0:00
So we have the hand and the gloves.
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0:00
You actually don't have to use a whole robot to be generating data to train the robot.
0:06
Right now we are actually having like more than 500 people in the US as contractors and that's how we train the robot without like needing to deploy it uh in the first place. Helper or Terminator.
0:17
Today's robotic startups are racing ahead and some are more style than substance being accused of fake demos and even suspicious data collection practices.
0:27
So today on Access, we've got Sunday Robotics founder Tony Xiao.
0:30
They're the creators of Memo, a lovely little household robot.
0:34
But unlike everybody else who trains on videos and simulations, they've also got the Memory Glove, an exact replica of Memo's hand, which over 500 trainers are using to collect data on dozens of household chores.
0:46
But first, we hear from Alex live in Davos at the World Economic Forum, where we discuss night caps with big wigs, what was thinking machines thinking, and much more.
0:56
Welcome to [music] Access, >> Alex.
1:03
What's going on over there?
1:03
You buried in snow and whiskey and heads of state >> pretty much.
1:08
I do not know what time it is. Uh, it's 12:30 a. m. here. What time is it in LA?
1:15
>> It is 3:30 and it's a nice peaceful afternoon.
1:20
I haven't been intimidated by any uh prime ministers today. >> Yeah.
1:24
Uh, I guess for those who don't know what we're talking about, I'm in Davos, Switzerland, uh, for the Illuminati IRL, the World Economic Forum.
1:35
Uh, here doing some live interviews for sources, which is fun.
1:40
And, uh, it is a trip being here.
1:40
This is my first time to Switzerland and obviously first time at the at the forum and the conference. And, uh, yeah, man.
1:51
I just uh took Papy Van Winkle shots next to Well, I am and the White House's senior tech adviser.
1:56
So, that's that's been my night.
1:59
>> I think they can arrest you for taking shots of Papy Van Winkle. That's expensive stuff.
2:02
Don't [laughter] let anybody see you doing that.
2:04
But yeah, you look you look cozy.
2:05
You got like a nice wood panel ceiling.
2:07
I see the flag in the background.
2:10
You don't look too much worse for wear, though.
2:12
There's kind of like a sepia tone over your entire frame.
2:16
I don't know if that's just like poor health in general or just the great of the light.
2:24
>> It's poor lighting and poor health.
2:26
[laughter] >> Well, is anybody actually talking about economics or is it just >> Oh, yeah.
2:32
>> circular money exchange as we've come to expect from the Illuminati. >> No. Yeah.
2:36
I mean, every It's kind of funny. I feel like Yeah.
2:37
I feel like I'm just kind of um on the sidelines here because it's such a political thing.
2:42
I mean, at least half of the G7 is here.
2:47
Trump will be here tomorrow.
2:47
And everyone's talking about, apparently, they jam all the cell signals when he's traveling in an area.
2:52
So, from like 1 to 4 tomorrow, there's going to be no cell service.
2:57
Uh, so everyone's like, "Does your Starlink upload work?"
2:59
Like, these are the kinds of things like you feel like you're in like a a TV show.
3:04
Like, it's it's this just weird bubble uh that is really hard to explain unless you're here.
3:11
I mean, I'm staying in a in a chalet that um I'm sharing with like the former head of the NSA. >> Why were you invited? I forgot to ask.
3:22
>> I don't even uh Well, >> they're like, "Yo, we got to have sources. news on the ground." [laughter] >> Yeah.
3:27
Um no, my friend Ash at Brunswick invited me and I'm doing interviews at the Brunswick.
3:33
Um, Brunswick is a PR firm, but I don't know what this says about me, uh, that I can go to a Davos happy hour and like know people there.
3:40
I think it means that I'm probably have overindexed on networking in my career.
3:47
Um, to a point where now I should probably just dial back and like hide in a hole somewhere.
3:50
Uh, or just go off on the mountain here and ski.
3:54
I mean, it's beautiful, man.
3:56
It's um, yeah, I'm like I'm staying on a ski slope.
3:58
Um, and uh the juxtiposition of like waking up every morning and seeing the most beautiful, you know, like mountains I've ever seen and then trekking to the uh the KPMG AI house uh to watch, you know, whatever's going on uh that day.
4:15
It's it's quite something.
4:17
Uh >> what's the highest quality water that you've consumed?
4:22
>> I mean, the Swiss are are very good with the water.
4:24
I will say the tap water is great.
4:26
Um they really love sparkling water. everything's sparkling.
4:28
They're an interesting people.
4:30
They're they're very hospitable, but also I would say independent, you know, like they it's it's just the kind of a >> that tracks with what I've read on Wikipedia about that country. >> Well, sure.
4:41
But like to get, you know, to get back tonight, I was like, "Oh, yeah.
4:45
I need to figure this out.
4:45
I need to hike up the mountain to to come back and pod with you."
4:48
So, >> what elevation are we at?
4:50
Is that helping with the newsletter writing or or uh hurting it?
4:54
>> I don't I'm It's like 6,000 ft, whatever that is in meters. Uh, it's pretty wild.
4:59
I mean, you'll walk down the street here and it's like it's like Eric Schmidt or like a head of state and like instead of an entourage, it's like one person with them.
5:08
You know, there's just it's such a small space to have so many high- netw worth powerful people whether it's from tech, you know, business, politics, whatever.
5:18
Everyone's like regulated to the same space in a way that is just very unusual.
5:22
I've never seen anything like it.
5:25
So, how do you like remember all the conversations?
5:27
Do you have like a rolodex that you bring with you in like a aluminum suitcase or like how do you how do you do that these days?
5:37
You've got to be like a contact app master.
5:39
>> My memory for now is okay.
5:39
I mean, we'll see after a few nights of this how it is, but my memory is uh still serving me well.
5:47
I mean, I have that I talked about this on our CS episode, like I have a scratch pad where I just kind of note app, jot things down, uh, which is only decipherable by me.
5:54
Uh, and Granola is my best friend.
5:57
I mean, I granola meetings and, um, you know, I'm kind of shameless about that now.
6:01
I don't really know how I could do like turn do a live interview and turn around a newsletter three hours later without it, you know, because like the the team has not even been able to process the footage uh, from the interview by that point. So, um, yeah, man.
6:15
I'm I'm uh I'm running hard and heavy.
6:18
It's [laughter] It's a lot.
6:20
>> Do you know the article that I'm most famous for, Alex?
6:23
>> It had probably something with airplanes. >> No.
6:26
Back at Business Insider, I incidentally stumbled on the world's best slideshow series of Android or iPhone.
6:33
Your favorite blank revealed.
6:38
And I did it for like politicians, VCs, executives, sports stars, and it just went absolutely gang busters.
6:44
And so I wish I was there to do a beautiful reprise of that series.
6:49
Find out who's actually using the Pixel phone [laughter] or not, or is it all iPhone Pro Max and orange? >> It's mostly iPhone.
6:58
Um I mean, you've got people from all over the world, right?
7:02
So there are actually a decent Android contingency.
7:07
You could do so many good slideshows like the dignitaries slipping on ice uh as they walk down the prominade here.
7:12
Uh the horrible tidelines on the on the houses.
7:17
Um, I mean they take this little ski town and like the companies build these huge facads on the front of them for one week only that become it's kind of I mean it's like CS a little bit but um the only store original store that is still intact and not has not been taken over is Rolex.
7:37
Uh and that's that's when you know like the clientele is a bunch of billionaires.
7:41
I put this in the newsletter, but it is the only place in the world where you're in the line for the bathroom and it's like a tech billionaire founder and a head of state and uh you know Matt Damon and that is kind of like this place in a nutshell.
7:54
I mean at a party tonight I literally watched like the co-founder of a very famous payment startup have to go like stand in a alt like wait line because he didn't have the QR code to get in and the dude's worth like $9 billion.
8:05
It's just like it's it's absurd man.
8:07
This this place is absurd.
8:09
[ __ ] love to hear that.
8:11
Everyone should wait [laughter] in a line once in a while.
8:16
>> But every I mean I will say like topic-wise everything here is AI.
8:17
Um and but it's like AI it's a bunch of people talking about AI who don't actually know how AI works.
8:25
So the few people who do you know like Daario from anthropics here uh I was watching him in a couple talks today like Demis who from Google who I interviewed for sources.
8:34
um some of the open AI people like they're like gods, right?
8:39
Like they're just all these hangarons and people just waiting just like tell me about you know the impact on the economy and it's like none of them actually know the impact on the economy right but they're here to talk about that and um I don't know it's just a sign of I think how much money and power is intact these days.
8:57
>> Is there a drug of choice at Davos or just trade trading uh GPU credits? >> I'm sure there is.
9:02
I haven't run into it yet.
9:04
I'm probably not at the right parties.
9:06
Uh for me it's Tylenol and and vitamin Z.
9:08
Um but uh there are stories of course.
9:13
Uh I'm sure that's happening.
9:15
Um I did have I had a good bourbon tasting tonight.
9:17
There was lots of good wine.
9:19
Uh but uh I don't I don't know.
9:19
I mean the Swiss are they don't seem like a druggy culture.
9:24
I mean maybe I'm wrong, but I don't I don't I haven't really sussed it out enough to know.
9:30
>> Hard to be precise when you're lagging behind.
9:32
Well, back here in the States, we've had some interesting news.
9:35
Some of which I've read on your newsletter.
9:37
I feel like the one that was most interesting to me was the one that I knew the least about, which was thinking machines seemingly quasi imploding before it even launches.
9:48
C can you tell me some more of that? >> Yeah.
9:52
Um, this was Mirror Morati.
9:52
So, she was the CTO of Open AI.
9:55
She was a CEO for like two days.
9:57
uh when Sam Alman was fired a couple years ago, she started this new Neo lab which is doing you know foundational research in AI and it's been kind of just kind of pottering around.
10:08
I mean they released something but it was a academic thing.
10:10
Um and they've hired really good engineers.
10:15
They've raised a ton of money.
10:15
I think the largest like seed round in tech history like $2 billion.
10:21
And um basically there was this implosion where two of the co-founders left uh around six employees have left out of like a team of 40 to 50 and when AI researchers are you know trading like NBA athletes like I guess that matters a lot more than it used to.
10:39
So people were super interested in this.
10:41
Uh and I think it's because a lot of people also look at companies like this that are pre-product pre-revenue but are like a talent play in AI and like what are you actually going to do?
10:51
And if your company is your talent and your talent leaves, what is left, right?
10:54
And so um I don't know, man.
10:58
I think it just says like how uh fragile all this is and there's so few people at the frontier and like you know they all boomeranged back to OpenAI.
11:07
The people who left, they all came from OpenAI.
11:08
they just went back and now this really buzzy AI lab is like thrown into into disarray.
11:15
So, uh I don't really know what happens next, but um I do I I don't think there's going to be as many um foundational kind of frontier AI labs that are doing well uh in the future.
11:25
Uh I just think there's going to be consolidation.
11:29
I just it's it's too expensive and there's too much attention on it and drama.
11:33
You know, there was like drama about like there was a relationship that wasn't disclosed or something and I'm like, "Hello, >> there is." >> But come on.
11:42
Like every AI researcher in San Francisco was in like a polycule like this like arguing that that's the reason you had let go of someone.
11:50
I mean, I remember like one of the open A co-founders like got married in the office.
11:55
Like this is just I I don't know.
11:58
There's just a lot of uh drama and like um dirt being slung around in the press on what happened.
12:02
And I I I think it's just um there's just so much money at stake.
12:07
>> Are you talking about yourself dirt being slung around in the press?
12:11
>> I don't think I contributed any of the [laughter] um salacious details.
12:14
I will say I said people were leaving, which is true.
12:19
[laughter] Uh but then some others reported like, oh, it was about a relationship or there was misconduct, but wouldn't say what the misconduct was.
12:28
Very clearly coming from, you know, the thinking machines camp. Um, I don't know.
12:34
When you've been doing this as long as I have, you can very clearly see where narratives are coming from.
12:36
And, uh, Marott's camp was very like, I think vehemently trying to um, push back on the fact that her company was imploding by like saying like, oh, there was a relationship that happened months ago and we're just now like doing something about and it's like, okay, like news at 11 like people in AI labs are in relationships with each other. I don't know.
12:57
Just didn't seem that big of a deal to me.
12:58
Yeah, it's interesting given that they haven't really talked about much about what they're building yet, right?
13:05
I mean, I feel like this is one of >> I don't think they know what they're building, >> right?
13:08
And this is like one of the refreshing things I feel like about having Tony uh on the pod who we're going to cut to in a minute here is that I mean he's like humble about it and as you'll hear, you know, I've like known him for a couple years, but like when I first met him >> Yeah.
13:24
And when I when I first started working with him, you know, he was being super humble about having this like apparently this goated research paper that a lot of people were ripping off and that they really were on the frontier.
13:35
And he isn't super loud and proud about it, but it's like wow, someone actually with a academic background and a technical innovation here.
13:43
And now a couple years later, they have an impressive product and brand.
13:45
It just kind of feels like the full story.
13:49
And that's at a moment where it feels like a lot of companies either have some very fuzzy technical idea or they don't have a technical innovation at all and it's just a pure, you know, vertical play or an audience play or some type of rapper play.
14:04
And uh yeah, it seems unusual these days.
14:10
>> Yeah, [laughter] for sure.
14:10
His approach is super unusual.
14:11
I would argue he doesn't really have a product yet.
14:13
I mean, it sounds like his product is the gloves that we talk about that people get paid to use to train the data.
14:18
I'm still very skeptical of like these household robots uh being in homes very soon.
14:25
Uh I don't know, I've just been talking to a lot of robotics founders here in Davos and recently and I just I think there's a lot of hard problems here, but I do think he's approaching it in a super original way and the branding is sick.
14:36
Uh the glove idea is very unique.
14:39
He's obviously an OG in the field.
14:41
Uh, and Sunday, I think, is considered in Silicon Valley to be, you know, one of the key companies and robotics that everyone's looking to.
14:47
So, definitely a great guest and, um, cool that you worked on that and helped him kind of position all that back in the day.
14:56
I don't know, it's it's cool to see it finally come out, right?
14:58
>> I mean, honestly, I like at some point a year, year and a half later, I don't even know what I contributed.
15:03
Sometimes sometimes these company I mean I work with so many startups at a time in various capacities that sometimes they launch and I'm like did I write that and I actually I actually have to search my notion for the tagline sometimes and I'm like sometimes it's there sometimes it's not. Yeah.
15:22
As Tony said even just kind of the helper verse terminator dichotomy.
15:25
Uh if if it was even just that I can I can be proud of that.
15:28
I mean that's what I try to do.
15:30
You know, obviously I can't sell it per se, but that's what I'm proud of is trying to guide people toward a more positive, less cynical vision.
15:39
And to the extent that people feel that it helped them, [clears throat] awesome.
15:44
Uh, but no, I did not I did not come up with the name.
15:49
Back then, we didn't actually talk about it on the podcast, but back then they were called Lebot.
15:52
Like, let me do that for you, which is pretty cute and catchy in its own way.
15:57
I think Sunday is just kind of badass because I mean it's just one of those names that's like super simple and evocative and all the chores happen on Sunday and so it's just a little too perfect.
16:08
Um but Lebot was cute as well. >> Let me cute. Yeah.
16:13
I mean it's like Access, right?
16:15
Like simple, evocative and you listen to Pods doing the chores on Sunday. >> I hope so.
16:19
Shall we cut to the conversation with Tony? >> Yeah, let's do it.
16:29
How are things going in in Robo World since I saw you last?
16:31
I feel like you've gone through a couple company names at this point since I last saw you when we worked together like I think it was September 2024.
16:39
I mean, I feel like so much has changed. >> Yeah. Yeah, it it was good.
16:42
Uh I think the the naming thing is kind of funny that we've went through like I would say like three to four names like a week before we announced out of stealth.
16:53
we're call Saturday and then that's when we already have all the marketing materials, all the website, all the logos, everything ready and then we realized we made a very dumb mistake of like hey there's this other company also called Saturday Robotics and they have this trademark there and [laughter] realized that hey we should probably not be doing it.
17:11
Um, and like I think everybody was like like it's all hands on deck. Let's change our names.
17:16
We have like seven days left.
17:18
Change all the swags, change all the websites. And I think we did it. Um, yeah.
17:22
And I I I actually think that Sunday might be a slightly better name than Saturday. >> Wait.
17:28
So, do you do you need a lawyer or do you need a new lawyer? >> Yeah. Like it's a fun journey. Yeah.
17:36
>> Well, I feel like you netted out with something kind of perfect.
17:38
I mean, my favorite brands and taglines are always subverting something unexpected, and Sunday is like the day of the week people hate the most.
17:48
And I think there are a lot of people who would say, oh, we want to avoid the negativity.
17:53
And it's like, well, not if you're going to solve it, you know, and uh I think it worked out well.
18:02
I mean, was that the intention behind the name?
18:05
>> You think about like laundry and you think about like Monday, right?
18:09
And it's extra bad if you are worried about Monday while at the same time you need to do all your chores.
18:13
Uh so yeah, I think our goal is to give the Sundays back to people.
18:18
Uh so can they can actually enjoy it just like Saturday? >> If [ __ ] Monday.
18:22
com we're preempting with Sunday. com.
18:27
>> Tony was was idea Saturday or what did he contribute?
18:30
Was did he have you on Friday at one point?
18:32
[laughter] >> Well, Ellis has been really helpful.
18:35
I think we we've been talking to each other like way before we are um even planning to be out of stealth, right?
18:41
I remember we were having this early conversation about like how should we position ourselves as a company, what kind of vibes um like our brand should have and why we should not be building another Terminator robot and try to put it in people's homes.
18:54
I think we align very well on this um on this front.
18:58
Yeah, we were talking about helpers, not Terminators, but I mean that was honestly just like full trust at that point because Tony had no robot and no brand at that point.
19:08
And I was just like, if it's going to look like a helper, then yeah, let's let's do that.
19:14
And um I feel like it it turned out to be so cute.
19:17
And I've been playing so many like Nintendo games with my four-year-old.
19:21
I've come to realize that like iconic ovalshaped Kirby eye shape that you're kind of borrowing from a little bit or like the eyes on all I mean Nintendo puts those eyes on everything whether in Mario Wonder it's like uh a pipe or a star or anything else.
19:38
How did you how did you arrive at that?
19:39
Who who designed the the face?
19:42
>> Yeah, so we we have an industrial designer in-house that did like all the designs.
19:45
But I I think the funny thing here is like it almost feels like slightly controversial that now the robot has a face because most of the human robots actually does not have a face.
19:56
You probably saw the G1 unry robot. It's it's hollow.
19:59
There's nothing or it's like a fa like a black mask, right?
20:04
So I think we're we're almost doing the obvious thing that like if there's going to be robots everywhere that you need to talk to them, they should have a face uh so that you can actually make eye contact.
20:14
Um and but again like you know all the great work coming from Shai our industrial designer and he kind of like I saw the evolution of this whole design kind of starting from the hand uh from the gloves that we're making that we probably should talk like a lot more about uh in this podcast as well.
20:32
But uh kind of seeing it kind of design style propagated to the arms which are also like kind of rectangular uh and and smooth uh and to the to the body and eventually the face and and the hats.
20:46
>> The other thing that jumped out at me from our collab which feels like a million years ago at this point was that your emphasis on time I think was a little different than a lot of folks in tech.
20:58
I mean, I don't want to like throw anybody under the bus, but it's like I've just worked with so many AI productivity apps that talk about saving you time.
21:06
And for the most part, when you're at work saving time, you're just going to do more work, right?
21:11
Whereas, when you're saving time at home, it's almost like, oh, I could just go do something else now.
21:18
It's like almost a completely different paradigm that tech isn't usually touching with that proposition. >> Yeah. Yeah. Absolutely.
21:24
It's kind of the the thing that actually people don't want to do.
21:31
Um, nobody want to do their chores.
21:33
Um, and yeah, I think there are just it would be kind of ridiculous if the doctors are the journalists or like scientists are replaced by AIs and we're left with auto chores.
21:47
It just doesn't make sense. >> Oh, hey.
21:50
Journalists replaced by AI. Oh god.
21:55
Alex, you're going to be you're going to be stuck doing laundry.
21:56
That's going to be your full-time profession.
21:58
It's going to be a subscription service.
22:02
>> My wife makes me do laundry.
22:02
So, it's, you know, it's nothing new. I I will happily do it. That's fine. >> Yeah.
22:08
Speaking of laundry, uh my very first job was at Urban Outfitters.
22:11
I don't know if either of you know this, but uh I was scolded early on for not folding shirts well enough.
22:19
And I will just like take that insecurity to my grave.
22:24
And like I go so [ __ ] hard folding my shirts now.
22:26
Like I started learning to fold with a board.
22:28
I don't know if you guys even know that there is a folding board.
22:32
>> Wait, you you do that?
22:32
You actually fold with a board?
22:36
>> No, I'm good enough now. I'm a 10x folder now.
22:40
>> But like I I mean I was wondering like Tony, is there a specific folding methodology that you are adopting?
22:44
Are there any haters for this methodology?
22:49
Uh it's kind of weirdly controversial depending on where you are in the world how you fold your shirts.
22:54
>> Yeah, I think voting is I think relatively people agree on what is a good f looking like.
22:59
Uh it doesn't like >> you haven't worked at Urban Outfitters, but go ahead.
23:05
>> But I think the the place you have the most amount of attention is how like dishwasher should be loaded essentially like especially when there are like a ton of dishes, right?
23:13
But I think when we um we actually need to design these tasks uh when we teach the robot, it's not just like hey uh we get a lot of people collect data whatever you want it to be and then we just put the whatever the end result is and into the robot. That's not what we do.
23:31
And it's actually a a big trade-off between uh whether uh the specific way we fold is easy to learn for the robot and whether on the product side is actually something people want.
23:42
And at the same time it shouldn't be like um like ridiculously difficult uh that it might take like infinite data to to learn.
23:51
Um and I think we see that commonly that um when we make all these decisions about a robot it always tends to be like three or four categories of uh things that we need to jointly consider and like even how to fold the t-shirt was like a big part of it.
24:08
>> I know there's drama around the dishwasher methodology as you uh cited.
24:13
Actually, I found a meme online that I see go around every so often. It was from Twitter.
24:17
This person said, "In every partnership, there is a person who stacks the dishwasher like a Scandinavian architect." That's me.
24:24
Uh, and the other person who stacks the dishwasher like a raccoon on meth.
24:29
I would apologize to my wife, but she's not listening.
24:34
[laughter] >> Oh, my Yeah, Chloe's the same way.
24:35
So, you and I are both like dishwasher orderly people.
24:40
That's interesting to know about us because we're different in a lot of ways, but we we do that the exact same way.
24:46
>> The right way, you mean? >> The right way. Glasses in the back.
24:48
You got you have to put the stuff that's dirty first in the back.
24:51
You don't want to put it in the front.
24:53
You can tell someone's personality by how they stack dishes whether it's in the front or the back in my opinion.
25:00
>> Well, does Memo have a point of view on whether you should rinse before loading or load with the food on?
25:05
because I know there's controversy around that as well with the Cascade Platinum Reddit crew.
25:11
>> To to add context here, there's this whole uh I think a whole debate, right, of like the dishwashers will be able to clean your dishes better if you don't rinse as much.
25:21
There's a whole like theory around it.
25:23
Um but I think for us um like when we teach a memo, I think it will rinse.
25:30
We we do not have like big chunk of food and inside a dishwasher which I think is a is more more there's more downside than upside it can give. Yeah.
25:40
>> Well, you know that's big dishwasher that tells you that you need to rent.
25:43
That's that's the lobbying operation.
25:45
[laughter] [gasps] >> That's uh Bosch uh and co for those who missed the last episode.
25:51
Bosch's tagline from CES.
25:53
The more you Bosch, the more you feel like a Bosch.
25:56
[laughter] That's not a joke.
26:00
That was their actual tagline from the last time around.
26:01
Well, I know like a lot of the models for robots and otherwise.
26:05
Um, we also talked to the CEO of Rivian recently.
26:08
They talk about how they don't actually program rules of any kind.
26:13
They just gather more data.
26:15
Um, is that similar with you guys as well with the glove? >> Absolutely. Yeah.
26:21
It's just that um I think if you want robots in the real world in the open world like you cannot if else all the things that can happen um and trying to engineer that is kind of you'll be kind of cursed uh if you try to enumerate all the things that can go wrong.
26:39
But the nice thing about data is that um all of these if and else are somewhat included inside the data.
26:44
Uh so if we can get very diverse high quality data and enough quantity of them uh we don't actually need to do much uh like real coding but the neuronet network will learn from the data and and that will ends up being better.
27:00
>> As someone who's very close to this stuff are you more or less optimistic than the average person about riding in a Whimo or Tesla self-driving? >> I own a Tesla.
27:10
Uh I self-drive to um to office every day.
27:15
Um, >> you used to work at Tesla, right? >> Yes, I I did.
27:18
Uh, I spent a summer there back in 2022.
27:21
Uh, that was actually before the whole like end to end uh scheme happening there.
27:26
Um, back when things are not working so well like these days they work very well.
27:31
I feel like I get more optimistic uh when of like going when seeing things that doesn't work and believing that it will go to like 99%.
27:42
But um going from that to robot taxi that is fully fully autonomous and can scalably be deployed in any place.
27:52
I think there people might have like underestimated how much work there.
27:56
So I think I'm more optimistic for always the the first chunk of it and the second lag is like uh more challenging.
28:03
>> If we could get a little more technical for a second and and robotics are in the brain for me Tony because today I interviewed uh Demisabus.
28:09
You used to work at Deedmind right?
28:12
Uh so yeah so Demis and then um the co of skilled AI which just raised a big round and is doing robotics in the enterprise.
28:20
Uh and everyone is approaching it differently but Demis and um and and PC from skilled were saying like they think enterprise is the right way to attack this problem from a data perspective.
28:34
It's interesting to see you do it from the home you know going towards the consumer route.
28:41
Uh, and I'm curious like how you landed on that as the way that you all were going to go versus just doing the more kind of boring but stable, safe enterprise route. >> Yeah.
28:52
So, I I think this is maybe a hot take, but I think there is a myth around like industrial automation or like in factories is an easier like thing for the robot to do.
29:07
And that might be intuitive for people, right?
29:09
intuitive for people, right? there's more constraints uh like there's clearly well- definfined things that you should do but I think the broader context here is more like if you want to do robotics like pretty much everything is hard like 100% of the things will be hard and putting in that context I think the
29:27
relative like the hardness difference between uh going for the industrial automation or going for like homes I think it's small in that perspective and then I think the question becomes like hey are we really asking the right question of like trying to find things that are easy because everything is hard. Um and what we end up with there
29:45
Um and what we end up with there is like I think we just want to work on something that is really inspiring and exciting to people and that will help us build the best team and that will help us gather people who are like truly excited about the mission and like putting the extra work putting like all the ingenuities uh into making that a reality.
30:05
Um, so I think that's the maybe the hot take here is um in the grand scheme of things I think being like 20% 30% harder or easier really doesn't matter that much.
30:17
Um, and I think there is also a a a different viewpoint here uh when it comes to home versus uh like like say factories, right?
30:29
Uh one of the big thesis we have is we believe robots will win on the availability before they win on the sheer like productivity or like the speed.
30:43
So you're thinking about like factory automations, you're essentially competing with hourly wage workers and essentially humans and human hands are so perfect after like millions years of revolution and we're so intelligent and it's actually really hard to beat the throughput and the precision that human has.
31:05
has. But these are absolutely critical in factories because if you're slow like the whole line will be blocked by you and if um your success rate is not high enough maybe you'll be damaging some parts and that's like causing real
31:18
economic damages right so if you think about it in in that context home actually becomes quite easy because the type of work that we need to do is not like rocket science it's not like assembling an iPhone is essentially things that a six-year-old knows how to do. And it's one of the few places that
31:38
And it's one of the few places that you can provide a lot of value by like maybe giving people a cup of water when they're tired and maybe refilling that water like time over time, making sure it's always like the perfect temperature and perfect uh volume there.
31:51
The chores are they come like every day.
31:54
Every day there is new like 30 minute to an hour of chores getting invented in your homes.
32:01
But you cannot just have a housekeeper to come in for like 30 minutes and you let them go, right?
32:04
You either are like super rich and you have someone in your home the whole time.
32:10
They're your like housekeeper.
32:10
You pay like a full-time salary for that or you actually need to do it yourself.
32:14
And so the time we save becomes not like competing with a hourly wage worker but becomes your opportunity cost of like hey if every day after war you have like three hours you can spend and if you can save one more hour there is actually quite significant.
32:31
So it becomes like almost like a labor arbitrage that if you're making more the robot will be more useful to you earlier and that allows us to kind of get into the market like gradually as opposed to like we're spot on competing with human dexterity like from day one.
32:51
>> I get so mad at people who have three hours after work every day cuz clearly they don't have kids or they're super rich as you said and they have their own assistant or butler or something. thing.
33:02
I was like, I have approximately like 43 minutes of free time per day.
33:05
And if I have some music on, you know, doing the dishes or whatever is not the worst thing in the world, but when that is very literally the time that I have to like read a book or play a game or spend the time with my wife, it's like that is very valuable time.
33:21
And so I do uh I do see some some need there.
33:25
And I know the rumor is that Apple's working on something like this as well, right?
33:32
>> Yeah, I think um pretty much everyone is.
33:35
Um if you look at the latest demos coming from Tesla, from Figure is all about the homes, right?
33:41
>> Do you think we actually have robots in the homes of like everyday normal people though in the next even five years? I don't know.
33:50
Like when you what you have is so well thought out.
33:52
It's like the videos, the branding, the positioning, it really resonates. It's very intentional.
33:59
Shout out Ellis, I'm sure, for a lot of that, too.
34:01
But when do you think this actually shows up for people in a way that's like costconscious, that's accessible, that's scalable? >> Yeah.
34:11
>> Yeah. So I think the way we think about it is that as the hardware costs get lower and as the capability of the robot in terms of like the AI the neuronet network expand over time let's say in the beginning can do one task but over time he can do like a thousand tasks right I think the product itself is
34:28
inevitable that if it cost you a few thousand dollars and he can do all the chores in your home I think everybody will love it right but then the question really becomes like how can we get there I think we all agree that the end goal is is very clear uh can we get the cost down fast enough? Can we get the AI
34:43
Can we get the AI capability to go up fast enough?
34:45
Um so I I think I would say like for us we certainly believe that home robots will be in your neighbor's home like maybe sooner than you think definitely within the next uh 5 years.
34:58
And we're actually running a beta program uh this year uh towards the end of it to be putting it in real people's homes.
35:05
That's actually the thing that like really drives us as the team is that it's not 100% sure yet.
35:13
It's not like set in stone.
35:13
So, we're almost like driven by these a like urgency uh to see how can we like optimize things a little bit better so we can get this out of the door like a little bit sooner.
35:25
Uh so I think it's u it's part of the excitement uh that we have is that it's not clear yet.
35:30
The Neo launch was super interesting to me because again it was really good branding.
35:36
Uh the keynote was awesome and then you realize it's like oh no they want to charge you like $20,000 to do data collection in your home with a tea operated robot.
35:47
I don't know if that's your strategy Tony with Sunday. Sorry if it is.
35:52
Uh that's I think that's not going to work at scale.
35:55
I think like you can sucker a few early adopters into doing that, but uh you have to have value and not like trick people into actually being the customer, right?
36:03
Uh in a way that they don't know that they're the customer.
36:08
Like you know, you can buy the robot, but you're actually feeding the data back to the company and that's the reason they're doing it.
36:14
You're actually giving them more value than they're giving you, right?
36:15
And you want to you don't want to do that, I assume, with Sunday. >> Yeah.
36:19
I I think for us is um our opinion is it's not worth shipping a not autonomous robot.
36:25
And the reality is like when people think about robots, robots should be autonomous.
36:30
It should be by definition autonomous, right? It's a robot.
36:34
It's not like a human like wearing the the shirt of the robot, right?
36:38
So I think what but uh the reality is right like if we do not have data like the AI isn't there and without the AI will not be autonomous.
36:47
So we have almost this like chicken egg problem there that without deployment you cannot get a robot to autonomous enough to be useful.
36:55
Um so we're actually like thinking about this problem since the early days of like how can we break this chicken egg problems and what we end up with is the whole idea of using gloves to collect data.
37:05
So essentially instead of like getting robot to your home and get someone else to like log into your robot and collect data in your home all we do is we build these gloves.
37:16
Uh, I actually have one here. Uh, it's right here.
37:20
>> It just gave us the finger. Thanks so much, Tony.
37:22
[laughter] >> So, it's uh we have the hand actually here and the gloves.
37:27
Um, so the cool part here is that you actually don't have to use a whole robot to be generating data to train the robot.
37:36
All you need is to wear these gloves and do the task and we'll be able to like leverage that to train the robot.
37:43
So what that allows us to do is we don't have to get data solely from our customers.
37:52
Right now we are actually having like more than 500 people in the US as contractors and it's part of their work to be like just wearing the glove and do the chores and we'll pay them for that and that's how we train the robot without like needing to deploy it uh in the first place.
38:08
>> How much are we paying for this?
38:08
I mean can you hook a hook a brother up?
38:09
I mean I'm an entrepreneur now. I need some side income.
38:13
I think they're they're actually paid very well.
38:15
Uh in terms of uh wall clock time, every hour they spend uh they get paid like $40ish which is very good.
38:24
Um and the additional nice thing I think it's actually a very good job as in like you don't need to leave your home.
38:31
You can just stay there and you can do it anytime.
38:33
You can do it for like 10 minute in the morning, 10 minute in the afternoon and 10 minute at night, right?
38:38
And we don't control for that.
38:38
uh all we need is to get like this amount of well-instructed uh like you know clean data um from your home which brings us the diversity and the dexterity that's needed.
38:51
>> Is that like an ongoing project in terms of the training or do you feel like at some point it's going to magically be done?
38:57
>> That's a very good question.
38:57
I think I think we'll be I think the gloves will be here to stay.
39:02
We're going to be using it for a very long period of time.
39:05
uh just because if you think about places where AI works the best, it is when it has almost access to unlimited amount of data, right?
39:16
We have the whole internet to train the large language models and these days people are still paying like mercury or like scale like crazy amount of money uh to label their data.
39:24
of money uh to label their data. uh I don't think robotics will be a um a kind of exception to that and is probably even in the other direction of like in robotics we do not have a whole internet scale robot data to start with so we
39:39
have even more work that we need to do so I think we need to like yeah like the data collectors and um who wear the gloves um we're expanding them actually quite uh quite a bit uh this year so that we can >> yeah how many people are wearing gloves loves doing dishes right at this very moment. Do you think? Do you think?
39:58
>> But how do their wives feel about it?
40:00
[laughter] >> Sometimes both them and their wife do it together, which is >> that's romance.
40:06
>> That's 80 bucks an hour. [laughter] >> Yep.
40:10
So uh at this point uh we have around like slightly above 500 people uh all in the US collecting data for us and we're going into the thousands this year. >> Yeah.
40:21
And I know there is the design of the product, the brand, uh some stuff on the software side, I'm sure, and how you interact with it.
40:28
But like what percent of your moat would you say is this data that you're collecting with the glove?
40:35
Cuz I'm not sure that anybody else is is doing that, are they? >> Yeah.
40:38
So I think our approach of building a glove that is co-design with a hand, I actually I don't think I'm aware of any company that is doing uh something.
40:47
I think there are similar approaches but uh not exactly the same.
40:51
>> Everyone in robotics talks about the hand as a problem, right?
40:52
Like it's one of the top problems but you're right like to my knowledge no one is doing this as a data source like you are. >> How can that be? Yeah.
41:01
And this is actually kind of reflective of a a broader thing of like how we think about systems in robotics.
41:08
So people tend to think about robotics as like singular things like the hand uh now the data and maybe the the legs and like something like that but it's actually a lot more interconnected uh than look like.
41:19
interconnected uh than look like. So for example uh if like it could be true that let's say the bottleneck is the hand and you can build a very very dextrous hand but the problem is like how can you get a lot of data for it like what if
41:34
without the data the whole thing doesn't work no matter how good the hand is right so what we do is >> you can make a really nice fake video if you want [gasps and laughter] >> I feel like many have been accused of that in the last year >> yeah I will not comment on that uh our videos. [laughter]
41:50
[laughter] >> Um, but I I think it's actually more about like instead of building the most dextrous hand and call it a day and that's the optimization that we want to do, we're like, hey, can we build a hand that is so cap is capable, but we can also get a lot of data to unblock the AI part.
42:08
So when we design the hand, it's actually not about like, hey, let's get the most amount of fingers out of it. Let's get five fingers.
42:13
Let's get five fingers. let's get like 22 27 whatever degree of freedoms and pack all the motors in but can we design it like together with the glove and the glove is something that people can wear and efficiently collect data so I think when it comes to like for us uh in terms
42:34
of the mode as a company I actually think it's less about uh whatever data that we have because we're going to have so much more than that but always approaching these problems with a global knowledge of like AI, of software, of hardware and like the whole thing together like controls. Um, and that
42:51
Um, and that kind of always steers us to making decisions that is slightly different uh from the rest of the the field.
42:59
And I think that will be the the kind of the core competency that will continue to uh to own and um that will continue to help us make bets that are somewhat unpopular in the beginning but are post obvious. >> Yeah.
43:14
I mean what can you tell us about the other stuff we've seen in the space?
43:19
I know there's been different demos that kind of have or haven't been real visualizations of robots capabilities.
43:24
I mean does that bother you when you see that stuff?
43:29
Like what type of stuff seems not real when you see it going viral on Twitter?
43:34
>> The way I think about it right now is like videos are easy but like uncut videos are slightly harder but live demo is the boss and real deployment is the final boss, right?
43:45
Um so if you look at videos like let it be from researchers or companies, it could be the one time that it works out of like a thousand tries, right?
43:56
[laughter] The success rate could literally be 1%.
43:57
Uh but as we encou uh there are companies that are doing live demos in front of like hundreds and thousands of people and I think that's something that I respect a lot uh because that is when you have the true confidence to like show it in front of people as opposed to like like hedging it and be like Hey.
44:28
Uh, as long as we got a video out, we're all good here. Yeah. Did we miss you at CES? >> Yeah. Were you there? We saw ping pong.
44:35
We saw they were they were boxing. >> Yep. >> Yeah, it was cool. >> It was cool.
44:41
>> Well, what what of that stuff is real and what isn't?
44:43
>> I would say the ones that I saw um that are SCS uh namely like the for example ping pong, the boxing uh and some of the like um I think grasping objects from the shelf.
44:55
I think they're all real. They're all uh 2. 0 is driven by real AI.
44:57
They're not like there's nobody.
45:00
Well, the boxing one has someone like kind of remote controlling it, but the controls of the like the fact that the robot is not falling down is autonomous.
45:08
And to my knowledge, the all the autonomous demos of the uh like there's a folding a windmill and like taking photos with the dextrous hand, those are all like real and autonomous.
45:21
>> Yeah, it can be tough to tell.
45:21
Sometimes the outside looks shinier than than you know, if you will, how well the inside works.
45:29
I mean, if you look at one of the ones that we saw at CES, the uh unfortunately named LG Cloyd kind of has a cute face.
45:37
I mean, is that the one that looks like most similar to Memo?
45:41
I'm not sure exactly what it can do or how it works, but it looks polished.
45:43
And I think as the average person, you're like, "Oh, it certainly looks like it works."
45:49
But I mean, are you basically telling us that without the glove data, some of these household chores just aren't possible yet for anybody else?
45:56
I think for now that totally seems to be the case.
46:00
Um, and I think I will have it would change my mind if they they're willing to live demo it like many many times in front of the like the audience.
46:13
>> Yeah, speaking of memo is definitely a better name than Cloyd.
46:15
Imagine welcoming a guest to your house. Please come on in.
46:20
Allow Cloyd to serve us some amuse bouch.
46:24
>> It sounds a little like uh I don't know like a some evil [laughter] things.
46:29
>> I mean the e the some are some are like actively going for the evil vibes or the transformers vibes like Optimus or this or that and like Optimus is going to fall over and squash your cat.
46:39
like Elon doesn't really seem to care about, you know, that stuff in the short term, but if you look at it, I mean, logically, the way that you design it shows you what its goal is.
46:54
And when I look at some of the others, the goal is to look impressive.
46:58
And I mean, I assume you guys looked at the legs and you're like, "Oh, we don't need that unless we want to be vain about it." >> Yeah.
47:05
And I think a lot is about really the safety.
47:08
Um, and if we want like flashy demos, legs are are are great.
47:12
Uh, they look like humans.
47:12
Um, but and I I think also people wouldn't disagree on the fact that like if you look 10 years into the future, like the robot may all have lacks.
47:22
Uh, we'll all converge into that eventually.
47:24
But I think the question is more about like do we can we earn the trust of real consumers uh or other customers with whatever the robot that we're building right now.
47:35
And that will give us the ticket to build that ultimate robot that is like can do everything right.
47:40
And I think just prioritizing safety uh in a almost like a passive way that like if you just unplug the robot it's battery uh while he's doing something it will not fall over um is going to be like helping us gain the trust uh so that people know that we consider those uh edge cases. >> Yeah.
47:59
I saw on your site you have something called compliant control which is that you could interrupt it and it won't just like fall over or this or that.
48:08
How exactly does that work?
48:08
Like can you just kind of shoe its hand out of the way or how does that work? No more drinks for Alex. He's he's being cut off.
48:17
>> I needed that tonight.
48:17
I needed [laughter] that tonight.
48:19
Guys, >> I I think the idea there is um like when when the robot is doing certain things, right?
48:26
is essentially delivering the hand to where it wants to be.
48:27
But the rest of the body, let's say where your elbow is, doesn't have to be at the exact elbow position where it is right now, right?
48:36
Let's say your you you have a kid and they bump into the robot.
48:37
The robot shouldn't be there like rigid, but it should kind of tucked in and maybe like yield when uh someone hit it.
48:44
like yield when uh someone hit it. Um so this is what we mean by compliance that it is not like a industrial robot that all it does is to follow the rigid instructions going from point A to point B and no matter what you put in front of it it will it will destroy it right
49:01
that's not how we think a home robot should be I think to achieve that is again a mix of like very um very deep into the system side that we need to choose the right hardware and we need to do build the right electrical system so that we can enable the hardware to be compliant and then our model also need to consider that. Uh so it's it's always
49:19
Uh so it's it's always like a complicated answer that involves other parts of the system that we need to like put in like right at the beginning.
49:26
I think I need to upgrade my hardware.
49:29
Uh because I break glasses and plates constantly, uh expensive ones.
49:34
And it I mean it just makes you wonder like are we going to see silverware, forks, knives, plates, bowls specifically designed for robot >> robot friendly? Yeah. >> Yeah. Robot friendly.
49:48
Or do we not have to worry about that because Memo can handle your grandma's china just fine?
49:53
I think that's a that's a very good question of whether there there exists like robot friendly uh like utensils or um >> Oxo good grips the most goated brand in all of kitchens worldwide with those little rubber grips on them. I love that brand. >> Yeah.
50:15
>> Yeah. And I think the maybe the broader question here is how can we it's like when we deploy a robot into people's homes I think the adaptation of course the robot will adapt to how you live your lives right but I think human will
50:31
also adapt their behavior to the robot for example like um let's say you have a butter knife that is very thin and for some reason after you eat you just really want to put it on a table and is just essentially flat with a table and robot really struggles to pick it up, right? And what you you could do is
50:47
And what you you could do is like, you know, keep doing this and the robot keeps struggling.
50:51
Maybe it take like five minutes to pick it up.
50:53
But what you could also do is put it on top of a plate and then it's infinitely easier for the robot to pick up without you actually intentionally trying to do things that are so different.
51:04
Um, so I think there will be things like this happening um in the future that we will also kind of evolve our lives a little bit in exchange for more convenience that a robot can bring us.
51:17
that a robot can bring us. Uh, for example, when you're choosing a new coffee machine and you realize there's one one of them uh the robot knows how to use but the other one doesn't and they maybe produce like a similar amount of like quality of coffee and I think people will go for the ones that uh they
51:34
can have the robot to do right um so I think it's a exciting future to for us as well is that we don't need to handle like a million types of uh coffee machines but we like only need to handle the ones that are more popular and uh we can kind of change users behavior a little bit by steering them into getting like the ones that works well. >> How long before we get to what you're
51:57
>> How long before we get to what you're talking about?
51:58
I mean, I've asked before like when are these going to be in homes, but uh are is this like a 10-year vision?
52:04
Is this a fiveyear vision?
52:04
Like how far out are we from this actually in your mind?
52:10
>> Yeah, it's definitely less than five years.
52:11
Um and >> really, >> yep. >> Wow.
52:15
Because like if if you think about where are the fundamental bottleneck is is data.
52:19
Like I think the I'll put another hot take here is I don't think robotics like training an AI to um to like do households right is fundamentally harder than like math olympiates or like all the crazy reasoning models that we see right there.
52:39
In terms of the fundamental AI architecture, I think the rest of the fields like large language models or vision are actually quite ahead.
52:46
And the only reason that the robots cannot do the choice right now is the lack of data.
52:54
That is almost like the singular biggest bottleneck that is hurting progress because without the data you cannot put in like do very meaningful research to iterate on figuring out what are the right methods uh and figuring out all the science questions.
53:08
we're just like blocked there.
53:09
But once we're able to unblock the data part, I think I think the capability will be more like an exponential growth than like we're kind of stuck somewhere.
53:18
Uh so that's like kind of how we think about it's all about the data and we want to get as much data as diverse data as possible.
53:26
And you think the best way to get the data is the hand versus just like training on videos of humans interacting with the real world or all the you know how Elon talks about FSD is the way that you're going to learn for Optimus like everyone has a theory of how to get the
53:43
training data right now right and yours is a very unique one I have to give it to you like I haven't seen anyone else approach it that way um and you obviously have conviction that that is the way and I'm curious like you probably evaluated all the other ones like are all the other ones just dead ends? >> I don't think that that's how I'll put
53:58
>> I don't think that that's how I'll put it.
54:00
it. uh is I think it's true that right now I think robotics is still at the at the fun part that uh people are debating right and these days in in large language models people don't debate about like whether you need training right you're like this is like give me
54:15
money [laughter] I'll give you the the corresponding results right so there's still a fun part that people disagree uh that um there are many fun ideas uh that are uh people are like kind planning to like combat each other with narratives essentially uh but given the amount of
54:32
information and the concrete evidences that we saw we think dealing with gloves like collect the data transferring to the robot is the best way but this is not it's a strong opinion that is I would say like weakly held as in if there is another breakthrough that we
54:49
see that changes some of the assumptions we're more than willing to like update our prior and figure out how to best leverage that right and that can come in a lot of different flavors let's say world models that people talk a lot about these days. Uh it could be
55:02
Uh it could be simulation, it could be like anything.
55:06
Um but the piece that will never change in my opinion is like real high quality data and that is going to be the concrete thing that we'll have either way uh that would differentiated us no matter what's the downstream algorithm is whether it be like training the robot directly with like reinforcement learning with imitation learning or more models.
55:25
I think we're largely open-minded about how to use it.
55:27
And in five years, do you have a sense of what market it's going to hit in first?
55:33
I mean, I feel like intuitively I would think, you know, elderly folks, like people who have the most trouble uh with the chores and may need it most.
55:44
Is that your instinct as well?
55:47
For the early uh customers, we do think there will be a period of time especially in the beginning that the robot will give them more trouble than it helps right and in the beginning maybe it's like um you need to learn how to do the things in the way that the user wants uh and in the it might make mistakes.
56:08
Um, so I would say when it comes to the early customers that we'll be addressing, I don't think we'll be aiming at people who like almost like desperately or like have to use it.
56:22
Uh, but we'll start with people that the robot is a very nice to have.
56:26
Uh, but you know just elevates their their standard of living uh by a lot.
56:31
Um, so I don't think we'll be going for like elderly care uh from day one, but that's eventually the goal for sure.
56:38
What's the robo narrative at Davos, Alex? >> Oh my god.
56:43
I mean, it's all like jobs, you know?
56:46
you know? It's like this is yeah this is such a different group than uh what we normally talk about on the podcast but um I I do think everyone's like oh you know if robotics really hits uh and there's millions of these robots out in the world you know these are a bunch of policy folk like how's that going to impact hiring and
57:08
GDP growth and all these things and um I don't know everyone in tech that's building this stuff seems to think that it's actually going to be an accelerant for everything that it's not going to hurt uh economies, but it will uh free up um more people, you know, to do things that they want to do or just create net new jobs. So, yeah, there's a
57:26
So, yeah, there's a lot of like dueling narratives and dueling viewpoints, but um I think everybody agrees that like robotics is at an inflection point, right?
57:34
And that's like that seems to be the consensus here for sure.
57:37
And Tony, like I I assume you agree with that.
57:39
You wouldn't be doing this company if you weren't, you know, uh believing that.
57:46
But um yeah, like how do you how do you feel about the jobs piece of it?
57:48
Do you do you feel like uh we're going to find different kinds of jobs, more fulfilling kinds of jobs that get taken away uh you know from robots or how does that work? >> Yeah, for sure.
58:01
I think the the whole mission for us is to give people time back so that they can enjoy they can spend time with their families, they can spend time with their interests, right?
58:10
Um, and I think there's almost like a like not human part of doing chores that are so repetitive, that is so boring.
58:16
It doesn't highlight what makes us unique as as humans.
58:20
Um, but at the same time, I think it's um like because of our uh positioning and the current type of work uh that we want to tackle is really not going to replace anyone's job at all.
58:33
uh is all about like saving people's time because they need to do it themselves, right?
58:40
We're replacing like software engineers time to do like chores uh as opposed to people who really need uh to um to feed their families to do like housekeeping.
58:50
I don't think we're touching their um kind of their uh unique proposition at all because again um it might be funny to say but we as a robotics company we do not bet against human dexterity because it's so good and we always find place that we can complement them as opposed to replacing them because like from hardware software AI we're not close [laughter] in terms of getting like a human hand let's say that's why people are talking about it.
59:22
Um like the hand is the bottleneck, right? Um yeah. >> Yeah.
59:27
I wonder what simple chore do you now have the most respect for after digging into the hand dexterity.
59:35
Uh or otherwise it's like oh man cleaning a toilet. Unbelievable skill. >> Yeah.
59:40
I I think just even just using like shirt folding as an example, right?
59:46
We can fold these shirts in in the midair.
59:49
We fold it in a very dynamic way. We like fling it.
59:51
We use our hand to like kind of tuck it in.
59:53
Um and these are things that are very hard to learn for the robots.
59:57
Uh that I think I definitely come to appreciate that more uh like as we work on these tasks.
1:00:02
Uh the other thing like as I said for for bathroom cleaning, there are like harsh chemicals uh that we really don't want to get to the robot, right?
1:00:11
It might like have beautiful paints on it.
1:00:12
if you got like the the bleaches on it, it will not look great.
1:00:16
Um, and so when there are like um I think when there are you need to interface with uh very harsh chemicals and you need to apply a high amount of force, those are still like um I think remains very difficult for the mechanical side.
1:00:35
Just had a vision of somebody walking into my apartment, me saying, "Sorry guys, my Cloyd is covered in [ __ ] I got to go wash him off. >> Oh god.
1:00:46
Maybe we should end it there. >> Oh, Cloyd.
1:00:48
[laughter] >> Well, Tony, thanks for joining us, man.
1:00:52
It's been great to uh reconnect after all this time.
1:00:54
And uh yeah, super fun to see what y'all are building.
1:00:56
I mean, it just, you know, there's so many problem statements in tech that either I mean, there's so many reasons things can be weird.
1:01:05
Whether it is someone who just comes straight from business school versus a researcher like yourself who spent, you know, a decade on this stuff or just attacking problems that just kind of don't really need to be solved quite yet, but uh I think we could all agree chores are annoying.
1:01:20
So, nice to have someone doing that on the show >> for sure. Good to meet you, Tommy.
1:01:28
>> Yeah, great to see you guys. Uh yeah, enjoyed it.
1:01:35
That's it for this week's show.
1:01:35
Thanks Tony for coming on the podcast and Alex for staying up late.
1:01:40
I know you have very important people to be meeting with.
1:01:43
So, uh, thanks for giving us your time.
1:01:46
>> No one as important as you, Alice. Always.
1:01:47
Uh, don't forget to like, subscribe everywhere you get your podcasts. We're access. show, too.
1:01:51
We have a website and you can find us in video at AccessPod on YouTube.
1:02:00
>> You are so quickly fading, Alex. It's hilarious.
1:02:02
[laughter] It's obvious that It's obvious that you've been in like back-to-back meetings for like the last 16 hours.
1:02:06
And if you like this episode, please leave a fivestar review or thumbs up and share with a friend on the internet or in your local Swiss Village.
1:02:15
You can find me at Hamburger on Twitter [laughter] and at meaning. comp.
1:02:22
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1:02:25
>> Access is part of the Vox Media Podcast Network.
1:02:27
This show is produced by Hooked Creators. Bye-bye. >> Bye.