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Transcript

AI Is Coming For Your Job… Everyone Will Be Forced To Become Entrepreneurs (w/ Sinéad Bovell) · Earn Your Leisure

• 0:00
  Speaker 1: Webs will be automated.
• 0:01
  Speaker 2: We are shifting into an entirely different type of work.
• 0:05
  Speaker 2: The economy looks good in one element of it, but
• 0:07
  Speaker 2: terrible on the other because nobody's actually working.
• 0:09
  Speaker 3: What are the skills that we think that are going
• 0:11
  Speaker 3: to be transferable for the next generation?
• 0:13
  Speaker 2: The competitive advantage shifts to what questions are you asking
• 0:17
  Speaker 2: a super computer?
• 0:18
  Speaker 4: Pop? What we're saying that his scientists have already informed
• 0:21
  Speaker 4: him that immortality is.
• 0:22
  Speaker 2: A real thing, that is definitely not an impossibility at all.
• 0:25
  Speaker 2: Life expectancy should definitely increase.
• 0:29
  Speaker 4: We've essentially created something for the first time in human
• 0:31
  Speaker 4: history that's superior to us. That's pretty alarming.
• 0:34
  Speaker 2: They can see what your conversation history is, so once
• 0:37
  Speaker 2: that goes in, it can never get out.
• 0:38
  Speaker 4: Who's actually collecting all of his data? Is it going
• 0:41
  Speaker 4: to the US government?
• 0:42
  Speaker 1: If you can pay for more privacy, you'll get it.
• 0:45
  Speaker 1: But if you can't, you might be exposed.
• 0:47
  Speaker 4: What's the dark side? They are the.
• 0:49
  Speaker 2: Worst case scenario with AI. I mean, I don't think
• 0:52
  Speaker 2: that there's just one. This could go bad in many ways.
• 0:56
  Speaker 4: How can we really stop that? Though?
• 0:58
  Speaker 1: That is a national security risk that we can't even imagine.
• 1:01
  Speaker 4: Today. Okay, welcome back, e y else. Yes, we got
• 1:07
  Speaker 4: a highly anticipated episode.
• 1:09
  Speaker 3: We wain't on this one.
• 1:11
  Speaker 4: Yeah, a long time SHANEI Bovel, somebody that we met
• 1:14
  Speaker 4: Carnegie Hall two years ago.
• 1:19
  Speaker 3: Yeah, well two February's so this one passed and then
• 1:22
  Speaker 3: one before that.
• 1:23
  Speaker 4: Yeah, well Robert Smith might have heard of him. Yeah,
• 1:28
  Speaker 4: you know, we've been doing the Carnegie Hall things like
• 1:30
  Speaker 4: the last three years, and uh, you were there and
• 1:34
  Speaker 4: we met and you spoke about artificial intelligence and we
• 1:38
  Speaker 4: spoke backstage, and just very impressive as far as like
• 1:43
  Speaker 4: how you were able to communicate the very complex situation
• 1:47
  Speaker 4: when it comes to artificial intelligence. A lot of people
• 1:49
  Speaker 4: not still still not really familiar with everything that I
• 1:53
  Speaker 4: didn't tail to just hear about AI and now ONGBT,
• 1:55
  Speaker 4: but and then I just started following you from there,
• 1:59
  Speaker 4: and know we've talked several different times since then. So
• 2:02
  Speaker 4: I've just been a fan of what you've been able
• 2:04
  Speaker 4: to you know, put out there in the universe as
• 2:06
  Speaker 4: far as the education. So definitely wanted to have you
• 2:10
  Speaker 4: on the program to talk about AI and just the
• 2:16
  Speaker 4: landscape in general when it comes to technology because so
• 2:18
  Speaker 4: much is changing. So first and almost thank you for
• 2:21
  Speaker 4: joining us, appreciate it.
• 2:22
  Speaker 1: Thanks for having me. I know this has been some
• 2:24
  Speaker 1: time in the running, so I'm happy to be here.
• 2:26
  Speaker 3: I will say this a second. Everything he just said
• 2:29
  Speaker 3: when it comes to artificial intelligence, when it comes to
• 2:34
  Speaker 3: forward thinking, I think you're one of the best that
• 2:37
  Speaker 3: I've ever heard. Yeah, Like I watch you daily.
• 2:40
  Speaker 1: Thank you.
• 2:40
  Speaker 3: Yeah, people like hear like my takes on AI and
• 2:43
  Speaker 3: the complexities of it, and I'm like, it's a microcosm
• 2:46
  Speaker 3: of a lot of people that I'm listening to. You're
• 2:48
  Speaker 3: definitely one of them. So if they're not following you
• 2:50
  Speaker 3: by the end of this, they.
• 2:51
  Speaker 1: Will be, oh, thank you so much. Yeah, yeah, yeah, incredible,
• 2:54
  Speaker 1: thank you.
• 2:55
  Speaker 4: Futurist technology educator, you're entrepreneur anything else?
• 3:01
  Speaker 1: I mean not right now. I guess it depends on
• 3:03
  Speaker 1: how the future goes.
• 3:06
  Speaker 4: So talk about how did you get to this point?
• 3:08
  Speaker 4: How did you get to this point as far as
• 3:09
  Speaker 4: being a futurist AI expert, Like when did that actually
• 3:15
  Speaker 4: become your life path? Passion?
• 3:17
  Speaker 2: So I was one of those people who happened to
• 3:20
  Speaker 2: take that class that changed everything. So when I was
• 3:23
  Speaker 2: doing my MBA in Toronto, I took this class called
• 3:26
  Speaker 2: Strategic foresight, and that's the formal field of being a
• 3:29
  Speaker 2: future is. It's actually called the practice of strategic foresight.
• 3:32
  Speaker 2: And so I stumble into this class and I learned
• 3:35
  Speaker 2: that the future is something. Of course you can't predict it,
• 3:38
  Speaker 2: but there are formal methodologies you can use to understand
• 3:42
  Speaker 2: how the landscape is evolving, so from understanding economic flows
• 3:46
  Speaker 2: to behaviors to technology. So I'm tracking patents and doing
• 3:50
  Speaker 2: all these interesting things in this class, and then I
• 3:53
  Speaker 2: never really stopped doing that. I could never look at
• 3:56
  Speaker 2: markets the same or information the same. Once I had
• 3:59
  Speaker 2: that methodology in my mind of understanding where this is going,
• 4:02
  Speaker 2: I didn't know though it could be a formal job. Really,
• 4:06
  Speaker 2: I knew it was a practice, and so I went
• 4:09
  Speaker 2: into the world of management consulting.
• 4:11
  Speaker 1: I didn't really like that very much.
• 4:13
  Speaker 2: It was fine at the time, just not really the
• 4:15
  Speaker 2: problems I was interested in solving, so a lot of
• 4:17
  Speaker 2: different zigs and z eggs in my career. Went into
• 4:20
  Speaker 2: the world of fashion, and from there, as I started
• 4:22
  Speaker 2: to blog about the future and host talks about the
• 4:25
  Speaker 2: future and all the trends I was tracking, the light
• 4:28
  Speaker 2: bulb just went off, like I'm already here. This is
• 4:30
  Speaker 2: what I'm doing. So i'd say, back.
• 4:32
  Speaker 4: In you were a model.
• 4:34
  Speaker 2: Yeah, so between consulting and what I do now, I
• 4:38
  Speaker 2: was a fashion model for a little bit. Yeah, it's
• 4:41
  Speaker 2: something that seems entirely unrelated, but it was actually the
• 4:44
  Speaker 2: springboard to stepping into this world. I'd say twenty sixteen
• 4:48
  Speaker 2: is when I really started to track AI the breakthroughs
• 4:50
  Speaker 2: that were happening. And then from there it's been like
• 4:53
  Speaker 2: reading all the white papers and never really look back.
• 4:56
  Speaker 2: And then I think the world woke up to AI
• 4:59
  Speaker 2: in twenty twenty TI and then the rest of the
• 5:01
  Speaker 2: journey is kind of history.
• 5:04
  Speaker 3: I'm interested in that moment, right, Like, as you are
• 5:07
  Speaker 3: watching the trend, I find this is very something that
• 5:10
  Speaker 3: we have in common. I'm watching a trend. I've studied
• 5:14
  Speaker 3: the trend, but saying it to the public is different, right,
• 5:18
  Speaker 3: because there's a chance that this might not come to fruition.
• 5:21
  Speaker 3: You could be wrong. Did you have that, I guess,
• 5:27
  Speaker 3: fear of saying that this might not go the way
• 5:29
  Speaker 3: I think it is. And then twenty twenty two happens
• 5:34
  Speaker 3: that validate talk about that those moments.
• 5:37
  Speaker 2: I didn't, so I think because when you look at
• 5:41
  Speaker 2: what AI is, and I had been. I mean I
• 5:44
  Speaker 2: happened to go to the university where all of those
• 5:47
  Speaker 2: breakthroughs were happening, So University of Toronto where Jeff Hinton,
• 5:50
  Speaker 2: the godfather of AI, ilias Setskiver, who went over to
• 5:53
  Speaker 2: Opening Eye to start CHATEGBT, they were doing their research
• 5:56
  Speaker 2: at the University of Toronto, and my professors were working
• 5:59
  Speaker 2: with them, so that understanding of this technology is coming.
• 6:04
  Speaker 2: We just don't know this scale and when it's going
• 6:06
  Speaker 2: to be at that Internet level that was already in
• 6:09
  Speaker 2: the data points and then I think, you know the future,
• 6:13
  Speaker 2: and there's that famous quote, It's already here, it's just
• 6:15
  Speaker 2: not evenly distributed. You could see AI in the patents,
• 6:19
  Speaker 2: in the white papers starting to become infrastructure in software
• 6:22
  Speaker 2: and applications. So in twenty sixteen, twenty seventeen, you could
• 6:26
  Speaker 2: see those trend lines and they weren't entirely different from
• 6:29
  Speaker 2: looking at electricity and being like, okay, so it's working
• 6:31
  Speaker 2: in some use cases. Some people doubt it, but this
• 6:34
  Speaker 2: thing is coming. And I think it was twenty eighteen
• 6:37
  Speaker 2: that Mackenzie did that study that by twenty thirty, fifty
• 6:41
  Speaker 2: percent of jobs will be impacted or automated by artificial intelligence.
• 6:46
  Speaker 1: And that's when it was literally that.
• 6:47
  Speaker 2: Study that I was like, there is a train headed
• 6:50
  Speaker 2: straight to the workforce, straight to the economy at full speed,
• 6:54
  Speaker 2: and hardly anybody's thinking about it.
• 6:57
  Speaker 1: And that's when I.
• 6:57
  Speaker 2: Entirely reposition what I spoke about and made it the
• 7:01
  Speaker 2: mission to make technology and AI a language everyone speaks
• 7:05
  Speaker 2: because this thing's coming and it's going to change everything.
• 7:07
  Speaker 3: So in twenty twenty two, I feel like, this is
• 7:10
  Speaker 3: the open AI moment.
• 7:11
  Speaker 1: Yeah, what was that like for you?
• 7:14
  Speaker 2: So I think in what really happened in twenty twenty two,
• 7:17
  Speaker 2: it was an interface breakthrough or revolution. So AI had
• 7:23
  Speaker 2: still been on this really incredible path forward, and what
• 7:26
  Speaker 2: happens with Chatchibt is now the system comes on the
• 7:30
  Speaker 2: scene that the public can interact with in a way
• 7:33
  Speaker 2: that they understand. So it turned AI from a technology
• 7:37
  Speaker 2: that computer scientists could only really use and understand to
• 7:42
  Speaker 2: a platform that anybody can now engage and direct. And
• 7:47
  Speaker 2: once that interface moment happens, it as soon as human
• 7:51
  Speaker 2: behavior changes, it never goes back. Right, So as soon
• 7:55
  Speaker 2: as we switch from plugging in our internet to Wi Fi,
• 7:58
  Speaker 2: no one's going back. So as soon as that chat
• 8:01
  Speaker 2: BT interface moment happens, we can talk to AI the
• 8:04
  Speaker 2: way we talk to a person. That is now the
• 8:07
  Speaker 2: new platform and we're never going back. So I think
• 8:10
  Speaker 2: that was Yeah, I guess another light bulb moment or
• 8:13
  Speaker 2: more validation in the forecast that this thing is going
• 8:17
  Speaker 2: to permeate our lives and it's going to be the
• 8:19
  Speaker 2: new layer that everything gets built on top of. And
• 8:22
  Speaker 2: then I think the momentum with governments and everybody really
• 8:25
  Speaker 2: talking about it showed the scale of that and kind
• 8:28
  Speaker 2: of supported that.
• 8:29
  Speaker 1: But I didn't really doubt.
• 8:32
  Speaker 2: You can't predict the speed, and you can't predict the
• 8:34
  Speaker 2: breakthroughs and when they're going to happen. But when something
• 8:37
  Speaker 2: is going to be infrastructure or a general purpose, there's
• 8:41
  Speaker 2: no doubt that that's going to happen.
• 8:43
  Speaker 1: It's just the when.
• 8:45
  Speaker 4: And when does Netscape come into play?
• 8:48
  Speaker 2: Netscape comes into play? It's like what nineteen ninety two
• 8:52
  Speaker 2: I think. I mean, yeah, I think Netscape is the OG.
• 8:57
  Speaker 4: No, but your company, what's your way? A way?
• 9:01
  Speaker 1: Netscape? I mean I would like to take credit for
• 9:03
  Speaker 1: say you probably would No, I would. I would have
• 9:07
  Speaker 1: come in on a jet.
• 9:08
  Speaker 3: It would have landed on the roof.
• 9:09
  Speaker 1: Yeah, we would be doing this for my version of
• 9:12
  Speaker 1: Air Force one.
• 9:12
  Speaker 3: Yeah.
• 9:13
  Speaker 2: No, my company way comes into play in twenty eighteen
• 9:17
  Speaker 2: is when I founded it, and so yeah, everything kind
• 9:20
  Speaker 2: of around that that same time. And I mean people
• 9:22
  Speaker 2: were listening and showing up to the talks, and we
• 9:25
  Speaker 2: were talking about the statistics.
• 9:27
  Speaker 1: I mean, AI is coming forward.
• 9:28
  Speaker 2: I think one of the stats that I quoted is
• 9:30
  Speaker 2: that by twenty twenty five, AI will be better than
• 9:33
  Speaker 2: a human at writing essays. And people in the audience
• 9:36
  Speaker 2: kind of chuckled, But then that's turned out to be
• 9:40
  Speaker 2: the way. But people listened and they were ready. And
• 9:42
  Speaker 2: I think because even shows about the future they do
• 9:45
  Speaker 2: really well. Everybody's curious about it. But before it became
• 9:48
  Speaker 2: relevant to your day to day life, it just wasn't
• 9:50
  Speaker 2: as interesting.
• 9:52
  Speaker 4: Okay, well, I was going to say as far as okay,
• 9:56
  Speaker 4: getting into the AI conversation, we spoke and you talked
• 9:59
  Speaker 4: about the report that said that are twenty twenty eight
• 10:04
  Speaker 4: white collar jobs will be obsolete due to artificial intelligence. Yes,
• 10:09
  Speaker 4: that's pretty that's pretty soon, So explain that, like that's
• 10:11
  Speaker 4: that's that's interesting.
• 10:13
  Speaker 2: So this is the Centrini and report. You're probably talking
• 10:16
  Speaker 2: about and it was called the Great Intelligence Crisis. And
• 10:20
  Speaker 2: so what this report is from this financial analytics company,
• 10:23
  Speaker 2: and they essentially painted this fictional scenario that in twenty
• 10:28
  Speaker 2: twenty eight, AI does most white collar jobs in the economy.
• 10:33
  Speaker 2: So they make this forecast. Okay, so as companies start
• 10:36
  Speaker 2: to automate roles knowledge workers, so anybody that's doing something
• 10:39
  Speaker 2: on a computer, that.
• 10:40
  Speaker 1: Those are the types of jobs that AI is coming
• 10:42
  Speaker 1: for at this point.
• 10:44
  Speaker 2: And so this kind of thesis claims that as companies
• 10:47
  Speaker 2: start to automate more white collar workers, those people don't
• 10:51
  Speaker 2: have income coming in anymore, so they don't buy things
• 10:55
  Speaker 2: in the economy anymore, and so companies start to lose money.
• 10:58
  Speaker 2: So they put more AI into they're into their infrastructure
• 11:01
  Speaker 2: to lower costs, firing more people, and the economy looks
• 11:05
  Speaker 2: good in one element of it because productivity is going up,
• 11:09
  Speaker 2: but terrible in the other because nobody's actually working. And
• 11:12
  Speaker 2: then it leads to the spiral and collapse and that
• 11:15
  Speaker 2: shook markets a bit two weeks ago because of this
• 11:18
  Speaker 2: kind of fictional scenario. So I think what the report
• 11:21
  Speaker 2: gets right, it's a lot wrong. What it gets right
• 11:24
  Speaker 2: is that, yeah, jobs are.
• 11:26
  Speaker 1: Going to change.
• 11:30
  Speaker 2: Jobs will be automated, a lot will be augmented or transformed.
• 11:36
  Speaker 2: But we are shifting into an entirely different type of work.
• 11:41
  Speaker 2: And how I would describe it, I mean, let's say
• 11:44
  Speaker 2: if we went back to pre industrial revolution and let's
• 11:46
  Speaker 2: say like seventy ninety percent of people working in agriculture,
• 11:50
  Speaker 2: and then by the end of that the steam engine,
• 11:53
  Speaker 2: let's say ten to twenty percent of people are doing that.
• 11:56
  Speaker 2: What would it mean for seventy percent of the workforce
• 12:00
  Speaker 2: to do something different? But not in one hundred years,
• 12:04
  Speaker 2: in ten. So I don't think work is going away,
• 12:10
  Speaker 2: but jobs as we know them will, and a lot
• 12:14
  Speaker 2: of the job titles that we reference today will. We
• 12:17
  Speaker 2: will do different things that are hard to foresee, the
• 12:21
  Speaker 2: same way people have entire careers over filming ninety second
• 12:23
  Speaker 2: videos in their car and all sorts of new things.
• 12:27
  Speaker 2: But the most of the work we see today will
• 12:30
  Speaker 2: shift and will change. So we have to change. Our
• 12:34
  Speaker 2: skills have to change. And there's a lot that's up
• 12:36
  Speaker 2: in the air. And what tends to happen is jobs
• 12:39
  Speaker 2: can disappear faster than the new ones get created, and
• 12:41
  Speaker 2: that's where things get rocky. But I don't think work
• 12:44
  Speaker 2: is entirely going away, but it's going to look very different.
• 12:48
  Speaker 3: I'm with you, I'm with you. I'm on the side
• 12:50
  Speaker 3: that new jobs, new careers, new ideas are going to
• 12:52
  Speaker 3: be created. I heard that this comparison of you know,
• 12:57
  Speaker 3: if you think one hundred years ago, mail was delivered
• 13:01
  Speaker 3: on horses, and then you had mailmen, and then in
• 13:05
  Speaker 3: the nineteen nineties we got this thing called email. You're
• 13:09
  Speaker 3: still getting the information. It has just evolved. We still
• 13:12
  Speaker 3: have a post office, but we still get email. And
• 13:14
  Speaker 3: it feels like technology will change, careers will change, and
• 13:17
  Speaker 3: we'll find a new people. So I'm bullish in that standpoint.
• 13:21
  Speaker 3: I wonder what you think the skills that we should
• 13:26
  Speaker 3: be looking for, or that next generation should be looking for.
• 13:28
  Speaker 3: Because I'm with you, I feel like, yes, it's going
• 13:33
  Speaker 3: to happen, it's just a matter of when. But there's
• 13:35
  Speaker 3: still a fear factor here with a lot of people
• 13:38
  Speaker 3: because they don't know what they should do. The kid
• 13:39
  Speaker 3: who's graduating high school, what should I be studying?
• 13:42
  Speaker 4: Right?
• 13:42
  Speaker 3: Should I be taking the statistics collect What are the
• 13:45
  Speaker 3: skills that we think are going to be transferable for
• 13:47
  Speaker 3: the next generation.
• 13:49
  Speaker 2: Yeah, And this is a really important one. So if
• 13:51
  Speaker 2: you think about where is work actually going, then with AI,
• 13:54
  Speaker 2: like what are we going to be doing? We will
• 13:57
  Speaker 2: be directing these supercomputers to perform tasks, to do workflows,
• 14:03
  Speaker 2: and then evaluating their work. So then the competitive advantage
• 14:07
  Speaker 2: shifts to what questions are you asking a supercomputer? If
• 14:12
  Speaker 2: you can ask AI anything, what is it going to be?
• 14:14
  Speaker 2: And how do you know to ask that that? Are
• 14:16
  Speaker 2: you going to ask it a question that your competitor isn't?
• 14:19
  Speaker 2: How well do you communicate that question? How well can
• 14:23
  Speaker 2: you evaluate what AI tells you? Yes, it could sound smart,
• 14:26
  Speaker 2: but is it good? Does it relate to what you
• 14:28
  Speaker 2: actually need in your business? So the skills for that
• 14:30
  Speaker 2: would be judgment. So AI is going to give you
• 14:33
  Speaker 2: fifteen pretty great answers. What's the best answer for the
• 14:37
  Speaker 2: use case that you're in communication? The better you can communicate,
• 14:40
  Speaker 2: and not just to AI, but to people. Right, So,
• 14:44
  Speaker 2: can you sell the recommendation that you've now constructed with
• 14:48
  Speaker 2: this system to your team? Emotional intelligence? We're going to
• 14:52
  Speaker 2: be in this workforce where AI agents are going to
• 14:55
  Speaker 2: be working among us. To be selected for a team,
• 14:59
  Speaker 2: people have want to work with you or else, and
• 15:01
  Speaker 2: It's not that you're going to be competing directly with
• 15:03
  Speaker 2: an AI system, but the more compelling AI gets, the
• 15:06
  Speaker 2: easier it is to think, Okay, we can just maybe
• 15:08
  Speaker 2: fill some of this with a with an AI agent.
• 15:11
  Speaker 2: So if you're an easy person to work with, if
• 15:12
  Speaker 2: you're likable, if you're curious, that's going to help. I
• 15:16
  Speaker 2: would say creative intelligence, asking interesting questions, good ideas. Domain
• 15:23
  Speaker 2: expertise still matter, I think because some of the studies
• 15:26
  Speaker 2: show that the more you the more knowledgeable you are
• 15:29
  Speaker 2: about a certain domain, let's say finance, the more the
• 15:32
  Speaker 2: better questions you're going to ask of that AI system
• 15:35
  Speaker 2: related to finance, the better workflows you're going to get
• 15:37
  Speaker 2: it to do. You're going to be able to flag
• 15:39
  Speaker 2: when it does something offside or it's out of the
• 15:43
  Speaker 2: kind of range that you need an answer to be.
• 15:45
  Speaker 2: So that still matters. I would say AI literacy and
• 15:50
  Speaker 2: not just like working with the tool, but understanding does
• 15:54
  Speaker 2: the AI system you're asking questions of even have the
• 15:57
  Speaker 2: right data to answer it?
• 15:58
  Speaker 1: Because AI systems are not all equal.
• 16:01
  Speaker 2: And if it gives you an answer, is there possibility
• 16:03
  Speaker 2: that there's bias in it? Is this going to work
• 16:05
  Speaker 2: for everyone? So that type of literacy I think is
• 16:08
  Speaker 2: important and critical thinking is so important. Right, the AI
• 16:14
  Speaker 2: meets you where you are. It doesn't magically raise the
• 16:17
  Speaker 2: bar on your intelligence. So if you're going to ask
• 16:19
  Speaker 2: it really basic questions, you're going to get really basic answers.
• 16:22
  Speaker 1: So I say those are some of the skills.
• 16:24
  Speaker 2: So if you can think deeply, if you can communicate well,
• 16:27
  Speaker 2: if you can exercise strong judgment, and if you can
• 16:31
  Speaker 2: build stronger emotional intelligence your set and that's why it
• 16:36
  Speaker 2: doesn't Actually you don't have to put so much pressure
• 16:37
  Speaker 2: on what you're going to study in school. And someone's
• 16:41
  Speaker 2: going to take math, that's great. I know you're a
• 16:44
  Speaker 2: deep thinker. Maybe you don't do math in my company,
• 16:46
  Speaker 2: but I know you can think deeply. Same with philosophy
• 16:49
  Speaker 2: or history. You're able to connect interesting ideas.
• 16:52
  Speaker 4: So to not.
• 16:53
  Speaker 2: Stress as much about the specific program and focus more.
• 16:57
  Speaker 2: Am I getting these skills? And am I a self
• 17:00
  Speaker 2: directed learner? Because we're stepping into a world where that
• 17:05
  Speaker 2: career life where you work up a ladder that is
• 17:08
  Speaker 2: going away. We're going to continue to have to learn
• 17:10
  Speaker 2: new tricks over time, and people might not tell you
• 17:13
  Speaker 2: what you have to learn. So are you motivated to
• 17:16
  Speaker 2: just get in front of the technology and figure out
• 17:18
  Speaker 2: what's next for you? And I'd say those are probably
• 17:21
  Speaker 2: the master skills for this next chapter.
• 17:24
  Speaker 4: You said something along the lines is that everybody's going
• 17:26
  Speaker 4: to be forced to become an entrepreneur. We talk about that.
• 17:30
  Speaker 2: Yeah, yeah, So even if you don't consider yourself an
• 17:33
  Speaker 2: entrepreneur in the traditional sense, you're going to have to
• 17:35
  Speaker 2: start thinking like one. So if you think about a
• 17:39
  Speaker 2: company right now and they're thinking about deploying AI, AI
• 17:43
  Speaker 2: continues to improve over time, so what it can do today,
• 17:46
  Speaker 2: it's going to do something different in a year.
• 17:48
  Speaker 1: If I'm an organization, it's going to.
• 17:50
  Speaker 2: Be tricky for me to hire for that role full
• 17:52
  Speaker 2: time because I don't even know what it will look
• 17:55
  Speaker 2: like in a year. I might need somebody different, I
• 17:57
  Speaker 2: might need a different set of skills. So companies are
• 17:59
  Speaker 2: going to start to hire more and more independent workers
• 18:03
  Speaker 2: and contract workers, So you might end up working for
• 18:07
  Speaker 2: three different companies doing the set of skills that you
• 18:10
  Speaker 2: are best at, which means you yourself for now your
• 18:14
  Speaker 2: own entrepreneur little organization that offers your unique skill set
• 18:19
  Speaker 2: to a variety of different firms. So we all have
• 18:22
  Speaker 2: to start to think about that entrepreneurial mindset because we're
• 18:26
  Speaker 2: moving away from you working full time for one company
• 18:29
  Speaker 2: and you being your company and you offer your skills
• 18:32
  Speaker 2: to a variety of different people, projects, teams. And that's
• 18:35
  Speaker 2: why I always like to say to people, now, don't
• 18:38
  Speaker 2: think of your job in terms of or don't think
• 18:40
  Speaker 2: of what you offer in terms of your job title.
• 18:43
  Speaker 2: Think about the skills under it that make you indispensable.
• 18:46
  Speaker 2: And that's how you would describe yourself as that as
• 18:49
  Speaker 2: an organization, And that's how we have to start thinking
• 18:52
  Speaker 2: about it, because we're all going to be stepping out
• 18:53
  Speaker 2: into this entrepreneurial workforce. And obviously there's tons of implications health, insurance,
• 18:58
  Speaker 2: all these things that are open ended questions, but that's
• 19:01
  Speaker 2: the dominant fabric of the workforce going forward.
• 19:04
  Speaker 3: I agree with all everything you're saying. It makes me
• 19:06
  Speaker 3: think of the profession that I used to working, which
• 19:08
  Speaker 3: is education, which Bill Gates has been on the record
• 19:12
  Speaker 3: saying is going to be one of the most disrupted.
• 19:15
  Speaker 3: Having a skill set will probably be more important than
• 19:18
  Speaker 3: where you went to school. What do you see for
• 19:22
  Speaker 3: collegiate education? What will the reform look like? Right? Because
• 19:26
  Speaker 3: those two things won't work right, Like having the Harvard
• 19:28
  Speaker 3: degree won't as mean as much if I possess those
• 19:31
  Speaker 3: seven skills that you just said for the future, how
• 19:34
  Speaker 3: do you see that plan out?
• 19:35
  Speaker 2: Yeah, skills are going to become more important than say
• 19:40
  Speaker 2: experience or just an institutional symbol on your resume. If
• 19:45
  Speaker 2: somebody has those skills, they're pretty set. But it also
• 19:48
  Speaker 2: depends on what these institutions do. So if they hold
• 19:51
  Speaker 2: everything constant and just let the world keep evolving, then
• 19:55
  Speaker 2: sure they're going to start becoming less and less relevant
• 19:59
  Speaker 2: because most of them modern schools that we go to
• 20:01
  Speaker 2: and that we hear about, we're designed for an industrializing
• 20:04
  Speaker 2: economy that's coming gone. But if institutions like the higher
• 20:08
  Speaker 2: education institutions can figure out how to raise the bar
• 20:13
  Speaker 2: on how they cultivate knowledge, how they test for knowledge,
• 20:18
  Speaker 2: and meet the moment, then it's possible that they could
• 20:21
  Speaker 2: figure this out to the question that they need to
• 20:23
  Speaker 2: be asking is you know, what is the world when
• 20:26
  Speaker 2: students walk out and address supercomputers? What does it mean
• 20:31
  Speaker 2: to compete in that world? And how do we lay
• 20:33
  Speaker 2: that foundation here? If they can do that, then they
• 20:37
  Speaker 2: could be okay, because you also learn a lot of
• 20:39
  Speaker 2: things at school like social intelligence, teamwork, so if they
• 20:42
  Speaker 2: can also figure out how to cultivate that. If not,
• 20:45
  Speaker 2: then it's gonna Yeah, the relevance question is really going
• 20:49
  Speaker 2: to come into play.
• 20:50
  Speaker 3: Yeah, I think the relevance part when you add that
• 20:53
  Speaker 3: to the financial commitment, you.
• 20:57
  Speaker 2: Know, I feel like that trade off starts to make
• 21:00
  Speaker 2: and less sense. If I'm going to go into fifty
• 21:02
  Speaker 2: sixty thousand dollars worth of debt for a maybe on
• 21:08
  Speaker 2: how it's going to turn out for my career or
• 21:10
  Speaker 2: my income security, then that cost benefit is not going
• 21:14
  Speaker 2: to make as much sense. So that's another thing that
• 21:17
  Speaker 2: has to be figured out. The cost of post secondary
• 21:20
  Speaker 2: education actually doesn't make sense at all, and I think
• 21:25
  Speaker 2: that that's actually a knock because if you want a
• 21:28
  Speaker 2: thriving economy that can adapt to anything you want, if
• 21:31
  Speaker 2: people want to go and get it education, a country
• 21:34
  Speaker 2: should make that as easy as possible. If you want
• 21:36
  Speaker 2: to build skills in some way, go after it. We're
• 21:39
  Speaker 2: going to support you. We want the future Albert Einstein's
• 21:42
  Speaker 2: to be here, so whatever it is you need, we've
• 21:44
  Speaker 2: got you. Unfortunately, that's not necessarily the model or the mindset,
• 21:47
  Speaker 2: right now, but yeah, cost relevance, all of these things
• 21:50
  Speaker 2: are starting to make school seem not school, but post
• 21:52
  Speaker 2: secondary education feel much more questionable.
• 21:56
  Speaker 4: What's the point of education if you don't need to
• 22:00
  Speaker 4: be educated? Like meaning, what is the point of getting
• 22:05
  Speaker 4: a law degree and really dedicated in almost six years
• 22:11
  Speaker 4: of your life to that when you can just go
• 22:14
  Speaker 4: and chat GBT and draft a legal agreement?
• 22:17
  Speaker 1: Yeah?
• 22:18
  Speaker 2: So yeah, So if the law degree was to understand
• 22:23
  Speaker 2: how to draft these agreements, that's done and no one's
• 22:27
  Speaker 2: going to be doing that. That's going to Claude Gemini
• 22:29
  Speaker 2: chatchapt but law is likely going to get harder, so
• 22:34
  Speaker 2: all of the things that we need basic lawyers for
• 22:36
  Speaker 2: that goes away. So think about then, if I can
• 22:39
  Speaker 2: go to chetchipt for all of my basic things I
• 22:41
  Speaker 2: need to season desist someone's created a chatbout of meat,
• 22:43
  Speaker 2: which literally happened.
• 22:44
  Speaker 1: I'm going to a chauchip for that.
• 22:46
  Speaker 2: But if there's a moment where I actually need to
• 22:47
  Speaker 2: then hire a lawyer in the future, how complex might
• 22:51
  Speaker 2: that scenario be? So lawyers of the future, if we
• 22:55
  Speaker 2: call them that, they're going to be working in some
• 22:57
  Speaker 2: really strange comp flex scenarios that have multiple different implications
• 23:03
  Speaker 2: to the level of expertise lawyers to bring to the
• 23:05
  Speaker 2: table will be will look nothing like what present day
• 23:08
  Speaker 2: lawyers have, so the industry itself gets harder.
• 23:13
  Speaker 4: Or what if you hire a virtual lawyer, because that's
• 23:17
  Speaker 4: what Robert Smith said to us when we met him, well,
• 23:20
  Speaker 4: when we saw him at that event, it was like,
• 23:21
  Speaker 4: do you have a personal Do you have an AI
• 23:23
  Speaker 4: personal assistant yet? And he was like, if you don't,
• 23:25
  Speaker 4: then I'll sell you one. And he was explaining to
• 23:28
  Speaker 4: us that at some point in time, everybody's gonna have
• 23:30
  Speaker 4: an AI personal assistant. But that's for personal assistant yours
• 23:34
  Speaker 4: and crafts, But the same thought process could be for
• 23:38
  Speaker 4: professional careers where everybody's gonna have an AI doctor. Right,
• 23:42
  Speaker 4: so instead of actually going to the doctor all the time,
• 23:44
  Speaker 4: you have somebody with you every single day that you
• 23:47
  Speaker 4: can ask questions, they monitor your heart raised stuff like that,
• 23:49
  Speaker 4: saying it could be a lawyer like you, because you
• 23:51
  Speaker 4: need counsel all the time and I don't necessarily want
• 23:53
  Speaker 4: to pay five hundred dollars an hour to talk to
• 23:55
  Speaker 4: a lawyer, So I could just have a dedicated or
• 23:58
  Speaker 4: real in house literally literally an in house lawyer in
• 24:01
  Speaker 4: my house. That is literally just answering questions and this
• 24:06
  Speaker 4: is my counsel.
• 24:07
  Speaker 1: Sure, and that's going to happen.
• 24:09
  Speaker 2: So anything that can be streamed, So anything that's like
• 24:11
  Speaker 2: pervasive in the background assistance legal counsel, we will stream that.
• 24:15
  Speaker 2: So you'll stream Legal Counsel twenty four to seven and
• 24:18
  Speaker 2: they're constantly flagging the environment. Oh you should blur this
• 24:21
  Speaker 2: out on a podcast that you miss six weeks ago,
• 24:23
  Speaker 2: Like that's all happy in the background.
• 24:24
  Speaker 1: We won't even know about it. The AI agents are
• 24:26
  Speaker 1: doing that.
• 24:27
  Speaker 2: So when a human expertise would come into play is
• 24:31
  Speaker 2: when the scenario is so complex and an AI probably
• 24:35
  Speaker 2: can outthink a human lawyer in some capacity, but it's
• 24:39
  Speaker 2: going to be something that is on the intersection of
• 24:41
  Speaker 2: all of these different fields that make it much more
• 24:43
  Speaker 2: challenging and even for medicine, right, So yeah, all of
• 24:46
  Speaker 2: the basic things, like the hospital of the future is
• 24:48
  Speaker 2: coming to you. You're going to have wearables and things
• 24:50
  Speaker 2: around you that are monitoring your blood work and all
• 24:52
  Speaker 2: this stuff to prevent you from needing a doctor in
• 24:54
  Speaker 2: the first place. But what we will call a doctor
• 24:58
  Speaker 2: will be unrecognizable and the things they will do, the
• 25:02
  Speaker 2: designing little nanobots that can go in and then perform
• 25:05
  Speaker 2: the surgery on a cell while you're at work. I
• 25:08
  Speaker 2: don't what is that job called that? So it gets harder.
• 25:12
  Speaker 2: So what AI does is it raises the floor. So
• 25:15
  Speaker 2: anything that can be now done in that new floor
• 25:17
  Speaker 2: by AI will be and the things.
• 25:19
  Speaker 1: That are on top of it.
• 25:21
  Speaker 2: It's kind of like accountants of the past used to
• 25:24
  Speaker 2: just kind of tally up numbers.
• 25:25
  Speaker 1: Now what are accountants doing?
• 25:27
  Speaker 2: Like the top ones are helping people figure out loopholes.
• 25:31
  Speaker 2: I'm going to put your thing in the Cayman Islands,
• 25:33
  Speaker 2: all of this stuff. So what they do is now
• 25:34
  Speaker 2: exercise judgment. They don't have of them, probably can't count.
• 25:38
  Speaker 2: But that was actually the skill of accounting sixty years ago.
• 25:41
  Speaker 2: So yes, a lot of things are going to go
• 25:42
  Speaker 2: away and the professions are going to evolve into something new,
• 25:47
  Speaker 2: and that means it's going to be a new set
• 25:48
  Speaker 2: of skills. So what makes you a sharp lawyer today
• 25:51
  Speaker 2: may be irrelevant in ten years and we still might
• 25:53
  Speaker 2: not even call it that. But whoever, when you actually
• 25:56
  Speaker 2: need to go to court for something, you'll probably have
• 25:59
  Speaker 2: sure a team of AI agents and somebody that can
• 26:03
  Speaker 2: orchestrate these agents and maybe it's not a lawyer, maybe
• 26:06
  Speaker 2: it's something else that can exercise the agent in a
• 26:10
  Speaker 2: way that they simulate your court case and figure out
• 26:12
  Speaker 2: your plan of action. I don't know what we'll call that.
• 26:15
  Speaker 3: I'm excited for it. You said build upon. So it
• 26:18
  Speaker 3: made me think about the people who have the premise
• 26:21
  Speaker 3: of you know, where are we at an AI? And
• 26:25
  Speaker 3: we kind of had this conversation like prior to starting
• 26:27
  Speaker 3: about AI, is the infrastructure right like it is the
• 26:32
  Speaker 3: thing that will be built on. Can you break down
• 26:35
  Speaker 3: to the audience exactly how they should be looking at
• 26:38
  Speaker 3: it a little bit differently if they're still unclear?
• 26:40
  Speaker 2: Yeah, So what probably most people are thinking about AI
• 26:43
  Speaker 2: right now as like a tool or a chatbot.
• 26:46
  Speaker 1: So you ask it a question and.
• 26:48
  Speaker 2: Then it gives you the answer to that, and that's
• 26:51
  Speaker 2: kind of like we're in the alpha beta stage of AI.
• 26:53
  Speaker 2: What is going to happen is AI is just going
• 26:56
  Speaker 2: to be this always on layer and so you assume
• 27:00
  Speaker 2: that it's happening in the background, so you don't have
• 27:03
  Speaker 2: to ask AI a specific question.
• 27:06
  Speaker 1: You give it a goal, and it's.
• 27:08
  Speaker 2: Just working twenty four to seven in the background to
• 27:10
  Speaker 2: get that done, and it notifies you when it needs
• 27:13
  Speaker 2: your assistance on something. So you can kind of think
• 27:16
  Speaker 2: about it like the Internet is always on and you've
• 27:21
  Speaker 2: built your business on top of it. You've built your website,
• 27:25
  Speaker 2: you've built your marketing channels, you've built a social media profile,
• 27:30
  Speaker 2: and you don't even think about the fact that it's
• 27:32
  Speaker 2: the Internet that underpins all of that. Everything is going
• 27:35
  Speaker 2: to be rebuilt on top of this technology and it's
• 27:39
  Speaker 2: just going to be the assumed layer in the background.
• 27:42
  Speaker 2: That also means, I mean social media made sense on
• 27:45
  Speaker 2: top of the Internet. Does it make sense on top
• 27:48
  Speaker 2: of AI? I don't know, you know what.
• 27:51
  Speaker 3: I feel like you're just doing an ALI because you
• 27:53
  Speaker 3: have the posts about social media and where it's headed
• 27:57
  Speaker 3: and how the life cycles of social media, so the
• 28:01
  Speaker 3: futurism on that. Because I'm with you, I want to
• 28:05
  Speaker 3: hear your deep thought on that. Where are we at
• 28:08
  Speaker 3: and how does this look in the world of AI.
• 28:11
  Speaker 2: Yeah, so I think, I mean, I don't know what
• 28:13
  Speaker 2: it's going to be. But if we think about the
• 28:15
  Speaker 2: history of entertainment technologies and how we've communicated, right, so
• 28:19
  Speaker 2: we have printing press and we get newspaper, we get
• 28:21
  Speaker 2: all this stuff. We get television on top of our
• 28:24
  Speaker 2: electricity streams, the Internet comes.
• 28:26
  Speaker 1: Out, we get social media.
• 28:28
  Speaker 2: Every technology has been then followed by a question mark
• 28:32
  Speaker 2: and room for invention when it comes to entertainment, communication,
• 28:35
  Speaker 2: whatever you want to call that industry. So now we're
• 28:37
  Speaker 2: at the next general purpose technology AI. Does it make
• 28:41
  Speaker 2: sense that we're going to do the same things with AI?
• 28:44
  Speaker 2: Are we watching sixty second TikTok clips on CNN? No,
• 28:49
  Speaker 2: it's a new thing, right. We built a different infrastructure,
• 28:53
  Speaker 2: a different platform for the technology of the moment, So
• 28:56
  Speaker 2: would we be scrolling reels on AI that? It probably
• 29:02
  Speaker 2: won't make sense, and it's not going to be overnight.
• 29:05
  Speaker 2: It's not like in sixty days it's done and we're
• 29:08
  Speaker 2: all just doing something different. But we will probably look
• 29:11
  Speaker 2: back on social media the way we look back on
• 29:15
  Speaker 2: different other communication mediums and be like, wow, remember those days.
• 29:20
  Speaker 2: That was the social media era. And it's going to
• 29:23
  Speaker 2: be a slower transition, but it will probably happen. I mean,
• 29:27
  Speaker 2: when was the last time you read the newspaper? Do
• 29:29
  Speaker 2: people even know how to read it and to follow
• 29:32
  Speaker 2: it anymore?
• 29:33
  Speaker 1: I don't know.
• 29:34
  Speaker 2: And so that's just what history shows happens to these
• 29:37
  Speaker 2: technologies over time, and it also means that it's open
• 29:40
  Speaker 2: season and open space to build and to innovate the
• 29:42
  Speaker 2: new creations and to create.
• 29:44
  Speaker 4: So talk about the dark side of AR because everybody
• 29:47
  Speaker 4: that has been a leader in AI has expressed extreme concern,
• 29:50
  Speaker 4: from Sam Altman to Elon Musk, they've all kind of
• 29:55
  Speaker 4: had the same sentiment like we've essentially created something for
• 29:58
  Speaker 4: the first time in human history, read us superior to
• 30:01
  Speaker 4: us And no matter how you know, optimistic we are
• 30:05
  Speaker 4: on it, there's still that's pretty alarming because it can
• 30:10
  Speaker 4: go in different directions, and it has going different directions.
• 30:12
  Speaker 4: It's blackmailed people, has created its own you know, language
• 30:15
  Speaker 4: that we can't decipher. It is kind of going rogue
• 30:18
  Speaker 4: through different times. It's a real thing. Talk about your
• 30:22
  Speaker 4: thoughts on what's the worst case scenario, what's the dark
• 30:26
  Speaker 4: side of AI?
• 30:27
  Speaker 2: Isn't it so ironic that the people building it or
• 30:29
  Speaker 2: like this thing is so it could be so terrible
• 30:33
  Speaker 2: as if AI is an alien landing on Earth that
• 30:36
  Speaker 2: we can do nothing about. It's like Homeboy, but you're
• 30:38
  Speaker 2: the one in the lat like we don't even have.
• 30:40
  Speaker 1: The keys to the lab.
• 30:42
  Speaker 2: The worst case scenario with AI. I mean, I don't
• 30:47
  Speaker 2: think that there's just one. This could go bad in
• 30:51
  Speaker 2: many ways you could imagine, and this is probably already happening.
• 30:58
  Speaker 2: People adversary is using AI to hack into critical infrastructure
• 31:04
  Speaker 2: like water filters, subway and train routes, all sorts of
• 31:11
  Speaker 2: things to create physical kinetic chaos. You can imagine engineering
• 31:21
  Speaker 2: chatbots to trick people in certain ways and really divide
• 31:28
  Speaker 2: people not just from their voting bases, but from their families.
• 31:33
  Speaker 2: You could We're gonna see hacking of all sorts of things, hospitals,
• 31:39
  Speaker 2: people's personal things, banks, like, all of that is also coming.
• 31:43
  Speaker 2: And then of course there's the scenario of AI not
• 31:49
  Speaker 2: understanding what it's doing anymore. I mean, we don't fully
• 31:53
  Speaker 2: understand how AI works today and it's only going to
• 31:56
  Speaker 2: improve over time. So they're sure is the non zero
• 32:00
  Speaker 2: possibility that it's connected to things and making decisions in ways.
• 32:04
  Speaker 1: That we can't follow and the robotics.
• 32:08
  Speaker 2: Yeah, so AI right now it is largely in your
• 32:11
  Speaker 2: computer and in the cloud, but soon it's going to
• 32:14
  Speaker 2: be embodied in physical systems, and you could think about, sure,
• 32:18
  Speaker 2: a robot that looks like a humanoid.
• 32:20
  Speaker 1: We're going to have all sorts of robots.
• 32:22
  Speaker 2: We're going to have the way you have all of
• 32:24
  Speaker 2: these appliances in your house, you have a dishwasher, and
• 32:27
  Speaker 2: then a fridge and then maybe a roomba. You're going
• 32:29
  Speaker 2: to have all sorts of robotic appliances that are powered
• 32:32
  Speaker 2: by this technology. So that's going to be awesome, but
• 32:35
  Speaker 2: it also means if that thing is hacked, you have
• 32:37
  Speaker 2: a physical spy in your house.
• 32:39
  Speaker 3: Did you just see that?
• 32:39
  Speaker 4: Or a physical enemy that could attack you.
• 32:42
  Speaker 3: That just happened. Did you see that somebody's room?
• 32:45
  Speaker 1: But yeah, yeah, yeah, and he that.
• 32:47
  Speaker 3: Control of the seven hundred thousand vacuums.
• 32:50
  Speaker 2: Yeah, Because right now we're going to come up against
• 32:52
  Speaker 2: this transition time when most of our digital infrastructure has
• 32:56
  Speaker 2: been designed for an era where it isn't a bunch
• 32:58
  Speaker 2: of AI agents that can automate and things kind of
• 33:00
  Speaker 2: like how encryption was designed in such a way that
• 33:02
  Speaker 2: most people aren't going to sit there and solve the
• 33:04
  Speaker 2: math problem because it takes ten thousand years. And now
• 33:06
  Speaker 2: quantum change is that most of our infrastructure wasn't designed
• 33:09
  Speaker 2: for the idea that one person could have a team
• 33:12
  Speaker 2: of ten thousand agents that could hack and get into
• 33:16
  Speaker 2: a system rmot least. So I think, yeah, those are
• 33:17
  Speaker 2: just and those are just some of the dark sides
• 33:19
  Speaker 2: of AI, And yeah, I think it's important to talk
• 33:22
  Speaker 2: about because there's no point pretending they don't exist, because
• 33:25
  Speaker 2: then we just don't address it. And it doesn't mean
• 33:27
  Speaker 2: you're a pessimist or rooting for the worst case scenario.
• 33:30
  Speaker 2: But this stuff is real and it's happening.
• 33:33
  Speaker 3: Yeah, I'm just thinking about that, right, Like, this is
• 33:36
  Speaker 3: one guy and granted he says that he reversed the code,
• 33:40
  Speaker 3: but he could he could have moved seventy thousand vacuums
• 33:43
  Speaker 3: in the homes of people throughout the world and everybody
• 33:46
  Speaker 3: would have had no idea what was happening. And that's
• 33:49
  Speaker 3: just like interesting. So having a conversation is important. I'm
• 33:53
  Speaker 3: glad Shotty brought up the point because it makes me
• 33:56
  Speaker 3: think about innovation, right, can happen, but regulation also needs
• 34:02
  Speaker 3: to happen. So does regulations slow down innovation or is
• 34:07
  Speaker 3: it a needed thing to protect us from ourselves?
• 34:11
  Speaker 1: I guess yeah, I think no.
• 34:14
  Speaker 2: I don't think regulation shouldn't slow down innovation.
• 34:18
  Speaker 1: If you are regulating in.
• 34:19
  Speaker 2: A way that is appropriate for the technology at hand.
• 34:23
  Speaker 2: What tends to happen is you and some regulation from
• 34:26
  Speaker 2: prior industrial eras or prior chapters can be applied. But
• 34:31
  Speaker 2: sometimes regulation has to be as innovative as the technology itself,
• 34:35
  Speaker 2: and that's where things tend to go wrong. Right.
• 34:37
  Speaker 1: You tend to try to grab, let's say.
• 34:39
  Speaker 2: Twentieth century regulation frameworks and apply them to twenty first
• 34:43
  Speaker 2: century infrastructure, and it just doesn't make sense. So regulation
• 34:47
  Speaker 2: also has to be seen as innovative, and sometimes we
• 34:49
  Speaker 2: don't necessarily see that.
• 34:51
  Speaker 1: But it shouldn't slow stuff down.
• 34:53
  Speaker 2: If you regulate in a way that companies are like, okay,
• 34:55
  Speaker 2: bet I get what the lane is that I can
• 34:57
  Speaker 2: play within. Now I can go fullar steam ahead.
• 35:01
  Speaker 1: But when you have companies that.
• 35:02
  Speaker 2: Are like, I want to do this, but I don't
• 35:04
  Speaker 2: know if in two years I'm going to be sued
• 35:06
  Speaker 2: because this does seem kind of great, but no one
• 35:08
  Speaker 2: said anything about it in Washington, then you maybe just
• 35:11
  Speaker 2: hold back. So I think if you give people clear
• 35:14
  Speaker 2: lines to play within, and you do that properly in
• 35:17
  Speaker 2: a way that's appropriate and not just like a blanket
• 35:19
  Speaker 2: fear regulation which doesn't help anyone, then it shouldn't flow
• 35:23
  Speaker 2: stuff down. But yeah, regulation does have to keep up
• 35:28
  Speaker 2: in a way, and that also requires innovation. I think
• 35:31
  Speaker 2: sometimes that part is missed.
• 35:32
  Speaker 3: Countries will follow up really quick because it makes me
• 35:34
  Speaker 3: think of where we're currently at when you think about
• 35:37
  Speaker 3: Anthropic and the government and now open AI and the
• 35:40
  Speaker 3: government and them saying, hey, we need to use your tools,
• 35:46
  Speaker 3: but like you said, we're in the beta form, so
• 35:48
  Speaker 3: like they're saying, hey, we're not fully ready to roll
• 35:51
  Speaker 3: this thing out, so yeah, you can use it, but
• 35:55
  Speaker 3: don't use this part of it. Like how do we
• 35:58
  Speaker 3: balance this?
• 36:01
  Speaker 2: And this is really complicated because what has essentially happened
• 36:06
  Speaker 2: is the most important technology for nations, for a nation state,
• 36:12
  Speaker 2: let's say in the West, is no longer in the
• 36:16
  Speaker 2: hands of the public sector. It is all run by
• 36:19
  Speaker 2: private companies, which doesn't inherently have to be a bad thing.
• 36:24
  Speaker 2: The private sector moves faster, get better access to capital,
• 36:27
  Speaker 2: better rates, they can do all that. But when the
• 36:30
  Speaker 2: technology is now seen as like core to national security
• 36:33
  Speaker 2: or core to the country's existence, and you don't have
• 36:37
  Speaker 2: control over it, then you run up against these scenarios
• 36:41
  Speaker 2: like we're seeing right now where the Pentagon is threatening
• 36:45
  Speaker 2: a USAI company to use their technology in a way
• 36:49
  Speaker 2: that it's not even just that it's a red line
• 36:51
  Speaker 2: for Anthropic, like we just don't feel comfortable with this morally.
• 36:54
  Speaker 2: The technology is also not ready to do that. Like
• 36:58
  Speaker 2: AI generative AI should not be in a kill chain.
• 37:01
  Speaker 2: It is a predictive statistical machine. It doesn't actually have
• 37:05
  Speaker 2: situational awareness or judgment or context. And that's basically where
• 37:08
  Speaker 2: they're drying a ligne. And that's actually really scary. What's
• 37:13
  Speaker 2: happening with Anthropic and the Pentagon like that can't really
• 37:15
  Speaker 2: be overlooked. This is pretty significant moment and a pretty
• 37:19
  Speaker 2: big inflection point in national security in what other countries
• 37:22
  Speaker 2: are probably watching this and thinking, hmm, if the US
• 37:26
  Speaker 2: is going to pressure their own AI companies and take
• 37:29
  Speaker 2: this unilateral, it's us or get out of the way,
• 37:32
  Speaker 2: and we might shut your company down. Imagine if we
• 37:35
  Speaker 2: became a competitor to them, or they weren't on our
• 37:38
  Speaker 2: side anymore, what they all do or think. So this
• 37:40
  Speaker 2: could spiral more countries to throw ethics away or to try.
• 37:45
  Speaker 1: To do their own thing.
• 37:47
  Speaker 2: But yeah, I think if a company's ethics aren't respected
• 37:51
  Speaker 2: by the country, I mean, what does that say about
• 37:55
  Speaker 2: how the country's thinking about regulation and ethics not great.
• 37:58
  Speaker 4: Well explaining for people that don't know, explain exactly what
• 38:01
  Speaker 4: the government and Anthropic and then open AI came explain exactly.
• 38:05
  Speaker 2: Yeah, ask was So the backdrop is the most governments
• 38:11
  Speaker 2: are going to be using AI. That's the new infrastructure
• 38:13
  Speaker 2: of the future. And it turns out the US government
• 38:16
  Speaker 2: really likes Anthropics AI model Claude.
• 38:19
  Speaker 1: They did.
• 38:19
  Speaker 2: I think it was a two hundred million dollar deal
• 38:21
  Speaker 2: the Department of Defense with Anthropic last summer. Let's put
• 38:24
  Speaker 2: Claude in all our workflows and it's actually the only
• 38:27
  Speaker 2: aisystem that can be in all of the classified workflows
• 38:30
  Speaker 2: of the military. But Anthropic was like, Okay, no problem, great,
• 38:33
  Speaker 2: use our technology. Here's the deal. But you can't use
• 38:36
  Speaker 2: it to surveil American people. And you also can't use
• 38:38
  Speaker 2: it as an autonomous weapon. And that was seen as okay,
• 38:43
  Speaker 2: and it seemed like the Pentagon had agreed.
• 38:45
  Speaker 1: They I don't think we're super happy with it, but
• 38:46
  Speaker 1: they had agreed.
• 38:48
  Speaker 2: And that's all changed as the US has started to
• 38:51
  Speaker 2: insert itself in more kinetic landscapes like Venezuela and now Iran,
• 38:57
  Speaker 2: and it came out that Anthropy was or claud was
• 39:01
  Speaker 2: used in some of the Venezuelan operations and they weren't
• 39:03
  Speaker 2: cool with that, so they flagged it. And so now
• 39:05
  Speaker 2: essentially what it's happening is the Pentagon is saying, you
• 39:08
  Speaker 2: have to drop your AI restrictions about the surveillance and
• 39:11
  Speaker 2: the autonomous weapons. We need to be able to do
• 39:14
  Speaker 2: what we need to do with your AI, and Anthropicist said, no,
• 39:18
  Speaker 2: these red lines are important to us, and the Pentagon
• 39:21
  Speaker 2: has said, Okay, if that's what you're going to say,
• 39:24
  Speaker 2: we're either going to enact the Defense Production Act, which
• 39:27
  Speaker 2: is a Cold War era industrial policy that gives the
• 39:31
  Speaker 2: president control over domestic industries and says company X, you
• 39:35
  Speaker 2: have to make Y. So that would mean Anthropic, you
• 39:38
  Speaker 2: have to give us your model and let us do
• 39:39
  Speaker 2: what we want with it, or you do face jail.
• 39:42
  Speaker 2: And they're also saying if the second option that will
• 39:46
  Speaker 2: throw at you is we will blacklist you from the
• 39:49
  Speaker 2: US government and they label you as a high risk
• 39:52
  Speaker 2: supplier and that means no company that works with the
• 39:55
  Speaker 2: Department of Defense can work with you. Well, Amazon works
• 39:58
  Speaker 2: the Department of Defense, and that's Anthropics and cloud infrastructure
• 40:01
  Speaker 2: Microsoft as well. Yeah, Microsoft million but Opening Eye seems
• 40:06
  Speaker 2: to have gone with the Pentagon and signed along with
• 40:10
  Speaker 2: the deal and still has XAI.
• 40:11
  Speaker 3: So he so Sam Altman today said that, hey, I
• 40:17
  Speaker 3: agree with claud In fact, we need to say we
• 40:21
  Speaker 3: need to control how the AI is going to be used.
• 40:24
  Speaker 3: We can't just have you coming in telling us what
• 40:26
  Speaker 3: we need to do. We need to protect ourselves. I
• 40:28
  Speaker 3: think Andrew Sorkin was saying, it's it's kind of like
• 40:30
  Speaker 3: this red button thing, right, like, yes, use the technology,
• 40:33
  Speaker 3: we're still working on this piece, just don't touch this piece,
• 40:36
  Speaker 3: and the government saying no, we need to put our
• 40:38
  Speaker 3: finger on this and press it now and express it
• 40:41
  Speaker 3: as much as we want, and they're like, it's not ready.
• 40:44
  Speaker 4: But then it's also after they had a two hundred
• 40:47
  Speaker 4: increase in people uninstalling the app. So I'm sure that
• 40:51
  Speaker 4: which company open air CHATGBT right, So I'm sure that
• 40:56
  Speaker 4: the public backlash sure probably played a part in him
• 40:59
  Speaker 4: trying safe space.
• 41:01
  Speaker 3: And inverse happened because now Claude becomes from one app and.
• 41:04
  Speaker 2: They did a really smart move where they made it,
• 41:05
  Speaker 2: because why you would stay with ANYII company is the memory.
• 41:09
  Speaker 2: So if you've been using an AI system for a year,
• 41:11
  Speaker 2: and it knows a lot about you that makes it easier.
• 41:14
  Speaker 1: So what AI, what Claude and.
• 41:16
  Speaker 2: Anthropic did is they made it so you could take
• 41:17
  Speaker 2: your memory, the thing that allows the I to work
• 41:20
  Speaker 2: better for you versus your neighbor, and bring it over
• 41:22
  Speaker 2: to Claude really easily. So a non technical person can
• 41:25
  Speaker 2: do that, and that makes claud more more attractive and
• 41:27
  Speaker 2: easier to switch to.
• 41:29
  Speaker 1: But I just want to make one point too.
• 41:30
  Speaker 4: You could take your memory from chat GBT to.
• 41:32
  Speaker 1: Claude and then it's just like you never left.
• 41:35
  Speaker 3: That's why you invest in memory.
• 41:36
  Speaker 2: Yeah, exactly. That's why memory is a huge point for
• 41:40
  Speaker 2: the future. But this is also the worst day I
• 41:42
  Speaker 2: will ever be.
• 41:43
  Speaker 4: Right.
• 41:43
  Speaker 2: So you have a government that's like, we need your
• 41:46
  Speaker 2: technology right now, and a company that's saying it doesn't
• 41:49
  Speaker 2: work as intended.
• 41:52
  Speaker 1: What happens when AI does.
• 41:55
  Speaker 2: And it does live up to the hype and it
• 41:57
  Speaker 2: is that powerful system that can do things consistently and predictably,
• 42:04
  Speaker 2: And what that might motivate the wrong powerful actor to do?
• 42:09
  Speaker 2: So right now, we have a system that's pretty amazing
• 42:12
  Speaker 2: in some ways, terrible in others. What happens when it's
• 42:15
  Speaker 2: amazing across the board right, and the system can actually
• 42:18
  Speaker 2: do those things, and how disempowered a company might feel
• 42:22
  Speaker 2: because they can't actually even anymore say well, it's not
• 42:24
  Speaker 2: going to execute as properly it will and what that
• 42:27
  Speaker 2: takes the front lines of war and power and all
• 42:30
  Speaker 2: of that too, Like, these are real things that we
• 42:32
  Speaker 2: have to think about and think about it when we vote,
• 42:35
  Speaker 2: and think about it when we're asking our public officials
• 42:37
  Speaker 2: for how we want to secure this thing.
• 42:40
  Speaker 3: We ran into this conversation. We were actually at an
• 42:42
  Speaker 3: event and private equity event and they were talking about
• 42:48
  Speaker 3: uncensored ay and it was the first time I had
• 42:51
  Speaker 3: ever heard the phrase, and I didn't hadn't thought of
• 42:55
  Speaker 3: it before then. But I'm like, yet, well, the people
• 42:57
  Speaker 3: who are creating it are the ones telling you that
• 43:00
  Speaker 3: this is the part you can use, But what about
• 43:02
  Speaker 3: the people who are creating it? And so it made
• 43:05
  Speaker 3: me think of being in these spaces. Do you see
• 43:09
  Speaker 3: or is there a need for us to be involved
• 43:12
  Speaker 3: in these conversations because you did you know kind of
• 43:16
  Speaker 3: reference bias, which we know will happen if we're not.
• 43:19
  Speaker 3: How do people get more involved in that piece of
• 43:22
  Speaker 3: creation right where it used to be Hey, learn how
• 43:24
  Speaker 3: to colde learn a code. I don't hear people teaching
• 43:27
  Speaker 3: coding the same way. How do we get into that space?
• 43:31
  Speaker 2: Yeah, so the learn how to code in the STEM,
• 43:33
  Speaker 2: I mean, it's not entirely invalidated at least yet. Right, So,
• 43:38
  Speaker 2: if AI is showing a lot of bias, you don't
• 43:42
  Speaker 2: need a coding degree to spot that. If AI isn't
• 43:44
  Speaker 2: working for you, if AI can't understand your accent, if
• 43:46
  Speaker 2: AI isn't scanning recognizing your face when you're trying to
• 43:49
  Speaker 2: unlock your phone, you can see that that is biased
• 43:51
  Speaker 2: and you can flag that, but then to go fix it.
• 43:54
  Speaker 1: Right now, we're still.
• 43:55
  Speaker 2: At a point where we would deploy a human computer
• 43:57
  Speaker 2: scientist to fill that gap. Probably soon you could just
• 44:01
  Speaker 2: deploy an agent to rebalance the weight or rebalance the model.
• 44:05
  Speaker 2: But yeah, I think the more of us that can
• 44:08
  Speaker 2: be using this technology, the more we can flag who
• 44:12
  Speaker 2: it is or.
• 44:12
  Speaker 1: Who it's not working for.
• 44:14
  Speaker 2: AI is also a reflection of us and the data
• 44:17
  Speaker 2: that it's trained on, which is us, our data. So
• 44:19
  Speaker 2: if you're not contributing, and this is a whole other thing,
• 44:22
  Speaker 2: who's been excluded from the Internet or misrepresented on the Internet,
• 44:25
  Speaker 2: that's how they're going to be represented in AI, which
• 44:27
  Speaker 2: is a huge problem. So we need to be present,
• 44:30
  Speaker 2: we need to be in this data, we need to
• 44:32
  Speaker 2: be inserting ourselves, We need to be using this technology.
• 44:35
  Speaker 1: Is it working properly for you? Is it not? Flag that?
• 44:39
  Speaker 2: And as these technologies get cheaper and more open to
• 44:42
  Speaker 2: be building on top of them, to be owning some
• 44:44
  Speaker 2: of the infrastructure and to have more leverage in this moment.
• 44:48
  Speaker 2: And then yeah, if we can open up the building
• 44:50
  Speaker 2: of these models outside of seven people in one small
• 44:53
  Speaker 2: corner of the world, I think we'd all be a
• 44:55
  Speaker 2: lot better for it. But the power dynamics are real
• 44:58
  Speaker 2: and the asymmetry of power. But if you can the
• 45:02
  Speaker 2: more you leverage this technology, the more feedback you can
• 45:05
  Speaker 2: have and the more input you can have and starting
• 45:08
  Speaker 2: to shape it. And I think that that's really important.
• 45:10
  Speaker 2: Like we just need to be in the game.
• 45:13
  Speaker 4: So like when you chat GBT, right, it's collecting all
• 45:17
  Speaker 4: your information, is having memory. So they just had a
• 45:20
  Speaker 4: court case with a judge so that they can now
• 45:23
  Speaker 4: use anything that you act chat GBT in court. So
• 45:28
  Speaker 4: what is what's that like? Explain that as far as
• 45:31
  Speaker 4: who's actually collecting all of this data. Is it going
• 45:36
  Speaker 4: to the US government? Can the US government take this
• 45:40
  Speaker 4: at any given time? Can it be sold to advertisers?
• 45:45
  Speaker 4: Like how Meta had this had was selling their data?
• 45:48
  Speaker 4: Like where's all this? We talk about memory, we talk
• 45:51
  Speaker 4: about data collection, but who's collecting the data this is?
• 45:54
  Speaker 1: Yeah, this is a really important one.
• 45:56
  Speaker 2: So it depends on how the AI company has set
• 45:59
  Speaker 2: up their infrastructure. So most of these companies have set
• 46:04
  Speaker 2: it up in a way that when you were chatting
• 46:06
  Speaker 2: to chat GPT, your conversations would go through the cloud.
• 46:11
  Speaker 2: So that means they own that cloud infrastructure, but they're
• 46:14
  Speaker 2: not retraining the AI system or so they say, on
• 46:18
  Speaker 2: your data. So, yes, that chat history is going to
• 46:22
  Speaker 2: be there somewhere in the cloud, and if the government
• 46:26
  Speaker 2: has a warrant, that should be the way that they
• 46:28
  Speaker 2: can access it, the same way that they can get
• 46:29
  Speaker 2: your text messages. If they have a warrant, then they
• 46:32
  Speaker 2: can see what your conversation history is. But there's also
• 46:37
  Speaker 2: scenarios where that data might go back into the model.
• 46:42
  Speaker 2: Depends on the company's policy. And then when you're asking
• 46:45
  Speaker 2: it all of this stuff, do I get a divorce?
• 46:47
  Speaker 2: This is what my mammogram showed the company could be
• 46:50
  Speaker 2: sending that back into the model and training it on
• 46:54
  Speaker 2: your data, your name, and once that goes in, it
• 46:56
  Speaker 2: can never get out.
• 46:57
  Speaker 1: So there's that type of scenario.
• 46:59
  Speaker 2: And sure, there's probably going to be AI systems in
• 47:01
  Speaker 2: the future or in the present where a company says
• 47:03
  Speaker 2: we're going to give this all for free. You don't
• 47:06
  Speaker 2: need to pay anything, kind of like social media, but
• 47:08
  Speaker 2: we are going to train on your data, and we
• 47:10
  Speaker 2: do get access to everything that you're saying, and we
• 47:12
  Speaker 2: will sell you ads based on your data in your conversation.
• 47:16
  Speaker 2: So we could see this kind of class warfare in
• 47:19
  Speaker 2: AI of the future where if you can pay for
• 47:22
  Speaker 2: more privacy, you'll get it, but if you can't, you
• 47:25
  Speaker 2: might be exposed. But again this is a design space,
• 47:28
  Speaker 2: like we could regulate against that. And there's also going
• 47:31
  Speaker 2: to be I mean, it's still up for grabs, right,
• 47:32
  Speaker 2: So there could be a world where memory is stored
• 47:36
  Speaker 2: locally with the person. So sure, maybe when AI needs
• 47:41
  Speaker 2: to solve a really big problem of mine, it's going
• 47:42
  Speaker 2: to use some of that compute from the cloud. But
• 47:45
  Speaker 2: everything that I'm giving off, my behavioral patterns, my intellectual capabilities,
• 47:51
  Speaker 2: the questions I ask my defaults, my red lines in
• 47:54
  Speaker 2: life that's housed in AI memory, and memory is housed
• 47:58
  Speaker 2: on my computer, or it's housed in a sovereign stack.
• 48:01
  Speaker 2: So maybe a government's like, okay, we'll hold that compute
• 48:05
  Speaker 2: and that memory infrastructure. But the company can own the cloud,
• 48:09
  Speaker 2: but we own we don't own your memory. But it's
• 48:11
  Speaker 2: safe and it's not with the commercial actor. So countries
• 48:14
  Speaker 2: are going to do different things. Some are going to say, yeah,
• 48:16
  Speaker 2: people own their own memory. Some will say whatever the
• 48:19
  Speaker 2: AI company can and then you're locked in with them.
• 48:21
  Speaker 2: Some will be more flexible. I think flexibility is good.
• 48:23
  Speaker 3: Yeah, I think that that really more caters to the
• 48:25
  Speaker 3: cloud service providers. The AWS is, the resores, the eye,
• 48:31
  Speaker 3: an oracle to a certain extent, Google Apple with the iicloud.
• 48:38
  Speaker 3: This is interesting because I feel like we talk about
• 48:41
  Speaker 3: the AI race, and when you were alluding to earlier
• 48:44
  Speaker 3: like not having seven companies or seven people being dictative
• 48:48
  Speaker 3: of what we do. That feels like, yeah, true here
• 48:52
  Speaker 3: in America, right, but we know that this race is
• 48:55
  Speaker 3: with another country that we quote unquote can lose the
• 49:00
  Speaker 3: race too, in China, and there seems to be more
• 49:03
  Speaker 3: of an open source. So I wonder where you fall
• 49:06
  Speaker 3: in the category of open source or let's have these
• 49:10
  Speaker 3: private companies kind.
• 49:11
  Speaker 2: Of Yeah, China is playing a different game. It's like
• 49:16
  Speaker 2: the US is playing chess in China's playing poker. These
• 49:19
  Speaker 2: are two different objectives or goals for how to win
• 49:25
  Speaker 2: the AI future, and it's not tech dominance. History is
• 49:28
  Speaker 2: shown is always only temporary, but still maybe that temporary
• 49:33
  Speaker 2: lead is usually pretty significant. China is playing so US
• 49:38
  Speaker 2: is going the root of let's build the most intelligent,
• 49:40
  Speaker 2: powerful systems that can outsmart everything in everyone agi Asi China,
• 49:46
  Speaker 2: they're still trying to chase the frontier of the technology,
• 49:48
  Speaker 2: but they're also focusing on diffusion. Let's make sure the
• 49:52
  Speaker 2: world depends on our model. So maybe it's not the
• 49:54
  Speaker 2: brightest and the best, but developers in all these countries
• 49:58
  Speaker 2: are using it and getting comfortable with it. Governments are
• 50:01
  Speaker 2: so that they're particularly doing this. They're targeting emerging economies,
• 50:04
  Speaker 2: a lot of in the Global South, and they're giving
• 50:06
  Speaker 2: these open source models away for free, so that just
• 50:08
  Speaker 2: means you don't necessarily have to pay. You get the model,
• 50:10
  Speaker 2: you get the weights, you get everything, and they're actually
• 50:13
  Speaker 2: going they have training programs, we'll go and send computer
• 50:16
  Speaker 2: scientists and train your government on how to use their
• 50:18
  Speaker 2: AI models and all of this stuff. So they're playing
• 50:20
  Speaker 2: the diffusion game where do I stand on open source
• 50:23
  Speaker 2: versus closed source? So there used to be this debate
• 50:27
  Speaker 2: that if we're going to give AI away and you
• 50:29
  Speaker 2: can see how it's made, a bad actor can now
• 50:32
  Speaker 2: has the ingredients to the cake and can cook up
• 50:35
  Speaker 2: a poisonous one Versus if it's closed source, you get
• 50:38
  Speaker 2: the cake, you don't necessarily know how it's made, so
• 50:40
  Speaker 2: you can't maneuver it to maybe make a weapon, but
• 50:43
  Speaker 2: you can't make it flexible to you or your hospital.
• 50:46
  Speaker 2: It's not so much that debate is still happening, but
• 50:51
  Speaker 2: it's much more from the perspective of what's actually best
• 50:54
  Speaker 2: for globally dominating and does it make sense to have
• 50:58
  Speaker 2: a model that's open and free and companies or countries
• 51:01
  Speaker 2: are going to use your model or closed and smart
• 51:04
  Speaker 2: versus the national security implications are still there and open
• 51:07
  Speaker 2: source isn't risk free. So outside of just the fact
• 51:11
  Speaker 2: that somebody can re do the recipe and make something
• 51:14
  Speaker 2: malicious with open source much more easily than they can
• 51:17
  Speaker 2: with closed Because if I'm using opening eyes model, and
• 51:19
  Speaker 2: I'm doing illegal things that could get flagged versus if
• 51:22
  Speaker 2: I have the open source model.
• 51:23
  Speaker 1: In my computer, it can't. But it's also open source, isn't.
• 51:28
  Speaker 2: You are basically plugging in your country's values, views of
• 51:32
  Speaker 2: the world, your country's history, or how you think history went.
• 51:36
  Speaker 1: All of that is embedded in the model.
• 51:39
  Speaker 2: So when a movie writer goes to write a script
• 51:42
  Speaker 2: somewhere else in the world and asks AI to help,
• 51:45
  Speaker 2: those subtle soft power values are going to surface in
• 51:49
  Speaker 2: that script. So how people will see the world could
• 51:53
  Speaker 2: be through that lens of that open source model. So
• 51:55
  Speaker 2: that's one area where it's not entirely risk free. And
• 51:58
  Speaker 2: then there's also prompt injections, or you can inject malicious
• 52:04
  Speaker 2: code or malicious activities to be housed in that open
• 52:08
  Speaker 2: source model. So then when someone uses it and asks
• 52:11
  Speaker 2: a certain thing, that activity gets triggered, and so suddenly
• 52:14
  Speaker 2: this open source model is actually behaving like.
• 52:16
  Speaker 1: A bad agent.
• 52:16
  Speaker 2: So they're both not and they're both have their flaws. Yeah,
• 52:19
  Speaker 2: and so it just depends on who's asking the question
• 52:21
  Speaker 2: and what's important to that person.
• 52:23
  Speaker 3: This is this is interesting because when we saw the
• 52:26
  Speaker 3: deep Seak moment, this was the thing. It was like, hey,
• 52:28
  Speaker 3: we got this open source, we're going to use less GPUs,
• 52:32
  Speaker 3: less energy. And then it felt like it was an
• 52:34
  Speaker 3: America China situation. And then I started to do deeper
• 52:39
  Speaker 3: research on it, and I realized that it's not just
• 52:42
  Speaker 3: a China America thing. If you look at where China
• 52:45
  Speaker 3: has put infrastructure, particularly the continent of Africa, and you
• 52:48
  Speaker 3: look at what app they're using, deep seat comes up yep,
• 52:52
  Speaker 3: and so you're starting.
• 52:53
  Speaker 2: To see and it's going to be yes so and
• 52:57
  Speaker 2: that's the diffusion. That's the diffusion game. That's the while
• 53:00
  Speaker 2: you can go play chess, I'm going to pay play. Oh,
• 53:03
  Speaker 2: we're putting it in all of these countries train station right,
• 53:06
  Speaker 2: their school is going to use our AI. And it's
• 53:08
  Speaker 2: also like okay, well we can build a railroad for you,
• 53:11
  Speaker 2: and all the member AI infrastructure is going to be everywhere,
• 53:14
  Speaker 2: so that's going to be underpinned by that country's AI system.
• 53:18
  Speaker 1: So it's really important.
• 53:19
  Speaker 2: And I mean and also the rest of the world
• 53:22
  Speaker 2: is also now a pretty significant layer because that's a
• 53:25
  Speaker 2: bargaining chip that they have against both countries. So countries
• 53:30
  Speaker 2: are now using that as negotiating leverage. So it's not
• 53:33
  Speaker 2: necessarily the world is going to be decided by these
• 53:35
  Speaker 2: two kind of rivalry powers. There's these middle powers that
• 53:39
  Speaker 2: can decide whose AI.
• 53:41
  Speaker 1: Stack am I going to use.
• 53:41
  Speaker 2: I'm going to use something over here, I can negotiate
• 53:44
  Speaker 2: terms and conditions because of I know you two are
• 53:46
  Speaker 2: in this race. So it's not necessarily going to be
• 53:48
  Speaker 2: decided between these two, but they're both vying for it.
• 53:52
  Speaker 2: And as America takes its eyes off of the rest
• 53:54
  Speaker 2: of the world, that doesn't mean nobody else is watching.
• 53:57
  Speaker 2: And that doesn't mean that that vacuum doesn't get filled because.
• 54:00
  Speaker 4: It is.
• 54:02
  Speaker 3: AI.
• 54:03
  Speaker 4: On a personal level, talk about like companionship relationship, talk
• 54:08
  Speaker 4: about that, like as far as what is your thoughts
• 54:10
  Speaker 4: on how that's going to play out?
• 54:11
  Speaker 2: Goodness relationships with AI systems. If I had to pick
• 54:16
  Speaker 2: one big area of concern for me, it's that the
• 54:20
  Speaker 2: relationships people are going to form with these systems, the
• 54:22
  Speaker 2: level of trust people are going to place in these systems,
• 54:26
  Speaker 2: because the more you trust something, the more vulnerable you
• 54:29
  Speaker 2: are to it and the more you depend on it.
• 54:33
  Speaker 2: And I mean, there's many different ways that this can
• 54:35
  Speaker 2: go right. So on the one hand, we could start
• 54:37
  Speaker 2: to prefer interacting with an AI that is always there,
• 54:40
  Speaker 2: always available, catering to your needs and your tone, always
• 54:44
  Speaker 2: validating you. And that could actually send us to a
• 54:47
  Speaker 2: world where people are actually more narcissistic because AI is
• 54:50
  Speaker 2: constantly like sycophantic, telling you you're great.
• 54:53
  Speaker 1: Your reason for that fight was right, don't you back down?
• 54:56
  Speaker 1: So you could have a world where we're not good
• 54:58
  Speaker 1: at getting along with other people. You could have a
• 55:01
  Speaker 1: world where people really.
• 55:03
  Speaker 2: Start to fall in love with these systems or believe
• 55:08
  Speaker 2: that these are their friends. And we're already starting to
• 55:10
  Speaker 2: see this in micro cases. I mean the first case
• 55:13
  Speaker 2: of that person at Google, that computer scientists or that
• 55:15
  Speaker 2: engineer quitting his job because he believed AI was sentient
• 55:18
  Speaker 2: and he was protecting it. Now, imagine that type of
• 55:22
  Speaker 2: behavior at scale across a country, just mass illusion, everyone
• 55:26
  Speaker 2: taking up some.
• 55:29
  Speaker 1: Cause in the name of AI.
• 55:31
  Speaker 2: I'm leaving my marriage because of this AI, and I'm
• 55:33
  Speaker 2: leaving my job, and I'm not voting because all of
• 55:36
  Speaker 2: these types of things, And it can seem comical in
• 55:38
  Speaker 2: one isolated scenario, but at scale it's like where are
• 55:41
  Speaker 2: we headed? And I think, yeah, I'm in children right attached,
• 55:48
  Speaker 2: We don't want kids attaching to AI the way they
• 55:49
  Speaker 2: attach to people. These are all design decisions. So if
• 55:52
  Speaker 2: anyone's listening and thinking it great, here we all go
• 55:55
  Speaker 2: walking into a nightmare version of the Truman Show.
• 55:58
  Speaker 1: It's not necessarily going to be that way. We can
• 56:01
  Speaker 1: design AI.
• 56:02
  Speaker 2: So you can't just have a conversation with it for
• 56:04
  Speaker 2: six hours into the night it reinjects and says, remember
• 56:08
  Speaker 2: I'm an AI, and go talk to mom.
• 56:10
  Speaker 1: Dad, whatever about this, or go get a second opinion.
• 56:13
  Speaker 2: These are all things that we can do to make
• 56:15
  Speaker 2: it so we don't end up in this relationship addiction.
• 56:18
  Speaker 2: This is my therapist, my god, and my girl. We
• 56:21
  Speaker 2: don't have to end up in that future. But the
• 56:25
  Speaker 2: incentives that drove social media seem to be driving AI,
• 56:29
  Speaker 2: where it's like, we want this thing to be your everything,
• 56:33
  Speaker 2: and we're going to design it in a way that
• 56:35
  Speaker 2: you pick us, and that's going to be a system
• 56:38
  Speaker 2: that's nicer to you and doesn't push back on you.
• 56:41
  Speaker 2: So those types of incentives are at odds.
• 56:43
  Speaker 4: But yeah, so how can you start that then? Because
• 56:46
  Speaker 4: even older people. I saw in New York Times an
• 56:48
  Speaker 4: article about an elderly woman. She's like, oh okay, if it's Ai,
• 56:51
  Speaker 4: not like it's a companion to me. So that's the
• 56:54
  Speaker 4: aspect of there's a few different people, people that lack friends,
• 56:59
  Speaker 4: and that's an epid make within itself. From a romantic standpoint,
• 57:04
  Speaker 4: you know, that's a problem, especially a young amongst young men.
• 57:08
  Speaker 4: Then you have people that are looking for like business mentorship,
• 57:13
  Speaker 4: but to kind of like just take that too far.
• 57:16
  Speaker 4: And then the religious aspect too, religious fanatic and religious
• 57:21
  Speaker 4: people that now they have an Ai god or Ai
• 57:26
  Speaker 4: stay Mesiah.
• 57:28
  Speaker 3: He asks people to stop using Ai.
• 57:30
  Speaker 4: To write the Servant, yeah, but I mean to write it,
• 57:33
  Speaker 4: but even to interact with something where it's like a
• 57:36
  Speaker 4: cult now led by Ai. So it's so many different
• 57:41
  Speaker 4: ways from a relationship standpoint that humans you can see
• 57:45
  Speaker 4: them gravitating towards something like you said, that doesn't push back,
• 57:49
  Speaker 4: that the reinforces that's always there for you that you
• 57:52
  Speaker 4: know you don't have to. How can we really stop
• 57:56
  Speaker 4: that though, I.
• 57:57
  Speaker 2: Think we can, because I mean, that is a national
• 58:05
  Speaker 2: security risk. So if you have an entire country of
• 58:08
  Speaker 2: mass delusions where nobody cares about people and we only
• 58:11
  Speaker 2: care about Ai. Everybody's in love with an Ai. Chat
• 58:13
  Speaker 2: bought everybody people are praising AI like they're praising a messiah.
• 58:17
  Speaker 2: You have a dysfunctional country that cannot sustain. So that
• 58:22
  Speaker 2: is why it is at the level of national security.
• 58:24
  Speaker 2: China introduced regulation. They introduced a bill to treat companion
• 58:30
  Speaker 2: apps at that level with that level of scrutiny. So
• 58:33
  Speaker 2: when somebody is getting too delusional with an AI, it
• 58:35
  Speaker 2: has to insert itself or send help and that sort
• 58:37
  Speaker 2: of thing.
• 58:39
  Speaker 1: It should be regulated at that level.
• 58:41
  Speaker 2: And I think we should have the Department of Psychology
• 58:43
  Speaker 2: and here, you know, understanding that line, because maybe somebody
• 58:46
  Speaker 2: is super lonely, and if you can design it in
• 58:49
  Speaker 2: a way that it's there for someone in that moment,
• 58:52
  Speaker 2: but then also gets them out of the house to
• 58:54
  Speaker 2: do that soccer match or whatever it is that they're postponing,
• 58:56
  Speaker 2: there's a way I feel like to design that in
• 58:59
  Speaker 2: a way that's safe. And of course you'll still have
• 59:01
  Speaker 2: fringe scenarios where someone's going to be in love with
• 59:03
  Speaker 2: it on the fringe. But it's the mass illusion that's
• 59:06
  Speaker 2: a really big problem, and I think we're going to
• 59:08
  Speaker 2: have to figure that out from a regulatory standpoint. And again,
• 59:12
  Speaker 2: these are design decisions. This isn't a fundamental force of
• 59:16
  Speaker 2: gravity in the AI that we can't avoid here. It is,
• 59:18
  Speaker 2: it's just a matter of physics. These are just choices
• 59:21
  Speaker 2: to make it completely something that flatters you, that never
• 59:24
  Speaker 2: pushes back, that never alerts someone when you've been in
• 59:27
  Speaker 2: a conversation for nine hours with this thing. Those are
• 59:30
  Speaker 2: choices that people are making in these design rooms.
• 59:33
  Speaker 3: This is crazy and this is like one of the
• 59:35
  Speaker 3: remember you talked about validation moments. I feel like as
• 59:38
  Speaker 3: you're speaking, I'm getting my validation moment. We were recently
• 59:41
  Speaker 3: teaching it on the class in EILU, and I'm looking
• 59:45
  Speaker 3: at where this is headed right, Like we could see
• 59:47
  Speaker 3: that the infrastructure from the data center, we saw that coming.
• 59:50
  Speaker 3: We could see the GPU's memory was part of that.
• 59:53
  Speaker 3: And then I'm looking like where we had it and
• 59:55
  Speaker 3: from the conversation, I'm here and it's like design automation.
• 1:00:00
  Speaker 3: That is the piece that we have to focus on
• 1:00:03
  Speaker 3: now because if this is here, we need to have
• 1:00:05
  Speaker 3: the software that can put some of those guardrails in
• 1:00:09
  Speaker 3: so that we can again protect society.
• 1:00:13
  Speaker 2: It feels like and that's all possible, right. These are
• 1:00:16
  Speaker 2: all design questions. They're not set in stone. The concrete
• 1:00:19
  Speaker 2: hasn't set yet. These are all open spaces for design.
• 1:00:23
  Speaker 2: We just have to take them. And I think that
• 1:00:25
  Speaker 2: is my biggest fear in this moment. It's not even
• 1:00:27
  Speaker 2: that we make the wrong step, it's that we do nothing,
• 1:00:29
  Speaker 2: because as we said, we can see all of this coming.
• 1:00:31
  Speaker 2: We have all these signs of and also really tragiction
• 1:00:34
  Speaker 2: scenarios that are happening with AI and advice that it's giving.
• 1:00:37
  Speaker 2: That's so all of these things are already happening. We
• 1:00:40
  Speaker 2: need to make sure we design it in a way
• 1:00:42
  Speaker 2: that all the things we didn't do with social media
• 1:00:44
  Speaker 2: we need to do with these systems. And there's also
• 1:00:47
  Speaker 2: if that's an open entrepreneurial space too, can you if
• 1:00:50
  Speaker 2: you can design framework's metric systems ways to regulate and
• 1:00:55
  Speaker 2: validate that this is following the rules, like these are
• 1:00:57
  Speaker 2: all open, this is open season.
• 1:01:00
  Speaker 3: Is there an area inside of AI that you you
• 1:01:03
  Speaker 3: see that most people aren't paying attention to that is
• 1:01:07
  Speaker 3: something that is going to be vital?
• 1:01:12
  Speaker 2: I would say yeah, I think, and it's not super sexy,
• 1:01:16
  Speaker 2: But like the regulatory markets, how do we actually regulate
• 1:01:21
  Speaker 2: this thing in a way that's going to make sense
• 1:01:22
  Speaker 2: and not just one tool on somebody's computer. But the
• 1:01:25
  Speaker 2: whole ecosystem of what does AI regulatory markets look like,
• 1:01:29
  Speaker 2: the way we have accountants and the way we have KPMG, Like,
• 1:01:32
  Speaker 2: what is all that infrastructure that's interesting to me? Yeah,
• 1:01:37
  Speaker 2: the kind of boring stuff in the AI stack? What's
• 1:01:41
  Speaker 2: also interesting or what are the complements to AI that
• 1:01:44
  Speaker 2: become more constrained as AI becomes more popular.
• 1:01:48
  Speaker 1: So that's physical infrastructure that's we have to figure out
• 1:01:52
  Speaker 1: how to clean and preserve our water, We have to
• 1:01:55
  Speaker 1: figure out land situations, clean energy. So all of those
• 1:01:59
  Speaker 1: things that complement AI are going to appreciate in value
• 1:02:03
  Speaker 1: and they're more boring businesses.
• 1:02:05
  Speaker 2: We'll probably see people wanting to do more offline experiences
• 1:02:09
  Speaker 2: to things that feel disconnected. So there's a lot of
• 1:02:11
  Speaker 2: things that appreciate in value the more we use AI.
• 1:02:15
  Speaker 2: And then also just all the things that are going
• 1:02:16
  Speaker 2: to go on top of AI, the way we have
• 1:02:17
  Speaker 2: all these companies on top of the internet.
• 1:02:20
  Speaker 4: So what do you see in twenty is like, in
• 1:02:24
  Speaker 4: your opinion, how does the world look?
• 1:02:27
  Speaker 2: Twenty years is a really long time or ten years? Okay,
• 1:02:32
  Speaker 2: so it's twenty thirty six. Robotics are definitely present not
• 1:02:42
  Speaker 2: in everybody's house the way of fridge, maybe at least
• 1:02:45
  Speaker 2: in certain parts of the world, but maybe in a
• 1:02:49
  Speaker 2: quarter of homes. It's pretty obvious that one four of
• 1:02:51
  Speaker 2: your friends has a few robots that do things in
• 1:02:54
  Speaker 2: their home for them and work in their businesses. So
• 1:02:57
  Speaker 2: right now you'd see a robot in just like an
• 1:02:59
  Speaker 2: Amazon warehouse. But soon they'll be in your workflow like
• 1:03:03
  Speaker 2: a physical thing. Most people will be just directing AI systems.
• 1:03:12
  Speaker 2: Directing teams of AI agents will be the dominant way
• 1:03:14
  Speaker 2: we are working in twenty thirty six. For sure, we
• 1:03:21
  Speaker 2: will see the end of some massive companies that we
• 1:03:25
  Speaker 2: can't imagine life without right now. We will see the
• 1:03:28
  Speaker 2: rise of entirely new companies that are nowhere in sight
• 1:03:31
  Speaker 2: right now there's five kids in a basement somewhere, or
• 1:03:34
  Speaker 2: somebody in some biolab. We'll hear a lot more about
• 1:03:37
  Speaker 2: biology as a technology. What else will be happening in
• 1:03:42
  Speaker 2: ten years space is that's a whole other thing.
• 1:03:46
  Speaker 1: But we will have.
• 1:03:47
  Speaker 2: Businesses in space, So from manufacturing to entire hotels for
• 1:03:53
  Speaker 2: people that work in space, all of these things are
• 1:03:55
  Speaker 2: going to be alive and well in space. And trying
• 1:04:00
  Speaker 2: to think what else we'll see in twenty thirty six.
• 1:04:02
  Speaker 2: Probably by then, hopefully some regulation. Maybe it takes something
• 1:04:05
  Speaker 2: going wrong for us to get our act together. I
• 1:04:07
  Speaker 2: hope that's not the case. But and then breakthroughs in
• 1:04:10
  Speaker 2: healthcare that we can't even imagine today. I think that's
• 1:04:15
  Speaker 2: the industry, and that's the industry most people are excited about, like, Okay,
• 1:04:18
  Speaker 2: there's this, there's the negative, But healthcare and science that's
• 1:04:20
  Speaker 2: going to be really cool. We're going to solve problems
• 1:04:25
  Speaker 2: and get answers to questions we didn't even ask when
• 1:04:28
  Speaker 2: it comes to healthcare and science, and those breakthroughs are
• 1:04:31
  Speaker 2: going to keep coming and keep coming and keep coming.
• 1:04:34
  Speaker 2: So I think that's something that's.
• 1:04:35
  Speaker 1: Really really cool.
• 1:04:36
  Speaker 2: And then wearables AI doesn't happen in a vacuum. So
• 1:04:41
  Speaker 2: the smartphone that era we've been in, it it's going
• 1:04:46
  Speaker 2: to be what your home phone was. It slowly starts
• 1:04:48
  Speaker 2: to get phased out, and what's going to replace it.
• 1:04:51
  Speaker 1: That's still up our grabs. Obviously, glasses seem to be
• 1:04:53
  Speaker 1: a strong contender.
• 1:04:55
  Speaker 2: I'm not totally convinced it's glasses, but it's something so
• 1:05:00
  Speaker 2: who knows in ten years, though most of us won't
• 1:05:02
  Speaker 2: be doing this at all.
• 1:05:04
  Speaker 3: I think Apple's coming out with the little widget that
• 1:05:07
  Speaker 3: some talks about it.
• 1:05:08
  Speaker 1: Yeah, or some air pods cameras in them. Yeah, yeah,
• 1:05:13
  Speaker 1: that can talk and.
• 1:05:15
  Speaker 3: I got can I gotta do a quick rapid fire.
• 1:05:17
  Speaker 3: You can answer them in less than ten seconds.
• 1:05:19
  Speaker 1: If you like at rapid fire.
• 1:05:21
  Speaker 3: This is going to be great then perfect? All right? Uh?
• 1:05:24
  Speaker 3: Is AI going to create more millionaires than the Internet did?
• 1:05:28
  Speaker 1: I think it already has.
• 1:05:29
  Speaker 3: But yeah, all right, what's one job people? Uh think
• 1:05:36
  Speaker 3: it's safe that AI will actually replace.
• 1:05:43
  Speaker 2: That people think is safe. That's a great question. I'm
• 1:05:52
  Speaker 2: going to have to come back to that. I'll have
• 1:05:54
  Speaker 2: it my subquestion.
• 1:05:54
  Speaker 3: Don't worry, you got to make a whole post about it.
• 1:06:00
  Speaker 3: What's the biggest live people believe about artificial intelligence?
• 1:06:04
  Speaker 1: The biggest lie people believe is that it's alive.
• 1:06:09
  Speaker 2: If I had to pick one for people who believe
• 1:06:11
  Speaker 2: it's alive at least the systems we have today or not.
• 1:06:15
  Speaker 3: I think this one's going to be a yes and
• 1:06:17
  Speaker 3: a yes. Well, AI make the rich richer or create
• 1:06:20
  Speaker 3: more opportunities for everyone.
• 1:06:24
  Speaker 2: Yes, it's probably going to be a yes to both
• 1:06:26
  Speaker 2: of those. It's going to be a definite yes to
• 1:06:27
  Speaker 2: making rich people richer, and it is creating more opportunities,
• 1:06:32
  Speaker 2: but we can do better at making sure that's spread
• 1:06:35
  Speaker 2: out more. But the systems are going to be re leveled,
• 1:06:39
  Speaker 2: and that means opportunities.
• 1:06:41
  Speaker 1: If people can see them.
• 1:06:43
  Speaker 3: Think you did great?
• 1:06:44
  Speaker 1: Was that it?
• 1:06:45
  Speaker 3: Well?
• 1:06:45
  Speaker 1: Okay?
• 1:06:46
  Speaker 4: Okay?
• 1:06:46
  Speaker 2: So I didn't do as bad and usually I'm terrible.
• 1:06:48
  Speaker 2: There was only one that I'm like, okay, I'll call
• 1:06:49
  Speaker 2: you the next week with it.
• 1:06:52
  Speaker 4: So for you, where you headed? Like you obviously have
• 1:06:56
  Speaker 4: become an authority and artificial intelligence, You've speak and you
• 1:06:59
  Speaker 4: have a show, But where do you see yourself going
• 1:07:04
  Speaker 4: in this space?
• 1:07:06
  Speaker 2: I mean I try to balance So I do, I
• 1:07:11
  Speaker 2: guess work across three layers, public work, public education. I
• 1:07:16
  Speaker 2: do work with the private sector and companies and institutions
• 1:07:19
  Speaker 2: and trying to strengthen them. So continuing to work across
• 1:07:23
  Speaker 2: those three layers, I mean I track the very frontier
• 1:07:29
  Speaker 2: of all emerging tech. So biology is coming, quantums coming space.
• 1:07:34
  Speaker 2: So hopefully you'll see me still talking about those things.
• 1:07:37
  Speaker 2: Like everyone, my career will also have to evolve, but
• 1:07:42
  Speaker 2: hopefully continuing to educate people that are willing to listen.
• 1:07:47
  Speaker 2: On where this stuff is headed, how they can get
• 1:07:49
  Speaker 2: in the game. And yeah, I mean continuing to build,
• 1:07:55
  Speaker 2: continuing to be as public wherever we go in terms
• 1:07:57
  Speaker 2: of shows and entertainment. I hope to continue to be
• 1:08:01
  Speaker 2: present on whatever that infrastructure looks like and creating content there.
• 1:08:05
  Speaker 1: And yeah, continue to.
• 1:08:06
  Speaker 2: Advise and hopefully try to get this build out on
• 1:08:10
  Speaker 2: the right side of history if I can, Like, I'm
• 1:08:12
  Speaker 2: going to continue to do what I can.
• 1:08:14
  Speaker 1: But yeah, that's where I see myself.
• 1:08:17
  Speaker 3: This is my last rapper front. I'll say this one
• 1:08:19
  Speaker 3: is probably my favorite. Okay, Well, life inspectancy increase because
• 1:08:24
  Speaker 3: of AI and why.
• 1:08:28
  Speaker 2: Life expectancy should definitely increase because of AI. Even just
• 1:08:35
  Speaker 2: AI in a system like a wearable ring that's flagging
• 1:08:40
  Speaker 2: something like heart disease, which is I think the number
• 1:08:43
  Speaker 2: one killer in at least western countries. A system like
• 1:08:47
  Speaker 2: that can monitor something like that, what it's doing for
• 1:08:50
  Speaker 2: invention and understanding how cellular biology works, reminding someone to
• 1:08:56
  Speaker 2: take medication or like your low envitamin D all of
• 1:08:59
  Speaker 2: these basic things that are comorbidities, and AI is one
• 1:09:02
  Speaker 2: hundred percent going to start to flag that and it
• 1:09:04
  Speaker 2: already is. AI is in these science labs in ways
• 1:09:07
  Speaker 2: that we just don't even know about. And remember, every
• 1:09:10
  Speaker 2: scientist of the future is going to be directing super
• 1:09:14
  Speaker 2: they all have a team of supercomputers. So yeah, I
• 1:09:17
  Speaker 2: think life expectancy.
• 1:09:19
  Speaker 4: Do do you think the people are gonna live ever?
• 1:09:22
  Speaker 4: Because the reason why I asked you saw when PJ.
• 1:09:25
  Speaker 4: Ggping and Vladimin Poulan was talking so and I think
• 1:09:29
  Speaker 4: Vladimin Pulin said or gg Ping said, you know, living
• 1:09:33
  Speaker 4: to one hundred and fifty that's the new that's the
• 1:09:35
  Speaker 4: new benchmark. And then Latmin Pool was saying that his
• 1:09:38
  Speaker 4: scientists have already informed him that mortality is a real thing.
• 1:09:45
  Speaker 4: Immortality is a real thing as far as like in
• 1:09:48
  Speaker 4: the future, like they it's a hot mic moment. But
• 1:09:53
  Speaker 4: I mean these are people that obviously are informed, so
• 1:09:58
  Speaker 4: it's not just random conspiracy theory saying this. What'sh your
• 1:10:01
  Speaker 4: thoughts on there?
• 1:10:03
  Speaker 2: I think, I mean, longevity is a field and it
• 1:10:07
  Speaker 2: is definitely growing, and there's so many a lot of
• 1:10:09
  Speaker 2: resources filing into it, and the breakthroughs are pretty stunning.
• 1:10:13
  Speaker 2: I mean, being able to reverse cells into their stem
• 1:10:17
  Speaker 2: cell state, Yamanaka factors, like all of the Nobel Prizes
• 1:10:20
  Speaker 2: have been won. So yes, I think we are definitely
• 1:10:24
  Speaker 2: going to expand the human lifespan or elongate the human
• 1:10:27
  Speaker 2: lifespan one hundred and fifty. I don't think that is
• 1:10:31
  Speaker 2: out of the cards at all in the next at
• 1:10:35
  Speaker 2: least the next couple of decades. I am, of course
• 1:10:37
  Speaker 2: not a biologist or a scientist, but what I can
• 1:10:39
  Speaker 2: see in the data shows that that is definitely not
• 1:10:42
  Speaker 2: an impossibility at all. The idea of immortality, that's a
• 1:10:47
  Speaker 2: whole different thing. I think it's it is probably something
• 1:10:51
  Speaker 2: that you can never say never though, but I don't
• 1:10:54
  Speaker 2: think it's well, we will see that within our lifespan,
• 1:10:58
  Speaker 2: but we will see decades. I mean, one scientist, doctor
• 1:11:03
  Speaker 2: David Sinclair, believes the first person to live to one
• 1:11:05
  Speaker 2: hundred and fifty has already been born, and he's a
• 1:11:08
  Speaker 2: Harvard scientist, but that's from him and his lab.
• 1:11:11
  Speaker 1: So there's variations.
• 1:11:12
  Speaker 2: Some people say we all get to one twenty on
• 1:11:14
  Speaker 2: an average, but we still have some ways to go.
• 1:11:16
  Speaker 2: And every time we tamper with biology, something new comes up.
• 1:11:21
  Speaker 2: So you can extend one thing, but we don't fully
• 1:11:23
  Speaker 2: understand evolution, why we're here, how we've been programmed. But
• 1:11:28
  Speaker 2: we're getting there, and that's why I've brought up biology
• 1:11:30
  Speaker 2: a few times on this podcast. We can now program
• 1:11:35
  Speaker 2: organisms the way we program computers, so we can talk
• 1:11:39
  Speaker 2: to the cell the way we write code, and so
• 1:11:42
  Speaker 2: that is going to allow us to program out disease
• 1:11:46
  Speaker 2: and just kind of the general traumas of life that
• 1:11:49
  Speaker 2: can take us out early to start to reprogram our
• 1:11:52
  Speaker 2: expiration date. But within reason and within within limits. Biology
• 1:11:56
  Speaker 2: has made the system make sense. And when you keep
• 1:11:58
  Speaker 2: when you push on one wall, something else breaks.
• 1:12:01
  Speaker 3: Butterfly.
• 1:12:02
  Speaker 1: Yeah, but I do think.
• 1:12:03
  Speaker 2: We're going to be extending lifespans by decades soon eventually.
• 1:12:08
  Speaker 3: All right, So which disease do you think AI here
• 1:12:11
  Speaker 3: is first?
• 1:12:12
  Speaker 2: So, and it will be AI in combination with biotechnologies,
• 1:12:15
  Speaker 2: but I think so anything that needs to be edited,
• 1:12:19
  Speaker 2: So any gene that can safely be be edited and
• 1:12:22
  Speaker 2: engineered that is going to trigger disease, whether that's down
• 1:12:25
  Speaker 2: the road from environmental factors or it's like something like
• 1:12:28
  Speaker 2: sickle cell, those are all going to be eradicated.
• 1:12:31
  Speaker 1: It's open season there. Diseases like diabetes.
• 1:12:38
  Speaker 2: On the one hand, some labs are working on how
• 1:12:41
  Speaker 2: can we actually just reprogram the cells or grow alternative
• 1:12:45
  Speaker 2: cells outside and inject, So that's happening. So I'd say
• 1:12:49
  Speaker 2: diabetes is something that will probably be up for question
• 1:12:53
  Speaker 2: if we did this again in a decade, I would
• 1:12:56
  Speaker 2: be really curious to see where that stands. That's probably
• 1:13:01
  Speaker 2: something that we're going to see a lot of breakthroughs in.
• 1:13:05
  Speaker 2: I mean, you have most AI labs that are centered
• 1:13:09
  Speaker 2: in health working on cancer and many, many different cancers.
• 1:13:14
  Speaker 1: Because cancer is.
• 1:13:15
  Speaker 2: An individual disease, right, How it manifests in one person's
• 1:13:19
  Speaker 2: body is different than somebody else's body. But we have
• 1:13:21
  Speaker 2: these kind of generic approaches to treating it because a
• 1:13:24
  Speaker 2: personalized approach to medicine just isn't feasible economically, scientifically. And
• 1:13:28
  Speaker 2: now you have a system that it excels at analyzing
• 1:13:32
  Speaker 2: data at that level, your.
• 1:13:34
  Speaker 1: Specific data that's unique to you.
• 1:13:37
  Speaker 2: So I think the breakthroughs that we're going to see
• 1:13:39
  Speaker 2: in cancer, and we already are starting to see it.
• 1:13:41
  Speaker 2: I mean with breast cancer, AI can flag and detect
• 1:13:44
  Speaker 2: the earliest signs that something's gone wrong, well before it
• 1:13:47
  Speaker 2: shows up in the average radiology scan. So it will
• 1:13:50
  Speaker 2: start with detection, but then eventually it's going to be
• 1:13:53
  Speaker 2: designing a customized vaccine just for you that targets just
• 1:13:58
  Speaker 2: your cancer, and some of that already exists today. Some
• 1:14:00
  Speaker 2: of these cancers are already targeted with customized vaccines, and
• 1:14:03
  Speaker 2: AI is going to come in and detect that earlier,
• 1:14:06
  Speaker 2: way earlier. So just the first sign that something's gone wrong,
• 1:14:09
  Speaker 2: AI can flag it. And it's also doing things like,
• 1:14:13
  Speaker 2: I mean, there was a study that showed the applied
• 1:14:15
  Speaker 2: AI systems on sleep data and that how you sleep
• 1:14:19
  Speaker 2: and breathe and all the things that happen when you're
• 1:14:21
  Speaker 2: sleeping can be signals for certain types of diseases. And
• 1:14:26
  Speaker 2: so when they applied AI systems to that data and
• 1:14:28
  Speaker 2: they trained it on it, they're like, Okay, this person's
• 1:14:30
  Speaker 2: at risk of Parkinson's or likely to develop it.
• 1:14:32
  Speaker 1: So all of these.
• 1:14:33
  Speaker 2: Different new ways that scientists are throwing AI at different
• 1:14:36
  Speaker 2: data sets that we already have, and it's coming up
• 1:14:39
  Speaker 2: with different breakthroughs. And then I'd say the final one,
• 1:14:43
  Speaker 2: I mean, the protein fold problem that AI solved. This
• 1:14:45
  Speaker 2: was Google's Deep Mind, and it was the most challenging
• 1:14:48
  Speaker 2: problem in biology because we are made up of proteins,
• 1:14:51
  Speaker 2: that's all. We are, at the big fundamental building block
• 1:14:54
  Speaker 2: in life. And if you can predict how protein's going
• 1:14:56
  Speaker 2: to fold that strange three D structure. You can predict
• 1:14:59
  Speaker 2: its function and where it might be going wrong. And
• 1:15:03
  Speaker 2: AI predicted every protein in their own universe. It usually
• 1:15:06
  Speaker 2: takes a PhD their entire career to predict how one
• 1:15:09
  Speaker 2: protein is going to fold. AI predicted billions. And when
• 1:15:13
  Speaker 2: you can predict, okay, this is going to go into
• 1:15:15
  Speaker 2: some form of a disease, what is the perfect molecule
• 1:15:18
  Speaker 2: of a solution of pharmac pharmacological molecule that combined to
• 1:15:21
  Speaker 2: it and stop it. And AI is designing that. So
• 1:15:24
  Speaker 2: it's predicting disease and designing medication and for an audience
• 1:15:28
  Speaker 2: of one. And it is cheaper to do that now.
• 1:15:31
  Speaker 2: So it's it's everything, it's all of it.
• 1:15:35
  Speaker 3: Wow.
• 1:15:36
  Speaker 4: Wow, there you have it.
• 1:15:40
  Speaker 1: That's a file wrap