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- [00:01]
A
How do you become AI native? In this episode, it is an under 60 minute masterclass for how to become AI native. This is the type of content that people charge tens of thousands of dollars for. But on this channel, we're giving away for free. We're giving it away for free because I believe that people who understand how to become AI native are going to be able to outperform 99.9% of people on the planet. These are the people that are going to get raises in an economy where job losses prevail in these are the people that are going to actually create the one person, $1 billion companies. So in this episode, we break it down in the most clear way possible. You'll learn everything you need to know about how to become AI Native. What does a skill chain mean? What are skills mean? How do I think about context? How do I pipe things into cloud and how it all works together? I brought on my co founder, Theo Taba. And Theo Taba leads the world around, advising the best companies on the planet to become AI native. And in this episode he spills the sauce. This is the stuff that, you know, he keeps for ourselves and our team. But I, I begged him to come on. He came on and he does an absolute masterclass for how to become AI native and explains it in the most clear way I've ever seen on the Internet. So enjoy the episode and I can't wait to see what you build. I beg Theo Taba to come on because I think that there's such a huge opportunity in becoming AI native. And everyone's saying this word, AI native this, AI native that. But how do you actually become AI native? So Theo is my number one call with this sort of stuff. So welcome Theo to the pod. By the end of this episode, what are people going to get out of it?
- [01:55]
B
Nice to see you, Greg.
- [01:57]
A
They're going to get three things.
- [01:58]
B
One, how to become an AI native Org. We all want to know. We're going to talk about it today. Number two are two workflows in action of how to turn this AI native system that we're going to talk through into speed that unlocks signal from your customers, which is what this is all about. And Greg, you're going to be our signal in this demo and these workflows. And then number three, we got to talk startup ideas. Right? So let's talk about a few service based startup ideas. I think, you know, I'm actually going to go out on a limb and say this is one of the hottest and fastest growing markets in our lifetime in terms of this space for startup ideas. So I don't want to come off as, you know, hyperbolic or overblowing anything, but I do think this is a huge opportunity for folks.
- [02:41]
A
So by the end of the episode, people are going to find out, what does it mean to be AI native? What does that concretely mean? And they're also going to be able to figure out, okay, if I want to be the one person, $1 billion company, or I'm working in a company, how can I turn that organization into AI native? And then for the first time ever, you're actually going to allow us to peek inside of some of the workflows that you are doing within the team that, you know, are things that used to cost millions of dollars to do that you're doing by just becoming AI native. And you're going to not hold back, you're going to share all the sauce. Give me an example of some of the workflows you're going to show just to give people the taste. And then let's get right into it.
- [03:27]
B
Sure. So here's a prototype. You see, this looks a lot like Spotify because that was the system. We used this prototype, cool feature idea. You can come and listen to music live with your friends. This is just a demo, of course. This is just one workflow that we're going to show how to build this in minutes. This is fully functional, code it up and then also have it in a full testing suite so you can get direct signal from customers. This is just one of a lot of the things we're going to talk about today. So that's one kind of hint of what we're going to be showing.
- [03:58]
A
And the trick there is it's because you run an AI native org that you were able to get such a high fidelity because beautiful prototype. And we'll get into that later. All right, let's go, let's go.
- [04:11]
B
If you'll indulge me for a minute and everyone else, I want to just tell a very, very quick story that ends with our dear friend Greg here. So rewind. We're going back to the 70s. There's a four year old kid, starts playing chess in North London, becomes a master at age 13. Absolutely crushing it. Uses his chess winnings to buy a Commodore Amiga, which is an old computer, new at the time. Teaches himself to code builds this amazing game, is a lead developer on this amazing game called, I believe it's called Theme Park. They sell over a million copies, makes money from that, goes to school for computer science. The, the, you know, person is becoming a little more apparent here, but goes to school for computer science, gets a job, runs a studio building AI native games, goes back to school, does his PhD in cognitive neuroscience, is fascinated with the brain, wants to know how it works, starts a company, gets funded by none other than Peter Thiel. And they do some incredible stuff, man. They do some really incredible stuff. They fold every protein on the planet. They create an AI that beats the world's best go player, which I think has an insane amount of combinations. More atoms, more combinations than atoms in the universe, something like that. And I think Google then buys them in 2014. This, to me, I heard, was why Elon started OpenAI. Because he felt like this amount of power in one company's hands was a little too dangerous. Fast forward to 2024. The dude gets knighted and wins a Nobel prize. So underachiever, you know, real underachiever. Thanks, buddy. Do you know who this person is, Greg?
- [05:49]
A
Well, I know because look at him. And I was just with him. That's them.
- [05:53]
B
You were just with him? Amazing. Yes. You were just with them. So this is you and demis@google IO. This is Demis Asabis, co founder of DeepMind. And who's this Greg with JD.
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A
I love that guy.
- [06:09]
B
Our boy.
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A
Yeah.
- [06:09]
B
JD Choi. Shut up. Demis had a killer quote at Google I O. Running 100 miles an hour in the wrong direction is worse than standing still. He did emphasize the importance of speed, but that again, direction is super important. And that I think ties back to an AI native org and what this is all about. So you can't just run fast for the sake of running fast. You can't just have speed for the sake of speed. You have to do it in service of your customer. And you have to know what you're running to. And this is the magic. When you can work so quickly, understand the signal, understand what you're working towards, and have this AI native system set up, you can deliver incredible value for your customers and quite frankly, build a moat that makes you unstoppable. So this is what I've broken down. An AI native org is one where people manage agents, agents can read and write to the company, and the company gets smarter over time. That's. Those are the three bullets. There's of course, a lot of detail buried in these that we're going to talk about, but this is the system that allows companies to move with speed and get signal from the market and that creates their moat.
- [07:16]
A
And just because you use ChatGPT does not make you an AI native company or an AI native person. Right. That's like the, the, the thing, you know, I speak to people and I'm. And they, they say they're AI native and then I, I look into their workflows and it's. They're just using ChatGPT.
- [07:36]
B
I, I couldn't agree more. It's like if you had a website and called yourself like a tech company, it's just the gap is massive. So you've good, it's good that you're using that stuff, don't get me wrong. But we want to really build this moat. That's what an AI native org can actually unlock is this system which is comprised of people, agents in context. And we'll get into each one of those that produces incredible speed where you can produce anything in minutes. I flashed a little teaser of that. We're going to do a couple of those and then signal where you actually get to hear from the market often really quickly. And then all of that feeds back into the system and this gets smarter and better over time. Tracking so far?
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A
Yes sir.
- [08:19]
B
Amazing. Okay, well let's get into the system. Let's break this down a little bit. So you had this in your newsletter. I love this. And it mapped really well of course to what an AI native org is comprised of. So you have people at the top. Very bullish on people. Get into that in a second for strategy, taste, judgment and of course that trust piece. You have agents interfacing with the context on behalf of those people. And that context is really key. I think you called out. You have to make your company readable to agents like AI readable or agent readable. A lot of people use different terms, consumable, legible, etc. Etc. But I like readable. Let's just stick with that. And this is that shared context layer where the agents essentially have perfect vision 2020 vision on what the company is comprised of. And that interface between you and that data becomes an incredible level up and that really allows you to move with speed and again get that signal to deliver for customers. All tracking. Still, let's talk. Okay, so let's talk about people. There's no AI native org without AI native people. Let's just be super clear, like obvious. But I think people jump right to like let's get the agents and the system. If your people aren't using this and they're not understanding or do not understand how to use agents and how to use the system, it doesn't matter. You can put all the tech you want in your company, you can put all the agents, all the AI, all the tools, it's not going to matter. And so the big reframe here is what the role of a person becomes. So we have this high level how things were pre AI, where a lot of your work was done in the middle on the execution part, and a little of it was spent on either side figuring out what to do, the strategy, et cetera. And then on the end, reviewing the work. Is it good enough? Is it not good enough? What needs to change? Who needs to see it, communicating that work. The funny thing though is like these bookends are actually really important. That some might say is the work. That is like the really important, meaty part of the work. The research and the draft, all of that, of course, we had to do. But the bookends are really important. So we have this thing where AI actually eats the middle. And now with AI, you're freed up to focus on the beginning and the end, while AI, quote unquote, eats the middle. It does all of that execution work on your behalf and you get to focus on executing and deploying your judgment, your taste, all of that accrued knowledge and all of the things that make you great as a professional at the beginning and at the end.
- [10:53]
A
Work dream come true. Yeah, I think a lot of people know this now. You know, I think a lot of people are like, okay, yes, I feel, I understand that my new job is to manage agents, but they're not sure what that really means.
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B
They're not. And I think that's a great point and that's what we're going to get into with agents. But I think the main takeaway here is that everyone is a manager now. And that reframe of making sure your agents are set up for success like a manager would with their team is the unlock in terms of how you look at this. It's not, I've got a new tool like Salesforce or I've got a new tool like Excel. It's very different than that because essentially you have unlimited employees at your disposal and you need to make sure they're set up for success. Because I think Andy Grove, you know, godfather of management, once said, you know, the success of a manager is the success of their team or, you know, judge based judge based on the output of their team. And to me, that is it.
- [11:50]
A
Or as Greg Eisenberg once said to my wife, I really like turn down services at hotels because a turndown service is like when they clean your room right before bed. And they basically. I have a lot of trouble sleeping. So, you know, the fact that like everything is optimized for the sleep, you know, and like everything is like perfect. Never met a turndown service I didn't like. So you want to, you know, and I find I sleep better like that. So
- [12:26]
B
I have to bring it back to Seinfeld very quickly. Are you a sheets tucked in or untucked? In a hotel room, Greg.
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A
In a hotel room?
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B
Yep.
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A
That's a great question. That feels like a personal question.
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B
It is personal.
- [12:46]
A
I'll share it with, you know, the thousands listening right now. I would say I'm an unpucked person. I don't, it's like I don't need the constraint in my life. Like, don't constrain me, you know, if I especially I'm six foot three. I know. You know, I know people watch me on YouTube. They're like, when they meet me in real life, they're like, whoa, you're large, you know, you know, you're tall.
- [13:09]
B
So they don't say large, they say tall. Come on, give your credit. Yeah, exactly.
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A
So I would say untucked and yeah, let's keep going.
- [13:22]
B
I'm the same. All right, let's talk about agents. That second layer you have done, I think probably the most comprehensive job on the Internet. And I, I'm not being, you know, you know, we work together. I think, you know, you know, I'm shooting straight compared to everyone on breaking this down. Ross, Mike, Remy, you've had some killer folks on who explain agents. So I'm not going to spend too much time here. I'm just going to do a quick refresher and then talk about why they're important agents or models using tools in a loop. This is from Barry Zhang at Anthropic. Great engineer. You got to give them an environment, you got to give them tools and you got to give them goals. And coming back to everyone's a manager now and how to think about that. I think this kind of overview is really what I wanted to focus on. So if we look at this, you want your agents right now there's probably three levels. You're just chatting with chat GPT, that's kind of base level or Claude or whomever number two is. You've actually got some agents running and you're sitting there clicking, waiting for the next question to pop up or permission to be granted or prompt or check in to happen in your cloud code or in your codex. Approve. Approve. Approve. Maybe you have auto edits on, but someone's just there waiting for an agent to ask you if the next step is okay. The next state is the agent autonomy. And think about it like a new hire. Right at the beginning, you're having to babysit them a little bit, giving them what they need. And then over time, they're actually coming to you with stuff. And they're running for days without your oversight, maybe weeks. And it's incredible because they're doing great work and they understand everything and they're. They're absolutely nailing it. This is what you want your agents to get to. And in order for an agent to have autonomy, they need these four things. They really need these four things. They need a clear goal, they need the skills, they need the tools, and they need the context. All of those to succeed and be autonomous again. I will bring it back to your first day on the job. If I walked in to a new company and was expected to put a board deck together for the following week, on day one with no management, what would I do? Maybe the goal is somewhat clear, but a little bit fuzzy. Do I have the skills to do that? Maybe from a past job and not so much do I have the tools? I don't even know where to start. It's my first day. Do I have the context? I don't know what's going on with this company I just started. I will fail at that job. And I think people get impatient with the models or AI because they don't get what they want right away with a very simple prompt or none of this baked in. And so this is really what I want to harp on again. I know you guys have covered some of this, but I think having all of this baked in, the right goals, the right skills for your agent, the right tools for them, and that context, which we're going to talk about next, is what unlocks agent autonomy.
- [16:13]
A
The, the. The other piece on that is, you know, you. And this can go into context, but you don't know what good looks like. So the concept of an eval, can you expand on what that is and why it's an important piece of this whole puzzle?
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B
So an eval is essentially your visibility into the output of an agent. So what did the agent do and can you see how they got there and what was produced?
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A
And the thing that is produced, how does it. Like. What is the evaluation of it in terms of like is an 8 on 10, a 9 on 10, a 10 on 10, and comparing it to a desired output.
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B
So that will come from your skills yeah, the goal and of course the context all together with the right tools. So what I mean by that is if you have a standard, a quality bar, an sop, this is what good looks like that can get folded into a skill. It can also get found in context. Right. It can be a reference document of something that is the pinnacle and the goal clearly defines what success looks like. When something is great, how to measure if it's great and when it needs to be great and when it needs to be done. And so when you combine these things together and then of course, give the agent the right tools, you actually get the output that you want to the degree or quality you want over and over and over again. So we have a skills library because again, this isn't all single player. Right. When you're an AI native org, you have to think about how the team will benefit from this. How can other folks use agents and how can those agents use skills? So LCA has a skills library for our org. This is a demo ish version, meaning it's not fully complete. We didn't want to show everything, but this is ours, as you know, our skills library. So we have a bunch of skills here that people can come in, learn about and get started. You already know what skills are, so I'm not going to go too deep into this. But still, our favorite reference is, you know, Neo in the Matrix when they upload kung fu or, you know, combat training directly into his brain. Thank God none of us have the, you know, wires in the back of our necks. But this is essentially what you're doing with agents for skills. Neo loves it. I love this movie, by the way. So we have a bunch of skills here. And inside you'll see something that has five skills together. That's an interesting skill and that's what we call skill chain. And again, skill chains aren't something brand new, but essentially allows you to fire a lot of skills sequentially to make sure that your output is even better. So you don't always.
- [18:45]
A
You're going to cover that later, right?
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B
We are. We're going to. We're about to get into a demo, man. We're about to jump right in and then I'll show you how the skill chains fire.
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A
Okay, cool. So, yeah, because I think skill chains is a really important concept that actually a lot of people haven't covered. So I'm excited for that.
- [19:03]
B
Yeah. As the agents get more autonomous and as the skills and the models get better and as skills can start to call another skill, you can start to have that agent autonomy really start to show up and play a huge role in how you do the work. And that's the difference again between that AI native org versus the one that's maybe more AI assisted or AI curious or aren't AI at all. You're just waiting there and essentially you're managing on hard mode. You're assuming every agent is like an ultra junior, super smart, but you can't unlock that intelligence and you're just constantly there trying to direct it, trying to steer it. And it actually just gets frustrating in the end, and maybe you abandon it instead of really having that autonomy. Skill chains allow you to have more autonomous agents. You've already covered skills and what they are. They're markdown files. You guys know this. And then skill chains, like I said, are running playbooks back to back. Essentially, it's a macro skill with skills inside of it. So skill one then fires. Call Skill two, Skill two fires, and then call skill three. Skill three fires, and then off we go. So I'm going to give you a demo and a workflow of one thing that we use. Now. This is a for sip workflow. So normally I wouldn't have to touch anything for this to fire. This fires automatically on a trigger. However, I didn't want to leave it to a trigger picking something up by chance in this hour. So we're going to fire it ourselves and we're going to just call something in this fake environment that, you know, we at lca, we work with clients and what we're going to do is pretend there's a new prospect out there. People have heard of Spotify. Let's just say Spotify is a new prospect. So we're talking to them. We've spoken to him over months, but we haven't actually closed the deal yet. And now they're ready to get a proposal from us. How are we going to work together? We've had meetings, we've spoken about it in Slack. We have figured things out that we need to get done. And normally this would fire only on the request for a proposal picked up in a meeting transcript or sent in my inbox. So it would scan and I'll get into that in the brain and the context very soon. It would pick up that trigger and then fire this skill chain that we were just talking about. So it fires three skills here, and I'm just kind of kicking it off manually. And in about three minutes, four minutes, we'll actually see the output of this proposal and I'll break down the skill chain that went into it, but let me jump ahead just to talk to you about that skill chain. So this proposal flow, we'll get into the capture in a moment when I talk about the brain and the curate. But in the execution phase, which I just triggered, it fires three skills, creates a proposal microsite. So, you know, used to send proposals, emails, raw text, sometimes that works, but not always. You maybe want something a little more elevated. Creates a beautiful microsite. Number two is a copy skill. So it makes sure that it sounds really tight, it doesn't sound like AI, it doesn't sound like someone else, it sounds like me and in the conversations we've had. And number three, a QA skill, so it reviews it all, make sure we're not over promising anything, make sure we're not saying something completely egregious and make sure we're not making anything up that is not pulled directly from transcripts or from the data. And so once it's done, it deploys it live on a link and I can see it and then it pings me in Slack. I'll pause there. Do you have any questions on this skill chain before I jump in and see how we're doing with Claude?
- [22:42]
A
I think just the whole concept of a skill chain is like people stop at maybe a skill, right, and they're missing the chain to actually get high quality stuff. I also think that a big reason why people, you know, stop using AI as a part of like their workflow is they say, well, it hallucinates. It hallucinates. And this kind of combats a lot of that. I would say totally.
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B
Right? It does. When people say AI hallucinates one imagine again like an eager new hire who wants to impress you and will just kind of do things to get the job done without considering that it might break, break trust.
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A
It's literally fake it until you make it.
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B
Exactly. Like exacerbated, like times a thousand. Exactly. So yeah, AI loves to fake it till they make it. And your, your job is to make sure that they don't fake it or you minimize that as much as possible.
- [23:42]
A
And I also want to say one more thing about this proposal thing is lca. You know, we don't talk about it very publicly, but LCA is, I mean, works with literally most of the biggest companies on the planet building AI products, designing, engineering it, and also building AI native orgs. And a big piece of like why we're able to close Fortune 2000,
- [24:10]
B
you
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A
know, so frequently is this like being, you know, your LCA is competing against Companies that aren't doing this right, that aren't creating these, like, personalized proposals going the extra mile.
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B
And,
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A
you know, this has been the result of this, has been, you know, millions of dollars of revenue because of this. So this is like a big deal.
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B
It is. And another thing I just want to add to that is you and many folks who are, you know, coming from a sales background or sales org, knows how important speed is to closing the deal or striking while the iron is hot. And this is critical, right? So what normally happens here, what could happen here for companies is someone says, I'd like a proposal. You have to then go back, review all the notes in between meetings. When you have the time, you have to get back to them, say you're on it, you'll get them something. Then you have to confirm with the team when there's availability, you have to talk through it. And it might be days before you get them that proposal. They might have cooled off or gone somewhere else. And that's just the reality of sales and the reality of the market that we're in, in the era that we're in. You can see this already created this. I will risk opening slack. And it is here, you can see at 10:37am so what is that a minute? Two minutes ago, I got a little note from. This is just something I set up. Z is my middle name. And this is like a cool. I don't. I don't want to get too deep into the story here, but this is my more future guy and he pings me every time there's a new proposal ready for me to review. So I'll click on this. And here we go. I'm not saying this is absolutely perfect, but this is the pass that I want. This is the speed to the signal for me. Is this something that I like and it's something that we want. So home and discovery sprint for new listener retention. Boom. This is again, a demo. This isn't a real proposal. Spotify has not come to us to ask for this. I just want to clear that up. But this is the proposal that gets created. So you've got the outline, you've got the opportunity, you've got the whole breakdown.
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A
So, like, if you. Sorry, if you scroll, if you scroll up, it's like, here's what we're going to do. We're going to embed some of our, you know, here's the opportunity. So you, you know, I can break this down.
- [26:29]
B
I'm going to break it down in a second because I think there's some really cool pieces here. I want to give an overview. It's a whole plan week by week, what we're going to do, the team, how we're going to do the work, a little bit about LCA and why us, the cost. You know, I made sure that in the skill we weren't going to show real numbers. So we just want Spotify Premium for the team. And then a little outro. What's the cheapest sprint of all time? What's cool is. So I'll give you a few things. One, it looks pretty good. Like it's pretty well organized. It's pretty dialed. It's in the Spotify branding mixed with LCA's branding. So I like that. The spacing, the hierarchy, it all looks pretty dialed, which I love. So what I like here, I'll bring your attention to it should I asked it to make sure that we bake in some context from the past calls that we've had with Spotify. So you'll see a line here. So a home that works feels like a record store clerk who knows you again and the one hands you a record says trust me. So why is this line important? I'm going to show you something as a preview to the brain or the context section that we're about to cover right after this. This is a. I spun up a brain, put it on GitHub for for this episode. We have a shared LCA brain, but this is one that I put here. So you can see a brain is just. Or context is just a bunch of folders with markdown files in them. Bunch of folders to help guide the agents. Read me. You're essentially guiding the agent through your tree structure of folders and files and then helping them land at the right information. There's a bunch of different search ways or ways to architect search to go about this, but this is how we've done it. And you can see here in Spotify you can see correspondence and you can see things like meetings. And you can see this one meeting intro called to Maya. Again, this is stuff that we put here to make sure that the proposal could pull from something. So this is the first ever conversation between me and Maya that happened months and months ago. I learned a little bit about her that call. She's a vinyl person, but here you can see she said the thing about record stores. The person behind the counter hands you something and says, trust me. That's discovery. That's the feeling. So this is a cool line that she said to me in a meeting That I probably would never really remember when I'm crafting the proposal later on and coordinating with the team. What's good about this omniscient AI who sees everything, has the perfect context, is it whips up this proposal and bakes in those little moments of connection that I would love to do, given more time, we would love to do. I, I do want to give this level of personalization and then you can see it here in these moments. So there's a few of those peppered throughout this proposal, I would hope, because that's what it should do. It's a direct ask and one of our skills to make sure that it pulls from the transcripts and layers in personalization you can see here. And good luck in November. Save something for mile 8. This is because we know. I think there's something in here. So I run training NYC marathon in November, you know, again, like. And then I think somewhere else she mentions mile 8 and how that's like the. The toughest mile. So again, you're baking this stuff in on top of a great proposal, on top of doing it in literally under five minutes from the moment it was requested. So that's kind of the magic here that we can start to see when you get to this level of AI native operating. And I think something that's cool is. Again, normally I just fired this in Claude, but it's magic for me when I don't even know a proposal was asked for because I'm on the road, I'm in meetings, I'm doing something else. An email comes into my inbox and someone says, okay, we'd love to learn a little bit more. I'd love to see a proposal or show me what this might look like. The brain will understand that, which I'll explain right now. Pull in that trigger and fire this all without me ever having to lift a finger or even know that I needed to do this. So it's crazy to get that slack ping and then be like, oh, proposals ready? What are they talking about? I already have a proposal. And then I go back and see what their reference of the breadcrumbs were. And I'm like, oh, amazing. I have it review, send it off. And they're amazed. Cause they're like, how'd you get this to me? I just asked for it. And you know, again, you gotta balance that. But I think it's really cool. I'm actually, what I'm gonna do is start another one quickly as I talk into context because I don't wanna miss out on this and this one I'm gonna speak to a little bit and I hope it works, but this is gonna be. I showed you that Spotify prototype. They. You saw that proposal. They want to increase retention. So I know people use whisper flow, people use a bunch of other things. I'm a Luddite when it comes to this. I just like using the native mic feature and to me I find it works fine. So let's do that. I want to create a feature for Spotify. I want it to help increase retention. I think it should be a daily mini playlist or a daily blitz of three songs. I should be able to access it from the homepage. And when I get in, there are three handpicked songs for me. I know why they were picked. I'm able to save that playlist, share it with a friend or play the music. And the goal of this is to build this in under 10 minutes, use all the context you have, make sure it's beautiful and matches the design system and make sure that I can test retention. Okay, so I have this. I'm just going to go backslash goal here because like I said, running this command helps make sure this goal. And I'm going to run the other skill chain that I was talking about. So we have this skill chain. So I'm going to run this again. This. I'm running it on medium effort and I'm running it on auto. I would never normally do this. I would have it on high effort or ultra high. I might even. And not for this episode, but I might even have the workflow feature in now where I have sub agents going and really trying to optimize this design by going and vetting other things. And we're not going to talk about that now, I don't think. And I would definitely have permissions on. I would want to review if it's building the right thing. I would want to review some of this for this high stakes work. I love autonomy, but there are levels and places where you want autonomy to start. So while this is building. You want to talk about context?
- [32:54]
A
Yeah, let's do it. I.
- [32:56]
B
You're luck, dude. This is what this whole episode should be. Let's go.
- [32:59]
A
No, I just. I'm a big. I think this is like such a key part of the whole puzzle. So yeah, let's. Let's go into it.
- [33:05]
B
It is. This is that foundational layer that powers the agents to make you truly AI native and therefore your org. So before Greg, could you tell me what LCA's SOP is for getting back to clients.
- [33:20]
A
No, I cannot.
- [33:21]
B
No. Could you walk into let's take you back stumble upon and know what their strategy was for 2014? When were we there? 2013, 14, 15. What their strategy was or their definitions, definitions of success were for 2014.
- [33:37]
A
No, I can't. Even though I was in those board meetings.
- [33:40]
B
Exactly. And could you tell me who just got hired at LCA two weeks ago?
- [33:45]
A
I could. No, I can't.
- [33:48]
B
No.
- [33:49]
A
Yeah.
- [33:49]
B
So even I struggle with some of these things. Right. Because there's so much going on and everyone's doing this and then multiply that. When you're at a bigger company by n number of teams and people, you're essentially blind to the organization. And I think, you know, big reveal the context layer. The context layer, like literally allows you to see every. Everything and not everything in a. In a.
- [34:15]
A
The eyes open.
- [34:16]
B
I didn't expect that at all.
- [34:17]
A
I know, I know.
- [34:18]
B
We, we're storytellers here at lca, as you know. But the, the true magic is, of course you can add permissions, you can make sure what's gated, you can make sure that people see the right things at the right times. But what's cool is you're essentially giving agents 2020 vision on your company. And so when you have these questions, when you want to know these things, when you're building stuff that requires this type of intel, you have it. You don't have to wonder, you don't have to send 14 messages or wait days for the answer. You have it. And that's the magic of this context layer. So I'm going to zoom out and just walk through it at a high level. High level. Let me take you through it. There's a capture stage, a curation stage, storing in your brain or this context layer, using it to execute and then having customers experience it and that all flowing back into itself. So let's talk about capture. You have a bunch of stuff going on in your company from a bunch of different tools, places. We have Slack messages, we have meeting recordings, we have emails, we have boards and linear. We have on and on and on it goes. All of this information contains context and a lot of it is actually really helpful in producing what you need to produce for customers. So I have a routine that runs to collect this and bring it into almost like an inbox for my brain. So every hour takes it in, maybe every two takes it in and leaves it there. It brings all this stuff in and you can give rules, you can say where it pulls from and what Folders to look at. Very easy. And you can just go and build this if you haven't already in Claude, you can jump in and create a routine right here in the Routines tab and you can set it up. It's a cron job. It essentially just runs regular. It's like cowork scheduled tasks, but on steroids. So you can run these and work with Claude and create it. It'll do it. Or work with Codex. It'll do it. So you bring all this stuff in. You don't want everything in your brain, right? You don't want all of the information from everywhere sitting in every folder come, like piling up, piling up, piling up. You want to curate it to a degree. So before you file it, you have like a cure, almost like a librarian. Okay, cool. What actually needs to be in here and what do we want in here? So it reads it, cleans it up, files, it decides what's to ignore. And then some of those things might be triggers, like the proposal I spoke to you about. What do we act on? So it detects some language, acts on it. So it's a curation step. And then you store it in this brain or this company. Readable agent, readable context layer this, memory layer this, brain that. Again, like I mentioned, there's some other companies solving this major enterprise level. Other levels, like a glean, for example, Notion AI. They're like search plus context plus an agent layer, chat layer on top. If you want to get locked into that provider. A little bit of a black box on how it all works, but sometimes really great for your use case. Awesome. We like to do some of the things ourselves, and this, to us, has worked really well. So the brain here is, like I said, is just a series of folders with a bunch of different files in those folders organized in a way that agents can search and retrieve and then write back to and improve over time. That's in your brain. You have agents, people managing those agents pulling from that to execute and do the work. So they leverage the context. So I think what I covered, you know, you want to bring in the context, you want to file the context, you want to make the context legible, and then you want to leverage the context. You can direct the agents and set goals for them. You can ideate and prototype, which is what we're actually doing right now. It's cooking in Claude. You can create these artifacts, you can run skills and tasks, you can review this work, ship it, and then if I zoom out, all of that flows back into the work, into the system itself, all of those little things. And I'll get into traces in a moment or exhaust as some people like to call it from that execution. You ship it out, people get to experience it. The context actually becomes value. And that's what you're trying to unlock as an AI native org is going from that system, using it to work at incredible speeds. And then you're getting signal, you're delivering value and you're getting signal back from customers. So I'll talk about a labs page in a second on how you can ship some of this stuff out. It can realize the value and again, all of that signal from the market goes back into the system and then gets curated and then back into the brain. One little note I'll add before. Oh, you know, I want to hear what you have to say or ask about. This is a lot of the work that gets done or produced, let's say that proposal or let's say this prototype. There are a lot of decisions made along the way, which is tough in big orgs or even in small orgs to keep track of of why did we make that decision? There's a lot of work that happens along the way, a lot of documents, explorations, etc that, that are actually really valuable. So that's like cutting room floor stuff. Those are the traces that's some people call the exhaust. That's really important to come back in and then make new artifacts based on that. Maybe there's a learning or a lesson in how to get to a decision like this or how to create something. And your brain can act on all of these traces to create this and store it instead of leave it in these, this graveyard of files that no one ever looks at again. So another really cool thing about this system,
- [39:38]
A
so what you don't want to have happen is you're bringing in the wrong context. So you basically don't want to have output that agents are doing and it flowing back into the capture because you want to make sure that the human has basically said this is good, this is bad, edit this, right? So is what you're saying, you know, the experience is what is the human layer that basically allows the right type of context to flow back into the capture section and the brain section.
- [40:16]
B
It's great. Call out. So a couple of things. One is here, the humans still manage the agents, right? So you still have to have some human in the loop and some judgment on what is good and what isn't. And when you do, whenever you're chatting, whenever you're managing that agent, you will be telling it stuff and it will be remembering it and writing it back, updating the skills, making sure it knows what's good and what's not, updating the memory and maybe even piling it or packaging up into lessons. So that's one piece here on the leveraging the context. And this is more from your customers and from the market. So you're going to see stuff on how they're reacting. Are they buying more because of the new feature that you just dropped? Are they churning faster because of your new landing page? All that stuff and that signal is what will flow back into your tools and then therefore fall back into your brain and then update accordingly.
- [41:06]
A
Makes sense.
- [41:08]
B
Let's check in on Claude. Okay, it's built. So what I didn't cover briefly is the labs page. I showed you this, but this was part of a labs page. We should see. Ah, there it is. The Daily Blitz. So we spoke about this. This is what we. Before this didn't exist. It's here now, which is awesome. But before, we had this live event thing that I showed you. Let's check out the Daily Blitz and see how it turned out. All right. Slow burn sounds funky, man. Let's. Let's dive in. So this is actually pretty nice. This is like pretty clean card. It's right on the homepage like I asked. And we can click in now. You have this playlist why we built this for you. Great. It tells me why. One, you love with two fresh picks. Love that. On a little playlist. And I can play this blitz very loud in my headphones. You probably didn't hear it, but I actually have the music playing live right now, which is super cool. And I can also share it, which is awesome. Oh, I've got some friends there and I can share it.
- [42:09]
A
So just for context, the reason why LCA is building these things is know you have two. There's two parts of the business, right? There's the how to make AI native Org stuff, which is what kind of like what you covered, which is like the skills and just helping companies figure this out. But the other part of the business is designing the next iterations of apps websites that are AI native, right? So a big part of your Proposal Pro process is. I mean, there's a million design firms out there, right? So you, you want to stand out. And a way that you're standing out is by sharing these prototypes with stakeholders at potential clients. And you're kind of just showing you're using basically what you're Doing is you're using all the amazing context from the team and all the years of, six years of work of working with the world's largest companies and you're kind of like putting it in there and that's helping you kind of inspire what these prototypes look like. Is that correct?
- [43:17]
B
Totally. Right. And the new unlock here is how fast you can get feedback from some of the stuff you're producing, which is actually, as you know, in the game of product, the whole game, right, is you want to produce things and check them out and see how they feel. As much as you want to write a however many page PRD and slowly build it and get it out there over weeks or months. If you can build a prototype in under 10 minutes, that looks like this allows people to feel it, get real reactions, that's like the game.
- [43:48]
A
Well, so my question is an obvious one which is, it's like, oh, someone listening to this is like, okay, great. Theo, you have all this amazing context because you have like a team of 55 people or whatever of some of the smartest people in AI and product. I don't have that context. So for people who don't have that context but who want good outputs, be it product, be it whatever, how do they bootstrap context?
- [44:21]
B
The world is a large and lovely place, my friend. So we are not the only people who have produced beautiful work. I think for net Lu stuff, we, we're among the best in the world of thinking about AI flows, conversational ux, how to design for trust, especially with agents. Not a lot of people have done that. If you're looking back on this, this is go to Maubin. Maubin as an mcp. Now, Maubin is a library of a bunch of beautiful apps, their flows and all of the different permutations. Get Spotify's design system or another one that's similar. Create a skill around it, plug into a mob in mcp and all of a sudden you can create this in minutes as well. So this isn't only LCA stuff. Maybe the idea, okay, cool, retention. You know, we had something called the Daily five back in an early startup and maybe these ideas are easier to come by for us or faster to come by from us. And maybe our agents are more plugged into that. But in terms of producing something like this, people can do it just by using the right tools and creating the right skills and then slowly loading in the right context over time.
- [45:24]
A
Right. So I think the takeaway there is once you figure out what your output is, you want to see, like, which MCP exists for that output and then see how you can kind of scrape some of these ideas and things that are, you know, working or trending and stuff like that. Such that the, you know, the output is good.
- [45:51]
B
Exactly. So those are the tools piece.
- [45:53]
A
Yeah.
- [45:53]
B
And you give them the skills piece as well. So create a skill around the Spotify brand or whatever company brand or new brand, maybe there's some great. There are a ton of great UI skills out there as well. You give it a clear goal and then the context you have, well, maybe you're light on that if you're going from scratch, but find ways to provide context on why this would be a great product or what would make it a great idea and then feed it those MD files or kind of give it that access. This is something that I wanted to do with you. I know we're, we're coming up on time, but maybe if we can, let's. Let's do it. So on the labs page, what we didn't show is this test, right? This is just the, the labs experiment is part one. The test is part two. You can flash your phone now, Greg, and do this if you want, or I can just copy this URL and send it to you. But I'm going to show you what this test looks like and I'm going to slack you the URL, if you're cool with that. And what we're going to do, I'm going to complete this test. So right now, this is part of the skill chain that I was talking about earlier. So we have a skill chain firing for this that essentially looks like these five skills. Right. Right away there's a hypothesis we're trying to test. You know, we cruise, we blitz through it in cloud code normally, like I said, I would go through these. Then the build prototype skill, then a usability test skill, which we're going to show right now, a feedback synthesis skill and then a V2 skill. So this was cool. Imagine getting feedback right away and building it on the spot. That's even cooler. And so that's what these skills allow us to do. So if you have it open on your. Excuse me, on your screen right now, you can start it. And this is essentially what you're going to go to. There's a little bit of a usability test. This is like what a researcher might do with someone and it asks you a few questions, how often you listen to Spotify, how do you find your music? I replay what I know. And now I can open this daily Bliss. And it tells me like high level what to do. I go through the workflow. It asks me questions along the way. Oh, I, you know, how much did you want to listen to these after looking at the songs? A lot more. I love them. Go through it, etc. Etc. And then I'm just going to kind of quickly. How would you like? Very likely, except I wish it had more. More options or something like that. How valuable. Let's just give it a five. Actually, let's give it a seven. I need new music. How easy is it to open? Let's just keep going. Yes, I did great recommendations. Can you say more? No, I can't. Okay. So not sure if you were able to fill it out also, but that was the prototype. I just filled in essentially a research report. You can see the signal tab right now. There's zero out of 10. Oh one completed. That was me. I just completed it. In theory, you could send this link to 5 people, 15 people, 40 people, whatever you want. Maybe you have a community on Discord or Slack that you want early testers. You send this out, people complete it and then all you have to do is now with one answer. We're probably not going to get a great synthesis, but all you have to do is click this, this is another skill. And it'll synthesize the results. And imagine when there's 50 results or 100. Oh, actually you do. Here's some lessons. It generates some lessons. One, expectation, discovery, validation. And right there I could click plan V2 and then execute it and I could have a V2 done in the same session.
- [49:40]
A
Wow. And then I think, you know, I've seen some people talking about like autonomous product building and stuff like that. Like that's where this comes in. Right. So on this, on the Startup Ideas podcast, I talk a lot about building companies via the ACP framework, audience, community, product. And in an AI world where this exists, you know your product, just like you have this deep connection between the community and the product and you're just able to create a, a product that has a high, higher probability of success when you have something like this. Right. Otherwise you're just kind of flying blind.
- [50:20]
B
Totally. And this like the fidelity of the insights that you get, again, that perfect 2020 vision because of the context is awesome. So you can go to results and eventually you can see everyone here. This is a little custom thing that we built, but again did it all with Claude. Wasn't that challenge. It's not like, you know, it was all elite engineers doing this. We were able to do it and then you can have a report generated and when 50 people, 20 people, 10 people have of gone in and tested, you've got that. So I think a really cool opportunity. You can see the speed impact on speed. There's a little grid here. We don't have to talk about them all. But a proposal might have taken up to three days and now takes minutes. You can saw that clickable prototype. Again, not a prototype in figma, not a design prototype. A functional prototype could take one to two weeks to figure out what to build, do it, get it out in people's hands. Took minutes not only to get the prototype done, but also collect feedback and then potentially synthesize into the second version. So a lot of cool stuff. If we have five minutes. Greg, do you want to jam startup ideas or are we out of time?
- [51:25]
A
Let's give a few startup ideas and yeah, I'm curious what you got?
- [51:32]
B
Okay, I want your feedback here. My take is based on what we just showed you and based on what we just talked about, this AI native system of people, agents and context that unlocks speed for companies, that gets them signal in real time, allows them to build better things and create a moat. This is a framework that we love, that we created, that we use. You can now go deploy this if you want in what I think is the hottest, best market right now for startup ideas. If you're into services and eventually you could create products. So TBD, if it's a 30 day sprint or an AI acceleration team, you're very incredible with offers. But the game here is to niche down which you always talk about. And the three vectors is in this are industry, function and company size. So industry could be pick your niche, commercial, real estate, dentistry, whatever it is. Picket restaurants are very hot, very, very hot niche right now because they are especially fragmented and can really use this now. You can't go too small, they won't have the budget. But as you go up function, who do you want to support in that, in that industry, which team and then company size and then get incredibly good at understanding those workflows. You might already have an unfair advantage in one of these and producing the right service offering to help bring the system to those companies. Does that track?
- [52:58]
A
I mean, yeah, this is like no brainer. This is like it's stupid how good it is. You know what I mean?
- [53:05]
B
It's like yeah, I. If you're like hey, what can Lechecka do? We want to spin up five new companies. It would literally be this times five with different industries.
- [53:14]
A
Like, yeah, I mean, yeah, I think like LCA in theory, like would do this, but because like LCA focuses on Fortune 2000, there's just so many other markets that people can go after.
- [53:31]
B
Yep. And a way to prioritize it. You had a similar 2 upgrade in your newsletter, which I loved. I changed it just slightly to go from niche to general and then low frequency, high frequency. So if you can find niche workflows, so force very specific niche, industry function, company size that are high frequency and you can create those workflows and show people those on a sales call, in a brief, in a proposal, in content. Keep doing that over and over. You will, you know, you will have a layup ahead of you and then you can go to general but still very important because once they get the niche stuff, they're going to want the stuff that they do all the time that's a little less niche. And then you can go into the high value niche, but low frequency, but might have higher roi.
- [54:20]
A
Love it.
- [54:22]
B
Cool, man.
- [54:22]
A
And that's the episode, right?
- [54:25]
B
We could cook for days, my friend. But that's the episode for now.
- [54:28]
A
Yeah, I think there's. We can go so much deeper into so many parts of this, but in under an hour, this masterclass of how to become AI native, showing some examples, this has been amazing. I'm putting you on the spot, but I'm going to include as my pin comment, if you're a company doing more than $10 million a year in revenue and you're looking for a free consultation from Theo or team. Yeah. You can go and grab it. Maybe you can give away like 10 or 15.
- [55:11]
B
Absolutely. Maybe not 15, but we'll give away a few for sure.
- [55:14]
A
Okay, we'll give away 10, 10, 15 minute consultations. If you're a company doing $10 million a year or revenue, go and click the link. Theo is a criminally underfollowed account on, on, on the. On social and X. I'll include where to find him on. On the Internet too. Theo, is there one thing you want to leave people with for this episode?
- [55:40]
B
I think like all this stuff about AI native and AI everything can be overwhelming and, and can sound like a lot. And it feels like you have to be a technical guru and genius to just get started and just kind of make dents in this progress. But really to become an AI native org, think through the lens of managing agents and what those agents need to succeed and you will be well on your way to being ahead of most companies in the world. So I think just get started. Don't be scared. Scrape your knee and get stuff done.
- [56:10]
A
And if this has been interesting, just let me know, you know, because I'd love to have Theo back on the podcast again, but I want to create stuff that is valuable for you. So please let me know in the comment section like this. Com. Like this episode, if. If you got an ounce of value out of it. And I'll see you in there. I read every single comment. I respond to a lot of them. And Theo, I hope. Well, I'll see you. I'll see you soon. But I hope people like this episode and you come back on again. Thank you so much.
- [56:40]
B
Would love it, man. Thank you. Cheers.