Speaker 1: AI agents, AI agents. I know you're big AI agents
Speaker 1: right now, let's talk about AI agents. How you came
Speaker 1: to this epiphany about AI agents. Explain what an AI
Speaker 1: agent is like, let's break down. Let's have this technology conversation.
Speaker 2: Absolutely, can y'all see me, I'm coming in and Claire
Speaker 2: serve all right? Bet well?
Speaker 3: First of all, for those who don't know nineteen keys
Speaker 3: well with the thought leader, I lecture around the country,
Speaker 3: and what my gift is is pattern recognition, Right. I
Speaker 3: see things before most people, and I then try to
Speaker 3: take that information, synthesize it and give it to the
Speaker 3: average person so that they can actually use it in
Speaker 3: some useful way. Most people are too busy to understand
Speaker 3: things that's going on in technology, science, economics, right even
Speaker 3: when we talk about cognition in a state of thinking
Speaker 3: as the lack of critical thinking today. And I'm trying
Speaker 3: to make sure that the culture is a breasting up
Speaker 3: to date and all of our global entrepreneurs who are
Speaker 3: focused on generational wealth right now, So that's that is
Speaker 3: that you have a Is that your phone?
Speaker 2: Yeah? That was the team hitting me up. Let me
Speaker 2: go on, So.
Speaker 3: AI agents and I want to keep this in simplest terms.
Speaker 3: And I think we was here before, and I was
Speaker 3: explaining why we're now in that era where you don't
Speaker 3: have to over explain things because the beauty of it
Speaker 3: is everybody does have AI, right, And if you're intelligent
Speaker 3: enough to say, okay, what is an AI agent, It's
Speaker 3: a semi autonomous right, artificial intelligent machine, right, That is
Speaker 3: software that can act for you. So you can set
Speaker 3: up an AI agent and say, every single day, I
Speaker 3: want you to tell me what the weather is.
Speaker 2: It can be something as simple as.
Speaker 3: That, or you can say every single day, and you
Speaker 3: can have more complex things where you can feed these
Speaker 3: agents what they call skills, and these skills allow them
Speaker 3: to do repeatable things, right.
Speaker 2: And so sometimes I have my AI agent hold on me,
Speaker 2: tell the team that just relax on the texting.
Speaker 3: Sometimes I have my AI agent send me briefs every
Speaker 3: single day going through all of my content. And what
Speaker 3: I've done is I built out what they call a
Speaker 3: second brain system. Now, I had predicted that these things
Speaker 3: were going to occur a few high level conversations ago,
Speaker 3: maybe about two to three years, right, And now yeah, yeah,
Speaker 3: and that was finally in that place where I can
Speaker 3: actually build out a lot of the things that we
Speaker 3: were predicting when at first it was we would tell
Speaker 3: you about them, but we didn't actually have the ability
Speaker 3: to act on them, right. And so the second brain
Speaker 3: system is essentially me using something called Obsidian, which is
Speaker 3: this boat where you can keep all your data in.
Speaker 3: And what I've ingested inside this boat is as much
Speaker 3: data as.
Speaker 2: I possibly can one myself were coming in clear, I
Speaker 2: know it sounds like there's a little that going that
Speaker 2: to be the case. We got one second Mike mead
Speaker 2: out the check and we're trying to cook today. Oh yeah,
Speaker 2: we got you, all right, bad bad bet all right.
Speaker 3: So we build out a second brain system, and everybody
Speaker 3: should write that down and put it in a chat
Speaker 3: if you can, and you use something called Obsidian vote.
Speaker 3: This Obsidian vote is essentially a space where you can
Speaker 3: collect all your data. So I ingested all my data
Speaker 3: from Twitter, I ingested my data from YouTube, Facebook, Mini
Speaker 3: chat analytics. I've done it across anything that I can
Speaker 3: possibly ingest, from claw to chat GPT. Therefore I can
Speaker 3: see all of the things that I've been working on,
Speaker 3: thoughts that I forgot about, patterns that I have that
Speaker 3: are reoccurring across time, all the way from the last
Speaker 3: ten years up until now.
Speaker 2: And what ended up happening is there's actually a graph
Speaker 2: that it shows you.
Speaker 3: You can barely see it, but I'll show you right
Speaker 3: here to where your graph is like a knowledge graph.
Speaker 3: Each one of these points is a piece of data, right,
Speaker 3: So this might be a tweet right here that then
Speaker 3: connects to something. Right, this one says, this work gets
Speaker 3: harder because we.
Speaker 2: Independent while I was cooking a day, understand me.
Speaker 3: But then each one of these is connected to more information.
Speaker 3: This might be YouTube, this might be Twitter, These might
Speaker 3: be data that is training, something on my dashboard, or
Speaker 3: something else might be from Instagram. And then what I
Speaker 3: do is I take each one of these and I
Speaker 3: connect the patterns across time. So then I could feed
Speaker 3: that to my agent and it becomes his mind. And
Speaker 3: so it's a little more technical on the back end
Speaker 3: as far as making sure it has a consistent memory
Speaker 3: so that every time you give it a task and
Speaker 3: remember what you told it to do. So therefore it's
Speaker 3: checking on that memory, and then it's growing as it's
Speaker 3: consistently working with you. So these AI agents are things
Speaker 3: that people are using inside texts inside telegram right, and
Speaker 3: they're running their businesses on it after they get it
Speaker 3: set up. The first system to set up is first
Speaker 3: getting it to become context aware. So you want to
Speaker 3: first start with the context of sending all your data
Speaker 3: in and once it have that data, then you can
Speaker 3: start utilizing it in a way where you're telling it
Speaker 3: what to do, and then you can start building skills
Speaker 3: upon it, and then it gets to a point where
Speaker 3: you can start being autonomous with it. One of my
Speaker 3: greatest use cases so far was me creating a CRM
Speaker 3: from all of my customer data over the last ten
Speaker 3: plus years. Right, So now I was able to find
Speaker 3: two hundred and forty six thousand customers to where I
Speaker 3: now get to see who are my top one hundred.
Speaker 3: I'm seeing people who'd involve fifty to one hundred products.
Speaker 3: I didn't see people who'd inbolt something from every single
Speaker 3: thing that I've bought then showed up in person because
Speaker 3: I was able to take all of that data then
Speaker 3: rearranging into this system to where I now know who
Speaker 3: exactly this person is when they bought right, et cetera,
Speaker 3: and now I can retarget them or just call them
Speaker 3: directly I'm literally about to call my top.
Speaker 2: One hundred customers.
Speaker 3: You feel me because we built out a system now
Speaker 3: where my AI agents can show me who those people
Speaker 3: are when they buy it, can send me a pin.
Speaker 3: You can tell me when their birthday is. I can
Speaker 3: have an AI agent send them a birthday card if
Speaker 3: I want to, or send them a gift card.
Speaker 2: You.
Speaker 3: Essentially, it's like having personnel working on your behalf that
Speaker 3: you're giving specific tasks to do certain things. And the
Speaker 3: agentic AI economy is about to boom, right. This is
Speaker 3: where the big opportunities are looming. And a lot of
Speaker 3: people are afraid because they're looking at the fact that
Speaker 3: AI agents are essentially autonomous systems. So anything that has
Speaker 3: a system can be automated. That's the whole idea. It's
Speaker 3: not that it's replacing jobs or humans. Rather it's replacing systems,
Speaker 3: and what people end up doing they end up working
Speaker 3: in at You're becoming called in the machine and so
Speaker 3: now they're looking at saying that, oh, we.
Speaker 2: Only need machines to do these jobs.
Speaker 3: So people that were doing machine like jobs are getting
Speaker 3: replaced rapidly and faster and faster.