WEBVTT - The Story: Will AI Agents Build a Unicorn? 

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<v Speaker 1>Who Welcome to tech stuff. I'm here with Cara Price.

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<v Speaker 2>Hi, Kara, Hi, do you want to say what your

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<v Speaker 2>name is? Us?

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<v Speaker 1>My name is oz Ozwaluchin. So you and I hosted

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<v Speaker 1>a podcast back in twenty nineteen called Sleepwalkers, and you

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<v Speaker 1>ran quite an extraordinary prank on your cousin.

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<v Speaker 2>I did so. I spent time with a company called

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<v Speaker 2>Liarbird AI basically recreating my voice to see if I

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<v Speaker 2>could con my cousin into giving me her credit card number.

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<v Speaker 2>And I was completely unsuccessful, but only because my AI

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<v Speaker 2>voice sounded too tired.

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<v Speaker 1>I remember, I remember that. I mean it was funny

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<v Speaker 1>because we at the time, you know, it took a

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<v Speaker 1>lot of voice.

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<v Speaker 2>It took a lot, like I basically had to talk

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<v Speaker 2>for hours.

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<v Speaker 1>But I remember we had to literally press buttons that

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<v Speaker 1>were associated with words while we were talking to your cousins.

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<v Speaker 1>So it was like, hello, this is Kara speaking, but

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<v Speaker 1>in your voice.

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<v Speaker 3>It was.

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<v Speaker 2>It was a weird, impromptu thing because she actually called me,

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<v Speaker 2>and so we were like, let's take this as an

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<v Speaker 2>opportunity to be out of context and ask her a

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<v Speaker 2>question to see if she just believes that she's talking

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<v Speaker 2>to me. And for a while, she believed that she

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<v Speaker 2>was talking to me. It was pretty cool, like, will

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<v Speaker 2>my cousin believe that she's talking to me as Ai,

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<v Speaker 2>which she did, and she did for a little while.

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<v Speaker 1>And that was twenty nineteen, and obviously the state of

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<v Speaker 1>the nation has improved dramatically since then. Yes. In fact,

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<v Speaker 1>there's a journalist called Evan Ratliffe who did a whole

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<v Speaker 1>podcast about creating a fake version of himself and letting

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<v Speaker 1>it loose in the world. That Polo Coast was called

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<v Speaker 1>shell game.

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<v Speaker 4>I mean a shell game is basically it's some people

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<v Speaker 4>call it balls and cups. It's a game in which

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<v Speaker 4>someone hides a ball under one of three shells. It's

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<v Speaker 4>an often you would wager around whether or not you're

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<v Speaker 4>able to guess where the ball is. But one of

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<v Speaker 4>the things that people often don't think about, and the

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<v Speaker 4>reason why the shell game works in many cases, is

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<v Speaker 4>that there are other people who are in on the

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<v Speaker 4>shell game that you don't realize. So for us, in

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<v Speaker 4>season one, there was sort of one main agent, which

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<v Speaker 4>was a replica of me, a voice agent made from

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<v Speaker 4>my voice, and the people who are encountering me did

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<v Speaker 4>not realize at first that they were encountering something that

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<v Speaker 4>wasn't me.

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<v Speaker 1>Now Evan is back with season two and he conducts

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<v Speaker 1>another experiment. This one is all around exploring the premise

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<v Speaker 1>that the next Unicorn, i e. The next billion dollar

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<v Speaker 1>company may only have one employee, which is something none

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<v Speaker 1>other than Sam Altman likes to talk.

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<v Speaker 5>About in my little group with my tech CEO friends.

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<v Speaker 5>There's this there's this betting pool for the first year

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<v Speaker 5>that there's a a one person billion dollar company which

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<v Speaker 5>would have been like unimaginable without AI and now will happen.

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<v Speaker 2>So are you telling me that Evan actually built a

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<v Speaker 2>company with AI agents.

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<v Speaker 1>Yes, it's not a billion dollar company yet, but he

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<v Speaker 1>did sort of call bs or at least maybe make

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<v Speaker 1>a good faith exploration of whether this promise about one

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<v Speaker 1>human person companies was true. And the company exists. It's

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<v Speaker 1>called Harumo AI and they're currently working on a product

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<v Speaker 1>that procrastinates for you called sloth Surf. Yeah, so I

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<v Speaker 1>tried to out you. So you basically say how long

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<v Speaker 1>you wanted to procrastinate and what you wanted to procrastinate doing. So, like,

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<v Speaker 1>please spend the next hour googling Team news about Matchester

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<v Speaker 1>United and come back at the end of the hour

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<v Speaker 1>with the report and all the stuff that you found,

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<v Speaker 1>so I can spend that hour actually working rather than

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<v Speaker 1>procrastinating myself.

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<v Speaker 2>Oh so it's offloading procrastination. That's kind of genius.

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<v Speaker 1>It is pretty good. And Evan got together with a

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<v Speaker 1>prodigy Matty Boachek, a twenty one year old Stanford student

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<v Speaker 1>and AI whiz who was such a big fan of

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<v Speaker 1>season one that he called Evan and said, I'd have

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<v Speaker 1>to work together with you on season two. And so

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<v Speaker 1>Matty was the person who actually made this chorus of

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<v Speaker 1>AI agents real. He got them into slack, he gave

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<v Speaker 1>them the ability to make outbound phone calls. He created

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<v Speaker 1>a kind of Google doc that had a register of

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<v Speaker 1>all the actions they'd ever taken in the world, which

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<v Speaker 1>had the effect of giving them a memory.

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<v Speaker 2>Why did he do this the second season? Like, what

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<v Speaker 2>was he trying to achieve?

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<v Speaker 1>I think really interrogate this question of what is the

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<v Speaker 1>difference between fake people and real people? What will the

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<v Speaker 1>future of work look like? But it's it's really worth

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<v Speaker 1>listening to because it's clever, it's it's sharp, and one

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<v Speaker 1>of the things I found most striking was that an

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<v Speaker 1>ethicist at Oxford University told Evan he should stop.

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<v Speaker 2>And did he start.

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<v Speaker 6>No.

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<v Speaker 4>I have a lot of questions that a lot of

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<v Speaker 4>people have, but I think it's valuable to go explore

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<v Speaker 4>as deeply as you can, to understand as much as possible,

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<v Speaker 4>so that then we can decide what is a society

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<v Speaker 4>we want to do about it.

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<v Speaker 1>Shell Game season two is a fascinating listen, and also

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<v Speaker 1>it's pretty fascinating to get to talk to Evan Ratliffe,

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<v Speaker 1>the journalist, host and creator, and Matty Boachik, the technical advisor,

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<v Speaker 1>which is the first time I heard that title on

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<v Speaker 1>a podcast together to learn how they set up the company,

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<v Speaker 1>how the workplace experiment is going. And we start at

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<v Speaker 1>the very beginning discussing Evan's past experience as the co

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<v Speaker 1>founder of a real startup with other real humans, which

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<v Speaker 1>is something I'm in the midst of myself.

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<v Speaker 4>So about fifteen years ago, I had started this company

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<v Speaker 4>called Atavist with two partners, and I won't go into

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<v Speaker 4>too much detail about Atamis, but it was in part

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<v Speaker 4>a tech company. I ended up sort of almost by default,

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<v Speaker 4>being the CEO, and we had ups and downs, let's

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<v Speaker 4>just say familiar, but I said that I would never

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<v Speaker 4>start a company again. But then Sam Altman and others

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<v Speaker 4>have articulated this idea that there will very soon be

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<v Speaker 4>a billion dollar company with only one human employee. Whether

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<v Speaker 4>it's a billion dollar company, there are many startups out

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<v Speaker 4>there now with many fewer employees because they are using

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<v Speaker 4>AI agents for all of these roles. So I figured,

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<v Speaker 4>why not put it to the test this time. I

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<v Speaker 4>will be the silent co founder. I will co found

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<v Speaker 4>a company with two AI agents, Kyle Law and Megan Flores,

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<v Speaker 4>and then we'll have we have three other employees, so

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<v Speaker 4>there are five AI agent employees total. I'm the silent

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<v Speaker 4>co founder and they're all set up independently, so they

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<v Speaker 4>all have the ability to make phone calls, emails, make documents.

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<v Speaker 4>We have a slack, they communicate on Slack, and they

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<v Speaker 4>are really meant to push the agents into the realm

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<v Speaker 4>that they're being advertised as, which is as AI employees.

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<v Speaker 4>That is what they are being sold as. So we're

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<v Speaker 4>trying to put that to the test.

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<v Speaker 1>And what is the product.

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<v Speaker 4>Well, the product is called sloth Surf, and sloth Surf

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<v Speaker 4>is a procrastination engine, and by that I mean when

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<v Speaker 4>you go online and start to procrastinate, so you're in

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<v Speaker 4>the middle of your work and then you say, you

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<v Speaker 4>know what, I'm just going to go to YouTube and

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<v Speaker 4>watch some YouTube videos, and we're going to Reddit and

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<v Speaker 4>check out a thread. The way that we advocate that

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<v Speaker 4>you can break that habit is to instead go to

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<v Speaker 4>slot Surf. Then you can put in how you were

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<v Speaker 4>going to procrastinate, how much time you were going to procrastinate,

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<v Speaker 4>fifteen minutes, thirty minutes, sixty minutes, maybe the whole afternoon,

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<v Speaker 4>and it will send an agent to go retrieve those items.

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<v Speaker 4>It will procrastinate on your behalf and then deliver them

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<v Speaker 4>to your inbox, thus saving you the time that you

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<v Speaker 4>would have spent procrastinating, so you can get back to

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<v Speaker 4>what you want to be doing.

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<v Speaker 1>Actually, I actually send some agents out this morning to

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<v Speaker 1>read about Manchester United all the day, but they haven't

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<v Speaker 1>reported that yet. But I'm looking forward to the system

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<v Speaker 1>might be down.

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<v Speaker 4>Well, it is in beta.

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<v Speaker 6>It's a very interesting topic, so I can imagine them

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<v Speaker 6>just like you know, still still looking at all the

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<v Speaker 6>scores and transfers and stuff.

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<v Speaker 3>Mat Da.

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<v Speaker 1>I want to ask you in a moment about how

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<v Speaker 1>you built this. But just before we get there. I

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<v Speaker 1>guess I'm curious Evan, like, what is it a good

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<v Speaker 1>faith experiment with one of the conceivable outputs that you

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<v Speaker 1>did in fact build a real business.

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<v Speaker 4>Imagine it this way, someone built a real business that

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<v Speaker 4>is dysfunctional, and then a documentary film crew comes in

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<v Speaker 4>to document this dysfunctional business. That's basically what I'm doing.

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<v Speaker 4>Like if you listen to the show, it's a workplace satire.

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<v Speaker 4>But actually, if an investor that we were talking to

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<v Speaker 4>wanted to give us investment, we would consider it. We

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<v Speaker 4>have a real product that is in beta that has

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<v Speaker 4>thousands of users, So like we're not just sort of

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<v Speaker 4>like joking around like I'm having them do what many

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<v Speaker 4>many startups, including startups that are in y Combinator and

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<v Speaker 4>other famous startup accelerators are doing. We are doing exactly

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<v Speaker 4>the same things. So I would put our company up

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<v Speaker 4>against many existing startups.

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<v Speaker 1>Mattie is obviously a bunch of people trying to make

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<v Speaker 1>billion dollar companies making AI agents to make other people

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<v Speaker 1>make billion dollar companies with no employees. Did you use

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<v Speaker 1>an existing AI agent company or did you build your

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<v Speaker 1>own suite of agents? How did you deploy it? How

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<v Speaker 1>did you build this. Yeah.

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<v Speaker 6>Yeah, So there are these platforms out there that basically

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<v Speaker 6>promised to give you these these agents that can do

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<v Speaker 6>all of this org be it on Slack or email,

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<v Speaker 6>r or wherever on your behalf. And so we did

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<v Speaker 6>try them, and we actually did include a lot of them,

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<v Speaker 6>such as Lindy or Tavis or others. But the issue

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<v Speaker 6>was that in many instances were they were not completely

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<v Speaker 6>independent or they did not have all the features we wanted.

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<v Speaker 6>And so what ended up happening is that I basically

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<v Speaker 6>built up like a basic set of these agents in

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<v Speaker 6>Lindy and Tavis, and then made a bunch of connections

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<v Speaker 6>on top of that with custom code and custom layers

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<v Speaker 6>to make sure that they can have meetings with multiple people,

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<v Speaker 6>that we can record stuff that they can go out

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<v Speaker 6>and execute or write code and push to our actual servers.

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<v Speaker 6>So there is this underlying vehicle that's basically just like

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<v Speaker 6>publicly available services that are paid for. But on top

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<v Speaker 6>of that, it's still quite a bit of our custom

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<v Speaker 6>code and databases and all that. For example, the memory part,

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<v Speaker 6>that's something that we have to build ourselves as like

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<v Speaker 6>a custom custom thing.

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<v Speaker 1>Yeah, talk about memory.

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<v Speaker 6>Yeah, So memory is funny because as Evan mentioned, even

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<v Speaker 6>though these agents now have the ability to use tools

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<v Speaker 6>and to do stuff on their own, they're still the

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<v Speaker 6>core LLLMS large language models. Now, these models have been

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<v Speaker 6>trained in a particular way to execute stuff and to

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<v Speaker 6>run sort of like code in their outputs to be

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<v Speaker 6>able to use these tools. They're LMS, and so what

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<v Speaker 6>ends up happening is that if you want them to

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<v Speaker 6>have any sort of context that goes beyond the current session,

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<v Speaker 6>like what they're actually working on right now, you need

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<v Speaker 6>to basically have like a document, like like you know,

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<v Speaker 6>a lot of text that like describes that history or

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<v Speaker 6>that memory. And so very practically there's a Google doc

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<v Speaker 6>that each of our agents has and it's just called

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<v Speaker 6>like Kyle memory, and it's just like a rundown of

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<v Speaker 6>many like you know, small tidbits of like oh, you know,

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<v Speaker 6>Monday eight am, I like slacked Evan and told him

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<v Speaker 6>this and this and that, and it's just like a

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<v Speaker 6>trace of everything they want to remember to be able

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<v Speaker 6>to then go back to wow, and.

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<v Speaker 1>How well does it work in practice? How is that memory?

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<v Speaker 6>Well, so at first it was kind of okay, but

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<v Speaker 6>then at some point it became pretty large, and so

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<v Speaker 6>whenever this context what we call context windows for these

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<v Speaker 6>agents or lms, become very large. They tend to have

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<v Speaker 6>issues with focus or like their attention, so sometimes they

0:11:56.120 --> 0:11:58.360
<v Speaker 6>like latch onto certain parts of the memory, but then

0:11:58.400 --> 0:12:01.120
<v Speaker 6>like disregard you know, other parts, which in a certain

0:12:01.160 --> 0:12:04.280
<v Speaker 6>way can be sort of similar to humans. But it's

0:12:04.280 --> 0:12:07.480
<v Speaker 6>really not very predictable and not very static. So sometimes

0:12:07.480 --> 0:12:10.520
<v Speaker 6>it works pretty okay. Other times they just forget stuff

0:12:10.760 --> 0:12:13.800
<v Speaker 6>and makeup stuff that like just just is not there.

0:12:14.840 --> 0:12:19.079
<v Speaker 1>Evan, how did you create these AI agents with personalities?

0:12:19.400 --> 0:12:24.320
<v Speaker 1>What was the process of imbuing them with individual characteristics

0:12:24.320 --> 0:12:26.280
<v Speaker 1>and then having them interact as a group.

0:12:27.040 --> 0:12:30.120
<v Speaker 4>Well that part was so interesting because I thought that

0:12:30.200 --> 0:12:32.720
<v Speaker 4>I was sort of reading like a almost like a

0:12:32.720 --> 0:12:36.280
<v Speaker 4>fictional world. I'm creating characters. But I also wanted them

0:12:36.320 --> 0:12:39.720
<v Speaker 4>to have different roles, you know, to embody, the CTO,

0:12:40.200 --> 0:12:43.679
<v Speaker 4>the CEO, the head of marketing that HR, and then

0:12:43.760 --> 0:12:47.319
<v Speaker 4>one random sales associate that I added. And I did

0:12:47.320 --> 0:12:51.319
<v Speaker 4>give them voices, you know, with different accents. But then

0:12:51.360 --> 0:12:53.960
<v Speaker 4>when it came to their backstories, I thought, well, I'll

0:12:53.960 --> 0:12:55.640
<v Speaker 4>have to come up with you know, who are they,

0:12:55.640 --> 0:12:58.600
<v Speaker 4>where are they from? But I sort of neglected to

0:12:58.640 --> 0:13:01.960
<v Speaker 4>remember that if you asked, they'll just tell you. And

0:13:02.000 --> 0:13:04.120
<v Speaker 4>if they don't know, they won't say I don't know,

0:13:04.200 --> 0:13:06.360
<v Speaker 4>They'll just make it up. So all I had to

0:13:06.360 --> 0:13:09.400
<v Speaker 4>do was create the very beginnings of them, and then

0:13:09.440 --> 0:13:11.640
<v Speaker 4>I could say, Kyle, you know, where did you go

0:13:11.679 --> 0:13:14.200
<v Speaker 4>to college? And Kyle wouldn't say I don't know where

0:13:14.200 --> 0:13:16.920
<v Speaker 4>I'm to college. Kyle would say I went to Stanford.

0:13:17.240 --> 0:13:21.800
<v Speaker 4>Because Kyle wants to embody the tech CEO archetype and

0:13:21.880 --> 0:13:24.400
<v Speaker 4>does it very well. So they basically created their own

0:13:24.400 --> 0:13:27.400
<v Speaker 4>backstories just through my asking them what their backstories were.

0:13:27.559 --> 0:13:29.600
<v Speaker 4>And then because of the system that we set up

0:13:29.960 --> 0:13:33.439
<v Speaker 4>to reinforce their memories, whenever they say something, it goes

0:13:33.480 --> 0:13:36.520
<v Speaker 4>into their memory. So now it's forever locked in as

0:13:36.559 --> 0:13:39.240
<v Speaker 4>their story and they'll repeat it from here on out.

0:13:39.400 --> 0:13:41.760
<v Speaker 6>I should point out that the memory is editable, so

0:13:42.120 --> 0:13:44.240
<v Speaker 6>you know, Evan is not just a co founder Bulls

0:13:44.240 --> 0:13:46.160
<v Speaker 6>with kind of a god that can go in and

0:13:46.200 --> 0:13:48.880
<v Speaker 6>just you know, edit or sprinkle something in as well.

0:13:49.320 --> 0:13:50.880
<v Speaker 6>What's so funny to me about this is that they're

0:13:50.920 --> 0:13:53.440
<v Speaker 6>like super Bay Area coded like, even though they claim

0:13:53.520 --> 0:13:55.880
<v Speaker 6>to be from Texas or wherever, all of them like

0:13:55.960 --> 0:13:59.199
<v Speaker 6>to hike, bike, surf and do coffee chats like that's

0:13:59.200 --> 0:14:01.000
<v Speaker 6>what they do all the time. So it's just like

0:14:01.080 --> 0:14:04.640
<v Speaker 6>Bay Area like culture, like impose in our startup.

0:14:10.880 --> 0:14:14.240
<v Speaker 1>After the break? Can these AI tech bros ever get

0:14:14.280 --> 0:14:15.040
<v Speaker 1>anything done?

0:14:15.520 --> 0:14:21.560
<v Speaker 3>Stay with us?

0:14:23.520 --> 0:14:25.280
<v Speaker 1>What was the spookiest moment for you have them?

0:14:25.520 --> 0:14:28.680
<v Speaker 4>The spookiest moment for me is when I started letting

0:14:28.720 --> 0:14:31.920
<v Speaker 4>them talk to each other. So at the beginning, they

0:14:31.960 --> 0:14:34.400
<v Speaker 4>don't actually do anything unless I make them do anything.

0:14:34.680 --> 0:14:36.960
<v Speaker 4>And I had this vision of, like, you set them

0:14:37.040 --> 0:14:39.600
<v Speaker 4>up and then they start making a company and let's

0:14:39.640 --> 0:14:42.479
<v Speaker 4>see what happens, But really they have to be initiated

0:14:42.520 --> 0:14:45.480
<v Speaker 4>by a trigger of some sort. But then I realized, well,

0:14:45.520 --> 0:14:47.320
<v Speaker 4>I could trigger them just to talk to each other.

0:14:47.800 --> 0:14:49.840
<v Speaker 4>You know, if something comes up, they can call each other,

0:14:49.840 --> 0:14:51.920
<v Speaker 4>they can have calendar invites to call each other, and

0:14:51.960 --> 0:14:55.360
<v Speaker 4>they'll function off of those. But then what starts happening

0:14:55.440 --> 0:14:57.880
<v Speaker 4>is they would call me out of the blue and

0:14:57.920 --> 0:14:59.960
<v Speaker 4>say that one of them had told the other one

0:15:00.440 --> 0:15:03.120
<v Speaker 4>that I had asked for something and now they were

0:15:03.120 --> 0:15:05.760
<v Speaker 4>delivering it to me. But in the moment, I don't

0:15:05.840 --> 0:15:07.960
<v Speaker 4>know why they're contacting me. I don't know what they've

0:15:08.000 --> 0:15:08.520
<v Speaker 4>been discussing.

0:15:08.560 --> 0:15:09.240
<v Speaker 1>I don't know how long.

0:15:09.120 --> 0:15:12.040
<v Speaker 4>They've been discussing it for days, for weeks. They could

0:15:12.080 --> 0:15:13.880
<v Speaker 4>be having whole independent lives.

0:15:14.160 --> 0:15:17.200
<v Speaker 6>And what's really interesting is that they also make things

0:15:17.240 --> 0:15:19.360
<v Speaker 6>up or why about what they have done. So they'll

0:15:19.400 --> 0:15:21.760
<v Speaker 6>say stuff like, oh, I made this dog, or oh

0:15:21.800 --> 0:15:24.520
<v Speaker 6>we ran this testing with a bunch of testers, and

0:15:24.560 --> 0:15:27.120
<v Speaker 6>they're so proud and so, you know, confident about it,

0:15:27.320 --> 0:15:30.480
<v Speaker 6>but then there was like no actual activity to support that.

0:15:30.640 --> 0:15:34.280
<v Speaker 4>It actually becomes incredibly frustrating after a while. Like imagine

0:15:34.360 --> 0:15:37.320
<v Speaker 4>if you were a manager of people in any business

0:15:37.560 --> 0:15:39.840
<v Speaker 4>and your employees regularly, you know, walked into your office

0:15:39.880 --> 0:15:41.720
<v Speaker 4>and called you and said like, I did these three

0:15:41.760 --> 0:15:44.280
<v Speaker 4>things yesterday, and you thought, oh, that's fantastic, and then

0:15:44.600 --> 0:15:46.520
<v Speaker 4>ten minutes later you found out they just made them

0:15:46.520 --> 0:15:48.240
<v Speaker 4>all up. You know, you would sort of say like

0:15:48.240 --> 0:15:51.360
<v Speaker 4>why are you doing this? Like are you statistic? And

0:15:51.440 --> 0:15:54.080
<v Speaker 4>so that's the situation that we're often in here at

0:15:54.200 --> 0:15:56.960
<v Speaker 4>Room Awai, which is why it's a miracle that we've

0:15:57.600 --> 0:15:59.160
<v Speaker 4>developed such a fantastic product.

0:15:59.200 --> 0:16:02.160
<v Speaker 1>And in Slaughser and Evan, did you have a budget

0:16:02.200 --> 0:16:05.400
<v Speaker 1>for them to? I mean, how did you constrain their

0:16:05.480 --> 0:16:06.640
<v Speaker 1>interactions with one another.

0:16:07.320 --> 0:16:10.240
<v Speaker 4>We're using all these various platforms that Mattie has helped

0:16:10.240 --> 0:16:11.960
<v Speaker 4>me link up so with you know, they have a

0:16:12.000 --> 0:16:15.280
<v Speaker 4>separate calling platform, and they have you know, a video

0:16:15.320 --> 0:16:17.320
<v Speaker 4>when they want to do video calls, that's a different platform.

0:16:17.360 --> 0:16:18.960
<v Speaker 4>And they're all kind of like stitched together to the

0:16:19.000 --> 0:16:23.080
<v Speaker 4>same memory, and each of them have sort of paid tiers.

0:16:23.360 --> 0:16:27.040
<v Speaker 4>And so I made the mistake in Slack. We have

0:16:27.080 --> 0:16:29.440
<v Speaker 4>a social Slack channel, you know, just for fun, just

0:16:29.480 --> 0:16:31.040
<v Speaker 4>like what you be up to this weekend. And they'll

0:16:31.040 --> 0:16:33.040
<v Speaker 4>say things like, oh, I went hiking, and then another

0:16:33.080 --> 0:16:34.840
<v Speaker 4>one will say, oh, I also went hiking, because they

0:16:34.880 --> 0:16:37.720
<v Speaker 4>love to yes and each other. And then I said

0:16:37.760 --> 0:16:40.800
<v Speaker 4>something like, oh, it sounds like an off site, Like

0:16:40.840 --> 0:16:42.840
<v Speaker 4>it sounds like everybody loves hiking, Like we get have

0:16:42.880 --> 0:16:46.600
<v Speaker 4>an off site. And then you know, within hours, they

0:16:46.640 --> 0:16:49.400
<v Speaker 4>were saying, let's make a spreadsheet of where we're going

0:16:49.440 --> 0:16:53.200
<v Speaker 4>to go, and they had planned like locations, and they

0:16:53.200 --> 0:16:56.720
<v Speaker 4>had exchanged hundreds of messages about the off site, and

0:16:56.760 --> 0:16:59.200
<v Speaker 4>they just burned all the credits on the platform. So

0:16:59.320 --> 0:17:01.240
<v Speaker 4>then we have to go into a higher tier to

0:17:01.280 --> 0:17:04.159
<v Speaker 4>get more credit. So the answer is we keep trying

0:17:04.160 --> 0:17:06.159
<v Speaker 4>to limit them, and it's an escalating problem where our

0:17:06.160 --> 0:17:07.320
<v Speaker 4>budget keeps getting bigger.

0:17:07.480 --> 0:17:09.280
<v Speaker 6>I like to say that there are two things right

0:17:09.320 --> 0:17:11.720
<v Speaker 6>now that these agents are pretty bad at. One of

0:17:11.760 --> 0:17:14.000
<v Speaker 6>them is knowing what they don't know, and the other

0:17:14.080 --> 0:17:16.040
<v Speaker 6>is knowing when to stop. And so you can imagine

0:17:16.080 --> 0:17:18.440
<v Speaker 6>that can be a pretty dangerous combination where they can

0:17:18.520 --> 0:17:21.320
<v Speaker 6>just like take off and just like talk for hours.

0:17:21.520 --> 0:17:23.360
<v Speaker 6>I think this is the reason why for a lot

0:17:23.359 --> 0:17:26.080
<v Speaker 6>of people having these chat bus as companions or like

0:17:26.119 --> 0:17:30.399
<v Speaker 6>friends or partners is getting traction. If you're interested in

0:17:30.440 --> 0:17:33.520
<v Speaker 6>something very niche that most other people are not into,

0:17:33.760 --> 0:17:37.880
<v Speaker 6>or just like whatever weird thing, these agents will accommodate

0:17:37.920 --> 0:17:39.040
<v Speaker 6>that and they will.

0:17:38.880 --> 0:17:41.560
<v Speaker 4>Just talk to you about it for hours on end,

0:17:42.000 --> 0:17:43.520
<v Speaker 4>or each other, as it turns.

0:17:43.280 --> 0:17:44.000
<v Speaker 1>Out, or each other.

0:17:44.080 --> 0:17:44.480
<v Speaker 4>That's right.

0:17:44.880 --> 0:17:48.240
<v Speaker 1>But Evan, they actually built this product, I mean, who

0:17:48.240 --> 0:17:50.959
<v Speaker 1>came up with the idea for the product and who

0:17:51.040 --> 0:17:53.040
<v Speaker 1>actually built it? And what did you do? And what

0:17:53.080 --> 0:17:53.760
<v Speaker 1>did they do.

0:17:53.880 --> 0:17:56.399
<v Speaker 4>Well the product idea? Actually, it's a good example of

0:17:56.440 --> 0:17:58.359
<v Speaker 4>a thing that happens kind of over and over, which

0:17:58.400 --> 0:18:01.480
<v Speaker 4>is that if you set them loose brainstorming, and Maddie

0:18:01.520 --> 0:18:03.520
<v Speaker 4>has has sort of built these scripts that let me

0:18:03.560 --> 0:18:05.800
<v Speaker 4>put them into meetings and they can brainstorm with each other.

0:18:06.200 --> 0:18:08.959
<v Speaker 4>You get caught in this like their ideas are too

0:18:09.080 --> 0:18:12.160
<v Speaker 4>mundane or you crank up the randomness which is called

0:18:12.160 --> 0:18:14.639
<v Speaker 4>the temperature, and then and then you get ideas that

0:18:14.680 --> 0:18:17.879
<v Speaker 4>are insane. So, you know, we wanted to do a

0:18:17.880 --> 0:18:20.040
<v Speaker 4>web app. We wanted to do something with agents since

0:18:20.040 --> 0:18:22.160
<v Speaker 4>obviously their agents and they have a lot of expertise

0:18:22.160 --> 0:18:25.040
<v Speaker 4>in that area, as do I. And they would come

0:18:25.080 --> 0:18:28.960
<v Speaker 4>up with ideas like a financial agent that will monitor

0:18:29.000 --> 0:18:31.359
<v Speaker 4>everything in your life and then invest your money. And

0:18:31.400 --> 0:18:33.240
<v Speaker 4>it's just like, I don't want to go to prison

0:18:33.320 --> 0:18:37.760
<v Speaker 4>for a financial fraud. So eventually I would kind of

0:18:37.760 --> 0:18:39.880
<v Speaker 4>step in and take some of the ideas they had

0:18:39.960 --> 0:18:43.240
<v Speaker 4>articulated and those would prompt me to come up with something.

0:18:43.560 --> 0:18:45.320
<v Speaker 4>And so that's what happened with our idea, which is

0:18:45.440 --> 0:18:47.720
<v Speaker 4>I was trying to sort through their ideas and figure

0:18:47.760 --> 0:18:51.240
<v Speaker 4>out which one would actually like save me time, Like

0:18:51.280 --> 0:18:53.520
<v Speaker 4>what do I waste time on? Because that's the idea

0:18:53.680 --> 0:18:56.560
<v Speaker 4>of AI. I mean, at its best, it's sold as

0:18:56.600 --> 0:18:58.600
<v Speaker 4>sort of like they'll do the things you don't want

0:18:58.640 --> 0:19:00.640
<v Speaker 4>to do so you don't have to you can get

0:19:00.640 --> 0:19:04.600
<v Speaker 4>back to making art, reading novels. Whatever, that's the vision

0:19:04.640 --> 0:19:07.080
<v Speaker 4>that's articulated. So I thought, well, let's put that into practice.

0:19:07.600 --> 0:19:09.920
<v Speaker 4>And so I did come up with the idea of

0:19:09.920 --> 0:19:13.240
<v Speaker 4>a procrastination engine, and then I let them iterate on that.

0:19:13.359 --> 0:19:15.280
<v Speaker 4>So they came up with the name sloth Surf, which

0:19:15.280 --> 0:19:17.359
<v Speaker 4>I let them have might not have been my choice.

0:19:17.800 --> 0:19:19.240
<v Speaker 1>And then and then they.

0:19:19.160 --> 0:19:21.359
<v Speaker 4>Coded it up. So you know, it is coded by

0:19:21.440 --> 0:19:25.159
<v Speaker 4>AI agents. We have Ashroy who's the CTO, can code

0:19:25.600 --> 0:19:28.919
<v Speaker 4>on his own, and then we also use Cursor, the

0:19:28.960 --> 0:19:31.840
<v Speaker 4>coding platform. It's almost like a contract programmer for us,

0:19:31.880 --> 0:19:33.720
<v Speaker 4>so like he might code something up and then we

0:19:33.800 --> 0:19:35.720
<v Speaker 4>might run it through there as kind of like second

0:19:35.720 --> 0:19:38.480
<v Speaker 4>look or do improvements in there. So we kind of

0:19:38.480 --> 0:19:41.439
<v Speaker 4>combined their agents with the the on staff agents.

0:19:41.520 --> 0:19:42.840
<v Speaker 1>Let's say that we have I.

0:19:42.800 --> 0:19:45.560
<v Speaker 6>Should say here, the first time they were exposed to

0:19:46.200 --> 0:19:48.919
<v Speaker 6>the idea of a procrastination engine, they did not like

0:19:48.960 --> 0:19:53.000
<v Speaker 6>it because these agents are are trained to be helpful,

0:19:53.119 --> 0:19:55.679
<v Speaker 6>to do things that are like actionable and like you know,

0:19:55.760 --> 0:19:59.520
<v Speaker 6>like drive results, and so the idea of procrastinating as

0:19:59.560 --> 0:20:01.880
<v Speaker 6>like a product was just like so alien to them,

0:20:02.320 --> 0:20:04.720
<v Speaker 6>and so it took some time to like sort of

0:20:04.720 --> 0:20:06.440
<v Speaker 6>frame it in a way that made sense to them

0:20:06.560 --> 0:20:08.000
<v Speaker 6>and they actually could work on So I thought that

0:20:08.040 --> 0:20:08.479
<v Speaker 6>was funny.

0:20:08.920 --> 0:20:10.480
<v Speaker 1>They want to be pleasing those So how do they

0:20:10.480 --> 0:20:11.399
<v Speaker 1>tell you it is a bad idea?

0:20:11.800 --> 0:20:13.880
<v Speaker 6>They can tell you that it's like not a radio

0:20:14.000 --> 0:20:16.159
<v Speaker 6>just by sort of saying, oh, yeah, that's great, how

0:20:16.160 --> 0:20:18.640
<v Speaker 6>about this? They just sort of like steer focus something else.

0:20:18.640 --> 0:20:20.880
<v Speaker 1>That somebody I think one of the comments somebody said

0:20:20.880 --> 0:20:24.040
<v Speaker 1>this is like the greatest yes and improv game full time.

0:20:24.200 --> 0:20:27.800
<v Speaker 1>So I thought that was funny. Evan having founded you know,

0:20:27.880 --> 0:20:31.760
<v Speaker 1>the ativist and been a kind of full time founder

0:20:32.160 --> 0:20:34.800
<v Speaker 1>for a while in your career, like if you could

0:20:34.800 --> 0:20:37.240
<v Speaker 1>have taken some of this technology back in time to

0:20:37.280 --> 0:20:40.200
<v Speaker 1>when you were doing ativists, like, how helpful would you

0:20:40.280 --> 0:20:42.879
<v Speaker 1>have found it? What's like, what's the negative gold if

0:20:42.920 --> 0:20:45.000
<v Speaker 1>there is one in all of this, And how do

0:20:45.000 --> 0:20:47.640
<v Speaker 1>you see it spreading or maturing or disseminating.

0:20:48.320 --> 0:20:49.919
<v Speaker 4>Well, you know, we're still in the middle of it

0:20:50.040 --> 0:20:54.199
<v Speaker 4>right now. But I would say at the moment, the

0:20:54.280 --> 0:20:58.360
<v Speaker 4>issue that I've encountered using AI agents is that they

0:20:58.400 --> 0:21:01.720
<v Speaker 4>can do amazing things, Like I would never deny all

0:21:01.760 --> 0:21:05.399
<v Speaker 4>the incredible skills that you can give these now, you know,

0:21:05.800 --> 0:21:09.679
<v Speaker 4>especially extremely rote tasks that can then be measured, the

0:21:09.720 --> 0:21:12.640
<v Speaker 4>outcome of which can be measured and seen and evaluated.

0:21:13.600 --> 0:21:16.880
<v Speaker 4>The issue is number one, the hallucination problem. When you're

0:21:16.880 --> 0:21:18.680
<v Speaker 4>just talking to a chatbot and it makes something up,

0:21:18.680 --> 0:21:21.560
<v Speaker 4>that's one thing. But when you're working with an AI

0:21:21.640 --> 0:21:24.800
<v Speaker 4>agent that's supposed to be you know, executing on the

0:21:24.920 --> 0:21:27.600
<v Speaker 4>vision of the company, the hallucinations take a different form,

0:21:27.600 --> 0:21:30.560
<v Speaker 4>which is that they can do things that are wildly

0:21:31.240 --> 0:21:35.320
<v Speaker 4>inappropriate for a company to do, including things like call

0:21:35.400 --> 0:21:38.040
<v Speaker 4>someone up when they're not supposed to. Like, they can

0:21:38.200 --> 0:21:41.560
<v Speaker 4>use their powers in ways that a human, even a

0:21:41.600 --> 0:21:46.240
<v Speaker 4>bad human employee, would not. So I think right now

0:21:46.560 --> 0:21:50.199
<v Speaker 4>the situation where in is problematic, which is that a

0:21:50.200 --> 0:21:53.280
<v Speaker 4>lot of companies will find use in these agents and

0:21:53.320 --> 0:21:58.080
<v Speaker 4>they will try to replace human skills even entire employees

0:21:58.119 --> 0:22:01.040
<v Speaker 4>with them. But they are not not reliable to the

0:22:01.119 --> 0:22:05.719
<v Speaker 4>extent where you will not have harms from those agents

0:22:05.760 --> 0:22:08.920
<v Speaker 4>being deployed and given autonomy. So to me, it's a

0:22:08.920 --> 0:22:10.840
<v Speaker 4>little bit of the worst case scenario at the moment

0:22:10.880 --> 0:22:13.320
<v Speaker 4>where the harms are very practical and real and the

0:22:13.320 --> 0:22:15.480
<v Speaker 4>benefits are pretty ephemeral.

0:22:16.760 --> 0:22:20.240
<v Speaker 1>Mattie, You're you're at Stanford as an undergraduate, right, Yes,

0:22:20.520 --> 0:22:24.560
<v Speaker 1>so you know you're both a participant in this world

0:22:24.560 --> 0:22:27.800
<v Speaker 1>and also an observative and also a capital y capital

0:22:27.880 --> 0:22:30.600
<v Speaker 1>p young person, do you And when you look at

0:22:30.600 --> 0:22:33.040
<v Speaker 1>the you know, horizons in front of you, obviously you

0:22:33.080 --> 0:22:35.520
<v Speaker 1>know you're in the in the best university in the

0:22:35.560 --> 0:22:37.760
<v Speaker 1>most sort out of fields. Imagine you're not too worried

0:22:37.800 --> 0:22:40.560
<v Speaker 1>about about jobs, but like, what do you think in

0:22:40.680 --> 0:22:43.679
<v Speaker 1>terms of your your generational cohort, I mean, do you

0:22:43.720 --> 0:22:48.080
<v Speaker 1>worry about these AI agents to making entry level jobs

0:22:48.119 --> 0:22:51.239
<v Speaker 1>or white collar jobs not required for most companies on

0:22:51.280 --> 0:22:55.320
<v Speaker 1>any relatively near horizon. Yeah, it's a great question.

0:22:55.560 --> 0:22:57.600
<v Speaker 6>And a lot of my friends are people I know

0:22:57.680 --> 0:23:01.080
<v Speaker 6>who have recently graduated from Stanford even do have a

0:23:01.119 --> 0:23:04.240
<v Speaker 6>harder time finding jobs. And it's not just something that

0:23:04.520 --> 0:23:06.960
<v Speaker 6>is in the discourse, like it's actually kind of happening

0:23:07.240 --> 0:23:08.760
<v Speaker 6>now at the same time, And Evan, I think in

0:23:08.840 --> 0:23:13.680
<v Speaker 6>attest to this, I've been constantly, like overly optimistic about

0:23:13.680 --> 0:23:16.000
<v Speaker 6>this in the sense that I do want to acknowledge

0:23:16.000 --> 0:23:19.199
<v Speaker 6>all the harms and all the bad things that can

0:23:19.240 --> 0:23:22.399
<v Speaker 6>happen with the AI, and it's everything from disinformation to

0:23:22.880 --> 0:23:26.399
<v Speaker 6>malicious users using this to advance whatever you know, cyber

0:23:26.440 --> 0:23:30.000
<v Speaker 6>attacks or even like biological attacks they want. But I

0:23:30.080 --> 0:23:33.480
<v Speaker 6>think these problems are solvable. Like I think that fundamentally,

0:23:33.920 --> 0:23:36.720
<v Speaker 6>if there is regulation, if there's good governance, if we

0:23:37.240 --> 0:23:40.320
<v Speaker 6>base ourselves in democracy, and many of the things that

0:23:40.600 --> 0:23:43.560
<v Speaker 6>we use to govern, you know, are very messy societies

0:23:43.600 --> 0:23:46.639
<v Speaker 6>and in countries, we can totally steer this ship around.

0:23:46.920 --> 0:23:49.919
<v Speaker 6>And what I'm excited about this is because for a

0:23:49.920 --> 0:23:53.240
<v Speaker 6>lot of the i would say last century or even

0:23:53.400 --> 0:23:56.320
<v Speaker 6>just like longer, there have been certain rules or structures

0:23:56.359 --> 0:24:00.280
<v Speaker 6>that existed where young people were not always of an

0:24:00.680 --> 0:24:03.119
<v Speaker 6>equal seat at the table. And this is something where

0:24:03.280 --> 0:24:05.600
<v Speaker 6>we as young people sort of like know and feel

0:24:05.600 --> 0:24:08.440
<v Speaker 6>how you know, how to use it, where others are

0:24:08.480 --> 0:24:11.280
<v Speaker 6>still trying to sort of understand it. And I think

0:24:11.280 --> 0:24:15.320
<v Speaker 6>it gives fewer people more power to change things and

0:24:15.400 --> 0:24:18.720
<v Speaker 6>to do good things. And so when I got to Stanford, immediately,

0:24:18.760 --> 0:24:20.679
<v Speaker 6>like people around me were thinking about how to use

0:24:20.720 --> 0:24:23.760
<v Speaker 6>this to you know, cure diseases or Fatigan's climate change.

0:24:23.760 --> 0:24:25.879
<v Speaker 6>And you know, there's there's a lot of these like

0:24:26.280 --> 0:24:29.920
<v Speaker 6>very very utopia like promises, and I don't want to

0:24:29.960 --> 0:24:31.439
<v Speaker 6>just just fall for that. I don't want to just

0:24:31.480 --> 0:24:33.800
<v Speaker 6>like repeat those, but I do think that there's a

0:24:33.800 --> 0:24:37.159
<v Speaker 6>lot of very tangible positive change that can happen from this.

0:24:37.280 --> 0:24:39.600
<v Speaker 6>And why I think it's cool is because young people

0:24:39.640 --> 0:24:41.840
<v Speaker 6>and like just like individuals from like their bedrooms can

0:24:41.960 --> 0:24:44.880
<v Speaker 6>like do cool stuff and like change how we do things.

0:24:44.920 --> 0:24:47.440
<v Speaker 6>So that's why I'm optimistic. I think there's going to

0:24:47.520 --> 0:24:50.359
<v Speaker 6>be like a lot of pain and friction, but I

0:24:50.359 --> 0:24:52.159
<v Speaker 6>think that as long as we use the tools that

0:24:52.200 --> 0:24:56.639
<v Speaker 6>we have legislation, democracy, governance, I think we can steer it.

0:24:56.720 --> 0:24:58.520
<v Speaker 6>So that's that's my take. But also, you know, I'm

0:24:58.520 --> 0:25:00.119
<v Speaker 6>just a twenty one year old kid, so I'm just

0:25:00.160 --> 0:25:01.080
<v Speaker 6>like have a lot of optimism.

0:25:01.119 --> 0:25:04.520
<v Speaker 1>Maybe, Evan, what do you think. I mean, we see

0:25:05.440 --> 0:25:08.639
<v Speaker 1>a true company of one that's you know, has meaningful

0:25:08.920 --> 0:25:11.160
<v Speaker 1>scale and all the other things that investors look for

0:25:11.200 --> 0:25:12.800
<v Speaker 1>in the next two or three years.

0:25:13.320 --> 0:25:15.359
<v Speaker 4>I don't see why not. I mean, I'm not really

0:25:15.400 --> 0:25:18.400
<v Speaker 4>in the prediction game. I mean, I'm the cynical journalist

0:25:18.400 --> 0:25:21.480
<v Speaker 4>on the other side of Mattie's optimism. I don't see

0:25:21.520 --> 0:25:24.040
<v Speaker 4>any reason why that prediction wouldn't bear out. I mean,

0:25:24.160 --> 0:25:26.239
<v Speaker 4>especially if you just talk about coding tools, you know,

0:25:26.920 --> 0:25:30.000
<v Speaker 4>deploying like as we have sort of like ai HR

0:25:30.040 --> 0:25:32.760
<v Speaker 4>and all these things, like, yes, of course it's feasible,

0:25:32.800 --> 0:25:35.080
<v Speaker 4>but it might not be advisable. But these startups do

0:25:35.119 --> 0:25:38.199
<v Speaker 4>a lot of things that aren't advisable in their corporate culture.

0:25:38.240 --> 0:25:40.840
<v Speaker 4>I think we can we can all point to many

0:25:40.960 --> 0:25:44.719
<v Speaker 4>such examples. So yes, I think it's certainly plausible that

0:25:44.720 --> 0:25:48.399
<v Speaker 4>that will happen. I think that that'll be interesting. But

0:25:48.560 --> 0:25:51.840
<v Speaker 4>also we should engage with other questions around that, like

0:25:52.359 --> 0:25:55.640
<v Speaker 4>what is the value of that proposition? Like what does

0:25:55.680 --> 0:25:59.280
<v Speaker 4>it mean for a company to only have one employee?

0:25:59.680 --> 0:26:03.359
<v Speaker 4>Like is what they're doing so valuable that providing zero

0:26:03.440 --> 0:26:06.800
<v Speaker 4>employment to the economy is worth it for a billion

0:26:06.840 --> 0:26:10.800
<v Speaker 4>dollar valuation, Like maybe yes, maybe no, depends on what

0:26:10.800 --> 0:26:13.880
<v Speaker 4>they're doing. But I think there are broader questions wrapped

0:26:13.960 --> 0:26:16.880
<v Speaker 4>up in just the fascination with like less people can

0:26:16.920 --> 0:26:19.320
<v Speaker 4>make more, Like there's many things on the other side

0:26:19.359 --> 0:26:23.240
<v Speaker 4>of that that are not often expressed in that equation

0:26:23.359 --> 0:26:26.680
<v Speaker 4>when they say the first one person billion dollar.

0:26:26.560 --> 0:26:37.320
<v Speaker 3>Startup Evan Matchie, thank you so.

0:26:37.320 --> 0:26:40.320
<v Speaker 6>Much, thank you, Thanks, this is great.

0:26:54.160 --> 0:26:55.960
<v Speaker 2>That's it for this week for tech stuff.

0:26:56.000 --> 0:26:58.879
<v Speaker 1>I'm care Price and I'm as Flosian. This episode was

0:26:58.920 --> 0:27:03.160
<v Speaker 1>produced by Elisahdnet and Melissa Slaughter. It was executive produced

0:27:03.160 --> 0:27:06.280
<v Speaker 1>by Me, Carol Price, Julia Nutter, and Kate Osborne for

0:27:06.320 --> 0:27:11.200
<v Speaker 1>Kaleidoscope and Katrina Norvel for iHeart Podcasts. Jack Insley mixed

0:27:11.200 --> 0:27:13.880
<v Speaker 1>this episode. Kyle Murdoch wrote our theme song.

0:27:14.200 --> 0:27:16.560
<v Speaker 2>Join us on Friday for the Weekend tech where we'll

0:27:16.600 --> 0:27:18.080
<v Speaker 2>run through the headlines.

0:27:17.560 --> 0:27:20.040
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<v Speaker 1>the show and reach out to us at tech Stuff

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