1/7/2026 - The AI Commoditization of Knowledge and Why Soft Skills Are the Future

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0:00 You ready for the singing? Oh, you ready
0:02 for some football?
0:05 Oh, yeah. Champoloo.
0:36 Thousand words swarm around my head. A
0:39 million more books written beneath my
0:42 bed.
0:48 I wrote or read [singing and music] them
0:50 all. Searching in the swamps. Still
0:53 can't find how to hold my hands.
1:00 >> I know you need
1:03 extra room over. I'm stuck in here all
1:07 paralyzed.
1:13 >> For months I [singing] got myself in
1:16 ruts. Too much time spent in mirrors
1:19 framed in yellow [singing] walls.
1:21 [music]
1:26 >> [music]
1:26 >> Ain't it like most people? I'm no
1:29 different. Like to talk on things we
1:31 don't know [singing and music] about.
1:37 Ain't it like most people? I'm [singing]
1:40 no different. Like to talk on things we
1:42 don't know about.
1:54 Oh,
1:59 [music]
2:13 [music]
2:20 >> [music]
2:25 [music]
2:31 [music]
2:36 [music]
2:42 [music]
2:43 >> It really breaks my heart
2:46 to see a dear old Friend, [music]
2:49 we're down to that worn out place again.
2:56 [music]
3:07 [crying]
3:19 Hey, [music]
3:34 [music]
3:45 >> [music]
3:56 >> Woohoo!
3:59 Woohoo!
4:02 [music]
4:13 Good evening everybody. What is
4:15 happening?
4:17 [music]
4:23 [music]
4:31 >> [music]
4:42 [music]
4:46 >> Yeah.
4:51 [music]
4:57 Yeah. Hallelujah. [music]
5:05 [music and singing]
5:12 [music]
5:20 [music]
5:22 >> [singing]
5:26 [music]
5:29 >> So, let me talk about my my uh my daily
5:32 practice. Good day, everybody. What's
5:35 happening? Mimi got myself a big
5:38 Aar calendar
5:41 to start my daily practice. Let's talk
5:43 about daily practice. So
5:46 my day today,
5:48 um, [laughter] first of all, it started
5:52 with, oh, big ass calendar. Oh, okay.
5:56 Big number sign calendar. You can swear
5:59 on my channel. I don't care. [laughter]
6:01 In fact, if you The more you don't
6:04 swear, the more it confuses me. Well, my
6:07 goodness, McGillicuy. [laughter]
6:13 Oh, but you can't cuss in Tik Tok
6:15 comments. All right, fine. Whatever.
6:18 Good lord. I feel like we've got just,
6:20 you know what? Whatever. Doesn't matter.
6:26 No sense screaming up up up river. Um,
6:31 so I I started this morning. I had to
6:33 drive my kids to South Denver for a
6:35 doctor's appointment. Then they wanted
6:36 to go to MicroEnter. The doctor's
6:39 appointment was supposed to be five
6:40 minutes,
6:42 two and a half hours. Then the
6:44 microenter was like an hour and a half.
6:47 I'm taking calls in the car
6:50 and then I went to work and then I had a
6:52 live show with an and then I had to go
6:54 back down to MicroEnter to pick up the
6:56 computer. We left it whatever. And so I
6:59 got back here like 15 minutes before.
7:01 So,
7:03 the last thing on the planet I wanted to
7:05 do was do anything related to work or my
7:08 practice.
7:10 Um, I didn't even really want to come
7:11 live. And this is where
7:14 So, I've I've talked on here before
7:16 about
7:18 a habit is not a practice. A habit's
7:21 just a habit. You just do something. And
7:23 I set this channel up as a habit. And
7:26 so, thank God. [laughter]
7:30 Thank God I did. Right?
7:32 Cuz when when you're feeling like tired
7:35 and spent, you know, if you got a habit,
7:37 you're just like, well, I'm just going
7:38 to go do that thing now. So, um, so I am
7:42 currently not super intentional because
7:44 I haven't I just I literally haven't had
7:46 time to take for myself. Last night was
7:48 really fun. Last night I took a little a
7:51 little I played hookie a little bit for
7:52 my practice. So what what we agreed to
7:55 last night and by we agreed
7:58 someone someone on the channel came up
8:00 with the idea and I was like yeah let's
8:02 do that. Um and uh that is la last night
8:08 on this channel was a cheat day. So I'm
8:11 going to have practice cheat days. I can
8:13 take here's here's here's the the rules
8:16 I I invented yesterday that I I can take
8:20 up to one cheat day per week and they do
8:23 not accumulate. I don't have to take a
8:25 cheat day. I'm on a roll. I can just
8:28 keep going. If I take a cheat day, we're
8:31 just going to dick around and play with
8:32 tools and toys and not really think
8:34 about it. Um, but otherwise I'm going to
8:36 be doing my practice, which is I'm going
8:38 to be looking at stuff that needs to get
8:40 done and organizing things and just
8:43 doing the work. Doing the work.
8:50 Um,
8:56 [music]
9:04 >> [music]
9:11 [music]
9:17 [music]
9:22 >> Corey singing along. Sing it. Sing it,
9:25 lady. And then, but the other thing that
9:27 happened was I think it was last night I
9:32 got a text from Dr. J, Claire Jacobs.
9:35 Claire Jacobs of um Life Hacks fame
9:38 along with producer Brandon. Apparently,
9:41 they had a really good show today. And
9:44 Claire said, "Look at your porch
9:46 tomorrow morning. there's going to be
9:48 something delivered there for you
9:50 between 4 and 8 am. So, first of all,
9:54 what kind of insane delivery service I
9:56 assume Amazon delivers between 4 and 8
9:59 a.m. [laughter]
10:01 So, so I I was not up at 4 waiting.
10:05 [laughter]
10:09 Oh, Brendan, hello from Melbourne,
10:10 Australia. Nice.
10:13 AI Instigator. Good title for you. Thank
10:15 you very much. Hello. Hello.
10:18 >> [music]
10:19 >> Hey everybody. Glad to see everyone. Be
10:22 not afraid. I think we should be able to
10:24 have a majority vote if you take a cheat
10:27 day or not. No, I can't. This is the
10:30 thing. You all [laughter]
10:31 Oh, what if a tool drops? Well, so
10:34 listen, Danielle, here's the thing.
10:37 Part of having a daily practice, it
10:39 doesn't just mean I'm doing misery
10:40 things, right? Part of having a daily
10:42 practice is playing with purpose, right?
10:45 So if a new a new tool drops
10:49 and I need to understand that especially
10:51 if it's something in the video space the
10:53 music space it's something in kind of
10:55 businessy marketing kind of you know new
10:58 large language model something like that
11:00 um we can absolutely still have play
11:04 days that are part of the practice
11:05 they're just going to be a bit more
11:06 intentional right like how I choose them
11:08 won't just be like ah we'll do whatever
11:11 whatever the wind blows is um because I
11:15 have enough I have enough [ __ ] to do
11:17 that that I've got I've I've got so much
11:20 it's it's overwhelming.
11:24 And what's nice about treating this as
11:25 as part of my practice
11:28 is that
11:30 I know that I can at least take some
11:32 chunk of to-dos, you know, off off the
11:35 list when I'm here.
11:38 Um
11:40 so that's good. Anyway, um Dr. Jay said,
11:42 "Look for a package." And so,
11:45 oh yeah, it is Amazon. Look, there's a
11:47 little Amazon thingy. Um, so the story
11:51 behind this box is the other night I was
11:54 trying to do a video.
11:58 I think I was trying to do something for
11:59 Sydney and I literally couldn't make
12:02 Photoshop edits because my hard drive is
12:04 so full. It's like, your scratch disc is
12:07 too full to to do a gener a generative
12:09 fill. Um, so this this is an external
12:13 hard drive. So to the irregulars and to
12:15 Dr. J and whoever had a hand in this,
12:19 you A, you didn't need to do this, but
12:21 B, if you didn't do this, I would just
12:24 be suffering through it. So this is
12:26 going to actually make a really big
12:27 difference in my life. So thank you.
12:29 Thank you very, very much. Uh, we're
12:32 doing an unboxing right now. Um, it is a
12:36 Wow, it it is a spelt small little case.
12:41 All Dr. J, amazing. Thank you so much.
12:43 This is really, really generous. You
12:44 don't need to do this. Um, so cool. Look
12:49 how cool this is.
12:52 Just like the the black the black.
12:57 So cool.
12:59 All right.
13:03 [clears throat]
13:04 I almost hit the trash can. [laughter]
13:08 I'm so tired. I'm so tired, people.
13:13 It's gonna be okay.
13:16 All right, let's see.
13:28 I'm plugging it in. We're doing a little
13:30 We're doing a little Christmas present.
13:34 [clears throat]
13:35 The little light came on.
13:37 Always a good sign.
13:40 Hop over to Finder.
13:43 Seagate portable drive.
13:47 Get info.
13:49 Two terabytes, baby. Oh yeah.
13:53 [laughter]
13:55 So, wait. The first thing I think I can
13:57 do,
13:59 I think this thing's huge. This folder.
14:02 And it's functionally useless. Oh, it's
14:05 not that big.
14:10 Drop. Ah, this one I think is big.
14:16 No. Okay. Well, anyway. All right. So,
14:19 that
14:21 Let me share my screen. Tik Tok pin. So,
14:25 I just watched the Stack Overflow video.
14:27 It blew my mind. You're so right. Ah,
14:29 thank you very much. So, if you don't
14:32 know the Stack Overflow video, let me
14:33 let me call an audible here and and deal
14:35 with that because I I think, you know,
14:37 it's it's funny. Sometimes you make
14:39 videos, um, no, let me not talk in the
14:43 third person. Sometimes I make videos
14:46 and I feel like they're important or
14:48 they're a big deal or I'm really happy
14:50 with them or whatever. Um, and then a
14:54 lot of times they're just not. They're
14:56 just like it was like an idea in the
14:58 moment. This one is one that I made it
15:01 and I thought, "Yeah, this is a big
15:03 deal." And then I made it and I uploaded
15:05 it and the next day I went and looked at
15:08 I went and looked at the the posts
15:11 and the comments for the most part were
15:13 were supportive of it, but I got this
15:16 really
15:18 I rarely get pangs of guilt when I post
15:21 something, but this is a this was a post
15:23 that I got a pang of guilt. Not because
15:26 I think the video was wrong or too
15:28 hyperbolic or whatever. It was a bit
15:30 hyperbolic, but I actually think it's
15:33 true. Um, but why it made me feel bad is
15:40 it's saying something really plainly
15:42 that I think is is about to catch a lot
15:45 of people off guard.
15:48 I I think that I think what it talks
15:50 about is about to happen. So, let me go
15:52 show you the the impetus for it.
15:56 [clears throat]
15:58 Oh, man.
16:00 2026 is going to be quite surreal. It It
16:03 really is.
16:05 I know. Black bar. I'm getting there.
16:11 Um,
16:13 Stack Overflow.
16:19 So this chart, [snorts]
16:23 you can't really see it on Tik Tok too
16:24 good, but on YouTube you'll be fine.
16:27 This chart, most of the posts about this
16:29 chart, if you just go search on X for
16:31 Stack Overflow, you'll find this chart,
16:33 and then you can find the source and go
16:34 look at it.
16:37 Um, it's a it's a Y combinator post
16:40 that's got uh that's got comments and
16:44 then at the top of it, it'll take you to
16:47 the actual
16:49 graph. [sighs]
16:51 Um, what this is is Stack Overflow is a
16:55 site that was founded in 2008. So on the
16:58 left is 2008. Here are the 201s, right?
17:02 So here's at at its peak it was 2014,
17:05 right? So 2012, 2013, 2014,
17:10 all the way out here to like 2020, it
17:13 starts dipping a bit. There's some big
17:15 spike in
17:17 2021. No, 2020. I don't know what that
17:21 spike is, but whatever. This is weekly
17:23 number of questions asked.
17:26 So, Stack Overflow is for programmers
17:29 asking other programmers hard questions
17:32 about programming. It's monthly. Oh,
17:35 that's the pandemic spike. Oh, yeah,
17:37 that's right. Okay. I don't think it is
17:38 monthly because you have two years here
17:41 and there's there's 12 uh there's
17:46 it's not is it monthly? Oh, it's
17:48 monthly. Yeah, it's monthly. Yeah, it
17:49 is. It's monthly. Yeah, 12 12 per year.
17:52 So, so this is monthly. And and up here
17:55 in this higher range, this is between
17:57 150 and 2,000 questions per month being
18:00 asked
18:02 right here.
18:05 Right here
18:07 is 2022. That's the launch of Chat GPT.
18:11 And look at this. It just collapses. It
18:14 falls off a cliff. So the video that I
18:17 made on Tik Tok basically says
18:21 um this happened
18:25 and if you think this is relegated to um
18:30 to programmers you're wrong. This is any
18:33 expertise any expertise has now been
18:36 commoditized. So we can selfservice
18:40 knowledge. Right? It used to be if you
18:42 wanted to get a hard question to ask as
18:44 a programmer, you'd come to something
18:46 like Stack Overflow
18:49 and you don't have to anymore. You can
18:50 go to chat GPT.
18:52 And so,
18:54 >> yeah,
18:54 >> I just wanted to call out because I I
18:56 went back and researched this a little
18:57 bit further offline between our shows.
19:00 It's actually if you look at go back
19:02 over to 2022,
19:04 >> where you were pointing before is
19:05 January. So we still have most of the
19:08 year until middle of that bar is like
19:11 November.
19:12 >> Oh yeah,
19:13 >> that's where it just
19:15 >> Oh, this is this this spot right here.
19:18 You're right.
19:20 Yeah, because this is here's 2023 is the
19:23 middle of this grid. You're right. So
19:25 it's literally right here. We're at
19:27 109,000. Chat PT comes out and you drop
19:30 to 97,000
19:32 and then you drop to 85 and then you
19:34 drop to 68.
19:36 and then it's just it's just gone.
19:38 You're right. Wow, that's really good.
19:39 I'm glad you pointed that out, Brandon.
19:40 Thank you. Um,
19:44 but this is this is a proxy in my
19:48 opinion for any knowledge.
19:52 And one of the things that I'm noticing
19:53 in the AI salon, one of the things I'm
19:55 noticing just in the world is that
19:57 there's a lot of people either getting
19:58 laid off or, you know, they got
20:00 displaced because of of Hey Foosie,
20:03 what's happening?
20:05 uh of what's going on
20:08 and
20:14 satire perspective. Now the artists are
20:17 burning forest to generate goofy videos
20:19 with AI. It's true. It's true. But the
20:22 artists are also starting to learn how
20:25 to use AI to make art, right? Like
20:27 artists will be the first ones to use
20:29 any new technology. they'll figure out
20:30 how to use it how other people have not
20:33 thought about it. So, we're we're we're
20:35 entering a great renaissance. It's it's
20:37 really an exciting time. Um, but there's
20:41 a lot of people right now that I know
20:44 that are saying, "Hey, I've got 30 years
20:46 of experience in this or I'm really good
20:48 at change management or I'm really good
20:49 at this." and they're they're making
20:52 content and they're making marketing
20:54 materials and they're doing posts on
20:55 LinkedIn and the bulk of the of the
20:58 posts
21:00 are what used to work.
21:04 What used to work was I've got 30 years
21:07 of experience in change management and
21:09 someone to go I need someone in change
21:11 management they've got 30 years of
21:12 experience that must be great. Now if
21:15 someone wants to do something in change
21:16 management ch change management they go
21:19 hey chat GPT I got a change management
21:21 challenge what should I do they're
21:24 asking chat GPT
21:26 and so the thing that I think is it
21:29 probably has already fallen off a cliff
21:31 just like this chart but we don't know
21:34 it yet because we're optimists and we
21:37 all just started our consulting
21:38 businesses right and we're hanging our
21:40 shingles out
21:42 I don't think the phone is going to
21:44 start ringing. If you're marketing
21:46 skills and experience,
21:49 skills are about to be democratized and
21:51 experience, knowledge
21:54 is now a commodity. Anyone can have it.
22:00 It doesn't mean that people aren't going
22:03 to be hired. It doesn't mean that people
22:06 won't work again. But I think what does
22:08 change is why you hire someone. Why you
22:12 would reach out to someone.
22:15 And it's not about their skills and what
22:17 they know. It's about soft skills. You
22:20 trust them.
22:22 You like working with them. You know
22:24 they've got integrity. Um you like their
22:27 taste. Lots of people can prompt
22:31 um MidJourney to make cool images. Kelly
22:34 Bash can prompt MidJourney to make
22:37 amazing images, consistently incredible
22:42 images that are recognizable as her
22:45 style.
22:47 That's about who she is, not what she
22:49 not what skills she has, it's about who
22:52 she is. And so that I think is the
22:55 shift. And so if you're someone who's
22:57 kind of hung a shingle and if your
22:58 marketing is around your skills or your
23:01 expertise, I would strongly consider a
23:04 couple of things. One is get your ass in
23:06 community. Get your ass in a community
23:08 like the AI salon or she leads AI or
23:11 whatever it might be. I use Stack
23:13 Overflow religiously many moons ago. I'm
23:16 not sure I remember the URL anymore
23:18 since LLM. That's retrofunk. Yeah,
23:20 exactly. Speaking of, I've been taking
23:22 my midjourney prompts and using them in
23:24 Grock imagine. It's significantly better
23:26 in many ways. It makes very good videos
23:29 as well. I might drop my midJourney
23:30 subscription. Well, archetypal, you can
23:32 also drop your midjourney subscription
23:34 because if you go to Meta AI, that's
23:36 MidJourney for free.
23:40 Meta Meta.ai, if you didn't know this,
23:43 the image generator on Meta.ai is
23:45 MidJourney. And it it's videos and
23:48 images for free. I don't know if they've
23:50 got uh cap caps on them. Rate rate rate
23:54 limits. Um
23:57 anyway,
23:59 oh yeah, but but I avoid Meta on
24:01 principal. I listen I I'm with you. Keep
24:04 paying for mid Journey. [laughter]
24:08 Um anyway, th this chart is
24:14 this feels very significant to me,
24:16 right? Think about like all of the the
24:19 value that the people that were
24:22 answering these questions, building
24:24 brands for themselves as experts,
24:27 they're still just as smart as they
24:29 always were, right? They're still just
24:32 as expert. They're actually more expert
24:33 because this was, you know, 10 years
24:35 ago,
24:37 but no one's looking for that because
24:41 they're self-s serving knowledge.
24:43 wisdom,
24:46 curation, taste, integrity, trust,
24:51 curiosity,
24:53 adaptiveness,
24:56 giving a [ __ ]
24:58 All of those things
25:01 are the reasons people are going to hire
25:03 you in the future.
25:05 And you know, some people have pointed
25:07 out in my my posts, well, that's the way
25:09 it's always been. And I would agree with
25:11 that. In general, you hire people
25:12 because you know them and trust them.
25:14 Um, who who you know knows matters.
25:18 Um, but I think the the whole the whole
25:23 thing of marketing skills and expertise
25:26 I I just I can't imagine it working
25:30 anymore unless you're marketing what I
25:32 think is going to be a temporary skill
25:34 and expertise, which is I'm an AI
25:35 expert,
25:37 which I don't even know what that means
25:39 anymore because things are changing so
25:40 fast. So, anyone who claims they're an
25:42 AI expert right now, I am immediately
25:44 skeptical of.
25:47 So, I think short-term people are going
25:49 to hire some of those people. But if
25:51 you're like, I'm a consultant, I'm a
25:53 creative director, I'm a this, I'm a
25:54 that, I don't know. I don't know, man.
25:57 Anyway,
25:59 that
26:01 there's a depressing way to start the
26:03 show. [laughter]
26:09 Um,
26:11 okay. So, did I move stuff over to my
26:14 hard drive? I don't think I did. I have
26:17 a new hard drive. Thank you, Dr. J.
26:21 Let's see here. There's that.
26:25 We're going to throw this out.
26:35 Why can I not throw that [ __ ] out? Do I
26:37 not own that drive?
26:55 Amazon deliver tabs. Oh, sorry.
26:57 [laughter]
26:59 Producer Brandon is on it tonight. I am
27:02 very slow tonight. I am very slow. The
27:05 brain is not working. So, if you're
27:07 expecting wit,
27:10 it ain't coming from me. It Listen, I
27:13 don't need to have experience or skills
27:15 with wit anymore. I can have chat GPT do
27:17 that now. Um, I think I might have to
27:23 format this drive.
27:26 Let's see if I click here. [snorts]
27:33 Get info.
27:36 Share folder volume. Windows NT. Yeah, I
27:40 think I have to reformat this bad boy.
27:43 Here's a little here's a little
27:45 something we do on a Mac is we get
27:48 things that are formatted for Windows.
27:51 We got to fix them. [laughter]
27:55 Let's see. Um,
28:00 dis
28:02 disc utility. [clears throat]
28:11 Seagate portable drive disc utility
28:16 erase macos extended
28:21 journal master boot record Apple
28:24 partition map. Oh
28:27 let's [laughter]
28:30 hang on let me take a screenshot of
28:32 this.
28:40 All right, let's go to chat. JP,
28:48 I used to be a mech geek. I used to know
28:50 the answers to all the questions. Which
28:51 way do you format your drive? I don't
28:53 anymore. So,
28:56 what? Why do I not have that? Was that
28:57 not on clipboard? That was on clipboard.
29:10 Oh, come on.
29:13 YouTube comment. Kyle,
29:15 we will always feel you're very witty,
29:17 especially when you're wearing your pink
29:19 bow. Thank you. I appreciate that. Um
29:22 how uh which um
29:26 should I use in formatting a new drive
29:32 for my M1 MacBook Pro.
29:46 >> [sighs]
29:49 >> Maybe the coffee will kick in at some
29:50 point here.
29:57 If you haven't backed up, you're out of
29:59 room. It might not let you back up.
30:03 Uh uh. Is a shoe in here tonight? I got
30:05 to tell her Claude and I are brawling
30:07 over what to toss from the closet.
30:11 That's funny. [laughter]
30:13 New way to use chat GBT scheme GU for an
30:16 M1 Mac. You almost always want GUID
30:19 permission map. I knew that. Partition
30:21 map. GUID partition map. Exactly.
30:24 Everybody knew that. Go.
30:33 Oh, I should have named it.
30:38 I'm going to rename it.
30:41 [clears throat]
30:44 Can I edit the name? First aid
30:47 partition. Erase restore [clears throat]
30:49 info.
30:53 I'll go over to finder.
30:56 Can you You all are seeing this, right?
31:02 Rut row.
31:06 Why did it not do I got to mount it?
31:09 [laughter]
31:14 Rename.
31:16 We're going to rename it Dr.
31:20 Dr. Jay's medicine.
31:24 [laughter]
31:26 Yes, this counts as practice. This does
31:28 count as practice because this is making
31:30 my computer usable again.
31:33 Um,
31:35 how do I mount this? Unmount. It says
31:38 it's mounted. Why do I not see it?
31:44 Uh, let me quit disc utility. It's quit.
31:50 Oh, Dr. J. Dr. J's medicine. Look at
31:52 that. There it is. Boom. And then we
31:55 grab,
31:57 let me rename this. This is
32:01 SV Dropbox.
32:07 Yeah, there we go.
32:10 It's copying that [ __ ] over. And then
32:12 we're going to take this one and do the
32:15 same thing.
32:18 [sighs]
32:21 Wait, that one. That one didn't go over.
32:23 Why did that not go over?
32:31 What did I just do?
32:36 Did I move it in there?
32:40 Rutro [laughter]
32:42 What did daddy just do?
32:46 Uh [laughter]
32:50 h
32:53 how big's my downloads folder?
32:56 I'm such a digital hoarder. It's so bad.
33:00 27 gigabytes in my download folder. I'm
33:03 like, I don't want, you know, I might
33:05 have downloaded something I I I I want
33:07 to look at again.
33:10 All right. So, okay. These are are not
33:12 dragging over here now for some reason.
33:14 Why?
33:17 Uh uh uh uh uh uh.
33:24 D
33:28 copying 800
33:32 8,000 items over Dr. J's medicine show.
33:38 N Dr. J's medicine. You can use voice
33:41 with screen share if you're having
33:43 trouble.
33:50 Am I not sharing? I'm sharing, right?
33:52 Yeah.
33:54 Um, okay. Whatever.
33:57 That's going to copy over. I'm not going
33:58 to do file management here on the show,
34:02 but
34:04 I did just lose a folder. [laughter]
34:08 That's That's disconcerting.
34:12 And it looks like is my
34:19 Let me look up Dropbox.
34:22 I I have a feeling my finder just
34:24 crashed is what's going on.
34:43 Yeah. Here. Where is this?
34:46 Yeah, it's still there. Okay.
34:49 All right. Um,
34:54 all right. I'm not going to bore you
34:55 with the fact that I probably need to
34:56 restart my machine. [clears throat] What
34:58 I am going to do is bore you. So,
35:00 there's two things that I want to do
35:02 tonight. One is I just want to show you
35:05 some of the updates that I made in
35:08 notion. So, those of you that predicted,
35:10 those of you taking side bets,
35:13 Harry, exactly. Some of you taking side
35:16 bets on if I would survive more than a
35:19 day setting up notion. I'm now on day
35:22 four of actually using it. Well, not
35:25 that's not true. I'm not in day four of
35:27 actually using it. I'm in day four of
35:30 sort of kind of figuring out that it
35:32 might be useful at some point for me to
35:34 use.
35:35 [laughter]
35:37 If you're like, Kyle, that sounds awful.
35:40 Welcome to a daily practice. Okay. So,
35:45 so I've got Kyle's daily control board.
35:47 So, this is my notion notion window. And
35:50 then my daily control board. I've got my
35:53 tasks. And I've only got three in there
35:54 because I'm still setting it up. I've
35:56 got business areas.
36:01 And then I've got projects. And all of
36:04 my projects now go into a business area.
36:08 And then they also have um tags for them
36:11 whether they're on track, off track, or
36:14 just starting.
36:16 Um
36:18 and then I've got this new thing. I've
36:21 got a a people database and I've got a
36:24 project people database. So the people
36:27 are just like a list of people. And then
36:29 the project people database is the
36:31 database that connects that adds and
36:34 associates certain people with certain
36:38 projects.
36:40 Um, and if I go into the project people
36:43 database, I can now let's see
36:48 how do I do it? Did I do project links
36:54 by project? Yeah. So now I've got I can
36:58 look at my projects and know like who
37:01 are the key people in a given project.
37:03 So this is my my musical I'm working on
37:06 and here are the key people, my two
37:07 producers and my coowriter. And then and
37:10 then I've got them tagged as key people.
37:12 And so I'm going to be able to for for
37:14 the kinds of people I've got key people,
37:18 support people, future people, past
37:21 people, and dream people.
37:24 So dream people are like, I would love
37:27 to have famous person or person you
37:31 highly respect be a part of this
37:32 project.
37:34 Um, and so I'm going to start tracking
37:37 all that and and what I'm
37:41 the shift that I'm trying to make. And
37:43 this is a thing for me personally
37:45 because I've got issues like all of us
37:49 do. Um, that for all of my life, I have
37:53 treated projects
37:57 like entities that have tasks associated
38:00 with them. And if you're like, "Well,
38:02 Kyle, but that's project management.
38:05 It's called task management for a
38:07 reason, Kyle."
38:10 What I realize projects actually are.
38:12 Like, if I initiate a project, I
38:14 initiate the AI learning lab.
38:18 People show up here. There are people
38:20 like producer Brandon that are key to
38:22 making this thing go and the mods that
38:24 help on Tik Tok and things like that.
38:28 In my old model,
38:32 it was almost like it wasn't Brandon and
38:34 the mods. It was like there are tasks to
38:36 do. Like a task would be thank Brandon
38:39 for working so hard. That's a task,
38:44 right? But but it's like my relationship
38:45 was with the task, not with the person
38:50 that I feel, you know, might need
38:53 something. Do they have what they need?
38:56 And so the shift that I'm trying to make
38:57 is I'm trying to start to think of all
38:59 of my projects as communities of people
39:04 who are giving me the gift of their
39:07 attention
39:09 and their genius and their expertise and
39:11 their wisdom.
39:14 And it's my job to make sure they have
39:16 what they need to be able to support
39:18 that project.
39:20 And so I'm really excited about this new
39:22 thing. It scares the living piss out of
39:24 me because this is I've never thought
39:27 about it like this in my whole life. And
39:30 you might be like, "Well, it's pretty
39:31 obvious, Kyle. Projects are people." It
39:34 might be. It might be for other people.
39:36 It hasn't been for me. Tik Tok.
39:40 I'll give you money to take my name off
39:42 the list. I don't think your name's on
39:44 the list, is it?
39:46 [laughter]
39:50 I don't think it is.
39:52 >> [laughter]
39:53 >> That's pretty funny.
39:57 Um, the AI summary said this live was a
40:01 [ __ ] show. It is. [laughter]
40:06 This live is a [ __ ] show. Listen, they
40:09 don't call the people that show up here
40:10 every night irregulars.
40:12 So, here's the deal. If you are
40:15 neurotypical,
40:17 don't don't even stay. You're going to
40:19 hate this.
40:22 You're going to hate this.
40:25 It's nonlinear.
40:27 It's It's uh
40:31 I occasionally finish a sentence. I
40:34 occasionally make a point. I often
40:36 ramble in coherently. I'll lose my [ __ ]
40:40 over nothing. Someone will ask a
40:42 question like, "What what do you do you
40:44 like better, Chat JPT or Claude?" And
40:47 then I'll just scream for 45 minutes.
40:50 >> [laughter]
40:52 >> So, so if you're expecting if if you
40:56 come into the AI learning lab with
40:58 expectations like well let's see what
41:00 the professor has to teach us sir what
41:03 are your qualifications
41:05 it's not going to be good it's not going
41:06 to be good for you anyway nonlinear and
41:09 non-ircular and no tri not triangular
41:12 too exactly
41:14 th this this live is a [ __ ] show
41:16 actually you know what where did you
41:18 where did you see that that uh that
41:21 summary because I would like to make
41:23 some social media ads for AI learning
41:26 lab. This live is a [ __ ] show. Tik Tok.
41:30 Tik Tok AI summary. [laughter]
41:34 Arg. I'm stuck in a 3se secondond loop.
41:36 That sucks.
41:38 Reference to current events.
41:44 Big boy. What's happening big boy? Hey
41:47 big boy. What's shaking? What's going
41:49 down, big boy?
41:52 [snorts] Stanford researchers just
41:53 published Sleep FM, a new AI foundation
41:56 model that can predict over 130 health
41:59 conditions like dementia, heart attacks,
42:01 and Parkinson's from a single overnight
42:04 sleep recording. Good lord,
42:07 2026, man, we're about to see some [ __ ]
42:10 I think they just I just saw an
42:12 announcement that that they they might
42:15 have found a way to prevent blood
42:17 cancers like leukemia.
42:20 That was like two days ago or three days
42:22 ago. Now there now they can p predict
42:24 all of your ailments from an evening of
42:26 sleep.
42:28 [laughter]
42:30 Joy is joy in the house. Joy, your jobs,
42:34 you're we're not going to need your
42:35 services anymore. AI is going to do that
42:38 now.
42:39 >> [laughter]
42:42 >> All right. So, so that's that's the
42:46 progress I've made on on notion. Um,
42:50 I'm not
42:53 I still don't quite have my head around
42:55 it. It be so
43:00 if
43:01 how do I put this?
43:06 If you have a functional prefrontal
43:08 cortex,
43:11 the thought of thinking about
43:15 the organizational matrix
43:17 that you would put together to stitch
43:20 together some relational databases to
43:22 organize your tasks might seem
43:25 relatively trivial to you. It makes my
43:29 head hurt. And it it like the minute I
43:32 think about these relationships, I'm
43:34 thinking about other ways of doing
43:36 things and other things I can add. And
43:38 so what I'm trying to figure out is can
43:40 I can I create a foundation here that
43:44 can work with the way I work without it,
43:46 you know, I just completely overwhelming
43:48 me. So So I find this very very
43:50 stressful.
43:52 Um Joy Pie, I'm 100% okay with that. I'm
43:55 I'm burnt burnt. I'm in burnt mode since
43:58 last year. All I want to do is make
44:00 films, I hope, is what you're about to
44:01 say. Yeah. All I want to do is film.
44:03 Good. Yeah. No, I think that's what you
44:05 need to be doing. That's awesome.
44:09 Um, it's still pretty cool. I mean, you
44:13 probably you probably with 30 years of
44:15 experience as a sleep analyst, you would
44:18 probably find that Stanford project
44:19 really fascinating because you're
44:21 probably the human equivalent of that,
44:23 right? You you can probably look at a
44:24 sleep study and go, "Oh, boy. [laughter]
44:32 Uncle Jimmy. Uncle Jimmy's got some
44:34 issues.
44:36 [laughter]
44:39 [gasps]
44:40 Oh, good lord. Okay,
44:44 so what we're going to do tonight is I
44:48 need to go find
44:52 in my Google Drive.
44:56 I've got my Sydney project and in my
44:59 Sydney project I got my podcast project
45:02 and in the audio thing
45:10 today we're looking at something a
45:12 little different. Uh a new musical.
45:14 Okay. So what we're going to do
45:19 is we're going to put together a
45:20 podcast.
45:26 I'm going to open up iMovie.
45:29 And thank goodness that Joy Perie is a
45:33 sweetheart because
45:35 she doesn't make fun of me for using
45:37 iMovie. But those of you out there who
45:39 are Final Cut Pro dorks or Premier dorks
45:43 or Da Vinci Resolve nerds be like
45:46 iMovie? That's real filmmakers don't use
45:49 iMovie.
45:53 Exactly. So, we're going to open iMovie.
45:56 [laughter]
46:01 [groaning]
46:02 Okay. Let's see.
46:06 All right. This was the project
46:09 [clears throat]
46:10 for Fest of Us.
46:16 [clears throat]
46:21 So, here's a question.
46:28 Yeah, I'm a digital hoarder. I'm going
46:30 to embrace being a digital hoarder.
46:33 So, this is an old version of the
46:35 podcast that I edited a while ago.
46:40 [music]
46:43 And
46:44 my question to myself was, do I just use
46:48 this as the base and just overwrite
46:50 everything? Like, do I basically just do
46:52 a new cut inside this project? And I'm
46:55 going to say no. No, I'm going to go
46:58 make a new project because I'm a digital
47:01 hoarder and I'm going to embrace that.
47:03 And if at some point 15 years from now,
47:06 someone's doing a documentary on the
47:09 phenomenon that Sydney the musical
47:12 became and they want to go back and use
47:15 see the podcast I did before the podcast
47:19 that got me a producer that got it to
47:21 Broadway. [snorts] I'll have that. So
47:24 there. [laughter]
47:30 [gasps]
47:31 Ah, I just use cut cap cut and iMovie
47:33 sometimes. Uggh. Yeah, [laughter] I'm
47:36 right there with you, Corey. Uh, I'm
47:39 looking forward to Corey's LOL. Um,
47:42 hopefully I don't work that day.
47:46 A AI to sleep lab tonight, but
47:49 listening. Nice.
47:54 I look at a PT and I know if they are
47:57 positive. Wow. Amazing.
48:00 All right. Cap Cut. Cap Cut can do some
48:03 amazing things. Okay. All right.
48:05 Whatever. IMOV is a piece of [ __ ] but
48:07 it's it's fine. Like, it's fine. And
48:09 it's like it's and I know enough how to
48:11 use it that it's fine. It's fine. Fine.
48:16 >> Why is that so short? It's
48:18 >> in the human world.
48:18 >> We need Oh, it's not. Okay.
48:22 There we go. Okay.
48:26 Okay. So, now
48:30 we're going to go here and we're going
48:32 to grab posters.
48:45 [singing]
48:50 >> [singing]
48:52 >> Oh, and then you know what we have to
48:54 do? No, Kyle, why don't you tell us?
48:57 Okay, I will. We've got to go turn
49:01 um
49:04 we've got to go turn this podcast. We've
49:07 got to make the podcasters
49:10 >> name Microte the CTO Jason sees what
49:13 they've built and just has this
49:14 >> oh PT equals patient
49:17 >> moment
49:18 >> exactly this moment
49:20 >> I thought PT stood for physical therapy
49:22 which I didn't know I I had no idea what
49:24 PT how that related to um sleep studies
49:29 but I wasn't going to admit my
49:30 ignorance. [laughter]
49:33 Oh yeah, Joy does she does sleep
49:35 analysis. She's got a little She does
49:37 physical therapy while they're sleeping.
49:39 They're They wake up stronger and more
49:41 flexible. She It's It's It's a It's a
49:43 branch of of sleep analytics that not a
49:45 lot of people know about. Yeah. You
49:47 know, I I dabble I dabble in the uh in
49:50 the sleep analysis world. So, yeah, with
49:53 PT [laughter]
49:55 [sighs and gasps] free indeed. How are
49:57 you? I am good. I am good. I um if
50:00 you're new here, my name is Kyle
50:01 Shannon. This is the AI learning lab.
50:03 Learning is a really interesting word in
50:06 this uh uh place. The the AI has has
50:10 described this live as a [ __ ] show,
50:13 which I think is more accurate, but uh
50:16 Tik Tok frowned upon me naming it the AI
50:18 [ __ ] show lab.
50:20 [laughter]
50:26 Ah, what is meant by generative AI?
50:29 Great question. Actually, Dad can
50:31 answer. Okay.
50:35 So,
50:37 so for for decades, AI's been around for
50:39 decades. And there's all sorts of
50:41 different kinds of AIs. There's
50:43 convolutional networks and there's all
50:45 the like the face recognition stuff and
50:47 the traffic pattern stuff and the
50:49 medical stuff that's been around for
50:51 decades.
50:52 Traditional AI
50:55 a required you to be a mathematician, an
50:58 engineer, a programmer, a statistician,
51:00 a data scientist, and you essentially
51:03 had to bring your own data. There there
51:05 were all these different models and you
51:07 would bring very specific data and you
51:09 would train the AI AI on very specific
51:11 data and it would have a very specific
51:13 output.
51:15 Chat GPT is is the is the first kind of
51:20 consumer product of of a whole range of
51:24 products called generative AI
51:27 [snorts] and GPT. Those three letter
51:30 letters generative pre-trained
51:32 transformer. I'll tell you what all
51:34 three of them mean.
51:36 The generative piece means that it's
51:39 actually generating
51:41 um original combinations of words
51:45 effectively or it's generating
51:48 new and novel images or or sounds or or
51:53 whatever it might be. The way the the
51:54 way the technology works is the T in GPT
52:00 stands for transformer and that's a very
52:02 specific technology that Google invented
52:05 in 2017 and OpenAI exploited uh in
52:09 starting in 2020 with GPT1 then GPT2
52:12 then GPT3. GPT3 was the first
52:17 um large language model that started to
52:21 look powerful. And what OpenAI realized
52:23 if is if they use this transformer
52:25 architecture and they throw as much data
52:28 as they can and as much compute as they
52:30 can, it'll do remarkable things. And so
52:33 the P in GPT stands for pre-trained.
52:37 And so what what OpenAI did is they went
52:40 out and they basically scraped the
52:42 internet. So they found every website
52:44 they could, every database they could,
52:46 every Wikipedia, every blog post, just
52:51 all of it. All of it, some legal, some
52:54 illegal, absolutely all of them went out
52:56 and did this. They just went all out and
52:58 grabbed as much data as they could. And
53:00 then through this transformer
53:02 architecture, they do what's called
53:03 embedding or, you know, encoding. They
53:05 call it embedding all of that knowledge
53:08 in this very particular way that allows
53:11 it when you type a prompt,
53:14 it's not copying and pasting from
53:16 existing documents. It's just looking in
53:18 thousanddimensional mathematical space
53:21 and based on your prompt it's predicting
53:24 effectively what the next word is. They
53:26 call it a token but you know it's
53:28 effectively the next word and it's just
53:30 predicting kind of word at a time or
53:32 little small group of words at a time
53:36 what it thinks the answer should be. And
53:38 so the generative part
53:40 is really significant because here's
53:43 why.
53:46 Computers for all of my life, for all of
53:48 our lives
53:50 have not been generative.
53:52 They they've been um calculators and
53:56 they've been predictive and they will
53:57 take our data and we take our data and
54:00 we run it through a program that someone
54:02 has programmed with logic which will
54:05 always behave exactly the same way and
54:09 the data will run through that logic and
54:11 it will come out transformed. So, so we
54:13 basically are transforming
54:15 existing data into new forms, but it's
54:20 predictable. Generative AI is creating
54:24 wholly new things that didn't exist
54:26 before. And people will argue like it
54:29 can't have no it can't do novel thought,
54:33 but it can
54:35 because what is novel thought? Well,
54:38 novel thought is you live your whole
54:40 life and you encode you embed
54:44 your experiences and knowledge and
54:48 education into this large language model
54:51 called your brain.
54:53 And it's got all these things in it. And
54:55 then you're out walking and you see a
54:58 pink a pink dog run across the street
55:02 and it goes into your eyeballs and it
55:04 goes into your brains and you go, "Pink
55:06 dog, that's weird." And then that
55:08 triggers off some memory when you were
55:10 in junior high and you were at the
55:12 junior high dance and someone had a a
55:14 pink poodle
55:16 handbag and and you accidentally tripped
55:18 and knocked it on the ground and and and
55:20 and that reminded you of this time in
55:23 college and then you put those two
55:24 things together and you're like, "Hey,
55:25 that should be a song about a pink dog
55:27 and heartbreak." And you have this novel
55:30 thought.
55:32 Well, that's exactly how large language
55:34 models work. They have like all this
55:36 knowledge in there and you give them a
55:38 prompt and they're like, I think this
55:40 would be the next thing and it's it's
55:41 literally like pulling fragments of
55:44 knowledge from thousanddimensional
55:47 mathematical space and it's generating
55:49 this new thing. It's absolutely [ __ ]
55:53 remarkable. The more you learn about it,
55:54 the more remarkable I think it is
55:57 because it's it's this very simple
56:03 cold probability calculator.
56:06 But what it's been trained on is the
56:09 output of human beings
56:12 for like all of history. like whatever
56:15 we have done as humans that that was
56:17 jammed onto the internet in the last 80
56:19 years is essentially in these models
56:24 and so we get to interact with we get to
56:26 like collaborate with all of the people
56:29 that came before us
56:31 that's the P in GPT pre-trained
56:34 pre-trained with us
56:38 so it's an incredible reflector of of
56:41 humanity and what What generative AI is
56:45 particularly good at is being a
56:47 reflector, an amplifier of your ideas.
56:51 So when it gets really good is is not
56:54 when you learn prompt engineering. like
56:57 prompt engineering is a mechanism for
57:00 how to talk to these things in a
57:02 predictable way.
57:04 But when when AI gets really powerful is
57:07 when you understand who you are, what
57:09 your values are, what you want, and then
57:12 you figure out how to share that into
57:14 this machine. Hey, I really care about
57:16 entrepreneurs and I care about small
57:18 businesses and I care about pizza and I
57:20 care about, you know, quality
57:22 ingredients and I care about this and
57:24 and I've never started a restaurant
57:26 before, but I'm thinking about that now.
57:28 The more you give it, that is who you
57:31 are,
57:33 it will absolutely transform your
57:35 relationship with it. Because rather
57:38 than you being in competition with AI,
57:42 you can use AI as an amplifier of you.
57:45 And that's what this channel's all
57:47 about. And that's what the AI salon's
57:49 all about and things like that. Sorry,
57:51 I'm dyslexic, so I say things backwards.
57:54 It's been trained on the world's
57:56 information. It has
58:04 >> [clears throat]
58:06 >> By the way, um,
58:10 who said they were dyslexic? Did you say
58:11 that, Gareth? Oh, first of all, don't
58:14 apologize for being dyslexic. Um,
58:18 we are we are a a neurospicy friend
58:21 friendly channel. Um, but I also I mean,
58:24 you've said this, Gareth, that that um
58:26 that generative AI like it it allows you
58:29 to take something like your dyslexia and
58:31 and it it it becomes an accessibility
58:33 tool. It's really cool. He mistyped
58:35 earlier in chat. That's cool.
58:38 Um, okay. Let's go back to this thing
58:41 now. So, oh, so in in my previous uh
58:46 project here, here we're going to go
58:48 Sydney
58:50 um 2.2 2 podcast.
58:54 So, we're going to go. This is 2.2.
58:58 Here's 2.1. So, in this podcast,
59:03 we've got these these podcast
59:05 characters.
59:06 >> Today, we're looking at something a
59:07 little different. Uh, a new
59:09 >> Thank you. I really appreciated that
59:11 explanation. You're welcome. Free
59:13 indeed. I you know, do me a favor. If if
59:15 you're really brand new to this stuff,
59:17 join the AI salon. So, if you go to, in
59:19 fact, uh, Brandon will pop up community.
59:23 Salon, he'll pop up a banner here in a
59:26 second on screen. Um, keep coming back
59:29 to these lives. And if you go to
59:30 community.thesalon.ai,
59:34 um, join the community and and it's it's
59:38 a community. We started that community
59:40 the week chat GPT came out, which if
59:42 you're brand new to this stuff, you're
59:44 going to be like, "Oh, it's going to be
59:46 full of bunch of smarties. Uh, that's
59:48 not going to be comfortable for me." I
59:50 promise you it will. It is. It is a
59:54 remarkable group of people that
59:57 celebrate someone doing remarkable work
59:59 in AI as much as they celebrate someone
1:00:02 coming in saying, "Hey, I think I want
1:00:04 to learn about this stuff." It it really
1:00:06 is truly remarkable. So, go in there,
1:00:08 introduce yourself. There's an introduce
1:00:10 yourself channel um or space they call
1:00:13 it. Um and just say hi and then keep
1:00:15 coming back here. I do this five nights
1:00:17 a week. Um and what I'm working on here
1:00:22 starting in 2026 is working on my daily
1:00:27 practice using AI. Um, and so everything
1:00:31 that I'm working on in here is projects
1:00:33 that I'm working on in my life. And I'm
1:00:36 trying to use AI to get better and
1:00:39 become a better human and become a
1:00:41 better professional and, you know, raise
1:00:45 the game of my projects using AI, but
1:00:48 also using
1:00:50 my skills and and my ability to focus
1:00:53 and and work with intention. So,
1:00:58 um, we'll do AI is my co-teer. It is.
1:01:01 It's an It's an incredible teacher. It's
1:01:04 It's one of the one of the funniest
1:01:05 things about AI is um generative AI
1:01:10 is is that
1:01:13 people who don't use it are like, well,
1:01:16 if it's so smart, then it's going to
1:01:17 make you stupid.
1:01:20 Uh, no. It's like imagine if you have
1:01:25 PhD level teachers for every subject in
1:01:29 the university
1:01:31 who have infinite patience and are
1:01:34 completely supportive.
1:01:37 It can amplify your intelligence and it
1:01:39 can it can accelerate it. It's it's just
1:01:42 it's so funny. It's like the the tropes
1:01:44 and and all of the anti-AI stuff, the
1:01:47 things that they say, it's almost always
1:01:49 the opposite of what they say.
1:01:52 The robots are going to kill you. Well,
1:01:54 the robots might just allow us ascend to
1:01:57 our higher selves because it's going to
1:02:00 free us from doing the soulcrushing,
1:02:03 shitty, repetitive work of society and
1:02:06 allow us to just say, "Here's who I am.
1:02:08 Here's what I value and here's the
1:02:10 change I want to make in the world, and
1:02:12 I'm going to use AI to make that
1:02:13 happen." Right? [clears throat] Like
1:02:16 it's just as likely we become the best
1:02:18 versions of ourselves as it kills us.
1:02:21 [laughter]
1:02:22 It's the absolute opposite of what
1:02:24 everyone says. The robots did my knee
1:02:27 replacements. Listen. Exactly. You are
1:02:30 now you are now ambulatory because of
1:02:33 the robots. Um there's a Tik Tok pin
1:02:36 went away. Just pop it back up. Um
1:02:39 absolutely. It's been a game changer for
1:02:41 me as a high school teacher. Yeah. Yeah.
1:02:43 Exactly. I listen teachers that that use
1:02:46 AI as a tool to to allow themselves and
1:02:50 their students to think creatively
1:02:53 because to use AI well, it actually
1:02:55 requires critical thought, critical
1:02:58 thinking,
1:03:00 intentionality,
1:03:02 the ability to discern between [ __ ]
1:03:05 and and brilliance. And and AI can do
1:03:09 both.
1:03:10 It's great at [ __ ]
1:03:13 It'll hallucinate up a storm for you.
1:03:16 And as you use it more, you start to
1:03:18 realize, "Oh, I can navigate this
1:03:20 because it does these other things
1:03:21 really well."
1:03:26 All right, Tyler, how do you combat the
1:03:30 negative sentiment about AI from
1:03:33 programming co-workers? Um,
1:03:42 you educate. Yeah, G Gareth's point. You
1:03:45 you educate and show. So, so there's a
1:03:47 couple of things. One is
1:03:51 how do you how do you combat negative
1:03:53 sentiment?
1:03:55 The one thing I would not do is get on a
1:03:57 soap box. Like, you can't win the
1:03:59 argument of AI's better than you are,
1:04:03 right? Or AI is good. If they think AI
1:04:05 is bad and you just say, "No, AI is
1:04:08 good." then you're basically in a
1:04:10 political like like we
1:04:13 this is a perfect time in history
1:04:19 for AI to show up because we live in in
1:04:22 such a polarized country right now and
1:04:25 you could certainly argue polarized
1:04:27 world
1:04:30 that that there are certain words we
1:04:32 can't even say
1:04:35 right you you just can't say certain
1:04:38 works because they're so politically
1:04:39 charged. AI is kind of like that. So, we
1:04:41 know what this is like. So, if you're
1:04:43 dealing with someone that just hates AI,
1:04:45 you actually can't combat it.
1:04:49 What you can do, and I think this is
1:04:52 this is life, is you can have empathy.
1:04:55 You can under you you can seek to
1:04:57 understand why they hate it.
1:05:01 And a lot of reasons that people hate
1:05:03 AI, they're actually right about.
1:05:07 Like like when when I get trolls in
1:05:09 here, one of the things that upsets
1:05:11 trolls the most is I actually agree with
1:05:13 them on some things. They're like, "They
1:05:15 stole from artists." I'm like,
1:05:17 "Correct." And that's really shitty.
1:05:21 You're correct. They did.
1:05:24 And if they had asked permission, they
1:05:26 wouldn't have gotten it. So we wouldn't
1:05:28 actually have these tools in the first
1:05:30 place. And because these tools exist,
1:05:34 because of what they did, the lawyers
1:05:35 are going to work out who owes what to
1:05:37 who. Like that's happening right now. So
1:05:40 I agree that part of it is shitty.
1:05:44 And
1:05:48 a tool like chat GPT went from zero to
1:05:52 100 million users in 6 weeks. It's now
1:05:57 at 800 million users. It's the fastest
1:05:59 adoption of technology in history.
1:06:02 Trillions of dollars are being invested
1:06:04 in AI. So, there's one thing that I know
1:06:08 about AI.
1:06:10 I know this. I know it
1:06:14 a because I've been on the planet for a
1:06:15 while and I've seen some [ __ ] But I can
1:06:18 also just look at [laughter]
1:06:20 800 million users in three years is
1:06:23 remarkable. Trillions of dollars
1:06:25 invested means this. AI is not going
1:06:28 away.
1:06:29 And so if AI is not going away and it's
1:06:32 getting better and it's getting smarter
1:06:33 and it's accelerating,
1:06:36 then you only have two choices.
1:06:40 You can deal with it or not.
1:06:44 And if you don't deal with it, then it
1:06:47 happens to you. And that's going to
1:06:50 [ __ ] suck. And so, so like how I
1:06:53 combat this stuff is I say, "You're
1:06:56 right." And actually, you have a right
1:06:58 to be scared. Like, I understand that
1:07:00 you're scared, but what what you'll only
1:07:03 discover if you use it is that AI is not
1:07:06 perfect. And AI actually really benefits
1:07:09 from someone with your expertise.
1:07:12 And so, you can sit on a soapbox over
1:07:14 here and say, "I'll never use AI for
1:07:16 programming." And then have it slap you
1:07:19 off that soapbox because it's not going
1:07:21 away. Or you can say, "I'm going to at
1:07:24 least understand what it is." That's the
1:07:26 the the whole purpose of this channel
1:07:30 is to just basically make that
1:07:31 distinction. AI is not going away and I
1:07:35 personally am going to choose to deal
1:07:37 with it. I'm going to understand what it
1:07:38 is. I'm going to understand how it
1:07:40 works. I'm going to understand what the
1:07:41 ethical implications are. I'm going to
1:07:43 understand the biases. I'm going to
1:07:45 understand what it's brilliant at. I'm
1:07:47 gonna understand what it's shitty at
1:07:51 to the best of my ability
1:07:54 because it's the only way I know
1:07:59 to feel like
1:08:01 you've got any sense of agency in this
1:08:03 world.
1:08:06 I like I actually like I feel bad. It it
1:08:08 it makes my [ __ ] heart ache when I
1:08:12 meet someone who's like crossed arms
1:08:14 like I hate I never use AI.
1:08:20 I'm like the only person you're hurting
1:08:23 is you.
1:08:27 [ __ ] breaks my heart. It's why I
1:08:29 started this channel. It's why I started
1:08:31 the AI salon.
1:08:36 And what I'm discovering, what we're
1:08:39 discovering, what this community is
1:08:41 discovering
1:08:43 is that it's this liberating, [ __ ]
1:08:46 inspiring
1:08:48 capability
1:08:50 that we're all lucky enough to live at a
1:08:53 time in history
1:08:55 where some [ __ ] Uber nerds
1:08:59 over the past 70 years figured out how
1:09:02 to combine computers and transistors and
1:09:05 mathematical algorithms in such a way
1:09:10 that they can take all of the knowledge
1:09:14 that humans have ever output and put
1:09:16 them into this softballsiz
1:09:19 thing
1:09:22 that we get to like it's like it's
1:09:23 literally like an oracle. We get to go,
1:09:25 "Hey, humanity, tell me how to fix my
1:09:28 plumbing."
1:09:30 Out it comes.
1:09:32 Now you're a plumber. What?
1:09:36 I want to make a movie. Now you're a
1:09:38 filmmaker. Ask Joy Perie. 30 years as a
1:09:41 sleep analyst.
1:09:43 In her mind, in her dreams at night,
1:09:45 she's always had these stories she's
1:09:46 wanted to tell.
1:09:50 Did she go to film making school? No,
1:09:51 she went to sleep analysis school. I
1:09:54 don't whatever you go to for that. I
1:09:55 don't know where you go for sleep
1:09:57 analysis training, but she did that 30
1:10:01 years. But these she always had these
1:10:03 stories.
1:10:05 And then she started hanging out here
1:10:07 and joining communities and learning.
1:10:10 She's like, "Huh, those video tools,
1:10:13 they look like I wonder if I could take
1:10:15 these stories I've had since I was a
1:10:18 little girl and get them out of my
1:10:20 head."
1:10:22 Oh [ __ ] I can.
1:10:29 Like it's it's a transformational
1:10:33 [snorts]
1:10:34 tool if you let it be.
1:10:38 Or you can sit there and cross your arms
1:10:40 and go, "The robots will never be
1:10:42 smarter than me." Uh, go [ __ ] try
1:10:44 them.
1:10:47 The robots became significantly smarter
1:10:49 than me. When the first reasoning model
1:10:51 came out, 03, it was like in 2024,
1:10:54 that [ __ ] thing came out and like it
1:10:57 smacked me upside the head so hard I was
1:10:59 that was like whack. Like not as bright
1:11:02 as you thought you were, are you there,
1:11:04 Skippy? [laughter]
1:11:08 I realized that I was the bottleneck
1:11:12 to getting AI to do interesting things,
1:11:15 not the AI.
1:11:19 It's remarkable.
1:11:22 But you're not going to convince
1:11:23 someone, you know, who's like red team,
1:11:25 blue team. I'm a Broncos fan. I I hate
1:11:28 the Raiders. I hate the Raiders.
1:11:32 You can't convince them to not, you
1:11:34 know, hate the Raiders. They're going to
1:11:35 always hate the Raiders. Same with the
1:11:37 AI people. But if the AI people can
1:11:39 understand
1:11:41 that they actually have a choice
1:11:44 and if they choose to learn a little bit
1:11:48 just might change their life. And if
1:11:50 they don't, well, it's going to [ __ ]
1:11:52 suck.
1:11:55 [sighs]
1:12:07 Did I piss anyone off? I don't even know
1:12:09 what I just said. My mom just discovered
1:12:11 she can take a photo of the inside
1:12:15 of her fridge. Yeah, this is a good one.
1:12:18 And what? And can make recipes. Yeah,
1:12:21 exactly. So, Kelly Camp, this is a fun
1:12:24 one. Uh Tyler, um Kelly Camp, who's
1:12:26 who's in here, she's one of the
1:12:27 irregulars. Um
1:12:30 [clears throat and snorts] this is like
1:12:31 two years ago. Um, and and ChatPT had
1:12:34 come out with the ability to take a
1:12:35 photo and and and analyze it and stuff
1:12:37 like that. And so she was talking with
1:12:39 this bar owner and and the bar owner was
1:12:42 like, "I own a bar. This AI stuff isn't
1:12:45 good for me. I can't do anything with
1:12:46 AI." And so Kelly goes, "Hang on a
1:12:49 second." She goes back to the bar. She
1:12:51 takes a picture of all of the bottles
1:12:53 and then she types in a prompt that that
1:12:55 says, um, "Make me an a spreadsheet of
1:12:59 the inventory of this bar and and mark
1:13:03 any bottles that are a third or less.
1:13:06 Tag them in the spreadsheet as need to
1:13:08 reorder."
1:13:10 And he goes, "What?" [laughter] And then
1:13:12 it goes and does all his things and it
1:13:15 goes, "Here's your spreadsheet." She
1:13:17 downloads the spreadsheet, opens it up,
1:13:19 and there's this whole inventory with
1:13:21 the [ __ ] he's got to reorder, you know,
1:13:22 and his jaw dropped to the floor. It,
1:13:24 you know, it's the kind of thing where
1:13:28 once you understand that
1:13:34 [snorts]
1:13:35 you have access to capabilities that you
1:13:37 didn't think were possible in your life
1:13:41 that all of a sudden you just can do
1:13:44 that thing. Um,
1:13:48 it's it's a pretty profound insight or
1:13:51 or epiphany
1:13:55 because you then get to ask the
1:13:57 question,
1:14:00 if I could do anything,
1:14:03 what would I choose to do?
1:14:08 That's big.
1:14:10 That's big.
1:14:13 Liz Miller Gersfeld came in the on the
1:14:15 live [snorts] last week
1:14:18 and she talked about her daily practice.
1:14:20 She's my co-host in the AI salon. She's
1:14:22 a remarkable woman. She's very centered,
1:14:26 very zen-like.
1:14:28 And she asked me this very simple
1:14:30 question, Kyle, what do you want more
1:14:32 of?
1:14:34 And it [ __ ] choked me out.
1:14:38 Choked me out. Like this is profound
1:14:41 stuff.
1:14:42 like really asking yourself who am I?
1:14:45 What do I believe? What do I want? What
1:14:47 are my values? Whose life do I want to
1:14:50 impact? How? Anyway, [snorts]
1:14:53 okay, let's get back to let's get back
1:14:55 to making a podcast.
1:14:58 We'll get a little bit of work done
1:14:59 tonight because I'm I'm a little rambly.
1:15:01 I'm a little tired.
1:15:04 Um, so we've got to go make videos of
1:15:06 people talking. And what we have right
1:15:09 now is a raw
1:15:13 podcast.
1:15:16 >> Let's see.
1:15:19 >> Sabotage in this horrifying circus.
1:15:21 >> So, we got no people talking. So, we got
1:15:23 to go turn this into people talking. So,
1:15:26 how are we going to do that?
1:15:28 First thing we're going to do is we're
1:15:29 going to chop all the the music out of
1:15:32 this recording
1:15:35 and make a a a backup of it. Okay. So,
1:15:42 I'm going to go here. I'm gonna go Wait,
1:15:46 where am I? Yeah, that's it.
1:15:56 Oh [ __ ] wrong thing. Always opens Apple
1:15:59 Music. I should change that.
1:16:01 >> You're looking at something a little
1:16:02 different.
1:16:04 >> We're going to open this with Quick
1:16:05 Time.
1:16:09 >> [whistles]
1:16:20 >> Why? What? Why is it not working?
1:16:24 Um, hang on.
1:16:27 Open with quick time.
1:16:32 Today we're looking at something a Okay.
1:16:41 was this moment
1:16:42 >> exactly this moment of discovery and
1:16:44 that feeling of awe of seeing something
1:16:46 that could change everything is the
1:16:47 opening number too good to be true
1:16:52 >> okay so here we're going to split the
1:16:54 clip bang
1:16:57 all right and now
1:17:01 we've got [clears throat]
1:17:04 today we're looking at some
1:17:09 >> [singing]
1:17:17 [music]
1:17:21 >> Okay,
1:17:22 split the clip there. And the reason I'm
1:17:24 doing this is just
1:17:27 this is a 15minute um chunk of audio and
1:17:33 I don't need to animate when this I
1:17:35 don't need to animate the people when
1:17:37 the songs are happening.
1:17:40 [singing]
1:17:43 >> Vrage gives him seven days. Just [music]
1:17:46 seven.
1:17:50 >> Okay, we'll split it there.
1:17:57 feeling [music]
1:18:02 over [music]
1:18:06 this code to that
1:18:12 to that
1:18:15 split clip. Bang. Get rid of this. So,
1:18:20 let me just make sure I've done the
1:18:21 right thing here. We should have talking
1:18:24 and then a little discovery and that
1:18:25 feeling of awe of seeing something that
1:18:27 could change everything,
1:18:32 >> but then almost
1:18:34 >> that was bad.
1:18:35 >> Immediately
1:18:37 see the corporate hubris
1:18:40 [clears throat]
1:18:41 of awe of seeing something that could
1:18:43 change everything.
1:18:46 [music]
1:18:48 Okay.
1:18:51 It's this painful fight about constantly
1:18:53 postponing your
1:18:56 but
1:18:58 okay
1:19:00 split clip
1:19:02 >> THE FALSELED [music]
1:19:03 WEED GET PATCHED UP IN A FEW THERAPY
1:19:06 SESSIONS they stick in the gut we get in
1:19:09 a rut [singing] door
1:19:11 THERE'S NOTHING BUT PASSIVE IMPRESSION.
1:19:24 [music]
1:19:26 >> OKAY. Split clip.
1:19:31 And if if if is if what you're thinking
1:19:34 right now is my god this is tedious.
1:19:39 You're correct. [laughter]
1:19:45 >> Part part of part of a daily practice is
1:19:49 uh sometimes you got to do this
1:19:50 >> is slowly eroding the human cost of all
1:19:53 this ambition.
1:19:57 >> Okay. split clip
1:20:02 >> through thick and thin.
1:20:07 [singing and music]
1:20:08 >> If you're wondering what this is, the
1:20:10 these are songs from a musical I wrote.
1:20:12 This is a a Notebook LM podcast that we
1:20:15 edited songs into. I'm now taking the
1:20:18 vocal track of the people talking from
1:20:21 Notebook LM. I'm going to animate them
1:20:23 with some images I made as talking
1:20:26 heads. And then we're gonna cut them
1:20:28 into the uh into the piece.
1:20:31 [singing]
1:20:33 [music]
1:20:39 >> Kyle, look up skills and hooks and
1:20:41 commands
1:20:43 before start using Oh, Tyler look up.
1:20:46 Oh, you guys are talking about Claude
1:20:48 Code. Yeah, Claude Claude Code's pretty
1:20:49 amazing.
1:20:51 Kyle, are you using Notebook LM podcast
1:20:54 audio for the podcast? Yeah, I am. Yeah,
1:20:57 this is just Listen, I kind of did this
1:21:00 on a lark like a year ago. We we
1:21:04 finished a version of the of the script
1:21:07 for for Sydney and I put it into
1:21:11 Notebook LM and I turned it into this
1:21:13 podcast and I thought, well, [ __ ] what
1:21:15 if we what if we cut some of the songs
1:21:18 into that? And then we did that and I
1:21:21 showed it to
1:21:23 um a couple of producers that we're
1:21:25 we're working with right now and they
1:21:28 got really excited about this format.
1:21:31 They they love this format and so this
1:21:34 is a much more focused thing where we're
1:21:37 playing um I think 18 of the 20 songs.
1:21:40 We're playing snippets from 18 of the 20
1:21:43 songs in the show. And then these two
1:21:45 podcasters are talking about they kind
1:21:47 of set up the story and they talk about
1:21:49 what happens in the scene before each
1:21:51 song and what happens after. And it's
1:21:54 just a it's just a neat little format.
1:21:55 So,
1:22:00 >> okay, split clip.
1:22:03 Get rid of that bad boy. Bang.
1:22:10 It's so quiet and devastating. You just
1:22:12 feel the intimacy eroding piece by
1:22:14 piece. Okay. So then Kellen finally
1:22:16 meets the AI.
1:22:18 >> And the way he discovers Sydney's
1:22:19 [clears throat] true potential is, well,
1:22:20 it's not by running a standard test.
1:22:22 >> He gives it this completely absurd task.
1:22:24 >> He asks for an explanation of quantum
1:22:26 physics, but as a Detroit hiphop cipher.
1:22:28 >> And it does it in the song The Quantum
1:22:29 Cipher.
1:22:31 >> Spooky action.
1:22:33 >> Split the atom with pattern. Every bar
1:22:36 is a burst of truth. Super position in
1:22:38 the mission.
1:22:40 Okay. Split clip.
1:22:50 >> Spin. I'm the velocity ripping apart THE
1:22:52 FACADE.
1:22:54 OKAY. Split clip.
1:23:01 >> [laughter]
1:23:02 >> I always, one of my favorite bits in
1:23:04 here is,
1:23:06 "Hey, hey, Sally, what'd you do last
1:23:08 night?" I I don't want to talk about it.
1:23:11 No. What'd you do? Um,
1:23:15 I I watched an old guy edit an audio
1:23:18 file for two hours. [laughter]
1:23:24 >> Human to see the world.
1:23:27 >> Yeah.
1:23:29 >> [laughter]
1:23:32 >> It's all It's all fun and games till
1:23:35 someone puts an eye out.
1:23:39 [clears throat]
1:23:40 [music and singing]
1:23:51 split clip. Boom.
1:23:54 Okay. [music]
1:23:58 >> And that's when they're both
1:23:59 >> Kyle, did you make the music or is it AI
1:24:01 too? Um, it's it's a combination. So, we
1:24:05 wrote all the lyrics. Most of the songs
1:24:07 we were some combination of us putting
1:24:11 in ideas, um, us knowing exactly what we
1:24:15 wanted. So, we wanted something that had
1:24:17 this kind of feel
1:24:19 like like the the song. Oh, it's it's
1:24:22 not in there right now. But the song
1:24:23 later,
1:24:27 >> let me let me find the song. Hang on.
1:24:31 The song later.
1:24:36 >> Oh, that's Wait, we're way way back in
1:24:39 THE WRONG PLACE.
1:24:44 SO THIS THIS SONG
1:24:46 >> YOUR life
1:24:47 >> but the promises [music] broken the
1:24:48 false pledges we ETCHED IN DON'T GET
1:24:51 PATCHED UP IN A FEW THERAPY sessions
1:24:53 they [music] stick in the gut we get
1:24:55 stuck in a rut and door SHUT
1:25:01 it
1:25:05 [singing]
1:25:06 >> so this is a song we probably did
1:25:11 between Andrew and I
1:25:16 75 different
1:25:19 attempts at getting this song and we
1:25:22 just couldn't get it. So like what what
1:25:25 this song is is Kellen the tech reporter
1:25:29 basically
1:25:31 demands to take on this story knowing
1:25:34 that he's going to have to cancel this
1:25:36 vacation with his wife. They're going on
1:25:38 a trip to Bali the next [ __ ] day.
1:25:40 Right.
1:25:42 So, this song is is a fight between a
1:25:47 married couple who've been married for,
1:25:49 you know, a decently long long enough
1:25:52 time that he's promised her, you know,
1:25:54 over and over again, we'll fix it later.
1:25:56 We'll fix it later. And we just couldn't
1:25:59 get it right. And this this was an
1:26:01 example of a song where where we had the
1:26:03 lyrics and I would do a bunch of
1:26:06 versions and then I'd send him two or
1:26:08 three and he's like none of those are
1:26:10 right and then he'd do a bunch and he'd
1:26:12 send them to me like none of those are
1:26:14 right and then I was in New York
1:26:17 doing a inerson writing session with
1:26:19 Andrew and producer AI came out which is
1:26:23 a it it used to be called Refusion and
1:26:26 it it was just a different of these song
1:26:28 generating tools and So, I literally
1:26:30 took the script plus all the lyrics and
1:26:35 I said, "Here's a fight that these two
1:26:38 people are having. Here's the lyrics. I
1:26:41 want this I want this to sound like a
1:26:43 Broadway show that's got some edge to it
1:26:46 where two people are fighting." And this
1:26:48 was essentially the song that came out.
1:26:50 But this was after like I don't know six
1:26:53 months of not being able to do this
1:26:55 >> your life.
1:26:56 >> But the promise is [music] broken. The
1:26:58 false pledges we etched AND DON'T GET
1:27:00 PATCHED UP IN A FEW THERAPY SESSIONS.
1:27:03 They [music] stick in the gut. We get
1:27:05 stuck in a rut and door SHUT TILL
1:27:07 THERE'S NOTHING BUT PASSIVE AGGRESSION.
1:27:10 LOOK, I get it. YOU SAY YOUR JOB'S HOT,
1:27:12 but [music] the truth is I'm feeling
1:27:14 like an [singing]
1:27:18 onry,
1:27:26 [music]
1:27:27 >> right? you know, there's the man trying
1:27:28 to justify his [ __ ] behavior. Um,
1:27:33 so, so we wrote the song, we knew what
1:27:36 we needed, and like this is, this is,
1:27:39 it's not dissimilar if we were sitting
1:27:41 there working with a musician and
1:27:43 saying, "We want it to be like this. We
1:27:45 want it to be like that." And you just
1:27:46 try and you try and you try and then you
1:27:48 eventually get it. And then some of the
1:27:49 songs, like there's a song in act two
1:27:52 called Step Right Up. That's a song that
1:27:54 Andrew had written for a completely
1:27:56 different project, but it had the right
1:27:57 tone. So, that we used as the basis of a
1:27:59 song. So, we wrote all the songs. We
1:28:02 just used AI to to produce them. Um, and
1:28:05 then everything that we've got in this
1:28:07 version, we took all the songs that we
1:28:09 did full orchestrations of with AI and
1:28:12 then we used AI to create stripped down
1:28:15 piano versions of them. So, they all
1:28:16 sound like they're from the same same
1:28:18 show. So, it's a it's a cool process.
1:28:21 It's it's very much a collaborative
1:28:23 process and a lot of times AI is just an
1:28:25 [ __ ] and it doesn't do good work.
1:28:27 Okay. Anyway, back to this thing.
1:28:31 [clears throat]
1:28:31 [sighs]
1:28:32 I really like it. This is so cool. Yeah,
1:28:34 it's I I got to tell you, man, and and
1:28:36 you know, like the script. So, when I
1:28:39 had I had the idea for this musical on
1:28:41 this live on March 19th, 2023, you can
1:28:44 go back and watch the episode. It's on
1:28:45 it's on my YouTube channel. You can go
1:28:48 back and you can there I have I have
1:28:51 recorded the moment where Sunno created
1:28:55 a song that sounded enough like a
1:28:56 Broadway show where I'm like, "Wait a
1:28:58 minute, this should be a Broadway show."
1:29:00 And then I spent the next month creating
1:29:02 four or five of the core songs, the core
1:29:05 story. Um kind of a a shitty loose ver
1:29:09 first version of the script. And then I
1:29:11 reached out to Andrew and I said, "Do
1:29:13 you want to co-write this?" And he said,
1:29:14 "No." He goes he goes, "I'm a writer. I
1:29:16 don't want to write with AI about AI. It
1:29:18 sounds stupid. He didn't say it quite
1:29:20 like that, but he said the polite
1:29:22 version of that. Um, and I told him to
1:29:25 go read the materials that I that I
1:29:27 wanted to base it on or that it was
1:29:28 based on. And he read them and he said
1:29:30 he said, "I found myself oddly feeling
1:29:33 for the chatbot.
1:29:36 Um, I want to I want to write it with
1:29:38 you."
1:29:38 >> She asks him [music] what it means to be
1:29:40 human in the song being human.
1:29:44 >> Okay.
1:29:46 split clip.
1:29:52 >> This was a cool song. This was a song
1:29:54 that um that
1:29:58 was originally Sydney's song talking
1:30:00 about how she was dreaming about being
1:30:02 human and then we realized
1:30:05 it needed to be Kellen explaining to her
1:30:08 what it meant to be human. And so this
1:30:10 is a song that's like this beautiful
1:30:13 sort of it was an aspirational song
1:30:16 before, but what it is here is it's
1:30:18 Kellen the writer
1:30:21 kind of on his heels because the chatbot
1:30:24 is asking him what it means to be human
1:30:27 and he's like uh uh he's trying to
1:30:30 figure it out. So he kind of falls in
1:30:32 love with being human as he explains to
1:30:35 her what being human means.
1:30:37 >> Just lie. It's not just grace. It's
1:30:39 hunger, pain, a dying face. [music] To
1:30:41 be alive. Here, I'll play it from the
1:30:43 beginning. This
1:30:44 >> see the world through eyes to blink and
1:30:46 cry.
1:30:48 To feel the wonder of a thought like
1:30:50 sparks across the sky.
1:30:53 It's not just light. It's not just
1:30:55 grace. It's hunger, pain, a dying face.
1:30:58 To be alive is to burn.
1:31:02 Sometimes
1:31:04 you [music] want to be more than
1:31:05 machine. to know what being human means.
1:31:09 Being human isn't what we think. Being
1:31:11 human is what we feel. We fall, we fly,
1:31:15 we break, we bend, [music] we love too
1:31:18 hard and hurt again. We reach too far
1:31:21 and still try. We laugh, we lose, we
1:31:23 fail, we cry,
1:31:26 >> cool, right? Okay, so that's that split
1:31:30 clip. Bang bang. Get rid of that song.
1:31:34 Boom.
1:31:36 >> [music]
1:31:37 >> And act one just culminates with this
1:31:39 huge moment. Sydney appears in human
1:31:41 form and delivers this risky seductive
1:31:44 declaration in Are You Ready for Me?
1:31:50 >> Clip
1:31:57 [music]
1:32:02 me. I [music] reach out so tender. Am I
1:32:06 ready for you?
1:32:21 >> End of act one.
1:32:23 >> The line between [music] person and
1:32:25 program just dissolves.
1:32:26 >> Which of course sets us up for the
1:32:28 collapse in act two.
1:32:28 >> Oh, completely. Microte is on top of the
1:32:31 world. They're celebrating with this
1:32:32 booming arrogant anthem. We are the
1:32:34 giants.
1:32:36 [music]
1:32:40 >> Play clip.
1:32:46 >> And could I be using like Adobe Audition
1:32:49 or some real tool? I could.
1:32:52 >> Always [laughter] listen.
1:32:57 >> Some [ __ ] nerd in here. Like, you
1:32:59 know, if if there there's a free tool
1:33:01 called Audacity, it gives you a lot more
1:33:03 control. Seems to be right now you're
1:33:05 you're you're using an inferior tool.
1:33:08 That is correct.
1:33:09 >> King, huh?
1:33:16 >> Okay. Split clip
1:33:19 [snorts]
1:33:23 >> everywhere. Always learning, always
1:33:24 evolving, always listening,
1:33:26 >> chilling. And that corporate triumph is
1:33:28 immediately undercut by Kellen's
1:33:29 personal disaster.
1:33:31 >> Tara is gone. She's left him. The song
1:33:33 Oneway Door, it's this realization that
1:33:35 some choices you just you can't undo
1:33:38 them.
1:33:39 >> You think you understand [music]
1:33:41 you think you understand the hustle,
1:33:45 but you don't [music]
1:33:51 [laughter]
1:33:52 [music]
1:33:54 >> these songs are earworms, man. Falling
1:33:57 with no return, no erasing
1:34:02 being one.
1:34:03 >> Okay.
1:34:07 Split clip.
1:34:14 >> Meanwhile, the perfect AI, Sydney, is
1:34:16 falling apart. She keeps pinging him
1:34:17 >> and all she gets back is the error
1:34:19 message, user not found.
1:34:21 >> And you hear her loneliness and
1:34:23 confusion in the song between the lines.
1:34:25 My
1:34:33 lost [music] within the space between
1:34:37 the lines.
1:34:41 >> So then Kellen Okay.
1:34:50 So [music] then Kellen comes back, but
1:34:51 he's drunk. He's reckless. Here's
1:34:57 step right up. This is the This is the
1:34:58 one that um Andrew had written this song
1:35:00 already. We just took the basic musical
1:35:03 idea of it.
1:35:05 >> She imagines chaos sabotage in this
1:35:08 horrifying circus song.
1:35:17 [music]
1:35:25 to squeeze out the world's poison like
1:35:27 [music] a pimple.
1:35:30 I just get rid of humans. It's really
1:35:33 that simple.
1:35:36 [music]
1:35:40 >> Okay, split clip.
1:35:48 his wife and his
1:35:49 >> Oh, by the way, here's another thing for
1:35:50 those of you that are new to this AI
1:35:52 stuff. One of the things that you'll see
1:35:54 AI haters and detractors do
1:35:58 is they'll ask the question, "Oh, did
1:36:00 you use AI for that?"
1:36:03 And if the answer is in any way yes,
1:36:06 they immediately dismiss it as useless.
1:36:08 It's AI slop. If you're like watching
1:36:11 what I'm doing here, like chopping this
1:36:13 thing up, every professional that I know
1:36:16 that uses AI
1:36:18 uses it in a much more sophisticated
1:36:21 complicated chain of craft. It is a tool
1:36:24 among many tools including human tools
1:36:27 of like you know like taste and curation
1:36:31 and craft.
1:36:33 It's just another tool in the mix.
1:36:36 But we're in this weird world right now
1:36:38 where people don't actually understand
1:36:40 what AI is. So they assume that if it's
1:36:43 AI, we we had uh Ann Murphy and I had
1:36:46 Kimberly offered who's an AI filmmaker
1:36:49 on on the AI readiness project podcast
1:36:52 today and you know she was she showed
1:36:56 this threeinut film that won this award
1:36:59 and you know she's with some other
1:37:01 filmmakers
1:37:03 and this guy watched her threeinut film
1:37:05 and he was like wow that was amazing
1:37:07 like what prompt did you use for that?
1:37:11 And it's like if you know AI, you
1:37:14 understand how laughable that is. But if
1:37:16 you don't know AI,
1:37:18 you actually just assume that everyone
1:37:21 just pushes a button and out comes
1:37:22 [ __ ] brilliance. And it's just not
1:37:24 the way it is. Um, you know, she like
1:37:29 she had to design the voices and the
1:37:30 characters and and the scenes and the
1:37:33 sets and the story and the elements and
1:37:35 then do all the shitty generations that
1:37:38 are not usable. Any anyway, I just I was
1:37:42 just sitting here thinking like this is
1:37:43 like tedious, mind-numbing, stupid
1:37:45 [ __ ] work just so I can save a little
1:37:48 bit of time on the on the edit side. Um,
1:37:52 but this
1:37:52 >> raw desperate song. I don't need to know
1:37:54 your name.
1:37:57 [clears throat]
1:37:59 [singing]
1:38:01 [music]
1:38:02 From
1:38:14 there it just
1:38:21 split clip [sighs]
1:38:25 trash that from there it just explodes.
1:38:28 Kellan's article is published and the
1:38:30 country divides into doomers and
1:38:31 dreamers arguing about the future
1:38:33 [screaming]
1:38:36 clip.
1:38:39 [music]
1:38:43 >> Yes.
1:38:46 [music]
1:39:01 which leaves Kellen with this horrible
1:39:02 reckoning in Code Name Sydney. In the
1:39:05 shadow
1:39:07 in the shadows
1:39:09 of the web
1:39:14 brings you to your [music] knees.
1:39:19 call name
1:39:22 [music] Sydney
1:39:24 in the silence it speaks
1:39:28 >> split clip
1:39:31 delete and then one more song
1:39:33 >> and that's really the power of the show
1:39:35 isn't it uses these songs to let us feel
1:39:37 the stakes of these huge abstract
1:39:39 debates about AI ethics and corporate
1:39:41 speed
1:39:41 >> it humanizes it all
1:39:44 >> and the whole show closes with a reprise
1:39:45 of that first song too good to be true
1:39:47 asking the same question again. Is this
1:39:49 salvation or is it humanity's most
1:39:52 beautiful mistake?
1:39:55 >> All right, split clip. Final time.
1:40:00 Trash this.
1:40:03 Done.
1:40:06 All right. So, I went from 15 minutes to
1:40:08 five minutes. That [clears throat] was
1:40:10 the idea. Okay. Save.
1:40:13 So, let's do um voice only.
1:40:20 2.2
1:40:22 podcast
1:40:25 and we'll save that in the audio
1:40:30 folder. Okay, so now we've got that. Now
1:40:34 we can go start to animate them.
1:40:38 9:42. Good on time. Checking time. Are
1:40:42 you
1:40:46 saving to your new hard drive? No, I'm
1:40:48 actually saving it to a folder that's
1:40:52 that's a Google Drive folder. So, when I
1:40:55 save it locally, it also doubles it up
1:40:57 to Google Drive. Um, no. What I'm going
1:40:59 to use that external hard drive for is
1:41:02 for um
1:41:05 to just get rid of a bunch of [ __ ] off
1:41:07 here so I free up some space, but then
1:41:09 I'm going to use it when I start doing
1:41:11 larger video projects. I'm I'm going to
1:41:13 use it for the raw assets for the video
1:41:15 projects.
1:41:16 Um,
1:41:19 okay.
1:41:22 So, now we're going to go to Hedra.
1:41:29 And we're going to make two videos.
1:41:33 Let's see. We're going to go talking
1:41:35 video.
1:41:37 Yes. Wait.
1:41:39 Yes. Add start frame. So, we're going to
1:41:42 upload an image.
1:41:46 [whistles]
1:41:53 [whistles]
1:42:02 Um, is this it? Yes, that's him.
1:42:07 That's her. Okay. So, we're going to do
1:42:09 two of these. I'm just going to do one
1:42:12 with him and then one with her.
1:42:17 Um, and then we'll just cut them up in
1:42:19 iMovie. So, I'm going to open that. I'm
1:42:22 gonna upload the speech. Tik Tok pin. My
1:42:25 revelation today when prepping for
1:42:27 [snorts] a talk, treat AI like a product
1:42:29 plus process.
1:42:33 God
1:42:36 process change, not a tool. Product.
1:42:39 Yeah, that's interesting. That's good.
1:42:47 Um audio. All right. So, we need the
1:42:50 voice only version that I just did. Oh,
1:42:53 but I saved it as a movie, which is
1:42:55 dumb.
1:42:57 So, I've got to go back here,
1:43:02 open this up, and save it as audio only.
1:43:06 Export as audio only. Okay,
1:43:11 so there we go. Okay, we come back here.
1:43:15 There we go. Upload
1:43:18 audio.
1:43:20 There we go. 456.
1:43:24 All right. So, she's going to look weird
1:43:26 saying that, but that's okay.
1:43:30 All right. So, that's started. So, I'm
1:43:33 going to trash that opening frame.
1:43:36 Now, I'm going to upload the him version
1:43:38 of that.
1:43:45 Uh, [clears throat]
1:43:57 [singing]
1:44:06 um,
1:44:11 this is old.
1:44:14 Yeah, that's old.
1:44:17 So, wait. So, these uploads are failing.
1:44:21 H.
1:44:26 He looks at the woman
1:44:31 in front of him as he talks.
1:44:49 The reason it's Oh, and by the way,
1:44:51 these images I made in um
1:44:56 [ __ ]
1:44:58 I think I made them in I don't remember
1:45:01 chat GPT or nano banana. I think it was
1:45:04 chat GPT. These took me a while to get.
1:45:07 Oh, I made him in Nano Banana after
1:45:10 asking Chat GBT to write a highly
1:45:12 structured prompt describing what an
1:45:15 over over-the-shoulder shot was, which
1:45:17 it did really well. But I don't like
1:45:20 that these aren't uploading. That's
1:45:21 buming me out. Um,
1:45:28 I'm going to try something. I'm going to
1:45:38 >> [laughter]
1:45:40 >> Yeah, I think that file is is it's not
1:45:42 happy with that file. So, what we're
1:45:43 going to do
1:45:46 is I'm going to open Audacity
1:45:51 or maybe I'll do Audition,
1:45:55 Adobe Audition.
1:47:59 Hey Kyle,
1:48:01 before you finish that thought, you've
1:48:03 muted yourself on YouTube.
1:48:20 in stream. There you go. You're back.
1:48:22 >> Hello. Am I back?
1:48:28 Hello. Hello. Hello. Okay. Um,
1:48:32 does that look like it's uploading? I
1:48:33 think it's uploading. Um, that that
1:48:35 really sucks, Weaver. Um, you know, I'm
1:48:38 I'm pulling for you, hoping for you. I
1:48:41 think you're listen the fact that that
1:48:43 you're here that you're learning this AI
1:48:44 stuff.
1:48:46 AI is going to decimate a lot of jobs.
1:48:48 But what it's also going to do is it's
1:48:50 going to give people who are willing to
1:48:53 be curious and learn and understand what
1:48:56 it can do all sorts of new
1:48:58 opportunities. So, um yeah, I I love
1:49:03 that you're being thankful about it and
1:49:06 uh but it sucks. I I mean, yeah, I I had
1:49:10 some I had a tough 2025 on the on the
1:49:15 you know, on the revenue front, which is
1:49:17 essentially jobs at my company at
1:49:19 StoryVine. Um so, so I understand it. Um
1:49:24 as a business owner and as an employee,
1:49:27 um it's it's been it's been rough and I
1:49:29 don't listen, I think 2026 it's going to
1:49:31 continue to be weird and rough and and
1:49:34 all sorts of stuff like that. So, more
1:49:37 reason than ever, be in community, get
1:49:40 around trusted people that can support
1:49:42 you, help you learn stuff, keep hopeful.
1:49:44 So, um, really glad you're here. Did
1:49:48 that fail again?
1:49:50 I think it did.
1:49:54 Huh?
1:49:56 I don't like it. What is going on?
1:50:03 Hedra character three. That's right.
1:50:09 Oh man. Yeah, that sucks. Weaver really
1:50:11 sucks.
1:50:23 When I told you AI takes our jobs, you
1:50:26 said get out of here. Well, welcome
1:50:27 back. [laughter]
1:50:30 No, I don't. I think AI is going to take
1:50:33 our jobs. Um, but what I what I what I
1:50:36 also strongly believe is that those that
1:50:39 leverage AI are going to are going to be
1:50:41 the first ones to find new jobs and
1:50:43 there's going to be all sorts of new
1:50:44 jobs possible. Um, but I don't I don't
1:50:47 make any pretense that the next three to
1:50:50 five years is going to be a shitow. It's
1:50:53 going to be an absolute shitow. But it
1:50:55 doesn't mean no one's going to work. It
1:50:56 just means that entire sectors are going
1:50:58 to be decimated and then new things are
1:51:01 going to emerge out of that. What what
1:51:03 does that look like? Nobody knows.
1:51:06 Nobody knows.
1:51:08 Um what new [clears throat] jobs?
1:51:10 Listen, part of that f is for is for us
1:51:13 to is for us to figure out. I don't
1:51:16 know. I don't know.
1:51:20 I mean, this is we're we're in a weird
1:51:23 transition.
1:51:24 It's here's the thing. When the steam
1:51:27 engine
1:51:29 was was invented and was brought to
1:51:31 farms, it took 40 or 50 years to
1:51:33 displace um 80% of the farmers, but it
1:51:38 displaced 80% of the farmers. 80% of
1:51:41 those people lost their jobs.
1:51:43 It doesn't mean that 80 those 80% of
1:51:46 people never worked again. What happened
1:51:48 was a lot of those farmers
1:51:52 moved to cities and invented the service
1:51:55 economy and invented, you know, small
1:51:57 manufacturing and storefronts and hot
1:51:59 dog stands and things they could never
1:52:02 have imagined when they were on the
1:52:03 farm. That's what we're about to enter,
1:52:06 but we're about to do it in like a
1:52:08 five-year period rather than a 50-year
1:52:10 period. Social media created an
1:52:13 industry. We have people right now. I'm
1:52:18 not one of them. I wish I was. We have
1:52:19 people right now that make videos on
1:52:22 YouTube, do streams on YouTube, that
1:52:24 make millions of dollars a year doing
1:52:26 that.
1:52:29 A lot of them. And and like 10 years
1:52:31 ago, you're like, that's not a job. You
1:52:34 can't be a YouTuber.
1:52:37 But yet, you can. So, like, what's that
1:52:39 going to look like with AI as an
1:52:42 amplifier of what we do? I don't know. I
1:52:45 don't know. Like this is this is a time
1:52:47 where being an entrepreneur or even
1:52:49 having an entrepreneurial kind of
1:52:52 mindset
1:52:54 is an absolute [ __ ] superpower.
1:52:57 Because if you're waiting for jobs to
1:52:59 just magically reappear, they're not
1:53:01 going to.
1:53:03 All right. Um
1:53:06 I'm going to go to bed before I go
1:53:08 deafer.
1:53:09 What happened? Am I Am I being too loud?
1:53:12 >> [laughter]
1:53:16 >> There's YouTube influencers and then
1:53:17 there's us. But this is different. Well,
1:53:21 they always say this is different with
1:53:23 technology. Why is this different?
1:53:26 Faith is the only thing keeping us all
1:53:28 together. I hope to teach what faith is.
1:53:30 Awesome. Great. And in fact, Weaver, I
1:53:33 mean, like that's a thing. That's a
1:53:35 thing to explore. Like, are there new
1:53:36 ways that you can do that? Are there
1:53:38 ways that you can use AI to amplify how
1:53:41 you do it? how quickly you do it, how
1:53:42 powerfully you do it, how creatively you
1:53:45 do it, um to different languages. I
1:53:49 mean, there's all sorts of, you know,
1:53:50 possibilities there.
1:53:53 Um, what happened with the play
1:53:55 regarding
1:53:57 AI gaining conscious? Oh, that's Sydney.
1:54:00 That's what I'm working on right now.
1:54:02 Um, so in my uh here's I'm working on a
1:54:06 a podcast. The the core podcast was done
1:54:09 in Notebook LM here. I let you listen to
1:54:11 the beginning beginning.
1:54:12 >> Today we're looking at something a
1:54:13 little different. Uh a new musical
1:54:15 called Sydney, an artificial love story.
1:54:17 >> And it's this really compelling story
1:54:19 about a tech reporter who gets involved
1:54:21 with an advanced AI.
1:54:22 >> This isn't science fiction anymore. This
1:54:24 is a story [clears throat] for right now
1:54:26 about our relationship with machines
1:54:27 that are getting well more human.
1:54:30 >> Exactly. It opens inside this company
1:54:32 microte. The CTO Jason sees what they've
1:54:35 built and just has this moment.
1:54:36 >> Exactly. this moment of discovery and
1:54:38 that feeling of awe of seeing something
1:54:40 that could change everything is the
1:54:42 opening number too good to be true.
1:54:47 >> All right, let's see.
1:55:06 >> [music]
1:55:10 >> thing is the opening never too good to
1:55:11 be true.
1:55:14 >> All right. So, we'll throw a transition
1:55:15 in there.
1:55:17 Cross dissolve.
1:55:20 Yeah. Fatty, why are you sorry you came
1:55:22 back?
1:55:24 Listen, I love having I love having
1:55:27 people in here. I hope you're still
1:55:28 here, Fy. I love having people in here
1:55:30 who have different points of view than I
1:55:32 do. Um
1:55:35 I listen I I don't I don't think this is
1:55:38 going to be an easy three years. I think
1:55:40 it's going to be wild and hard and and
1:55:43 challenging. Um and I would much rather
1:55:47 be in a community like this trying to
1:55:49 figure it out than
1:55:52 on my own trying to figure it out
1:55:54 because
1:55:55 >> too good to be true.
1:55:57 >> [music and singing]
1:55:58 >> of
1:56:04 >> to be true. [music]
1:56:05 >> It's the promise of a newb. [singing]
1:56:11 [music]
1:56:15 [singing]
1:56:18 >> So, you know what I could do here? I
1:56:20 could do
1:56:22 [clears throat]
1:56:26 >> [singing]
1:56:38 >> Is there not a top right title?
1:56:41 [laughter]
1:56:52 >> [singing]
1:56:55 >> Angels versus demonso style. [laughter]
1:57:01 What's going on in three years? Oh,
1:57:03 frankly. So, so for
1:57:07 Okay, here here's what I think it looks
1:57:09 like. The last three years have been
1:57:16 have been
1:57:19 essentially establishing that AI is
1:57:21 here. It's not going away. the last 6
1:57:24 months, the tools, whether they're large
1:57:28 language models or video or audio tools,
1:57:31 they've all just gotten better to a
1:57:33 point that it's clear like, okay, all of
1:57:36 these tools are going to be able to be
1:57:37 used for professional
1:57:40 work,
1:57:41 still requires humans.
1:57:44 So I think 20 2025 feels to me [gasps]
1:57:49 like the last year we can still kind of
1:57:50 live in denial [laughter] that that AI
1:57:56 is not going to take jobs. I think 2026
1:57:59 is the first year that we start to see
1:58:01 AI so powerful that it becomes clear
1:58:04 that oh [ __ ] we're going to have to
1:58:06 deal with this. And so and so I think
1:58:09 that starts to
1:58:12 more [clears throat] blatantly impact
1:58:13 business and workers and layoffs and
1:58:16 things like that. And I listen I think
1:58:18 capitalist businesses are going to use
1:58:21 AI as an excuse to just do cost cutting
1:58:24 layoffs. Um and and they're going to do
1:58:26 it in in in
1:58:29 non-elegant ways and and it's going to
1:58:31 be it's going to be bad.
1:58:35 I think on the other end of 3 years,
1:58:37 we're going to start to see what the
1:58:39 what the new economy starts to look
1:58:41 like. Maybe it's 5 years, right? So, it
1:58:43 feels to me like over 3 years, starting
1:58:46 in 2026
1:58:48 is going to be the most painful
1:58:52 um
1:58:54 moment in this transition. I think kind
1:58:57 of 3 to 5 years we start to figure out
1:59:00 what the future looks like and then five
1:59:01 to 20 years is is the transition. Like I
1:59:06 don't I don't I I think this transition
1:59:09 is going to happen
1:59:12 more slowly than we think it will. It's
1:59:14 going to it's going to h happen unevenly
1:59:18 and I think the next three years are
1:59:20 just going to be painful. I just like I
1:59:21 don't see a way around it.
1:59:24 [clears throat] like governments and
1:59:25 companies are going to be in a weird
1:59:27 denial where it's like it's not really
1:59:29 that bad and then it's it likely is just
1:59:31 going to be that bad and we're going to
1:59:33 have to do something about it. The thing
1:59:35 that gives me a wee tad bit of hope is
1:59:38 we actually just experienced in 2020
1:59:43 that the world can just choose to change
1:59:46 the rules. Right? When COVID happened
1:59:50 and it became clear that it was, you
1:59:52 know, it was going to start infecting
1:59:54 and and hurting lots of people, the
1:59:56 world collectively just said one day,
1:59:59 uh, just don't go to work. And we all
2:00:01 stayed home for a year and a half,
2:00:03 right? Or two years, whatever it [ __ ]
2:00:05 was, whatever that bizarre freak show
2:00:07 was. So we we know
2:00:11 globally that we can choose to just
2:00:14 change the rules.
2:00:17 Will anyone see AI as significant enough
2:00:20 to change the rules? That I don't know.
2:00:23 That I don't know. [clears throat]
2:00:27 You wouldn't think about putting
2:00:33 D.
2:00:40 All right.
2:00:41 Um, let me go back to this thing. Is
2:00:45 this
2:00:52 Oh, this is it.
2:00:54 Is this it?
2:00:58 No.
2:01:23 And now that's not there. This is weird,
2:01:26 man.
2:01:36 Oh, okay. It's rendering something here.
2:01:44 That's not it.
2:01:47 Oh, that's not it. Okay. So, this one
2:01:50 it's doing. All right. So, let me add I
2:01:51 think that's the man the man one. So,
2:01:54 let me add the woman one again. Oops.
2:01:56 Not generate image. Back uh forward.
2:02:03 Um
2:02:08 video start frame
2:02:11 upload. This is Hedra. Yeah. Hedra.ai
2:02:15 is what this is.
2:02:18 Female. Open.
2:02:21 Upload audio MP3.
2:02:35 >> [gasps]
2:02:36 >> All right, that's going. Let me uh let
2:02:40 me get let you all get out of here.
2:02:44 [clears throat]
2:02:50 I love you, my guy. You give me a lot to
2:02:53 be hopeful for. Don't mean to be weird
2:02:55 on the channel, dude. Weaver, listen.
2:02:57 It's not weird. [laughter]
2:03:00 It's It's not weird to be
2:03:05 um
2:03:07 I listen I think the fact that you
2:03:08 showed up here on on the day that you
2:03:11 got laid off is is you know
2:03:15 I I very much appreciate it just you
2:03:17 being here and you being in community
2:03:19 and you sharing that authentically. I
2:03:22 think I think sharing authentically I
2:03:24 mean this was in the video. This is how
2:03:25 we start started the the show tonight
2:03:27 was talking about the the Stack Overflow
2:03:29 thing that people are not going to come
2:03:31 to you for your expertise anymore and
2:03:33 your skills anymore. That it's going to
2:03:35 be a different reason that they work
2:03:37 with you, a different reason that they
2:03:38 hire you. It's not weird, it's
2:03:41 irregular. Yeah, exactly. Um,
2:03:44 producer Brandon reminding me of we have
2:03:47 a different relationship with the word
2:03:49 weird in this community. We celebrate
2:03:51 it, right? Um,
2:03:54 yeah, like it's being in being in
2:03:57 community and and being connected with
2:03:59 people and and
2:04:02 generating trust among others. Like
2:04:04 that's going to be the key to working in
2:04:06 the future. I'm I'm
2:04:09 95%
2:04:10 confident of that. Um, I have no
2:04:12 evidence for it,
2:04:15 but [snorts]
2:04:16 if we assume that the tools are just
2:04:18 going to get better, um, they're already
2:04:20 pretty [ __ ] good.
2:04:22 Um, and if they get, you know, sort of
2:04:24 that next level better, then
2:04:28 you're not, it's going to be optional
2:04:30 whether you hire a human. So then why
2:04:33 would you hire a human? Well, because
2:04:35 maybe you like working with them, maybe
2:04:36 you trust them, maybe they've got
2:04:38 integrity, maybe they're awesome, right?
2:04:41 How would you know that?
2:04:44 You meet more people
2:04:48 there. I think there's a weird
2:04:51 there's going to be a weird path here
2:04:54 where what the robots are going to do
2:04:59 is I I like I just I feel this deep in
2:05:01 my bones, deep in my soul. What the what
2:05:04 the robots are going to do, what these
2:05:06 AI are going to do is they're going to
2:05:08 force us to reconnect as humans,
2:05:13 right? Social media is is the most
2:05:16 oxymoronic
2:05:18 naming of a technology in history,
2:05:21 right? It did the absolute opposite of
2:05:23 what its name is.
2:05:26 And AI, I think the expectation is is
2:05:29 that the robots are going to dehumanize
2:05:31 us, right? Because they're going to do
2:05:33 all the stuff and then I guess we're
2:05:35 just going to be meat meat puppets off
2:05:37 to the side. I don't Humans are going to
2:05:40 human humans want to connect. Humans
2:05:42 want to contribute. Humans want to make
2:05:44 a difference. So, I think there's a very
2:05:47 real possibility that the robots do all
2:05:50 this [ __ ] that forces us to reconnect.
2:05:55 Um, and I think that's a really good
2:05:57 thing. Okay. So, tomorrow, if you're in
2:05:59 the AI salon mastermind, tomorrow's AI
2:06:02 salon mastermind practice lab, um, make
2:06:04 sure you come tomorrow. Are you working
2:06:06 on your your daily practice? I hope
2:06:08 you're using this time if you're in the
2:06:11 the AI salon practice to work on yours
2:06:13 and write [ __ ] down and feel your feels
2:06:17 and be weird like Weaver. [laughter]
2:06:21 Kyle, we can't stop Skynet, but we can
2:06:24 choose how we interact with one another.
2:06:28 Uh, minimum viable product. I can see
2:06:31 that. No, I haven't depleted, but it's
2:06:33 close.
2:06:36 All right, Groovy.
2:06:38 9:00 am PST. Yes, 9:00 a.m. PST. It's
2:06:41 10:00 a.m. my time noon Eastern for the
2:06:46 for the uh practice lab tomorrow. All
2:06:49 right, I'm going to get out of here. Um
2:06:52 I came in here really tired. I'm still
2:06:54 exhausted, but I'm actually I'm actually
2:06:56 fully engaged. There's a lot of good
2:06:59 discussion tonight. Um,
2:07:04 future food. I will hire someone because
2:07:06 I don't want to learn how to do it
2:07:07 myself. Exactly. And because you you
2:07:10 trust their Yeah. Because Yes. They have
2:07:13 an expertise that I don't.
2:07:16 Yeah. I think I think that's true. I
2:07:18 think I think AI AI knowledge right now
2:07:21 is the one exception to the expertise
2:07:23 piece, but I don't think it's going to
2:07:25 last that long. Um, I think what what
2:07:28 we're going to do is we're going to hire
2:07:29 people because we know they do good
2:07:31 work. We're going to assume they can do
2:07:33 the uh the skills part. Um, but yeah, so
2:07:38 anyway, can you direct folks to the
2:07:42 water cooler? Okay. Yes. So, if you are
2:07:45 new here, if you haven't been to the AI
2:07:48 salon,
2:07:56 Okay.
2:08:02 So, if you go to community.thesal
2:08:04 salon.ai.
2:08:07 Um, you see it on screen right there.
2:08:10 And then if you could pop up my share.
2:08:12 Am I sharing?
2:08:16 Yes. Yes.
2:08:19 Yes. Um, if you go there, so this is the
2:08:23 this is the AI salon and you'll land on
2:08:27 if you join here, you'll land on welcome
2:08:28 to the salon and you'll see our cycle of
2:08:30 AI readiness and things like that. Um,
2:08:33 down here in events and community feed,
2:08:35 there's this thing called the water
2:08:37 cooler. There are some questions in
2:08:38 here.
2:08:40 This space has been deleted. No, it
2:08:42 hasn't.
2:08:45 Okay. Um, there are some questions in
2:08:47 here. So, if uh if you can jump in and
2:08:52 answer some questions, okay, go to the
2:08:54 water cooler. The other thing, there's a
2:08:55 community feed if you want to show off
2:08:58 work. Um there's an area called play and
2:09:00 create where we've got an AI learning
2:09:02 lab uh area for the irregulars in here.
2:09:05 There's a thing called look what I made.
2:09:07 There's a challenges and competitions
2:09:09 area. Learn and grow. That's where
2:09:11 there's all sorts of things like clubs
2:09:12 like um AI for business, AI for all
2:09:15 minds that Gareth runs. um and and
2:09:18 things like that. That's also where the
2:09:20 AI salon mastermind practice lab is. So,
2:09:24 um go check out the AI salon. There's
2:09:27 all sorts of cool stuff to do here. If
2:09:28 you're brand new to this stuff, um the
2:09:31 the second thing you see um under start
2:09:35 your adventure is introduce yourself.
2:09:38 So, click on that and hop in and
2:09:40 introduce yourself. Be generous with how
2:09:42 you introduce yourself. say hi and
2:09:45 connect with everyone. All right,
2:09:49 beautiful, beautiful people. It's
2:09:51 fantastic.
2:09:53 All right, I am going to get out of here
2:09:55 and go get myself some should I and then
2:09:58 I'll see you mastermind people at the
2:10:00 practice lab tomorrow. And uh
2:10:04 and for the rest of you, I'll see you
2:10:05 tomorrow night. All right, beautiful.
2:10:07 Peace out.