Dan Shipper talks with Kieran Klaassen, general manager at Cora and creator of Every's compound engineering methodology, about where humans fit in an AI-driven world. Kieran and collaborator Trevin Chow's four-step framework—plan, work, review, compound—treats an AI workflow like a sandwich: agents do the deep work in the middle, while humans frame the problem at the start and review the outputs at the end. Topics include why agentic coding's "work" phase is essentially solved, why setting the frame of a problem remains hard for agents, whether simulated personas can replace human judgment, Dan's bar for AGI (an agent worth running 24/7 with no off switch), and what Kieran's background as a classical composer taught him about performance, polish, and finding joy in work. Timestamps: 00:00:00 – Introduction and the AI sandwich metaphor 00:02:33 – What compound engineering is and how it's evolved 00:04:27 – The "work" phase of agentic coding is essentially solved 00:06:27 – Why humans belong at the beginning and the end of an AI workflow 00:11:06 – Why agents can't change frames—and how this keeps us employed 00:16:51 – Full automation is a moving target 00:23:21 – Musical composition as a model for human-AI collaboration 00:26:39 – Finding your place in an AI-accelerated world by leaning into what brings you joy # Channel Presentation Observations: "The AI Sandwich: Where Humans Excel in an AI World" ## Explicit Self-Description Evidence ### Show Identity - The outro (apparently AI-generated, given its exaggerated tone) names the show **"AI and I"**: > "you absolutely, positively have to smash that like button and subscribe to AI and I" - It identifies the host: **Dan Shipper** — "a journey into the future with Dan Shipper as the captain of the spaceship." ### Host's Claim About Guest's Affiliation - The host frames both participants as connected to **Every**: "what we've found internally at Every is we're absolutely not [losing software engineer jobs]... we still hire software engineers." - Guest (Kieran) is introduced as **GM of Cora** and **creator of "Compon Engineering"**, a framework "everyone inside of Every uses." ### Claimed Editorial Scope - The host explicitly claims scope beyond engineering: > "I think we can also translate that to the rest of AI and its impact on work." > "I think that also applies to the rest of knowledge work." - Framing of software engineers as early indicators: "software engineers are a little bit of the canary in the coal mine." ## Observable Presentation Patterns ### Format - **Interview/conversation podcast** with host-led questioning, guest expertise, and a recurring segment structure (greeting, explainer, objection discussion, sign-off: "See you next time"). - Contains **two mid-roll sponsor reads** for the host's own organization's products: Granola ("granola.ai/every") and Monologue ("Monologue from Every"), plus the tagline "Evry, the only subscription you need to stay at the edge of AI" — indicating the channel promotes Every's product ecosystem. ### Intended Audience (Demonstrated) - **Practitioners working with AI coding agents**: the plugin is described as something "everyone who's really coding with agents is at least aware of if not using." - **Knowledge workers concerned about AI's impact on jobs**: "we have all these questions about... is everyone going to lose their job." - **Product/engineering professionals**: discussion of code review, PRs, spec-driven development, product polish, and role evolution ("product manager, product engineer"). ### Tone and Positioning - Optimistic-pragmatic framing on AI and work: "the simple answer is ride the bottle... be the bread in the sandwich... you're going to be fine." - Encourages audience self-reflection: "what brings you joy? ... you should do more of that using LLMs in your work." - Host acts as curator of insights: "every once in a while you have a thing that you do or you figure out that I'm like, 'That's definitely the future.'" ## Notes on Source Reliability - The outro's hyperbolic style ("I'm absolutely, hopelessly in love with you") appears to be a scripted/AI-gimmick segment, so its claims about the show's quality are promotional rhetoric, not neutral description. - Statements about Every's internal practices ("we still hire software engineers") are creator claims, not independently verifiable from this material. # Delivery Observation: "The AI Sandwich: Where Humans Excel in an AI World" ## Format Overview This is a **two-person interview** (host Dan interviewing Kieran, GM of Cora and creator of Compound/Compon Engineering), with two mid-roll ad reads and an ironic AI-generated outro. Delivery evidence below focuses on the two speakers. ## Host (Dan) — Delivery Patterns ### Affirmation-Heavy Listening The host frequently acknowledges with brief affirmations between guest turns: > "Yeah, exactly." > "Yeah, I agree." > "I think that's totally right. I love this art angle that you have." ### Analytical, Structured Framing The host favors layered analogies and explicit spectrum-building: > "All of work exists on this spectrum from it being totally rote to it being art." He also pre-frames audience objections: > "And I want to start with an objection that I think people will have, which is like, 'Okay, like for now, agents can't do the idea in the brainstorm, but pretty soon they will.'" ### Concrete Analogy Use He explains abstract points with everyday examples (the hurting knee / Advil / IT band example) and self-checks for clarity: > "I know it's like a little bit hand-wavy, and the knee hurting thing is a little hard to hard to understand, but does that does that resonate for you?" ### Metaphor Mixing, Acknowledged > "It just feels very much to me like the simple answer is ride the bottle. Um or to mix the metaphor, be the be the bread in the sandwich." ## Guest (Kieran) — Delivery Patterns ### Repeated Phrase Stuttering A striking, recurring habit: restarting or doubling words mid-sentence ("the the," "it it," "and and"). This appears throughout: > "But, we start to realize more. Like first of all, the work phase is kind of done. Like it it works." > "So, uh this is kind of what we realized like the beginning and the end, the middle is kind of solved..." **Note:** This repetition is dense enough to be a stable speech pattern, not isolated moments. ### The Word "Beautiful" as a Core Vocabulary Anchor Kieran repeatedly returns to "beautiful" and "beauty" — likely a stable vocabulary preference: > "make it more beautiful, make the design better" > "lean into making beautiful stuff, and whatever that means to you" > "that can be beautiful code, beautiful abstractions, beautiful architecture, beautiful design, beautiful copy" ### Personal Experience as Teaching Device He grounds abstract workflow ideas in personal history (building Cora, Pomodoros, classical composition study): > "this is something I've learned from doing Pomodoros where ideally if you do Pomodoros, the old school way..." > "I have a music background. I studied classical composition..." ### Rhetorical Questioning He teaches through question-asking rather than direct assertion: > "So, slop comes out or whatever you call it... but how do you know it's good?" > "if everything works, like where do I work? What is still the bottleneck?" ### "Kind of" and Hedged Certainty A frequent hedge, "kind of," softens claims: > "the work phase is kind of done" > "the middle is kind of solved and can be automated pretty well" > "It's kind of like a sandwich." ## Shared / Cross-Speaker Patterns ### Enthusiastic Agreement Loop Both speakers repeatedly affirm each other ("Yeah, I agree" appears from both sides), creating a consensus rhythm rather than debate. ### Repeated Key Phrases Across the Episode Several phrases are deliberately or habitually repeated: - **"The bar is high. The bar will always get higher."** — said twice by Kieran (opening clip and mid-episode), suggesting it's a signature line. - **"It's kind of like a sandwich"** — attributed to Trevin, reused as the episode's central metaphor. - **"Slop"** — used by both speakers as shorthand for low-quality AI output ("slop comes out," "it will be all slop, all the same"). ## Uncertainty Notes - Timestamps in the transcript are fragment indices, not verified playback times; no time-based delivery observations are made. - The opening [0]–[33] segment duplicates content from [522]–[544] (likely a cold-open recap); repeated phrasing there is editing, not a speech habit. - The ad reads and outro ("smash that like button...") are separate scripted segments, not evidence of either speaker's conversational delivery. # Observation Record: "The AI Sandwich: Where Humans Excel in an AI World" ## Speakers - **Dan Shipper** — host, refers to himself as Dan; references Every and his own Granola sponsorship reads. - **Kieran Klaassen** — guest, introduced as GM of Cora and creator of "Compon Engineering" plugin/framework (transcript spelling). - **Trevin Chow** — referenced, not present; contributor to Compon Engineering, credited with the "sandwich" metaphor. ## Central Metaphor: The AI Sandwich - **Kieran**: "Humans are the bread in the sandwich and the AI is in the middle." Attribution note: Kieran credits **Trevin** with originating the sandwich comparison ("Trevin at some point said, 'Oh, it's kind of like a sandwich.'"). - **Dan**: "The AI is whatever you put on your sandwich." (playful extension) - Core idea: humans should occupy the **beginning** (framing, ideation) and **end** (validation, polish) of work; the **middle is automatable**. ## Compon Engineering Framework (Kieran) Four original steps plus additions: 1. **Planning** — make a clear plan of what to build. 2. **Work** — the agent implements; Kieran: "the work phase is kind of done... I don't need to think about it. I trust it... LLMs are very good at just following steps, doing deep work... hours, days even." 3. **Review** — assess output quality, iterate. 4. **Compound** — store learnings in the repository so future agents avoid past mistakes. Kieran: "that's by far the most powerful thing." - Additions by **Trevin**: **brainstorm** and **ideate** steps before planning; the top of the process requires the human "super well in the loop," while planning itself can proceed without the human. - **Kieran's disagreement with alternatives**: spec-driven development "assume[s] 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." - **End-of-process human role**: after automated tests pass, a human clicks around and adds feel/polish — compared to Pomodoro "extra time" deepening where "something beautiful happens." ## Human Roles at the Edges ### Beginning: Setting the frame (Dan) - **Frame-shifting argument**: any problem ("my knee hurts," "feature is broken") has a larger frame above it (stretch IT band, stop running on hard surfaces). "Humans are very good at flipping and changing frames... Our job is to set the frame." Dan admits this is "a little bit hand-wavy." - **Kieran's qualification**: ideation will likely be automated too ("persona of 100 people and run simulations... probably you'll learn something from that"). His reservation: "if you want it to be your own... you cannot fully automate everything. It's maybe a little bit like art... it needs to be from you or somehow be connected." - **Dan's supporting points**: - Simulated personas "would still only cover a small set of the decisions." - Rare feedback loops: "You may only get a couple moments in your career where you gather the data that helps you decide about a particular thing... very hard to get into language models." - LLM outputs are generic ("superintelligence kept in a box... no idea what's going on in the world"); cites "it's X not Y" AI-writing tics. - "One deep reason... it's just not going to be yours." ### End: The performance / polish (Kieran) - Drawing on his classical composition background: practicing a piece 100 times "is not very creative... this is the middle part." The performance — bringing it into the world — is the special human moment, analogous to the final polish step; the start (composing from nothing) is the other special moment. ## Claims About Employment and Work Change - **Dan**: Every still hires and needs software engineers; the work "looks a lot more like managing." Software engineers are "the canary in the coal mine"; the same pattern applies to copywriting, strategy, design. - **Kieran**: engineers become "more of a manager, but also more of a product person — product manager, product engineer." Advice: "lean into what is beautiful to you" — beautiful code, abstractions, architecture, design, or copy — so LLM use "gives you energy instead of drains you." - **Dan's spectrum claim**: all work runs from **rote to art**; rote parts get automated, the frame keeps moving, and "the final thing that's not automatable is... art made by humans who feel something." - **Kieran's caveat**: "It's still scary because what if you're in the middle and you want to move"; he believes everyone has a source of joy to lean into ("what lights a fire in you?"). ## AGI / Capability Predictions (Dan) - **Personal bar for AGI**: "whenever it is economically profitable... to run an agent 24/7 where it never turns off." Notes Open Claw (transcript name) runs on a schedule/heartbeat, not continuously. - Current long-running tasks "aren't changing frames... not picking the next one." - Predicts "fundamental changes to the language model architecture... to let them learn better" are needed first. "We're not even close." - Tension preserved: Dan says models "will get better at doing this" but are "much further than we think" from end-to-end capability; Kieran agrees. ## Preserved Tensions and Qualifications - Kieran simultaneously predicts ideation automation will increase **and** that full automation is impossible for work that must be one's own. - Dan's frame argument is self-described as partly "hand-wavy." - Automation of newly human tasks is expected to continue ("yes, those will get automated too"), with the human role framed as perpetually shifting rather than fixed. ## Non-Content Segments - Sponsored reads by Dan: **Granola** (AI notepad) and **Monologue** by Every (dictation). - Closing outro includes an AI-generated-style subscribe appeal, ending with a joke declaration of love toward Dan. # Speaker Attribution Record ## Video **Title:** The AI Sandwich: Where Humans Excel in an AI World **Show (per transcript outro):** AI and I ## Speakers & Roles - **Dan Shipper** — **Host / Creator**. Interviewer; identified in outro as "captain of the spaceship" of the show. References working with the guest and internal practices at Every. - **Kieran** — **Guest**. Introduced by host as **GM of Cora** and **creator of Compon Engineering**, an engineering framework/plugin described as used internally at Every and known among developers coding with agents. - **Trevin Chow** — **Referenced third party (not a speaker)**. Identified by host as a contributor to Compon Engineering; credited by the guest with adding the brainstorm/ideate steps and coining the "sandwich" metaphor. - **Outro voice (unnamed)** — **Promotional narrator**. Delivers the closing like/subscribe pitch in exaggerated promotional style; ends with a personal declaration to Dan. Not identified by name. - **Ad narrator(s) (unnamed)** — Deliver mid-roll advertisement reads (see below). Identity not specified. ## Promotional Segments (Flagged) - **Granola ad** (mid-roll): AI-powered meeting notepad. Reads: personal testimonial from the narrator, feature descriptions, offer of **3 months free with code "every"** at **granola.ai/every**. - **Monologue / Every ad** (mid-roll): "Be a dictator" sketch promoting Monologue dictation app by Every; describes Every as "the only subscription you need to stay at the edge of AI." - **Outro promo**: Like/subscribe call for the show "AI and I" with praise of host Dan Shipper. ## Referenced Sources & Third-Party Claims (as stated by speakers) - **Compon Engineering** — guest's framework/plugin; four-step process (plan, work, review, compound) plus Trevin's added brainstorm/ideate steps. - **Cora** — product the guest manages/builds (GM). - **Spec-driven development** — cited by guest as an approach assuming humans always in the loop, which he disagrees with. - **Pomodoro technique** — guest cites as source of his insight on extended polishing time. - **Suno** — cited by guest as AI music generator, contrasted with live performance. - **Open Claw** — cited by host regarding 24/7 agent operation; claims it runs on a schedule/heartbeat, not continuously. - **Every (company)** — host claims internal hiring: "we still hire software engineers." ## Provenance Notes - No curated metadata supplied beyond transcript; speaker names/roles derived solely from transcript dialogue. - Comment availability: not indicated in source material. Humans are the bread in the sandwich and the AI is in the middle. 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 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. \>\> and the end, the middles can be automated pretty well and uh Trevin at some point said, "Oh, it's kind of like a sandwich." Which was like very funny. Kieran, welcome to the show. Hello, Dan. Happy to be here. So, for people who don't know, you are the GM of Cora. Um and you are also the creator of Compon Engineering, the engineering framework and plugin that everyone inside of ever uses and everyone who's really coding in with agents is at least aware of if not using. Um and so, a pleasure to have you on the show. Thank you. Yeah, it's always great. 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, "That's definitely the future." And you just figured something out along with Trevin who also helps out Trevin Chow who also helps out on Compon 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 Compon Engineering plugin that you've rebuilt the engineering workflow for how you should work with agents. And in thinking about that and thinking about where a human is used and where a human is like should not be present inside of that process, I think you've you've found something like really interesting and deep about in general how how humans and and AI are going to interact with work. So, do you want to explain a little bit about Compon Engineering and that and and the process that you've created and then also explain this this insight about where humans fit. Yeah, absolutely. So, Compon Engineering is uh like a philosophy of like doing engineering work, but we realize it applies to more than just engineering work. It's product work as well. It's design work. It could be knowledge work. It could be other things. But, how I build it is while building Cora I had AI and I was like, "How can I use AI to do better work more quickly?" And the initial version of Compon Engineering really evolved around four steps, which is planning first. You make a great plan, so it's very clear what you need to build and do. Then do 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. The third is review. So, slop comes out or whatever you call it, some something beautiful comes out. One of the two, like something comes out, but how do you know it's good? And traditionally there's like a code review or like a PR that you review and see like, "Hey, this can be improved." And there's some iteration going on there. 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 like, "Oh, this is this is a good learning. Probably we'll run into this again." You can compound that knowledge back into the system and we store that as knowledge inside the repository. 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. And that's really the power like that's by far the most powerful thing that uh is in this plugin. But, we start to realize more. Like first of all, the work phase is kind of done. Like it it works. If you have a good plan, it does the work and is is pretty good. And then the review it makes it a little bit better. \>\> And by that you mean like uh having an entire phase dedicated to work in this whole system doesn't necessarily make 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 to care. Well, like I don't need to think about it. I trust it. And this is not like uh trust me, bro. Just works, but this is like I've seen if you put in a good plan like it does the plan. Like it executes on the plan. LLMs are very good at just following steps, doing deep work, like working for hours, days even now. And that's that thing is kind of solved. And the review starts to get there too. And the planning starts to get there too. And then there's this next step is like, "Okay, so so if all these things work, where where do I have to do anything?" Because like did I just automate my job? Yeah. Yeah, did I automate myself out of a job? If everything works, like where do I work? What is still the bottleneck? And there are two things we started to know. Like Trevin he's a very very great contributor to the Compon Engineering plugin. Like he is a product person and he is like, "I need more on the product side." Which is like before the planning phase. So, he added first a brainstorm step and an ideate step. And the ideate step is like really going wide. Like it's like, "Okay, let's come up with ideas in a in a room full of interesting people with angles." Brainstorm is more like I have a problem that 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 like the top is very important to be super well in the loop with a human. And really like ask a lot of questions and really think hard. Like the human should think hard, the LLM should support the human. But then after that, the planning phase, if you have a good brainstorm and idea what problems you solve like 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 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 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 \[snorts\] at the moments where we need to think harder. And that's the first one. So, the other one comes at the end. So, like something comes out, how do you validate it's good? Well, it's already tested because we have browser automated testing. It 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. Like, "Oh, this is this this doesn't feel good. We can polish it even more. We can make it even better. We can increase like or like 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 like 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. Um and 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, uh this is kind of what we realized like the beginning and the end, the middle is kind of solved and can be automated pretty well. And uh Trevin at some point said, "Oh, it's kind of like a sandwich." Which was \[laughter\] like very funny. And and Dan is now referring to the AI sandwich, which which I think is is is very cool. Like and and I think the sandwich here is like when do you need to think about what you do and really use your brain versus offload it to the LLM. 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 \[music\] the important detail. \>\> Yeah, forget the decision, forget the action item, let something important slip through the cracks. That's why I love \[music\] 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. Granola 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 sort of just transcribing all the important moments in my work life, \>\> \[music\] \>\> 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 \[music\] was, how you act as a leader, how you performed in particular difficult conversations, and how you can do better. It's really a power tool for anyone who cares about their meetings and also cares about how they show up in those meetings. It also has this \[music\] thing called a recipes, which are pre-made prompts for common tasks like negotiating, coaching, or summarizing. I even have a recipe that I made that's even a lot that you should check out. Once you try it on one meeting, it's really really hard \[music\] to go back. The notes are always better than what you could do manually, and it helps me be much more \[music\] present instead of frantically typing all the time. Head to granola.ai/every for 3 months free \[music\] with the code every, e v e r y. That's granola.ai/every for 3 months free. And now, back to the episode. Humans are the are the the sandwich, the bread in the sandwich, and the AI is in the middle. Yeah, the AI is whatever you put on your sandwich. \>\> Yeah, exactly. And I I think that's really interesting and really cool because a, it it 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, like what are a agents going to do?" And is it are everyone going to is everyone going to lose their job and all that kind of stuff? And I think um 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 like we we still hire software engineers. We need software engineers. But uh but the the the way that you're working and what you're doing looks a lot more like managing, if you're doing it well. But you're still involved, but you're involved at the beginning and the end of sort of the sandwich. And uh and I think the same is going to be true of every other kind of work, whether that's um you know, copywriting or strategy or design. And and I think there are deep reasons why that is the case that I think it will be interesting to talk about. Um and I want to start with an objection that I think people will have, which is like, "Okay, like for now, agents can't do the idea in the brainstorm, but pretty soon they will. So, then what then what happens?" Um there are the now they're starting to do the beginning of the of that process. And I think that there's something interesting here where um if you look within any given local frame of a problem. 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 uh this uh feature is broken or um customers are anxious about this part of the product or whatever, any problem. If you take that frame and you say, "Okay, well, the solution is maybe for your if you're if we're taking the knee hurting thing, the solution is take Advil." Um any part of that process, you know, getting to the store or whatever can be automated. Let's say DoorDash can go do it. But there's always even even once you've once you've solved it in that way, there's always a larger frame within which to think about the problem. So, an example is if your knee hurts, you might need to stretch your IT band. Um or you might need to stop running on hard surfaces every day. Um and each one of these is sort of addressing the same problem at a different level of the stack, at a different from a different frame. And um humans are very good at flipping and changing frames like that. And our job is to set the frame or set the bounds within which we solve the problem. And um I I think it's very it's going to be very very hard for agents to do that well by themselves, and there are there are deep reasons for that. But does that Do you get that? Like I know it's like a little bit hand-wavy, and the knee hurting thing is a little hard to hard to understand, but does that does that resonate for you? Yeah, for sure. Yeah, it's like this this all comes down to uh building an environment where the agent will thrive, and you do that by like picking the right things. And and and this is why it's so important to have humans with uh experience and humans with taste and humans that just want to click around and like say this is or this is great, and why it is or great. And and I think I think it's similar to like the Advil example. Like if if you keep doing that, it's probably your friend will say, "Yo, that's messed up. Just go like go fix the problem instead of like denying the problem." And and maybe it will work for you for a little while, but you need someone to shake you up, and in that case, like that's the human or that's the the other person. Um but I do think like it will also like be more automated. Like also the ideation, you can say, "Okay, 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, too, where we run simulations of like millions of people and see how things work, and probably you'll learn something from that. 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 it's maybe a little bit like art, like making art. Like if you want it your own, you need to just it needs to be from be from you or somehow be connected. Uh 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 um are very important. Yeah. I agree, and and I think um yeah, you can imagine it being like okay, yeah, we're going to simulate a 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. It's all it's a moving target. Like we always get something new, and then again, there's 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. 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, uh that that like sort of rare expertise that is encapsulated in an expert who has a personality and a world view is hard to hard to get, and and you're right, it's also always moving. And um I don't know, that that makes me like very excited about this stuff cuz I feel like we've been we've been wandering in the woods for a long time on like, "Okay, what is AI 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. Um or to mix the metaphor, be the be the 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. And and it will be different for different people because and 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, um you need to find your way of writing code. Like yes, you can write code, but maybe it's about beautiful code. And maybe you find uh also lots of uh value in just seeing beautiful code. Like someone looks at a UI and says, "Oh, this is beautiful. This works great." Maybe you want that for code. Some people don't care about that, but they're like, "Oh, but the UI should feel great." And and just really polish it, go extra. Like wherever you feel joy, but also it's way more product focused. So, as an engineer, you're going to become either more of a manager, but also more of a product person. So, it's it's I think like a a product manager, product engineer, like it's 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 code, beautiful abstractions, beautiful architecture, beautiful design, beautiful copy. Uh I 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. \>\> \[music\] \>\> You can speak faster than you can type, so dictators choose to do so whenever possible. While those confined to keyboards deal with finger cramps and input lag, ow, voice allows dictators to convey ideas as naturally as they sound in their heads. \>\> Hey, yeah, that's exactly what I meant. And in \[music\] the future, as AI tools improve, we will see a rise of dictators around the world. \>\> \[music\] \>\> More and more dictators are choosing Monologue from Evry. It learns, transcribes, \[music\] and translates across different disciplines and languages, adjusting its output format to match \[music\] your context, allowing you to stay in flow. Be a dictator, an idea by Evry. Evry, 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 There's the 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. Um but another deep reason is you can think of language models as being a superintelligence that has been kept in a box for the last year and has no idea of what's going on in the world um 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, yeah, 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 of that. And to truly solve a problem well or to truly make art or to truly make a product that resonates with people, um 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 at the 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 My My My general bar for AGI is whenever it is economically profitable um or makes economic sense to run an agent 24/7 where it never turns off. And Open Claw is like pushing in this direction, but it's it's doesn't run 24/7. It runs on a schedule. It has a heartbeat. But it's not like you just say, "Hey, like Open Claw, just go and just do a bunch of stuff and just work all the time, and spend tokens all the time on on stuff, and it is is worthwhile." It just It's 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 5 minutes, and then the next one I'm going to spend 4 days on it." It's like It's We're not even close to that. And I think we're going to need some fundamental changes to the language model architecture to like let them learn better for them to get to a point where they're they're um 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 I agree. One other way to look at it, so I 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 a 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 like you see something, you express something. 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 I wanted to change it a little bit with with a step at the end." And suddenly you're like a kind of performing or iterating or you're making stuff. You're putting something in the world, and and I feel that's 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. Uh 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, and there is a little bit of a link for me with doing this bullish 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 elements as well for me. I think that's totally right. I love this art angle that you have. And another Another way to say this is all of work exists on this spectrum from it being totally rote to it being art. And art itself has many tasks within it. Any kind of creative work has many tasks within it that that are more more rote or less rote. And um if you're trying to map work on that spectrum, the stuff that is more rote is just going to be stuff that you're not going to have to do anymore. 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. So as as certain things get rote, 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 the final thing that's not automatable is the is like art 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 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. Uh if you're an old an artist or even 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. Like you should do more of that using LLMs in your work because that's good. I agree. Kieran, always a pleasure. Thank you. Yeah. Let's see where this goes. See you next time. See you. Bye. \>\> \[music\] \>\> Oh my gosh, 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 like 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.