00:00:00
If Apple doesn't provide me a large
00:00:01
language model to talk to in a way, I
00:00:04
don't care.
00:00:04
>> I saw it took me like half an hour to
00:00:06
watch the video because I kept pausing
00:00:07
it and laughing so much.
00:00:08
>> I'm a little scared about Whimos on New
00:00:10
Jersey highways.
00:00:11
>> It's a different type of driver.
00:00:13
>> They say AI is going to change
00:00:15
healthcare. What does that really mean?
00:00:16
AI is going to change our streets, our
00:00:18
highways. What does that really mean?
00:00:20
Oh, we're going to get humanoid robots
00:00:22
in our homes. That is going to change
00:00:23
our lives forever. What does that really
00:00:25
mean?
00:00:29
>> Joanna, thank you for joining me on the
00:00:31
way home podcast. Yeah.
00:00:33
>> All the way here in Kernney, New Jersey,
00:00:34
which is 5 minutes away from my house.
00:00:36
>> Yeah. Well, we like having people in
00:00:38
person on the podcast more than virtual
00:00:39
cuz it's just more personal. It's more
00:00:42
fun.
00:00:42
>> We're right here. Yeah.
00:00:43
>> We're This You know what? The two most
00:00:45
powerful people in New Jersey tech media
00:00:48
are in this room.
00:00:50
>> I am willing to accept that. Do you
00:00:52
think there's anyone else
00:00:53
>> in New Jersey? I haven't thought about
00:00:55
that actually. I'm not really sure.
00:00:56
There's got to be other
00:00:57
>> in tech media. We know there's a lot of
00:00:58
important media people in New Jersey.
00:01:00
>> Tech media. I don't know certain people
00:01:03
where they're from, but I'm I'm just
00:01:04
going to go ahead and accept the crown.
00:01:06
>> I'm pretty sure it's just us.
00:01:07
>> I'm willing to take it.
00:01:08
>> I mean, there's a lot of others and we
00:01:10
are the top is what I'm saying here.
00:01:12
>> I like it. I like the confidence. We
00:01:14
should talk a little bit about tech
00:01:15
because there's there's a bunch going
00:01:16
on. We have your book here in front of
00:01:18
us. We're also going to talk about this
00:01:19
I'm really excited about.
00:01:20
>> This is my new way of promoting the
00:01:21
book. It's just
00:01:22
>> is just holding it up to your face
00:01:23
during the entire podcast.
00:01:24
>> It makes all the podcast editors very
00:01:26
angry the block. Yeah,
00:01:28
>> we were just talking about the cover, by
00:01:29
the way. The title is very and the
00:01:31
colors and the shapes and I have to talk
00:01:33
to you about humanoid robots as well.
00:01:34
>> Oh, I would love to.
00:01:35
>> I have so many takes.
00:01:37
>> I know you do.
00:01:37
>> They sometimes get me in trouble, but
00:01:38
I'd love to hear your
00:01:39
>> I love your takes and they're mostly
00:01:40
right. So, yeah.
00:01:41
>> Perfect. So, tell me about this book.
00:01:43
It's called I am not a robot. What is
00:01:46
What is
00:01:47
>> my year using AI to do almost
00:01:49
everything? You can search that all. You
00:01:52
really just have to search I'm not a
00:01:53
robot, but
00:01:54
>> Joanna Stern because if you search I'm
00:01:55
not a robot, then you just get captas
00:01:56
and it's a disaster. Totally.
00:01:58
>> Um, great idea for the name of a book.
00:02:01
Not actually a great idea when you go to
00:02:02
sell the book
00:02:03
>> for SEO.
00:02:04
>> Um, yeah, it's terrible SEO capture
00:02:06
situation. Um, yeah, this is about my
00:02:08
year trying to use AI in as many parts
00:02:10
of my life as possible.
00:02:12
>> And um, that that's my pitch. Go buy the
00:02:15
book. giving it a shot, being
00:02:17
optimistic, open to it being either
00:02:20
awesome or terrible, just fully fully
00:02:23
immersing yourself.
00:02:24
>> Fully immersing myself. And I don't
00:02:25
define AI as just generative AI. It's
00:02:27
not just chat bots. You've got
00:02:28
self-driving cars in here, humanoid
00:02:30
robots,
00:02:32
healthcare, tons of different
00:02:34
applications of, you know, what once was
00:02:36
called deep learning, but now everyone
00:02:38
just calls AI or, you know, chat bots or
00:02:40
generative AI.
00:02:41
>> Yeah, there's neural nets. There's all
00:02:43
sorts of things. It's just all
00:02:44
artificial intelligence. Yep.
00:02:46
>> All the time.
00:02:46
>> Yep.
00:02:47
>> Awesome.
00:02:48
>> Not only do I have a book out, I have a
00:02:49
new media company, which we're know
00:02:51
we're going to talk about, but it's
00:02:52
called New Things, and you can go to
00:02:54
thenewththing.com.
00:02:56
>> Or
00:02:57
>> look at these sound effects.
00:02:59
>> We got a sample.
00:02:59
>> Or you can go to my YouTube channel,
00:03:02
Joanna Stern.
00:03:03
>> PERFECT.
00:03:04
>> We'll link everything below so people
00:03:06
can find it. First, we can talk.
00:03:07
Actually, there's a little bit of tech
00:03:08
news that we haven't talked about a ton
00:03:10
yet on the podcast, which is Apple's new
00:03:12
CEO. That's a fun one. What do you think
00:03:14
about
00:03:15
>> you feel like you haven't talked about
00:03:16
that a lot on this podcast?
00:03:17
>> Well, we have talked about it, but I
00:03:18
haven't gotten other people's thoughts
00:03:20
outside of the podcast. So, now I'm
00:03:21
excited to start doing that
00:03:22
>> because I've been listening to this
00:03:23
podcast in my car and I'm I'm pretty
00:03:24
sure you've you've done a lot of
00:03:25
coverage of this.
00:03:26
>> We did have a lot of optimistic musings
00:03:28
about the future of Apple and I'm c Do
00:03:29
you agree with our thoughts on the on
00:03:31
John Turnis being the new CEO, the
00:03:33
product guy?
00:03:34
>> I do. I do. I think look, we we love
00:03:38
products. That's our focus. Tim Cook is
00:03:42
kind of he likes products. I think we
00:03:44
you say he's not the product guy, but
00:03:45
he's like he likes them.
00:03:46
>> Yeah. He likes using the products to be
00:03:48
good at his job of making the company
00:03:50
really good.
00:03:51
>> He loves using products to make money.
00:03:53
>> Yes. Exactly. Right. Great. Yeah.
00:03:56
>> We do too.
00:03:57
>> But we also just love using the
00:03:59
products. Like even if someone told me
00:04:00
you're not going to make money using
00:04:01
this tech product, I would love to still
00:04:03
use it.
00:04:04
>> Yeah.
00:04:04
>> And I think it's I love your guys
00:04:06
optimism. I'm optimistic. I think it
00:04:08
just means that the most senior leader
00:04:10
at the company is going to be able to
00:04:12
talk a little bit more thoughtfully
00:04:14
about these products, why they made
00:04:16
these decisions, what goes into it.
00:04:20
>> You and me have both met Tim Cook over
00:04:22
the years, and he's great to talk to. He
00:04:25
clearly loves Apple. He loves the
00:04:27
products they're making.
00:04:28
>> Yeah.
00:04:28
>> This is going to get like deep into the
00:04:30
products.
00:04:31
>> You've interviewed a bunch of Apple
00:04:32
execs over the years, and I get I mean,
00:04:34
you've talked to Tim. Do you ever talk
00:04:36
to him about products or it's more just
00:04:37
like global company vision, big picture
00:04:40
stuff? That's usually what he's good at.
00:04:41
>> It feels like that's where the
00:04:42
conversation usually goes. Yeah. Right.
00:04:44
When you I mean I've watched your
00:04:46
interviews with him too.
00:04:47
>> Yeah. With the CEO.
00:04:48
>> Yeah. With the CEO. That's But there are
00:04:51
other companies where the I would say
00:04:54
what's a what's a good example of
00:04:55
>> I mean I talked to YouTube CEO in the
00:04:58
weeds of the products all the time. I
00:04:59
haven't done an interview with him in a
00:05:00
while on camera, but we did do one and
00:05:02
that was very product focused. Um, and I
00:05:05
I think in general just when I talk on
00:05:07
camera about stuff, I'm trying to
00:05:08
connect it to the viewer and that is
00:05:10
through the products that we're both
00:05:11
using. So, we have experiences with the
00:05:13
products. We think there are things that
00:05:14
are great about it. We think there are
00:05:15
things that could be better about it and
00:05:16
then the competition, you know, unveils
00:05:18
some shortcomings and we get to compare
00:05:20
and contrast. So, that's what's fun to
00:05:22
me.
00:05:22
>> I feel like actually what's really
00:05:23
interesting is that right now in the
00:05:25
tech industry, the closest CEOs to the
00:05:28
products are the AI companies because
00:05:30
they're the founders.
00:05:32
>> True. Yeah.
00:05:32
>> Right.
00:05:33
>> Yeah. um like you've got the Microsofts,
00:05:36
you've got the Amazons, you've got
00:05:37
Apple,
00:05:38
and they're not the founders anymore,
00:05:40
right? Right. And they're not they're
00:05:42
they're making these big decisions about
00:05:44
cloud infrastructure and all the things
00:05:46
that are not the consumer product. But
00:05:47
when you go and talk to
00:05:49
>> the Daarios or the Sam Alman's of the
00:05:51
world right now, they're also really
00:05:53
deep still into the product that they
00:05:54
helped create. Yeah.
00:05:56
>> So, you have these
00:05:57
>> conversations.
00:05:59
Look, there are plenty of conversations
00:06:01
these guys are having right now, right?
00:06:03
they're on the circuit and it's everyone
00:06:04
is talking to them.
00:06:06
>> But if you're a person like me or you,
00:06:09
you can get really good conversation
00:06:11
around the products because they're in
00:06:13
it.
00:06:14
>> Yeah. Yeah. That that makes a lot of
00:06:15
sense. Also, it's there's a lot of AI in
00:06:16
your book on actually give us the give
00:06:19
us the 10,000 foot view of how you used
00:06:22
AI for an entire year. Well, well, you
00:06:25
had the day, but also the entire year in
00:06:26
the book. How did that go? What
00:06:28
happened? You don't have to spoil
00:06:29
everything, but how did it go? Oh, it
00:06:32
went. It went. Uh, that's that's that's
00:06:35
the top uh that's the big answer.
00:06:37
>> It happened.
00:06:38
>> I want Look, there seemed to be a moment
00:06:42
last year, 2025, even end of 2024, where
00:06:46
there were so many new products coming
00:06:47
out that had AI in them.
00:06:48
>> Yep.
00:06:49
>> And there were all these grand
00:06:51
proclamations being made by these CEOs
00:06:53
saying AI is going to change our lives.
00:06:55
It's going to be amazing. And I was
00:06:57
like, why don't we just try to live that
00:06:59
and see if that's true? and can I live 5
00:07:01
years into the future and see what these
00:07:03
people are talking about using today's
00:07:04
technology and not giving people a
00:07:06
perfect view, right? Because the models
00:07:08
have been getting better, the products
00:07:10
have kind of been getting better. Um,
00:07:12
and see what what life is like. And I
00:07:15
tried a big thing that I think is
00:07:18
different about this book versus any
00:07:19
other AI book is first of all, it's
00:07:22
meant for consumers, the people who are
00:07:24
using this stuff. It's not just
00:07:26
generative AI. It's not just chat bots.
00:07:28
We get into self-driving cars. There's
00:07:30
humanoid robots. There's medical AI. And
00:07:33
I wanted to look at all these parts of
00:07:35
life and say, "Okay, they say AI is
00:07:38
going to change healthcare. What does
00:07:39
that really mean? AI is going to change
00:07:41
our streets, our highways. What does
00:07:42
that really mean? Oh, we're going to get
00:07:44
humanoid robots in our homes. That is
00:07:46
going to change our lives forever. What
00:07:48
does that really mean?"
00:07:49
>> It actually means nothing because
00:07:50
they're really not ready. Um,
00:07:53
>> and so that's I I And the book is like
00:07:55
done by the year. So, it's I wrote this
00:07:57
in 2025. It starts in winter of 2025,
00:08:01
you know, New Year's Day. And I just
00:08:03
tried to throughout the year use AI as
00:08:05
much as possible. I do want to say like
00:08:08
there there were places where I couldn't
00:08:10
use AI. I say in the beginning of this
00:08:11
book,
00:08:12
>> if I went full throttle, I would be
00:08:15
divorced and I would have lost
00:08:16
everything that was important to me.
00:08:18
>> Wow.
00:08:18
>> So, I used that in a tempered way,
00:08:21
right? like I wanted to still be a human
00:08:24
and a good parent and
00:08:26
>> a good spouse and all of these things.
00:08:28
Um but I really Yeah. I mean,
00:08:31
>> name it, I tried it.
00:08:32
>> Yeah. There's so many interesting things
00:08:33
in that. Like I first I've wanted
00:08:35
there's a video I've wanted to do cuz I
00:08:37
in the back in the day I did a Google
00:08:40
versus Siri versus Alexa video. And now
00:08:44
it feels like the modern version of that
00:08:46
would be to do GPT versus Gemini versus
00:08:50
whatever other like Perplexi, all the
00:08:52
best ones, Claude. But every time I
00:08:54
think about trying to do that, I always
00:08:55
feel like there's some massive update
00:08:57
around the corner that as soon as I put
00:08:58
out my video, it would be out of date.
00:09:00
And so I don't know if I can make that
00:09:02
video or if it's even worth making
00:09:03
because it's not useful anymore. How do
00:09:05
you feel about those compar? There's no
00:09:07
one model that seems to forever be
00:09:09
number one. It feels like it's not a
00:09:10
useful comparison. Yeah, I think that
00:09:12
would be a tough video for you to do.
00:09:13
Like it would have a very short shelf
00:09:15
life and you would hear from a lot of
00:09:16
people saying like I just switched to
00:09:18
Gemini. Oh, I just switched to that.
00:09:20
Like every every week it's different.
00:09:22
And I'm very clear in this book to say I
00:09:24
never actually mention model names.
00:09:26
Maybe there's one or two places where
00:09:27
you know I mentioned 40 or something
00:09:29
like that. But I really because I knew
00:09:31
that if you know writing a book you you
00:09:34
only want to sell it for like a day. Um,
00:09:36
I mean
00:09:38
I or apparently successful authors don't
00:09:40
want it to be sold for just a day. I'm
00:09:41
not really I don't know yet. I don't
00:09:43
know if I'm going to be a successful
00:09:44
author. Um, but so I but I think the
00:09:46
themes of it, right? So if I was using
00:09:49
an image generator, right, I knew that
00:09:51
wasn't going away. Yes, there's some
00:09:52
examples in this like where the image
00:09:54
generator just like completely gaslights
00:09:56
me and keeps saying like I was
00:09:57
generating a picture for my son of five
00:10:00
hamsters and it kept being like,
00:10:02
>> "Yeah, no, there's five hamsters in this
00:10:04
image." and be like, "No, there's six
00:10:06
hamsters in this image." You know, and
00:10:08
then they'll be like, "No, no, look, I
00:10:09
did it again." And then it' be like,
00:10:10
"Seven hamsters." It was and some stuff
00:10:12
like that has gotten better. Yeah.
00:10:14
>> But
00:10:16
>> yeah, thematically I really tried to
00:10:18
just stay on that and not be mentioning
00:10:20
specific models because if I had like at
00:10:23
the time, I don't know,
00:10:24
>> GPT would have been the best and then,
00:10:26
you know, today people believe Claude or
00:10:28
Gemini or, you know, Gemini is probably
00:10:30
getting updated in a few weeks at IO.
00:10:32
Exactly.
00:10:32
>> We we can't say.
00:10:33
>> Yeah. So to your point, AI is a lot more
00:10:36
than just the chat bots. There's the
00:10:37
self-driving cars, there's the medical
00:10:39
breakthroughs, there's all kinds of
00:10:40
stuff. What was the most surprising
00:10:42
thing that you maybe found in looking at
00:10:44
all these different things? Cuz the word
00:10:45
AI now it's
00:10:47
>> it includes a lot of things we used to
00:10:48
call uh what did we call it before?
00:10:51
There was like the the machine learning.
00:10:53
>> Yeah, machine learning. Exactly. Or even
00:10:54
neural links or whatever all these other
00:10:56
stuff. What are you what are you
00:10:57
surprised by?
00:10:59
Um, well, there's a lot of surprising
00:11:01
moments in the book just in terms of my
00:11:03
usage. I think one of the big messages
00:11:05
that I want people to know in this book,
00:11:07
and I think maybe your viewers already
00:11:08
know it or listeners already know it,
00:11:10
but there are so many places right now
00:11:13
where we hear about this hate of AI. You
00:11:15
might hear it from your friends or from
00:11:18
your listeners or viewers.
00:11:20
>> And the truth is AI is already in your
00:11:22
life. There's just no way you're going
00:11:24
to be able to say no to it, right? And
00:11:26
you've made this point before like about
00:11:28
the image processing and the AI and the
00:11:30
algorithms that go into so many
00:11:31
different things. But I think even
00:11:32
broader and deeper than that, for
00:11:34
instance, one example is I have a big
00:11:36
chapter in here about AI reading my
00:11:37
mammogram.
00:11:39
>> And I sought that out. But many are
00:11:41
going to get their X-rays or mammograms
00:11:44
or ultrasounds and they're already being
00:11:46
read by AI. There is a radiologist
00:11:48
behind the scenes using AI to say
00:11:51
actually that looks like cancer. Mhm.
00:11:53
>> Um, so that was one thing that I didn't
00:11:56
really realize how ingrained it was
00:11:59
already in parts of our lives that we
00:12:00
don't think about. I think even like
00:12:02
self-driving cars,
00:12:04
you we know they're in specific cities,
00:12:07
>> but that AI is affecting your life
00:12:09
because your car is driving next to it.
00:12:12
>> Yeah. I saw Whimo out here the other
00:12:13
day, which I thought was insane. Yeah.
00:12:15
It was like right around the corner, and
00:12:16
I saw that they're starting to test in
00:12:18
New York City
00:12:18
>> and I'm a little worried about that. for
00:12:21
them. Actually, mostly.
00:12:22
>> This is making me mad because now you
00:12:24
are the top New Jersey tech reporter.
00:12:26
>> Oh, really? I mean, I I tested Whimos in
00:12:29
Austin and I tested the robo taxis in
00:12:31
Austin. That's the only place I've ever
00:12:33
ridden in a like fully self-driving taxi
00:12:35
service.
00:12:36
>> Really?
00:12:36
>> Yeah. And then I heard that they were
00:12:38
bringing it to New York City and I
00:12:39
thought that sounds bold. And then I saw
00:12:41
one and I thought, "Oh, that's
00:12:42
>> Did you see it in New York or in New
00:12:43
Jersey?"
00:12:43
>> I saw it right here on this block in New
00:12:45
Jersey.
00:12:45
>> Really?
00:12:46
>> Yeah. So, it must have been somebody
00:12:47
coming from New Jersey. Tech New Jersey
00:12:50
reporter. This is insane. New breaking
00:12:52
in New Jersey one or whatever it is. You
00:12:55
I'm so jealous of the job you're about
00:12:57
to get there.
00:12:58
>> So, they're out here.
00:12:59
>> No, I had no idea they were in New
00:13:00
Jersey. I knew they were like in
00:13:01
Brooklyn and they've been testing them
00:13:03
>> around the city.
00:13:04
>> Wow. And you're sure it was a Whimo?
00:13:06
>> It was definitely Whimo. Yeah. And I I
00:13:08
saw that because I recognized the
00:13:10
headline that I had just seen and I'm
00:13:11
like, "Oh, okay. Confirmed is true
00:13:12
because I see one here probably dropping
00:13:14
someone off at the helicopter tours or
00:13:16
something like that."
00:13:17
>> Interesting.
00:13:17
>> Yeah. I'm a little
00:13:18
>> I'm a little scared about Whimos on New
00:13:20
Jersey highways.
00:13:22
>> It's a different type of driver that you
00:13:24
have to deal with and that you and I
00:13:26
feel like in each one of these cities,
00:13:27
the self-driving car, I guess something
00:13:29
not people don't always talk about is it
00:13:30
has to assimilate to the driving style.
00:13:32
So in California, these big multi-lane
00:13:34
highways, all this stuff, and then of
00:13:35
course the neighborhoods, well-paved
00:13:38
roads, usually nothing too insane, at
00:13:39
least from the footage I see. Austin,
00:13:42
similar stuff, slightly different
00:13:43
intersection types. You come to New York
00:13:45
and New Jersey and the way taxi drivers
00:13:47
drive, the way Uber drivers drive, the
00:13:49
way people on scooters fly past you and
00:13:51
bicycles in the bike lane
00:13:52
>> and jug handles in New Jersey
00:13:54
>> and all roundabouts and all these other
00:13:56
crazy things. I I'm curious. I'm curious
00:13:59
how it handles that stuff.
00:14:00
>> So, I in the book go to Phoenix and go
00:14:02
with my whole family. I have two sons
00:14:04
and my wife. We went to Phoenix for
00:14:06
spring break. We called it our Whimo fun
00:14:07
vacation.
00:14:08
>> Okay. And um we went there for a week
00:14:10
and we took about 40 Whimos and I had
00:14:12
been in them in different cities, but I
00:14:14
really felt like I got when you're
00:14:16
really in one city and you you can pick
00:14:18
up all of the little things the cars do.
00:14:21
Yeah.
00:14:21
>> Um you really feel like you're starting
00:14:23
to understand that driving style.
00:14:25
>> Yeah. The city the city specific driving
00:14:26
style is is fascinating and I wonder
00:14:28
Yeah. They all the cars all have to talk
00:14:30
to each other and get better at driving
00:14:31
in each place. So if a Whimo starts to
00:14:33
move, because this is the thing, if
00:14:35
you're a Whimo in New York and then you
00:14:36
do a drop off in New Jersey and then you
00:14:38
pick someone up and they want to go to
00:14:40
South Jersey and then Philly and now
00:14:42
you're in now you're a Philly Whimo. Now
00:14:44
what is that like?
00:14:45
>> This is so many years away, but um I
00:14:47
would say in 5 years something like that
00:14:49
could be
00:14:50
>> could happen.
00:14:51
>> You have the book here. You decided to
00:14:52
write a book. I'm curious about the
00:14:53
decision to write a book in the first
00:14:55
place because you have this long history
00:14:56
of you've been obviously you've done
00:14:59
podcasting, you've done writing for your
00:15:01
website, you've done video stuff. Why
00:15:03
the book and why now for a book?
00:15:05
>> Yeah, it's a really good question. Why
00:15:07
did I do this book? As I am exhausted
00:15:09
from being talking about this book, um,
00:15:12
>> it's made you think about it more.
00:15:14
>> I should ask my bot version of myself
00:15:15
why I did this book. Um, I thought this
00:15:18
was going to be a moment in time. Like
00:15:19
the beginning of this book, I talk about
00:15:21
the internet and this idea that in 1995,
00:15:25
let's say,
00:15:26
>> were you even born?
00:15:27
>> I was I was 2 years old,
00:15:28
>> right? Okay. I had a feeling. Um I was
00:15:31
in fifth grade. I wasn't that much older
00:15:33
than you. I mean, a little bit. Um like
00:15:36
if someone in 1995 came up to you as
00:15:39
2-year-old Marquez and you were like, I
00:15:42
can't even speak.
00:15:43
>> Hi.
00:15:44
>> Yeah.
00:15:44
>> I only know three words.
00:15:46
um and told you everything you do is
00:15:49
going to be on the internet, right?
00:15:50
Every you're going to shop on the
00:15:52
internet, your mail's going to be on the
00:15:53
internet. All these parts of your life
00:15:55
are going to be going through a
00:15:58
computer, you would have been like,
00:15:59
"Hell no."
00:16:00
>> People of the time should certainly
00:16:02
didn't believe it,
00:16:03
>> let alone want it,
00:16:04
>> right? Yeah.
00:16:05
>> You would just be like, "No." And then
00:16:06
and then if you told them, "Actually,
00:16:08
and 10 years after that, it's all going
00:16:10
to be on a little screen that you put in
00:16:12
your pocket."
00:16:13
>> Mhm. And then they told 2-year-old
00:16:14
Marquez, "You're going to review those
00:16:16
screens. You're going to be
00:16:20
>> pumped."
00:16:21
>> They'd be like, "You're going to be a
00:16:22
famous reviewer of little rectangles in
00:16:26
your screen in screen rectangles."
00:16:29
>> You've been like, "Okay, I want to be an
00:16:31
astronaut."
00:16:32
>> Yeah,
00:16:33
>> mommy.
00:16:33
>> Yeah.
00:16:34
>> Yeah. Yeah. And so my point is is that
00:16:37
with the book I thought everyone keeps
00:16:39
saying we're on this at this moment
00:16:41
where AI is going to make that change.
00:16:44
>> It feels like it's good to have this
00:16:46
piece that like will stand the test of
00:16:49
time
00:16:50
>> to either be completely wrong or
00:16:52
completely right.
00:16:53
>> So this is the moment in time where we
00:16:55
are there's spectacular specul specul
00:16:58
spec h speculating speculation
00:17:02
speculation.
00:17:02
>> Edit that out. spectacular speculation.
00:17:05
>> Yes,
00:17:06
>> about about whether or not this is the
00:17:08
future basically. And we're all living
00:17:09
in the time where okay, we know the
00:17:11
world as it is. We know the search
00:17:12
engines. We know the structures of the
00:17:13
internet. We know how we sort of switch
00:17:14
on and switch off, but this AI thing is
00:17:16
going to change everything. And we're
00:17:18
all skeptical.
00:17:18
>> What if we and this is the premise of
00:17:20
the book. What if machines are a part of
00:17:22
every part of the fabric of our lives?
00:17:24
>> Yeah.
00:17:24
>> Every part. Just like the internet
00:17:27
became.
00:17:27
>> Yeah. But now machines that have smarts
00:17:30
that have that are smarter than humans,
00:17:33
these people say.
00:17:34
>> Mhm.
00:17:35
>> Are going to change our lives.
00:17:37
>> Yeah.
00:17:37
>> So, look, I'm covering that every day
00:17:39
and you are too and it's going to seep
00:17:41
into but I thought maybe a book form
00:17:43
would be good. And also, I'll be honest,
00:17:45
like I really want to hit a different
00:17:47
audience with this book.
00:17:48
>> Yeah. What is it? How is this audience
00:17:50
different than your normal like video
00:17:52
audience? I am here talking to your
00:17:54
audience and I hope they will read the
00:17:55
book or I'm really hoping that your
00:17:57
audience will tell
00:17:59
>> someone in their life where they're like
00:18:01
you know your audience like heard us
00:18:03
debating if you should do that video on
00:18:05
Gemini and Claude and they start getting
00:18:06
mad like we know in their head they were
00:18:08
like no man
00:18:09
>> wait for 5.7. Yeah, exactly. 5.7 5.5
00:18:12
with the image manner better than this.
00:18:14
You know, nano banana
00:18:17
>> sentence that didn't exist a year ago,
00:18:19
>> right?
00:18:19
>> Nano banana is better than my five point
00:18:21
whatever.
00:18:22
>> I want them to tell their person in
00:18:25
their life that isn't super deep in
00:18:27
>> you should go read this book because it
00:18:29
summarizes and gives you a really good
00:18:31
understanding of what AI could do for
00:18:33
you.
00:18:33
>> Okay. Yeah.
00:18:34
>> I do also want let me look at this
00:18:37
person. Nano banana nerd. I I want you
00:18:39
to also buy the book, but you could buy
00:18:40
it for a friend.
00:18:41
>> There is a lot of uh I guess there's the
00:18:44
two versions of the way people think
00:18:45
about this future. I always have my own
00:18:48
nuanced version of this, which maybe
00:18:49
we'll get to, but there is clearly a lot
00:18:53
of positive use of artificial
00:18:54
intelligence, especially in things like
00:18:56
pattern recognition, like in the medical
00:18:58
field like we talk about. Oh, it'll look
00:19:00
at this uh MRI. It'll look at this tons
00:19:02
and tons of data in a way that a human
00:19:04
can't and find a pattern. and maybe that
00:19:06
actually means something that we didn't
00:19:07
know about before. So, there's a ton of
00:19:09
upside, but then there's also a bunch of
00:19:10
downside. And you hear stories about
00:19:12
this all the time about the chat bots
00:19:13
talking crazy to people about all sorts
00:19:15
of other negative things. How do you
00:19:17
think about the positive versus negative
00:19:19
trade-off of all of our lives being
00:19:22
filled with artificial intelligence?
00:19:24
>> I wish that I had a better answer at the
00:19:25
end of this book. I mean, you should
00:19:27
still read the whole book, and spoiler
00:19:28
alert, I don't have a better answer at
00:19:30
the end, but you should still read the
00:19:31
whole book. Even you, Nana Banana
00:19:33
friend. Um, similar to any other tech
00:19:37
tool, and I know you've made this point
00:19:39
in your videos,
00:19:40
>> Mhm.
00:19:41
>> there's going to be good and bad. And
00:19:43
so, like, there is a very good chance
00:19:45
this is totally worse and has far more
00:19:49
negative consequences than any other
00:19:51
technology. Like,
00:19:53
>> I feel like I sound like Sam Alman
00:19:55
sitting here.
00:19:56
>> Yeah. But,
00:19:56
>> but I need to learn how to do like a
00:19:59
great Sam Alman impression. Like, he
00:20:00
thinks so thoughtfully off to the side.
00:20:03
>> Mhm.
00:20:04
You know how you study people when you
00:20:05
interview them?
00:20:06
>> Oh yeah. You always watch a bunch of
00:20:08
interviews of other things they've
00:20:10
talked about before and then you realize
00:20:11
they're going to do the same thing.
00:20:12
>> Yeah. It's like looks up really hard.
00:20:14
>> Does a few calculations
00:20:15
>> and it tells you the good news.
00:20:17
>> Mhm. Um
00:20:20
um like the job displacement is real and
00:20:23
people right now I mean I'm just started
00:20:26
this new media company and the amount of
00:20:28
incoming I have from young people out of
00:20:31
college who just can't get a job because
00:20:33
they feel like AI has taken over these
00:20:36
basic tasks whether it's video editing
00:20:39
whether it's design
00:20:42
writing that feels real and people are
00:20:44
furious about it and they're like why do
00:20:47
we need this technology.
00:20:48
>> It's hard to answer now and that's the
00:20:50
point of being in this moment in time.
00:20:52
>> I have I have my nuance take which I say
00:20:54
it kind of relates back to smartphones.
00:20:57
I saw like the time before smartphones
00:21:00
where I was like playing around with the
00:21:01
VHS camcorder and there was a bunch of
00:21:02
different tech and then you know
00:21:04
smartphones came along and they brought
00:21:05
all these different technologies into
00:21:06
this one supercomputer in your pocket
00:21:08
and it's gotten really really really
00:21:10
good and also it's sort of slowed down
00:21:12
in improvement
00:21:14
and I have a hard time picturing a post
00:21:17
smartphone world meaning we've moved on
00:21:20
from this form factor of the rectangle
00:21:21
in your pocket to something else. Uh, a
00:21:24
lot of these big tech companies are
00:21:26
trying to prepare themselves for a post
00:21:28
smartphone world. Oh, what if it's a
00:21:30
glasses thing? What if it's a computer
00:21:31
on your face? So, they're all trying
00:21:33
that stuff, but I personally still have
00:21:36
a hard time imagining that the
00:21:37
smartphone isn't the center of that
00:21:39
universe. Do you have a hard time with
00:21:41
that or do you feel like we could just
00:21:43
move on from smartphones?
00:21:44
>> I have a picture at the end of the book.
00:21:46
Bring it back to the book.
00:21:47
>> Okay,
00:21:49
>> we can we get a tight shot of that
00:21:51
specifically.
00:21:52
So, for audio listeners, there's a then,
00:21:54
a now, and a soon.
00:21:56
>> Is exactly what I was talking about. The
00:21:58
then is someone sitting down in front of
00:22:00
a computer with a tower PC. The now is
00:22:03
just holding the supercomputer in your
00:22:04
pocket. And the soon is this girl with
00:22:07
some cool glasses on, which are that's
00:22:10
you with which is clearly smart.
00:22:13
>> That super cool smart girl.
00:22:14
>> Yeah. And that's the soon that's the
00:22:16
soon. So, that's the post smartphone.
00:22:18
>> Post smartphone.
00:22:19
>> Not alongside the smartphone. alongside.
00:22:21
You know what? Actually, we need to
00:22:23
revise this illustration in addition
00:22:24
number two. The the phone is in her
00:22:26
pocket.
00:22:27
>> Okay.
00:22:27
>> Or in her purse or, you know, it's
00:22:29
sitting in the car or something. It's
00:22:30
close by.
00:22:31
>> Yeah.
00:22:31
>> Um because the the phone powers this and
00:22:33
I I one of the reasons I Well, if you
00:22:36
read all of this text here, it basically
00:22:38
says we went through this, but we didn't
00:22:39
lose these other things, right? I mean,
00:22:41
we don't Well, actually, I was just by
00:22:43
your desk. You do still have like a
00:22:44
giant tower.
00:22:45
>> Maybe not for long, but I do.
00:22:47
>> Maybe not for long, but you know. Yeah,
00:22:49
sure. you're going to get like a Neo,
00:22:51
you're it's going to power everything
00:22:52
you do. Um,
00:22:55
>> we still have these, right? So, we we
00:22:56
have this long history of tech where
00:22:58
things don't get replaced. They just we
00:23:00
get added.
00:23:01
>> We get augmented
00:23:02
>> augmented and and our focus shifts,
00:23:04
right? I mean, certainly the smartphone
00:23:05
and this idea that the smartphone will
00:23:07
be replaced. I I totally am with you.
00:23:09
This is I I can't see it ever like this
00:23:11
is the per like the devices keep getting
00:23:13
more perfect.
00:23:14
>> It knows everything about us. It fits in
00:23:16
our pockets. we get to put it down and
00:23:18
then pick it up, which I think is
00:23:20
actually something we don't think about
00:23:21
a lot, but it's super important. And
00:23:23
then there's obviously all the thoughts
00:23:24
about how often do you pick it up, how
00:23:25
often do you put it down, but the face
00:23:27
computer, maybe there's a world where
00:23:29
you use the face computer a lot and you
00:23:31
use your phone a bit less because it's
00:23:32
more convenient. You take photos and
00:23:34
videos with it, maybe you talk to it,
00:23:35
but you then check the the rectangle in
00:23:37
your pocket a little bit less. But
00:23:39
that's still the thing that has all the
00:23:40
power, all the best compute, all the
00:23:43
best form factor. So, I just I don't
00:23:46
know. I don't see it going away.
00:23:47
>> Okay. So, in the book I write a lot
00:23:49
about the wearables. Um, and this is
00:23:52
perfect segue. So, we'll talk about this
00:23:54
one. Did you ever try this out?
00:23:55
>> No.
00:23:55
>> This was the B. This is the B.
00:23:57
>> Okay.
00:23:58
>> Okay. It was Amazon actually ended up
00:23:59
buying it.
00:24:00
>> Looks kind of like a Fitbit.
00:24:02
>> Yeah. It it the hardware is
00:24:03
unremarkable. It's fine. But this I wore
00:24:06
for most of the year
00:24:08
>> and it has a microphone and it records
00:24:10
everything you do.
00:24:12
>> Right now, I press the button and green
00:24:13
means this is recording. Mhm.
00:24:15
>> And so everything that's being said goes
00:24:17
to Amazon's cloud,
00:24:19
>> they transcribe it, they pull it back
00:24:21
down, they get rid of the audio,
00:24:24
>> and then I get a summary of our
00:24:25
conversation, and it also gives me
00:24:27
to-dos about our conversation. So if
00:24:29
I've told you like, Marquez, I'm going
00:24:32
to apply to be the N the New Jersey One
00:24:35
tech reporter.
00:24:36
>> Number one,
00:24:37
>> yeah, I don't is
00:24:38
>> top three.
00:24:39
>> I actually meant that it's the channel
00:24:41
name. I don't know. Is there a New
00:24:42
Jersey TV network? There might be.
00:24:44
>> Yeah. Anyway, um this will all be in
00:24:47
there and it will say you need to go
00:24:48
apply to be the New Jersey number one
00:24:50
tech correspondent,
00:24:52
>> right? Everything from this conversation
00:24:54
will have been summarized by I by AI and
00:24:57
it will give me
00:24:59
>> this crazy to-do list, a detailed to-do
00:25:01
list. And you can keep this on all day
00:25:02
and it happens. Yeah.
00:25:03
>> It's basically another brain in a way,
00:25:07
right? I'm outsourcing my memory. I
00:25:09
don't need to remember what we did here.
00:25:11
I don't have to actively go and put that
00:25:13
in my to-do list.
00:25:14
>> Yeah. Yeah. We were just talking about
00:25:15
this. Was it last episode they were
00:25:17
talking about? This of um I already sort
00:25:20
of augment part of my brain in a way
00:25:23
where I've just given up on remembering
00:25:25
certain things and I use well right now
00:25:27
it's a task app and I just something I
00:25:29
have to do. I immediately take out my
00:25:30
phone and I open the task app and I
00:25:32
write it down just to make sure I don't
00:25:33
forget because my memor is not perfect.
00:25:35
And everyone is willing to draw the line
00:25:38
in the sand in a slightly different
00:25:40
place about how much they're willing to
00:25:41
augment or offset their own brain. Um,
00:25:44
when you're going all in on everything,
00:25:48
do you feel like it's making you I guess
00:25:50
we thought about this as like less smart
00:25:53
or less useful?
00:25:54
>> Less human.
00:25:55
>> Does it? Yeah. Less human. Does that
00:25:57
feel bad to like not use your brain as
00:25:59
much or is it convenient and then you
00:26:00
can do all sorts of other random stuff
00:26:01
you didn't think about before? There are
00:26:03
moments where you're like, "Oh, don't
00:26:04
worry about it. My bracelet got it."
00:26:06
>> Yeah.
00:26:06
>> Like I don't have to worry about that.
00:26:07
>> And that's nice kind of.
00:26:08
>> That's nice kind of, right? Because
00:26:10
there are certain times where you know
00:26:12
you're missing things.
00:26:13
>> There's of course the flip side where
00:26:14
it's like I don't want to record
00:26:15
everything. I don't want to live in my
00:26:16
own surveillance state.
00:26:18
>> Yeah.
00:26:19
>> And what I need to remember to do is not
00:26:22
that important. It's like change the fil
00:26:25
water filter in my house.
00:26:26
>> Yeah. But if it's unimportant, I'm going
00:26:28
to forget. Well, that was one of the
00:26:30
findings in here is that I say like
00:26:31
especially to my wife like I say I'm
00:26:33
going to do all these things and I never
00:26:34
do them
00:26:35
>> or I don't it's not I don't ever really
00:26:37
do them but um also I just don't have I
00:26:39
don't remember to do them.
00:26:41
>> Mhm.
00:26:41
>> Right. Um and so I think this idea of
00:26:45
some passive computing that is on us
00:26:50
>> but works handinhand with the smartphone
00:26:54
is coming. I I mean, and well, I'm gonna
00:26:57
do something next because I wanna I want
00:26:59
to get your take, but I I I mean, I know
00:27:00
how you felt about many of these
00:27:03
>> devices.
00:27:04
>> Yeah, I I'm I'm willing to give them a
00:27:06
shot. This is my thing. And I know that
00:27:08
the the main trade-off that I think I
00:27:09
think the most about, and I'm probably a
00:27:11
lot of people watching think about is
00:27:12
the trade-off between
00:27:14
the data and the privacy and the
00:27:16
convenience. And it's almost like a
00:27:18
sliding scale. The more convenience you
00:27:20
get, the less privacy you get. If you're
00:27:22
going to have it remember everything you
00:27:23
said and give you a summary of it, it
00:27:25
has to listen to a lot of what you're
00:27:27
saying and it's going to be super
00:27:28
convenient because it gives you that
00:27:29
back in functionality. And so, how far
00:27:32
do you want to slide that? Ah, you know
00:27:33
what? I'll let it listen to everything.
00:27:34
I'll let it see everything through my
00:27:36
face cameras and I'll let it hear
00:27:39
everything through the microphones and
00:27:40
then I'll be the most productive human
00:27:42
ever alive.
00:27:43
>> Or the other side of that which is I
00:27:45
just want it once in a while just to
00:27:46
remember one or two things and that's
00:27:48
fine. And I think that's going to be the
00:27:51
calculation going on at every tech
00:27:53
company, right? At Apple, at Amazon, at
00:27:55
Google.
00:27:56
>> If we're not providing a great tool and
00:28:00
utility, people are not going to deal
00:28:03
with the privacy trade-off. Right? So,
00:28:06
the glasses I think are a great example
00:28:08
right now. And a lot of people don't
00:28:11
trust the meta glasses,
00:28:12
>> but there's a good enough utility there,
00:28:16
especially on the camera front,
00:28:18
>> right? that we're willing to put these
00:28:20
cameras on our face in spec I mean I use
00:28:24
them in specific situations right places
00:28:26
I wouldn't want to be holding my phone
00:28:28
and so there's a good enough utility
00:28:30
there and a good enough benefit but once
00:28:32
you start I don't know always recording
00:28:35
or always having those glasses on what
00:28:38
is the benefit
00:28:39
>> it you need to prove that
00:28:41
>> and that's the conversations that I'm
00:28:43
sure meta apple etc are having about
00:28:45
what is the features that we can build
00:28:47
in here to make that privacy trade-off
00:28:48
off work.
00:28:49
>> Yeah. Yeah. Every company has to has to
00:28:51
find a different place for that line in
00:28:52
the sand. That is also one of my I guess
00:28:55
it's more it's less of a theory, but
00:28:56
it's kind of proving out which is that
00:28:58
Apple is
00:29:01
not winning the AI race, but because
00:29:05
they've basically not competed at all,
00:29:08
they it's come around the other side
00:29:10
where people like that about them and
00:29:12
they can focus on being the hardware
00:29:13
that we run the AI stuff on at some
00:29:15
point. Um, but they have had a focus for
00:29:18
a long time on the privacy and because
00:29:21
of that they do not offer as much
00:29:23
convenience. C, Siri, Apple
00:29:25
intelligence, etc.
00:29:26
>> I actually don't mind on the Apple
00:29:28
intelligence stuff. I think you're
00:29:29
totally right. If Apple doesn't provide
00:29:31
me a large language model to talk to, in
00:29:34
a way I don't care.
00:29:35
>> Yeah,
00:29:35
>> but Siri is so atrocious. It is so
00:29:38
terrible.
00:29:39
>> It's pretty bad
00:29:39
>> that
00:29:41
>> it can't do the basic things that were
00:29:43
really been promised. And I talk to Siri
00:29:45
all the time.
00:29:46
>> Yeah.
00:29:47
>> Yeah. I mean, what other choices? Like
00:29:49
using CarPlay. I mean, we have you
00:29:51
probably have deep thoughts on this, but
00:29:53
yeah,
00:29:53
>> it's just a different podcast. Let's cut
00:29:55
it there. Um or like I need Siri to just
00:29:59
play a song or tell me some basic
00:30:01
information in the car or use my
00:30:03
HomePod.
00:30:04
>> Stuff that doesn't require a ton of data
00:30:06
and information.
00:30:06
>> I tell the story all the time. every
00:30:08
morning. I just want Siri to play NPR
00:30:11
pod uh news in my bathroom while I'm
00:30:13
just washing my face and putting on my
00:30:15
makeup.
00:30:16
>> Why do I have to ask five different ways
00:30:18
and have to perfectly explain the name
00:30:21
of the podcast and the like that is
00:30:23
insane.
00:30:24
>> Yeah,
00:30:24
>> it is 2026.
00:30:26
>> So, we had to growing up learn how to
00:30:29
Google search. Yeah.
00:30:30
>> Which was oh, you you kind of use
00:30:32
certain words in a certain order and
00:30:33
then you will find what you need. And as
00:30:36
they got better, it became less
00:30:38
important to know those skills. The AI
00:30:40
is kind of the same way. Like you have
00:30:41
to learn how to prompt it. You have to
00:30:42
learn how to ask it to get a certain
00:30:44
result. But after a while, they should
00:30:45
be good enough that you don't you should
00:30:46
just use natural language and it should
00:30:48
just know
00:30:48
>> conversational AI. We have the very
00:30:50
basics on all of the other chat bots
00:30:53
now.
00:30:53
>> Yeah.
00:30:53
>> And look, Apple's going to do this.
00:30:55
They're going to do it this year.
00:30:56
>> But
00:30:57
>> I'm with you. like you don't need to
00:30:59
give me a chatbot that knows everything,
00:31:01
but just please like one that can easily
00:31:03
play NPR in the morning or just tell me
00:31:06
the weather or you know just just some
00:31:09
of the really basic things like we got
00:31:11
timers right I'm f like I spent a lot of
00:31:13
time yelling at Apple about that we got
00:31:15
the timers finally got timers I don't
00:31:17
mean to be really selfish and you know
00:31:19
spoiled and say not look I'm very
00:31:22
thankful for the timers Apple
00:31:24
>> multiple timers
00:31:25
>> multiple timers right But now we we need
00:31:28
a little
00:31:28
>> now. Yeah. Yeah. Proud of that.
00:31:31
>> I wanted to bring this to you on the
00:31:32
podcast. I think it's gonna be the
00:31:34
hottest AI wearable of the year.
00:31:36
>> Okay. The hottest AI wearable of the
00:31:38
year.
00:31:38
>> Ah,
00:31:39
>> it's a pin.
00:31:40
>> It is a pin. And
00:31:42
>> I know you've had feelings about pins.
00:31:43
Tech pins.
00:31:44
>> Well, this pin uh is probably better
00:31:47
than the other tech pin.
00:31:49
>> Yeah, this is why I brought this. I
00:31:50
wanted a review. I wanted the Marquez
00:31:52
review of the product that I made.
00:31:54
>> Okay. So, it's uh it just has some text
00:31:56
on it. It says verified human. I am not
00:31:59
a robot. Now, I didn't have to do
00:32:01
anything to earn this pin and prove that
00:32:03
I'm a human. This is like a trust basis.
00:32:05
Like you believe that I'm a human.
00:32:08
>> I've spent a few minutes with you today.
00:32:10
I feel feel like you're a human. But we
00:32:13
we you know, one way I was going to have
00:32:14
you tested is that if you poke yourself
00:32:16
and see if you bleed.
00:32:17
>> That could be simil I mean, I could
00:32:19
simulate that. You're saying I passed
00:32:22
the Turing test.
00:32:23
>> Let's see. Let's see if there's blood.
00:32:24
So far, so far so good.
00:32:26
>> We gave Mark has the blood update last
00:32:28
week, so
00:32:29
>> yeah, that was a pretty solid show.
00:32:30
>> Oh, I saw that room back there. I was
00:32:32
worried about that room.
00:32:33
>> They had to get that blood.
00:32:35
>> Has any other guests made you bleed on
00:32:36
the show?
00:32:37
>> No, no, we haven't beta tested the like
00:32:39
making me bleed live yet,
00:32:42
>> but uh I feel like it would do a pretty
00:32:44
good job. I mean, I would bleed for
00:32:46
sure.
00:32:46
>> Okay. Well,
00:32:47
>> yeah,
00:32:48
>> then you guys have really advanced
00:32:49
Marquez bot here to amazing potential.
00:32:55
team.
00:32:55
>> Yeah.
00:32:55
>> Yeah. The back room is full of a bunch
00:32:57
of like non- workinging prototypes that
00:32:58
are kind of just like sparking and it's
00:33:00
it's a whole thing back there. But
00:33:01
>> um what else are your other impressions
00:33:03
of this pen? You look it's been really
00:33:05
rough for other pin makers out there.
00:33:07
>> Yeah.
00:33:08
>> And I'm this review means a lot to me.
00:33:11
>> So So the other pin that I think you're
00:33:13
referring to also has the word human.
00:33:16
>> Yes.
00:33:16
>> Um with like an extra letter or
00:33:18
something, I think. But uh it says human
00:33:20
on it. Um, that one had a bunch of other
00:33:23
claims of things that it said it could
00:33:25
do. This one, I don't think there are
00:33:27
any claims of things that it should do.
00:33:29
It should just verify that I am a human.
00:33:31
>> Yep.
00:33:31
>> Which importantly should mean that if
00:33:33
I'm a robot, I should not be able to
00:33:35
wear this. Like a humanoid robot should
00:33:38
not be able
00:33:39
>> Oh, good point.
00:33:40
>> to like dawn the pin.
00:33:41
>> We can take that in.
00:33:42
>> So, maybe some security some security
00:33:44
steps in there.
00:33:45
>> This is a decent review.
00:33:46
>> Yeah.
00:33:47
>> Um, how how about the battery life? Oh,
00:33:50
it seems like it'll never turn off,
00:33:53
which is really nice. The tech that
00:33:55
lasts forever is, and this should last a
00:33:57
really long time, is underrated in my
00:33:59
opinion.
00:34:00
>> Wow. This means I almost could cry. This
00:34:02
is a huge
00:34:03
>> I've worn like
00:34:05
>> like two or three, you know how uh
00:34:07
>> the swag you get at tech events, you
00:34:09
always get a pin.
00:34:10
>> Yeah.
00:34:10
>> Like that. This all I know. You don't
00:34:12
get the like little Apple pin like WWDC
00:34:15
or like random and we used to make a
00:34:16
pin, too. Like this. This feels like
00:34:18
right in line with like the
00:34:19
>> Well, I really modeled these after I
00:34:21
mean again, you might be a little No, I
00:34:23
don't think you're too young. Do you
00:34:24
ever go to TGIF Fridays where they had
00:34:25
like all the flare?
00:34:26
>> Of course. Yeah. Yeah. My birthday was
00:34:29
uh I was born on a Friday.
00:34:30
>> That Oh, really?
00:34:31
>> Yeah. And so I used to go to Fridays on
00:34:33
my birthday every year. Even if it
00:34:35
wasn't a Friday. Uh just somewhere in
00:34:36
New Jersey wherever I was.
00:34:38
>> Yeah.
00:34:39
>> I loved CJ and they're like not around
00:34:41
that much anymore.
00:34:42
>> They're Yeah, unfortunately. I mean, I
00:34:44
get it. Can't miss. That sampler was
00:34:47
amazing.
00:34:48
>> The bread sticks went crazy.
00:34:49
>> Yeah.
00:34:49
>> But yeah, this is this is a solid
00:34:51
>> truly one of the best samplers. Okay.
00:34:53
>> Yeah.
00:34:53
>> It means so much.
00:34:55
>> Yeah. I don't know how every tech seems
00:34:57
like you have a bunch.
00:34:58
>> Well, I mean, it's free for you.
00:35:00
>> Oh, thank you.
00:35:00
>> Actually, the promotion we ran for the
00:35:02
pre-order was that you had to pre-order
00:35:03
the book and I'd send you a free pin.
00:35:05
>> Okay.
00:35:06
>> Which the shipping on the pin was more
00:35:07
than the pin.
00:35:09
>> Yeah, that's that's what we learned when
00:35:10
shipping our pin as well is we had to
00:35:12
figure out the pin economics. But if you
00:35:15
two want the Well, we got to get like a
00:35:18
quote. I'm going to have to put your
00:35:19
quote on the pin review page. I know
00:35:21
that's going to cost.
00:35:22
>> Battery life seems infinite.
00:35:24
>> Yeah. In close.
00:35:24
>> Battery life seems infinite. If you
00:35:26
would like to get this Marquez approved
00:35:30
pin.
00:35:31
>> Mhm.
00:35:31
>> You just have to pre-order and then you
00:35:33
go to joastern.com
00:35:34
and get your pin.
00:35:35
>> Now you know.
00:35:36
>> Although I'm frightened because I'm
00:35:37
actually like doing my Well, my AI
00:35:39
agent, it deals with all the orders and
00:35:41
sometimes it messes up and it's a mess.
00:35:44
I'm sure it'll do it'll do great for
00:35:45
this on the Okay. On the bottom.
00:35:47
>> Is that true? You have an AI agent doing
00:35:48
all of the back end on your
00:35:50
>> Yeah, on the pin.
00:35:51
>> Oh, just the pin.
00:35:52
>> Well, I mean on also it built my website
00:35:54
and everything. But yeah.
00:35:56
>> Yeah,
00:35:57
>> it's actually beautiful website. I have
00:35:58
to say
00:35:58
>> that I believe. That I believe.
00:36:00
>> Yeah. But no, it like if you submit a
00:36:02
form the or you email the AI agent puts
00:36:05
it into the spreadsheet and then sends
00:36:06
an email to my publisher to say there's
00:36:08
a new pin order.
00:36:17
There's a little humanoid robot at the
00:36:19
bottom of the pin.
00:36:20
>> How do you feel about humanoid robots?
00:36:22
>> I have so many thoughts. It's a whole
00:36:23
other podcast. Um,
00:36:24
>> no, it's this podcast.
00:36:25
>> It's it's this this waveform. This is
00:36:28
the one.
00:36:29
>> Um, I really want them. It's weird
00:36:33
because I feel like I'm really excited
00:36:36
about this category because
00:36:39
I've lived through so many new tech
00:36:42
gadgets and hardware and this is one
00:36:44
that we've all seen for so long. We've
00:36:47
seen it in in cartoons and we've seen it
00:36:49
in movies and it's always this promise
00:36:52
and so now we like see them. They're
00:36:55
really being made by these companies or
00:36:57
Tesla or
00:36:59
>> Chinese companies, which is my recent
00:37:01
YouTube video, and we want them to work.
00:37:04
>> Mhm.
00:37:05
>> But they don't.
00:37:07
>> Yes.
00:37:08
>> I know you also have thoughts of like,
00:37:09
why is this form? Like, is this the
00:37:11
right form?
00:37:12
>> Yeah.
00:37:13
>> But I don't even care. It's cool.
00:37:16
>> And it's so fun.
00:37:17
>> I I do think it's fun. I have this
00:37:21
thing. So, like you said, I don't know
00:37:24
that this will be the most efficient
00:37:27
form for a functional multi-purpose
00:37:30
robot. Let's say it's in your home or
00:37:31
something like that. You just want it in
00:37:32
your home and it does things like
00:37:33
laundry and puts the dishes away and
00:37:35
cleans stuff or whatever. Having it be
00:37:38
an upright bipedal
00:37:40
10fingered thing with eyes and a head
00:37:43
and stuff. It's like it's it is fun and
00:37:45
you do get that like Jetson's feel or or
00:37:47
the futuristic uh C3PO is a tall one
00:37:51
right in Star Wars. Like
00:37:52
>> it's cool, but it's also a little bit
00:37:56
creepy when we anthropomorphize
00:38:00
things that are clearly not human.
00:38:02
Almost like an animatronic friend.
00:38:04
>> And it has to get through that um
00:38:08
>> what's the valley called? Uncanny. It
00:38:10
has to get through that uncanny valley
00:38:12
of being a little creepy before it gets
00:38:14
to being really nice. And I don't know
00:38:16
if I'm willing to deal with that.
00:38:17
>> I have spoken to so I spoke to a lot of
00:38:20
robotics experts for the book and
00:38:22
there's definitely this to there's a
00:38:25
there's a two sides. There are the
00:38:28
people that say we should have custom
00:38:30
single utility robots that do the things
00:38:33
that you're talking about and are really
00:38:34
good at them,
00:38:35
>> right?
00:38:36
>> And then there's a side that the world
00:38:38
is built for humans. This is the form
00:38:40
factor. This is what we look like.
00:38:42
>> I've been thinking about this a lot. I
00:38:44
thought so. I agree with that. I also
00:38:46
think that we as humans built the world
00:38:48
around all of the shortcomings of the
00:38:50
human form. And we can do better.
00:38:53
>> Literally short like we can't even reach
00:38:55
things. Our arms don't extend.
00:38:57
>> Driving is the perfect example for this,
00:38:59
right? So you have a car. Let's say it's
00:39:02
not a self-driving car. You could
00:39:03
theoretically have a human a humanoid
00:39:06
robot sit in the driver's seat of your
00:39:08
car and hold the steering wheel and
00:39:11
press the pedal and drive it. That's one
00:39:13
version of this solution. Now you have a
00:39:15
operated car from a robot, but it still
00:39:18
has the same blind spots. It still only
00:39:20
has a set of eyes on the front. It still
00:39:21
only has the reaction time of things
00:39:23
that it can see and hear. Or your car is
00:39:28
covered in sensors, covers all the blind
00:39:30
spots. It has this neural link that like
00:39:32
maps all this information together, can
00:39:34
see way further around all the sides of
00:39:35
the car, and has instantaneous response
00:39:38
and doesn't have to move through the
00:39:39
steering wheel and the pedal. And it's a
00:39:41
much better self-driving car operated by
00:39:45
a robot in that case. So, even though
00:39:47
yes, we did design the car form around
00:39:49
the human, I think that is actually a
00:39:51
limitation that we can do better uh by
00:39:54
designing the single-use robots. So
00:39:57
yeah, the world may not look the way it
00:39:59
does in many years if we have a bunch of
00:40:01
really good robots instead of the the
00:40:03
world like built the way it is.
00:40:05
>> I love that idea and I think I see it re
00:40:07
I see you see it really clearly in the
00:40:08
book when I I I don't know if anyone in
00:40:11
the world knows as much about laundry
00:40:12
folding robots as I do. Okay.
00:40:14
>> How many CES laundry folding robots have
00:40:16
you talked to?
00:40:17
>> I have talked to so many. I've also had
00:40:20
a laundry folding robot in my house.
00:40:22
>> Wow. And the interesting thing about the
00:40:25
laundry folding robots talking about
00:40:26
CES, there are those demos where it
00:40:28
doesn't use hands, right? The shirt goes
00:40:30
through a different way. It goes through
00:40:32
a conveyor belt, it folds, does these
00:40:33
things.
00:40:34
>> What the humanoid companies want to do,
00:40:36
and even some other startups that are
00:40:38
doing some wacky things with laundry
00:40:40
folding,
00:40:41
>> they want to give it hands.
00:40:42
>> Yeah. And
00:40:43
>> the hands, they want to train it on
00:40:46
thousands of hours of folding. Right.
00:40:48
So, I had this this robot in my house,
00:40:50
and it's just two robotic arms, but it
00:40:53
doesn't it's like clenching. I keep
00:40:54
doing this. Like, that's how they look,
00:40:56
right? They're like the grabs in like
00:40:58
the, you know, in the arcade where you
00:40:59
it comes down in the claw that you don't
00:41:02
get the free thing,
00:41:03
>> which is actually it's probably better
00:41:04
for folding things
00:41:05
>> kind of, but like
00:41:07
>> some things.
00:41:07
>> Some things.
00:41:08
>> Yeah.
00:41:09
>> If there was a lot of I learned so much
00:41:12
about the complication of folding
00:41:13
laundry. It's really simple for us,
00:41:15
right? And there's this idea of Morvex
00:41:17
par paradox where things that are really
00:41:19
simple for humans are really hard for
00:41:20
robots and things that are really hard
00:41:21
for robots are really simple for humans.
00:41:24
>> This robot like it struggles so much to
00:41:28
fold because it doesn't have the hands.
00:41:30
It doesn't have all of the the right
00:41:32
moves like when the when the the shirt
00:41:34
falls it doesn't quite know it because
00:41:36
every time a shirt falls it looks
00:41:38
different, right? It doesn't know where
00:41:39
the arms are. It doesn't know where the
00:41:40
neck is.
00:41:40
>> Yeah. It could also only fold t-shirts,
00:41:43
which is a problem, you know, for, you
00:41:44
know, if you only wore t-shirts, it's a
00:41:46
problem for people. Um,
00:41:48
>> sorry, we just had this argument uh last
00:41:50
week on the podcast about how
00:41:52
>> about how about just about how garments,
00:41:54
the nature of fabric and garments are so
00:41:57
complex that I have no faith that an ML
00:41:59
model is anywhere close to understanding
00:42:00
how they work.
00:42:01
>> And and that's what was proven by this.
00:42:03
And so now you have millions of dollars
00:42:06
going into solving a problem of folding
00:42:09
garments
00:42:10
>> with that are trying to simulate the
00:42:13
human way of doing it, right? Like
00:42:15
you're and I would like found myself
00:42:17
cheering this robot in my basement. It
00:42:18
was two giant arms hooked up to a big
00:42:20
laptop. It's got all the things and I'm
00:42:22
like standing in my basement like you
00:42:23
can do it. You can fold my t-shirt in
00:42:25
under 10 minutes. you know, like
00:42:29
why not create a robot that doesn't have
00:42:31
arms that folds laundry a different way
00:42:33
and which we know some companies are
00:42:34
doing.
00:42:35
>> Yeah. Yeah. This is ex and and I
00:42:36
remember like talking in the in the
00:42:38
Tesla factory about there are things
00:42:39
that they have humans do that that the
00:42:41
robots simply can't do like when they
00:42:43
have to connect like a hose for some
00:42:45
system to another one. It just sort of
00:42:46
dangles and the robot just misses it.
00:42:49
Doesn't like is trying to calculate the
00:42:50
position has no idea what to do. The
00:42:52
human just grabs it, grabs it, connects
00:42:53
it and it's fine. So there are
00:42:54
definitely things that are probably
00:42:57
forever going to be better for humans,
00:42:59
but yeah, for the things that are like
00:43:02
small mundane tasks, like I don't want
00:43:03
to clean, like the we have robot
00:43:05
vacuums. They aren't a human. It's just
00:43:06
a thing that rolls around on the ground.
00:43:07
That's perfect. That's all it needs to
00:43:09
be. The the the single-use robot thing,
00:43:11
I think that gets stronger and stronger
00:43:13
in my head every time I think about it.
00:43:15
>> And I think for certain tasks, it's
00:43:16
going to make sense for sure. Driving,
00:43:18
right? We that that one's already here.
00:43:21
Uh it took a long time for us to get
00:43:23
here. Um but a lot of these other single
00:43:26
tasks that we want in our homes are just
00:43:29
so hard. I mean like the dishwasher. I
00:43:31
know you know the scene of the Neo robot
00:43:33
went viral of it doing the dishwasher.
00:43:36
>> Struggle.
00:43:36
>> Yeah.
00:43:37
>> Right. I mean because that's like a very
00:43:40
h like the dishwasher was built for a
00:43:42
human.
00:43:43
>> I was laughing at your video for like a
00:43:45
It took me like half an hour to watch
00:43:46
the video cuz I kept pausing it and
00:43:48
laughing so much because it was really
00:43:50
it was tough. I'm like thinking about it
00:43:52
and it's fine that it's slow because I'm
00:43:54
not doing it so it can just happen in
00:43:56
the background but just watching the
00:43:57
robot like try to bend over at the
00:44:00
>> I thought it was when we were filming it
00:44:01
I mean I was like you know you know
00:44:03
you're live in this moment and I was
00:44:05
like yelling at my producer David come
00:44:07
over here like you know we can't miss
00:44:08
this it might fall got to get from like
00:44:10
I thought it was going to fall into the
00:44:12
dishwasher you know and it was
00:44:15
>> um and last last week we posted a first
00:44:18
video one of my first videos on my own
00:44:20
YouTube channel. Marquez, I'm really
00:44:21
trying to
00:44:22
>> Yeah.
00:44:22
>> live in your
00:44:24
>> We have a soundboard.
00:44:25
>> We I'm I'm aware you have a soundboard.
00:44:28
I'm surprised we haven't used it more
00:44:30
here. Um it's very impressive
00:44:31
soundboard.
00:44:32
>> I got you.
00:44:33
>> Um Grock context, please.
00:44:35
>> It's a new one.
00:44:36
>> That's a new one. Yeah,
00:44:37
>> that's a brand new one.
00:44:38
>> Um where was it going? Oh, so I wanted
00:44:43
to do this story on these Chinese
00:44:45
robots, the Unit G1 that you see going
00:44:47
viral everywhere on the internet.
00:44:49
>> Yeah. How is that?
00:44:50
>> Um, well, the video is doing pretty
00:44:52
well, but most people are angry that I
00:44:54
I'm really talking about the fact that
00:44:55
they're all coming from China. I mean,
00:44:56
the the the thing that I wanted to talk
00:44:58
about there was that we're letting these
00:44:59
Chinese robots into the US
00:45:01
>> and we're so worried about Chinese EVs
00:45:03
and we're so worried about Chinese
00:45:04
phones, but yet like we're like, "Oh,
00:45:06
let's let these giant humanoid robots
00:45:08
that are 80 pounds and can do kung fu
00:45:10
come into America." Like,
00:45:11
>> yeah,
00:45:11
>> that seems odd. Um,
00:45:14
>> and it's a really interesting
00:45:16
exploration of why China is obviously
00:45:18
ahead of the US on manufacturing these
00:45:20
because they're ahead of us on
00:45:21
everything on manufacturing in terms of
00:45:23
electronics.
00:45:24
>> Um, but what was just funny, you should
00:45:27
watch that one too, is like
00:45:28
>> the robot can't do anything. I mean, it
00:45:30
can do some things. It can do it can
00:45:32
dance, which my kids love, and it can do
00:45:34
kung fu, which my kids love.
00:45:36
>> Um, but other than that, it just like
00:45:38
sat in my house doing nothing.
00:45:40
>> It can do choreographed things. Yeah.
00:45:42
Yeah. Yeah. I think this is what I've
00:45:44
seen about a lot of the uh AI promises
00:45:46
is there's so much training left to do
00:45:48
and there's so much learning left to do
00:45:50
by the robots that were sort of selling
00:45:52
the promise of the future.
00:45:53
>> I made the humanoid robot bet that there
00:45:56
would not be a single humanoid robot
00:45:58
shipped to a customer in 2026 that can
00:46:01
do all the autonomous things.
00:46:02
>> Oh, it can't.
00:46:03
>> Oh, yeah. That's part one. You're wrong
00:46:05
about I think they will ship.
00:46:06
>> They will ship it.
00:46:07
>> Do you think the 1X is going to get
00:46:08
shipped? I think that they will ship it
00:46:10
to select few. I don't think that I will
00:46:13
be one,
00:46:13
>> but it won't do all the things.
00:46:14
>> I don't think Marquez is going to be
00:46:16
select few.
00:46:16
>> No.
00:46:17
>> Um, let's say this. If I get in New
00:46:21
Jersey packed,
00:46:22
>> Yeah.
00:46:22
>> New Jersey tech media packed right here.
00:46:25
>> Okay.
00:46:25
>> If one of us gets the 1X
00:46:27
>> Uhhuh.
00:46:28
>> we have to go. We have to share a New
00:46:30
Jersey
00:46:30
>> 100%. Yeah. You know,
00:46:32
>> you're welcome to try to ask it to do
00:46:34
whatever you want.
00:46:34
>> You're welcome to come to my house.
00:46:36
>> Yeah. and sit with it in the car. It
00:46:39
sounds like you want your humanoid to
00:46:40
ride to drive your car.
00:46:41
>> It's gonna be really bad at that.
00:46:43
>> We will shut down the town for that.
00:46:44
>> Yeah.
00:46:45
>> Um and so I think they will, to answer
00:46:48
your question, Alice, I think they will
00:46:49
ship them autonomous. Zero chance. No
00:46:52
way. Maybe it does one thing
00:46:54
autonomously, which is like open the
00:46:55
door.
00:46:56
>> Yeah.
00:46:56
>> It won't do that. Um maybe maybe it will
00:47:00
do one task autonomously.
00:47:03
>> Yeah. Yeah. We had a
00:47:05
>> actually that's what we will do. You can
00:47:08
control my 1X from here in your VR
00:47:10
headset.
00:47:11
>> Oh yeah. I'll just start doing the
00:47:13
dishes.
00:47:13
>> Yeah.
00:47:14
>> Through through the headset with the the
00:47:16
handle. Yeah. That'll be great. We had a
00:47:18
Amazon Astrobot here for like two or
00:47:22
three maybe longer. Three or four years.
00:47:23
>> No. It's still here.
00:47:25
>> It's still here. It's still here.
00:47:26
>> That's so unfortunate.
00:47:27
>> It's just unplugged.
00:47:27
>> The poor thing. It's
00:47:28
>> I had one too in my house.
00:47:30
>> Oh, really?
00:47:30
>> So you're aware of how bad it is at most
00:47:33
things. like it would sort of just roam
00:47:34
around being a security camera, which I
00:47:36
guess is technically a successful
00:47:39
>> it does what it says it will, but all
00:47:41
the other things it's supposed to be
00:47:42
able to do, it would not be very good
00:47:44
at. And then it would just kind of trip
00:47:45
over things and get stuck on wires. So,
00:47:47
it was this little robot, big wheels.
00:47:49
>> It was just a like a Lex on wheels to
00:47:51
like play music.
00:47:52
>> Yeah. That that's the closest we've
00:47:54
gotten so far, which
00:47:55
>> Yeah. And the whole idea of like it
00:47:56
would take the drink to the to the get
00:47:58
like to the other room.
00:48:00
>> Yeah. made no sense to me because like
00:48:03
it couldn't grab the drink.
00:48:04
>> A person would have to be in both rooms.
00:48:06
>> At which point you could just ask the
00:48:08
person,
00:48:09
>> right? Right. Which
00:48:11
>> Yeah.
00:48:12
>> Yeah.
00:48:12
>> made it a tough cell.
00:48:14
>> Yeah.
00:48:14
>> So, yeah, I do I tend to think we are
00:48:16
quite far from the humanoid robot
00:48:18
future, but I'm curious to see how that
00:48:21
plays out. If it'll be humanoid robots
00:48:22
or if I'll be proven right, vindicated.
00:48:24
It'll just be a bunch of specialized,
00:48:26
smaller, really efficient, hyper adapted
00:48:29
robots for individual tasks everywhere.
00:48:32
Your dishwasher will just have an army.
00:48:33
>> Or the or the humanoids will just be in
00:48:36
factories and industrial settings.
00:48:38
>> Even that, I don't think needs to be
00:48:40
humanoid cuz even those are like connect
00:48:42
this hose or like put the door on the
00:48:44
car. At this point, it's just a a an
00:48:46
arm. Like we have this robot arm in the
00:48:48
studio which we've put a camera on and
00:48:49
we can teach it to do things. But in
00:48:51
this factory, it doesn't have to learn
00:48:52
anything. It just has to grab the
00:48:54
windshield and put it on the car or grab
00:48:55
the next windshield. Doesn't have to be
00:48:57
shaped like a like C3PO. So,
00:48:59
>> yeah, I guess and that's the argument
00:49:01
that Amazon's made too, right? They've
00:49:03
got millions of robots and they're not
00:49:04
humanoids.
00:49:05
>> We'll see. I'm very curious. Um, all
00:49:08
right. What I have another question
00:49:09
about just going independent as a media
00:49:12
person because you've done now writing
00:49:15
and video as part of like The Verge and
00:49:18
Wall Street Journal and having your own
00:49:19
YouTube channel. How does that differ?
00:49:22
Like what what was the choice to to go
00:49:24
independent and how does it like compare
00:49:26
to being a part of a a larger structured
00:49:28
corporation?
00:49:31
>> Yeah. No, like look, I'm only four weeks
00:49:33
in. Well, like I'm two months, three
00:49:35
months in, but we just launched the
00:49:37
YouTube channel. We just did that last
00:49:38
week, two weeks ago. Um
00:49:40
>> which boy, you know, I'm
00:49:43
really making it's a lot of work. You've
00:49:45
been working hard here.
00:49:47
>> Yeah. Yeah, it is. You know, video
00:49:49
production is one of those things where
00:49:51
>> you probably heard my octopus analogy.
00:49:53
You end up wanting to do the video
00:49:55
stuff, but there's a ton of stuff around
00:49:57
the video stuff that you also have to do
00:49:59
now, which is, you know, the behind the
00:50:01
scenes, the inbox, the accounting, the
00:50:03
the, you know,
00:50:04
>> the bookkeeping, the insurance,
00:50:06
>> all that stuff.
00:50:07
>> I underestimated that. People told me,
00:50:10
but I really underestimated that work.
00:50:13
Um,
00:50:14
>> so but now I'm finally getting past some
00:50:16
of that where I can actually get back to
00:50:17
the content. I mean, I'm launching a
00:50:19
book in the middle of all this, which do
00:50:20
not recommend. Um, but one of the
00:50:22
reasons I did do this was because this
00:50:24
book was coming out. I was going to be
00:50:25
on things like this and I wanted to be
00:50:27
able to talk about the new thing. So,
00:50:29
it's called The New Thing. It's called
00:50:30
The New Things. Go subscribe. It's
00:50:32
Joanna Stern is the YouTube channel. Um,
00:50:36
>> but a lot of it like truly, you know,
00:50:38
inspired by people like you. For so
00:50:40
long, I had been publishing to YouTube
00:50:43
and I became really obsessed with videos
00:50:45
on YouTube, you know, a few years ago at
00:50:47
the Wall Street Journal because I
00:50:48
started realizing this is where the
00:50:50
audience is, right? The Wall Street
00:50:52
Journal is made, the audience is
00:50:54
amazing, but they're older. They have
00:50:57
more money and I felt in many ways I was
00:50:59
not being as accessible to all the other
00:51:02
people in the world that want to know
00:51:03
about tech. Uhhuh.
00:51:04
>> And so I started focusing a lot on my
00:51:08
YouTube videos at the journal and those
00:51:10
would appear on the Wall Street
00:51:11
Journal's website too, but I was really
00:51:13
looking at the data on YouTube.
00:51:14
>> Yeah.
00:51:15
>> And more and more I wanted control over
00:51:17
that. I wanted control over the
00:51:18
audience. I wanted control over what we
00:51:21
do because you know you have editors and
00:51:23
editors are amazing and I still have
00:51:25
some editors that I'm hired and are
00:51:27
working with at the new thing. Um, but I
00:51:30
just wanted the freedom to do even more
00:51:33
stuff and weirder stuff.
00:51:35
>> Yeah. Well, even weirder than what you
00:51:36
are already some of your I really like a
00:51:39
lot of the interesting video idea
00:51:41
concepts that you came up with and
00:51:43
decided to pull off at the Wall Street
00:51:44
Journal. I wonder what even weirder
00:51:47
looks like. What is your favorite
00:51:48
>> I mean
00:51:49
>> weird idea to
00:51:50
>> I don't know if it's even weirder, but
00:51:52
like I just don't have to ask permission
00:51:54
to do certain things. Totally.
00:51:56
>> You know?
00:51:56
>> Yeah. And you get to sort of experiment
00:51:58
with uh what works because it connects
00:52:00
with the audience, but also just what
00:52:01
what you want to do and just to see if
00:52:02
it works,
00:52:03
>> right?
00:52:03
>> Yeah. What was uh what was the process
00:52:05
for coming up with ideas for that
00:52:08
audience? Maybe there's a little bit of
00:52:09
a different audience for the new thing
00:52:10
versus the journal, but how do you come
00:52:12
up with an idea of what makes a good
00:52:14
video?
00:52:15
>> I mean, for me, it's just curiosity.
00:52:17
Like, if I have a question about
00:52:18
something like this book, like I just
00:52:20
What does the world look like when AI is
00:52:22
everywhere? Okay, let's go out and
00:52:23
answer that question. Let me go and
00:52:25
report that out. let me go talk to
00:52:26
people. Let me go test it. I mean, I
00:52:28
think the major overlap that both of us
00:52:30
have is that we test things. We don't
00:52:32
just like go and talk to the companies
00:52:34
making it. We wait for us to be able to
00:52:36
use a lot of this. And so, I want that
00:52:38
to be a guiding principle of my coverage
00:52:41
forever. Um, I think the the tough thing
00:52:46
about tech is like you it's all
00:52:49
overlapping now in so many ways, right?
00:52:52
So, if I want to not, you know, like,
00:52:54
oh, I mean, I don't want to talk about
00:52:55
AI, which is not what I want to do, but
00:52:57
I, you know, think that there might be
00:52:59
too much AI coverage. How do I figure
00:53:00
out how to put those pieces together in
00:53:03
a in stories that are still interesting
00:53:05
to people because people can be very
00:53:07
oversaturated right now with YouTube,
00:53:09
AI, YouTube?
00:53:10
>> Um, and that's I don't want to only be
00:53:13
AI YouTube. I really don't.
00:53:14
>> Yeah. Um, like I don't want to be just,
00:53:17
you know, every day like here's 10
00:53:18
prompts that you can do to
00:53:20
>> that channel is already out there.
00:53:21
>> That channel's already out there. Um, so
00:53:23
I think my guiding principles is like
00:53:25
things that I'm curious about, things
00:53:26
that are going to affect our lives with
00:53:28
tech and, you know, not be super
00:53:32
technical, but be technical enough that
00:53:34
I can still talk to the people who love
00:53:36
tech, but also like with the same thing
00:53:38
with this book, the people in our lives
00:53:40
that are still curious. We all use
00:53:42
phones, like we all use tech. How do I
00:53:45
help make your life better and explain
00:53:46
something to you?
00:53:47
>> Yeah. Do you have a a big project idea
00:53:50
or a big video idea that you haven't
00:53:52
come haven't done yet that you think you
00:53:53
could now pull off
00:53:56
>> or is it all top secret?
00:53:57
>> We need a little bit more time to get up
00:53:59
and running. Right now it's just like
00:54:00
get through the book launch.
00:54:01
>> Yes. Which is
00:54:03
>> get through WWDC.
00:54:04
>> Okay. June.
00:54:06
>> Yep.
00:54:06
>> Yeah. IO.
00:54:07
>> I have a feeling September is going to
00:54:09
be very busy.
00:54:10
>> Usually ends up being a little chaotic.
00:54:12
>> Yep.
00:54:12
>> Yeah. So like you know
00:54:15
>> yeah I kind of think of uh I've talked
00:54:16
about this as like this we have the
00:54:18
waves of like the offseason the on
00:54:20
season like September will be on October
00:54:22
will be on and then you'll have like J
00:54:24
post CES January is a nice little little
00:54:27
uh resting period you can sort of lick
00:54:30
your wounds reset a little bit. Yeah,
00:54:32
this year it was like Samsung pushed
00:54:34
back a little bit. It just kind of kept
00:54:35
happening. But you can uh you can sort
00:54:37
of think of random new ideas and fun
00:54:39
stuff you want to try that isn't
00:54:41
>> I mean I have a lot of those ideas. Is
00:54:42
it just figuring out the flow? I mean, I
00:54:45
could talk pick your brain on for hours
00:54:47
how you manage all of the different
00:54:48
things and channel. And I'm also writing
00:54:50
a newsletter. So, like that's, you know,
00:54:53
twice weekly newsletter, YouTube videos,
00:54:56
shorts,
00:54:57
>> events, all of those things. And how do
00:54:59
you think about it all cohesively?
00:55:01
>> I tend to go down the rabbit hole of
00:55:03
that often, like content strategy. And I
00:55:05
don't have a newsletter and I'm not also
00:55:08
doing like a book tour. So I I'm mostly
00:55:11
just focused on the content strategy for
00:55:13
me is like long form versus short form
00:55:16
or now short long form. That's actually
00:55:18
something that's a meta in my head. Uh
00:55:21
three to five minute yeah
00:55:23
>> long form videos kind of died in the
00:55:25
last four or five years. And why do they
00:55:27
die? Everyone's optimizing for long
00:55:29
watch time, but people want to watch our
00:55:31
videos.
00:55:31
>> Casey Neistat was talking to me about
00:55:33
this and he said Marquez is doing this
00:55:34
well.
00:55:35
>> Yeah.
00:55:35
>> Yeah.
00:55:36
>> I mean I I was doing long shorts, which
00:55:38
were kind of weird, but I want to do
00:55:39
shorts. just have an idea and like you
00:55:41
know what people might want to hear me
00:55:43
on this and it doesn't have to be 10
00:55:44
minutes.
00:55:44
>> Exactly.
00:55:45
>> Yeah.
00:55:45
>> I think that I think we need more of
00:55:47
that.
00:55:47
>> Okay.
00:55:48
>> So there's a thought for the world.
00:55:50
>> There's a thought.
00:55:51
>> Free.
00:55:52
>> Yeah.
00:55:52
>> I gave you a free pin.
00:55:54
>> Yeah. And I gave you a free uh content
00:55:56
strategy idea. Perfect.
00:55:57
>> Amazing.
00:56:06
>> All right. I got a couple rapid fire
00:56:07
questions for you.
00:56:08
>> Oh gosh. Um, feel free to take
00:56:10
>> it's about the magic mouse.
00:56:13
>> I I'm going to throw one in there about
00:56:14
the magic mouse. Uh, are you
00:56:16
>> I have two of them.
00:56:17
>> You is this your This isn't your mouse
00:56:19
of choice, is it?
00:56:20
>> It is.
00:56:21
>> Okay, maybe this isn't rapid fire.
00:56:22
>> Everyone is explain.
00:56:24
>> Everyone is rolling their eyes.
00:56:25
>> Explain yourself. You You intentionally
00:56:27
use And it sounds like you have two. So,
00:56:29
you bought another magic mouse where you
00:56:31
>> Because when I'm charging, I like to
00:56:32
have them in and out.
00:56:33
>> That's Now, you know, you could just get
00:56:35
one mouse and just
00:56:38
>> What? Right. Like you could just keep
00:56:40
using it while it's charging.
00:56:42
>> Yeah. It's I love the scrolly feel. I
00:56:45
like the clicky part. I like it.
00:56:49
>> Okay. Have you
00:56:50
>> Have I tried other mouses? No. Never.
00:56:53
>> Never.
00:56:54
>> But do you not like the other mouse?
00:56:56
>> Only using the magic mouse. In fact, in
00:56:59
1995 when you were 2 years old and I had
00:57:01
my first computer, I had a prototype of
00:57:04
the Magic Mouse.
00:57:05
>> They've always had
00:57:05
>> Johnny I have sent it to me. They've had
00:57:07
bad mice for a long time actually. Like
00:57:09
I have I unboxed some of those old like
00:57:11
iMac G3s and the mouse even from those
00:57:14
times was notably unique.
00:57:17
>> The see-through the the plastic
00:57:19
see-through ones.
00:57:20
>> Yeah,
00:57:20
>> it's awesome mouse.
00:57:21
>> It's an awesome mouse.
00:57:22
>> I mean, it's just cool looking. I don't
00:57:24
know.
00:57:24
>> Thank you. Thank you. Thank you. Insane
00:57:27
mouse chest. Okay. Yeah.
00:57:29
>> Yeah. Desktop or laptop person?
00:57:30
>> Am I going to be cancelled for the magic
00:57:32
mouse stuff? Should we edit this out?
00:57:34
>> I No. No. I'm We have I'm not going to
00:57:37
name names, but we have people who work
00:57:39
here.
00:57:40
>> It's me and Michael also.
00:57:41
>> We like the Magic Mouse. I knew that we
00:57:44
shared a bond except for this whole
00:57:45
basketball thing that you're always
00:57:47
talking about.
00:57:48
>> Oh my gosh.
00:57:49
>> Just something about the magic mouse
00:57:51
that's that brings out
00:57:52
>> Yeah.
00:57:53
>> I just get mad for no reason. Uh
00:57:56
>> maybe John Turnis is the first thing
00:57:57
he's going to do.
00:57:58
>> No, that's actually huge. Wait, he has
00:58:00
to know the magic mouse is bad. He's a
00:58:01
product guy. He has to know the magic
00:58:04
mouse is back.
00:58:04
>> Marquez, please talk into the mic.
00:58:06
>> Tim Cook,
00:58:07
>> I was like,
00:58:08
>> podcast alert. Podcast alert here.
00:58:11
>> Did you see that? You've seen the clip
00:58:12
where I asked Tim Cook about the magic
00:58:13
mouse and he's like, we make a mouse
00:58:14
like that. He hasn't thought about that
00:58:16
in years.
00:58:17
>> Tim,
00:58:18
>> wait. No, I haven't seen the Turnis one.
00:58:20
>> Well, I haven't asked him about it, but
00:58:21
I know that he knows him first.
00:58:23
>> He knows the magic mouse is bad.
00:58:26
>> Of course, he knows it because he also
00:58:27
has two. And this is exactly what he
00:58:29
does.
00:58:29
>> He takes over at true. That's not I have
00:58:31
not reported that out. Please, as a
00:58:33
reporter, I will confirm that. Actually,
00:58:36
Apple PR will not confirm.
00:58:37
>> September 1st when he he becomes a new
00:58:39
CEO. I'm starting a countdown timer to
00:58:41
when Apple releases a good mouse.
00:58:43
>> Okay.
00:58:43
>> Cuz there's a lot of stuff I want Apple
00:58:45
to make. That's maybe at the top of my
00:58:47
list.
00:58:48
>> And honestly, I'm fine with that mouse
00:58:49
just having a port someplace else.
00:58:51
>> That would be a really nice start,
00:58:53
>> right?
00:58:53
>> Yeah.
00:58:54
>> I mean, Ellis agrees.
00:58:55
>> Even if that's the only change they
00:58:56
make,
00:58:57
>> even if it's still an ergonomic
00:58:58
nightmare, just move the port. Okay. Do
00:59:00
you have hands?
00:59:01
>> Yeah.
00:59:02
>> How is it an ergonomic nightmare?
00:59:03
>> Cuz it's I have a like a hand that is
00:59:07
maybe three times the size of the hand
00:59:09
they designed it for. That's true.
00:59:10
>> That's true.
00:59:11
>> So, what if they just made Magic Mouse
00:59:12
XL?
00:59:12
>> Yeah. What if they made Magic M?
00:59:14
>> That would solve one of the critical
00:59:16
issues.
00:59:16
>> Ultra.
00:59:17
>> Oh, yeah. Magic Mouse Studio.
00:59:19
>> That would solve one of the two horrible
00:59:22
things about the Magic Mouse. If they
00:59:23
could just
00:59:24
>> $27,000
00:59:26
to get that mouse, what would you buy
00:59:27
it?
00:59:28
No, I would just use an MXM, which is
00:59:31
just fine.
00:59:32
>> Oh my gosh. If you spend an extra $600,
00:59:33
it has wheels.
00:59:35
>> All right, I'm in.
00:59:36
>> No, stainless steel. It's little feet
00:59:39
with it just walks around little scary
00:59:42
>> and a tail.
00:59:43
>> I'm pretty sure John Turnis is listening
00:59:45
and taking me.
00:59:46
>> He's got it right now.
00:59:47
>> John, if you just watch this part of the
00:59:48
podcast, Magic Mouse 2, please. You can
00:59:51
do wonders. Um,
00:59:52
>> okay. Rapid fire. Laptop.
00:59:54
>> Rapid fire. Laptop. Laptop or desktop?
00:59:56
>> Laptop always? laptop always. Okay. No
00:59:58
desktop at all.
00:59:59
>> I have a I have a Mac Mini running
01:00:01
stuff, but
01:00:02
>> totally. Uh what what browser do you
01:00:04
use? Are you an AI browser person or are
01:00:06
you a regular browser person?
01:00:07
>> I have a lot of browsers. Um right now
01:00:09
Chrome vertical tabs. I know you have a
01:00:11
lot of feelings on that.
01:00:12
>> I like Chrome. I like
01:00:13
>> I heard a lot about Arc on this podcast.
01:00:15
I don't know what those I guess
01:00:16
>> Arc is nice. Arc is not the AI browser,
01:00:19
but it is the beautiful functional
01:00:22
browser that a lot of us like. Um, but
01:00:24
then there's like the Perplexity Comet
01:00:26
and
01:00:26
>> I've been using Comet a lot. Okay.
01:00:28
>> Yeah, I've been using Comet a lot. Um,
01:00:30
you know, here's the true story. I used
01:00:32
Microsoft Edge for a long time
01:00:34
>> on a Mac.
01:00:36
>> I know it hurts people. It hurts when I
01:00:38
say it, but they had this great feature
01:00:41
called collections, which everyone else
01:00:43
ripped off, but you could group your
01:00:46
tabs or you could group your shortcuts
01:00:48
together, and it was amazing. Especially
01:00:49
like when I was working like I'd be
01:00:51
working on a project like the book and
01:00:53
I'd have 10 documents and 10 different
01:00:56
things and I just wanted them in one
01:00:57
collection.
01:00:58
>> Oh, like a tab group.
01:00:59
>> It's a tab group but it's saved.
01:01:01
>> Oh, okay. So, okay. It's persistent.
01:01:03
>> Yeah, it's like a bookmarks manager but
01:01:05
it was better than that.
01:01:06
>> And then every other browser was like
01:01:08
we'll add that and then you were like
01:01:09
finally no more Edge. I don't see
01:01:12
>> Can I share a quick I've never met
01:01:14
someone else who uses Perplexity Comet.
01:01:16
Um, I don't use it anymore, but I wanted
01:01:18
to share the the the last time I used
01:01:20
Purplexity comment, which was me asking
01:01:22
it to search. I gave it a a bunch of
01:01:25
things that I I I was making a mood
01:01:26
board and I was like, can you go and
01:01:28
collect these images for me? And I gave
01:01:30
it this big list of everything I was
01:01:31
looking for and it's like, go out and
01:01:33
find these images. And it came back and
01:01:34
it found me 20 images. And I was like,
01:01:36
that's fantastic. And I was like,
01:01:38
>> pictures of the magic mouse.
01:01:40
>> Sure. For for for this for this story,
01:01:42
it could totally be. But then I asked
01:01:44
it, okay, now here's an updated version
01:01:46
of the list. Go out and find images. And
01:01:49
it said, I don't have the ability to
01:01:50
find images. And I spent an hour where
01:01:52
it was just like, I can't do this,
01:01:54
Ellis. I don't have image.
01:01:55
>> You're like, I you did it.
01:01:56
>> The in are the images you found me. And
01:01:59
then I was just like, I deleted off my
01:02:01
computer. I was like,
01:02:01
>> I used Comet a lot in the book um
01:02:03
because it became like a reporting
01:02:05
assistant where I could say,
01:02:07
>> write to this company, see if they're
01:02:09
willing to do an interview about this.
01:02:10
And it could just do that in the
01:02:12
browser. H
01:02:13
>> yeah, comet's good. And I'm I'm testing
01:02:15
personal computer by um Perplexity now,
01:02:17
which is pretty good, too.
01:02:18
>> Okay, I'll redownload it.
01:02:19
>> Yeah.
01:02:20
>> Yeah.
01:02:20
>> Okay. Sorry. Um
01:02:21
>> these are not rapid fire
01:02:23
>> task app or pen and paper.
01:02:26
>> Both.
01:02:28
>> Both.
01:02:28
>> Yeah. Like when I'm doing something like
01:02:31
if I have like a day of video
01:02:33
publishing, I have like a lot of things
01:02:34
to do or you know, column or whatever
01:02:36
I'm have going that day.
01:02:38
>> I have like lots of tasks that I need to
01:02:40
do at the moment. They're like immediate
01:02:42
tasks and I write those on a paper on my
01:02:45
on my desk.
01:02:46
>> Okay.
01:02:46
>> Um but then
01:02:49
>> I have this crazy notion to-do list
01:02:51
thing that I've made. It's really not
01:02:52
great. Um do you use notion for to-dos?
01:02:54
No,
01:02:54
>> not for to-dos. We use it for uh project
01:02:57
management for like a per video basis.
01:02:59
>> Yeah. So do we. Well, we started doing
01:03:00
that and then I just because I was like
01:03:02
I'm already here. Let me try to use the
01:03:04
AI to build a to-do list thing and it
01:03:06
did a pretty good job. But
01:03:08
>> um because I'm also doing meeting notes
01:03:09
in there. So, I'm doing a lot in there
01:03:10
right now. Um, but that like has a big
01:03:14
list and that's like further that's like
01:03:16
stuff I need to do for the day, but it's
01:03:18
not like I need to do at this hour.
01:03:21
>> Okay, that's a Yeah, good distinction.
01:03:23
All right.
01:03:24
>> Uh, I haven't used a pen and paper in
01:03:26
years, I think.
01:03:28
>> Since 1992,
01:03:29
>> basically. Phone phoning your pocket
01:03:32
right now?
01:03:33
>> No, it's on in my backpack on the side.
01:03:36
>> Oh, which one is it? It's a iPhone 17
01:03:39
Pro.
01:03:39
>> If you had to use a different phone,
01:03:42
which one would you pick?
01:03:44
>> iPhone 17 Pro Max.
01:03:46
>> It's a good answer. What if you couldn't
01:03:47
use What if you couldn't use any Pro?
01:03:50
What if you couldn't use any iPhone?
01:03:51
>> Samsung Galaxy. No, I'd probably do a
01:03:53
Pixel. I don't know. I I have been
01:03:55
trying to I have been playing around
01:03:56
with a lot of the Android foldable
01:03:58
phones.
01:03:59
>> Yeah.
01:03:59
>> Um my issue, and I'm sure I'm going to
01:04:01
have the same issue with the foldable
01:04:03
iPhone. It's very big for me.
01:04:05
>> Yeah. Well, it's it's interesting the
01:04:06
way they're trying to allegedly
01:04:08
allegedly it's going to be like this
01:04:10
much smaller.
01:04:11
>> Yeah.
01:04:12
>> Passport style and then wider. So, it
01:04:14
could be it could be smaller and fit in
01:04:15
your pocket better than the current Pro.
01:04:17
>> Yeah. I just do a lot of texting on like
01:04:21
the fly when I'm commuting and stuff
01:04:22
like that. And then it's like, okay, I'm
01:04:23
putting it in my pocket. It's too big.
01:04:24
And then I love being able to open it up
01:04:27
and do much more with the bigger screen.
01:04:29
But I don't take advantage of that in in
01:04:32
that many scenarios. And I think I would
01:04:35
more if I actually had a smaller
01:04:38
footprint.
01:04:39
>> Interesting. Okay.
01:04:40
>> Yeah.
01:04:41
>> Um, how fast can you type the alphabet?
01:04:44
>> Like, I don't know, 90 words. I don't
01:04:46
know, 80 words per second.
01:04:47
>> Is this your keyboard of choice? So,
01:04:49
it's a bit of a tradition around here
01:04:50
for our guests on Wave Forum to simply
01:04:53
type A through Z,
01:04:55
>> as fast as they can. Uh, it doesn't
01:04:56
matter. We have a leaderboard. No
01:04:58
pressure. You'll have three tries. And
01:05:00
we also offer any of these fine,
01:05:03
beautiful keyboards over here if you'd
01:05:04
like. I don't know if you typically
01:05:06
>> think I win.
01:05:06
>> No, if you uh if you want to get a
01:05:08
faster score, if you type faster on any
01:05:10
of these keyboards, we can swap it out.
01:05:12
>> No, I think I can do with this.
01:05:13
>> Okay.
01:05:14
>> Oh boy.
01:05:15
>> No pressure. I'm going to point my
01:05:17
microphone. You just So, the way this
01:05:19
works, so before you take your three
01:05:20
attempts, the way this works is you, as
01:05:22
soon as you type the letter A, it starts
01:05:24
counting. Yeah. And you just go ABC to
01:05:25
E. If you miss a letter, you still have
01:05:27
to hit that letter. And then that's how
01:05:28
you get all the way through Z.
01:05:31
Can I tell you something else?
01:05:34
>> Okay. No, no. I'm gonna I can do this. I
01:05:36
can do this.
01:05:36
>> You got this. The first one you'll just
01:05:37
figure out how it works and then the
01:05:39
next two you'll you'll lock in.
01:05:40
>> All right. Ready?
01:05:41
>> I'm ready.
01:05:52
>> Oh, you have to press it. Okay.
01:05:55
>> You got your first
01:05:56
>> Yes.
01:05:56
>> First rodeo. So, now you've seen it.
01:05:57
>> 10.5.
01:05:59
>> Okay. I won't tell you the scores unless
01:06:00
you want to hear them. But
01:06:01
>> I see it says best time right under
01:06:03
five.
01:06:03
>> Oh, no. No. That's cuz I did it earlier.
01:06:05
But you have two more shots.
01:06:06
>> You did it earlier.
01:06:07
>> I just did it to make sure it worked.
01:06:09
>> But we have a leaderboard of everyone
01:06:10
who's ever been on the podcast and how
01:06:12
fast.
01:06:12
>> Well, you're at the top or you're on
01:06:14
there at five, so I know at least
01:06:16
>> I my official time might be slower than
01:06:18
that. I don't remember. They'll find the
01:06:20
leaderboard, but you got you got extra
01:06:22
shots to get even faster.
01:06:24
>> All right. Yeah, we go again.
01:06:26
>> Go for it.
01:06:30
I want to go again. I restart it.
01:06:33
>> Okay.
01:06:34
>> Wait, how do I restart?
01:06:35
>> I think you hit enter and it restarts.
01:06:36
>> Okay, got it. All right.
01:06:37
>> Yeah.
01:06:38
>> Sound effects. Go.
01:06:44
I'm going again.
01:06:49
>> Go.
01:06:50
>> Is that how it works?
01:06:51
>> I think. Yeah. Just send it. Just send
01:06:52
it until
01:06:54
until you get to see.
01:07:06
Oh, so much slower this time.
01:07:10
>> I was doing better when I wasn't looking
01:07:11
at the
01:07:13
>> Are you like home row just like banging
01:07:15
keys or are you trying to like
01:07:16
>> I'm trying to touch type but I'm like
01:07:18
then I realize
01:07:19
>> some of them are in weird places.
01:07:20
>> Well, you realize you skip a a letter
01:07:23
and then you're like, "Oh, I hit that."
01:07:24
Like that's what's
01:07:25
>> Yeah. You got to make sure.
01:07:26
>> I'm like, "Oh, I've moved on." And it's
01:07:27
like, "Oh, no." Like, "You didn't get,"
01:07:29
you know,
01:07:29
>> be Yeah. Yeah.
01:07:30
>> Cuz like I should just be looking at the
01:07:31
screen and touch typing without looking
01:07:32
at the keyboard.
01:07:33
>> Yeah. That's high difficulty.
01:07:37
>> All right.
01:07:39
>> Also, you know, this is making me
01:07:40
realize like they my kids don't know how
01:07:41
to type. I have an eight-year-old who
01:07:43
does not know how to type.
01:07:45
>> What about on a phone screen?
01:07:46
>> On a phone screen kind of, but or on an
01:07:49
iPad, but like mostly they just do
01:07:50
voice.
01:07:51
>> Oh, that is
01:07:53
>> And I'm really upset about it. and I'm
01:07:54
going to start working with him on
01:07:55
Saturdays on typing
01:07:56
>> cuz something we talked about is
01:07:57
something that quote kids these days
01:07:59
don't know anymore. They don't really
01:08:01
have to know file structure or how or
01:08:04
that files are even a thing
01:08:06
>> and they also don't really have to know
01:08:07
how to Google things. And I guess they
01:08:09
don't really have to know how to type
01:08:11
very much.
01:08:11
>> No.
01:08:12
>> Wow.
01:08:13
>> I mean they know how to type like um
01:08:16
they know they know how to type emojis.
01:08:18
>> Of course. Well, that's important
01:08:20
because you can't just say well you can
01:08:21
say the emoji but you got to really find
01:08:23
the perfect one. All right, one more
01:08:24
time. Go for it.
01:08:25
>> This is very embarrassing.
01:08:38
>> Okay, I beat my time there. 9.3.
01:08:40
>> 9.3. Lock it in.
01:08:42
>> All right. I feel like if I did this a
01:08:44
few more times, I would be way better.
01:08:45
>> Totally. I think that's usually
01:08:47
>> I think we proved that I'm a human pin
01:08:49
on it.
01:08:49
>> Yeah, exactly. That's very important.
01:08:51
Uh, do you want to know leaderboard
01:08:53
scores or do you just
01:08:54
>> Yes, I do.
01:08:55
>> Okay.
01:08:55
>> Is there anyone good on the leaderboard?
01:08:57
>> It's in Slack. I think I'll tell you
01:08:58
who's around you in in your time.
01:09:00
>> Let's see. I got a DM from Adam. Boom.
01:09:03
Okay. Your 9.3 is exactly in between our
01:09:07
own cinematographer Brandon and David
01:09:10
Blaine.
01:09:11
>> Wow.
01:09:12
>> Right. Nestled right in between those
01:09:14
two.
01:09:14
>> David Blaine doesn't have magic tricks
01:09:15
to be faster.
01:09:17
>> He was He was also like, I'm going to be
01:09:19
super slow. And then he got faster and
01:09:20
faster as he went. So there it is.
01:09:22
>> Well, that I mean I feel like I would
01:09:24
also Okay, tell me what you are. What is
01:09:26
it's that five?
01:09:26
>> Um I ended up I This is the the whole
01:09:29
leaderboard. Uh
01:09:31
>> Cleo's at four.
01:09:32
>> Yeah,
01:09:33
>> that's amazing.
01:09:34
>> Tom Scott had a 3.5. Insane. Some people
01:09:38
are just insanely fast typists, but
01:09:39
>> didn't didn't Tom Scott do it on like
01:09:41
his Dell laptop?
01:09:43
>> Yeah, it was like like his keyboard was
01:09:45
the loudest thing.
01:09:45
>> I'm coming back and I'm going to
01:09:47
practice. This is not good.
01:09:48
>> That's good. No, that's perfect.
01:09:50
>> I would really love to come back with
01:09:51
the OneX robot and
01:09:53
>> Oh, maybe they'll type
01:09:56
>> see how fast they can type.
01:09:57
>> Joanna, thanks for coming on. Thanks for
01:09:58
talking to me. And uh I'm I'm going to
01:10:01
point people towards watching your
01:10:03
videos and reading this book because I
01:10:05
think it's all it's all very
01:10:06
interesting. People need
01:10:07
>> about the best AI wearable in the world.
01:10:09
>> And of course, well, they can get their
01:10:11
hands on this wearable if they get the
01:10:12
book, of course.
01:10:13
>> That's right. And they can.
01:10:14
>> I noticed you stopped wearing it. I I
01:10:16
can't I feel I feel bad lying.
01:10:18
>> Uhhuh.
01:10:20
You can't put it on.
01:10:21
>> No, I just It would imply that
01:10:23
>> Oh, you feel bad lying that you're I
01:10:25
see. Okay, that's so true. Yeah.
01:10:27
>> Yeah. I'll get Marquez to wear it.
01:10:29
>> Yeah, you should get
01:10:29
>> I'll give him the pin.
01:10:30
>> Get Get the real Marquez to wear it. And
01:10:33
I so appreciate that Marquez bought for
01:10:35
you being so honest here.
01:10:36
>> I I appreciate the pin. I will. I
01:10:38
>> You know what we should really do as a
01:10:39
test? Can you fold your t-shirts?
01:10:41
>> I can't fold anything. Yeah, I just kind
01:10:43
of have clothes. You fold laundry?
01:10:45
>> I haven't fold. No, I actually genuinely
01:10:47
know. I can put stuff on hangers
01:10:49
sometimes.
01:10:51
>> But yeah, I avoid the folding stuff.
01:10:52
>> Yeah, we should test that. It would
01:10:53
really be it. That's That's the touring
01:10:55
test.
01:10:55
>> Me versus CES robot. That actually is
01:10:57
basically the touring test. The caption
01:10:59
means nothing anymore. It's fold fold my
01:11:02
>> humans can fold the laundry. You're
01:11:03
human.
01:11:04
>> There it is. Now we know.
01:11:05
>> Great.
01:11:05
>> We'll have you back on at some point.
01:11:07
Thanks again and see you soon.
01:11:18
Good thing I recorded this on my B
01:11:19
bracelet.
