Fable 5 is taking over, AI isn't getting cheaper, AI backup plan and WTF Loops? | Ep 17

[00:00:00]Ladies and gentlemen, you are tuned in to rate limited podcast with your host Ray Fernando, Adam Larson, and Nathan Snell. And together we are now the new rate limited podcast. Um, Eric Provence has graduated and has now been adopted by the OpenAI family and uh, we huge congratulations to Repor and Eric Provence and uh, it's really amazing that he's been able to be a part of our our show and uh, we're going to try to see if we can sneak him out from time to time or maybe see if we can get uh, permission from some of those folks because he's just really deep in the bowels of the AI movement and uh, has been able to provide lots of value. So, please drop any comments below. Uh, and, uh, you know, wish him really well and, and we're really excited about the stuff that's going on there as well. Uh, but joining us today, we have, uh, Nathan Snell. And, uh, we also have, you know, Adam Larson, who, uh, I'm just going to hand the baton to because, uh, Adam actually knows him very, very well, and it's going to be a really exciting episode from we're going to talk about Fable 5, obviously. Uh, what's going to happen with all of our jobs when it's gone. No, I'm just kidding. This is not a doomer podcast. This is here to like really talk about building. If you're here because uh someone probably sent you this and they care a lot about you because we're here to really build and kind of figure out what this next movement of things are. Uh our backgrounds we have probably decades of experience in the industry with building software and working on so many intricate systems. I think combined all of us has probably touched our software has probably touched a billion people's devices or something like that. So it's really exciting. Uh you'll get to hang out with us and kind of have this like live conversation. Uh so without further ado, Adam, please go ahead and take it away. All right, Nathan, thank you so much for joining and Bray, it's always a pleasure. Nathan, why don't we do a quick 30 second background on you? So, Nathan and I have been working together for about eight years. Uh, he was a co-founder with me for the company Railon that was acquired. So, Nathan has been very AI forward from the very beginning. He will tell you he's not a coder, but he is actually a coder. Uh, back in the early days of Railon, him and I were both writing code together to get that company off the ground. And with AI, like it's really unlocked a lot of the stuff that he does. He leads a some pretty big initiatives on the AI side today. Has a lot of insights into how to design and build products. So Nathan, anything you want to add there?

[00:02:14]>> No, I think that's uh yeah, that describes it well. Definitely AI pills or AI Maxi depending on on how you want to describe it. So it's I mean you can't you kind of can't see around, but I've got like my local LLM machine. I've got another machine. It's like my goal is I want as many agents running as possible for as much as possible. So here >> this is why it segus into a an amazing first topic. So early on or late in 2024, early 2025, Nathan, myself, and a couple other uh folks that worked with us at Railon, we had some major debates.

[00:02:44]One of the debates was about the cost of AI. There was a big firm belief that the cost of AI was going to go to zero. And I remember arguing very adamantly against that. So Sam Alman just came out about a week ago and said for the very first time, AI is actually AI costs are actually a huge factor. and he's never seen this before. It never came up until now. I think companies more than ever are trying to rightsize based on the ROI they're getting from it. Uh so Nathan, what are your thoughts on that? And it's okay if you want to say that I was right and you were wrong. I'm okay with that as well. [laughter] >> So uh I so I don't know. I'd say um I still contend that I think that uh certain AI prices are going to go down.

[00:03:27]I mean, I think the the original part of that argument, right, was uh which I which I think still holds true. So, I'm I'm not I'm not ashamed to say that you're right should it happen. We'll see over the next few episodes. Uh but I do think right the continued frontier models, I think we'll see those prices go up. But I but at the same time, right, if you look at it, uh Google just announced or I think they're rumored to announce the actual the price drop on their plus plan, right? So I'd say like at the same time that you know that Altman is talking about how prices are going to be going up you also have Google and others dropping prices right and sort of and taking I'd say certain models right probably not quite the same Frontier models but certain models down in terms of the price uh and what it costs or you know to I guess get access to them and that sort of thing. So you know I think if we're talking again on on kind of the token cost overall I do think we'll continue to see elements of those prices go down but also as we look at things like Fable or Mythos or others. I mean, I think as models become more kind of nichely focused, um, you know, I suspect we'll see some of those prices, you know, go up. Um, but I don't know. I still tend to think that we're going to see overall overall access and cost, uh, going down.

[00:04:32]>> Interesting. Ray, what's your take on this?

[00:04:35]>> Um, I look at it in two different perspectives. One is like the talking point that he's throwing out that they want us to anchor to, [laughter] right, to actually discuss the point about costs. And you know, I think like you said, the costs are just going to go up for frontier knowledge. But the the the back side of it is like as the people who actually are the controllers of the market. They set the price. So, you know, I don't think they're crying uh to their investors saying, "Oh my god, we're not profitable." I they I think they can see the writing on the wall.

[00:05:04]They're extremely profitable. Um this is just hearsay. And this I'm not speaking for anyone. I'm just kind of a spectator in this whole thing. I don't have any skin in the game. I don't have any like back-end investments in any of these companies. Uh, you know, this is just us talking as as like a for me just talking personally. And um I I'm just really curious like at what point that things kind of start to become embedded into an operating system. Apple just announced the iOS, you know, versions of 27 and they're doing their own custom infra, partnering with Google, doing some really cool stuff, making much smaller models that do really cool things that are embedded into the OS, which to me is like, oh, I didn't see that really coming from Apple. Uh, and you know, for them, the costs are like sort of narrowly scoped and um sort of integrated and taking advantage of the existing systems. So I I think there are different architecture plays here and I'm really curious. It sounds like it's a much more nuanced discussion in terms of costs for things.

[00:06:03]So I think like onetoone infra, you know, >> intelligence stuff. I'm just going to send a blast of documents for a law specific task may cost a lot because you're going to expect higher um you know degree of like um you know technicalities that you need to actually get right for case law versus something that's just like I'm vibe coding GTA 6 or something like that you know in one HTML file you know maybe not that much of a cost over time you know so I think that's probably where people are going to start these businesses I'm curious for you guys like how are you guys scoping work for tasks to be done.

[00:06:39]>> Yeah, I think Gray like you make some good points. I think I I joke that I agree. I think there's a lot more nuance to this entire thing. Like there are going to be smaller models that >> but from a coding perspective it does feel like it's just up at this point u from a coding perspective just to be very fair and one of the things that I think we missed early on I know I missed I I had kind of saw that the companies are highly subsidized and coders are always going to want the best model. You can see that with Fable, which we'll talk about a bit here in a minute. But what we missed is like the length of these agent loops that are actually starting to be had. And I think this is a very interesting segue into our next topic. Um, which is around and maybe we'll close this out for a second before we jump into it, but which really is around the craziness that people are going into around token maxing, writing loops that run. I' I was talking to somebody and they've been running one for 35 hours, which is just mind-blowing to me. Um, so anyway, when you start when you're a company and you have people employees doing this and you don't see the ROI on the output of all the spend that's actually happening, how do you look at that, Nathan?

[00:07:48]>> I mean, so I mean, I'd say for me, I mean, I think there there's two parts of it, right? It's like one on a very personal level, like you with employees, you know, where I have like where I see their token budget and everything else.

[00:07:58]Um I mean like honestly like I internalize it as like the leaderboard where I'm like actually I want to see the usage. Um because like I do see really strong ROI. Um you know now like if I didn't you know then then I think to your point like if I was not seeing the ROI then like then I do think that it begins to become more of like a budget management thing right where it's like hey like maybe it doesn't make sense to increase your limit to where you can spend $5,000 a month on tokens or you know or 10 or whatever it might be and kind of having it uncapped almost in a way. Um, you know, I do think there's a point, right, where like where productivity versus cost becomes, you know, becomes a factor. But at least today, like I haven't seen that challenge. Um, I think when it hits, you know, then it then kind of like anything else, it just becomes a factor of like, okay, it probably doesn't make sense, you know, to have, you know, a token allowance larger than X until sort of that ROI returns and that sort of thing.

[00:08:45][snorts] >> Yeah. Ray, do you have any any other thoughts on that? Um, I'm curious about you guys in like harness engineering because I know that like part of the reason why some of the limits started to get actually set were from these Korean kids that I actually met um almost like 6 months ago plus. Like they were literally spending two plus billion tokens a day, but they automated the whole sweet workflow. And one of the guys is a quant trader. So he basically ported over all of his methodologies and like made this crazy system. I don't think he realized how smart he was, but like made a whole agent operating system and, you know, just slid in whatever provider they could do work for.

[00:09:23]>> That seems to have evolved when I started talking to the folks at Factory AI, uh, cuz they made the whole Droid missions thing. So, that thing can actually run for several days and it creates tests and it kind of loops on itself. Uh, and it's all like, you know, basically done via prompting. But what's crazy about it is like the longer that it runs, the more that it kind of starts to keep its own guardrails because their agentic harness is so well kind of built out from the ground, you know, going up and stuff like that. And then I'm seeing latest with the latest versions of clawed code and stuff where it's like starting to do that launch sub aents to keep context really small and these different companies are already sort of optimizing that maybe because they understand that uh a little bit more of these workflows. But I'm just curious about your guys' take on that like you know uh are they going to be the bigger factor here for aiding costs or how are you guys thinking about those things too?

[00:10:12]>> So so what's interesting to me is I I look at all the companies like um you know Uber came out recently and said that they blew through their token budget in four months but they didn't see an output um they didn't see an output relative to how much was spent.

[00:10:26]>> I talked to folks that I've worked with in the past. their company went from all in on AI to like, hey, we need to be a little bit more conscious about what we're spending here.

[00:10:36]>> So the So what is ROI? Like is ROI us saving time as individuals or is ROI actually getting things out to the customer that equate to revenue? I think it's the latter. I think like if we're not able to actually drive more revenue for the company, there is no ROI. And I can't really think of a company that has actually been able to crack that to a enormous scale yet other than like the big players like Anthropic and and OpenAI. I don't think we're seeing we're you know companies are spending many millions of dollars on tokens and we still are limited by the same factors which is deciding what to build how to build making sure customers are happy they're getting what they need on it and I think there's a lot more to to to figure out here on the ROI side when it comes to the particular case you're talking about Ray I think it what's interesting about that is like how are they affording that like I I don't understand like you know it's it's wild to me like I just don't understand >> like you don't just have a trillion dollars in your I mean, [laughter] bro, you're broke. Like, come on.

[00:11:39]>> What about you, Nathan?

[00:11:41]>> Yeah. I mean, I think you're right on the ROI side. And I mean, to me, there's um and I'm again, I'm probably somewhat biased just like, you know, as you and I are even like actively building agentic, you know, agentic, you know, products is there's an element too where it's like I think to build a really great agentic product, you have to be a really really active user of the tooling, right? So like I also think that there's that element where it's like you know if you're a company and you're not building like you know agentic products as a whole then I think that sort of you know that ROI value question that you brought up Adam is spot on which is like okay like are we actually delivering you know more you know more impact to a customer that's driving more revenue and ROI for them and for the company and if the answer to that is no then like something needs to be looked at right and and like and I don't know maybe I'm too maybe I'm too bullish on AI but like I would even say if the answer to that is no it's probably not a harness token. problem, right? It's probably an operational system problem, right? Because like at the end of the day, I'd say like working in an AI native way, right? Not even building an AI product, but working as an AI native organization, I think done properly ends up moving the bottleneck kind of as you're describing, right? So like if the bottleneck is still in engineering, you know, then like it's, you know, then it may mean, you know, and and often times means that like that um like that it hasn't been sort of adopted really well yet or like the right systems haven't been put in place such that pressure can then be put on the design team, right? Or then onto the product team and so on. So I think to me it's like you know I would almost liken it to it's probably not like an AI token challenge as much as it is an operational challenge that sort of is manifesting itself in like an overusage or over reliance on AI in in such a way to where it's not actually delivering on the results right because at the end of the day like an agent isn't responsible for delivering the results the people are today still as much as you know as much as we all might like to be like oh yeah an agent's just going to do everything like I'm still on the hook for delivering particular results and impact and like if I don't do my job it's not the agents fault, right? It's it's my inability to properly manage the team or drive the right processes and that sort of thing. So, that's sort of that's kind of my thinking.

[00:13:41]>> Yeah. I think my my closing thought um on this entire thing, it is relatively simple. I think like at the end of the day, everything's going to be rightsized and we're going to end up start spending the the money that needs to be spent there. And it's just going to be interesting to watch like how all this ends up playing out long term. And in my in my mind there's a bit more hype and and when we think about like the pro so I was working with one of the big AI companies and we there was a bug that needed to be fixed very simple bug and like a relatively simple bug and their their comment was well that's not on the road map we have it on the engineering backlog okay but if you have all the AI in the world how is there a backlog like why is there a bug that can't be fixed right now So until that goes away, whatever is stopping that from being the case, I don't see like the ROI being at the level that, you know, it's being touted by like there's that company that did a 100x organization. You know, they wanted to do 100x output. So it's interesting that it should be a goal we strive for, >> but we also need to be realistic that the bottleneck isn't on the code right now. It's on the operational side, and we need to figure that out. And I don't know if AI fixes that. uh very easily right now. Yeah, I mean that's that's one of the things that I've uh you know even internally with like you know at you know add into it you know and even with sort of friends that are running companies one of the big sort of distinctions I've tried to drive is that bifurcation of like AI helps with the mechanics right so like it it makes the mechanics of doing work faster it doesn't stop the operational inefficiencies right so it's like you know you you can you know and even like for example on the product management side right it's like we can have PMs that do tons of stuff really quickly and they vibe prototypes and they do research and they do all this and they can be ready to go but then if operationally they have 17 report ups and they have to do you know program review like and all these other things like like AI can't solve the operational inefficiencies u you know it can it can help sort of make it can drive efficiency but at the end of the day it still requires somebody saying hey we want to operate as an organization in a more lean way or more efficient in these ways and like it's a person that has to make that decision and then try to use AI to like to solve maybe whatever gap they were trying to solve before but But I think you're exactly right of like mechanically AI is great. Operationally like there's change that has to happen to really drive results there.

[00:16:04]>> That's perfect. All right, let's segue into the next topic. The creator of cloud code creator of cloud cloud code said, "I don't write prompts anymore.

[00:16:15]I write loops."

[00:16:18]So Ray, I feel like this is a perfect topic for you, so I'm going to let you take this one first.

[00:16:23]>> I was like, where you been, bro? like these Korean kids have been doing it for like a year plus. I think uh I think the writing was on the wall a little bit and I think that's kind of why people were starting to see like slash commands and I remember cursor talked about like grind mode back in the day and people were trying to get longunning stuff with you know Ralph Wiggum and everything like that and I think a lot of people's gut reactions initially were like of I I can barely get output that's reliable from my agent. How come people are just saying this out loud and with the introduction of the latest version of cloud code with Fable 5? It's, you know, I've sent it on a really complicated task to do tons of research and come back and and look through data that would just literally take me several days even with AI because I have to keep all of this in my head, you know, beyond a million token context window. And to see it do that and launch like 12 sub aents and and like really do and write code on the fly for this and parse data and analyze things and keep those things and think through all those processes.

[00:17:24]It's just been very very impressive and to me my um it's like wow I think everything uh if you com get a smart model if you have a really great harness that can um basically keep track of memory keep track of the context do automatic compactions and and do these techniques where it can either write its own files to kind of you know keep itself towards a goal you know it can do a lot of great work right and I think for me as a person with a lot of experience in software engineering I'm trying to think about how can I make either a codebase or things that I used to do in different stages of software development. uh how can I sort of codify them so that these agents will just naturally pick them up and just start to adopt them into things >> and so like that's kind of the way a little bit back to Nathan's point about operational speaking like there are operations that usually happen engineers work on code they hand it off to the QA team the QA team does their job they make reports and then somebody has to review it and then you know there's like this discussion in terms of impact for the release and then what timelines you have left you know if you only have two weeks left you have a month left how do you then fix all the bugs that you have left and which ones should you actually focus on and regression impacts. So those are the things that like literally there's or organizations that are built for this stuff to handle this you know and and can I make my own mini version of that with these agentic loops >> so that they kind of start to do that themselves and do they have enough taste or do I need to like what parts of me do I need to instruct to give them that vision uh and that's where I think loops are perfect because then they can do those things I get the feedback and keep improving and I think um this is a uh I I love I love that it's kind of going in this direction. It's it's it's it's going to cost a lot of moneywise, but uh we're just curious to hear what you guys experiments are or kind of what you guys have been thinking about how you've been thinking about loops and stuff. Yeah.

[00:19:15]>> Yeah. Nathan, let's hand it over to you before I give my probably counter take to all of this.

[00:19:19]>> Yeah, I think there's a few thoughts to me there. I mean, one, if you go back to like the cost side that you brought up at the very beginning, you know, there is sort of an interesting element of like even if the token cost goes down, if the overall consumption goes up, like it doesn't really matter, right? I mean, it's like it can be cheaper, but like it can still cost more. Um, I don't want to dil us back into that, but this is one of the things that sort of strikes me there. Um, you know, one of the things I was actually surprised about on the loop piece, you know, and and like and I think conceptually and everything that you were saying, Ray, I mean, I I agree with him like I've, you know, I've ran my own loops and continue to run my own loops on different things. Um, you know, I was sort of surprised that this is getting the attention in a way that it is just because, um, you know, it's not like, and I don't know, and maybe I'm too sort of far into it, but it's like, it's not sort of novel to me in a way.

[00:20:04]And like, to me, the like I guess where I've been anticipating more time being spent is on like the proactive side, right? Because it's like from a loop standpoint, like, okay, I still have to do the mental load, right? I still have to get out of reviewer mode and be like okay do the thing it can and kind of drive the AI into something versus like I think the value not that it's not in a loop and there's not value there because there certainly is but it's like I think there's a big difference in that versus the value of like hey like I'm proactively looking at something I'm then kicking off that loop you know however long or short it may be and like and I'm kind of actively beginning to do that work and I don't know so to me it's like I feel like the value isn't necessarily the loop per se as much as it is like the proactive you know actions that can begin to happen because even on like the process side we were talking about the example that you gave Ray of like okay like you know we only have so much time like you know what capacity can we actually fit into this time like that's something that like that an agent could very easily proactively go into Jira for example estimate sort of the average velocity whether by tickets or points or what have you and go hey we have this challenge going on here's what I suggest right and like you know I don't know if you need a loop for that per se but I feel like that to me is where I've anticipated things to go from a value standpoint like the ROI you comment that Adam brought up earlier versus like, you know, I don't know, the idea of like, oh, let's like let's maximize this as much as possible, getting these loops running and all that sort of stuff. So, I don't know.

[00:21:23]>> Yeah, my take is very similar to I would say both of yours. Um, but I I I just feel like we sound so dumb talking about loops like like it's it's been something that been we've been doing this for years already now. And I remember I remember back in like early 2025, I would be like, >> "Hey, keep going until you're pass all the unit tests." That's a loop. Like, what the heck are we talking about here?

[00:21:48]My post on um X was, "I feel like collectively we as engineers sound dumber and dumber every day." This nonsense, this nonsense about writing loops is really just nonsense. Why are we acting like it's the greatest thing ever? And I say, "Learn to code, folks.

[00:22:03]Learn to use AI efficiently. Don't burn tokens for no reason. look at your code and please always look at the incentives for anyone giving advice. And I said, this is coming from someone that is very pro AI. Like I agree, like I think loops are are great, but it's not anything new. It's not anything crazy. The people that are running these 35 hour, 50 hour loops and burning a millions of dollars in tokens. That is not normal. And it is something that like the output of that is really is like how do you even measure that you got a million dollars worth of value out of that? I'm not seeing these new products pop up that are showing me that someone did a loop that launched this brand new thing out of nowhere. Uh I'm just not seeing that that side of it. Go ahead, Ray. I saw you unmute there. Uh fear, fear, fear, fear. We got to push the fear. We got to push the sales. We got to get more enterprise sales in the back end. Uh how do we do this? Oh, security. Ah, yes.

[00:22:57]Everyone can break into your systems now with these intelligent models. So, buy our stuff to protect you.

[00:23:01]>> Yep. It's all about incentives. like we always got to look at like the incentives behind, you know, all of this. Uh, all right. So, in the last topic here, we're going to talk about Fable 5. Ray, I know you actually I don't want to put words in your mouth, but are blown away by it. I know Nathan, you've got a very interesting take. So, we're going to it's going to be fun to talk to you guys about this. So, Ray, why don't you start talking about your uh hands-on experience with Fable 5 or I guess what was known as Mythos.

[00:23:31]>> Yeah.

[00:23:32]blown away I think is um what I've seen and kind of what the task I threw at it made me think that there is more intelligence that's happening between thinking and the next set of steps that need to happen than um you know in terms of like if you were to like tell someone to just go from New York to California and there's no roads or nothing and go figure it out like I feel like this is that point where we can actually kind of point someone in that direction. They're like, "Okay, I know enough about weather. I know enough about how to, you know, feed myself and know enough about the world to kind of start to navigate in certain areas. Uh, and if that's my sole goal, then I'm I'm just going to do it, you know, and just go and like I don't know how long it's going to take, but something's going to happen." And I think to me, I I can actually not start to see that. Like that was kind of the wish and the goal from the very beginning. And this is to me I'm seeing some things more specifically in agentic like coding practices that uh that just like just the basic stuff you need. And I think it's just like literally just researching autonomously being able to um like write code to look at large pieces of data uh split that tasks out to different agents so that they can understand the task that they need to do uh and then come that bring that back into an orchestrator which is a basic agent loop right. So those are like probably the four key big pieces that it can do uh on its own um so that it can kind of navigate the software world and start to build these software factories.

[00:25:06]So the biggest problem I threw at it was like I have an open claw with like like over 300 plus megabytes in the database of just text and I said I've had all these conversations. Can you just go specifically look at the agent that's working on health? You'll have like tons of data that you need to work with. uh I'm trying to, you know, get down to 10% body fat. Go and like I want to build the whole system around this and analyze all of the different conversations I've had. And it did like an amazing breakdown spinning off these different agents, thinking through things and and then like the next step I said, okay, let's go ahead and act like put on our senior architect hat. Um I want to make a data model that I want to store in a central data store because I'm going to make, you know, widgets. I'm going to make, you know, uh, open like Telegram versus little conversations. I'm going to have real-time voice and then I'm also going to have like a full full-blown iOS app to do different aspects of this whole journey. Can you start to model the data that we would need to have these different interactions? And it just like started to think through and keep all of the stuff in the context because like it started to organize itself in terms of like oh keeping a food logs these things over here should be transactional these things should be atomic these things should be and I was like oh and it just continued that throughout the entire process for like almost like an hour and some change and that's the intelligence that that really started to blow me away because I started to think about like I don't want to refactor my databases like I'm already seeing memory problems with Open Claw that I know what need to be fixed, but I don't want to fix them, you know? So, it's like uh but I want to kind of design my own system for this.

[00:26:47]And it's like I I think we're there at that point where and I'm trying to explain this to my my my parents and other folks. It's like McDonald's made restaurant factories and it's not the healthiest of food, but they can produce a burger that tastes the same wherever you are in the world. And they don't need a PhD level person to make this burger. you know they can hire someone off the street. So the this new models have the capabilities to make these software factories in which you can start to point them at operational problems or different things and have them try to go figure those things out and then once they build the things they can start building the smaller things or delegate those tasks. So that's kind of why I feel like this is you know scratching the surface. I haven't had, you know, weeks or months with this model. I've just public whatever has been publicly available, but I can already see that um from what's in there. So, >> that's awesome. Nathan, you had a different take. And so, this is I I want to give you a little background on Nathan. So, Nathan and I over the years have like always jumped on whatever new model comes out because it's always like exciting to test it out. We would always talk about it. But Nathan saw this one and what was your reaction to that, Nathan?

[00:27:56]>> So, yeah. So, I was like, I mean, honestly, my reaction was like me. like not not that excited and and I think a little bit more backstory to that too.

[00:28:04]So, I've been a Claude Maxi for I don't know 8 12 probably 8 or 12 months and uh and I think with with Chad GBT's 5.5 drop and I've been using Claude code you know you know you know since the beginning and uh and Adam actually you you know you were like look like I know you're using this all the time you have to go go try codeex and try 5.5 on it it is so good and I'm like h I don't want to do that I like I've already got all my systems set up they're running on cloud code it's fine you're like just just try it so like I did and it is awesome Awesome. And I'm like, so I moved everything over. I've used the goal features and every and I'm like, this is amazing. Like I'm like I'm a heavy codeex user now. Not as much on the cloud side at this point. And like so when Fable came out and I was like like okay. Yeah. I'm like I've got goals and codecs like I've got I'm like you know what like I'm fine. So like I I am now like I am trying it but as a whole it's more of like I'd say it's more to like learn and experience. Um I don't know like it'll be interesting to see uh to see sort of like how it plays out.

[00:29:01]But this is also I feel like one of the first instances in a while just as a kudos to OpenAI where like OpenAI has had sort of like the goal concept and they've actually led here. So to me it was actually exciting to see like okay like OpenAI I think they've been sort of maybe a little bit less scattershot and more focused in areas you know and now um now Enthropic in a way is actually playing catch-up where I feel like for the last six to eight months OpenAI has been playing that a bit more. So >> yeah, for me I feel like Fable it's a great model. Like I think it does a great I've done quite a few like long running tasks on it. It's slow and it's expensive and I don't know if the if that equates to like the value you're getting from it for most things. There are things that I think it totally makes sense to point it at. I do have a funny trivia question for you all. So we have the creator of Claw Code, you know, saying he writes loops now. And we have Fable 5 Mythos, which is the best model ever that that we should be scared of.

[00:29:59]How many open issues do you think there are on cloud code right now? [laughter] If you had to if you had to guess like how many let's >> 582.

[00:30:10]>> Okay. What do you think, Ray?

[00:30:13]>> I want to say like 5,000 because it's all automated. 8,545.

[00:30:19]So >> that's wow. Like I mean it's 80% right.

[00:30:21]I mean I guess is is it the fax machine happening now? Right. But >> 8,545.

[00:30:28]So anyway, like it just goes to show you that no matter what we have and you know some of those issues probably are not legit. We do not we do not have coding solved. Like loops aren't going to fix things. We've got the company with the best access to every model still having trouble keeping up with their backlog of stuff that's happening and cloud code has always got bugs that that pop up into it constantly. Um, so it is just a funny it's a funny like narrative that is pushed and I will say I agree with you. Uh, GPT5.5 still my go-to model. It's fast. It's good. It's got really good design taste.

[00:31:04]And it it is the first GBT model that I actually feel like I've enjoyed coding with that I can actually remember. I remember using 03 for like my planning and reasoning back in the day. I like that model.

[00:31:17]>> Yep. And this model is just like my regular coder. It does good at planning.

[00:31:21]It does good at coding. It's got good design taste. And it's fast and fairly inexpensive compared to, you know, some of the Opus and and I guess now Fable 5.

[00:31:31]Um. All right. I think that's probably a good place to wrap. Uh Ray, do you have any closing thoughts before we call this episode?

[00:31:40]>> I think if this model with Mythos and like PPT, you know, six comes out and it's just as crazy as we think it is or whatever.

[00:31:52]What is your guys's like next phase of life? You know what I mean? Like I'm just curious like where you like your jobs are gone, something happens. It's like what do you do next? Well, I mean, what is it like? I mean, what are you thinking, Nathan? [laughter] >> So, uh, so I've always been in like the red team mentality, right, where it's like, okay, like anytime I'm working on something, it's like, all right, like how would I like how would I sort of beat myself? Like, how would I how would I like how would I beat my own company?

[00:32:19]That sort of stuff. Just because I think from like a product standpoint, it, you know, it keeps me sharp. So, I've done this, especially with like with, you know, all the generative AI stuff. I've done this on the life side. So um so I'd say one of the things that I was thinking about is like okay what's really hard for AI to replicate and it's like in physical goods right I mean like very difficult for AI to replicate physical goods uh because like you know like if it's software related which has been you know my life for two decade you know two plus decades I was like yeah it probably needs to not be software related um so any so I I'm actually getting ready to launch an e-commerce brand for for like a niche category that I found and part of actually even the reason that I'm investing in that honestly is because it's like working in the frontier of There's times where you're like, "Holy smokes, like this is crazy." Like you change the operating models, you change the story, you see the models, and you're like, "This is going to change things." I like, and being honest, I'm like, there's certain things where I feel like I have a minor amount of clarity, but honestly, it changes so fast. I'm like, I don't know. So anyway, so my my sort of uh you know, I don't even call it a side hustle, per se, but like my my sort of counter against myself. It's like if uh if I if I don't win, right, if I win in creating better frontier model stuff and everything else, it can and it ends up putting me out of a job, I'll always have an e-commerce brain. That'll be great, you know? Or like best case is like I don't put myself out of a job and I have two things that are doing great because of AI. So that's sort of that's my that's my kind of counter to myself. So, >> I've always told myself if I have to get out of software, I'm probably going back to some service job. And so, back when college, I worked my way through building like doing a lot of woodworking and stuff like that. So, I probably I'd probably fall back to that. Um, I I do also have e-commerce brand that that we're building out as well. Uh, both.

[00:33:57]So, there is that that's happening as well. But, it it is like one of those things where you do you really do got to kind of take that seriously. Now, I think there is a 95% chance that engineering is not going to be done away with. I think it's a very high likelihood. I could be wrong though.

[00:34:13]I've been wrong about several things.

[00:34:15]For example, I predicted React was not going to be useful and it has ended up being incredibly useful. So, that's like the worst take of my life. Um, >> pricing. You were wrong about pricing.

[00:34:24]I'm just kidding.

[00:34:24]>> Pricing. I think I'm pretty spot on with that. [laughter] But anyway, yeah, that's my that's probably what I would do. I I really think engineering is like going to be fun over the next 20 to 30 years. And I think if you're getting into it, the job market is going to suck for you coming out of college, but that's going to turn around. And I remember after the dot crash, which is when I came into the industry, I ended up having to take an unpaid internship to get started because it was that hard to actually get a job. No one was hiring and you had to get in and kind of prove your value.

[00:34:54]>> So Rey, what's your backup plan?

[00:34:57]>> A couple of things. Like I spend time between the islands and the Bay Area. So I get out of the bubble quite a bit. And there's something so simple just about, you know, being in nature and just kind of chilling. And you know, I do a lot of outrigger canoe paddling, which is just amazing. You know, I could be on the water all day. And like those people who hang out on the water all day have a very different mentality about life, right? Like their whole ecosystem is completely different. You know, they feed from the water, they live by it, they hang out. It's just a whole like different vibe. They don't even >> they >> don't have fishing loops that they're just constantly trying to maximize and sorry >> like the fishing is like the thinking, you know? It's like they're just being on the water. It's like you're kind of taking the wave. You're you feel your skin you can change like I can feel the temperature changes and I'm like, "Oh, it's going to rain the next 5 minutes, you know, without even looking at a phone. I don't have my phone with me or even a watch anymore. I don't even wear a watch." And um I worked on the Apple Watch, which is kind of crazy, right? So it's like, oh, I'm getting more in touch with uh myself as a human and what humans already have built in as far as technologies. And so I'm uniquely trying to combine like how can I be more human in this AI world? And then is AI just a tool like I'm using for fishing, for paddling, for being on the water, for getting stuff done and how is the world coming to me or or how how am I going to shape things around me? And uh that's a question I really don't know yet. And I feel like the only counter to that is live streaming. And so if I stream live, what I'm working on thinking about, you know, that's AI has to take that knowledge, index it, serve it to a model, and like, you know, make weights and that takes time and that's a delay.

[00:36:36]So being real time is actually kind of a mode that I feel like I want to just keep, you know, plugging away at and figure out what this means or, you know, being human in the age of AI. So >> yeah, Ray, you are the live streaming master. I feel like I always see you live on uh X or or YouTube or something.

[00:36:52]It's awesome.

[00:36:54]>> Well, guys, this has been an awesome episode and Nathan, thank you so much for joining. I hopefully we can keep you hopefully you're willing to keep doing this with us and hang out. We didn't scare you away too much. [laughter] >> Uh all right, everyone. That's the end.

[00:37:08]Take care. Thank you.

[00:37:09]>> Take it easy, y'all. We'll see you on the next rate limited