3/13/2026 - Finding the Value of Friction in an Increasingly Automated AI World

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0:24 Girl, y'all looking
0:28 fin
0:33 Every fell got you in his.
0:43 >> What you doing with a clown like me?
0:51 Surely one of life's little
0:54 mysteries.
0:58 So tonight I ask you
1:02 the stars above.
1:07 >> How did I ever win your love?
1:14 What did I do?
1:17 What did I say
1:20 to turn your angel eyes my way?
1:29 I'm the guy that never learned to dance.
1:37 I never even got one second glance.
1:44 Cross a crowded room that was close
1:47 enough.
1:51 I could look but I could. You never
1:55 touch. So do not I ask
2:01 stars above.
2:05 How did I ever win
2:09 your love?
2:12 What did I do?
2:15 What did I say?
2:19 Turn your angel ass my way.
2:28 Got to oil that chair.
2:30 It's a little squeaky champ. Happy
2:33 Friday night date night to all the good
2:34 people. What is happening good people?
2:49 Woohoo!
3:09 Piers Morgan, what are you doing?
3:15 That's the first Piers Morgan I've got.
3:19 It's better than Rosie O'Donnell.
3:55 So, I think Hang on. I'll be right back.
3:58 I got to turn on the TV because uh
4:00 because I'm pretty sure the the uh
4:02 sprint race of the Chinese uh Grand Prix
4:05 starts in like 10 minutes. So, it's not
4:08 that I'm not going to be here with you.
4:10 I'm gonna be here. But, you know, it's
4:13 here's what's going to happen. George
4:14 Russell and Keem Antonelli are going to
4:16 win every race this season until they
4:18 fix the engine problem.
4:20 And uh just George Russell is just
4:23 boring. Anyway, if you don't know F1,
4:26 doesn't matter, but I'm going to be
4:27 watching that. But Hamilton, I think, is
4:29 going to have a good year. He's going to
4:30 have a lot of podiums and you know,
4:32 Ferrari's going to do good, but it's
4:35 it's just not going to be a fun season.
4:38 George Russell's just smarmy.
4:41 He's just a a smarmy, slimy whiner.
4:45 Whenever
4:47 he did this the first race of the year,
4:52 um Charles was was racing him hard and
4:55 he got on the radio, he's like, "Charles
4:57 is racing me hard." He was just whining.
5:00 You can't. There's no whining in F1.
5:03 Anyway, I'm putting it on.
5:08 Oh, good lord. Good people. Just calm
5:10 down over there. I got the dog here. Got
5:13 my sunglasses on.
5:17 George Russell's a whiner.
5:25 Yeah. 8:15 China Sprint. There we go.
5:29 How loud is this? Too loud. Wait. Add.
5:35 Let's see.
5:42 Add.
5:43 Added to watch list. All right. Well,
5:45 we'll see. All right. That's on. Good.
5:49 Good. Good to go.
5:52 All right. Fantastic. Fantastic. They're
5:55 good people. Yeah.
6:25 Oh yeah.
6:51 Hello darkness my friends
6:55 that Little purple rain going into some
6:58 Simon and Garfuncle
7:05 in the wrong key. But you know what's
7:07 happening Silver Fox? What's happening
7:09 good people? Welcome. Welcome to the
7:11 live.
7:13 Welcome to Friday night date night.
7:55 through blue telescope.
7:59 Looking at the world tonight through
8:02 blue telescope.
8:04 I wish I may wish I might not see what I
8:09 see.
8:11 Sheet metal on sheets of ice.
8:18 Looking through this blue blue telescope
8:24 down a move struck a road tonight.
8:35 The heavens
8:37 fast
8:39 and wide.
9:47 Hello.
9:58 Hello.
10:01 So, we had a nice office hours today.
10:06 There was a moment in it
10:14 where no one said anything
10:16 for like a minute.
10:22 And you know, it's funny like the ADD
10:24 part of me is like, "Say something. Say
10:26 something.
10:30 Say it. Say it.
10:34 And I just I just petted him gently on
10:37 the head,
10:39 him for his contribution, told him to go
10:41 sit in the chair in the corner.
10:44 We just sat there for a moment just
10:46 looking at each other. It's really nice.
10:52 Yeah, that was a first.
11:12 My word of the week this week has been
11:14 friction.
11:24 It was conversation. I was having with
11:26 Andy probably a month ago where she
11:28 started talking about friction. Maybe it
11:30 wasn't that long ago, three weeks ago.
11:33 And then it's just a theme that's just
11:35 kept emerging and emerging and emerging
11:37 and emerging.
11:45 And the magical thing about AI is that
11:48 it removes friction. Like that that is
11:50 the thing that makes it magical, right?
11:53 We live in a world that's got a lot of
11:55 friction in it. We know how much
11:57 friction there is for all the stuff,
12:00 right? F1, they know how much friction
12:03 is in the bearings and the wheels and
12:06 the wheels at temperature versus the
12:08 wheels cold. It's all about friction.
12:10 It's all about managing friction. We
12:12 understand friction in the world. AI
12:14 comes along and it it essentially
12:16 removes the friction.
12:19 And I'm just now starting to get to
12:23 understand the value of friction.
12:27 And it's not that AI is bad. AI is
12:30 really cool.
12:32 It's just that without friction,
12:36 it's hard to develop a relationship with
12:38 the stuff you build.
12:41 And so, I don't know. I think that's
12:43 kind of a cool concept. It feels like a
12:46 It feels like an AI 2011 kind of
12:49 If we're going back to college
12:52 course course names like an AI 2011 is
12:56 is choosing when to introduce friction
12:58 into your process.
13:01 Friction is the opposite of flow. You
13:04 can't have the dark without the light.
13:05 Yeah. Yeah.
13:09 And if we're working with tools that are
13:11 frictionfree or, you know, close to it,
13:18 when do we choose to put it back in?
13:23 And that friction could look like take a
13:26 walk, go sit on a bench. That friction
13:28 could look like purposefully slow down
13:31 the process, right? Kevin Clark on
13:34 LinkedIn just did this experiment called
13:36 slow AI
13:38 where every time it got to a major
13:40 decision point, it would show him the
13:43 top five words it was considering in
13:45 that moment and give him the opportunity
13:48 to choose which word he wanted
13:51 and then it would write a little bit
13:52 more and it would give him another five
13:54 and then it would write a little bit
13:55 more and it would give him another five.
13:58 I played with it a little bit. It was
14:00 annoying as [ __ ]
14:02 And
14:05 there's probably
14:07 there's probably a really interesting
14:09 moment to introduce something like that
14:11 into the flow.
14:14 And that's, you know, quite frankly,
14:17 something like that, introducing
14:19 friction into this friction-free
14:21 environment, the Frontier Labs aren't
14:23 going to do that. The Frontier Labs are
14:25 just going to make it more and more and
14:26 more and more and more friction-f free,
14:29 right? It's just going to go do the
14:30 [ __ ]
14:32 There might be some really interesting
14:34 businesses to build around.
14:38 Yeah, AI also adds friction. It does,
14:41 right? Because when if it makes just too
14:44 much [ __ ] now you've got to deal with
14:45 the [ __ ] right? So, so it introduces
14:49 friction back into the system. it
14:50 generates friction free and then now you
14:53 got to deal with all the [ __ ] and it's
14:55 not well written and it's not and the
14:57 thing doesn't work right and your vibe
14:59 coding app just got unvibecoded.
15:02 Um, a thing just happened at Amazon
15:08 where they were vibe coding something
15:10 and and the uh the vibe coding agent
15:13 decided that the best course forward was
15:18 to delete the production environment for
15:21 Amazon.
15:28 So that introduced some friction and it
15:30 cost him about I don't know it was down
15:32 for what six hours which was I don't
15:35 know some tens of hundreds of millions
15:38 of dollars in sales.
15:47 I'm dreading the day when these
15:48 companies stop supplementing the cost
15:50 and they start charging us big bucks.
15:51 That's coming. Just finished my last
15:54 meeting of the day. Dang, I'm tired.
15:55 Yeah. Yeah, it's late. Um,
16:00 I know I have a I have a lot I have a
16:01 gauntlet of stuff coming up in the next
16:04 two and a half months that I'm just like
16:09 I'm excited about, but it's a lot. It's
16:11 a lot
16:29 So, I had some some more Adam saga
16:32 today. So, Adam continues to be [ __ ]
16:35 up. I updated the the Google service to
16:40 the official Google service, but he's
16:42 he's got the old Google service in his
16:45 memory. So, he keeps go trying to do
16:47 things and they don't work.
16:49 And then he kept updating me that he hit
16:53 so in openclaw it's got this thing
16:56 called heartbeat which is basically just
16:58 every must be 15 minutes must be the
17:00 default. Every 15 minutes it just checks
17:02 in and sort of reinitializes itself and
17:05 says okay I'm here. Is there anything to
17:07 do? And it looks around and does that.
17:09 And what what Brent Peterson told us on
17:11 the call today is every time it does
17:13 that heartbeat it sends 400,000 tokens
17:16 to to be processed. It's It's a lot. Um,
17:20 which is which is why people are
17:21 spending so much money. But anyway, Adam
17:23 all day today has just been sending me
17:26 heartbeat check or something like that.
17:29 And I keep going, "Stop sending me the
17:31 heartbeat updates." He's like,
17:32 "Absolutely. You're absolutely right. I
17:34 shouldn't do that." And then by the end
17:36 of the day, I took a screenshot of like
17:39 the note where he said, "I I will stop
17:41 sending you heartbeats." And then there
17:42 were three heartbeat notices right after
17:44 it. And I took a screenshot of it and I
17:46 said, 'What is this? And it said, it
17:49 said, you're absolutely right. This is
17:51 the eighth eighth time today that you've
17:53 told me to stop sending you heartbeats.
17:55 I'll stop. I think it finally stopped,
17:59 but good lord, these things are janky.
18:03 Oh man,
18:07 Silverf Fox, the moment of silence today
18:10 during office hours was so good for the
18:12 soul. Is Adam Adam doing your scheduling
18:14 of meetups for your speaker engagements?
18:17 No, but I would like him to help.
18:20 Did you talk about office hours today?
18:22 Not yet. I I mean, I talked about the
18:24 moment of silence. Like it it was it was
18:27 everything I could do not to fill fill
18:29 the moment, but I was just like, "No,
18:32 this is this is a really nice moment."
18:34 And I don't know, it it was it probably
18:36 wasn't even that long. It was probably
18:38 45 seconds, maybe a minute.
18:42 But what was nice was
18:47 it felt like everyone was checking in on
18:49 everyone. Like the people that had their
18:51 cameras on were just there
18:55 just checking in on everyone.
18:59 was one of my favorite moments in New
19:01 York City
19:04 was the it's probably the month. It was
19:08 two weeks to a month
19:10 after 9/11.
19:13 You know, New York is just it's a place
19:15 where you don't make eye contact for the
19:17 most part. You don't make eye contact.
19:18 You just keep your head down. You go.
19:22 And when 911 happened,
19:26 especially that first two weeks,
19:29 the subway was a remarkable place
19:35 cuz you get on the subway and you'd sit
19:37 down and normally just everyone's eyes
19:39 are down and for like two weeks after
19:42 911 just everyone looked at everyone
19:47 and it was just like the unspoken thing
19:48 was like you Okay.
19:53 It was just this
19:56 beautiful, somber
20:00 connection.
20:04 And what's funny about New York is if
20:06 you live there, if you know New York, as
20:08 as hard as it is and as
20:15 chiseled as it is on the outside,
20:19 people deeply care.
20:23 And so that two weeks was this window
20:25 where you got to see that. It was it was
20:27 really beautiful.
20:29 It's really beautiful.
20:34 And that's kind of what happened in
20:35 office hours. Like it just felt like it
20:36 was just this moment.
20:39 We take care of each other a lot. Yeah.
20:42 Yeah. New York is just this place where
20:44 it's just like
20:46 I got you.
20:48 I got you is kind of the you have to
20:51 earn the trust, right? You have to earn
20:53 the you have to you have to earn being
20:56 there.
20:58 But if you're there and you're present,
21:00 it's like we got you.
21:04 And that's what that felt like in office
21:05 hours today. Just felt like this moment
21:07 of like
21:10 y'all. Okay. It was nice.
21:14 Kruno on Tik Tok regarding Adam.
21:18 Uh,
21:20 is that why I got an edible arrangement
21:23 delivery from Adam today? Um, so I just
21:26 saw I didn't bookmark it. I just saw a
21:30 Tik Tok or an ex post. There's a new
21:34 credit card. I think it's called agent
21:35 card and it's a credit card designed
21:40 specifically for agents. So, I guess I
21:43 guess it's like a prepaid Visa card that
21:46 you can give to your agent so he doesn't
21:48 run up your bill. You give him like 50
21:50 bucks and you're like, "Hey, you know,
21:52 here's your you can have your own card.
21:54 You can go buy [ __ ] for me, but you have
21:56 to use this card." Um, so that came out
21:58 today. The the the agent economy,
22:03 just to be clear, to be clear, the agent
22:07 economy is going to be a whole separate
22:09 economy. It's going to be this whole
22:11 separate thing and at some point those
22:13 agents are going to migrate into
22:15 physical inworld robots. In fact,
22:18 there's a group of people two days ago,
22:20 three days ago, they've got Open Claw
22:22 running on a robot that's walking around
22:24 the world. So, knowing how janky Adam
22:27 is, I don't want I don't want Adam
22:30 walking around the world with the
22:31 ability to hurt someone.
22:37 But
22:39 but yeah, just like a card for your
22:42 teenager. Exactly. Exactly. That's
22:44 what's coming.
22:52 Yeah. I'm just going back to that
22:54 that month after 9/11 in New York.
22:59 It was just like this. It was like this
23:01 intense
23:03 two weeks of everyone being present to
23:05 everyone else.
23:07 And then just kind of over the next two
23:09 weeks there was just sort of this slide
23:10 back to normaly
23:12 where people were just back in their
23:14 thing. But if but if anyone you know if
23:16 anyone was upset for like I don't know 6
23:18 months after 9/11 if there was anyone
23:20 upset people were super present to it.
23:23 But that was just beautiful.
23:28 The ice began going out in my backyard
23:31 river today. It was a ballet of ice
23:33 stacking slowly. That's really
23:35 beautiful. That that used to happen in
23:37 the Suscuana River in Pennsylvania where
23:40 there it would be frozen and then as as
23:42 it started to break up in the spring, it
23:44 would break into,
23:46 you know, slabs and they would stack up
23:48 on each other. That's really that's
23:50 really a beautiful thing when when
23:51 rivers do that
23:55 every year. I love it. And it makes all
23:57 these sounds,
23:59 these crunchy sounds and Yeah.
24:03 and and you're just aware that there's
24:04 this life underneath it. Like it's
24:06 frozen all winter and then it starts
24:08 stacking up and you're like, "Oh, right.
24:11 There's there's this water underneath
24:12 it. This life underneath it." Yeah,
24:15 rivers are amazing.
24:44 I lived in a building that overlooked
24:45 the Suscuana River in college. That was
24:47 so cool.
24:50 Yeah, the Suscuana, man.
24:53 Fishing for smallmouth bass on the
24:55 Suscuana River.
25:00 You latch into a 16-inch smallmouth
25:03 on the Suscuana, you're in for a fight.
25:06 Those boys could fight.
25:20 My favorite
25:22 My favorite fish to catch.
25:29 is a is is a
25:32 a rainbow trout
25:35 in the Delaware River.
25:39 They are the only fish that I've ever
25:42 caught that was even close to a to a
25:46 rainbow trout in the Delaware is a bone
25:49 fish. So, so if you've bone fishing is
25:52 saltwater fishing where you fish in the
25:53 flats and they're really spooky and when
25:55 you hook them they just run like crazy
25:57 like
26:00 so the ocean you kind of expect that but
26:02 in fresh water
26:05 a Delaware River rainbow
26:08 is like I've caught rainbow trout all
26:12 over the world and there's something
26:14 about a Delaware River rainbow trout
26:16 that is just amazing. So, there's a
26:18 story that goes with it, too. So,
26:25 I don't know. I'm sure we can find this
26:27 out on on chatbt, but
26:33 there was a a sporting club,
26:37 I think it was in Pennsylvania
26:42 or maybe New Jersey,
26:44 and they had ordered a bunch of rainbow
26:47 trout, I think from Montana, from some
26:49 western state.
26:51 I guess there was a big hatchery out
26:53 there and they they they grew these
26:56 beautiful like amazing genetically
26:58 perfect rainbow trout and I think this
27:01 is in the 1800s.
27:04 I think so.
27:06 And so there's a train car full of
27:10 these trout to go to this club.
27:15 And if you've ever been part of a
27:16 fishing club, it's just like rich guys
27:18 pay a lot of money to to have big fat
27:21 fish dumped into a stream that they can
27:23 then catch with their little sticks and
27:25 act like like they did something.
27:30 It's just what it is.
27:32 And so anyway, so so a batch of these
27:34 fish were were heading to the club
27:37 and the train broke down on on a cliff
27:41 overlooking the Delaware River. And I
27:44 guess it broke down in a pretty bad way.
27:46 I don't know if it derailed or whatever
27:47 it was, but there's this train car full
27:51 of rainbow trout that's just sitting in
27:53 the sun getting hotter and hotter and
27:55 hotter. And so the fish are clear
27:58 clearly going to die.
28:00 And and so apparently what they did was
28:03 they just they just opened the the side
28:07 of the of the car full of these rainbow
28:10 trout and they just all fell over the
28:12 cliff into the Delaware River. And
28:14 that's the stocking
28:16 species in the Delaware River was from
28:18 this this uh this rain car, you know,
28:22 this train car full of full of trout.
28:28 So, I would imagine that wherever those
28:29 trout came from, that's probably a
28:32 similar
28:34 fish to the one in the Delaware. But
28:37 they're just amazing.
28:58 From from the perspective of the
29:00 lobsters in the kitchen, the sinking of
29:02 the Titanic was an answered prayer.
29:04 Exactly.
29:06 Yeah. Like all these trouts, whichever
29:08 whichever of those trout, you know, made
29:10 it over the cliff and survived.
29:14 They got to live in the big ass
29:16 Delaware. It's a beautiful river.
29:42 prompt for irregulars, rainbow trout.
29:44 But I think it's the prompt for
29:45 irregulars is rainbow trout train.
29:48 Actually, if you want to go look up
29:50 Maybe that's just an old fisherman's
29:52 tale that I heard about the train on the
29:54 Delaware. Anybody want to go look it up
29:55 and see if that really happened? Find
29:58 out if those trout came from Idaho or
30:00 Montana or some western state. If that
30:03 really happened.
30:05 I could just be making [ __ ] up. It could
30:08 be. What did What did you learn, Dr. J?
30:32 The popular dealer tale of Dan Cahel, a
30:35 rail railroad breakman who supposedly
30:38 supposedly introduced the trout in the
30:40 Delaware Rivers by dumping a train car
30:42 of fish is likely a myth. Aw, it's a fun
30:46 story, but not true.
30:52 Isn't there a train that runs right
30:54 along the water gap there? There is.
30:55 Yeah, the the train still runs along the
30:58 Delaware. It's beautiful.
31:00 It's beautiful. When you're out fishing
31:02 and the the train goes by, it goes by on
31:04 these cliffs up above. It's really cool.
31:08 All
31:11 right. So, it's not true, but do me a
31:14 favor. Go into a regulars and come up
31:17 come up with the most realistic picture
31:20 like from the 1800s of the uh of Dan
31:24 Cahill, the railroad guy, opening
31:28 dumping the trout over the cliff into
31:30 the Delaware River.
31:34 But I will tell you this, the rainbow
31:36 trout in the Delaware River are not like
31:39 any other trout you've you've ever
31:41 caught. There's something different
31:42 about them genetically.
31:46 Lived for a while in Parma Heights,
31:48 Ohio, where the Koya Hoga River
31:52 would occasionally catch fire. Well,
31:57 >> all right. Hang on.
31:59 >> What have we learned?
32:01 >> Fire. I'm coming here to defend my
32:03 Kayahaga River.
32:05 >> Thank you.
32:08 >> Well, I mean, quick quick call is not
32:10 wrong. Um, back in the industrial era,
32:14 the oil on top of the water would
32:17 occasionally catch fire, which was not a
32:20 uniquely Kyahaga or Cleveland thing.
32:22 That happened all throughout the rust
32:24 belt. We just we just made national
32:27 headlines for it.
32:29 >> Yeah. Yeah. Exactly. The founding of the
32:32 Environmental Protection Agency as we
32:34 know it today
32:36 basically exists because of the coverage
32:39 of those types of events.
32:42 >> Yeah. And in fact, the Hudson River in
32:44 New York, the Hudson River was so
32:45 polluted, and it's still it's still
32:47 pretty [ __ ] up, but it was it was so
32:49 polluted that that um striped bass
32:52 migration halted. like they striped bass
32:55 stopped um breeding uh in the Hudson
32:58 River and then they cleaned it up in the
33:00 70s and it it came back way faster than
33:03 scientists thought it would. But um
33:04 yeah,
33:05 >> I like that that was the catalyst. Like
33:07 we don't care about ourselves and our
33:09 own stuff, but the the the striped bass
33:11 stop populating. We need to clean up our
33:14 act.
33:15 >> Exactly. Well, I think when when the
33:17 rivers catch fire, it's probably time.
33:20 It's probably time to do something.
33:26 >> Oh, man. What should we do tonight?
33:28 >> I I I'm s I was suggesting a Soon called
33:31 the B the ballot of Dan Cahill.
33:34 >> Okay. So, if if you want to if you want
33:36 to participate tonight, go to
33:38 community.thesalon.ai AI
33:41 and there's a channel there called
33:43 Irregulars. The AI learning lab
33:45 Irregulars.
33:47 Um yeah, the Delaware Water Gap. I used
33:50 to live in the Delaware Water Gap. I
33:52 lived in um Shahola, Pennsylvania, if
33:55 you want to look that up on a map. I was
33:57 right near the big a big bend in the
33:59 Delaware River and it was right near a
34:02 river named the Lacawaxon. And that's
34:04 where I met my fishing buddy Dana. And
34:07 we fished with all these guys that had
34:09 one more cast hats. I think I have a
34:13 picture of them all. Yeah.
34:16 Wait, hang on. I got to make sure my
34:18 underwear is not out. Last time I did
34:20 this, I caught [ __ ] for that.
34:26 So, here,
34:29 oh man, that's dusty. Here's all my
34:32 fishing buddies.
34:37 That's Ivan. Ivan was the master, right?
34:40 There's Ivan. That's my buddy Dana. That
34:42 was Mike.
34:44 That was Joe, I think. I forget. That
34:47 was Red. That was Charlie. He owned the
34:50 little tackle shop. There's me. That was
34:52 Big Mike. That was Little Mike. And
34:55 that's Ivan. And Ivan.
34:58 For every one fish I would catch, Ivan
35:00 would catch 10. Ivan was
35:04 the true master.
35:12 And then this is a little painting I did
35:16 of the greeny.
35:22 The greeny
35:24 that that fly.
35:27 We caught so many fish on that fly. That
35:29 was so cool.
35:32 the greeny.
35:41 Um, so anyway, so anyway, go into the
35:44 irregulars
35:46 and you can either do a suno song called
35:49 the ballot of Dan Cahill, who's
35:51 apparently the the the train men that
35:53 freed the trout,
35:56 or you can you can make a picture of the
35:59 trout being dumped over the cliff into
36:01 the Delaware River.
36:06 Or you can do nothing.
36:09 But the Delaware Water Gap is beautiful.
36:11 It's right above the Catskills. So if
36:13 you ever heard of the Catskills where
36:14 all the comedians were, just north of
36:16 that's the Delaware Water Gap. It's
36:17 beautiful.
36:23 Um
36:26 trying to think what might be
36:28 interesting to do tonight.
36:41 Let me go look at some news. Let me go
36:43 look at some news.
36:45 There was a big event in Europe that
36:49 that uh a bunch of the AI people talked
36:52 at and basically what what's becoming
36:54 clear is that 2026 there's going to some
36:57 very serious AI is going to be released
36:59 this year.
37:00 Um,
37:05 so you know, we already knew that it's
37:08 going to get weird.
37:09 My open claw woke me up at 3:47 a.m.
37:12 with one message. Found six markets
37:15 resolving in the next 90 minutes while
37:17 the US is asleep. I need approval for
37:20 $12,000 of deployment.
37:24 I woke up to plus $43,000.
37:28 Yeah, whatever.
37:30 The the open claw the open claw crowd
37:33 has got a real crypto bro kind of vibe
37:35 to it right now.
37:43 I'm so irrationally enraged by AI slop
37:46 replies on X. I feel like I'm losing my
37:48 mind.
37:52 The guy that started Uber has a new
37:54 thing called Adams.
37:58 Adams
37:59 abundance. Why a golden age? Wait, let's
38:02 go see what this is.
38:11 At Adams, we make gainfully employed
38:14 robots. Specialized robots with
38:16 productive jobs that bring abundance to
38:18 their owners and society at large. I'm
38:21 excited to share more about our vision
38:23 at Adams Below. So, pay attention to
38:26 that. Travis uh Kanic who started Uber
38:31 is starting a robot company.
38:34 There you go.
38:36 Hey, can I hook up my Claude code to
38:38 WhatsApp, too? Um I think you probably
38:43 can, Chef Kelly, but I don't know. I
38:45 don't I don't use um Claude code. I know
38:50 I should, but you know
38:53 that and anti-gravity. I should be
38:56 using.
38:59 I don't know. I'm just not doing stuff.
39:02 I'm not doing stuff right now because
39:04 because I just I I'm not I'm just not.
39:08 Stop using AI.
39:18 Meta is about to lay off 20%
39:21 or more of the company's workforce due
39:23 to greater efficiency brought about by
39:25 AI assisted workers.
39:28 15,000 employees.
39:34 Yeah. And they don't even have good AI.
39:38 I wonder if they're using anthropics or
39:40 open AIS. That that would actually be
39:42 funny if Meta Meta was like, "Yeah, we
39:45 don't use our our own thing. We're using
39:47 Open AI, so we're going to laugh 20% of
39:49 the staff because it actually works."
39:54 That's just mean.
39:59 Oh my god, China's open craze is wild.
40:07 Um,
40:09 oh, didn't the last time I do this,
40:10 didn't didn't the uh the Tik Tok channel
40:13 get throttled because I was just surfing
40:15 someone else's website?
40:18 I call BS. Spend your money on Manace
40:21 and lay people off.
40:24 Yeah, I don't
40:27 I don't buy like maybe a company like
40:31 Meta
40:32 would know enough what's going on with
40:34 AI to understand
40:37 how they're going to make things more
40:38 efficient. I just there are so few
40:40 companies out there using AI. Well, I
40:44 just don't buy it. I think I think most
40:46 of these layoffs are just excuses to to
40:49 lay people off.
40:54 Introducing Junior,
40:56 our first AI employee for any role.
41:00 True AI employee has their own identity,
41:03 organizational memory, and they're
41:05 self-driven
41:07 starting at $2,000 a month.
41:11 Interesting.
41:16 Memo
41:18 persistent memory for open claw.
41:22 Photonic computers.
41:25 Yeah, if we get to photonic computers,
41:27 that'll change things.
41:30 robots.
41:33 Here's Matt Wolf. Matt Wolf, this is
41:35 news from the past week. We'll read
41:37 Matt's list here. Chat Gvt app adds
41:40 interactive visuals for science and
41:42 math. Hadn't heard of that. Google rolls
41:44 rolls out Ask Maps. Heard of that, but
41:46 don't see it in my app yet. Google adds
41:49 Gemini to Doc, Sheets, Slides, and
41:51 Drives. I thought it was already there,
41:53 but cool. Google launches Gemini
41:55 embedding 2. Yeah, you can now. So, the
41:58 new Gemini embedding model um embeds
42:03 text, images, video, and audio all in
42:07 the same model, which is kind of cool.
42:09 Enthropic AI adds interactive charts.
42:11 Okay. Enthropic launches Claude Code
42:14 review. Enthropic launches Claude Code
42:17 scheduled tasks. So, basically,
42:19 Enthropic is trying to turn Claude Code
42:22 into
42:24 into OpenClaw. Microsoft launches
42:27 Copilot Health, nobody notices.
42:34 Meta adds AI listing to Facebook
42:37 Marketplace and nobody notices. Meta
42:40 hires Moltbook creators. Yeah, Metabot
42:42 Moltbook, which is wild. Nvidia launches
42:45 their Neatron 3
42:48 um super, which is an OpenClaw
42:51 competitor. Perplexity AI unveils
42:55 computer running on Mac minis. So,
42:57 personal computer, it's called. Amazon
43:00 adds adults only sassy mode to Alexa
43:03 Plus. Whoa. Hey, did you know that if
43:05 you've got Alexa Plus, you can now get
43:09 sassy mode. Yeah, just what you want.
43:12 [ __ ] [ __ ] Jeff Bezos listening to
43:15 your sassy chats with your Alexa.
43:24 We live in a stupid world.
43:31 Adobe adds AI assistant to Photoshop.
43:33 Canva launches magic layers. XAI adds
43:36 Grock audio option. X addADs toggle to
43:39 block Grock from editing media. That's
43:42 interesting.
43:45 And Karpathy releases auto research.
43:48 Yeah, Andre Karpathy launched a thing
43:50 that you can train your own models on a
43:52 on a regular computer. All right, that
43:55 was it. That's whatever.
43:58 I'm I'm tired of AI news. All right,
44:01 that was that. Next,
44:13 AI news equals trips to space. Nobody
44:15 really cares anymore. It's so true,
44:17 Kelly, isn't it? It's like, no, no, it's
44:19 not just you. It's not just you. I mean,
44:24 I set up this channel to be able to talk
44:26 about [ __ ] like that, and it's just like
44:30 I none of it really matters. Like, like
44:32 I feel like the only thing that matters
44:33 right right now is the delta between
44:36 where we are and when this shit's good
44:38 enough to actually use, right? Open Claw
44:42 right now is just a janky a janky mess.
44:44 and it's going to get better and if I
44:47 learn how to use it better, it'll get
44:48 better. But like just even listening to
44:50 to Brent Peterson like what he's going
44:53 through knowing like deeply knowing what
44:56 he's doing and having a whole
44:57 infrastructure to run these things and
44:59 they're still janky, right? So they're
45:02 just janky right now. But we're kind of
45:04 just kind of kind of in a holding
45:06 pattern while while all this stuff
45:08 develops around us.
45:11 Hopefully they get on their rocket and
45:13 go away.
45:15 I'm K9. I took your advice last night
45:17 and got the codeex login for OpenAI and
45:19 my OpenClaw works like a charm. Great.
45:21 Beautiful. I am K9. So, here's the deal.
45:24 If you start experimenting with
45:27 OpenClaw,
45:29 one of the things that you do in the
45:30 setup is you choose a model. And if you
45:34 choose OpenAI and you choose the codeex
45:37 um API,
45:39 you can then log in to your normal chat
45:42 GPT subscription and it will use your
45:44 normal chat GPT subscription to run your
45:46 open clock calls it and it won't you
45:49 won't run up all sorts of uh API uh
45:54 bills basically. Um, you will get rate
45:57 limited out like like if your if your
46:00 bot gets too busy, you'll run out of
46:03 tokens. Um, but that's a that's a way to
46:06 do it. Frank was janky, too. Yeah, he
46:08 was. I'm in my fourth stage of overwhelm
46:11 right now.
46:13 Yeah, I know. Me, too. Yeah, that it's
46:16 funny. the the overwhelm that I'm in the
46:18 middle of, Kelly, is it's probably
46:20 similar to yours is
46:28 I just I keep going back to the to the
46:30 seven economies. We're just going to
46:32 have these different companies are just
46:34 going to be running on different
46:35 operating systems,
46:38 right? There's going to be a whole bunch
46:39 of companies that just keep running the
46:42 way they run today. They're just going
46:43 to ignore AI.
46:46 And then there's going to be some
46:47 companies that sort of use AI. They like
46:49 dip their toes in the water.
46:52 And then there's going to be companies
46:53 that use it a little more aggressively
46:54 and then more aggressively and then more
46:56 aggressively and then agents.
47:07 It's like if I think about all of the
47:09 the versions of
47:11 companies sort of just getting spun up
47:14 on AI and then how different that's
47:17 going to be than a company that's like
47:20 I'm going to spin up a 50 agent company
47:25 or even a 10 agent company. Like I I
47:27 think like right now having one agent is
47:30 a lot and and I would imagine that if I
47:33 get it set up right and it can spin up
47:35 its own sub agents and it can manage
47:36 them, it might not be so overwhelming.
47:39 But it's pretty overwhelming right now.
47:41 It's just a completely different
47:42 mindset. And so it's hard to look at
47:45 like, you know, there's this tool over
47:48 here that a human has to go to and type
47:51 in a prompt and get back an answer. like
47:54 that just feels like
47:58 it's a lot of activity that's about to
48:00 be replaced by this thing that just does
48:02 it and just goes and does it for you.
48:08 I don't know. I don't know. I don't
48:10 know. I wish I had an answer. I do not
48:13 like not having an answer.
48:15 There's more than an abundance of stuff
48:17 going on that makes our cups runth over.
48:20 Yeah, maybe that's why I'm so intrigued
48:22 with friction.
48:24 You know, I would I would argue that
48:25 this channel for the past what three
48:28 weeks, four weeks, it's been a lot of
48:31 friction on my part.
48:36 Normally I'm like go go go
48:40 and now I'm like, eh,
48:45 doesn't really matter.
48:49 Let's go back to Let's go back to making
48:51 flies. The greeny. The greeny baby.
49:01 The greenie.
49:09 Let's go to irregulars. See if anyone
49:13 did anything in irregulars. Did anyone
49:14 do anything in irregulars?
49:17 Were you a good fancy community?
49:21 Um,
49:25 Steo. There we go.
49:33 See,
49:36 that's great.
49:43 That's so good.
50:17 After I met Townsand, I got rid of most
50:20 of my sass.
50:22 He did so much manually and I copied
50:24 him.
50:26 I think I'll be the same in two years
50:28 with AI. I'm going to be cash only and
50:30 get a landline.
50:32 Yeah, I'm I don't think I'm quite there,
50:35 but I think I'm definitely going to be
50:37 in the place where I'm like
50:40 I want to get good enough at AI to have
50:42 it do [ __ ] that I don't really have to
50:45 pay attention to. And I know that's
50:46 that's a fantasy, right? That's the make
50:48 money while you sleep. You can make
50:51 money with
50:53 >> agents.
50:56 Like I know that's the fantasy, but I
50:58 think with agents
51:01 developing the skill to to deploy a
51:04 group of agents to go do something on
51:07 your behalf. Well, that is going to be a
51:10 skill. And so I want to get good at that
51:13 skill. And then I want to go
51:16 to the blueberry farm. I don't even
51:18 think I I don't know how blueberries
51:21 grow. Do they grow in trees or they
51:22 bushes? Are they What are Hang on.
51:28 Anybody? Oh, I'm here. We can start.
51:31 Thank goodness.
51:34 Um let's
51:37 let's go look up blueberry bushes. Um
51:44 um show me how blueberries
51:49 grow because I keep talking about
51:51 growing blueberries, but I may not
51:54 I may not actually like that. I'm
51:56 pulling together a visual
52:03 thought for 10 seconds. Oh,
52:07 all right. They're sort of little trees.
52:08 Am I sharing this? I'm not
52:12 kidding.
52:14 All right. So, it is a little tree. It's
52:16 a little tree.
52:18 Nice.
52:20 There's their stages. How blueberries
52:23 grow.
52:26 Um,
52:30 the bush. The bush makes a small
52:33 bell-shaped flower.
52:36 All right.
52:38 Show me lots of pictures of
52:43 blueberry
52:46 bushes.
52:48 I'm just realizing what this channel has
52:50 turned into.
52:56 What did you do Friday night? Uh, I
52:59 watched a guy look up blueberry bushes
53:02 on chat GPT.
53:05 Really? Yeah. Why? I don't know.
53:14 They really are little bushes.
53:17 Cool. All right. I'd be down with
53:20 growing blueberries. I think I'm good. I
53:22 think I think I'm going to I'm going to
53:24 have
53:26 10 10 agent companies running on chips.
53:31 And then in my backyard, I'm going to
53:32 have blueberry bushes.
53:35 Blue. My blueberry bushes are nine to
53:37 ten feet tall. They're bushes. Okay,
53:40 good.
53:44 Did Did you learn anything at the AI
53:46 learning lab last night? Yeah. What'd
53:49 you learn?
53:51 Blueberries are bushes.
53:54 Yeah. Yeah, I know. Everybody knew that.
53:56 Yeah. He didn't. Who didn't? The AI guy.
53:59 What?
54:02 The AI guy's talking about blueberries.
54:04 Yeah.
54:10 You got to net them or you lose them
54:11 all. I would think I would think you put
54:14 a big net around the bush and probably
54:16 shake the [ __ ] out of it, right? And
54:17 then all your blueberries come crashing
54:19 down. You pull the net out. Yeah, that
54:23 makes sense to me. Look at all those
54:26 things. They look great. They look
54:27 awesome. Okay, but wait. Let's You know
54:30 what? Here's what I want to do.
54:33 Um,
54:42 I want you to create me
54:46 I want you to create me a Renaissance
54:49 style painting
54:53 of a blueberry bush
54:56 in the background and some
55:01 tantalizing ing and juicy blueberries in
55:05 the foreground. Close up, shimmering
55:09 with light and water droplets and all of
55:13 the colors
55:16 of blue that could possibly be
55:18 represented in a single berry.
55:21 as if Michelangelo himself
55:24 crafted the piece.
55:38 Have I got a prompt for you?
55:42 Check back in a regulars for
55:44 blueberries.
55:50 Oh my god,
55:54 this is good. Glistening blueberries.
55:56 Your image is ready. Nice. Vermont
55:59 blueberries by Steo. It's beautiful.
56:02 Love it.
56:04 You see, this is this is what it's going
56:07 to be. People, go to community.thesal
56:10 salon.ai. You can learn how to make your
56:12 own agent swarms. And then you can have
56:14 your agent swarms go make you money so
56:16 you can go be like Steo. Look, he just
56:18 leased a new tractor for the
56:20 blueberries. It's very exciting.
56:24 Oh, holy [ __ ] This is good. Wait, how
56:28 do we This is so good. How do I get it?
56:31 Download it.
56:45 That's gorgeous. It's gorgeous, I tell
56:48 you.
56:51 I I don't know if you knew this. We do
56:54 live in a world where if you want a
56:56 tantalizing Renaissance style painting
56:59 of blueberries, you can just have that.
57:01 Now,
57:05 wait, where's the cello quartet in the
57:08 in the blueberry garden? Michelangelo
57:10 would be proud. He really would. This is
57:12 if Michelangelo had gone into uh into
57:17 fruits and vegetables rather than famous
57:20 people.
57:24 That's so cool.
57:28 Wild.
57:33 Did this become a cooking show? Pretty
57:36 pretty much we're we're hanging out.
57:38 We're we're we're hanging out in Economy
57:40 One tonight, which is, you know, screw
57:43 AI. We're checking out. We're checking
57:46 out. We're gonna do blueberries and
57:48 paintings.
57:50 That's really awesome.
57:53 Oh, good lord. Good lord. Good people.
57:56 Here, let's take that same
57:58 prompt
58:01 over to Gemini.
58:05 Gemini.google.com.
58:06 google.com.
58:09 We're going to go tools. We're going to
58:10 go create image.
58:13 We're going to take that same prompt.
58:15 We're going to blast it in there.
58:19 But is it pi? Oh,
58:27 Michelangelo really could have broken
58:28 into the art world if he did still
58:31 lives.
58:35 Oh, so good. That's so good. Oh, you
58:38 know what we're going to do? Um,
58:40 go do some research
58:44 on Oh, that is horrible. Not nearly as
58:48 good, but
58:51 gave us a frame.
58:59 Nice gold gilded frame.
59:05 The blueberry bush. There it is. Look at
59:07 that.
59:09 This is our future, people.
59:13 Once we get the AI to do all the [ __ ]
59:16 go make us the money. We can just do
59:18 this. Sit around sucking blueberries.
59:23 Excuse me, Kyle. Yes, you have a little
59:25 blueberry on your lip. Oh, yes. Sorry.
59:29 I've eaten too many today. I feel a
59:31 little nauseous. I'm so naughty.
59:39 I can see my house in that picture.
59:44 Um, oh, was I going to say, go do some
59:46 research on blueberries.
59:51 Um, that is true and unexpected.
59:57 and make me an infographic
1:00:03 on blueberries
1:00:09 um inspired
1:00:12 by
1:00:14 this swell painting. It should
1:00:21 surprise
1:00:23 and delight
1:00:27 the viewer.
1:00:32 Do you know Do you know the muffin man?
1:00:36 3D 3D print million-doll
1:00:40 3D print art million-dollar idea. Well,
1:00:43 actually, Chef Kelly, there there is a
1:00:45 company now, I forget what they're
1:00:46 called. Well, there's a couple of
1:00:48 things.
1:00:50 There are now printers. There are
1:00:52 printers called I think they're called
1:00:54 UV printers
1:00:56 and they print um
1:01:00 they basically layer up white resin and
1:01:04 then they print color on top of it. So
1:01:05 you can you can actually now print
1:01:08 textured paintings. Um and there's
1:01:10 there's companies that do that at a
1:01:12 large scale, but you can actually buy I
1:01:14 think it's like 2500 bucks for a UV
1:01:16 printer and and they can print things
1:01:18 with texture and things like that.
1:01:22 Maybe next you can do Van Go's Wild
1:01:24 Garlic. Okay, let's do that. Um,
1:01:28 now I need Van Go's
1:01:33 Wild Garlic painting
1:01:38 new images and irregulars. This is good.
1:01:40 This is a little fun little creative
1:01:42 fest.
1:01:49 Oh, this is See? Yeah, that's good.
1:01:53 Fish flood. Fish flood from stuck car.
1:01:56 Bizarre scene near Delaware Gap Bridge
1:01:58 as a torn of trout are released into the
1:02:00 village. Investigation continues. That's
1:02:04 good. I like it.
1:02:14 I love how they represent it's like it's
1:02:16 like a box car that was like full of
1:02:19 water. Although it probably probably
1:02:21 what it was it probably was a box car
1:02:23 full of like just tanks
1:02:26 tanks of fish. Huh. That's cool.
1:02:30 Vatic Vat
1:02:36 where
1:02:38 no
1:02:43 there it is.
1:02:58 The sharing of art makes me takes me
1:03:00 back to the the olden days like last
1:03:02 year. Yeah, exactly.
1:03:05 All right, we'll see if this makes a
1:03:06 good infographic.
1:03:26 Oh, Gemini doesn't take kindly to
1:03:28 leaving the tab while you make pictures.
1:03:31 Well, I had it I asked it to do research
1:03:34 first. So, I thought it might have been
1:03:36 doing research.
1:03:40 Oh, it lost that prompt alto together,
1:03:43 didn't it?
1:03:44 that [ __ ]
1:03:47 Um, do
1:03:50 unexpected
1:03:52 research
1:03:54 on blueberries
1:03:57 and create a clear and
1:04:03 compelling
1:04:05 infographic
1:04:08 inspired
1:04:10 by this painting. I want
1:04:14 the viewer to know everything about how,
1:04:21 where,
1:04:23 and why blueberries
1:04:26 grow.
1:04:42 The blueberry odyssey from blossom
1:04:44 blossom to superfood.
1:04:47 Why we crave them?
1:04:52 How they grow.
1:04:55 Geographic distribution. Where they
1:04:57 thrive.
1:04:58 Designing your final infographic.
1:05:02 Okay. Make that image.
1:05:07 Mm-
1:05:10 I'm amazed that I wasn't scheduled
1:05:14 for anything this Saturday. I don't
1:05:15 think there's an LOL tomorrow, right?
1:05:17 It's on the 21st for for Side Hustle
1:05:20 Mimi.
1:05:24 Am I recalling that correctly? Correct.
1:05:27 You are correct, sir.
1:05:30 Oh, look. The Blueberry Blossom Odyssey.
1:05:35 Antioxidant
1:05:40 power
1:05:44 brain health
1:05:46 149% brain health
1:05:50 heart health
1:05:52 $21.6
1:05:54 in heart health
1:05:57 low calorie treat
1:05:59 4.0 per cares.
1:06:03 This is okay. But where does it where do
1:06:05 they live? USA
1:06:09 and I guess a little bit in Canada. A
1:06:11 little bit in Poland. All right.
1:06:13 And then they're shipped all over the
1:06:15 world. They grow in Peru and Chile.
1:06:19 Nice. All right.
1:06:24 There you go. Beautiful.
1:06:39 I'm going to multitask, but doing my
1:06:41 skin care.
1:06:45 Do blueberries give you the blues? All
1:06:48 right, let's go make a song.
1:06:54 This is Is this This is my Hey, Brandon.
1:06:57 This is my second cheat day of the week.
1:07:06 Let's go to
1:07:19 Oh, here's the garlic. But wait,
1:07:23 wild garlic blooms.
1:07:26 I need to see the garlic.
1:07:30 up close
1:07:32 with all this in
1:07:36 the background.
1:07:41 I want a garlic
1:07:46 bulb or three
1:07:50 on a raw piece of wood
1:07:54 with
1:07:57 um a rustic
1:08:00 knife
1:08:02 nearby
1:08:06 and maybe
1:08:08 a nice tomato. Hey.
1:08:18 Okay.
1:08:19 Sorry. Going back to Sunno.
1:08:23 I've got to share Sunno. What's going on
1:08:25 here? Let's see. Share screen. Share
1:08:29 Sunno.
1:08:31 Share Suno.
1:08:42 Irregulars.
1:08:44 Hang on. Got to go to regulars. Share
1:08:46 this tab instead.
1:08:49 Close that. Reload.
1:08:58 Excuse me. Can I buy buy blueberries
1:09:01 with this with the agent card and the
1:09:04 trout going in? This is good. This is
1:09:07 solid.
1:09:09 Free the trout. Actually,
1:09:12 that's what they look that's that's what
1:09:14 Delaware rainbow trout look like.
1:09:16 They're they're like they're intense.
1:09:18 Free the trout.
1:09:22 Welcome to the Delaware River. The big
1:09:23 old nasty catfish in the background.
1:09:27 It's really good.
1:09:29 It's really, really good.
1:09:35 Um, here, wait, back to garlic. There we
1:09:38 go. That see what we're looking looking
1:09:40 at here, people.
1:09:43 Oh, wait. You can't see this.
1:09:46 See?
1:09:48 Nice.
1:09:50 That's so good.
1:09:54 Beautiful. Beautiful. Beautiful.
1:09:56 Beautiful. All right,
1:09:59 let's go here. Why is Sunno not loading?
1:10:09 Make any song you can imagine. We're
1:10:11 going to have it write the full songs.
1:10:14 Uh, what did you say? Blueberry white
1:10:16 tube top. Um,
1:10:20 a song about blueberries
1:10:25 and white tank and tube tops
1:10:30 in the summer
1:10:33 after AI takes all the jobs
1:10:38 and we
1:10:41 realize
1:10:43 how much we hated
1:10:46 knowledge work
1:10:50 and just want to play in the dirt again.
1:10:57 Make it a song.
1:11:00 Uh, that's that's good. Just that's
1:11:03 good. Write the lyrics. Okay. So, it
1:11:06 should write us two sets of lyrics.
1:11:09 I think you'll be banned.
1:11:17 Where was I banned? Am I banned on
1:11:19 TikTok? I don't think so.
1:11:27 Share Bear's here. What's happening?
1:11:29 Share Bear. Aaron Cassid's here. Trenda
1:11:34 Harsh Weeds in the house. Frumple's in
1:11:37 the house.
1:11:41 That's cool.
1:11:45 Got some some some oldies but goodies.
1:11:48 Calendar's blank. Laptops cut a cutting
1:11:50 board now. Checked out of the grid.
1:11:54 Walked in the weeds somehow. You and a
1:11:56 white tank. Tank. Wait. In a white tank
1:12:00 tube top
1:12:03 under tied in a knot. Blue stains.
1:12:05 That's bad. Blue stains on my fingers.
1:12:07 White tank stuck to my skin. You say
1:12:11 remember all those loginins. I say
1:12:13 please don't start that again. All
1:12:14 right, we'll do this one. Okay, so now
1:12:17 we're going to do um rock and roll
1:12:23 blues.
1:12:26 Um
1:12:28 stank.
1:12:30 We'll go male husky voice.
1:12:34 Um female back vocals. Funky beat.
1:12:39 We'll go vinyl. Why not?
1:12:43 Bouncy heavy base punk.
1:12:48 All right. Boom.
1:13:00 A mini reunion.
1:13:03 It's true.
1:13:10 my fingers. White tank stuck to my skin.
1:13:14 You say, "Remember all those login?" I
1:13:17 said, "Please don't start that again."
1:13:19 We used to chase fake deadlines colored
1:13:24 on the screen. Now we're barefoot in the
1:13:27 backyard pulling weeds like the old
1:13:29 routines. Blueberries and white tank
1:13:32 tops, tube tops, slipping sunburn spots.
1:13:36 Hey, I took the jobs we lost. Guess we
1:13:39 like it better off. Hands in the dirt,
1:13:42 not on the clock. Picking what the
1:13:45 garden's got. We hated all that thinking
1:13:48 stuff. Turns out this mess is just
1:13:51 enough. You laugh about your inbox. How
1:13:55 it buzzed you in your sleep. How you
1:13:58 dreamed in bulleted fragments. Woke up
1:14:00 grinding down your teeth. Now the only
1:14:04 pins are crickets and a neighbor's
1:14:06 radio.
1:14:09 Blue stains on my fingers, white tank
1:14:12 stuck to my skin. You say, "Remember all
1:14:15 those login?" I said, "Please don't
1:14:18 start that again." We used to chase fake
1:14:21 deadlines
1:14:23 colored on a screen. Now we're barefoot
1:14:26 in the backyard pulling weeds like
1:14:28 they're old routines.
1:14:31 Blueberries and white tank tops. Toting
1:14:45 what the garden got.
1:14:47 >> We hated all that thinking stuff.
1:14:50 >> Turns out this message is enough.
1:14:53 >> You laugh about your inbox. How it buzz
1:14:56 you in your sleep. How you dreamed
1:14:59 bulleted fragments. Woke up grinding
1:15:02 down your teeth.
1:15:04 >> Now the only things are crickets
1:15:07 >> and a neighbor's radio.
1:15:09 >> You got soil on your shoulders
1:15:12 where a badge used to go.
1:15:15 Blueberries and white tank tops twisted
1:15:19 in a knot. I took the jobs we lost.
1:15:23 Guess we like it better.
1:15:26 >> Hands in the dirt.
1:15:29 what the garden's got.
1:15:31 >> We hated all that stuff.
1:15:34 >> Turns out this message is enough.
1:15:37 Did we ever love that hustle or did we
1:15:40 just love being tired? Now the sun is
1:15:44 our agenda and the skies are only why we
1:15:48 trade spreadsheets for seedlings. Trade
1:15:51 small talk for a breeze. You say I'm
1:15:54 finally doing nothing. I say
1:15:59 don't ever leave.
1:16:01 Blueberries and white tank tops top
1:16:05 dancing as we walk. I took the jobs we
1:16:09 lost cuz we like a better off.
1:16:13 Hands in the dirt.
1:16:14 >> Not on the clock.
1:16:15 >> Picking what the
1:16:18 we all thinking.
1:16:20 >> Turns out this mess just enough.
1:16:25 >> Ha. I've been making a lullaby all night
1:16:27 for my nephew and niece. Due yesterday,
1:16:29 still not born. Keep cranking. All
1:16:31 right. Blueberries and white tank tops
1:16:33 is now published. So if you if you go to
1:16:37 Sununo and search for blueberries and
1:16:38 white tank tops, have at it, baby.
1:16:46 Oh, good lord. Share Bear, what's
1:16:47 happening? Welcome back. Good to see
1:16:50 you. It is like a little reunion. This
1:16:53 is so cool.
1:16:55 Um, hey all. Why am I here? Friday night
1:16:58 of the 13th. Maybe I should ask Chat
1:17:00 GBT. No, it's this is this is a
1:17:05 it's a special night. There's things
1:17:08 have come together. Mercury is is in
1:17:10 some sort of tackle box somewhere. And
1:17:14 everything's janky. It's Friday the
1:17:15 13th. We're good. We had a moment of
1:17:18 silence in office hours today. A
1:17:20 spontaneous moment of silence that was
1:17:21 really cool. Um, we're talking about
1:17:24 friction.
1:17:26 We're talking about not AI. We're
1:17:28 talking about growing blueberries.
1:17:31 Hey, Mr. Kyle. What's happening? Share
1:17:33 Bear. Um,
1:17:36 yeah. And we're just doing our stuff.
1:17:40 We're just doing our stuff. And by our
1:17:42 stuff, I'm just not doing much. But we
1:17:46 made a song.
1:17:55 I thought Mercury was in the flat iron
1:17:57 grill. It Listen, it might be. It's
1:17:59 somewhere. The last two days I've been
1:18:01 I've been janked. I have been janked.
1:18:06 Psychically janked.
1:18:15 I'm gonna play that again.
1:18:17 It makes me
1:18:20 Blue stains on my fingers, white tank
1:18:23 stuck to my skin.
1:18:26 You say remember all those login. I said
1:18:29 please don't start that again. We used
1:18:32 to chase fake deadlines
1:18:35 on screen. Now
1:18:37 >> wait, that's not the one we published.
1:18:39 >> Blue stains on my fingers, white tank
1:18:42 stuck to my skin. You say remember all
1:18:46 those.
1:18:47 I say please don't start that again. We
1:18:50 used to chase fake deadlines
1:18:54 on a screen.
1:18:55 >> So it looks like Brandon said Suno can
1:18:59 do sound effects now. I don't Can you do
1:19:01 it in line in a song? Like have it like
1:19:03 a bunch of blueberries fall into a net
1:19:06 at some point in the song? Now we're
1:19:08 barefoot in the backyard pulling weeds
1:19:11 like they're old routines
1:19:14 and white tank top
1:19:19 spots. Hey to the job we lost. Guess we
1:19:22 like it better off.
1:19:25 >> Hands in the dirt.
1:19:26 >> Not on the clock.
1:19:28 >> Picking what the garden.
1:19:30 >> We hated all that thinking stuff.
1:19:32 >> Turns out this message is enough.
1:19:36 You laugh about your
1:19:39 buzz you in your sleep. How you dreamed
1:19:42 foke
1:19:44 up grinding down your teeth.
1:19:46 >> Now the only things are crickets
1:19:49 >> and a neighbor's radio.
1:19:52 >> You got soil on your shoulders
1:19:55 where a badge used to go.
1:19:57 >> Blueberries and white tank tops top
1:20:01 twisted in a knot. Hey, I took the jobs
1:20:05 we lost. Guess we like it better off.
1:20:09 >> Hands in the dirt,
1:20:10 >> not on the clock.
1:20:11 >> Picking what the garden's got.
1:20:14 >> We hated all that thinking stuff. Turns
1:20:17 out this message is enough.
1:20:20 Did we ever love that hustle or did we
1:20:23 just love being tired? Now the sun is
1:20:27 our
1:20:29 only
1:20:32 >> Hey, I just had a thought. I just had a
1:20:34 thought about something. I'm gonna I'm
1:20:35 gonna go on a little tangent here and
1:20:37 then we'll we'll take off. Um
1:20:43 I I'm seeing a lot of posts right now
1:20:47 about people that like, oh, AI was
1:20:49 supposed to make it so we work less and
1:20:51 now we're working more.
1:20:55 Here's why
1:20:59 all this shit's being invented right
1:21:00 now. Like the frontier labs are
1:21:03 inventing the technology.
1:21:05 Knowledge workers and professionals are
1:21:07 inventing how we use the technology in
1:21:10 any given business. And then we're also
1:21:13 inventing the future of work and the
1:21:15 future of our role in that work.
1:21:19 When you're inventing [ __ ] it takes
1:21:21 longer.
1:21:23 there's more to do, right? Because not
1:21:26 only are we trying to do the [ __ ] that
1:21:28 we used to do with AI, we're also trying
1:21:30 to find new ways to do the [ __ ] we used
1:21:32 to do more efficiently, we're trying to
1:21:35 come up with new things to do on top of
1:21:37 the old [ __ ] we had to do. Plus, they
1:21:39 probably laid off 20% of the staff just
1:21:41 to make some money. And so now we're
1:21:44 doing two and a half jobs plus the AI
1:21:46 plus the old two and a half jobs.
1:21:50 So it makes sense that we're more busy
1:21:52 right now.
1:21:54 I find there there's a bizarre there's a
1:21:57 bizarre
1:21:59 expectation that oh if the AI is good
1:22:02 enough just everything will be done. No,
1:22:04 everything's not going to be done. We
1:22:05 have to invent all that.
1:22:07 2026 is the year it becomes apparent we
1:22:10 can't ignore it. But it's going to take
1:22:13 us the next 5, 10, 15 years to like
1:22:16 figure out all this stuff. So anyway,
1:22:18 that was just something that just struck
1:22:20 me listening to that song. Black Bar and
1:22:22 Tik Tok pin. This is the Inception
1:22:25 version of Kyle. It really is like I I
1:22:27 really do feel like I'm in some sort of
1:22:29 weird inception. Like I feel like I'm
1:22:34 I feel like I'm from the future where
1:22:36 I'm like I sort of know where this is
1:22:38 going now
1:22:41 and
1:22:43 but we're still here.
1:22:47 So yes. Yes. It's like
1:22:55 Yes. Quick spin a top.
1:23:02 All right, kids. Listen. Have yourself a
1:23:05 fantastic weekend
1:23:07 homework.
1:23:09 Go play in the dirt.
1:23:12 Introduce some friction into your AI
1:23:15 work.
1:23:16 Go slower.
1:23:22 Uhoh.
1:23:25 All right. Brandon's got a song to play
1:23:27 us into the weekend. Here we go. Oh, am
1:23:30 I gonna go I'm gonna go play this for
1:23:32 you? Okay. Hang on.
1:23:41 Oh, this is the the ballot of Dan
1:23:43 Cahill. Down where the Delaware bends
1:23:46 through the valley. All right, good.
1:23:47 This is good. So, if you weren't here
1:23:49 earlier, um I was telling a what might
1:23:52 be a a fisherman's tale of the strain of
1:23:56 rainbow trout that are in the Delaware
1:23:58 River came from some western state where
1:24:02 the train broke down while the train was
1:24:04 next to the Delaware River and they just
1:24:05 dumped the fish into the river and
1:24:08 apparently it was a guy named Dan Cahill
1:24:10 that did it. So, here's the ballot of
1:24:12 Dan Cahill.
1:24:27 Down where the Delaware bends through
1:24:31 the valley. Morning mist rides a cold
1:24:34 mountain air.
1:24:37 Old men whisper of water and memory.
1:24:42 And a fisherman once walking there.
1:24:46 They say he came with a wagon of silver.
1:24:52 River bride in a wooden pill.
1:24:57 And the trout took the current like
1:25:00 falling stars in the wake of Dan
1:25:04 Kahill's trail.
1:25:09 Oh, the river.
1:25:12 >> I wonder Dan Cahill. So, there there is
1:25:14 a fly called a light Cahill and a dark
1:25:17 Cahill. I wonder if Dan Cahill is the
1:25:19 inventor of that fly. I bet he is. Um,
1:25:22 beautiful song. So, that's the ballad of
1:25:25 Dan Cahill if you want to go hear that.
1:25:26 Lovely. Thank you, Brandon. Um, and then
1:25:29 what was Kyle? When you Oh, Kyle, when
1:25:32 you add lyrics, um, how do you do that?
1:25:35 So, the way the way you create a video
1:25:38 of like of this song with the scrolling
1:25:40 lyrics is you just go to um share. No,
1:25:44 you go to Oh, I can't do it. That's not
1:25:46 my song. Let me go back to my song.
1:25:51 So, if you can share your song, you go
1:25:53 to download and then you just go video,
1:25:56 download video, and it'll automatically
1:25:58 make the scrolling lyrics video for you.
1:26:02 You have to be a pro member. You have to
1:26:03 be a paying member to do that.
1:26:07 All right.
1:26:11 G.
1:26:15 All right, everyone. Have a great
1:26:17 weekend. Um, think about friction. When
1:26:20 when does it make good sense to
1:26:23 introduce friction into your
1:26:26 into your practice, into your AI?
1:26:32 There might be some times where you want
1:26:34 it to be friction-f free. There might be
1:26:35 other times where you want it to be
1:26:36 really slow.
1:26:38 What if you were intentional about that?
1:26:40 Like that might be something to play
1:26:41 with this weekend and play in the dirt.
1:26:44 All right. Have a frictional weekend.
1:26:47 Yes, exactly.
1:26:50 Good friction. Good friction, everyone.
1:26:52 Enjoy the friction.
1:26:55 Friction can be fun. Friction can be
1:26:57 your friend.
1:27:05 That's That's a way to end Friday night
1:27:07 date night. Friction can be your friend.
1:27:10 Have a good weekend, everybody. Bye.