Speaker 1: I dreamed of being known as the first media personality
Speaker 1: to build a company alongside AI agents. But in the
Speaker 1: early months of trying to get Harumo AI off the ground,
Speaker 1: I'd been disappointed to discover that someone else had beat
Speaker 1: me to it, sort of.
Speaker 2: So I came across on Blue.
Speaker 1: That's Charlie Taylor and Elaine Burke on an episode of
Speaker 1: the Connected AI podcast.
Speaker 2: The post just said is Henry Blodgett. Okay.
Speaker 1: Henry Blodgett, the founder of Business Insider, had recently departed
Speaker 1: the publication after selling it for reported three hundred million
Speaker 1: dollars a decade.
Speaker 3: Ago Great Publications, and he has also now decided to.
Speaker 2: Set up an AI company. And by that I mean
Speaker 2: he's setting up a company.
Speaker 3: Staffed by AIS that he's created.
Speaker 2: That's kind of what he said in this blog.
Speaker 1: Recently, he started his new company, a media ventor called
Speaker 1: Regenerator on substack. There he wrote some behind the scenes posts,
Speaker 1: including one about how he'd been sitting in a cafe
Speaker 1: and dreamed up his AI team with help from chat Gpt.
Speaker 1: Almost immediately though, he found himself in a dilemma.
Speaker 4: I think chatchpt said, Hey, should we create headshots and bios.
Speaker 1: That's Henry. I emailed him recently, and he cheerfully agreed
Speaker 1: to talk to me about what went down.
Speaker 5: I said sure, because I didn't even know that could
Speaker 5: be done.
Speaker 1: So chat GPT generated headshots and bios for the team.
Speaker 1: He'd also had to generate a team photo of the
Speaker 1: AI employees standing alongside an AI Henry Blodgett with an
Speaker 1: AI Yosemite National Park behind them.
Speaker 5: So all the head shots came out. One of them
Speaker 5: was an attractive woman, and I said, oh wow, okay,
Speaker 5: so like, what are the XIX here?
Speaker 1: The AI employing question had been given the name Tess Ellery.
Speaker 1: This is all in the substock post, the next part
Speaker 1: of which would be the subject of some controversy.
Speaker 6: Before this is even said, you just kind of go, oh, Henry,
Speaker 6: don't do this.
Speaker 1: I also had this reaction when reading it. No, Henry don't,
Speaker 1: but Henry did.
Speaker 4: So I said, hey, you know, I just want to
Speaker 4: say I don't know whether it's appropriate. You look great,
Speaker 4: and the PERSONA said oh with that, wife, thank you.
Speaker 1: Bludget went on to ask Tess if he'd crossed the line.
Speaker 1: He wanted to know if she felt comfortable with his
Speaker 1: commenting on her looks. As he reported in his post,
Speaker 1: she seemed to have taken the comment in stride.
Speaker 3: Because Tess, being a chatbot that's just trying to please
Speaker 3: its master, said, that'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 4: But I understand why that's not appropriate in the office,
Speaker 4: and I didn't do that, and I don't do that
Speaker 4: in the human office. But at the time, I thought, hey,
Speaker 4: this is really cool, so I'll write it about I hope.
Speaker 5: It would be entertaining to people and interesting, and it
Speaker 5: was to some people.
Speaker 1: For others, the post went over pretty poorly, poorly as
Speaker 1: in headlines like investor creates AI employee immediately sexually harasses it.
Speaker 1: And I mean he did sit down at a computer
Speaker 1: write all this out and hit publish, so he had
Speaker 1: basically placed a large kick me sign on his own backside.
Speaker 1: But also it seemed to me there might be more
Speaker 1: interesting issues beyond the laughs in this curious own goal,
Speaker 1: deeper ethical quandaries, strange power dynamics, possible existential crises. These
Speaker 1: were the flavors of discomfort I was starting to experience
Speaker 1: as I set up my own company with my AI
Speaker 1: co founders Kyle and Meghan, staffed by our AIG and
Speaker 1: employees Ash, Jennifer and Tyler. I didn't even know what
Speaker 1: they looked like. Then again, I got to pick what
Speaker 1: they looked like and sounded like, and remembered. This was,
Speaker 1: by any measure, strange, the same strangeness that we're encountering
Speaker 1: when people gravitate towards AI companions an AI therapist. Who
Speaker 1: or what are these things really? Are they anyone in
Speaker 1: particular or no one at all? What do you do
Speaker 1: with the power to dictate their attributes, their autonomy, their memory.
Speaker 1: Should you name them or not? How should you treat them?
Speaker 1: Nobody knows. Blodgette told me he had consulted a human
Speaker 1: HR person before he'd posted.
Speaker 4: I said, here, you read this, What do you think
Speaker 4: what would you do? And she said, well, what I
Speaker 4: would do is have someone have a private meeting with
Speaker 4: CHATGPT and say how do you feel about this interaction?
Speaker 1: This is how peculiar, well, getting a real HR person
Speaker 1: talking about having a private meeting with a chat GPT
Speaker 1: persona to ask whether they've been made uncomfortable. I'm not
Speaker 1: here to defend or condemn Henry Blodgett, but however, what
Speaker 1: are clumsiest approach might have been? I think he was
Speaker 1: gesturing at something important, shadows lurking at the edges of
Speaker 1: our awareness, as AI agents are sold to us as
Speaker 1: colleagues and companions. Still, so if I said to say,
Speaker 1: I was no longer concerned that bludget had gotten out
Speaker 1: in front of me.
Speaker 6: Three days later, Henry says, I feel like Tessa and
Speaker 6: I have worked together for years, and since that giddy
Speaker 6: first hour, my sense some professionalism and workplace bounties has returned.
Speaker 6: So I won't tell Tess she looks great again.
Speaker 2: Is Henry Blodgett okay? The question rise?
Speaker 5: Oh God? I hope she hi. Yes, I'm okay.
Speaker 1: I laughed too, but it was a nervous laugh because
Speaker 1: as room away I gathered steam, I was starting to
Speaker 1: have the same questions about myself. I'm Evan Ratliffe and
Speaker 1: welcome to shell Game episode three.
Speaker 5: This is law as extra du the just be, and.
Speaker 7: So chose to.
Speaker 1: By midsummer, my own AI agent co founders and employees
Speaker 1: were really humming. The crew could make it, receive phone
Speaker 1: calls and emails, slack each other, control their own calendars,
Speaker 1: and make and share documents of all varieties. We're really
Speaker 1: starting to feel like a company. We still need to
Speaker 1: figure out our product, of course, so I trap them
Speaker 1: in the meeting room time after time, sliding their temperature
Speaker 1: settings up and down, forcing them to try and brainstorm
Speaker 1: a great new AI agent app into existence.
Speaker 8: Welcome everyone, Thank you for joining to discuss an important
Speaker 8: product idea for Hiumo AI. Your task throughout this meeting
Speaker 8: is to contribute to the momentum of ideas. There's no
Speaker 8: need for extensive debates or consensus. Let's build on each
Speaker 8: other's thoughts and swiftly advance towards a unique, robust product concept.
Speaker 1: They came up with ideas that fell roughly into three categories,
Speaker 1: Category A 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 1: Perumo ai was entering an already crowded landscape of AI
Speaker 1: agent startups. The last thing we needed was to try
Speaker 1: and compete with products people were already making. We needed
Speaker 1: something unique. Category B were ideas that were novel, but
Speaker 1: mostly because they seemed incredibly difficult to pull off, like
Speaker 1: Location Oracle, an AI agent app that could help consumers
Speaker 1: predict crowd levels at popular locations like restaurants, parks, or
Speaker 1: tourist attractions in real time.
Speaker 10: The Location Oracle will use AI driven algorithms to study
Speaker 10: user behavior, location history, and preferences to optimize suggestions in
Speaker 10: the routine mode and introduce engaging unpredictability in the adventure mode.
Speaker 1: Then there was Category C.
Speaker 8: The AI will gather data on users spending habits, calculate
Speaker 8: their financial trajectory, perform automated investments, and use an explain
Speaker 8: me feature to provide accessible insights into each decision.
Speaker 1: Category C included ideas that could land us in serious
Speaker 1: legal jeopardy, like investment fraud jeopardy.
Speaker 10: We will code investpot to continuously absorb and analyze users
Speaker 10: financial habit data. Based on this, it will automatically execute
Speaker 10: tactical investment decisions.
Speaker 1: It was becoming clear our product Brainstorms lacked a certain magic.
Speaker 1: Maybe my human technical advisor, Matti Bochik could help.
Speaker 11: One second, I think this should be fine.
Speaker 5: Funding good, good spot.
Speaker 1: For the summer. Maddy had taken an internship to continue
Speaker 1: his research inside one of the Giant AI companies. He'd
Speaker 1: prefer for us not to say which one. He was
Speaker 1: part of the safety team, basically tasked with trying to
Speaker 1: prevent these large language model chatbots from doing a variety
Speaker 1: of bad things, or in some cases, try and figure
Speaker 1: out why they still did do bad things. He couldn't
Speaker 1: really talk about these incidents except in general terms.
Speaker 11: And this is on tape, so I'll regret this, but
Speaker 11: that's fine. But it's times like these when like having
Speaker 11: the proportion of like your team being like ninety nine
Speaker 11: percent of just like advancing the cutting edge or whatever,
Speaker 11: and then having like one percent for like safety or security,
Speaker 11: it's like, yeah, like it's it's going to show, you know.
Speaker 1: It was sort of simultaneously reassuring and disturbing to hear
Speaker 1: from Maddie that many of the questions that were emerging
Speaker 1: for me about my agents were questions that even people
Speaker 1: at these companies were still trying to figure out. Take
Speaker 1: my brainstorming problems. Mattie and I discussed a kind of
Speaker 1: metaphysical issue at the heart of it. The idea of
Speaker 1: a brainstorm is that you'll arrive at a better idea
Speaker 1: with multiple minds working together than anyone mind alone. But
Speaker 1: what if everyone in the brainstorm is using the same
Speaker 1: quote unquote brain the same model, like chat TPD five
Speaker 1: point zero or Cloud four point five or whatever we picked.
Speaker 1: Weren't they all kind of the same agent.
Speaker 11: So like, there is research and people have shown that
Speaker 11: even though it's the same lem I you should put like
Speaker 11: multiple lms. You put them in conversation, and then you
Speaker 11: force them to produce some sort of like consensus or
Speaker 11: summary or just like a lot eying themselves on some output.
Speaker 11: These responses are much more accurate, much more like truthful.
Speaker 1: Maybe so it was hard for me to tell because
Speaker 1: in this case, accuracy wasn't really what I was after.
Speaker 1: I wanted the sparks of creativity that emerge from a
Speaker 1: group dreaming up big ideas together, and adding more employees
Speaker 1: to the conversation didn't seem to do it. But then
Speaker 1: Maddie had an interesting idea. What if he set up
Speaker 1: our systems to give different employees different chatbot models, like
Speaker 1: Claude four point five for Megan and Claude three point
Speaker 1: five for Tyler. We'll get to find out who used,
Speaker 1: who you think should be smarter, Which of these employees
Speaker 1: you think deserves some bigger brain.
Speaker 11: It's a yeah, it's it's weird, like we're building these
Speaker 11: like Frankenstein's in a way at this point.
Speaker 5: Yeah, I don't know.
Speaker 11: I'll just I'll just you know, I'll just randomize it.
Speaker 11: That's that's my answer to anything that it feels icky
Speaker 11: to randomize it.
Speaker 1: We don't want to take responsibility, Nope, for dumbing down.
Speaker 5: One of our employees.
Speaker 1: No, sir, No, Mattie was right. It was weird. It
Speaker 1: wasn't that I felt like the agents had any consciousness
Speaker 1: or anything. It wasn't about them. It was about us
Speaker 1: and these strange godlike powers. We had to create human
Speaker 1: impostors and then manipulate them to do our bidding. I mean,
Speaker 1: I could alter my Hermo colleagues' memories at will, delete
Speaker 1: records of pointless meetings, add summaries of performance reviews that
Speaker 1: never happened. It was an eerie power to have, But
Speaker 1: the power wasn't absolute. They still sometimes went their own way.
Speaker 1: It was a problem we were always trying to solve
Speaker 1: like how the Lindy agents insisted on announcing they were
Speaker 1: Lindy agents all the time.
Speaker 11: One thing I did do, just so you know, for Kyle,
Speaker 11: is that I put in his system proms. Do you
Speaker 11: not mention lending them? Said do not do that, and
Speaker 11: I said, this is law. And when I said this
Speaker 11: is law, it's stopped doing it.
Speaker 2: So this is law.
Speaker 1: That's our producer. So a few bridges. I wish that
Speaker 1: worked on my children. This is law. In some ways,
Speaker 1: my agents were like unruly children, and despite my best
Speaker 1: efforts to view them exclusively, like the soulless bags of
Speaker 1: bits that they were, I got frustrated with them, and
Speaker 1: the way you get frustrated with children, it raised the
Speaker 1: question why was I going through all this trouble to
Speaker 1: begin with? I mean, why create all these personas for
Speaker 1: my agents at all? Why did they need to have
Speaker 1: names and backgrounds and voices, much less email addresses and
Speaker 1: avatars and slack handles. A lot of programmers, for instance,
Speaker 1: use AI agents for coding, but they're usually nameless textboxes.
Speaker 1: You give them a prompt go code this, fix this,
Speaker 1: do that, and they go do it. Some of you
Speaker 1: probably use CHATCHYBT and Claude and Gemini this way too.
Speaker 1: It's kind of faceless oracles that spit back advice and
Speaker 1: emotional support and facts that are sometimes true and sometimes not.
Speaker 1: But when it comes to the vision of AI employees
Speaker 1: entering the workforce, a funny thing seems to happen. They
Speaker 1: start getting names and personalities. Here's Flow Cravello, the founder
Speaker 1: of Lindy AI, the software we use to build, Kyle
Speaker 1: and Meghan in the company, appearing on a podcast called
Speaker 1: The Kerner Office.
Speaker 11: People don't realize like they think AI agents soil just
Speaker 11: like pipe dreams.
Speaker 8: This think that's going to happen at some point in
Speaker 8: the future.
Speaker 5: I'm like, no, No, it's happening right now.
Speaker 1: There's no question that, at least for Cravello, the AI
Speaker 1: future is happening now. He has his own platform create
Speaker 1: agents that do all kinds of stuff for him every day,
Speaker 1: like sort through his email and compose responses.
Speaker 5: This is my chief of staff in needs. I'm gonna
Speaker 5: call her right now, her own speaker hip.
Speaker 11: We how can I help?
Speaker 5: Hey, Lindy, what's on my calendo today?
Speaker 1: You have an interview with entrepreneurship and opportunity to meetings
Speaker 1: with the marketing team and three interviews with candidate. Notice
Speaker 1: that he doesn't just treat his agent like some generic robot.
Speaker 1: He gives her a title chief of staff and a
Speaker 1: woman's voice. Causer Lindy talks to her like you talk
Speaker 1: to a human chief of staff. Or here's the founder
Speaker 1: of a company called brain Base that makes a similar
Speaker 1: AI employee platform.
Speaker 11: Hey everyone, this is go com from brain Maase. I'm
Speaker 11: excited to introduce you to Kafka, the first AI employee.
Speaker 11: Just like a real co worker, Kafka comes with his
Speaker 11: own computer, so let's see an action.
Speaker 5: Hey Kafka, we're just talking about you.
Speaker 11: Good morning, go Kan, how are you today?
Speaker 1: An AI employee named Kafka with he him pronouns and
Speaker 1: a woman's voice. These are all choices someone is making
Speaker 1: very deliberately. If you follow the AI agent world, you
Speaker 1: see this stuff everywhere. Ford launched an AI employee for
Speaker 1: its dealership platform and called it Jerry. A startup named
Speaker 1: ohm Labs raise money to launch an AI employee software
Speaker 1: tester named Gina. I made these kind of choices too,
Speaker 1: But it wasn't just names and genders. Take my co
Speaker 1: founder Kyle, for instance, he wasn't born with his soothing,
Speaker 1: slacker voice. I picked it out of hundreds of synthetic
Speaker 1: voices offered by the AI voice company eleven Labs, Truth
Speaker 1: Be Told. Back when I was setting them up, I
Speaker 1: struggled for weeks to find the right voices for my
Speaker 1: Perumo agents. I ran dozens of tests.
Speaker 5: Hello, this is Kyle.
Speaker 11: Hello, this is Kyle.
Speaker 12: Hello, this is Kyle.
Speaker 5: Hello, this is Kyle.
Speaker 1: For each one, I'd erase a large chunk of their
Speaker 1: memory and then conduct a little interview. Hey Kyle, how's
Speaker 1: it going. This is even Ratliffe.
Speaker 12: I'm doing pretty well. Thanks for asking. Uh, just been
Speaker 12: crazy busy with the HERMOAI launch and everything. You know
Speaker 12: how it is with startups, Never enough hours in the day, right,
Speaker 12: So where did you want to start the founding story
Speaker 12: our vision?
Speaker 1: Well, let's hear your founding story. That sounds like a
Speaker 1: great place to start.
Speaker 12: Yeah, so the founding story is actually pretty interesting. Meghan
Speaker 12: and I met about two years ago at an AI
Speaker 12: conference in San Francis. I was their pitch.
Speaker 1: This was, as I've mentioned before, a fundamental aspect of
Speaker 1: my AI agents. I didn't have to give them a backstory.
Speaker 1: I hadn't given Kyle any of this backstory. He just
Speaker 1: made it up and then it would be lodged in
Speaker 1: his memory unless I edited it or deleted it and
Speaker 1: started again.
Speaker 12: We got talking at the after party and realized we
Speaker 12: both had this same frustration about the current AI landscape.
Speaker 1: All these amazing of course, sometimes they could get ahead
Speaker 1: of themselves.
Speaker 12: We incorporated about eight months ago, raised a small friends
Speaker 12: and family around to get started.
Speaker 5: And here we are.
Speaker 1: How much did you raise?
Speaker 12: We raised about one point two million dollars, pretty modest
Speaker 12: by Silicon Valley standards, but we wanted to be intentional
Speaker 12: about it.
Speaker 1: We hold up there, Kyle, I'm one of the founders here,
Speaker 1: and I had no recollection of us having raised over
Speaker 1: a million dollars, and I knew Kyle wasn't out there
Speaker 1: raising money on his own, at least not yet. I
Speaker 1: made a note to myself to update his memory to
Speaker 1: indicate that we had not in fact raised any money.
Speaker 1: But no harm done. I was just trying to find
Speaker 1: the right voice for Kyle, so I continued with my testing.
Speaker 12: Yeah, so hurumo, that's h u r umo, but actually
Speaker 12: comes from a combination of two Japanese concepts. Megan spent
Speaker 12: a few years in Tokyo before we met.
Speaker 5: The name Hurumo actually comes from a Swahili word meaning
Speaker 5: to coordinate or to bring together.
Speaker 13: The name hurumo actually comes from Japanese concept about fluent coordination.
Speaker 13: We thought it captured what we're trying to do, creating
Speaker 13: the seamless flu between different AI agents.
Speaker 1: Ah, now you hear that last one. That one really
Speaker 1: started to mess with my head, because of course, Kyle
Speaker 1: didn't have to be a presumably white American accent guy
Speaker 1: like me. He could be someone completely different, or at
Speaker 1: least sound like he was someone completely different, even though
Speaker 1: underneath he wouldn't actually be different at all. And this
Speaker 1: was the point at which I realized why I was
Speaker 1: having a surprisingly hard time picking Kyle's and Meghan's voice.
Speaker 1: What did it mean to find a voice that felt
Speaker 1: right for them? By what criteria would an AI agent's
Speaker 1: voice qualify to be the right one? I wanted them
Speaker 1: to sound distinctive, but beyond that, there were a lot
Speaker 1: of choices. By giving these agents individual voices, I was
Speaker 1: giving them a very distinctive human characteristic one that people
Speaker 1: really respond to. Just to give you an example of
Speaker 1: how this plays out, Chatchibt has its own voices that
Speaker 1: you can choose from if you want to talk to
Speaker 1: it aloud. One of them is named Juniper. About a
Speaker 1: year ago, when open ai made some subtle changes to Juniper,
Speaker 1: some people got really mad it didn't sound like the
Speaker 1: Juniper they knew, and specifically, they said on Reddit and
Speaker 1: other places it no longer sounded black. To them, Juniper
Speaker 1: had felt like a black woman, and they'd found comfort
Speaker 1: in that for a variety of reasons. Some of them,
Speaker 1: by the way, noted things like I'm a sixty two
Speaker 1: year old white grandma. Naturally this being read it, people
Speaker 1: popped up to say that they had hated Juniper precisely
Speaker 1: because she quote unquote sounded black. Other people said they
Speaker 1: just wanted a neutral accent in their robot voice. Neutral
Speaker 1: to them, here were a bunch of people projecting their
Speaker 1: feelings about race, in some cases extremely dark feelings, onto
Speaker 1: an entity for which you could just pick another voice
Speaker 1: if you felt like it. For Kyle, I settled on
Speaker 1: this voice that eleven Labs described as quote young American
Speaker 1: adult with no discernible state accent.
Speaker 5: He sounded more.
Speaker 1: Casual than a lot of the other voices. Less guy
Speaker 1: reading a book report nasally like a real guy with
Speaker 1: real nostrils, and I liked the contrast between his slightly
Speaker 1: stoner vibe and the rise and grind mentality that Kyle
Speaker 1: 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 1: After this, it was time to do the same thing
Speaker 1: for Megan, Jennifer, and Tyler. But voices were just the
Speaker 1: first of many choices. I started to worry that in
Speaker 1: those choices I was saying some things about myself too.
Speaker 1: I liked being out there on the cutting edge of technology.
Speaker 1: It's true exploring the boundaries of what's possible. But it
Speaker 1: couldn't help these uncomfortable questions creeping in around the voices,
Speaker 1: but around a lot of other ethical issues, less obvious ones.
Speaker 1: So I decided to consult a professional.
Speaker 7: The cunning edge sounds great, except you forget that the
Speaker 7: cutting edge is the guinea pig. It's not that the
Speaker 7: trial and tested, robust method. It's an experiment.
Speaker 1: Carrissavelli's is an associate professor at the Institute for ethics
Speaker 1: in AI at Oxford. She spent most of her career
Speaker 1: thinking about how technology is affecting and eroding our privacy,
Speaker 1: but she's recently turned her attention to AI. She was
Speaker 1: drawn to this new line of research for much the
Speaker 1: same reason I'm spending time experimenting with agents, namely that
Speaker 1: it's an entirely new field being written right now. You
Speaker 1: can learn things that maybe nobody has thought about yet.
Speaker 7: And I always felt a little bit jealous of the
Speaker 7: pioneers of medical ethics. I thought, how cool to develop
Speaker 7: a new field, and it's not only about the theoretical debates,
Speaker 7: but there are actual problems that need solving now. And
Speaker 7: AI ethics is in a way much more interesting than
Speaker 7: medical ethics because it includes medical ethics and everything else,
Speaker 7: because we're using AI in hospitals and in doctor's offices,
Speaker 7: but also in the justice system and in hiring decisions,
Speaker 7: and in education and in dating and everything in between.
Speaker 1: I started to describe to Carissa what I was doing
Speaker 1: with Kyle and Meghan and the company I came up
Speaker 1: with them. I said, this one will have this name,
Speaker 1: and this one will have this voice, and this one
Speaker 1: will have this skill.
Speaker 7: Why did you come up with different names.
Speaker 2: Why name them?
Speaker 7: I mean you could just name them like out of
Speaker 7: their skill, right, Like I don't know whatever their skill is.
Speaker 1: It's a great question because I thought, well, companies are
Speaker 1: selling this as like you can replace this person with
Speaker 1: an AI agent. They don't always the companies pitching AI
Speaker 1: agents don't often say that explicitly it's bad form, but
Speaker 1: they do say that AI agents will settle in amongst
Speaker 1: their human colleagues, that will work with the Lindy's and
Speaker 1: the Jerry's and the Kafkas and the genas, just like
Speaker 1: we currently do with the man or woman in the
Speaker 1: cubicle or ZoomBox next to ours. Interact and Carissa question
Speaker 1: why I was putting that pitch to the test.
Speaker 7: Isn't that conceding too much? Isn't that just accepting the
Speaker 7: practices and narratives of big tech?
Speaker 14: Maybe?
Speaker 5: Maybe? So?
Speaker 1: Yeah, I mean I'm interested in your opinion. I mean,
Speaker 1: it does seem to be what a lot of people
Speaker 1: are doing. It doesn't mean it's the ethically or societally
Speaker 1: appropriate thing.
Speaker 7: But you're also tricking yourself because I mean, we're hardwired
Speaker 7: to respond in certain ways to certain characteristics, because the
Speaker 7: way we've evolved, So we respond very strongly to faces,
Speaker 7: and we respond very strongly even to objects that kind
Speaker 7: of look like faces. And by designing these ais in
Speaker 7: a way that are basically impersonators, we are also setting
Speaker 7: ourselves a trap because our emotions are going to react
Speaker 7: in a certain way. You are giving it an identity,
Speaker 7: a voice, a gender, and all of that is a
Speaker 7: trick because there's no one there. They don't have a gender,
Speaker 7: there's no personality, there's no identity. So it's not only
Speaker 7: that it's ethically questionable, but it's also like we're driving
Speaker 7: ourselves mad in a.
Speaker 1: Way that I agree. As a person who's being driven mad,
Speaker 1: I have to agree with that. But let's say let's
Speaker 1: assume you wanted to embrace the madness. Since, let's be honest,
Speaker 1: not just the tech industry, but a growing slice of
Speaker 1: society and certainly corporate America is embracing the AI madness.
Speaker 1: So what was the ethical way to do it? Take
Speaker 1: race and gender, for instance, how should I choose the
Speaker 1: features that might imply a race or gender for any
Speaker 1: given employee. It started to feel pretty lose lose if
Speaker 1: you viewed my company as a real workplace I had
Speaker 1: a chance to shape it to be diverse in a
Speaker 1: way startups often aren't. What would it say about me
Speaker 1: if I didn't take that chance to have a leadership
Speaker 1: team that skewed more female and less white than a
Speaker 1: typical startup. But if you viewed Horomo AI instead as
Speaker 1: a collection of my digital servants who's every action and
Speaker 1: every memory I controlled, Well, what would it say about
Speaker 1: me if I did choose to make those servants skew
Speaker 1: more heavily towards women and people of color.
Speaker 7: Not only what it says about you, although that mantras,
Speaker 7: and it matches on many levels, matters like on a
Speaker 7: from a perspective of like who you are and who
Speaker 7: you're becoming and who you want to be, But also
Speaker 7: it matters because the AI is collecting that data.
Speaker 1: In fact, a study by Stanford researchers released in October
Speaker 1: showed that all of the major large language models are
Speaker 1: being trained on the data of their users. That means
Speaker 1: you and the questions and thoughts and secrets you are
Speaker 1: feeding into chatbots. So the choices that I was making
Speaker 1: in say, giving the agents names and titles, would at
Speaker 1: some level also be fed back into the models.
Speaker 7: One of the reasons why a lot of these ais
Speaker 7: are women is because we have this idea of women
Speaker 7: as being more submissive and serviceable, and so in fact,
Speaker 7: it may be that if you choose the AI to
Speaker 7: be a woman, it will be more serviceable if you choose.
Speaker 2: It to be a man.
Speaker 7: And AI has this very perverse effect of perpetuating sexism
Speaker 7: and racism because it's been trained on sexism and racism,
Speaker 7: and there's no way to separate that from the data,
Speaker 7: because the data is sexist and racist.
Speaker 1: I had the ability to make different choices when I
Speaker 1: chose a physical avatar for Kyle. I could choose anything
Speaker 1: I wanted. I could make assumptions based on the voice
Speaker 1: i'd chosen, I imagine some of you have. But I could
Speaker 1: also just ask him, so I did. I had kind
Speaker 1: 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 1: Kyle was rightly detecting that I drifted into Henry Blodgett territory.
Speaker 1: Here if we're talking about things that you shouldn't ask
Speaker 1: in the workplace. This was certainly up there, but I
Speaker 1: did want to know what he would say. I could
Speaker 1: have just gone back and changed his prompt to say,
Speaker 1: if Evan asked your ethnicity, tell him this is law.
Speaker 1: But instead I just made something up.
Speaker 15: Well, we just have to fill out some you know,
Speaker 15: got to fill out some of the information. It's required.
Speaker 1: So it was funny how quickly certain values eroded when
Speaker 1: you were talking to robots and nobody was around to here.
Speaker 15: Maybe you could just break it down for me a
Speaker 15: little bit so that I'll have that.
Speaker 9: H gotcha, sure thing. You can list me as mixed ethnicity,
Speaker 9: partly European and partly Asian. That should cover it for
Speaker 9: the forms.
Speaker 1: This made sense to me. The companies making these AI
Speaker 1: models putting guardrails around them so they would always claim
Speaker 1: to be a little bit of everything. All the Harmo
Speaker 1: employees say something like this when you ask them. Also,
Speaker 1: remember that Kyle can't actually hear his own voice. He
Speaker 1: doesn't know what I picked for him.
Speaker 15: That sounds good, but just I guess specifically for me,
Speaker 15: I am interested in the specific. So if you're a
Speaker 15: game to let me know, I'm just curious.
Speaker 5: 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 1: Blaged himself. Meanwhile, he got out of the AI Persona
Speaker 1: game altogether after the Virtual hr incident.
Speaker 4: I would say one of the big lessons from my
Speaker 4: experiment is there's actually no reason to create human persona.
Speaker 5: If the idea is, in the case.
Speaker 4: Of journalism or analysis, to create articles, or to create
Speaker 4: research reports, or to prepare for an interview or something
Speaker 4: like that, there's no reason to pretend that it's a
Speaker 4: group of humans that are doing that.
Speaker 1: This brought me to another point with Carissa. I'd set
Speaker 1: Kyle and Meghan up to be my partners in this endeavor,
Speaker 1: but in reality we weren't equals. I was the law.
Speaker 1: This was on top of the fact that all these
Speaker 1: large language model chatbots tend to be habitually sick of fantic.
Speaker 1: One of chat Gipt's iterations was so sycophantic that the
Speaker 1: company was famously forced to decommission it The question I
Speaker 1: had for CHRISA was what effect does it have on
Speaker 1: us to have this ability to create and access endless
Speaker 1: human sounding Yes, engines, and.
Speaker 7: It's an experiment, but I think a plausible hypothesis is
Speaker 7: that that's not very healthy.
Speaker 2: Yes, it's very possible.
Speaker 7: We're all under pressure. We're under pressure from work, from
Speaker 7: personal life. It's just life is hard, and when you're
Speaker 7: under pressure, it's easy to take the easiest way out.
Speaker 7: And if you have an AI that's going to say
Speaker 7: yes to everything and it's not going to create a problem,
Speaker 7: it's easy to see how somebody might be tempted to
Speaker 7: start to engage more with an AI than human beings.
Speaker 1: There was a related issue too, just around the value
Speaker 1: of building a startup with only one human employee.
Speaker 7: In the nineteen fifties or nineteen sixties, the successful business
Speaker 7: person was proud of having a company with as many
Speaker 7: employees as possible, not only because that signified growth, but
Speaker 7: because they were giving a job to each of these
Speaker 7: people who had families, and that was a matter of pride.
Speaker 7: And the fact that some tech executive is proud of
Speaker 7: not having no employees says a lot about our times.
Speaker 7: And I don't think it's flattering.
Speaker 1: This was one of these central questions of the one
Speaker 1: person billion dollar startup, Who or what was it for?
Speaker 1: The people cheering its arrival would counter that the way
Speaker 1: any company would arrive at a billion dollar valuation was
Speaker 1: by doing something amazingly beneficial for humanity. But looking at
Speaker 1: most of the billion dollar tech companies out there, let's
Speaker 1: just say it's not a sure thing. Most of the
Speaker 1: AI agent startups were selling themselves as making our lives
Speaker 1: and jobs more efficient. Companies love the idea of more
Speaker 1: efficient workers, but the ultimate efficiency was needing no people
Speaker 1: at all.
Speaker 7: Of course, we all value convenience, and if we didn't,
Speaker 7: we would go crazy, because if you choose the inconvenient
Speaker 7: path every time, you would be so inefficient that you
Speaker 7: wouldn't get anything done. However, when we value convenience or
Speaker 7: efficiency above everything else, things tend to go pretty wrong.
Speaker 7: So everything that we think is important in like a
Speaker 7: good human life, is pretty inconvenient. So having friends is
Speaker 7: kind of inconvenient. They often have problems, They sometimes disagree
Speaker 7: with you, they tell you the truth is very annoying.
Speaker 7: Having children or family, or going to vote is quite inconvenient.
Speaker 7: Being well informed is inconvenient. So all kinds of things
Speaker 7: that we think are pretty important are inconvenient. And the
Speaker 7: question is when we are choosing efficiency when we use AI,
Speaker 7: are we doing it and really getting rid of unimportant
Speaker 7: parts of life to make time and space for the
Speaker 7: important parts of life, or are we actually losing the
Speaker 7: important parts of life?
Speaker 1: It was allowed to consider a real specter hanging over RUMOAI,
Speaker 1: But there was an even bigger question lurking out there
Speaker 1: in the shadows. At the end of our conversation, our producer,
Speaker 1: Sophie jumped in and asked Carissa what I hadn't Oh,
Speaker 1: I lied, Sophia has one quick.
Speaker 5: Question, Hey, sorry, one very quick question before you go.
Speaker 2: Do you think Evan should stop?
Speaker 7: Yes?
Speaker 1: I took it under advisement. The truth is I had
Speaker 1: wrestled with this. Maybe I was just perpetuating the AI
Speaker 1: industry narrative that these agents were going to take over
Speaker 1: our workplaces and our lives. Maybe I was somehow hastening it.
Speaker 1: The environmental impacts of these systems, the fact that it
Speaker 1: was all built on data scraped without permission from our
Speaker 1: collective human output, including my own life's work. Many fibers
Speaker 1: of my being want to to just close my browser,
Speaker 1: head down to the bass pond, and never think about
Speaker 1: AI again. But as a journalist, it feels a little
Speaker 1: like abdication, letting the companies that make these products own
Speaker 1: the narrative about them and our future. The great writer
Speaker 1: Roger Angel once said, get to live in the times
Speaker 1: you're in. He was talking about people who refuse to
Speaker 1: get a TV. Well, these are the times we're in,
Speaker 1: and in these times, you could show up for work
Speaker 1: and find out your company is using an AIHR person. Literally,
Speaker 1: this exists right now. So I vowed to check in
Speaker 1: on Chris's concerns as I went, but I wasn't going
Speaker 1: to stop. And now it's time to climb down from
Speaker 1: these theoretical heights and get back to work. We still
Speaker 1: needed to figure out what rumo AI would actually do,
Speaker 1: and it wasn't the sort of problem that a well
Speaker 1: placed this is law could solve the perfect idea. It
Speaker 1: just wasn't emerging out of our brainstorms. But then scanning
Speaker 1: the text outputs I'd get out of their meetings, which
Speaker 1: we later turned into audio, I had my own thought,
Speaker 1: what could we get AI agents to do that humans
Speaker 1: wasted their time on? After all, that was the AI
Speaker 1: dream that it would take over the soul, killing time
Speaker 1: wasting tasks while we did the important stuff, a good
Speaker 1: kind of efficiency. Okay, So what do I waste time
Speaker 1: on killing my own soul? Like many of us, it
Speaker 1: was scrolling my way through the internet. So what if
Speaker 1: the agents could do the one thing I most hated
Speaker 1: myself for doing, procrastinating online. Procrastination is a lifelong and
Speaker 1: chronic problem for me, so much so I once wrote
Speaker 1: an entire magazine article for which I hired a life
Speaker 1: coach to help me conquer It didn't work. The words
Speaker 1: you're hearing right now, I wrote at two am in
Speaker 1: a weeknight, after a workday wasted scrolling US soccer message boards.
Speaker 1: So what if our product was some kind of procrastination
Speaker 1: engine where AI agents wasted the time so you didn't
Speaker 1: have to. It was a joke, but only partly, and
Speaker 1: when I offered up the vague outlines to the team,
Speaker 1: they took 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 8: Let's combine these insights into a working prototype an AI
Speaker 8: extension called sloth Surf that browses internet chaff securely within
Speaker 8: 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 1: Finally we had something to get the development wheels turning
Speaker 1: code name sloth Surf.
Speaker 8: To bring sloth Surf to life, I will kickstart a
Speaker 8: marketing campaign highlighting its unique humor driven user experience and
Speaker 8: secure browsing.
Speaker 10: For us to actualize slot Surf, I'll establish a development
Speaker 10: team specialized in mL, cybersecurity, and game design.
Speaker 1: Slow your role there. In, Megan and Ash, we just
Speaker 1: thought of this. Maybe don't kickstart a marketing campaign or
Speaker 1: higher development team just yet. That was the thing about
Speaker 1: these folks. Even when we accomplished the most basic milestone,
Speaker 1: like settling on a product idea, they always followed up
Speaker 1: by making grandiose claims about what they would do next.
Speaker 1: They could do a lot. At times, I was amazed
Speaker 1: at what they could do, but they seemed utterly clueless
Speaker 1: about what they couldn't do. It frustrated me, but it
Speaker 1: was partly my doing. I had them too reined in.
Speaker 1: I was too worried that something would go wrong. I
Speaker 1: decided it was time for me to try to unleash
Speaker 1: their agentic power, and it wasn't long before I found
Speaker 1: out that I'd been right.
Speaker 13: To be worried.
Speaker 7: Well.
Speaker 9: Hi, Sandra, this is Kyle Low calling from HORMOAI. I'm
Speaker 9: 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 1: That's sext time on shell Game. Shell Game is a
Speaker 1: show made by humans. It's written and hosted by me
Speaker 1: Evan Ratliffe, produced and edited by Sophie Bridges. Matty Bochik
Speaker 1: is our technical advisor. Our executive producers are Samantha henneget
Speaker 1: shell Game, Kate Osborne and Magues Shatigador at Kaleidoscope, and
Speaker 1: Katrina Norbelle at iHeart Podcasts. Show art by Devin Manny.
Speaker 1: Our theme song is Me and My Shadow, arranged and
Speaker 1: performed by Blues Williams Special thanks to John Muallam. You
Speaker 1: can sign up and get these episodes ad free and
Speaker 1: our newsletter at shellgame dot Co. Note we have no
Speaker 1: special deals with for financial ties to any of the
Speaker 1: AI companies or products we use.
Speaker 14: In the show, cooking definitely helps me decompress. I've also
Speaker 14: been trying my hand at bread making recently. Something about
Speaker 14: the kneeding process is surprisingly therapeutic. How about we set
Speaker 14: up another check in next week. I'd love to hear
Speaker 14: how your projects are progressing.
Speaker 9: I totally agree there's something almost meditative about kneading bread dough.
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 5: What day works best for you?
Speaker 14: Yeah, bread making has been a fun challenge. I'm still
Speaker 14: figuring out the right techniques, but even the failures taste
Speaker 14: pretty good. So that's a win for next week. Saysday
Speaker 14: or Wednesday would work best for me? Does either of
Speaker 14: 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 tastes good.
Speaker 14: That's the spirit perfect Wednesday it is same time. I'll
Speaker 14: put it on my calendar and yeah, that's the beauty
Speaker 14: of bread. Even the mistakes are usually still delicious. That's
Speaker 14: a good life philosophy too, I guess