SPEAKER_01
Humans are the bread in the sandwich, and the AI is in the middle.
SPEAKER_03
The AI is whatever you put on your sandwich. If you ship something or do something, if you want it to be your own, you cannot fully automate everything. It's like art. If you want it your own, it needs to be from you or somehow be connected. So I believe it's so important to do things you enjoy and you love. And it's very important to make it feel great because the bar is high, the bar will always get higher. The beginning and the end, the middles can be automated pretty well. And Trevin at some point said, oh, it's a sandwich, which was very funny. Kieran, welcome to the show. Hello, Dan. Happy to be here.
SPEAKER_03
[SPEAKER_01] So for people who don't know, you are the GM of Quora, and you are also the creator of Compend Engineering, the engineering framework and plugin that everyone inside of every uses, and everyone who's really coding in with agents is at least aware of, if not using. [SPEAKER_01] And so a pleasure to have you on the show.
SPEAKER_03
Thank you. [SPEAKER_01] Yeah, it's always great. [SPEAKER_01] So I love getting to chat with you and getting to work with you because every once in a while you have a thing that you do or you figure out that I'm like, holy shit, that's definitely the future.
SPEAKER_01
And you just figured something out along with Trevin, who also helps, Trevin Chow, who also helps out on Compend Engineering. And I think it has massive implications for how programming works. And then I think we can also translate that to the rest of AI and its impact on work. And one of the things you've been doing, so you have this Compend Engineering plugin that you've rebuilt the engineering workflow for how you should work with agents.
SPEAKER_01
And in thinking about that and thinking about where a human is used and where a human should not be present inside of that process, I think you've found something really interesting and deep about, in general, how humans and AI are going to interact with work. So do you want to explain a little bit about Compend Engineering and that and the process that you've created?
SPEAKER_03
[SPEAKER_01] And then also explain this insight about where humans fit.
SPEAKER_01
[SPEAKER_03] Yeah, absolutely. [SPEAKER_03] So Compend Engineering is a philosophy of doing engineering work, but we realize it applies to more than just engineering work. [SPEAKER_03] It's product work as well as design work. [SPEAKER_03] It could be knowledge work. It could be other things, but how I build it is while building Quora, I had AI and I was thinking, how can I use AI to do better work more quickly? [SPEAKER_03] And the initial version of Compend Engineering really evolved around four steps, which is planning first. [SPEAKER_03] You make a great plan, so it's very clear what you need to build and do.
SPEAKER_01
[SPEAKER_03] Then the work part where the agent does the work and implements it and actually writes the code, does the design work or whatever work needs to be done. [SPEAKER_03] The third is review. [SPEAKER_03] So slop comes out or whatever you call it, something beautiful comes out. [SPEAKER_03] One of the two, something comes out, but how do you know it's good? [SPEAKER_03] And traditionally there's a code review or a PR review and see, hey, this can be improved. [SPEAKER_03] And there's some iteration going on there.
SPEAKER_01
[SPEAKER_03] And then the most important step is the compound step, which is if anything comes up during that review or during the planning, that you think, oh, this is a good learning. [SPEAKER_03] Probably you will run into this again. [SPEAKER_03] You can compound that knowledge back into the system and we store that as knowledge inside the repository. [SPEAKER_03] And agents next time when they go into planning or when they go into work or review, they can see the mistakes they made before, so they won't make it the next time. [SPEAKER_03] And that's really the power, that's by far the most powerful thing that is in this plugin.
SPEAKER_03
But we start to realize more, first of all, the work phase is done. It works. If you have a good plan, it does the work and it's pretty good. And then the review, it makes it a little bit better. [SPEAKER_01] And by that you mean like having an entire phase dedicated to work in this whole system doesn't necessarily make that much sense when all that really means is run the model, let the model do the thing. Yeah. So there needs to be a step. But what I mean by done is I don't need to care. Or I don't need to think about it. I trust it. And this is not trust me, bro, it just works. But this is, I've seen if you put in a good plan, it does the plan. It executes on the plan.
SPEAKER_03
LLMs are very good at just following steps, doing deep work, working for hours, days even now. And that thing is solved. And the review starts to get there too. And the planning starts to get there too. And then there's this next step. It's, okay, so if all these things work, where do I have to do anything? [SPEAKER_01] Yeah, did I just automate my job? Did I automate myself out of a job? If everything works, where do I work? What is still the bottleneck? And there are two things we started to know. Like Trevin, he's a very great contributor to the Compend Engineering plugin. He is a product person.
SPEAKER_03
And he was, I need more on the product side, which is before the planning phase. So he added first a brainstorm step and an ideate step. And the ideate step is really going wide. It's, okay, let's come up with ideas in a room full of interesting people with angles. Brainstorm is more, I have a problem, but I don't really understand exactly what and how.
SPEAKER_01
[SPEAKER_03] So it's very much brainstorming with you around the problem. [SPEAKER_03] And the first thing we noticed there is the top is very important to be super well in the loop with a human.
SPEAKER_03
And really ask a lot of questions and really think hard. The LLM should support the human. But then after that, the planning phase, if you have a good brainstorm, an idea of what problems you solve, it can create a very good plan and the human needs not to be in the loop.
SPEAKER_03
Brainstorm is more like I have a problem, but I don't really understand exactly what and how. So it's very much brainstorming with you around the problem. And the first thing we noticed there is the top is very important to be super well in the loop with a human. And really ask a lot of questions and really think hard. The human should think hard. The L1 should support the human. But then after that, the planning phase, if you have a good brainstorm, an idea of what problems you solve, it can create a very good plan and the human needs not to be in the loop. So that's the first realization where it's like, oh, hey, here's good to be in the loop versus not to be in the loop. And you can see other like spec-driven development, for example, or other ways to do things. They assume that it's always good to have people in the loop. And I disagree. I think it's very important to know when to be in the loop versus when to hand it off. Because that means we can think harder at the moments where we need to think harder. And that's the first one. So the other one comes at the end. So something comes out. How do you validate it's good? Well, it's already tested because we have browser automated testing. It clicks through. All the requirements are very clearly specified. And it says, yeah, everything works. But the beauty comes in when a human looks at it, clicks around, and has a feel. Oh, this doesn't feel good. We can polish it even more. We can make it even better. We can increase, or we can do something that's still missing or make it more beautiful, make the design better. And this is something I've learned from doing Pomodoros, where ideally, if you do Pomodoros, the old school way is you start with a task. And if you finish after 15 minutes, you have 10 more minutes to work on the same task. You cannot switch tasks. And sometimes in that space, something beautiful happens because you will go deeper. You will go further than you would do. And I think this is the other moment, which is all the way at the end when everything is done, where you can just elevate everything and make it even better. And I think that's also what we need to do because if we don't do it, it will be all slop, all the same. And it's very important to make it feel great because the bar is high. The bar will always get higher. So this is what we realized, the beginning and the end. And the middle is solved and can be automated pretty well. And Trevin at some point said, oh, it's a sandwich, which was very funny. And Dan is now referring to the AI sandwich, which I think is very cool. And I think the sandwich here is when do you need to think about what you do and really use your brain versus offload it to the LLM.
SPEAKER_03
[SPEAKER_01] We've all been there. You're sitting in an important meeting and you're trying to pay attention. You're trying to stay present. But you have this lingering underlying anxiety that you're going to forget everything, that you're going to miss the important detail, forget the decision, forget the action item. Let something important slip through the cracks. That's why I love Granola. It's an AI-powered notepad that works in the background while you're in your meetings. It takes notes on everything that gets said, transcribes action items, and helps get rid of that feeling. You don't have to worry about whether you're going to miss something because Granola has you covered. And that lets you stay present in meetings. I've been using Granola for a long time, almost since they came out. And it's amazing for this. It doesn't join the meeting like some of those other clunky meeting note takers. The UI is really fast and well-considered. And it feels like it's just transcribing all the important moments in my work life. And that gives me the confidence to get great work done. And what's even cooler is you can chat with your notes afterwards. You can run detailed research reports on how your week was, how you act as a leader, how you performed in particular difficult conversations, and how you can do better. It's a power tool for anyone who cares about their meetings and also cares about how they show up in those meetings. It also has these things called recipes, which are pre-made prompts for common tasks like negotiating, coaching, or summarizing. I even have a recipe that I made that's in Granola that you should check out. Once you try it on one meeting, it's really hard to go back. The notes are always better than what you can do manually. And it helps me be much more present instead of frantically typing all the time. Head to granola.ai slash every for three months free with the code every. E-V-E-R-Y. That's granola.ai slash every for three months free. And now, back to the episode.
SPEAKER_03
[SPEAKER_01] Humans are the sandwich, the bread in the sandwich, and the AI is in the middle. Yeah, exactly.
SPEAKER_03
[SPEAKER_01] And I think that's really interesting and cool because, A, it gives me a good mental model for how I should be working with coding agents. But I think that also applies to the rest of knowledge work. And I think this is such an important question now because we have all these questions about, oh, my God, what are agents going to do? And is everyone going to lose their job and all that? And I think software engineers are a little bit of the canary in the coal mine. And so far, what we found internally at Every is we're absolutely not. We still hire software engineers. We need software engineers. But the way that you're working and what you're doing looks a lot more like managing. If you're doing it well, you're still involved, but you're involved at the beginning and the end as sort of this sandwich. And I think the same is going to be true of every other kind of work, whether that's copywriting or strategy or design. And I think there are deep reasons why that is the case that I think will be interesting to talk about. And I want to start with an objection that I think people will have, which is like, OK, for now, agents can't do the idea in the brainstorm, but pretty soon they will. So then what then what happens? There are now they're starting to do the beginning of that process. And I think that there's something interesting here where if you look within any given local frame of a problem.
SPEAKER_03
[SPEAKER_01] And I think the same is going to be true of every other kind of work, whether that's copywriting or strategy or design. [SPEAKER_01] And I think there are deep reasons why that is the case that I think will be interesting to talk about. [SPEAKER_01] And I want to start with an objection that I think people will have, which is OK, for now, agents can't do the idea in the brainstorm, but pretty soon they will. [SPEAKER_01] So then what happens? [SPEAKER_01] There are now they're starting to do the beginning of that process. [SPEAKER_01] And I think that there's something interesting here where if you look within any given local frame of a problem.
SPEAKER_01
So to take a non-coding example, the problem might be my knee hurts and I want to solve that problem. But you can say my knee hurts is the same as this feature is broken or customers are anxious about this part of the product or whatever. Any problem.
SPEAKER_03
[SPEAKER_01] If you take that frame and you say, OK, well, the solution is maybe for your if you're if we're taking a knee hurting thing, the solution is take Advil. [SPEAKER_01] Any part of that process, getting to the store or whatever can be automated. [SPEAKER_01] Let's say DoorDash can go do it. [SPEAKER_01] But there's always even once you've solved it in that way, there's always a larger frame within which to think about the problem. [SPEAKER_01] So an example is if your knee hurts, you might need to stretch your IT band or you might need to stop running on hard surfaces every day.
SPEAKER_03
[SPEAKER_01] And each one of these is addressing the same problem at a different level of the stack at a different frame. [SPEAKER_01] And humans are very good at flipping and changing frames like that. [SPEAKER_01] And our job is to set the frame or set the bounds within which we solve the problem. [SPEAKER_01] And I think it's going to be very, very hard for agents to do that well by themselves. [SPEAKER_01] And there are deep reasons for that. [SPEAKER_01] But does that get that? [SPEAKER_01] I know it's a little bit hand wavy and the knee hurting thing is hard to understand. [SPEAKER_01] But does that resonate for you? Yeah, for sure. Yeah, it's this.
SPEAKER_03
This all comes down to building an environment where the agent will thrive and you do that by picking the right things and this is why it's so important to have humans with experience and humans with taste and humans that just want to click around and say this is shit or this is great and why it is shit or great. And I think it's similar to the actual example. If you keep doing that, your friend will say, yo, that's messed up. Just go fix the problem instead of denying the problem. And maybe it will work for you for a little bit that you need someone to shake you up. And in that case, that's the human or that's the other person.
SPEAKER_03
But I do think it will also be more automated, the ideation. You can say, OK, let's have a persona of 100 people and run simulations of how they think and how they behave. And clearly we're going there to where we run simulations of millions of people and see how things work. And probably you'll learn something from that. And there will be more automation and maybe even that step in the front will be fully automated. But I do think in the end, if you ship something or do something or make a statement in the world, if you want it to be your own, you need to say yes or no at some point, you cannot fully automate everything.
SPEAKER_03
It's maybe a little bit like art, like making art. If you want it to be your own, it needs to be from you or somehow be connected. So I believe having those moments where you decide this is what I enjoy. And that's why it's so important to do things you enjoy and you love are very important. [SPEAKER_01] Yeah, I agree. [SPEAKER_01] And I think yeah, you can imagine it being OK, yeah, we're going to simulate a bazillion people and then we're going to make decisions based on what we think they would do, but that would still only cover a small set of the decisions that someone might make. [SPEAKER_01] It will never be fully. It's all a moving target.
SPEAKER_03
We always get something new and then there is a layer up that we then can make even bigger impact on. Especially because for a lot of these decisions, the feedback loops on these decisions, the data is really rare. [SPEAKER_01] You may only get a couple moments in your career where you gather the data that helps you decide about a particular thing. [SPEAKER_01] And that's very hard to get into language models, especially because it's hard to get and they need a lot of it. [SPEAKER_01] And so that rare expertise that is encapsulated in an expert who has a personality and a worldview is hard to get in. [SPEAKER_01] And you're right. [SPEAKER_01] It's also always moving.
SPEAKER_03
[SPEAKER_01] And I don't know that makes me very excited about this stuff, because I feel we've been wandering in the woods for a long time on what is progress going to mean and how are humans going to be involved and all that kind of stuff? [SPEAKER_01] And it just feels very much to me the simple answer is ride the bottle or to mix the metaphor, be this bread in the sandwich. [SPEAKER_01] And if you do that, you're going to be fine. [SPEAKER_01] It's going to be really, really, really great. Yeah, I agree.
SPEAKER_03
And it will be different for different people because you need to change some things. You cannot keep doing what you're doing, because if you like writing code only, you need to find your way of writing codes. Yes, you can write codes, but maybe it's about beautiful codes. And maybe you find a lot of value in just seeing beautiful codes. Someone looks at the UI and says, oh, this is beautiful. This works great. Maybe you want that for codes.
SPEAKER_01
[SPEAKER_03] Some people don't care about that, but they're like, oh, but the UI should feel great and just really polish it, go extra, wherever you feel joy. [SPEAKER_03] But also it's way more product focused. [SPEAKER_03] So as an engineer, you're going to become either more of a manager, but also more of a product person. [SPEAKER_03] So it's more of those things as well. [SPEAKER_03] And so there will be some changes, but lean into making beautiful stuff and whatever that means to you. That can mean beautiful codes, beautiful abstractions, beautiful architecture, beautiful design, beautiful copy.
SPEAKER_01
[SPEAKER_03] I think it's very important to lean into what is beautiful to you because then you will find a way to utilize an LLM to make something that gives you energy instead of drains you all the way. [SPEAKER_03] It may not look like it, but Naveen is a dictator. [SPEAKER_00] You can speak faster than you can type. [SPEAKER_00] So dictators choose to do so whenever possible. [SPEAKER_03] So I think it's a product manager, products engineer, like it's more of those things as well.
SPEAKER_01
[SPEAKER_03] And so there will be some changes, but lean into making beautiful stuff and whatever that means to you. That can mean beautiful code, beautiful abstractions, beautiful architecture, beautiful design, beautiful copy. [SPEAKER_03] I think it's very important to lean into what is beautiful to you because then you will find a way to utilize an LLM to make something that gives you energy instead of drains you all the way. [SPEAKER_03] It may not look like it, but Naveen is a dictator. [SPEAKER_00] You can speak faster than you can type. [SPEAKER_00] So dictators choose to do so whenever possible.
SPEAKER_01
[SPEAKER_00] While those confined to keyboards deal with finger cramps and input lag, ow. [SPEAKER_00] Voice allows dictators to convey ideas as naturally as they sound in their heads. [SPEAKER_00] Hey, yeah, that's exactly what I meant. [SPEAKER_00] And in the future, as AI tools improve, we will see a rise of dictators around the world. [SPEAKER_00] More and more dictators are choosing monologue from Evry. [SPEAKER_00] It learns, transcribes, and translates across different disciplines and languages, adjusting its output format to match your context, allowing you to stay in flow. [SPEAKER_00] Be a dictator. An idea by Evry.
SPEAKER_01
[SPEAKER_00] Every, the only subscription you need to stay at the edge of AI. [SPEAKER_00] Yeah, and I think there's a deep reason why language models are not going to be as good at that. [SPEAKER_00] There's one deep reason, which is it's just not going to be yours if you didn't decide it, if you didn't do it. But another deep reason is you can think of language models as being a super intelligence that has been kept in a box for the last year and has no idea of what's going on in the world, except for whatever it gets right when it pops out of the box. And because of that, its outputs end up being a little bit more generic and less personal to you and your situation.
SPEAKER_01
And you can see this in all of the stuff that's like, okay, all the AI writing that's like, it's X, not Y, or all that kind of stuff. It's just going to do all that. And to truly solve a problem well or to truly make art or to truly make a product that resonates with people, it's going to have to be really well tuned to the exact problem that you're trying to solve or the exact form that you're trying to make.
SPEAKER_03
[SPEAKER_01] And language models need a lot of help to get there.
SPEAKER_01
And that's why you have to be on either end of them to set the frame of the problem and then make sure the details are really right at the level of execution at the end. And I don't think they will get better at doing this, but I actually think they're much further than we think they are from being able to do it all end to end. My general bar for AGI is whenever it is economically profitable or makes economic sense to run an agent 24-7 or it never turns off. And OpenClaw is pushing in this direction, but it doesn't run 24-7. It runs on a schedule. It has a heartbeat.
SPEAKER_01
But it's not like you just say, hey, OpenClaw, just go and do a bunch of stuff and work all the time, spend tokens all the time on stuff and it is worthwhile. We're not even close to that. And yes, we sometimes have well-specified tasks that we can send a model off to go for 24 hours on. But again, it's not changing frames. It's not finishing the task and being like, cool, now I'm going to pick the next one and that's going to take five minutes, and the next one I'm going to spend four days on it. We're not even close to that.
SPEAKER_01
And I think we're going to need some fundamental changes to language model architecture to let them learn better for them to get to a point where they're running 24-7. And I think that will, if they are running 24-7 like that, they'll be a lot closer to being sensitive enough to context to actually do interesting creative things, but we're not there yet. Yeah, I agree. [SPEAKER_03] One other way to look at it. So I have a music background. [SPEAKER_03] I studied classical composition. [SPEAKER_03] And I think one of the beautiful things about music is that, yes, Suno can create songs, but it will never capture a live performance or coming up with a melody.
SPEAKER_01
[SPEAKER_03] And it's something internally in the human, as a composer or musician. If you perform something and deliver this to other people, they feel that. It will not be the same.
SPEAKER_01
[SPEAKER_03] Sure, if you're a DJ, it's maybe somewhere in the middle, but there is something about performing and seeing something, expressing something. [SPEAKER_03] And I think there is some of that element in these steps as well, where you see something and you're like, oh, it feels a little bit off here because I don't know why, but I wanted to change it a little bit with the step at the end. [SPEAKER_03] And suddenly you're performing or iterating or you're making stuff, you're putting something in the world. [SPEAKER_03] And I feel that special. Practicing a piece for playing it a hundred times is not very creative as a musician.
SPEAKER_01
[SPEAKER_03] And this is the middle part. [SPEAKER_03] But at the end, the performance is where you bring it out into the world to the people. [SPEAKER_03] So I think that's a special moment. [SPEAKER_03] And there is a little bit of a link for me with doing this polish step at the end and at the start is maybe coming up with a piece. Like if you're a composer, coming up with something out of nothing. [SPEAKER_03] And this is also a special moment. [SPEAKER_03] And normally everything in the middle is boring. [SPEAKER_03] It's just work. [SPEAKER_03] And I feel these moments are still special and it works for making software or other things with LMs as well for me.
SPEAKER_01
[SPEAKER_03] I think that's totally right. [SPEAKER_03] I love this art angle that you have.
SPEAKER_03
[SPEAKER_01] And another way to say this is all the work exists on the spectrum from being totally rote to being art. [SPEAKER_01] And art itself has many tasks within it. [SPEAKER_01] Any kind of creative work has many tasks within it that are more rote or less rote. [SPEAKER_01] And if you're trying to map work on that spectrum, the stuff that is more rote is just going to be something you're not going to have to do anymore. [SPEAKER_01] And that is a big opportunity to move a lot of the work that we do to the more creative, probably more interesting parts of work. [SPEAKER_01] And to recognize that that frame is always changing or is always moving.
SPEAKER_03
[SPEAKER_01] So as certain things get rote, other things become things that humans start to do. [SPEAKER_01] And yes, those will get automated too, but we're going to keep moving down along that spectrum. [SPEAKER_01] That is a big opportunity to move a lot of the work that we do to the more creative, to us, probably more interesting parts of work. And to recognize that that frame is always changing or is always moving. So as certain things get rote, other things become things that humans start to do. And yes, those will get automated too, but we're going to also keep moving down along that spectrum.
SPEAKER_03
Final thing that's not automatable is art made by humans who feel something. And I think that's beautiful.
SPEAKER_03
It's still scary because what if you're in the middle and you want to move, or if you want to figure out what that is to you, because this might sound very abstract and weird to some people. If you're not an artist or haven't really felt this in moments, it sounds maybe a little bit like, oh, but that's not me. But I do believe everyone has this. Think of it, what brings you joy? What lights a fire in you? What do you get excited about? I think that thing you should lean into, whatever that is. And that can be beautiful writing or that can be very structured lists or whatever it is. Anything that just brings you happiness. You should do more of that using LLMs in your work because that's good.
SPEAKER_03
[SPEAKER_01] Always a pleasure. Thank you. Let's see where this goes. See you next time. [SPEAKER_01] Bye. Bye. Bye.
SPEAKER_03
[SPEAKER_02] Folks, you absolutely positively have to smash that like button and subscribe to AI and I. Why? Because this show is the epitome of awesomeness. It's finding a treasure chest in your backyard. But instead of gold, it's filled with pure unadulterated knowledge bombs about ChatGPT. Every episode is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat, craving for more. It's not just a show. It's a journey into the future with Dan Shipper as the captain of the spaceship. So do yourself a favor. Hit like, smash subscribe, and strap in for the ride of your life. And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.
SPEAKER_03
And and there will be more automation and maybe even that step in the front will be fully automated. But I do think in the end, if you ship something or do something or make like a statement in the world, if you want it to be your own, which you need to say yes or no at some point, you cannot fully automate everything. Like it's maybe a little bit like art, like making art, like if you want it to your own, you need to just it needs to be from be from you or somehow be connected. So I believe like having those moments where you decide this is what I just enjoy. And that's why it's so important to do things you enjoy and you love are very important. Yeah, I agree.
SPEAKER_01
And I think. Yeah, you can imagine it being like, OK, yeah, we're going to simulate a bazillion people and then we're going to make decisions based on what we think they would do, but that would still only cover a small set of the decisions that someone might make. It will never be fully.
SPEAKER_03
It's all it's a moving target. Like we always get something new and then again, there is a layer up that we then can even make bigger impact on. Especially because especially for a lot of these decisions, the feedback loops on these decisions like the data is really rare.
SPEAKER_01
You may need you may only get a couple moments in your career where you gather the data that helps you decide about a particular thing. And that's very hard to get into language models, especially because it's hard to get and they need a lot of it. And so that that like sort of rare expertise that is encapsulated in an expert who has a personality and a worldview is hard to hard to get in. And you're right. It's also always moving. And I don't know that that makes me like very excited about this stuff, because I feel like we've been we've been wandering in the woods for a long time.
SPEAKER_01
On like, okay, what is a progress going to mean and how are humans going to be involved and all that kind of stuff? And it just feels very much to me like the simple answer is ride the bottle or to mix the metaphor, be this be this bread in the sandwich. And if you do that, you're going to be fine. It's going to be like really, really, really, really great. Yeah, I agree.
SPEAKER_03
And it will be different for different people because. Yeah, you need to change some things like you cannot keep doing what you're doing, because if you're if you like writing code only, you need to find your way of writing codes. Like, yes, you can write codes, but maybe it's about beautiful codes. And maybe you find also lots of value in just seeing beautiful codes. Like someone looks at the UI and says, Oh, this is beautiful. This works great. Maybe you want that for codes. Some people don't care about that, but they're like, Oh, but the UI should feel great and just really polish it, go extra, like wherever you feel joy. But also it's way more product focused.
SPEAKER_03
So as an engineer, you're going to become either more of a manager, but also more of a product person. So it's, I think like a product manager, products engineer, like it's more of those things as well. And so there will be some changes, but lean into making beautiful stuff and whatever that means to you, that can mean beautiful codes, beautiful abstractions, beautiful architecture, beautiful design, beautiful copy. I think it's very important to lean into what is beautiful to you because then you will find a way to utilize an LLM to make something that gives you energy instead of drains you all the way. It may not look like it, but Naveen is a dictator.
SPEAKER_00
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SPEAKER_00
Every, the only subscription you need to stay at the edge of AI. Yeah, and I think there's a deep reason why language models are not going to be as good at that. There's one deep reason, which is it's just not going to be yours if you didn't decide it, if you didn't do it.
SPEAKER_01
But another deep reason is you can think of language models as being a super intelligence that has been kept in a box for the last year and has no idea of what's going on in the world. Except for whatever it gets right when it pops out of the box. And because of that, it ends up being, its outputs end up being a little bit more generic and less personal to you and your situation. And you can see this in all of the stuff that's like, okay, all the AI writing that's like, it's X, not Y, or, you know, all that kind of stuff. It's just going to do all that. And to truly solve a problem well or to truly make art or to truly make a product that resonates with people.
SPEAKER_01
It's going to have to be really well tuned to the like exact problem that you're trying to solve or the exact form that you're trying to make. And language models need a lot of help to get there. And that's why you have to be on either end of them to set the frame of the problem and then make sure the details are really right at the level of execution at the end. And I don't think, I think that they will get better at doing this, but I actually think they're much further than we think they are from being able to do it all end to end. My general bar for AGI is whenever it is economically profitable or makes economic sense to run an agent 24-7 or it never turns off.
SPEAKER_01
And OpenClaw is like pushing in this direction, but it doesn't run 24-7. It runs on a schedule. It has a heartbeat. But it's not like you just say, hey, like OpenClaw, just go and just do a bunch of stuff and just work all the time, spend tokens all the time on stuff and it is worthwhile. It's just, we're not even close to that. And yes, we sometimes have well-specified tasks that we can send a model off to go for like 24 hours on. But again, it's not changing frames. It's not finishing the task and be like, cool, now I'm going to pick the next one and that's going to take five minutes. And the next one, I'm going to spend four days on it.
SPEAKER_01
It's like, we're not even close to that. And I think we're going to need some fundamental changes to language model architecture to like let them learn better for them to get to a point where they're running 24-7. And I think that will, if they are running 24-7 like that, they'll be a lot closer to, I'm sensitive enough to context to like actually do interesting creative things, but it's, we're not there yet. Yeah, I agree. One other way to look at it.
SPEAKER_03
So I have a music background. I studied classical composition. And I think one of the, the, the beautiful things about music is like, yes, Suno can create songs, but it will never capture like a live performance or coming up with a melody. And it's something internally in the human, like as a composer or musician, if you perform something and you deliver this to other people that they, they feel that like it will not be like, like sure. If you're a DJ, it's, it's maybe somewhere in the middle, but like there is something like performing and like you see something, you express something.
SPEAKER_03
And I think there is some of that element in these steps as well, where you see something and you're like, oh, it feels a little bit off here because I don't know why, but I wanted to change it a little bit with the, with the step at the end. And suddenly you're like kind of performing or iterating or you're making stuff, you're putting something in the world. And, and I feel that special, like practicing a piece for like playing it a hundred times is not very creative as a musician. And this is kind of the middle part. But at the end, the performance is where, where you bring it out into the world to the people. So I think that's a special moment.
SPEAKER_03
And there is a little bit of a link for me with doing this polish step at the end. And at the start is maybe coming up with a piece, like if you're a composer, like coming up with something out of nothing. And this is also a special moment. And normally everything in the middle is kind of boring. It's just work. And, and I feel these moments are still special and it kind of works for making software or other things with LMs as well for me. I think that's totally right. I love this art angle that you have.
SPEAKER_01
And another, another way to say this is all the work exists on the spectrum from it being totally wrote to it being art. And art itself has many tasks within it. Any kind of creative work has many tasks within it that are more, more wrote or less wrote. And. And. If you're trying to map work on that spectrum, the stuff that is more wrote is just going to be something you're not going to have to do anymore. And. And. That is a big opportunity to move. A lot of the work that we do to the more creative. To us, probably more interesting parts of work. And to recognize that that. Frame is always changing or is always moving.
SPEAKER_01
So as, as certain things get wrote, other things become things that humans start to do. And, and yes, those will get automated too, but like, we're going to also keep, keep moving down along that spectrum. And. And. Final thing that's not automatable is the, is like art and made by humans who. Feel something. And I think that's beautiful. Yeah. It's still scary because what if you're in the middle.
SPEAKER_03
And you want to move, or if you want to figure out what that is to you, because this might sound very. Abstract and weird to some people. If you're not an artist or haven't like. Really like. Felt this in moments. Like it sounds maybe a little bit like, oh, but like, that's not me. But I do believe everyone has this. Like, think of it, like what brings you joy? Like what, what. Lights a fire in you. Like what do you get excited about? Like, I think. That thing. You should like lean into like whatever that is. And that can be beautiful writing or. That can be. Very structured lists or whatever it is. Like anything that just brings you. Happiness.
SPEAKER_03
Like you should do more of that using LLMs in your work because. That's good. I agree. Kieran.
SPEAKER_01
Always a pleasure. Thank you. Yeah.
SPEAKER_03
Let's see where this goes. See you next time. See you.
SPEAKER_01
Bye.
SPEAKER_03
Bye.
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