AI Value Keeps Moving [Raw Session]

00:04 — What Does the Market Actually Value?

Hey, welcome back to Slow Builds.

This one is me trying to keep pulling on that same thread I had in one of my other videos about what the market is currently valuing.

The last one was really about what I considered always to be the staples of what to invest in and then how things like attention and what is grabbing people’s attention seems to be getting all those incentives now.

And so I keep coming back to the question over and over again about what does the world really value at this moment?

Like what are people in…

So this one’s going to be about the investing part of it.

And not what we say matters and not what sounds important, but what actually gets rewarded.

Where money goes, where investors place their value, where the market seems to think that going and the more I think about it the harder it is for me to separate the actual importance of something from the investment value of something because those are not always the same thing.

Food matters.

Power.

Shelter.

Medicine.

Finance.

Insurance.

Infrastructure.

Those are the things people people need.

Those are the things that make the world function.

But the market does not always reward what matters most.

It rewards what grows.

Every definitely rewards what scales.

It rewards what controls people’s attention, the world’s attention.

It rewards what controls access and I think that is the part I’m trying to wrap my head around because I’m looking at a and I’m gonna bring this back to AI just like most of the videos and I keep asking myself where does the value actually go?

Is it the AI companies themselves?

Is it like the hardware like the chips?

The RAM?

Is it the data center?

So the infrastructure part of it?

Is it the power that powers everything?

Is it the companies that already have customers?

So to the information to build the models off of?

Or does the value in this situation keep moving?

And it’s the question I keep sitting with.

Where does the value go when the bottleneck keeps changing?

02:37 — AI Looks Like Code

The strange thing about AI is that on the surface it feels like software, it feels like code.

It is code, no getting around it.

Models, agents, chatbots, tokens, images, videos, all these things detect the prompt box, the response, ones and zeros.

And because I’m a developer part of me looks at that and thinks, okay, this is just software.

It is very advanced software, very expensive, and extremely powerful.

But still, it’s just code in the end.

It’s just software in a box.

And software has this weird property or once something works it can spread very quickly.

It can be copied.

It can be improved, rebuilt.

It can be wrapped into other products.

It can be made smaller, cheaper, and it can be made more specific.

It can be made open source.

It’s a big thing people I haven’t heard too much about.

There are some open source models now and they’re getting stronger and better.

So when people talk about like AI is going to be one winner, one company, one model, one giant system that owns everything.

I don’t believe that at all.

I have a very hard time accepting that.

Maybe there will be a few huge winners.

And right now we’re seeing that with the two or three major players in the game, maybe four major players.

And each hold their place and I believe that.

And they will be very valuable.

But I do not think AI is going to be one thing.

I think AI is going to become many things, big models, small models, local models, company specific models, agents, personal assistants, medical tools, financial, legal, customer service, creative tools, pieces and models that are small enough and efficient enough that they work within a phone, inside apps, directly on a chip, models inside operating systems, cloud-based models.

There’s all these different aspects of it.

And models are not even visible.

Models anywhere because they are just built into the workflow.

So that makes investing in the technology kind of hard.

You can, I’m going off a little bit so for loose spot.

You can go with what currently are the leaders in the market, but that changes.

And that makes me go back to the beginning of the web, like AltaVista, Yahoo, try to use some of the other ones, Locust, all the old search engines.

And they dominated the web, Netscape.

But then search got modified.

Google came around and they took ownership of it really.

Bing tried to take over, Yahoo’s still kicking around, but DuckDuckGo for anonymous.

But I’ve always said for a long time that search itself is gonna be replaced sometime, which is why these companies like Google, they expand and build other things.

They use the revenue from the ads that are from the search and they use that to build other parts of the company and grow bigger.

’Cause I still think Google is gonna be one major players that win this AI race.

But, so back to it.

So it makes, it does make investing very hard because if AI becomes everywhere, then where is the durable value?

Is it in the model itself?

Or does the model become like electricity?

Something running in the background?

Something everyone uses?

Something that becomes expected.

Something that becomes less special over time.

Something becomes copied and rebuilt and reconfigured and just changes mold basically and multiplied across many different companies and owners and players.

06:48 — Right Now, the Money Is Physical

And this is where it gets strange because AI looks digital, but right now the money that is going into AI is going into the physical things.

Like I said, the chips, the data centers, power, cooling, LAN, all the infrastructure set up, the manufacturing, and it makes perfect sense because right now the bottleneck is physical.

You need all these things to make it work.

You need that infrastructure and all the chips and the hardware to run these models.

So even though AI feels like software, the value right now is being placed on the physical layer underneath the software.

And that is what I’m trying to understand because for retail investors, like small-time investors like myself, that is where a lot of the access is.

You cannot easily buy into the current AI companies.

They’re all privately held.

Some IPOs are coming.

You cannot, a lot of the pure AI model companies, like all of them are private at the moment, except for Google really.

And the public market starts chasing infrastructure around it.

The chip companies, the cloud companies, data center, retail, real estate companies basically, energy, nuclear is a big one at this point.

And that makes sense.

If the gold rush is happening, you cannot buy the gold mine.

Maybe you buy the picks and the shovels.

But then I keep asking, is that permanent?

Or is that just where the bottleneck is right now because if chips are scarce and chips are variable.

Same with data centers.

If land and infrastructure is scarce then whatever is available becomes valuable.

Then all of a sudden becomes land.

Then it becomes all the building around building new data centers.

It becomes around nuclear to get cheaper energy.

But what happens when those constraints change?

What happens when there are more chips, there’s more when models get smaller and don’t require as much energy, when they can run locally on a phone and don’t require the big data center and you want more specific.

What happens when local AI, like I said, local AI is good enough for most tasks.

And what happens when every company has access to good enough AI?

Where does the value move then?

09:08 — The Value Keeps Moving to the Bottleneck

And that’s probably the biggest idea in the video right there.

The video, the value keeps moving to the bottleneck.

At first maybe the bottleneck is the model, then the best model, the best research, the smartest systems, then the bottleneck becomes the chips, who has the GPUs, who has the supply chain, who owns it, who can get enough compute from their chips.

When the model becomes data centers, the buildings, the land, the cooling systems, the infrastructure, all that stuff.

And then it becomes, back to the data mode, it becomes who has the data, who can build the best models, who already has customers, workflow, who already sits inside the business processes.

And then maybe the bottleneck becomes trust.

Who do people actually really rely on who they believe and allow access into their life who gets embedded deeply enough that switching away becomes hard.

I think there’s one winner in that situation at the moment and when most people the average person thinks of AI they do think of Jet GPT so open AI has a strong name advantage on that one.

It’s not just picking the best AI company.

It’s figuring out where the bottleneck is now and where it might move to next because if you buy the bottleneck too late maybe you’re buying the thing after the market already priced like price it to its top and if you buy the wrong layer maybe the value moves somewhere else.

So it’s a very it takes a lot of research a lot of market but understanding the ecosystem that’s happening around AI.

I didn’t mean to make this about AI but it’s just showing how the value proposition and where it moves and how it navigates around even this technology that is changing the world and changing how we invest this change in how we day-to-day lives.

11:20 — Does Infrastructure Stay the Winner?

So right now infrastructure feels like the obvious place and I get it.

I understand all the things it’s not magic to figure that one out it’s not floating around in air, AI has to run somewhere, and so there’s a physical layer.

But the thing I keep asking is, does the infrastructure stay the winner?

Or is the infrastructure just the first obvious investment wave because real AI companies are private?

Because that is a big difference.

If infrastructure is permanently scarce, then maybe it keeps winning.

But if infrastructure gets built out, if chips become more available, energy gets cheaper, get smaller, AI becomes way more efficient, then maybe the value shifts away from the infrastructure.

Maybe it moves back towards the software or maybe it moves towards companies that already have distribution or maybe it moves into more normal business that use AI.

That is where the whole thing starts to feel unstable.

Not unstable like it’s all fake but unstable in the sense that the center of value may not stay in one place.

The market might be rewarding chips today because chips are scarce.

Then it might reward power tomorrow.

Then it might reward the AI companies.

And then it might punish model like the AI companies if models become cheaper and more interchangeable.

I think they’re going to get punished too if they don’t come up with a token strategy to the average Joe and the smaller companies not run out of token so quickly.

And that brings you back to the boring stuff because maybe the answer is not that AI replaces food, finance, insurance, healthcare, energy, logistics.

Maybe AI becomes those companies, maybe AI makes those companies better.

Maybe the value eventually goes back into the old world, but through the new efficiency layer.

Now see, that’s where AI right now…

I’ll get to this…

My next section is that.

So I’m just going to keep reading.

13:31 — Maybe AI Becomes a Feature

This is one of the things I keep thinking about.

One of AI does not say special.

One of AI becomes the feature.

A feature.

Because that happens with technology.

At first something is the product, then eventually becomes a feature inside every product.

The internet was special.

Then every company had a website.

Mobile apps were special.

Then every company had an app.

Cloud was special.

Then every company and every software is non-Cloud based.

And AI is probably gonna go the same route.

Right now AI is the thing.

Everyone is talking about it, trying it, investing in it.

And every company is trying to explain how they’re gonna use it.

But everything, eventually maybe AI just, It’s just part of the software stack.

And this goes against a lot of taking jobs.

Not really, because it’s still part of that, ’cause it brings value to companies.

I just watched the one where AI’s pretty much gonna replace all drive-through intercoms.

And the people, so there’s a job gone.

Now the people inside, so far they’re liking it, but it’s a pain point at the moment, but it’s one of those things that’s gonna happen.

So that’s a place where that becomes a feature that a company can, like Wendy’s or Burger King or McDonald’s, they can use that to make their ordering more efficient, to make one less employee they have to pay for or put that employee, still pay that employee, but add them to the queue so they can manage more drive-through customers at once than a single at a time.

That’s a smarter way to be, if I was the marketing company for one of those, that’s the way I would look at it.

Your drive-through guy now can take both orders instead of one, speed up the process.

But yeah, that’s where it becomes like an automated system.

So like, but it becomes part of the stack within the company.

It is inside banks, it’s inside insurance companies, grocery, logistics, hospitals, call centers, counting software, it’s already in development tools.

I use it every day.

I ran out of tokens today, so I had to come do my video.

So it’s inside everything at the moment, but it’s just not being utilized to make the companies always more efficient at the moment or being, like I say, boring, it’s just part of the process.

And maybe the models get cheaper.

Maybe we start having like again, the local models for each company, specialized versions.

So picking a winner becomes pretty much almost impossible in a software game like that.

‘Cause it’s not one winner, it is many winners in many places.

So maybe the better question is not which AI company wins, maybe the better question would be which companies can use AI to improve their business they already have.

And that is a different way to look at the value that AI brings and where the market, where the value would shift to.

16:43 — Tesla, SpaceX, and Physical-Digital Companies

And this is why companies like Tesla and SpaceX, they don’t get out of my head.

Not because I’m saying they are automatic winners, I’m not saying that, but they are interested because they are not just software.

They are physical and digital at the same time.

Tesla has manufacturing energy, it has cars, it has robots, it has the software, it has the data.

It says it has AI here but SpaceX has the AI and it has satellites with the Starlink, it has communications, it has the infrastructure, it also has the solar, it has many things that you put all that together, you have an entire ecosystem.

And then also like the software and they have the data, they have the attention, and they have the full infrastructure and ecosystem wrapped all around it.

So when I look at companies like that, part of me thinks, okay, maybe that is closer to where value might be sitting.

Not only in code, not just in infrastructure, but in the companies that combine physical capability, software, data, distribution, attention, the power, did I say communication?

Yeah, because they literally have it all.

They even have the boring company.

They can put the holes.

Like it’s scary when you think about it.

And it does not make it, and I see, It does not make it safe because it is scary.

It does not mean it’s not overvalued.

Does not mean it’s going to work out.

It’s not a guarantee.

But it explains why my brain keeps going there because the company is not just selling a digital thing.

It is building physical systems and software systems together.

Maybe that combination matters more in the next phase.

The companies that win are not only the ones with the best models, maybe they’re the ones with the best connection between the model and the physical world.

18:56 — The Retail Investor Problem

And then I come back to the retail investor problem.

Where do you put your money?

Not in a financial advice way.

I’m not giving any advice at all here.

And I’m just thinking through the problem, talking out loud, reading my notes today I wrote for me.

Because if you’re a normal investor, you’re always trying to figure out what you actually access to.

You might believe AI is going to be huge, but can you buy the companies you actually believe in?

A lot of them are private, so you buy the public companies around them, chips, cloud, infrastructure, consulting, power, say land companies that already have AI exposure, SpaceX if you want it, but also land.

Land is going to be anything nuclear for the power also.

But then you had to ask, am I buying the real value?

Am I buying the temporary bottleneck in the flow where the value is going to end up?

That is the uncomfortable question that has to be answered because sometimes the temporary bottleneck makes a lot of money and it does.

But you got to know when to get out and when to get in.

Sometimes it’s the right place to be, but sometimes by the time everyone sees the bottleneck, market has already moved on and then the value moves again.

So maybe investing in AI is not just about believing AI will matter.

That part feels extremely obvious to me.

The harder part is knowing where the value settles before the first wave is over.

When the first wave is finished and when we start moving into the second and third wave of this new world that we’re going to be living in and where the calm waters are going to be?

Does it stay in the chips, the cloud, the power?

Does it stay in the infrastructure and land?

Does it stay with the software companies building the AI models?

Does it move to the data modes with the companies that own the data that build the best models off and know you and know your company and has the trust of the consumer?

So it now makes it very hard.

21:04 — What If AI Gets Smaller?

And then there’s another side of it too, like if AI gets smaller, it gets cheaper.

Again, we have the local models.

What if companies don’t always need the biggest model?

What if most tasks do not require the top of the line?

Because that seems possible.

I know it’s going to happen.

I can see it in a way I envision things and how to build things and how to be selective on when you use a model, what you use a model for, how deep you need it to be, it makes a big difference in the long run.

So there’s ways to cut costs, to save on all aspects and all frontiers.

There’s ways to move it into the phones, the handhelds, move it into wearables, small workflows within your system, little nodes that sit somewhere between that doesn’t even need internet access for some of it.

Maybe there’s enough information and data to process an incoming form that has pre-designed answers and it knows, you know, it’s limited to what’s there so it can make decisions without requiring outside sources and it can make itself smarter.

I’m just me thinking out loud.

So a small company answering internal questions that always need the biggest system.

Small business doing support is not always needed either.

A developer working inside its own code base, sometimes just needs a little help with syntax and knowing where a method may live or is there a test for this type thing.

It doesn’t need the best model all the time.

So if AI spreads out that way, then the value might not concentrate as much as people think, it might fragment.

It may become more specialized and more embedded.

And that changes the whole investing story because the big infrastructure build makes sense everyone needs massive centralized computer for compute forever.

But if a lot of AI moves closer to the user, closer to the device, to the business, smaller models, smaller applications, then maybe the infrastructure thesis changes a little bit.

It does not disappear, but it changes.

And maybe the question becomes who benefits when AI gets cheaper?

Is it the company?

Is the customer?

Is it the model provider?

Is it the business using the model?

Is it the chip company still?

Is it the power companies?

Is it the company that no longer needs as much compute?

And that’s a very, that changes the question again.

23:42 — Maybe the Value Goes Back to the Old Staples

And this is where I start looping back to the old safe investments, food, shelter, finance, insurance, healthcare, energy, utilities, infrastructure, logistics and banks.

The things that used to feel obvious, because maybe I does not replace those things.

We can’t replace all of those things.

Maybe I’ll be makes them more efficient.

Maybe I helps grocery chains manage inventory better.

Helps insurance companies analyze risk better.

Helps banks automate support and fraud detection.

Helps healthcare systems process information faster, clients.

A help logistic companies route trucks better, helps with fleet management, helps energy companies manage demand better, find bottlenecks there, break up the grid, better sources, know when to pull energy, when to pull it back, how to manage price better.

AI helps manufacture reduced waste.

AI helps normal companies do more with fewer people.

And if that is true, then maybe the value eventually shows up in the boring companies again.

Not because they’re AI companies, but because they use AI well.

And that is where it gets interesting again to me because maybe the market is chasing AI as a category, but the real long-term value might come from AI becoming invisible inside other businesses.

The same way electricity is not exciting by itself anymore.

It’s just part of everything.

Same way the internet became part of every company.

AI is gonna become part of every company in my mind.

And then the question becomes, which companies actually get better because of it?

That might be harder to see than buying the obviously AI names, but it might also be where the long-term value is gonna sit.

25:40 — Why Picking Winners Is Hard

So this is why I keep going in circles on this, because I can see both sides.

I see why infrastructure matters.

So yes, there’s value there.

But I also see how that value, in my mind is going to shift.

Because chips, energy, the models, all that’s going to price will drop, they’ll get cheaper, they’ll become more available.

If every company gets access to the same similar tools and the value may not stay in the obvious places.

It may move to distribution, trust, customer relation, the data modes, companies that already own the workflow again.

And that is why picking the winners is extremely hard because there is no single winner here.

Maybe there are hundreds of winners.

Maybe the winners are not even the companies we think of as AI companies.

Maybe the winners are the companies that quality use AI to become better businesses, more efficient businesses.

And maybe some of those companies that look like winners today are just sitting on the current bottleneck.

And it does not make them bad investments.

It just means the questions is more complicated than AI is the future, so buy AI.

26:55 — Where Does AI Create Durable Value?

So to finish this up, maybe the hardest part about investing in AI is that the value keeps moving.

First it moves into all those things that we’re currently seeing the market flood to.

And then maybe in my mind it moves away.

It’s definitely going to move when those companies go public.

The value is going to shift there pretty quickly.

And then after that, maybe it moves into companies that learn how to harness the models to make them cheaper and more common and allow companies to harness the power of them without requiring all the large infrastructure and scale that they currently require.

And then maybe it moves more than whoever owns the customers, the workflow and the data.

And eventually maybe it moves back into normal businesses, food, banks, insurance, like all those things, healthcare, groceries, like you got to have groceries.

You have to have energy, you have to have shelter, you need to have healthcare.

So those things, like whichever companies learn how to harness it extremely well, to in my mind, those are still through the old staples in the new era, in the AI era that learned how to reduce costs, to leverage AI to make their companies more efficient.

And hopefully as being more efficient, you become more customer satisfaction goes up also.

So maybe the question is, where does AI create durable value once it stops being special?

And that is the part we’re all trying to figure out.

Say AI is code in the end, but right now the money’s going into all the things wrapped around that.

And that changes the value, moves with it as always.

So where do you invest when the bottlenecks keep changing?

I don’t have the clean answer to that.

And I think that the honest place to leave it, because part of me still trusts the old stuff.

But I also cannot ignore that the market is chasing something else right now.

Is chasing the scarcity, the control, the scale, the compute, the attention.

I’m not doing, my other video is gonna just be on the attention part of it, ‘cause again, that seems to be where people are putting their value and it hasn’t gone away, it hasn’t gone down.

And the old boring stuff that I always believed in and still put my money into mostly, still grows, but it grows steadily, slowly, slow builds.

And maybe that is what makes this moment so hard to understand is the physical world still matters.

The software layer is exploding again.

The infrastructure is extremely expensive.

All the models are private.

So the retail investor is trying to figure out which layer actually captures the value.

I do not know the answer is one company.

I do not know the answer is one sector.

Maybe the answer changes over time, and it always does.

And maybe that’s the whole point, the value moves.

The hard part is figuring out whether you’re investing in where it is now or where it’s going next or you’re already too late, where it was.

That is what I’m trying to always think through.

I spent a lot of time analyzing in my brain trying to just play different scenarios because it’s not perfect and there’s never a final answer.

I’m just trying to understand.

And this all came about me trying to figure out where people place value.

How is value calculated?

What puts the real evidence behind why money is moving in the different areas that it is currently?

Because a lot of it doesn’t make sense.

But then if you go deeper, it does.

But then if you go a little deeper, it doesn’t.

30:55 — Closing Thought

My thing is a lot of these companies are not run the most efficient.

They burn through a lot of cash, they burn through a lot of money.

A lot of them don’t treat their employees always the best.

They don’t treat their customers always the best, but yet they’re strict and they but they are striving for something better.

Hopefully their intentions are in the right place and when you find a person or a company that is trying to make the world a better place, more efficient, help people, but they might be ruthless in trying to get there and misunderstood.

I believe that is a great place to invest in my mind, but you never know.

Thanks for watching.

Bye.

Watching AI Grow Up in Real Time [Raw Session]

00:00 — AI Still Feels Massive

Hey, welcome back to slow builds the key come back to this idea that maybe we’re looking at our at AI a little too close up at the moment right now.

It feels extremely massive. It feels very expensive.

It feels like it needs very specific things to work chips data centers cooling power infrastructure and token management.

It needs all these new systems just decide which model to use which tools we’re gonna call what data should go, what should run locally, what should go to bigger models and what’s actually what worth paying for.

And I get why companies are focused on that right now because the cost is real.

Infrastructure energy hardware all of it is real and it’s expensive.

Space.

Money.

Wondering if we’re making the same mistake people often make with new technology. We look at the early version and assume that that’s the shape it’s going to be forever. What we see today is what it will always be and I don’t think that usually it’s never how it works.

The early version of technology is often very big, very awkward, overly expensive and hard to use.

Then over time things get smaller, cheaper, they get more normal, they get standardized, it moves into the background.

And everything eventually people forget how strange it was to use or how it looked in the very beginning.

So that’s what I want to think through in this video a little bit.

Maybe it’s not in its final form right now.

Maybe I AI is still still in this room size computer era where and what that means is like IBM back with the old big machines that took up entire rooms when your mobile phone in the car was the size of your head.

That’s the idea here.

Like right now we see AI and it takes up all this space.

It’s this massive thing that it costs a lot of money.

02:10 — Early Limits Feel Permanent

So like I said, when you’re living through a technology shift like we currently are, the limits feel permanent.

And that’s the trap.

Whatever technology requires today starts to feel like part of the technology itself.

So with AI right now, we say it needs CPUs.

It needs massive data centers, huge amounts of power, extensive cooling systems, tons of water, high speed connections, expensive model calls.

It needs all this infrastructure around it.

And all that is true right now.

I’m not saying that part is fake, but the question is whether those are permanent requirements or just the early stages of what we’re beginning to develop.

Because those are very different things.

If you went back for enough for in computing, computers were not something you casually carried around.

They were machines, physical machines, large machines.

They began in special rooms, special environments, needed special operators.

Had full on instruction manuals around them.

Eventually computers became something that could sit on a desk.

I remember when I had my Tandy 1000, like it was, to me it was small and was a home computer and I could plug it in and I loved it.

Now that feels so clunky.

My wife, she uses a laptop for work.

I said, well, we can get you a desktop from work.

We have a bunch and they actually run a lot faster.

And to her like that’s, it doesn’t make sense.

Why would I have that big machine in the house?

But that’s the way it used to be.

And in a Tandy 1000 that I had, that was considered to be a small machine.

And even some of the old laptops we had, those were considered small.

And now like they’ve seemed so bulky compared to like a MacBook Air.

’Cause like I said, everything moved to something you put in a bag.

And with the phone, it’s something you can put in your hand.

And now we really even think of a phone as a computer, really, even though it’s more powerful than any computer ever had growing up and that’s a very strange thing to think about.

Once technology matures enough we stop seeing the infrastructure.

We just see the use.

We don’t think about all the engineering required to send a message, load a video, make a call, use GPS or even stream music.

It’s just normal.

It’s part of life but it did not start normal and I think AI might be in one of those early periods where the machinery is still very visible.

We’re still looking at the wires, the power bill, the server rooms, our token management, and because we can see all that we assume that is what AI is, but maybe it’s not.

Maybe that’s just what early AI looks like.

05:02 — The Internet Went Through This Too

I saw some of this with the Internet. The Internet era too, really, like not the earliest academic version of the internet.

But the business version, the period where everyone started realizing they needed a website.

Every company needed to be online.

Every company needed some kind of server setup.

Needed people who understood networking and hosting and security.

And for a while, it felt like every company had to build its own little technology fortress.

Servers in the building, locked rooms, special permission, data closets.

It’s people who could go into the room that other people couldn’t even walk past.

That was me.

There’s a few buildings I was in and I couldn’t even… certain hallways I wasn’t allowed down.

It had this serious, almost bunker-like feeling.

And I think that matters because when you were inside that moment, it probably felt like that was just what the internet required.

If you wanted to be a serious company, you needed all this infrastructure.

Servers, rooms, people, physical control.

But then the shape changed, hosting changes.

Salesforce came around and created SaaS, Basecamp.

Cloud changed everything really, and everything moved to a SaaS infrastructure.

Managed services changed things.

A lot of the infrastructure moved away from average companies, not all of it, but enough that the average business stopped thinking about the internet in the same way.

Eventually the question was no longer, how do we build and protect all this infrastructure ourselves?

The question became, do we want to be online?

And I think AI is going through the same confusion right now.

Companies know AI matters.

But they don’t know the shape yet.

They don’t know how much should be internal, how much should be local.

They don’t know how much should be used in the big models.

They don’t know which workflow should be redesigned, what should be automated.

Watch it still have a person as part of the process.

So everyone is trying to figure these things out.

And the models is a big one ‘cause everyone thinks they gotta be on the biggest and latest model, but really like that’s not always the case.

And it goes back to the AI fatigue, it goes back to everything else.

Every time a new model comes out, do you gotta rework everything, rebuild everything?

Well, sometimes if things work and they work, you keep them as they are, but that’s a whole nother video.

So let’s get back to this one.

And a lot of it probably, so, hmm, let me get back.

So everyone is trying all these different things.

Some of it will last, a lot of it probably won’t, but that’s how these phases seem to work.

The early infrastructure feels like the right answer.

Then later you realize it was just the scaffolding that builds the whole thing, puts it all together.

07:52 — Token Cost Is Today’s Bandwidth Problem

One of the clearest places you can see that right now, again, is token cost.

A lot of companies are starting to worry about how much AI is actually costing them on the end.

And they should.

It’s a real problem.

If you have employees using AI all day, agents running workflows, documents being summaries, meetings being processed, support tickets, chat bots, code reviews, bug errors, century investigation, Jira’s being processed basically.

Like there’s a whole lot of stuff going on.

Calling a model that the spend can get very out of control very quickly.

So now we’re seeing another layer appear.

Before a prompt even gets answered, something else decides where should it go?

Should this use the expensive model, the cheaper model, a local model?

Should we cache all the answers?

Shorten the context?

Should we summarize it first?

Just all these different things.

Can we route it through another tool?

Can we avoid the call entirely?

And that’s a real business problem.

But it’s also reminds me of how people used to think about bandwidth.

There was a time when sending data was expensive enough that people thought differently.

Images were heavy, videos were unrealistic, storage mattered more.

Everything had to be crest and limited.

And then over time, the cost curve changed, not because cost disappeared completely, but because the cost dropped enough that behavior changed.

People stopped treating every bit of data something precious.

The internet became something people assumed.

I wonder if tokens are in a similar place.

Right now tokens feel like a metered resource.

They feel like something companies have to watch carefully.

But models get cheaper, hardware improves, inference gets more efficient, local models get better, and routing gets smarter, then maybe the behavior changes.

Maybe the question stops being how do we reduce AI usage and becomes what happens when AI usage is assumed and that’s a very different world.

09:57 — Thinking Past Today’s Version

My next videos, I had to break this one up in the double videos because I have, it’s too much.

This one really hits me in a way where I hate how we get stuck thinking about things as we see them today knowing that no just from being in the world and being a person and watching things evolve it will not be what we see today a thousand percent like there’s no getting around it.

So I really wanted to take my time with this video and I have the other one written I just can’t remember what the details are and I might try to do it tonight too so I can have it queued up.

So I don’t lose the momentum on this.

So back to this one we’re talking about what happens when AI usage is assumed and like how that is different.

And I also don’t think the future is necessarily one giant model answering everything that seems way too simple and too expensive.

It seems more likely to me is a layered system a company might have a bunch of smaller internal AI systems like trained workers.

Non-conscious, non-magical, just focus systems that know a specific job.

One handles invoices, support tickets, internal policies, code checks, HR help questions, FAQs, summarizes all the meetings, watches Slack, checks logs, looks at Sentry.

You can have all these small ones using small models, a lot of cash stuff, a lot of memory, and you’re not relying on your token usage then.

It’ll certain moments and let me highlight where I am at the moment.

One searches internet documents, one cleans up messy information.

So like what N8N, I use that a lot on my own.

I have my own installation, it’s on my open cloud server and I find that.

I will build up the workflow with almost no AI involved at all.

It’s making all the decisions and then AI is used in certain pieces of it for analyzing something, giving me something to post on X, giving me a LinkedIn post to do or letting me know that there’s an idea for a response to a YouTube comment.

Keep the comments coming.

I don’t get many and I do read them all because there’s not that many to read but I’m very thankful for the ones I get.

So AI is part of this system not the entire system is what I’m getting at there.

So like I said one cleans up information before it goes anywhere else and then only when the work is hard enough or ambiguous enough it gets passed to the bigger thing and that feels more realistic to me.

Not every task needs the smartest model in the world.

Some tasks just need a competent small system with the right context.

And that is a very, that’s where local models and company-trained systems become interesting because maybe the heavy lifting does not always happen on that biggest layer, that outsourced layer.

Maybe a lot of work happens before the big model.

And when I say big model, I mean, sending it out to an anthropic or throwing it out to open AI.

And so a lot of work happens before you even see that being happening, which is what I was getting out of my innate end.

And even my, there’s another one I use too, I can’t remember, pipe dream.

I think that’s what it’s called.

And a smaller model fits.

Another one summarize.

Another one classifieds, another one retrieves the right data.

Another one checks whether the request is even worth escalating.

Then the expensive model only handles the part that actually needs it.

That sounds less exciting than saying one AI will do everything.

But it sounds more like companies actually, how companies actually work.

Layers, routing, permission, specially tools, internet knowledge, cost control, compliance, escalation paths, that’s boring infrastructure.

But boring infrastructure is usually where technology belongs.

14:14 — AI Still Needs People Involved

And before I jump into the next part, that’s what I’m saying.

As much as I feel employment and jobs will be squeezed.

And then I did the videos about how AI is widening the path, your career path.

So I do believe there’s going to be a shift.

People will lose jobs, undoubtedly.

New jobs will be created.

People will find their new, if they’re willing to learn and willing to do things, people will find their new path.

And I believe that’s what that does.

It shows that AI is a tool.

It’s not going to replace everybody.

It’s not going to replace everything.

People still need to be involved.

We still need the human touch basically.

So let’s get back to this.

15:01 — Solar, EVs, and the Maturity Curve

Solar and EVs and the maturity curve problem.

That’s the title of this next section.

This is where I think about solar and EVs.

Not as the exact same thing, but as another example of technology, maturity changes the decision.

Solar always made sense as an idea.

The sun is there, the energy is there, concept is easy to understand, but the early economics were different.

The panels were different.

The storage was different.

The grid problems, the installation, the incentives were not the same.

And depending on when you bought in, you might’ve paid a lot for a version that later improved quickly.

That does not mean early adopters were stupid or made bad choices.

They were part of the process that they proved demand.

They found the problems.

They helped move the technology forward.

But from a normal person perspective, being early can mean paying to learn an immature version.

My friend’s going through it now with solar panels on his house.

Paid a lot of money, got a lot of incentives and rebates, but now he could upgrade his solar panels to have much more powerful, more efficient ones, but he loses all cost advantage of what he locked into.

So same with EVs.

The idea made sense before the product felt safe, enough for everyone.

The range, the battery capacity and the charging capacity, the repair knowledge, trust had to improve.

At some point technology gets boring enough that a normal person can look at it and say, “Okay, I can probably live with this.”

That is an underrated milestone.

When technology becomes boring, it becomes much easier to adapt.

And I think AI is not boring yet.

It is still changing too fast.

The tools are changing.

The cost is constantly moving.

The models are all over the place.

The hardware, now we’re in space.

We’re going in the deserts.

We’re using all the pond water.

The workflows around it are changing.

The tools that we use are changing.

The assumptions are very different from day to day.

So everyone feels behind.

But I don’t know if anyone is really permanently ahead.

So who can build the perfect AI workflow today?

And it might look outdated.

I said right here in a year, but it could look outdated in a couple of hours, to be honest with you.

Someone can panic, learn one tool today and six months later, that tool might be absorbed into something else.

Actually, it’s a little bit less than that ’cause I think pipedream is the one I said.

I was using it for expenses, I had tools going, automating my email process, invoices and workday bought it.

And that happened within like two months.

A company can build a whole system around saving tokens and then the price of tokens might collapse.

And I have a video about token collapse and price, and I might try to throw out into the next video also.

Cause I really believe token.

The price and consumption of tokens is not going to be as like, think about compare it to data on your phone.

It’s just, I remember when I used to travel on how expensive it was to go to US or even travel like across the Canada or anywhere else in the world where how much data and just minutes was so expensive outside of your zone and now it’s not even a second thought so compare that to token usage day and what it possibly will be in the future cost drop but but the ground is still moving so much that everyone is still starting over.

18:35 — Today’s Requirements May Not Be Tomorrow’s Requirements

The biggest mistake might be assuming that what AI needs now is what AI will always need.

Right now it needs certain chips, cooling, data centers, speed and connections, cloud setups, machinery.

But why do that changes?

Why are the future AI’s not just data centers forever?

Why do it becomes local?

What if it runs on your phone?

What if it’s distributed?

Why do smaller models become good enough for most daily work?

Why do of phones, laptops, computer servers, vehicles, appliances, internal systems all start having useful AI built inside of them.

What if the cloud model is still there but it’s not the only place intelligence happens?

And I know where I was going with that and it’s part of my other video.

That seems plausible to me because that is what happened with computing in general.

Computers did not just get bigger.

They also got smaller, spread out, personal, mobile, invisible.

Just think about the amount of technology in your AirPod Pros compared to what your first computer, my Tany 1000 was.

Huge difference, Atari, Super Nintendo.

It’s amazing how small things get, how powerful they get as time moves forward and we get better at what we’re doing and how much the price drops.

They moved in like all this AI like I said it moves invisible it’s inside your cars your phones watches TVs doorbells thermostats like it’s unbelievable.

And so when I look at AI today and I see all the heavy infrastructure I try to remind myself this might not be the final form.

It’s not going to be the final form this might just be the visible machinery phase.

And that matters because if we mistake the machinery for technology, we might misunderstand where this is going.

We might think the future belongs only to whoever owns the biggest data center.

And maybe some of it does, but maybe another part of the future belongs to whoever figures out how to make AI smaller, cheaper, more specific, more private, more embedded into normal work and normal devices and normal things fit into your life basically.

And that might be just as important.

I think it’s going to be more important.

21:04 — AI May Help Improve AI

And there’s another part to all of this that makes AI different from previous technologies really because AI may help improve AI itself.

Not in a science fiction way.

I mean in practical terms.

AI can help write code, test code, optimize systems.

It can help design chips.

It can find patterns.

It can do all the research.

It can do optimization.

It can help engineers compare designs.

It can help find the waste.

It can help automate boring parts.

It can help be more efficient, build faster tools.

It can help build the tools to help build itself, improve cycle within that might compress.

With older technology, humans were still doing the work with the tools available at the time.

With AI, we may have a tool that helps improve the next version of the tool.

It’s like recursive efficiency.

And that doesn’t mean everything becomes easy.

It does not mean progress is guaranteed and it doesn’t mean cost vanishes.

But it does mean the curve could feel strange.

Things that look impossible or too expensive right now might become normal faster than people expect.

It’s already happening.

That’s why I’m cautious when people take today’s AI limitations and project them straight into the future.

They might be right about the current problem, but wrong about how the problem stays in that form.

Power matters, chips, cooling, cost, but efficiency also matters.

And people forget that part.

Everything gets more efficient.

Well, not everything, not perfectly and not smoothly.

But over time technology tends to find ways to waste less.

And if enough money, attention, competition, and engineering talent move into one area, the early version usually does not stay in that version very long.

23:10 — The Personal Takeaway

So I think the personal takeaway is not ignore AI, obviously not.

That would be the biggest mistake because it’s here to stay, it’s here, it’s part of our lives.

It’s as much as like the iPhones part of your life, AI will be a massively more part of your life.

But I also don’t think the takeaway is panic and rebuild your whole life around today’s tools.

That might also be a mistake.

The better posture is somewhere in the middle.

Use it, learn it, pay attention.

Build small things with it.

Understand what it can do, understand where it fails.

Notice how it changes your work.

Notice what it makes easier.

Notice what it makes more confusing.

But don’t worship the current version.

Don’t worship it at all.

Always consider it to be fluid, changing, but stay on top of it.

Don’t assume today’s tools are the final tools.

Don’t assume today’s prices are the final prices.

Don’t assume today’s workflows are the final workflows or today’s infrastructure.

Because we are probably still very early in the shape of all this.

Not early in the hype sense.

Not really like everyone is about to get rich.

I mean, early in the awkward sense, early, like the machinery is still exposed.

Companies are still building the wrong things.

Normal people are trying to figure out what, what is useful for him.

What is just noise?

Like everyone feels behind, but no one really knows what the mature version looks like yet.

And I think that is an important distinction because feeling behind can make you desperate.

But realize that the analogy is still immature, it can make you feel, help you be a little more patient.

You can keep learning without pretending that the current version is scared.

24:58 — Don’t Mistake the Early Machinery for the Final Form

I think that’s the thought I keep coming back to.

We might be mistaken in the early machinery for the final form.

We see the data centers, the chips, the giant models, the complexity.

And we think this is what AI is.

But maybe this is just what early AI is.

Maybe the future is small, it’s local, specialized.

Maybe every company has its own internal AI system doing small pieces of the work.

Maybe the big models still matter, which they will, but they become one layer in a much larger system.

Maybe AI becomes less of a website you visit or an app you open, but more something like built into the way we just live, part of our work, part of our life.

I don’t know exactly where this lands.

I don’t think anyone does but I do think the current version is not the final version.

And when I look at the history of computers the internet solar EVs and all things technology related that seems to be the pattern.

The early version looks too expensive way too awkward very limited.

Way too big too hard to justify then the investment investments come in the tools get improved the infrastructure changes the cost moves.

The adoption happens the assumptions break and eventually people look back and say I can’t believe we thought that was the way it had to work.

That’s where I think we are with AI.

We’re not at the end.

We’re still looking at the big machine in the room and one day maybe sooner than we think we’ll look back at this phase realize how early it actually was not because nothing mattered, but because everything was still being figured out.

I hope you like this one. Thanks. Bye.

Revisiting AI Fatigue: Learning to Step Away [Raw Session]

00:00 — Revisiting AI Fatigue

Hey, welcome back to Slow Builds.

I wanted to revisit AI fatigue, but not the same way as the last video.

Last time I talked about this, it was more about around coding and constant feeling that we got.

And the fatigue it caused, AI has been causing, and it’s causing a lot of people, and I still believe it’s a massive issue.

How AI changes the feeling of programming.

you can go from being deep in the code to like now you’re not a coder anymore, you’re more like a manager, a supervisor, overseeing your agent’s coding really. You’re reviewing, checking, testing, and verifying. You’re managing the work instead of always being inside of it.

That part is still true for me, but this one is a little different. This one’s more about what happens after that because once AI makes more things possible you start feeling like maybe you should always be doing something and that is part I’m trying to deal with right now not whether AI is useful because it’s extremely useful it’s making making things that seemed impossible or so on are now within grasp basically so it’s not whether AI saves time it definitely saves time but But what happens when the time it saves just turns into more pressure?

What happens when every quiet moment starts feeling like unused productivity?

And what happens when stepping away feels like you’re falling behind?

And that is what I want to talk about through in this one because I think I’m learning that just because AI lets us work more does not mean we have to work all the time.

01:47 — AI Makes Work Easy to Start

The thing that feels different with AI is how easy it is to start work now.

Before if you wanted to build something, there was a lot of friction involved.

You had to research it, plan, document, write it, test it, constant fixing, rethinking, and even possibly just completely scrapping everything and starting over.

There were natural barriers in the way, and those barriers were annoying, but they always they slowed you down. Now a lot of that is condensed. You can have a rough idea and turn into a plan. You can have messy notes and turn them into structure just like these videos in a way. You can have a half-built project and get help cleaning it up quickly, getting it to a proper MVP. You can ask for test cases, refractoring, full architecture, get complete second opinions. You can even have it go out and research to see if the idea is even possible. A full rebuild doesn’t take weeks, weeks or months. It takes hours and minutes sometimes and again that is very useful but it also means work is always sitting there one prompt away and that is the part that gets me. I do not even need to fully sit down and enter work mode anymore. I can just send a quick message to a telegram or dispatch through Claude. One small prompt, a loose idea, an unfinished thought, and something’s moving. Something’s being outlined, written, planned, even built.

So then the question becomes, if it’s that easy to start something, why wouldn’t I always be starting something?

Why wouldn’t I always have an idea moving forward?

And why wouldn’t I always have a project being cleaned up and being ready for testing and users.

Why wouldn’t I always have AI working on something in the background for me constantly, continuously having my agents just running nonstop.

And that is where the pressure creeps in.

03:57 — This Pressure Did Not Start With AI

And I don’t think this is pressure starting with AI.

I think this has been around for a while.

I think AI is just a new version of something that we’ve seen before.

An email became normal in companies, it changed the pace of work.

People started feeling like they had to respond faster.

Before that, not everything needed an instant reply.

Then cell phones headed a whole other layer on top of that.

Now you can be reached even when you’re not at your desk.

Then the Blackberry took it further.

Work email was always with you on the go and be able to take the phone calls.

And then smartphones, especially the iPhone.

I took everything to a whole like just exponentially more productivity within your palm of your hand, email, text, calls, full on calendars, apps, every tool, every document.

Your whole job was just sitting there in your palm.

And if you let it, there was almost no downtime anymore.

You could always check something, always reply, always look something up, always fix one small You’re staying half connected 100% of the time.

And AI feels similar to me, but in a slightly different way.

Email made us reachable, phones made us available, smartphones made us portable.

AI makes us startable.

And that is the part that feels new.

It’s not just that someone else can reach me, it’s that I can reach back into work at any moment.

I can open another loop, I can start another idea, I can create motion even when I should probably be resting.

And if you’re the kind of person who already has a lot of ideas, I can get heavy fast.

05:46 — Too Many Open Loops

And that’s where I feel my life is right now.

I have a lot of things on the go, like my steady app, the book idea I’m working on, these videos, there are tools I wanna build with my kids, there’s a bunch of side apps, I got the open cloud going.

There’s just a couple of websites.

I’m helping other people or at least I’m trying to.

And there are random ideas that just pop up every single day.

And on top of that, there’s actual work work, like my real job, real responsibility, deadlines, real mental energy being spent.

And AI makes all of those things feel more possible.

And that is the good part about it.

But it also makes all of those things feel more present.

Like they are all sitting there waiting to be touched at any moment.

And if I have a free 30 minutes, I start thinking maybe even five minutes, two minutes before I run out, like I can jump on and send a prompt.

I like, I should touch the book, clean up steady a little bit, check in on my open-claw agents, should write something, plan another video, automate something.

Should I ask AI to organize my ideas?

I should get one small thing done, one small thing, and one small thing is fine, but when every break becomes one small thing on top of another, you never actually stop.

And that is the problem.

The work does not have to be huge to wear you down.

Sometimes it’s the constant opening and closing of loops, the constant touching of everything, the constant feeling that something could be moving if you just gave a little push, a little prompt.

That’s really all it is.

07:32 — Not Everything Has to Move Every Day

I think one thing I’m trying to learn is that not everything has to move every single day or every single moment.

And it sounds simple, but it’s hard when you care about the things you’re building.

If the book sits for a few days, I feel it.

I really do.

Study does not move and has moved in a while.

And the app ideas that I have are not touched.

I feel they’re being wasted.

But it does not mean I should work on them all the time.

can sit, some things can wait, and some things can be unfinished without even being abandoned really and that is a big distinction here. Unfinished does not always mean neglected.

Sometimes unfinished just means not today. So I’m trying to spread things out more. Maybe I touch the book a little bit each week. Maybe I touch open clock a couple times a week.

Maybe I move one app forward slowly.

Maybe I help someone when I actually have capacity, not just because I feel the urge or in the moment.

I need to step back and take my time.

It’s basically what I need to do.

Maybe some ideas just stay in notes for a little bit.

And that doesn’t mean they are dead.

It just means I’m not trying to carry all of them at the same time.

And I think that matters more now because AI makes it so easy to pretend you can carry everything. But you still have one brain, you still have one body, you need sleep, you need some quiet, you need time where nothing is asking anything from you. And AYA does not remove that.

09:04 — Work Work Has to Have a Line

The other place I feel this is my actual work because with software there’s always more to do. There’s always another bug, cleanup, an integration, edge cases, new testing, just another thing to think through basically.

And with AI it’s easy to keep going. You can tell yourself I’ll just ask it one thing. I’ll just clean this up. I’ll just outline this for tomorrow. I’ll just test one more path. Compare one more approach and then 30 minutes turns into an hour.

An hour turns into late nights and then you gotta be back up again in the morning and your body you like it causes bad sleep it causes little sleep and you’re waking up early to try to catch up on what you thought you were hoping to finish the night before and you start the next day tired and if you do that enough it catches up mentally and physically and that’s the part I think people are miss they’re underestimating at the moment this kind of work might not look physical but it still hits your body.

Bad sleep hits your body.

Constant context switching hits your body.

Living in work mode all the time hits your mind and your body.

So I’m trying to be more careful with after hours work. Not perfect. I still do it. I’m making these videos at night. Sometimes on the weekends. Well I have to because I can’t do work but but even like my other ideas and stuff like they’re done at night. I do a lot of extra work at night so I need downtime.

I still push into the evening sometimes. Sometimes 30 minutes here or an hour there. I always do at least probably five to ten hours an extra week of my real work on top of my other stuff that I try to do. Sometimes I want to get something out of my head and sometimes I want to move something forward but I’m trying to notice the difference between making progress and refusing to stop because those are not the same things.

Sometimes I’m not being productive and I’m just avoiding the discomfort of leaving something unfinished.

11:13 — Downtime Has to Stay Downtime

The main thing I’m trying to remind myself is that downtime has to stay down.

If I go for a walk, it does not have to become a planning session.

If I go for a run, it does not have to become a productivity tool.

If I watch a show, I do not have to feel guilty because I could have been building something.

If I sit there and do nothing useful for a little while, does not mean I’m wasting my life.

It means I’m taking a break and I need to take more breaks.

And I think for people who work a lot, distinction matters because your brain can start measuring everything.

Rest, quiet, family time, walk, show.

Everything gets filtered through whatever is moving something forward.

And that is a bad place to live.

You need time where you’re not producing.

You need time where you’re not optimizing.

You need time where your mind can relax, even numb time.

You think of them like my wife watches those, like Bachelor and Love is Blind shows, like those are just turn your mind off garbage TV basically.

And we all need that.

Me, I go to Seinfeld, I go to King of Queens.

I have my shows that allow me to just go to a place that I don’t have to think.

I know it sounds strange, but I think it’s real.

Sometimes your brain just needs to not be solving anything, not improving anything, not learning anything, not building, just coming back down.

That’s not a waste of time.

That is part of staying healthy enough to keep going.

12:47 — AI Should Give Us Breathing Room

The strange thing is that AI should give us breathing room.

At least that is how I want to use it.

If it saves time, I do not want every save minute to become more work.

If it helps me rebuild faster, I do not want that to mean I have to rebuild everything constantly.

If it helps me write faster, I do not want it to mean I should write every idea as soon as it pops in my head.

If it helps me code faster, I don’t want that to mean I should always be coding.

The code cannot just be more output forever.

At some point, the time saved should actually become time saved, time to breathe, think, make better choices, recover, step back and ask if the thing even matters because otherwise AI does not give you freedom.

It just speeds up the treadmill.

And I think that is a trap.

We look at the tool and think I can do more now, which is true, but maybe the better question is what should I not do now?

What can I leave alone?

What can wait?

Why do I actually need less motion, not more?

And that’s the part I’m trying to learn.

13:57 — Learning to Step Away

Like, so I guess that’s where I’m at with AI fatigue at the moment.

The first layer was realizing AI change how work feels, especially with coding, less flow, more review, more supervision, more verification.

It’s just a lot more mind work rather than doing.

But this next layer is different.

It is learning how to step away when the work is always available, when the tools are always ready, when the ideas are always one prompt away.

When everything can technically move because everything does not need to move.

Not today, not all at once, not at the cost of sleep or the cost of health, and definitely not the cost of never feeling present.

I still think AI is useful.

I know it’s useful.

It’s changing the world.

I’ve done many videos on it.

We’re going to see great things happen.

And I’m super excited about what we’re going to see and what I get to build.

I’m happy with what I get to do.

and I still think it’s going to change the whole world.

But I also think we have to be careful with it because just because we can work not stop does not mean we should.

Just because we can produce more does not mean every quiet moment is wasted.

And just because AI can help us start again does not mean we always have to start something new or start over.

Sometimes the work is stepping away.

Sometimes the progress is letting your mind relax.

Sometimes the healthiest thing you can do spend. Just don’t do another prompt. Don’t open another loop. Don’t start another project.

Just stop for a little bit and let downtime actually become downtime. Learn how to relax and not constantly think, “Well, I can spin this up. I throw this out. I see what happens in the morning. I should have my agents working nonstop.”

We all need to take a little break, take a breath, relax.

All right. Thanks for watching. I hope you like this one.

When Investing Stops Feeling Like Building [Raw Session]

00:00 — When Investing Feels Like Building vs Protecting

Hey, welcome back to So Builds. I’ve been thinking about the idea lately. When you have a little,

investing feels like building. When you have a lot, investing sometimes feels like

protecting. I guess the best way to do it is, I was thinking, let’s just say there’s a stock

and you think about putting in it. Let’s just take SpaceX for example to start with. So if you put,

say right now like if you had very little and so $2,000 you put 2k in that feels like

Hope you have hope that 2,000 will turn into something great now

Let’s say you have a fair amount of money. So you want to put in like you’re looking at

$20,000 well that

20,000 more or less feels like a risk

there’s a little bit of your worryness of is that money gonna grow or am I gonna lose it and

I think that explains a lot about money stress because we usually assume that having more money makes investing easier and

In some ways that obviously does because you have the money to put in

If you have more money you have more options you have more margin you can absorb mistakes better

You can get access to things other people can’t but emotionally I don’t think it’s it’s that simple because once you have something to lose

The whole feeling changes. It’s not just about getting ahead anymore. It becomes more about not falling backwards

01:32 — The Penthouse and Basement Idea

You know, I got this from I was watching Bloomberg this morning and a rod was on and he was talking about

from the penthouse to the basement and

And that really hit me like thinking about it. And that’s what made this whole video come about

So he’s talking about like as a superstar and athlete. He basically lived in the penthouse

his big paycheck, he was treated like a superstar, but then when he retired and he had to move

into the business world, he’s not starting from the lobby or the third floor.

He said like I’m starting from the basement.

I don’t have any contacts.

I don’t have any room.

I got to learn from the beginning.

I’m starting from nothing really because that is really what money can feel like.

If you start with very little, you are trying to climb.

If you’re trying to get from the basement to maybe the main floor, then maybe one day

to somewhere better.

But if you already have a lot, or if you have built a lot, the fear changes.

You’re not only looking up, you’re looking down.

You’re thinking, “What if I mess this up?

What if I lose what I’ve built?”

You trust around people, you make the wrong choices, you take the wrong risk, and you

end up back where you started.

completely different emotional game in my mind. Climbing is stressful but trying

not to fall is more or just as stressful in a different way.

02:59 — Investing With Little

So when you’re

investing with little, investing can feel more hopeful. Not easy, it’s not

painless, but there’s always that hope, that glimmer of someday hopefully I’ll

make it. You put a small amount of money, maybe 25, 50, 100, maybe you buy one ETF

Maybe you buy a few shares.

Maybe you’re just starting to learn about investing.

And every little bit feels like proof that you’re moving.

You’re building something that was not there before.

You’re not managing a fortune.

You’re not building, but you are building the habit.

You’re trying to learn the languages.

You’re learning how to take the risks and how to manage the risks.

You’re learning what it feels like when the market goes up and down.

You’re building confidence, one small decision at a time.

in the dividends, when a dividend comes in, it’s like a yay, like I made the right choice.

The stock goes down, you’re feeling down on yourself, but then all of a sudden, ch-ching,

it’s like, oh, well, that’s a little bit of a win.

So when you’re starting with little, the first win is not the return.

The first one is becoming someone who invests.

And that’s a great way to look at it, I believe.

04:08 — The Useful Lessons From Having Less

But then the useful lessons from having less, coming from less can teach some useful things.

always healthy things but useful. You learn to watch prices, you learn to wait for sales,

and you learn to ask if something is worth it. You learn that money represents work.

You learn that a dollar is not just a dollar, it’s time, effort, it’s stress. It’s a choice

you had to make. So when you invest, you pay attention to it. You do your research. You’re

You may not assume the person in the nice suit knows more than you do.

You may be forced to develop your own filters.

And so having less can make you careful because carelessness is very expensive

and you don’t want to go through that.

You, you work hard for your money and you want to be careful for where you,

where you put your money to work for you.

05:05 — Investing With More

Um, but when you have more, the numbers change.

And even if the percentages are the same, the motion weight is not the same.

losing 10% of a thousand’s a hundred.

That hurts, but it’s very survivable.

Losing 10% on a million, that’s a hundred grand.

The same percentage, but it hits a little different.

And that is where investing is stopped feeling

like building and starts feeling like you’re protecting,

you’re protecting the numbers,

you’re protecting the lifestyle, the future,

you’re protecting your family.

The version of yourself that feels like

you finally made progress and you don’t wanna lose that.

That can make people more careful.

very risk adverse, but can also make people more anxious.

And anxiety does not always create better decision.

The math may be percentage based,

but the fear is dollar based.

05:58 — Why Having More Can Make You Worse

And why having more makes you worse.

That’s a weird title to have.

This is the part people do not always talk about.

Having more money can make you better equipped,

but it can also make you worse if you’re not careful.

because now there are more doors open.

More people want to pitch you,

more people want to devise you.

There’s more private deals that show up all of a sudden.

More business ideas sound possible.

More people around you seem successful.

And when everyone around you is talking in bigger numbers,

bigger risks, those risks and the advice they’re giving you

and everything, it kind of starts to feel normal

and that’s dangerous because the size of the opportunity

distract you from the quality of the decision.

Bigger money can create bigger mistakes.

It creates more access,

more access does not mean better judgment.

More confidence can turn into overconfidence pretty quickly.

You think everything you do is a winner.

You everything you touch turns to gold.

But really, you need to be very careful about those decisions

and continue to do the research and the due diligence

that you were doing when you had nothing.

More advice can become more noise

and more opportunity become more temptation.

And that’s a big problem.

You’re tempted to be part of everything,

but you need to be selective.

Having more options is only useful

if you still know how to say no.

07:27 — Athletes, Lottery Winners, and Inheritance

And this brings up a couple of different things

about like athletes and lottery winners and inheritance.

So, and this is why those old stories

about athletes going broke after retirement hit differently.

I’m careful.

I’m careful with the exact stats about this because some of the numbers people

repeat are probably not clean, they’re not true, but the pattern makes sense to

me. Someone makes a lot of money in a short window, the lifestyle grows, friends

and family depend on them, advisors show up, business opportunities start coming

out of the woodwork, and put them the career ends. It could be an injury, it could be a

trade, it could be any number of reasons, so the income stops, but that lifestyle

stop. You’ve built up this lifestyle and dependency and that’s not just a spending

problem that’s an identity problem. Same with lottery winners. The money arrives

before the skill. The money arrives before the habits were built and the

money arrives before you’ve had spending boundaries because now you have an

abundance of money that you didn’t have before. You used to live within your

limits or as close as who knows maybe you were living outside of them and that

lottery win is just a it’s going to continue those bad habits someone who

was used to thinking and hundreds or thousands of something expect to make

decisions and millions and that is not normal same with inheritance you can

inherit the money without inheriting the discipline that’s built with it you can

receive receive the result without receiving the scar tissue that goes along

with building that wealth. Money without habits is very, it’s fragile.

09:10 — The Opposite Problem: Quiet Wealth

And the opposite problem,

quiet wealth. But there’s another side to it too. Like some people do the opposite. They save

everything. They spend almost nothing. They build wealth quietly. They never look wealthy.

They never act wealthy. And sometimes that is impressive. But sometimes it raises another

question. Do they control the money or did the fear control them? I had a friend growing up whose

family look like they were just getting by. Mother worked in the cafeteria, father was

the school bus driver, old cars, same old house, a very small house, hand-me-downs,

and nothing crazy. Just normal life from the outside. And I say that because we all lived

like that back then. And we all went to the same school and we all had like, I’m not going

to say it was a mix between blue collar and white collar, like low white collar.

We grew up not hand-to-mouth, but we grew up within our means.

But then later, like in my friend’s situation, her parents passed.

And all of a sudden, people realized her parents had serious money.

She was instantly a millionaire because it was all left to her, an only child.

And that made me think because from the outside we thought they had less, but really they

were living far below what they could afford.

And maybe that was wise, maybe some of it was fair, maybe both were true, maybe they

were just going off what they were taught and just putting the money away.

And there’s a difference between not having money and not spending money.

10:45 — My Own Version of This

And my own version of this, this is the way I feel.

I think about this in my own life because in a lot of ways I still like, I live like

And my wife especially lives like that. We look for sales

My wife will return something and rebuy it just to save a couple dollars

She does not like paying full price unless the values there. Well, we will pay for value where it’s deserved

We have older vehicles that have been paid off for years

15 16 years old vehicles repairs are annoying, but they usually still cheaper than having monthly payments

we have a small lease car that we

Really hate the car, but it gets the job done. It’s cheap. It’s reliable and

It’s within warranty so anything goes wrong the kids drive it everyone’s happy

It’s from A to B and that’s it and we don’t like the car and we hate the car, but it’s cheap

Our house is not fancy

Compared to new houses probably feel small outdated not modern

We do have an in-ground pool, but like a defense that goes around my yard

It looks like it’s falling down from the outside the gate the gate

I don’t even know how we got past inspection with the gate for the pool the deck

It’s a bit slanted and some of the boards need to be replaced and when you took an aerial picture of our backyard

It needs to be painted pretty bad

and honestly the

Deck probably needs to be replaced

But it’s still standing and it still works and we’re not trying to win some imaginary lifestyle competition

12:26 — Spending on What Actually Matters

But we do spend at the same time. We do spend money. We spend money on trips. We spend money on memories

We spend on things we actually care about we put we put that in ground pool and because my wife loves to swim

I love to swim I do my laps in the mornings

My love my wife loves going out between her calls at work and she’ll be altered till 7 doing her swim dance

She loves being in the pool

So for us that that matters to her that adds something real to our lives we explore we travel

We build moments with the family, so it’s not about never spending. It’s about knowing why we are spending

We could do both we could take the trips have the pool upgrade the house buy newer vehicles and still probably be fine

But just because we can afford something does not mean it’s worth buying it just because money is available

does not mean it needs to be used.

We could spend more and still be fine,

but being fine is not the same as being wise.

Some of these things AI puts in, I love.

13:29 — Quality Over Quantity

And quality over quantity.

I think this is where, for us,

it’s not just with the stuff, it’s with life also.

More things does not automatically mean a better life.

More expensive does not automatically mean better.

Newer does not, I mean, meaningful.

A bigger house might be nice.

A newer car would definitely be nice.

A clean deck, a deck that we’re not afraid

is gonna fall over, it would be awesome.

But every upgrade has a cost.

Like we have appliances we need to replace.

We’ve had a microwave that, it’s a built-in microwave

and it’s been broken for years.

So we use it as like another cupboard.

Our stove, the main big burner has been broken for five, six years.

So we’ve learned to deal with three burners.

Our fridge doesn’t make ice anymore and now it’s leaking.

But they’re going to give out pretty soon.

But they work.

So we’re fine with that.

And we’ll live with it.

And I’m not saying we have to.

It’s more or less we can’t find what we want.

We’re not just going to replace it with just something to replace it.

As long as we can get by, we’ll get by until we find what we want.

And we’re willing to pay for what we want, but we’re not willing to settle.

We’re not willing to just put whatever in there.

So the things we buy, we care for.

We take care of them.

We don’t break them.

We don’t throw them out.

We reuse them.

We donate them.

We try to sell them.

And we try to keep them as long as possible.

If something breaks, even if it’s out of warranty, we will make the phone call to see if we can

get some compensation. It’s happened right now with one of the things in the pool.

They’re gonna send me a new motor for free to let me try. So it’s not about

it’s not about like I said it’s it’s it’s it’s quality over quantity and like

every upgrade has a cost so we’re careful with what we do upgrade. It’s not

just the price the cost is also more pressure maintenance expectations money

leaving every month, the mental load of having to make the payment. So the question becomes,

does this actually improve our life or does it just make the outside look better? And

that’s where money can either serve you or start managing you. I do not want, I do not

want we can’t afford it to become the reason for every decision.

16:01 — How This Ties Back to Investing

And how this ties back

to investment. And this is all, it’s all about investing really, because investing is not

only about picking the right stock or ETF or business to put your money in, it’s about

what kind of person you become around money. If you are careless when you have little,

more money will probably make you more careless. If you are fearful when you have little, more

money may not fix the fair. It’s definitely not going to fix the fair. If you spend for

image more money gives you more image to buy. If you save from panic more money

gives you more to protect. So the real work is not just building portfolio the

real work is building the judgment to handle the portfolio. The hardest part

about having more money might be keeping the lessons from having less.

16:55 — The Middle Path

And that’s

true. The middle path is probably what I’m trying to figure out. I don’t want to

live from fair. I don’t want to hoard every dollar. I don’t want to save so much

that life passes by but I also don’t want to drift into lifestyle creep. I

don’t want to spend because other people would. I don’t want to invest in things I

don’t understand just because someone with confidence says it’s a good deal or

a great idea or it’s not gonna blow up. I want the money to create options. I want

fewer payments, less pressure, more freedom. I want more ability to take care

I want more ability to make memories. I want a more ability to help where it actually makes sense and

That is different from trying to look rich. I don’t want to be house poor. I

Do not think I want money so I can look wealthy. I want money so life has more room

I love the people a lot of people like when I show up there’s a all I look he looks homeless

I drive an old Jeep that looks like it’s beat up.

My pants have, I wear shoes that are falling apart

and my pants have holes in them.

I don’t look like, I don’t walk around like,

I don’t try to look like I’m rich and I’m fine with that.

I don’t want that stigma.

And I don’t have any reason for anyone

to try to impress anyone.

I have the people in my family around me

and we all know each other and my friends know who I am

and that’s what matters most to me.

18:26 — Final Reflection

So from the final reflection here,

so when I hear that line about the penthouse

and the basement,

I think the goal is not just escape the basement.

It is to not forget about the basement,

what the basement taught you,

because coming from less can teach you value.

It can teach you patience.

It can teach you to be cautious.

It can teach you how to stretch money.

But you also have to be careful

not to carry the fear forever.

And if you ever do build something

inherent something or receive a windfall or reach a point where you have enough

the question changes. Is it no longer just can I build? It becomes can I protect

without panicking? Can I spend without showing off? Can I invest without

gambling? Can I save without hoarding? Can I use money without letting money

become the whole of my whole identity? That is the part I’m still trying to

think through. Maybe having little makes investing feel like hope and having more

feel like responsibility. I think it makes it feel like risk. The other one is

easy. They are just different but I think the danger is assuming that more money

more money automatically means less stress. It does not. Sometimes more money

just gives you a different kind of stress. A quieter one. A much heavier one

to be honest.

19:47 — More Money, Heavier Decisions

And not that I have a lot of money. No, I’m still worrying about like can I

ever retire. I’m hoping for that day sometime. I’m hoping some of my stocks and some of my

picks and some of my investments are going to pay off someday. And even then I think

I still want to work. I don’t want to stop what I do. I enjoy what I do and I love it.

And I want to build and create and that’s a beautiful thing in my mind. So I don’t,

Even if we had money tomorrow, I don’t think I would stop what I’m doing.

I might take a little less more time off because I don’t take much time off at all.

But anyway, back to it.

More money does make your decisions in your life a little more heavier sometimes because

now you are not only trying to build a life, you’re trying not to lose the life.

And maybe the real goal is to keep the useful parts on both sides, keep the hunger from

having keep that hunger from having less keep the caution keep the respect for

money that’s the big one you got to respect money but also learn to enjoy

the parts of life that money is supposed to support when you have little

investing feels like building when you have a lot investing feels like

protecting and maybe the work is learning how to do both without letting

fair or ego make the decision.

I hope you like this one.

Thanks for sticking around.

Bye.

Seeing Life As Unfinished, Not Broken [Raw Session]

00:00 — Seeing Life as Unfinished, Not Broken

Hey, welcome back to Slow Builds.

Lately I’ve been thinking about how easy it is to look at your own life and assume something is wrong, just because everything kind of looks unfinished.

Nothing feels like it’s really falling apart. Your life isn’t ruined. It’s not hopeless.

There are just so many unfinished projects and tasks that you want to get done, and it becomes overwhelming in a way.

I think that distinction matters more than we give it credit for.

Because a lot of the time, when we look at other people, we see the finished version, or at least the version they want us to see.

The clean version. The edited version. The part where the decision already worked out.

The app already launched. The business already makes money. The debt is already gone. The body already changed. The system already makes sense.

When we look at our own life, we see the scaffolding.

We see the browser tabs open, the note app full of half thoughts and unfinished ideas.

We see the budget that still has all kinds of gaps in it, and it’s not being met or finished yet, and not being followed.

The fitness plan that worked for three weeks, and then it got interrupted. You got tired. It was vacation, work, sick. It just didn’t follow through, but the plan is still there.

You see the project that’s still missing the feature that you want to get rolled out, or the last couple of bugs that need to be tested and tightened up.

We see the video idea. This one is pretty good for me. It sounded clear in my head, but it comes out messy when I try to talk through it. I get lost. I’m going to get lost in this one too, guaranteed.

Because we are inside it, it can feel like evidence that we are falling behind.

We’re not where we want to be, or we compare ourselves to where everyone else might be.

But maybe it’s not always that.

Maybe some parts of life are not broken.

Maybe they’re just not finished yet.

02:12 — Some Things Really Are Broken

I don’t mean that in a motivational way.

I don’t mean everything is secretly going to be great. It’s going to turn out just the way you want it.

Some things really are broken.

Some decisions really do need to change. Habits really are damaging. Some relationships, systems, jobs, projects, and regular routines need to be looked at honestly and revisited, removed, tweaked, or just started over.

But I think we also create a lot of extra pressure by treating every incomplete thing as a personal failure.

That is something I keep running into with software, money, health, this YouTube channel, all kinds of family stuff, and especially with AI.

I always feel like I’m falling behind. Touching on that, I actually think the system is built in an odd way, and maybe falling behind is part of the process anyway. We can get to that in a different one.

We’re just trying to build a life that feels a little more intentional than it did before.

There are so many areas where the work is happening, but the result is not clean yet.

And when the result is not clean, it is tempting to think the work does not count.

I think that is a wrong way to look at it.

03:35 — Software Starts Ugly

One thing software has taught me is that almost everything useful starts very, very ugly.

You think you understand what you’re building. Then you actually start building it and suddenly the clean idea in your head turns into a pile of edge cases, incomplete prompts, bad links, bad buttons, errors, incomplete databases, missing flows, and a page that looks half done.

Then the user doesn’t use it the way you expected it. There’s no validation. It’s all kinds of mess.

But that stage is still not garbage.

That stage is the point where the idea starts becoming real.

Reality is where the clean version gets challenged.

That is true in code, and I think it’s true in life also.

A lot of plans look great before they touch reality.

A money plan looks good before the car breaks, or in my case, the deck breaks, the fence falls down, or the car needs to be fixed.

Fitness plans look good before you get a bad night’s sleep and don’t want to get up early. Or you plan on running outdoors and it’s raining, too windy, or too cold.

A content plan looks good before you actually have to sit down after work and record something.

A family boundary looks obvious until real emotions show up.

An app idea feels simple until you have to think about onboarding, security, privacy, payments, support, data privacy, and everything anyone actually cares about.

Once reality hits, we often assume the plan failed.

But maybe the first version just met production.

That is probably the best way I could put it.

A lot of life is your first version hitting the road. Wheels hitting the road. Foot on the gas. Production is messy.

There are real users, real bills, fatigue, interruptions, old habits, people with different needs, and always limited time.

There is your own mood too, which is not always as reliable as you wish it was.

So when something gets messy, it does not automatically mean the whole thing is wrong.

It might just mean you’re finally seeing what needs to be adjusted.

06:16 — Money Does Not Become Simple

That has been a useful way for me to think about a lot of things, especially money and investing.

I used to think of money more as a final state.

At some point you’re supposed to have it figured out. You save enough, you invest enough, you make enough, you can finally relax.

But I don’t know if it really works that way.

At least it has not felt that way for me at all. Not even close.

There’s always another layer.

You pay off one thing, then notice another thing.

You increase your income, then taxes become more visible.

You start investing, then realize you have to manage the risk that goes along with it.

You start paying attention to the news and world events, and at the same time notice how that affects your ups and downs.

You buy a house, maintenance shows up. People don’t realize that owning a home is not cheap.

You own a rental, people become part of that equation. Sometimes it’s family, which gets real messy real quick. Or maybe it’s people that know how to game the system.

But sometimes you luck out. I’ve had it every single way.

You try to help family, then realize helping and enabling are not the same thing.

You start thinking about retirement, then you have to think about health, kids, timing, inflation, and how long life actually is.

So even when things improve, they do not necessarily become simple.

They just become more detailed.

And I think that is part of progress people do not talk about enough.

Progress does not always make life feel lighter right away.

Sometimes progress gives you better problems, bigger problems.

That sounds negative, but I don’t mean it that way.

It’s just more honest.

08:11 — Problems Change Shape

When you’re broke, the problem might be survival.

When you start earning more, the problem might become stewardship.

When you’re unhealthy, the problem might be just moving.

When you start getting healthier, the problem might become sustainability, continuing it.

When you have no projects, the problem might be boredom and wasted potential.

When you start building things, the problem becomes focus, maintenance, shipping, and deciding where to place your attention.

So the problem does not disappear.

The problems change shape.

They morph into something else.

If you expect progress to feel like the end of problems, you might misread the next stage.

You might think, why am I still dealing with friction? Why am I still so tired all the time? Why do I still never feel done? Why is there uncertainty in everything I think about or touch or get involved in?

Maybe the answer is because done was never really the deal.

Maybe life is mostly maintenance and adjustment.

It doesn’t sound flashy, but it feels very true.

You do not build a body once. You do not build a marriage once. You work on your body. You work on your marriage.

You do not build a career just one time. You’re constantly moving through your career.

You do not build a financial life once. There are always ups and downs.

You do not build the software once. You build, maintain, fix, rethink, and sometimes tear it down and start over.

If you’re waiting for the moment where everything is finally locked in and complete, you might spend most of your life feeling like you’re failing, even when you are actually making progress.

That is the part I’m trying to be more careful with.

10:12 — The Big Pile of Everything Undone

Because I can do this thing where I look at everything that is not done and mentally turn it into one big pile.

The app is not finished. The YouTube channel is way too small. The house needs work. Vehicles. Fitness. Money. Family. Business. All the automation stuff I want to build with AI. All my notes are scattered. Taxes. Accounting. Organization.

I’m getting overwhelmed just going through the list and feeling like so much of my life is undone.

When you stack it all together, it feels like complete chaos.

But if you separate it out, some of it is just normal life.

Some of it is active construction.

Some of it is waiting for the right session.

Some of it is not urgent at all.

And a lot of it is not even a real problem. It’s just problems we make on our own. It does not need my attention.

Sorting these things makes a difference mentally, emotionally, and even physically to some extent.

Because when everything unfinished feels equally urgent, you end up exhausted before you even start.

You’re like, why even bother?

You do not know whether to fix the app, organize the money, clean the house, record the video, or go for the run.

I had to go for a run. I had just finished a call and wanted to try to get this video out. Trying to knock off little things makes a big difference.

But even then, I’m going to go back on this and it’s not going to be done. I’m still going to feel incomplete.

So your brain treats the whole thing as danger.

That is where I think a lot of people get stuck.

Not because they are lazy. Not because they don’t care. But because they’re caring too much.

There are too many loops without knowing which one actually matters today.

Which one is the most important one to tackle?

12:22 — What Slow Building Actually Means

I think this is where slow building helps.

Not because it makes everything easier. It does not.

Nothing gets easy.

But because it gives you permission to stop pretending everything needs to be solved at once.

Slow building is not about moving slowly for the sake of moving slowly.

It’s more about refusing to panic just because something takes time.

It is saying, okay, the thing is not finished. What is the next honest piece?

Not the perfect piece. Not the impressive piece. Not the piece that makes the whole thing look great. Not what you envision in your mind.

Just the next real piece.

That could be one feature, one walk, paying off a bill, having that hard conversation, spending an hour cleaning, putting some time aside and doing that.

Get one video recorded. Just record it. Don’t worry about editing it. Don’t worry about anything else. Just record it and get it ready for the next step.

There’s a lot of dignity in that.

Even if it does not look like much from the outside.

Maybe that is why I keep coming back to this channel overall.

I’m not trying to present the finished version.

I do not think I could even if I wanted to. I really can’t because I don’t know how to do videos. I don’t know how to do anything. That’s why they’re all unedited raw sessions.

I mix up. I mess up. You see it.

And that’s okay because that is where most people actually live.

They live in those messy moments.

14:06 — Most People Are in the Middle

Most people are not in the launch video.

Most people are not in the before-and-after picture.

Most people are not in the final chapter.

They are somewhere in the middle of 20 different things.

Trying to make money, stay healthy, keep the relationship intact, understand AI, keep up without getting swallowed up by everything, and keep their head above water.

They’re trying to build something useful.

Trying not to waste their time, trying to be less reactive, trying to become a little more stable.

And it’s easy to think, once I get this part handled, then I can start.

Once the house is clean, the schedule is better, the app idea is cleared out, I have more energy, the weather changes, there is less stress about money, I understand how to use the tools, life calms down.

But it never does.

I’m not sure life ever calms down. Not in the way you imagine.

You get moments. It changes. Some things get easier. Other things become more complicated.

At the same time, you either build inside the unfinished life you have, or you keep waiting for a clean starting point that is never going to show up.

You just have to get up and do it.

It sounds simple, but it’s hard to accept because an unfinished life does not feel very inspiring.

It feels inconvenient.

You have to build around appointments, work, family, bad decisions, repairs, fatigue, your own inconsistencies.

Around the fact that some days you just do not feel like the person you thought you were becoming or supposed to be.

That can mess with your identity and your mental state, because we like to believe progress should make us more consistent and bring us to that vision of where we expect to be.

But sometimes progress reveals how inconsistent we still are.

16:10 — Returning Faster

You start running, then miss a week.

You start eating better, then have a bad stretch.

You start building an app, then avoid it.

You start making videos, then question the whole thing.

You start using AI, then feel overwhelmed by how much there is to learn and how much other people are doing it differently than you are.

You start organizing money, then make impulse purchases or unexpected purchases show up.

The old version of you wants to use that as proof.

See, you’re not really changing.

But I do not think that is always true.

Sometimes change looks like returning faster.

Not never slipping.

Not never drifting.

Not never getting tired.

You notice sooner. Come back with less drama. Make the next useful move without turning the mistake into your entire identity.

That is another place where software gives a decent metaphor for me.

A bug does not mean the entire application is worthless.

It means something needs to be inspected.

Maybe the logic is wrong. The input was unexpected. The assumption that was made when it was built was incorrect. There was bad test coverage.

We do not delete the entire project every time something breaks.

At least you shouldn’t.

You debug it. You isolate the issue. You fix what you can. You learn something about the system overall, and then you keep going.

But with ourselves, we are often much harsher.

One bad day becomes, I have no discipline.

One awkward video becomes, I’m not good at this at all.

One missed workout becomes, I always fall off.

One missing month becomes, I cannot manage money.

One unfinished project becomes, I never finish anything.

That is not analysis. That is just a bad error message.

It tells you almost nothing useful.

18:19 — Less Shame, More Inspection

A better message would be more specific.

What broke?

Where did the system fail?

Was the goal too vague?

Was the schedule unrealistic?

Was the environment working against me?

Was there too much friction?

Were you tired?

Did you get enough sleep?

Were you avoiding something because it was unclear?

Or did you just not want to meet the conflict or friction that came along with it?

Did you even actually care about the thing?

Or were you just chasing the idea of being the kind of person who does that thing?

Those questions are more useful than shame.

That’s probably one of the biggest shifts I’m trying to make.

Less shame, more inspection.

Not in a soft way. Not in a pretend everything is fine way. But in a practical way.

Shame is usually bad at system design.

It makes you want to hide, quit, or overcorrect.

Inspection gives you information, and information is something you can work with.

That applies to AI today also.

A lot of people are trying to figure out where they fit now.

Developers are wondering what coding even means when AI generates huge chunks of it in fractions of the time.

Creators are wondering whether anything they make will stand out compared to the constant AI junk thrown out there, and how realistic that junk is becoming.

Businesses are wondering which tools matter and which ones are noise.

Regular people are trying to understand if they are falling behind the curve. Are they part of that restructuring process that’s going to happen within their business, their life, and the whole thing?

It feels unfinished because it is unfinished.

20:07 — Living During the AI Shift

We’re not living after the AI shift.

We’re living during it.

That means a lot of our conclusions are probably very early.

Some of the fears are justified. Some excitement is justified. Some of the predictions are very wrong.

I’ve talked about this before. Jobs are going to change. Career paths are going to change. Some people gain access they never had before. Some people will get extremely overwhelmed and anxious.

Some tools are going to disappear. Some workflows will become normal so quickly we forget they were new, or that we even use them because they are automated behind the scenes.

In that kind of environment, trying to have a perfectly settled opinion might be the wrong goal.

Maybe the better goal is to stay engaged without pretending to know the full shape of it yet.

That’s another form of living with unfinished systems.

You can use AI without making it your whole identity.

You can be cautious without being frozen.

You can be optimistic without being naive.

You can admit the tools are powerful without pretending they solve every human problem.

You can build with them and still care about understanding what is happening underneath.

That balance is very hard.

I think a lot of people are tired because they’re trying to resolve something that is not ready to be resolved yet.

They want the final answer.

Is this good or bad?

Is this opportunity or is it threat?

Is this the future or is it just all hype?

Is this freeing people or making them more dependent?

The annoying answer is probably yes to parts of all of it.

Which means we have to live in the tension for a while.

And that is extremely uncomfortable, but it’s also real.

22:05 — Judgment Takes Longer Than Information

Not everything important gives you a clean answer right away.

Sometimes you have to keep observing, keep testing, keep adjusting, keep asking better questions.

That is not weakness.

That is how you build judgment.

And judgment takes longer than information.

Information is cheap now.

AI can give you information instantly, but judgment still has to develop through contact with reality, through mistakes, repetition, noticing patterns, and seeing what actually happens when an idea leaves your head and enters the world.

That might be one of the reasons I still care about building slowly.

Because slow building creates contact with reality.

You cannot just live in the imagined version forever.

At some point you have to record the video. Push the code. Send the invoice. Make the phone call. Do the workout. Check the numbers. Have that conversation.

Then reality gives you feedback.

Usually not as cleanly as you hope, but it gives you something.

Then the question becomes, can you keep working with that feedback without making it too personal?

That is hard for me.

I think it’s probably hard for a lot of us because feedback can feel like judgment.

A video cannot perform very well and it feels like people rejected that idea.

A dislike hurts.

An app has no users and it feels like the project is pointless.

A plan slips and it feels like you failed.

A conversation goes badly and it feels like nothing can change.

But sometimes feedback is just data.

Not always. Sometimes it does mean something serious.

But not every weak signal deserves a dramatic story.

Sometimes the video title was unclear.

Sometimes the app needs better onboarding.

Sometimes timing was just wrong.

Sometimes you’re tired.

Sometimes the idea needs more time.

Sometimes the thing is not ready yet at all.

And sometimes maybe it’s not worth continuing.

But you usually need calm attention to tell the difference.

Panic is not very good at making that call.

24:15 — Unfinished More Than Broken

That is why I like the phrase unfinished more than broken.

Broken makes me want to either fix everything immediately or throw the whole thing away.

Unfinished makes me ask what stage it is in.

Early? Stuck? Neglected? Waiting? Is this actually done enough?

That last one matters too, because some things stay unfinished because we keep moving the finish line.

A video could always be better. An app could always have another feature. A budget can always be a little more optimized. The room can always be cleaner. The plan can always be more complete.

At some point, unfinished becomes an excuse to avoid releasing, deciding, or accepting.

So I’m not saying unfinished means harmless.

Sometimes unfinished is where we hide, and that is worth being honest about.

There’s a difference between a slow build and avoiding a build.

A slow build still has movement.

It may be small. It may be inconsistent at times. It may pause when life gets heavy, but there’s still some contact with the work.

An avoided build becomes something we only think about.

We talk about it. Research it. Rename it. Plan it. Imagine it. But we never really touch it.

I’ve done that too.

Sometimes planning feels like progress because it’s cleaner than execution.

Execution creates evidence.

Planning creates possibilities.

Possibilities feel better because it has not failed yet, but possibility also does not become anything unless it gets tested and started.

That is a tension I keep noticing.

I do not want to rush everything, but I also don’t want to use slow building as a polite way to avoid shipping.

So maybe the question is not, is this finished?

Maybe the question is, is this alive?

Is there still movement here?

Does it have my attention?

Is there still a reason to continue?

Is the next step small enough that I can actually do it?

That feels more useful, because some things in life will be unfinished for years.

26:20 — Keeping the Important Parts Alive

Your health is never finished.

Your finances are never complete.

Your relationships are never finally finished.

They are all still living things.

Your skills are never learned. You’re always learning new ones.

Your understanding of the world keeps changing as the world changes and you get more experience and more knowledge.

Even your identity changes from time to time.

It’s always moving.

So maybe the goal is not to finish your life.

Maybe the goal is to keep the important parts alive. To maintain what matters, repair what you can, stop caring about what no longer belongs to you, and build something slowly enough that it can actually become part of your real life, not just your fantasy life.

That is where I think this matters.

Because if you believe your life is broken every time it is unfinished, you will constantly feel behind.

When you constantly feel behind, you start making bad decisions.

You rush. You compare. You buy things you don’t need. You chase shortcuts. You copy people whose lives are nothing like yours. You abandon things too early or cling to things too long because you don’t want to admit they’re not working.

Both can happen.

But if you can look at your life more honestly and say, okay, this area is unfinished but it’s not broken, then you can breathe a little. You can work with it.

You can make a smaller decision.

You can stop turning every open loop into a crisis.

That’s probably the point of this video.

Not that everything is fine.

Not that unfinished is always good.

Not that slow progress is automatically noble.

Just that we need better categories.

Broken. Unfinished. Neglected. Growing. Paused. Done enough. Not worth continuing.

Those are different things.

If we mix them all together, we make life harder than it already is.

28:15 — Living Inside the Build

For me right now, a lot of things are unfinished.

The channel is unfinished. The software ideas are unfinished. The way I use AI is evolving.

The money plan is better than it was, but there is no perfect final step or system to put in place.

Health is constantly ongoing. Family stuff is always complicated. Houses always need repairs. Vehicles always break down.

There are ideas I have not acted on yet.

There are habits I still have to protect and habits I have to undo.

There are days where I feel clear and days where I feel like I just reacted to whatever was in front of me.

But I do not think that means everything is broken.

It means I’m living inside the build.

Maybe that is where most of us are.

Not at the start or the finish. Inside of it.

Trying to keep enough awareness to not drift too far left or right.

Trying to keep enough patience to not quit too early.

Trying to keep enough honesty to not lie to ourselves.

Trying to keep enough humanity when reality shows us something.

That is not a clean story, but it might be a real one.

I think there is some comfort in that.

Not comfort as in everything works okay. Comfort as in maybe the mess does not automatically disqualify you.

Maybe the half-filled parts are not proof that you failed.

Maybe they’re just where the work still is.

Maybe the next right move is not to reinvent the whole life.

Maybe it is to pick one unfinished thing and touch it honestly.

Not solve everything.

Not become a new person overnight.

Just touch the work.

Look at it.

Look at it without flinching.

Ask what stage it’s in. Ask what it needs next. Then do that piece.

That is slow, but it’s not nothing.

Over enough time, not nothing can become quite a bit.

That is all I have for this one.

I’m still thinking it through, but the phrase that keeps sticking with me is maybe it’s not broken.

Maybe it is still being built.

It’s unfinished, and maybe that applies to more of life than we really think.

30:37 — Closing Note

Again, AI wrote this entire one for me.

I went through it. I did. I ad-libbed quite a bit.

It’s longer than I expected, but I think it’s a message that needs to be out there.

I like this message.

I use ChatGPT for a lot of these and I told it, take what I have and make me one. I want to see what came out of it.

I’m happy with that because it is unfinished and it’s a work in progress. That’s the way we have to look at things.

Small goals. Take a big goal and turn it into small ones. Knock those little ones out here and there. Be honest about it and celebrate the small wins, because those small wins lead to big victories.

Alright, thanks for watching.

AI Widens the Path [Raw Session]

00:00 — Thinking About AI Differently

Hey, welcome back to Slow Builds.

I’ve been thinking about AI a little bit differently lately.

I’m always thinking about it. Obviously I use it all the time, but I’m thinking about it differently. Not completely different. I still think a lot of the same concerns that we have are real.

Jobs are going to change. We’re going to lose jobs. Some work is just going to completely disappear in my mind.

I still think companies are going to use AI to do more with fewer people. Inevitable, in my mind. I do think new jobs will appear that we have no idea about yet, but that takes time. I don’t think there’s any honest way around any of this. AI is going to disrupt the workforce in general.

But I also think there’s another side to this that I haven’t really given enough attention to in my mind.

AI widens the path.

The career path. Not in a cheesy way. Not in an “everyone is going to get rich and build a company overnight” kind of way. I don’t believe that at all.

But I do think AI changes what one person can attempt.

It changes how far someone can get before they need a full team. It changes what kind of skills become valuable and important. It changes who gets to participate in work that used to be locked behind credentials, departments, budgets, years of waiting on permission and building experience.

That part feels positive to me.

It’s still complicated. It’s messy, and it’s full of all kinds of risk.

But it’s positive, because for a long time if you wanted to build something you had to stay in your own lane.

You were a developer, designer, marketer, entrepreneur, support person, accountant, lawyer, founder in general. And yes, people crossed over from time to time, but for most people their career path narrowed pretty quickly.

Your career path might have got longer, but it didn’t get wider. You learned a skill, you got paid for that skill, and you kept doing that same skill. If your idea required five other skills, then you either needed money, a team, connections, or a whole lot of extra time.

AI does not remove all of that, but it does widen the path to allow one person to take bigger strides and wider steps.

02:52 — AI Is Not Only a Replacement Story

Most of the AI conversation still seems to be around replacement.

Developers, designers, writers, customer support, accountants, paralegals. And the honest answer probably is yes. In some ways it will replace some tasks, most tasks, or parts of tasks. It will reduce the number of people needed for certain kinds of work.

Some jobs may become a lot smaller. Some roles may disappear completely. There may be fewer people in each position. There may be more skilled people, or more manager-type roles. It’s going to change the whole landscape, I believe.

And especially when you put AI together with physical AI, that changes a whole other dynamic of robotics, automation, sensors, and machines that can actually interact with the physical world. That disrupts a ton of things.

I don’t think we should pretend that it isn’t going to happen, but replacement is only one side of the story.

The other side is expansion.

And it’s very exciting to me because AI also lets people do things they could not do before.

It lets a developer explore design. It lets a designer explore development.

It lets founders explore legal, accounting, content, support, research, marketing, flows, everything. You can build out your full product spec, your pitch deck, and get legal drafted up. Everything’s done. You put a couple of prompts together and set up the proper agents.

That does not make you an expert in any of those areas. You still need eyeballs to oversee it and make sure it’s done correctly. That’s an important distinction. But it gets you enough to start.

Starting is where a lot of ideas used to die, and they still die there.

Not because people were lazy. Sometimes.

Not because they didn’t care. They probably care, but they didn’t have enough passion to follow through.

But because the path was too narrow. That’s the real reason, I think.

They had the idea but not the team. They had the product instinct but not the technical ability or skills. They had the design sense but not the back-end knowledge to build it out. They had the business idea but not the money to hire people and build the team around it.

So the thing never moved.

AI changes that quite a bit. It doesn’t guarantee success. It just gives more people a way in and gives more of an opportunity to be successful.

05:36 — The Career Path Gets Wider

I think this matters for careers, too.

I think it matters for careers in a big way because for a long time career growth was often about being very specialized.

You pick a thing. You become good at that thing. You become very good at that thing. Then you try to move inside of that thing higher up and become more specialized. The more specialized you are, the better your pay got, and the more you’re the expert.

I don’t think that goes away.

But AI is probably going to shift what companies value when they hire. Instead of only hiring someone who can do that one narrow task, companies may start looking for people who understand that domain, that task, and that work skill, but at the same time know how to use AI to their advantage.

They can use AI to stretch across not just that task, but all the other tasks involved with doing the business or the development or whatever it is.

And that’s a different kind of person.

That’s not someone who knows nothing and just types prompts. That’s not what I mean at all.

I mean someone who has real knowledge in an area that a company needs. But that person can use AI to multiply that knowledge across adjacent areas.

Are you a UX designer who understands product flows, friction, user behavior, and visuals?

You may now be able to move much deeper into the development process. You may not be able to produce a full-stack app. You can’t replace a full-stack developer, obviously, but now you can get a real MVP moving out the door.

You can get it in people’s hands. You can work through screens, interactions, front-end code, and test flows. With testing and UI and AI, you can find where the experience breaks and build out something that people can actually use.

You can get a prototype out pretty quickly, and that’s a big shift. Before, the designer may have needed a developer just to see if the idea was even possible to build. Then you needed a marketer to get it out in front of people.

Now they can build something from the idea all the way to where testers can touch it. Then they can bring it to a pitch deck, possibly try to get investment, build a team around it, get actual traction behind it, and get real feedback.

That feels like a healthier path in some ways.

Instead of needing the full team before you even know if the idea is workable and if it matters, you can test more ideas early.

You can get the first versions out faster.

You can learn before spending as much money.

That’s a big difference.

And you can make smaller bets.

That matters too, because now you’re not wasting your cycles. You can have multiple fires burning at the same time and see which one catches.

And then you can build a team.

09:00 — This Applies Beyond Developers and Designers

I don’t think this only applies to developers and designers.

I see the same thing with small law firms.

A lot of people get into law and they really want to take on the large companies to help the smaller people. They want to do those class action suits.

But that’s the kind of thing that takes a lot of time and effort. It takes a lot of research, and usually they can only take on one at a time if it doesn’t take up their full workload.

AI doesn’t replace the lawyer and the judgment behind it, but it helps a small firm take on those heavy, long, task-driven lawsuits. Now they can take on more of them because AI helps with the research, summaries, document intake, witness statements, and analyzing everything.

All of the lawyer stuff that has to happen, a lot of that can be done quickly through AI and then brought to the front for review. They can take on more cases and try to help more people.

Same thing with accountants. An accountant can suddenly do their intake, processing, and formulas. They may be able to take on more clients and bring it down to a point where, in the end, they just review before filing to make sure everything is done right.

That allows them to take on more people and possibly hire more people.

Same with lawyers.

Maybe it feels like a single person can do more, but then it allows them to bring in more people so they can help more people.

So a small firm that uses AI can suddenly start growing with AI.

That’s a different shift to think about.

10:56 — Developers, Designers, and the Advantage of Knowing the Work

I think, like I said, maybe I put this one in twice.

Designers have the biggest advantage. I said that because designers understand everything about that.

But then it comes down to the designers at the end needing to build a team. They can get the prototype out there.

But I believe developers might have the biggest advantage. Maybe it’s just me. I’m biased because I am a developer.

But developers know where all the bodies are buried here.

AI is built by developers, technically. We know what a working demo is. Taking an idea to MVP, we know what a demo really is. We know what lipstick on pigs are. We know an app can look amazing but it’s all just popsicle sticks.

That’s where it hits for me, because I can tell AI to build something and it can look great. Now I can use AI as my designer. I can use it as my marketer. I may not understand that database, or I may not know how to do an iOS or Android app currently, but I can have this do it for me. I can click the buttons.

But I do know that I can’t really put it out there for the masses because I can’t trust the authentication. I can’t trust personal data not being leaked. There are going to be edge cases that I don’t know about.

So it can help me move fast. It can generate the code, write the tests, and do all that stuff.

But I still have to judge it.

That’s what matters.

That’s one of the reasons I don’t think AI completely replaces developers, designers, and people for all the work. Not in the way people are talking about.

AI can throw that product out there, but it doesn’t know every edge case and what humans are going to do to it.

I don’t even know what all my users are going to do. But if something happens, I can try to anticipate what those oddities are.

AI can try to predict things, but we really have to have a team around it.

So once the product is out there and we have that alpha, this is where it becomes: now we need real people.

13:58 — From Idea to Alpha Is Different Than Alpha to Product

This is the part I keep running into personally, because AI makes it much easier to get from idea to alpha, or even beta in some cases.

And that is real.

I can have an idea and get something visible pretty quickly now.

A product can go from sitting in my head to running on a server. That still feels strange.

Honestly, it’s one of the most positive parts of AI to me.

Because I’ve had ideas sitting around for years. Most developers probably have. Little tools, apps, business ideas, experiments. AI lowers the cost for me to get those out there.

But there’s a big difference between an alpha and a finished product.

That’s what I’m trying to get at here.

An alpha is something people can try. They can play with it. I can get feedback. I can let people click it.

But taking real money from different people, holding real information in there, and having users depend on the thing is totally different to me.

That’s the threshold I struggle with.

Not the starting. The finishing, the hardening, and the trusting.

I think a lot of AI-built products are going to hit that wall. They all look good. Demos are good. Landing pages are great. They take your money. They have quick little dashboards. But they don’t have all those nuances that true products have. Those gotchas and weird things you have to have, but no one ever talks about.

That’s where the path gets wider.

And that’s where the work still lives.

AI widens the path, but it does not remove that last mile.

So even as a developer, if I go through that process, I am going to have to get a team.

I am going to have to get someone who understands social media and marketing better than I do to make sure it’s pushed out right.

I’m going to have to bring in security people to help me make sure I’ve got all my holes tightened. A support team to help manage chatbots or whatever we decide to do.

Because I think a lot of small apps and small businesses are going to be created where more jobs may get created. It’s just going to take a little time, I think.

16:18 — The New Hiring Question

I think this also changes how we look at how we hire.

In the past, I’ve always hired someone who’s very good at a specific thing. A marketer, a designer, security, a lawyer, an accountant. And it still makes perfect sense to me.

But now what’s more valuable, in my mind, is someone who has a strong base and a strong knowledge of those things, but they know how to use AI on top of that.

You may not be an actual certified accountant. You may not be a 20-year senior developer. You may be more of a junior developer, but you grasp and understand development.

I’d almost rather hire a designer who has a small background in development, so they get it.

But then you also need to have your database person who understands security and models and code.

You mix all that together. You’ve got a hodgepodge team of misfits, basically, but by using AI they become super-powered.

But it’s not someone who just says they use AI.

That won’t be enough.

Everyone is going to say that, and they already are.

The difference will be whether they can show how AI makes their actual work better.

Can a designer use AI to move an idea closer to MVP?

Can a developer use AI to ship faster?

Can a marketer use AI to test more angles without turning everything into generic AI slop?

Can an operations person use AI to automate boring workflows without breaking the business?

Can a support person use AI to find patterns in customer issues and feed that back into product?

Can it all trickle back in properly?

That’s the career opportunity I think AI is going to help create.

Not AI expert as a vague label, but a person who has domain judgment and knows how to extend that judgment with AI.

That may become one of the most valuable combinations.

Skill plus AI. Taste plus AI. Experience plus AI. Context plus AI.

Because AI by itself is still missing something.

It needs direction. It needs correction and judgment. It needs someone who knows when the answer is technically correct but pretty much wrong.

It puts you on the right path to the answer.

And that’s where people still matter.

19:15 — The Path Gets Wider, But Not Easier

I don’t want to make this sound easier than it is, because widening the path does not mean the path becomes easy.

It may actually become more confusing.

Before, your lane was narrower. You knew what you were supposed to do.

You were a developer, you developed. Designers designed. Marketers marketed.

Now the edges are blurred.

A designer can build. A developer can design. A founder can write code. A solo person can launch a product. A small team can do what used to require a much larger team.

And that is extremely exciting to me.

This video was made to bring a positive outlook. Not job replacement as the whole thing, but new career paths and new opportunities that are existing.

I don’t believe we’re going to have to go out and get a full university degree and become certified in everything.

I have a Java certification from many years ago. What does that mean now? Nothing.

I can get AI to write Java.

As long as I have a basic understanding of coding and how coding languages work, I can use AI to help build any language. It makes no difference.

But I still need to have the understanding of how it works. So when something goes wrong, or I want to review it to make sure it’s built properly, that’s important.

It also means people have to think more clearly, because when you can do more, you also have to decide more.

You have to decide what is worth building.

Is it good enough for alphas?

Is it safe for real users?

You have to decide when to bring in those experts.

You have to decide when AI is helping and when it’s just producing more stuff.

I hate that part.

And that’s a real problem.

AI can create momentum, but it can also create noise.

It can make you feel productive when you avoid the hard questions.

Does this thing actually matter?

Does anyone want it?

Can they trust it?

Can they support it?

Should it even exist?

Those questions don’t go away.

If anything, they become more important because now building is easier.

So judgment becomes more valuable.

21:38 — Why This Still Feels Positive

The positive part to me is not that AI makes everyone successful at once.

Most products are still going to fail. They won’t work. They’ll get no traction. Distribution is hard.

But more people get to try. They get to put those ideas out there to see what sticks, and that matters.

More people can build first versions.

People can learn how to make things and build things now.

More people can cross over into areas they were curious about and add that to their repertoire, their toolkit.

More people can test ideas without waiting years.

More people can become useful in ways their job titles never allowed.

And that feels like a widening of opportunity.

Not equal opportunity. Not perfect. But wider than before.

I think that’s worth paying attention to because if we only talk about AI’s replacement, we miss the people who will use it to become more capable.

The designer who’s more technical.

The developer who’s more product-minded.

The small business who can finally automate admin work and focus on the business and the customer.

The person with no team who can finally test his ideas out.

The employee who can move beyond their job description because they know how to use AI well within their job.

That’s real.

And it might be one of the better parts of the whole shift that we’re seeing happening right now.

23:12 — Closing Reflection

So I think that’s where I’m landing.

AI is going to replace work. No getting around it. I don’t think we should ever pretend otherwise.

But it also widens that career path.

It changes what one person can attempt. It changes what small teams can build. It changes how careers may grow in the future. It changes who gets to move closer to the work they actually care about.

And that’s a big one, because if you love what you do, it’s not work.

A UX designer may not become a full-stack engineer overnight, but they can build an MVP and get a product out the door pretty quick for testing. They can get it in the hands of the developer and give them a better idea of what to build and a framework to start from.

Then the developer can use AI to help build it out quicker by doing the tests, documentation, modeling, database, and all that. You’ve got a small team building great products at that point.

A developer may not become a lawyer or an accountant, but they may be able to get far enough. They’ll have the small pieces to add in there to do the proper conversions of what legalese needs to be here, what boundaries am I crossing, is this product legal in certain states or countries, what conversion of money and taxes across all the places. A lot of accounting things and tracking can get the basics done.

But you need the real specific people to help you.

So once you get to that level and your idea gets traction, then you need to build a team.

I think jobs will come around. It might turn into a gig-chores type marketplace where you don’t have a normal career anymore, but your skills are needed across multiple apps and multiple businesses.

That’s a great way to think about it, I think.

And that’s the part I find encouraging.

Not that AI removes the need for people, but it may let people step into work they were previously blocked from.

The path is wider now.

The hard parts are still there. Trust is still hard. Security is still hard. Marketing is very hard. Marketing is the key, because I talked about attention and getting that attention. Once you have it, keep it. Once you’ve got it and you can hold it, that’s when the money comes.

Getting people to care is still hard. Finishing is still hard.

But starting is different.

And for a lot of people, that may be enough just to change the direction of your life.

Not all at once and not overnight, but slowly. One idea, one test, one small build at a time.

I did this one quick because with everything going on, I want to have more positive videos about this. I’m trying to take a step back to see what’s really happening.

AI is really going to help us, I think, have different career paths and a lot of new ideas.

I’m very excited for this.

Thanks for watching.

What Does the World Value Now? (Raw Session)

00:30 — Opening Thought: The Old Way Doesn’t Jive Anymore

Unless it makes sense at all.

The old way, just the way I understood it and grew up, just doesn't jive anymore.

And I don't mean in a dramatic way, and in a world way or anything like that.

I just mean the basic things.

'Cause like I said, like I invest money, I buy stocks, and whatever I felt was important doesn't seem to matter anymore.

The way money moves, the way companies are valued, the way people decide what is important, the way safe investments used to feel safe, and the way attention seems to be worth more than anything else in the planet at the moment.

The way food can be expensive, more expensive than it's ever been, but the people that are producing it and creating it and growing it, they're struggling and there's not many of them left and they're having a hard time.

The way medicine can be one of the most important things in the world along with food and energy.

And people, you got to try really hard convince them to even trust it.

And at the same time, those same people will take something that they heard about online because it helps them look better or helps them feel better.

It just feels like the value system is off.

Or maybe it's not off.

Maybe it's working exactly the way it's designed to work.

And that's the part that kind of gets me.

Because I'm not trying to be conspiracy theorists or anything like that.

I'm just trying to grasp and wrap my head around.

If I need to invest money, I need to plan for future.

Where do I put my money?

Like what I'm used to may not be, or what I think I know may not be reality anymore.

02:25 — The Old Safe Places

Because when I grew up, I always had the basic idea in my head from investing like there's certain things that you got to have.

You got to have food, power, fuel, shelter, medicine, heat, transportation.

And those kinds of things felt like the foundation.

So even when economy's bad, there's a recession, things are hard, times are hard.

Those areas still made sense.

You still had to have shelter, you still had to have heat, you still had to have water, had to have food, transportation even just to get around.

Those things matter.

You need to have medicine, doctors, hospitals.

So when you think about a safe place to put money, not guaranteed, because nothing's guaranteed.

But they felt safe, steady, like a long-term place to put your money.

They were institutions that possibly weren't gonna go away, like food companies, utilities, energy, health care, banks, infrastructure.

Really like the boring stuff you can think of.

Still, to my mind, feels like the safest place.

But even in times, they lose money.

And they don't grow the same.

And they cut dividends.

03:41 — Where the Growth Seems to Be Now

But when you look at where the excitement is, where the insane evaluations.

SpaceX.

Where the growth is, it feels the world has shifted.

I'm not saying that like always new something new comes out something big.

Big change in the market industry.

Yes, that's where that's brand new. That's exciting. It's a new IPO coming out. Yes, that is going to get an initial track.

It may have a lot of ups and downs, volatility, but in the end there's a market being created or disrupted it makes sense to me.

So it's going towards, in my mind.

The money is not just going toward what people need anymore.

It's going towards whoever controls people the attention.

Which was to feed the cloud, chips, the data, the model.

Whoever controls the layer between people and the rest of the world controls the layer between the food, the medicine, the fuel.

Whoever controls that is who's technically more of the money should go I guess at this point.

04:57 — Free Is Not Really Free

But even those things could shift because like think about Facebook's free, Instagram's free, YouTube search, a lot of AI tools have free versions.

Even if you pay a lot of it feels free.

Gemini, Copilot. Open AI has a free one.

But they're not really free.

None of it's free and we all know that.

If you're not paying for something you are the product.

The attention is the product.

The behavior, the data, the habit, those are the products that people are paying for.

And whoever can hold your attention the most, where everyone is fighting for your attention, that becomes the most valuable part.

And that's where the people are placing their value at the moment.

And it's more valuable than the thing they're selling.

So if you can grab the attention, hold the user, hold the eyeball, get the click, you are worth more than the thing that people are trying to sell to you.

I understand the business model.

I understand the logic behind it.

Advertising, scale, data, recommendations, AI, global reach.

I get why it works.

But emotionally, as a regular person looking at the world, it still feels backwards.

Because food keeps people alive.

Power keeps the lights on.

Fuel and energy move the world.

Keeps you healthy and alive.

But the companies treat it like the future or the companies controlling the screen in front of your face.

The phone in your hand.

And that's hard for me to really process and move my investments to match that reality.

06:55 — Thinking Out Loud While Following the Script

And that's why I'm doing this ramble right now.

So I'm trying to stick the script but I'm going off at the same time.

And I know I keep bringing that up in my video.

But I want people to know that there is a big distribution between reading the script and me just thinking out loud at the same time.

So yeah, this is the hard part for me to wrap my head around.

And one example.

This is what made me really think about this.

On Instagram the other day a post came up about Facebook Reels.

Just the Reels.

A TikTok competitor.

Not even their main product.

Just a part of Facebook.

Just stuff people scroll through while waiting in line, sitting on the couch, avoiding whatever else they should be doing.

That's what I like to say it.

And that one feature inside one company is generating more revenue than Airbnb.

Netflix.

This is what gets me.

They make about the same amount of revenues Tyson Foods.

They make more money than Nike.

This one blew my mind.

Reels generates more money than Coca-Cola.

That's insane.

08:23 — Reels, Coca-Cola, and Tyson Foods

Not because Reels has no value.

Obviously, apparently it has value.

People use it every time.

Keeps attention, moves products, shapes culture, changes habits.

But still, Coca-Cola is everywhere.

Coca-Cola.

The polar bears at Christmas.

Your name on the Coke bottle.

Like Coke literally is everywhere.

I own Coke.

Warren Buffett is one of the first investors.

He loves Coke.

Reels makes more money than Tyson Foods.

They put the food on the table man.

They have physical products.

They feed the world.

Factories, trucks.

It's in the grocery stores.

And Reels makes just as much money as them.

And Reels is not even the company.

It's just a product within a product.

Man oh man.

That's, you kind of take a second to think about that.

And somehow that's valued like one of the biggest businesses in the physical world.

Wow.

And that's where I keep coming back to this question.

What does the world actually value now?

Because it's not that the food stopped mattering obviously.

Food matters more than almost anything.

09:51 — Food Still Matters, But Where Is the Money Going?

You see it every time you go to a grocery store.

Beef prices are through the roof.

Groceries prices in general through the roof.

Everything costs more.

And yes, there are very good reasons for a lot of that inflation.

Transportation.

Fuel cost.

The packaging.

Labor.

Which I think is going to dramatically drop.

So I've already talked about that, but I think there's a few more videos coming around that one.

Interest rates, supply chain, all of that adds up.

All of that is needed.

But when you look at the people closest to the actual food, farmers, ranchers, producers, a lot of them are the ones, they're not getting rich.

A lot of them are actually shutting down.

They could really stay afloat.

So the consumer pays more.

The grocery store is making money.

Grocery stores are a big part of my portfolio, but they're not growing like Reels.

But yet you go there every day, or at least once or twice a week.

And they're making money, but they're not.

Where's the money going?

The processors move huge revenue, but the people producing the thing that we actually need to stay alive, and they're trying to sell those on the shelf, those people are not making any money.

And the people that are making the money are like Facebook because they're the ones showing us the product on the grocery store and getting us to go to the grocery store.

This is me rambling here now.

So the platform that is used to grab my attention, to show me the product that gets me to the store, is the tool that's valued the most at this point.

Wow.

11:39 — Attention Scales Differently Than Food

So I'm going to go back to this and then a platform that sells attention can scale in a completely different way.

And that's true.

They can scale a different way.

And one more person watching videos does not cost the same as feeding another cow or buying more land to plant more stuff, get a tractor, get feed.

Understand that adding more eyeball, getting more eyeballs to stay on your screen longer does not cost the same as putting another box of cornflakes on the shelf.

And one more ad impression does not cost the same as raising cattle, producing the beef, processing the beef, shipping it in packaging.

So I understand why market values the tech different or the farmer different or the grocery store different or the transportation truck different.

But understanding does not make it feel less strange.

Because you think the value would go to the thing that you truly want, not the thing that told you you wanted or made you think you wanted it.

Because right now the thing that matters most can be low margin and the thing that distracts us can be high margin.

I said something.

I don't know exactly what.

But it says something.

And you can see this in other places too.

13:02 — AI Still Needs the Physical World

So this is where I go and it like AI feels digital and abstract.

It's a piece of code basically, but underneath it is still physical.

There's chips.

There are data centers and needs cooling.

It requires power, needs building, which means land, wires and infrastructure.

It's all these things.

So even the future that feels like software still depends on all the boring infrastructure, the physical items.

But the energy that runs it may be valued less than the model itself.

The building may be valued less than the company that's renting the space to compute.

The power grid may be valued less than the chips.

And the chips, the chip is valuable now because it's scarce.

Maybe one day, if there are enough chips, the value moves somewhere else.

It moves to the code.

It moves away from the chips themselves.

So all the physical items are not valued the same as the digital items.

And that's the part I keep trying to understand.

The value keeps moving up the chain.

From the physical thing to the system around the thing to the platform above the system, whoever controls the attention, who controls the access or the bottleneck.

Because right now chips are the bottleneck, they get the most.

But then once that's fixed, then it goes back to the model.

It goes back to the code.

It goes back.

Then it doesn't even go there.

Goes to who has the attention.

14:24 — Medicine, Trust, and What People Can See

And I don't know, it's like medicine has a version of this too, because you still need medicine, you need doctors, you need vaccines.

I believe we need vaccines to help prevent disease and spread.

We need hospitals to help people and help the sick.

But trust in medicine now gets filtered through mostly social media, gets filtered through fear, politics, and whatever story someone's seeing online.

People begin to trust the advice they get from those Reels.

Instead of trusting the doctors and scientists and chemists, the studies, the results, the tests, they see an influencer showing weight loss, muscle gain, puffy lips, perfect skin.

So they are willing to take the pill, inject the needle, put the cream on, do whatever because the results they see in the Reels are visible and they have their eyeballs, they have their attention.

They can see it.

They can measure it.

They can post it.

Maybe that's probably the same problem.

We seem to value what we see, what we can measure.

We value what gets our attention and what can be sold and what we can turn into growth.

But the things that quietly hold everything together are harder to value.

15:50 — Invisible Until It Breaks

Food is invisible until the price goes up.

And I say because we just go buy stuff randomly.

We know what we're gonna get and we don't notice it until we look at the bill basically.

Power is invisible until the lights go off.

You just pay the bill, the lights are on, you're on the internet, you're good to go.

Medicine is invisible until you get sick.

You don't need it.

You don't think about it.

Infrastructure.

That's invisible until there's a road blockage, or there's a flood in a building.

Until all that happens you don't even notice it's there.

You just take it for granted.

And maybe that is why this all feels so strange to me.

The world still depends on the boring physical things.

Food, power, medicine, shelter, infrastructure.

But the biggest rewards seem to go to the layer above, the attention layer.

I don't know, reverse this first data layer, platform bottleneck, but really it's the attention and the thing that controls that attention.

The thing that sits between people in the world, the Reels, the Facebook, the Instagram, the stories, the YouTube.

I'm on YouTube right now.

I'm not great name was attention.

I got a few of you and I'm happier here.

17:10 — What Gets Rewarded

So I don't think I'm saying the old things stop mattering.

I think I'm saying they still matter maybe more than ever, but the world does not always reward what matters most.

It rewards what scales.

It rewards what captures attention.

What controls the choke points.

It rewards what can turn human behavior into revenue.

Whoever controls the eyeballs, the clicks.

Those are the people that are valued the most.

And that's what feels off to me because when you grow up you think important things should be valuable.

The basic parts of life to keep you alive should be the most valuable things.

And they are.

Because they keep you alive.

But from your wallet, your checkbook, and your retirement plan, they don't seem to be the most valuable thing anymore.

It's not always the people closest to the work.

Not always at the same level.

Not always at the same level as the company controlling the system around them.

18:12 — The Main Question

So I don't really have a clean answer here.

I'm just trying to sit with the question.

What does the world value now?

Not what do we say we value, not what sounds important, but what actually gets rewarded?

What gets funded?

What gets scaled, protected?

What gets the attention?

And when I look at it that way it feels like the answer is not just food, power, medicine, infrastructure, even though we still need all the things.

The answers seem to be attention, access, control.

Being able to scale something.

Keep your margins high.

Margins low.

The layer between people and reality.

And I don't know if that means the world does not make sense anymore.

Or if it makes perfect sense and I just don't like it.

I don't know.

It's, uh, hmm.

19:15 — Looking Back at Facebook

I go back to when Meta went, well Facebook at the time went public.

To me, it made no sense.

They don't make any revenue.

They make no money.

They're losing money.

But then quickly they become one of the most profitable companies and you try to reevaluate how you look at what is considered valuable and important in the world.

From a financial point of view, I guess.

It's a difficult one because I still banks, grocery stores, food processing.

But because all these companies now, I believe AI is going to help them be more efficient, help them do better breakthroughs.

But I also think a lot of them are following these trends.

They're coming up with things that aren't things that make you live longer.

They're coming up with things that make you feel better and look better.

And I'm sure there's a lot of people in there that are still making things that keep you alive and keep you from getting sick and help you get better that way and live longer.

But they're also not naive either because they're public companies and they have to think about the shareholders.

20:41 — Closing Thought

So I am holding on to the ones that I have just because I do believe that at some point this will turn back into normal reality.

At the same time, I'm hoping to get a little piece of the pie at SpaceX because I strongly believe that that is going to be a very important company.

But to me, that has value.

It has a product.

It's actually trying to do something.

But at the same time, it also has AI built into it.

And it also has the communication level of Starlink.

It has the power with the soldiers and the satellites.

So there's a lot of physical things within that one IPO along with the attention layer.

It has the eyeballs right now just because of how big it is.

Anyway, there's a lot of rambling going on.

So I'm going to finish this one.

And I am going to do more videos because this is a deep thought.

I just woke up and started thinking like, "What is going on?"

So I had to make this video.

So thanks for watching.

We’re About to See Things Humanity Has Never Seen Before | Thinking Out Loud

00:00 — Opening / What this video is about

Hey, welcome back to Slow Builds.

So this video is gonna be sort of about the idea that we’re about to see stuff we’ve never seen before.

And I don’t even think our minds can imagine it.

It kind of goes off another video I did recently about if everyone has access to AI.

So what happens then?

The creativity that comes from that.

The people that had zero access, all of a sudden they have access.

So this goes along the lines with that one.

This is kind of a continuation from that one.

00:33 — Survival pressure and unused creativity

Basically, I was talking about how there are probably millions of people in the world with ideas and creativity and potential, but most of their energy gets consumed just trying to survive life.

Bills.
Stress.
Work.
Exhaustion.

Just trying to stay alive.

Who knows what situation people are going through, from third-world countries to developed countries.

And because of that, a huge amount of human creativity probably never fully appears.

Not because people aren’t capable of it, but because they never really get enough room to explore it.

They don’t have the time, the energy, the effort, the space to just let their brains be creative.

01:23 — Why this moment feels different

Lately I’ve been thinking a lot about what happens if that starts changing.

Because honestly, I think we’re entering one of the most exciting periods humans have ever lived through.

More exciting than the industrial age.

Well, all those ages had to happen to get here, but the speed they took to become known to the world and for the world to take advantage of them took a lot longer than what we’re seeing happening today.

And I also think a lot of people still don’t fully see what’s happening yet, and how quickly it’s happening.

02:01 — Big changes used to take time

Obviously humanity has gone through all these things before.

The internet.
Television.
Color.
Remote controls.
Mobile phones.

Those changed society completely.

They really did, and they got us to where we are today.

Most of those changes came slow.

They took time.

Distribution of the new systems, the impact it had, getting it up and running.

It didn’t all happen super quick all the time.

Even the internet itself took years to hit normal life.

Everyday people getting access to it.

But that is a stepping stone to where we are today.

02:44 — The infrastructure is already here

AI feels totally different because now all that infrastructure is in place.

And we’re even building it more while we’re using it.

AI is helping us decide how to build it for itself.

That’s the scary part about it.

The phone already exists.
The computers, we already have them.
The chips, we’re building more.
Satellites are going up.
We have solar power.
We have data centers.
Nuclear plants are coming back online.

We have everything in place that we need.

We need more, but we have a lot of it.

So the improvements now spread almost instantly.

A new model comes out and millions of people have it already.

A method goes out, it gets leaked, and then millions of people have it open.

Everyone has access to do whatever they want.

Create their own crypto, have their own agent go out and sell it and buy it and do whatever.

So the speed that we have access to things now, that’s a big difference.

A big changer.

A big barrier breaker.

03:57 — Who gets access this time

I think the real important part is who gets access this time.

Historically, powerful tools belonged to a small group of people.

People with money.
Education.
Connections.
Large companies and corporations.
People with status.

You usually had to get invited to the party to have access to a new system or a new thing, or even to be able to purchase it.

But AI feels different because suddenly regular people can now do things that used to require entire teams, years of training, and experience.

You can learn faster.
Research faster.
Build software.

The new Google one, I’ve got to get my hands on it, could build a game just by talking.

Art is a questionable one.

There are new movies out.
There are new videos.
Music and stuff like that.

So I think art is being created quickly, but I think art is one of those things that will still stay authentic in different forms.

Same with writing.

But even writing now, people can quickly do up manuscripts.

They can get ideas pumped out pretty quickly and organized.

Brainstorming.

But I still don’t edit these.

Man, I hope maybe someday I can figure out how, but I’m trying to avoid that at the moment.

Starting businesses, translating ideas into something real happens almost instantly and in real time.

And yeah, it’s still rough around the edges, but the barrier between an idea and execution is collapsing incredibly fast.

And that’s the part that feels historic to me.