Why Opus 4.5 Just Became the Most Influential AI Model

[00:00] The world changed last week. Opus 4.5 is the first time where I've been able to vibe code and it just keeps going without tripping over itself. It just keeps building stuff and it doesn't have errors. If there are errors, it fixes it. I kind of knew we were headed in this direction. Somebody on Blue Sky was like, I don't think anyone should have any opinions on AI until they spend two hours in the Opus room. [00:22] And I think that's right. I no longer feel I can in good faith say human skills are going to be relevant. One way to look at this and be like, hey, you can take your craft, you can evaluate the outputs of this, and you can make sure that the people in your world are being getting good stuff faster, but also make sure it's safe and on rails. That just isn't how humans work, man. Humans want to type in the box and get a thing. And if it kind of works, they'll be like, I did it. I don't know if society will completely reorder itself, although in a way it seems to be trying to. [00:52] is... [00:53] learning how hard it is for humans to metabolize change. Everybody thinks lots of thoughts about me and themselves and their disciplines, like, [01:01] I'm a front-end engineer. I'm a product manager. To see all of those categories blur and all of the things that allow people to say where their value is... [01:11] is frankly really overwhelming.

[01:27] This podcast is sponsored by Google. Hey folks, I'm Ammar, product and design lead at Google Define. We just launched a revamped vibe coding experience in AI Studio that lets you mix and match AI capabilities to turn your ideas into reality faster than ever. Just describe your app and Gemini will automatically wire up the right models and APIs for you. And if you need a spark, hit I'm feeling lucky and we'll help you get started. Head to ai.studio slash build to create your first app. [01:57] Oh, welcome to the show. [01:59] It's great to be here. Thank you. [02:01] I am so excited to get to interview you. For people who don't know you, you're the co-founder of Abord, which is an AI-powered software delivery platform for businesses. But closer to my heart, you are a fantastic writer. Thank you. You wrote a piece when I was in college. It's like the piece when I think of that era called What is Code for Bloomberg. I would love to revisit that piece in a second. [02:31] Oh, Dan, that's fine. You drink some milk and talk to me here, then. That's great. I have to do stretches now. I didn't have to do stretches before. At least you can do the stretches, dude. Enjoy it. [02:48] So it's super excited to talk to you, but I think the thing that we are both super excited about is Cloud Code. [02:55] And in particular...

[02:58] Wait, wait, I'm super excited about my own product. But yeah, Cloud Code, let's talk about it. You know what the hell just happened? [03:07] Yeah, the world changed last week. And I think people... People don't know yet. It's like they just don't know. It changed. Can you articulate it? I have my own thesis, but what do you think it is? It's Opus 4.5 and Sonnet 4.5. Inside of Cloud Code was a step change. [03:26] How would you describe it? I will say the most immediate thing that I noticed is for a long time, we've had the ability to vibe code something in one shot that looks like a passable app. [03:39] But Opus 4.5 is the first time where I've been able to vibe code and it just keeps going without tripping over itself. It just keeps building stuff and it doesn't have errors. If there are errors, it fixes it. [03:56] fully featured iPhone reading app that it's the coolest thing. I can take little pictures of books I'm reading and it will do an analysis, but then I can kick off a research agent that will go and download the source text and do a close reading study of it. And then it'll generate a custom introduction for me and a custom reading profile based on all of the stuff in my photos. It's crazy. It's a fully featured app that would have taken months to build that I have no idea how it works. [04:26] what you're seeing. Very similar. So I've been, we have a tool that built, if you go to aboard.com, you can use it on the web. Like you build software for businesses at the prompt. And we've been, you know, trying to wrap things

[04:41] guardrails around the chaos of vibe coding because it doesn't finish things. It's the last mile is really long. It tends to leave a lot of loose ends. [04:48] And so we've been very, very involved in this space and stayed really connected to it. And then about two weeks ago, right, like something changed, and they sort of released their models. And I think what I would say is that Cloud Code is – [05:02] I would go so far as to say it's the first true product built on top of an LLM. There are a lot of – and, you know, I want to believe that we're in there too and so on. But what we're all trying to do is build constraints and systems and kind of recursive methods of understanding what the output is and making it better and making the LLM actually work the way people expect it to without all the sort of strange endings. [05:28] And Cloud Code feels like they took that seriously. [05:32] represent some giant step change in the capability of an LLM. Like it feels like [05:38] Yeah, like Sonnet and Opus are better, but they're not like 9,000 times better. But they added in a layer of kind of agent-style stuff. [05:49] Um, [05:50] It's thoughtfulness to the product, so it's constantly evaluating its own outputs and then improving them, which leads to these really, really complex outcomes when it comes to writing code. And so I'm in the same boat. I have a set of benchmark projects. There's one called – there's this document. It's got a terrible – not document. It's a database. It has a terrible name. It's called iPads. And a friend of mine asked if I could work with it like a year ago using AI. And it's a government-produced database of every –

[06:18] college, they have to fill it out. And it's like, what are their majors? And what's the gender and race breakdown at the school? What is tuition? And so on and so forth. And [06:29] Um... [06:31] And it's grisly. It's Microsoft Access databases and huge data dictionaries, and it's the sort of thing that literally I wouldn't have touched at an agency without hundreds of thousands of dollars to staff a team of engineers. Like it was a horrible, horrible programming problem to sort of take this, transform it, and put it on the web in a sort of modern way. And, man, I just knocked it out. It wasn't easy. Like I still had to kind of know a lot of stuff, but it did a really great job, and it built me a nice visualization with smart search. [07:01] I had to create an AI-enhanced search tool. I've been using it to set up a pipeline to build little musical synthesizers just to see how that could work. And today I was like, hey, clone a TR-808 drum machine, and it did it in 20 minutes, right? And it's just sort of like – now, I spent – [07:17] Whole day is creating that pipeline, right? But that used to be like the work of a company, right? [07:23] And so – [07:25] I think what's tricky, I don't know if you have this experience, what's really tricky is you go, wow, I'm powerful. And then you realize, like, no, this is everybody now. Like, you feel like you've captured something. Like, you got the ultimate Pokemon, but everyone's getting the same Pokemon, like, shoved into the mailbox. This is me trying to come up with an analogy that connects with you as someone who's a lot younger. Yeah.

[07:49] Thank you. [07:49] Thank you for being so relatable. Yeah, this is part of my job. [07:55] I think I totally agree. My experience, I actually did a whole presentation for our team this morning on what I think has changed about programming. And I would be curious, I think you're the perfect person actually to talk to this about. [08:13] So the thing that is really interesting, the design principle that I think made CloudCode makes CloudCode so powerful is that anything that you can do on your computer, CloudCode can do. [08:25] And it has a set of tools that are below the level of features. They're low-level tools. They're command-line tools. It's bash, it's grep, it's all this stuff. [08:41] And what that allows you to do is it creates this system that is very composable and very flexible that you can build on top of and use in ways that they couldn't necessarily predict. And what's also really important is that means that the programs or the features of Cloud Code are actually just prompts. They're slash commands and subagents. [09:11] which lets you iterate faster as a company and also lets your users make their own features. And I think that that is a general principle that you can start to apply to any

[09:22] based application as a product principle, which is anything in our application that [09:28] Anything that a user can do, AI can do. And generally, we're trying to move what used to be product functionality that is written in code into prompts that the agent uses low-level tools to accomplish the feature outcome. And that opens up all of these interesting, cool, new doors for software development. I agree. Look, I think the patterns in this kind of programming, this kind of thinking are really, really different. [09:58] As a company that's building a tool along these lines, I think the patterns are... [10:03] are emerging kind of for everybody in sort of all the LLMs. It's just that Claude Code just really bundled them up very, very efficiently, and it kind of hit its core audience of engineers, which is them. Just like right across. It's just a slap across the face because they literally were like, here it is. Here's the future. It's going to look like this. And we all went, yeah, all right, man. Okay, you got it. Yes, Mr. Claude. [10:29] There's a few patterns, right? [10:33] up as sentences. There's another aspect of it, and it integrates with the existing system. So it's not like, it's not this world apart. And actually what I found over the last week is where I normally would go to a command line and start typing, I start typing in English and forgetting I haven't gone into Claude, right? Like I'm just, it's so immediate because it's so much better at building and orchestrating. And you know, it's funny, it really, I'll give you an example. I wanted to

[11:03] So I went to like fly.io, which is a very fast deployment environment. And I was like, you know, because I bet it will be able to coordinate well here. And then I just was like, wait a minute. I have this random server just like sitting somewhere that I use for scratch projects. Can you just SSH into that and just deploy this thing for me? And it's like, yeah, no problem. And it just like jumped onto the boxes and like looked around. It's an Ubuntu server. Yeah, let me update your Nginx. Oh, you need to get the certificate installed here. Let's go ahead and do that. [11:33] And 10 minutes later, and then... [11:36] The killer was I was at Thanksgiving and my friend's dad was like, boy, I really need to make a searchable index of this one politician's newsletter for oppo research. I was like, man, that's something he's like, yeah, I've been cutting and pasting into Google Sheets. And I'm like. [11:53] Is it all available on the web? And he's like, it sure is. I open up Claude Code on my phone and literally between turkey and dessert, I built and shipped it. It was SQLite on the back end. It works just fine. He's going to do his oppo research. Don't worry. He's on the right side. And like... [12:10] And so I shipped a pretty complicated search-based full-text search. I know that whole architecture really well, so it was really easy to instruct it. But off we go, and it also is good with dealing kind of like [12:23] I didn't have to use all the new custom fancy stuff. I could just use an old server that was sitting around because it knows. And so there's all of that going on. And I think that as I've been working with it,

[12:36] What I'm finding is – [12:39] Thank you. [12:41] You've got to think not just in terms of solving the problem, but in terms of one level of abstraction up. [12:47] I had it build a little musical synthesizer for me that emulated like a Moog synth. [12:52] something I know a reasonable amount about. And it did like an okay job and had a lot of caveats. And the remaining work on it would be hard and I didn't do it. But then I was like, okay, one level up. [13:05] You need some more information about digital signal processing. So I'm going to go spider some books that are available free online, and I'm going to put them into a database. Whenever you have a question, [13:14] search this little tiny SQLite database and refer to it. So then I give it a reference source. And then I was like, wait a minute, you keep writing code, and, Claude, you have to calm down because your code's okay, but it's not that great. I want you to go find all the open source libraries that are really good about digital signal processing, which is really edge casey. [13:32] And I want you to make a list of them. And I want you to only build based on those things. You should adapt and create a library. And then you should implement based on that library. And as like five or six things, five or six things at that level of abstraction unfolded, I'm now able to say, hey, make me a synth that's like this and come back 20 minutes later. [13:52] And, and, [13:53] That is a lot. Actually, it's a little emotional and confusing to process after 200 years as a software person. But if you work at that level, and I think that's the skill that's going to be emerging. Yeah, I agree. I want to stop you there at the like the sort of emotional level of.

[14:13] 200 years of software engineer. Um, [14:17] And, you know, [14:19] I think that there's probably, there's, there's just a lot of people who are, um, professional, who are professional software engineers who love the craft of code and who maybe are, um, [14:31] Pretty skeptical of AI because they're like, well, I can't write like... [14:34] the, the, the well-crafted code that I can write, you know, it has, it does all these things like it does all these things that are, you know, the code is not efficient and it's maybe not as dry as it needs to be. There's all this like stuff, right? Um, yeah. [14:50] And also, if someone like you uses it, you can move to this level of abstraction where to some degree that code doesn't matter. [15:00] Or it doesn't matter as much as it used to. Like, how do you... [15:05] How do you square that sort of like craftsman mindset about code with what is now possible? [15:11] Dan, man, I don't know. I don't know this week. I mean, I think two weeks ago, I would have been able to be like, I gotta tell you, I mean, I've been watching all this stuff real closely. And I know how albums work. I did the homework and so on and so forth. And I kind of knew we were headed in this direction. [15:31] But again, it's a step change in product. It's not a step change in technology. Like the technology is still roughly the same. It just feels like they're – but there's also this element of like – [15:42] One of the things we haven't talked about yet is you can instruct it to get better. You can be like, hey, if you were Claude, if you were, I was like, if you're a really good

[15:49] engineer at Anthropik. [15:52] take a look at this code base and tell me how to make it more efficient. And it's like, well, I would do these things and get this stuff out of this file and put it over here and make this more searchable. And let's make a command over here and let me write you some code. And so it's self-referential, which means it can accelerate. And so [16:07] What I'm getting at is... [16:10] Thank you. [16:10] I no longer feel I can in good faith say, hey, calm down and take it as it comes. [16:15] Humans are human skills are going to be relevant. I don't know if this is going to be a really good time for everybody because, you know, [16:22] You've got 600,000 jobs in Accenture alone. There's like 50 million devs in the world. There's a glut case to be made, which is, hey, everybody can clean up their roadmap, and it's a real great time for engineering to capture the value here and bring that acceleration to the organizations that they service, and everybody can have their thing. And that is really exciting and motivating. And I think that would be one way to look at this and be like, hey, you can take your craft, [16:52] Make sure that the people in your world are getting good stuff faster, but also make sure it's safe and on rails. [16:59] That just isn't how humans work, man. Humans are just like, humans want to type in the box and get a thing. And if it kind of works, they'll be like, I did it. Just like you with your app or me with my app. It's like, they might be crap. You might be looking at this and you might have like app glaze all over it, just like we see with images and text. But you can't see it yet because it's so shocking, except that it's software that

[17:20] And it's like, it's not like, there's no API glaze. It's like it pulls from the database or it doesn't. So it's just this very confusing moment where it's doing really practical, really difficult things that used to be really expensive. All I can tell people to do is like, somebody on Blue Sky, I don't know who it was, just was like, Blue Sky doesn't love this stuff, was like, [17:42] I don't think anyone should have... [17:45] Any opinions? [17:46] on AI until they spend two hours in the opus room. [17:50] And I think that's right. Like, you got to just give it two hours and see where you get. And then you can be as grumpy as you want. But, like... [17:58] You got to give it a go. [17:59] I agree. I think, um, and I would love to get to some of the like social implications, but I, [18:06] I'm mostly interested at first because I think the best way to understand the larger implications is to understand the implications on yourself. How is it changing how you process the world and how you think about yourself? And so I'm curious about that for you. You know, it's funny. I'm building an AI company with a wonderful business partner who I've worked with forever. I'm looking out. We have a nice office and we have a great team. [18:36] And we have clients and we work with them and we're doing what I just described. We are moving their roadmap along and we're bringing them. [18:44] tools much more cheaply and much more quickly than we used to be able to. [18:48] And I think it'll get faster, right? Like we want to...

[18:51] We want to drive that value out. And so... [18:54] In some way, things are pretty normal in that I come to work on the train every day and [19:00] In some ways, they're not in that, [19:02] There was so much friction built in, for good reasons, into the software development process. And the software development process is social, you know, like... [19:12] Engineers say no a lot, and they say no for good reason, and I used to train them to say no. [19:16] Because clients would ask for things and it would blow up the scope and then the whole project wouldn't ship. And then they'd call me on a Saturday and yell at me. And I didn't want that to happen. And so I'd be like, we've got to say no up front. And my co-founder has a wonderful maxim, which is there's no bad news 90 days out. If you see something failing and you tell somebody, hey, like, I think we're going to have a problem. I'm not going to be able to build your thing. But it's three months ahead. And you say, let's work together to find a solution. People tend to be very accommodating and understanding. [19:46] It's only like three days before when you're like, we're going to miss the deadline that they freak out. [19:52] And so my whole life has been architected around the fact that everything I do is exhausting, takes time and involves some of the most difficult people who have ever existed on the face of the earth who usually hate me and each other. [20:05] Okay, and like that is my day to day and I'm pretty good at it and everybody thinks lots of thoughts about me and themselves and their disciplines and people are very, very anchored to their disciplines, right? Like I'm a front end engineer, I'm a full stack engineer, I'm a designer, I'm a product manager.

[20:21] And to see all of those categories blur and all of those rules change and all of the things that allow people to say where their value is. [20:31] is frankly really overwhelming. And I don't want to devalue that emotional response because I've been kind of coming in and being like, hey, let's all do this together and let's move forward. But boy, I don't know about you, but there are elements of this that are just a freaking smack across the face. You probably lose so much time in the gaps between tools. You design in one place, you write and manage content in another, and then you publish somewhere else. Every jump is a chance for work or [21:01] Framer is different. Framer already built the fastest way to publish beautiful, production-ready websites, and it's now redefining how to design for the web. With the recent launch of Design Pages, a free canvas-based design tool, Framer is more than a site builder. It's a true all-in-one design platform. From social assets to campaign visuals to vectors and icons, all the way to a live site, Framer is where ideas go live from start to finish. [21:31] version, looks like your mock-up. [21:33] What you make is what goes live. Framer isn't a stripped down demo. It's a free, full feature design tool. [21:39] You get vectors, 3D transforms, P3 colors, SV animation, unlimited projects, and collaborators. Are you ready to design, iterate, and publish all in one tool? [21:49] Start creating for free at framer.com/design and use the code DAN for a free month of Framer Pro.

[21:55] That's framer.com slash design and use the promo code Dan. [21:59] Rules and restrictions may apply. [22:00] And now, [22:01] Back to the episode. It's interesting. I've definitely, I've had moments of that, both on the writing side and on the coding side. [22:10] But I think that... [22:12] we're so in the center of just figuring out, okay, what do we do now? That it has quickly shifted to like, there's so much to do. So, so it's, I think, [22:22] I'm familiar with the emotional experience. Well, you chose to jump in, right? You're like, I'm going to build infrastructure and community here. [22:30] in order to address this change. We built a lovely office. You should come visit. [22:35] Literally because we know that New York City is not ready for AI. [22:38] And we're like, okay, let's at least have a place where people can like, and we've been having not-for-profits in and lots of folks who like are going to get ignored. [22:45] So that we can talk about this. I think that part feels really good. I think it's just like, it's a lot of change. Like we're coming on, we got GLP's pandemic and now this. Writing is funny for me too, because I'm like... [22:58] I actually see the writing is because like it doesn't write for me. I kind of don't get it to write for me. It just, [23:04] It can't be me. I just am what I am as a writer. But I see a lot of people who aren't writers, and my God, it's good for them. It gives them access to a world and to kind of… [23:16] entree into a more formal style of communication that they didn't have before. And so to me, writing is supposed to empower. And if the robot helps you, that's good. If the robot thinks for you, that's bad.

[23:27] Yeah. [23:27] I think, um, [23:29] I've been trying to sort of process like, okay, what are those moments where I had that existential freak out? What is that like? Because I had that a few times sort of during this process. And each time I've – once I got over it, I felt like, okay – [23:46] There was something there that I missed and I'm trying to like update my intuition or my analogies for like so I can understand those experiences better. And. [23:55] There's that moment where the present sort of like collapses into the past and everything that you used to know looks really old and you're like, what's next? [24:08] And the intuitive experience that I think matches to this most closely is before we had really good sea travel, we used to think that if you went into the ocean, there would be like an edge that everything would fall off. [24:25] And that's our intuitive notion in a lot of ways of what happens when you get to the horizon. And what we found when we got to the horizon is that there's more horizons. [24:37] And my experience, I think that that maps pretty well onto my experience with AI is like each time I encounter this new thing, I'm like, oh, my God, I'm at the edge of the world. And there's like it's a cliff and it's just going to like drop off. And then each time I sort of step into. [24:51] over the horizon. And I'm like, whoa, there's this whole new territory, which is not to say that there are no bad effects and there's not complicated social issues to work out. But it is to say that

[25:05] I've learned to catch that edge of the world intuition and try, and I've tried to update it with, there's probably not an edge. There's just a new horizon. That's a good way to look at it. I agree with that. I think for me, it doesn't, I don't think human beings are going to change. I don't know if society will completely reorder itself, although in a way it seems to be trying to. So that part's tricky, but I, [25:31] Yeah. [25:33] I think what's wild to me is – [25:37] Learning how hard it is for humans to metabolize change. [25:42] For me, the moment [25:45] The one that blew my mind, the last time I felt this way, just exactly like this, was my doctor put me on Manjaro very early. I needed it. [25:56] What's Manjaro? It's like a Zen pick. It's a GLP-1. Okay. So suddenly, after a lifetime of not being able to lose weight, I lost like 70 pounds in a hurry. [26:06] And I was very dangerously big. I'm still pretty big, but like... [26:10] My health changed. And it was really after a lifetime of being told, like, this is how this works. This is the only way it works. You can only do surgery. There is willpower and so on. So all these rules and this whole social system and things that I heard from doctors, and it was one day they went, okay, [26:27] Beep. [26:29] And it was really confusing. I'm an adult man, and it was really confusing to go from this is the system of the world, this is what weight is, this is what obesity is, and these are the only ways that things can change. And then to hear the next day that actually it kind of was a medical condition, whoops, and then knowing that this would push through the world and this would change the way that we talk about our bodies completely.

[26:59] I just like, I knew in that moment, like, oh, yeah. [27:02] We're not going to put this back in the box. This is going to be very different. People are going to have very strong opinions about it. Oprah's going to do a special, and here we go. And I feel that way about this. [27:12] That we can't process the change, but just a year or two, which is how long it's going to take for like the idea that you can just have code by typing in a box and it's pretty advanced and it does things like ship apps, is nowhere near enough time to process that. Like it's just nowhere near and it's actually going to look like that horizon. It might take a couple of years for people to figure out that they can have any software they want anytime. [27:42] that are floating around. Like my company, Aboard, is all about taking latent software and making it real and getting into people's hands. And so we've been trying to coach people along and they're very confused. And now you're about to see, like, you know that OpenAI is going to build their own and you know that Copilot's going to get smarter and you know that there's going to be Super Bowl ads, if not this year, then next year, about how you can have anything you ever wanted. And I'm, [28:08] We just rebuilt the whole society over the last 30 years around software, right? Like software is eating the world was this whole idea. And now it's eating itself. And so like, look, you're right. Like, are we going to be okay as a species? About as okay as we ever are. Will there still be jobs? Yes. Right? Like I don't, I'm not actually a pessimist, but I am pessimistic.

[28:33] After the pandemic and GOP-1s and Trump and everything, I'm just like very nervous about the human ability to tolerate change. And we've created the ultimate change engine that sits in the middle of our global economy and spews out change like at an unbelievable rate. And we've we just created the number one change accelerator possible, which is move software much, much faster. And so. [28:58] I don't think we're going to see... [29:02] It's not going to be familiar. Parts of it will be very familiar, but I think parts will be very, very weird. And it's going to be really, really strange to watch. [29:09] I love the GOP one example. Um, and it's interesting that you listed GOP ones with Trump and the pandemic, but I assume you're, which, you know, in my world, those are two pretty negative things, but GOP ones, I assume you're, um, you have a positive experience with them. So it's sort of interesting. Change is hard, man. I was in client services for 20 years. It is hard. I still am. It's hard. [29:36] I have a really good product that can really help people. I have an organization that can really help people. I see Claude Code showing up, and I'm showing it to people in my world. Because similar to you, I'm like, whoa. And they're like, well, hold on a minute. And I'm like, no. And it's not me saying I want you to use this. I literally just want to say, it was like this when I was writing. I just want to show you so that you can figure out what to do next. And what I have found over and over in the course of my life is that merely by showing people,

[30:06] They tend to panic. [30:08] They don't want this change. And they say they do. They want the output. They want the value. Everybody wants to be an app developer. But what they want is it to run – [30:16] The way it used to. I don't know if you've noticed this, but every product manager, you know, is now building their own app and every engineer is building their own app without product managers. And the product managers are building without engineers and the designers are trying to figure out how to ship. [30:29] And they're all really happy to get everybody out of their world, right? [30:34] and [30:35] they're pretty sure they're going to be able to capture the value of the revolution. [30:39] And they wanted to follow the rules that used to be there. [30:43] It won't. [30:45] No, you won't. And so you can be, I like, I don't know what we are. Are we, [30:50] all pipeline builders? Are we all coders now? Are we all app builders? And like everybody's having the experience you and I are having who is deep in on this, but we're about to find that everything we created is probably more disposable and less exciting than we thought it was like two weeks from now. And so I am puzzling that. I think it's I think this is. [31:10] Going to be a rough one deep down. [31:13] An exciting one with an enormous amount of good things. And I can't, I'm so excited for everybody to have all the software they ever wanted because that's always been my dream. [31:22] But now that it's here, I'm a little scared. Isn't that interesting? I've been thinking about that too a little bit. If I take a step back and – [31:32] re-round like seven years or 10 years and I said, there's just going to be a thing where you type into it and it just makes whatever you want. Yeah. I would have been like, that's great. That's definitely not scary. It finally happens. Yeah. They've been promising this. They have been promising this for 70 years. And then it just happened. And then you're like,

[31:51] It makes me question if anything could happen that would be an unalloyed good. [31:58] Hmm. No, that's been the lesson of the last like 15 years. No is the answer. And that's. [32:05] I don't know. Like... [32:07] That's also the lesson of adulthood. [32:09] Right? [32:10] And it's also the lesson of working with people. When you work with people, their best qualities are always their worst qualities. You know, I'm good at thinking big thoughts, but often terrible at delivery. So you have to pair me with somebody who's good at delivery. Yeah. [32:24] You know, because I get distracted. [32:30] You know, it's funny that I'm tangential to that. [32:34] The promise of software, if you go back to like the Xerox PARC days, even before LISP programming language and so on, is that we would have sets of composable objects that could interact and that an average human being would be able to learn the system and build whatever they wanted. [32:49] That was the whole point of like Alan Kay and the Dyna book in the 70s. If you don't know what this is, like it's very legible. It's essentially like a laptop that kids can use to build any software they want proposed in the 70s at Xerox PARC. [33:03] Go look at the Wikipedia page. It's kind of what we thought. And we thought that was going to be the iPhone, right? We thought that was particularly the iPad to the point that like Steve Jobs and Alan Kay were kind of like talking about that. [33:18] as the iPhone was being rolled out. Like, "Hey, I think we're getting closer." You know? And it was, the idea was you'd manipulate code in ever more abstract ways. And what happened is LLMs, computers continued to suck and suck and suck and be horrible and never work. And our solution was actually to simulate humans so that they could do it for you rather than make the computer really, really usable or figure out how to make really, really robust code.

[33:45] And there's good reasons for that, but I don't want to go into them right now. But like that people have been trying for decades. And so suddenly we have it. We have the fantasy of the 70s. [33:54] I could sit. [33:55] I can train anybody, I think, at this point. [33:58] To think... [33:59] algorithmically and structurally enough about applications, you know, and there's going to be a lot of [34:04] retooling around how we educate people about what software does. But I think in about two weeks, you could start to build really, really meaningful stuff. And I think in about two years, you can probably build just about anything. And that used to be the work of 20 years. [34:18] That is great. [34:20] It is great. I don't want to like freak out too much. I just spent all Thanksgiving weekend as we're like just ended and I just spent too much time on the computer, I think. [34:31] But I want to I want to stick there because I love this. That's the story of adulthood, because. [34:39] The you're absolutely right. [34:43] And... [34:44] That is my problem with a section of the AI discourse that is, I would say, more the mainstream section, which is has this hidden underlying assumption that anything that could have negative effects. [35:00] is bad and is looking for only those, more or less, as opposed to a little bit more, [35:09] like in adulthood, you're like, there's some really good stuff here and there's some problems here. And it's sort of this like, like,

[35:18] wonderful and terrifying mix of things. And our job is to acknowledge the good stuff and deal with the bad stuff as best we can. And I think that's what's difficult to access when you're at the edge of the world. Oh, okay. I know exactly what you're talking about here. I see it differently. So you've got a variety of discourses, right? So let's take one, which is the [35:43] And the one you're talking about is like very left adjacent, very much shows up on Blue Sky. Right. In some ways, that's kind of my home base. I guess my family, the way I was raised. [35:53] you've got one group that is like AGI is coming, get ready, the computer is God. Okay. And so like, we've all kind of learned to make our peace with them. [36:03] They don't live here in New York City. We're just going to like, they seem good. It's a lot of guys, a lot of polyamory and good for them. I wish they would. Acro yoga, you know. Yeah, and they also really like, they've also kind of all shut up about AGI because there's so much money to be made. Like, you know, Sam Altman cracks me up, right? Because he wants to be Steve Jobs, but he's Steve Ballmer. He just kind of got the wrong Steve. And it's just like, here we go. Okay, commerce, capitalism. That is a hot take. [36:33] Tell me if I'm wrong. I would love for you to unpack that. I think it's a great line. [36:41] Oh, do I even need to? He's a really, really good salesman. He's a really good deal guy. He told us we were headed towards AI Jesus, and now we're getting shopping.

[36:49] Right. Like he's he's a commerce guy. I don't actually I think he's good at that. [36:55] I think Anthropic, it's funny if you compare the two companies. OpenAI is very much Microsoft. Whatever you want, whatever you want. We're going to sell this to you and you're going to have it. God, let me give you more. [37:08] Anthropic is Google. And it's actually funny because look where they're buying their chips. Anthropic is literally buying Google TPUs. I thought you were going to say Anthropic is Apple. No, nobody's Apple because nobody's really – Cloud Code is great, [37:25] It has nothing to do with human beings. It has to do with it. It's still for engineers. [37:29] You can't put anyone, you can't put a civilian in front of that interface. It makes no sense. [37:34] That's true. You just can't. Now, could they get there? Maybe. I just don't think they even want to. I think they want to just accelerate, accelerate, accelerate engineering and let everybody go run off. And then they'll figure out how to productize along the way. [37:48] Whereas, like, I think OpenAI wants to make a play for the whole shebang. They want to be the operating system. [37:55] And the apple in the middle, the people like, [37:59] So what's it going to look? The thing about Apple is it made the computer disappear. So who's going to make the LLM disappear? [38:05] and just sort of align it with what people want to do today. And I don't know if we're even there yet with this technology. I don't think so either. Oh, so wait. So that's group one. Okay, we got group one. And then...

[38:20] Here is my, I'll actually give some advice, which is, [38:26] Silicon Valley in particular dropped this absolutely bizarre thing, told everybody it would solve every possible social ill and didn't really come with a plan. And there were real harms that emerged and people panicked. [38:41] And the harm frameworks weren't clear. And I think what we got to do, because I'm in there too, man. I love this stuff. I use it every day. And then I go on BlueScribe where like 80% of my feed is people saying how much they hate everything that I'm touching all day long. And I get it. I get it because I also hated the tech industry. [39:00] I think you got to just like let them burn it out. There will be people who just hate this shit. [39:07] for the rest of their life. Um, and what you'll find, cause I'll tell you, here's what's wild. And this is actually as someone who's very much kind of on there, I feel I'm on their side. Um, [39:20] I got my kind of progressive type literary types from my, you know, I used to be an editor at Harper's Magazine, right? And so, like, there's a whole world there for me where those people want nothing to do with this. They want their pros untouched by a robot, and they want a certain world and a certain vision of the world to persevere. And this is all noise and distraction from that, just like everything is, just like the tech industry is, just like the web is, like blogging was.

[39:50] care. [39:50] And okay, like – [39:52] That's what they want. [39:54] But then I think there's – but then there's this very tricky thing going on. There's a lot of people who are like, this is just an unalloyed evil and we have to reject it. And at the same time, I'm sitting here in my nice office in New York City, but I'm hearing from – [40:06] And working with children's health charities and scientists and real do-gooders and climate types who are like, this can accelerate our roadmap and we want to do it. We want to use these tools to achieve our mission. And their mission is unalloyed what I believe to be positive in the world. They see the value. They're often coming to it like as scientists, they see the risks and they're like, let's please use it. [40:29] in order to get that done. And they are not, software is not the star of the show for them. Their work is, their community, their donors. And they're like, what can we do to aggregate the data or deploy the platform or manage the content or do this stuff in such a way that we can do more of the other thing we want to do, which is we believe in unalloyed good for the world. And they're super excited and motivated. And so what I see when you're talking about that stuff, [40:59] that I have a really good ethical model for what humans need, and I believe we have to reject this outright. And then there's another group that is like, I believe that, and it's my day-to-day job. And group A is like, keep this out of everything. [41:13] And group B is like, I can't wait to use more of this. And it's very, very confusing. And I think that tension is going to just keep rising. And at the same time, there are people who are like, I'm a professor. I teach research methods. I don't want this near my students. I need their brains to work. And I get that. I actually think that's right. Like, good. Okay. Draw that line. Make them figure it out. They're going to go use it anyway. They know that.

[41:36] But like if you want to put them in a box for a minute so that they actually learn the history of how to think and what to do and you feel that that's important as an educator, I'm not going to second guess you. I respect that. So I think it's trying to find a balance in all this. But ultimately, the balance is like you're there with that prompt and it does something for you that's really useful. [41:55] and kind of knowing what's good and what's bad about it and then going on with your life. Because if you even try to engage with any of the discourse around this technology, you're just in hell. [42:05] which I mean, I'm glad I didn't start a business totally focused on that problem. [42:11] This is why I stay off of Blue Sky. I can't imagine being you on Blue Sky. It's like it sucks. [42:19] I get a funny hall pass with this stuff because I'm an old and, you know, I'm just like, I still get yelled at on a regular basis. But yesterday I was just like, Simon Willison, who I'm guessing many of your listeners should know, is just like, wow. [42:35] the hornet's nest by talking about how AI was changing coding. And I just did a like, he's right, you should listen. [42:42] post and you know like half the people what happens is everybody comes out and they're like yep yep and then the other half are like no there's this one time and it's this and it's that let them fight man let them fight in your mentions i think this is actually a very typical [42:57] Um, [42:59] basically reaction to a paradigm shift and to some degree, um, [43:04] People who have... Who are like...

[43:08] know how they do things and want to keep doing it that way are just going to keep doing it. And it's the same thing. You also got people coming in from the West Coast telling you how it must be done forevermore. [43:19] Yeah. And that's it feels real bad. And they just dismiss your concerns. Right. We're used to it. We're tech nerds and we're used to we're used to nerds just kind of like stumbling in. Nerds never actually fully acknowledge how much power they have in a room. And so they're like, well, why is everybody so upset? This is really cool technology. [43:49] And they're like, oh, whatever, UBI. And like that... [43:53] That whole thing, that's how that comes across. [43:56] It's just this tin ear on the West Coast. And it is pretty hard for people, I think, to be told over and over how it's okay that they're being devalued. [44:06] without being celebrated in any way. And so you end up with stuff like Anthropic having to pay $1.5 billion to publishers, right? Because of all that stuff, you know, it's just like these – [44:19] they feel vulnerable and then they feel attacked and then they're going to use what power they have. And one of the powers they have is to just complain. Um, um, [44:27] And I don't know, I think you got to, we have to own that because we got to keep all the money. [44:32] Well, let's unpack that a little bit. First of all, let's bring in some employees to unpack it with us as the leaders of our companies. Hey, guys, come on in. Let's talk about how we are working.

[44:49] I love the – they feel vulnerable, and if you feel vulnerable and something new comes along, it's like – it's an obvious immediate reaction to be like, this is bad. I don't like this. I don't want this, right? It doesn't help that they all went to the White House and kumbaya with Donald Trump, including Jensen Huang. I mean, it doesn't like help the vulnerable people feel less vulnerable. Let's just – just putting that out there. Anyway, go on. That lost my mom, which is – For real, right? Yeah. Yeah. But I think – [45:19] you're you're you're replaying my thanksgiving conversations um no they're my mom is much uh much she's very proud um she should she should be very proud but she wants me to be careful you better be careful with the league um yeah i think um but let's [45:42] Let's annoy ourselves, you know, in between worlds type people where we like the tech stuff and then we also care a lot about writing in the humanities. And so – [45:52] And ideally, because we're amazing New York tech people, we can kind of be the bridge that's missing between these two camps. And what I want to understand, let's say we're trying to explain. Literally, you and I can do an event. I have a nice space. We can bring them all here. I would love that. That would be amazing. We're going to do that. [46:14] We're going to do an event where humanities people can come yell at us. [46:18] I didn't sign up for that. You're the one on Booskite. You get the help. No, no, no, no. We're doing it. We're going to bring in the angriest overpaid professors from the most expensive schools in America to tell us how bad we are. Here's what I want to understand. Let's take the balanced perspective for a second and say, we want to examine the arguments of the people on the left who are loudest about this being bad.

[46:48] And, you know, [46:49] Um, [46:51] Like, what are the what are what do you think are the actual real bad things that have happened or are happening or will happen that a reasonable person who loves this technology should care about? [47:03] That's a very good question. Let me think for a second before running my mouth. [47:09] Thank you. [47:10] Because I think, look, [47:12] There are a lot of stories and narratives about specific harms. You see them in the paper. [47:17] And, you know, it'll be... [47:22] Chat CPT encouraging suicide in teens. [47:28] I think there's an element, I have a tricky reaction to that because as a technologist, [47:33] I've watched and I'm... [47:35] I'm 51, right? So I've watched like two or three generations of internet technology and [47:41] These harms just spill out at scale. [47:44] And it's really not stopping the harms is not always possible. You have a new technology, [47:51] You see ways that – and I think what happens is you see these orgs, they get – [47:56] They get a narrative of their own importance in the world because they're getting constant positive feedback. The money's pouring in, people are saying, "My God, this really helped my daughter, this really helped my son, we're using this in all sorts of exciting scientific ways." [48:11] And then they're shocked when something bad happens, right? Because there's so much good pouring in and it's coming with so much money. And they're shocked. And then they do like a full court press. And then you end up in this like bizarre cycle where, you know,

[48:25] It always ends up with somebody getting really into MMA as a CEO. [48:30] I really think that's them. They feel so attacked and they feel so vulnerable because people keep telling them that they're kind of evil. They're like, I'm going to become a fucking cage fighter and that's going to show them. [48:46] Kite surfing is the gateway drug to that. It's just a whole thing that happens. [48:51] So you've got this whole cultural dynamic playing out inside of giant tech orgs as the money pours in. [48:57] And it's like a whole thing. And then you've got the press issue. [49:01] desperately seeking for very specific harms to get a story that can turn into a narrative that can be a little bit broader. And you smash those two things together, and it's pretty hideous. And the only way that you resolve that is through regulation and oversight. [49:17] our society is at least a little bit collapsing and it just doesn't seem interested in that. And so, so now, [49:24] What would be... [49:26] a way to do with this. First of all, I don't want to, what would be a thing to do here? First of all, I don't want to put LLMs back in the box. [49:34] I would say that [49:36] When we're talking about harms, [49:38] Not specific harms. [49:40] The lack of provenance is bad. I would like to know what goes into my meat. [49:45] I want some nutritional guidelines as to what's in my anthropic [49:51] LLM. [49:52] and what it's using and where that data came from. I don't want to be surprised by huge copyright cases. I shouldn't be. I should know what

[50:00] I'm using. [50:02] I know that Google is the web, roughly. And Google doesn't go into secret parts of the web, and it honors robots.txt. That is a contract that Google made with the web, and when it doesn't honor it, it's really bad. And, in fact, there have been technologies where Google kind of, like, tried to sidestep the open web, and people got really upset, like AMP pages and things like that. [50:22] Oh, you and I are drinking a Spindrift Tropical Lemonade. Love it. Looks like I am too. [50:30] Great minds. [50:32] Spindrift. The brand of New York liberal tech nerds. God, it's so bad. [50:41] It's a terrible place to be in technology in New York City. The – [50:45] All right. [50:45] So, [50:47] Anyway, coming back to it, right? [50:51] What is the harm that's been done? [50:53] We won't know the real harm, not the specific harm, but the broad. I don't see it as harm. I just see it as change. [50:59] What kind of society do we want to have to deal with the kind of change that is coming? [51:04] A 50 million person underpinning of the entire global economy, the tech industry, the [51:12] You've got giant consulting firms, you've got tech integration firms and software companies. Their core product has been radically devalued. What do we think about that? Who gets to talk about that? [51:25] Like who is going to, the AI folks are going to be like, it's great. It's the best thing that ever happened. Everybody gets their software. I'm going to say that because I'm building a product along those lines. But like if we're going to have this level of change, it almost feels like you're not even, what I think is going to shock people is how,

[51:42] how people see it coming but then don't really plan for it. Like everybody, and that's what actually panics me a little bit, Dan. I like, [51:51] Because people are like, well, you're still going to need engineers for this. And everybody is like, well, but. [51:56] when they see this new technology. And I think we have to start internalizing. Actually, horizons aside, this will change a lot of the ways that people do things. And it might change the way they make money, and it may change... [52:09] what their lives were like. So what's that going to look like? [52:12] And – [52:14] Ironically, I had... [52:15] Claude, make me a... [52:17] prediction model for the future of the consulting industry and write me a little stories. [52:23] What's that? What'd you get? What did it say? Dude, they were really sad. I was like, no, because I literally was like, okay, you know what, Paul? You get a little cynical. Just say mild bearish. Mild bearish. Okay? And it was like Rahul thought that he had made a good choice by going to computer science. [52:44] One after the other, it was like... [52:47] How to draw a Sankey chart. I can share it with you. You can share it with – like I published it as an artifact. Here, let me just give it to you. Let me show you this thing. Please. Hold on because I want you to see it. [52:57] Um... [52:59] One sec. [53:02] There we go. You see that? [53:04] Thank you. [53:06] Yep, I do. Okay, so this is, I didn't give it this title. And in fact, I tried to really hedge, I was like, hey, it looks like AI might really change the consulting industry. And I want you to make a Sankey chart.

[53:19] and tell me. What is a syncy chart? It's one of these guys. Okay, so it's like. This is the IELA chart. Yeah, yeah. Stuff comes in on the left, and it gets turned into work on the right. So like financial services clients, you know, feed in, and then they make this much money off of consulting. So right now we're looking at Deloitte, giant consulting firm, and it does audit and assurance and consulting and tax and legal. Let me see if I can zoom in a little bit. [53:47] Oh, yeah, I just zoomed in on you. Here we go. [53:49] Okay, so [53:52] Mostly, and like I said, I said mild, bearish case. [53:56] It does seem like this could really affect these industries. Just show me kind of what might happen if AI was going. It's kind of ironic to ask Claude. And so I was like, let's look at McKinsey. Everybody loves McKinsey, everybody's favorite company. So $16 billion in revenue, $45K. [54:12] employees headquarters New York City. And in 2024, their revenue is about $16 billion. Now, I didn't have it do deep research. It was just very hand-wavy. So I'm guessing all this is kind of wrong. Let's be clear. Like it's not. But it says that by 2035, McKinsey's revenues, if it loses digital services, are going to get down to $4 billion. [54:31] And so you can see that here if we switch to $4 billion, the whole chart shrinks and we go, you know, let's go back to right now we're making our money through corporate strategy operations and so on. So I had it right, employee stories for each company. And so Alexander Torres. I found everything right. Oh, yeah, everything. Stanford undergrad, Harvard MBA, McKinsey Associated 27.

[55:01] I gotta say, Claude just decided to burn the shit out of McKinsey. Like it just, again, like, [55:08] I'm not grinding an ax here. I was just like, you know, just write little stories about what's up. [55:13] The dirty secret of strategy consulting was that the frameworks weren't magic. They were structured thinking applied to ambiguous problems, and structured thinking turned out to be exactly what AI was good at. By 2027, a CEO could upload their company's data, describe their strategic question, and get a McKinsey quality analysis in an hour, complete with market sizing, competitive dynamics, and three options with tradeoffs. It wasn't as polished, didn't come with the McKinsey name, but it was 90 for 5% as good at 1% of the price. [55:43] McKinsey tried to go out market. We don't sell analysis. We sell judgment, the partner said. We sell access, relationships, implementation support. But implementation was getting automated, too, and relationships only mattered if you had something valuable to offer. Alex made partner in 2029, just as the firm started its long contractions. She was one of the last. By 2032, McKinsey was a quarter of its former size, serving only the largest clients who needed the brand for board cover. [56:13] client, chief strategy officer at Midcap Industrial Company, less prestigious, more stable. She actually got to see her decisions play out, which was novel. Oh, my God. Sometimes she missed the intellectual intensity, the feeling of being the smartest people in the room. Then she remembered it the smartest thing in every room now was the computer.

[56:32] Incredible. Incredibly. I'll share this with you so you can share it with your listeners. But it's a cloud code artifact that I built yesterday for fun. [56:43] somebody works at one of the firms and they're like, eh, I got the numbers a little bit wrong. And then they were just really quiet for a minute and they went, [56:49] Interesting. [56:51] But yeah, Accenture, Rahul had spent 15 years. Armies got smaller. They disappeared. And then at the end, he took a buyout at 45, started a small consultancy, helping mid-market companies with the human side of AI adoption, change management, the squishy stuff the AI couldn't do well. It's a living. Some weeks he almost believes he's adding value. Mild bearish. Anyway, you know, I think – [57:17] Is it a little ridiculous that I'm using AI to explore this particular part of the world? Sure. Is it... [57:26] Do I buy this? No, because I actually – I do – [57:30] your horizon thing is real. Nobody knows what's on the other side. [57:34] Right. This is a the mild bearish case is is that an economic contraction won't have a sudden flowering of new opportunity and that people won't figure out what to do next. And they'll just be captured in this kind of like shrinking world while robots do more. [57:50] For the rest of our lives. And that's not actually how humans and societies work. Like, but, but I do think it is a change at that level of magnitude that we're going to have to react to. [58:00] I agree. I love that. I think that's so interesting. And I think it's actually a good...

[58:06] Um, [58:08] example of why language models are so powerful and what makes them, [58:12] sort of special. And in that is an interesting example of why I think consulting firms, oddly, are going to still be valuable and important. Let's bring that in. Let's hear what you got. Great. So the... [58:30] The thing that... [58:33] Bye. [58:34] It seems to have picked up on in its mild bear case is that you can get the analysis and the judgment for, you know, 1% of the cost. [58:46] And obviously, the thing is like, oh, it's not buying analysis and judgment or whatever. But there's something, I want to just stick with the analysis and judgment for 1% of the [58:56] I have done this too. I have... [58:58] put all of our company financials into Claude and had it write our investor update and it did a fucking phenomenal job. Yeah. I mean, that's so good. Anything kind of bureaucratic, it's just magical. Yeah. And I've also done a lot of strategy stuff with it. And I, [59:14] I think you can break up human thought or just – [59:22] ways of solving problems into two broad categories. [59:26] In one category, there's a right answer, and it's extremely rare, but there's only one. It's a needle in a haystack, which is what traditional programming is actually quite good at. [59:35] It's math, it's logic, it's all that kind of stuff. - Give me an example, like just kind of like Excel spreadsheet

[59:42] Exactly. Okay. Yeah, yeah. [59:45] uh, you know, uh, how profitable were we, were we this, this quarter is later. There's like a, you know, there's a set of rules that you can apply and there's a, there's one right answer because you have very precise definitions of what right is. Make me a pie chart of what we're selling. Exactly. Um, and on the, on the other end is, um, um, um, um, um, um, um, um, um, um, um, um, um, [1:00:08] And the literary analogy is the Borges story, like the Library of Babel, where it's like every book is there, but there's infinitely many books between where you are and where you want to get to, and they're all nonsense. So you're just always in nonsense, basically, unless you've artificially constrained the search space. [1:00:31] The other... [1:00:33] the other thing, [1:00:35] you know, I don't know, branch of human thought or way, a way to think about the way the world is, is, um, instead of this live, this infinite library where, um, [1:00:47] you're sitting in this sea of nonsense, but you know that if you get through enough nonsense, you'll get to the right answer, and there is a right answer because there's only so many pieces of hay between you and the answer. You're going to get to the needle in the haystack. On the other end is a library where every single book [1:01:07] is meaningful and has a story. [1:01:11] But there are infinitely many books between where you are and where you want to be.

[1:01:15] It's not countably infinite. It's just you're just sort of in this enchanted forest of stories that you can go read. And each of them has a plausible sounding answer. [1:01:27] And you have to use your own human intuition or judgment and feedback from the world to move your way to generally the right direction. [1:01:38] area, but there's no right answer. And, um, when we're thinking about a question, like when we're thinking about a question, like, um, [1:01:48] What's going to happen to consulting businesses or what strategy could consulting businesses take or what's the mild bear case? [1:01:56] I think we're much more likely to be, especially if we're looking at a Claude answer in the regime of there's infinitely many meaningful stories. And we're looking at one of them, but sort of treating it like it like it's the this other one where it's like there is a right answer and Claude just found the right answer. [1:02:14] Because if you change your prompts slightly. [1:02:17] Claude could write you a great story about why consulting businesses are going to do really, really well. [1:02:24] It was a mirror of my anxiety at the moment, but you're absolutely right. I'm one, literally five minutes away, and if it hadn't told me that I was running out of Opus credits, I probably would have done it. Which I wasn't, by the way, a little product problem there in case people from Anthropic are. I had 20% left, and it's like, hey, we're almost done here. And I'm like, really? Because I have a problem, but it's not that profound. So...

[1:02:48] Anyway, but yeah, you're right, right? Like the mirror... [1:02:52] The mirror of it [1:02:54] And that's the tool of it, and that's a really hard thing to convey, because what people are used to is putting words in a box, like with Google, and getting a response and being able to trust and evaluate that response. [1:03:06] You're putting words in a box and it's translating your idea into another form. And that is simply going to mirror what was inherent in the idea as according to the rules of the LLM, as opposed to actually being an answer to your question. But it's suspiciously like an answer. And so this is such a subtle thing. [1:03:27] And again, this is where I get, if you ask me kind of what, [1:03:31] Going back to harms, the greatest harms that the LLM companies do, and I actually think that Anthropic does a better job here, [1:03:38] is to anthropomorphize the bots. [1:03:41] That has caused like the fact that it looks like it's answering rather than statistically translating a question into an answer and then that answer into code and then that code into other code. If they had emphasized translation as opposed to chat. [1:03:57] I think we'd be in a much better place with this technology, and I think we'd have a better understanding of it. [1:04:02] What would that look like from a UI in a way that would make sense? [1:04:07] You know, I think what would be useful is instead of a, it's a good question. I don't have an [1:04:12] immediate answer, but my instinct is you would keep [1:04:18] You know, I mean, this would be really nerdy, but more like a GitHub commit log. Like...

[1:04:22] You put this in, and actually this is what Claude Code and other things end up looking like, which is here was our state. [1:04:30] And then I evaluated it. [1:04:32] And I did a bunch of queries in my internal database, and I transformed it into this new state. I've saved the old state because we want to go back to it. But here we are now today. So we have a whole new kind of context. And we've actually changed the way that we're working. Where do you want to go from here? Well, I want to do this, and I want to do that. Great. I'm going to update the state again. And I'm going to keep a really clear log, and I'm going to keep the relationships between where I was when we started doing this and where I am now. [1:05:02] this works and how [1:05:05] to do this and how to do it repeatedly and how to do it on guardrails and how to do it in such a way that you have confidence that it will be the same today as it was yesterday. [1:05:15] And if you gave me that, [1:05:18] which [1:05:20] Does an average human being really want that? I don't know, but I do. [1:05:24] right? And is that going to work better than chat? No, probably not. It probably won't get you 700 million users, but, um, [1:05:32] I think that LLMs are complicated. It's really hard to learn how they work. [1:05:37] I actually had ChatGPT write me a medieval quest in which a – [1:05:42] a magic spell was said, tokenized, and sent through the different layers of the LLM. I highly recommend it. Like, find an analogy that works for you, and then make it explain LLMs in the context of, like, a quest or a journey. Yeah, because otherwise you don't – there's a lot of things that just get –

[1:05:57] go missing, like the fact that there's zillions of layers happening and each layer is kind of like talking back and forth to the other layers and sort of, it's not like your question is being answered. Your question is being broken up. [1:06:11] and spread across sort of like [1:06:14] a zillion metadata bases that are then coming back and forming something that looks like an answer [1:06:22] but without consciousness. And like that, you know, I don't know how to explain that to people. I got to, I got to stop you there. I, what there's, I have someone, we could do a whole other podcast on this, but I just want to, let me, let me respond. And then I'm curious, I'm curious what you think. And then I think we should, we should definitely do a part two of this conversation, but what I hear, what I'm hearing is, [1:06:44] is... [1:06:45] We could do a live event, too. That'd be fun. We could record. Let's do it. Yeah. We can invite all the liberals and they can yell at us, as you said. Yeah. [1:06:56] Um... [1:06:58] What I hear you saying or almost yearning for sounds like traditional code. [1:07:04] Yeah. [1:07:05] You know what you're going to get. You know if you do it today, it's going to be the same as it was yesterday or tomorrow. It's going to be the same as it was today. It's very traceable. [1:07:15] And I also hear a little bit of like it's not actually giving you an answer. It's more of like a stochastic parrot type thing. A little bit. I hear a little of that. Keep going. I'll respond. Keep going. Yeah. [1:07:27] and

[1:07:30] My feeling about this is actually... [1:07:35] we are extremely well equipped to work with the way language models work. And we're much better equipped than we are to work with code for people who are non-experts. And that's because the, and I think it's actually a good thing that they're anthropomorphized because, um, [1:07:53] we have models, very advanced models for how to deal with human beings. [1:07:57] And human beings are like this. They are squishy. They do not necessarily give you the same answer today as they did yesterday. And there are specific kinds of people that are particularly like language models. So people pleasers as a people pleaser. [1:08:15] I'm very much like a language model. I have a lot of empathy for my language models. That's true. Yeah. And, and, and you, you, you get that sense from a people pleaser where like, you know, [1:08:26] other people pleasers in my life. Like I can just see when they're kind of like doing that thing where they're just telling me what I want to hear. And I'm like, stop. Like, I just want to know what you think, you know? And so I think we have a lot of basically innate biological machinery for dealing with this kind of interaction and that, um, yes, there's, there's an adjustment period. And yes, like, for example, if you, we should be detecting if you're in delusional state [1:08:56] go along with your delusions. Right. Um, but I think people will, will very naturally learn because there's a, a, a really, uh,

[1:09:07] close analog, they'll very naturally learn to use it and then very naturally learn to separate it from other types of things and put it in its own sort of category. And I think that's why I think it is actually kind of genius that it is a chat and it is a little bit anthropomorphized and it is. [1:09:25] interacting in that kind of way. [1:09:28] Thank you. [1:09:30] Hmm. [1:09:36] I don't know. I see it. I get it. [1:09:43] I just don't know if we can handle this, man. I don't know. I think the humans are pretty. When I'm talking about making it reproducible, that's me as a kind of. [1:09:53] Programmer, outliner type. I get that. [1:09:56] But I think what's... [1:09:58] What's tricky and what's thorny is when you talk to businesses and orgs, [1:10:02] ones that really want to use it, not ones that are just like trying to figure out what generative AI means. That lack of reproducibility is really scary because they need to know that something – [1:10:14] You know what I think? Here's what it sounds like you're saying to me and push back on this. [1:10:19] Paul, it sounds like you want it to work like computer. [1:10:23] But it doesn't work like computer. It works like new thing. [1:10:27] And you should get used to new thing instead of expecting it to work like computer. [1:10:34] It's not quite right. It's close. The slight...

[1:10:44] Um, [1:10:45] The slight change I would make is it works like new thing. [1:10:50] That is very close to thing that is older and more innate for you to interact with than computer. [1:10:57] Um, [1:10:59] And that gives you a lot of innate biological, cultural machinery for how to deal with new thing productively in a way that you actually did not have with computer. [1:11:15] Um, [1:11:16] And, you know, [1:11:17] Comes with costs. [1:11:19] It's not cost free. You may confuse new thing with person. [1:11:26] But... [1:11:28] It is also part of its power and beauty and part of the reason why it has been adopted so closely. [1:11:38] heavily and makes me optimistic that we will, [1:11:43] also start to naturally separate out new thing into a, into a clearly new category that we know how to deal with because we know how to do that with people. [1:11:52] We know how to do that with the people in our lives who like, [1:11:55] act a certain way we know we have to like deal with. And that's why actually I think some of the outrage or some of the news articles or whatever is productive because, you know, [1:12:05] It's the only way to, or it's not maybe the only way, but it is one good way to get people to just like pay attention and just be like, okay, I got to like be a little bit suspicious of chat, but I'm still going to use it. Um,

[1:12:16] And so I think that's, you know, I would write the articles differently. I would write the headlines differently, but I think, um, [1:12:23] What we're trying for is some... [1:12:27] um, some way to differentiate between person and new thing. But I, I think that's a productive process that's going to happen. I mean, um, [1:12:37] Interesting. Okay. I'm puzzling it out. [1:12:40] Because what is my actual my criticism here is that. [1:12:44] But here's what I want. What I want if I am a business or a not-for-profit or I manage a lot of electronic health records, if you want me to use NewThing, [1:12:56] I need to know that it works like computer because I trust computer. Computer, [1:13:01] is encrypted and saves the data and it's good. And you're telling me the new thing will let me have more of this, but I need to know that it's going to be the same today as it was yesterday as an interface, as a way to get to that stuff. And maybe the way to get to that stuff is you get the new robot to write the code, [1:13:19] And as a result, you have this very reproducible environment. Maybe it can stand up things that [1:13:25] repeat. [1:13:26] But [1:13:27] That ambiguity, it's not really just my ambiguity. I think that's the ambiguity that a lot of – [1:13:32] organizational thinkers are dealing with, right? Like, how do I trust this? I know I can do stuff with it, but how do I trust it? [1:13:42] And what you're giving back to me is like – [1:13:45] At some level, it feels like you're saying you can't because it's like people.

[1:13:50] And companies run with people. Yeah, but they'd love not to. See, this is the fantasy, right? So take a second, if we have a second, and tease this out, because I think this is really important. The fantasy of this technology, which I think I agree with you is not actually what it's for, is that it will give me the interface to human beings, but the discipline and predictability of the computer. [1:14:15] you [1:14:16] And that isn't working yet. Absolutely not. And what's happening, I do think that OpenAI is saying, just give us a minute. [1:14:24] Just give it. We're going to get you that. We're going to get you the people that you don't have to pay that do exactly what you tell. We just need a little more time. [1:14:32] And at some level, I feel like that's where AGI has landed as a concept. [1:14:39] a cohort of disciplined bots. [1:14:41] Where do you think we're going? I'm saying this. I'm sort of watching your face do funny things. What are you thinking? Well, I have a whole AGI take. [1:14:54] But... [1:14:55] The really important thing is do exactly what you tell them. [1:14:59] And exactly what you tell them is... [1:15:02] That's the whole ballgame. [1:15:05] like what are you going to, what are you going to tell them? Um, um, [1:15:10] And I think – [1:15:12] The way that our intuition fails us is – [1:15:16] Well, if it does exactly what I say, it's going to be the perfect thing. [1:15:20] And that's actually just not true because you often don't know what to say. Like it's a process. It's a creative process of figuring out what to say with experience with other people with the machine.

[1:15:33] Yeah. [1:15:34] And I think also – [1:15:38] Um, there, uh, [1:15:42] That is the actual value of this thing, is that it generates constructive confusion. [1:15:48] Thank you. [1:15:49] And you have to address it with it, but then you can iterate through confusion and get to goals. And that is very, very real. And it is not... [1:16:00] Saleable. [1:16:01] That's not what anybody wants to buy. [1:16:04] I think that there's – [1:16:07] So we do a lot of consulting too. [1:16:09] with big companies. And I think there, there is, there's room for AI inside of big companies. However, I think, [1:16:17] Thank you. [1:16:17] It may actually be that, and this should actually be a positive thing if you're afraid of AI adoption being too quick, is I don't think that you can – it's very hard to be totally AI native retrofitting into a big company. I just don't think that that happens, really. [1:16:37] And so even though – Explain that because, I mean, literally, that's kind of – we're trying to build that bridge, and it's hard. I think I know what you're talking about. [1:16:46] The exact thing that I'm talking about is exactly what you're saying. [1:16:49] exactly what you're saying, which is like, well, they want it to be, [1:16:52] predictable and do the same thing today as it did yesterday. And that's just not how this technology is. [1:17:02] And so at its best, like I think there is a way to make things very predictable, but you're saying like at its best. Yeah. At its best. That's not what it is. Okay. And so, um,

[1:17:13] So big companies can use this and can start to adopt it, but because they have all these [1:17:19] forces and constraints that make it difficult to use things that can't totally be trusted and are totally new. It's difficult to use it to its maximal extent. [1:17:30] Um, but I think, so a, I think that will, uh, lead to less change than might be intuitive, um, to those of us who are sitting around at Thanksgiving being like, holy shit, like Ovis 4-5 just changed the entire world. [1:17:43] I have this debate constantly with my business partner because I'm like, man, that's it. Death is coming. Just shut up. Have you seen bureaucracy? And he's right. I've worked with some of the largest bureaucracies in the world. It takes a long time. [1:17:59] We were up for a project with America. [1:18:04] years and years ago, Obama era. And they're like, "Yeah, God, if you guys could do it, we could give you 20 grand if you could just take an Amex." And we're like, "We'll do it. We'll help America." And then a week later, they call back and they're like, "Nah, we're just going to give the Navy $2 million." [1:18:20] It was for like essentially a glorified RSS feed reader. Like it wasn't. [1:18:25] It would have been like a $50,000 project. We were going to take a hit. [1:18:31] But it's madness, right? And so like the largest bureaucracies have never had a sense of value [1:18:40] being and money sort of like and the actual delivery being all that way, all that connected as much as like an individual developer might feel. So I think, excuse me.

[1:18:50] I think you're not going to change that. I think that is right. [1:18:54] The only thing I think, though, Dan, is like... I want to finish, though, because I think there's this other component to that, which is pace of change is slower. But companies like ours that are right now are about 20 people, [1:19:08] like sub 20 people that are growing up in this world where every single person is using cloud code across the organization for every single thing you're you're creating all these new primitives for how to work with this squishy technology that is not about how do we make it like so predictable that it doesn't take risks it's like how do we do the most we possibly can with it because um [1:19:32] Okay. [1:19:32] Because we're small enough and young enough that we can take those kinds of risks. And those kinds of companies, I think, they're only a small number right now, but they're going to be a lot of companies like that over the next five or ten years. And they're going to become big companies and be acquired by big companies. And so that's the... [1:19:53] That's the other side of it is instead of trying to make the technology legible to someone who's like running a multibillion dollar company. [1:20:04] you can, you're actually going to get the best out of it by making it like the most useful thing for these, this like small group of early adopters that are figuring out, like, how do we use the squishiness to our advantage? [1:20:13] Yeah, I mean, I think – [1:20:17] I think there's going to be, it's like anything, it's so big and this space is so big. It was already so big. And we're dropping such a big change into it that it's going to express multiple different ways. I completely buy that there will be lots of AI native orgs, especially now that I'm seeing CloudCode and the actual promised future of accelerated delivery is here.

[1:20:42] Our thing, too, you can build a business app in five minutes and it used to be five months. [1:20:47] and that's true of 3D rendering, and that's true of all these categories that were really, really complicated before. [1:20:56] And so I think there'll be this huge layer of acceleration from relatively small organizations that can deal with that, take it in, learn it and apply it and have a desire to like share the value. They want to like do more, get paid less, but move faster. I think like there's huge opportunities there. I think where people are screwed is if they're like, cool, now I can engineer 10 times faster. I'm going to go on vacation and I'll just get all my work done in like five minutes and nobody will know. That is going to come by you. [1:21:23] But I also do – I think, though – [1:21:28] It's too big of a change, right? [1:21:31] And people are going to want some of that for themselves. Like I'm just sort of thinking about, [1:21:36] really big orgs I've worked with where the engineers just say no all the time. And the CEO is really frustrated, but that's just life. That's just how it goes. That's what it's always been like. And then somebody shows up and they're just like, it doesn't have to be that way. [1:21:52] You know, you can have everything. That's going to feel so good. It's going to feel so good. And they're going to throw it by the wayside. It's going to be like a live, laugh, love kind of like trip to Italy for them. They're going to just, you know, they're going to abandon their family because they can suddenly, like, the supply chain SAP integration that was scheduled for 36 months now takes three. Oh, my God.

[1:22:22] big enterprise software, but they also don't have CTOs. They still know what to do in the middle. And they can have really good tools now, which means for them that instead of implementing Salesforce, they can buy a summer home. [1:22:36] That's sort of where that equation plays out. So I don't think... [1:22:40] Because what you're saying here is all true up until the point that you realize that a vast amount of spend on technology goes to like five companies. And everybody kind of hates those five companies. Like – [1:22:53] Unless they make money from them. [1:22:56] They hate them. Like they come to us and they say, I hate this company and I will do anything to never work with their software again. [1:23:05] Given that being out there, I think there's a lot of drama ahead as people decide if they want to spend millions of dollars on SaaS or not and sort of heavy enterprise builds. [1:23:19] I think it's kind of a yes to everything as well as status quo because it's such a big space. It's not going to change. But I think we've got to watch the margins. I think stuff is going to shift really weirdly in ways that we weren't expecting. [1:23:31] I agree. And I think that's a great place to leave it. Paul, fantastic conversation. It was really great to get to chat with you. [1:23:38] Yeah, let's hang out, Dan. I would love to do that. If people are looking for you, where can they find you on the Internet? They should check out our website, Abort.com. We have a really, really nice – think of it as like super pro vibe coding platform that lets you build stuff. But we build it with you. We don't just give you a tool. We make sure that –

[1:23:59] We have good product managers. We call them solution engineers who listen, and they will help you out. So that's enough shilling. You can send me an email, paul.ford at aboard.com. You can find me on LinkedIn. You can find me on Blue Sky. I'm off of Twitter. All the regular places. I'm pretty easy to find. [1:24:14] Awesome. [1:24:15] Thanks, Paul. Yep. Anything you need, let me know. [1:24:45] is a roller coaster of emotions, insights, and laughter that will leave you on the edge of your seat. [1:24:50] 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. [1:24:58] So do yourself a favor. Hit like, smash subscribe and strap in for the ride of your life. [1:25:03] And now, without any further ado, let me just say, Dan, I'm absolutely hopelessly in love with you.