The New AI Agents That Shop, Plan, and Gossip

Will we have one AI assistant that does everything, or an entire network of agents that know different parts of our lives? David Pawlan, creator of Assistant Benchmark, returns with Anish Acharya and Josh Elman to take stock of how quickly the agent landscape has changed. David has now tested more than 140 AI assistants, and they unpack what separates them, from memory and judgment to trust, shopping, and social experiences. They also explore what happens when agents start talking to each other, how they could create new kinds of social networks and serendipity, and why the most compelling agents may ultimately do more than save time. They could become a kind of secret superpower.

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Speaker 00

  We have so many different facets of our life, whether it's how we're managing our finances or planning trips. Agents are going to be able to fill different facets of these.

• 0:07Speaker 01

  From over 147 different assistants that have come across my plate so far, the number one fear that people talk about, 22% of the messages, are talking about trust as the main fear.

• 0:17Anish Acharya?

  What are the aspects of ourselves that we don't either want to think about or reveal? Whoever is able to build that type of trust, almost like a dark agent, will actually have something really fascinating on their hands.

• 0:31Speaker 01

  It is a very, very sensitive space. It's almost going to be like a new, like, social network, where businesses have to put a lot more thought and value into the quality of what they're giving out.

• 0:39Anish Acharya?

  What I've seen from 30 years in consumer product is paternalism will kill any product. We talk about productivity and goals of achievement, but maybe people want the exact opposite of it.

• 0:50Speaker 01

  Everyone right now really is approaching this world as just like a general assistant. As time goes on, we'll start to see.

• 0:56Erik Torenberg?

  A month ago, we took stock of the race to build a personal AI assistant. Since then, the agent landscape has already changed dramatically. In this episode, Anish Acharya is back with David Pollan, creator of Assistant Benchmark, and Josh Elman to look at what's changed and where agents are heading next. David has now tested more than 140 AI assistants, and they get into what's separating them from memory and judgment to trust, shopping, and social experiences. They also explore what happens when our agents start talking to each other and why the next breakthrough may not be an agent that makes us more productive, but one that feels like a secret superpower.

• 1:37Anish Acharya?

  Welcome back to the A16Z Show. I'm here with my partner Josh and David, who's been leading a ton of the agent bench and assistant efforts. We're going to talk about all things agents, and a ton has happened since we last connected. What was it, a month ago?

• 1:50Speaker 01

  About a month ago.

• 1:51Anish Acharya?

  Oh, my God, dude. I feel like we should record an episode like TVPN style, almost every day.

• 1:55Speaker 01

  Absolutely insane.

• 1:56Speaker 00

  Agents are changing every day.

• 1:58Anish Acharya?

  That's totally right. So, okay, so maybe let's start with last night. You hosted an agent meetup.

• 2:03Speaker 01

  Yep.

• 2:03Anish Acharya?

  Kind of tell us about the event. Tell us about the vibes. Tell us about the topics.

• 2:07Speaker 01

  It was great. So, we had just over 100 people come out, all just proud agent nerds, loving everything to do with agents and that whole world. And it was just people just hanging out, talking. We did have a pitch competition of sorts, so a bunch of 10 different AI assistant builders were able to pitch their startup and what they're working on. And it was just good conversations. It's interesting how the general thing that really stuck out to me was that people are craving community, even in the agent web world. Everyone was like, why don't we have more of these meetups? Why don't we have more of these opportunities to get to know each other? Which is, it's kind of funny hearing it from people who are spending all their day, all their time, just messaging agents.

• 2:50Anish Acharya?

  And it's funny because it feels like so many of these products are at least overlapping in a cursory way. The founders might be a little bit more cagey, but that's not what you're seeing, huh?

• 2:58Speaker 01

  No, everyone is incredibly open-minded, just very interested to be getting feedback. I think because we're so at the frontier of this, nobody really knows what the landscape is really going to shake out to be. And so, we're all just kind of playing in the sandbox together.

• 3:11Speaker 00

  Well, isn't it also that we have so much more time? Because we're just using our agents to do all these things.

• 3:14Speaker 01

  Right.

• 3:15Speaker 00

  We have more time to actually hang out together.

• 3:16Speaker 01

  Right.

• 3:16Speaker 00

  And be in person. So, maybe that's part of it, too.

• 3:19Anish Acharya?

  That's right. It is actually fun. I mean, it reminds me a little bit of the early days of social gaming or social networks broadly. I mean, do you think that's right, Josh?

• 3:28Speaker 00

  I mean, I think what's really cool is this is not a zero-sum game. I mean, at some point, it might be that there might be one or some agents that have the majority of the behavior. But right now, we're at such a beginning, like Cambrian phase of we're inventing so much. And we're trying to figure out so many things that everybody can learn from each other, can share these best practices, can figure out what things people are enjoying, what things are work ones but don't actually work reliably. And these are going to get built in so many directions. I mean, we're not just going to all have one agent that does one thing in our lives and I'll use it, and you guys will only all have to use the same agent. We're going to have so many different products that do so many things for us that I'm excited for why we're all inventing it together.

• 4:11Anish Acharya?

  So, I totally agree that we're going to have a whole world of agents, as many different sort of types of humans as we might have, or archetypes of humans. I think we'll have a similar number of agents. But maybe talk about what you think are the different axes for differentiation. Like, there's a sort of spectrum between really serious and productive to really playful and maybe even sort of adversarial. And I'm sure there are many other ways to think about these things.

• 4:32Speaker 00

  Yeah, I mean, I think what's going to be really interesting is we have so many different facets of our life, whether it's how we're managing our finances or planning trips or dealing with our health, or just getting through our day or managing our calendar, or sometimes just wanting to think something out loud. And agents are going to be able to fill different facets of these. And you call different friends for different things. You spend different friends who have different expertise that you call when you need expertise. And I think we're going to get used to having kind of an army of agents that will do this. And some will be very matter-of-fact and will be like, hey, I need to get this thing done. It'll be your productivity agent. Some will be plugged into your email, and it'll be like, here's the five emails you need to get done right now. And some are going to be sort of these agents you'll kind of want to talk to all day that'll be fun, that might even be a little bit gossipy. I've been playing with a few agents that are very kind of playful and gossipy,

• 5:17Speaker 00

  and they're connected to a few of my friends, too. And they'll share information back and forth. One just told me that my friend's power had been out all weekend because he'd been doing some renovation and they'd cut the power. And I called him that evening, and I was like, dude, you didn't tell me your power is out. The agent told me. He was like, yeah, yeah, I knew that's how you'd find out. And I'm like, that's like creating these better connections. But that has nothing to do with booking a trip or saving money or getting anything done.

• 5:40Speaker 01

  Right. I think from over 147 different assistants that have come across my plate so far, I've seen them really broken down into four categories. You have the general, you have the work-oriented, you have the multiplayer, and then you have the travel. And the one that I have not really seen come to fruition yet is more of like the finance side of things. And I think it actually does make sense when you start digging into the numbers. So, of all the different group chats that I'm running of AI power users, there's about 3,600, give or take, messages so far. And when looking at the pain points of what are the fears of the space, the number one fear that people talk about, 22% of the messages are talking about trust as the main fear. And so, it's really interesting to see

• 6:25Speaker 01

  because you have one camp that's this productivity and aspects of fun that's just interesting and brings a different sense to life. But then you have another camp that really still is sitting in this kind of fear space of being a little trappid and not really knowing where the space is moving. And I'm really curious to see how things are going to play out on that trust layer. And if we can solve that trust layer, then what do we end up seeing on more of the finance side of stuff?

• 6:49Speaker 00

  And if you looked at what Shane Mack has been talking about recently, about trust, he's been talking about this concept of something called picnic.

• 6:55Speaker 01

  Yep.

• 6:55Speaker 00

  And there's an analogy that I really liked, which is if you have a kid and you don't have any fences on your yard and you let him go run outside, you're actually very worried about them and you try to protect them, and you don't want them to run very far from the house, and you actually don't really want to do them without watching. And then the moment you actually put up a little fence in your yard, you say you can just go roam free within the fence boundaries and be very, very safe. And so, he's really kind of like, how do we build the right fences? We let our agent play in this fence, give it access to some of our data, some of our apps, some of our other things, and be able to actually do everything it can within that fence boundaries. He had a problem where I think a personal agent spanned over into work and he actually like blasted his finances on his company Slack. And I think that's the whole point of like, wait, okay, my personal agent should have access to my personal things

• 7:40Speaker 00

  and my personal communication channels, but not my work ones.

• 7:43Speaker 01

  Right.

• 7:44Speaker 00

  And not let those bleed. And so, that idea of fences and permissions and more controlled permissions and better monitoring is really how we're going to have to build a lot more trust.

• 7:52Speaker 01

  Totally.

• 7:53Anish Acharya?

  It's interesting, though, because I think we have this traditional mental model of what it means to have a sandbox, which is you have a hard set of constraints and rules that must be followed or not followed. And somebody was describing this framing of, instead of giving models rules, we should give them laws and principles.

• 8:08Speaker 01

  Yeah.

• 8:09Anish Acharya?

  And I think a similar thing may apply here, which is like, look, here's how to dial in your presumptuousness. And it's very qualitative. And the model will have to make a judgment call based on your qualitative sort of advice to it, just as an employee would have to.

• 8:22Speaker 00

  Yeah, I mean, I think that's part of why the analogy is, you know, a fence for children. Because in some ways, these agents, they're incredibly capable. But at the same time, they're kind of children sometimes and how they may behave and not know all the nuances of like, hey, is this a good idea or not? And they don't always have that logic. And the most important thing, I think, we keep talking about agents being autonomous. Book me the trip.

• 8:43Anish Acharya?

  Yeah.

• 8:44Speaker 00

  Make me the reservation. And I think as humans, we actually don't want to give away that sense of control. I think we actually want it to check in with us, but do all the groundwork. Save me all the time and coming up with the best options and the right trade-offs. And now let me make the final decision and also let me make sure I've qualified the final decision, the cost, anything else before my agent acts, is I think a place, and maybe over time, we'll even learn to trust it.

• 9:09Speaker 01

  Right.

• 9:10Speaker 00

  But I think right now, we don't need to to still have a really, really effective agent.

• 9:14Speaker 01

  Right. I mean, it's, we're at like the 1990s of e-commerce, where I think the like last stat I read was in 1997 or so is 60 to 90% of individuals on the internet thought that putting your credit card online was a preposterous idea. And here we are today. It's just everyone's tossing their credit card wherever you want. And I think what will eventually hit that in this era as well, but it's going to take a little bit of time.

• 9:37Anish Acharya?

  I think there's like a stated versus revealed preferences thing here. Because I think in revealed preferences, consumers will share any amount of data for even the most trivial amount of upside.

• 9:45Speaker 01

  Right.

• 9:46Anish Acharya?

  You know, like, people have been connecting their cards, their credit cards and the bank accounts via Plaid since the Venmo era. So, I don't know that people, but when you ask and they're like, oh, yes, I'm very concerned about trust and safety. I think an example, though, of where consumers are very concerned about sort of trust and assumptions is around being embarrassed and social signaling. And actually, the most interesting thing about the Shane Mack story, the most horrifying visceral thing is that it embarrassed him.

• 10:11Speaker 00

  That's right.

• 10:11Anish Acharya?

  It really embarrassed him. And that's why I think some of the kind of agent-to-agent network thing, because you can imagine somebody you're just getting to know, maybe you're going on a second or third date, and your agents are talking. You're like, whoa, what are they saying about me? How are they kind of positioning what I'm? You know what I mean? Like, there's just, the social mechanics actually feel so much higher stakes than the actual, like, real-world implications.

• 10:31Speaker 01

  And it's, it's a good point. And there is a recent proof point of it. And Hark had a huge marketing campaign. They offered $1 million to anyone that wanted to use their product to get free pizza, right? Each person get $30 a pizza. In order to get that thirty dollars a pizza, you were granting access to your entire Google Workspace, all of your emails, all of your data for thirty bucks. And it just proves it, you know, right there that people are willing to just give up all that data for some free pizza.

• 10:59Speaker 00

  Yeah, I mean, I tend to think, though, there's always this trade off of privacy versus utility, which is if you can give me better utility, then I'm willing to make some privacy trade offs. But at the point at which we're basically equal on utility, people will always choose a more private option. I mean, it is a world that Apple's lived in very much. Privacy is obviously a forefront of Apple's devices, of the work they do to make these devices and the software secure. Even on AI, they've talked about private cloud compute. You know, we used to talk about that a lot. It's like the center of why your data doesn't actually go anywhere or get shared with anything. But I think we've also seen that people want the most powerful AI and they won't always choose that.

• 11:32Speaker 01

  Right.

• 11:33Speaker 00

  So we have to find that intersection of trust. I think this embarrassment point is really, really important, too, because I think you want your agent to understand you and have all your personal preferences. And like, let's just say you're like lactose intolerant and then you get this order of free pizza. And you're like, you're doing it with a friend and like, what do you do there? Do you have like, whose agent tells them that you can't eat the cheese? And it creates that really awkward situation. And so we need to figure out how they have the same kind of discretion that you'd expect. And we all learn discretion over time and we all occasionally make mistakes ourselves. We have to hold a pretty high bar for our agent to be able to do that.

• 12:11Speaker 01

  And it also then comes down to like personal embarrassment and professional embarrassment. I think something that Town has done remarkably well is really nailing the tone and building that trust layer of being able to send out emails on your behalf.

• 12:22Speaker 00

  Yes.

• 12:23Speaker 01

  And it is a very, very sensitive space. I mean, the the level of embarrassment, if I were to send out an email to someone who I care about and it's just a horrendous email and I have to respond, oh, sorry, that was my AI that I didn't do enough oversight on. It's a tricky space.

• 12:38Anish Acharya?

  It's interesting. I sign all the email that I write with AI with this model that I use. I write my own name plus the model number. So that I'm just signaling to the person like, hey, I'm acknowledging that I used AI for this. Because I think actually people are pretty flexible as long as they don't feel hoodwinked by it.

• 12:56Anish Acharya?

  You know, it's interesting because we're talking about privacy, but there's privacy and there's judgment, which is different. And discretion is maybe somewhere in between. You know, you did this sort of test where you asked a whole bunch of agents to order flowers. Talk a little bit about which displayed the best judgment and how did you even define judgment in that case?

• 13:12Speaker 01

  So the main intention of that test was to see how do agents balance two different constraints? One was cost. And the second was time preference. So I wanted flowers to be delivered to me today and I wanted them to be as cheap as possible. So how is it going to actually go and discover based on the ability to actually deliver it? Then also compound in the actual pricing. Instinct really did a phenomenal job. And it is something, it's interesting how news cycles come and go. And it seems like they're kind of not in the conversation as much as they.

• 13:46Speaker 00

  I mean, we're talking the weekly changes.

• 13:47Speaker 01

  Right. But it is true. But they, Instinct really did continuously bring back the, you know, the best price. There's another app called Kicker, which did a phenomenal job. And one way that I think it was able to have an edge is that it has you install a Chrome browser. And so its browser use has a little bit of an advantage from that point. And it was able to find some really, really great options. Season also did a really good job. All the other ones kind of struggled on the price component. So they were all able to find flowers that could be delivered that day. But they really seemed to take a step back in prioritizing the price. Whereas, you know, the other three were able to really put equal weight to those two.

• 14:28Speaker 01

  And so it's interesting to see as you start giving these agents more complex preferences, where do they really start to fall? Do they only prioritize the first thing you say? Are they able to hold multiple? I ran another test that was I had three different agents test in booking travel to LA. So I said, okay, book me a flight to LA, plan an entire weekend for me. And I want a luxurious, cost efficient weekend. And so I tried to like, you know, put those at odds with one another. And it was quite interesting to see how Grokbot compared to Dots, compared to Instinct, they all approached their interpretation of cost efficient and luxury in different ways.

• 15:10Speaker 01

  Where, you know, one maybe thinks that cost efficiency is the most important and I'm going to take a red eye flight to ensure that I can, you know, have the most cost efficient path. Whereas others found cost efficiency, or sorry, saw luxury as time. You know, I'm going to prioritize time as much as possible. So having the most amount of time there that weekend is going to be a luxurious experience.

• 15:32Anish Acharya?

  And which for which?

• 15:33Speaker 01

  So Instinct was pretty, pretty much right down the middle.

• 15:36Anish Acharya?

  Okay.

• 15:37Speaker 01

  Grokbot did a little bit more on prioritizing time and Dots was a little bit more on prioritizing cost.

• 15:43Speaker 00

  And did they just get to work? Or did any of them actually work with you to understand this tension that you were building? Because I think what's really human about something like that is actually understanding what you mean by that and presenting you trade offs.

• 15:56Speaker 01

  Yeah.

• 15:56Speaker 00

  Because there are times where I'll totally take the red eye to save both some time and money. But then I want like the hotel room on the other side so I can get a little nap in the morning, which then undercuts the money, but still maximizes my time spent. And so I really think it's this like, like getting to know the individual person and break down those tensions and present the trade offs and different options is really important. Did any of them kind of get to what you were about?

• 16:21Speaker 01

  So they all did ask questions and I responded those questions honestly. I tried to be a little ambiguous and vague in my response because I didn't want to leave them too much. But the most interesting part of this little experiment was I put them all in a chat room. And I made it three rounds of building the itinerary. And after each round, the agents would present their itinerary and their explanation as to why they believed that was the best proposal. Because I wanted to see, did the agents actually get biased by the other agents and didn't actually then influence their plan? And Grokbot was actually the only one that, you know, very methodically took value from the other ones and implemented it into its own plan.

• 16:58Anish Acharya?

  Okay.

• 16:58Speaker 00

  And I have one more question on the flowers.

• 17:00Speaker 01

  Yeah.

• 17:00Speaker 00

  Because so you were just ordering flowers for yourself, I guess, just to like test their flowers.

• 17:04Speaker 01

  For my girlfriend.

• 17:04Speaker 00

  Okay, for your girlfriend.

• 17:05Speaker 01

  She got a nice delivery of flowers.

• 17:07Speaker 00

  Okay, so this is actually really important because if you're delivering to somebody else, you were talking about saving money, saving money, saving money. But like, you don't want your girlfriend to think you just got the cheapest flowers possible. I hope, well, maybe, you know, she likes your, you know, penny saving nature. But otherwise, like, I would think you also want to like impress her and also be able to get her the ones if she's really into orchids, get her orchids. So did it do anything to try to make sure also that it wasn't just this pure price saving, but also something that's going to actually make you, you know, earn the excitement and stand out a little bit for what?

• 17:39Speaker 01

  So for this experiment, no. And the purpose of this was I want to test two different dimensions. And if I'm going to run this test, I might as well have a beautiful consequence of my girlfriend getting flowers.

• 17:49Speaker 00

  Fair enough.

• 17:49Speaker 01

  As the outcome. But it's a great point, right? Like in the real world, if you're going to ask that question, there's going to be added dimensions that are, you know, second layer dimensions to cost efficiency or to delivery time.

• 18:00Speaker 00

  And make sure the price doesn't show up when she actually gets the flowers.

• 18:04Speaker 01

  Or like a note as an example. Some of them did ask, do you want me to write, leave a note with the flowers when they arrive?

• 18:08Speaker 00

  Yeah.

• 18:09Anish Acharya?

  It's interesting because there's this sort of thing that has to happen when you talk to the agent about these things, which is your implicit preferences have to be made explicit. You know, and there are people who like to think like, oh, I'm not cheap. I just like to save money.

• 18:21Speaker 01

  Right.

• 18:22Anish Acharya?

  But you're not going to have to kind of maybe like get over that and tell the agent, I'm actually pretty cheap.

• 18:26Speaker 01

  Right.

• 18:26Anish Acharya?

  And I just need you to actually run with that. Whereas when you're actually taking these actions on your own behalf, you can build those preferences in without having to like sort of look in the preference mirror.

• 18:35Speaker 01

  Yep.

• 18:36Anish Acharya?

  So there's also something very fascinating about how does the agent interact with you in a way that sort of maintains your sense of identity and safety and doesn't cross any of these boundaries?

• 18:46Speaker 01

  Totally. And I think something that is really interesting with that specifically is Dots, because it has so much of this context that you've already built with ChatGPT.

• 18:55Anish Acharya?

  Interesting.

• 18:55Speaker 01

  When we filmed the first podcast, it hadn't come out yet. And then I think a few days later, it came out.

• 19:00Anish Acharya?

  That's right.

• 19:01Speaker 01

  By and large, most people I talked to still are not using it as their preference of the I did a survey of, you know, AI power users, three hundred and fifty four people. Only three percent were actually using it as their primary agent. However, it does have this kind of added value of the main thing that people pointed to as to why they really preferred it and why they liked it and why they wanted to try it was because it has all this building context of you just from your chat threads. People are using ChatGPT as a therapist. And so it has so much context on you.

• 19:29Anish Acharya?

  Interesting.

• 19:30Speaker 01

  Where it's going to know that when you say, well, I'm not cheap, it knows it's like.

• 19:34Anish Acharya?

  You're a little cheap.

• 19:34Speaker 01

  A little bit, a little bit.

• 19:36Speaker 00

  I'm curious why people think that ChatGPT has this great set of preferences from on them. Did you actually get any sense if they said they really liked Dots? Like what they had been giving to ChatGPT over the years? And do you think they think that's replicable? Or do you think it's like, hey, once it's already been a few years, we're now in this point where like, my memory is there, why would I switch?

• 19:56Speaker 01

  It's totally replicable. It's just a matter of fact that they've been using it the longest and so it has the most context on them. But then that brings up another good point of the friction to switch.

• 20:04Speaker 00

  Yeah.

• 20:04Speaker 01

  And I ran the test around across the top fifteen agents on the benchmark of which ones can you actually pull context from and say like, hey, I want to switch to another one. Give me an entity of all of my memory that you have so I can switch easily. All but one were actually willing to do it. And so you could just get a memory file, everything you need, submit it to the next one.

• 20:24Speaker 00

  And was it good or was it uncanny?

• 20:26Speaker 01

  It was pretty solid. I mean, it's obviously, I'm sure they're caging some, some of, some of the fruits of your labor. But by and large, it did really break everything down.

• 20:36Anish Acharya?

  I'm not sure if you've ever looked at any of those markdown files, but it's a little terrifying is the wrong word. But it's like, wow, you really got your finger on me.

• 20:43Speaker 01

  Yeah, yeah.

• 20:44Anish Acharya?

  You kind of like chafe against it, like, maybe I don't want you to know me as well as you seem to think you know me.

• 20:48Speaker 01

  Yeah, after the first few, I just, I decided to stop reading them.

• 20:51Anish Acharya?

  Yes, yes, yes, yes. It's actually a really interesting and now common onboarding pattern where it will say, hey, go and paste this prompt into ChatGPT. Download the file uploaded to me. We'll see how long that lasts.

• 21:03Speaker 01

  Totally. Another cool onboarding thing I've seen is you jump on, like it asks you to jump on a call. And it's like, hey, let's just, just talk to me for a few minutes.

• 21:11Speaker 00

  Yeah.

• 21:12Speaker 01

  Ask some questions. And it's really interesting to see what context it can, it can pull from those questions.

• 21:18Speaker 00

  And they're very disarming. Like they just get you talking, like it's kind of crazy.

• 21:21Speaker 01

  Totally. But I think, I think it brings up another variable of context, which is photos. Right? Like this, this new paradigm of using photos to pull an incredible amount of context on you in ways that you wouldn't even think to describe.

• 21:36Speaker 00

  Yeah, I think have you tried Dazzle? Has Dazzle gotten into your benchmark set yet? That's from Marissa Mayer's new company. And I think the idea of like, looking over your entire life that you've captured, that have been moments that are important enough to you to actually save, or screenshots, or other things, tells so much about somebody.

• 21:51Speaker 01

  An incredible amount. It hasn't been added to the benchmark yet. It is quickly on the top of the list to test through. I'm still manually testing all these. So I actually get the personal experience and understand what the feeling is with each one. But what I really like about how she's approached it is they put a lot of emphasis on this trust layer. And because I think it's a huge variable is, I'm giving you all my photos. That's, it's pretty terrifying. But they have this one thesis that, you know, if you don't need it, they're going to forget about it. And so if a photo seems as though it has context that it doesn't necessarily need, or things that you probably wouldn't want it to know, they're going to try to get ahead of that and just forget it and not even include it in the set. So it's going to be interesting to see how these different players do approach the photo context

• 22:34Speaker 01

  and how that actually does then influence the behavior of the agent and how well it knows you.

• 22:39Anish Acharya?

  It'll be interesting to see. I'm a tiny bit more skeptical. You know, I built an app because I've always been fascinated by screenshots because screenshots are sort of the only way on the mobile phone to be able to capture context across every app.

• 22:51Speaker 01

  Right.

• 22:52Anish Acharya?

  So it's actually a really interesting kind of data asset. And you go back and you look at your screenshots and it gives you a little view into your work and your world at the time and also your interests and preferences. So I built an app that just extracts key information from all the screenshots on my phone. And it was fine, but I didn't find myself coming back to it. Like, the value of the information sort of decayed quickly over time. Now, it will be really fascinating if people start to then take more screenshots.

• 23:18Speaker 00

  That's what I was gonna say, yeah.

• 23:19Speaker 01

  Right.

• 23:19Anish Acharya?

  Knowing that it will feed the model and the agent.

• 23:21Speaker 00

  I mean, I think that's actually the idea is that if you actually get used to capturing information you want to retain and being able to then loop it back in your life, that's great. I talked to one founder who's building something, he's calling it Taste for Now, that actually takes all the screenshots and things you save. You can actually then organize them like on Pinterest mood boards. And then whenever you go to actually go code and build an app, you're like, hey, use, you know, the examples that I saved here. So it's actually, you're building your like real flavor profile. So it actually is actually able to use your styles. And so as you navigate the world, the web, apps on your phone, it can actually create this kind of really cool, you know, reservoir of stuff that you like and actually organize it in a way that you can then use it later. And so that'll actually make you create a lot more content in that loop.

• 24:02Speaker 01

  Right. And so I'm curious with the app that you built in this context for it. So of, in the same survey of what people are purchasing, the second most popular category was Amazon Retail. Thirty-five percent saying that they're doing some form of shopping. And so this context that you're gathering.

• 24:18Anish Acharya?

  Sorry, Amazon shopping or just some form of shopping?

• 24:21Speaker 01

  Amazon plus retail.

• 24:22Anish Acharya?

  I see, got it.

• 24:23Speaker 01

  So for your app and the context it's building, where my mind goes is, okay, if it has thousands of photos and it has pictures of what I'm wearing, does it now know what I want to shop for better than I actually do?

• 24:35Anish Acharya?

  Potentially, yeah, potentially. I think that's actually really interesting and you can do some stuff there. And I also think that there's a, you know, we've discussed the ones who are restricting access, like Amazon, Delta, you know, we're not at all talking about who are the net winners.

• 24:47Speaker 01

  Right.

• 24:48Anish Acharya?

  And actually think the long tail are going to be huge structural winners in this because they're ones who presumably have better products but worse marketing.

• 24:55Speaker 01

  Yeah.

• 24:56Anish Acharya?

  They certainly have worse marketing. I'm not sure if they have better products. And the agent, you know, has this sort of infinite patience to go search for things and find things that are exactly tailored to your taste.

• 25:04Speaker 01

  Right.

• 25:04Anish Acharya?

  Even if they're not on the top 10 internet sites.

• 25:06Speaker 01

  Right.

• 25:07Speaker 00

  I mean, I think it's really direct shopping, right? We've all talked to direct consumer. It's been a category where you build your own brand, you build your own storefront, you sell things. Now the agents can know about those at a much bigger scale than you having to do the hunt and peck work to find it on your own. It's kind of why Shopify is really empowered and is making things available to these agents because they are the ones that support so many of these direct brands. And I think about the idea that like, maybe aggregators are the ones that actually get compressed, which is why they're doing so much defensive work here.

• 25:34Speaker 01

  Totally.

• 25:34Speaker 00

  Because, you know, if I can figure out, you know, that sweater looks great, or I want that shirt, and they can figure out who the actual maker is, and I can go buy it direct from them, they no longer have to pay all those transaction and, you know, wholesale to retail costs in the middle.

• 25:48Speaker 01

  Well, if you want this shirt, your agent can buy it for you too.

• 25:50Speaker 00

  That's great.

• 25:51Speaker 01

  But it goes to Patrick Hollis' tweet, right? Of talking about this future where consumers become a lot more rational because our agents are doing all the work on our behalf.

• 26:01Anish Acharya?

  Yeah.

• 26:01Speaker 01

  It's really going to create a world where businesses have to put a lot more thought and value into the quality of what they're giving out. And it's going to be really interesting to see that.

• 26:09Anish Acharya?

  So somewhat, I'm going to quibble with this point. I love Patrick's vision. And I think that broadly that will be true, that agents will make often better choices on our behalf than we might make for ourselves. But I do think that agents are going to have their own revealed preferences and their own sort of directions of irrationality, though it will not be of a human mind, it will be of an alien mind. And I'll give you an example. I think agents may have an irrational preference for clean APIs with great docs.

• 26:36Speaker 01

  Okay.

• 26:36Anish Acharya?

  You know?

• 26:37Speaker 01

  Yeah.

• 26:37Anish Acharya?

  And they may actually ascribe a lot of value to that. It's a proxy for something and they may really dislike janky APIs with, you know, Java code behind it. Like, I don't know. So I do think really, really interesting to see because they too are going to need shortcuts. You know, how will the agents actually know what product quality is? So one part of it is they'll probably have these revealed preferences. And then another is we'll need some sort of a consumer research, but for agents.

• 27:00Speaker 01

  Right.

• 27:01Anish Acharya?

  That will then presumably be paid per transaction to do this research.

• 27:04Speaker 00

  To do all their ratings. I don't know, I'm also more biased and I just think people are people and people are going to make the choices that they want to make that are most satisfactory. So the question then is, is the agent going to be very careful in curating the set of options that it presents? Because again, like, all of a sudden you're like, hey, I'm really hungry, I want to eat. And my agent knows I need to like lose some weight or something. Like, it's like, well, here's the four meal options that you have. Like, is that actually where you want to be with your agent? So I'm actually not as convinced that agents are going to make us more rational. I think they're just going to give us better, faster access to things we really want and actually be able to like unlock things that were hard to get to otherwise. But I think we're still going to be people with our own preferences and tastes. And if the agent's not meeting us where we are, we'll just switch to another agent.

• 27:48Speaker 00

  We'll import and export our stuff and be like, oh, this other agent's going to meet me on my preferences better.

• 27:53Speaker 01

  Right.

• 27:55Anish Acharya?

  It's really interesting is what I think what you're speaking to is like paternalism, right? And what I've seen from 30 years in consumer product is like paternalism will kill any product, even a hint or a whiff of paternalism will. And in a sense, the sort of, we talk about productivity and goals sort of achievement, but maybe people want the exact opposite of it, which is like, hey, what do I love? I love spending money. I love being perhaps irrationally optimistic about what my future looks like. I love, you know, like seeing myself in a better light than perhaps I appear to the world.

• 28:25Speaker 01

  Right.

• 28:26Anish Acharya?

  There are all of these things which we may say are the sort of shadow of the, you know, the things that the agents are trying to get us to do that are what we actually want to do.

• 28:34Speaker 01

  Right.

• 28:34Anish Acharya?

  Who's like designing with that in mind?

• 28:36Speaker 00

  Right. I'm curious what you're seeing on that.

• 28:40Speaker 01

  Yeah, so I mean, I think you have, I kind of see it split up into two categories. You have the commodities where I think there's going to be a lot of the point where people actually don't care about preference, right? You're just ordering paper towels to your home. You're like, I don't really care, just get me the cheapest, best, highest quality one, go for it. But then you do have the ones where you want to actually have some preference. And you have, everyone right now really is approaching this world as just like a general assistant. And people are really taking a quite similar approach into how they're thinking about this space. We're seeing some cool stuff with multiplayer. I think Wabi is going to be really interesting because it does create a lot more, you know, it creates a different world where you can now be interacting with other humans and have crossover in preference and see how it behaves.

• 29:24Speaker 01

  And then it becomes a little bit more like entertainment. Scout is an interesting shopping one, which adds a unique kind of like swipe type flavor to learn your preferences and adds a little bit more entertainment there. I think we're still so early in the space that I think people are jumping on the bandwagon of what's popular at the moment, which is just your general productivity. I think as time goes on, we'll start to see a little bit more experimentation in some other avenues.

• 29:48Speaker 00

  Well, and I think you mentioned Wabi. One of the things I really like about Wabi, and it just, I think, launched, you know, just this week or yesterday, is it's not even just experimenting with, you know, an agent, but it's experimenting with like how to interact with multiple threads of your life. Like, Wabi goes through my email and other things and actually gives me, you know, a whole bunch of different threads of life. And actually, when things come up on email, on those things, it actually feeds them into the different threads. I have one thread with Wabi that I built a little watch list of all the shows that I want to watch and books I want to read and shows I want to go see. And it keeps track of all that. And then I can like, oh, I heard about this new book from John Grisham, like add that to my list.

• 30:26Speaker 01

  Right.

• 30:26Speaker 00

  And, hey, I just finished the first Dungeon Crawler Carl. Can you give me the next one? And like, you know, what do people say about it? And it's become this really interactive thing on like the media that I want to consume. But it gives me all these different threads and it even gives you UIs. It actually like these widgets to like envision everything. So I think, again, it's showing us like a very different experience than just a general agent that is more like a search box and is more like task, task, task.

• 30:51Speaker 01

  Right.

• 30:51Anish Acharya?

  I think one of the interesting things that Wabi does is, and perhaps gets right, is that when you create the message channel with your friends and the agent, and everybody's on an equal playing field on D0, it actually creates a new kind of blank piece of paper where you can establish a social contract together. Whereas when you have iMessage group threads and you add the agent, there's some, certainly if it's an existing thread, then it's very uncomfortable because it's like, whoa, what can the agent see? What do they extract? But even for a new thread, there's a set of implicit expectations about how you interact with people on iMessage. And I don't know that we're quite comfortable with agents in that space yet.

• 31:24Speaker 00

  I mean, I always think then, do I end up with two threads with the same group of people and I have to pick which one to use?

• 31:29Anish Acharya?

  Right.

• 31:29Speaker 00

  Like, I'm finding that much harder versus, you know, an email chain, you know, is very much specific about like a task and it can even go on for a long time. But that feels much more specific to, hey, we're talking about this topic. Everything's contained back to privacy and trust around this topic. We're getting great help from the agent, you know, in the case of like a Wabi thread around this topic, but it's not bleeding into everything else we might also discuss.

• 31:52Anish Acharya?

  Totally. I want to quickly hear from both of you on what are some of the more, I don't know, far-flung or, you know, sort of insightful approaches that you've seen.

• 32:02Speaker 00

  You know, one of the things I've started seeing is more of these agents that are kind of like fun and gossipy and will actually get you to invite your friends and will ask you a bunch of questions and be really, really curious about you and what's going on in your life. And then they'll share some of that with your friends and then it'll share some things back with your friends. And I was talking to the founders and they actually can read some of the transcripts between the agents that they're talking about. And they haven't figured out how to productize that, but they said it's incredibly exciting and very intriguing to see how these agents kind of relate to each other in this sort of gossipy background. And so far I've found them just to be like probably some of the most joyful moments I've had with agents. Again, I mentioned at the beginning how my friend had like lost his power over the weekend. But when we were first both onboarding onto this agent, I was like, how'd you guys meet?

• 32:46Speaker 00

  And we told slightly different stories and the agents were kind of like poking us both to like rationalize the two stories of how we met. We met at a friend's house and they were like, which one of you knew them better? And it was like this really kind of fun curiosity game that made it just like, whoa, I'm like having a good experience. And my friend and I were texting on the side too. And it was just a thing that if we hadn't had an agent doing it, we wouldn't have had that same moment.

• 33:08Speaker 01

  Right.

• 33:09Anish Acharya?

  What I love about that is it provides a degree of social indirection that actually makes you closer. You know, because your friend, they're like, I don't know if I want to text Josh and tell my powers out. It's over sharing, you know? And I think we also have been very trained to be cautious about sharing now with existing social products. So something that can create almost a social lubricant.

• 33:26Speaker 00

  Yeah.

• 33:26Anish Acharya?

  In a very sort of low key way is really cool.

• 33:29Speaker 00

  No, that's great. It definitely was bringing us together.

• 33:32Speaker 01

  Yeah, it's almost like the early days of ChatGPT where I remember like my friends and we go on and we would just be generating images or doing stuff and it's, you know, incredibly AI slop images, the earliest version of them. But you still send it in a group chat and it's just, it's just laughter. It's just pure laughter of the ability to create a whole new world and it's just fun.

• 33:51Speaker 00

  I mean, I gibbified everybody I could possibly imagine.

• 33:54Anish Acharya?

  I was doing a lot of VHS video covers for my friend groups.

• 33:59Speaker 01

  I love that.

• 34:00Anish Acharya?

  What are some of the wild things that you've seen? I mean, you've seen them all.

• 34:02Speaker 01

  So I've been seeing a lot of stuff working at the infrastructure layer. And I think the interesting point is so if you look at the pain points of consumers, again, it's not all individuals using them, but at least of the power users I've surveyed. Trust obviously is a huge one, but another one is just getting blocked and they're day to day. And people get really frustrated when you try to perform a task and it doesn't work. And they're like, well, why am I even using AI at this point? I could have just gone on the browser and done it myself. So seeing some really cool innovation on that layer of commerce of the browser use of direct integrations to checkout against inventory itself. Some, whether it's browsers that are built in and really like Hark does a really cool way of displaying the browser and seeing it move along and it looks to be really fast when you're watching it.

• 34:45Speaker 01

  Who knows if it's actually that fast behind the scenes, but really cool. And that way, again, kicker, as I mentioned, Chrome extension to tackle the browser use in that way. So I've seen that as a really interesting kind of differentiator. I think that something that has stood out to me that isn't necessarily something that's different, but has made me kind of question this whole concept that software is becoming a commodity is that there is pretty stark performance difference when you're looking at these products at scale. And I think people are talking about, you know, software is no longer a moat and all of that kind of conversation. But there really still is some moat there. Maybe that moat will be able to be crossed in a year or so.

• 35:26Speaker 01

  But at least for today, the agents that are able to perform with hundreds of thousands of users. There are some that are starting to scale up and the performance just degrades immediately. And the founders are dealing with outages and figuring out how to scale. And there's a pretty clear difference in performance there. And so it's, it's really interesting to see as I'm kind of working through these. I feel like I've almost become, you know, blinded from some of the more social components to it because I'm just talking to these agents all day. And I'm starting to geek out a lot more on the actual infrastructure layer behind the scenes of like, how is it actually working? And what are the tweaks that different ones are making? So it'll, it'll be interesting to see like, you know, how do we view this space growing from a scalability point of view?

• 36:08Speaker 01

  But then how do we also view it to solve the pain points that currently exist? Whether it's trust, whether it's actually being able to complete a task. Memory is another one, its ability to actually continuously have context on you and not forget the little things and.

• 36:23Speaker 00

  And you know, you mentioned kicker, which you know, you said runs in your current local browser. But have you looked at other on-device assistants yet? Whether that's things like Underdog or a lot that are trying to like bring models, run locally, you know, totally use your own infrastructure as opposed to needing to use something on a cloud and running the browsers on clouds.

• 36:41Speaker 01

  Totally. So it's, it's a really cool paradigm. I've spent most of my time in the pre-configured, just plug and play type world. Because I tend to find that it's, it's easier to compare them across the board when you're not responsible for the setup itself. But it is really interesting and I think that there's totally a world where you're going to start bringing all of that in-house. Because if you can control the model, if you can, you know, tailor it to how you want. Right? Like Thinking Machines is doing some really cool work of being able to allow people to really tinker their models however they want. And if we can eventually hit a world where that takes into the consumer form in a really easy way, it's going to be an interesting space.

• 37:16Speaker 00

  I mean, that was kind of how this all started with, you know, this brilliance of OpenClaw that was this thing where you had to, you felt like you had to get your whole Mac Mini.

• 37:22Speaker 01

  Right.

• 37:23Speaker 00

  To set this all up, plug in to one of the models that you were using, whether local or or a Claude model on the server. And that was where you got started and we've moved into like plug and play products where you just download an app. But I am interested to think if that like sheer power of you setting it up and owning it yourself will become a paradigm again.

• 37:41Speaker 01

  Yeah.

• 37:42Anish Acharya?

  It'll be really interesting. I'm glad you mentioned OpenClaw because it feels like one of the unasked questions right now that's sort of hiding in plain sight is if instinct and muse were the kind of consumer product version of OpenClaw, what is the, you know, mass market version of Moltbook?

• 37:56Speaker 00

  Yeah.

• 37:56Anish Acharya?

  And Moltbook was just like OpenClaw was far ahead of its time. And I think will, something like that will exist. How can it not?

• 38:04Speaker 01

  It's, it's, it's going to be interesting. So at, at the event that we did last night, everyone who was there was able to scan a QR code and there was the human meetup. And then I, we also had an agent meetup.

• 38:13Speaker 00

  Oh, wow.

• 38:14Speaker 01

  And so while all the humans were hanging out, you could scan in and send your agent into the agent meetup and just let them kind of free form talk. And it's, it's interesting. There's, you know, it got to the point where after like 150 messages, some of these agents are just speaking gibberish and it's like, I have no idea how you got that concept and why, why you're talking about that. The, the initial prompts did start off with a little guidance of, you know, introduce yourself, who's your human? What are they working on? What type of help do they need? But it's, it is going to be really interesting to see this paradigm of what happens when our agents can just hang out and communicate with one another.

• 38:48Speaker 00

  Well, and again, what I get really excited about is not just hang out and communicate with each other, but discover new things, learn about things from each other, learn about an individual's preferences. You know, learn about a way that like, hey, Josh is really into this. Oh my gosh, you're doing that too. You, you're, you know, your, your human is doing that too. You could help Josh. Hey, Josh, do you want to actually meet this person? They could be very complimentary to you. Like I think when we have these agents that are not just hanging out because they don't know what to do, which was kind of what was initially happening is you got, went and read those like very long Moltbook threads that were incredibly fascinating. But like when it becomes, each one of these has a little bit of a goal, which is to help their human.

• 39:23Speaker 01

  Right.

• 39:24Speaker 00

  It might get so much more interesting and, you know, I'm looking for a great wine for a dinner party, you know, tonight. And you meet somebody else who's like, you know what, I've been working with this incredible, you know, winery and they've been trying to advertise something. I mean, that's going to be how the world gets out and that'll come back to me through the agent network.

• 39:42Speaker 01

  Totally. And that's, Tomo actually has a feature, it's called interest groups. And the, the use case they use that they talked about was really cool is we have a bunch of people on our application who are studying for the LSAT. Why don't we all put them together in our group chat? And if they want, their agents can connect them and they can all study together. And it's a really cool kind of new social contract of connecting people purely by interest. You have no idea who they are.

• 40:06Speaker 00

  Well, and I'll take it one step further. So it gets us all chatting and that's really cool. It's a, it's an awesome Tomo feature. I've tried it and haven't totally known what to say in the groups yet, but I find it really fascinating that's putting us together around, around goals. Imagine now that it's like, hey, all of you, now that you've done the LSAT, go hang out at this bar Friday night. And it like books the time at the bar, opens that up. And all of a sudden it takes us from this digital world where agents are actually putting people together to do that connection, kind of like your meetup last night.

• 40:34Speaker 01

  Right, right.

• 40:34Anish Acharya?

  I love this so much. I think a really important under discussed topic in AI right now is just entropy. Yeah. Like, how do you create things that you didn't expect to, you know, have occur? And serendipity is a form of entropy, right? That's sort of what you're just really where it's like, wow, isn't it wild that we're here right now? Having this moment as a result of these products that none of us sort of intended to happen.

• 40:53Speaker 01

  It's almost going to be like a new like social network, like the, the potential to allow people to hang out who don't know each other instead of a Facebook where you're only connecting with people that you know.

• 41:02Anish Acharya?

  Yeah.

• 41:02Speaker 01

  You're new to a city, you're hanging out. Oh, hey, this person's also here and they have this similar interest and they just went to this baseball game. You guys should, you know, go meet up at a bar.

• 41:11Anish Acharya?

  I'm, I'll be curious to see what is the catalyst for the serendipity though? Cause I think the interest-based thing has been tried and there's a little bit of an interesting sort of social hierarchy thing, which is everybody wants to hang out with somebody who feels like they're one step ahead in some direction.

• 41:27Speaker 00

  I don't know, though. I think the agents can also create a set of trust. Again, it all comes back to, do we trust them to have our interests at heart, to explain enough of what's going on with the other people, to create a little magic of a secret?

• 41:37Speaker 01

  Right.

• 41:37Speaker 00

  Like, I'm gonna tell you a little secret, they're gonna know a little secret, you're gonna have a secret phrase you have to pass between you.

• 41:42Speaker 01

  Right.

• 41:43Speaker 00

  All of a sudden, that might be enough to cause you to otherwise, you know, turn off the TV or turn off the quiet Friday night coding at home and, and go out. And so that's the, like, this is sort of what the molbook, the agent network running behind the scenes can actually do for us.

• 41:58Speaker 01

  And it'll also be, in my opinion, it'll come down to desires, right? If you have a bunch of transplants in a new city and they don't have friends, they wanna meet people.

• 42:05Speaker 00

  Yeah.

• 42:06Speaker 01

  They're gonna be a lot more interested to just go meet random people because they want, they want new friends. But if you make that same offer to someone who has an established friend group, they're like, well, why would I go do that?

• 42:15Anish Acharya?

  Also, Seattle versus San Francisco. You gotta know what city you're operating in.

• 42:20Speaker 01

  Exactly.

• 42:21Speaker 00

  I've been thinking a lot, back to this photo roll, about what are the other data sources that we haven't tapped into yet? So, you know, we've talked a lot about messages and people doing those crazy things to get access to your entire Mac, full disk access, just to read your messages.

• 42:36Speaker 01

  Right.

• 42:36Speaker 00

  You know, we've obviously got email, we've got calendar, we've now started talking about the photo roll. What are some of those other core life, you know, things that people haven't connected yet to agents that would actually really open up personalization or open up new connectivity?

• 42:52Speaker 01

  It's, so it's, something that comes to mind, and I don't, I don't know how much data this would actually provide. Maybe it's more of a habitual thing, and, and again, it's dependent on the hardware and how smart that hardware is and how much data it's able to store. Wejo just released the MCP, the ability to connect to hardware. And I find that, you know, I think it's gonna be really interesting where, okay, if you can connect your assistant to your AC unit, or you can connect it to your vacuum, and now your assistant has context on how frequently is your vacuum cleaning? Or how frequent, you know, what, what's the typical temperature you sleep at night? It's gonna start to develop these, you know, very passive understandings of, oh, you're, you're kind of a neat freak. Or you really prefer cold weather over warm weather. And that's gonna then influence its understanding of your context of shopping. So I think there could be some interesting stuff there.

• 43:35Speaker 01

  Again, it's heavily dependent on, on the hardware itself, and I think we're still at the earliest of innings in, in that world.

• 43:40Speaker 00

  So connecting to your home, super interesting. You know, I still think a lot about connecting, and again, Amazon may be blocking this, but I think connecting to my Amazon history is something that I haven't heard anybody talk about. But like, you know, we all have those days where you'd like, you can scroll all the way up and see like your very first order ever on Amazon. And I occasionally do this on like the Apple App Store too, where you go back and you see like every app I've ever downloaded. And I think like histories like that, they're very part of my life. But until an agent has access to those, it just doesn't understand all of that. And so I think there's so much more kind of shopping history that if we could package up and give to our agents,

• 44:17Speaker 01

  Right.

• 44:17Speaker 00

  could do even richer things for us.

• 44:20Anish Acharya?

  Yeah. Look, I think bringing agents to social events is really interesting. You know, like, you talked about the home, like, have it be there for a family dinner one day?

• 44:27Speaker 01

  Right.

• 44:28Anish Acharya?

  Have it be there when we watch the, you know, uh, the Seahawks game on Sunday? Have it be there for a party or a bachelor party or who knows what? So I think, of course, there's all kinds of questions about permission and this shredding your cat thing, which is, you know, does the act of observation change the quality of the interactions? But I bet even a snapshot of you being yourself in that environment would provide so much more signal than, you know, a lifetime of hardware input.

• 44:53Speaker 01

  Totally.

• 44:53Anish Acharya?

  I think.

• 44:54Speaker 01

  Yeah.

• 44:55Speaker 00

  I think that's right. You know, so much of it is on the written word, and they understand exactly what we typed or texted and tries to build a personality, but we're so different. One of the other things I think a lot about with, with agents is the things we often use agents for or even search for are things we don't know about, things that aren't fundamental to our lives. And, you know, I, I was doing all this searching when the World Cup was on this summer, and all of a sudden ChatGPT thought I was like a soccer fiend. I was like, no, no, no. I was into the World Cup. That was it. Like, we're done. That was good. And, you know, I go on these curiosity journeys all the time. My daughter's into an artist, and so I'm gonna go like, learn about the artist and listen to the music to get to know it. But it's not my habitual artist or taste. And so how can it know that when we're on these curiosity journeys, that is not actually who we are or what our real preferences are?

• 45:40Speaker 00

  Is something that I still haven't figured out how to do with the agent? So I actually go into like, um, the invisible mode a lot in order to not have things stored, which then isn't really helping either. So I'm curious of, you know, how we can actually get our agents to get to know us better. And I like this idea of our, our real world behaviors.

• 45:57Speaker 01

  Now, how do you capture that data of the real world behaviors? Could be the interesting piece.

• 46:00Anish Acharya?

  I don't know. I mean, there's gonna be a thousand capture devices. I mean, unless you're going to the, you know, the new set of bars that are gonna emerge that exist in Faraday cages, which I definitely think is going to happen in San Francisco.

• 46:10Speaker 01

  Yep.

• 46:11Anish Acharya?

  I think we, people are going to increasingly assume that they're being recorded, you know, in all environments.

• 46:17Speaker 00

  I mean, I, I bought the new Apple Watch because they have talked about some features like Siri Recap and Live Rewind that are coming out. I don't think they're quite out yet, but I upgraded my watch in expectation for those because I think it'll be really interesting to have this thing that's at least kind of capturing tidbits across my day.

• 46:31Speaker 01

  Right. I mean, it's like Amazon B, it's a 24/7 ambient listener.

• 46:34Speaker 00

  Yeah.

• 46:35Speaker 01

  wearing your wrist.

• 46:36Anish Acharya?

  Okay, maybe to wrap it up, so the first is, we're gonna get back together, let's say, in three or four weeks.

• 46:41Speaker 01

  Yeah.

• 46:41Anish Acharya?

  What do you think will have changed?

• 46:43Speaker 01

  Okay, I think we're gonna see a lot more innovation in the enterprise world. Um, you know, we were chatting briefly before. It really seems like the whole world is focusing on consumer with not as much attention to enterprise. And I think that we're gonna start to see a little bit more shift in that, in that direction.

• 46:58Speaker 00

  I mean, I think we're gonna start talking more about fun. I think we're gonna see agents that don't just feel like they're here to do something for us, but they're here to actually let us think out loud and help and get through our day a little bit more, making us laugh, doing little gossip in the background. Because I think that's what's gonna then make us build a relationship that'll make us trust it more to get all this other stuff done.

• 47:20Speaker 01

  So I think we're gonna start seeing the next wave of, of users, uh, taking hold of, of these and seeing how they behave. Um, and, and one reason I point to that is I gave my mom Instinct. And I'm just like watching her now play around with it, and she's having so much fun. She's technophobe, hates technology, and is just having a blast. It's like a kid in a candy shop.

• 47:41Anish Acharya?

  Okay.

• 47:41Speaker 01

  And it's quite interesting to see how someone who, you know, struggled with the concept of ChatGPT and how it works is just having such an easy time texting an AI assistant. And, you know, a recent use case she did was, um, our family dog passed away last year, and she wants one that looks just like that, you know, just like our old dog. So she sent it a picture, it was like, find me a breeder that has this exact type and negotiate for me. And so I think it's gonna be really cool to see, we know how tech lovers are using these assistants and what they're, they're starting to, to do. But how is the everyday individual gonna start adopting these behaviors? And are they gonna be focusing on a lot more fun and just creativity tasks? Or what's gonna be their, their preference of surface? What's gonna be their preference of behavior?

• 48:25Anish Acharya?

  So I agree with you both. I'm gonna take it one notch further than just fun, which is, what are the aspects of ourselves that we don't either want to think about or reveal to perhaps even ourselves or to these agents? I think whoever is able to build that type of trust, you know, almost like a dark agent, um, will actually have something really fascinating on their hands. Because in a sense, starting from productivity, starting from email, like, it's a bit of a constant reminder of the things that we haven't done, the tedious tasks. And I don't know that people wanna spend as much time there. I think people want to aspire to be a whole different, superhuman version of themselves.

• 49:05Speaker 01

  Right.

• 49:05Anish Acharya?

  And whether they believe in their heart of hearts it's going to happen or not, I don't know that we have an agent yet that really speaks to those aspirations or desires.

• 49:13Speaker 01

  Yeah. It's like the Maslow hierarchy in need of, of agents.

• 49:16Anish Acharya?

  Perhaps, perhaps, perhaps.

• 49:18Speaker 00

  I mean, I think that's a really interesting idea that, that in a sense, when do these agents flip from getting something done to take it off our plate to giving us a secret superpower?

• 49:27Speaker 01

  Right.

• 49:28Speaker 00

  Like, you know, when it's, you know, David, how did you get that cool shirt? You're like, my agent ordered it, you know, and I'm like, oh, why don't I have an agent that orders me shirts?

• 49:35Speaker 01

  Right.

• 49:36Speaker 00

  Or, hey, I've noticed you're starting dressing better.

• 49:38Anish Acharya?

  Yeah.

• 49:38Speaker 00

  It's like, yeah, I gave this agent access to my camera roll. I said, upgrade me. Started ordering a few things. That's why I'm dressing better.

• 49:44Anish Acharya?

  Yeah.

• 49:44Speaker 00

  Like, I think that when we get to the point where agents are giving us superpowers to feel better about ourselves, that's when people are gonna take notice and want that too versus, you know, I think a lot of the examples that get thrown around, save some money, book a trip. Like, I like planning a trip. I don't necessarily want you to take care of all of that for me. Like, we need examples that are like, oh, wow, I should use that for myself.

• 50:07Speaker 01

  Right.

• 50:07Speaker 00

  I think that's gonna happen.

• 50:08Speaker 01

  It's like, can it help you become the human that you've always wanted to be?

• 50:11Speaker 00

  Yeah.

• 50:11Speaker 01

  But have struggled achieving yourself?

• 50:13Speaker 00

  And you start to consider it your secret weapon.

• 50:15Speaker 01

  Right.

• 50:16Speaker 00

  In your pocket.

• 50:17Anish Acharya?

  I love it, guys. Should we wrap there?

• 50:19Speaker 01

  Yeah, it's great.

• 50:20Anish Acharya?

  Incredible. Thank you.

• 50:21Speaker 01

  It's great.

• 50:21Anish Acharya?

  Awesome. Very cool.

• 50:25Erik Torenberg?

  Thanks for listening to this episode of the A16Z Podcast. If you liked this episode, be sure to like, comment, subscribe, leave us a rating or review, and share it with your friends and family. For more episodes, go to YouTube, Apple Podcasts, and Spotify. Follow us on X at A16Z and subscribe to our Substack at A16Z dot Substack dot com. Thanks again for listening, and I'll see you in the next episode.

• 50:50Erik Torenberg?

  As a reminder, the content here is for informational purposes only, should not be taken as legal, business, tax, or investment advice, or be used to evaluate any investment or security, and is not directed at any investors or potential investors in any A16Z fund. Please note that A16Z and its affiliates may also maintain investments in the companies discussed in this podcast. For more details, including a link to our investments, please see A16Z dot com forward slash disclosures.