Your AI Profile Is Coming… And Who Owns It? [Raw Session]

00:00 — Convenience Has Another Layer

Hey, welcome back to Slow Builds.

This video is connected to the last few videos I did around convenience, grocery delivery, when things come to you, that kind of stuff.

And I think there is a whole other layer to that.

I think there is a whole new business opportunity too, which I’m not sure I fully understand yet. I have to investigate it a little bit more.

The idea is your profile.

Because the problem with a lot of convenience right now is not always the delivery of it.

It is that the person or the system doing the task does not really know you.

They do not know how you like your things, your judgment, your little preferences.

And that sounds small, but I think it might become a much bigger idea and a great opportunity.

Because what happens with technology is not just a store you log into.

What happens when it stores how you like things?

That is what I mean by an AI profile.

Not just your account, or your passwords, or payment information, or identity.

I compare that to Bitwarden because I use that.

But a version of you that moves from one store to another, one car to another, one salon to another.

Basically moving from robot to system with you.

And I do not fully know how I feel about this yet.

Because part of it sounds incredibly useful.

And part of it sounds extremely uncomfortable and scary in a way.

01:44 — Grocery Delivery Shows the Problem

A simple place this started for me was groceries.

I talked about this in my other videos, about how my wife hates grocery delivery in one way.

We like how it works.

We like the convenience of it and the simplicity.

But then there is someone picking your groceries for you.

They do not know how you like your avocados or your bananas.

They do not know which brands you like, or what you might switch based off a sale price, or if something is not available.

Sure, you can pick your options.

You can pick your alternatives.

But there is a whole other aspect to it where, I know it sounds kind of silly, but it is judgment and taste.

It is that household experience.

It is years of little decisions compressed into something you do without thinking.

And right now, that does not transfer very well.

You can make a list.

You can write the notes.

You can choose your replacements in the app.

But the system still does not really know how you choose.

It knows what you ordered.

It does not know why you picked what you picked, why there was a difference, or how you changed your mind.

And that is a massive difference between making a list and actually completing the list.

03:08 — What If Grocery Preferences Became Trainable?

So then I started thinking, what if that becomes trainable?

Maybe at first it is very simple.

You order groceries a few times and the app learns your substitutions.

Or maybe you reject certain items and it learns what you did not pick.

Or maybe one day there is a robot picker, an Optimus-type robot at the grocery store, and you go in and train it.

You go to the store.

You pick the produce.

You reject some things.

You choose others.

You touch it, smell it, look at it.

And over time, maybe very quickly, because with a robot you just set it and forget it, they learn your patterns.

Not just, this person buys bananas.

But this person likes bananas at this stage of ripeness.

This person avoids this brand.

They accept this substitution.

They do not want wilted greens.

They check the expiration dates.

This person would rather skip the item than replace it with the substitution currently available, based on price, flavor, brand, or who knows why.

And that starts to become more than a grocery list.

It becomes your grocery profile.

04:24 — Does the Profile Belong to the Store or to You?

Then the bigger idea is, does that profile belong to the store?

Or does it belong to you?

Because if it only belongs to the store, then one store knows you.

But if it belongs to you, maybe you can take it anywhere.

Maybe you can take it to a different grocery store.

You can go to a grocery store in any city, maybe even any country, and the system knows you.

That is a strange idea.

Because then the value is not just the robot.

The value is the profile.

The robot is just a worker.

The profile is the thing that knows you.

04:57 — Travel Makes the Profile Even More Powerful

Before I jump into the next part, this got me really thinking about vacations.

We go to different countries.

We do not speak the language.

So now it could open up a complete possibility where I do not need to know the language.

I can literally take my profile, and as long as stores have the same kind of setup, I can make my list in my language, send it to the grocery store, and then it shows up at my house, or my Airbnb, or my hotel, or wherever.

The profile aspect of it could be so powerful in my mind.

And once you see it in grocery stores, you can see it everywhere.

05:38 — Haircuts May Be an Even Better Example

The big example would be haircuts.

Again, with my wife and with a hairdresser.

Honestly, hair might be an even better example because a lot of people have trouble getting the same haircut, the same color, and the same style every time.

Even if they go to the same salon.

Even if they go to the same person.

They show pictures.

They explain exactly what they want.

There is still interpretation.

There is still memory.

There are variations.

One person cuts slightly different.

The color is off a little bit.

Maybe the style looked good once, but nobody quite remembers exactly what changed.

So what happens if that becomes a profile also?

Your haircut profile.

Your color.

Length.

Layers.

Shape.

What you liked last time.

What you did not like last time.

What products worked.

What looked good after two weeks.

What grew out badly.

And again, maybe this is not a robot at first.

Maybe it is just a better digital record for a salon.

But eventually, maybe it is portable.

You go to a different salon, and instead of trying to explain yourself again from scratch, your profile comes with you.

Or one day, maybe there is a machine or robot in your house that can repeat the same cut or maintain the same style.

That would be pretty awesome.

That sounds futuristic, but the concept is not that strange.

It is just taking something humans already try to remember and making it repeatable.

07:09 — Convenience Is One Thing, Repeatability Is Another

This is where it starts to feel different.

Convenience is one thing.

Repeatability is another thing.

People do not just want things easier.

They want things done the way they like them.

And that is your profile.

07:22 — Cars Already Have a Small Version of This

Cars are obviously another example.

Cars already have small versions of this.

Seat memory.

Mirror memory.

Temperature.

Radio.

Pairing your phone.

Bluetooth devices.

Those are driver profiles.

But imagine it becomes much deeper than that.

You get into any car and it knows you.

It knows your seat, your mirror, your temperature.

It knows your music.

It knows if you prefer sportier rides or softer rides.

It knows if you are in a rush.

It knows what kind of driver you are.

It knows whether you are cautious or fast.

And if the car is autonomous, or even semi-autonomous, maybe it knows how you like to be driven.

Smooth.

Fast.

Direct.

Efficient.

Avoid highways.

Give more following distance.

Take your time.

Music on or off.

Silence.

Talk radio.

All these things become part of this profile, this different level.

08:26 — Waymo and the First Signs of This

One example of that would be my friends who were in San Francisco recently for a conference, and they were blown away by Waymo.

I might be saying the name wrong.

They got into one autonomous car, like a taxi, and they synced their playlist.

Then when they got out, they went and got something to eat.

Then they jumped into another one.

And they were blown away that when they got into the next one, it literally picked up mid-song from the last one.

Now that is a profile.

They are already doing that.

That already shows this is something people are thinking about.

That extra little level.

But I am thinking about it from a whole different ballgame.

09:17 — Airbnb, Travel, and Showing Up to a Place That Already Knows You

Say an Airbnb where you travel around a lot.

All of a sudden, the way you like your bed.

The groceries you like to have stocked ahead of time.

Toiletries.

Fabric softeners.

All these different things.

It knows who you are and what you like, depending on where you are going.

A ski trip.

A beach trip.

A city trip.

All these things are kind of laid out for you as soon as you show up because your profile follows you.

09:46 — Bitwarden Is the Closest Thing I Can Think Of Right Now

The closest thing right now that I can think of is Bitwarden.

I use Bitwarden for passwords, logins, notes, payment, identity, and it fills everything else for me.

It carries basic pieces of me from site to site.

And I think most of us already accept that now.

We do not want to remember every password.

We do not want to type our address a million times.

Same with payment information.

But we also do not want to store all these things on individual sites because they get hacked.

So you pick one secure place and allow it to be the maintainer of everything.

In some ways, it is already porting my identity.

But it is still mostly administrative.

It knows how to log me in.

It knows where I live.

It knows what cards to use.

It has secure information.

But it does not know my taste.

It does not know my preferences, habits, or decisions.

I think the next version could move in that direction.

From password management to preference management.

From identity management to personality management.

That sounds dramatic, but I do not think it is that far off conceptually.

10:56 — AI Systems Are Already Moving Toward This

AI systems are already trying to learn tone, style, preferences, memory, routines, and context.

I see that myself.

I set my instructions for writing the book.

I set how I like the code at work.

I have my own private memory where every time I do something at the end of a feature or bug, I ask if there is anything we should add to our memory that was found.

Sometimes it comes back with nothing.

But sometimes it comes back and says, this is something different we did that we have not done before, and it sounds like you have repeated it before, so we should add this.

So it starts to learn how I code and how I read.

Streaming platforms already suggest what they think you would like.

Cars have driver settings.

Phones know your habits.

Apps know your behavior.

And if you are not paying for it, you are the product.

The difference is that right now, those profiles are fragmented.

Amazon.

Google.

Apple.

Netflix.

Spotify.

Your hairdresser.

But the bigger shift would be those pieces becoming one portable layer.

One version of you that can be used across services.

That is where it gets interesting.

And to be honest, it gets very risky and scary.

12:23 — The Useful Side Is Obvious

The useful side is obvious.

You would not have to explain yourself all the time.

You would not have to start over with every service.

No rebuilding preferences for every app.

You would not have to teach every system separately.

Things would feel smoother.

Grocery picking.

Salon.

Car.

Restaurants.

Hotels.

Your Airbnb.

The robot going around your house doing all your work for you.

Cleaning.

Knowing what temperature to put the washer on.

Knowing when you want things turned on or off.

When the lights should be on.

Where to put stuff in the fridge.

How to organize things.

All these simple little decisions that are made unconsciously are now being stored and saved.

And honestly, for some people, it could be more than useful.

It could be accessibility.

For elderly people.

Disabled people.

Busy families.

People with health issues.

It helps.

There are so many different levels of how this can help in so many ways.

When you really look at it, the world could adjust to you a little bit more.

That is the good side.

And I do not want to ignore that because sometimes we talk about these things only in end-of-the-world type situations.

But convenience does solve real problems.

Automation does solve issues.

Profiles reduce friction.

The issue is not that it is useless.

The issue is that it is extremely powerful.

14:01 — The Uncomfortable Side Is Ownership

The uncomfortable side is ownership.

Who owns your profile?

That is probably the main question here in my mind.

If grocery stores learn your preferences, does the grocery store own that data?

At the salon, is it you or is it the salon?

The car.

The robot in your house.

Is that going up into the cloud somewhere?

Or is that still your own personal profile?

It becomes your AI assistant.

What you prefer.

What you buy.

What you regret.

It will remember those things.

And what you respond to.

So who controls all that?

Because this is not just data in the old sense.

It is not just name, address, email, and phone number.

It is behaviors.

Preferences.

A version of your tastes and habits that may become more valuable than your basic identity.

Because knowing who I am is useful.

But knowing how I choose is a whole other level.

And that can serve me, or it can be used against me.

15:03 — Personalization Can Become Manipulation

This is where manipulation comes in.

If a system knows what I like, it can help me.

But it can also steer me.

It can show me the products I am most likely to buy.

It can frame choices in a way I am most likely to accept.

It can slowly shape my defaults.

It can make substitutions that are good for the company, not for me.

It can learn when I am tired.

When I spend more.

When I avoid thinking about my choices.

What kind of messages work best when trying to manipulate me.

That is a different form of normal advertising.

Normal advertising guesses.

Today it already goes deeper than that because of how sophisticated it is.

But this brings it to a whole different level.

It scares me to think of Meta taking over something like this.

Meta glasses are watching everything you are doing.

I am going off script here, but I do not trust Facebook at all.

That is a personal thing.

But if everyone starts wearing their glasses, then it is not just knowing what I like and what I do.

It is watching what I am doing.

It is watching how I make those decisions and how I interact.

And they are definitely capturing that information.

There is no way around it.

That feels uncomfortable.

Not because all personalization is bad.

But because personalization has a shadow side.

It can serve you, or it can narrow you.

16:36 — The Lock-In Problem

There is also a lock-in problem.

If one company builds the best profile of you, leaving that company becomes harder.

Not because you cannot leave technically.

But because everything works worse somewhere else.

Groceries are worse.

Recommendations are worse.

Routines break.

Saved preferences disappear.

That is a different kind of lock-in.

It is not only that your files are trapped.

It is that your learned self is trapped.

And that might become one of the biggest competitive advantages.

Companies may not just compete on product.

They may compete on how well they know you.

And once they know you well, you may stay because starting over feels annoying.

That already happens a little bit.

People are connected through photos, messages, ecosystems.

I am an Apple user.

I am an Amazon user.

But an AI profile would take that further.

Because now the thing you lose is not just access.

You lose your accumulated understanding.

All your preferences.

All your knowledge.

So then it becomes:

Can I export my profile?

Can I carry it with me?

Is it stored locally?

Do I have full control of it?

Does someone else have access to it?

Can I modify it?

Can I reset it?

Those questions matter.

And if I delete it or reset it, does someone else still have fragments of it somewhere?

Because without control, an AI profile could become another thing we rent from a platform.

A version of ourselves that we do not fully own.

18:08 — Cloud, Local, and Permission-Based Profiles

This is why I keep thinking about different ways the profile could exist.

Cloud-based is the easiest way to look at it.

You sign in and your preferences follow you.

But cloud-based also means someone else stores it.

Maybe there is a local version, something stored on your phone or on a device you control.

Maybe the robot can access it temporarily.

The car can read it when you are driving.

The salon can upload it when you go there.

The grocery store can pass it around.

I do not know.

Maybe it is like a permission system.

You do not give every company all of you.

You give a part of you.

This is me thinking through what happens here.

Design matters.

This could become one of those areas where the boring details are actually the whole thing.

Permission.

Portability.

Encryption.

Local storage.

Exporting.

Deleting.

Auditing.

Resetting.

Changing it.

Maybe there is a middle version.

Maybe your profile is not one giant thing.

Maybe it is broken up.

Maybe all these different things have a piece of it, and you control the main part that unlocks it.

Maybe you have the main key that is you, and each grocery store holds a piece of it that connects to you.

I do not know.

Some of these things you may want to connect.

Some you may want to keep separate.

In the end, who gets access to it?

Signing in with one big platform and letting it know everything about me is convenient.

But it is also extremely scary.

19:39 — Do I Even Want Everything to Be Repeatable?

That brings up a deeper question.

Do I even want everything to be repeatable?

Because part of me likes the idea.

Get the same haircut.

Get groceries picked correctly.

Have systems understand me.

But part of life is also change.

Trying something different.

Being surprised.

Changing my taste.

Having a human suggest something.

Realizing you do not like what you used to like.

If your profile is too strong, does it freeze you?

With deeper profiles, correction becomes important.

The profile needs to know that people change.

What I liked before may not be what I like now.

Maybe I like something new now.

What I choose under stress may not represent me.

What I bought once may not be part of my identity.

Maybe it was a mistake.

Maybe it was one of those regrets.

And that matters because a model of you is always going to be incomplete.

We are always evolving and changing and trying new things and disliking some things.

It is not you.

It is a representation of you.

A useful one, maybe.

But still incomplete.

20:59 — The Direction Feels Real

This is where I land.

I think this is probably coming in some form.

Maybe not exactly like this.

Maybe not one universal profile.

Maybe not as clean or smooth as the thumbnail makes it look.

But the direction seems real to me.

More memory.

More personalization.

More automation.

More systems that do not just respond to commands, but learn your preferences.

And I think the important question is not just, can we build this?

The important question is, who controls it?

If I have an AI profile, I should be able to see it.

I should be able to correct it.

Move it.

Delete it.

I should have full ownership of it.

I should be able to choose which parts are shared.

I should be able to give companies parts of it, or a full version of it, depending on what I feel comfortable with, or how deeply I want to be integrated with that system.

That feels like the line.

The profile should serve the person.

The person should not become trapped by the profile.

And maybe that is the real concern.

Not that AI knows you.

But that AI knows us through systems we do not control.

22:10 — From Password Management to Preference Management

That is the thought right now.

We have password management.

Eventually, we may have preference management.

And maybe after that, personality management.

Systems that know not just who we are, but how we like things done.

Everything.

And that could make life easier.

It definitely would make life easier.

It could make things more repeatable and less stressful.

But it could also make us easier to predict, influence, and lock in.

So the question I am left with is pretty simple.

If there is going to be a portable version of me, do I own it?

Or does someone else own it?

Because that might be one of the bigger technology questions coming.

Not just what AI can do.

But who controls a version of us that AI learns?

And this goes back to one of the other videos I did about how we are giving ChatGPT and Claude everything at the moment.

We are asking every single private thing, public thing, business thing, idea, whatever.

And that is being stored somewhere.

So again, that is probably one of the most valuable things people are going to have to learn how to control and be part of.

It is a scary thought.

I would love to see what people think about this.

Thanks.

Bye.

52 Videos Later: What I’ve Learned So Far [Thinking Out Loud]

00:00 — Six months in, still awkward

Hey welcome back to Slow Builds.

So I’m about six months into this channel now, and this is around video 52. I’m trying to do two a week, so I think I did a few more in the beginning, but I’m pretty spot-on here.

And I know it sounds like enough videos that I should probably feel more comfortable just doing this, but honestly, I still feel extremely awkward.

I only record in this room. I did one other room once, and that was because there were too many people around and I had an opportunity. I don’t edit. I do the beginning and end. I still haven’t shared this channel with anyone outside of my immediate family.

I’m not sure if it’s because I’m scared, or I’m still not comfortable at all with this.

So in a way, I’ve been very consistent. But in another way, it feels like I’m standing still at the starting line of all this.

I set out a goal for this channel because I want to see if authenticity can win out in the end here. With AI and videos and faceless channels and the trust aspect of things popping up online, I think there’s something there.

I’m just trying to prove a point.

But at the same time, it’s kind of therapeutic doing this. It allows me a place to let my mind ramble a little bit.

01:50 — I’m not a creator, I’m just pressing record

Like I’m saying, I’ve done this, and I’m not a creator. I’m not a digital content creator. I’m not a YouTuber.

I’m just a guy who presses record.

And the thing I’ve learned is I can press record a little bit faster now. I’m not scared of messing up as much.

I have one video where the cat was meowing. I’m next to a bathroom, so sometimes the toilet flushes. Sometimes people upstairs are a little extra loud. I don’t edit that out. I don’t try to do anything.

So I’ve learned that the videos don’t need to be perfect. They just need to exist.

I’ve gotten better at noticing ideas throughout the day. I started with 52 ideas that AI helped me produce, and I’ve gotten way off track. I’ve done a lot of them, but most of the ones I’ve done have been outside of that.

Obviously this is 52, so either I’ve gone through all of them, or I still have a few left in the bag.

I’m starting to understand what topics keep pulling me back and allowing me to have a voice, or at least try to do something.

I don’t have a voice. I’m just rambling.

03:06 — Making videos feels less impossible now

The biggest change is probably not that the videos are good.

It’s that making one feels less impossible now.

It doesn’t feel like climbing a mountain. At the beginning, even recording felt like a big event. Now it’s still extremely uncomfortable, but it’s more familiar.

I can sit down, talk through an idea, upload it, and move on.

And that is something.

It’s a little bit reassuring that maybe, as long as I can keep the ideas coming, keep the scripts flowing, and continue to work on my workflow, I might be able to continue on.

Well, I’m going to continue on anyway for the year. I know it’s a big commitment to say that, but I’m going to do my best.

03:56 — The parts that still feel stuck

There are parts that still feel very stuck.

I’m still not fully in control on camera. I still don’t really move around. As you can see, there are noises in the background. There’s no editing or overlays. There are no shorts.

And maybe the biggest one is that I still haven’t really shared it with anyone.

That tells me something, because it means part of me is still treating this like a very private experiment.

Even though it’s public. It’s online. Anyone can find it. Anybody can see it.

But with the billions and millions of videos going up, and different channels, and no real link back to me personally in a way, I think if my friends looked hard enough they would find me.

But emotionally, I’m still half hiding this.

At this point, 52 videos in, I shouldn’t feel too bad about it.

04:54 — Commitment versus voice

That’s the commitment versus my voice problem with the 52 videos.

I’ve noticed something with the schedule I’ve been trying to keep. It’s helped me because without some kind of commitment, I probably would have found reasons to skip a lot of them, or take big breaks in between.

But there’s a downside to it also.

Sometimes I need to get a video done.

And when that happens, I may have a few ideas ready, some notes ready, or a script that AI helped me hash out from my own thoughts.

But I have not really sat with the idea long enough yet.

So I go into the video very blind. Just winging it, really.

The thought might technically be mine, but it does not always fully feel worked through yet. And I can feel that when I record.

Instead of it sounding like a conversation, or just ramblings like I was calling them for a while, it can start to sound more formal. Very robotic.

More like I’m reading a finished thought instead of working through an unfinished one, or just tinkering with it in my head unedited and live.

That’s something I want to watch.

Because the commitment matters, but I do not want the commitment to quietly change the voice of the channel.

The idea might be mine, but the shape of it is too clean until I get to get my hands on it.

There’s a difference between using AI to organize my thoughts and using AI to move faster than my thoughts.

06:38 — Using AI without letting it make everything too polished

That brings up the AI stuff.

AI helps me organize everything, but sometimes the first initial scripts feel way too polished and way too formal. I’ve tried to rewrite my instructions a few times for it to help me rewrite and tweak them.

And that’s not me saying it’s a bad thing, because without it, there’s no way I could write all these scripts.

I use AI all the time.

A lot of these videos start with my messy thoughts. Usually I’m working out, or running, or driving in the car, or even walking, and I’ll just be talking to it, throwing stuff out there until I say, do we think we have something here?

Then we’ll go through the process.

It organizes it. It gets the comments. It gets the description. It gets the thumbnail idea.

That part is extremely useful.

But I think there’s a difference between organizing a thought and replacing the process of thinking through the idea before, during, and after.

Sometimes if I record too soon after the script is made, I really feel it.

And that’s me just trying to get videos out the door to keep on schedule.

The idea is mine, but I have not carried it around long enough in my head. I haven’t noodled with it. I haven’t said it out loud enough.

08:06 — Why I use notes, and why that can get messy

Some of that is really because it takes a lot of time.

I come up with the ideas, and this video is half me reading what’s in front of my face that AI helped me turn into jot notes, and half me going off like I am right now.

The reason why I do it is I’ll talk into the phone, we’ll come up with a rough idea or jot-note idea, and then I could sit down and do the video straight up.

But my problem is I’m afraid I’m going to miss some of those nuances and little nuggets that I really want to bring up.

As I’m going through it, I keep coming up with more and more.

So that’s why I use the notes.

But it takes so much time sometimes, and then sometimes those videos become extremely long. This one might go a little long. It’s video 52, so that’s fine. I’m allowed to do that, and this is a rambling video in a way.

I don’t like missing out. I don’t like forgetting stuff. I don’t want two videos to be too similar.

But maybe that’s okay.

I always looked at it like a TV show, like it’s a rolling script and it goes in order. But on YouTube, does it really matter? It probably makes no difference.

I could do a million videos and 30 percent of them could be almost the exact same topic. There’s still a crazy amount more. And what order they are in probably doesn’t matter.

09:43 — The AI topic trap

That brings me back to the AI topic trap.

There has been a lot of focus on AI so far, and I am scared of being stuck there.

The channel is not supposed to be about just that.

I notice how much of the channel is about AI in a way, and it makes perfect sense because it is changing our world. It is new tech that is everywhere. We are all using it. It is changing our lives.

I’m thinking about it. I’m working with it at work. I’m working with it on my own. I’m using it personally.

So it’s everywhere.

But I also don’t want this channel to become only that. I don’t want to accidentally trap myself in one topic just because that is where the early videos went.

And because those are the videos that get the traction.

The channel was never supposed to be about just AI. I’m not an AI expert. I try my best to use it.

A lot of the videos I put out around AI lately are me thinking through where I think we’re headed, or what I think could happen.

I like to get those out ahead of time because maybe I’m predicting something that is going to come true, or maybe I’m going to fall flat on my face. But at least I want to get my ideas out there to see how they compare later.

I myself will look back at these to see what things I said that may have come to light, or may have just gone by the wayside.

But I don’t want this channel to become just AI.

It’s hard when it is such a part of my life, but I also have family. I have kids. I want to talk about financial stuff. I want to talk about patterns of change, working on yourself, fitness, habits, how to live a better life.

Everything life related, really.

I really wanted the channel to be based off the book I’ve been trying to write, and I haven’t touched it in a while. I’ve talked about that AI fatigue and not feeling bad about it, but you still have to give yourself a little nudge every now and then.

12:14 — The traction side

I can talk all I want, but if I’m not seeing traction, there is that side of it.

I’m not seeing a huge lift from it.

I do all this work, and there are comments here and there, and some people showing up. That really matters to me. It makes me feel good.

I haven’t seen too much in the last few. I see people watching them, but not too many people. But enough that it makes me feel okay.

At this point, 52 videos in, I have about half as many subscribers.

You would think you would hit a point where it might grow a little quicker, but I’m hoping that with consistency and time, and maybe coming up with some sort of pattern, I can keep these things at a certain length.

I don’t want to go too long.

I do find my videos are usually 18 to 25 minutes. I haven’t really hit 30 minutes, and I feel comfortable with that. Maybe I need to make them shorter, or maybe I need to do shorts to try to promote them and get a little more traction.

Right now, it’s small. Very small.

And it messes with my head a bit because I start asking whether the work is adding up, or whether I’m just talking into the void.

Which is fine. Like I said, it’s therapeutic.

It helps me get my ideas out, putting them on video like paper video, and it helps with the uncomfortableness of being able to do a video and put it up there without being too scared if someone finds it or sees it.

It’s easy to say you want to build something slowly.

It’s harder when it actually is slow.

14:41 — Views versus purpose

The biggest problem I get caught up in is views versus purpose.

One thing I find myself fighting is what happens when a video stands out.

Sometimes a video gets more views than the others. A lot more views, a lot faster. And I can usually see why.

The “AI helps me cheat” one got a lot of views fast. “AI fatigue” got a lot. There are a couple other AI ones too.

Maybe the topic was clear. Maybe the title was better. Maybe the thumbnail made a big difference. Maybe it just happened to hit at a time when a lot of people were searching for the same topic.

And when that happens, there is an urge for me to lean into it heavy.

Not even in a fake way. Just in a practical way.

You see something work and part of your brain says, okay, do more of that.

But then I have to stop myself. I really do have to stop and take a breath, because that is not really the purpose of this channel.

I want to learn from what works, but I do not want to be owned by what works.

There is a difference between noticing a signal and letting that signal become a command.

A video doing better tells me something. It does not automatically tell me what the whole channel has to become.

I don’t want to confuse a signal with a command.

I want to learn from what works, but I don’t want to be owned by it.

Because I don’t want people to attach to it because I hit a nerve, then people watch, subscribe, comment, and then I do two or three more along the same lines and get that same response.

Then all of a sudden I go back to what I want the channel to be, and I don’t want to let people down. I don’t want to mislead, if that makes sense.

I want to be honest about this the whole way.

16:59 — What the first six months proved

What this first six months has proved to me so far is that I can keep going.

It’s very imperfect.

Not every day. Not perfectly. Not with some polished system.

My system is crap.

But it is enough to build a body of work. And maybe that matters more than I would have thought at the beginning.

The videos are not where I want them to be. The channel is nowhere close to where I want it to be, but it is probably doing better than I thought it would, as bad as it is doing.

I’m not as comfortable as I want to be yet, obviously.

I am more comfortable, but not to the point where I can sit here with the door open, with people listening or walking around. I’m not there yet.

I’m not there to go out in public yet.

But there is something happening that did not exist when I started.

There is a confidence. There is an awareness. There is a simpler system.

18:07 — What the next six months may need

So what the next six months may need is not to make it too polished or too goal-setting. Just honest direction.

Slowly get more comfortable on camera.

Try recording outside the room. I want to use my phone. I want to go around. My kids got a DJI stick and they’re recording everywhere, and I want to try to get out of here.

I want to do more without relying on the script.

I want to get more into light editing.

I want to share the channel. I do work with a rowdy bunch, so I know there is going to be a lot of razzing if it gets to those guys. But to my other crew, if I stay within that little group, I’d be happy to share it at the moment, I think.

I don’t want to get away from AI, because my ideas and thoughts on AI are not always around the coding process. They’re not always around how it is embedded in systems.

It is really about the psychological part of it, the theory, the social part of it.

I see things around elderly people, disabled people, medicine, keeping a part of ourselves, privacy, world problems, investing. It is all connected to what I want to get to.

So AI is always going to be in there for the time being, because I don’t think it is going away.

I want to keep the raw session format, but improve in clarity.

Maybe shorten them. Maybe have a more formatted layout that I can stick to with time limits, and hit my nuggets harder.

There are a few things I want to do.

I want to sit with ideas a little longer before I record them, so I don’t need to have the script in front of me.

I also want to build an app where I can have the notes in front of me as I’m talking, and it is smart enough to know when to keep moving. I don’t like using the mouse. I don’t like using my finger. I lose my place.

I also want to build up a backlog so I can breathe a little bit. This is 52, but I already have three in the pipe coming out before this one that are scheduled.

That gives me time to breathe a little, especially because it’s summer, friends are coming up, and I might have to go away. I want to make sure there is no break.

21:03 — Small steps, not overproduction

I don’t think the answer to all of this is to suddenly turn it into some overproduced thing. That would probably kill the part of it I’m actually trying to do here.

But I do think the next six months need a little more movement.

Maybe not a full studio setup. No heavy editing. Just small steps.

Get out of the room. Try a different atmosphere. Maybe a little more editing. Some new lighting. Some shorts. Things like that.

And I do need to tell people about it.

Maybe I also need to give the ideas a little more time before I record them.

I try my best. I do go through them.

I have videos I’m excited to do. “Underestimating AI.” “It’s Okay to Be Lazy.” Something around the world starving itself. World value. AI reputation and scarcity. “You Still Have to Ride the Bike.”

That one is interesting, and I’m very excited for it. I even told my wife about it, even though she hates this channel and doesn’t want anyone to know I’m doing it.

Even she said, that’s interesting.

22:42 — Still figuring it out

So that is where I’m at.

Six months. Video number 52.

As you can tell, I’m awkward. I’m off script. I’m all over the place. I’m very unsure.

I’m uncomfortable doing it, but not uncomfortable that I’ve messed this one up.

I’m still not seeing any kind of signal that this is working, except for the few subscribers that seem to be dedicated. I’m very happy for that.

I kind of look at it like maybe they see something. Maybe they want to be able to say later, I started following him when.

If I do get a little bigger and get more subscribers, those subscribers from the beginning can chime in like old friends.

That makes sense to me.

That is why I love the feedback. I love the comments. I read everything.

I don’t even know what it would be like if I did take off and there were a lot of comments. I don’t know if I could do it. I’d have to get one of my kids to help me or use AI to help review them so I don’t miss any.

Because I don’t want to miss any.

24:19 — Protecting the reason I started

I’m still figuring this all out.

I’m trying to figure out how much to use AI, what topics to cover, and how to not chase the high-performing videos.

I want to keep a good mixture. I want to stick to my plan.

But I’m also still here.

And maybe that is the part I should pay attention to, because the point was never to look like I arrived.

The point was to keep showing the process while it is still unfinished.

And right now, it is very unfinished.

Maybe the lesson from the first six months is not that I need to become more polished.

Maybe it is that I need to protect the reason I started.

The consistency to me is what matters.

I’m trying to prove consistency and structure.

Because you can’t just randomly show up all the time and expect someone to watch you sitting in a room with bad lighting rambling about nothing.

Unless I’m going to move around, go to different places, and show different things, I need to have stronger topics and better flow.

When I say structure, I mean structure to the videos: the opening, ending, intro, and maybe a part I would edit where I do a quick recap of what is coming up in the video.

That way, within the first 10 or 20 seconds, the video has something that can hold attention longer.

But that is not the point of this video.

The views do tell me something. They show that people are paying attention.

But none of this can become the whole point.

Because if this channel turns into me chasing whatever worked last week, or reading thoughts I haven’t actually worked through yet, then I may keep publishing videos, but slowly lose the thing I was trying to build.

What I’m trying to build is consistency.

I’m trying to build authenticity.

I can’t speak that well. I try my best. My friends know where I’m from, and they call the way I speak “Foster-ese.”

Anyway, I just want to keep this going. I want to keep a consistency. I want to get the year done.

So 52 more to come.

That’s a big number.

It was a big number to get here.

Thanks for watching, and have a good one.

Why We Still Choose to Do Things Ourselves [Raw Session]

00:00 — When Optional Things Still Matter

Hey, welcome back to Slow Builds.

This one goes along with the utility videos I’ve been doing. It’s the other side of the conversation.

The last couple videos were about the idea that more and more things are becoming optional. Shopping comes to us now. Food comes to us. Groceries come to us. Work can happen through a laptop. You do not need to physically be anywhere. Meetings happen through Zoom or Teams. Maybe eventually transportation becomes something we access instead of something we personally own.

But there is another side to all that.

Just because something becomes optional does not mean people stop doing it.

Sometimes they keep doing it. And sometimes, once something becomes optional, it becomes clearer why they were doing it in the first place.

That is the part I keep coming back to.

When something is mandatory, you do not always know if people actually value the thing itself, or if they are just doing it because life requires them to do it.

But once the obligation disappears, the reason changes.

If you still do something after you no longer have to, then maybe there is something inside the act itself that matters to you.

That is what I want to get at with this video.

The things we still choose to do ourselves.

Not because they are always efficient. Not because they are always logical. Not because there are no alternatives that accomplish the same goal.

But because the physical version, the slower version, the human version still gives us something.

And I think that is going to matter more as life gets more automated.

The question will not only be:

What can technology do for us?

The question will also be:

What do we still want to do ourselves?

01:58 — Grocery Shopping and Hidden Preference

A simple example is grocery shopping, because grocery shopping is becoming optional for a lot of people.

Not everywhere. Definitely not perfectly. But more than it used to be.

You can order groceries online. Someone can pick them up for you and drop them off. It saves time. Depending on what it is, it can save money. It avoids the hassle of stores, possible accidents, gas, and all kinds of things it removes from your life.

And for some people, that is genuinely helpful.

If you are busy, sick, elderly, disabled, overwhelmed, or just need a break, delivery can be a real support.

I am not against that at all. I do not do it much myself, but I have used it.

But I also understand why some people still want to go themselves.

My wife is the perfect example of this. For certain things, she still wants to pick the food herself. And I fall into this category too.

The point is basic.

They do not know how I like my food. They do not know what produce I would pick. They do not know what substitutes I would choose. They do not know which brand is okay if one is not available.

Sometimes something technically matches the order, but it is still not really what I wanted.

And she is right.

Sometimes you get there and change your mind. You see something. You see a sale. You change what you want to make that day or the next day.

That is not stubbornness.

It is preference. Judgment. Taste. Experience.

The grocery app can know the item, but it may not know the decision behind the item.

It may know bananas, but it does not know how ripe I want them.

That is the part that is hard to automate.

The task looks simple from the outside: get food and put it in the house.

But inside the task are hundreds of tiny preferences, tiny judgments, and tiny decisions you make without thinking.

And when someone else does it for you, you start to notice how much judgment was hidden inside it.

I think that is probably true for a lot of things.

We think the task is simple until we hand it to someone else.

04:20 — Utility Versus Ritual

That is the difference between a utility and a ritual.

A utility is about the result.

A ritual is about the experience.

If I need food in the house, grocery delivery can solve that.

That is utility.

Food arrives.

But if I want to pick the food myself, walk the aisles, say hi to people, see what looks good, and check the sales, that is different.

That is ritual.

That is not only about groceries. It is about participation.

And I think a lot of modern convenience misses this distinction.

It assumes the result is the whole point.

Sometimes it is.

Sometimes the result really is all that matters.

If I need toilet paper, I do not need a meaningful experience. I just need toilet paper.

If I need batteries, I probably do not need to wander through the aisles and reflect on life and which batteries are best. I just need batteries to get the flashlight or remote working.

But not everything works like that.

Some things carry more human weight than the task suggests.

Food is probably the easiest example, but I think the same thing shows up in driving, going to the office, going to the market, or sitting in a restaurant instead of ordering food.

Those things are not only about acquiring the thing.

They are about the surrounding experience.

That is why making something efficient does not automatically make it better.

It depends what part you are trying to preserve.

If the goal is only the outcome, efficiency usually wins.

But if the experience matters, efficiency can strip something away from it.

05:53 — Weekend Markets and Useful Friction

Weekend markets are probably another great example of this.

Most people do not go to a weekend market because it is the only possible way to get vegetables.

They go because it feels good to go.

You walk around, look at things, see people, say hi, touch the produce, talk to vendors, get a coffee or a treat, buy something you did not plan on, and support local.

There is something social in it. Something physical. Something local. Something inefficient in a good way.

And that is the point.

If the only goal is food, the market is probably not the most efficient way to go about it.

But if the goal is getting out, seeing people, being in your community, and having small rituals, then efficiency is not the main measurement.

That is the hard thing to preserve in a world that wants to optimize everything.

Because optimization usually asks:

How do we make this faster?

How do we remove friction?

How do we remove all the steps?

How do we make it a single tap?

Those are useful questions, but they are not the only questions.

Sometimes the better question is:

What was the friction doing?

Was it only wasting time?

Or was it creating contact?

Was it creating movement?

Was it creating small decisions?

Was it creating a reason to leave the house?

Was it creating memories?

Because once you remove the friction, you might remove more than the inconvenience.

You might remove the reason people like the thing.

A market is not better because it is efficient.

It is better because it is not only a transaction.

It is a place that matters.

07:28 — Restaurants Are Not Just Food

It is also why restaurants and food delivery are not the same thing.

Food delivery is useful.

There are nights where it makes complete sense. You are tired, busy, do not have time to cook, do not know what to cook, or just do not want to go anywhere.

The food comes to you.

Great.

But going to a restaurant is not only food.

It is leaving the house. Sitting somewhere. Trying something new. Being served. Hearing the room. Talking to other people. Having a conversation that feels different because you are not sitting at your own kitchen table.

You are out among other people.

Delivery solves hunger.

A restaurant can create an experience.

Those are related, but they are not identical.

And I think this is where a lot of replacement conversations go wrong.

People ask:

Why go to the restaurant if you can get the food delivered?

Why go to the store if you can order online?

Why go to the office if you can join a Zoom meeting?

Why drive if a vehicle can take you?

Now we are getting into Tesla self-driving and robotaxis.

But those questions are incomplete because they assume the official purpose is the full purpose.

The official purpose of a restaurant is food.

But the human purpose might be connection.

The official purpose of a market is buying goods.

But the human purpose might be ritual, conversation, interaction, and socializing.

The official purpose of driving is transportation.

But the human purpose might be freedom, control, enjoyment, privacy, identity, going on long drives, or going somewhere without having to plug in your destination.

Maybe it is just driving for the sake of driving.

The official purpose of the office is work.

But the human purpose might be trust, mentorship, social contact, social context, or in my case, pair coding.

If you are sitting with someone, it is a lot easier to talk through a programming problem or walk through a possible new feature when you are with the person in the room and can see how they see it.

That is different than talking through video like I am doing right here.

So when we replace the official purpose, we do not always replace the human purpose.

And that is the gap.

09:56 — The Office After Remote Work

The office is a great example.

Zoom and Teams made a lot of office presence optional.

Not all of it. Not for every job. But for many knowledge workers, the office stopped being the only place work could happen.

Files are online. Meetings, messages, code, documents, calendars — work moved into the network.

And for a lot of people, that was a huge improvement.

Less commuting. More flexibility. More time with family. More time at home. Less stress. More control over your day. Less wasted office performance.

There is a lot of time wasted driving back and forth, stopping, taking breaks, and just moving around the old system.

But that does not mean the office has no value.

If the work can happen anywhere, then going to the office has to be about something more than access to a desk.

It might be about mentorship, trust, reading the room, hard conversations, team identity, onboarding, creative friction, casual conversations that do not happen in scheduled calls, or feeling like you are part of something.

Those things are harder to measure.

But they are real.

And I think this is the same pattern again.

When the office was mandatory, people went because they had to.

Once it became optional, the reason had to become more honest.

Are you going because there is real value in being together with your coworkers?

Or are you going because someone is trying to recreate the old system?

That is a very different question.

And I think a lot of companies are struggling with that right now because they are trying to bring back the obligation instead of understanding the ritual.

They are saying:

Be here because this is where work happens.

But for many workers, that is no longer true.

So the office has become something else.

Less mandatory infrastructure. More intentional gathering place.

And if it cannot become that, people will resist it.

Because once people experience optional, it is hard to go back to mandatory without being given an actual good reason.

12:06 — Driving When Transportation Becomes Optional

Driving works the same way.

The official purpose of driving is transportation.

You need to get somewhere, so you drive.

That is the basic utility.

But for a lot of people, driving is more than transportation.

It is control. Independence. Privacy. Music. Being alone. Clearing your head. Road trips. Your first car.

For me, the old truck.

It is the sound. The feeling of operating something physical. The habit of getting in and going.

So if robotaxis or autonomous transportation make driving optional, that does not mean driving disappears.

It means driving changes categories.

For some people, it becomes unnecessary.

For others, it becomes more meaningful.

Because now they are not driving only because the world requires it.

They are driving because they want the experience.

That is why I think the horse comparison kind of works here.

Horses did not disappear. They just stopped being the default transportation.

They became sport, recreation, lifestyle, identity, work in specific contexts, and maybe nostalgia.

Cars may go through something like that.

Not completely. Not everywhere. Not for everyone.

But for enough people, the meaning changes.

Some people will still own vehicles because they need them: rural people, tradespeople, families, and people in places where alternatives do not work.

But some people will own vehicles because they love them: collectors, enthusiasts, people who love old trucks, working on engines, manual transmissions, motorcycles, and the control.

Some people will not own vehicles at all because what they wanted was never the car.

It was mobility.

That distinction matters.

Some people love driving.

Some people just need to get somewhere.

When driving becomes optional, we find out which is which.

13:55 — When the Human Version Becomes Premium

There is also something interesting about human service in all of this.

We usually think automation makes the robot the premium thing.

The robot assistant. The robot driver. The autonomous system.

But sometimes, once automation becomes normal, the human version becomes a luxury.

Mass-produced clothing made clothes cheaper, but handmade clothing became premium.

Processed food made food easy, but chef-prepared food became premium.

Digital photos became unlimited, but film photography became aesthetic.

Automation does not always destroy the old thing.

Sometimes it turns the old thing into status.

So maybe one day robotaxis are normal. They are the utility layer: cheap, available, practical.

But a human driver becomes expensive.

Not because a human is better at every driving decision, but because the human provides something else.

Discretion. Judgment. Conversation. Security awareness. Help. Running errands. Reading the situation. Knowing your preferences. Being accountable.

A robot can move you.

A human can understand the day.

And that is probably true in other areas too.

AI can answer questions, but sometimes people still want a human advisor.

Delivery can bring food, but sometimes you still want to go to the restaurant.

Remote work handles the meetings, but sometimes just being around other people makes a big difference.

Automation can handle the utility.

But the human layer still has value.

The question is where that value is real and where it is just nostalgia.

Because not every old way deserves to survive.

Some things were only done manually because there was no better option.

But some things had value inside the manual part.

And we need to be able to tell the difference.

15:44 — Not Every Old Way Was Better

That is probably the hardest part, because it is easy to romanticize the old way.

It is easy to pretend everything was better when people did everything themselves.

But that is not true.

A lot of the old way was inefficient.

A lot of it was exhausting.

A lot of it excluded people.

A lot of it wasted time.

And a lot of it was only manageable because someone else was doing invisible labor.

So I do not want to say we should do everything ourselves.

That is not realistic.

I do not even think it is desirable.

Convenience can be good.

Automation can be good.

Delivery is great.

Remote work is awesome.

Autonomous transportation is going to change the world.

These things can give people access, time, safety, and flexibility.

The problem is not that things become easy.

The problem is when we stop noticing what disappears with the difficulty.

The store trip was not only a store trip.

The office was not only your desk.

The drive was not only the distance.

The market was not only about getting vegetables.

And the restaurant was not only about eating or consuming calories.

Some of those things carried structure, contact, movement, control, and meaning.

And if we remove them, we may need to replace those things intentionally.

That is the part I think we underestimate.

We are very good at asking:

Can this be made easier?

We are less good at asking:

What did the harder version give us?

Sometimes the answer is nothing.

Sometimes the harder version was just worse.

But sometimes the answer is that it gave us a reason to leave the house.

It gave us contact with people, movement, control, pride, a memory, an experience, a sense of participation in the world, and gratitude.

Those things matter, even if they are inefficient.

17:42 — VR and the Body Layer

This also connects to VR in a way.

For a while, there was this idea that virtual reality could replace physical experiences: vacations, meetings, events, virtual worlds.

Some of that is useful.

VR can show you a place. It can let you preview something. It can make inaccessible experiences more accessible. It can create things that cannot exist physically. It can be educational. It can be fun.

I have been using the Meta again lately because I wanted to try some of these things.

But it does not fully replace being there.

Seeing a beach is not the same as being on the beach.

Seeing a city is not the same as walking through the streets and smelling the air.

Seeing people is not the same as feeling like you are with them.

There is a body layer that technology has a hard time replacing.

Smell. Weather. Temperature. Tiredness. Randomness.

Randomness is a big one.

And I think this helps explain the broader point.

Technology can replace information more easily than it replaces experience.

It can show us things. It can deliver things. It can simulate them. It can automate them.

But that does not always mean it gives you the same human value.

Sometimes the value is in the inconvenience, in the body, in the place, in the people, and in the fact that you actually went there.

That is why vacations do not become obsolete just because you can see beautiful places on a screen.

And it is why a lot of physical life will probably survive automation.

Not because it is efficient.

Because it feels different.

19:17 — Utility Version and Ritual Version

The more I think about this, the more I think the future splits a lot of things into two versions:

The utility version and the ritual version.

The utility version is about getting the result.

The ritual version is about doing the thing.

Grocery delivery is utility.

Going to the store and picking it out yourself is ritual.

Same with going to the office, going to a restaurant, driving, shopping, and all these things.

AI assistance is the utility.

Thinking something through yourself may still be the ritual.

And I do not mean ritual in a religious way.

I mean it is a repeatable action that carries meaning beyond the result.

Something that gives shape to life.

It makes you feel involved.

Sometimes it connects you to your body, your place, or other people.

The danger is that if we only optimize utility, we may flatten everything.

Things become faster, easier, and more available.

But not everything becomes better.

Because better depends on what you were actually trying to get.

If you only wanted the object, delivery might be better.

If you wanted the experience, delivery is incomplete.

That is the distinction.

20:24 — What Do I Still Want To Do Myself?

This is where the question becomes personal.

What do I still want to do myself?

Not what should everyone do.

Not some universal rule.

Just what is still worth doing manually in my own life?

Maybe I still want to bike to return something because the errand gives me a reason to move.

Maybe I want to go to the market because it gets me out of the house.

My wife wants to pick certain groceries because she has preferences.

Someone else might still want to write, think, build, or learn manually even though AI can help.

That might become more important, not less.

Because as more things become optional, we need to decide what kind of person we become when nobody is forcing the old habits on us.

If I do not have to move, do I still move?

If I do not have to go out, will I leave the house?

If I do not have to learn the skill, will I intentionally try to learn new skills?

Same with talking to people.

If I do not have to do the slower thing, is there still a reason I would?

Those are not only technology questions.

They are life questions.

21:34 — When Obligation Goes Away

I do not think the future is that we stop doing things.

I think the future is that more things stop being mandatory.

And in many ways, that is good.

But it also means we have to be more intentional because obligation used to make some decisions for us.

The errand made us leave the house.

The grocery trip made us walk around.

The commute made us move through the city.

The office made us see people.

The store made us wait.

The car made us learn a skill.

The market made us interact with someone.

Not all of that was good.

But not all of it was meaningless either.

When the obligation goes away, we often get freedom.

But we also lose some of the structure that came with it.

And then we have to decide what to keep.

That is the part I do not think we talk about enough.

We focus on what technology removes: the errand, the commute, the wait, the manual process, the friction.

But that might be where the honest answer is.

Because if you still do something when it is optional, maybe that is a clue.

Maybe it is not just a task.

Maybe it is a ritual, a connection, a preference.

Maybe it is health.

22:40 — Convenience Is Not the Enemy

So I think that is where I land on this.

Convenience is not the enemy.

Automation is definitely not the enemy.

Delivery, remote work, robotaxis — they are not the enemy.

But none of those things are neutral either.

They change the default.

And when the default changes, behavior changes.

When groceries can come to you, grocery shopping becomes a choice.

Same with food and restaurants.

Same with the laptop and the office.

Same with transportation and driving around.

And once something becomes a choice, we need to ask why we still do it.

Maybe we stop.

Maybe that is fine.

Maybe we keep doing it.

Maybe that tells us something.

Maybe the slower version still matters.

Maybe the human version still matters.

Maybe the inefficient version still matters.

Not always.

Not for everything.

But for some things.

And that is probably going to be one of the strange parts of the future.

Not deciding what technology can replace, but deciding what we still want to preserve.

Not because we have to.

Because something about doing it ourselves still keeps us human.

It keeps us involved in the process.

It keeps us part of life.

Alright, thanks for watching.

Bye.

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.