Latest / What's Uptin? / We’re entering the most disruptive era in human history: Reid Hoffman tells me everything

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We’re entering the most disruptive era in human history: Reid Hoffman tells me everything · What's Uptin?

0:00
Bill Gates, Sam Altman, Ray Dalio all kind of cautioning
0:04
about an AI bubble. Do you think we're in an AI
0:06
bubble? I already see two or three
0:08
things where today AI transforms industries, you know, in Silicon
0:13
Valley probably would you historically have cost you $5000
0:17
in legal fees. You do that in 10 minutes of
0:20
interacting with an AI that goes along with your subscription of
0:23
$20.00 a month. What keeps you up at night?
0:25
Probably the biggest thing is. Breed Hoffman Co founded
0:30
LinkedIn. He was an early investor in
0:32
Facebook, Airbnb, and he's one of the most connected people in
0:36
Silicon Valley today. He's putting everything he has
0:40
on AI, but not just to change the way we work, but to even
0:44
change how long we live. Is this time different?
0:47
It is different insofar as it's a new, much more general purpose
0:52
technology than earlier technologies.
0:54
I want to find out from him, Are we in an AI bubble?
0:57
Will humans still work in 10 or 20 years?
1:01
And where should we be investing right now?
1:03
This current administration has ended the post World War 2
1:06
American World War. Do you think the damage can be
1:09
reversed? This is what's Upton with Reid
1:12
Hoffman. Reid Hoffman, thank you so much
1:14
for sitting down with me. My pleasure.
1:16
So I want to start with LinkedIn be naturally.
1:19
I was at GTC in NVIDIA this week and I met a girl.
1:22
She has 350,000 followers on LinkedIn from Germany.
1:26
No Instagram, no YouTube. She's a LinkedIn influencer.
1:28
This is like become a thing. Did you ever imagine this
1:31
happening like the LinkedIn influencers?
1:36
No, I think it's one of the things we learned during the
1:39
path in terms of what happened. We thought that would be the
1:43
sharing of of business intelligence and content because
1:46
part of the thing about navigating the modern world,
1:49
professionals like, well, what should I be learning?
1:52
Who should I be talking to? What are the career changes?
1:55
What are the industry changes? We knew that sharing like the
1:59
collaborative intelligence of the network would be useful.
2:03
The notion of having influencers and having that be a follower
2:08
concept, that was something we learned by looking at the rest
2:10
of the social web. And saying saying it'll it'll
2:13
eventually come to LinkedIn. Yeah, and part of the and part
2:16
of the theory on LinkedIn is to say, well, there's a bunch of
2:19
things that are very useful within the social web.
2:21
There's things where you go, hey, that's entertaining,
2:24
talking about sports, social games, other kinds of things.
2:29
But what are the things that help people with their work?
2:32
What are things that help people with their careers?
2:34
And that's what we do at LinkedIn now.
2:37
When we see something of the social website, actually there's
2:40
a version of that that would be helpful for, then we do it.
2:44
Talk about video, because you know, 2020 brought the emergence
2:48
of TikTok, YouTube brought YouTube Shorts, Instagram Reels,
2:51
etcetera, etcetera. LinkedIn brought its own version
2:54
of short form vertical video. Anecdotally, it was like really
2:58
big for maybe six months and then it stopped being big.
3:02
And now kind of a lot of people are wondering, is it, is it
3:05
going to be, I mean, do you see that as a big part of LinkedIn
3:07
going forward? Well, I think for sure part of
3:12
what we have to do is we have to figure out what the version is
3:15
that works for the network. And the reason why I think it's
3:19
for sure is because the same notion where you have like
3:22
LinkedIn influencers, you have either business leaders or, or
3:27
experts or content leaders or coaches or any of these things
3:32
who are establishing A presence and talking to a set of people
3:35
and having that content be useful for people navigating
3:38
their careers and work. The same reason that like a post
3:42
or an essay, which usual video is also useful.
3:45
You see the person now, I don't think we'll be seeing TikTok
3:49
dances. I think that would be a bit of a
3:51
surprise. I don't know what a business
3:53
rain dance looks like, for example, But I do think you'll
3:58
see the reason I was like, oh, do I do I find you compelling as
4:04
a as a potential leader and understanding about, you know, a
4:08
transformation like AI or something else action back
4:11
seeing you interacting with you getting that sense video is part
4:17
of what brings that tangibility for human connection.
4:20
And so I think that that will resist what version of it we
4:24
continue to iterate on to say what works for the network.
4:27
What do you think it was about LinkedIn that made it successful
4:30
when other apps, you know many other apps failed?
4:33
So, well, that's decades of work, but the, I think part of
4:41
it was staying very true to the mission, right.
4:43
So the mission is we try to be time saving, not time spending.
4:48
We try to be, what are the things that matter to you in
4:52
terms of what you're doing in the work?
4:54
So for many, many years people were like, well, Linkedin's
4:57
boring. It's like, well, we're not
4:59
trying to be exciting, we're trying to be useful, right?
5:02
So. It's that's almost counter
5:04
intuitive to other platforms which you know, inherently are,
5:07
are consuming more of our time because they're, they're
5:09
addictive, right? Yes, and and, and our thing is
5:13
no, no, it's how do you get the most bang for your buck in the
5:18
shortest time frame. It doesn't mean that we say, you
5:21
know, zero time frame, but it's kind of like the question of, of
5:24
what are the things that are most helpful to you?
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And you go, ah, good. I'm, I've got the things I need
5:29
right now. I'm moving on.
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It's great because part of being kind of work focused is like
5:36
you, you'd rather work 8 hours than 10 hours.
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If you can get the same stuff done.
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Same thing in terms of kind of use of LinkedIn.
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And so so that focus and then also being like, for example,
5:48
like one of the things is like LinkedIn, one of the principles
5:52
is civility. We don't permit you to be like
5:58
hostile right to people. That's just not the right
6:03
environment. And you say, well, freedom of
6:05
speech, you're like, great, go yell on whatever social platform
6:09
your of your choices. That's not what we do here.
6:12
Yeah, I was going to say I never see that kind of content on
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LinkedIn. I'm not sure if it's because
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people are representing their professional selves or if
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LinkedIn is doing a good job of suppressing it or.
6:22
Short answers both. OK, there you go.
6:24
Let's talk about some of your AI companies.
6:27
You have inflection Manus, Tell me about tell me about them.
6:31
I mean, what, what you know, you're you have a lot of options
6:34
of how you want to spend your time.
6:35
What brought these into into, into light?
6:37
So inflection first because Mustafa Suleiman and Karen
6:42
Swanian and I had the idea that chat bots are going to be
6:45
important. This was pre ChatGPT.
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So ChatGPT came out while we were building part of the idea
6:53
with inflection, which you know, this kind of great still going
6:56
company, not as big as chat CBT because they got out there first
7:00
and that's great. But the but like part of The
7:05
thing is, is what you do as an entrepreneur is you picture the
7:08
future. And part of picturing the future
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is we will all have multiple AI systems that we're close to that
7:16
help us navigate our life. There'll be things obviously in
7:19
the LinkedIn context like work life and so on, but then also
7:22
like call it a travel assistant or life assistant or everything
7:26
else. And one of the things we
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realized was that everyone in the AI space was focusing on IQ
7:32
and IQ is only one important element of that kind of lifetime
7:38
assistant. EQ is also important.
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Like how do we establish trust is because isn't because, well,
7:45
you seem to be smart, there's a lot of smart people you don't
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really trust. It's because you seem to be in
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tune with me. You will, you have a
7:52
understanding of who I am, what I want and it's, and it's it,
7:57
it, it is much more natural to interact with you if the, the
8:00
emotional tone is right. So we were the first out at the
8:04
substantive EQ portion. And now of course, there's other
8:08
cues that have been added to this too.
8:09
There's, you know, some with inflection, but like AQ, like
8:13
can I act on your behalf action quotient SQ Am I socially
8:18
intelligent within the group? Right, There's a sack of these
8:21
things going. That was inflection.
8:22
Inflection was the first one. Modest is partially like what
8:28
are the really important things about AI that are beyond chat
8:31
bots, beyond coding assistance, beyond work, you know, kind of
8:35
work process automation that can make a massive difference in
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human life and health is obviously one of them.
8:42
Now, one part of it is you have a medical assistant who can say,
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hey, you know, I've got this rash on my on my hand, You know,
8:49
should I be alarmed? Should I do anything?
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What should I do about it? Take picture.
8:52
And but like if you can cure critical diseases that are
8:57
killers like cancer or other things, that makes a massive
9:01
difference to all people, young, old, rich country, poor country,
9:06
etcetera, etcetera. And so so Doctor Mukherjee and I
9:09
Co founder Manus because AI is the right technology that gives
9:18
us the first real shot we have of curing something that is as
9:22
difficult as cancer. How is it going?
9:27
Oh, it's going well. It's early days, these take
9:31
years to do. We built some pretty interesting
9:35
fundamental technology about how do you kind of identify targets
9:39
because one of the things that's weird about cancers is a cancer,
9:42
like there's thousands of cancers.
9:44
So it's kind of a description of it.
9:46
So what are the targets that are the different kind of things
9:49
that are doable, which molecules might be might be a therapeutic
9:56
in this and even in this earliest prototyping work, we've
10:00
actually already discovered some potentially interesting
10:02
therapeutic molecules. So it's very early, right?
10:05
So it's kind of like there's this thing about, you know, you
10:07
file like this like drug development form and you kind of
10:11
say, Hey, we're going to look at this drug.
10:12
And and so we've done that and that's, that's just prototyping
10:16
the the AI technology that we have are in the process of
10:20
building. You have a Bill Gates, Sam
10:23
Altman, Ray Dalio all kind of cautioning about an AI bubble.
10:28
It's the talk of so many, you know, headlines right now.
10:31
Do you think we're in an AI bubble?
10:33
Categorically, no. I think that the look, I think
10:38
to some degree bubble is kind of an expectation question.
10:40
So are there some financings of billions of dollars in the AI
10:46
companies that will fail? Yes, that's not a bubble.
10:50
A bubble is when like the promise of an industry has
10:55
overly grown and the whole thing falls down.
10:58
The the, the reason why I think categorically no is I already
11:02
see two or three things where today AI transforms industries.
11:10
There's obviously all the coding stuff which will be very intense
11:12
this year and the coding will then spread to others.
11:16
But like, for example, if anyone hasn't tried to say, hey,
11:19
there's this, I'm going to use, you know, Microsoft Copilot,
11:23
Chachi UT Claude Gemini as my legal assistant.
11:28
Try it doesn't mean that there aren't room for lawyers.
11:31
But like you go, oh, I'd like to, I'd like to offer you a term
11:33
sheet for something You can literally go in and say, here's
11:38
the things that I think should be in the term sheet.
11:41
Can you provide me a draft term sheet?
11:43
You look at it and go, oh, add in some of these clauses and
11:47
withdraw those and another version that you know in Silicon
11:51
Valley probably would you historically have cost you $5000
11:55
in legal fees. You do that in 10 minutes of
12:01
interacting with an AI that goes along with your subscription of
12:04
$20.00 a month. Sure.
12:07
Well, so on that note, you know, I have a few friends been laid
12:09
off within AI or tech companies within the past year.
12:13
You also have, you know, 10s of thousands of layoffs happening
12:16
at the Amazon sales forces. What do you say to people who
12:20
say that, you know, these jobs are not going to come back?
12:23
And some people are saying we could hit 1010% unemployment,
12:26
20% unemployment. So, OK, I think that the
12:31
transition period is always difficult, so I don't want to
12:33
undersell the difficulty. And so the very first thing for
12:37
a lot of companies is to go, oh, I can save 10% of my cost with a
12:45
10% workforce reduction because I've got this kind of
12:47
productivity and all the rest. But by the way, the reason why
12:50
companies grow employees is not because they, they're always
12:56
like looking to trim employees. They're looking to have like
13:00
kind of high revenue and business impact per employee.
13:02
And they hire more employees as they can have a more high
13:05
business impact. So it's not like the, the the
13:08
usual way that people chart this is they think there's a fixed
13:11
amount to work and oh boy, now the work's going to be done with
13:14
80 people rather than 100 people.
13:16
So 20 people are getting laid off and it's like there's no
13:18
such thing as a fixed amount of work.
13:20
There's for example, when you say even if this company says,
13:24
hey, I only need half the engineers that I had before, by
13:27
the way, it's going to take them years to get there, just to be
13:29
clear. And the other, you know, the
13:32
kind of the layoffs, all a square, you know, kind of block
13:37
and everything else, those are all we mishired.
13:39
There's nothing about AI in that right now.
13:41
I mean, there's nothing that shows that.
13:43
But if you say, hey, I'm going to have half what?
13:46
What did the other half do? Well, the other half go and join
13:49
other companies. The other half go and join other
13:51
startups and so forth like companies or.
13:53
Start their own companies, right?
13:54
Exactly. So that, but it doesn't undercut
13:57
the transition difficulties. So there are transition
14:00
difficulties and nearly for sure all of us are going into those
14:06
transition difficulties. And my general recommendation to
14:08
people is Start learning to use AI because how are you on the
14:13
right side of that? Because as companies are
14:16
adjusting, they're like, we need people to help us with AI.
14:19
Great, that can be you. Be one of them.
14:21
Yeah, you, you seem to be in a like an advocate for using AI to
14:25
be superhuman. And I, I think a couple of
14:28
people say this now, but it's like you won't get replaced by
14:31
AI. You'll get replaced by someone
14:33
who does AI really well or who knows AI really well and.
14:36
He was working with that. And he was working with AI.
14:38
How about though, like long term, let's say 10 years from
14:41
now, should governments be experimenting with universal
14:44
basic income? I mean, do these because that's
14:47
the whole point of generative AI, right?
14:48
It's like it's, you know, I love the analogy of the, the
14:51
elevator, right? Or the car didn't replace the
14:53
this, right? The jobs came back or it took
14:55
half of America to do farming for the other half.
14:58
I get all of that. But then when you bring up the
15:00
generative nature of AI, all of a sudden you're like, wait a
15:03
minute, is this time different? Well, it is different in so far
15:08
as it's a new much more general purpose technology than earlier
15:12
technologies. So and it affects not just one
15:16
industry, but like all industries and we're in a
15:20
network connected environment. So it affects them faster and
15:24
broader. So all these things make it new.
15:26
And what's more, as a Gen. tech technology, it's the first
15:31
beginning where you go, you know, for example, cranes did
15:37
replace human workers and what they're doing, but it was kind
15:40
of a very specific focused way of doing that here with agents.
15:46
You can replace human workers, say customer service agents with
15:50
just AI and that there will be a bunch of replacement or if that
15:53
Agentech technology like plays that way.
15:57
So it is different speed, size of impact, etcetera.
16:03
But what I think is not different is that like, it's
16:08
the, you never can say fully like, well, what's the job?
16:11
What are the new jobs that are going to get created?
16:15
And you never say that, that that gets evolved out.
16:18
Like you don't throw, you don't know, right?
16:21
So, and they go, well, but maybe this time there isn't new ones.
16:23
And they're like, OK, there is some chance we'll have to
16:26
navigate the episode, But I, I, I would be willing to take a
16:29
fairly substantive bet that there will be new jobs.
16:32
Now, the transition's still really difficult.
16:35
So we said what should governments be doing?
16:36
Should they be doing Ubi should be doing actually, in fact, what
16:40
they should be doing is workforce transformation, right?
16:43
And, and, and many different experimental things to see which
16:48
ones, because you know, when you tend to talk to government,
16:51
people say, great, we'll set up the Community College, we'll
16:54
have community colleges that have AI education, you know,
16:57
like, it's not going to be that simple, right?
16:59
So you have to be dynamic and trying with a number of
17:02
different things. And, you know, I, some of the
17:03
projects I, I support, like opportunity work and others are
17:07
trying to figure out that kind of a, of a nimbleness of network
17:11
to be adaptive to this. And that's what I think, well,
17:13
we need to do. Now, just as a last note on
17:16
this, what you want to be doing is monitoring what's actually
17:21
happening, not trying to build for what you theoretically think
17:26
will happen, because just about everybody who theoretically
17:31
thinks they know what'll happen are only right by accident.
17:34
Whether you're an expert, a government person, an economist,
17:38
we don't really know. And a lot of people find that
17:41
scary. But by the way, and I appreciate
17:44
that, I understand, but by the way, in that scary, like as an
17:48
entrepreneur, I look at that as an interesting opportunity
17:50
because those transformations, it's like when we brought in
17:53
electricity in a society, everyone thought it was really
17:56
scary. Can you imagine the modern world
18:00
without electricity? The opportunity there is huge
18:05
transition, very difficult. So in that case, I mean, you,
18:08
you hear people from Silicon Valley once in a while come out
18:11
with this doom and gloom prediction and they're dropping
18:14
out of the board or whatever. I mean, do you, you're obviously
18:17
much more optimistic than that, but but what makes you
18:19
optimistic on that? Well, so a couple of things.
18:22
One, history, like the doom and gloom that you've heard, you
18:26
know, like, by the way, if you went back and look at the
18:28
discourse around the printing press, it's very similar to the
18:30
discourse around AI. Oh, it's going to collapse
18:32
society. People are going to have a whole
18:35
bunch of misinformation. They're going to this is social
18:38
infrastructure is going to collapse.
18:40
So and by the way, human beings and disruptive change do create
18:44
problems. So after printing press 90 years
18:47
of religious war, right? So you know, we humans have to
18:50
navigate well. So that's one history is that 2
18:55
is I'm a very strong believer that the future is what we make
19:01
it, right? And so we can steer like, I
19:04
don't go, oh, things are going to be bad.
19:07
I go, oh, steer well, right, like like engage, do things.
19:12
And it doesn't mean that there aren't, you know, potholes and
19:15
you know, traffic accidents and other kinds of things as you're
19:18
going. But staying in the game and
19:23
steering towards a much more positive future is how you get
19:27
there. The way you get to a positive
19:28
future is not by avoiding bad futures, but by steering towards
19:32
positive ones. And to what role has government
19:35
regulation helped or prevented where you think AI should be
19:41
heading toward this transformation of of many
19:44
industries? So I think primarily what you
19:46
want to be doing when you have even as a amazing like this
19:51
massive tsunami of AI technology, you want to be doing
19:55
two things. One, prevent things that are
19:57
genuinely. Like just no, no exceptions,
20:02
catastrophic. So you go bioterrorism,
20:06
cybersecurity, say, OK, we should try to prevent that
20:11
stuff. And in preventing that, even if
20:14
we have bad or clumsy regulation that has some other unforeseen
20:18
costs that's worth preventing those things, then you want to
20:22
be that's the top thing because like a general proxy uses, you
20:27
should regulate when bad regulation is better than no
20:29
regulation, right? So why do you have regulation in
20:32
the financial system? Because even though it impedes
20:35
the development of new financial system technology, the financial
20:39
system breaking is a serious problem for society.
20:42
So bad regulation better than no regulation.
20:45
The second thing you should be doing is monitoring.
20:47
As Lord saying, well, I'm insightful and I think that
20:53
we're going to have a problem with people who are working on
20:55
the customer service agents, you know, cover service agency, yes,
20:58
probably. So I'm going to pre predict what
21:01
all the transition should happen like well, no one can do that
21:05
effectively, including that person who thinks that.
21:08
So what you should do when you say I have a worry, a specific
21:11
worry, whether it's, you know, job transitions, misinformation,
21:16
other kinds of things, say well, how do we measure that?
21:18
How do we monitor that? So what should regulation be
21:21
doing? It should be saying generate
21:23
that measurement, generate that kind of like here's the thing, I
21:26
want you to be measuring and I want you to be reporting.
21:29
And what's more, if I'm really concerned about it and I don't
21:33
think you'll necessarily reporting honestly, I want you
21:36
to have your auditors report it, right?
21:38
So the third party, that's why we have auditors is saying, yes,
21:42
this is what's happening on this concern in our in our
21:46
measurement thing so that you can say, oh, look, this is
21:48
really growing. We have a problem here, now
21:52
let's start addressing it as opposed to pre guessing every
21:54
problem. Is college still worth it?
22:01
Yes, but for strange reasons. The strange reason is a
22:04
combination of the network of people that you meet at college
22:09
and the fact that you spend some time getting ready on the
22:14
launchpad for society. There are many things that are
22:18
broken on college. It's it's a question around, for
22:23
example, the, you know, the degree system, what you're
22:28
learning. Like, for example, one of the
22:29
classic things is most universities are saying no AI
22:32
because AI is cheating. You're like, everyone's going to
22:33
be using AI for everything that they're doing, including, by the
22:36
way, graduate students and professors.
22:38
You're adopting an idiotic thing.
22:40
It's like saying no driving on the highway.
22:43
You just have to run right? Like it's like, it's like this
22:45
doesn't work. So, so there's a bunch of
22:47
transformation that's going to need to happen, but it is still
22:49
useful. Now the problem is, is that our
22:52
college system has kind of gotten to a place where it's,
22:55
it's like it should be had at any price and everything has an
22:59
ROI to it. And while, for example, liberal
23:02
education is a very good thing to get, it's not again, at any
23:05
price. So if you have graduates who
23:07
are, you know, this is a little bit of a extremist thing, but
23:11
like they're graduating with an urban studies degree with
23:15
$300,000 in debt, that's a very bad challenge to start from.
23:21
So then what would you major in? Or what would you advise someone
23:24
to major in? So, well, generally speaking,
23:31
again hard to predict the future, there will be more and
23:35
more software transformation. So you say, well, CS, well, CS
23:38
sort of. You want computational thinking
23:42
as is demonstrated this year. You don't really need to learn
23:44
coding per SE. That's like a lot of CS measures
23:47
tend to be the I learned, you know, I learned how to, you
23:50
know, write in Java or I learned how to write in C++ or whatever.
23:53
Rasta, a whole bunch of different frameworks, Python.
23:57
What you actually need to be is thinking about how software
23:59
amplifies your work, whether your work is I'm a historian or
24:04
your work is I'm a journalist or your work is, you know, I'm a
24:07
financial analyst or anything else.
24:09
So you should be learning that and you should also be learning
24:12
the kind of what are the set of skills that would be important
24:17
for the future. Now, one part of it is human
24:19
skills will be more important. So like, for example, we know
24:21
that AI will be later in how are we doing?
24:25
Like for example, a sales thing of you and I interacting
24:28
together. We will be that's not a thing
24:31
that AI will be anytime soon in. So like how do we work together
24:34
as a team? So you want to be learning all
24:37
of those kinds of things too, but like still thinking still,
24:41
you know, LMS are terrible at math, right?
24:43
You know, there's so yes, you know, so, so it's like there's a
24:46
whole stack of stuff you should be learning, but it's not like,
24:49
you know, I don't know if you, you know, stating my age, but
24:54
like one of the things was there's this old film called the
24:56
Graduate and they say plastics. There's like there isn't a
24:59
specific kind of thing where you go do this, just learn this,
25:03
yes. Yeah, it seems, you know, I saw
25:05
seven or eight AI CEO's on stage yesterday, many of them
25:10
billionaires. But seeing them interact like
25:13
was really interesting because the you're talking about social
25:17
skills. I mean, I was really shocked the
25:20
way they interacted and the way they were kind of introverted.
25:23
And I mean, I shouldn't be shocked because it's nothing new
25:25
necessarily to Silicon Valley, but it almost made me, it almost
25:28
made me a little scared. Like is this, if these people
25:31
are running the next wave of the transformation of every single
25:35
industry, you know, is there going to be enough empathy or,
25:39
you know, the human side of, of understanding what they're
25:42
actually building? So how does someone, I mean,
25:44
there's a lot of genius people out there that are total nerds
25:47
or introverts. Like what should they be doing?
25:50
So well, it's there's still a lot of room for people who are
25:54
on the spectrum and people building stuff as we see a lot
25:57
of great technologists building the current AI wave.
26:00
That's of course, part of the reason why it inflection we did
26:03
EQ because it's actually really important and it is the thing
26:06
that, you know, part of what I've been trying to whether it's
26:09
writing super agency or other things is to kind of try to
26:15
bring a certain humanism back to the construction of of new
26:21
technology, new technology businesses and how how do people
26:25
think about it? What are the things they do?
26:27
So, for example, a classic one is, you know, in, you know, kind
26:32
of my book before super agency impromptu was like, it's
26:36
amplification intelligence. How do we think about AI is
26:39
amplifying human capabilities? It doesn't mean that we won't
26:41
also do replacement, but put as much energy or more into how
26:47
does that make us better in terms of as humans, in terms of
26:50
what we're doing in a full spectrum, Not just IQIQ, of
26:54
course, super important, but like wisdom, right, or like even
27:00
compassion as as ways that you're operating.
27:03
And I think, you know, the way that you do that is you add that
27:07
into the market, you get that into market demands, you get
27:10
that into the general discourse, you go on podcasts and talk
27:12
about it. And that's part of how the
27:15
industry starts responding to that more.
27:18
Yeah. You know, when you think about
27:20
college, I almost wonder if the whole model needs to be
27:23
rethought of because my dad, you know, studied civil engineering,
27:26
got his PhD, was in the same office for 40 years, right,
27:30
Using what he learned in his PhD.
27:32
I mean, he's doing research and all of that.
27:34
But, you know, versus me in my 20s, I think I had seven
27:37
different jobs, right. And so it almost begs the
27:39
question of now when you think about what's going to happen,
27:42
even what's happening right now, what you're learning in the
27:45
university, in every industry is going to be changing, you know,
27:49
every few years. Have you thought about that?
27:51
I mean, have you thought, like, do you think that we'll see the
27:54
the sort of evolution of what it means to even go to university,
27:57
whether it's more certificates or, you know, I don't know.
27:59
Yeah. Well, for sure, I think that the
28:04
look, so one of the things that opportunity work does is say
28:07
there's a whole bunch of jobs and skills that a university
28:10
degree is not the actual it's like says nothing about your
28:13
capability of doing it. It's not positive, it's not
28:15
negative, it's just irrelevant. So and by the way, that means
28:18
that that lots of people like say, for example, you didn't
28:21
have the economics to go to the Super expensive universities.
28:25
You were working to put your family or yourself, you know,
28:28
but you still have the skills. She's a little talent.
28:30
And and so we both want to make an ability to construct all of
28:34
those to to learn those skills and deploy them and also an
28:38
employment environment that'll that that encourages a lot of
28:41
that happen because we want talent to get there.
28:43
And that's part of the reason why I like projects like
28:46
opportunity work. Tear the paper ceiling.
28:49
You know what are stars like? People who are like, highly
28:52
talented, talented people who do not have college credentials
28:56
that can work for things. All of those things are an
28:58
important part of the future. Last year, you expressed
29:02
concerns that US tariffs and spending cuts could
29:05
inadvertently help China's tech industry and damage America's
29:09
competitiveness in AI. I've been to China multiple
29:12
times. I've been to Tencent and
29:13
Alibaba. I went to the Shanghai auto show
29:15
last year and saw crazy, you know, Chinese EV companies that
29:19
you and I, well, you've probably heard of, but most people never
29:22
heard of. Where do you stand on that now?
29:24
I mean when it comes to US, China and AI.
29:27
So, you know, look as as as as American, I'm very like, I want
29:32
the US society to prosper and kind of continue to have a great
29:37
influence the world order because I believe in kind of a
29:40
lot of our fundamental American values.
29:43
Now, that being said, the various things that the current
29:46
administration's been doing, tariffs being one of them, are
29:50
kind of causing the rest, like even former friends to no longer
29:54
like us. And you can see Europe, Canada,
29:59
other places that are formally like us being their closest
30:02
partner. They're now like, well, you're
30:05
treating us badly. So we'll take a look at who is
30:08
our best trading partners. And if it's China's our best
30:10
trading partner, great. China's not coming in and
30:12
saying, you know, do this for us or pay this additional tariff
30:16
and so on. They just say, hey, great, we'd
30:18
love to do business with you. That's how you do business,
30:22
right? You don't you, you do business
30:24
collaboratively. You do business not by ordering
30:26
people around, not by trying to play like a mob boss with
30:29
protection money, you know, etcetera.
30:31
You do it by collaborating. So I think we have actually
30:34
already have done serious damage to not only your average
30:41
American citizen doesn't understand.
30:42
It's like, look, if if the ability for a bunch of US
30:45
industries to to including the tech industry to sell overseas
30:49
diminishes, then less money flows into the US.
30:53
And if less money flows into the US, that's less money
30:55
everywhere. It's less money for the schools,
30:57
that's less money for taxes, that's less money.
30:59
Like there's all kinds of things where that gets to less money
31:01
for. And that's the reason why
31:03
something that they may think is abstract, like trading
31:06
partnerships, is actually necessary for the health of
31:10
their communities and the health of their families.
31:12
Do you think the damage can be reversed or we have to wait till
31:16
future presidency for that? So I think this current
31:21
administration has ended the post World War 2, American World
31:24
War. So I don't think it's
31:26
reversible. I think there's a
31:28
reconstructible, but it will take a lot of work to do that.
31:31
Interesting. I want to speak about, you know,
31:34
a little bit about Reed and the advice that Reed would give.
31:37
So let's start with LinkedIn. I mean, what would your advice
31:39
be to grow on LinkedIn? So on LinkedIn, well, it's a
31:43
little bit like also AI, which is start using experimenting.
31:46
OK, what people don't realize is if you do just a fair outlook a
31:52
little bit, it can actually really benefit you now say for
31:55
example, the most basic fill out a profile have some real
31:58
information to break you go I did that nothing happened yes,
32:01
because when you do that, it's like filling out a thing when
32:05
when people are searching, do they find you right And who
32:08
knows maybe they find you tomorrow.
32:10
Maybe they find you 6 months from now, but it's so little
32:13
work to do like filling out a relatively robust LinkedIn
32:17
profile is under 10 minutes right It's relatively
32:20
straightforward. So that's one thing.
32:23
Another thing is when you connect to people who, and this
32:28
is one of the things people like who should connect to is like,
32:30
well, you know, expect the people to know you and trust
32:32
you, right? And it could be by the way, you
32:34
know, your friend's uncle met you and liked you.
32:38
And so women you know, connect with because then when you look
32:43
through like, OK, who would be people that would be useful for
32:46
me to talk to about what I might be doing with my career, my jobs
32:50
and so forth. And you start doing searches at
32:53
2° and you realize your friend's uncle knows somebody at this
32:57
company that you're interested in about and you can ping them
33:00
and say you line if you get me connected.
33:02
So I could just talk to them and see if there's a good shit.
33:06
And and so sure that your friend's uncle's, you know,
33:08
colleague may not understood them, but because you come into
33:13
an interruption that's oh, you're not head watching solid,
33:16
smart, etcetera. Great.
33:19
Then I may not be the right person, but this other person in
33:22
the company is the right person to talk to him because I'm
33:25
introducing you'll do that. So, right, that kind of
33:27
engagement starts creating serendipity and what you want to
33:30
have is you want to increase your serendipity of something
33:34
that could be magical, that would help you with a career,
33:36
and those would be very first things.
33:39
Yeah, absolutely. I always when I was AI worked at
33:42
MTV News actually back in the day.
33:44
And I was like, you know, I was covering Justin Bieber and, and
33:47
stuff that I didn't enjoy. So I I said, you know what, I
33:49
want to work in, in business news.
33:51
And so I started every time I would meet someone at a party or
33:53
a networking event or whatever, I would say, yeah, I like my
33:56
job, but I'm looking to do this. And it was amazing how many
33:59
times people would say, you know, look me up on LinkedIn,
34:02
see how many, you know, if I have someone you want to meet,
34:05
you know, so it's kind of like this digital library of, of, of
34:08
a database that I could kind of be like, Hey, do you actually
34:10
know this person? When it comes to posting,
34:12
though, I want to talk about Chachi PT.
34:14
There's this thing that I've noticed where, and people are
34:17
now starting to create memes and make fun of the, the LinkedIn
34:20
post where it's like, and I'm not talking about the, I'm
34:22
thrilled to announce like that's been there for a while, But the,
34:26
the M dash, the, you know, it's not, it's not challenge.
34:30
It's the redirection, you know, And so there's these instant
34:33
things where you're scrolling Chachi BT and you're like this,
34:36
I'm sorry, there's these instant scenarios where you're scrolling
34:39
LinkedIn and you can say like this guy, use Chachi P, not just
34:43
use, but probably just copy and paste it at Chachi PG prompt and
34:46
then again and again and again. And I almost find myself and
34:49
some other people I know. It's almost like our unique
34:52
selling point as LinkedIn creators is not even opening
34:56
Chachi BT And like, let me just write this from my own voice to
34:59
ensure 100 percent no one assumes it's it's AI.
35:03
So I mean, in terms of creation, you have your podcast, your
35:06
books. I mean how do you think about
35:07
that differentiating the AI sounding content from breed?
35:13
Well, I think the most central thing now you're asking a
35:15
person, as you know, who created a digital twin of themselves,
35:18
who wrote Co, wrote the first book with GBT by the impromptu.
35:25
So now I think it's important to be transparent.
35:27
So I think going to ChatGPT, Co π whatever and producing
35:32
something and then just pasting it in, I think that's kind of
35:36
dishonest and it is me that's horrible.
35:39
But it's like part of what you want to be is when you're
35:41
establishing trust and branded people like trust, like no, no.
35:45
I think this is worth your reading.
35:49
Like I looked at it, I touched it, etcetera.
35:52
Now, I have no objection to someone producing a, a, a, you
35:56
know, Claude or Copilot or Gemini thing and then saying,
36:01
look, I'm looking around and then like touching it up to
36:04
something that they think is really good because it's like
36:06
here, I recommend this to you. Yeah, it's it's actually a value
36:08
to the your audience. It's a value to you.
36:10
It saved you time. It enhanced your.
36:12
But what you should do is say what I'm posting.
36:17
I am taking a moment of trust, like am I, am I taking your
36:24
attention with some care that I really recommend that you pay
36:27
attention to this? I, I own this in terms of what
36:31
I'm talking to you. And so I think that's really
36:33
important and that's how I think people should approach it.
36:36
So it's not that I actually think one should use AI tools,
36:41
but you should see as like, is this good enough that this is
36:48
what I would want people to be reading that I produced when all
36:54
of my friends had to sit there and listen to it.
36:57
And when I hold my head high versus go, oops, sorry about
37:01
that. You know, like that's how you
37:03
should look at it when you're doing posts.
37:05
What do you think about today's new tech talent, the new
37:07
graduates coming out of Stanford?
37:09
What do you make of them? Well, OK, I think every
37:12
generation is smart. I think that they, you know,
37:18
part of what I think is this is generation AI.
37:20
So I think that part of what they are is just like each, each
37:24
new generation has has is kind of native in the current tech.
37:28
And I think that's something that's super important.
37:30
I look forward to learning more from them about this.
37:35
And so, and you know, look, I, I have some sympathy of like, wow,
37:41
this is a lot more chaotic than when I read, you know, kind of
37:44
graduated, which, you know, and, and by the way, that's both
37:47
disorienting for to go, but it's also opportunity.
37:50
So I kind of encourage them to think about the opportunity of
37:53
it. Chaotic in terms of funding and
37:55
meetings and the way things are moving just faster.
37:57
What's what, what's what's a good thing to do with your
38:00
career? It used to be kind of like much
38:05
more simply tracked, and now it's like it's a very chaotic
38:09
melee. But you're optimistic, it seems
38:11
in in the talent in the next generation?
38:13
Yes, absolutely. No, no, it I think anybody,
38:17
anybody who goes, oh, this generation has problem X And
38:20
even if that generation does have problem X, they're like,
38:23
you know, that's what the other generation said about you.
38:25
So, you know, come on. That's true.
38:26
They're always judging each other.
38:28
Yeah, that's true. The last question I want to ask
38:30
you is what keeps you up at night?
38:33
Probably the biggest thing is, I mean, there's a whole set of
38:39
things and it's not just world chaos, you know, war in the
38:42
Middle East, you know, I mean, it just like it goes on and on
38:45
and on. Yes, right.
38:48
But probably the biggest one is let's not fumble the opportunity
38:52
about how magical AI can be for us.
38:56
And we can, there's all kinds of ways we can fumble it.
38:59
And part of the reason I'm being very positive is not because
39:02
there won't be traffic accidents and you know, things going
39:06
wrong. Of course there will be, there
39:09
was things going on with every launch of technology, including
39:12
electricity and all the rest. But like the possibility, like,
39:16
for example, like it's really a question of which year do we all
39:21
have a medical assistant on our smartphone?
39:23
It could be this year that's better than today's average GP.
39:28
And think of what makes the difference.
39:29
Like relatively few people have access to a doctor, and almost
39:35
nobody at a moment's notice. That's massively transformative
39:40
to human life. That's the kind of thing I don't
39:43
want to fumble. Do you think we will?
39:47
Like there's a lot, I mean, I've been reading articles, there's a
39:49
lot of like longevity research happening right now.
39:53
Do you do you see a time where we're living TO140150?
39:59
I think we will be figuring out how to live longer and longer.
40:02
I don't think it's kind of the quick unlock.
40:06
Because the principal ways that we've expanded our average
40:11
lifespans, which we've done a lot of low hanging fruit on, is
40:15
eliminate various forms of child mortality that really helps with
40:19
age, improve basic nutrition and a bunch of other things.
40:24
You know, that helps a lot. Measure basic things that kind
40:27
of kill a bunch of us, heart attacks, that kind of thing.
40:30
And try to do that. Now it starts getting into the
40:33
hard part of it, right? Which is OK, so we're all in a
40:37
version of we're all dying from cancer.
40:39
Just the question is, does something else get us first,
40:42
right. And So what are the things that
40:47
we can do to kind of create that cellular, cellular rejuvenation?
40:54
And I think that's going to be a hard path.
40:56
I think AI will help with that. I think other things, but I
40:58
don't think it's a, you know, some people are like, no, no,
41:01
no, we're all going to be living there 100, you know, in, you
41:05
know, but naturally in a couple years, like I don't think the
41:08
science is there yet. Great, thank you so much.
41:11
This is a really insightful conversation.
41:13
I learned a lot and I really appreciate your time.
41:15
Thanks for listening to this episode of What's Up 10 where I
41:18
bring you inside the minds driving tech, money and power.
41:21
Follow along on YouTube, Spotify, Apple, or wherever you
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