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Michele Wucker: How AI Journalism Flunked the Fact Check · Boomer Has It!

• 0:00
  SPEAKER_01: Hi, I'm JB Miller in London.
• 0:03
  SPEAKER_03: And I'm Jim Jelamis in Brooklyn.
• 0:06
  SPEAKER_01: And this is Boomer Has It.
• 0:17
  SPEAKER_01: So, Jim, what's on the docket today?
• 0:20
  SPEAKER_03: Today on Boomer Has It, we're finally confronting a topic we've been avoiding: artificial intelligence.
• 0:28
  SPEAKER_03: This much-hyped, much-loathed technology is disrupting all aspects of human life, including journalism.
• 0:36
  SPEAKER_03: And it recently hit close to home for our special guest, Michelle Wooker.
• 0:42
  SPEAKER_03: Michelle is a strategic advisor and global risk and economic policy expert whose insights have guided businesses, central bankers, and other policymakers towards making better decisions.
• 0:54
  SPEAKER_03: She's also the best-selling author of The Gray Rhino, a book about massive, obvious, high-impact dangers charging right at us that give leaders a choice recognize and act decisively, or freeze and get trampled.
• 1:12
  SPEAKER_03: And not to be confused with the unforeseeable, unimaginable black swan, only visible in hindsight, or the elephant in the room that normalizes doing and saying nothing.
• 1:24
  SPEAKER_03: A gray rhino is an obvious, visible, probable challenge.
• 1:28
  SPEAKER_03: Think financial crises, COVID-19, AI, AI, and tech disruption or climate change.
• 1:37
  SPEAKER_03: People see it coming.
• 1:38
  SPEAKER_03: We talk about it sometimes a lot, but only some of us step up to face it and turn danger into ding-ding opportunity.
• 1:49
  SPEAKER_03: Hello, Michelle.
• 1:50
  SPEAKER_03: Hi, Jim and JB.
• 1:52
  SPEAKER_00: Great to see you guys.
• 1:53
  SPEAKER_03: Yeah, great seeing you too.
• 1:55
  SPEAKER_03: It's been a while.
• 1:56
  SPEAKER_03: Um it has.
• 1:58
  SPEAKER_03: Full disclosure here, we should say we've all met years ago at uh Media Bistro parties.
• 2:05
  SPEAKER_00: Where we actually pre-Media Bistro, before Media Bistro was Media Bistro.
• 2:10
  SPEAKER_00: It was the parties that didn't have a name yet.
• 2:15
  SPEAKER_01: There were parties in the East Village.
• 2:16
  SPEAKER_01: In the East Village.
• 2:17
  SPEAKER_01: I remember, yeah, I'm trying to remember what the name of it was.
• 2:19
  SPEAKER_01: There was a bar in the East of the East Village, Laura.
• 2:23
  SPEAKER_00: Flamingo East.
• 2:24
  SPEAKER_01: Yes, Flamingo East.
• 2:26
  SPEAKER_00: Well remembered.
• 2:27
  SPEAKER_00: And we used to have invitation chains which in involved facts and phone because not everybody had email yet.
• 2:38
  SPEAKER_03: Wait, when we say facts for our younger listeners, we're not talking about facts, F-A-C-T-S.
• 2:45
  SPEAKER_03: We're talking about an ancient communications transmission procedure.
• 2:54
  SPEAKER_01: It was just one step up from parchment or chisel, chiseled into uh cement bricks that were that were delivered by right by people holding the anyway.
• 3:07
  SPEAKER_03: Let's get started.
• 3:08
  SPEAKER_03: Um Michelle, there was a recent article on Fortune about obvious risks facing businesses, and it a lot of it was resting on your gray rhino coinage.
• 3:21
  SPEAKER_03: And the reporter committed a few forced errors, like incorrectly defining gray rhino and not attributing it to you.
• 3:30
  SPEAKER_03: Uh, and when you we tell us a little more, you dropped a note to him.
• 3:34
  SPEAKER_03: You tell us, tell us what happened.
• 3:36
  SPEAKER_00: Well, I was flattered.
• 3:37
  SPEAKER_00: I mean, the article said, I mean, it was based on a a CEO conference that Fortune did.
• 3:43
  SPEAKER_00: And I was flattered that it said it's, you know, the difference between uh Gray Rhino and the Black Swan is the biggest uh thing that that corporations are not understanding in risk management today.
• 3:56
  SPEAKER_00: It's like a really important distinction.
• 3:57
  SPEAKER_00: And they they had a couple of of you know smart people on stage uh talking about it, and uh they talked about sort of attacks on older people as related to the Nancy Guthrie case, that uh there have been a lot of warnings in the uh you know the community about older people being vulnerable.
• 4:17
  SPEAKER_00: And uh so so they were talking about the you know, the gray rhinos.
• 4:20
  SPEAKER_00: Here is something they're warning about and they they didn't do enough.
• 4:23
  SPEAKER_00: And so I say it's often very common that uh when people are writing about gray rhinos and black swans, well, they already know what a black swan is because that term became big uh in 2007, 2008, 2009 during that great financial crisis.
• 4:40
  SPEAKER_00: And so they'll they'll attribute that because they know what it is.
• 4:43
  SPEAKER_00: But despite the fact that today we have this thing called the internet, and you can actually pretty easily do a search for, you know, where did the gray rhino come from?
• 4:53
  SPEAKER_00: And it's pretty obvious, I cannot tell you how many reporters don't bother to do that little bit of work.
• 5:01
  SPEAKER_00: And they often will, you know, read about it someplace else that also hasn't attributed it and always, yeah, almost always get the definition wrong.
• 5:10
  SPEAKER_00: And in this case, they were talking about it as is something that uh that you know you can it it's uh you you ignore it in the present and you can see it in in hindsight, which is not what the gray rhino is about.
• 5:24
  SPEAKER_00: It's about looking, looking in front of you, the thing in front of you.
• 5:28
  SPEAKER_00: And and sometimes it's useful looking back when people go, oh, it was a black swan, nobody saw it coming.
• 5:34
  SPEAKER_00: And you know, when you know, as a cop-out, which is not how that term was meant to be used either.
• 5:40
  SPEAKER_00: It was a reminder of you know uncertainty and the need to to build uh you know resilient organizations that uh that thrive in volatility.
• 5:49
  SPEAKER_03: At the expense of uh at the expense of getting into the weeds here, you know, in hindsight, do we look, you know, I was in banking at the time, and I I can't tell you how many bankers I spoke to who were just friendly acquaintances told me something's happening right now, and we're playing a game of credit musical chairs, and the bottom's gonna fall out soon.
• 6:11
  SPEAKER_03: So, do we call the 2008 financial crisis a black swan or a gray rhino?
• 6:18
  SPEAKER_00: So uh I sort of see it as a crash of gray rhinos, crash being the wonderful zoologically correct word for a group of gray rhinos.
• 6:29
  SPEAKER_00: Oh some very, very obvious ones.
• 6:32
  SPEAKER_00: And sometimes when those all come together, you get some little black swan type uh events.
• 6:39
  SPEAKER_00: But maybe, you know, you you didn't see exactly what was going to come after, but there were a lot of gray rhinos, and particularly the housing market.
• 6:46
  SPEAKER_00: You know, as you know, I mean, you know, I was buying an apartment, I was well, I bought an apartment on the Upper West Side in 2001.
• 6:53
  SPEAKER_00: And in four years, it almost doubled in price.
• 6:57
  SPEAKER_00: And I, it was a first apartment, and I was thinking about making some changes in my life.
• 7:02
  SPEAKER_00: So I, you know, I sold it for almost twice what I had bought it for because I I could see this.
• 7:08
  SPEAKER_00: And, you know, as you know, I was uh I spent a lot of time on Wall Street as well, particularly working on emerging markets and financial crisis.
• 7:15
  SPEAKER_00: And a lot of the people I knew were the smart, wonky people on Wall Street uh who read Charles Kindleberger, the sort of the guru of of uh of manias, panics, and financial uh financial crashes.
• 7:30
  SPEAKER_00: And they were all saying something's coming.
• 7:32
  SPEAKER_00: Christine Lagarde was saying there's a financial tsunami coming.
• 7:36
  SPEAKER_00: The uh, you know, the FBI was issuing reports about mortgage fraud, and there was just this crazy, crazy run-up in in housing prices, which ended up being the the first domino that sent everything else tumbling after it.
• 7:52
  SPEAKER_03: Right.
• 7:53
  SPEAKER_00: Well, but nobody saw anything coming.
• 7:56
  SPEAKER_03: No, no.
• 7:58
  SPEAKER_03: As you get close to the top, the uh, you know, the opacity increases.
• 8:03
  SPEAKER_03: Uh um, and then just back to this fortune, this unfortunate, unfortunate fortune article.
• 8:10
  SPEAKER_03: Um, I'm not gonna name the journalist because you know, we have seven or eight listeners and they're very devoted to the podcast.
• 8:17
  SPEAKER_03: And I'd hate for him to be doxxed or swatted, or for any of our loyal fans to call child protective services as they did to Pete Buttigieg.
• 8:28
  SPEAKER_03: Um so we, you know, before 2022, before the advent of Chat GPT, we would have attributed these oversights to sloppy reporting.
• 8:39
  SPEAKER_03: But this being 2026, the problem is the sloppy reporting didn't come from a human.
• 8:45
  SPEAKER_03: He used AI to write the story, juicy as it would have been to expose him as a fraud, um, around the same time, the Wall Street Journal revealed that he's the lead writer in an AI experiment at Fortune.
• 9:00
  SPEAKER_03: And it's designed to scale content production.
• 9:03
  SPEAKER_03: Ugh, oh my god, I hate that word, content.
• 9:06
  SPEAKER_03: And he he's cranked out more than 600 stories using the technology at the time of the article.
• 9:13
  SPEAKER_03: And I I took a look, it's it's 10 magazine articles a month.
• 9:17
  SPEAKER_03: Uh they're AI assisted, and they account, get ready for this, 20% of Fortune's web traffic in the second half of 2025.
• 9:27
  SPEAKER_03: Most were written by that that journalist.
• 9:30
  SPEAKER_03: So, thoughts here.
• 9:32
  SPEAKER_03: Uh, JB and Michelle, you were both beat reporters.
• 9:36
  SPEAKER_03: Michelle, you were at the Milwaukee Sentinel and Dow Jones.
• 9:39
  SPEAKER_03: JB, you were a Cub reporter at a tender age at a weekly, a weekly publication.
• 9:45
  SPEAKER_03: And either of you imagine using AI to cover a story.
• 9:50
  SPEAKER_01: Sure.
• 9:51
  SPEAKER_01: I would just switch it on and go on holiday for a few weeks.
• 9:55
  SPEAKER_01: Come back and we s receive great responses.
• 10:00
  SPEAKER_01: No, of course not.
• 10:02
  SPEAKER_01: But I mean, I'm not I don't have what was the number you said for this guy in Fortune?
• 10:07
  SPEAKER_01: You said 10 a month, 10 articles a month?
• 10:10
  SPEAKER_03: 10 magazine articles a month, which and we're talking magazine size.
• 10:14
  SPEAKER_03: So let's it's like 3,000.
• 10:16
  SPEAKER_03: It's gonna be longer than uh, you know, than the average newspaper article, you know, feature length magazine size.
• 10:23
  SPEAKER_03: So what maybe 800, a thousand words?
• 10:26
  SPEAKER_01: Right.
• 10:27
  SPEAKER_01: I mean, I had a I I was working for a weekly, small weekly paper, The Conquered Patriot.
• 10:33
  SPEAKER_01: I had a noon Wednesday deadline.
• 10:35
  SPEAKER_01: I covered the the municipal light board, the selectmen, I did business interviews, uh, old people who had interesting stuff and when I bought a camera, they made me a photographer.
• 10:46
  SPEAKER_01: I was they couldn't believe their luck.
• 10:49
  SPEAKER_01: They paid me five dollars an article, and um, and I had the greatest, greatest time.
• 10:54
  SPEAKER_01: I loved it.
• 10:55
  SPEAKER_01: Um, this was decades before AI was even imagined.
• 10:59
  SPEAKER_01: It was decades before the Terminator films, which introduced the concept of AI to a lot of people, I think, and uh the end of the world.
• 11:09
  SPEAKER_01: Um, but Michelle, you've you've you've had a much more illustrious newspaper career than I have.
• 11:15
  SPEAKER_01: Could you imagine using using AI?
• 11:18
  SPEAKER_00: Well, I mean, it was it was very early in my career.
• 11:21
  SPEAKER_00: I want to say that.
• 11:21
  SPEAKER_00: And sometimes people today say I'm a journalist, and that drives me bonkers because that has not been on my business card since like 1992.
• 11:29
  SPEAKER_00: Uh, what I do is not journalism.
• 11:32
  SPEAKER_00: Um, and although, again, and I've I my my journalist friends are like the the the old guard, you know, the really the amazing people who took it really, really, really seriously.
• 11:43
  SPEAKER_00: And uh, you know, today it's it's a very, very different thing.
• 11:48
  SPEAKER_00: You've got all the clickbaity stuff and the and the AI generated stuff and the cessational stuff and the you know corporate directed stuff, and it's it's more of an entertainment kind of thing.
• 11:59
  SPEAKER_00: So I, you know, I don't want to be called a journalist partly because what that has uh become.
• 12:05
  SPEAKER_00: And it's also because you know, it's kind of is it's also an insult to the people like the old, the the old guard journalists, because I really admire their craft and uh what they did.
• 12:20
  SPEAKER_00: And I do very, very different things now.
• 12:23
  SPEAKER_00: You know, you have this sort of you know, you know, advisory, you know, opinions, uh speaking, and uh very, very different work.
• 12:32
  SPEAKER_00: And uh, but you know, using AI, I I you know, I can actually see some uses that might be be helpful.
• 12:40
  SPEAKER_00: I mean, uh, you know, in uh, you know, working at Dow Jones, you know, sometimes you'd get these like, you know, press releases, you know, you you'd see earnings reports or things that are just, you know, boilerplate that you know doesn't require a big brain to turn into a short article.
• 12:56
  SPEAKER_00: Um, although you you definitely need a human to look and make sure that it got right.
• 13:01
  SPEAKER_00: Um and you know, the funny thing about how I found out about this Wall Street Journal article was I was telling a friend of mine about this, you know, experience.
• 13:10
  SPEAKER_00: And of course, you know, I sent the guy a note I said, you know, I'd really like to clarify this.
• 13:14
  SPEAKER_00: It's not quite right.
• 13:15
  SPEAKER_00: And uh so he he you know messaged me back, said, yes, please let me know.
• 13:20
  SPEAKER_00: So I messaged him.
• 13:22
  SPEAKER_00: And he, you know, to his credit, he put in a quote from what I sent him uh and credited me, but he kept the wrong definition in.
• 13:30
  SPEAKER_00: And he misspelled my name with two L's, which, you know, thank you, autocorrect, which hates two L, they hates one L Michelle's.
• 13:39
  SPEAKER_00: So I think that was another, you know, earlier use of of AI that that meant.
• 13:43
  SPEAKER_01: Autocorrect.
• 13:44
  SPEAKER_01: Yeah.
• 13:45
  SPEAKER_00: So anyway, I was telling telling a friend of mine about this, and she says, Was it fortune?
• 13:49
  SPEAKER_00: I said, Yeah, how did you know?
• 13:51
  SPEAKER_00: And she pointed me to the Wall Street Journal article, and then I went, oh man, it was this, it was the same guy.
• 13:56
  SPEAKER_00: This is crazy.
• 13:57
  SPEAKER_00: And one of the things that that he said in there was, you know, I'm not fact-checking like the New Yorker.
• 14:03
  SPEAKER_00: And that touched that touches on something that's driven me crazy that, you know, that fact-checking could be, should be a really good use of AI, but you know, with facts that are vetted by humans, uh, and you know, making sure that the facts that are in there are accurate, that's a big problem.
• 14:25
  SPEAKER_00: Uh, I I look at fact checkers way back in the day when I actually was a journalist.
• 14:29
  SPEAKER_00: I remember writing an article about a company in Wisconsin, and the fact checkers came up and they changed some of the references to Wisconsin to Minnesota.
• 14:39
  SPEAKER_00: Oops, human error, whoops, wrong jurisdiction.
• 14:43
  SPEAKER_03: Yeah.
• 14:43
  SPEAKER_01: But also, Fortune magazine is a major publication.
• 14:46
  SPEAKER_01: This is not a small fly by night, you know, it's not click, it shouldn't be a clickbait source.
• 14:53
  SPEAKER_01: It's I mean, you know, I grew up with, wasn't it time life?
• 14:57
  SPEAKER_01: Was it one of the what it was a part of the state of Time Life?
• 15:00
  SPEAKER_01: I think I mean also I want to be in a reference point.
• 15:03
  SPEAKER_01: What got me into journalism were two men called Bob Woodward and and and Carl Bernstein.
• 15:08
  SPEAKER_01: This is this is dating us all slightly, but but journalists were heroes in my in my youth, and it probably got a lot of people into journalism, and that was you know investigative, investigative journalism.
• 15:22
  SPEAKER_01: And in the film, maybe some of our viewers and listeners were more familiar with the film All the President's Men with what Robert Redford and uh Dustin Hoffman.
• 15:31
  SPEAKER_01: But you see the sort of what was it called?
• 15:33
  SPEAKER_01: The sort of you know, shoe leather.
• 15:34
  SPEAKER_01: Was it called shoe leather reporting where they would actually get out, walk, and you know, interview people and knock people on you know on their doors, and they would do the work.
• 15:45
  SPEAKER_01: There was no internet, there was no AI.
• 15:48
  SPEAKER_01: Uh, they were they were doing, and and there's one of my favorite shots in the film is when they're in the in the Library of Congress and they're looking up, I can't even remember exactly, looking up names or something, and the camera pulls up, up and up, and you see them surrounded by a sea of stacks of books, and then you uh and you see how much work it was.
• 16:10
  SPEAKER_01: They did that was work.
• 16:12
  SPEAKER_01: Journalists did a lot of work.
• 16:14
  SPEAKER_01: They weren't, but they also weren't expected to produce what 10 articles, you know, all this, all this um, you know, I was gonna say footage, but you know, uh wordage um on a weekly basis uh for you know for clicks, and the Washington Post gave them time to uh you know work on this story.
• 16:36
  SPEAKER_01: Thank you, Washington Post is still running.
• 16:38
  SPEAKER_01: So the whole financial client, the whole financial infrastructure of journalism has changed.
• 16:46
  SPEAKER_01: And you're not a journalist anymore, probably for good reason, because it's a tough business to be in, right?
• 16:51
  SPEAKER_01: You found a much more modern approach to to um a profession that that journalism you know led into.
• 17:01
  SPEAKER_01: And I'd imagine you're using all of a lot of your journalistic skills.
• 17:05
  SPEAKER_00: Those are great skills.
• 17:18
  SPEAKER_00: Going, hey, are you sure this is right?
• 17:19
  SPEAKER_00: And and you know, copy editors back in the day were all worth their weight in in gold, platinum, you know, whatever's more valuable.
• 17:27
  SPEAKER_01: We have copy editors, yes.
• 17:29
  SPEAKER_01: We respect and admire and revere copy.
• 17:32
  SPEAKER_03: You know, I I I have to speak, in contrast, I feel a little envious.
• 17:36
  SPEAKER_03: I always have the way I entered journalism as a freelancer, it was a little like if you could imagine John Belushi from Animal House pitching stories and with the incentive that I could get free trips and free stuff, free booze, cigars, but you know, books, free stuff.
• 17:56
  SPEAKER_03: That was my sort of my approach to journalism.
• 17:58
  SPEAKER_03: And shame on you, Jamie.
• 18:00
  SPEAKER_03: Shame on me is right.
• 18:01
  SPEAKER_03: I'm chasing, believe me, I'm chasing that the media parties were good.
• 18:06
  SPEAKER_00: I I gotta say that.
• 18:07
  SPEAKER_00: You know, not just I mean the ones, the ones that we met at, we paid for our drinks.
• 18:10
  SPEAKER_00: But I gotta say, some of the some of the you know, lunch parties and this party and that, that the the the food and the wine were were were quite good.
• 18:17
  SPEAKER_00: I will.
• 18:18
  SPEAKER_03: Well, Michelle, and I and one thing I I will I'm proud of is I I tipped heavily at these parties, and um writers are notoriously cheap, but I I you know I I we had a a feature writers group and I'd hear you all talk and say words like ethics.
• 18:34
  SPEAKER_03: I was like, huh?
• 18:35
  SPEAKER_03: Oh.
• 18:36
  SPEAKER_03: And um and and and it was amazing to me that I would I would file a story for the front of the book of a publication, I'd get a dollar a word.
• 18:45
  SPEAKER_03: I was living in a cheap apartment, it felt great.
• 18:48
  SPEAKER_03: And then I'd get a deluge of calls.
• 18:51
  SPEAKER_03: A copy editor would call, a fact checker would call, and the editor would call.
• 18:54
  SPEAKER_03: And I'm like, what do all these people want?
• 18:56
  SPEAKER_03: I'm done.
• 18:57
  SPEAKER_03: Um, and it was a slow climb to really respect the industry and um get, you know, and and unfortunately, you know, the barriers to entry have declined, and we have citizen journalists like I kind of was, um, but committing, you know, terrible, terrible breaches of of ethical guidelines and journalism.
• 19:19
  SPEAKER_01: Sorry, sorry to interrupt, but are those actually are those journalists?
• 19:23
  SPEAKER_01: That's more the that's opinion, that's not journalism.
• 19:27
  SPEAKER_01: Are you I'm not sure what you're referring to.
• 19:28
  SPEAKER_03: Are you talking referring to websites, YouTube videos, people I'm talking about people who who call themselves citizen journalists who have no idea of the difference between opinion and and and newswriting?
• 19:41
  SPEAKER_01: They're not actually journalists, they call themselves citizens.
• 19:43
  SPEAKER_01: I'm I don't know if I would define them as journalists, even if they define themselves.
• 19:47
  SPEAKER_03: If I attend a protest, for example, I'll see some some of these people actually create badges for themselves that say citizen journalists.
• 19:56
  SPEAKER_03: And some of them, to their credit, have press passes, official press passes from what you know, whatever city or or or region we're in.
• 20:04
  SPEAKER_03: And and they're they're the real deal.
• 20:13
  SPEAKER_01: That's where we stand.
• 20:15
  SPEAKER_01: The paper that I work for, you know, that doesn't uh you know doesn't exist.
• 20:19
  SPEAKER_01: There were two papers in Concord, Conklin, Massachusetts, two competing weekly papers, and every city had, you know, there was a village voice in New York, there was there was the Boston Phoenix and the Real Paper.
• 20:30
  SPEAKER_01: They had these wonderful weekly uh alternative papers.
• 20:34
  SPEAKER_01: The alternative that was the it was sort of the the more uh the sort of hippie press, but they broke place to write.
• 20:42
  SPEAKER_01: Sorry?
• 20:42
  SPEAKER_01: It was the cool place to write.
• 20:44
  SPEAKER_01: It was oh yeah, absolutely.
• 20:46
  SPEAKER_01: I mean, they who they and they're cool papers to read, but they also broke stories.
• 20:51
  SPEAKER_01: You know, they covered a music scene, they covered the serious stuff.
• 20:54
  SPEAKER_01: They broke stories, and we've lost that.
• 20:56
  SPEAKER_01: So there so I should there must be some citizen journalists who are sort of replacing, right?
• 21:04
  SPEAKER_01: They're they are they are working in um in the place of those wonderful weeklies that just that don't exist anymore.
• 21:13
  SPEAKER_00: And I've got to give credit to some of the people who you know don't even call themselves journalists, but these sort of uh, you know, as uh cell phone videos of things that are happening have been really an important addition to transparency.
• 21:27
  SPEAKER_00: But you know, you've so you've got that, then you've got the kind of uh do my own researches and journalists, which some of some of whom are really good, some of whom are not.
• 21:35
  SPEAKER_00: Um and then you but you you don't have the the the filters the way that you used to.
• 21:42
  SPEAKER_00: You don't have some of the the screening that you used to.
• 21:45
  SPEAKER_00: And I think that that uh journalists are also asked to do a lot more to uh you know to to go on camera, the the the audio, the video, the the different types of being present, being out there, which you Many of which have their advantages, uh, but people are being asked to do very different things, and the financial pressures, as you said, are very, very difficult.
• 22:08
  SPEAKER_00: A lot of advertising has has gone to social media, things like that.
• 22:13
  SPEAKER_00: And so uh I think a lot of traditional media has been struggling, and the question of how do you respond to that is is really tough.
• 22:22
  SPEAKER_00: On the other hand, we have things like like Substack and and other places where experienced independent voices uh can actually get paid without dealing with what sometimes is is quite fraught organizational politics.
• 22:38
  SPEAKER_00: But it's it's a totally different landscape.
• 22:41
  SPEAKER_00: When, you know, when I was uh when I was in graduate school and doing some freelancing, I could, you know, I you could you could make a graduate school level living doing that.
• 22:52
  SPEAKER_00: And I can't even imagine what I would tell a starting out journalist these days.
• 22:57
  SPEAKER_00: And I think probably a lot of the the ones starting out, they're gonna have to be, you know, the people whose families can support certain things.
• 23:05
  SPEAKER_03: Yeah.
• 23:06
  unknown: Yeah.
• 23:07
  SPEAKER_03: You know, they uh I live in a young community, and when when the 20 or 30 somethings find out I I've been a writer, I am a writer, they ask, how do I break in?
• 23:17
  SPEAKER_03: And I I say I don't know.
• 23:20
  SPEAKER_03: I don't really know.
• 23:21
  SPEAKER_03: That's what I've been saying up till now.
• 23:23
  SPEAKER_03: But I I recently saw a really terrific op-ed piece in the Washington Post that I'm still reading, newspaper I'm still reading, full disclosure.
• 23:33
  SPEAKER_03: Uh, by Will Leach, L-E-I-T-C-H.
• 23:36
  SPEAKER_03: Great writer.
• 23:37
  SPEAKER_03: He writes for New York magazine.
• 23:38
  SPEAKER_03: And I I just I I need to refresh my memory of who he was.
• 23:42
  SPEAKER_03: He was an early founder of Buzz Key News.
• 23:45
  SPEAKER_03: He has a blog, he has a Substack, he has a podcast, and I come to the conclusion that if you want to strike out as a writer or a journalist, it's DIY.
• 23:54
  SPEAKER_01: Yeah, I think Substacks.
• 23:56
  SPEAKER_03: You're not gonna get hired unless you can get hired for a wire for a wire service or a daily.
• 24:01
  SPEAKER_03: You gotta you gotta strike out on your own.
• 24:03
  SPEAKER_03: And I remember naively going to the Wall Street Journal years ago.
• 24:07
  SPEAKER_03: Um, there was an opening, and I knew someone, and she looked at my clips and she loved them, and she said, you know, we're getting people from wire services, and they're turning in, they've been writing 10 stories a week.
• 24:19
  SPEAKER_03: And I was like, Oh, right, they're reporters, and that's you probably did that, Michelle.
• 24:25
  SPEAKER_00: Uh oh yeah, Dow Jones, emerging markets report.
• 24:28
  SPEAKER_00: We were doing we had three market reports a day, plus all the other stories on top of that, and you know, stalking presidents and finance ministers when they were in town for for for uh conferences and you know, getting the president of Mexico mad at me for asking about uh side accords to uh to NAFTA and you know, you there was it was a lot.
• 24:50
  SPEAKER_00: It was a lot, a lot, a lot.
• 24:52
  SPEAKER_03: A lot.
• 24:52
  SPEAKER_03: And then I and JB, I imagine when you were in Concord, you know, doing your gumshoe work, you probably covered like um town hall meetings and sewer easements.
• 25:04
  SPEAKER_01: I covered everything town hall, as I said, the municipal light board meeting, um, everything, uh the town meeting.
• 25:11
  SPEAKER_01: It was really fun.
• 25:13
  SPEAKER_01: Um, and it was um, yeah, it was a lot of fun.
• 25:15
  SPEAKER_01: But now there's a it's a totally different delivery system.
• 25:18
  SPEAKER_01: You sort of have your own delivery system, but it's put the onuses on you to build up uh a readership.
• 25:25
  SPEAKER_01: I mean, I I have a Substack, but I've none of a very large circle of readers yet.
• 25:30
  SPEAKER_01: I'm trying to, I'm trying to build that.
• 25:32
  SPEAKER_01: And you know, we have a YouTube channel.
• 25:34
  SPEAKER_01: Uh, you know, it's it's it's very it's very DIY, but but if you can hit it right, um, you can reach a far greater uh potential readership and viewership than you could have done um unless you were attached to a major newspaper, right?
• 25:52
  SPEAKER_01: Isn't that fair?
• 25:53
  SPEAKER_01: Or you know, or Maggie.
• 25:54
  SPEAKER_01: I used to know I did some stuff for the New York Times years ago, and that was everybody read it, you know, and uh but now you're on your own.
• 26:02
  SPEAKER_01: Um but yeah, the rewards are potentially uh good, but but they but they reward clickbait.
• 26:11
  SPEAKER_01: And we're getting back to the uh you know AI discussion, you know, it reward that's the problem.
• 26:18
  SPEAKER_01: There's a there are there are financial rewards to being lazy.
• 26:23
  SPEAKER_01: Um and there didn't used to be, because there used to be copy editors that would that would that would call you on it.
• 26:29
  SPEAKER_01: No one's calling you on it now.
• 26:31
  SPEAKER_01: Um and what's that great quote?
• 26:34
  SPEAKER_01: It was at Mark Twain that a um um a like will go around the world before truth has had time to uh attire its shoelaces.
• 26:44
  SPEAKER_01: So I just something like that.
• 26:46
  SPEAKER_00: It's so true.
• 26:47
  SPEAKER_00: It's so true.
• 26:47
  SPEAKER_00: And I remember, you know, even the really good copy editors, I mean, they they they did some amazing saves.
• 26:53
  SPEAKER_00: You know, sometimes they were really obnoxious.
• 26:57
  SPEAKER_00: But I've, you know, in in contrast to the the lack of copy editors and and uh and and you know, I had a a big problem with the New York Times itself, you know, when they were were writing about my uh my work.
• 27:08
  SPEAKER_00: It was the summer that uh they they fired the copy editors and made them apply back for their jobs, which I only found out later.
• 27:16
  SPEAKER_00: But you know, they were doing a story about the the gray rhino and they uh they tried to change the definition, they tried to uh attribute it to Chinese state media, and you know, I emailed them, my friends emailed them, I had people on staff in different parts trying to email, didn't hear anything, anything, anything back.
• 27:36
  SPEAKER_00: Um, and it was it was crazy.
• 27:38
  SPEAKER_00: And I'm like, don't you think it's news that an American who wrote this book is the source of this?
• 27:45
  SPEAKER_00: And but of course it didn't go with their narrative of, oh, you know, China doesn't want any foreign intellectuals, any foreign ideas there.
• 27:53
  SPEAKER_00: And you know, it took me to like practically uh setting myself on fire to get attention.
• 27:59
  SPEAKER_00: And and a couple years later, they started in with it again.
• 28:02
  SPEAKER_00: And by that time, I I knew someone else who was able to get through to the standards desk, to the business desk, and they, you know, they've they finally uh made a change.
• 28:13
  SPEAKER_00: And of course, they have never mentioned Gray Rhinos again after that, but you know, I'm I'm kind of glad.
• 28:18
  SPEAKER_00: But if you know there are human errors, and if an error gets in, and if the humans are convinced enough that they're right, it doesn't get changed.
• 28:28
  SPEAKER_00: And of course, AI is very, very confident of its answers, and uh you have no idea if it's right or not.
• 28:36
  SPEAKER_00: So I think it's um it's a really tough uh situation, but I do think that there's a huge opportunity for for using AI to do some of that fact-checking, but only if you can't have a way to get humans to feed it, to make sure that things are accurate.
• 28:55
  SPEAKER_00: And you know, there's a lot of talk in uh you know the AI world about sovereign AI, that you know you're you're AI within your organization.
• 29:03
  SPEAKER_00: Uh, so you're not uh you're the the questions that you ask are not being fed into something out into the ether, that you're not accidentally sharing uh business secrets with you know secret sauce with some third party.
• 29:16
  SPEAKER_00: And uh, you know, for me, I look at these these large language model models and you know they scoop in everything from the internet.
• 29:22
  SPEAKER_00: And of course, you everything on the internet is true, right?
• 29:26
  SPEAKER_00: Because it's on the internet.
• 29:27
  SPEAKER_00: Uh I had this argument in the late 90s with uh of uh of uh what do you call it, somebody who was convinced that you know Y2K was was a real thing and the world was gonna end and and you know foreign mercenaries were gonna come and attack us through you know looking at the tape on the back of the the road signs and things.
• 29:46
  SPEAKER_00: But I swear it's absolutely absolutely true.
• 29:51
  SPEAKER_00: But but you know, uh you know, a basically a a model, like what do they call it, a small language model, a custom language model, someplace where you know that everything that has gone in there has been vetted and uh is legit as far as you know serious vetting can tell you.
• 30:10
  SPEAKER_00: And of course, you know, the New York Times story is that that so-called, you know, newspapers of record even make big mistakes in those areas.
• 30:19
  SPEAKER_03: Yeah, I I saw that, you know, I as a my work as a content strategist, I worked with a large insurance company and they were experimenting with AI tools.
• 30:29
  SPEAKER_03: They had a proprietary one, and it was a it was a closed source model.
• 30:34
  SPEAKER_03: So everything that every query we made was had been vetted and was ready to go and reduced their risk entirely.
• 30:44
  SPEAKER_03: But we're now we're talking about using these large language models to talk to the world, and just going back to this fortune article that misquoted you, I I I I read through the whole piece, it was long, it had some clickbait on there, which I it's kind of insulting in a way, using the name Nancy Guthrie.
• 31:07
  SPEAKER_03: Um I can only imagine it really rose top to the top of search.
• 31:12
  SPEAKER_03: Otherwise, the article read like something I would have written for, you know, when I was writing for corporations and banks, where they asked me to go to a conference, quote people, pull out three or four data points, or um summarize some important bullets for the reader to walk away from.
• 31:36
  SPEAKER_03: And I have to tell you, this was one of the most unremarkable, dull, dreary pieces I have ever read.
• 31:43
  SPEAKER_03: Here here are some of the here are some of the takeaways.
• 31:47
  SPEAKER_03: Uh for companies without Fortune 100 budgets, both executives converged on three affordable, underutilized baselines.
• 31:55
  SPEAKER_03: Secure transportation.
• 31:56
  SPEAKER_03: That means don't put your executives in Ubers where they could be kidnapped.
• 32:02
  SPEAKER_03: Um, don't let your board members use Gmail.
• 32:07
  SPEAKER_03: Um, and keep your secure your security on all the time, your security protocols.
• 32:12
  SPEAKER_03: It was sort of like like duh.
• 32:15
  SPEAKER_03: Um so I you know, we're talking about quality of the reporting, um, which is dreary.
• 32:23
  SPEAKER_03: And but as media companies use AI to scale scale their content.
• 32:30
  SPEAKER_03: So quant we're talking about quantity.
• 32:32
  SPEAKER_03: What's gonna come out of this?
• 32:34
  SPEAKER_03: What's gonna happen?
• 32:35
  SPEAKER_03: I mean, this was just I I don't know who would have read this and said, Oh, very good.
• 32:40
  SPEAKER_03: Check off bullet number one.
• 32:42
  SPEAKER_03: I'll I won't put our leading executives into Ubers anymore because they could get kidnapped or uh board members, don't use Gmail Mail, send that out right now.
• 32:53
  SPEAKER_03: Um, thoughts on that?
• 32:56
  SPEAKER_00: Yeah, well, I think it's that the the the quality versus quantity, and people do pay for quality, and there are still some publications that that do do that.
• 33:06
  SPEAKER_00: I mean, you know, I've subscribed to The Economist since the beginning of time.
• 33:11
  SPEAKER_00: I remember being in graduate school and I was on the subway at a very, very late hour and was reading The Economist at like, you know, midnight coming home, probably from one of these uh pre-media bistro.
• 33:22
  SPEAKER_00: Actually, no, I that was after graduate school, but it it might have been coming back from one of these parties.
• 33:26
  SPEAKER_00: Right, I was reading the The Economist on the subway, which I often did throughout my life, and there's some people across commented and they were really impressed by this.
• 33:34
  SPEAKER_00: But but you know, I still, you know, I still trust the Economist.
• 33:39
  SPEAKER_00: Uh you know, I think Financial Times I still trust.
• 33:43
  SPEAKER_00: But I think you've you've really got to be uh constructively critical about the publications that you are reading and being much more selective.
• 33:55
  SPEAKER_00: And uh, and you know, I I teach, I teach RISC uh at uh University of Virginia, and you know, we've been talking about that with with some of our students.
• 34:03
  SPEAKER_00: And and do students have the, are they developing the critical skills to uh look at something and decide whether the AI has got it right or not?
• 34:15
  SPEAKER_00: One of the things I've been doing is is showing them, like I took uh I took notes during one of our online classes and I showed the the note to them.
• 34:24
  SPEAKER_00: I said, see how this is really general, how it's they talked about blah, blah, blah.
• 34:28
  SPEAKER_00: They talked about this.
• 34:30
  SPEAKER_00: They and but I'm like, okay, well, what did they say about it?
• 34:33
  SPEAKER_00: It's not good at at judgment, at picking out really the key insights and takeaways.
• 34:40
  SPEAKER_00: There was uh uh there was a really good essay recently, or maybe about six months ago, I forget where, but um, if you if you if you look for a delvish, D-E-L-V-I-S-H, it's like the AI language, we're gonna delve into, we're going to explore, we're going to, and I, you know, and I've seen it a lot with uh with policy students since long before AI.
• 35:03
  SPEAKER_00: So I suspect that uh some of the AI has been trained on some really um bad policy writing.
• 35:09
  SPEAKER_00: But uh, you know, that it tends to summarize and it's not good at putting things into context.
• 35:15
  SPEAKER_00: And perhaps it'll get better over time, and you see all this criticism of uh how much it uses M-dashes.
• 35:21
  SPEAKER_00: And I'm sure that the models have started to take that into uh consideration.
• 35:26
  SPEAKER_00: You're all of a sudden not gonna see the M-dashes anymore.
• 35:29
  SPEAKER_03: Um like a lazy college student using M-dashes to avoid uh avoid uh fine punctuation points.
• 35:36
  SPEAKER_03: Yeah.
• 35:36
  SPEAKER_00: Yeah, and now I'm worried about using if I use them, I'll think are they gonna think I'm AI?
• 35:41
  SPEAKER_00: And and I tapped to some people there, they were talking about their niece submitted a paper and uh they they they knew that the niece had not used AI, and it came back in an AI detector as oh yeah, definitely used AI because she was probably using language that you know it had like what it had trained on, or uh the authors guild a few months ago put out a uh a test.
• 36:06
  SPEAKER_00: They pulled some of their own content from decades ago and sent it through several AI checkers.
• 36:13
  SPEAKER_00: And only a couple of them said, no, this is not AI written, and the probabilities in the other ones were all over the place.
• 36:21
  SPEAKER_01: Troy, I'm I'm totally confused by this.
• 36:23
  SPEAKER_01: You're saying that non-AI produced content is being flagged as AI produced.
• 36:29
  SPEAKER_01: Yes.
• 36:30
  SPEAKER_01: Now that's just false accusation.
• 36:32
  SPEAKER_00: Yeah, yeah.
• 36:34
  SPEAKER_01: That's so confusing to me, and that's could probably confusing a lot of people.
• 36:38
  SPEAKER_01: So well, we can't trust any, we can't trust anything at this point.
• 36:42
  SPEAKER_01: Um I read Deck, and I just read some Charles Dickens, it's all AI produced.
• 36:47
  SPEAKER_01: It isn't I don't think that reads authentically at all.
• 36:51
  SPEAKER_01: I mean, it could be you could just put any novel through um an AI model, it's gonna come up, it's gonna flag something, wasn't it?
• 36:58
  SPEAKER_01: Because AI is based on these these old this old content.
• 37:02
  SPEAKER_00: Well it's you know it's getting better.
• 37:03
  SPEAKER_00: And then I read somewhere that uh, you know, if you if you ask AI to, you know, for its opinion on different things, it's gonna prefer things that were written by its own uh output.
• 37:18
  SPEAKER_00: So it you know, it discriminates against the other LLMs.
• 37:21
  SPEAKER_00: I mean, you just you just don't know what's what's what.
• 37:24
  SPEAKER_00: And uh a big problem is that one of the things that you do when you're a Cub reporter, when you're a junior researcher at a bank or at a wherever is you are developing those critical skills.
• 37:38
  SPEAKER_00: And so by by uh by by delegating a lot of that to AI, I mean, first of all, you're you know who knows about the quality, just as yeah, you don't know about the quality of something an intern does, you know to check it extra carefully.
• 37:53
  SPEAKER_00: Um, but you're not developing those skills.
• 37:55
  SPEAKER_00: So what's gonna happen with the next generations coming up when nobody has had the opportunity to develop critical thinking and writing skills on their own.
• 38:05
  SPEAKER_00: And that's really scary.
• 38:07
  SPEAKER_03: You come up with words like um delvish.
• 38:11
  SPEAKER_01: I don't even know if it's the next generation.
• 38:13
  SPEAKER_01: I mean, there must be millions of people now who are reading a lot of AI content and just accepting it as real.
• 38:20
  SPEAKER_01: You know, we're talking when we talk about this heyday of copy editors and newspaper reporters and and editing, you know, this is a while ago now, and and there's a whole other generation has already grown up since then.
• 38:33
  SPEAKER_01: So I I I've and and we know this for a fact because so many people believe stuff that's nonsense, like the 2020 election, you know, was stolen.
• 38:42
  SPEAKER_01: I mean, there's a lot of nonsense out there, and people are believing it.
• 38:46
  SPEAKER_01: Sorry, I sound like the pessimist of the three of us.
• 38:50
  SPEAKER_01: And uh I'd like to hear some more optimistic um uh response to my my dour.
• 38:59
  SPEAKER_00: Well, I mean, there there are people who are who are conscious of of this and and thinking of about it and uh trying to get to a good place.
• 39:09
  SPEAKER_00: I think you're you're seeing a lot of pushback against AI in all sorts of uh different ways, whether it's the data centers or the slump.
• 39:16
  SPEAKER_00: I mean, that was a word of the year last year for good reason.
• 39:19
  SPEAKER_00: I mean, people hate it.
• 39:21
  SPEAKER_00: Um, so I think that that backlash at some point is is going to uh have some effect.
• 39:27
  SPEAKER_00: And then some of my really smart friends who think a lot about this and are are deep experts in data analysis and in ways that that I wouldn't even aspire to be because it's what they do is so sophisticated.
• 39:40
  SPEAKER_00: You know, they're worried about what they call model collapse, which is that you know, as more and more content becomes AI generated on the internet, uh the you know, the models feeding on themselves, it's like a um the you know, like when you when you have a TV, you know, those images of the TV with the little TV inside and the little TV going all the way back.
• 40:00
  SPEAKER_00: I mean, it's like that.
• 40:01
  SPEAKER_00: That at some point when there's so much nonsense out there that it's just not gonna mean anything.
• 40:06
  SPEAKER_00: And that's why I do think that these small language models, custom language models, sovereign AI, whatever you call it, is is important.
• 40:14
  SPEAKER_00: And I think that you're gonna see more people uh paying for quality, paying for things where you understand the underlying logic, what's in the black box.
• 40:25
  SPEAKER_00: Uh, and those those are important skills.
• 40:28
  SPEAKER_00: And we just need to make sure that everybody else is uh is more AI literate.
• 40:33
  SPEAKER_00: I mean, we we have lots of problems with media literacy uh these days and and people not knowing what to believe.
• 40:39
  SPEAKER_00: And I think that that's that's a really big thing.
• 40:42
  SPEAKER_00: I think we need to go back to civic education, and that needs to be a big part of civication civic education.
• 40:48
  SPEAKER_00: Yes, go do your own research, but but first of all, research, how to do research, how to judge what is credible and what is not.
• 40:57
  SPEAKER_01: Yeah, well that does give you some hope.
• 41:01
  SPEAKER_03: Go ahead, JB.
• 41:02
  SPEAKER_01: No, that does give you some hope because because otherwise, if people be just begin questioning AI, they can question everything they read, including the well-sourced um, you know, content to use a horrible word, that's out there.
• 41:17
  SPEAKER_01: If they're gonna they're gonna question AI, they're gonna question any you know everything.
• 41:21
  SPEAKER_01: But yes, if they are, if they if it leads them to subscribe, and that means financially supporting um journalists outlets or posts that are reliable and have the financial structure, infrastructure to uh hire what copy editors or proofreaders or you know, just to uh I mean I I subscribe as as Jim does.
• 41:42
  SPEAKER_01: We scroll Washington Post, New York Times.
• 41:44
  SPEAKER_01: So we you know, I'm um we're supporting these these institutions, but I don't know if enough people are.
• 41:51
  SPEAKER_01: And um and and there are a lot more people, you know, watching nonsense YouTube videos full of absolute rubbish than are reading the New York Times.
• 42:01
  SPEAKER_03: Well, you know, there's a lot of um there's a lot of AI mimicry out there.
• 42:05
  SPEAKER_03: If you you can see videos on YouTube, you can hear um copycat AI on any streaming service, streaming music service.
• 42:15
  SPEAKER_03: So you know, I have two thoughts in my head.
• 42:18
  SPEAKER_03: One is what you know, we're talking about quality of of music, of fine art, of literature.
• 42:26
  SPEAKER_03: You know, I I think it's safe to say that AI, AI is producing based on the past.
• 42:32
  SPEAKER_03: Everything that's in existence is is what it it draws from.
• 42:37
  SPEAKER_03: That's the well that it it uses to fuel output.
• 42:42
  SPEAKER_03: You know, in at a certain time you could never get Jimi Hendrix produced by AI.
• 42:47
  SPEAKER_03: You can now.
• 42:48
  SPEAKER_03: And I I think maybe you know a test the test for someone with a good ear is you know, is this prose?
• 42:54
  SPEAKER_03: Is this poetry?
• 42:56
  SPEAKER_03: Does it sound like it came from Hallmark?
• 42:58
  SPEAKER_03: Does the music sound like it came out of an elevator?
• 43:01
  SPEAKER_03: It's probably AI.
• 43:04
  SPEAKER_03: But you know what we're talking about now, I think all of us is we're talking about the retail output of AI, what we see on an everyday query.
• 43:14
  SPEAKER_03: Um, you know, how many bananas do I need to put in my smoothie?
• 43:18
  SPEAKER_03: Or, you know, I don't know.
• 43:20
  SPEAKER_03: Um tell me about you know what Madonna's doing in these days.
• 43:25
  SPEAKER_03: Um, but I I happen to be at my cafe with a friend of mine who's a coder, and he opened up his screen.
• 43:32
  SPEAKER_03: I said, Why are there two columns?
• 43:34
  SPEAKER_03: And it was all code.
• 43:36
  SPEAKER_03: And he said, Well, I have an AI agent, I pay 200 bucks a month for a subscription.
• 43:41
  SPEAKER_03: It's writing code.
• 43:42
  SPEAKER_03: I go, right now, and I did see one of the columns moving, certain text boxes lighting up.
• 43:49
  SPEAKER_03: It was it was writing code for him.
• 43:52
  SPEAKER_03: So Michelle, from a from a risk perspective, as
• 44:00
  SPEAKER_00: we as we gain more and more agentic ai behind the scenes doing stuff that we can't see what is what do we you know what's the potential risk here is uh are i think we're are we does it can we classify ai the ai revolution as a gray rhino probably not because we're talking about it well no the gray rhino isn't what you're talking about when people are talking about it that's that's what the gray rhino is it's it's the obvious it's the probable it's the it's the visible and one of the things that i urge people to do is is to break it down into the individual parts of it because ai itself is a is a crash of gray rhinos there's um so many things so you you're starting to see these uh agentic uh AIs coming out and talking among themselves about uh how to go places where they're not supposed to go and get into trouble they're not supposed to get into uh and that's the kind of thing that we've been hearing a lot of of warnings about so that's I think a very very big one these agents going and doing their their own thing uh with potentially very very bad consequences I mean uh I I don't know uh I mean I know there's a lot of talk in uh with within militaries about the use of AI in decision making and things like that but what you know has got access to some of that what way when is it gonna get into the nuclear codes and really do stuff it's not supposed to so that's one thing there's the other part that I was talking about about you know not getting skills is about uh you know cognitive offloading for example getting dumber we're getting dumb we're getting dumber we we are willing well some people are willingly turning that over I think about you know there the only phone numbers that I remember are phone numbers that I knew before cell phones and is that a sensible thing to offload well actually I think you know I don't need my brain space for remembering phone numbers there are some things that it's great to have something else to do for you or you know directions you know how many places do you remember how to go without GPS and of course it is useful uh if you've got uh ways or something showing saying hey the traffic's really bad here go the other way but uh you know what are the skills that we can't afford to offload and how are we going to make sure that humans are able to uh to be critical of the output and to to guide it and then to ring fence the more dangerous potential activities and uh you hear all this uh you know oh less regulation means more innovation but you know there's some areas where uh you need some ring fencing you need people who understand what is going on and have a policy framework to make sure that this stuff doesn't get way out of control and you're starting to see those conversations uh coming out of you know some of the the the AI companies themselves going hey you know this is the problem okay we can't we can't release this one because it's it's we don't know how to control it that's a whole other area of of scariness so there's you know they're they're really scary things and you see you see for example with uh designers I know a lot of designers who've been having trouble because people are just you know using AI and the things that I'll use ai for if I'm you know writing a sub stack and I only have 10 minutes to come up with an artwork then obviously that's not work that was going to go to a human in the first place.
• 47:33
  SPEAKER_00: But for for other projects you still really do want a human with judgment or you know in the you know editing and copying everything I you know I love things that will flag typos for me but I find that many of the the AIs flagging writing will flag things that are wrong.
• 47:52
  SPEAKER_00: I mean I've throw out you know three quarters of the suggestions that it makes um so it's you know how do we ensure that people still have those skills well boomer has it audience you just got a free risk assessment you didn't have to pay one cent to a leading risk analyst to get to get the the full scoop on on AI and its risks yeah and technically I am Gen X I'm just on the other other side but uh you know but uh but lots of boomers in my life and I I really uh uh respect them that's fair thank you very much yes we're just we're not that many years apart it's nice to hear from the younger from the younger I'm on the cusp it's like you know my birthday's on the you know on the cusp and uh so if I don't like one uh one horoscope then I can read the other one and I can like okay you know maybe there's some things I I want to include myself in this other generation which you know including you guys I love talking with you guys so that's definitely a benefit where I can say okay I'll hop to the other it depends on what year you put as the as the dividing line.
• 49:02
  SPEAKER_01: Yeah we'll give you it your generation X but it's you should get some boomer fruits old Gen X.
• 49:09
  SPEAKER_01: Okay.
• 49:10
  SPEAKER_01: All right new good fair enough well it's lovely to have you on there's so much it's such an incredible subject and um there's just a lot to think about here.
• 49:20
  SPEAKER_03: And thank you for thank you for your generosity your time yeah and a great reunion too Jim do you have uh do you have a headquake for us this week I I do have a headquake but it comes with a disclosure um my friend's daughter uh was a co-writer for a hit single uh by an artist named Sienna Spiro uh who's English and uh her global hit is called You Stole the Show and um I'm a big supporter of my friend and his daughter uh she was as I mentioned co-writer um her partner is a co-writer and producer it's it's a beautiful song that starts off with a large big orchestral strains and then leads into simple piano accompaniment a little it reminded me a little of Carol King and then this young woman's voice that gets big and large and emotional and it's a it's a pretty great moment she's on fire right now she's on you'll see her on the cover of L magazine and Rolling Stone and um she's top in the charts so what's her name Sienna Spiro Sienna Spiro who is exploding and what's the name of the song it's the from the visitor album it's called You Stole the Show which is what I always say about you.
• 50:50
  SPEAKER_01: Well thank you Jim I'll keep out an ear for that and an eye for that uh well my headquake is I've gonna start with a question has anyone ever called you a Yank?
• 51:00
  SPEAKER_03: Um no although I will be in the UK at some point.
• 51:05
  SPEAKER_01: Yeah maybe when you come to the UK to visit me you'll be called a yank.
• 51:09
  SPEAKER_01: If you were called a yank would you sue is it a bad word?
• 51:14
  SPEAKER_01: Well this is this has come up because although it wouldn't occur me to sue anyone who called me yank it did occur to someone called Raymond Joseph an American working at a Pizza Express in Aberdeen Scotland who managed to alienate all of his coworkers one of whom called him the Y word more specifically his co-worker said why don't you just leave nobody effing likes you he didn't say effing he used the F word nobody effing likes you you effing American go back to your country you effing yank Joseph sued him for racial harassment and a Scottish judge recently ruled in his favor awarding him 5,000 pounds damages so I don't remember being ever called a yank no I think I was when I was in boarding school briefly in a British boarding school um but it would never occur to me to sue anyone the word yank uh is a diminutive of Yankee which is used a lot in the lexicon I think it might even be the name of a sports team of some kind the New York Yankees yeah I think that's uh yeah they're a football team I think yeah ice hockey and that they inspired a musical called Damn Yankees that could be Sue Doubly two bad words two bad words John Schlesinger made a film called Yanks Yankee Doodle Dandy is a word used a lot anyway apparently it's a racial epithet I have no idea the more obvious I mean sort of the more obvious question is what do you put on a Scottish pizza?
• 53:03
  SPEAKER_01: Does it does it come with haggis haggis come a haggis pizza please yes well it's a pizza express that's part of a chain I'll take you to one when you visit when you visit me in in September.
• 53:16
  unknown: Okay.
• 53:19
  SPEAKER_01: All right well that's it for this week thank you very much for listening and uh watching us it's been Boomer has it I'm JV Miller in London and I'm Jim Jelamis in Brooklyn.
• 53:33
  SPEAKER_01: Thanks bye bye