Become AI Native in less than 60 mins

How do you become [music] AI native? In

this episode, it is an under 60inute master [music] class for how to become AI native. This is the type of content

AI native. This is the type of content that people charge tens of thousands of dollars [music] for. But on this channel, we're giving away for free. And

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 [music] people on the planet. These are the people that

the planet. These are the people that are going to get raises in an economy where [music] job losses prevailent.

These are the people that are going to actually create the one person $1 billion companies. So in this [music]

billion companies. So in this [music] episode we break it down in the most clear way possible. You'll learn

everything you need to know [music] about how to become AI native. What does

a skill chain mean? What are skills mean? How do I think about context? How

mean? How do I think about context? How

do I pipe things into [music] claude?

And how it all works together. I brought

on my co-founder Theo Taba and Theot Taba leads the world around advising the best companies on the planet to become AI native. And in this episode, he

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 [music] I begged him to come on. He came on and he does an

absolute master class 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

internet. So enjoy the episode and I can't wait to see what you build.

[music] I beg Theo Taba to come on because I think that there's such a huge opportunity in 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?

Nice to see you, Greg. They're going to get three things. 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 unlock 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 I'm actually gonna 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.

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

concretely mean? And they're also going to be able to figure out, okay, if I'm a 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

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.

Sure. So here's a prototype. You see

this looks a lot like Spotify cuz 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 coded 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

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

today. So that's one kind of hint of what we're going to be showing.

And and the trick there is it's because you you run an AI native org that you were able to get such a highfidelity beautiful prototype. And we'll get into

beautiful prototype. And we'll get into that later. All right, let's go.

that later. All right, let's go.

Let's go. 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. as

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

Amigga, 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 um I believe it's called Theme Park. They

sell over a million copies, makes money from that, goes to school for computer science. Oh, the the you know person is

science. Oh, 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 Teal.

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 in an insane amount of combinations, more atoms than 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 Open AI because he felt like this amount of power in one company's hands was a little too dangerous.

[snorts] Fast forward to 2024, the dude gets kned and wins a Nobel Prize. So

underachiever, you know, real underachiever. Thanks, buddy. Do you

underachiever. Thanks, buddy. Do you

know who this person is, Greg?

Well, I know because look at him and I I was just with him. That's Demis.

You were just with him. Amazing. Yes,

you were just with him. So, this is you and Demis at Google IO. Uh, this is Demis Asabis, co-founder of DeepMind.

And who's this, Greg?

It was JD from the team. I love that guy.

Our boy. Yeah. Jamie Choy, shut up.

Deis had a killer quote at Google IO, "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

important. And that I think ties back to an AI native or 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 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 or 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

over time. That's those are the three bullets. There's of course a lot of

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 mode.

And just cuz you use chat GBT 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 chatbt. [laughter]

chatbt. [laughter] 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 or can actually unlock is this system which is comprised of people, agents, and context. 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?

Yes, sir.

Amazing. Okay. Well, let's get into the system. Let's break this down a little

system. Let's break this down a little bit. So, you had this in your

bit. So, you had this in your newsletter. I love this. And it mapped

newsletter. I love this. And it mapped really well, of course, to what an AI native or is comprised of. So, you have people at the top. I'm very bullish on people. Get into that in a second for

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

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

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

of. And that interface between you and that data becomes an incredible level up. And that really allows you to move

up. And that really allows you to move with speed and again get that signal to deliver for customers.

All tracking still let's rock.

Okay. So let's talk about people.

There's no AI native org without AI native people. Let's just be super

native people. Let's just be super clear. like obvious, but I think people

clear. like obvious, but I think people jump right to like let's get the agents in the system. And 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

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 preai 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 etc 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

work. The funny thing though is like these bookends are actually really important. That some might say is the

important. That some might say is the work. That is like the really important

work. That is like the really important meaty part of the work. The researching,

the d all of that of course we had to do, but the book ends are really important. So we have this thing where

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 acrude knowledge and all of the things that make you great as a

professional at the beginning and at the end of the 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.

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

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

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

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 ba uh judge based on the output of their team and to me that is that is it

or as Greg Eisenberg once said to to my wife uh 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, uh you know the fact that like everything is

optimized for the sleep, you know, and like everything is like perfect. Uh

never met a turndown service I didn't like. So, um you want to you you know

like. So, um you want to you you know and I find I sleep better like that. So

I have to bring it back to Seinfeld very quickly. Are you a sheets tucked in or

quickly. Are you a sheets tucked in or untucked in a hotel room, Greg?

In a hotel room.

Yep.

Um, that's a great question. That feels like a personal question. [snorts]

It is, but I'll I'll share I'll share it with, you know, the thousands listening right now.

Um, I would say I'm an untucked uh person. I I I don't It's like I don't

person. I I I don't It's like I don't need the constraint in my life. Like

don't constrain me. You know, if I if Especially I'm 6'3. 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're tall." So

they don't say large, they say tall.

Come on, give yourself credit. Yeah,

exactly.

So, uh, I would say untucked. Um, and

yeah, let's keep going. [laughter]

[gasps] I'm [clears throat] 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

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

are models using tools in a loop. This

is from Barry Zang at Anthropic, uh, great engineer. You got to give them an

great engineer. You got to give them an environment. You got to give them tools

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 [snorts] 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

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 checkin to

happen in your cloud code or in your codeex. Approve. Approve. Approve. Maybe

codeex. 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

it. This is what you want your agents to get to. And in order for an agent to

get to. And in order for an agent to have autonomy, they need these four things. They really need these four

things. They really need these four things. They need a clear goal. They

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

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 but 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

company. I just started. I will fail at that job. And I think people get

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. [snorts] The the the other

autonomy. [snorts] 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?

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 and and and the thing that is produced does how does it like what is the evaluation of it in terms of like is an eight on

10, a nine on 10, a 10 on 10 and comparing it to a desired output. So

that will come from your skills, 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, uh this is what good looks like that can get folded into a skill. It can also get found in

a skill. It can also get found in context, right? that it can be a

context, right? that 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.

[snorts] So we have a skills library because again, this isn't all single player, right? When you're an AI native

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 work. This is a demoish version, meaning it's not fully complete. We didn't want to show

complete. We didn't want to show everything, but this is ours, as you know, our our skills library. So, we

have a bunch of skills here that people can come in, learn about uh and get started. you already know what skills

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. Um, thank god none of us have

brain. Um, 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. Um,

Neil loves it. I love this movie, by the way. Um, so we have a bunch of skills

way. Um, 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 a 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

you're going to cover that later, right?

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.

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. 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 a one that's maybe more AI assisted or AI curious or aren't AI at all. Uh you're just waiting

there and essentially you're managing on hard mode. You're assuming every agent

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. [snorts]

agents. [snorts] Um, 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 backtoback. Essentially, it's

playbooks backtoback. Essentially, it's a macro skill with skills inside of it.

So skill one then fires calls skill two, skill two fires and then call skill three. Skill three f fires and then off

three. Skill three f fires and then off we go. So I'm going to give you a demo

we go. So I'm going to give you a demo uh and a workflow of one thing that we use. Now this is a for SIP workflow. So

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

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 them over months,

them. We've spoken to them over months, but we haven't actually closed the deal yet. And now they're ready to get a

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

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 in 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

talking about. So, it fires three skills here and I'm just kind of kicking it off manually. And in about 3 minutes, four

manually. And in about 3 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,

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

curate. But in the execution phase which I just triggered, it fires three skills.

Creates a proposal micro site. So you

know used to send proposals, emails, raw text. Sometimes that works but not

text. Sometimes that works but not always. You might want something a

always. You might want something a little more elevated. Creates a

beautiful micro site. Number two is a copy skill. So it makes sure that it

copy skill. So it makes sure that it sounds really tight. It doesn't sound like AI. It doesn't sound like someone

like AI. It doesn't sound like someone else. It sounds like me and in the

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 overpromising anything. Make sure we're not saying

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. Um, 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?

Um I think uh 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

stuff. I also think that you know 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

hallucinates. It hallucinates and this kind of combats a lot of that. I would

say totally right. It does. When people say

totally right. It does. When people say AI hallucinates one, [snorts] imagine again like an eager new hire who wants to impress you um and will just kind of do things to get the

job done without considering that it might break break trust.

It's literally fake it until you make it. Right.

it. Right.

Exactly.

Like exacerbated like times a thousand.

Exactly. Um, 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. And I also want to say one

possible. 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

native orgs. And a big piece of like why we're able to close Fortune 2000s,

you know, so frequently is this like being, you know, you're LCA is competing against companies that aren't doing this, right? Um that aren't creating

this, right? Um that aren't creating these like personalized uh proposals, going the extra mile. And

um you know this has been the result of this has been you know millions of dollars of of revenue um because of this. So this is like a big deal. It is

this. So this is like a big deal. It is

I 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 or 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,

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,

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

something. Then you have to confirm with the team when there's availability. You

have to talk through it. It might be days before you get them that proposal.

They might have cooled off or gone somewhere else. And that's just the

somewhere else. And that's just the reality of sales and the reality of the market that we're in and the AI era that we're in. You can see this already

we're in. You can see this already created this I will risk opening Slack. And it is here. You can see at 10:37 a.m. So, what

here. You can see at 10:37 a.m. So, what

is that a minute, two minutes ago, I got a little note from this is just something I set up. Ziz is my middle name. And this is like a cool I don't I

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 um a new proposal ready for me to

review. So, I'll click on this, and here

review. So, I'll click on this, and here we go. I'm not saying this is absolutely

we go. I'm not saying this is absolutely perfect, but this is the path that I want. This is the speed to the signal

want. This is the speed to the signal for me. Is this something that I like

for me. Is this something that I like and is something that we want? So, home

and discovery sprint for new listener retention. Boom. This is again a demo.

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.

[snorts] You've got the whole breakdown.

So, like if you go sorry, if you scroll if you scroll up, it's like here's what we're going to do. We're going to embed uh some of our you know here's the opportunity. So you you know

opportunity. So you you know yeah I can break this 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

cost. You know I made sure that in the skill we weren't going to show real numbers. So we just want Spotify premium

numbers. So we just want Spotify premium for the team. And then a little outro.

What's uh cheapest print of all time?

What's cool is so I'll give you a few things. One, it looks pretty good. Like

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,

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

"Trust me." So, why is this line important? I'm going to show you

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

this episode. We have a shared LCA brain, but this is one that I I put here. So, you can see a brain is just or

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. Readmes.

You're essentially guiding the agent through your tree tree structure of folders and files and then helping them land at the right information. There's a bunch of

information. There's a bunch of different search ways or ways to 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 call 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

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

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."

that's discovery. That's the feeling."

Mhm.

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

omnisient 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.

[snorts] So, there's a few of those peppered throughout this um proposal, I would hope, because that's what it should do. It's a direct ask in one of

should do. It's a direct ask in one of our skills to make sure that it pulls from the transcripts and layers in personalization.

Um 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 eight

and how that's like the the toughest mile. So again, you're baking this stuff

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

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

do this. So, it's crazy to get that Slack ping and then be like, "Oh, proposal's ready. What are they talking

proposal's ready. What are they talking about? I already have a proposal." And

about? I already have a proposal." And

then I go back and see what the reference of the breadcrumbs were and I'm like, "Oh, amazing. I have it."

Review, send it off. And they're amazed because they're like, "How'd you get this to me? I just asked for it." And

uh, you know, again, you got to balance that. But I think it's really uh cool.

that. But I think it's really uh cool.

I'm actually what I'm going to do is start another one quickly as I talk into context because I don't want to miss out on this. And this one I'm going to speak to a little bit and I hope it

works. But this is going to be I showed

works. But this is going to be I showed you that Spotify prototype. They you saw that proposal. They want to increase

that proposal. They want to increase retention.

So I know people use whisper flow.

People use a bunch of other things. I'm

a lite when it comes to this. I just

like using the native mic feature and to me I find it works fine. Uh, so let's do that. I want to create a feature for

that. I want to create a feature for Spotify. Uh, I want it to help increase

Spotify. Uh, I want it to help increase retention. I think it should be a daily

retention. I think it should be a daily mini playlist or a daily uh, blitz of three songs. I should be able to access

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. Um, and the goal of this is to build this in under 10 minutes, use all the context you

have, uh, make sure it's beautiful and matches the the design system, and make sure that I can test uh, retention.

Okay, so I have this.

I'm just going to go back slashgo here [snorts] because, like I said, running this command helps uh, make sure this goal.

I'm going to run the other uh 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. Um, and I would definitely have

think. Um, and I would definitely have permissions on. I would want to review

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.

Yeah, let's do it. I

You're luck, dude. This is what this whole episode should be. Let's go.

No, I just I'm a big I think this is like a such a key part of the whole puzzle. So, yeah, let's let's go into

puzzle. So, yeah, let's let's go into it. It is. This is that foundational

it. 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?

[gasps] No, I cannot.

No. Could you walk into let's take you back uh Stumble Upon and know what their strategy was for 2014? When were we there? 2013, 14, 15. what their strategy

there? 2013, 14, 15. what their strategy was or their definition definitions of success were for 2014.

No, I can't. Even though I was in those board meetings exactly. And could you tell me who just

exactly. And could you tell me who just got hired at LCA two weeks ago?

I could No, I can't.

No.

Yeah.

So, even I struggle with some of these things, right? Because there's so much

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 uh the context layer like literally allows you to see

everything and not everything in a in a exact the eyes open that at all. I know, I know. We We're storytellers here at LCA,

know. We We're storytellers here at LCA, as you know. Um, but the uh 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 letter. So I'm going to zoom out and just walk through it at a high level. High level. Let me take you

high level. High level. Let me take you through it. There's a capture stage, a

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 in 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 clot. You can jump in and create a

in clot. 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 co-work scheduled tasks but on steroids. So you can run these and work

steroids. So you can run these and work with claude and create it. It'll do it.

Or work with codeex. 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 cur almost like a librarian. Okay, cool.

What actually needs to be in here? 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 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

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

the context. You want to make the context legible. And then you want to

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,

for them. You can ideate and prototype, which is what we're actually doing like right now. It's cooking and claude. You

right now. It's cooking and 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

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

experience it. The context actually becomes value. And that's what you're

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

getting signal. You're delivering value and you're getting signal back from customers. So I'll talk about a labs

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 I you know I want to hear what you have to say or ask about this is [snorts] 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

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. [snorts]

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 and it flowing back into the capture because you know you want to make sure that the human has

basically said like this is good, this is bad, edit this, right? So are 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 and the brain section. It's

a great call out. So a couple things, one is here the humans still manage the agents, right? So you still have to have

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

they're reacting. Are they buying more because of the new feature that you just dropped? Are they churning faster

dropped? Are they churning faster because of your new landing page? All

that stuff. And that signal is what'll flow back into your tools and then therefore it will back into your brain and then update accordingly.

Makes sense.

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. Um, all right.

Slowburn sounds funky, man. Let's Let's

dive in. So, this is actually pretty nice. This is like pretty clean card.

nice. This is like pretty clean card.

Uh, it's right on the homepage like I asked. Um, and we can click in now. You

asked. Um, and we can click in now. You

have this playlist. Why we built this for you? Great. It tells me why. One you

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. Um, and I can also share it,

super cool. Um, and I can also share it, which is awesome. Oh, I've got some friends there, and I can share it. So,

just for context, the reason why LCA is building these things is, you know, you have two there's two parts of the business, right? There's the how to make

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 uh with stakeholders at 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 um and all the years of of you know 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? 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'd 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.

Well, so my question is is is is is an obvious one which is it's like oh someone listening 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 [sighs and gasps] you know don't have that context but who

want good you know good outputs be it product be it whatever how how do a bootstrap context.

The world is a large and lovely place my friend. So we are not the only people

friend. So we are not the only people who have produced beautiful work. I

think for net new stuff were 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 Mobin. Mobin has an MCP now. Mobin is a

Mobin. Mobin has an MCP now. Mobin 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 and MCP and all of a sudden you can create this in minutes as well. So this isn't only LCA stuff.

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

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.

Right? So I think the 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 and things that are you know working or

trending and stuff like that such that the you know the output is good.

Exactly. So those are the tools piece.

Yeah.

And you give them the skills piece as well. So create a skill around the

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.

Um, this is something that I wanted to do with you. I know we're we're coming up

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 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 uh earlier. So, we have a skill chain

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

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 um 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

test. This is like what a researcher might do with someone. And it asks you a few questions. How often you listen to

few questions. How often you listen to Spotify?

How do you find new 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, 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 uh very likely

except I wish it had more more options or something like that.

Mhm.

How valuable? Uh let's just give it a five. Actually, let's give it a seven.

five. Actually, let's give it a seven.

I need new music always.

How easy is it to open? Let's just keep going. Yes, I did.

going. Yes, I did.

Uh great recommendations.

Can you say more? No, I can't. Okay.

So, not sure if you uh were able to fill it out also, but that was the prototype.

I just filled in essentially a a research report. You can see this signal

research report. You can see this 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 five 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 on 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.

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

like that. Like that's where this comes in, right? So on this on the startup

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 you have this deep connection between the community and the product. Um, and

you're just able to create a a product that is has a high higher probability of success when you have something like this, right? Otherwise, you're just kind

this, right? Otherwise, you're just kind of flying blind.

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

everyone here. This is a little custom thing that we built but again did it all with Claude. Wasn't that challenged.

with Claude. Wasn't that challenged.

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 kind 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

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

minutes. You can saw that clickable prototype. Again, not a prototype in

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,

cool stuff. If we have five minutes, Greg, do you want to jam startup ideas or are we out of time?

Let's give a let's give a few startup ideas. Um, and and yeah, I'm curious

ideas. Um, and and yeah, I'm curious what what you got.

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 uh incredible with offers but the game here is to niche down which

you always talk about and the three vectors is industry function and company size. So industry could be pick your

size. So industry could be pick your niche. Commercial real estate,

niche. Commercial real estate, dentistry, whatever it is, pick it. Restaurants are

a 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? Uh, which team? And then

that industry? Uh, 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 I mean, yeah. This is like no-brainer.

This is like it's it's stupid how good it is. You

know what I mean?

It's like Yeah.

I If you're like, "Hey, what can Leecha do? We're going to spin up five new

do? We're going to spin up five new companies." It would literally be this

companies." It would literally be this times five with different industries.

Like that's literally do.

Yeah. I think like LCA in theory like would do this. Um, but because like LCA focuses on Fortune 2000, there's just so many other markets that

people can go a after.

Yep. And

for a way to prioritize it, you had a similar uh two-up grid in your newsletter, which I 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

frequency. So if you can find niche workflows, so for 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.

Um, 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.

Love it.

Cool, man.

And that's the episode, right?

We could cook for days, my friend, but that's the episode for now.

Yeah. I think there's we can go so much deeper into so many parts of this. Um,

but in under an hour, this master class of how to become AI native showing some examples. This has been amazing. Um, I'm

examples. This has been amazing. Um, 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. Uh,

yeah, you can go and and grab it. Um,

maybe you can do give away like 10 or 15. Um,

15. Um, absolutely. Uh maybe not 15, but we'll

absolutely. Uh maybe not 15, but we'll give away a few for sure.

Okay, we'll give away 10 10 15 minute consultations. If you're a company doing

consultations. If you're a company doing $10 million a year revenue, go and click the link. Theo is a criminally

the link. Theo is a criminally underfollowed uh account on on on the on social and act. I'll include [snorts] where to find

act. I'll include [snorts] where to find him on on the internet, too. Um Theo, is there one thing you want to leave people with uh for this episode?

I think like all this stuff about AI native and AI everything uh can be overwhelming 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

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.

And and if this has been interesting, just let me know, you know, because uh 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

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

it. And I'll see you in there. I read

every single comment. I respond to a lot of them. And uh Theo, I hope well, I'll

of them. And uh Theo, I hope well, I'll see you I'll see you soon. But I hope people like this episode and uh you come back on again. Thank you so much.

You too. Would love a man. Thank you.

Cheers.