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- [00:00]
A
You might have heard of engineering loops. They've been going viral on Twitter and everything like that. And I think they're really interesting, but they're way more interesting to use to actually run your business. There's a way to use loops to actually get customers, get SEO, be seen by LLMs and actually improve your product 24 7. Now, I haven't seen anyone cover how to actually implement these loops, so I created a tutorial how loops work, how you can use it to run your business, and how you can use click Claude code or codecs to actually implement it. In this episode, I share everything with my friend Ellie, and you'll see and understand completely how to do it yourself so that you can get traffic, get customers, build a startup today. My favorite loop is actually the last loop that we share. Enjoy the episode and I'll see you at the end. The startup ideas podcast.
- [00:57]
B
It's dipping time, baby.
- [01:01]
A
Ellie, welcome to the show. By the end of this episode, what are people going to learn?
- [01:07]
B
Yeah, so you're going to learn how to use loops to better automate your business. Loops have been really popular over the last few weeks. People are using loop engineering to better develop products, but it can go a lot further than that. You can use it for SEO, for Facebook ads, really, to automate almost every part of your business. So that's what we're going to talk about today.
- [01:26]
A
Okay, cool. So, yeah, people are using loops to basically build products, but you're basically saying there's a way to use loops that could, you know, you could run your business on it, basically, and that's going to help you get customers, that's going to help you build a more efficient business. And what I'm asking for you, Ellie, is if you can clearly explain how to actually do this thing and then show some examples so that people can actually just copy some of these workflows. And then by the end of the episode, they're going to understand the loops for, you know, how do you use roops to run your business, but also how they can get started today. Can you make that commitment, Ellie?
- [02:07]
B
Yeah, yeah, sure. I'm going to show you how to use loops to run your business. We're going to talk about it at a high level, like sort of what the concept is, where you can potentially use it. But then I'm also going to show you how it actually runs in practice. So it's not just theoretical. I'll show you how you can actually improve your SEO massively using loops. It's the sort of thing a lot of you might be doing today if you have anything running on a schedule that's a form of loop. But we're going to sort of really push it far. And I think the state of AI today, you can really do quite a lot with a loop over a long period of time. Most of the time we're talking about loops. Maybe, you know, they run in half an hour, an hour here we're talking about loops that might last for months or even years.
- [02:52]
A
Let's do it. All right, let's get into it.
- [02:54]
B
Cool. So, yeah, around a month ago, loop engineering got really popular. Boris from Open from, well, Boris from Claude Code started tweeting about it. Also, Peter Steinberger from OpenClaw started tweeting about loop engineering and everyone sort of was like, wow, what is this loop engineering thing? Overall, it's quite a simple concept, but it's really blown up. And I guess it's nice that sort of, it's got a term now, loop engineering. Before someone could have described this concept and it didn't have sort of a one word explanation. Now it does. Shortly after this whole hype cycle started, a friend of mine, Demetro, he went and tweeted this in 2026, you don't prompt anymore. Your software should be able to build itself and achieve product market fit on its own. Your only job should be to find money to pay for tokens and take care of yourself. So he was definitely joking when he wrote this. I think he was making fun of this whole idea of loop engineering. We had like, you know, prompt engineering, context engineering, harness engineering. Every month we've got another hype cycle. But if you read the tweet, I found it funny. I thought it was a great tweet. But then the question is, wait, could you actually do this? What would it look like to actually have everything running on a loop in your business? So that's what we're going to speak about today. And the idea of loops, honestly, it's not that new. Maybe even like 10, 15 years ago, the Lean Startup book was pretty popular. And a big part of that book was this loop where you build something, you'd measure how it does, then you'd learn from it, then you'd build a bit more. But basically, if you break down a business or you think about Dimitri's question, how could an entire business run as a loop? It's basically, let's go build something, let's get feedback, let's improve it and just keep that cycle going of like, build and learn, build and learn, and I guess measure as part of that as well. And you can do the exact same thing with AI. And it's not just sort of high level for like the business to build and get feedback that might be on the product, but you can do this for so many parts of your business. And it's actually what you do already. If you're improving your SEO, you're seeing, okay, where do I rank today? Where do I want to rank? What are the things I can do to improve it? Who is ranking above me? You do all these experiments and then you try and rank higher and you have accurate measurements from Google coming back to you. And, and so that would be an example of a great loop that you can run. It's a loop that I'm running today in production. If you're sort of familiar with like Lean manufacturing where. Or the sort of the Toyota story where a lot of this stuff became popular as well, that's also a loop where basically you're just constantly iterating and trying to make things better. And so these aren't new concepts. I think we're all familiar with them. When it got paired with loop and loop engineering, it was like, wow, what is this? But I think it's something we all understand quite well and it's just how do we take these ideas and get our agent to do the same thing?
- [05:56]
A
Right. Yeah. And the Toyota example, I think that was the basis of the Lean startup book. Right. I think Eric Reese looked at the Toyota example and basically said like, hey, there's this Japanese company and the way they manufacture, how are they able to create such reliable, consistent cars? And it was through that, the loop mechanism that they had this assembly line that was just highly efficient. And because of that they were able to just create incredible products at a good price. What Eric looked at, he said, okay, well you can actually build a startup in that same way. Before that, people weren't building startups in that loop way. It was more artistic. They'd kind of like just put out a product and change it as they go. But I think, you know, Toyota lean manufacturing process, applying that to startups was one of the reasons why we had such successful startups post 2005. So what are we talking about when we're talking about loops with AI agents?
- [06:58]
B
Yeah. So I think maybe the best way to explain it is to jump into that first example I spoke about. Well, I guess let's talk about sort of a loop loop engineering first and then jump into specific examples. But I would say if the loop for the Lean Startup Is build, measure, learn. We have very similar steps here with an agent we have this build step which is like telling an AI, hey, go build me my new SaaS, for example. Then we have this verify step where if you're building product, the verify step might be that all tests work or the agent has used a browser and to make sure it can click through everything. Or there's some other agent that's running. If you're doing this in Claude code and you use slash goal. So that's basically running this loop and it's got this other agent checking has it actually finished or not? Is it working? And if it's not working, it's just going to tell the sort of the main builder agent to just keep looping and looping and looping till it's fully working. Some other examples of this within engineering. So you always have this stop condition. You don't want the AI just to loop infinitely. There needs to be some sort of result that you converge on. So some stop condition examples. One is like the feature works in the browser, I want sign up. Like the goal is to have sign up working. You know, you slash gold, make signup work and then once it's working in the browser, it's what you know, it's passed and that's sort of the end of the loop. Another one for anyone building AI products. I run a product called Inbox Zero. So this product, like it manages, it's an AI that manages your inbox and this one is super important for me. Basically we have evals. The evals are sort of tests for AI. Like, how well does it do? In the case of Inbox Zero, it would be how well does this model categorize emails? So for example, I just got a newsletter email that came in. Does it get categorized as newsletter? So evals are basically like the tests, the evaluations to check how well the AI is doing. Different models will perform better or worse. Depending on what prompt you have, it will perform better or worse. So your goal is to sort of get your evals really high. It might be choosing the right model or adjusting your prompt. And so you can run this as a loop as well. And what that would look like is tell your agent, hey, I want our tests or evals to pass. Like get a score of 90% and above.
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A
And.
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B
And so it can keep running the prompt over and over and it can keep adjusting it. And if it sees, oh, I'm only passing 88% of the time, it can try again. And each time it will try and do a Bit better till eventually it gets past 90% accuracy. And so if we take this, that's sort of on the engineering side, how you're always sort of building and verifying. But it's verify step. It doesn't just have to be related to product. It could really be anything. And really what you want is some sort of input back into the system, some sort of objective metric. And so in the case of SEO, which is the first example I mentioned, the objective metric is where do you rank in Google search? So right now, if you search for the term inbox zero on Google, Inbox zero ranks first. But some other term, AI email assistant, we really want to rank high for that as our business. But we ranked, I don't know, position 30 or so. So what we can do is run a loop that runs every month, for example, and tries to push us further and further up until basically we're on that first page. And honestly, this is a loop that never really has to end. Maybe this loop ends when we're in position one, but this isn't a loop that's running, let's say for half an hour straight. It's running once it's taking its step, and then a month later, it will continue its process and try and push us further up.
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A
And.
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B
And so we can go into detail, like what this actually looks like and what is needed to make a loop like this work. Because this example, I think, like, it's a good example because it applies to a lot of other things in the business. Facebook ads, for example. You know, you're spending $100 a month on the ads or $100 a day. You know, you want it to get to profitability. So it's all the same ideas. So how can you get an AI agent to get there? It is basically the ideas, the idea here that we're trying to understand.
- [11:18]
A
Yeah, and with SEO, I think the way you would typically do this is you would hire an agency or you'd hire a freelancer to essentially do this loop. Right. So what you're suggesting is you kind of don't need to hire that person, at least to start. You can hire, build a loop that has a KPI, in this case, Google ranking, which is, you know, isn't gray, it's black or white. Either you moved up this month or you move down, or you stayed the same. Right. And you're able to basically say, okay, am I doing a good job? Am I not doing a good job? And then based on that, actually, you know, perform actions. So the only question mark Is can agents at the time of recording, can agents, are they smart enough to actually work in as good, if not better than hiring an agency or a freelancer? Because ultimately, as a business owner, you care about moving up in the rankings, right? So you don't want to have a loop just for the loop's sake.
- [12:25]
B
Yeah, exactly. And I would say also, even if you do try this experiment and it doesn't work, you haven't necessarily lost anything. A lot of us aren't necessarily going to be hiring the SEO expert anyway. So it's just like you could run this experiment and worst case scenario, you see, oh, it's actually had negative impact. What a loop like this would do would be like, let's say we move from position 20 to position 30 in like sort of Google rankings. You could just undo the change, basically. So none of this is really set in stone and it's sort of just experiments that we're running and hopefully like sort of long term will push us up. But if anything goes wrong, we can always revert. Nothing is set right.
- [13:06]
A
I mean, so I guess, do you think that agents are good enough today such that they can actually impact Google ranking and get you more traffic?
- [13:19]
B
So having run it myself, it's definitely having positive results. It's going to take a few months to sort of really have the impact that I want. But yeah, for sure. Like before that we did this recording, I took a look at the numbers and I can see a whole bunch of numbers are going in the right direction. Some of them, you know, I might be moving from page three on Google to page two for a certain term. So it's, I guess it's valuable, it's getting there. But obviously the ultimate goal is to get to a first page ranking. I do think it depends on lots of different factors. Like, you know, inbox zero domain rating might be like 63 or so last I checked, 64, something like that. For a new business that sort of has a super low domain rating, maybe, you know, it would work out differently. But yeah, to me, I'd happily run this. Whether it's like an established business or you know, a new business that you're starting to set up, basically.
- [14:15]
A
Well, that's the thing with SEO is don't expect to do SEO and it works in 24 hours, you know. Yeah, SEO is something that takes months, not days. That's just in general. So this is the type of loop that you, you kind of want to have working in the background while you're doing other things too, right?
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B
Yeah, exactly.
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A
So that way you might wake up on month four, like nothing's really happening. Then month four, all of a sudden, bam, bam, bam. You're on page one. And that's happened to me in the past where it's just like, SEO wasn't really working for some amount of time, but you're doing the things necessary to rank well and then all of a sudden that compounds and it starts to really bear fruit. So yeah, let's go deeper into this and see some examples.
- [15:05]
B
Yeah, exactly. And it's definitely something that compounds over time. And I think everything you do, marketing wise, is all going to have an impact. Also, we've been speaking about SEO here, but all of these things obviously benefit your LEO or GEO for ranking in search in LLMs as well. So it's still super valuable even if you're not someone using Google search that much anymore. Like, going sort of deeper into this, like, how would you actually set this up? So the example you brought of having an agency that sort of running your SEO, well, I'm not an SEO expert, but what they would likely do is run certain experiments. They do an audit of your website. This is something I think you should get AI to do for you. Regardless, just get an audit done. It will say like, oh, we should improve these meta tags, or you've got these JSON lds, which could give you a small boost. So go and do all of that. Maybe you don't have a sitemap. There are a lot of things AI can just get fixed immediately and quick win for most websites, I would say. But after that, what sort of that agency might do is start to experiment with certain terms. It's seeing, okay, you're ranking quite well for AI email assistant, but you're not on the first page yet. What is happening there? What can we fix? And so this whole thought process, you don't even need to worry about it too much. The AI will go into it and be like, okay, you might be cannibalizing your own links because you're sharing the link power between two different links on your website. But whatever the AI comes out with or the SEO agency, what they're going to do is make those improvements. And they're not going to see results immediately. They're going to come back a month later and see, okay, we have moved up, we have moved down. And so the exact same thing that the SEO agency is doing, that's what we want our own agent to do for us. And so the first thing we need to do is give it access to all the Tools it needs the main ones, I would say one is Google Search Console, that where you can basically see all your data. And Google Search Console has an API, so it will show you exactly where you're ranking for Google rankings. I'm going to go to that. So here we're looking at my Google search console for DraftFantasy.com. this is a business I started around 12 years ago. It still runs today. It's not my main focus, but because of the World cup, it's had quite a lot of activity recently. And here you can sort of see how it's ranking. It's had 10 million impressions over the last three months on Google Search. Down here we can see sort of some of the queries that it's ranking for. For the38.0 search term right now, it's had 120,000 clicks, which is actually quite insane. You can see it's got a million impressions. This is actually not a business that I've been running. There's sort of this loop agent on. I didn't want to go into the numbers behind Inbox zero, but I'm happy to sort of share what's happening with DrawFantasy.com right now. And you can see it's ranking well for a bunch of terms. But like what I did around two days ago is basically tell my, you know, my Claude code, go and do the same loop engineering thing we're doing for SEO for Inbox zero. Let's just have it run for draw fantasy as well, because why not? It will run in the background. I don't really need to think about it. It will make good updates over time and it will remember what it's done and then go and make more improvements. And so here you can sort of see like lots of data around, like, you know, where your search terms are ranking, where is it? Let's say average position. This is like a big one. So, for example, over here you can see I'm ranked fourth for the term 38. 0. But let's say I want to push that up to 1. Like the AI can basically look at all of this data that I have here on screen. It can connect via the Google API and all this data will come into it and it can make a really smart decision, honestly, a lot better than me, even, and decide, okay, these are the terms, like bringing a ton of traffic right now. How can we change things so we can rank even higher? So this is 4.4 right now. If I can get this up to 3 or 2, imagine this wouldn't be 120,000 clicks, this might be a half a million clicks. So it can drive just a ton of value and there might be some really low hanging fruit that can go and sort of fix up and make it work. So yeah, the first thing to do and just across your business, whether you're doing loops or not, I think one of the easiest tricks is just connect your AI to your different tools, your real data. The tools here would be Google Search Console. Another one would be data for SEO. That's like an SEO API similar to Ahrefs and Semrush, I believe, and it will show you how you're ranking against competitors. So Google Search Console will just show you, okay, you're ranking fifth over here, but like, what are the four articles that are ranking higher than yours for this term that you're really after? And so the more information you can give to your AI, obviously the better it can do. And so what this loop actually then looks like is it makes improvements. It can check an objective metric, which is your Google ranking, where you ranked. It can learn from that, which you know you can do immediately, and it can continuously iterate. And the idea is every month or maybe every two weeks, it looks back at what it's done, it's noted everything down. This is another important part of it, like have it remember, have let's say a markdown file with everything that's happened the last time it made improvements. And then it can basically check its experiment. Did it do well or not? It decided to change the description of the page. Did it, you know, did that description change? Did it rank our article higher or lower? And so it can look back at what was tried, what wasn't tried, and it can iterate on that the same way as an SEO agency would do for you.
- [20:59]
A
So if someone wants to actually create this SEO, SEO loop, today is the easiest way to do it. Basically screenshot this, paste it into your Claude code or codex and be like, I want to create an SEO loop. I want to give you access to my Google Search Console data for SEO. And I want you to be judged on the objective metric of the Google ranking. So check the metrics. Is that what people should be doing or how would you optimize that?
- [21:39]
B
Yeah, I think if you did that, honestly you could go quite far with it. I can show you an example if you go to atomeave.dev this is another website I put out not so long ago, but here there's actually a real example of this SEO improver, or just people don't need to use this if people are familiar with eve, which is a Vercel project that just came out, or FLU framework by the Astro team. So this is sort of like, you don't need to use these to build agents, but this is one way of building agents. But either way, even if you don't use this, you could honestly copy and paste this URL into your Claude code or Codex and just say, hey, I want you to go and sort of copy the ideas here. But here you'll see basically a prompt that does the same thing, which is like, this is my Google Search Console. This is the data for you to get into the API key for you to get into data for SEO. And then here's a full prompt that you can go and copy if you want.
- [22:40]
A
Oh, wow, this is great. This is awesome. This is a expanded upon version of basically what I just said. This is basically, you're the SEO improver. You're going to be judged upon these three metrics. And it's. Yeah, an instructions MD file for the specific job, right?
- [23:01]
B
Yeah, exactly. So you can see, for example, it's saying when you apply changes, select the subset of this week's recommendations and that cleanly to files and the blog repo. You can read through it if you want. But the basic idea is exactly what we said. And if you want to play with the cli, you can even run this command or even copy this prompt honestly into claude code. This is a prompt and it will set that up for you. You don't need to use this. It might actually complicate things for some people, like using EVE or Flow. I might just show you this in my own Claude code quickly.
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A
Yeah, cool.
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B
So here's my own Codex. Just running it in a terminal on my machine. I've actually gone and taken the idea we had here and just taken a screenshot of the chart we had before. And that's the loop we basically want to have running. But yeah, if I say, hey, I want to set this up for myself, I want to create an SEO loop, basically. Honestly, even with that, we should be able to get quite far. Maybe like if you're doing this yourself, speak to the AI a little bit more about it in terms of what actually needs to happen. Maybe you can use plan mode, but like, it literally is as easy as that. It will guide you through, like how you have to connect Google Search Console. If you're running it on your own computer, that's the easiest. There's a CLI you need to install or use the Google API. So there's like a few steps you need to go through, like to give access to your data. But once you've done that, honestly it should be quite easy and you know, say something like we want to improve our SEO, that would sort of be the main thing. Maybe even do it on your own repo. Depends where your blog is, how this is done exactly. If you have a WordPress blog, maybe you want to give access to WordPress if it's, you know, on some other System, if it's GitHub, then you could do that differently. But you give AI access to your blog, everything you're doing, all your data and then honestly from there it should be able to run on its own. The one step afterwards, what you really want is to have some sort of automation set up. So like if you're a Claude user they have I think are they called routines on Claude right now?
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A
Yes.
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B
And Cursor has automation. So and I think Codex is also called Automations so you can run one of those. And the idea is just every few weeks it should pick up where it left off basically. And yeah, if you want a much deeper example, then use what we showed for Atom Eve basically.
- [25:27]
A
Cool. So I had my friend Ross, Mike on the POD recently and we talked a lot about loops. And his perception about loops is he's an engineer, he's a front end engineer. So he's looking at it from an engineering perspective. He basically was like, I don't really believe the hype around loops. I think the people that are going to get rich from loops are the token providers because people are just going to be burning tokens. Now we didn't talk about any business use cases. We were talking specifically around engineering. If I were to implement an SEO loop, would it be smart to basically say a click to me is worth 3 cents or a customer to me is worth $100? You know, stop, like, you know, stop, stop, basically stop the loop if these things happen. Right? Because you basically what's going to happen with these loops is that it's going to cost money and it might be $50 a month, $100 a month, $200 a month depending on what you're actually doing. And you might just decide like it's not worth it. So I'm curious how you think about cost benefit analysis for loops.
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B
Yeah, so I watched Mike's video, your guys video together and it was great. And I like, I definitely agree with a lot of what he's saying. That's like, you know, the unnecessary hype around these terms. Also in terms of cost for sure. Like he mentioned that Peter steinberger works for OpenAI now, spending $1.3 million a month on AI credits. You know, it might even be more at this point. So I fully agree with that. For this loop, I would actually say it's quite cheap, so you should very much do it. You really shouldn't worry about cost, especially if you compare it to what this would cost if you hired an SEO agency. The reason I say it's so cheap is like it's not each run in this loop, it's happening once a month. For example, I wouldn't be shocked if this like cost you less than $5 in tokens to basically go and run this one time right now. Like what? I just ran it in the background. So each of these runs, they're not that deep. It's not that it's an like sort of an AI getting itself into an infinite loop. It sort of is, but like it's infinite over time, meaning it will run once every month for the next two years or five years. Honestly, for me, I'd be happy for it to just keep going. Do that once a month thing. You might even want to sort of, you might want to have the AI update you in between. This is something else I do myself. Every time one of these runs, I need to know it's running. So I'll get it to ping me on Slack basically whenever it's done a run. And then I can look over things and I can sort of give a quick approval if I like it or don't like it. And so I'm very happy to get these like once a month updates for things we can improve in SEO. And yeah, the overall cost is going to be small. The other thing I'll mention that Mike didn't is that if you're on a max plan, you are getting tens of thousands of dollars per month in your like 100 or $200 per month subscription if you know you're really tight on budget and on a $20 plan. And yeah, you need to be much more wary of tokens. And I think about like using open source models that are cheaper for this sort of thing, like GLM 5.2 type thing. But if you're lucky enough to be on sort of 100 or $200 per month max plan, you really, you've got thousands and thousands of tokens there. And so I wouldn't be worrying about cost for something like this. It should be fairly cheap, honestly.
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A
Cool. All right, so we looked At SEO loops. What are other loops that people could be thinking about?
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B
Yeah, so another really good one would be a Facebook ad loop. So you're running ads on Facebook. Maybe even the AI is generating its own ads. And it's just, it's looking at the data it's put out like an experiment with three different variants. It sees, you know, variant A is doing super well and so it pushes more in that direction. And so this is exactly what you'd be doing if you're hiring an ads agency as well, they're going to be experimenting with lots of different copy, lots of different graphics and images or videos and so on. And so you could run the same thing basically with an AI. Where this might get a little bit challenging is that the content that gets created by the AI, it's not always going to be amazing. If you're doing video content generation with AI or graphics being generated with AI, it won't necessarily, you know, be as good as what a human can put together. I'm sure there are some very good AI generated ads running by right now. But if I had to guess, the human generated ads are running better, but things like changing a line of copy, for example, that it's very easy for an AI to go and change and then, yeah, see how it's performed and then improve on it. Or if we're talking about Google Ads where, you know, you don't have images necessarily, you're just trying to rank on Google search ads. I can very easily change the copy, basically.
- [30:37]
A
Yeah. Or, you know, it's funny because like the humans are becoming the API layer in the sense of like create a folder and every day have like create a new ad where you're yapping for 30 seconds and then let AI kind of edit it and let AI go into that folder and edit it from there versus going and creating a fully AI ad. Less context, less human layer. You know, I think my belief is the best ads are actually, I mean, if you have millions of dollars to spend, yes, the best ads are hiring the best humans on the planet to go and do that. But not everyone has millions of dollars to spend or hundreds of thousands of dollars to spend on the best agent, you know, ad agencies on the planet. We're not making super bowl ads here. So the way to do it is a mix of humans plus AI to get you to a really, really quality level. And I just think that, yeah, if you just integrate this into your ads loop, you kind of get the best of both worlds. You're getting the human feeling of an ad, but you're getting the AI optimization around it. And the game around Facebook ads in general is a game of volume. You know, people forget this, but you know, it's really this, this game around a bunch of different narratives and hooks and seeing which one works. So it's basically taking your one product but trying different angles and hooks and different types of, you know, people. A female, a male, an older person, a younger person, and then seeing how the algorithm reacts to, to it, and then cutting the losers, doubling down on the winners. And I could see how this loop could optimize this.
- [32:26]
B
Yeah, exactly. And frankly, we are doing this loop regardless whether you're doing it yourself or the AI is doing it. You might even have a thing in todoist. I often put schedules in todoist every three days. Remind me to look at this thing. You're basically doing that exact same thing with AI. Go look at Facebook ads in three days from now. You don't need to be on top of it every hour of the day, every day or two. Do you need to look back at what just happened and try different angles? And so if you want to try a thousand angles as a human, that's difficult. As an AI, it's pretty easy to do, to just try as many variants as possible. Obviously budget plays a part of it as well. You need to give enough budget to each variant to sort of make a decision as to whether it worked or not.
- [33:11]
A
Ellie, do you have time to show one more loop?
- [33:14]
B
Yeah, the ultimate loop, which is sort of interesting, like product feedback loop. If you actually wanted to have your entire business run on AI, just like an AI that builds itself and also gets feedback from users and then builds itself, that would be something like that. Yeah, maybe.
- [33:32]
A
That's really cool. So what you're saying here, I'm just looking at this. So this is really cool. This is basically you have an AI agent that's reading customer feedback, that's looking at your analytics, like your post hog, looking at your logs, your sentry. And based on that, it's prioritizing, it's finding out the biggest pain points, it's learning and it's prototyping features, fixing bugs, and then it looks at the actual. I don't know if it's DAU or revenue, I guess.
- [34:03]
B
I mean, you could decide.
- [34:04]
A
Yeah, you can decide. Sometimes it's nps, sometimes it's retention, so sometimes it's virality. So you can decide or you can even let the agent decide, basically. Say for each feature, pick the best possible KPI and maybe you have to approve it. But I think that could also make sense because there's certain features. Actually, the way I would think about this, Ellie, and tell me if I'm wrong here, I would actually do a bug loop separate from a feature loop. So the bug loop would be around uptime, the objective metric would be more around uptime and things like that. But the product feedback loop might be around core metrics like dau over MAU or retention or virality, stuff like that.
- [35:06]
B
Yeah, for sure. Yeah, I think that would be a great way to look at things. Yeah, this loop is sort of. It's almost like sort of the ultimate loop. It's the loop, maybe the lean startup loop. But everything you would do to run a business is like, how can we give as much information back to the AI to sort of build itself? I think this would be like, sort of a true policy, like a true company builder, where it's like the idea and everything is like, on the agent itself. I think this would be risky to do on a real business, but I'm sure we're going to see a lot of companies come out which try and do something along these lines. You just throw in a line like, hey, go build me a business that helps real estate agents. It starts building something, and if it had access to enough tools to market itself, to get feedback from users, you know, and that feedback might just be in the analytics or in the database or, you know, whatever it has access to, that would sort of be the ultimate loop. And I'm sure we'll start to see some really good businesses built like this in the next year. I've even seen, like, early experiments of it happening right now. I assume none are doing incredibly well. But yeah, like, this does feel like the future. Like, anything that can be done at a computer and AI can do so, you know, why can't it, like, even. Why can't it decide on its own features and experiment and adjust its product over time the same way humans do?
- [36:26]
A
Okay, so we've done product feedback loop, the holy Grail loop. We've done the ADS loop, we've done the SEO loop. Just take us home, Ellie. What are other types of loops that we can use this for? Is the sky the limit?
- [36:44]
B
Yeah, I think so. I mean, there are limitations to AI, but it does feel like every part of your business you could potentially set on a loop. You, as sort of the founder of your business, you wake up every day, you know, you've got your schedule, the alarm clock goes off. You are that agent Starting your loop. You're thinking, today, how can I improve my business? It's the same for the AI. How can we get it to sort of be in that same mode? You might be doing social media, video content, cold outreach, whatever. It supports all of these things that you're doing and, you know, checking every few hours or improving it and looking at some objective metric. For example, on social media, how many likes did I get? How many impressions did I get? You know, how many conversions did I get? That all of that could theoretically be fed into the AI to help it improve and iterate on itself, learn from it, and do better next time. You know, there are definitely things here which it won't do incredibly well. I'd be skeptical that you could get an AI to get like 100,000 Twitter followers, but, you know, there are a lot of parts of the business where I'm certain it can have massive impact. And, you know, you don't really lose anything for trying.
- [37:49]
A
Well, yeah, I think to me, like, you know, I wouldn't give it a loop around. Go find 100,000 X followers. You know, you kind of want to start with a smaller loop, right? Like the minimal viable loop, the mvl, in the sense of first, start by just creating incredible posts and just optimize around the posts. And maybe the verifiable outcome isn't 100,000 followers, but it's 10 likes.
- [38:30]
B
Yeah, no, I agree 100%. The outcome should not be 100,000 followers. I think even for me, it would be. I mean, impressions you're getting on a post, for example, would be, what, what, like likes, impressions? Something like that. Like, yeah, every piece of content you put out, how well is it performing? Obviously, the number of followers should go up over time. It's difficult to go backwards, but, like, how many views are we getting on average per week? That's sort of the metric I'd be trying to push up. And it's the same thing I do for myself. You know, I put out 10 tweets this week. Nine of them didn't do very well. One did do well. Why did that one do well? How can I do it better next time? And you're obviously great at this. You have a much larger social following and, you know, you must be doing the exact same thing. So it was like, could. Could we get an AI to sort of run that same process itself?
- [39:21]
A
Ellie, thank you for coming on for explaining loops, for. For opening our eyes, for sharing examples. I'll include links for where to follow Ellie on social media in the description in notes. And, Ellie, thanks again for coming on, being generous with your sauce. And I'll see you next time.
- [39:41]
B
Yeah, it's been great. Speaking. Thank you.