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What Now? With Trevor NoahWhat Now? With Trevor Noah

Hilke Schellmann: Is the Algorithm Hiring the Wrong People?

AI isn’t just coming for your job — it might already be your manager. Trevor and Eugene sit down with investigative journalist Hilke Schellmann to examine how artificial intelligence has quietly infiltrated the workplace. From hiring software that analyzes your facial expressions to productivity trackers that monitor everything from your writing style to your bathroom breaks, Schellmann explains what these systems actually do — and what they get wrong. Do they eliminate bias, automate it, or just hide it better? And what happens to human work when the algorithm is watching? You won’t want to miss this episode. SurveyMonkey takes the fear out of asking—and the doubt out of deciding. If you haven't already.... Subscribe to the channel here: http://bit.ly/SubscribeTrevorNoah Or follow the podcast on your other favorite platforms... SiriusXM - Apple Podcasts - https://bit.ly/WhatNowOnApplePodcasts Spotify - https://bit.ly/WhatNowOnSpotify 00:00:00 - We Have a Confidence Problem and It's Mostly Men 00:01:08 - Welcome to What Now? Our Guest Has Been in the Same Apartment for 16 Years 00:02:38 - Why Do People Learn "I Don't Speak Your Language" In Your Language? 00:03:27 - Meet the Woman Who Investigates AI for a Living 00:05:31 - From Pakistan to AI: How Hilke Became Obsessed 00:08:01 - Spoiler: Your Face Cannot Tell Anyone If You're Good at a Job 00:09:50 - Eugene Finds Out How Much of His Life Is Already AI 00:11:38 - Your Book Shook Me Up in the Best Way 00:14:21 - The Junior Lawyer Problem: AI Is Eating the Entry-Level Jobs 00:15:24 - Ad Break: SurveyMonkey and Eugene Wants to Survey Who Doesn't Like Trevor 00:17:28 - Two Truths and a Lie: Eugene Has Never Heard of a Zip 00:19:25 - How Your Resume Gets Judged Before a Human Sees It 00:21:17 - One-Way Video Interviews: Talking to a Screen That Doesn't Talk Back 00:23:37 - Humans Are Bad at Hiring and AI Is Also Bad at Hiring 00:28:24 - AI Is Hiring People Who Used AI to Beat the AI 00:31:44 - The Amazon Story: How the Word Women on Your Resume Got You Downgraded 00:37:56 - The Space Bar Game and Other Weird Ways Companies Test You 00:44:28 - Could AI Remove Bias or Does It Just Make Bias Scalable 00:55:59 - Your Spotify Playlist Could Get You Fired 01:02:43 - Big Brother at Work: Productivity Theater, Rubber Rooms, and Bladder Breaks

Hilke SchellmannguestTrevor NoahhostEugenehost
Feb 26, 20261h 20mWatch on YouTube ↗

EVERY SPOKEN WORD

  1. 0:00 – 1:08

    We Have a Confidence Problem and It's Mostly Men

    1. HS

      [upbeat music] What I have learned by, like, bringing AI into the talent acquisition hiring space, I learned, like, how bad our old processes are. Like, job interviews, actually really bad, um, because you are, uh, it sort of filters out the people who are good about talking-

    2. TN

      Mm

    3. HS

      ... about doing the job.

    4. TN

      As opposed to doing the job.

    5. HS

      So we have this, like, competence versus confidence problem. Like, people who, like, come off as-

    6. TN

      Yes

    7. HS

      ... like, confident, we often think like, "Well, that person speaks so confidently about-

    8. TN

      They must be-

    9. HS

      "They must be really good." It turns out, like, that are more often than not men. Um, and that doesn't mean actually they're competent. So we sometimes complain-

    10. TN

      What? Us men?

    11. HS

      [laughs]

    12. TN

      No.

    13. HS

      Never. Uh [laughs]

    14. TN

      What?

    15. HS

      [laughs]

    16. TN

      Us?

    17. HS

      So, you know. [laughs]

    18. TN

      Mm-hmm. You load us, Hilke? No.

    19. HS

      [laughs]

    20. TN

      [laughs]

    21. HS

      As always, not all men, but a lot. Um.

    22. TN

      Us little men, ooh, acting like we know more than we do. Us?

    23. HS

      [laughs]

    24. TN

      Ooh. Come on, Hilke. Ooh. [laughs]

    25. HS

      Uh, mansplaining. What? [laughs]

    26. TN

      [laughs] [upbeat music]

  2. 1:08 – 2:38

    Welcome to What Now? Our Guest Has Been in the Same Apartment for 16 Years

    1. TN

      This is What Now? With Trevor Noah. [upbeat music] You based here? Where are you based?

    2. HS

      Uh, yeah. Um, based at NYU, 20 Cooper Square.

    3. TN

      Okay.

    4. HS

      I live in Brooklyn.

    5. TN

      Okay. Oh, what part of Brooklyn?

    6. HS

      Greenpoint.

    7. TN

      Greenpoint.

    8. HS

      It's a old-

    9. TN

      So that's-

    10. HS

      ... Polish neighborhood.

    11. TN

      Greenpoint's-

    12. EU

      Why did your voice go down when you said Greenpoint?

    13. HS

      [laughs]

    14. TN

      I thought that she did.

    15. EU

      I thought that she did.

    16. HS

      [laughs] He caught me.

    17. TN

      Yeah, no. Greenpoint.

    18. EU

      No.

    19. HS

      Um, well, it's v- very different. I've been in the same apartment for 16 years. Been beautiful 16 years ago. It's still kind of beautiful, but the neighborhood is changing a lot. Um-

    20. TN

      But isn't it becoming cooler and younger?

    21. HS

      Yeah. But then-

    22. TN

      Oh, that's what you don't like about it

    23. HS

      ... well, I like that it was, like, kind of Polish, and you walk into a store-

    24. TN

      Ah

    25. HS

      ... and people talk to me in Polish.

    26. TN

      Okay.

    27. HS

      And I don't really know Polish. The only thing I know is, like, one line-

    28. TN

      [laughs]

    29. HS

      ... that's, like, "Nie mówię po polsku" and that is really bad in Polish saying I don't understand Polish. But I kind of like that.

    30. TN

      Wait, that's, that's Polish for I don't understand Polish?

  3. 2:38 – 3:27

    Why Do People Learn "I Don't Speak Your Language" In Your Language?

    1. TN

      I-

    2. HS

      You know?

    3. TN

      I, I've never understood why people learn the phrase, "I can't speak your language," in another language.

    4. HS

      'Cause you wanna be polite.

    5. EU

      And I think first-

    6. TN

      But just speak your language

    7. EU

      ... like, it's a test on yourself to see-

    8. TN

      Yes, but-

    9. EU

      ... how much you can learn.

    10. TN

      Okay, but now think about this to, think about what this says to the other person.

    11. EU

      Yeah.

    12. TN

      You've said to them-

    13. EU

      Yeah

    14. TN

      ... in their language-

    15. HS

      Yeah

    16. TN

      ... you can't speak their language.

    17. HS

      Yeah.

    18. TN

      To me, what it shows me is you just don't wanna speak my language because you've learned enough to say you can't speak it, and then you won't learn the rest.

    19. HS

      No, I think that's like-

    20. EU

      No

    21. HS

      ... being really polite.

    22. EU

      That is not, no.

    23. HS

      You're, like, visiting them-

    24. TN

      No, that's not, that's not nice. No

    25. HS

      ... and, like, you want to be nice to them.

    26. TN

      Think about it. You've literally walked up to somebody, and you wa- someone came up to you, and they're like, "I don't speak English." And then you're like, "Well, you did a great job there." And they're like, "Mm, that's enough for me."

    27. HS

      [laughs]

    28. TN

      [laughs] Think about it.

    29. EU

      I think we've done enough for the day.

    30. TN

      You're like, "Nah, that's enough for me."

  4. 3:27 – 5:31

    Meet the Woman Who Investigates AI for a Living

    1. HS

      me.

    2. TN

      Thank you so much for joining us. This is, I, like, you know, sometimes, and maybe, maybe it's confirmation bias, sometimes you'll see a thing in the world that confirms the feeling that you're having and the idea. And I, and like a lot of us will be like, "It's a sign. It's a sign." Literally coming here into the, into the studio today, I saw these posters that are all over New York. It's a little QR code.

    3. HS

      Uh-huh.

    4. TN

      And it says, "AI, who are the winners? Who are the losers?" And it's a QR code, and I don't know what's happening, and there's all these different ones everywhere. And then they say, "Is your job next? Is your job next?" And it's all, like, ominous and it feels like it's promo for a movie, but it's not, I think.

    5. HS

      So what is on this QR code? Did you check?

    6. TN

      I'm not gonna scan a random QR code.

    7. HS

      [laughs]

    8. TN

      This is how your phone gets hacked. I'm not gonna scan the QR... I was just, I wa- I just wa- looked, and I was like, "Yes."

    9. EU

      I wish I did. Yeah.

    10. TN

      I was like, we're talking to the perfect person today because you have dedicated more time in your life than most people into answering this question. Like, basically, like, who are the winners and who are the losers? So, like, be- before we, we, we delve into it, like-

    11. HS

      Yeah

    12. TN

      ... if, if, if you were to explain to somebody who you are and what your passion is in and around the topic of AI and it, how it relates to work, how would you introduce yourself to them?

    13. HS

      Oh, wow. Um, I guess I feel like, you know, I'm an investigative journalist, and I have... You know, I used to investigate all kinds of things, and now I just investigate AI, and I'm trying to understand, like, how does it work in society and maybe who are the winners and the losers. Uh, but also, like, you know, I really think about, like, the way it's changing the world of work, and I saw it eight years ago starting, and I was like, "Oh, I don't know if people are aware of this, and somebody needs to look into it." And there was kind of nobody else there who was, like, looking into it, so I was like, "Might as well look into it." Um, I'm just driven by, like, sort of curiosity, and I'm like, "What is going on here?" So now it has a little bit involved. Like, I investigate AI, um, not only AI in hiring and in the world of work. I also build AI tools. I think about, like, how journalism will be impacted by AI and how we can maybe save journalism or a factual-based society-

    14. TN

      Yeah

    15. HS

      ... um, when everything can be generated. Um, so those are kind of things and questions that I think about.

    16. TN

      I love the idea of being an investigative

  5. 5:31 – 8:01

    From Pakistan to AI: How Hilke Became Obsessed

    1. TN

      journalist, doing everything and then focusing on one thing, 'cause then it makes me go, what was it about this one thing that you thought supersedes everything else? Like, what, what were the other topics you were covering before this that you, that you just-

    2. HS

      Uh, yeah, I mean, I, I covered-

    3. TN

      ... cast aside

    4. HS

      ... uh, you know, like, uh, uh, violence against women in, in Pakistan. I went to Pakistan.

    5. TN

      Yeah.

    6. HS

      I looked at, like, South Asia. I did all kinds of things. Um, and I don't know, I had, like, one Lyft ride in 2017 in the fall. I was in Washington, DC, trying to get from a conference that has nothing to do with AI, uh, to the train station. I got in the back of the car and asked the driver, "How are you doing?" And he said I've, I've had a weird day. In the history of me taking Lyfts, no one has ever said that. [laughs] And I was like, "Really? Well, what happened?" He's like, "I had a job interview by a robot, n- with a robot." And I was like, "What? A job interview with a robot?" Um, he's like, "Yeah." You know, he had applied for a baggage handler position at an airport, and he got a call from a robot that asked him three questions, and he was really weirded out. This was in 2017, so we are, you know, light-years, uh, further down the road of AI now.

    7. TN

      Yeah.

    8. HS

      Uh, but I was like, "I have never heard of this," so I started looking into it, and here we are. And then I went to a conference, and I was like, wait a second, there are all these, like, AI vendors in HR, and, like, it's being used everywhere, and no one talks about it. And whoop, down the rabbit hole I went. Um, and somehow it never... It doesn't let me go. I'm thinking about, like, the next four books on AI, the next research studies on AI. It just doesn't, um... I don't know how I... I don't know. I'm very, I'm very bad at predicting the future, uh, but I could tell that this is, like, a transformative technology-

    9. TN

      Yeah, yeah

    10. HS

      ... that we need to pay attention to, and not only how the technology work, but it's, like, societal implication. What does this mean if we use AI-

    11. TN

      Hmm

    12. HS

      ... in hiring? What is it... What are the consequences of this? If we use it in journalism, how does our world change or maybe not change, and how does it improve the world or maybe not? And I was surprised that there isn't maybe a whole lot of improvement as we wish it would be, at least in hiring.

    13. TN

      Mm-hmm.

    14. HS

      So I think that was a little bit surprising, sadly, um, that when I first saw... Like, the first time I went to a conference and somebody was explaining how they do, like, emotion scanning on their faces and, like, checking the intonation of your voices to find out if you're gonna be good at a job and, like, the words that you say, and I was like, "Wow, who knew that, like, facial expression in s- in job interview could be predictive of your success at a job? Like, what, what a way, like, a new way of science." And then, you know, we trust but verify as a journalist, so I trusted that information, and then I went on to verify it and talked to a lot of experts who are like,

  6. 8:01 – 9:50

    Spoiler: Your Face Cannot Tell Anyone If You're Good at a Job

    1. HS

      "What? Emotion and faces?"

    2. TN

      No ways.

    3. HS

      Like, that doesn't exist to predict how good you are at a job. And I was like-

    4. TN

      No ways

    5. HS

      ... I was like, "Oh, that's too bad." Um, intonation of our voices, we can't really tell what kind of emotions you have. Like, we can sort of, like, make a prediction, but that's not always really the case. Like, you know, it's kind of like-

    6. TN

      Yeah, but-

    7. HS

      ... when I'm in a job interview and I say I'm nervous and... Uh, sorry, uh, when I'm, when I'm in a job interview and, and I smile, and people... You know, a facial, uh, emotion-scanning algorithm would say, like, "Oh, yeah, she's totally happy. She's smiling." And I'm like, "I'm fucking nervous." [laughs]

    8. TN

      [laughs]

    9. HS

      I'm not, I'm not happy in a job interview. Who in their world has ever been happy in a job interview? Uh-

    10. TN

      Oh

    11. HS

      ... so that's kinda like, you know... It is a prediction, uh, but we're using it to, like, sort of select people, uh-

    12. TN

      Yeah, it's just like your intuition-

    13. HS

      ... in rejection

    14. TN

      ... fr- from what I hear, it's like your intuition as an investigative journalist was basically to say, "There's something deeper that's happening here."

    15. HS

      Mm-hmm.

    16. TN

      "There's a world..."

    17. HS

      Hmm.

    18. TN

      Do you know what I mean?

    19. HS

      Yeah.

    20. EU

      Hmm.

    21. HS

      Totally. And we... And somebody has to look into it. And for some reason it just sometimes happens to be me who's standing right there, so I have to, like, take it on. It's like, you know, when the, when the, uh, chairwoman of the Equal Employment Opportunity Commission, when I was talking to her about AI and hiring, and she's like, "Yeah, I do wonder. Now we have these, like, one-way video interviews," and, and, you know, the, the companies use the recording, run them through a transcription service, like speech-to-text transcription like you have-

    22. TN

      Uh-huh, uh-huh

    23. HS

      ... on your phone, uh, and then the AI predicts upon that transcription. And she was like, "I wonder how good the transcription software works for people with accents, people with speech disabilities." I'm like, "Yeah, totally, and you have, like, a federal agency. You should totally look into that and study that." And she's like, "Oh, yeah. I don't know." And I was like, "Okay, there's no one here," so I started to study it with the help of a research team, a computer scientist, sociology professor. I don't do this work alone. Um, but, um, yeah. So that's kind of the work that I do.

  7. 9:50 – 11:38

    Eugene Finds Out How Much of His Life Is Already AI

    1. EU

      You know, the more you speak, I realize this is how it sounds like whenever I speak to Trevor about technology. He knows so much about technology. I only know how to send texts.

    2. TN

      But you send them very well.

    3. EU

      Very well.

    4. HS

      [laughs]

    5. EU

      Sometimes I send pictures as well-

    6. HS

      Wow

    7. EU

      ... with those texts. And an emoji-

    8. HS

      Wow.

    9. TN

      Don't get me started.

    10. EU

      You know-

    11. TN

      I've actually never heard you talk about AI now that I think about it.

    12. EU

      Never. Because also I don't understand how much of it is in my life-

    13. HS

      Hmm

    14. EU

      ... and I don't also understand how much it scares people. So I'm even scared to ask people, "What is it about AI that scares you?" Because I don't interact with technology that much, so how would you explain to me what scares people and how much I've been using without even knowing I've been using?

    15. HS

      Yeah. Um, well, we use it in everyday life. Do you have a spam filter on your email?

    16. EU

      Niks. [laughs]

    17. HS

      [laughs]

    18. TN

      [laughs]

    19. EU

      Hilke, I specifically said to you.

    20. HS

      [laughs]

    21. TN

      [laughs]

    22. EU

      [laughs]

    23. HS

      Uh, well, you know, it's, it's like sort of the, the rise of A- of, of AI has been everywhere, right? And it's really, like, software really when it comes down to it. It's just sort of, like, maybe software on steroids. It does things better than we're used to, where we say like, "Oh, if this, then do this." Like, we have now self-learning, uh, tools that can sort of do translations, um, from... You know, we could now be talking in German or French, and an AI could just translate that in our voices, uh, and an AI can generate that. Um, so we see it kind of everywhere, moving into everything. Um-

    24. TN

      That's crazy. You... Like, so wait, you're saying with the technology now, out of nowhere we can just go from speaking English and then we just switched into another language?

    25. EU

      In real time.

    26. TN

      In real time? That-

    27. HS

      I don't know if it works in real time, but we can definitely do it.

    28. TN

      Das ist unglaublich.

    29. HS

      I could definitely do that. Yeah. [laughs]

    30. TN

      Und dann du sprichst Deutsch und, und dann Eugene sprichst Deutsch. Du auch?

  8. 11:38 – 14:21

    Your Book Shook Me Up in the Best Way

    1. HS

      the AI

    2. TN

      ... ice broken nothing. [laughs]

    3. HS

      [laughs]

    4. EU

      [laughs]

    5. TN

      You know, you, uh, your book, your book really, um, I think shook me up in, in, in, in the perfect ways-

    6. HS

      Hmm

    7. TN

      ... because you've written extensively about, about the world of AI, and I, and I... What I wanted this conversation to do, because I, I try to talk to people like Eugene, funny enough-

    8. HS

      Hmm

    9. TN

      ... who I realize don't have the handle or the, or, like, the passion for tech that I have, you know? And, and sometimes I think if you love tech too much, you're just focusing on, like, the tech side of it than talking-

    10. HS

      Yeah, yeah

    11. TN

      ... and you're like, "Wow, the engineering-

    12. HS

      The new tech, the new tech, yeah

    13. TN

      ... and the this." And then when I speak to a person who's not into tech, they just go like, "Wait, wait, wait, wait." What does it do for me? What does it do against me? And how do I need to think of its role in my life? And y- your book really broke it down, because one of the first things I noticed a- about your writing is AI is fundamentally gonna change what the word job means. Do you know what I mean? Like, like, job has-

    14. HS

      Yeah

    15. TN

      ... constantly had, like, evolutions over time.

    16. HS

      Mm-hmm.

    17. TN

      Like, people used to go like, "A job is this," and, you know, like, it meant using your hands. And be like, "That's not a job." And the first people on a computer were thinking, they're like, "That's not a job." And then now people go, "That's not..." But fundamentally, from everything I've seen you write, and obviously everything that's happening in the world, it seems like job itself is gonna change. What have you found in, in your investigations on, like, how AI is changing what jobs actually are or aren't, like in different fields, lawyers, doctors-

    18. HS

      Yeah

    19. TN

      ... et cetera?

    20. HS

      Yeah. I mean, I think we already see some of it coming down. Um, uh, uh, you know, we see... We, we already see some of the consequences of, like, AI infiltrating our, our daily lives.

    21. TN

      Hmm.

    22. HS

      We see a lot of, uh, way less, like, sort of early career hiring, 'cause I think a lot of times people who use AI a lot sort of, uh, describe it as like, "Oh, yeah, I have, like, a little intern with me-

    23. TN

      Yeah, yeah

    24. HS

      ... who does, like, a lot of job for me," right? Like, they can write code for me. They can do, uh, you know, you can generate a research report of stuff that I need to know. Like, I can generate emails, newsletters, like, stuff that I have to, uh, write that-

    25. TN

      And these are all the-

    26. HS

      That we maybe were gonna giving to-

    27. TN

      These are all the early jobs that people would-

    28. HS

      Um-

    29. TN

      Set my calendar, book my flights.

    30. HS

      Um, yes. Yeah, all that.

  9. 14:21 – 15:24

    The Junior Lawyer Problem: AI Is Eating the Entry-Level Jobs

    1. HS

      um-

    2. TN

      But how do we, how do we... That, that seems to be the conundrum, right, is law firms. Most of the people who start out in a law firm start out, they've got their law degree, they go and work at a law firm, and it's-

    3. HS

      Yeah

    4. TN

      ... like your job is just to, like-

    5. HS

      They do research, yeah

    6. TN

      ... go through the paperwork and do-

    7. HS

      Yeah

    8. TN

      ... the research and write up briefs and do this. But you're working for someone. But in that process, you're learning, and they're teaching you what they're looking for, and they're trying to... But y- if we cut off that level, then where does the expertise come from?

    9. HS

      Yeah.

    10. TN

      You know? Because we say upskill, but then who is doing the up of the skill?

    11. HS

      Yeah. Yeah. I mean, I think it's, like, sort of a, a, um, you know, what I sometimes fundamentally think of, and, you know, we don't have all the answers yet to some of these questions, if I may say that, is, like, sort of like what, what stays as a human, uh, in the age of AI, right? If, like, uh, AI can do sort of what we think as, like, very human-

    12. TN

      Yeah

    13. HS

      ... uh, things, like, if AI can write better than I do, how can I express myself? Like, and what does it mean for humans in a world of AI? Like, what do we bring to the table now that AI can do so many things for us? [upbeat music]

  10. 15:24 – 17:28

    Ad Break: SurveyMonkey and Eugene Wants to Survey Who Doesn't Like Trevor

    1. TN

      Don't go anywhere, 'cause we got more What Now? after this. This episode is brought to you by our friends at SurveyMonkey. You know, Eugene, I don't know if you've ever thought of starting a business, but every time I think about people who start businesses, I'm like, man, you need a specific kind of bravery, and by bravery, I mean terrifying uncertainty. Because think about it, right now, everywhere in the world, there are people making massive life-altering decisions based entirely on a vibe, just a feeling in their gut.

    2. HS

      That was me when joining the podcast.

    3. TN

      You know what I mean?

    4. HS

      Mm-hmm.

    5. TN

      But what if you actually knew the truth when you started a business? What if you knew your direct reports weren't just nodding along but actually had thoughts that they were too polite to tell you?

    6. HS

      Hmm.

    7. TN

      Or what if you knew that your best customers were already halfway out the door to a competitor? Because let's face it, we like to say follow your heart, but in business, ignorance is a very expensive hobby. The good news, Eugene, is that SurveyMonkey takes the fear out of asking and the doubt out of deciding. It's the difference between walking into a meeting with a hunch and walking in with 500 validated opinions, which mathematically is better for your blood pressure. The truth might hurt for a second, but it's cheaper than being wrong. So let's get you a survey and some actual answers. Sign up for an annual plan at surveymonkey.com/whatnow, and use the code WHATNOW for two months free. That's two months free with code WHATNOW at surveymonkey.com/whatnow. You have any questions?

    8. HS

      Is it too late for me to run a survey about who doesn't like you in the podcast?

    9. TN

      What do you mean who doesn't like y- you? Like, like me?

    10. HS

      Like you, yes. Specifically the person in front of me.

    11. TN

      I think surveys are supposed to be anonymous so that people's feelings don't get hurt.

    12. HS

      But I did type anonymous there.

    13. TN

      Well, I mean, now I'll know that you typed it because do, do you have a problem with the working conditions?

    14. HS

      I haven't sent it.

    15. TN

      But you wanna make a survey about-

    16. HS

      I haven't sent it yet.

    17. TN

      You can just use the code WHATNOW to get two months free when you do it.

    18. HS

      Still have two months to complain about you.

    19. TN

      I feel like this is got personal.

  11. 17:28 – 19:25

    Two Truths and a Lie: Eugene Has Never Heard of a Zip

    1. TN

      All right, Eugene.

    2. HS

      Mm.

    3. TN

      Let's play a little game. You know, make something fun. Two Truths and a Lie.

    4. HS

      Mm-hmm.

    5. TN

      Here we go. One, I've had to tell a world leader that their fly was undone. Two, when getting dressed, I don't do sock, sock, shoe, shoe. I do sock, shoe, sock, shoe. Three, I've been a Verizon customer for 11 years. What do you think?

    6. HS

      Hmm. Very confused. First of all, why would a world leader own a fly? 'Cause those things just come uninvited. Secondly, lying to your friend is not cool. It's never been a game.

    7. TN

      No, Eugene, f- a fly is for... Like, the zip is what... And then it's, it's g- it's not a lie. It's a game where I'm try- it's like I give you inform- okay, I lied. All three are true, Eugene. And in case you were thinking, you know, Verizon isn't as expensive as you think. In fact, if you bring in your AT&T or T-Mobile bill, they'll give you a better deal. And the reason I've been with them for this long is just because I travel so much. I need a network that's reliable. That's right, a better deal on the best network with the most ways to save on plans, streaming, and phone deals. Take your AT&T or T-Mobile bill to your local Verizon store today. Get your better deal and start saving for real. Based on RootMetrics' best overall mobile network performance, US second half 2025. All rights reserved. You must provide recent consumer mobile bill in the name of the person redeeming the deal. Additional terms, conditions, and restrictions apply. So do you understand how two truths and... Do, do you understand it now?

    8. EU

      I understand that you didn't have to lie first before telling me that Verizon is the best.

    9. TN

      No, I wasn't lying f- Eugene, it's not a lie. I wouldn't lie to you. It's, it's a game. Okay, I'm sorry.

    10. EU

      I lied.

    11. TN

      Ah. [laughs]

  12. 19:25 – 21:17

    How Your Resume Gets Judged Before a Human Sees It

    1. TN

      In, in the job space actually, I, I would love to know, like-

    2. HS

      Yeah

    3. TN

      ... you've done a lot of investigating, and I wanna get into some of the stories because I th- I think people will be fascinated by how humans have been affected by AI already. Is there, is there, is there like a concrete number on how much hiring is actually done by AI now and how much is human? Because a lot of people out there, if you told them, "Oh, hey, your job application, your CV, your resume, whatever you type up, it's not even seen by a human in some companies."

    4. HS

      Not?

    5. TN

      Yeah, nothing.

    6. HS

      Yeah, sorry. Um, so we think about the... [laughs]

    7. TN

      How do you think you got here? You think if I knew you were coming you'd be here?

    8. HS

      If I looked at your resume. [laughs]

    9. TN

      This was AI. [laughs]

    10. HS

      You now have to say it was, like, shitty AI or something. [laughs]

    11. TN

      [laughs]

    12. EU

      This freaks me out ev- at every, at every turn. Wait, wait, so someone applies for a job.

    13. HS

      So you, like, upload your resume, or you don't even have it uploaded.

    14. EU

      Yes.

    15. HS

      Or like you already have it on LinkedIn and you just t- hit the one click.

    16. EU

      Present.

    17. HS

      Yeah. So, um-

    18. EU

      To the company I'd like to work for.

    19. HS

      Yeah.

    20. EU

      Right?

    21. HS

      So, like, all of these big platforms, they all use some form of AI. That I can tell you. We don't have, like, a central register where companies-

    22. EU

      Mm

    23. HS

      ... have to register and say, like, "We use this AI tool or not." We just know this from surveys and sometimes me calling companies. Um, so I know that they use AI. So you have to think about, like, the beginning of the hiring process you often have thousands of people applying for a job, right? We call this like sort of a big funnel. And, uh, some companies, you know, this is, um, a couple years old, but I talked to Google, they get over three million applications. IBM gets five million, over five million applications a year. So it's a lot of resumes that come into this funnel. So what we now see is, like, um, a lot of companies, and usually large companies, a lot of Fortune 500s, uh, use AI to reject people, to sort of cull the herd of all these applicants. And, like, so we see in the early stages, uh, rejection, rejection, and, like, a few people, uh, going on the yes path for AI.

  13. 21:17 – 23:37

    One-Way Video Interviews: Talking to a Screen That Doesn't Talk Back

    1. HS

      And then, you know, doing, like, one-way video interviews, and now we have video avatars interviewing people.

    2. TN

      Just ex- just break down what is a one-way video interview? 'Cause I think a lot of people... I didn't know what that was until I-

    3. HS

      Yeah, totally

    4. TN

      ... until I read your work.

    5. HS

      Uh, yeah, I hear you. Uh, I've done so many. [laughs]

    6. EU

      Until 30 seconds ago. What are you talking about?

    7. TN

      No, but I didn't know.

    8. EU

      Me.

    9. TN

      Yeah. [laughs]

    10. HS

      Yeah. [laughs] So, like, a one-way video or audio interview, like, uh, you know, there's now a traditional way to do this, which is, like, six or seven years old, uh, where you don't have anybody else, uh, on, like, you know, you kinda log in, you get a link, um, do this, uh, video interview if, if you want the job in the, in the next 48 hours. So you click the link, and then instead of a human on the other side, it's on a Zoom call, um, you just, like, get maybe a video of somebody saying, "Hey, welcome to company X. We're so delighted you are here. We have a couple of tests for you." And then you get a question like, "What are your strength and weaknesses? Why do you want this job?" And then you tape yourself basically. You get, like, a couple minutes to prepare, and then you tape yourself, like, saying like, "My strength and my weakness is this." Uh, and then I think all of the, uh, applicants I've spoken to think that, like, a human watches all these vi- videos. Bless their hearts if they do. And some companies actually do have humans watch it, all of these, but some companies also use AI, uh, to, uh, rank people and, uh, uh, do that. So we see that more and more, and we see this often, like, entry-level jobs. We see this in, like, uh-

    11. TN

      Hm

    12. HS

      ... retail companies, fast food, like, um, it's called, uh, high turnover, high... No. High volume, high... I don't remember. Um, so it's in, in, in job space-

    13. TN

      Jobs, jobs that generally have people, where people are coming in quickly and leaving quickly.

    14. HS

      Exactly.

    15. TN

      Like, they're not gonna be there. It's not a career job. So people are going-

    16. HS

      Sometimes it's a career job.

    17. TN

      Oh, but it's just-

    18. HS

      We see, yes-

    19. TN

      ... like, high turnover

    20. HS

      ... uh, but it's, it's a high turnover or you have, like, lots of candidates that you have to go through.

    21. TN

      Okay, got it. Got it, got it.

    22. HS

      Um, so for example, like, Goldman Sachs had, um, a f- a few years ago for their summer internship, they had, like, over 100,000 applications. Um, so th- they have to, like, go through these applications and, like, narrow down the pool. So you use, like, resume screening AI. Uh, you use, like, uh, video interviews.

    23. TN

      Yeah, yeah, yeah.

    24. HS

      You can use games.

    25. TN

      Yeah.

    26. HS

      Uh, we see, like, uh, personality. Uh, these games are supposed to find your personality, um, while you're clicking on balloons, pumping up balloons, they find your personality.

    27. TN

      It's, it's, it's-

    28. HS

      All kinds of ways to assess you without maybe putting in a whole lot of work because humans are expensive to do this work. And also,

  14. 23:37 – 28:24

    Humans Are Bad at Hiring and AI Is Also Bad at Hiring

    1. HS

      sorry to say this, but a lot of humans, they do suck at hiring because we have bias.

    2. TN

      So that's... Yeah.

    3. HS

      We have human bias.

    4. TN

      But that's, so now that's the conundrum.

    5. HS

      And I suck at hiring. Um-

    6. TN

      But this is the conundrum though.

    7. HS

      Yeah.

    8. TN

      So, so, so this is, this is the thing that's, like, weird now just for this part of it is my refle- my reflex when I hear something like that is to go, "Oh, no, this is, this is not good. How can you have AIs screening people's interviews and..." But then on the other hand I go, if you have 100,000 people applying to a job, let's be honest, I don't think there's any human who's gonna get through those 100,000 application. I don't think there's any humans, and I wouldn't be shocked if there were, like, a bunch of humans who were skipping through this before because they were just like... It's like auditions in a way. At some point the person's tired.

    9. HS

      Yeah, totally.

    10. TN

      You know, you, you wanna get them when they're fresh. You wanna get them when they're in the mood.

    11. HS

      Yeah, not when they hungry or-

    12. TN

      And I won- I wonder is there a world where, like, does the AI make it better then?

    13. HS

      You know, I wish I could tell you that. Um-

    14. TN

      So we don't know

    15. HS

      ... and I have... We don't know. I've asked-

    16. TN

      Oh, wow

    17. HS

      ... many, many companies, um, to let me come in as a researcher and, like, sort of, uh, look at, like, here's your traditional way of hiring, here's, uh, your AI hiring, and have this-

    18. TN

      And what do they say?

    19. HS

      ... like, run at both times, and then sort of double-check, like, you know, the people that the AI said that would be high performers, did they actually turn out to be high performers?

    20. TN

      Yeah.

    21. HS

      And I have not seen a company do this or want to share this with me or with anyone.

    22. TN

      Huh.

    23. HS

      I think it's because, I don't know, there's, like, a lot of turnover in HR, like, these processes, um, don't work that, that well, and I think what we already know... So what we know from a survey of, uh, C-suite leaders, like, sort of leadership in companies, um, of over 2,000 in Germany, the UK, and the US, uh, when they asked them, "If your company uses AI tools, um, do they reject qualita- qualified, do they reject qualified candidates?" And almost 90%, um, of the leadership said yes. So they know that their tools reject qualified candidates.

    24. TN

      Hmm.

    25. HS

      They still use it because I guess the efficiency from, uh, using AI versus humans, it's just much, much more greater. Um, but it's not that we know that one process is better-

    26. TN

      Yeah

    27. HS

      ... than the other. I mean, we do know that, like, uh, humans are very biased in hiring, and even the best anti-bias training is not gonna get out of it.

    28. TN

      Yeah.

    29. HS

      Um, and you know, y- we all know the shortcuts, right? If you see somebody on your resume that they went to Harvard, you're like, "Oh, they must be smart." No.

    30. EU

      They're not?

  15. 28:24 – 31:44

    AI Is Hiring People Who Used AI to Beat the AI

    1. TN

      use the-

    2. HS

      Uh-

    3. TN

      ... I'm gonna use the AI-

    4. EU

      Mm-hmm

    5. TN

      ... to apply for the job.

    6. EU

      Yes.

    7. TN

      They're gonna use the AI to grade me.

    8. HS

      Yeah.

    9. TN

      I'm gonna use the AI to pass the grades.

    10. EU

      Yes.

    11. HS

      Exactly.

    12. TN

      Then they're gonna... I mean-

    13. HS

      I'm gonna try to use AI to, like, outsmart the AI.

    14. TN

      Huh. Yeah.

    15. HS

      There's actually AI programs that now apply for you, um, so you don't even have to do anything. Um, so there's all kinds of stuff. But, like, the question is, like, well, what are we then doing here? Like, like-

    16. TN

      Yeah, like what are we?

    17. HS

      Yeah. What are we doing?

    18. TN

      In, in fact, that is a good question.

    19. HS

      That is, that is a great question. Like, what are we doing?

    20. TN

      That becomes the question, what are we do- because if the AI is hiring the people who are using the AI to get the job, that the AI has hired the people, th- then we, that's what I mean, is, like, we have to ask the fundamental question, wait, what was the point of this process in the first place? Because multiple studies have shown humans are terrible at predicting the future, especially when it comes to hiring, right? A lot of the time when you're hired, you're hired because the person sitting across from you saw something in you that they considered-

    21. HS

      Hmm

    22. TN

      ... correct for the company, but a lot of the time it's just wrong.

    23. HS

      Yeah.

    24. TN

      You know what I mean? It's just, it's wrong. And then people don't do well, and they go like, "Well, that, that didn't work." But, uh, but the, the prediction is wrong.

    25. HS

      Mm-hmm.

    26. TN

      You know what I'm saying?

    27. HS

      Yeah.

    28. TN

      And so now I almost feel like we, we forgot what the whole point of an interview was. Like, I, I'm not a historian, but if I was to bet, I would think an interview was just to be like, "Let me see what your vibe is."

    29. EU

      It was a vibe check.

    30. TN

      It was a vibe check.

  16. 31:44 – 37:56

    The Amazon Story: How the Word Women on Your Resume Got You Downgraded

    1. TN

      that you wrote about.

    2. HS

      Yeah, the Amazon story is one of them.

    3. TN

      The Amazon one was pretty insidious.

    4. HS

      But, um-

    5. TN

      Wait

    6. HS

      ... so this was, this was, like, if you had the word, uh, woman or women-

    7. TN

      Yes

    8. HS

      ... um, on your resume, you got downgraded. 'Cause, you know, the, the, the t- the tool had learned over time, you know. You s- you s- you give it, um, uh, uh, resumes of people who currently work here or, or who maybe made it to the last round of hiring, sort of labeling thas- them as these are the successful people. Well, if you work in a tech company and you probably have a gender disparity already, uh, built in, uh, from maybe previous bias, um, you kind of replicate that, right? If the people who are in the role, if you use their resumes, the, the ma- sh- machine does what it does best. It looks for patterns, and it finds out-

    9. TN

      And it finds the patterns

    10. HS

      ... wow, women are less successful here, so we should downgrade them in the hiring process. So-

    11. TN

      Yeah, there, there were some applications in the story where Amazon was hiring people, and their system basically went on its own, doing its job as it had been told, and it went, "Oh, I've noticed women's soccer team, women's baseball, women's anything does not match with the people who are currently at the top of Amazon."

    12. HS

      They don't have that word on their resumes, basically.

    13. TN

      Exactly. So this person is less likely to be like that person, so we're gonna downgrade that. But this had nothing to do with your actual qualifications.

    14. EU

      Wait, did AI do that, or did someone who put the input to the AI do that?

    15. TN

      No, AI did that.

    16. HS

      Yeah, there was no input. This was, this was a machine l-

    17. EU

      There was no input at all.

    18. HS

      Yeah. You have to think about, like, you know, sort of, uh, present day AI, what we do is, like, we give, uh, uh, the AI just the data we have and l- have it, like... We call it unsupervised learning. Have it, like, figure out, uh, what do these people have in common and who should we hire.

    19. TN

      The best fit for, yeah.

    20. HS

      Um, so, yeah.

    21. TN

      Yes.

    22. HS

      So it looks at, like, patterns in the, the, the resume lake, um, that you give it, and I guess it scans all of the words, and then, then it does what it does best. It does, um, um, a pattern, um, analysis and finds out, you know... And one other example was, like, if you had the word Thomas on your resume, you also got more points.

    23. TN

      If you had the word what?

    24. HS

      Thomas.

    25. TN

      Thomas?

    26. HS

      Thomas.

    27. TN

      Like the m- the name Thomas.

    28. HS

      Thomas, like the name Thomas. Um-

    29. TN

      [laughs]

    30. HS

      Or, like, in an- another case it was, like, words like Syria and Canada. Um, so the-

  17. 37:56 – 44:28

    The Space Bar Game and Other Weird Ways Companies Test You

    1. HS

      He didn't get fired, uh, but he did apply to a job.

    2. TN

      Okay.

    3. HS

      Um, he was based in, uh, Barcelona and, and, and pla- and, uh, he was based in Barcelona and applied to a job in, in London. Um, and he got a link immediately after applying saying, like, "Hey, go to this link." Um, and you know, I, I sort of feel like we as, uh, job applicants, we are sort of forced consumers of this tech, right?

    4. TN

      Mm.

    5. HS

      Because if you want the job and you get an email with the link saying, like, "Hey, you have 48 hours."

    6. TN

      What are you gonna do?

    7. HS

      "Click on this link."

    8. TN

      Yeah.

    9. HS

      "Play this game," what are you gonna do? You're gonna do it.

    10. TN

      Yeah, you're gonna play the game.

    11. HS

      Even though you were like... And he was like, while he was doing it, he was like, "This is weird. Why is it asking me all these questions?"

    12. TN

      It sounds like the beginning of a horror movie.

    13. HS

      [laughs]

    14. TN

      "Do you want to play a game?"

    15. HS

      Why do I have to do this? Like, it sounds great, and I think a lot of applicants technically like it better than answering 100 questions about, like, "Are you the life of the party?" Like, I'd rather pop up balloons. Um, but when you realize, wait, is this the only criterion I'm gonna be judged on, how well I, like, pop balloons? Or, like, uh, in, in, in, in, in one of the games I had to hit the space bar as fast as possible, and while I was doing that, I was like-

    16. TN

      Just like da, da, da, da, da, da, da

    17. HS

      ... ba, ba, ba, ba, ba, you get, like, 15 seconds or so to do that, and I was like, "What does that have to do with the job?" Like, in what jobs do you have to hit the space bar as fast as possible? Like-

    18. TN

      Maybe it's like a company where, like, there's, like, big gaps between people's names. Maybe there's like-

    19. HS

      [laughs] One of the, one of the-

    20. TN

      Maybe you're working at a company where it's like Suspenseful Pause Incorporated.

    21. HS

      [laughs]

    22. TN

      Maybe it's like... I mean, I wanna know what this job is now-

    23. HS

      Yeah

    24. TN

      ... where somebody out there is just like, "Ugh."

    25. HS

      Yeah. But, but you sort of think-

    26. TN

      Maybe it's a company, maybe it's a company that had to cut costs because all the enters, the enters on the keyboards were broken, and now they have to hire people who can use space-

    27. HS

      [laughs]

    28. TN

      ... to get to the next line, 'cause you can't just press return.

    29. HS

      Well-

    30. TN

      You can't just press, come on, come on. And then that boss is like, "You know what we need? We need people who can press the space bar."

  18. 44:28 – 55:59

    Could AI Remove Bias or Does It Just Make Bias Scalable

    1. HS

      Uh-

    2. TN

      Yeah, but, but now that the, but now that the machines-

    3. HS

      [laughs]

    4. EU

      You're right

    5. TN

      ... but now that the machines are doing the job, could it be possible, and I know, I'm not saying it will, but I'm saying could it be possible that the AI... 'Cause, 'cause here's, here's what I think about in, in what you're talk- in what you're saying. We're living in a world where we know that biases exist.

    6. HS

      Mm.

    7. TN

      We know, right? So whether it's, uh, in courts, whether it's in law enforcement, whether it's in jobs, whether it's-

    8. HS

      Mm-hmm

    9. TN

      ... in schools.

    10. HS

      Yeah.

    11. TN

      Doesn't matter, we know that bias-

    12. EU

      Social settings

    13. TN

      ... social set-

    14. HS

      Mm

    15. TN

      ... bias exists, right? Now, AI's gotten involved, and we see the AI mirroring many of our biases.

    16. HS

      Yeah.

    17. TN

      But the difference is, with AI, we can actually see it. We couldn't see it before, and we couldn't, like, prove it. We had to conduct, like, weird studies.

    18. EU

      Yes.

    19. TN

      We had to... Before, you couldn't say-

    20. EU

      Yes

    21. TN

      ... this company didn't hire anyone because they didn't say baseball or because they had women-

    22. HS

      Mm-hmm

    23. TN

      ... or because they said Black.

    24. HS

      Mm-hmm.

    25. TN

      But now you c- you can actually look at the data and go-

    26. HS

      Yeah

    27. TN

      ... "Oh, damn."

    28. EU

      Mm-hmm.

    29. TN

      And I, I sometimes wonder if it'll be easier, and again, this could be the optimistic side of me, but I, I sometimes wonder if it could be easier for us to address bias in society-

    30. EU

      Mm

  19. 55:59 – 1:02:43

    Your Spotify Playlist Could Get You Fired

    1. EU

      That's me. [laughs]

    2. TN

      No, really.

    3. HS

      [laughs]

    4. TN

      And so-

    5. HS

      Yeah

    6. TN

      ... but now when I-

    7. EU

      No way

    8. TN

      ... when I think of that-

    9. HS

      Yeah

    10. TN

      ... I'm like w- like, are we heading towards a world where a company can hire you or fire you-

    11. HS

      Based on-

    12. TN

      ... looking at your Spotify playlist, going, "Oh-

    13. HS

      [laughs]

    14. EU

      [laughs]

    15. TN

      ... this? Oh, no.

    16. EU

      That's good.

    17. TN

      Oh, no."

    18. HS

      Oh, yeah, yeah. I mean, l- look, some psychologists say-

    19. EU

      [laughs]

    20. HS

      ... that, like, the way we behave is very predictive. And they can certain, find certain ways... Like, there was a, um [lips smack] there was a, a, a, a big finding a f- a, a few years ago, and I think it was, like, uh, that a lot of computer scientists are really into manga comics. Um, so-

    21. EU

      Mm

    22. HS

      ... the question is, like, well, if you look at, then, resumes, should you hire the people that like mangas? And, um, because you know they're gonna be good computer scientists.

    23. TN

      Mm-hmm, mm-hmm.

    24. HS

      But what is it with the people who are great computer scientists who just are not into manga? Like, that's not fair, uh, to those people, right?

    25. EU

      Mm.

    26. HS

      So, like, that's sort of the problem with these shortcuts. But I sort of do feel like-

    27. EU

      And, and there's-

    28. HS

      ... there is a dystopian-

    29. EU

      ... different points

    30. HS

      ... vision that, like, um, you know, I sort of felt like at one point, I was like, wow, maybe at one point we're just not even gonna do a job interview anymore. A company will just tell you if you're hired or fired or if they don't want you based on all of the social exhaust, the data exhaust we sort of leave around. And, and companies can predict who we are. Um, turns out we did test the, uh, uh, sort of personality testing that is being used on social media. It doesn't work. Um-

  20. 1:02:43 – 1:20:11

    Big Brother at Work: Productivity Theater, Rubber Rooms, and Bladder Breaks

    1. TN

      Your work really delves into keeping the job, which I think a lot of people-

    2. EU

      Mm

    3. HS

      Mm

    4. TN

      ... aren't aware of, and might even be more terrified to find out about.

    5. HS

      Oh, yeah.

    6. TN

      Like-

    7. HS

      What we see with the surveillance at work.

    8. TN

      Yeah, like, like for instance-

    9. HS

      Yeah

    10. TN

      ... and, and I, I know there was an explosion of this during COVID.

    11. HS

      Mm-hmm.

    12. TN

      Once people were working remote, and then companies were like, "We need software to know whether people are actually in their underpants or not, and we need to figure out, like, what people are doing at home." But now-

    13. EU

      [laughs]

    14. TN

      ... companies are starting to deploy AIs that not only see how, like, active you are, but they try to predict whether or not the company should fire you, not based on what you're doing now, but what the company thinks you might want to-

    15. HS

      Mm

    16. TN

      ... maybe do or not do.

    17. HS

      Yeah.

    18. TN

      Like-

    19. HS

      I mean, I think it's often, like... You know, it's called, like, a, um, a, a digital neighbor or something. Like, sort of like the, the, the idea is, like, you are a vice president of sales of North America, so there might be a vice president of sales in, in Europe.

    20. TN

      Okay.

    21. HS

      And one of them is, like, uh, might be more, um, successful or not. That's actually kind of vague and hard. Um, but for this, the sake of this, this example, we'll assume, okay, maybe, maybe the, the, uh, the European person is, is better at their job. And so then, an AI will, like, sort of take in all of the digital traces that you leave, how many emails you send, how many Zoom meetings you attend. Are you a bully in Zoom meetings? Do you speak up? Like, it can kind of, uh, assess, um, a lot of different things, and then tell the person in the US, like, "Hey, the person that has your job in Europe and, like, sells more or whatever-

    22. TN

      Wow

    23. HS

      ... like, is more successful, they do this. Why aren't you doing that?"

    24. TN

      Damn.

    25. HS

      It's sort of like a clone of, like, looking at all of their... Everything that gets recorded. And, um, you know, it's sort of like, I don't know. We have different ways to be successful. Like, maybe you write-

    26. TN

      Yeah

    27. HS

      ... 500 emails. The next person is successful by doing, like, 100, uh, in-person meetings a week. That's probably not possible, but, you know, maybe they do 50 a week. Who knows? Um, but we sort of... And, you know, what does it mean to be successful? Like, we had this, like, whole thing, um, probably don't remember this, and I might be dating myself, but there used to be, like, algorithms in New York City to assess teachers, like, 20 years ago or so. Like, every, uh, parent was like, "I wanna know how good my teacher is." Well, it turns out, like, these algorithms were terrible.

    28. TN

      Mm.

    29. HS

      And, and a lot of p- teachers were, like, put in rubber rooms, uh, because their, their students didn't gain enough knowledge in a year. Um, but it could be that they were already at the top.

    30. TN

      Wait, the teachers were put in what?

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