The AI Reflexivity Loop (this moment will define you)

Welcome ladies and gentlemen. What we

are about to go through is a comprehensive breakdown of how AI is rewiring the global economy, what it

means for your portfolio, what it means for jobs, what it means for businesses.

And by the end of this presentation, you will have a complete map of all the different moving parts. And the place

that I want to start is where exactly the problem is. The biggest

technological shift in human history is without question happening right now.

Most people are watching it from the sidelines because they can't see how the pieces fit together. They see chatbt, they see Nvidia stock going up, they see robots on Twitter, but they have no

framework for understanding how it all connects or what it even means for them.

And so the presentation for today is for two types of people. Number one is investors and traders. And you want to

have a clear idea of where exactly do I put my capital if AI is changing the actual structure for some of these companies and understand where exactly

do the supply chains begin to function.

How do I think about timing and signals within this? and how are all these

within this? and how are all these different moving pieces actually fitting together? The actual names of the

together? The actual names of the company's data and framework, not just hype around headlines. And then the other type of person this presentation is for is for business owners or

employees. And I have both of those

employees. And I have both of those because what you want to do is know if your industry, your company, your job is about to get disrupted. And if it is,

you want to know what do you do about it before it's too late. And so you need a map for what is changing and when both

groups need the same thing, a complete map of all the moving parts. And so by the end of this presentation, you will have a framework that organizes all of

AI into three simple buckets. Number two

is an understanding of how those buckets connect GDP, rates and markets and a company by company map of who is winning, who is losing and why exactly

that is happening. And then number four, the specific signals to watch to know when things are changing. This is really key because you don't want to just have an idea or a headline. You need to

connect that to tangible signals to say where are we at in this entire process?

How much risks exist? and how do we think about the escape velocity of all these changes that are taking place? And

then number five, a clear picture of which jobs sectors and businesses are at risk and which ones benefit. And so the three forces that are about to merge

into a self-reinforcing loop that will reshape every single sector, every job, and every portfolio on Earth. And here's

the thing, it's likely to happen by the end of 2026 into 2027. So there are three forces and this is what I break everything down into for thinking about

AI right now. It's not that complicated.

But at the end of the day, what happens is that there's only going to be two outcomes. There's going to be the

outcomes. There's going to be the disruptors and the disrupted. AI

eliminates the middle ground. Every

company, every job, every sector, it gets pushed to one side. And so the goal of this is to say what is the map so that you end up on the right side. You

know I was just listening to uh the recent podcast by Naval on his channel and he breaks this down incredibly well

where he talks about AI basically destroys the ability for people to be average in a space. Basically AI creates so much leverage that the top performers

basically become the best, take all market share and creates more of a winner take all effect. So if you're in a domain that you are not actively

striving or have a significant edge and moat in being the best in that domain and you're not just an average person in that domain, then you have a significant problem that you're going to begin to

face. And so the question is, are you in

face. And so the question is, are you in the category of becoming a disruptor or be getting you're being disrupted? And

so the map is going to be laid out in this presentation. As a couple quick

this presentation. As a couple quick definitions, you'll hear some terms right here that you want to begin to understand on software. As a quick note, this entire slide deck presentation will

be linked below and I will have some other things for you at the end of this presentation that will help you even more. and then an entire breakdown of

more. and then an entire breakdown of how to think about more of these moving parts as well as if you stay till the end, I also have a live stream that I'm going to be doing Monday that further

breaks down these things and says where are the opportunities that exist? I'll

be sharing some proprietary models on that live stream for everyone and everyone who is on that live stream will be able to get all of those 100% free.

And so you have to be on the live stream to be able to get that. And so the couple terms you'll hear here here is about GPUs, APIs, ARR, LLMs. These are

very simple terms. They're not meant to complicate anything. I have the

complicate anything. I have the definitions right here just for graphic processing units. API just means

processing units. API just means application processing interface hardware terms. ASIC, application specific chip. It's just a a chip that's

specific chip. It's just a a chip that's for a specific application. That's all.

Same thing with some of these other explanations right here. And I'll

explain them as we go. so that none of them are going to be complicated. Again,

check the link below. If you're not a subscriber on the Capital Flows research substack, you're going to want to go be there and get all of the breakdowns and research, especially the live stream that we're going to have on Monday. And

then this entire slide deck will slide deck will be linked below. Okay, let's

jump right into everything. Everything

in AI fits into three very simple buckets. And I break these down into

buckets. And I break these down into these three buckets because this is really how I've approached the market and the research that I am doing because

what I want to know is how do I break down these really complex moving parts into simple buckets that tell me where exactly we are going and the first

bucket is software and this just means what are the actual AI programs Chad GBT claw gemini deepseek what's their revenue right now Right. So this is

really the brain of the system. If you

remember when chat GBT first came out, you know that first version, you can see it in AI videos or just the output compared to what we have now is, you

know, we have a insanely better software side. We have an insanely better AI side

side. We have an insanely better AI side of what exactly we have. And so what has happened over the last three weeks and one of the reasons why I I've spent so

much time over the last three weeks just retooling every single thing that I've been doing because the changes that we've seen in AI over the last 3 weeks

are in my opinion pretty close to the way that it felt when Chachbt first came out. So if you remember Chacht came out

out. So if you remember Chacht came out and everyone was just blown away.

They're like how in the world is this possible? Everything is going to shift.

possible? Everything is going to shift.

this is going to completely move around everything, you know, and and it's just kind of this reorientation that just feels surreal. What people don't really

feels surreal. What people don't really realize is that exact same thing has happened over the last, you know, I would say 3 weeks ago now. And it's

still not appreciated because it hasn't impacted every single sector yet because they're still trying to make the user interface of it a little bit more easy to interact with. But once that happens,

which I think is going to be in the next 2 weeks or less, people are going to begin to say, "Oh, wow. That's something

really massive that I need to understand and be on the right side of or else I'm going to have some significant risks."

So, three buckets. The software side, that's number one. The hardware side, that's number two. So, chips, data centers, power, Nvidia GPUs, server

farms, electric grid, right? $666

billion is being spent to build out this in 2026 alone, just for this year. This

is the infrastructure. This is really key. Without the infrastructure, all of

key. Without the infrastructure, all of this bucket number one, the software can't run. And then number three is all

can't run. And then number three is all the physical machines, robots, self-driving cars, drones, Tesla

Optimus, Whimo, Ouro trucks. 100 100,000

humanoid robots are expected to ship in 2026. That's this year. This is the

2026. That's this year. This is the hands and feet. And so here's the thing.

When you have all three of these buckets operating in a reflexive feedback loop with each other, that is when the economy is going to get retoled. And we

are in the process of beginning to see this take place. And it is critical to understand each of these because with only one of these, you wouldn't have a major retooling. But if you have all of

major retooling. But if you have all of these combined and a reflexive feedback loop between them, that's when everything changes. And so it's when

everything changes. And so it's when each part of the system makes the other part stronger and it accelerates. So

when you have better software, it needs a you know more and better hardware and that better hardware makes smarter AI and then as a result that allows smarter

AI to control robots better and then you begin to have this you know reflexive feedback loop where all of these are feeding on each other. Once this loop starts spinning on its own it

accelerates everything. That's the

accelerates everything. That's the escape velocity moment. In my view, that moment is going to occur sometime at the

end of this year. And in my view, we are still not seeing an actual change in the underlying economy, whether that is from the interest rate side or the equity

side that's even pricing any of this.

And so, where are we on this loop right now? You have on the software side and

now? You have on the software side and hardware side, you have companies spending $660 billion this year. This

link is very strong. So software and hardware are connected. They still need to be more connected with all the capback spending, but there is a connection there, right? A lot of these data centers are being built and a lot

of the hardware that's getting, you know, created right now is being used.

Every AI model generation demands two times more compute. That's kind of the idea. So hardware and software side

idea. So hardware and software side that's beginning to have a strong connection now with the software and the physical side emerging. AI can control

robots but you know you're still in this kind of pilot mode of waiting for the full release of everything. So you are still not having a complete retooling of

every single thing where software is actually controlling all robots.

Everyone has a robot in their house.

That doesn't exist yet. And then also with the physical side impacting hardware, we're not seeing hard, you know, robots actually building data centers at scale yet or even helping in the process. You have to realize, you

the process. You have to realize, you know, people are trying to be reductionistic about this. They're

saying everything is going to change or nothing's going to change or, you know, you don't get it. Robots can't do XYZ.

What you have to recognize is that these incremental processes are going to accelerate. And if someone recognizes

accelerate. And if someone recognizes that they can decrease their costs significantly by firing half their people and having supervisors oversee a whole fleet of robots instead, that is

going to drive a massive amount of rotation away from some jobs and into others. And so that's the key thing to

others. And so that's the key thing to begin to think about. Now, here's where the money really shows what is happening right now. Here are all the major

right now. Here are all the major companies and their capital expenditures, which just means capital expenditures is just what am I using to invest money. So just think about it

invest money. So just think about it like this. If you understand real

like this. If you understand real estate, you understand that you need to go build an actual apartment complex or a multif family complex before you can actually rent it out, right? And so

capex, you know, if you if you put up money for an investment and you go build the building, it's not rented out yet, but it's being built, right? Capital

expenditures is kind of like that. its

companies spending money to invest in the buildout of these different investments so that they can have a return on their investment so they can in a sense lease out all of the

different things that they are building.

Now notice here 2024 in blue 2025 in this orange right here and then 2026 estimate right here. Notice that every single major company AWS, Google,

Microsoft, Meta, and Oracle, all of them have a very very clear trend where they continue to put a ton of money into capex. And again, this is a very

capex. And again, this is a very important chart to have saved. Uh, you

know, that's why this entire slide deck is going to be available and it'll be linked below. But, you know, I would

linked below. But, you know, I would encourage you spend time thinking about where is this capex taking place? How

can you benefit from it? How can you think about that?

The $600 billion question is, okay, we're building all this stuff, how much money are we going to make from it? So,

for example, like going back to the real estate example, if you build an entire multif family property, well, how much money you going to make from that? You

need to have an estimate of that. And

how long is it going to take to lease up? How long is it going to take to rent

up? How long is it going to take to rent out those units? That's what you need to have a really clear answer for. And so,

when you look at this, it's very similar where you're saying, "Okay, here's what we're investing or here's what we've we've invested. How much money are we

we've invested. How much money are we making from it right now? Well, when you look at all of these different companies and you aggregate them together, this is what we are seeing right now. Massive

capex in orange, but revenue is growing, but it's still a massive gap. Uh the gap has been shrinking, right? But it's

still a really significant gap. Right?

Here we currently have or you know for 2027 the expectation is a 8x gap, right?

Right now we have an expectation of a 12x gap and you know we have a 18x gap in 2025 that took place. So this is really key to think about because if we're not making money on all this AI

infrastructure then that's when a recession could take place. That's when

you could actually have the risks that people are talking about. But again when people are talking about these risks on the upside or downside or whatever it might be all we have to do is say hold the phone. What is the data that we need

the phone. What is the data that we need to look at to understand where we're at in this entire space, in this entire cycle? And this is the data that you

cycle? And this is the data that you want to be looking at. And so, why might this just not be another dot bubble? Why

isn't this just a speculative mania that, you know, everything is taking place in? Well, several reasons. Number

place in? Well, several reasons. Number

one, the buyers are the richest companies in history. Apple, Microsoft,

Google, Amazon, they all have over $500 billion in cash combined. they can

afford to overspend for 3 to four years without going bankrupt. Now, that

doesn't mean we can't go into a recession or we can't have issues or something like that. But is that going to be a 2000 bubble type of situation?

No. That's not on the table. The cost of not building is actually higher than the cost of building. If Google doesn't build out AI infrastructure and Microsoft does, Google loses. And here's

something that people don't really understand, and it is that these companies are under a constraint. If

Google doesn't adapt and invest, they could lose market share and you could have another company come in and take over as they become irrelevant. Now, I

don't think Google's become irrelevant just because of the leadership, but one of the reasons why is because they're actually investing. They're taking that

actually investing. They're taking that bet. And again, people who are just

bet. And again, people who are just always going to be conservative or whatever that might be or always bring up the bearish case, they're not thinking about the trade-offs. They're

not the risktakers that are allocating money as the CEOs. They're not actively under the gun making the decisions about the tradeoff they have to take. Not, oh,

is this a good or bad decision, but is this a tradeoff for another thing and another force that's going to begin to impact them? Competitive pressures force

impact them? Competitive pressures force spending regardless of for regardless of short-term returns. And so AI is

short-term returns. And so AI is generating real productivity games already. JP Morgan had a a breakdown

already. JP Morgan had a a breakdown where they talked about how AI tools to do day the daily work of 11,000 employees and how that that's really

already taking place. We're already

seeing layoffs of some employees in this entire space. You know, Amazon's robots

entire space. You know, Amazon's robots have, you know, saved 10 billions in logistics in 2025 alone. You know, those are some really big numbers and significant changes that we were seeing taking place. And so the signals that

taking place. And so the signals that tell you if the thesis is right or wrong. So this is how you want to break

wrong. So this is how you want to break down things. Again, when you are

down things. Again, when you are operating in financial markets, right?

Like that is my job. That is what I focus on. That's what I do. If you are

focus on. That's what I do. If you are operating financial markets or the economy where you're running a business, you don't have the luxury to just say, "Oh, I only want to know the bull case that confirms my view. I don't want to know the bare case or I don't want to

have a redundancy plan." That works for people who are trying to get clicks. It

doesn't work for risk takers. And if

you're hearing a risk taker, you understand how important it is to understand these signals. On the

positive side, you want to look for tech companies continuing to raise spending targets for capex. That's why, do we begin to realize why the Nvidia earnings

call next week is going to be so important? It's because it's going to

important? It's because it's going to give us a view into that entire thesis that's taking place. You also want to look at TSMC chip factory revenue. Is

that growing month- overmonth consistently? It's actually one of the

consistently? It's actually one of the companies that has monthly fundamental data. You should probably watch that

data. You should probably watch that because it's so integrated with all these companies. More than 30% of big

these companies. More than 30% of big companies deploy AI at scale. So you

want to look at how are companies actually using AI and is AI revenue exceeding the 25% of cloud revenue. So

think about that those changes that are taking place and then look for are we having successful IPOs of anthropic open AAI at current valuations. Those are

going to be massive catalysts this year, I think, are going to take place sometime this year. And so the red flags that you want to be looking for, and if you actually understand both of these changes and these things, then you'll

understand the the system and the moving parts of the system that we're in. So

any tech company that's cutting their spending budget, even 10% for capex, that's a big red flag. Chip equipment

orders, you want to watch those very carefully. AI revenue, it needs to stay

carefully. AI revenue, it needs to stay really significant while spending hits 600 billion. So, we need to see revenue

600 billion. So, we need to see revenue continue to rise. And then we actually need to see companies reporting. If

they're not reporting that AI is actually saving money, that's an issue.

And then finally, when you have all these companies take out so much debt, for example, Oracle, then if you have a spike in interest rates, that can actually have a massive impact on the financing abilities of again companies

like Oracle. And I'll give you a

like Oracle. And I'll give you a complete breakdown and dashboard of this at the end. The exact dates, exact sources, the exact catalyst that you want to look at. Okay. So going back, we have these three buckets right here of

okay, we have software, we have hardware, we have the physical machines.

Now what I want to do is I want to break down each of these so that you understand them. First bucket again, AI,

understand them. First bucket again, AI, software, the brain of the system. Okay?

They build AI models. So software can read, write code and analyze and think, right? Businesses pay monthly fees. They

right? Businesses pay monthly fees. They

get, you know, monthly recurring revenue. Now, check this out. Open AAI,

revenue. Now, check this out. Open AAI,

they make CHBT, you have Anthropic with Claude and Google and Gemini, right?

Open AAI is making $20 billion a year right now. Anthropic 14 billion and you

right now. Anthropic 14 billion and you have Google making around 15 billion plus their cloud revenue service.

Combined, you have basically 30 billion in annual revenue. That's two years ago it was near zero. So, think about that.

30 billion at, you know, nearly two years ago is zero. So they're spending, you know, 660

zero. So they're spending, you know, 660 billion and they're making about 30 billion. If you had it have another, you

billion. If you had it have another, you know, crazy number like a 10x and you start making 300 billion, right? Like

that's a massive return in revenue based off of those type of capex numbers, right? We don't know how fast it's going

right? We don't know how fast it's going to go, but I'll break down that escape velocity for you. And so the major AI companies that you want to watch is

OpenAI, Anthropic, Google, Meta AI, XAI, uh, Mistal AI, and then Deep Seek. These

are the annual revenues of all of them.

These are the key stats that you want to be watching, the valuations that we sit at and what they make right here. And so

one year ago, CHAGBT, you have the race for this market share. CHAGBT was about 86% of market. Now it's at 64%. That's

actually important to note because this market share is one of the things that's driving markets, financial markets right now because open AI has so many of the commitments for the changes that are

taking place. When you have this type of

taking place. When you have this type of market shift, that can impact how companies view credit risk. If everyone

is long or has exposure to OpenAI and these other companies, for example, any of the large companies, if they have a lot of agreements with OpenAI and then things shift to Claude or Gemini, that

can create a massive shift in the market, right? And so Chat GBT's share

market, right? And so Chat GBT's share has market share has dropped. So the

market is not really settled about okay, who exactly is going to take dominant market share. And so the winner of this

market share. And so the winner of this race will be worth trillions. But we

don't know who the winner is yet.

Anthropic, the breakout that you need to know about this, $40 billion a year in revenue, up from 2.5 just one year ago.

Again, massive growth rate in the billions right here. 2.5 comes from one product, Claude Code, the AI coding assistant. I will say that over the

assistant. I will say that over the last, you know, several weeks, all the new updates with cloud code have fundamentally changed all of the work

processes that I am running in markets, in you know, just business in all of the different research that I'm doing. And

so now 70% of Anthropic's own engineering code is written by AI. There

was a I think interview with one of the guys from Anthropic talking about you know creating Claude and how they're creating Claude by using Claude and they're working in an iterative feedback

loop to use AI to create AI and that's what we're really seeing right now. So

when we had that launch of co-work you actually had $2 trillion in global software stock stocks crash in just one day right like that is a real impact

right the the shift now that needs to take place is people adapting to these changes in software right like that's why the IGV ETF is down so much right

now and so the cost of intelligence as well is collapting collapsing so the AI API pricing is collapsing and in decline before it was you know about two years

ago. So you will notice that we have had

ago. So you will notice that we have had such a massive change from 2023 where you had GBT4 and then 2020 uh or 2024

right here. You have the price for API

right here. You have the price for API tokens dropping. Now you know we're

tokens dropping. Now you know we're 2025. We're incredibly low much lower

2025. We're incredibly low much lower than we were a couple years ago or even a year ago. And so what you're seeing is the cost of AI drop 97% in two years. So

what cost 30 million uh you know 30 million words and cost of in 2023 is now 15 cents today. Why this matters because you're having demand explode but it's

getting cheaper to use. And so this is really where we come to the entire China situation because you have deepseek in China. It costs 5.6 6 million to build

China. It costs 5.6 6 million to build and it now costs 28 cents per million words and you know high math score built by a hedge fund with 248 ships. On the

other hand, OpenAI in the US cost 500 million to build and it has a higher cost and a lower math test score backed by hundred billion in funding. Now to be

fair, there are some differences but the takeaway here is that the cost of building these is beginning to drop in one sense. um or excuse me, the cost of

one sense. um or excuse me, the cost of using them is beginning to drop in many ways. And so what you need to begin to

ways. And so what you need to begin to understand is these different operating expenses for these different things. So

it's, you know, 90x cheaper to build, 9x cheaper to use, and you have higher test scores for DeepSeek right here. That's

why it actually impacted Nvidia when it actually was launched. And so the key tension is how much it costs for all of these different moving parts. And so

while the training cost in AI those are actually growing. So there's all this

actually growing. So there's all this money being spent on actually training and growing the actual companies and the software but the inferences cost using

AI again right here using it is dropping. And so this is why the

dropping. And so this is why the hardware investment side keeps growing because when you want to build frontier models you have to use and invest more.

That's why we have all of this capex beginning to take place and why demand is exploding. So that's why we've had

is exploding. So that's why we've had this rally in stocks during this period of time. And so you have all these

of time. And so you have all these different names making money during this period of time on the a in the AI space.

And so when you have this type of change where you have a lot more spending taking place in certain sectors and demand in all this buildout, but then you also have disruption and retooling.

That's why you have this divergent here where the semis are at all-time highs, but then you have the software ETF at lows. And then you'll notice that you

lows. And then you'll notice that you have things like utilities actually rallying to all-time highs right here and actually pushing up because electricity costs are surging because of

all this buildout. Well, those are very important things to understand. So, what

does that mean exactly? If you're an investor, the AI software race is not won yet. The market share is shifting

won yet. The market share is shifting fast and the pure play AI companies are you know preipo still but they will be the biggest IPO in history. How the

market accepts those is going to tell us a lot about where we are in the cycle.

So public plays like Google or Meta or Microsoft or Palunteer these are already the pure plays that we have in many ways for AI or for this capex buildout. So

you want to watch the IPO calendar for 2026 and 2027 as that comes out if you're a business owner. AI tools are now 97% cheaper than, you know, really in two years ago. There's still more

options that you can pay more for if you want to get more tokens to do more things, but the the pricing on just the baseline is decreasing. So, if your competitors adopt this and you don't,

you're falling behind. So, Claude Code, C-Pilot, Gemini, they already can do the work of junior analysts, coders, and writers. The question isn't if, it's how

writers. The question isn't if, it's how fast. And so if you're an employee,

fast. And so if you're an employee, here's what you want to think about. AI

can now write code, handle customer service, create content, and analyze data. If your job is primarily doing one

data. If your job is primarily doing one of those, add skills right now. You

really need to think about how are you differentiating yourself within that process. How are you having something an

process. How are you having something an ability that other people don't have? So

to zoom back out, we have this entire software side. It is, you know,

software side. It is, you know, incredibly advanced. You have $30

incredibly advanced. You have $30 billion in annual revenue and then you have you know 800 million weekly users.

So bucket one is advanced. The

intelligence exists. This is already taking place right now. The key is what is powering this? And that's what really brings us to bucket number two, the hardware and chips and data centers

power side. So key companies in the data

power side. So key companies in the data or the power side. Nvidia obviously is the major player here. They own 80 to 90% of all the AI chips. the most

they're the most important AI hardware company in the world. That's key though.

They're the hardware company. And so

TSMC, this is the factory that builds Nvidia's chips. They make 92% of the

Nvidia's chips. They make 92% of the world's most advanced chips. That is

going to be an incredibly important fact to understand for a couple slides I have down the line. Remember that number, 92%.

ASML in the Netherlands makes the only machines that can print tiny chips. 100%

monopoly. No substitute exists. So you

want to be watching ASML very carefully.

And then final company right here in Korea SK Highix makes 60% of all the specially memory chips. So key concepts here is you have data centers. These are

the giant warehouses of computers. So

you need compute and all of these warehouses to be able to run all the software that we have. And then we also need the high bandwidth memory. So these

are just ultra fast memory chips. Just

think about it as you need different chips for different things. All of these special chips are sold out through 2026.

So in your mind, if you're saying, "Ah, you know what? I don't understand all these different chips and things and whatever those might be." Just

understand that you have a lot of different specialized chips in a lot of different places and all of those are sold out through 2026, the end of the year right now. That shows you how much

demand there is. And then you have uh COWS which is TSMC's packaging tech that connects AI chips to memory. That is

key. The tightest bottleneck in AI hardware. That's really key. And so when

hardware. That's really key. And so when you understand these different moving parts, these five companies have near monopoly control. Now here's the thing.

monopoly control. Now here's the thing.

If any of these breaks, if any of these break, the entire AI buildout stalls. So

all these companies have taken out a ton of debt and they made a ton of commitments and they're trying to do all these different things with capex. If

one of these companies has issues that can cause a ripple effect across every major company and also different sectors. So for example, if you have a

sectors. So for example, if you have a retooling and people begin to say, "Oh, you know what? I need to start laying off people." And then one of these

off people." And then one of these companies fumble somehow and they say, "Oh, we we can do it, but just not in the timeline that you said. It's going

to be a year from now." then you might actually have companies have to rehire people right so you want to think about these different scenarios and tensions that is taking place everything really starts with Nvidia because they are the

most important company in AO at a $4.5 trillion market cap Nvidia data center revenue is the the fastest product ramp

up in history and so what you're seeing is Nvidia's data revenue hit 5.12

or excuse me 51 1.2 billion in a single quarter up 60% 66% year-over-year their product black well chip is ramping up as

the fastest product launch in history that is in just I mean it's massive right and so the key thing to understand is that as we are continuing to ramp up

more and more the direction is continuing to be to the upside we continue to surprise expectations on all these metrics and the demand for Nvidia chips

is only going to grow in the future.

This is not a one-time event. It is only going to grow into the future. You know,

I'm not going to go into all of it right now, but right now we're only talking about how do we retool our economy right now and and begin to shift things for AI. We haven't even talked about, okay,

AI. We haven't even talked about, okay, what about all the robots? And then we haven't even talked about how are we going to transition any of that stuff into all of the SpaceX stuff that Elon is doing and all the shifts that we're

seeing in space. or we haven't even brought any of that onto the table. This

is just for how do we use and ramp up the AI that we currently have to retool and bring more efficiency. And again, in the live stream that I'm going to do on Monday, I'm going to break down more of that. So, if you're saying, "Oh, I'd

that. So, if you're saying, "Oh, I'd really like to understand how the kind of future of some of that stuff plays out and how to think about, well, how much goes to robots versus SpaceX and space changes or Optimus or things like

that." You're gonna want to be there on

that." You're gonna want to be there on Monday to we'll break that down a lot more. So Nvidia is connected to

more. So Nvidia is connected to everything in all three buckets. What I

will just say is that if there is anything that comes out that's negative about Nvidia's operations, something happens where they get really screwed up somehow that is going to cause a a

market selloff across everything because it's going to create a bottleneck in the entire AI space. So Nvidia is the universal node. It touches every part of

universal node. It touches every part of the AI economy. the bucket one of software, bucket two of hardware, and bucket three of the physical side of AI.

And so you have customer concentration that's pretty significant right now. And

so the five companies that control the entire supply system are right here broken down. And you want to understand

broken down. And you want to understand and watch these companies very closely.

If you want to understand these companies and have a better understanding of each of them, again, below linked below are four technical reports. And they're not even that

reports. And they're not even that technical. They're just a little bit

technical. They're just a little bit more dense and they have data behind them. I'm not even going to call them

them. I'm not even going to call them technical because they're not really that technical. It just has all the data

that technical. It just has all the data and sources to back everything up, that's all. And they break down each of

that's all. And they break down each of these companies even further. And so you want to understand all of these choke points in the AI supply chain because if

either any one of these have issues, that's going to cause an issue in the entire supply chain. On the flip side, if they begin to ramp up, right, if we have positive Nvidia earnings, what is

that going to probably affect? TSMC,

ASML, and all these other companies. So

it works on both sides. And again, this trend that we are seeing is one of the strongest we've ever seen in history.

It's why these stocks are melting up right now. So TSMC, the factory that

right now. So TSMC, the factory that makes 92% of AI chips, Taiwan semiconductor manufacturing, a $1.2 trillion market cap, they make chips for

Nvidia, Apple, AMD, and Broadcom. All of

them are the sole source. They have no alternative factory and no alternative place to go. it is the tightest bottleneck for that the COWS advanced

packaging. So COWS uh connects AI chips

packaging. So COWS uh connects AI chips to their memory. And so that the the idea here is that these are important.

If you don't understand any of that, totally fine. It's not really that big

totally fine. It's not really that big of a deal. All you need to know is that TSMC is at a critical bottleneck and they're what connects the entire

geopolitical risk. So Taiwan is 80 miles

geopolitical risk. So Taiwan is 80 miles from China. This is the number one

from China. This is the number one supply chain risk in AI. And here's the thing. People are saying, "Oh, well that

thing. People are saying, "Oh, well that doesn't matter because China's building AI and everything's going to be fine."

That is okay until the end of this year and beginning of next year. Because

again, these three buckets when you begin to have a shift where you have this positive reflexive feedback loop where hardware, software, and all the data centers and

compute are all in a positive reflexive feedback loop. What happens when the US

feedback loop. What happens when the US begins to say, "Oh, you know what? Why

don't we just build a factory with a bunch of robots and they can just make all of our goods and services for us and we don't need to import stuff from China? It's actually cheaper if we do

China? It's actually cheaper if we do that and it's vertically integrated without any geopolitical risk." Would

you do that? I mean, it's a clear thing where if that begins to take place, China is going to have to make some type of move. I don't know if that's going to

of move. I don't know if that's going to happen or not. I don't know if they're going to invade Taiwan or not. No one

really does. Everyone has their view on that. But what is important is

that. But what is important is understanding if that takes place, what would force them to do that and what would the consequence be? And so,

you know, TSMC, their COWS has one of the tightest bottlenecks in terms of demand versus capacity. So, again,

there's a lot of demand for their systems and their products and there's a massive shortfall right now. You can see here's demand in red and then capacity in blue is below that. So we're

continuing to have a shortfall there.

And so you have a very similar dynamic taking place in these other markets as well. I'm not going to get into too much

well. I'm not going to get into too much depth on these. All these slides are here for you. But the idea is that there is a shift coming where custom chips are

growing incredibly fast in other companies as well. And this is going to be important to watch because if you have the change and you have these other companies be able to create chips that

could cause them to rely on Nvidia a little bit less. And so custom AI chips are growing 46% versus Nvidia's GPU shipments of 16%. So companies like

these are are creating their own chips.

So it doesn't kill Nvidia. training

still needs GPUs, but it does mean Nvidia Nvidia's monopoly can have some a little bit of deterioration over time. I

don't think that's going to take place on a broad basis because all the chips are different, right? And no one's going to pee with that outright quality, but it's an important thing to think about, especially from a competitive

perspective. All right, on Broadcom,

perspective. All right, on Broadcom, another important company, $ 1.6 trillion market cap, $73 billion AI backlog, massive backlog. And so the

design they design custom AI chips for the biggest tech companies again Google, Meta, OpenAI, Binance also dominates networking 70% of Ethernet switching chips cost for data centers. They have

20 billion in AI revenue. They're an

important company to watch in this entire process. And so you have all of

entire process. And so you have all of these different AI networking names that you want to watch because they are growing incredibly fast as data

centers upgrade their so to faster connections. I was digging into some of

connections. I was digging into some of these companies again they'll be you know all linked below but if you're thinking about oh what are the different you know AI infrastructure plays and picks and shovels these are very

interesting names to look at right here.

I would be watching all of these and beginning to understand what exactly drives them. And again, you maybe not

drives them. And again, you maybe not even have heard of these names. Probably

a reason that you want to look into them and try to find some opportunities. And

again, all the details for them will be linked below. And so, let's go over the

linked below. And so, let's go over the power side and how there's a binding constraint of not just chips, not just money, but power. And you will notice

that AI workloads the demand is increasing really significantly which just means that AI needs more power. US

data center power demand is growing from 62 gawatt to 134 by 2030. AI share is rising from 14% to 40% of all data

center power. This matters because power

center power. This matters because power capacity prices spiked 10x from $29 to $33 per uh mega gigawatt a day. And so

Microsoft has a$80 billion backlog it cannot fulfill because there isn't enough electricity. Just think about

enough electricity. Just think about that. There's not enough electricity for

that. There's not enough electricity for an this massive backlog to be filled.

Does it make sense why utilities are rallying, electricity companies are rallying so much? I mean, that's why you have these massive plays in all of these companies like Constellation Energies and, you know, all these other names

right here. Here's an entire breakdown

right here. Here's an entire breakdown of all of the power and data center infrastructure, the new AI plays that are connected to that. You want to watch all of these different names. And again,

they'll all be linked. They're here. You

can get them in the slide deck. They'll

all be linked below as well. So, let's

talk about how all of these kind of pieces come together. The AI supply chain takes 3 to six years to go from chip design to AI revenue. So the full cascade is right here. Here's how you

want to think about that. So the total is 36 to 72 months from design to revenue. This means ASML, TSMC, Nvidia

revenue. This means ASML, TSMC, Nvidia move first. AI revenue companies move

move first. AI revenue companies move last. So just think about it like this.

last. So just think about it like this.

If you going to say, okay, well, when is AI going to begin to disrupt jobs in the economy? Well, what you would need is to

economy? Well, what you would need is to say, let's first start with where we're at with foundry production, equipment orders, and equipment delivery. If we're

not near having these begin to make significant progress and go through the supply chain, then the disruption and the retooling of the entire economy is

probably a little bit farther away. And

that's the whole reason why we have had AI get launched, but people are saying, "Oh, well, where is it? Where is where is this actual thing?" And you have to recognize that we're still in that ramp

up process right now. And so, here is really the paradox. Every big company, every big tech company is both Nvidia's customer and competitor in one sense.

And so, this is really where it comes to understanding the different chips, custom chips, and the key tensions around them. I'll leave this slide here

around them. I'll leave this slide here for you to you guys can go over yourself. But the interesting thing to

yourself. But the interesting thing to note is that all of these data center reats are an interesting play in this context, right? You have digital realy

context, right? You have digital realy trust and then these other names right here which are a very interesting way on the real estate side to play the infrastructure. And so these

infrastructure. And so these infrastructure plays, they have contractual revenue already. And you

know, you already have guiding for 10 billion in revenue in 2026 and plans to double capacity by 2029. Massive

changes. And the other part of the supply chain you want to be thinking about is copper. You have in white here the copper price and then in blue Freeport Macaran. And so the each of the

Freeport Macaran. And so the each of the different data center uses $60 million in copper. The global copper deficit is

in copper. The global copper deficit is expanding. Freeport macaran is the

expanding. Freeport macaran is the largest publicly traded copper producer.

Why it matters? Copper prices signal AI infrastructure buildout 6 to 12 months ahead. It's a leading indicator, right?

ahead. It's a leading indicator, right?

The reason why some things lead and lag others is because they are linked and on different parts of the supply chain that are systematically linked. So rising

copper prices means more construction, FCX, you a lot of other names are contributing the materials for this AI buildout. That's why we have materials

buildout. That's why we have materials rallying. So when you see materials

rallying. So when you see materials rallying in your mind, you should say, "Okay, I know that the buildout is taking place." I know that probably in 6

taking place." I know that probably in 6 to 12 months things are going to look different. That's why when we talk about

different. That's why when we talk about escape velocity and where we're at with everything that there's a really good chance we see a merging of these three buckets sometime this year. So you know how they're paying for it, how all of

these different changes is all these companies are taking out $1.5 trillion in debt over the next 5 years. This is a massive issuance. And so one of the key

massive issuance. And so one of the key things is saying, "Oh, where is this debt getting distributed? How do I think about that?" That's one of the things

about that?" That's one of the things I'm going to cover in the video on Monday because the debt that's getting taken out is very aggressive and you're getting all these companies that are getting actually pretty levered. And so

you're seeing, you know, capital intensity actually increase pretty significantly. So bucket two, the bottom

significantly. So bucket two, the bottom line, what does this mean for you? If

you're an investor, highest conviction plays are these different names right here. Look at Nvidia, TSMC, SML. Watch

here. Look at Nvidia, TSMC, SML. Watch

these names, especially on the energy side as well. These have revenue visibility that most, you know, other tech stocks that are waiting don't have yet. And also on the material side, you

yet. And also on the material side, you want to watch these watch materials and then watch materials in connection with these other names like Nvidia, TSMC, ASML and also these buildout names for

the data infrastructure and then all the capex spending. Watch those very

capex spending. Watch those very carefully because as that continues to take place, copper prices are going to continue to go up. If that begins to slow, whether that's because there's a debt issue or things are not happening

fast enough, guess what? It could cause a market crash in copper. Right now,

we're not seeing that and there's still demand on the horizon, which is why copper is likely skewed to the upside.

If you're a business owner, if your business depends on energy, computing or manufacturing, costs are about to change. And you probably already know

change. And you probably already know that the data center power demand is going from 4% to 8 to 9% of US electricity. That affects everyone's

electricity. That affects everyone's power bill. That's why we're actually

power bill. That's why we're actually seeing it transmit into a lot of other places in the economy with higher energy costs. So cloud computing cost may rise

costs. So cloud computing cost may rise as well. And then if you're an employee,

as well. And then if you're an employee, here's actually something very interesting. Data centers, construction,

interesting. Data centers, construction, electrical engineering, nuclear energy, and power grid rules are hiring. We've

seen that with a lot of different backlogs for people just trying to hire.

One of the things that I would say is if you're an investor, looking for private credit deals in these spaces is very interesting. Um I think there's a lot of

interesting. Um I think there's a lot of really interesting deals taking place.

If you're an employee, look at what the different opportunities exist across these different names, right? You can

just go to chatbt or claude or whatever it might be and say, "What are the different investments that are taking place? How are those connected to jobs?

place? How are those connected to jobs?

Point me to the specific companies and jobs that are taking place in my immediate area. How do I connect

immediate area. How do I connect connected with those people?" Like solve your own problem. Even if that is in a certain skill job or if that is in a higher skill job or a leadership role,

right? There's going to be jobs across

right? There's going to be jobs across all of these different changes. And so

let's zoom out. Remember this framework.

We have two buckets. Number one is software, all of the things that are taking place. We have all of the revenue

taking place. We have all of the revenue that it's bringing in and it needs hardware and compute with that capex. So

just remember capex is right here in bucket two. And then you have software

bucket two. And then you have software in bucket one. You have five choke points in bucket two where if those mess up, this is going to cause this whole

house of cards to fall if those choke points screw up somehow. And power is being is is the binding constraint here, right? You don't have enough power for

right? You don't have enough power for all of that. So, bucket two is deploying right now. The money's flowing.

right now. The money's flowing.

Factories are building. Now, what

happens when AI enters the physical world? And that brings us to bucket

world? And that brings us to bucket three. physical machines, robots,

three. physical machines, robots, self-driving cars, and drones. And

here's what I'll say at the forefront.

My view is that when you have hardware and software merged together, that is when things are going to take place that people just can't even comprehend.

Because software overall has always been connected to the digital world. Yes, it

interacts with the physical world, but it's a different layer. It's

disconnected in many ways, right? And

the a lot of the development that's taken place over the last decade, especially in the 2010s, was all about software. It was all about SAS, was all

software. It was all about SAS, was all about being long all of these high compounders in the SAS space. When you

begin to link that with robots, with self-driving cars, with drones, with all of these autonomous aspects of hardware and that operates in a reflexive

feedback loop, the data centers and compute are the fuel that provides the foundation for that. If those begin to take place, which we're not seen yet, that if that begins to take place,

consumption patterns, businesses, and everything is going to retool because everyone's going to start thinking about the world completely differently. Right?

If you have an Optimus robot or a self-driving car, you know, one of the things that I was talking to uh a friend about recently is about how some

apartment complexes are now actually not having any parking spaces because everyone just expects to take an Uber or Whimo somewhere, right? And that might

seem like a dumb idea right now, but if in 5 years less people own cars, what do you think that's going to do to things like Uber? What do you think it's going

like Uber? What do you think it's going to do to traditional car companies that are making regular cars? And don't get me wrong, I like driving a car. I like a regular car. I like my car. And the the

regular car. I like my car. And the the thing is though, if we have a shift, the consumption is always going to follow where the cheapest price is. If it

becomes almost nothing and cheaper to get driven around or have a self-driving car than it does other cars, then capital is going to flow that direction.

So bucket number three is all about the physical machines. And so why bucket

physical machines. And so why bucket three is a game changer is bucket one and two are software running on hardware inside buildings. That's separate.

inside buildings. That's separate.

Bucket three is when AI starts doing things in the physical world. Once we

cross that chasm, there is no going back. And also once we cross that chasm,

back. And also once we cross that chasm, that's when you're going to have a really an arms race geopolitically in a sense, not just for the AI side, but

also for the robot side. And I mean I just cannot express enough how how much of the robot side which is coming you

know to a reality this year this year how much of that is going to shift global trade right there you know there's a lot of interesting things like you know Aurora uh with 250,000 miles

zero fault accidents 100% delivery time driving truck tucks without humans again there's always going to be humans driving some trucks in some areas okay this is not trying to make you know blanket statements performing surgery,

intuitive surgical 3.4 uh 1.5 million uh procedures in 2025. And then you have assembling cars. So figure AI, you know,

assembling cars. So figure AI, you know, robots will be working 10-hour shifts at BMW and contributing 30,000 vehicles, fighting wars, we're already seeing this. This is what we're already seeing

this. This is what we're already seeing right now. And so the when bucket three

right now. And so the when bucket three scales, it changes jobs, trade, GDP, and geopolitics. I I mean just think about

geopolitics. I I mean just think about what happens when if you onshore even more and you take all the jobs out of a country, a country could actually begin

to have massive issues and it could begin to get antagonistic in some ways or it could begin to get pushed to develop more if the US supports it or something like that. So, you know, these

different changes and trade-offs are important to think about. All right, the humanoid robot costs are crashing toward working salary levels. Here's the very interesting thing. When we look at all

interesting thing. When we look at all of the different costs for robot again, for example, you have the humanoid robot causing costs 6K

to 140K. The average is approaching

to 140K. The average is approaching $40,000, which is near an annual's worker salary. So cost fell 40% in 2025

worker salary. So cost fell 40% in 2025 alone versus, you know, 15 to 20. And

Goldman revised its humanoid market forecast 6x up toward, you know, 338 or 38 billion by 2035. So, you know, just think about if you can spend $25,000 and

get an Optimus robot, let's say the end of this year or beginning of next year, how is that going to change your life and what you do? Whether it is how you

spend money, how you run errands, how exactly you just conduct your day. How

is that going to shift as you have an Optimus robot or any of these other robots that are in the economy? The

major humanoid robot companies are right here. I'd encourage you to go through

here. I'd encourage you to go through these and begin to think about how are these connected to the changes that we're seeing. What countries are they

we're seeing. What countries are they in? What are the partners that they

in? What are the partners that they have? All of those are laid out here.

have? All of those are laid out here.

Whimo is a very good example that self-driving cars work at scale. Now, we

know that Whimo doesn't work in every single city because of some changes whether it's weather and streets and things like that.

However, to be clear, a lot of these things are automating and changing.

They're improving. Whimo autonomous ride hauling services at scale, their target is 1 million rides a week by the end of 2026. And they're on track to do that.

2026. And they're on track to do that.

So 1 million rides a day, excuse me, rides per week by the end of 2026 across 20 cities. And you already have the

20 cities. And you already have the company valued at $126 billion. Then you

have another example, you know, Aurora, the first commercial driver or commercial driverless trucking company in the United States or US history. They

launched driverless freight in April 2025 in the Dallas Houston corridor.

Again, is this going to go everywhere?

No. But think about some examples like how you have this entire system with the unions and with hours and the laws around hours for driving. And you could,

if you've ever had any type of transportation experience or import export, you know that if a driver is moving in a certain direction and he hits his hour limit, even if he is 5

minutes away from you, he has to stop and wait and go to sleep and wait until he can come and, you know, finish out his, you know, timing and come and deliver those things to you. If you can shift that around a little bit, just

think about how more efficient the entire world becomes, how that shifts delivering rates, how that shifts profits, revenues, and things like that.

This is a very interesting example that I'd encourage you to look for. Uh

defense spending on AI, very very interesting. I mean, you know, I would

interesting. I mean, you know, I would say Palanteer has one of the largest contracts. It's one of the companies

contracts. It's one of the companies that I'm most bullish. um you know AI drones for you know warfare in different areas and the US defense budget is already approaching a trillion dollars.

Um I mean when we see that the US is using um claude to conduct its actual military operations which we saw for the entire Venezuela thing when we saw that

oh yeah they're actually using claude to conduct that. I mean that tells you a

conduct that. I mean that tells you a lot right if that you know if that is happening at the highest level it just tells you how much of an impact all of

these changes are having so bucket three bottom lines what does this mean for you if you're an investor asymmetric bets with you know a lot of upside are in these names and especially the humanoid

supply chain especially Tesla is the highest optionality but highest execution risk defense very interesting names here watch out for these these IPOs, especially Andrew. I think that's

going to be very interesting um to yeah, as a long-term play um company. If

you're a business owner, think about these things. If your business involves

these things. If your business involves driving warehouses manufacturing any repetitive task, automation is coming faster than you think. Humanoid robots

and all these different changes. So now,

which tasks can robots handle in the next two to three years? Ask yourself

that if you're employee, just ask yourself all of these different questions, right? And I'm not saying

questions, right? And I'm not saying that all truck drivers are going away.

Just think about how can you differentiate yourself? How can you have

differentiate yourself? How can you have an edge in the market? How can you be in a market that is expanding instead of shrinking? Right? Timeline for that is

shrinking? Right? Timeline for that is about 2 to 5 years in my view. All

right, remember the framework we're at.

Bucket one, software, right? We have the 30 billion of revenue and costs are shrinking. Bucket two, all the compute,

shrinking. Bucket two, all the compute, all the capex buildout. You have all the choke points. And then bucket three is

choke points. And then bucket three is all the physical AI, all the humanoid robots and all of those changes. Now

let's connect that to a combination of how it's changing everything. And this

is the effects of feedback loop. So you

have the connection between software and hardware, right? The bottleneck

hardware, right? The bottleneck is that we are not seeing software and physical automation and physical automation and then also the hardware capacity. We're not seeing those in full

capacity. We're not seeing those in full force yet. These could take place this

force yet. These could take place this year where the the physical side and hardware capacity begin to match and the software and and robots begin to merge at the end of this year. We're already

ramping up into that. And so you want to look at the different changes in these sensitivity changes as we have movements across this supply chain. So you're

saying, okay, well, how do we know if we're going to hit that this year? You

want to look at these companies and see how fast they're producing all that because the faster they produce all that, the faster we're going to hit escape velocity. And then again, the

escape velocity. And then again, the macro context is the interest rate in the dollar. If we move up in interest

the dollar. If we move up in interest rates, which I actually don't think is going to happen. That's not my view. I

think interest rates are actually skewed to the downside on a cyclical basis, then but but if they do move up, that can create some issues on the financing side. And so, no single country on a

side. And so, no single country on a geopolitical basis has a full sack AI capability. you still have it

capability. you still have it diversified across you know several companies which is again TSMC you know Korea and things like that. When you

look at this no single country has a full you know technology stack especially with China and the different company uh countries and companies in China is the largest

threat to the United States and if they get pushed into a corner they will act and that's what you really need to be thinking about as we have these changes

and the retooling. So AI could replace repric global trade entirely. This is

what I think is the thing that no one is talking about and no one appreciates at all. I mean it's probably one of the

all. I mean it's probably one of the biggest misconceptions or misunderstood points in the global financial markets and economy today. The core idea is if autonomous agents and robots make it

cheaper to manufacture in the US than to ship from overseas factories than using human labor then the entire logic of offshoring reserve uh reverses. This

would change trade deficit and surpluses for every major country, capital flows, and which countries have economic leverage and where companies choose to build factories. So, China's counter

build factories. So, China's counter leverage is they have some metals and refining capabilities, especially on the rare earth side. That could be very interesting. So, AI doesn't eliminate

interesting. So, AI doesn't eliminate geopolitics, but it reshuffles the deck.

That's how you want to think about it.

All right, let's talk about you.

Everything so far was a setup to understand those three buckets, how they connect and where we are in that loop.

Now, what does that mean for your money, your career, your business? Well, what I want to start with is think about sector performance. Think about where AI

performance. Think about where AI winners are in traditional sectors. You

have obviously a lot of benefits in SMH, the semiconductor space. And again,

notice right here, XLU for the utilities ETF, XLI, the industrials ETF, right?

Those are both directly connected to this theme, energy and buildout on the that basis. And again, this is the

that basis. And again, this is the 2-year total return by ETF sector.

Notice what is the main sector that is down on a two-year basis. It is the software sector. If the software sector

software sector. If the software sector begins to retool and adapt, which I actually wrote a report on this on the substack, which I think it it is going to, you know, you don't have, you know,

here's what I'll say. IGV is not down 3.2% over the last two years and the rest of the market up 100%.

Or, you know, over 50% like all these other sectors. That doesn't happen

other sectors. That doesn't happen because of just short-term selling pressure. This is a real fundamental

pressure. This is a real fundamental driver. The question is, does it have

driver. The question is, does it have persistence? I think that the IGV ETF,

persistence? I think that the IGV ETF, you need to be able to pick winners in it because some of those companies are not getting disrupted at all. They're

just getting sold from sector and factor flows, but they're still up significantly over the last couple of years. And so, you want to begin to find

years. And so, you want to begin to find those names and pick out those names, which again, I've done on the Substack and I've explained some of the thesis for that. And again, I'll be explaining

for that. And again, I'll be explaining a lot more on Monday as well. The labor

market, let's talk about that. The

economy's number one risk. This math

should concern you because the service sector is 70% of US GDP 70%. You know AI if you have AI on the software and

hardware side merge the main thing that is at risk is the service sector because it can begin to disrupt the largest sector of the economy impact GDP and inflation and

employment could be massive. One of the things that I talked about and I think Twitter spaces and then offline with a

couple other people is that if you have these changes in the service sector in the labor market that

could begin to really cause disinflation, right? If this this takes

disinflation, right? If this this takes place and you have escape velocity, yes, that can cause disinflation and bonds to bid. That's why you want to understand

bid. That's why you want to understand these signals because they can indicate the timing for that. All right. So jobs

most at risk in the market over the next two to three years. I have some breakdowns here. Uh you know new growth

breakdowns here. Uh you know new growth cases. I would be looking into all of

cases. I would be looking into all of these sectors, all of the investor playbooks. I have three tiers of

playbooks. I have three tiers of conviction here. You have a total

conviction here. You have a total breakdown of all of the major names that you want to be watching. Again, all

these will be laid out. You can download this slide deck as well. Here are all the questions you want to be asking yourself if you're a business owner. And

then if you're an employee, you want to look for these warning signs. that your

job is being automated, right? You want

to look it what's your company doing with AI? What is it about to do? How is

with AI? What is it about to do? How is

it beginning to lean into this? The

window to reskill is now. Right now, in 2 to 3 years, competition for safe roles will be absolutely fierce. I cannot even

begin to explain how important it is to just say, "Let me begin to spend some time developing a skill and one that is

not replaceable with AI." Find a skill that is amplified by AI, not replaced by it. All right. All of the monitoring

it. All right. All of the monitoring dashboard for all of these changes. So,

what are all the catalysts you need to be watching is here. So, you can find all of these different moving parts right here. Again, Nvidia earnings next

right here. Again, Nvidia earnings next week going to be the most important thing on a bigger picture basis. Any IPO

by OpenAI or Anthropic, massive, massive changes. Um, I think that there's a lot

changes. Um, I think that there's a lot of interesting things happening in the background that I'll talk about on Monday because uh, you know, we're going uh, you know, have a lot of stuff we're

going over in in today's thing. So,

three buckets one last time. AI software

again, status that's taking place right now. Bucket number two, hardware. All of

now. Bucket number two, hardware. All of

this capex build out. You have choke points, but power is a binding constraint. This is being deployed. And

constraint. This is being deployed. And

then physical AI, physical things, humanoid robots, you have all the self-driving, you know, driverless trucks, things like that. Again, I think the robot thing is is the most interesting. It's inflecting. Once each

interesting. It's inflecting. Once each

of these align and they're all at this stage of advanced and they all work in a reflexive feedback loop, the escape velocity in my view is late 2026 and

early 2027.

2028 is what I think would happen at the latest. The ultimate result is you are

latest. The ultimate result is you are the disruptor or disrupted. Whether

that's in financial markets, in the companies that you're betting on, or in the actual economy, the middle disappears. I think the average the

disappears. I think the average the people that are striving to be average or are average are just going to get absolutely crushed. But if you're

absolutely crushed. But if you're putting in that extra work, if you're putting in intentionality and how you're thinking about things, that is going to be one of the largest differentiating

factors for you. Map the signals position for the flows. The convergence

of intelligence, infrastructure, and physical automation represents either the most consequential industrial transformation since electrification or the most capital intensive speculative

cycle in history. I think it is the first, not the second. The data

indicators in this presentation give you a monitoring framework that tells you the difference. Now you know the supply

the difference. Now you know the supply chain, each major company, and how to watch them and think about them as we move forward. So, this is where we go to

move forward. So, this is where we go to the full picture. I have linked below comprehensive you know reports that break down on a technical basis all the data points for

these. So if you want to say oh you know

these. So if you want to say oh you know I want to trade these names actively I want to maybe look for a job at one of these names or something like that go to the reports linked below and then again the link for the next live stream will

be linked below as well. So whoever is on that live stream, I will be sharing quantitative models with the latest kind of AI things that I've been building and especially that I've been building in

the last 3 weeks. So a lot of stuff has changed in the last 3 weeks in my view.

I'm going to be sharing the models that I've built in the last 3 weeks with all these developments. And if you are here

these developments. And if you are here after kind of like all this time and uh you know end of the video and things like that if you are on that live stream you will get uh the the models that I

have been building and I will be sharing those with you and it'll only be for those people who are on the live stream.

So if you're there you will get them. If

you don't well there well they'll be available for paid subscribers on the substack but if you want to you know get those models you have to be on the live stream. The links are below and then

stream. The links are below and then again the full reports that I wrote are right here. This entire slide deck is

right here. This entire slide deck is linked below. And with that, thank you

linked below. And with that, thank you for being here for the Capital Flows presentation on AI.