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Speaker 1: One of the first examples that I saw was a

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Speaker 1: really long memo with this style that we talk about

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Speaker 1: as purple prose.

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Speaker 2: It's like when people.

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Speaker 1: Just use really flowery language that's elaborate and long.

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Speaker 3: Kate Niedhoffer is a social psychologist and the vice president

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Speaker 3: of Betterop Labs, which recently conducted a study about a

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Speaker 3: new phenomenon in the workplace AI WORKSLOP.

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Speaker 1: We ask people to share examples of workslops that they've received.

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Speaker 1: They'll tell us like, there's this weirdness, like there's something

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Speaker 1: off about this, and feeling so confused and unsure what

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Speaker 1: the content is really about. But then it's like I

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Speaker 1: don't know what to do about this. I don't know

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Speaker 1: if I should just redo it myself, start from scratch,

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Speaker 1: tell the person, ask the person. Sometimes there's a power

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Speaker 1: dynamic that makes it really complicated to even engage, and

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Speaker 1: so it leaves people sort of paralyzed. It's like I

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Speaker 1: don't know what this is, what it means, or what

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Speaker 1: to do about it, and I still have to do

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Speaker 1: the work.

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Speaker 3: What Kate's study found is that contrary to what AI

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Speaker 3: companies have been promising, AI is creating more work for

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Speaker 3: people we're having to spend time reading emails written by

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Speaker 3: chat GPT, watching automatically generated PowerPoint presentations, and dealing with

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Speaker 3: computer code that looks okay but doesn't actually run properly. Meanwhile,

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Speaker 3: Mark Zuckerberg is telling his workers to use AI to

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Speaker 3: work five times faster, and a bunch of companies are

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Speaker 3: still laying off employees under the assumption that they can

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Speaker 3: save money by just using AI to do that work.

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Speaker 1: What we're doing is slashing the human and eliminating that

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Speaker 1: whole productivity potential. I think it's backfiring because you're having

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Speaker 1: a well being tax on the people who stay, who

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Speaker 1: have lost their peers, who are taking on all of

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Speaker 1: this work. It's just a compounding negative effect.

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Speaker 3: This study went kind of viral and made some headlines,

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Speaker 3: and if you just read the headlines, it can seem

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Speaker 3: like the results prove that AI shouldn't be used in

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Speaker 3: the workplace at all, But that wasn't really the point

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Speaker 3: of the story. Betterup's main thing is that they give

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Speaker 3: advice to companies. Usually that advice isn't just given out

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Speaker 3: for free in public. Companies pay money for this insight.

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Speaker 3: So my interview with Kate was an opportunity to see

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Speaker 3: what kind of conversations are actually happening behind the scenes

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Speaker 3: at workplaces that are experimenting with AI, the kind of

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Speaker 3: conversations that might also be happening where you work. Kaleidoscope

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Speaker 3: and iHeart podcasts.

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Speaker 4: This is kill switch. I'm dexter Thomas, I'm sorry, goodbye.

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Speaker 5: How would you define workslap workslop is AI generated text

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Speaker 5: that looks like it completes a task, but.

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Speaker 1: Upon further inspection reveals that it lacks context and all

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Speaker 1: the cognitive effort that is really necessary to complete a task.

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Speaker 3: So, in other words, it's when you get sent an

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Speaker 3: email or a presentation that's very clearly written by chat GBT,

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Speaker 3: when there's a bunch of M dashes and bullet points,

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Speaker 3: flowery language phrases like delve into the stuff that at

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Speaker 3: first glanced kind of looks like a well thought out

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Speaker 3: piece of work, but if you keep reading, it's obviously robotic.

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Speaker 1: We see a lot of examples of emails, reports, code,

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Speaker 1: a lot of like vibe coded code, even apps. So

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Speaker 1: people talked about the creation of prototypes, for example, that

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Speaker 1: are based on prompts, and so they're sort of brittle

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Speaker 1: experiences that don't really have substance underneath them.

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Speaker 3: Where did this phrase work slob come.

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Speaker 1: From My colleague Jeff Hancock at the Stanford Social Media Lab.

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Speaker 1: And we had been studying the ways that managers make

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Speaker 1: decisions about delegating tasks to humans versus AI, and we

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Speaker 1: knew about AI slop, you know, that already existed, and

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Speaker 1: so we were thinking about, like, but what is it

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Speaker 1: in the workplace and why is it so different? So

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Speaker 1: Jeff came up with that first definition of AI generated

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Speaker 1: content that fulfills the appearance of completing the task, but

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Speaker 1: it doesn't actually have this like substantive cognitive work or

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Speaker 1: the decision making that a task really requires. And so

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Speaker 1: we went out with this definition we started to study it.

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Speaker 3: Kate and her team worked with Stanford Social Media Lab

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Speaker 3: to study this. They surveyed over one thousand workers across

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Speaker 3: fields from finance to medicine to government.

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Speaker 1: We start by kind of like funneling them into it

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Speaker 1: by saying, like, we have some questions about your experience

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Speaker 1: with AI at work. We explain what AI is so

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Speaker 1: we're all on the same page, and then we ask

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Speaker 1: them how often do you use AI at work? And

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Speaker 1: we ask if people in their company have required them

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Speaker 1: to use AI. As you know, many people are mandated

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Speaker 1: to use AI, so we want to get a sense

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Speaker 1: of what their policy is. And then we start asking

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Speaker 1: questions about how often they send or share work with

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Speaker 1: colleagues that uses AI to help produce it. And then

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Speaker 1: we say, how much of the AI generated work that

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Speaker 1: you send to colleagues do you think is actually unhelpful,

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Speaker 1: low effort, or low quality?

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Speaker 2: And people readily admit to that.

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Speaker 1: And then we say, well, how much of the AI

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Speaker 1: generated work that you send a colleagues do you think

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Speaker 1: is helpful and high quality? So you know, we have

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Speaker 1: to make sure that we're not being biased. And then

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Speaker 1: we say our most important question to give us the

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Speaker 1: prevalence is in your job, have you received work content

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Speaker 1: in the last month that you believe is AI generated

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Speaker 1: that looks like it completes a task at work, but

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Speaker 1: is actually unhelpful, low quality, or seems like the sender

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Speaker 1: didn't put in enough effort.

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Speaker 3: So you asked that question, which is essentially your definition

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Speaker 3: of WORKSLOP without actually using it for them so as

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Speaker 3: not to bias them. How many people answer yes.

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Speaker 2: To that question forty percent?

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Speaker 3: Forty percent in the last month. That's a really high number.

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Speaker 3: I can only imagine that's going.

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Speaker 1: To go up.

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Speaker 2: Yeah, I agree.

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Speaker 1: It's something that we're really interested in tracking. We also

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Speaker 1: wanted to know how frequently people experience it. So if

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Speaker 1: you think that that's the prevalence that forty percent of

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Speaker 1: people have experienced it, then how much of the work

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Speaker 1: that they receive do they think fits this description? And

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Speaker 1: it's about fifteen percent. So fifteen percent of the work

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Speaker 1: that you receive is AI generated work that looks like

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Speaker 1: it completes the task but really doesn't.

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Speaker 3: Okay, so this sounds a little annoying, but aside from

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Speaker 3: the mild annoying, so the deep annoying somebody might feel

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Speaker 3: from getting an email that pretty clearly is written by Chadgibt,

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Speaker 3: we're seeing a presentation that was written by chadgpt. What

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Speaker 3: else are the effects we should.

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Speaker 4: Think about here?

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Speaker 1: So it's a lot of time wasted, that's the first thing.

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Speaker 1: It has an emotional impact that's pretty strongly negative emotions

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Speaker 1: like being annoyed, frustrated.

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Speaker 2: You've been confused.

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Speaker 1: And then I think the most insidious impact of all

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Speaker 1: is this interpersonal judgment and evaluation that you think that

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Speaker 1: the person who sent you the workslop is less capable,

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Speaker 1: less creative, less trustworthy. It's like you're immediately casting judgment

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Speaker 1: on your ability to work with this person over time

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Speaker 1: and to collaborate effectively. And that's so important. That's the

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Speaker 1: most important thing in the workplace is that we believe

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Speaker 1: that our colleagues are competent, capable people with shared goals

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Speaker 1: that we can align with and do our best work.

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Speaker 3: So the respondence to your study, you're telling you this

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Speaker 3: that when they get workslop from somebody, they don't trust

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Speaker 3: that person anymore.

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Speaker 1: They trust them less.

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Speaker 3: Yeah, you know, so I've gotten pitches asking me to

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Speaker 3: cover things. So it's hey, dexter, I've seen that you

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Speaker 3: covered X. Here is this thing that I do, or

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Speaker 3: here's this thing that my client does. And it was

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Speaker 3: always in the same format. It's like a three paragraph format,

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Speaker 3: and it would be a brief sentence telling me how

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Speaker 3: cool I am, and then a couple of paragraphs telling

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Speaker 3: me what their client's product or project was, and the

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Speaker 3: third paragraph telling me why it's so interesting in giving

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Speaker 3: me suggestions for how I could cover it. And I

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Speaker 3: was seeing the same format over and over and over again.

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Speaker 3: And that made me not only dislike the PR person,

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Speaker 3: but dislike the person who I'm supposed to be covering it.

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Speaker 3: So I start to think, whoever this person is, I

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Speaker 3: don't want to have anything to do with them because

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Speaker 3: they're having some PR person who's just running their stuff

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Speaker 3: through perhaps chat Gibt, perhaps Claude, whatever, and so I

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Speaker 3: start to Yeah, I start to have a pretty negative

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Speaker 3: opinion totally.

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Speaker 1: I think that's the first phase of it. It's like

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Speaker 1: you recognize some linguistic cues that seem repetitive or template,

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Speaker 1: and like, to me, I experience it as so deceptive.

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Speaker 2: There's something first.

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Speaker 1: That's empty about the text and hard to read. But

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Speaker 1: then that second experience, which is so emotional. It's like

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Speaker 1: I'm confused, I'm frustrated, I'm annoying, angry, I feel deceived.

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Speaker 1: And then there's like that third process, that interpersonal process

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Speaker 1: that kicks in and you're like, I don't want to

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Speaker 1: work with this person, I don't trust this person, I

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Speaker 1: don't like this person in your case, and that's when

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Speaker 1: we saw those results in our study. That's when I

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Speaker 1: was like, oh God, we're in for something bad here.

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Speaker 3: The feeling of anger or frustration that you get when

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Speaker 3: someone dumps a bunch of AI generated slop on your desk.

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Speaker 3: This is what Kate would call the emotional tax of

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Speaker 3: work slop. About half the people surveyed said that when

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Speaker 3: they receive workslop from a colleague, their opinion of that

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Speaker 3: colleague changed, and at interpersonal level, this is obviously a

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Speaker 3: bad thing, but this is also the sort of thing

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Speaker 3: that a company would want to avoid because it translates

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Speaker 3: into less money for the company.

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Speaker 1: People are spending just under two hours on each incent

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Speaker 1: of workslop, and so if you think about you know,

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Speaker 1: the forty percent of people who have experienced it, the

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Speaker 1: rate with which they experience and set it can cost

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Speaker 1: up to nine million dollars for an organization. So it's

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Speaker 1: a very very costly phenomenon right now. In addition to

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Speaker 1: the cost of using these tools.

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Speaker 3: The MIT Media Lab recently reported that despite thirty to

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Speaker 3: forty billion dollars being invested in the generative AI, ninety

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Speaker 3: five percent of organizations are getting zero return on that investment.

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Speaker 3: And on top of that, Kate's study suggests that it's

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Speaker 3: actually even worse. A lot of businesses are losing money

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Speaker 3: to work slab so two questions, why is work slab

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Speaker 3: happening and whose fault is it? The second one you

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Speaker 3: might think, you know, the first one that answers a

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Speaker 3: little deeper, that's after the break So is there an

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Speaker 3: age demographic that's more likely to be getting or receiving

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Speaker 3: WORKSLOP in your study?

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Speaker 1: I don't have a demographic breakdown right now. It's something

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Speaker 1: that we're working on. We know from our previous research

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Speaker 1: that older people trust AI more, and we know that

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Speaker 1: trust in AI, especially when a sort of blind trust,

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Speaker 1: can lead to this over reliance and can produce more WORKSLOP.

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Speaker 3: So that right there is really interesting because the tendency

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Speaker 3: with any kind of new technology, with almost any new

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Speaker 3: technology in the past, we've expected for young people to

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Speaker 3: be the early adopters, and that I think societally we

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Speaker 3: start to blame young people for all of the ills

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Speaker 3: that that new technology brings. But what you're saying about

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Speaker 3: trust in AIS is really interesting.

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Speaker 1: So I think there is something really interesting happening with

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Speaker 1: age here. You know, agency understanding how to use the

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Speaker 1: tools in a discerning way, knowing when to use them,

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Speaker 1: how to use them. That's a really good positive predictor

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Speaker 1: of not producing WORKSLOP, and so it's really possible that

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Speaker 1: maybe a more tech native person would have higher agency

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Speaker 1: and be less likely to over rely on the tools.

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Speaker 1: There's this really interesting distinction and the way that people

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Speaker 1: use AI. We call it the pilot mindset. So people

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Speaker 1: who are high agency, high optimism in the way that

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Speaker 1: they use AI, and we see that pilots are not

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Speaker 1: producing as much WORKSLOP as the inverse, which is passengers.

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Speaker 1: Passengers are low agency, low optimism. They're either not using

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Speaker 1: AI at all still or they're using it to create

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Speaker 1: a whole bunch of workslop.

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Speaker 3: So this is something that people are telling you because

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Speaker 3: you also asked not only have you received workslop, but

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Speaker 3: you're asking people fairly directly, are you generating AI work

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Speaker 3: that maybe is unhelpful and just passing it off like

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Speaker 3: it's your own work? And people are saying yes to this. Yeah.

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Speaker 1: What's incredible is that even with the self report or

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Speaker 1: biases that exist, people are still admitting to producing, you know,

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Speaker 1: just under twenty percent of their work it's WORKSLOP.

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Speaker 3: And I am going to assume that there are people

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Speaker 3: who are doing this and they're just not willing to

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Speaker 3: admit it.

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Speaker 2: I completely agree.

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Speaker 1: I will say that what we're starting to see is

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Speaker 1: the biggest predictor of creating WORKSLOP is actually being kind

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Speaker 1: of burned out. So people say that, like the reason

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Speaker 1: why they're generating it themselves as authors of WORKSLOP is

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Speaker 1: because these tasks that they're using AI to do are

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Speaker 1: one of many things that they're responsible for doing, so

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Speaker 1: they have a really full plate. You know, there have

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Speaker 1: been layoffs in the past few years, and so people

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Speaker 1: have increased span of control. They're having to do the

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Speaker 1: work of multiple people, and that makes it really tempting

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Speaker 1: to try to get a tool to do work on

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Speaker 1: your behalf. Also, I don't know if this feels true

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Speaker 1: for you, but a lot of what we hear from

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Speaker 1: our members and organizations is that, like everything feels really urgent,

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Speaker 1: and that's another predictor of producing work slow. It's just

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Speaker 1: the pressure to perform right now is really high, and

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Speaker 1: people are really depleted and haven't recovered from COVID.

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Speaker 2: It's really tempting.

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Speaker 1: When you have a very powerful tool to rely on

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Speaker 1: it to help you out. It's just that people are

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Speaker 1: doing that in a really blind way.

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Speaker 3: I mean, I would go further than saying it's tempting.

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Speaker 3: I would say it's the logical decision.

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Speaker 1: Right.

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Speaker 3: If you were at a job that you're watching people

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Speaker 3: getting laid off, you think you might be next, and

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Speaker 3: you're doing multiple people's job and you're not getting paid anymore,

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Speaker 3: why not use AI to get some of that stuff

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Speaker 3: off of your plate. What we're talking about here is

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Speaker 3: we're talking about fundamentally, I think in empathy thing which

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Speaker 3: is workslop. Maybe just the sign that, Yo, this person's overworked,

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Speaker 3: that's what I should think, But my immediate knee jerk

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Speaker 3: reaction might be this person's lazy, that're a bad person,

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Speaker 3: they don't respect me, right, And how do you even

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Speaker 3: get around that?

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Speaker 1: It's such a compassionate reframe on the situation, and maybe

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Speaker 1: you just came up with a new intervention. We could

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Speaker 1: try to do an experiment on that of just you know,

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Speaker 1: when people, you know they're about to start judging people,

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Speaker 1: that maybe we could just introduce the idea that people

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Speaker 1: are producing workslop because they're overworked, because everything feels urgent

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Speaker 1: and important, and maybe just by telling people that it

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Speaker 1: can initiate a dialogue that's more about hey, can I

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Speaker 1: help you approach these tasks in a different way.

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Speaker 2: Can I take something off of your plate?

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Speaker 3: I love your optimism.

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Speaker 2: My collaborator is Canadian. I can't help it.

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Speaker 3: I mean, I mean real, just real, Because maybe the

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Speaker 3: workslop thing isn't necessarily a sign that everybody's using chatgabt

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Speaker 3: or the chatchabt is bad or the claud is bad.

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Speaker 3: Maybe that's not really it. Maybe it's more of a

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Speaker 3: deeper fundamental societal thing, which is that yo, people are overworked.

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Speaker 2: I think that's exactly right.

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Speaker 1: Like that has been the focus of our research for

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Speaker 1: the last year, is about this crisis that we're in.

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Speaker 1: We've seen this over the past ten years in our

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Speaker 1: data that people are decreasing in the amount of resilience

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Speaker 1: and agility and these foundations of well being they have.

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Speaker 1: We're an all time low. So people really lack motivation, optimism, agency,

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Speaker 1: and we're just hungry for compassion and we just need

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Speaker 1: someone to refuel us so that we can be motivated

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Speaker 1: and feel like we matter and our work matters.

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Speaker 3: Again, basically, the conditions that are causing the problem here,

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Speaker 3: like we are going to continue to create those conditions.

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Speaker 3: The product is the output, which is the product which

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Speaker 3: is the output with this sounds like a spiral to.

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Speaker 2: Me, a vicious cycle. I think there's a way out.

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Speaker 1: We are thinking a lot about what type of interventions

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Speaker 1: are possible to prevent work slog and there are a

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Speaker 1: few things that are possible.

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Speaker 3: We'll get into that after the break. So Sam Altman said,

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Speaker 3: I believe, paraphrasing him, that this year we may see

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Speaker 3: AI basically quote unquote join the workforce. You've studied how

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Speaker 3: actual employees feel about basically AI kind of joining the workforce.

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Speaker 3: Where are we in terms of that right now?

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Speaker 2: Actually, you may be surprised.

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Speaker 1: I think that people are really excited about AI joining

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Speaker 1: the workforce.

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Speaker 2: When we ask people about.

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Speaker 1: This idea of managing a hybrid workforce of humans and agents,

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Speaker 1: for the most part, they express positive emotions like excitement, optimism, confidence.

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Speaker 3: Well, when you say people here, are we talking managers?

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Speaker 3: Are we talking CEOs, bosses? Are we talking workers?

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Speaker 1: Okay, that's a great question. Primarily when we ask managers,

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Speaker 1: they're more excited than individual contributors about AI joining the workforce.

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Speaker 1: And what people are wanting is training, And so we

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Speaker 1: have done a lot of work trying to understand what

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Speaker 1: type of training people need in management skills to best

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Speaker 1: manage AI, and for the most part, relational skills training,

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Speaker 1: a much more human type of training providing context, listening,

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Speaker 1: empathy is far more effective than task based training, where

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Speaker 1: you're giving directions on how to prompt, how to specify style,

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Speaker 1: how to evaluate the AI's output accurately. There are all

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Speaker 1: these courses you can take right now in AI literacy.

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Speaker 1: They're all about these task based training, hard skills to

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Speaker 1: learn how to do this. We find then if you

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Speaker 1: teach people to be really good managers, they're going to

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Speaker 1: do a really good job managing the AI and have

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Speaker 1: better results.

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Speaker 3: So really a big part of reducing workslot falls on

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Speaker 3: the managers and the CEOs. They're the ones responsible for

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Speaker 3: creating an environment where people can be comfortable with how

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Speaker 3: AI is being used, or where people can be comfortable

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Speaker 3: speaking up if something's wrong.

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Speaker 1: One of the things that we're seeing is that having

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Speaker 1: an environment that's psychologically safe, where people have the ability

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Speaker 1: to ask questions and take risks in a way that

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Speaker 1: feels safe, so they don't have to be worried about

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Speaker 1: whether using the AI is demanded or appropriate or permissible,

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Speaker 1: you know. So, I think part of that is like,

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Speaker 1: can I try this out? Can I disclose that I've

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Speaker 1: used it here? Can you give me some feedback on

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Speaker 1: whether this quality is high enough? Also training those mindsets,

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Speaker 1: so the pilot mindset, teaching people to have these skills

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Speaker 1: to be augentic over their usage of AI and also optimistic,

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Speaker 1: so curious and confident and willing to explore different ways

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Speaker 1: to use it to be more creative as opposed to

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Speaker 1: feeling like you have to use it. And then I

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Speaker 1: often like to say that AI is a multiplayer tool,

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Speaker 1: multiplayer game. And if we remind people that there is

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Speaker 1: a human recipient on the other end of your work,

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Speaker 1: and your goal is to collaborate with another human, and

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Speaker 1: you think about the potential negative impact of producing thoughtless

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Speaker 1: work that's AI generated, then I think even that psycho

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Speaker 1: education and the salience of knowing that there are humans

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Speaker 1: on the other side of the tool can be really

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Speaker 1: helpful to having positive collaboration.

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Speaker 3: Again, I think the study in some ways is a

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Speaker 3: kind of a warning. And basically, right now I can

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Speaker 3: totally understand why somebody would generate work slap, and it

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Speaker 3: would be great if their bosses or their company would

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Speaker 3: recognize the root cause of why they're using chat GBT

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Speaker 3: to write code or write a presentation that would be amazing.

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Speaker 3: Let's be realistic, that probably won't happen. And so your

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Speaker 3: study finds forty percent of people have received work slap

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Speaker 3: in the last month. I see that going up. Let's

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Speaker 3: talk about a five year in the future period. What

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Speaker 3: does that look like.

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Speaker 1: Yeah, it's a really complicated moment that we're in right now,

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Speaker 1: and I think the pressure to do more with less

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Speaker 1: is increasing, and there are so many unknowns about the

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Speaker 1: future of humans and the way that we work together and.

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Speaker 2: The tools that we use.

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Speaker 1: And they don't mean to be pollyannish and thinking about,

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Speaker 1: you know, the way that we can help and the

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Speaker 1: way that we can train people. I do think there

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Speaker 1: are deeper issues to bring like politicians and policymakers in.

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Speaker 1: But I have to say that, like, we've been through

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Speaker 1: this before. So think back to the early two thousand

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Speaker 1: and tens when social media came around and people had

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Speaker 1: to people meeting organizations and the people within them had

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Speaker 1: to learn to communicate and collaborate in a new way.

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Speaker 2: You know, it reshaped the whole.

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Speaker 1: World of just as an example, marketing and advertising, it

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Speaker 1: was a totally enormous shift in the way that we

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Speaker 1: do work, the way that we relate to each other,

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Speaker 1: the way that we use tools to get our work done.

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Speaker 1: And I think that gives me hope that you know,

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Speaker 1: this is a really, really powerful tool. We can create

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Speaker 1: a ton of value. We may need to slow down

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Speaker 1: a little bit, but you do have to stay relevant

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Speaker 1: with the tools that we all have access to right now,

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Speaker 1: to test them out, to try them out, to figure

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Speaker 1: out if you know, in the same way that years

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Speaker 1: ago we learned code, maybe no longer as relevant, but

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Speaker 1: I think you have to figure out how to use

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Speaker 1: these tools.

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Speaker 3: I mean, yeah, that's kind of the wild thing, is

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Speaker 3: I mean, I remember when a journalist lost their job,

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Speaker 3: you know, somebody to get up on Twitter and say, hey,

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Speaker 3: learn to code.

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Speaker 1: Ha ha ha ha ha.

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Speaker 4: Right.

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Speaker 3: Ironically, some of those same people I think are probably

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Speaker 3: now looking for a gig along with the rest of us,

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Speaker 3: because entry level coding gigs, I mean Claude and chat

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Speaker 3: GBT kind of has that on lock. It may be

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Speaker 3: generating terrible code, but it may be quote unquote good enough,

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Speaker 3: or as you say, it may be workslop and then

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Speaker 3: somebody gots to come in and clean it up. But

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Speaker 3: the company hasn't quite figured that out yet.

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Speaker 1: Right, So maybe there's a new industry of people who

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Speaker 1: have strong critical thinking skills and the ability to evaluate

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Speaker 1: the quality of code and to detect these signals and

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Speaker 1: to point that out. You know, I could see that

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Speaker 1: being an unexpectedly useful ability. I noticed that, Like, my

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Speaker 1: kids are way better at detecting AI generated images whenever

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Speaker 1: they are those games.

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Speaker 2: You know, AI generated or not.

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Speaker 1: I get them wrong every time, and my kids know

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Speaker 1: every single time, Like it's so obvious.

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Speaker 2: So maybe there are.

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Speaker 1: Some cues that you know, younger demographics, people who have

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Speaker 1: more hourly jobs can develop to help them evaluate and

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Speaker 1: work on their critical thinking skills to help the rest

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Speaker 1: of us prevent it.

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Speaker 3: I mean, I'm starting to wonder if maybe the one

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Speaker 3: prediction that we could make is that there might be

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Speaker 3: some kind of new category of job created, which is

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Speaker 3: basically work slop manager, like somebody who can who can

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Speaker 3: catch the work slop and say, I know what that is,

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00:25:58,400 --> 00:26:02,359
Speaker 3: let me fix that workslop Jim workslop janitor. You gotta

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00:26:02,400 --> 00:26:05,520
Speaker 3: be kidding me, man, You're right, I think some people

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00:26:05,520 --> 00:26:06,439
Speaker 3: are that right now.

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00:26:06,760 --> 00:26:09,840
Speaker 2: I think they are for sure that it's.

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00:26:09,720 --> 00:26:13,159
Speaker 3: A little bleak, but maybe it's necessary. Maybe this is

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00:26:13,200 --> 00:26:15,840
Speaker 3: where we're headed. Maybe not everybody figures out a way

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00:26:15,880 --> 00:26:18,320
Speaker 3: within their company to make it so the workslop isn't

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00:26:18,359 --> 00:26:21,480
Speaker 3: being generated, and so just understanding that this stuff is

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00:26:21,480 --> 00:26:23,040
Speaker 3: going to be generated, and then we got to have

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00:26:23,080 --> 00:26:29,280
Speaker 3: somebody clean it up before it gets put out. You know,

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00:26:29,440 --> 00:26:31,159
Speaker 3: Kate and I didn't realize this when we were talking,

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00:26:31,200 --> 00:26:33,919
Speaker 3: but it turns out that the phrasing of janitors for

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00:26:34,000 --> 00:26:37,280
Speaker 3: AI workslop is actually already a thing. I was just

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00:26:37,320 --> 00:26:40,320
Speaker 3: searching and there is a site called code janitor that'll

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00:26:40,359 --> 00:26:42,639
Speaker 3: charge you a couple thousand dollars to clean up the

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00:26:42,680 --> 00:26:45,560
Speaker 3: AI generated code so that your app will stop crashing

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00:26:45,600 --> 00:26:48,480
Speaker 3: and actually work. This is a niche job that I

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00:26:48,560 --> 00:26:51,080
Speaker 3: think is probably going to spread out to other industries,

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00:26:51,440 --> 00:26:54,040
Speaker 3: maybe even the industry that you work in, to which

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00:26:54,359 --> 00:26:57,040
Speaker 3: you might say, well, wouldn't it just be better to

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00:26:57,160 --> 00:26:59,480
Speaker 3: have hired somebody who's good at their job to do

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00:26:59,520 --> 00:27:03,600
Speaker 3: it right there the first time? Yes, probably, but if

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00:27:03,640 --> 00:27:06,399
Speaker 3: you're not the boss, that decision isn't always up to you.

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Speaker 3: Thank you for checking out another episode of kill Switch.

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Speaker 3: If you want to email us we're at kill Switch

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00:27:14,640 --> 00:27:18,760
Speaker 3: at Kaleidoscope dot NYC, or we're also on Instagram at

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00:27:18,960 --> 00:27:21,840
Speaker 3: kill switch pod. And if you like the show, which

468
00:27:21,920 --> 00:27:23,880
Speaker 3: hopefully you do because you're all the way at the end,

469
00:27:24,240 --> 00:27:26,720
Speaker 3: think about leaving us a review. It helps other people

470
00:27:26,760 --> 00:27:29,200
Speaker 3: find the show, which helps us keep doing our thing.

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00:27:29,760 --> 00:27:31,840
Speaker 3: And once you've done that, did you know that kill

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00:27:31,840 --> 00:27:34,200
Speaker 3: Switch is on YouTube? So if you like seeing stuff

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00:27:34,240 --> 00:27:36,280
Speaker 3: as well as hearing it, you can have the link

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00:27:36,359 --> 00:27:39,119
Speaker 3: for that. In the show notes, kill Switch is hosted

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00:27:39,119 --> 00:27:43,159
Speaker 3: by Me Dexter Thomas. It's produced by Sena Ozaki, Darluck Potts,

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00:27:43,359 --> 00:27:47,640
Speaker 3: and Julia Nutter. Alexanderveld also helps with production on this episode.

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00:27:47,840 --> 00:27:50,440
Speaker 3: Our theme song is by me and Kyle Murdoch and

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00:27:50,560 --> 00:27:54,360
Speaker 3: Kyle also mixed the show. From Kaleidoscope, our executive producers

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00:27:54,400 --> 00:27:58,840
Speaker 3: are Ozma Lashin, Mangesh Hajikadur, and Kate Osborne. From iHeart

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00:27:58,880 --> 00:28:02,880
Speaker 3: Our executive producers ur Katrina Norvile and Nikki E. Tor Oh.

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00:28:03,000 --> 00:28:05,120
Speaker 3: And one last thing, I don't want people to get

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00:28:05,119 --> 00:28:08,120
Speaker 3: the impression that workslop is just people turning bad work

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00:28:08,160 --> 00:28:12,040
Speaker 3: into their bosses. The study shows that workslop also flows

484
00:28:12,160 --> 00:28:15,600
Speaker 3: down the organizational ladder too. Kate, give me an example.

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00:28:16,680 --> 00:28:20,440
Speaker 1: There are some people who talk about receiving them from

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00:28:20,640 --> 00:28:21,320
Speaker 1: their ceo.

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00:28:21,880 --> 00:28:22,960
Speaker 2: Here's an example.

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00:28:23,359 --> 00:28:25,800
Speaker 1: The CEO of our company sent over some notes for

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00:28:25,880 --> 00:28:28,399
Speaker 1: fundraising strategy. It was just a whole lot of words

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00:28:28,440 --> 00:28:32,159
Speaker 1: saying nothing Chat GPT puke he sent over would have

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00:28:32,200 --> 00:28:34,439
Speaker 1: been so much more helpful as a paragraph written in

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00:28:34,480 --> 00:28:38,520
Speaker 1: his own words. The style of writing, reliance on bullet points,

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00:28:38,720 --> 00:28:42,200
Speaker 1: the fact that our CEO readily admits using GBT for everything.

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00:28:42,800 --> 00:28:46,120
Speaker 1: He had just gotten back from vacation. He was asking

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00:28:46,160 --> 00:28:48,960
Speaker 1: GPT for parenting advice. So it's just like.

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00:28:48,920 --> 00:28:49,760
Speaker 2: Going on and on.

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00:28:51,440 --> 00:28:56,720
Speaker 3: I'm sorry, Chat GBT for parenting advice is you know.

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00:28:56,760 --> 00:28:59,040
Speaker 3: I'm gonna leave that alone because that's an entirely different episode.

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00:28:59,040 --> 00:29:03,400
Speaker 3: I don't want to touch that right now. Wow, I really

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00:29:03,440 --> 00:29:05,200
Speaker 3: hope that we do not need to make an episode

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00:29:05,240 --> 00:29:08,840
Speaker 3: about people using chat GBT for parenting advice. Please do

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00:29:09,000 --> 00:29:11,880
Speaker 3: not make us make an episode about that anyway.

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Speaker 4: Catch on the next one. Good Bye,