The Real Cost of AI Depends on the Job [Raw Session]

00:00 — Opening / Tokens Are the Visible Cost

Hey welcome back to Slow Builds. So thinking about AI cost but not in the usual way people look at it. Most of the time people are talking about tokens.

It’s always tokens and this comes up a lot for me too because at work and paying for personal stuff and things like that. So how much does a prompt cost? How much does the output cost? Which model is cheaper? How many messages do you get.

And that makes sense because that is part of the part right now that we can actually see in front of us and what people are going through between using ChatGPT, Claude, and just using some APIs.

And the cost shows up. It shows up as tokens, credits, usage limits, depending on what model you’re using. But I keep wondering if that’s just way too simple right now. Because not all AI usage is same kind of usage.

A person asking a chatbot questions is one thing. A company training a model is something else. A doctor using AI to double check symptoms is different from a video generator running overnight. A developer fixing production outage is different from a developer asking agents to slowly work through new features or backlog bugs.

Those are all AI but they are not the same kind of work. Some maybe the future cost of AI is not one price. Right now it’s not one price either but I imagine it’s going to be broken up a lot more granular.

Maybe it depends on the job itself and maybe that’s where this whole thing starts to split apart a little bit.

01:45 — What Sits Behind the Token Price

So right now tokens are the easiest thing to talk about because they are measurable. Input tokens, output tokens, your context window, your API bills, your message limits. That is the visible units that we’re all seeing on our subscriptions, on our work invoices.

But behind every token there’s a bunch of other costs that we do not really think about when we are just typing in the box. Which model is being used? What hardware? How much power consumption? What is the speed that we expect a response? How reliable? How consistent? What privacy is involved?

There’s an amount of reasoning that we need. How many times can we try the same thing? They determine the right answer. There’s an amount of checking going on in the background.

So two AI tasks might both use tokens but they’re not really the same cost. One job might need the best model available right now with very little room for error. Another job might be fine with a small model slowly working away in the background.

And that distinction really matters because it really determines the cost that’s involved and what outcome and what speed and timing and consistency is required. Because if we’re only talking about a token price we missed the bigger question, what kind of work is the AI actually doing?

03:20 — Some Jobs Need the Best Model

And I think this is kind of where AI usage starts to split.

And then AI is messing this one up.

Some jobs need the best model.

Some jobs do not.

If you’re making a serious architecture decision in software or thinking through a medical case or reviewing something legal or financial, you probably want a stronger model, a very consistent model with a strong background, vast knowledge, and very precise.

You might want more reasoning, more checking, better context, and maybe a human involved along the way for checking.

But a lot of everyday AI work does not need that.

Summarizing a short email, renaming files, sorting notes, pulling dates out of documents, clean rough text, organizing simple lists, a list does not need the most expensive models in the world.

It could actually run on a local model running on your machine for free. Well free like you do have to pay for your your energy and consumption that way but other than that you’re not paying tokens and that is where I think the future gets way more interesting because of using one big model for everything more of this is probably becomes routed.

Cheap model first, better model if needed. Specialized model for narrow work. Local model for private or repeatable work. Expensive only when the task actually deserves it.

That feels different from how people often use AI right now, where you just pick the best model and use it for everything. But that might not make sense long term.

It is like using a full-size truck to pick up groceries. Just one bag. It works but it’s probably not the most efficient way to do it every single time.

05:03 — Urgency Changes the Cost

Urgency, like the biggest piece for me is the urgency because speed costs money, reliability costs extra money, low latency costs money. If AI needs to answer right now, that’s a different kind of system.

A surgeon using AI cannot wait around for background job to finish. A self-driving car cannot send something away and hope the answer comes back before the light turns red. A robot in a factory, a security system, or a production outage all have that same kind of problem.

They need low latency, they need consistent results, they need them fast and accurate. The answer has to be close. It’s probably going to be more expensive, but not everything is like that.

A family doctor using AI to think through diagnostics. Diagnosis may need accuracy, but not necessarily a half second response. A developer building features or working on bugs or non-production bugs may not need every answer instantly.

Business owner asking for a report might be fine getting it in the morning.

An AI agent reviewing documents overnight does not need to act like a live chatbot.

It doesn’t need to have the answer instantly.

It can run in the background slowly.

You can run on a local model like video generation, simulations, big code cleanups, document reviews. Like a lot of these things, they don’t need to be overly expensive and overly consuming.

So the question becomes pretty simple. Does this AI need to be fast or does it just need to be done?

That one question changes the cost. And I think a lot of future AI work is going to fall into the category. Not urgent, still valuable, but you don’t need it right away.

You don’t need the best model. You may not even need, like I said, you could probably run it locally. You don’t need to be outsourcing it through an API and burning through tokens. A lot of it can just be running in the background on your own machines and your own system, maybe even on your own laptop.

I have a Ubuntu running out in the room and it does plenty of stuff for me that I don’t need to spend money on besides the energy that it takes to run it.

07:32 — The Can Wait Category

I think the can wait category is much bigger than people realize because there’s a lot of valuable work that does not need to happen in real time.

Reading documents, checking contracts, looking through old notes, watching for changes, generating reports, testing code, cleaning data, comparing options, building prototypes, reviewing logs, summarizing meetings, most of this does not need instant response.

I wanted to read through those because there’s a lot of different cases where when you really think about it you could throw it into a job. You don’t, as long as you get an update and overview and a response at some point and you know you’re going to get it, it’s almost like set it and forget it type thing.

You don’t need to sit around waiting for the prompt to return the results instantly

And that is probably going to be 80-90% of all the AI work that happens.

It just needs steady background compute.

And if it can run in the background, it can probably be cheaper.

It can use different models, it can run at different times, it can be queued, it can be batched.

Like, just, I don’t know, highlight this.

Because like, even think about like the power grid where different times cost different money for energy. So if you have a bunch of things, you can hold off and run it at the low times so your energy is cheaper.

Maybe you live in a place where you can collect a lot of solar energy, enough that you can run a machine off of.

So during the day, you’re collecting the energy and at night you run the models.

There’s all kinds of ways around this.

You can run when compute is cheaper or less busy.

Look, I had that right in the next thing.

That is a very different cost model than sitting in front of a chatbot waiting for an answer or to appear word by word.

09:21 — Cheap Background AI Changes Usage

And I think that changes how we use AI because when something’s expensive and immediate, you’re careful with it.

But when something is cheap and can run quietly in the background, you start using it differently.

You start asking it to watch things, check things, compare things, tell you what changed, look through files, review code, prepare drafts, clean up messy work.

Not because it’s dramatic, but because it becomes very useful.

So you can optimize your life, you can improve your systems, you can improve your workflows, you can produce more output without breaking the bank or without like, you know, burning down a forest.

Like you allow these things to run slowly in the background on their own and it’s slowly improving your life without you doing heavy lifts, without you spending a lot of extra.

but a lot of little things are adding to the final product.

10:21 — Placement Matters: Local, Cloud, and Remote Compute

And this is where the infrastructure part comes in, but I do not think the infrastructure is the whole point.

The point is placement.

Where should the AI job run based on what job is needed?

Some AI will run locally on your phone, laptop, home server, inside a company network.

I’m gonna highlight this again because it’s funny

Now I’m seeing more online in the last few days or a week about running, getting out of the cloud, bringing things back in locally into companies and infrastructure within their organizations.

Not massive, huge, like it used to be, like massive in-house data centers and server rooms, but more run a lot of things locally rather than outsource everything ’cause it does cut down on the bills.

maybe they have their own power sources that they can control and they can maintain and they’re not relying on others.

Maybe they have enough RAM and GPU running around in their company that they have down times.

So there’s lots of different things and the fact that we went from massive computers down to small computers and laptops and then we moved to the cloud and like it always shifts.

and I do other videos about that where the value is moving, what the world values now and how it constantly moves around.

It all depends on the bottleneck and where people’s attention lay.

So another video is coming on that.

But it’s just always interesting how everything’s always changing and a lot of things always come back to the same thing in the end.

So like I said, and companies are doing that.

They’re moving it back into their local networks now.

And that makes sense for private work, simple work, repetitive work, or even just personal stuff.

Stuff where you do not need the biggest model and maybe you don’t want the data leaving your device or you want to keep it contained within your own privacy.

Some AI will run in the cloud and it will always run in the cloud.

And that makes sense for harder tasks, larger models, business systems, shared tools, anything that needs more compute than your own device can handle.

And in some AI might run more remote places where power is cheaper, cooling is easier, where infrastructure is built specifically for background compute.

Maybe that is near a hydro, maybe that is near a nuclear, solar heavy regions, maybe one day some of it is space based compute.

Whoever thought that was going to happen.

But the point is not space.

The point is that not every AI job needs to run in the same place.

If it needs speed, keep it close. If it needs privacy, maybe keep it local.

If it needs massive models, send it to the bigger infrastructures. If it can wait, send it somewhere where it’s cheap and can run in the background at low cost.

And that’s your cost splitting, you’re breaking it up based off the role that’s required and the output that’s needed.

13:31 — AI Cost Becomes Routing

So maybe the future is not one simple AI price, maybe the future is routing.

The system asks what is the task? How hard is it? How urgent is it? How private is it? How accurate does it need to be? Can it use a small model? Does it need a frontier model? Can it wait? Can it run locally? Can it run overnight? Can it run somewhere cheaper?

And then the work gets sent to the right place and maybe there’s multiple places along that workflow flow before you get the final answer.

And that’s a different way to think about costs because today we mostly see the front end. We type in the box we get the answer.

But behind the scenes the future might be much more layered. Small models doing small jobs, big models doing hard important jobs, local doing private, fast systems doing very urgent, and background just doing the slow jobs, the invisible work and the cost depends on where the work lands.

That feels like a more realistic version of where this goes.

Not one AI, not one price, not one model doing everything.

More like different layers of intelligence used for different kinds of work.

And that’s what I said before. This is a me off script again. I don’t think there’s one winner in AI. Yeah, the AI model, the data model. I talked about that, but they all seem to do a little bit different ones gonna be better at testing one’s gonna be better at documentation and content one’s better at writing code and so it all comes into play where I think they’re all going to be part of the process rather some are like I said some are gonna run local some are gonna be small some are gonna be big for the privacy some are gonna have to go out to where we had the data access somewhat like it’s going to be distributed across many places depending on the flow and requirements of the request and this so and this is why I guess get back to this now why cheap background AI matters

15:46 — Why Cheap Background AI Matters

this matters because because once some AI gets cheap enough people stop thinking about it the same way.

They stop asking should I spend tokens on this and they start asking can this just run in the background.

That’s a massive shift because a lot of useful AI work is boring.

It’s not always a big impressive demo.

It’s more like check this, watch that, summarize, clean, find anomalies, look for patterns, tell me what changed, look through files, review code, run tests, prepare drafts, and you do it all while you sleep.

There’s no, like you might set a deadline, but you’re not overly concerned.

That kind of AI could become normal if the cost gets low enough.

Not because every model is cheap and not because AI is free, but because the right kind of work gets routed to the right kind of compute.

And that’s probably where the usage grows.

Not only from people chatting more, but from AI running quietly around the edges of everything.

Watching, checking, sorting, comparing, cleaning.

Doing the slow work that people often avoid because it’s boring.

And boring always wins.

Boring, repetitive, it just requires too much attention.

So I think about reading, studying for reading a biology book for your exam and falling asleep.

It’s that kind of work.

That is the part that feels and I’m sorry about the biology part. I just had to do that for university myself when I was younger and I remember that specifically and that is the part that feels easy to underestimate.

17:26 — The Real Cost Depends on the Work

So I do not think the real cost of AI is just token counting.

Token count matters but it is only one piece of it.

The real cost depends on the kind of work being done.

Some AI is expensive because it needs speed, because it needs the best model.

Reliability needs a huge amount of data to get the right context.

And it also depends on how high the stakes are.

Like I said, doctors, driving, flying, any kind of high risk, instant answer type thing is gonna be expensive.

But some AI probably becomes cheap because it can wait.

Smaller models, local, running in the queue, overnight.

It can run when triggered rather than always on.

Run when power’s cheaper rather than on all the time.

And that part I think is easy to miss.

We talk about AI costs like it’s one thing, but AI usage is not one thing.

Like I said, like surgeons, family doctors, a developer doing production versus developer building a feature.

Students summarizing notes, the company’s analyzing documents, lawyers preparing briefs, accountants doing tax returns.

A lot of it all depends on the current situation and what’s expected of the result.

So maybe the future is not one AI price, maybe it’s different layers of intelligence with different costs, fast, cheap, local background, high trust, good enough.

And the more those layers split apart, the more AI usage changes.

Because once the cheaper layers get good enough, people stop treating AI like something they only use carefully.

They start treating it like something that is always working around them.

19:20 — Watching AI Become Embedded Everywhere

As part, and that is the part I’m trying to watch, and I want to see, I’m actually starting to see it a little bit more.

Not just what the best model can be, but what happens when the right model is in the right place, at the right speed because becomes cheap enough for the job.

And I really see this because my initial reactions, I still think are valid where this is gonna change everything, the world’s gonna look different and AI is gonna change and it already has.

But I really believe that it’s the people who are on top of it and learning it and in it and seeing how it can be used I think is going to have a big difference in what I always looked at taking jobs it’s not just going to take jobs it’s going to create new jobs obviously there’s going to be less of what we see today so what people are going to school for what they’ve been doing for the last 10 20 30 years is going to change but I do believe everything’s going to change for a good way there’s going to be some downfall but there’s going to be a brighter future in my mind and I can see how AI is going to be embedded in everything which it already started has and is getting more and more constantly.

This is not a normal technology that’s just coming out.

It’s world-changing and we’re seeing those effects already.

So I believe it’s going to be broken up. People are starting to use it differently.

We’re going to see it spread out sort of like how we saw a cloud compute, SaaS companies. That was a foreign form before Salesforce, but now it’s the norm and we’re rolling into that I believe.

So anyway I’m very interested to see what happens and thanks for watching bye.

When Everything Comes to You

00:00 — Convenience Changes the Default

Hey, welcome back to Slow Builds.

This video is more about convenience.

I talked before about how AI, software, all the things that are happening kind of make everything becomes like utility.

So this video is not…

It’s about convenience in a way, really.

But not in a simple convenience is good or convenience is a bad way.

I use this stuff too.

Amazon food delivery, easy returns, all of it.

So this is not me pretending I’m above it.

The thing I keep thinking about is more subtle than that.

More and more of life is being designed, so we do not have to leave the house.

Shopping comes to us, food comes to us, groceries come to us.

Returns are handled, but almost zero effort and zero thinking about it.

Work can come through laptops and phones and whatever else.

Meetings can happen through a screen.

Entertainment’s already in your hand.

And now even random little cravings can show up at your door.

At first, it feels amazing, but convenience is useful.

It saves time, energy, when you’re busy, too tired.

If you’re sick, you can avoid people and spreading diseases.

If you don’t have a car, if your life is just too full.

But I also think there’s a quiet cost to all this because some of the friction we are removing was not just inconvenience.

Some of it was movement, structure, a reason to go outside, a reason to see people.

And some of it was a reason to participate in the physical world, just to be part of it and active and mentally.

And when everything comes to you, those things do not happen automatically anymore.

You have to choose them on purpose.

And that is what this video is kind of about.

Not that convenience is bad, but that convenience changes the default.

And once the default changes, we change too.

02:03 — Amazon and the One-Click Habit

And Amazon is probably the clearest example, at least for me, because it did not feel like some giant social shift at first.

It just felt useful.

You needed something, you searched for it, ordered it, one click by, and eventually it showed up.

Even back when we first had it where we are, like shipping took like a week or four days or something like that, it wasn’t a big deal.

But especially, but there’s a difference between driving around looking for something and just ordering it.

Even if you have to wait because it removes the hunt and the use of gas, it removes the uncertainty.

And the prime, and then prime, like with two day shipping, now that’s pretty much everywhere and almost, most things are almost like next day.

The speed became part of the habit.

You stop thinking, “I should go see if the store has it.”

You start thinking, “I’ll just order it.”

That shift matters because running to the store is not actually one action, it’s a chain of actions.

You get ready, find your keys, get in the car, deal with traffic, gas, parking, walking through the store, looking for the item, wrong size, trying to order it from them.

The cost is more than you expected and then like the whole thing becomes this

Becomes a lot of friction basically an Amazon could press a lot of that into a single button

And then they worked on the return side of it

And that is the part that really changed the entire loop for me in my mind

Because online shopping used to have a penalty if the item was wrong returning was was annoying

You had to worry about paying and worry about the shipping the labels all that stuff

Yeah, but now

Returns have become so easy

Sometimes you barely need to pack you don’t even have the package you what Amazon sometimes. There’s a local drop-off point

The whole thing feels way too simple

So now the loop is smooth need something order it arrives wrong thing return it

Order another one every time that loop gets easier the old habit gets weaker the old habit was I need something

I should go somewhere the new habit is I need something I should just check my phone

That is a massive massive change in my mind not because one Amazon order change your life

But because millions of small decisions start pointing in the same direction less going out less browsing

Less asking someone in the store walking around carrying things parking

less moving through the world and more waiting for the world to come to you.

04:45 — Food Delivery and Moving Cravings

And then food delivery takes it to even further really what Uber Eats, DoorDash, Grubhub, and then throw in Postmates.

All of those services normalize something that used to feel like a treat.

Restaurants, food showing up at your house.

At first that was mostly pizza, Chinese food,

A few local places that already had delivery now it can be anything fast food coffee sushi desserts

groceries just random snacks

And in some places almost anything you can think of I remember the first time I realized I could get

movie theater popcorn from the movie theater

And that one struck me pretty hard because it’s such a small ridiculous example.

It’s not medicine or groceries

There’s nothing urgent.

It’s just the popcorn you buy when you go to the movies

It’s usually a real treat

But now I can just click a button and it shows up my house so I can watch a movie at home

So now you got candy drinks cravings, and I’m not saying that to judge it.

I understand it

Sometimes you want the thing but it shows up for it shows how far the expectation has moved

The old version was if I want that I have to go there

The new version is if I want that maybe someone can bring it to me and that’s a very different mental model because now the question is not

Is it worth leaving the house for the question becomes can I get it delivered?

How long is it going to take?

And if the answer is yes, the barrier drops if it’s not just food.

It’s a desire become

It is desire becoming logistical

You want something and the system figures out how to move it.

That is what these apps are really doing

They’re not just food apps movement apps

They are movement apps.

They they move once they move cravings.

They move small impulses through the city

Someone else drives someone else waits someone else parks someone else carries it and you stay where you are

That is convenient, but it’s also changes your relationship the world outside the door

06:53 — Outsourcing the Physical Act of Living

TaskRabbit, an errand service part of the same pattern because once food and produce can come to you the next thing is labor.

Someone can assemble it, someone could pick it up, drop it off, they can take care of everything like you order on IKEA, it shows up, they pick it up, they put it together, they get rid of the garbage, they leave.

And someone can wait in line for you, someone can do the small tasks you don’t want to do.

And again, there are real benefits here.

Some people do not have the time, the ability, they don’t have the tools.

Some people just really overwhelmed elderly people, disabled people.

Some people are working too many hours, so they just need that break.

So I don’t know what to pretend the old way was always better.

A lot of friction was just friction.

A lot of errands were just annoying.

A lot of inconvenience was not character building was just inefficient.

But when enough of these services start stacked together, life starts to feel different.

Amazon brings the product food apps bring the meal grocery delivery, delivery brings all your groceries to stock, you can even get tasks rabbit, they do the services and the labor and they can even like put the groceries in the cupboards for you in the fridge, right apps bring the cars remote work brings the office streaming brings the entertainment.

So in the world not something you you’ve got to go into as much as you used to.

It is something routed to you and that feels like progress and in many ways it is but it also means we are outsourcing more of the physical act of living.

Before robotaxi fully arrives we already have a version of transportation as utility.

It’s not just always transporting us, it’s transporting our wants and that is part that feels bigger than anyone at.

We are building a world where the first question is not where do I need to go it is can I get this can I make someone bring it to me and this is where Wally always go back to Wally the movie.

08:57 — WALL-E and Optional Movement

Because I think the future is literally people floating around in chairs while robots do everything because Wally exaggerates something that already exists.

Everything comes to them food entertainment movement information

Comfort they do not really have to do anything and the movie works because the direction is recognizable

We already understand the temptation why why move if something can come to you

Why cook if I can just order it?

Why leave the house if the house contains everything I want and I can have or if it doesn’t I can have it brought to me.

And again, it’s not always bad.

I don’t want to say that there are cases where this is important and it makes a big difference.

But if it becomes a default for everything, the movement becomes optional.

And when movement becomes optional, a lot of people will move less not because they’re lazy, not because they’re bad people or bad habits, because the environment no longer requires it.

And that is important part.

We like to frame this as individual discipline.

But a lot of movement used to be built in the life.

You walk through the stores, you walk through the grocery store, carrying your groceries, bring them to your car, returning stuff, going to the office.

You move between places.

You stood in lines, you ran errands, you interacted with people.

It was just life.

And now more than more of that can be removed.

And once it’s removed, you have to rebuild it intentionally.

And that is hard because intentional movement requires a decision.

The old friction mode made some of those decisions for you.

Now you have to do it yourself.

10:32 — Using Errands as a Reason to Move

And I noticed this with myself.

Sometimes I use I use errands as an excuse to ride my bike.

I have to return something.

I might bike there.

If I get to get some small groceries I’ll bike there.

And some if something’s local I might just I might choose to get it myself.

Not because I have to.

That is the whole point.

I usually do not have to.

I could probably make it easier.

I could order more, I could get more delivery, I could sit at home and let the system do more of it.

But the errand gives me a reason to move.

It gives me a reason to get outside, it gives small purpose and that matters because going for a bike ride, just to go for a bike ride is good but sometimes it’s easier when there’s a reason attached to it.

Drop this off, pick it up, go get that, take the scenic way, move your body because the task gives you

Excuse and I think that is one of the hidden lot losses when everything comes to us.

We lose excuses

We lose small reasons we lose the friction that pushes us into motion and then we wonder why everything feels more

It just doesn’t feel right like we wonder why people feel more isolated

We wonder why the day has less is as less

Shape to it and again, I’m not blaming delivery apps for all of that

That would be too simple

But I do think the pattern matters if life keeps removing reasons to move then we have to create reasons ourselves

12:03 — Convenience Can Hide the Real Cost

There’s also money side to this too and I don’t want to go too deep into it

But and this is probably like I said, it’s gonna be I could probably build a whole video on it

But it’s hard not to mention it because when everything is one tap away

It gets easier to confuse access with affordability.

Just because you can order does not mean you can afford it.

Just because the app lets you move, have it, does not mean your budget can absorb it.

Food delivery is expensive.

There’s tips, there’s service fees.

Menu prices are normally higher.

Sometimes the total is almost absorbed compared to picking it up yourself.

But the friction is so low that you can use it.

And I think that is where people get into trouble.

You see people eating out all the time.

I tell my kids it’s the worst way.

That’s the killer of your finances right there.

Ordering in all the time, getting coffee delivered, getting snacks, buying things constantly.

And it is easy to wonder how are people affording this?

And the uncomfortable answer may be a lot of them are not.

They are using credit, they’re carrying balances, they’re using the buy now, pay later.

Like everyone, I want this McDonald’s and I’ll pay $3 a month for the next six months to get this Big Mac.

I say that because my kid has a friend who just did that.

And it’s like, what are you doing?

They’re not seeing the full costs because each purchase feels small in the moment.

Convenience can hide costs.

And that is one of the dangers.

When you physically go somewhere, there’s no friction.

You have to decide is it worth the trip.

You have to get up, you have to drive, you have to wait,

You have to check the prices, you have to scan your card, you have to carry it home.

With apps, the purchase can feel less real, tap, confirm, wait.

That’s really it and that is very dangerous because the system is very good at making the desire feel reasonable.

I want it, it is available, I can have it.

That loop is not neutral.

It trains expectations and over time, it can make ordinary waiting feel unacceptable.

14:08 — Convenience Can Also Be Access

And, but there’s another side to it too.

Convenience can give people freedom.

And this is the good part about it.

I do not want this video to turn into some old man complaining about delivery apps, because this is not the point.

For some people, these services are genuinely helpful for the elderly, grocery delivery, task rapid to put stuff away.

For the disability, same thing.

Delivering reduces a real barrier that a lot of them have.

If you’re sick, getting food and medicine and not spreading it and being able to recover.

If you are a parent with young kids, not dragging everything through the store and reducing that stress and not,

reducing the stress ’cause when you bring your kids to the store and things get out of control, you start yelling, so it reduces a lot of anger and stuff like that also.

If you do not drive, these services can open up full access to everything that you normally have to rely on a ride and extra complications on top of simple tasks.

That burden’s gone.

If you live somewhere with limited options, online shopping brings you things that allows you, allows you to have that normally you would either have to pay extraordinary prices for and just not be able to get.

So it’s not that simple.

Convenience is not automatically bad.

Sometimes convenience is dignity.

Sometimes it’s access, relief.

Sometimes it’s the thing that makes life manageable.

And that is why I think the better question is not, it’s convenience good or bad.

The better question is, what happens when convenience becomes a default?

Because when it is used intentionally, it can help.

But when it becomes automatic, it can hollow out parts of life without us noticing.

And that is the balance I’m trying to think through.

15:56 — When the World No Longer Forces You

Maybe the future is not that we stop doing everything.

Maybe the future is that we stop being forced to do as much.

And in a lot of ways that is good because it also puts more responsibility on us because the world no longer forces you to move.

You have to choose movement.

If the world no longer forces you to leave your house, you gotta make that mental choice to get up and go out.

If the world no longer forces you to wait, you have to build patience somewhere else.

If it doesn’t force,

if the world no longer forces you to delay purchases, you have to create financial boundaries for yourself.

And that is not easy because friction used to do some of that work for us.

Not perfectly, not always fairly, but it did.

The store being closed made you wait.

The long drive made you reconsider.

The effort of going out made you ask if you really need it.

The physical lack of shopping made spending feel more real.

The errand gave you a reason to move.

When those barriers disappear, some things get better and some things get harder, just in a quieter way.

And that is the trade off I keep thinking about.

It means it removes friction, but some friction was giving us movement.

Some friction was giving us structure.

Some friction was giving us contact with the real world and other people.

And everything comes to us.

The movement effort and participation become choices.

And maybe that is the future.

Not that we cannot do things anymore, but that we no longer have to.

And once we no longer have to, we have to be honest about what we still choose to do.

We have to be conscious about it.

We have to put systems in our own way to force us to do these things.

We have to watch our budget.

We have to physically move.

We have to pay attention.

And I think that’s a big thing.

17:51 — AI, Utility, and What Comes Next

And then there’s another part to this video that I’m going to add about you know you

AIs utility along the same thing so this one’s a really about the convenience of it

And how it forces us to look at the world a little different

when we add in a whole other aspect of that of of how AI and robots and

The way things are become more of a utility because there is right now.

There is a friction and

and a hesitation say just from grocery shopping.

And I think there’s a way of profiles and AI

and the way we can build that out

to make that more consistent.

So anyway, thanks for watching

Cheap Abundance Won’t Save the Middle Class [Raw Session]

00:00 — Cheap Does Not Automatically Mean Prosperous

I open by connecting this video to the previous one about people underestimating AI. This one goes deeper into the idea of AI abundance: software, services, education, entertainment, support, admin work, workflows, and even physical goods getting cheaper over time.

The main tension is that cheaper access does not automatically mean a better life. If everything gets cheaper but people lose stable income, bargaining power, ownership, and independence, then abundance may become a cheaper version of dependence instead of real prosperity.

02:04 — AI Plus Energy Becomes the Production Layer

I talk about why AI alone is not the full story. The bigger shift is AI plus cheap energy, robots, automation, logistics, infrastructure, software systems, and physical hardware.

At that point, AI stops being just a chatbot or a coding tool. It becomes part of the production layer of society. It can help produce, coordinate, manage, design, optimize, and deliver things with fewer people in the pipeline.

The key question becomes: what happens when labour is one of the costs being reduced?

03:35 — The Uncomfortable Side of Efficiency

This section gets into the uncomfortable part of AI-driven efficiency. We like the idea of AI making things cheaper and more affordable, especially when affordability is such a major issue.

But wages, salaries, and human time are also costs. When companies talk about efficiency, that can mean fewer people, smaller teams, less admin, less support, fewer junior workers, fewer repetitive tasks, and less middle management.

The squeeze does not have to happen all at once. It can happen slowly through companies not replacing people who leave, hiring fewer juniors, automating workflows, and using AI to absorb work that used to justify another role.

04:23 — The Slow Squeeze Inside Companies

I use Salesforce as an example of what this could look like, while being clear that I may be speculating based on what I’m seeing in the news.

The point is not that every company fires everyone overnight. It is that companies may train AI systems and agents inside the business until fewer people are needed in those roles.

This can show up through attrition, fewer internships, fewer junior hires, and seniors using AI agents to handle work that used to be assigned to newer employees.

That is the slow squeeze.

06:31 — Why the Middle Class Feels This First

The middle class is built on income, jobs, training, consistency, and the belief that hard work can lead to a stable life.

If AI reduces the value of normal knowledge work, the old ladder does not disappear instantly. It just gets weaker. That can be more dangerous because people may not notice until they are already stuck.

I also connect this to the earlier idea that AI widens the path for people who are willing to learn it. If you use AI inside your job, broaden your skills, and become more productive, you can become more valuable. But ignoring the shift is risky.

07:12 — AI Can Make You More Valuable If You Use It

I talk about how workers in almost any field can use AI to strengthen their position.

Developers, call center workers, accountants, lawyers, and many other roles can use AI to write notes faster, build FAQs, research, organize information, improve workflows, and reduce mental load.

The point is not to work all the time. AI can help people breathe a little easier while still becoming more productive.

But if someone refuses to learn it and loses their job, employers may choose someone else who understands AI and can help the company move forward.

09:08 — The Middle Class Is the Pressure Point

I explain why this is not just a poor versus rich issue.

The poor already live closer to dependence. The rich already own assets. The middle class is the fragile layer in between.

The middle class depends on stable jobs, career growth, home ownership, retirement accounts, small businesses, skill premiums, professional identity, and the belief that work turns into security.

That bargain is already harder than it used to be. If jobs become less stable, skills lose value faster, ownership concentrates, and housing stays expensive, the middle-class path starts to break.

10:27 — Ownership Determines Who Benefits From Abundance

AI abundance sounds good if everyone shares in it, but that is not usually how systems work.

The people who own the productive layer benefit first. That means ownership of AI models, chips, compute, energy, robots, platforms, data, distribution, capital, and real estate.

Prices may fall in some areas, but control can still centralize. People may get more tools, cheaper services, entertainment, and convenience, while owning less and having less security.

Abundance means there is more stuff. Prosperity means people have stability, agency, ownership, and a real path forward.

11:33 — Why UBI or CBI Starts to Make Sense

I get into the touchy subject of UBI or CBI.

I do not think income support comes up only because people want to help. At a system level, it may become practical. If AI weakens enough jobs, the system still needs consumers, businesses still need buyers, governments still need stability, and people still need housing, food, energy, healthcare, transportation, and basic dignity.

The basic idea is simple: if work no longer distributes enough income, something else has to.

In the short term, that support can be real help. If someone is drowning financially, income support can remove real stress.

13:01 — Support Can Become Dependence

The risk is what happens after support becomes normal.

Support can become structure. Structure can become dependence. Dependence can start to feel normal very quickly.

Dependence does not always feel bad at the beginning. Sometimes it feels like relief: finally being able to breathe, pay bills, catch up, and feel like there is a floor underneath your feet.

But if the jobs are not there, ownership is out of reach, and the system gives people just enough to survive but not enough to build, people may settle into that.

Not because they are stupid or bad, but because most people follow the path put in front of them.

14:20 — The Darker Side of Cheap Abundance

The middle class may not collapse dramatically all at once. It may be quietly reshaped through a slow normalization of having less control.

That is the darker side of cheap abundance.

People can have more access but less power. More convenience but less ownership. More support but less independence.

That is the part that feels scary when you really think about it.

14:43 — The Optimistic Part: AI Lowers the Cost of Trying

I shift into the optimistic side.

The same tools that squeeze the middle class may also open doors for certain people.

AI lowers the cost of trying. It lowers the cost of learning, writing, coding, design, researching, planning, launching, building small businesses, creating content, and making tools for other people.

Things that used to require a team can sometimes be started by one person. Things that used to require money can sometimes be tested with almost nothing. Things that used to require permission can now be attempted directly.

That does not mean everyone wins. But some people will, and that matters.

15:38 — From Survival Mode to Building Mode

Some people who were buried under bills, stress, exhaustion, and survival mode may finally get enough room to relax, think, and build.

If an income floor removes some pressure, the question can change from:

“How do I survive this month?”

to:

“What can I build now that I have room to think?”

That is a completely different question.

For people who still want to improve their lives, AI may become one of the biggest leverage tools they will ever have.

16:13 — We May See Things We’ve Never Seen Before

This connects to the bigger idea that we may see things we have never seen before.

People can build the app, start the business, create the channel, write the book, learn the skill, serve a niche, help their family, create something useful, and move themselves one layer up.

Not instantly. Not easily. Not guaranteed.

But the door is more open than it used to be.

16:42 — The Future Divide May Be Active Versus Passive

Maybe the future divide is not only rich versus poor.

Maybe part of the divide becomes active versus passive.

People who use the tools versus people who are managed by the tools. People who build versus people who only consume. People who learn versus people who wait. People who use support as a floor versus people who become trapped by it.

I do not mean that in a judgmental way. Life is hard, stress wears people down, and not everyone starts from the same place.

But room is not the same as direction.

Some people will use the room to disappear into comfort. Some people will use it to build.

17:39 — Abundance Is Not Enough

I come back to the main tension.

AI may make things cheaper. It may create abundance. It may reduce suffering. It may give people access to tools they never had before.

But abundance by itself is not enough.

If people lose income, ownership, agency, and a path to improve their lives, cheap goods do not solve the deeper problem.

The middle class does not just need cheaper products. It needs a path, work that matters, ownership, stability, and the belief that effort still compounds.

18:13 — The Final Question

This is where I land.

AI may weaken the old path, but it may also create a new one for people willing to use it.

I am worried about the squeeze. I am worried about dependence. I am worried about ownership concentration.

But I am also optimistic for the people who decide not to drift with it.

If AI lowers the cost of building, learning, and trying, there will still be openings. Maybe not for everyone, and maybe not evenly, but enough that it matters.

The real question is:

When things get cheaper, do we become freer?

Or do we become more dependent on the people who own the system?

That is what we need to watch.

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.