price entry level prius the cheapest prius new you can buy yeah you think it's going to be over under that price under under Ooh, I think you made a bad bet.So I have the over.Okay.I think it's going to be like 50 grand for the 75 grand.No way.
SPEAKER_03: No way.
SPEAKER_01: Really?
SPEAKER_03: No way.
SPEAKER_01: Okay, it's the entry price of the robot versus the entry price of the Prius, which I think is how to look at these things.I think these things are the equivalent of a Prius.Yeah.And I think it's got to be before January 1st, 2027. gotta be it's gotta be before that an apple vision pro which has all the the the the the brain and sensors is only three thousand largest bet in the history of this week in services there's 20 grand on the line here 10 is a big bet 10k and 10k i mean it could cancel out or something sweet 20 dimes yeah oh my what are we doing here on this podcast i don't know i mean this is getting out of control our poker game is making its way into this it's making its way into I mean, are we doing this podcast in order to place bets?Feels like it.And that's okay.We're making it interesting for us.We have a bet that now is going to go through the rest of 24, 25, and 26.
So it's a two and a half year bet, folks.It's almost a three year bet here.But I like it.We'll be sitting here with more gray hair.Yeah.And yeah.Yeah.So I give this thing a C plus as done here.I want to see you guys make a salad.Okay.
I think...
SPEAKER_03: If my salad challenge, they should come on the show and, and maybe, or we can go on site and see what it can do.
SPEAKER_01: Let's go on site and see him make a salad.
SPEAKER_03: That's like a real life.Cause we haven't done one of those yet with someone on site.Yeah.
SPEAKER_01: I can get somebody like, uh, to do like a really good, like get it.We get a red camera or one of those really good HD cameras.We'll get makeup.We'll get some old man makeup going.Yeah.
SPEAKER_02: Okay.
SPEAKER_01: I like it.All right.Let's do another demo and then we'll wrap here.We've got a lot done today.
SPEAKER_03: Well, the next one I want to talk about as well is not a demo, and then I have an actual demo.
SPEAKER_01: All right, so we're just doing all topics today, it feels like.
SPEAKER_03: Well, no, I know I have five queued up, but these are big ones.Okay, so you remember last year, you were starting to get excited about auto GPTs.
SPEAKER_01: Yes.This was, I think, baby GPTs and auto GPTs.Yes.
SPEAKER_03: Exactly.So this company, which was founded by a couple of amazing engineers, the CEO is this guy, Scott Wu.Interesting background.This guy was a competitive coder.And so he had won competitions doing competitive coding.Incredible.And what this team did was they basically took the idea of auto GPTs And they really took it to the next level.And there's a few examples here.I'm not going to play the videos because I think everyone who has seen it at this point, if you haven't seen this yet, this is Devin.
Yeah.But what they did, which I thought was, and it's really, really insightful. Everybody else was using auto GPTs in a very constrained box, like either in a terminal or something like that.And it could kind of figure things out, but it was really hard for it to think holistically.What they did was they kind of flipped it around, in my opinion.They gave Devon an IDE that was built so as an integrated development environment. So it has access to code.It has access to a terminal.This would be like Replit.Great example.
They basically gave it a custom Replit that their AI can drive.And so by giving that, the auto GPT, in their case, Devin, had access to a much different set of tools.So it's much more powerful.Yes. And so because of that, and then they obviously took a model and they fine tuned it, did all the good stuff and a great scoring on that model.They really broke through what I believe was the barrier in the, you know, you sit down somewhere and you're like, Hey, I want to make a site that basically does news and this is how it should work and all those kinds of things.And so they, in their IDE have like a chat bot, they have a terminal, they have a web browser. They have all the pieces that you need to go off and do this.And so I really think that they've done something incredible here for that particular revolution of like the auto GPT style of applications.
SPEAKER_01: Yeah, it's super impressive.And this is the future.I think, you know, we have verticalized apps.OK, I can be, you know, big blue chess.I can be Kasparov.Great. Really programmed well.Okay, what's next?Co-pilot.Okay, really interesting.
I'm working, the co-pilot's finishing my sentence, giving me an outline of my blog post or working with me on my code.And then we're going to agents, right?We called them baby GPTs when we started this journey.But basically, it's a role. It is a worker.And so, the way we should think about this is we don't use the term slave anymore.I also got corrected, by the way, the language police.I call something a master bedroom.Oh, really?You can't call it a master bedroom anymore because it's hard.
Oh, wow.Okay.
SPEAKER_03: I hadn't thought about that.
SPEAKER_01: So, I think they call it primary.So, now they're calling it primary.Okay. rooms okay i get it um and so but essentially you're creating a slave right and that's what they used to call these things in programming code right you have this slave so this is like a role i'm going to just call it a role a job function and if this thing can operate through a role what i described on the last all in on the one before was what i believe will be what i'll call maestro The maestro is coming, the conductor.
SPEAKER_03: Oh, great name.Great name.
SPEAKER_01: So the maestro is coming.And what the maestro is going to do is I'm running my one-person company.And I just say, okay, I need a developer.Oh, I need a little designer over here.Okay, I need a copywriter over here.And then what is my job every day?Well, I'm sitting there with these roles, right?With these agents.And I've got these virtual employees, right? And I'm just pushing them along.
Hey, show me a new design for that app.Hey, I want to add, you know, to this app, Twitter login and Google login.Right now we're logging through phone, but I want to add the Google login code.Boom, it does it.Okay.Hey, copy team, let's write a blog post about this and do a tweet.Okay, boom, we got that queued up.Okay, now I want everybody to build the launch plan.Give me the plan that we're going to launch this new, you know, login with Google and I want a press release.Okay, boom.
And it's like, whoa. Now we start thinking about what would modern day entrepreneurship be?It's going to be being the maestro.
SPEAKER_02: Maestro.
SPEAKER_01: You're just going to be a maestro with a bunch of virtual assistants.And then you might bring somebody in.And so there's going to be a new class of company, I predict, where it will come in and it will be like, hey, you're using this AI to make your marketing plan.So we're going to have a human review it with them, right?A marketing maestro.Now you got a marketing maestro at the company.What do they do?Okay, they got the copywriter AI.They have the ad buying AI.
They've got the... You know, logo and the brand building AI, you know, the tone of voice AI, the social media.I mean, this is going to get really interesting.And I think this is where we get to a 10 person company, you know, making $100 million.Each employee makes $10 million.And then you got 10 person company, 100 million in revenue equals a unicorn, right?10 times revenue.And what would the margin on that company?If you pay each of those people a million dollars a year, because why not?They're maestros.
Yeah. yeah 90 and you spend 10 million let's say you spend 10 million on other expenses servers whatever make 20 million on operations got a 70 percent margin make a 70 million dollar profit company with 10 employees 70 million times a 20 times ebita it's 100 1.4 billion dollar company that's the future bro i think this is the future i completely agree with you you know definitely it's an a plus right yeah it's a plus
SPEAKER_03: The only reason I'm not going to give any pluses, I don't have access yet.
SPEAKER_01: So you go A, I go A plus.I go A plus.I'm going to give it to them on vision and be in the first out of the gate.
SPEAKER_03: And look, they're awesome.And what you're saying is super fascinating.There you go.This is their scoring on the software engineering bench. What I would say is when you just said what you were saying, I think about all the windows you kind of have open.And so what you're basically taking it to the next level is you'd have kind of like your Google Docs, you know, and you can sort of do this today because, you know, if you enable in your Google workspaces or your personal Google Docs, they've put in some of the co-pilot features now. And then you can do it within your tweet deck equivalent as well.And this is a really fascinating idea that take all the startup functions and you basically allocate a co-pilot to it.And then initially, one person is driving in.And then over time, you maybe layer it with some other people.
I really like that.
SPEAKER_01: Maestro is the company I want to incubate.So if somebody out there wants to do Maestro... Really interesting times.Should we do one more demo?You know, we had a lot of catch-up news that we had to get to, so.
SPEAKER_03: Yeah.Well, you know, since we're on news, I'm just going to keep it on that topic.Okay, keep going.Because, you know, this is one that you and I were talking about earlier.
SPEAKER_01: I mean, we can do news today, and I'll do another episode with you this week, but I'm so deep in the AI.We can just do a demo episode this week.
SPEAKER_03: Yeah, we'll do a demo episode because there was a lot of news to catch up on.
SPEAKER_01: A lot of news to catch up on.
SPEAKER_03: Yeah, but this was interesting because you and I have a couple of bets going with Apple, and then there's bought off the press's news as well.So what's interesting is this is a research paper that was released by Apple folks that talked about their work on a multimodal LLM pre-training and what they had done there. And so it's an excellent paper.It talks about, you know, relatively small sizes.And, you know, you can see here as an example, I'm going to zoom in where they're asking their thing is like, how much should I pay for all the beer on the table according to the price on the menu? Right.And so, yeah.Right.And you can see they kind of compared it to different chats.Right.
Which is their set.Twelve dollars.And this other Emos 37B said fifty ninety nine.And then Lava basically came up with a different number.Right.And so explain why.And it said here, look, there's two beers on the table.Each card costs six according to the table.So six times two is twelve bucks. This is a really, really well done.
SPEAKER_01: So amazing.
SPEAKER_03: Yeah.
SPEAKER_01: And this is where it gets really interesting.It's not just like, hand me the apple and the apple's the only thing.It's like, it's really doing some logic here.Yeah.There's two beers.The price on the menu is $6.Therefore, I mean, the ability to show your work is, I think, going to really help speed this up.So what we can learn here, dovetailing with the other breaking news.The other breaking news is there was a Bloomberg story by a very credible journalist over there at Bloomberg. I think perhaps the most credible journalist covering Apple, that Apple and Google are talking about a partnership where Apple uses Google's Gemini in the iPhone.
This would be colossal.What do you think is going on here?Because Apple... Not having AI means the end of Apple, in my mind.They have to master AI.They can't give it to Google, can they?
SPEAKER_03: So I believe that this is slightly different, and it's related to something you guys talked about, even on the all-in pod.This, I believe, is a business development deal versus a technology deal.Okay.Explain the difference.And this is a business development deal... Because one, the exchange in value of the search deal is ridiculous.I think it's on the order of $15 or $20 billion a year now.And so for both of these organizations, there is a data need and a channel need, I'd say, for Google in terms of that's why they pay so much for that default search in Safari.And... On the flip side, that's a significant revenue stream for Apple today.
So this is a business development deal to try to keep the status quo in place as much as possible.Where Apple's like, man, I think our investors would really probably be upset if we lost.Because you got to think about that $20 billion or so that comes in.Do you know what the number is?It's a big number.
SPEAKER_01: Oh, I think they might be up to $30 billion for the search deal.And this story in Bloomberg from Mark Gurman, who is known for being one of the great people covering it.So you could be correct here in the framing is they're just going to put Gemini on the iPhone.There should be a Gemini app preloaded. And it lets you interact with your phone in some way at a deeper level.So when you say, oh, Siri, I want to do this, maybe it would do, you know, it might use Gemini to help.I don't know.But so that's what you think.This is a carriage deal as opposed to replacing Siri.I think so.
SPEAKER_03: It's use your distribution mechanism that you're buying today to put yourself in place to then, because there's cascading effects here by having this deal.If Google and Apple work this deal out, Apple gets to keep that revenue stream.No startup can pay them that.OpenAI can't pay them that.No one can pay them that amount of money. I would think there's exactly one company in the world that can pay them that amount of money, which is Google.On the flip side, if Google can get that data into their ecosystem, then what that really does is it helps them continue to get a moat around the experience and what users are doing to build a better product. So in a world of like, you know, all this regulatory stuff that challenges startups today, they probably couldn't go by perplexity right now, which is, you know, they'd get sort of challenged on all this.
SPEAKER_01: So here's what it says.The two companies are in active negotiations to let... to let Apple license Gemini, Google's set of generative AI models, to power some new features coming to the iPhone software this year, said the people, blah, yada, yada, yada.Apple recently held discussions with OpenAI and has considered using its model, according to People.If a deal between Apple and Google comes to fruition, it would build upon the two companies' search partnership, as you talked about, So this is kind of interesting because the way this is being framed, I think, is that this they say it's for like a certain set of features.I wonder if this is just like image correction or, you know, search or something or an extension of the search thing.So when you search, it does something a little more intelligent, you know, in the search results.Right.So I don't know, man, it.
Or do you think it's white labeling it and to power Siri's behavior?I bet on the latter.Really?
SPEAKER_03: You think this is going to power Siri?I think if you put anything else in the market right now that doesn't have capabilities like we talk about on this show, I think you're going to put yourself behind.And so my guess is that holistic... And there's a couple of parts to doing this at scale, which I think people... are underestimating you can pay chat gpt or open ai 20 a month you do and it only lets you send 40 questions every couple of hours yeah okay you can go to bard and use it as much as you want to so google has and is probably only one of the only few companies that can operate the infrastructure at a scale that can also power these things yeah
SPEAKER_01: So it's a cloud play as well.Anyway, this is fascinating.It's a whole ecosystem play.You got to think, you know, with the Lena Khan Biden administration, you know, and the EU and the UK is because obviously UK is not in the EU anymore.The UK's regulatory departments, there's going to be a bunch of microscopes on this real quick. Because these two own 100% of the smartphone market.So this is very strange.The search deal is already under scrutiny.This is now, well, AI plus 100% of the search of the mobile phone market.And remember, there was a rumor...
That Johnny Ive, formerly of Apple, who created the iPhone, and Sam Altman, amongst the many deals floating around that Sam was involved in, a consummate dealmaker, obviously, shout out Sam Altman, that, hey, this was going to be, he was going to make an opening iPhone.So this is really fascinating.Yeah. Maybe this is a defensive play for them to keep the duopoly.Yeah, competition makes for strange bedfellows.
SPEAKER_03: There's a lot of angles to it.
SPEAKER_01: A lot of angles.Well, we have our bet that there will be a model built into the iPhone and the Android, two smartphones, and you're going to win that one, I think, hands down.Yeah. I think that's an easy bet for you to win.So congrats on that.All right, everybody.If you want to watch the show, because we had so many video references, please just type in this week in startups, go subscribe, hit the bell.You'll get the notification when a new episode comes up four days a week right now, AI Tuesdays, and we're going to do a demo show.So we'll book another show this week.We'll try to get the demo show out later in this week.
Follow Sundeep, x.com slash Sundeep, x.com slash Jason for me. If you want to be a mensch, go ahead and write a review on your favorite podcasting app, rate, subscribe, all that nonsense.You can follow us also on all the socials, TWI Startups.See you all next time.Bye-bye.
