Speaker 1: Welcome to Tech Stuff by'm moz Veloschen here with Cara Price.
Speaker 2: Hello. Hello, So I just read this piece about how
Speaker 2: AI has already started displacing jobs, but it's actually not
Speaker 2: the jobs you would think.
Speaker 1: So do we have something to celebrate this upcoming Labor Day?
Speaker 1: AI not stealing our jobs?
Speaker 2: Well, notebook LM, as you know, is trying to make
Speaker 2: podcast hosts obsolete. But no. According to MIT's State of
Speaker 2: AI and Business Report, AI is replacing jobs that are
Speaker 2: usually outsourced to other countries. That's the short term. Long term,
Speaker 2: around twenty seven percent of white collar jobs will be eliminated. Yeah.
Speaker 1: I know a number of people who are worried about
Speaker 1: the security and longevity of their jobs. But I also
Speaker 1: know people who are thinking about the kind of glass
Speaker 1: half full perspective about new jobs that AI might create.
Speaker 1: And we're sharing a conversation with one of those people today.
Speaker 2: Yeah. You and I recently spoke with journalist Robert Kapps,
Speaker 2: who has been reporting on what jobs could exist in
Speaker 2: an AI driven future, and he actually came up with
Speaker 2: a list of twenty two jobs that don't necessarily exist
Speaker 2: yet but are likely to exist when humans in AI
Speaker 2: work sort of in a hybrid capacity.
Speaker 3: Yeah.
Speaker 1: He lists a few jobs that sounded pretty interesting, and
Speaker 1: then wrote up this article for the New York Times magazine.
Speaker 1: I think my personal favorite was probably AI plumber. That's
Speaker 1: the person who will figure out why an AI system
Speaker 1: might not be working the way it should be, and,
Speaker 1: as Rob puts it, we'll snake the pipes.
Speaker 3: Yeah.
Speaker 2: And I was happy to hear him say that tastemaker
Speaker 2: jobs will last a while.
Speaker 1: Is that because you consider yourself a test maker?
Speaker 2: Yes, a nondescript creative professional. And he says that those
Speaker 2: will continue to exist, So I'm very excited. He mentioned
Speaker 2: one job called a world designer, where a person fabricates
Speaker 2: an entire universe, complete with fictional characters and locations, which
Speaker 2: could apply to everything from marketing campaigns to video games.
Speaker 1: Yeah, I remember him talking about that. He also said
Speaker 1: that he wrote the first draft of the piece using
Speaker 1: AI to see what would happen and his Robert describing
Speaker 1: how that went down with his editor, I.
Speaker 3: Thought it would be a funny joke to play on
Speaker 3: my editor, Bill Wassick that you know, he assigned me
Speaker 3: the article and then like two hours later, I'm like,
Speaker 3: here you go, here's my invoice. I love this new future. No,
Speaker 3: it was a little bit of a luck, but it
Speaker 3: was also you know, you're thinking about AI and jobs,
Speaker 3: and you're thinking about how what are the future jobs?
Speaker 3: So I just thought it was a logic place to
Speaker 3: start with. Okay, well what will my role be as writer?
Speaker 3: And you know, freelance journalism is a hard place to
Speaker 3: be I and we're not extremely well paid people anymore.
Speaker 3: So it's if I can write more, I can make
Speaker 3: more money, you know, just as a purely capitalistic play,
Speaker 3: like that's the dream, right, But of course that wasn't possible.
Speaker 3: It wasn't nearly good enough. And I would suggest this
Speaker 3: like to anybody out there who's very worried about it.
Speaker 3: It's like, just go have it, do your job. Just
Speaker 3: try it, because like it'll teach you a lot about
Speaker 3: how far it still has to go, which can be
Speaker 3: surprising in our current type cycle.
Speaker 2: Right to absolutely no one's surprised. The AI version of
Speaker 2: Robert's story was not published because he couldn't risk his
Speaker 2: reputation on an article where AI may or may not
Speaker 2: be hallucinating, or may or may not be exaggerating things
Speaker 2: or understating things.
Speaker 1: You know, We've talked about cognitive offloading a bunch on
Speaker 1: this show, basically atrophying your own skills by using AI
Speaker 1: too much, and that's something that Robert said he's very
Speaker 1: conscious of.
Speaker 3: When you start to really work with the AI, at
Speaker 3: some point you start to hand over your sense of
Speaker 3: taste and your sense of uh, like is this good.
Speaker 3: You sort of hand over the authority to the AI,
Speaker 3: and the AI doesn't have any capability to accept that authority.
Speaker 3: It will tell you that like, oh, yeah, this is great,
Speaker 3: this is true, this is but like it doesn't It's
Speaker 3: just a machine, right, so it doesn't. It doesn't really
Speaker 3: have the ability to accept that moral responsibility. Like at
Speaker 3: some level there are just these elements that like have
Speaker 3: to come from the human because the AI is just
Speaker 3: not capable of providing them.
Speaker 2: You know what I love the most is that after
Speaker 2: using AI, his next step was to reach out to
Speaker 2: a bunch of people, which I know is basic reporting,
Speaker 2: but I think it really speaks to the core of
Speaker 2: his argument that AI will need the human touch for
Speaker 2: quite some time to come right.
Speaker 1: Human work will continue, but the nature of the work
Speaker 1: may adapt to the AI Revolution and Robert have some
Speaker 1: interesting speculations about what the jobs of the future might
Speaker 1: look like and how they fit into three distinct buckets, trust, integration,
Speaker 1: and taste.
Speaker 2: So we started out by asking how he came up
Speaker 2: with these three buckets, And here's the rest of our conversation.
Speaker 3: The first person I called for the article was the
Speaker 3: Ethan Molloch, who wrote the book Cointelligence, who's Professor Wharton
Speaker 3: and who thinks and writes a ton about AI, and
Speaker 3: I just wanted somebody to talk with about like, Hey,
Speaker 3: how should I even be approaching this? And like right away,
Speaker 3: even from that first conversation, I knew that this was
Speaker 3: going to be way more philosophical than anticipated, because he wasn't.
Speaker 3: He was basically like, there's no way I can tell
Speaker 3: you specific jobs.
Speaker 1: Why did he say that he couldn't tell you specific jobs?
Speaker 1: What was the kind of intellectual exercise or missing step
Speaker 1: from where we are today to knowing what those jobs
Speaker 1: will be part of?
Speaker 3: It is just too fast moving, you know. One of
Speaker 3: his big phrases they told me a couple of times,
Speaker 3: was like it depends on how good the AI gets
Speaker 3: and how fast. Right. But then there are some things,
Speaker 3: and you know, he talks a lot about the jagged
Speaker 3: frontier of AI, which is sort of like the AI
Speaker 3: can be really good at some things and then just
Speaker 3: like really horrible at some things that you would expect
Speaker 3: even the base like estimating the word count of an article, right,
Speaker 3: like you expect any human to nail that pretty easily.
Speaker 3: But when you think about all the different jobs, or
Speaker 3: even your job, or any job, what level are you
Speaker 3: bringing some sort of moral or technical or whatever responsibility
Speaker 3: to that job, Like you are signing off on something,
Speaker 3: you are the person saying like, yes, this is good
Speaker 3: and right and best and you know whatever it be.
Speaker 3: And that can exist in a lot of different things.
Speaker 3: In writing this article, it's I'm accepting responsibility for the
Speaker 3: truthfulness and accuracy of this article, and the AI can't.
Speaker 3: It's just not part of our moral world. Right. You
Speaker 3: can't turn to the AI and blame it when something
Speaker 3: goes wrong. I mean you can, but it doesn't care.
Speaker 3: And so you know, you can think about the like
Speaker 3: extreme far end of that, in like autonomous warfare or
Speaker 3: something where like AI robots kill somebody, they can't accept
Speaker 3: moral responsibility. So the first category was trust and where
Speaker 3: are humans gonna still be very necessary? Whereas AI need
Speaker 3: them to authenticate and to provide that sort of trust.
Speaker 1: I mean, to your point. This piece appeared in the
Speaker 1: New York Times magazine. And when we think about like
Speaker 1: the history of newspapers, or the history of publishing, or
Speaker 1: the history of publishing houses, like as this concept of impremoto,
Speaker 1: like I know if this article or this book or
Speaker 1: this movie comes from this studio or this producer or whatever,
Speaker 1: that I can trust it to a certain extent. The
Speaker 1: output of AI is often entirely divorced from its creator.
Speaker 1: But you know, you mentioned in the piece some of
Speaker 1: the roles that may emerge in this trust bucket, like
Speaker 1: AI auditors, trust authenticators, AI ethicists, And I wanted to
Speaker 1: ask you, obviously, one of the things you have to
Speaker 1: think about is how much cultural demand will there be
Speaker 1: for these types of things. I mean, we're living in
Speaker 1: a world now of alternative facts and conspiracies, and I'm
Speaker 1: just wondering, do you think the demand signal will be
Speaker 1: there from the world, even if it should be morally?
Speaker 3: I think that there. I think that there are a
Speaker 3: whole lot of tasks and a whole lot of things
Speaker 3: that we really would love AI to do, right like
Speaker 3: that that we would be just fine with AI doing
Speaker 3: and in fact, we probably already do a lot of them, right,
Speaker 3: Like I have AI transcribe my interviews, and I don't
Speaker 3: think twice about the moral implications of doing that. And
Speaker 3: I think that like, as humans, we sometimes jump to
Speaker 3: these very extreme cases, right, Like I just used a warfare,
Speaker 3: but like you know, replacing human creativity and things like that,
Speaker 3: and yeah, we're gonna have to think very carefully about
Speaker 3: the lines there. But I think that there's a whole
Speaker 3: bunch of tasks that you're that you're just fine with.
Speaker 3: And so you know, one example that I kind of
Speaker 3: reference in the piece in Trust is that, like, oh,
Speaker 3: I thought that like HVAC repairmen might be some of
Speaker 3: the last people to be affected by AI, But in fact,
Speaker 3: HVAC repairmen have to do a lot of paperwork, right, right,
Speaker 3: and a lot of things that's not really core to
Speaker 3: what they enjoy about the job. But if they're using
Speaker 3: AI to do their contracts and to do their paperwork,
Speaker 3: at some point someone needs to validate that, like those
Speaker 3: contracts are accurate, that they're legally fair, because you can't
Speaker 3: trust the AI, the AI is not worthy entity. So
Speaker 3: like even trust comes in there, like somebody needs to
Speaker 3: validate that, and it becomes a little bit different when
Speaker 3: you haven't created the thing, the contract or the whatever yourself,
Speaker 3: like the same problem I was having with the article.
Speaker 3: It's like because you can't. You have to have a
Speaker 3: sort of slightly different set of skills to be able
Speaker 3: to be like I'm very familiar with where the jagged
Speaker 3: edge of AI what kind of mistakes it makes, and
Speaker 3: there really can be very weird mistakes, right, like unexpected
Speaker 3: not like it's not like backreading something a human rope,
Speaker 3: you know, and you can scale it up where it's
Speaker 3: more complicated than HVAC contracts, and it's something in an
Speaker 3: organization where they're where they have a whole chain of
Speaker 3: systems and somebody has to really know the AI well
Speaker 3: enough to be able to go through that chain and
Speaker 3: give it the like human approval of like, yes, this
Speaker 3: is this is trustworthy and accurate. And I think when
Speaker 3: I talk about ethicists, you know, a big organization might
Speaker 3: be integrating AI enough into the organization that they sort
Speaker 3: of have to have some level of explainability, right and
Speaker 3: some level of justification of like we let the AI
Speaker 3: make these decisions and not these decisions, and here's why
Speaker 3: we do it, and those things need to be explainable
Speaker 3: in a way that is satisfying to all kinds of constituents, right,
Speaker 3: so investors, customers, you know. But it can be like
Speaker 3: if the organization ends up in court, right, they have
Speaker 3: to explain it to a judge and a jury in
Speaker 3: a way that they're like, okay, that's rational and ethically sustainable. So,
Speaker 3: you know, one of the things I like to say
Speaker 3: is that like the AI boon might actually be a
Speaker 3: big boost for philosophy majors who can think through the
Speaker 3: sort of philosophical implications of how the AI is used
Speaker 3: in an organization and create a rational chain of ethical
Speaker 3: explainability for why it's done that way. Because so much
Speaker 3: of large corporations comes down to as they get bigger,
Speaker 3: they're just there's just more and more liability everywhere.
Speaker 2: Maybe one day there will be an AI hallucination interpreter,
Speaker 2: which would be very interesting. I think, I don't know.
Speaker 2: The second bucket is integration, and these jobs seem to
Speaker 2: be more technical in nature. Can you talk a little
Speaker 2: bit more about the integration category and what those jobs
Speaker 2: will be like?
Speaker 3: Sure? So another expert I talked to was a fellow
Speaker 3: named Robert Siemens, who's a professor at New York University
Speaker 3: who studies a lot of this, and he's like, well,
Speaker 3: certainly there's going to be some technical rules, right, like
Speaker 3: people need to understand the AI, and not just like
Speaker 3: we know how to build models. We know how to
Speaker 3: build AI. But these are sort of the most immediate
Speaker 3: jobs that you can see coming up that'll be very
Speaker 3: big for the next some amount of years, you know,
Speaker 3: and they may shrink as the AI changes, right, we
Speaker 3: may need less integrators over time, but like right from
Speaker 3: the jump, you need someone at your organization who really
Speaker 3: understands the models, can really dig down, can really understand
Speaker 3: what the I is doing and why, and can help
Speaker 3: map it to you know, the specific company's peculiarities and
Speaker 3: needs and wants and desires, right, And so one of
Speaker 3: the first ones he talked about was just the AI auditor,
Speaker 3: someone who can go and just really create some sort
Speaker 3: of understanding of the AI for people in the business.
Speaker 3: So you know, you can almost think of these as
Speaker 3: like a sort of twist on or an addition to
Speaker 3: your typical IT manager, but isn't quite so. Just technically focused, right,
Speaker 3: They're just like, Okay, our sales teams and models are
Speaker 3: not showing this, We're not hitting the optimization right, So
Speaker 3: just keeping up with the models and like which which
Speaker 3: one is actually best at algebra right now? Right? Like
Speaker 3: which one is actually best at writing right now? Right?
Speaker 3: It changes every few months. Just even keeping up with
Speaker 3: that if it changes at this pace, can be a
Speaker 3: pretty substantial job. So you can see AI being a
Speaker 3: great optimizer and a great tool, but someone needs to
Speaker 3: understand the system enough to work with the ad to
Speaker 3: make sure that like, oh, we're having this problem, like
Speaker 3: people aren't washing their hands enough. How can AI help
Speaker 3: us in a hospital setting? Someone needs to be thinking about,
Speaker 3: like how to make the AI get to the outcomes
Speaker 3: that they want to see. Because it's a very powerful
Speaker 3: tool for some of these things, but you know, it
Speaker 3: needs someone kind of prompting it and helping and integrating
Speaker 3: it to these very very complex, big organizations.
Speaker 2: After the break, why taste making will be the industry
Speaker 2: of the future, stay with us.
Speaker 1: So the first bucket was trust essentially human in the loop,
Speaker 1: a category of jobs around ultimately like taking responsibility right
Speaker 1: or being the final arbiter of like what is just
Speaker 1: what is legal, what is correct. The second bucket is integration,
Speaker 1: which is essentially like, how do we know enough about
Speaker 1: these tools to harness them, effectively.
Speaker 3: Optimize them and harness.
Speaker 1: The third bucket is taste, and this one I think
Speaker 1: relates most directly to what me, you and Kara do
Speaker 1: for a living. And I was curious, how did you
Speaker 1: choose that word and what does it mean in the
Speaker 1: context of your pend the jobs that may emerge.
Speaker 3: Yeah, I mean, I think taste is going to be
Speaker 3: very core to a lot of things, and not just
Speaker 3: creative jobs. It's also the one that I feel like
Speaker 3: people kind of perk up because it's it's also sort
Speaker 3: of humanly appealing. But you know, as I was just
Speaker 3: thinking about it and talking to people, and I use
Speaker 3: this in the piece, I just had this viral e
Speaker 3: clip of Rick Rubin in my head right of him
Speaker 3: on sixty Minutes and Anderson Cooper talking to him and
Speaker 3: saying like, do you know how to work a soundboard?
Speaker 3: And he's like, no, you know, want to play instruments?
Speaker 2: No?
Speaker 3: Do you know anything about music?
Speaker 2: No?
Speaker 3: And he's like, well, what what do you do? And
Speaker 3: his answer, I'll paraphrase, I don't have in front of me,
Speaker 3: is is you know, I am very confident in my taste, right,
Speaker 3: and so like I had this in my head because
Speaker 3: you know, just thinking about what do we still need
Speaker 3: humans for it? And at a base level, like the
Speaker 3: AI doesn't want anything on its own, right, Like, unprompted,
Speaker 3: it will just sit there idle. Right. So at some point,
Speaker 3: the basic human AI interaction is the human asking the
Speaker 3: AI for something. And to me, one logical route you
Speaker 3: go down if you explore that enough, is that the
Speaker 3: human is providing the taste for what it wants created
Speaker 3: and by I mean created. It could be a creative
Speaker 3: thing like a piece of music, or it could be
Speaker 3: a non creative thing like a business process. Right, But
Speaker 3: at some point somebody is looking at it and saying like, hey,
Speaker 3: I have the vision for what I want. But what
Speaker 3: they are really doing is they're making creative choices. And
Speaker 3: they're not wrong or right choices. They're choices of aesthetic
Speaker 3: or their choices of function, right, Like, so there's multiple
Speaker 3: ways to find a solution, but they're sort of using
Speaker 3: their taste and their judgment to make these creative decisions
Speaker 3: to get to their outcome.
Speaker 1: Cara I'm curious for your take here because you know,
Speaker 1: wearing your other hat, you're a TV producer, successful TV producer.
Speaker 1: How do you think about this idea of taste being
Speaker 1: a key place of human irreplaceability.
Speaker 2: Taste is certainly I think the final frontier to be
Speaker 2: messed with. I try to think of it. Does AI
Speaker 2: have taste? AI has taste insofar as what is fed
Speaker 2: to a large language model. If you feed a lot
Speaker 2: of Beethoven to a large language model and it's spinning
Speaker 2: out music that is supposed to be modeled after Beethoven
Speaker 2: or Bach or whoever, you still need a human being
Speaker 2: who's going to decide is this music good. I think
Speaker 2: things that are synthetically made by large language models can
Speaker 2: be good. But I still think there's someone who's deciding
Speaker 2: ultimately if that thing is good or not. And that's
Speaker 2: a person.
Speaker 3: And that's what I mean, at some level of someone's
Speaker 3: making those decisions. And I think, like, again, there's these
Speaker 3: far out examples of like, oh, I use the AI
Speaker 3: to do everything, and that's going to have a certain quality.
Speaker 3: But there's like, oh, I use the AI to do something,
Speaker 3: and I do some things myself, right, I do something's
Speaker 3: analog and that's going to have a different quality, right,
Speaker 3: Like those can both exist?
Speaker 2: Do you find that quality that qceing has become a
Speaker 2: harder job? Like I do notice that just even in
Speaker 2: LinkedIn posts or in Instagram posts, people are relying more
Speaker 2: on chatch ept to create the language that they're using.
Speaker 2: And there is a sort of en shitification of things
Speaker 2: because we have become used to just accepting that things
Speaker 2: aren't as good anymore because people are using chat ept
Speaker 2: to produce content. I know, is there a job that
Speaker 2: exists to push back on in shitification?
Speaker 3: I guess. I mean, like I've done enough writing experiments
Speaker 3: now that I can tell, especially like LinkedIn is just
Speaker 3: rife with it. Like there's just certain constructions M dash shit.
Speaker 3: The m dash is one but like I love m
Speaker 3: dasher is the one that gets me is the like
Speaker 3: it's not X, it's y. So like when you and
Speaker 3: you'll see these once I tell you this, you'll you'll
Speaker 3: see this on LinkedIn like every single post. It's like
Speaker 3: the future is not you know, apples and oranges, it's bananas.
Speaker 3: It's like that that construction just standing on its own
Speaker 3: is like when the AI is trying to be like
Speaker 3: a muscular copywriter, especially Claude. It just it just it
Speaker 3: just loves that construction, right, And yeah, I think that
Speaker 3: it's funny because when I think about AI in creative fields,
Speaker 3: and I try to think about it sort of more
Speaker 3: optimistic in writing, Like there's this extreme example of like
Speaker 3: I just use it to just write the whole thing,
Speaker 3: or I use it to do whatever. Like there's a
Speaker 3: lot of room between where we are and that being
Speaker 3: the outcome, and there's a lot of like sort of
Speaker 3: positive room between there. And so like one of the
Speaker 3: things that I also do is I work on documentary
Speaker 3: films and I'm making a documentary film right now about
Speaker 3: AI weapons. And it's going to cost a little over
Speaker 3: a million dollars to make that film, right, And that's
Speaker 3: that's relatively inexpensive for a documentary film. And would I
Speaker 3: love it if AI could help me make that film
Speaker 3: for five hundred thousand dollars. Like there's a lot it
Speaker 3: can't do. It's not gonna like DP my shoots and stuff.
Speaker 3: But like, you know, I have an editor, and you
Speaker 3: know it's gonna take months and months to get the
Speaker 3: edit together, and I wouldn't want to replace that editor
Speaker 3: because that editor is a valuable story collaborator who's taste
Speaker 3: I love. But what I love the tools to help
Speaker 3: him be able to do in ten weeks what takes
Speaker 3: him twenty because it can like do sort of first
Speaker 3: cuts quickly for him, and it can sort of like
Speaker 3: let him try things much faster, like so therefore we
Speaker 3: can make that documentary faster and we can move on
Speaker 3: to another one. I would absolutely love that, right, like,
Speaker 3: and I think that he would too, but like to
Speaker 3: go right to like, well, just let's just have the
Speaker 3: AI edit the whole thing and it'll be super cheap. Yeah,
Speaker 3: but it it'll suck.
Speaker 1: Now, But what about what about with this piece though?
Speaker 1: Because V one you knocked up in two hours using
Speaker 1: AI a little bit maybe came in a little bit
Speaker 1: of a strung man in the piece, right, But on
Speaker 1: V two that you wrote yourself, like, were there places
Speaker 1: where you helpfully leveraged AI to make it better? And
Speaker 1: if not, like, when do you imagine starting to do
Speaker 1: that and in what way?
Speaker 3: So I didn't use AI in this piece for that,
Speaker 3: Like I didn't, And I would say one of the
Speaker 3: reasons is I have editors at the New York Times
Speaker 3: magazine who are very good.
Speaker 1: And even better than GPT five.
Speaker 3: But what I will say is I've done a lot
Speaker 3: of experiments with writing in both fiction and nonfiction, of
Speaker 3: trying to figure out where to use the AI, and
Speaker 3: what I find is that for me it becomes unglued
Speaker 3: very quickly in that even Male talks about this in
Speaker 3: the piece which I very much agree with. He says
Speaker 3: that he never lets the II create a first draft,
Speaker 3: and that's something that people have talked about, like, oh,
Speaker 3: use it to get over your writer's block, how it
Speaker 3: created a first draft. But like his point, which I
Speaker 3: find for myself too, is that the AI starts to
Speaker 3: dominate your thinking. It puts you in the AI box
Speaker 3: and then you're like, well is this where I would
Speaker 3: have gone without the II? And it's sort of like
Speaker 3: you end up in your own exist crisis. But then
Speaker 3: I find even as I use as an editor, I'm
Speaker 3: sort of saying like, hey, is this good? Is this?
Speaker 3: And it'll start to give some suggestions like oh, that's great,
Speaker 3: like you could probably lay on this point a little harder,
Speaker 3: or like maybe this is a good opportunity to introduce
Speaker 3: some moral ambiguity or whatever. But as you as you
Speaker 3: start using it to do that. You're handing that taste
Speaker 3: to the AI, right in the same way that like
Speaker 3: when I write a piece of New York Times, I
Speaker 3: give it to my editor because I'm so close to
Speaker 3: it that I'm like, is this is this even any good?
Speaker 3: Am I any good? Have I ever been any good
Speaker 3: at anything? In my life? That's sort of the writers
Speaker 3: just sent into madness. But you're trusting your editor now
Speaker 3: to tell you, like, yes, this is very good, right
Speaker 3: that this part's working. This part isn't working. I find
Speaker 3: it really fraught to hand that to the AI because
Speaker 3: it really doesn't know, but it will tell you that
Speaker 3: it knows.
Speaker 1: When we look at the three buckets in this article
Speaker 1: which I'll trust, integration, and taste, one of the things
Speaker 1: that strikes means is that these have a somewhat pyramid
Speaker 1: like structure, right, So like, ultimately, when it comes to trust,
Speaker 1: somebody has to take responsibility legally, morally or otherwise for outputs.
Speaker 1: When it comes to integration, like somebody has to diligence
Speaker 1: the tools and decide which ones are helpful and how
Speaker 1: to integrate them. And when it comes to taste, somebody
Speaker 1: has to be a tastemaker. Somebody has to be a
Speaker 1: talented creative or an experience creative, whether it's in music
Speaker 1: or organizational design or writing or whatever it is. So
Speaker 1: I guess my question is how much of how many
Speaker 1: of the jobs, because you also came up with a
Speaker 1: concept of the AI plumber, right, how many of the
Speaker 1: jobs that will emerge in the AI revolution are kind
Speaker 1: of very much for the top of the pyramid types
Speaker 1: of jobs, And was that a consideration the piece, Like,
Speaker 1: even if there are these new jobs created, will they
Speaker 1: make up for the jobs lost?
Speaker 3: You know, I would say that, like the piece is
Speaker 3: going to be kind of and I'm going to be
Speaker 3: kind of woeful at answering that because I think while
Speaker 3: I did it under the structure of like listing some
Speaker 3: new jobs, like really my hope was to sort of
Speaker 3: like help people philosophically think about like where new jobs
Speaker 3: will be and maybe even what their own job will be,
Speaker 3: and like what interacting with AI will be like. As
Speaker 3: I say, I think that part of it is like
Speaker 3: thinking about where the AI needs humans. It's not so
Speaker 3: much like I try to resist the idea of its
Speaker 3: humans serving the AI, because again, the AI doesn't want
Speaker 3: anything if you follow the chain far enough, eventually you
Speaker 3: get to a human that wants the thing. Right, So
Speaker 3: it's like, where does the AI need the human? It
Speaker 3: needs some trust, and it doesn't really understand your business.
Speaker 3: It needs some integration, and it doesn't really understand what
Speaker 3: you want, so it needs your taste. So there, it's
Speaker 3: hard to say, like how much will be sort of
Speaker 3: AI plumber versus like sort of setting the taste. You know,
Speaker 3: I think that probably ultimately, like the taste maker part
Speaker 3: of it is so fundamental to human creation of any kind,
Speaker 3: that like that will be the longest lasting, right, Like
Speaker 3: I think integration might be the shortest lasting, but like
Speaker 3: could still be decades. It's interesting because every single person
Speaker 3: I talk to express serious trepidation about where we are
Speaker 3: headed in terms of jobs. All of them would say
Speaker 3: AI is going to result in more prosperity, is going
Speaker 3: to increase wealth. But will that prosperity and wealth accrue
Speaker 3: to capital or will that or will it accrue to labor? Right?
Speaker 3: And that's the big unknown that I think people really
Speaker 3: kind of wring their hands about. And I think that
Speaker 3: like we are definitely at the place where it could
Speaker 3: go either way. I am a little bit of a
Speaker 3: firm believer in you know, sort of the phrase the
Speaker 3: cars go where the eyes go, we will go where
Speaker 3: we sort of collectively point ourselves to go. Right, None
Speaker 3: of this is foreseeing the technology will not It does
Speaker 3: not make it guaranteed to go one way or the other.
Speaker 3: I think that in theory, right you have a bunch
Speaker 3: of companies working on this, A bunch of them are
Speaker 3: playing with open source. In theory, the tools will be commodities,
Speaker 3: which is to say, unless open AI and a less
Speaker 3: anthropic and everybody decides that they get to a model
Speaker 3: that's so good that they just close it off for themselves.
Speaker 3: Like we'll all have access to these tools. That should
Speaker 3: in theory, and this might not be very comforting, but
Speaker 3: in theory, that should be democratizing, right, like the Internet was.
Speaker 3: That should enable more of us to be able to
Speaker 3: do more things, and that should be actually threatening to
Speaker 3: bigger organizations. So like, my hope is that we should
Speaker 3: be entering a very entrepreneurial age where like small teams
Speaker 3: of people can take on big encumbered people. But I
Speaker 3: don't think that like that. We're in this weird quirk
Speaker 3: area right now where doesn't look like that, right, Like,
Speaker 3: what it looks like is we have these massive monolists
Speaker 3: that are just getting stronger and more impenetrable, which I
Speaker 3: think is part of why it seems so scary. Right,
Speaker 3: They're just accumulate cash till the cows come home, and
Speaker 3: the rest of us are going to be poor. And
Speaker 3: I do think that, like in this sort of one
Speaker 3: sense of like being a corporate cog in one of
Speaker 3: those massive enterprises, Like there's never been a worse future
Speaker 3: for that. But like if you can start to think entrepreneurial,
Speaker 3: if you can start to think about, like, hey, how
Speaker 3: can I use these tools to do something that excites me? Right,
Speaker 3: that I'm really thrilled about, Like I want to make
Speaker 3: documentary films, right, Like, and it's so expensive, but like, hey,
Speaker 3: maybe they can come down and I can start to
Speaker 3: tell the stories I want to tell in the ways
Speaker 3: I want to tell them, right, Like that's just for me. Like,
Speaker 3: you know, my hope is that like there's all sorts
Speaker 3: of stuff that we don't see coming that is going
Speaker 3: to be really empowering to people. I don't know that
Speaker 3: that's true, right, there's certainly enough worry about just it's
Speaker 3: just capital building on itself right, which will require some
Speaker 3: other kind of intervention that is fairly bleak about. But
Speaker 3: again I think the tools will be commodities, not the people.
Speaker 1: Robert Capps, thank you for joining us on tech Stuff.
Speaker 3: Thank you, thanks for having me.
Speaker 2: That's it for this week for tech Stuff.
Speaker 1: I'm Kara Price and I'm mos Valasha And this episode
Speaker 1: was produced by Eliza Dennis, Tyler Hill and Melissa Slaughter.
Speaker 1: It was executive produced by me, Kara Price and Kate
Speaker 1: Osborne for Kaleidoscope and Katria Norvelle for iHeart Podcasts. The
Speaker 1: engineer is Beheid Fraser and Jack Insley mixed this episode.
Speaker 1: Kyle Murdoch wrote our theme song. Please do rate, review
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