I Tried to Switch From ChatGPT to Gemini [Raw Session]

00:00 — Why Gemini Made Sense

Hey, welcome back to Slow Builds.

So the other day I tried to switch from ChatGPT to Gemini, and on paper it made complete sense to me.

Google has Search. It has YouTube, Maps, Gmail, Docs, Drive. It just has basically my entire life for the last however long Google’s been around.

And the plan also came with large amounts of extra storage. Plus, the entire thing could be shared with up to five people, and that was a big selling point to me because now my kids can take advantage of it.

They could use it because they use their Gmail, they run their business online, and I thought it’d be a great thing for them.

Plus, a little side bonus: it gave me limited ads on YouTube. So I was very excited for that.

And I already use Gemini. I use it a lot. Basic searches, YouTube research. I use it a lot for second opinions. I use it for Notebook. I love it.

And I was very excited to see what unlocking the model in the paid version would get me.

01:18 — Slowing Down and Questioning the Subscription

But at the same—and at the same time—I’ve been noticing a lot of friction with ChatGPT lately.

I was getting outdated information often enough that I felt like I had to double-check everything.

I also wasn’t using it for coding as heavily as I have been before. Like, I’ve done videos on burnout and fatigue and trying to keep up, so I’ve been trying to slow down.

So my focus has become more: take my time with it. So I didn’t need as many tokens. I didn’t need as much heavy usage for the building part because I want to let that focus go towards my actual job and work, which I want to do.

I love my job and I love what I do, and not burn myself out like I’ve been doing.

So this is why I started wondering: maybe I don’t need to pay for the higher subscription for ChatGPT anymore.

Maybe Gemini was looking like it could cover most of my life in a single plan, and I was able to share it, and it seemed like the switch was going to be quite easy.

02:31 — Going All In

But once I actually tried—I made the switch, and to be honest, I went all in. I bought the year because it gave me an even deeper discount. It gave me instant access, and it gave me a little bit more.

But once I actually started trying to use Gemini, pretty quickly the advantages that I saw when I compared it, and what I was hoping I was going to get, were not the advantages I was feeling.

And that’s what this video is really going to be about.

This isn’t me trying to prove that ChatGPT is better than Gemini for everyone. That’s 100% not true. Everyone has their own caveat. Everyone has their own choice. Everyone has different workflows.

And I don’t think these tools stay still long enough to make even that statement anyway.

Things change so quickly. Things move all the time in this new era that we’re part of.

03:33 — Familiarity and Being Understood

So this is more about what it takes to switch away from a tool once it’s become part of how you work and how you live.

Familiarity. I really have trouble saying [unclear] sometimes.

And that’s just me. That’s just the way I am.

And I love that I can use—and that’s another thing—ChatGPT, and even Gemini to some extent. And Gemini, not as good because it doesn’t know me as well for these scripts and these videos and stuff, or as much random chats.

But ChatGPT knows me. It knows I can’t spell. It knows I have a hard time speaking, and it corrects those things based off the history that we have.

And again, that is being familiar with me, and that’s part of it.

But in that case, it wasn’t the main—that’s not the mainly about…

Familiarity is part of that, but in my case it wasn’t mainly about history or ChatGPT knowing me.

Here we go.

04:33 — The Output Was Different

The output itself was different.

I could see something usable from ChatGPT, and with Gemini, when I was doing the same thing, it felt like I was trying to turn scraps into the thing I’d asked for.

It wasn’t giving me what I wanted. It was giving me piecemeal. It was giving me—not even nuggets. It was just giving me—I don’t want to say fool’s gold, but it just didn’t feel right.

So before I switched, though, Gemini really did look like the logical choice.

Like, it really felt it was going to give me everything I wanted, and it was going to do it.

It was a little more expensive, I think, for the plan I was going to go to, but I was able to share it. I was able to integrate it into everything that I use on a day-to-day basis, and that’s really what I was so excited about.

05:25 — Current Information and the Google Ecosystem

But ChatGPT’s—because ChatGPT’s biggest weakness for me was current information.

I use it every day, but if I ask it about something recent, I often had to verify the answer. I would ask it to search it again, check the date, or confirm the details somewhere else.

One example was SpaceX. It felt like it thought SpaceX was still a private company, and that’s just a random thing.

So then when you see that, it’s like, can I trust everything [unclear]? So you end up having to review, revisit, and double-check everything it says.

So you’re using it more as a guide rather than truth.

Whereas Gemini is connected to Google, so my assumption was that current information and search would naturally be one of its strengths.

Then there was the rest of the ecosystem that Google has. Like planning a trip. Google has the places, reviews, the driving distances, flight access, hotels, and Maps. Maps was a big one.

And I use it for research and videos. Like, Google owns YouTube. So, like, doing videos, finding trends, going through what I’ve already done. It should be pretty straightforward.

And to be able to create—go through my email. It should be able to create a document. It should be able to, like, create a spreadsheet, link it all together.

And to me, that’s a massive advantage in the theory that Gemini was going to win this.

And the plan was more than just Gemini. There was also the addition of the storage, sharing with my family, and access to more tools across Google.

I wasn’t just comparing one chatbot subscription with another. I was looking at the larger package and thinking this might be more useful to the whole family.

07:13 — Familiarity Has Value

And I’m not—I’m also not someone who refuses to switch just because I’m used to something.

I do become comfortable with tools. I use an iPhone, and I don’t enjoy switching to Android. I hate using an Android.

But I can use an Android if I have to, but I do notice the friction.

I’m comfortable with my internet provider because I understand the tools, the service, the people, the system. Like, I’ve tried to switch before and I was not happy. And where I live, there’s not that many options.

Like Fire TV. Like, I don’t want to use a native cable box. My TVs all use Fire TV. I don’t want to switch to anything else. I love it. It’s my favorite thing for getting all my content.

And it’s a big selling point for me on which cable company I go with, because if they have the app or whatever…

That familiarity has value. It removes small decisions and lets me get on with what I was trying to do.

But I’m still willing to change. I just need a good reason.

A different product has to give me something that makes the adjustment worthwhile. It cannot only be cheaper, and it cannot only look better in a list of features.

I’m willing to pay more money for a better experience, if that experience makes the work easier or gives me better results.

08:38 — Paying Didn’t Change the Results

And that was the test for Gemini.

And almost immediately I started running into the same limitations—oh my goodness—I had experienced with the free version.

The paid plan gave me higher access, more benefits around Gemini and the ecosystem, but the everyday results didn’t suddenly become more useful.

I was still getting answers that felt thin. I was still having to go back repeatedly to explain what was missing.

The Google ecosystem was all around it, but I didn’t feel as if those pieces were working together.

09:10 — Planning a Family Trip

And the clearest example is a trip I’m planning for the family.

I wanted to lay out all the places we were considering going. Break it all down by days and stuff like that.

And I imagined being able to plot locations on a map, separate them by day, and see whether the schedule made sense.

Which places should go which day? Which one fits with what? Link it all together. Give me multiple maps. Give me a single map. Get different layers.

I just figured it’d be straightforward. Google owns Gemini. Google owns Maps.

It couldn’t give me the map I pictured in my mind. I asked several times. It said, “I don’t have access.”

And to be fair, ChatGPT couldn’t create the exact map either, which is fine. I expected that.

And this isn’t—so it’s not a point for ChatGPT.

The difference is that I expected Gemini to be able to do it because the map and the AI both belong to the same company. That connection was part of what I thought I was buying.

What happened instead was ChatGPT still handled more of the surrounding work in a way I could use.

It could research locations, create the daily list, organize a spreadsheet, build the documents, and help me keep refining the plan.

It didn’t deliver everything, but the output moved the work forward.

With Gemini, I kept seeing the potential without experiencing the benefits.

10:31 — YouTube and Scripts That Fell Short

The same thing happened with YouTube. Google owns it.

So I expected Gemini to be especially useful for my channel. I thought it would be able to look at my website, go through all my YouTube videos, and understand what I had already made.

And help me develop ideas that actually fit in the direction of the channel. Come up with these topics, help me do scripts, help format them.

But YouTube Studio, for some reason, still seems to be its own thing. It wasn’t part of the package.

And so Gemini wasn’t getting access to the analytics in there. It wasn’t giving access to the things that helped me build it out a little bit better.

So when I asked for video ideas or tried to build scripts, the results often felt generic and incomplete.

It wouldn’t give me what looked like an outline for a 15-minute video. But parts labeled as two or three minutes might contain only a couple sentences.

That isn’t a small difference in interpretation. Two sentences are not two minutes of natural speech. Sometimes they aren’t even 20 seconds.

I would point that out and ask it to expand the section and give me realistic timings. Like, redo it or tell me what the real times are.

It would agree, make another attempt, and still come back way off. And I’d keep prompting it.

But at that point, I was spending my time trying to make the tool understand the size and shape of the output I wanted instead of working on the actual idea.

ChatGPT normally gets much closer, [unclear] me having to go back and forth, do whatever. It understands that a calm 15-minute video and what needs to be in there.

It gives me something I can read aloud, react to, cut apart, and turn into my own.

It isn’t perfect, and I still rewrite it, as you can see in this video. This video was written by ChatGPT, but I am going off script quite often. I’m all over the place.

But ChatGPT gives me the material rather than the description of the material.

That difference matters more to me than which company has access to the largest amount of data.

12:47 — Having Data and Producing Useful Work

The image results felt similar too, because Gemini could give me the images, but I wasn’t getting images or thumbnails that were as useful for what I wanted.

And again, this wasn’t about one failed prompt. It was the repeated feeling that I had bought access to—I bought access to a larger ecosystem while the part directly in front of me still wasn’t producing better work.

Now, I want to be fair about Google’s advantage because I do think it’s real, and it’s going to make a massive difference in the future.

It’s probably making a massive difference now.

Google has an enormous amount of information. It has public information through Search, Maps, YouTube. Even though I didn’t see this, those things are connected within the company.

And it connects all that with your personal information through your Gmail and your Drive. And that is a major, major advantage.

But having the data and turning the data into a useful personal experience are different things.

In my case, personal Gmail is not a large part of my working life. My work is handled elsewhere.

I keep my personal inbox as simple as possible with filters, and I use other tools like OpenClaw, n8n, to help me monitor my email and tag things and push it to the side.

So even if Gemini can do more inside Gmail—which it can, and I’ve seen it do it—that feature does not solve any important problem for me.

Someone running their business through it, yeah, it makes a big difference. It makes perfect sense.

I have a friend who uses Gemini. He loves it. He pays for it. But his email and his inbox are his job.

The Google interaction—integrations—could matter far more, like, to him than it does to me.

But even then, the integration still has to appear in the work. It has to save time, improve the result, or let you do something that was difficult before.

The fact that all the services have Google logos does not automatically make them feel like one intelligent system.

14:48 — What ChatGPT Helped Me Do

And that’s where I think ChatGPT currently has managed for me.

Like, in that situation, ChatGPT has access to my Google ecosystem. It already created a spreadsheet. It already plotted everything out.

It gave me the links to the map location in a spreadsheet. It made a spreadsheet with multiple days and broke them all up. It listed everything. Like, it did all these things in the Google ecosystem.

And it was also able to check my Gmail to find my flight itinerary. It was able to go find my Airbnb. It could do those things for me.

I just expected Gemini to do it a lot more seamlessly. A lot more—a lot more ease, more intuitive.

But anyway, the thing is, why ChatGPT?

So this is where I think ChatGPT currently has managed for me.

It doesn’t own my search history. It doesn’t own YouTube or Maps. It doesn’t control my email and documents in the background.

But the interaction feels more polished for me. The output feels more tailored to what I asked for, and I can usually move from an idea to something usable with less friction.

ChatGPT itself has changed a lot. There is regular ChatGPT, Codex, Work, different models, tools, connectors, different ways to approach the same task.

Sometimes I have to pay more attention to where I am and which [unclear] I’m using.

It’s not the single simple product it once felt like, but despite those changes, I still tend to get the kind of output I’m looking for.

I can get a spreadsheet, document, research. I can get all these things.

I may have to move between different parts of the product, but the finished result is usually closer to something I can use.

16:32 — Why I Switched Back

And that’s why I switched back.

It wasn’t because ChatGPT had years of secret knowledge about me that I couldn’t replace. It wasn’t because I believe Gemini is bad, and it wasn’t because I’m loyal to one company.

I switched back because the supposed advantage of Gemini remained mostly theoretical in my actual workflow.

The free version had already been useful to me for quick searches, YouTube ideas, and getting different perspectives.

Paying for it didn’t change its role. It was still useful as another tool, but it didn’t become the place where I want to do the main work.

17:08 — A Second Tool Isn’t Always a Primary Tool

And maybe that’s the mistake I made.

I assumed that because Gemini was valuable as a second tool, the paid version would naturally become a better primary tool.

Those are not the same things.

A tool can be worth using without being the tool you want to build your whole process around.

Gemini can still help me check current information, look at a topic different, or research something connected to Google.

Claude can be useful when I want deep second review. When I want to work, do deep code, code reviews, architectural designs, security designs.

Claude, I love using that for that. And it’s part of our work ecosystem because I am finding ChatGPT—in my mind, it’s better. But that’s just me.

ChatGPT—and ChatGPT remain on top of all that. It’s going to remain my place, my personal place, where I take all of that and actually shape it into the thing I’m making.

This isn’t the clean one-subscription answer I was looking for, but it’s closer to how I actually work.

18:10 — The Value of What Already Works

There’s also a larger point here about switching anything.

We often treat familiarity as if it’s laziness. We tell ourselves we should be willing to try the new phone, move to the cheaper provider, replace the software, or rebuild the workflow.

Sometimes that is true. Sometimes we are holding on to something only because changing feels uncomfortable.

But familiarity can also be part of the value of a product.

Knowing where things are, understanding how the tool responds, and trusting the shape of the result all save time.

A replacement doesn’t merely have to match the old tool’s feature list. It has to overcome the practical value of everything that already works.

And for me, Gemini didn’t do that.

18:58 — I Need to Feel the Advantage

Google may eventually connect all of its information in a way that makes the advantage impossible for me to ignore.

And if it can genuinely understand the public work on my channel, use my information with all the right permissions, and connect the maps for my trip planning and YouTube and turn it into better output, I would try to switch again.

I’m not against—not switching. I’m not committed to ChatGPT forever. These products are changing too quickly for that.

But there has to be a real reason to move.

Not just a discount. Not just more storage. Not because I can share it. Not just the promise that everything is part of the same ecosystem.

I need to feel the advantage when I sit down and try to get something done.

With Gemini, I didn’t. At least not yet.

I would love to hear comments on the AI tools you use. Which ones bring you value? What ones do you use for different tasks? And which ones are you willing to pay for?

Thanks for watching. Hope you like this one. Bye.

Every AI Knows a Different You [Raw Session]

00:00 — Opening

Hey, welcome back to Slow Builds.

In one of my earlier videos, I was talking about the idea that maybe my digital self isn’t really me. Maybe it’s just the version of me that’s easiest to measure.

After I finished recording, though, another thought hit me: I don’t even think I have one digital self. I don’t think anyone has one.

I think we all have a bunch of them, and every company, every AI, and every service is building a completely different version of who they think we are.

It’s easy to imagine that somewhere there’s this complete profile of us. One giant file that says, “This is you. Here’s everything you like, everything you do, and how you think.”

But when you stop and think about it, no one actually has that. Not even close.

Instead, everyone has tiny pieces or different versions of you.

00:51 — Different Versions of Me

Take AI, for example.

Gemini probably knows things about me that ChatGPT doesn’t. Gemini sees my Gmail, bills, appointments, calendar, and travel confirmations. Maybe it understands that side of my life: my organization and day-to-day activities.

Claude knows me completely differently. It knows my work, integrations, coding style, architectural decisions, and problem-solving skills. It probably thinks software is my entire personality.

Then there’s ChatGPT. This one might know me best from a personal point of view.

Gemini probably knows me best from an organizational and day-to-day perspective. Claude probably knows me best from my work life.

Another service probably knows my health activity better than anybody. It also knows which apps I like. It doesn’t know what’s happening inside those apps, but it knows which ones I use and where I spend most of my time.

My bank knows how I spend money. Amazon knows what I’m buying. Google knows what I’m searching for, and YouTube knows what I’m watching.

02:36 — Different Windows Into My Life

Each company is building its own little profile, separate from the others. Every one of those profiles is built through a different window into my life.

It’s almost like asking ten different friends to describe you. None of them are lying, but none of them can describe the whole person.

Then you realize something else: we’re actually making this harder.

02:54 — Intentionally Breaking Connections

For years, everyone has said—and they still say it—be careful online. Protect your privacy. Don’t share everything. Use a VPN, block trackers, browse in incognito mode, use different email addresses, and keep work separate from your personal life.

Don’t use your real birthday everywhere. I do that one. And I lost my spot again.

But I do all these tricks. I change the answers to my security questions. I don’t connect every account. I use passkeys for many things. I use burner email addresses.

That sounds like you don’t trust the systems, and the funny thing is that all of that advice is good.

But every one of those decisions breaks another connection. It makes another piece of your digital life invisible to the other parts.

03:48 — Personalization Versus Privacy

That’s the tension I can’t stop thinking about.

On one hand, everyone wants AI to become more personal. We want it to understand us, remember us, know our preferences, save us time, help us make better decisions, and give us better recommendations.

On the other hand, we’re spending more effort than ever making ourselves harder to observe.

We’re hiding behind VPNs and blockers. We’re limiting social media, using fake accounts, deleting old posts, and separating our work and personal lives.

These two ideas are pulling in opposite directions.

We’re asking technology to know us better while intentionally revealing less of ourselves.

Honestly, maybe that’s exactly how it should be.

Privacy and protecting yourself are important. Understanding how these systems use your information, allowing different parts of your life to remain separate, and not relying completely on these systems are all good things.

But it also creates that tension.

04:43 — Fragmentation as Protection

I don’t know if I want one company to know everything about me.

Maybe I’d rather no one know the whole picture. Maybe I’d rather every AI understand only one part of my life.

Maybe fragmentation isn’t a flaw. Maybe it’s actually protection.

If one company had access to my work, finances, health, conversations, travel, purchasing, thoughts, and even relationship information, that would become something very different.

It could become something capable of duplicating or impersonating you, and that’s very scary.

Maybe this separation isn’t a flaw. I believe it has a purpose, but it changes how we should look at these systems.

05:30 — A Different AI for Each Part

I’m off script here, but if you want an AI that understands your work habits, maybe that’s your work AI. It’s like having a separate work phone.

Then maybe you have something else for cooking, chores, and the practical parts of your home life. Something that helps you plan, reminds you to put out the garbage, knows which meals you like, or notices what’s missing from the fridge.

I think there may be different places for everything.

06:04 — Intentionally Scattered

Back to the script.

I have another question: maybe there will never be one AI that really knows me.

Not because the technology isn’t good enough, but because I’ve intentionally scattered myself across different services, devices, accounts, conversations, and parts of my life.

Honestly, maybe that’s true for almost everyone.

We talk a lot about personalized AI, but at the same time, we’re becoming less observable than we’ve ever been before.

06:32 — Dozens of Incomplete Versions

I think that’s a really interesting tension, even though it creates a lot of friction between these systems.

Maybe there isn’t one digital version of me. Maybe there never will be.

Maybe there are dozens of versions of me. Each one convincing, each one useful, but each one a little incomplete.

Maybe that’s exactly how it should stay.

If one system knows you completely, then technically, it is you. Maybe that’s a scary thought.

It’s also a useful thought. It might be something we could strive for, but maybe it isn’t where we should want this technology to go.

I believe we should keep some personality and humanity in it.

Like I said in my previous videos, my memories are my memories. Things happen in my life that aren’t digital, so an AI will never know them.

I believe that’s what keeps the humanity there.

All right. Thanks for watching. Bye.

AI Knows Your Data, Not Your Life [Raw Session]

00:00 — Opening

Hey, welcome back to Slow Builds.

I've had this idea bouncing around in my head for a while now, and I still don't fully know what I think about it.

We keep hearing that AI is getting more personal. It knows what we buy, what we stream, every search we make, everything we're watching, and even how we talk. It knows our personalities. It recommends your music, your books, when you travel, places to eat, places to go.

And it answers pretty much every question in a way that can feel like it actually understands what you're asking, and who you are.

For me, it knows how I speak, and that I don't know how to spell very well. It's not that my vocabulary is that bad. It's just that my spelling is.

And that's probably only going to get more personal and more precise over time. The more it knows you, and the smarter the models get, it's just going to learn you.

So the better they get, the more it starts to understand you, instead of just giving general answers to general questions.

01:12 — The Me That Can Be Measured

But doing that makes me wonder a little bit.

Is the version of me that exists online actually the real me? Or is it just a version of me that happens to be the easiest for systems and AI to measure?

Because it has all this stored data. It has all this information from what I do online with my phone, my computer, my browsing history. Every bit of data is collected.

Even on Meta, it goes down to every click, every hesitation, things like that. It knows what you're doing. It knows how long your mouse is hovered over a button, or a link, or an image. What made you scroll back up real quick, or pause your scrolling.

It knows these things.

It knows how long, or how many times, you read a page on your Kindle.

And if you think about it, companies already have all this information about you. They can see parts of your online life. They know more about you from your online presence than you might even know about yourself. Maybe more than your partner, spouse, family, or closest friends even know.

And that behavior starts to look like a fingerprint of you. Your identity.

Not perfect, but probably good enough to make some pretty impressive predictions.

And honestly, that part is kind of amazing, but it's also kind of scary when you think about it.

02:49 — Observation Is Not Experience

The more I think about it, the more I realize that this is still observation. It's not experience.

Watching me do something through the lens of a computer or data is not the same as understanding why it mattered to me, or what was going on in my head.

Some of my favorite moments in life have almost no digital footprint at all.

I remember going to concerts where I ended up liking the opening act more than the band I actually paid to see. Nobody recommended it to me. Nobody predicted that I would like them so much. I was just there, in the moment, experiencing it.

Sometimes I'll spend an hour walking around used bookstores, not looking for anything specific. Or record stores, same idea. I'm just seeing what catches my eye. What leaflet, or cover, or footnote I pick up to read that grabs my attention.

Maybe I'll leave with nothing. Maybe I leave with a book that changes how I think about something else. Maybe it influences me.

I've bought vinyl the same way, because the album cover looked interesting. I've found clothes in little thrift stores that I probably would have never clicked on or searched for online.

04:02 — No Digital Footprint

And when my wife and I travel, I actually enjoy disconnecting a bit.

You go offline. You turn off your phone. You don't take it with you, and you're just exploring a new city, a new town, and all these brand new experiences.

You're walking instead of planning every stop. Finding little coffee shops because it smelled good. Talking to people who lived there. Eating somewhere because it wasn't on the list. You just happened to pass by, it was busy, you liked what you saw, it was a special, or you got invited in by the owner or a server.

Some of the best moments I've ever made started with absolutely no plan at all.

So there's no digital footprint. There's no way for a system, or AI, or data to get that.

And when you think back on those experiences, I don't remember looking at my phone.

I just remember being there.

04:56 — Measurement And Meaning

One thing I've noticed over the past year is that I keep coming back to the same idea from different directions.

I've talked about attention. I've talked about productivity. I've talked about AI. I've talked about convenience.

And underneath all those topics is this question:

Are we starting to confuse the things we can measure with the things that actually matter?

Attention is measurable. That doesn't make it valuable.

Money is measurable. That doesn't automatically make someone successful.

Productivity is measurable. That doesn't mean you're making progress.

And now I wonder if data is becoming another version of that.

Just because we can measure someone's behavior doesn't mean we've measured the person.

Those feel like two very different things.

05:41 — Memories That Stay Offline

So then I started thinking about memories.

Some of the memories that shaped me don't exist anywhere except inside the people who were there with me.

A conversation over coffee. A road trip. Laughing so hard with my wife and family that neither of us could finish the story. Something one of my kids said years ago that I still think about.

Those things are in my head. They're not written down. They're not digital.

Like helping someone for 10 minutes and then never seeing them again for the rest of your life.

One example is a guy I met when I was out jeeping. I came across a Jeep with a flat tire, and now he has my spare tire. I have his flat, but he has my spare.

But that's nowhere in my digital history whatsoever. I guess now it is, but until then it was never anywhere.

The location, the person's name, who was with me, what time of day it was, what GPS was showing. None of that information is anywhere.

That was just an experience that happened.

These experiences don't become data. Nobody uploads them.

If every hard drive in the world disappeared tomorrow, those memories would still be part of me. They're part of everyone that was involved.

And honestly, some of these memories shape you. They shape who I am more than my thousands of Google searches, or my random searches that mean nothing.

07:17 — Not Everything Has To Become Content

I've never really enjoyed social media that much. Not because I think it's evil.

Well, I do think it's evil.

I don't know why that was put in there. AI put that in there, but I do believe it's evil. I think it's a downgrade for society. It's hurting minds. It's causing attention issues. It needs to be controlled.

I know it has its place. It always had its place. But the value needs to be taken out of it and used as a tool, or whatever. It is what it is.

So I do think it's evil.

But I don't use it anymore because I don't want to document everything.

If I find a great restaurant, maybe I'll tell a friend over coffee. If I hear an amazing band, I'll send it to someone I think would appreciate it. If I have a great trip, most of those stories get shared around the dinner table.

Not everything has to become content.

Sometimes I think keeping the experience small actually makes it more meaningful.

It belongs to the people who were there, not to everybody.

08:22 — A Higher Resolution Version

Then my mind wanders somewhere else.

People are already talking about things like Neuralink, brain-computer interfaces, technology that one day might understand us at a much deeper level than what we type into a keyboard.

And I'm not saying that's good or bad. I honestly don't know yet.

But it made me wonder, even if technology could see everything I look at, everything I hear, and maybe even patterns in how I think, would that actually become me?

Or would it just become a much higher resolution measurement of my behavior?

I'm not sure if those are the same things anymore.

09:00 — The Parts That Never Become Digital

Maybe my digital self isn't really me.

Maybe it's just a version of me that's easiest to measure.

The real version might exist somewhere else.

In conversation. In memories. In mistakes. In random discoveries. In the places where I wander without a destination. In the songs I found by accident. The books I picked up because something about them felt interesting.

In the experiences that never needed to be uploaded to matter.

If AI is learning from digital life, what happens to the parts of me that never become digital?

I think that's the question I've been sitting with lately.

Because so much of our lives are touch, feel, and seeing with our eyes. Not stuff that we type down, put into a note, or upload to Facebook or Instagram.

They're all things that we experience as they happen.

And those are things that I don't think AI can always get.

I think there are ways that it's going to get more of it, but I still think there are parts of it that are going to be ours and ours only.

All right, thanks for watching. Bye.

How AI Built My Burnout (And How I’m Fixing It) [Raw Session]

0:00 — AI Fatigue Feels Different

Hey, welcome back to Slow Builds.

I’ve talked before about AI fatigue, and I still think that’s real.

There are days when I’ve been using AI for hours. I’m going through code, reading responses, testing things, jumping between chats, and eventually my brain just gets tired.

That kind of tired makes sense.

Take a break.

Go to sleep.

Come back at it tomorrow.

Usually that’s fine.

But lately I’ve realized what I’ve been feeling is different.

I don’t think I’m just tired from using AI.

I think I burned myself out on AI a little bit.

And I don’t mean I’m done with it.

I’m not done with AI at all.

We can’t be.

It’s part of our lives now. It’s part of everything we do.

I still think it’s one of the biggest changes I’ve seen for building software, changing processes, and just being part of normal life.

There is value in it.

It’s not going away.

It’s here to stay, so we all have to learn it and learn how to use it.

But I also think I pushed it too far.

Not in one dramatic way.

More like slowly, one extra prompt at a time.

One more tool.

Another workflow.

Another idea.

Starting another project.

Eventually I got to a point where opening some things back up started to feel heavy before I even did anything.

That’s when I realized this was not just fatigue anymore.

It was burnout.

2:02 — I Went Too Deep

The first thing that happened is I went too deep.

When AI started moving this fast, and when everyone started talking about it, I felt left behind.

I’ve done videos on that.

I didn’t want to be left behind.

I didn’t just want to use the tools.

I wanted to understand them.

I wanted to understand the models, the agents, the workflows, coding with it, proper prompts, context, instructions, memory, all of it.

And all of it was useful.

It helped me see AI as more than just a chat box.

Once you start looking at tools like n8n, Pipedream, or Gumloop, you realize a lot of this really is workflow design.

Something comes in.

AI does one or two parts here and there.

It gives you responses.

You make decisions.

Then something gets pushed out.

That changed how I thought about what was possible.

But then there was always another layer.

Once I understood one thing, there was another tool.

Another thing to learn.

Another thing to figure out.

Once I understood that tool, there was another model.

Now I had to figure out which model worked best for which operation or task.

Then another coding agent comes out.

Then another way to structure instructions.

Another way to change prompts.

You can see where that goes.

You start going real deep.

And it always feels like, “I should probably understand that.”

That is a dangerous thought, because there is always going to be something else.

There are always changes.

Everything is moving forward.

And if you think you have to stay at the forefront of it, watch every video, read every doc, and test every new tool, it never ends.

So it has to reach a point where I can say:

I know enough to build with what I have right now.

I can learn the next thing when I actually need to.

4:13 — I Went Too Wide

After going deep, then I had the problem of going way too wide.

And this might actually be the bigger problem for me.

AI removed a lot of friction between having an idea and turning it into something.

Before AI, an idea had to survive a little bit.

I had to think about it.

Google it.

Write it down.

See what was out there.

Most ideas would probably just fade away.

And honestly, that was probably healthy.

They might still exist somewhere in a notebook, or an email, or somewhere in my brain, but a lot of them just died.

Because if I wanted to take an idea seriously, I had to spend real energy on it.

Now that first step is almost free.

The speed to research something, figure out an MVP, or see what exists is almost instant.

I can have an idea and immediately start researching it.

I can look at domains.

I can think through the business model.

I can see what already exists.

I can think through what might be legal or not.

And because AI will keep going with me, that idea starts becoming real before I decide whether it deserves to be real.

That can happen with software.

But it can also happen with a book, a product, a physical business, a game, a video, almost anything.

AI can help put the idea into reality.

Everything has somewhere to go now.

That sounds exciting, and it is extremely exciting.

But it also means fewer ideas naturally die now.

And I think I needed some ideas to die.

Not because they were all bad.

Just because I’m one person.

I didn’t sit down and decide I wanted twenty things going on at once.

I just stopped letting every idea end.

Eventually each idea had its own little world around it.

A chat.

A name.

A folder.

Research notes.

Sometimes a domain.

Sometimes a prototype already built.

Maybe a half-finished thing sitting somewhere that I now feel responsible for.

That’s where the width of the whole thing becomes exhausting.

It’s not just the number of projects.

It’s the number of open loops.

They are sitting there waiting for me to do the next prompt, push the next button, and move them forward a little bit.

6:42 — AI Creates Work Too

The part I didn’t really understand at first is that AI does not only remove work.

It also creates work.

A friend said to me the other day, “If AI is supposed to help us work faster and better, why does it feel like I’m doing more work than I ever did before?”

And sometimes that work is worth it.

But it is still work.

Especially with code.

AI can build something faster than I can build confidence in it.

It can write a feature in a few minutes, or even a few seconds.

But then I have to read it.

Understand it.

Test it.

See what else it changed that it should not have changed.

Make sure my tests are actually working.

Make sure the tests are built right.

Make sure it didn’t build the test for the new feature and ignore the old stuff.

If people are going to put their information into something I built, I can’t just trust it because the code looks convincing.

That review process takes a lot of energy.

And then if I ask AI to fix one issue, it might fix that issue and touch other things.

So now I’m reviewing everything all over again.

Checking everything again.

Testing everything again.

Trying to remember what I had already signed off on in my head.

That loop can go on forever.

It never ends.

Writing has its own version of this too.

AI can rewrite a chapter forever.

The book can be constantly rewritten, changed, fixed, rewritten again, changed again.

It can suggest structure changes forever.

It can make the writing smoother.

But then I have to check every single thing it says and make sure it still sounds like me.

Are the timelines right?

Is the meaning still what I wanted?

Did it change something it should not have changed?

Did it give me five more kids that I didn’t know I had?

That is work.

That is a lot of work.

It can produce a lot of content quickly, but it is a lot of work for me to make sure it lines up with my vision, my words, and my truth.

It is a different kind of work, but it is still work.

And after a while, the thing that was supposed to make everything easier started making everything feel like there was never an end.

8:51 — The Real Problem

I think the real problem is that I was letting AI decide how much work existed.

And according to AI, there is always more.

There is always another improvement.

Another feature.

Another tool.

Another test.

Another idea to throw in there.

Another thing I should probably look into.

AI does not naturally stop and say, “That’s enough for your life right now.”

It doesn’t know I have a job.

It doesn’t know I have a family.

It doesn’t know I need to work out, sleep, eat, and still have some kind of normal life outside of building things.

It can keep going.

I can’t.

And for a while, I think I was trying to work at its speed.

That is the part that burned me out.

Not AI itself.

Trying to live at the pace AI makes possible.

Because once you’ve seen that speed, normal progress starts to feel slow.

You can have an idea in the morning and something running by night.

That is extremely exciting.

But it can also mess with your sense of what a reasonable pace actually is.

Suddenly taking a week to do something carefully feels extremely slow.

Leaving a feature out feels like you are falling behind.

Letting a project sit feels like failure.

And I have a few sitting around, and it really does feel like failure.

But maybe it isn’t.

Maybe that is just what it looks like to build at a human pace again.

10:09 — Slowing Down

So I’m trying to slow down.

I don’t have a perfect system for this.

This isn’t me saying I solved AI burnout.

I definitely haven’t figured that out.

But right now it means being more intentional.

What am I actually working on?

What needs to happen next?

What is enough for this version?

And when am I done?

That last question matters more than I realized, because AI is not going to tell me when I’m done.

I have to decide that for myself.

Some projects might sit for a while.

Some features might never get put in.

Some ideas probably should stay as ideas.

Some chats should just end as chats.

And that’s hard for me, because I like building.

I love learning.

I like seeing what’s possible.

But these are all side projects.

They matter to me, but they are not worth burning myself out so badly that I have nothing left for the rest of my life.

AI did not remove my limits.

It just made them harder to see.

My time is still limited.

My attention is limited.

I have limited energy.

My ability to understand and trust what I’m building is still limited.

So maybe the answer, at least for me right now, is not to stop using AI.

It’s to stop letting AI set the pace.

I’m still building.

I’m still learning.

I’m still using it.

I’m just doing it slower.

And right now, slower feels a lot more sustainable than trying to keep up with something that never has to stop.

Thanks for watching. Hope you liked it.

You Shouldn't Trust AI's First Answer [Raw Session]

00:00 — Opening

Hey, welcome back to Slow Builds.

I've been thinking about inconsistency in AI responses, not in a dramatic sense of AI is useless or you can't trust anything it says. More in the practical sense.

If you put the same prompt into a model two times, five times, ten times, you usually don't get the exact same answer back.

Sometimes the meaning is close, sometimes the structure is close, but the wording changes. The examples change, the order changes, and sometimes the emphasis on what it focuses on as the answer changes.

I think that is one of the easiest ways to remember what these systems are actually doing and what they actually are.

They're not pulling one fixed answer from a shelf. They are generating a response based on patterns, weights, probability, context, and whatever instructions are active at that moment.

Even when the prompt is the same, there can be more than one reasonable path for the answer to take.

00:59 — Testing Variation

A simple test would be asking for a random, less common Bible verse, nursery rhyme, slogan, or asking it to generate some other random line or idea, and then running that same prompt several times.

An even better way to do it would be to tell it not to use external sources. Don't go to the web. Don't go out and search websites. Use your own data set.

Because something that's been repeated millions and millions of times, something that's been indexed and is in its thing, it's less likely to make any variation or mistakes on certain common things that are known throughout the entire world.

But some random, and that's why I say the Bible, because there are many verses that are not common. There are some that are extremely common, and then there are others that are very less known or repeated or out there.

So if it has to take its own context and go against its own data set, odds are you're going to get close to what the variation would be, but each time you may get a different variation. You're probably never going to get the exact same wording, just because it's the way that the model works.

So it's not that the problem changed, but the output probably is going to change. And that makes sense, because the model is not choosing from permanent answers. It's moving through possibilities.

02:24 — The Bigger System

But I also think there's another layer to that that people don't always talk about. Maybe they don't even realize it or see it.

The model is part of a bigger system.

There's also routing and hardware and infrastructure. There's load, there's caching. There's all these different aspects that go into a prompt going in and the answer coming back.

There's a big story and adventure that your prompt goes on before it returns something to you.

So the caching and stale data, syncing delays, network issues, any kind of failure along that route could cause the answer to be different.

And when a response feels different, it may not only be because the model generates something. It could also be because the surrounding system is acting differently.

And this is one of the parts I keep thinking about.

What happens when there's a huge amount of load?

Maybe my request goes through exactly the same infrastructure, or maybe it doesn't. Maybe requests get routed to different hardware. Maybe some get queued. Maybe capacity changes. Maybe a provider can route certain requests differently based on location, my token usage, what pay grade I'm on.

Just certain words within my prompt may force it to go in a different direction. Maybe I get put on a lower model. Maybe I'm accessing an old data set.

And I don't actually know what's happening inside every AI provider at every moment. And that is really part of the point here.

Unless I control the infrastructure, I'm trusting the thing behind the name on the screen is behaving the way I think it is.

If I run my own model on my own infrastructure, control my data sets, control where it pulls in data, and control the whole configuration along the pipeline, I can start removing a lot of those variables. I can start making it very consistent.

And I think that's going to be an important role in a lot of very high-level critical systems because it helps consistency, and also lower cost and lower latency.

But that is not how most AI is used.

I open an app, I choose a model name, I type something into a box, and there's an enormous amount between that box and response that I do not control.

I may not control the exact model version. I don't control the servers, the routing. I don't control the data set. I don't control the caching. If tools are involved, I don't control whether every tool returns exactly the same information. And if outside data is involved, that data itself can change.

At some point, I'm trusting the provider and the system around the model.

Maybe the same model was used, but the request was routed differently because the system was busy. Maybe a tool had fresher data one time and older data another time. Maybe some connected system was delayed. Maybe the network was slow. The model had the same name, but some part of that pipeline around it was not exactly the same.

And I'm not saying that that's a conspiracy theory. I'm saying it because AI does not exist as a clean box where a prompt goes in and an answer comes out.

It is a stack system. And once you start seeing it that way, the inconsistency feels less mysterious.

The infrastructure around it is dynamic. The data it touches may be fresh, stale, incomplete, delayed, or cached. The tool it uses may work perfectly one time and fail another time.

But I think they fail them gracefully. I think they do it in a way that we still get a response that could be close. So there are fail-safes. There are backup plans.

06:00 — Code Review Friction

And I run into this a lot with work, with code reviews.

I'll write my code and I'll get our system to run our skill. I'll run it until I think it's passing. I want to run the skill the same way the code reviewer is going to run it.

Its job is to review the code.

And so depending on how that skill and prompt are written, they always seem to find something.

So no matter how many times I run it myself and think I finally got everything, once that gets passed off in a PR and the other code reviewer runs the exact same skill, it notices something. It notices a security thing. It'll focus on security. Maybe it'll focus on the naming. It'll find some weird edge case.

And then, again, I'll try to fix all those.

And some of those findings are legit. And that's what makes this complicated, because as much as I go through it over and over again, it will always find more work.

And it gets frustrating, because you could be changing something small. You could go through that feature yourself and you know you got everything covered, but it will always find more work.

And even when you pass it all yourself and submit it, the code reviewer will run that same skill and it will find more work. It will find more problems or more suggestions.

And it's frustrating to a point because the thing will never be green. It will give me green test results, but it'll never give me a full green code pass.

So there comes a point where you just have to trust that you covered all those cases. You have good code that can go out.

But there are times it does find stuff that you missed, and it can make good suggestions. But it can also disagree with itself.

And that's a big thing.

I find that a lot where depending on how many times you run it, and what prompt you give it, or what you tell it to look at or don't tell it to look at, it will contradict itself from time to time.

And that's where I get it to start to feel this fuzziness about, am I running through the same model? Am I running through the same infrastructure? Did this skill look at the same code? Did it use the same memory context and instructions?

And that's where this all kind of comes, because you need a piece of personality behind it. And there needs to be a real person still checking stuff and running the checks and balances, basically.

08:37 — Using Variation on Purpose

And outside of code at work and our system, I don't mind these inconsistencies that much.

I actually use it on purpose.

So for the book that I'm working on, for the thumbnails for these videos, the scripts, personal code projects, I will pass it around. I will ask the same model multiple times to get different responses and check.

I will take one response, I'll ask multiple models, I'll ask multiple systems. I will give the answer from one to another to see what it says about it.

I want to get those things. And sometimes I'll take the answer, and I actually like that they don't give me the exact same thing because I don't necessarily want AI to give me the answer and then I just accept that answer.

I want to see the different directions it goes.

It lets me be the one who makes a choice.

One model might catch something another one missed. One might give me an idea I hadn't even thought about or considered before. And another might push it in a direction I don't like.

So I can move back, or I can go into an uncomfortable stage and figure out how to work that in. But I'm the one who makes the decision. I'm the one who decides where I want this to go.

And with the book especially, this matters.

It is supposed to be my thoughts, my experiences. I don't want the model deciding what I meant just because it produced this clean, beautiful paragraph.

I need to review it. I need to push it into other things. I need to get my thoughts in there and make my decisions.

I can get three different approaches to solving something, look at the trade-offs, test them, decide what actually fits what I'm building or writing.

So the inconsistency isn't always a weakness. Sometimes I intentionally create more of it.

I just don't want to confuse the AI giving me an answer with me making the decision.

10:38 — When AI Sounds Certain

And that matters because people often treat AI output like it is the truth.

They ask once and assume the answer is the answer.

But sometimes you need to ask again. Sometimes you need to compare responses. Sometimes you need to check the source, test the code. You need to slow down and remember that confidence in the wording does not always mean confidence in the process.

That is part of what I keep coming back to when I look at this.

The answers may sound clean. They may sound complete. They may sound certain. But behind that answer is a system that can vary from moment to moment.

If we're using AI for brainstorming, it can actually be useful. The variation gives you different angles.

But if you're using it for facts, code, money, health, legal questions, things that matter and need precision, timeliness, and consistency, that variation is something that has to be managed. It has to be considered.

And in certain situations, you can't just put it into OpenAI or ChatGPT and accept what it says.

It has to be built properly, and it has to be the right kind of system. It can't just be me chatting and getting random answers about what the chemicals in my pool should be, or what's the weather tomorrow?

Because the goal is not to pretend AI is perfectly consistent, and it's not to reject it because it's imperfect.

The goal is to understand what kind of tool it is.

It's powerful. It's useful. But it's not static.

The same prompt can lead to different answers because the model is working through probability. And the same system can behave differently because the infrastructure around it is always moving.

12:24 — Repeatability and Judgment

That is why I think repeatability matters. Verification matters. Running the same prompt more than once matters.

And sometimes intentionally asking another model the same question matters.

Not because I'm trying to find the model that tells me what I want to hear. You can't do that.

It's actually the opposite. It always tells you what you want to hear. It always tells you you're the best, you're the greatest. And you have to step back from that and see what it's doing and why it's doing it.

So you have to take that with a grain of salt.

I try to force the models to tell me I'm wrong. I want it to tell me a different answer, how to look at something different, so I can't just trust what I'm getting.

So I'm not trying to find the one that tells me what I want to hear. I want the disagreement. I want to see where they differ.

Then I can look at those answers, use my perspective, use my likes and dislikes, test what I can test, and decide what I actually think.

Because AI is not just a brain in a box. It is a process. It's a system. It's infrastructure, hardware, and it's just code in the end.

And sometimes the process changes the answer. Sometimes the data set changes the answer.

So you have to be careful. You can't accept it blindly.

You have to use your human intuition and review what you're getting.

But at the end of that process, I want to be the one who makes a decision, and I want to make it mine.

I don't want to just blindly assume that I'm getting the right answer. I want to use it to help me make faster decisions.

I want to use it to help me speed up the process of finding the answers, getting through the code, writing the book, doing a YouTube script, like this one here.

I've read this, redid this one a few times. I've added my own stuff on top of it. And I've gone off script more than once, and I do that all the time.

But that's okay.

It's there as a guideline. I didn't use this as a replacement and just robot it through. I use this as a guide to get me to where I want to go.

Alright, thanks for watching. Bye.

I’ve Been Putting the Wrong People First [Raw Session]

00:00 — Opening

Hey, welcome back to Slow Builds.

I've been thinking a lot lately about what people actually mean when they say, "Put yourself first."

For the longest time, I never liked the phrase.

I still don't like the phrase technically, because it does sound selfish.

It sounds like you're ignoring people.

It sounds like deciding that your needs matter more than everyone else around you.

But the more I look at it and think about it and see it in my life, I don't think that's what it really means anymore.

I think putting yourself first is really about having priorities and being willing to live by those priorities and rules that you put for your life.

Because if you say yes to everything, if you try to keep everyone else happy and keep the situation feeling comfortable for everyone in it, eventually you stop making decisions based on what matters to you and what matters to the people around you, your family, your closest people.

You start making decisions based on avoiding disappointing someone else. Disappointing someone who's not even part of your immediate family or your closest inner circle.

And I think I've done that more than I realized.

01:23 — Saying Yes Is Easy

One of the focuses in the book that I've been working on and trying my best to get out is be willing to say no.

Not because saying no is powerful, but because saying yes is way too easy.

Someone asks. Someone needs something. Someone has expectations.

You don't even stop to think. You just say yes.

It's almost like an automatic response that we're all trained to, depending on how you were raised and where you grew up and what you were taught.

Saying yes and not letting other people down becomes the normal default.

Because the uncomfortable part isn't saying no.

The uncomfortable part is watching someone be disappointed.

And that's why you say yes.

Because you don't want to see that disappointment on someone's face. You don't want to let someone else down.

Most of us would rather inconvenience ourselves for months, years, go into financial problems, make our closest people be disappointed, than make a stranger disappointed or feel uncomfortable.

We'd rather do that than sit through thirty seconds of awkwardness.

And I started realizing that's completely backwards when you look at it from the other point of view.

02:46 — Choosing My Wife First

Here's something. I do have some small examples of this, and one is a recent one that happened earlier this summer.

I have a friend who was coming to town and wanted to just stop by. He didn't even want to stay at our house. He just wanted to stop by and see my home, because I've known this guy for a long time and he's never been to where I live.

So he wanted to see my surroundings.

He's a friend that lets me stay with him when I go visit his area.

Normally, someone coming to the house isn't a big deal, but my wife's never seen this person, never talked to him, and she gets uncomfortable around strangers really.

And she doesn't enjoy having people over because she always feels uncomfortable.

So immediately I felt stuck.

I was thinking about disappointing my friend.

I was thinking about making things awkward.

I was trying to figure out how to make everybody happy.

Then I realized, basically what it was, was I wasn't going to say no to him and I wasn't going to let my wife know he was coming.

So how do I navigate this awkward situation?

And then I realized I was solving the wrong problem.

Who comes first?

And for me, that's an easy one in that situation.

Once I put it in that perspective, the one person I have to make sure is happy is my wife.

That doesn't mean I don't care about my friend.

It doesn't mean I don't want him to see my home and see where I live. He lets me stay at his place. I get to go see where he lives.

It means if those two priorities are in conflict, I should already know, and I do already know, which one always wins.

And the funny part is, as soon as I accepted that, the stress on me disappeared. The stress on my wife disappeared.

My friend made fun of it a little bit, but he understood. I could tell he understood.

And in the end, we found a way.

As soon as that stress and discomfort disappeared, my wife was more open because she didn't feel obligated. So once that was taken away, it became a much more friendly, easygoing situation.

So he came over. He got to meet my wife for the first time.

It's funny because I have friends from a lot of different places because I traveled a lot during my work. So I have all these friends across North America, really.

And my wife has heard of all of them, but she's probably never met 90% of them.

She's heard of them. They've seen her. They've been in the room when I've talked to my wife. They've been in videos, like FaceTime, where my wife has seen them and said hello.

But only from a digital social aspect like that. Never in person or shake-a-hand type thing.

But back to it. Now she got to meet one of my friends, and he got to come see my house and say hi to my wife.

And it worked out pretty good.

Because as soon as I understood where to put my priority, once it becomes a simple yes or no based off your priorities, your rules, and your guidelines, you take all that discomfort and thought process out of it.

Because it becomes, I'm following my rules.

Answer one. Answer two. Answer three.

And when they all add up, then that's the winner.

There's no analytical part about it.

So that made it really good.

That one came up because I had one friend who said, we were out watching a game, and my friend got to meet some of my coworkers. It came up about coming to my house.

And one friend said, "Why won't you let him over?"

I said, "Because I'd rather my wife be happy. I'd rather disappoint my friend and keep my wife happy than make my friend happy and have my wife mad at me."

When you put it in perspective, it's just, who are the most important people in your life?

And I'd rather make those people happy and comfortable.

07:18 — Knowing My Edges

Here's another one that came up.

It's pretty close around the same group of friends because they live far away, and every year I go there.

A few videos ago, I've talked about not drinking anymore and how that affects my life, why I do it, and things that have come out of it that make life a little bit better. A lot better, actually.

But every year I do take a trip with those friends. I go visit them because there's a big music festival. We do a lot of barbecues, backyard pools, go to the beach.

And we do a lot of hanging out because there's a lot of friends in that area. I spent many years working there.

Some are old coworkers. Some are friends I met along the way.

But there's always drinking involved.

So since I stopped drinking, I've been looking at that trip totally different.

My friends would absolutely respect my decision, and they've already told me that. They'd never put pressure on me.

One of them even said they're willing to pay for my trip, pay for all my expenses, just to make sure I come and I'm part of the annual trip basically.

And that means a lot to me.

This isn't about bad friends.

It's about knowing myself.

There's a chapter in the book about knowing your edges, and this is what that revolves around.

I know what the environment that I'll be putting myself in is. I know how fresh I am off not drinking, and how easily drinking becomes part of that fun time basically.

I know the habits that are connected and how slips can happen easily.

And I know my edges. So once a slip happens, I know how quickly it slides and gets progressively worse.

Then it's a restart, which I know I can do, and I've done it many times, but I just don't want to go there again.

And knowing that I don't want to do it again, I know I want to protect myself.

That means I have to say no, as much as it disappoints, as much as people are willing to go out of their way to make me not feel that way.

I just don't want to put myself in the situation. I don't want to put them in the situation.

It's not that I would become some kind of monster. It's not that.

I just set goals. I set priorities. And I want to stick to them.

So I think next year is going to be a different story.

Even this year, I went to a few parties, barbecues, did the camping thing. I haven't gone to a concert. I've been to work events. We've gone out boating, things like that.

So obviously, I don't have the problem of saying no to everything. I don't have the problem of staying away.

I'd just rather limit my exposure, which is really me saying no.

And I have to feel okay with that because I put myself first, then I put my family first, then I put my friends first.

So in this situation, it's me, then my family, and my friends have to get a no, which is uncomfortable and disappointing.

But they're all fine with it.

10:41 — Working From Home

Another example in this, putting family first, is my working condition.

I do work from home. I do have an office I can go to, but I work remotely and my wife works from home.

For years, I've always gone to a friend's house, a coworker's house. Even when we weren't coworkers, I still went to his home.

And that's where I work.

I have an office there. I have my computer, monitors set up, my own desk. I have my snacks there.

It's more or less to have company in the same area so we can work off each other with coding and business decisions and testing and things like that.

But lately I started asking myself, why am I spending so much of my week somewhere besides my home?

Why am I spending time away from my wife and my kids?

Because my kids work from home or do school, so they're always here. My wife's always here.

So there's spontaneous walks, spontaneous lunches, going in the pool, errands around the house, making supper.

There's all kinds of things that I could easily add to our life as a family just by being present, being here.

Removing that commute time to there and back home. Removing being away and only talking through text or messages to see what the plan is.

Or missing a walk because I wasn't here when the perfect time happened.

So I know my friend is possibly taking this a little bit personally.

I think because he's so used to it. It's been such an ingrained thing for over a decade.

And now all of a sudden, I've kind of abruptly stopped it.

Usually in the summertime I do anyway, because in summer we do have the pool. It's beautiful here. I get to go for runs, go for bike rides.

If I have a break between meetings and work, I can just hop on my bike and go out. Or like I said, my wife and I can go to the park and go for a walk.

So I'd rather be here for the spontaneous moments to create memories and life and connection than be at a desk in my friend's home away from my family and the people.

So it's uncomfortable, but I have to put myself first and my family first in that situation.

Because it's just the way I look at how much time's left. How many summers we have.

And there's another part where I talk about this.

I don't want to miss those opportunities just because of me putting the wrong people first.

13:29 — Stay Calm and Solve the Right Problem

This comes back to another time recently this summer.

Stay calm, solve problems. That's in the book.

I realized it applies here too, because the problem usually isn't that someone asks something of you.

The problem is that you immediately take responsibility for their feelings.

You start trying to fix everything.

And I do have an actual example with my kid for this.

You start trying to make every outcome perfect.

Not just the outcome for the people closest to yourself. You're trying to make the outcome perfect for everyone involved.

Instead, you need to stop and ask, what's actually happening here?

Who's responsible?

What belongs to me and my family?

What doesn't?

Once you answer those questions, the decision usually becomes extremely clear and stress is removed.

14:22 — Family First in a Panic

Like I said, my kid recently took a big trip helping their partner or friend move from one part of the U.S. to another part.

Along the trip, we became aware that there was no place to stay.

The apartment or condo or whatever they were planning on living in once they moved there was not real. It was sort of a fake scam thing.

So now they're traveling in the middle of packing up a life in one city, state, to move completely across the continent to another state and city where there's no destination at the moment.

There is no place to park and unload.

So once that happened, I get a call of panic, and the mind automatically goes everywhere at once.

You start thinking about what's going to happen. What are we going to do?

And you start thinking about the friend, about the situation, what's going wrong and what can go wrong.

Who should have planned better?

There's a lot of blame that happens.

But my first thought, once I heard it, I was driving and I was told about this, I stopped and I said, this is okay.

Is our kid okay?

Is our family okay?

That's my first priority.

Is there any issue with my child?

Is everything going to be fine?

And everything's fine, yes.

They're driving. They have hotels already booked along the way. They have Airbnbs along the way once they get to the final destination.

And it's not because everyone else doesn't matter in this situation.

It's because in that moment, I need to know where the center is.

Where are the things that I put closest to my life?

What situation are they being put in?

And is that controllable? Is that safe? Is it comfortable? Is it what we need?

If we need a hotel, we'll get a hotel.

If we need an Airbnb, I'll look it up.

And that's the first thing I did.

I looked up Airbnbs. I looked up hotels to see what was available and what I could get.

And once I had that under control, it was fine.

I even said, well, we could change flights.

I looked up changing flights, what the cost would be, what was available.

All that's fine.

So once that was clear, now we can breathe.

We can get a hotel. We can get an Airbnb. We can even change the flight.

Sure, that leaves the other friend in a bad situation, but my family, my people, are first and taken care of.

Now we can take time. Now we can look at helping the overall situation because we know we're safe and we're fine.

So we can look at places to live, different contacts to deal with. We can weed through the apartment options. We can figure out a solution.

But the order mattered here.

Family first did not mean no one else gets help.

It meant we stopped pretending every concern had the same weight.

And I think that's the part I'm starting to understand.

Putting yourself first, or putting your family first, is not always about walking away from people.

Sometimes it's about getting the priorities straight enough that you can help without losing the plot.

17:32 — Trusting the Bigger Feeling

I think part of this is age.

I look at life a little different now than I used to.

Not in a dramatic way. Just in a more honest way with myself.

With age, obviously there are only so many summers.

There's only so many winters. Only so many trips we can take.

There's only so many certain years where I'm going to be able to do the physical activities I want to do, like go paddle boarding, kayaking, hiking, any kind of thing that's strenuous.

Once you reach a certain age, obviously a lot of those mobility and risk-taking things are not part of your life anymore.

And you have to stop saying, I'll go next year.

Because before next year starts, it becomes less guaranteed than it used to feel.

Every year takes one out of the bucket.

I just took a trip back home, and honestly, part of me didn't want to go.

I like being home, especially in the summertime.

The weather here is great. There are beaches. I have my pool.

I love my routine of running outdoors and biking, and I knew where I was going, I wasn't going to get that.

But there was part of me that looked at the trip and thought, do I really want to leave right now?

But then there was that other part that asked a better question.

How many more chances am I going to have to go home?

How many more times am I going to get back there?

How many summers are going to line up where I can spend a week or two with friends and family with no obligations or pressures, have a place to stay, everyone's there, everyone's healthy?

And when I looked at it that way, the answer felt different.

But it also felt easy.

Missing some good weather at home matters, but it doesn't matter as much as the memories and making other people who want to see me.

My mother really wanted to spend time with me.

My nephews are getting older, and I had a chance to see all of them play their favorite sports, play together, hang out with them.

And I got to hang out with my friends and their kids.

So the weather didn't matter as much as seeing people.

And it didn't matter as much as choosing something I knew would probably mean more in the long run.

I think I trust that gut feeling more than I used to now.

Not every gut feeling.

Some gut feelings are just fear.

Some are just avoidance.

Some are just wanting comfort right away, ignoring the uncomfortable, being scared of it.

But this was a gut feeling to go home.

I knew I had to do it.

Not because I felt that something was going to happen or go wrong, but the timing. I knew that the timing was right.

I knew that I hadn't been home in such a long time, or hadn't seen these people in such a long time, that it was going to mean just as much to them as it was to me.

And that's a feeling that says this matters.

So I paid attention to it, and I did it.

And it was an amazing time.

So don't let a short-term flinch make decisions for you, because I think that happens more than people realize.

We avoid being uncomfortable for a few minutes or days, and then we miss something that could have stayed with us for years.

We say no because the logistics feel annoying.

We stay home because leaving feels uncomfortable, inconvenient.

We avoid the conversation because it feels awkward.

We keep the routine because changing it doesn't feel right.

But really, sometimes you've got to shake it up.

And sometimes comfortable is good.

Sometimes staying home is the right choice.

But sometimes the flinch that's telling you not to do it is just the first layer.

Because you feel like it's uncomfortable, you just automatically say no.

Which is the opposite. You say no to yourself, you say yes to others.

It's pretty funny how it works out.

And when we let that first layer control everything, we can end up building a life that's technically comfortable, but quietly smaller than it needs to be.

And that's what I'm trying to pay more attention to now.

Not just what feels easy today, but what I'll be glad I chose later.

22:02 — Closing

I'm still learning all this.

I'm still playing with it.

Obviously I'm getting older, so I'm trying to say yes to more things, or try more things, and ignore that little voice that's telling me not to, and trust the gut that says, "Go for it."

I don't think putting yourself first means being selfish anymore.

I think it means being honest about the life you're trying to build.

For me, that means my health, sobriety, marriage, family.

Those things have moved higher than they used to be.

That doesn't mean I care less about my friends or how they feel.

I'm not ignoring anyone or putting things to the side.

It doesn't mean I'm trying to push those people away or any people away.

It just means I finally have a better order in my mind.

I'm prioritizing things a little different to make my life set up in a better way.

And it helps me make my decisions easier and clearer.

Some people are going to be disappointed with that, and that's okay.

Because if your friends or the people around you are disappointed, that's a good thing in a way because it means they care and they want to spend time with you and they enjoy your company.

But it also means hopefully they respect it.

And once they understand what you're doing, then they know that you're putting your priorities first. You're putting your family first, yourself first.

And they're not excluded from that.

It's just that there are times when you and your family are higher on the list.

Because disappointing someone isn't always the same thing as doing something wrong.

Sometimes it's simply the cost of building a life that's actually aligned with what matters most to you, and making sure you make a lasting life further in the future.

Your family is the one that's going to be with you the longest.

My wife is going to be the one that's with me pretty much forever.

Same with my kids.

So I need to make sure that my priorities are put in line with that and them.

Thanks for watching.

Bye.

AI Is Creating a Trust Gap [Raw Session]

00:00 — Opening

Hey, welcome back to Slow Builds.

I recently, on my vacation back home, was watching my nephews use AI. And it made me realize how differently people are experiencing this whole thing.

Because when adults talk about AI, we talk about it like it is this massive turning point.

I believe it is. I think it is one of the most game-changing technologies we are going to see for a long time. It changes jobs, industry, creativity, trust, and almost every aspect of technology.

Those things might all be true.

But when I watch younger people use it, I do not feel like they see it the same way at all.

00:41 — Kids Treat AI Like An App

They were not staring at it like some miracle. They were not sitting there having a deep conversation about what it means for humanity.

They were just using it.

Homework question? Ask AI.

Need something summarized? AI.

Want to mess around with a picture or an answer or just a joke? Ask AI.

It is not amazing to them in the same way it is amazing to someone else who remembers life before the internet was always in your pocket.

To them, AI is almost just another app.

But the part that stood out to me is that they also do not really trust it.

They know it can be wrong. They know it can make things up. They understand hallucinations. They anticipate it. They know it gives weird answers. They know it can sound confident and still be completely off.

So there is this strange mix happening.

They use it casually, but they do not respect it deeply.

It is useful, but not authoritative. It is convenient, but not trusted.

And I think that is a very different relationship with technology than the one a lot of adults assume kids are having.

02:02 — Leverage, Not Truth

The easiest way I can describe it is that younger people seem to treat AI like leverage, not truth.

Not wisdom. Not a teacher they fully believe.

Just leverage.

It helps them get through something faster. It gives them a starting point. It gets them unstuck.

And to be honest, as I am reading this and talking it out loud, they seem to be using it the way you might use Google or Reddit. You still have to use your mind to decipher what is authentic, what is real, and what has been fudged a little bit.

Sometimes it probably helps them cheat. Sometimes it helps them avoid doing the hard part. And sometimes it is just entertainment.

And I know that can sound like some old person complaining about kids these days, like some back-in-my-day thing where kids are ruining everything.

I do think there are risks there, especially if they never learn the underlying skill.

If every first draft, every explanation, every hard thought gets outsourced too early, something is lost.

There is another video I want to do on that, where we lose the ability to think for ourselves.

But watching the kids use it, I kind of believe there is still a little bit of hope that these kids are seeing it for what it really is.

Not seeing its full potential, but starting from a point of distrust and slowly using it to help them get an edge. Like I said, leverage.

But I also think they are seeing something honestly.

03:42 — Growing Up In A Messy Internet

They are growing up in a world where the internet has always been messy.

They already know that not everything on screen is true. They already know people fake things. They already know apps are trying to get their attention. They already know online realities are unstable.

So when AI gives them an answer, they do not necessarily receive it like truth.

They receive it like output.

And that distinction really matters.

A lot of adults are still shocked that a computer can speak with confidence and be wrong. But younger people seem less shocked by that.

They almost expect it.

And maybe that is good. Maybe that skepticism protects them. And I believe it does.

But there is another side to it.

If everything becomes output and none of it is trusted, then what happens to the idea of actually knowing something?

That is the part I keep getting stuck on a little bit.

04:32 — Older People See The Risk First

Then I think about the older generation, and the relationship can be completely different.

For a lot of older people, and I do not mean my age, I mean older, like my mother, seniors basically, the first thought is not, how can this help me?

The first thought is, how can this hurt me?

And honestly, I understand that also.

They have been scammed. They came late to the technology game. They never know what to trust.

Email scams still work. Text scams still work. Any kind of scam still works.

And AI making phone calls, text messages, follow-ups, and emails better is a constant burden.

If you are already uncomfortable with technology, AI does not feel like a helpful assistant.

It feels like the internet got even harder to trust.

Now the scam emails can be better. The fake voice can sound more real. The fake image can look more believable. Even the videos look real. The text, subtitles, everything can become almost indistinguishable from the real thing.

So you have to come in with skepticism about everything.

It is a sad way to live, but you almost have to assume everything is fake until you can prove that it is not.

The person pretending to be your bank, your kid, or your grandkid can sound more polished than before.

So from that perspective, AI is not a playground. It is a threat surface. It is another open window.

If you already felt like the online world was confusing, AI does not make it feel simpler. It makes it feel like the floor moved again.

06:19 — Fear Is Not Irrational

I think this is where a lot of people in tech miss the emotional part.

We just say, just learn the tool.

But if someone's first experience of the tool is fear, that is not a small thing.

You cannot just explain that away with features, because for them the question is not whether AI can summarize a document or write an email.

The question is whether it can trick them, expose them, impersonate them, or make the world less safe.

And that fear is not completely irrational.

We even have to worry about that. Even young people worry about that. My first instinct is not to trust everything either.

But I still want to have optimism that there are good people and good intentions.

06:58 — The Middle Group

Then there is the group in the middle.

This is where I sit. This is probably where most of my time is spent thinking about it.

Old enough to remember life before all of this, but young enough to know I cannot just ignore it.

I use AI. I build with it. I think with it. I code with it. I shape ideas with it. It helps me move fast. It makes me a little lazier sometimes. It gives me clean answers that still need my judgment.

And sometimes I ask it something and realize the important part was not the answer. It was me figuring out what I actually meant to ask.

That is the weird middle place.

You can see the opportunity. You can also see the cost. You can understand why younger people use it so casually. But you can also understand why older people are scared of it.

And I do not think the answer is to be blindly pro-AI or blindly anti-AI.

That is too easy.

The harder thing is learning how to stay awake while using it.

08:00 — Staying Awake While Using AI

The real danger for me is not that AI exists.

The danger is that it becomes so convenient that I stop noticing which parts of my thinking I am handing over and which parts I am doing myself.

This is where the school example becomes interesting.

If a kid uses AI to finish homework, did they learn?

Maybe. Maybe not.

It depends what happens in their head.

If they use it to understand the problem, compare explanations, test their own answer, or get unstuck after trying, that could be useful.

But if they use it to skip the part where their brain had to struggle, then they got the result without the formation.

And that is not just a school problem.

Adults do this too.

We use AI to write the email before we know what we want to say. We use it to summarize the thing before we have sat with it. We use it to make the plan before we have felt the constraints. We use it to sound clear before we actually are clear.

I am including myself in that one.

I use it a lot to help me form text messages, emails, and responses in general. But I use it to help me pause in most cases and reflect.

There have been plenty of times where AI helped me in that situation.

But there have also been times where I could feel myself reaching for it too early. Not because I needed help, but because I did not want to sit in the messy first part of it. I did not want to waste my time, in a sense.

And that messy first part is usually where the real thinking is.

That is the part where you find out what you believe. That is where you notice the contradictions. That is where you realize the first version of the idea was too simple.

AI can help after that.

But if it replaces that completely, I think something starts to weaken.

09:39 — The Real Generation Gap Is Trust

So maybe the generation gap is not really about who uses AI and who does not.

Maybe it is about trust.

Kids use it, but they do not trust it.

Older people often do not use it because they do not trust the world around it.

And the middle is trying to figure out when trust is earned, when skepticism is healthy, and when fear is just keeping us from learning something useful.

That is a harder skill than prompt engineering.

Knowing when to believe an answer. Knowing when to check it. Knowing when to ask better questions. Knowing when the output sounds good but does not actually match reality. Knowing when speed is helping and when speed is flattening the work.

That is the skill I think people underestimate.

Because AI makes output cheap.

But judgment does not become cheap just because output is cheap.

If anything, judgment becomes more important.

When everyone can generate text, images, code, summaries, plans, and opinions, the valuable part becomes knowing what is worth keeping.

And that is the slow work.

That is attention, taste, context, and lived experience.

That is being able to say, this sounds right, but it is not actually true.

Or, this is technically correct, but it misses the point.

10:52 — Playground Or Prison

That is why the playground-or-prison idea keeps coming back to me.

For some people, AI feels like a playground. You can make things faster, explore ideas, build tools, learn new skills, and turn a blank page into something you can react to.

For other people, it feels like a prison.

More scams. Fake content. Pressure. Job fear. Confusion. More systems they do not understand, but still have to live inside.

And I do not think either reaction is completely wrong.

The same tool can open doors for one person and create anxiety for another.

The same tool can help someone learn and help someone avoid learning.

The same tool can make work better or make work thinner.

This is why I do not want to talk about AI like it is automatically good or automatically bad.

I think the real question is: what kind of relationship are we building with it?

Because if the relationship is just convenience, that has a cost.

If the relationship is just fear, that has a cost too.

And somewhere in the middle is a more difficult path, where you use the tool but keep the judgment active.

11:50 — Keeping Judgment Active

I do not think my nephews are wrong for using it casually.

I do not think older people are wrong for being cautious.

And I do not think people in the middle are wrong for feeling conflicted.

That might actually be the honest place to be.

Because this technology is useful and it is risky. It can help you think and it can help you avoid thinking. It can make things more accessible, or it can make the world harder to trust.

So maybe the goal is not to pick one simple position and defend it forever.

Maybe the goal is to stay aware.

Use it, but do not worship it.

Question it, but do not hide from it.

Let it help with the work, but do not let it replace the part of the work that changes you.

That is the part I keep coming back to with Slow Builds Lab.

The output matters.

But the person being shaped by the process matters too.

And if AI is going to be part of the process now, then I think the challenge is learning how to use it without disappearing inside of it.

Alright, thanks for watching.

Hope you liked this one. Bye.

AI Replication Won’t End Scarcity [Raw Session]

00:00 — Opening

Hey, welcome back to Slow Builds.

So this one's about replication and AI, and abundance and scarcity, and who controls it.

This all starts with me thinking about Oreos.

Of all the things, it's a strange place to start a conversation about AI, manufacturing, ownership, and all those things. But Oreos are what bring it back for me.

00:25 — Why Food Makes Replication Easy To Understand

I think food is probably one of the clearest ways to understand what AI replication might actually do to us and for us.

Right now, if you want an Oreo, you buy Oreos. If you want Ruffles, you buy Ruffles. There's something very normal about that, almost too normal to notice.

You walk into a store, you see the package, you recognize it. You already know exactly what it's going to taste like once you take that first bite.

The brand is not just a logo. It's a memory. It's being a kid, the lunchbox, the road trips, someone's cupboard. The sound of the bag opening. The exact crunch, the exact salt, the exact filling, the way the cookie twists apart.

That is why this gets interesting, because AI does not just make new things easier to create. AI also makes existing things easier to copy.

And I think that changes the relationship between us and the things we buy. Not all at once, not perfectly, and definitely not tomorrow morning. But directionally, replication is going to get easier and easier.

01:38 — From Creative Work To Physical Things

Right now, we mostly talk about AI replication with creative work.

AI can copy writing styles, generate visuals, create music that feels close to something else, summarize books, imitate voices, mimic formats, and reproduce patterns.

That conversation is already very uncomfortable in a lot of different areas.

But I think a bigger version of this is when replication starts moving deeper into the physical world, not just the digital world or the copy.

Food is where you start to feel the possibilities and what could happen.

Imagine taking a Ruffle chip and giving AI enough information about it: the ingredients, the taste, the shape, and all the different things that make a Ruffle what you think it is. The way the flavor hits at the beginning and then fades away.

At first, maybe AI just gives you a recipe. Not a perfect one. Not the real trade secrets. Just something close.

Then you make it and you tell the system why it's wrong. It's too salty. It's not crisp enough. It's too thick. The flavor is close, but the texture is off. And the system adjusts.

That alone is a different world, because now the thing that used to require company factories, research labs, packaging, distribution, and shelf space starts becoming something a regular person can approximate. They can play with it and try to figure it out on their own.

Maybe not perfectly. But close enough is powerful. A lot of the world runs on close enough. We see that all the time.

03:20 — When Products Become Patterns

This is the part that feels bigger than snacks.

An Oreo stops being only a product. It becomes a pattern, a texture profile, a flavor target, a process, a memory that can be aimed at.

And once something becomes a pattern, AI is very good at working with it.

Maybe the first version is manual. AI gives you a recipe and you make it yourself. Then the tools get better. Kitchen machines get smarter. Small food printers get less strange. They become part of life. Local manufacturing improves.

Maybe restaurants use AI to recreate discontinued foods. Maybe families can use it to rebuild recipes that were never written down. People with allergies use it to make versions of their favorite snacks that they can actually eat without worrying about reactions.

And that part is very interesting because it's not just convenience. It is memory.

A lot of food is memory. You're not always trying to eat the objectively best thing. Sometimes you're trying to return to a feeling, a moment, the snack you had as a kid, food from a place you moved away from, the meal someone used to make before they were gone.

Or the discontinued thing you still remember, even though it probably was not nearly as good as you remember it being. But the memory makes it seem so much better.

AI replication can make some of that easier to reach. And that is both beautiful and extremely weird at the same time.

04:52 — What Happens To Brands

This is where brands start to change.

Today, part of what a brand gives you is access. If you want that specific thing, you go to the company that makes it. They control the recipe, the process, the supply chain, the packaging. They don't control the shelf space completely, but they have that shelf space. They're in front of you. You know where to go get it.

But if replication gets easier, the brand may become less about the object itself. It becomes about nostalgia, trust, authenticity, and permission. It becomes about wanting the original, even if the copy is close.

And that's not a small thing to toss aside.

There are probably cookies better than Oreos. There are probably chips way better than Ruffles. There are bakeries and restaurants and home cooks who can make better versions of almost anything you find in a grocery store.

But that is not always why people are buying it.

Sometimes people are buying the memory, the package, the name, the version they already know.

That is why a knockoff can taste fine and still feel wrong. It can be close, but not emotionally correct. The colors are wrong, the name is wrong, the whole ritual around the product is not the same. The memory does not attach to that other version the same way as the original.

So maybe in a replication-heavy world, brands do not disappear. Maybe they become nostalgia containers.

They become the official version, the childhood version, the trusted version, the version that feels real, even if the copy technically is an exact replica or so close that you can almost not tell the difference in a blind taste test.

06:42 — Authors, Stories, And The Real Version

I think this also connects to authors and stories, because AI is already doing this with creative work.

If AI can read a body of work and generate something with a similar shape, then the author becomes partly a creator and partly a source of authenticity.

You can imagine a future where there are endless books that feel kind of like your favorite author. Endless songs, endless shows, all of these things. And that raises the same question as food.

Why are you actually attached to the pattern? The feeling? The person? The real version? The original? The fact that it came from a real life?

I do not think the answer is simple.

Sometimes people just want the feeling. Maybe AI can give them a story that feels like a certain kind of book. Maybe that is enough.

But sometimes it will not be enough.

Sometimes the reason the story matters is because someone lived enough life to write it. Because it came from a specific person at a specific moment.

Food matters because it connects you to a place, a family, childhood, or a version of the world that does not exist anymore except in your mind.

Replication can copy the surface. It can copy more and more of the pattern. But it may not copy the history around the thing, or the real feeling you get from reading it, looking at it, or tasting it.

08:10 — Abundance Is Not The Same As Freedom

This is where I'm going to go a little off track into abundance.

The end-of-scarcity idea gets complicated, because on the surface, AI replication sounds like abundance.

In some ways, it might be. If people can recreate food locally instead of relying on giant supply chains, that is useful. People can make cheaper versions of things. Someone with allergies can create a childhood snack safely. They can create copies of recipes they love, but with ingredients they do not react to.

If small businesses can manufacture products without massive factories, that could open a lot of doors.

I do not want to flatten this into only a scary technology story. There is real possibility here.

But abundance is not the same thing as freedom.

We already live in a world with abundance in some categories, and some people still struggle to access what they need.

There is plenty of food in the system right now, but not everyone eats well. There is more content than anyone can consume, but attention is still scarce. There are more tools than ever, but ownership often stays concentrated.

So if AI makes replication easy, the question is not only, will there be more stuff? There is definitely going to be more stuff.

The better question is: who controls the ability to make it?

Who has access to it?

If food can be copied, scarcity moves to ingredients. If recipes can be copied, scarcity moves to machines. If designs can be copied, scarcity moves to the materials that make those designs. If styles can be copied, scarcity moves to reputation. And if experience can be copied, scarcity moves to authenticity.

And if everything becomes easy to make, ownership becomes a real question.

Who owns the recipe, the training data, the machines, the cartridges, the flavor profiles, the brand likeness?

Who gets sued for making something too close?

Who gets access to the technology?

Who is allowed to replicate abundance?

10:25 — Controlled Abundance

That is the part I keep thinking about from the bigger point of view.

If the future version of a food printer only works with licensed cartridges, then abundance is still controlled.

If the recipe database is locked behind a platform, abundance is still controlled.

If the machine refuses to make anything too close to a trademarked product, again, abundance is controlled.

If only wealthy people can afford the tools, then abundance is controlled.

So the world may feel more abundant and more locked down at the same time.

And that really makes you want to pay attention.

11:01 — The Search For The Original

The more copyable things become, the more some people will search for what still feels original.

Bringing it back to the original topic here, I wanted to go off track a little to talk about the ugly side of what could happen, and what most likely does happen, because greed always comes into play.

But back to the copying of everything, and how people still search for the original. That is the strange part.

Replication might make things cheaper, but it might make nostalgia more valuable.

If every snack can be copied, the old package matters more. If every story style can be imitated, the real author matters more. If every brand can be replicated, then the original memory matters more.

Not for everyone.

Some people will choose the cheapest copy, the healthiest version, or the version that is easiest to get. Some people will not care if it's the official original.

But some people will care more than ever.

They will want the real thing. Not because the real thing is objectively better, but because it connects to something.

That is what nostalgia does. It turns an object into an anchor.

A cookie is not just a cookie. A chip is not just a chip. Stories are not just words on paper.

These things carry memory, identity, and continuity.

And if AI can copy the object, we may start noticing how much of the value was never only in the object.

12:35 — What Still Matters

I do not think this ends with brands disappearing.

I do not think it ends with everything becoming free.

And I do not think it ends with some clean future where everyone prints whatever they want at home.

The future is probably way messier. It always is.

More things will be available. More things will be copied. More things will be close enough.

And then we will have to decide over and over again what still matters about the original.

Maybe we wanted the taste, the convenience, the feeling, the memory.

Maybe we want to prove that something from before is still here.

That is why the Oreo example sticks so hard for me.

It sounds small, but it points at something bigger.

AI replication may change food, manufacturing, brands, stories, authorship, ownership, and scarcity. But underneath all of that, it asks a very human question.

If AI can copy the thing, what part of the thing was never really in the object?

Because maybe the cookie was never just a cookie. Maybe that chip was never just a chip.

And maybe abundance does not end scarcity. Maybe it just shows us what we are really scarce for.

13:51 — Closing

So I think we are going to be able to copy things. We are going to be able to create things. There is going to be abundance.

But who controls it?

And then what is important to us?

Is it how much something costs, how available it is, or the memory that comes with it and how it makes us feel?

Alright, thanks for watching.