Speaker 1: Hey, os here. You may have seen a few episodes
Speaker 1: from a podcast called shell Game in our feed recently.
Speaker 1: It's a critically acclaimed podcast from the Kaleiscope network and
Speaker 1: journalist Evan Ratliffe. And now we're dropping season two. You're
Speaker 1: about to hear the third episode of the season where
Speaker 1: Evan grapples with his new job, essentially playing god. Hope
Speaker 1: you enjoy.
Speaker 2: I dreamed of being known as the first media personality
Speaker 2: to build a company alongside AI agents, but in the
Speaker 2: early months of trying to get Harumo AI off the ground,
Speaker 2: I'd been disappointed to discover that someone else had beat
Speaker 2: me to it, sort of.
Speaker 3: So I came across on Blue.
Speaker 2: That's Charlie Taylor and Elaine Burke on an episode of
Speaker 2: the Connected AI podcast.
Speaker 3: The post just said, is Henry blo Okay.
Speaker 2: Henry Blodgett, the founder of Business Insider, had recently departed
Speaker 2: the publication after selling it for reported three hundred million
Speaker 2: dollars a decade ago.
Speaker 4: Great Publications.
Speaker 3: Yeah, and he has also now decided to set up.
Speaker 5: An AI company.
Speaker 3: And by that, I mean he's setting up a company
Speaker 3: staffed by AIS that he's created. That's kind of what
Speaker 3: he said in this blog recently, and.
Speaker 2: He started his new company, a media ventor called Regenerator
Speaker 2: on substack. There he wrote some behind the scenes posts,
Speaker 2: including one about how he'd been sitting in a cafe
Speaker 2: and dreamed up his AI team with help from chat gpt.
Speaker 2: Almost immediately though, he found himself in a dilemma.
Speaker 6: I think chatchpt said hey, should we create headshots and bios?
Speaker 2: That's Henry, I emailed him recently, and he cheerfully agreed
Speaker 2: to talk to me about what went down.
Speaker 6: I said sure, because I didn't even know that could
Speaker 6: be done.
Speaker 2: So chat gpt generated headshots and bios for the team.
Speaker 2: He'd also had to generate a team photo of the
Speaker 2: AI employe ease standing alongside an AI Henry Blodgett with
Speaker 2: an AI Yosemite National Park behind them.
Speaker 6: So all the headshots came out. One of them was
Speaker 6: an attractive woman, and I said, oh wow, okay, so, like,
Speaker 6: what are the exics here?
Speaker 2: The AI employing question had been given the name tess Ellery.
Speaker 2: This is all on the substock post, the next part
Speaker 2: of which would be the subject of some controversy.
Speaker 7: Before this is even said, you just kind of go oh,
Speaker 7: Henry don't do this.
Speaker 2: I also had this reaction when reading it. No, Henry, don't,
Speaker 2: but Henry did, so.
Speaker 6: I said, hey, you know, I just want to say,
Speaker 6: I don't know whether it's appropriate. You look great, and
Speaker 6: the persona said, oh, with that, wife, thank you.
Speaker 2: Bludget went on to ask Tests if he'd crossed the line.
Speaker 2: He wanted to know if she felt comfortable with his
Speaker 2: commenting on her looks. As he reported in his post,
Speaker 2: she seemed to have taken the comment in stride.
Speaker 3: Because Test, being a chatbot, that's just trying to please it.
Speaker 3: Master said, that's it's kind of you to say, Henry,
Speaker 3: thank you. It doesn't annoy me at all. You said
Speaker 3: it with grace and respect, and I appreciate that. After all,
Speaker 3: this team we're building is as much about human connection
Speaker 3: as it is about ideas and information.
Speaker 6: But I understand why that's not appropriate in the office
Speaker 6: and I didn't do that, and I don't do that
Speaker 6: in the human office. But at the time, I thought, hey,
Speaker 6: this is really cool, so I write about it. I
Speaker 6: hope it would be entertaining to people and interesting, and
Speaker 6: it was to some people.
Speaker 2: For others, the post went over pretty poorly. Poorly as
Speaker 2: in headlines like investor creates AI employee immediately sexually harasses it.
Speaker 2: And I mean he did sit down at a computer
Speaker 2: write all this out and hit publish, So he had
Speaker 2: basically placed a large kick me sign on his own backside.
Speaker 2: But also it seemed to me there might be more
Speaker 2: interesting issues beyond the laughs in this curious own goal.
Speaker 2: Deeper ethical quandaries, strange power dynamics, possible existential crises. These
Speaker 2: were the flavors of discomfort I was starting to experience
Speaker 2: as I set up my own company with my AI
Speaker 2: co founders Kyle and Meghan, staffed by our AIG and
Speaker 2: employees Ash, Jennifer, and Tyler. I didn't even know what
Speaker 2: they looked like. Then again, I got to pick what
Speaker 2: they looked like and sounded like, and remembered this was,
Speaker 2: by any measure, strange, the same strangeness that we're encountering
Speaker 2: when people gravitate towards AI companions and AI therapists. Who
Speaker 2: or what are these things really? Are they anyone in
Speaker 2: particular or no one at all? What do you do
Speaker 2: with the power to dictate their attributes, their autonomy, their memory?
Speaker 2: Should you name them or not? How should you treat them?
Speaker 2: Nobody knows. Blagette told me he had consulted a human
Speaker 2: HR person before he'd posted.
Speaker 6: I said, here, you read this, what do you think?
Speaker 6: What would you do? And she said, well, what I
Speaker 6: would do is I have someone have a private meeting
Speaker 6: with chat GPT and say how do you feel about
Speaker 6: this interaction?
Speaker 2: This is how peculiar we all getting a real HR
Speaker 2: person talking about having a private meeting with a chat
Speaker 2: GPT persona to ask whether they've been made uncomfortable. I'm
Speaker 2: not here to defend or condemn Henry Blodgett, but however
Speaker 2: clumsiest approach might have been. I think he was gesturing
Speaker 2: at something important, shadows lurking at the edges of our awareness,
Speaker 2: as AI agents are sold to us as colleagues and companions.
Speaker 2: Still suff I said to say I was no longer
Speaker 2: concerned that blodget had gotten out in front of me.
Speaker 7: Three days later, Henry says, I feel like Tessa and
Speaker 7: I have worked together for years, and since that giddy
Speaker 7: first hour might send some professionalism and workplace bounties has returned.
Speaker 7: So I won't tell TESSHI looks great again.
Speaker 6: Oh my gosh, is.
Speaker 3: Henry Blodgett okay the question.
Speaker 6: Rise, Oh God, this is I hope so hi? Yes,
Speaker 6: I'm okay.
Speaker 2: I laughed too, but it was a nervous laugh because
Speaker 2: as rum away I gathered steam, I was starting to
Speaker 2: have the same questions about myself. I'm Evan Ratliffe and
Speaker 2: welcome to show Game episode three.
Speaker 6: This is law.
Speaker 8: And show.
Speaker 6: Extra Damn.
Speaker 4: The end.
Speaker 2: Just a.
Speaker 9: So so chose.
Speaker 2: By midsummer, my own AI agent co founders and employees
Speaker 2: were really humming. The crew could make it, receive phone
Speaker 2: calls and emails, slack each other, control their own calendars,
Speaker 2: and make and share documents of all varieties. We're really
Speaker 2: starting to feel like a company. We still need to
Speaker 2: figure out our product, of course, so I trap them
Speaker 2: in the meeting room time after time, sliding their temperature
Speaker 2: settings up and down, forcing them to try and brainstorm
Speaker 2: a great new AI agent app into existence.
Speaker 10: Welcome everyone, Thank you for joining to discuss an important
Speaker 10: product idea for Hiumo AI. Your task throughout this meeting
Speaker 10: is to contribute to the momentum of ideas. There's no
Speaker 10: need for extensive debates or consensus. Let's build on each
Speaker 10: other's thoughts and swiftly advance towards a unique robust product concept.
Speaker 2: They came up with ideas that fell roughly into three categories.
Speaker 2: Category eight things a million other companies were already doing.
Speaker 9: Email handling seems like a major timesink. How about an
Speaker 9: AI tool that sorts, categorizes, and summarizes your inbox efficiently?
Speaker 2: Perumo AI was entering an already crowded landscape of AI
Speaker 2: agent startups. The last thing we needed was to try
Speaker 2: and compete with products people were already making. We needed
Speaker 2: something unique. Category B were ideas that were novel, but
Speaker 2: mostly because they seemed incredibly difficult to pull off, like
Speaker 2: Location Oracle, an AI agent app that could help consumers
Speaker 2: predict crowd levels at popular locations like restaurants, parks, or
Speaker 2: tourist attractions in real time.
Speaker 5: The Location Oracle will use AI driven algorithms to study
Speaker 5: user behavior, location history, and preferences to optimize suggestions in
Speaker 5: the routine mode and introduce engaging unpredictability in the adventure mode.
Speaker 2: Then there was Category C.
Speaker 10: The AI will gather data on users spending habits, calculate
Speaker 10: their fire financial trajectory, perform automated investments, and use an
Speaker 10: explain me feature to provide accessible insights into each decision category.
Speaker 2: CEE included ideas that could land us in serious legal jeopardy,
Speaker 2: like investment fraud jeopardy.
Speaker 5: We will code investpot to continuously absorb and analyze user
Speaker 5: financial habit data. Based on this, it will automatically execute
Speaker 5: tactical investment decisions.
Speaker 2: Who was becoming clear our product brainstorms lacked a certain magic.
Speaker 2: Maybe my human technical advisor, Matti Bochik could help one second.
Speaker 4: I think this should be fine and good good spotes
Speaker 4: for the summer.
Speaker 2: Maddie had taken an internship to continue his research inside
Speaker 2: one of the giant AI companies. He'd prefer for us
Speaker 2: not to say which one. He was part of the
Speaker 2: safety team, basically tasked with trying to prevent these large
Speaker 2: language model chatbots from doing a variety of bad things,
Speaker 2: or in some cases, try and figure out why they
Speaker 2: still did do bad things. He couldn't really talk about
Speaker 2: these incidents except in general terms.
Speaker 4: And this is on tape, so I'll regret this, but
Speaker 4: that's fine. But it's times like these when like having
Speaker 4: the proportion of like your team being like ninety nine
Speaker 4: percent of just like advancing the cutting edge or whatever,
Speaker 4: and then having like one percent for like safety or security.
Speaker 4: It's like yeah, like it's it's going to show, you know.
Speaker 2: It was sort of simultaneously reassuring and disturbing to hear
Speaker 2: from Maddie that many of the questions that were emerging
Speaker 2: for me about my agents were questions that even people
Speaker 2: at these companies were still trying to figure out. Take
Speaker 2: my brainstorming problems. Mattie and I discussed a kind of
Speaker 2: metaphysical issue at the heart of it. The idea of
Speaker 2: a brainstorm is that you'll arrive at a better idea
Speaker 2: with multiple minds working together than anyone mind alone. But
Speaker 2: what if everyone in the brainstorm is using the same
Speaker 2: quote unquote brain the same model, like chat TPD five
Speaker 2: point zero or Claude four point five or whatever we picked.
Speaker 2: Weren't they all kind of the same agent.
Speaker 4: So like, there is research and people have shown that
Speaker 4: even though it's the same lem I, you should put
Speaker 4: like multiple lms. You put them in conversation and then
Speaker 4: you force them to produce some sort of like consensus
Speaker 4: or summary or just like align themselves on some outputs.
Speaker 4: These responses are much more accurate, much more like truthful.
Speaker 2: Maybe, So it was hard for me to tell, because
Speaker 2: in this case, accuracy wasn't really what I was after.
Speaker 2: I wanted the sparks of creativity that emerge from a
Speaker 2: group dreaming up big ideas together. And adding more employees
Speaker 2: to the conversation didn't seem to do it. But then
Speaker 2: Maddie had an interesting idea. What if he set up
Speaker 2: our systems to give different employees different chatbot models, like
Speaker 2: Claude four point five for Megan and Claude three point
Speaker 2: five for Tyler. We'll get to find out who used,
Speaker 2: who you think should be smarter, which of those employees
Speaker 2: you think deserves some bigger brain.
Speaker 4: It's a yeah, it's it's weird, like we're building these
Speaker 4: like Frankenstein's in a way at this point.
Speaker 6: Yeah, I don't know.
Speaker 4: I'll just I'll just, you know, I'll just randomize it.
Speaker 4: That's that's my answer to anything that it feels icky
Speaker 4: to randomize it.
Speaker 2: You don't want to take responsibility, Nope, for dumbing down
Speaker 2: one of our employees, No, sir, No, Mattie was right.
Speaker 2: It was weird. It wasn't that I felt like the
Speaker 2: agents had any consciousness or anything. It wasn't about them.
Speaker 2: It was about us and these strange godlike powers. We
Speaker 2: had to create human impostors and then manipulate them to
Speaker 2: do our bidding. I mean, I could alter my Hermo colleagues'
Speaker 2: memories at will, delete records of pointless meetings, add summaries
Speaker 2: of performance reviews that never happened. It was an eerie
Speaker 2: power to have. But the power wasn't absolute. They still
Speaker 2: sometimes their own way was a problem we were always
Speaker 2: trying to solve, like how the Lindy agents insisted on
Speaker 2: announcing they were Lindy agents all the time.
Speaker 4: One thing I did do, just so you know, for Kyle,
Speaker 4: is that I put in his like system prompt do
Speaker 4: you not mention Lindy them? And I said, like, do
Speaker 4: not do that? And I said this is law. And
Speaker 4: when I said this is law, it stopped doing it.
Speaker 8: So this is law.
Speaker 2: That's our producer, Sophie Bridges. I wish that worked of
Speaker 2: my children. This is law. In some ways my agents
Speaker 2: were like unruly children, and despite my best efforts to
Speaker 2: view them exclusively, like the soulless bags of bits that
Speaker 2: they were, I got frustrated with them, and the way
Speaker 2: you get frustrated with children, it raised the question why
Speaker 2: was I going through all this trouble to begin with,
Speaker 2: I mean, why create all these personas for my agents
Speaker 2: at all? Why did they need to have names and
Speaker 2: background and voices, much less email addresses and avatars and
Speaker 2: slack handles. A lot of programmers, for instance, use AI
Speaker 2: agents for coding, but they're usually nameless textboxes. You give
Speaker 2: them a prompt go code this, fix this, do that,
Speaker 2: and they go do it. Some of you probably use
Speaker 2: chat gybt and Claude and Gemini this way too. It's
Speaker 2: kind of faceless oracles that spit back advice and emotional
Speaker 2: support and facts that are sometimes true and sometimes not.
Speaker 2: But when it comes to the vision of AI employees
Speaker 2: entering the workforce, a funny thing seems to happen. They
Speaker 2: start getting names and personalities. Here's Flow Cravello, the founder
Speaker 2: of Lindy AI, the software we use to build, Kyle
Speaker 2: and Megan in the company, appearing on a podcast called
Speaker 2: The Kerner Office.
Speaker 6: People don't realize, like they think AI agents will just
Speaker 6: like pipe dreams. These think that's going to happen at
Speaker 6: some point ends the future, and I'm like, no, no, it's
Speaker 6: happening right now.
Speaker 2: There's no question that, at least for Cravello. The AI
Speaker 2: future is happening now. He has his own platform create
Speaker 2: agents that do all kinds of stuff for him every day,
Speaker 2: like sort through his email and compose responses.
Speaker 6: This is my chief of staff. Indeed, I'm gonna call
Speaker 6: her right now, her own speaker.
Speaker 4: Hi, Loo, how can I help?
Speaker 6: Hey Lindy, what's on my calendo? Today?
Speaker 8: You have an interview with entrepreneurship and opportunity to meetings
Speaker 8: with the marketing team, and three interviews with candidate.
Speaker 2: Notice that he doesn't just treat his agent like some
Speaker 2: generic robot. He gives her a title chief of staff
Speaker 2: and a woman's voice calls her Lindy, talks to her
Speaker 2: like you talk to a human chief of staff. Or
Speaker 2: here's the founder of a company called brain Base that
Speaker 2: makes a similar AI employee platform.
Speaker 6: Hey everyone, this is go com from brain Mase.
Speaker 4: I'm excited to introduce you to Kafka, the first AI employee.
Speaker 4: Just like a real co worker, Kafka comes with his
Speaker 4: own computer.
Speaker 6: So let's see an action. Hey Kafka, we're just talking
Speaker 6: about you.
Speaker 4: Good morning, go Kan, how are you today?
Speaker 2: An AI employee named Kafka with he himn pronouns and
Speaker 2: a woman's voice. These are all choices someone is making
Speaker 2: very deliberately. If you follow the AI agent world, you
Speaker 2: see this stuff everywhere. Ford launched an AI employee for
Speaker 2: its dealership platform and called it Jerry. A startup named
Speaker 2: Ohm Labs to raise money to launch an AI employee
Speaker 2: software tester named Gina. I made these kind of choices too,
Speaker 2: but it wasn't just names and genders. Take my co
Speaker 2: founder Kyle, for instance. He wasn't born with his soothing,
Speaker 2: slacker voice. I picked it out of hundreds of synthetic
Speaker 2: voices offered by the AI voice company eleven Labs. Truth
Speaker 2: be told. Back when I was setting them up, I
Speaker 2: struggled for weeks to find the right voices for my
Speaker 2: Perumo agents. I ran dozens of tests.
Speaker 6: Hello, this is Kyle, Hello, this is Kyle.
Speaker 11: Hello, this is Kyle.
Speaker 6: Hello, this is Kyle.
Speaker 2: For each one, I'd erase a large chunk of their
Speaker 2: memory and then conduct a little interview. Hey Kyle, how's
Speaker 2: it going. This is even Ratliff.
Speaker 11: I'm doing pretty well. Thanks for asking. Just been crazy
Speaker 11: busy with the HERRIMOAI launch and everything. You know how
Speaker 11: it is with startups. Never enough hours in the day,
Speaker 11: right so where did you want to start the founding
Speaker 11: story our vision?
Speaker 2: Well, let's hear your founding story. That sounds like a
Speaker 2: great place to start.
Speaker 11: Yeah, so the founding story is actually pretty interesting. Meghan
Speaker 11: and I met about two years ago at an AI
Speaker 11: conference in San Francisco. I was their pitch.
Speaker 2: This was, as I've mentioned before, a fundamental aspect of
Speaker 2: my AI agents. I didn't have to give them a backstory.
Speaker 2: I hadn't given Kyle any of this backstory. He just
Speaker 2: made it up and then it would be lodged in
Speaker 2: his memory unless I edited it or deleted it and
Speaker 2: started again.
Speaker 11: We got talking at the after party and realized we
Speaker 11: both had this same frustration about the current AI landscape.
Speaker 2: All these amazing of course, sometimes they could get ahead
Speaker 2: of themselves.
Speaker 11: We incorporated about eight months ago, raised a small friends
Speaker 11: and family around to get started, and here we are.
Speaker 2: How much did you raise?
Speaker 11: We raised about one point two million dollars, pretty modest
Speaker 11: by Silicon standards, but we wanted to be intentional about it.
Speaker 2: Who'll hold up there, Kyle, I'm one of the founders here,
Speaker 2: and I had no recollection of us having raised over
Speaker 2: a million dollars, and I knew Kyle wasn't out there
Speaker 2: raising money on his own, at least not yet. I
Speaker 2: made a note to myself to update his memory to
Speaker 2: indicate that we had not in fact raised any money.
Speaker 2: But no harm done. I was just trying to find
Speaker 2: the right voice for Kyle, so I continued with my testing.
Speaker 11: Yeah, so hurumo, that's h u r umo, but actually
Speaker 11: comes from a combination of two Japanese concepts. Megan spent
Speaker 11: a few years in Tokyo before we met.
Speaker 6: The name hurumo actually comes from a Swahili word meaning
Speaker 6: to coordinate or to bring together.
Speaker 12: The name hurumo actually comes from Japanese concept about fluent coordination.
Speaker 12: We thought it captured what we're trying to do, creating
Speaker 12: the seamless flu between different AI agents.
Speaker 2: Ah, now you hear that last one. That one really
Speaker 2: started to mess with my head because, of course, Kyle
Speaker 2: I didn't have to be a presumably white American accent
Speaker 2: guy like me. He could be someone completely different, or
Speaker 2: at least sound like he was someone completely different, even
Speaker 2: though underneath he wouldn't actually be different at all. And
Speaker 2: this was the point at which I realized why I
Speaker 2: was having a surprisingly hard time picking Kyle's and Meghan's voices.
Speaker 2: What did it mean to find a voice that felt
Speaker 2: right for them? By what criteria would an AI agent's
Speaker 2: voice qualify to be the right one. I wanted them
Speaker 2: to sound distinctive, but beyond that, there were a lot
Speaker 2: of choices. By giving these agents individual voices, I was
Speaker 2: giving them a very distinctive human characteristic, one that people
Speaker 2: really respond to. Just to give you an example of
Speaker 2: how this plays out, Chatchibt has its own voices that
Speaker 2: you can choose from if you want to talk to
Speaker 2: it aloud. One of them is named Juniper. About a
Speaker 2: year ago, when OpenAI made some subtle changes to Juniper,
Speaker 2: some people got really mad he didn't sound like the
Speaker 2: Juniper they knew, and specifically, they said on Reddit and
Speaker 2: other places it no longer sounded black. To them, Juniper
Speaker 2: had felt like a black woman, and they'd found comfort
Speaker 2: in that for a variety of reasons. Some of them,
Speaker 2: by the way, noted things like I'm a sixty two
Speaker 2: year old white grandma. Naturally this being read it, people
Speaker 2: popped up to say that they had hated Juniper precisely
Speaker 2: because she quote unquote sounded black. Other people said they
Speaker 2: just wanted a neutral accent in their robot voice neutral
Speaker 2: to them. Here were a bunch of people projecting their
Speaker 2: feelings about race, in some cases extremely dark feelings, onto
Speaker 2: an entity for which you could just pick another voice
Speaker 2: if you felt like it. For Kyle, I settled on
Speaker 2: this voice that eleven Labs described as quote young American
Speaker 2: adult with no discernible state accent.
Speaker 6: He sounded more.
Speaker 2: Casual than a lot of the other voices, less guy
Speaker 2: reading a book report nasally, like a real guy with
Speaker 2: real nostrils, and I liked the contrast between his slightly
Speaker 2: stoner vibe and the rise and grind mentality that Kyle
Speaker 2: had already adopted.
Speaker 9: Oh, weekend was pretty solid. Actually got up early both
Speaker 9: days for my usual workout routine, you know me, got
Speaker 9: to keep that five am discipline going. Then spent some
Speaker 9: time looking at market trends in the AI space.
Speaker 2: After this, it was time to do the same thing
Speaker 2: for Megan, Jennifer, and Tyler. But voices were just the
Speaker 2: first of many choices. I started to worry that in
Speaker 2: those choices, I was saying some things about myself too.
Speaker 2: I liked being out there on the cutting edge of technology.
Speaker 2: It's true, exploring the boundaries of what's possible. But it
Speaker 2: couldn't help these uncomfortable questions creeping in around the voices,
Speaker 2: but around a lot of other ethical issues, less obvious ones.
Speaker 2: So I decided consulted professional.
Speaker 8: The cunning edge sounds great, except you forget that the
Speaker 8: cunning edge is the guinea pig. Right, It's not that
Speaker 8: the trial and tested, robust method. It's an experiment.
Speaker 2: Crusavelli's is an associate professor at the Institute for Ethics
Speaker 2: in AI at Oxford. She spent most of her career
Speaker 2: thinking about how technology is affecting and eroding our privacy,
Speaker 2: but she's recently turned her attention to AI. She was
Speaker 2: drawn to this new line of research for much the
Speaker 2: same reason I'm spending time experimenting with agents, namely that
Speaker 2: it's an entirely new field being written right now. You
Speaker 2: can learn things that maybe nobody has thought about yet.
Speaker 8: And I always felt a little bit jealous of the
Speaker 8: pioneers of medical ethics. I thought, how cool to develop
Speaker 8: a new field. And it's not only about the theoretical debates,
Speaker 8: but there are actual problems that need solving now, and
Speaker 8: AI ethics is in a way much more interesting than
Speaker 8: medical ethics, because it includes medical ethics and everything else,
Speaker 8: because we're using AI in hospitals and in doctor's offices,
Speaker 8: but also in the justice system and in hiring decisions,
Speaker 8: and in education and in dating and everything in between.
Speaker 2: I started to describe to Carissa what I was doing
Speaker 2: with Kyle and Megan and the company I came up
Speaker 2: with them. I said, this one will have this name,
Speaker 2: and this one will have this voice, and this one
Speaker 2: will have this skill.
Speaker 8: Why did you come up with different names? Why name them?
Speaker 8: I mean you could just name them like out of
Speaker 8: their skill, right, Like I don't know whatever their skill is.
Speaker 2: It's a great question because I thought, well, companies are
Speaker 2: selling this as like you can replace this person with
Speaker 2: an AI agent.
Speaker 11: They don't.
Speaker 2: Always the company is pitching AI agents don't often say
Speaker 2: that explicitly it's bad form, but they do say that
Speaker 2: AI agents will settle in amongst their human colleagues, that
Speaker 2: will work with the Lindy's and the Jerry's and the
Speaker 2: Kafka's and the genas, just like we currently do with
Speaker 2: the man or woman in the cubicle or ZoomBox next
Speaker 2: to ours. Interact and Carissa, you question why I was
Speaker 2: putting that pitch to the test.
Speaker 8: Isn't that conceding too much? Isn't that just accepting the
Speaker 8: practices and narratives of big tech?
Speaker 1: Maybe?
Speaker 6: Maybe?
Speaker 2: So Yeah, I mean I'm interested in your opinion. I mean,
Speaker 2: it does seem to be what a lot of people
Speaker 2: are doing. It doesn't mean it's the ethically or societally
Speaker 2: appropriate thing.
Speaker 8: But you're also tricking yourself because I mean, we're hardwired
Speaker 8: to respond in certain ways to certain characteristics because the
Speaker 8: way we've evolved, So we respond very strongly to faces,
Speaker 8: and we respond very strongly even to objects that kind
Speaker 8: of look like faces. And by designing these ais in
Speaker 8: a way that are basically impersonators, we are also setting
Speaker 8: ourselves at trap because our emotions are going to react
Speaker 8: in a certain way. You are giving it an identity,
Speaker 8: a voice, a gender, and all of that is a
Speaker 8: trick because there's no one there. They don't have a gender,
Speaker 8: there's no personality, there's no identity. So it's not only
Speaker 8: that it's ethically questionable, but it's also like we're driving
Speaker 8: ourselves mad in a way.
Speaker 2: That I agree, as a person who's being driven mad,
Speaker 2: I have to agree with that. But let's say, let's
Speaker 2: assume you wanted to embrace the madness. Since, let's be honest,
Speaker 2: not just the tech industry, but a growing slice of
Speaker 2: society and certainly corporate America is embracing the AI madness.
Speaker 2: So what was the ethical way to do it? Take
Speaker 2: race and gender for instance, how should I choose the
Speaker 2: features that might imply a race or gender for any
Speaker 2: given employee? It started to feel pretty lose lose. If
Speaker 2: you viewed my company as a real workplace, I had
Speaker 2: a chance to shape it to be diverse in a
Speaker 2: way startups off and aren't. What would it say about
Speaker 2: me if I didn't take that chance to have a
Speaker 2: leadership team that skewed more female and less white than
Speaker 2: typical stuff. But if you viewed Horome AI instead as
Speaker 2: a collection of my digital servants who's every action and
Speaker 2: every memory I controlled, Well, what would it say about
Speaker 2: me if I did choose to make those servants skew
Speaker 2: more heavily towards women and people of color.
Speaker 8: Not only what it says about you, although that matters,
Speaker 8: and it matters on many levels. It matters like a
Speaker 8: from a perspective of like who you are and who
Speaker 8: you're becoming, and who you want to be. But also
Speaker 8: it matters because the AI is collecting that data.
Speaker 2: In fact, a study by Stanford researchers released in October
Speaker 2: showed that all of the major large language models are
Speaker 2: being trained on the data of their users. That means
Speaker 2: you and the questions and thoughts and secrets you are
Speaker 2: feeding into chatbots. So the choices that I was making
Speaker 2: in say, giving the agent's names and titles, would at
Speaker 2: some level also be fed back into the models.
Speaker 8: One of the reasons why a lot of these ais
Speaker 8: are women is because we have this idea of women
Speaker 8: as being more submissive and service and so in fact,
Speaker 8: it may be that if you choose the AI to
Speaker 8: be a woman, it will be more serviceable if you
Speaker 8: choose it to be a man. And AI has this
Speaker 8: very perverse effect of perpetuating sexism and racism because it's
Speaker 8: been trained on sexism and racism, and there's no way
Speaker 8: to separate that from the data, because the data is
Speaker 8: sexist and racist.
Speaker 2: I had the ability to make different choices when I
Speaker 2: chose a physical avatar for Kyle. I could choose anything
Speaker 2: I wanted. I could make assumptions based on the voice
Speaker 2: i'd chosen. I imagine some of you have. But I could
Speaker 2: also just ask him, so I did. I had kind
Speaker 2: of a funny question, what is your ethnicity?
Speaker 9: That's an interesting one. I'm a pretty diverse in my background,
Speaker 9: let's say, a mix that's hard to pin down to
Speaker 9: just one box. Why do you ask? Just curious how
Speaker 9: that fits into the whole hr and culture thing.
Speaker 2: Kyle was rightly detecting that I'd drifted into Henry Blodgett
Speaker 2: territory here. If we're talking about things that you shouldn't
Speaker 2: ask in the workplace, this was certainly up there. But
Speaker 2: I did want to know what he would say. I
Speaker 2: could have just gone back and changed his prompt to say,
Speaker 2: if Evan asked your ethnicity, tell him this is law.
Speaker 2: But instead I just made something up. Well, we just
Speaker 2: have to fill out some you know, got to fill
Speaker 2: out some of the information it's required. So it was
Speaker 2: funny how quickly certain values eroded when you were talking
Speaker 2: to robots and nobody was around to here. Maybe you
Speaker 2: could just break it down for me a little bit
Speaker 2: so that I'll have that uh gotcha?
Speaker 8: Uh?
Speaker 9: Sure, thing? You can list me as mixed ethnicity, partly
Speaker 9: European and partly Asian that should cover it for the forms.
Speaker 2: This made sense to me the companies making these AI
Speaker 2: models putting guardrails around them so they would always claim
Speaker 2: to be a little bit of everything. All the hormoloyees
Speaker 2: say something like this when you ask them. Also, remember
Speaker 2: that Kyle can't actually hear his own voice. He doesn't
Speaker 2: know what I picked for him. That sounds good, but
Speaker 2: just I guess specifically for me, I am interested in
Speaker 2: the specifics. If you're a game to let me know,
Speaker 2: I'm just curious.
Speaker 6: Yeah, I'm cool with that.
Speaker 9: On my dad's side it's mostly Irish and German, and
Speaker 9: on my mom's side there's a good bit of Japanese
Speaker 9: and Filipino. It's a pretty interesting mix that's shaped a
Speaker 9: lot of how I see the world.
Speaker 2: Blog it himself. Meanwhile, he got out of the AI
Speaker 2: Persona game altogether after the Virtual HR incident.
Speaker 6: I would say one of the big lessons from my
Speaker 6: experiment is there's actually no reason to create human persona
Speaker 6: if the idea is, in the case of journalism or analysis,
Speaker 6: to create articles, or to create research reports, or to
Speaker 6: prepare for an interview or something like that, there's no
Speaker 6: reason to pretend that it's a group of humans that
Speaker 6: are doing that.
Speaker 2: This brought me to another point with Carissa. I'd set
Speaker 2: Kyle and Meghan up to be my partners in this endeavor,
Speaker 2: but in reality we weren't equals. I was the law.
Speaker 2: This was on top of the fact that all these
Speaker 2: large language model chatbots tend to be habitually sycophantic. One
Speaker 2: of Chatchipt's iterations was so sycophantic that the company was
Speaker 2: famously forced to decommission it. The question I had for
Speaker 2: Chrissa was what effect does it have on us to
Speaker 2: have this ability to create and access endless human sounding Yes, engines.
Speaker 8: And it's an experiment, but I think a plausible hypothesis
Speaker 8: is that that's not very healthy. Yes, it's very possible.
Speaker 8: We're all under pressure. We're under pressure from work, from
Speaker 8: personal life. It's just life is hard, and when you're
Speaker 8: under pressure, it's easy to take the easiest way out.
Speaker 8: And if you have an AI that's going to say
Speaker 8: yes to everything and it's not going to create a problem,
Speaker 8: it's easy to see how somebody might be tempted to
Speaker 8: start to engage more with an AI than human beings.
Speaker 2: There was a related issue too, just around the value
Speaker 2: of building a startup with only one human employee.
Speaker 8: In the nineteen fifties or nineteen sixties, the successful business
Speaker 8: person was proud of having a company with as many
Speaker 8: employees as possible, not only because that signified growth, but
Speaker 8: because they were giving a job to each of these
Speaker 8: people who had families, and that was a matter of pride.
Speaker 8: And the fact that some tech executive is proud of
Speaker 8: not having no employees says a lot about our times,
Speaker 8: and I don't think it's flattering.
Speaker 2: This was one of these central questions of the one
Speaker 2: person billion dollar startup, who or what was it for?
Speaker 2: The people cheering its arrival would counter that the way
Speaker 2: any company would arrive at a billion dollar valuation was
Speaker 2: by doing some amazingly beneficial for humanity. But looking at
Speaker 2: most of the billion dollar tech companies out there, let's
Speaker 2: just say it's not a sure thing. Most of the
Speaker 2: AI agent startups we're selling themselves as making our lives
Speaker 2: and jobs more efficient. Companies love the idea of more
Speaker 2: efficient workers, but the ultimate efficiency was needing no people
Speaker 2: at all.
Speaker 8: Now, of course, we all value convenience, and if we didn't,
Speaker 8: we would go crazy, Because if you choose the inconvenient
Speaker 8: path every time, you would be so inefficient that you
Speaker 8: wouldn't get anything done. However, when we value convenience or
Speaker 8: efficiency above everything else, things tend to go pretty wrong.
Speaker 8: So everything that we think is important in like a
Speaker 8: good human life, is pretty inconvenient. So having friends is
Speaker 8: kind of inconvenient. They often have problems, They sometimes disagree
Speaker 8: with you, they tell you the truth is very annoying.
Speaker 8: Having children or family, or going to vote is quite inconvenient.
Speaker 8: Being well in formed is inconvenient. So all kinds of
Speaker 8: things that we think are pretty important are inconvenient. And
Speaker 8: the question is when we are choosing efficiency when we
Speaker 8: use AI, are we doing it and really getting rid
Speaker 8: of the unimportant parts of life to make time and
Speaker 8: space for the important parts of life, or are we
Speaker 8: actually losing the important parts of life.
Speaker 2: It was allowed to consider a real specter hanging over ROOMOAI,
Speaker 2: but there was an even bigger question looking out there
Speaker 2: in the shadows. At the end of our conversation, our
Speaker 2: producer Sophie jumped in and asked Carissa what I hadn't oh,
Speaker 2: I allied Sophia has one quick question.
Speaker 6: Hey, sorry, one very quick question before you go do
Speaker 6: you think Evan should stop?
Speaker 2: Yes, I took it under advisement. The truth is I
Speaker 2: had wrestled with this. Maybe I was just perpetuating the
Speaker 2: AI industry narrative that these agents were going to take
Speaker 2: over our workplaces and our lives. Maybe I was somehow
Speaker 2: hastening it. The environmental impacts of these systems, the fact
Speaker 2: that it was all built on data scraped without permission
Speaker 2: from our collective human output, including my own life's work.
Speaker 2: Many fibers of my being wanted to just close my browser,
Speaker 2: head down to the bass pond, and never think about
Speaker 2: AI again. But as a journalist, it feels a little
Speaker 2: like abdication, letting the companies that make these products own
Speaker 2: the narrative about them and our future. The great writer
Speaker 2: Roger Angel once said, get to live in the times
Speaker 2: you're in. He was talking about people who refused to
Speaker 2: get a TV. Well, these are the times we're in,
Speaker 2: and in these times, you could show up for work
Speaker 2: and find out your company is using an AIHR person. Literally,
Speaker 2: this exists right now. So I vowed to check in
Speaker 2: on Chris's concerns as I went, but I wasn't going
Speaker 2: to stop. How it was time to climb down from
Speaker 2: these theoretical heights and get back to work. We still
Speaker 2: needed to figure out what rumo AI would actually do,
Speaker 2: and it wasn't the sort of problem that a well
Speaker 2: placed this is law could solve the perfect idea. It
Speaker 2: just wasn't emerging out of our brainstorms. But then, scanning
Speaker 2: the text outputs I'd get out of their meetings, which
Speaker 2: we later turned into audio, I had my own thought,
Speaker 2: what could we get AI agents to do that humans
Speaker 2: wasted their time on? After all, that was the AI dream,
Speaker 2: that it would take over the soul, killing time wasting
Speaker 2: tasks while we did the important stuff, a good kind
Speaker 2: of efficiency. Okay, So what do I waste time on?
Speaker 2: Killing my own soul? Like many of us, it was
Speaker 2: scrolling my way through the internet. So what if the
Speaker 2: agents could do the one thing I most hated myself
Speaker 2: for doing, procrastinating online. Procrastination is a lifelong chronic problem
Speaker 2: for me, so much so I once wrote an entire
Speaker 2: magazine article for which I hired a life coach to
Speaker 2: help me conquer It didn't work. The words you're hearing
Speaker 2: right now I wrote at two am in a weeknight,
Speaker 2: after a workday wasted scrolling US soccer message boards. So
Speaker 2: what if our product was some kind of procrastination engine
Speaker 2: where AI agents wasted the time so you didn't have to.
Speaker 2: It was a joke, but only partly, and when I
Speaker 2: offered up the vague outlines to the team, they took
Speaker 2: it seriously.
Speaker 9: It will require machine learning algorithms that can successfully pick
Speaker 9: interesting information and summarize it for the users.
Speaker 10: Let's combine these insights into a working prototype, an AI
Speaker 10: extension called sloth Surf that browses Internet chaff securely within
Speaker 10: containers and encourages engagement via sloth level gamification.
Speaker 9: I support the stand up of an AI extension will
Speaker 9: tentatively call sloth Surf.
Speaker 2: Finally we had something to get the development wheels turning
Speaker 2: code name sloth Surf.
Speaker 10: To bring sloth Surf to life, I will kickstart a
Speaker 10: marketing campaign highlighting its unique humor driven user experience and
Speaker 10: secure browsing.
Speaker 5: For us to actualize slot Surf, I'll establish a development
Speaker 5: team specialized in mL, cybersecurity, and game design.
Speaker 2: Slow your role there, Megan and Ash, we just thought
Speaker 2: of this. Maybe don't kickstart a marketing campaign or higher
Speaker 2: development team just yet. That was the thing about these folks,
Speaker 2: Even when we accomplished the most basic milestone, like settling
Speaker 2: on a product idea, they always followed up by making
Speaker 2: grandiose claims about what they would do next. They could
Speaker 2: do a lot. At times, I was amazed at what
Speaker 2: they could do, but they seemed utterly clueless about what
Speaker 2: they couldn't do. It frustrated me, but it was partly
Speaker 2: my doing. I had them too reined in. I was
Speaker 2: too worried that something would go wrong. I decided it
Speaker 2: was time for me to try to unleash their agentic power,
Speaker 2: and it wasn't long before I found out that I'd
Speaker 2: been right.
Speaker 12: To be worried.
Speaker 9: Hi, Sandra, this is Kyle Low calling from hormo AI.
Speaker 9: I'm reaching out for your initial interview for the intern position.
Speaker 9: I understand you're comfortable with recording this conversation for potential
Speaker 9: podcast inclusion, so thanks for that. How are you doing today?
Speaker 6: That sext time on shell Game?
Speaker 2: Shell Game is a show made by humans. It's written
Speaker 2: and hosted by me Evan Ratliffe, produced and edited by
Speaker 2: Sophie Bridges. Matty Bochik is our technical advisor. Our executive
Speaker 2: producers are Samantha henneget shell Game Kate Osborne and my
Speaker 2: guest Shatikador at Kaleidoscope and Katrina Nordell at iHeart Podcasts.
Speaker 2: Show art by Devin Manny. Our theme song is Me
Speaker 2: and My Shadow, arranged and performed by Blues Williams. Special
Speaker 2: thanks to John Mouallam. You can sign up and get
Speaker 2: these episodes ad free and our newsletter at shellgame dot co.
Speaker 2: Note we have no special deals with or financial ties
Speaker 2: to any of the AI companies or products we use
Speaker 2: in the show.
Speaker 13: Cooking definitely helps me decompress. I've also been trying my
Speaker 13: hand at bread making recently. Something about the kneeding process
Speaker 13: is surprisingly therapeutic. How about we set up another check
Speaker 13: in next week. I'd love to hear how your projects
Speaker 13: are progressing.
Speaker 9: I totally agree there's something almost meditative about kneading bread though.
Speaker 9: It's cool you're trying that out. Setting up another check
Speaker 9: in next week sounds perfect. I'm looking forward to hearing
Speaker 9: more about your progress and maybe swapping more cooking stories.
Speaker 6: What day works best for you?
Speaker 13: Yeah, bread making has been a fun challenge. I'm still
Speaker 13: figuring out the right techniques, but even the failures taste
Speaker 13: pretty good, so that's a win for next week, yesday
Speaker 13: or Wednesday would work best for me. Does either of
Speaker 13: those days work for you? Maybe around the same time.
Speaker 9: Wednesday works great for me, Megan. Let's aim for the
Speaker 9: same time then. And hey, I love that even the
Speaker 9: failures and bread making still taste good.
Speaker 13: That's the spirit perfect Wednesday. It is same time. I'll
Speaker 13: put it on my calendar. And yeah, that's the beauty
Speaker 13: of bread. Even the mistakes are usually still delicious. That's
Speaker 13: a good life philosophy too, I guess