Newsletter and Transcript of Rabbit Hole of Research Episode 83: The Mini: The Cloud

Fire Amoeba, Downtown Chicago Data Centers, Tempe Lake evaporation rates, and it’s confirmed, David is the first three time guest!

In Episode 83: The Mini, Joe, Nick, and Georgia recap Episode 82: Data Centers: Are We Really Blowing Up the Planet for AI Slop and Porn? with confirmed, three-peat returning guest and Friend of the Pod, David Pincus, PhD. The crew follows up on the CoreWeave/Hammond deal, corrects the Tempe Town Lake numbers, and it’s way more than Joe thought. Georgia talks about discovering a massive data center in downtown Chicago. In Science Holes, Joe discusses the fire amoeba and Nvidia claims to reduce data center water use to near zero. Plus the crew takes you down the Media Hole of what they have been watching, reading, and playing.

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Episode 83 Transcript


Episode 82: Data Centers


Order the Touch of Noir 2027 Wall Calendar by Jim and Georgia—

12 months of all-original art by Jim and Georgia; pre-orders open now, with mailing expected in September


Words // Friends with Sara: Replay on YouTube

Jump to Joe’s Reading (But you should listen to all the amazing authors)


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Hang with the Crew:

Podcast Cross-Appearances:

Events & Conventions:

Joe:

Georgia:


Upcoming Episodes

*The Mini will now be every other episode!

  • Episode 84: ETGuest: Chris Guzman (@theboxinggloveartist)
  • Episode 86: Disability in HorrorGuest: Grace Daly (Bram Stoker nominee)

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What the Crew is Digging, Links, Resources, and Topics Mentioned in mini and/or full episode:

Links & Resources:

Science Holes:

  1. Newly Discovered Fire AmoebaArticle: Rappaport, H. B. et al. Preprint at bioRxiv https://doi.org/10.1101/2025.11.24.690213 (2025).A newly discovered organism, Incendiamoeba cascadensis, nicknamed the fire amoeba, was found living in the hot springs of Lassen Volcanic National Park in Northern California’s Cascade Mountain Range. It can thrive at temperatures up to 64°C making it the most heat-tolerant eukaryotic cell ever discovered (they use electron microscopy!). This challenges the long-held notion that complex cells with a nucleus can’t survive extreme temperatures the way bacteria and archaea can. The current bacterial heat record holder is Methanopyrus kandleri at 122°C. The upper heat limit for human cells is around 43°C/ 109°F, and not for long. A core body temperature reaching 40°C / 104°F causes heat stroke, and reaching 42°C / 107.6°F can cause irreversible organ damage or death, long before your core temperature gets to 43°C. And to Nick’s hot tub point: the comfortable hot tub temperature is 100°F – 102°F (37.8°C – 38.9°C) which is preferred by most Jacuzzi users lasting 15 to 20 minutes.2) AI Data Center Designed to Run Hotter and Uses Less WaterArticle: Nvidia says its AI data center design runs hotter to use a lot less water(The Verge)
    Nvidia claims that by switching to dry cooling combined with liquid loops they can run their servers at higher temperatures, up to 113°F (45°C). Which means their new data centers can cut water use to near zero. Amazon has also claimed higher heat tolerances for its air-cooled data centers. Nvidia’s does discuss the cost of building this style of data center compared to conventional air-cooled alternatives.

Science Terms

  • Eukaryote — an organism whose cells contain a nucleus and organelles; includes all animals, plants, fungi, and protists. Our cells are eukaryotic.
  • Prokaryote — an organism whose cells lack a nucleus; includes bacteria and archaea.
  • Extremophile — an organism that thrives in extreme environmental conditions such as high temperature, acidity, or pressure.
  • Protist — a diverse group of eukaryotic organisms that are not plants, animals, or fungi; amoebas fall into this category.
  • Cyst — a dormant, protective form some organisms can form to survive harsh conditions.
  • Archaea — single-celled microorganisms similar to bacteria but genetically distinct; many are extremophiles.
  • Liquid cooling — a method of removing heat from hardware using liquid (typically water or coolant) circulated through pipes directly in contact with heat-generating components.
  • Dry cooler — a heat exchanger that uses outside air and fans to cool a circulating liquid, without using water evaporation, unlike a cooling tower.
  • Cooling tower — a conventional system that uses water evaporation to dissipate heat from industrial processes and data centers.
  • Liquid loop — a closed circuit of coolant that absorbs heat at the source and carries it away to be released elsewhere.

Media Holes: What the Crew is Digging:

TV and Films

  • Trying — Apple TV+
  • Dark Matter Season 2 — Apple TV+
  • Lanterns — HBO/Max

Movies

  • Vacation Friends — 2021

Books

  • Bird by Bird — Anne Lamott, 1995
  • Intrigue — Silvia Moreno-Garcia, 2025 
  • The Girls We Send Away — Meagan Church, 2024

Video Games

  • 007: First Light — IO Interactive, 2025

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Transcript

[00:00:00] joe: Hey, welcome back to the Rabbit Hole of Research down here in the basement studio for the mini

[00:00:09] Nick: The mini bomb

[00:00:12] joe: Da-da-dun-da-dun. You’ve got me, Joe

[00:00:16] Nick: You got neck

[00:00:17] joe: got Nick.

[00:00:18] georgia: Georgia

[00:00:19] joe: got Georgia here talking about the cloud. We had David Pincus on with us talking

[00:00:29] Nick: us in the cloud here,

Joe?

[00:00:31] joe: center, I can find you in the cloud. Wanted to clean some stuffs up out of that episode. People ask questions, so I confirmed that David was the first three-time guest, so he’s officially in the lead.

But we got Bill Halliar and Reed locals Melissa are hot on his heel with their third appearances coming in October. [00:01:00] So who will

make it to

[00:01:03] Nick: doesn’t, last long, does it?

[00:01:05] georgia: He’s coming.

[00:01:05] joe: No. Who will make it to four? So

that was,

[00:01:10] Nick: my money’s on David.

[00:01:12] joe: David.

He… You’re right

[00:01:13] Nick: he’s gonna come on early next year. He’s gonna be like, “Hey, I heard my title’s at risk,” and he’s gonna be like, “Yeah, this is where we’re

[00:01:20] joe: And Bill and Melissa usually are in the horror month, so that’s when they usually come in. So that, that puts them in October. It’s a little disadvantage. I don’t know. We’ll see.

[00:01:29] georgia: see

[00:01:29] Nick: Gonna have to do two two episodes

[00:01:31] joe: Yeah,

[00:01:31] Nick: Halloween.

[00:01:33] joe: Have to get in there.

[00:01:36] georgia: So

[00:01:36] joe: So there was another thing that came up was about how many jobs were created by data centers after their construction is done.

So that was a question we didn’t have a answer on the mini. But the Brookings Institute, a little more than five, I just wanna thank you, Sid, on the episode. They’ve done some studies on this, and most, operational facilities, they employ seven [00:02:00] dozens to roughly 150 people on site for security, maintenance, and IT systems.

The larger campuses can support up to 200 permanent roles. So we’re talking somewhere between , several dozen to 200 people running these data centers. So not tons of jobs.

So, we were talking about local politics. CoreWeave in Hammond, Indiana, were trying to make a deal to build a big data center, and that vote was going to happen after the release.

And so following up on that, the deal actually expired, so it has not been approved. So it was a expiration of data center development agreement happened,

[00:02:46] Nick: Oh, yeah

[00:02:47] georgia: But that could come back.

[00:02:49] joe: They could make a different agreement, but the deal that Mary was talking about on that episode fell apart

And I’ll put all the links to all this stuff in the show notes. [00:03:00]

And we mentioned data centers mostly going up into dry places, and we kinda talked about it, but just reenter. They usually go there because of evaporative cooling. So you’re taking advantage of the very dry environment there.

And on that point about evaporative cooling, I had talked about the Tempe Town Lake in Arizona, and I said it loses 1 million gallons of water a year. I was off a little bit. It’s actually by this town, city of Tempe’s own reporting about 1.7 million gallons of water loss every day- What?

Leading to, you know, 600 million, 650 million gallons a year that never go back into the Colorado River.

[00:03:44] georgia: What? And

[00:03:45] joe: yeah. So that’s right. So the v- it’s kind

of this artificial

[00:03:49] Nick: huge– Like don’t they do environmental checks and all that? Like

[00:03:54] joe: Yeah, they pay credits and they fill it back up and,, people go fishing. They stock it with trout and stuff like that, [00:04:00] so everyone’s happy to have a artificial lake in the middle of the desert. And to put that in context,

context,

a medium-sized data center can consume up to 110 million gallons of water every year, and large facilities can be between one million and five million gallons of water every day peak demand.

So that’s about 350 million to 1.8 billion gallons of water for a big facility. So these data centers are, some of these artificial lakes that are out there are, in that same wasteful kind of, of water loss area. And for houses, a single home uses about 300 gallons of water per day,

And about 70%, this is Nick, you had asked about how that water is used at a home, 70% is indoor use. The bathroom is the largest portion of that. And outdoor water is about 30% for watering gardens and things like that. In regions like Arizona, Georgia, I [00:05:00] mentioned flood kind of irrigation of lawns it goes up to about 60% of the home’s total water usage is for watering lawns out there in the sunny state of Arizona.

[00:05:12] Nick: Just saying So

you’re responsible.

[00:05:15] joe: So yeah, so that’s our how we use water. So when we talk about these data centers and water use, at a personal level, we don’t use as much, like we’re not get- approaching millions of gallons a day, but we still are fairly wasteful. But just something to think about .

So yeah, that was all, that’s what I had. Some of the things we talked about and putting some facts and numbers to it, just put it all in perspective. But

[00:05:43] Nick: I feel like this is– this wasn’t as dark of an episode as we could and have gone for some of these

[00:05:51] joe: Yeah, no, it was pretty good. I thought it was well-balanced the pros and cons of it all, like we talked about.

[00:05:59] georgia: [00:06:00] I do think data centers are necessary in the fact that we’re using them for computing But do we need exponentially- Right, Mm-hmm … increase

Yeah, no, it was pretty good. I thought it was you know, it was well-balanced the pros and cons of it all, you know, like we talked about I mean, I do think data centers are necessary in, in, in, oh, I mean, in the fact that we’re using a lot of computing and we are using a lot of storage, but do we need to exponentially- Right

increase them and make all these huge data centers? And it seems like it’s only to support these, billionaires and these people that I feel like it’s just gonna be a bubble that’s gonna get burst anyway. And so who’s gonna end up really paying for that- Right … and the cost of that?

[00:06:34] joe: Right. Yeah. Yep. No, I think

[00:06:38] Nick: always seems to be the, the final question is what is the actual cost?

[00:06:43] joe: And what are we using it for, and is it worth that cost, right? ‘Cause I think there are things Talking about water and then when we

[00:06:49] georgia: Whatever you’re using it for, you know, is it worth that cost? Right, and especially talking about water. I know when we lost power recently I thought, and I really do feel like, I mean, it was bad and it was like 10, 11 days without power, but all I kept thinking was like, [00:07:00] as long as we still have water, you know what I mean?

I think not

[00:07:05] Nick: Oh, 100%.

[00:07:06] georgia: having water is- Right … yeah. So it’s just really scary to think about, but.

[00:07:13] joe: I mean, we need water to live. Can’t live very long without it, so it, of course, it’s gonna be top o’ mind when we think about it, so,

[00:07:22] georgia: But I did walk past a data center when I was in Chicago. It’s in the neighborhood of M- McCormick Place, and I’m just walking by and then , my friend that lives there is like, “Oh, what is this building?” And it’s this really historic building. But it was a data center and you really couldn’t hear anything.

Mm-hmm. But I guess what it was is it was built into this existing building-

[00:07:50] joe: Mm-hmm

[00:07:51] georgia: and in such a way that it was so isolated inside that you didn’t end up hearing it.

[00:07:59] joe: it, right. [00:08:00] Was it a small… probably a

smaller- Yeah,

Huh?

A smaller data center?

[00:08:04] georgia: I think that was a really pretty big building. One of those really big historic buildings- Mm-hmm

and they took up the whole… But I should have probably done more research before this

[00:08:16] joe: to

[00:08:16] georgia: like say which data center and how big it was. Yeah. But I was really surprised that-

[00:08:22] joe: Yep

[00:08:23] georgia: Yeah … that it was right there in the middle of the city- Mm-hmm … and I would’ve had no idea. Yeah.

[00:08:29] joe: Yeah.

[00:08:30] georgia: Mean-

[00:08:30] Nick: Yeah, I don’t think I would’ve known

[00:08:32] georgia: but I do think, so it really depends on some urban planning and, how these things are done.

And then we talked about this in the episode a little bit, but also how can those data centers be built in a way that can be using some of the byproducts,

wisely,, like a greenhouse or… You know what I mean? Like we talked a little bit about that.

[00:08:58] joe: So quickly [00:09:00] looked, it’s the Lakeside Technology Center.

It’s right next to the McCormick Place Convention Center, managed by Digital Re- Realty. The building was originally a printing facility for telephone books which no one uses those, Me

[00:09:16] georgia: which, who uses those? Repurpose … now housing a giant data hub,

[00:09:17] joe: a giant data hub,

and it really… I was gonna mention, usually in the cities, it acts as a major carrier for the hotel as internet connectivity hub for the Midwest, powering cloud services, social media, and city infrastructure.

So that’s right there.

[00:09:33] georgia: Yeah.

[00:09:35] joe: People don’t even realize that, so yeah. But right downtown, yeah. It’s cool looking at some of the pictures. Put those links the

the in the show notes

[00:09:45] Nick: Oh, is it a small– So it is a smaller one though, right? Like

[00:09:48] georgia: pretty

[00:09:49] joe: Yeah, it looks like a fairly large…

It’s 1.1 million square feet of space. Cooling racks, tanks outside hold 8.5 million gallons of liquid, and [00:10:00] so they’re not using fresh water directly from Lake Michigan. They’re using a closed loop system that they’re using to chill probably, yeah, so they chill the water down using water chillers, and then that’s distributed through in this kind of closed loop system, so.

, probably can do a little more research, but I’ll throw this link in there, and folks can go down the rabbit hole.

[00:10:22] Nick: Hell yeah

[00:10:24] joe: so as these things pop up, you might see it in your neighborhood near you

[00:10:30] Nick: I hope not.

[00:10:32] georgia: Right

[00:10:32] joe: yeah. Or at least hopefully they plan for it or they do something like we talked in the episode, community gets involved

[00:10:38] georgia: know what?

[00:10:39] joe: there’s benefits somehow.

[00:10:41] georgia: you know, so Just looking at how, I’m not gonna name names, but how certain utility companies Mm-hmm It seems… Anyway

[00:10:50] Nick: are we not allowed to drop

[00:10:52] joe: looking at how, I’m not gonna name names, but how certain utility companies Yeah It seems… Anyway. Going out about it and dropping names. But, yeah. I’m just,

[00:10:53] georgia: I’m just saying I don’t know if I trust-

[00:10:55] joe: I don’t know … trust- But we’ll see. I may not sound… [00:11:00] Industries build and you gotta take what you can and control what you can.

So- Yeah … either they come in and you don’t ask for anything, and then they still come in and you get nothing or, work, try to work with them and hold their feet to the fire and make it work, so.

[00:11:13] georgia: Yeah, I don’t know.

[00:11:14] joe: Yeah, I don’t know. But yeah. Otherwise, got some events coming up. After this release, I’ll be doing some readings at Printers Row Wine, September 19th, an art show October 10th, and-

[00:11:29] georgia: print festival October 3rd … a

[00:11:31] joe: 3rd.

Print Festival Georgia that’s going out, the inaugural 4th Street Print Fest. So we’ll put links to all of these. There’s a family day at the Smart Museum. I’ll be giving some demonstrations and a lecture. And in December, I’ll be doing a return to an art and science series, do another lecture about the science of zombies. and we’ll remind you of all these, let’s have it, if you subscribe to the newsletter you’ll see all these dates in there, and we’ll be updating it.

[00:11:58] Nick: Oh, [00:12:00] dang.

[00:12:00] joe: be a lot of fun, but Revisiting zombies. So yeah. So bunch of things

coming

[00:12:05] Nick: don’t you also have a calendar that is coming out or came out, right?

[00:12:10] georgia: thanks. Yeah. Yeah, well, we got it from the printer, so it is available. Touch of Noir 2027 wall calendar.

[00:12:19] joe: And people who, if you’re subscribed to the newsletter, get that email. It’s been in there for a little bit. People got the pre-orders. I know a few people have gotten their calendars already that ordered on there, and it’s still time to get a calendar, so- Plenty of time before the- Yep

[00:12:34] georgia: know a few people have gotten their calendars already that ordered on there, and there’s still time to get one. Right. Plenty of time before the new year

[00:12:35] joe: You’ll see

[00:12:35] Nick: Oh, wait, one more thing. Hey, Joe, do you can you also throw, go ahead and throw in the link to your reading that you did what, last week?

[00:12:43] joe: Oh, the yes, with Sarah. Yep, friends.

[00:12:47] georgia: And I really

[00:12:48] joe: I knew it The Zoom link. Yes, I, I, yep, yep, I actually

[00:12:51] Nick: ‘Cause,

[00:12:51] joe: can do that, yeah

[00:12:53] Nick: I didn’t listen to it live, but, , going back and using that link for the, you know, already recorded session,

[00:12:59] joe: [00:13:00] Mm-hmm. On

[00:13:00] Nick: just as good.

[00:13:01] joe: Yeah.

[00:13:02] georgia: I knew it tasted good Yeah,

[00:13:02] Nick: one knows.

[00:13:03] georgia: wasn’t it good?

[00:13:05] Nick: one knows the difference that it wasn’t live.

[00:13:07] georgia: it was just some great writing. Wow

[00:13:10] Nick: But

[00:13:11] joe: Yes. That was really fun. I had a good time. Read poetry for the most part. So I’ll throw that link in there so that’s there.

Yeah, Sarah did putting that together really great, and I think it’s a series that she does every quarter it

[00:13:25] georgia: it sounds like. It’s called Words…

[00:13:27] joe: yeah. Words, friends, and- Words

[00:13:30] georgia: With Friends? Yeah

[00:13:32] joe: just Words and then a couple backslashes, Friends with Sarah. So, but yeah, I’ll put the link to the YouTube channel with that so that you can go check it out and see me read and see the other four poets that yeah, did a great job.

It was very inspiring and cool. All right. You guys wanna keep motoring [00:14:00] along? Go down the science holes

[00:14:03] Nick: Yeah, let’s go on down.

[00:14:05] joe: Let’s do it. All right. You

wanna

[00:14:07] Nick: honest with you, I didn’t prepare for this week.

[00:14:10] joe: You didn’t

prepare

[00:14:12] Nick: is prepared.

[00:14:12] joe: Well,

[00:14:13] Nick: George, are you…

[00:14:16] georgia: that I wanted to visit, but then I didn’t give any heads up on it, so then I feel like I have to wait on those, but- All right.

[00:14:23] joe: have some science holes that I wanted to visit, but then I didn’t give any heads up on it, so then I feel like I have to wait on those, but- All right. I got two really quick since, it’s

[00:14:26] georgia: since, Like- Do you want me to pull out one of mine?

[00:14:28] joe: I’ll go through my two, and if you feel motivated, we can do it.

So cool and is that the newly discovered amoeba that likes it hot. So this am- the amoeba- I

[00:14:40] georgia: it

[00:14:40] joe: it … we had talked about it. It’s in… It’s kind of a protist, so it’s not plant, fungus, bacteria. It’s in its own eukaryotic family, a little different, and it’s called the fire amoeba Incendieamoeba

[00:14:54] georgia: amoeba

[00:14:56] joe: cassidinis, which translates to fire amoeba.[00:15:00]

It was discovered at the Lassen Volcanic National Park in Northern California’s Cascade Mountain Range. What’s really special was that a lot of eukaryotes, so remember eukaryotes have a nucleus, they have organelles. We’re– Our cells are all eukaryotic versus prokaryotic, which are like bacteria cells, and don’t have a nucleus.

One of the distinguishing features that separate the two. And so most eukaryotes cannot live at extreme temperatures like that. But this organism can live up to 64 degrees Celsius.

[00:15:35] georgia: I ask a question?

[00:15:37] joe: Sure. Is

[00:15:38] georgia: Is it an extremophile?

[00:15:40] joe: And that’s exactly right.

It

[00:15:41] Nick: did I know that’s where

[00:15:43] joe: It would be… I was getting there. It would be an extremophile. Exactly right. And if we think of bacteria, like the bacteria and archae extremophiles they can withstand even much hotter temperatures all the way up to, like, 122 degrees Celsius. [00:16:00] So, a few species of fungi and red algae held the previous record for eukaryotes, and the upper limit for humans and other mammalian cells seems to be around 43 C. Kind of warm there. So 43 Celsius is about 109 degrees Fahrenheit,

[00:16:18] Nick: I have a question for you, Joe.

[00:16:19] joe: Yes

[00:16:21] Nick: I can go from these two extremes. Does that mean I’m an extremophile?

[00:16:25] joe: Went too extreme. Sorry

[00:16:27] Nick: you know, going really cold or up to 109.

[00:16:31] joe: No at 109 you’re gonna be dying, so

[00:16:34] Nick: No, you’re not. You’re at, like, a nice hot tub

[00:16:36] joe: No, that 109 Fahrenheit that’s above a fever. I’m talking about your core temperature. You can’t live at that. That’s … Or long. I mean, it’s pretty hot. Yep.

[00:16:46] georgia: Yeah.

[00:16:47] joe: 63 degrees Celsius is about 145 degrees Fahrenheit, so that’s there.

So, but yeah, that’s, that was cool to see that, and I talked about the record 122. [00:17:00] You’re getting up to,

251 degrees Fahrenheit, so that’s like, you know,

[00:17:04] georgia: close

[00:17:05] joe: close to boiling. So above boiling, yeah, ’cause 100 degrees Celsius is boiling water. So yeah, y- the bacteria can live in very high temperatures, some of them.

So that’s really fascinating that they discovered, and this, sets, you know, you kinda think about it, you go, oh we talk here all life and the boundaries of it, but here’s just this new thing which kind of questions the notion of eukaryotic life what, how extreme conditions can be tolerated by organ- organisms that we thought we had a handle on.

So yeah, kinda cool

[00:17:38] georgia: that’s cool.

[00:17:40] joe: I’ll put the links to that. The other one that’s related to the episode was Nvidia. So they make all of these chips, the, the GPUs that kind of power the data centers. They say its AI data center design would run hotter and it can use a lot less water. [00:18:00] And so they had, like, this blog post.

They talk about this new style of data center using their new chips, and they can cut water use to near zero. And so because they can run their data servers higher, as high as 113 degrees Fahrenheit or 45 degrees Celsius, that they can have their chips, and they can use, these liquid loops operating at these higher temperatures you can run then and have much more flexibility when it comes to ambient air and evaporation and things like that. So it was kind of, you know, blog posts. They really didn’t talk about how much it costs to build a center like this or, you know, anything else or comparisons or compare it to a, a air cooling less efficient kind of data center.

So really just a teaser, but it’s interesting that… I know David had brought up about trying to find new ways to be more efficient, so this might be the start of that. But we’ll see what happens with that. But just thought I’d throw [00:19:00] that out that Nvidia says they are

[00:19:02] Nick: and while we’re on NVIDIA I do have a gripe with them still for raising all of the GPU prices

[00:19:10] joe: yeah.

[00:19:12] Nick: I have a– I’m very upset by all this.

[00:19:15] joe: And I will say that Amazon had similarly touted higher heat tolerances as part of making its mostly air-cooled data centers more efficient. So, yeah, I don’t know. Maybe these companies are listening, trying to do something, or are they just being more efficient so they can pack more in and, meet demand?

Kinda we talked about that on the episode, that the more efficient, then the more use. So I had those two. I don’t know. We can put a pin in it till next science hole.

[00:19:47] georgia: Mm-hmm. Okay. All

[00:19:48] joe: All right. Well, that’s it. How

about- All right.

[00:19:52] Nick: What kind of what, what kind of media have you guys been consuming lately?

[00:19:55] georgia: Well we caught up in the, on the show Trying [00:20:00] Mm-hmm. Yeah we went through all five seasons.

[00:20:04] joe: seasons. So the new season, ’cause they were, they had the fifth season was being released, and then the finale. So we watched, like, the day before the finale, we finished, and then it was late enough in the evening that the finale had dropped, so we just went ahead and watched it.

So no, it was really good. Liked the show. Yeah, really a good story. I think, Georgia, you mentioned had that Gilmore Girls vibe

[00:20:30] georgia: Yeah, ’cause it had like com- it was funny, but realistic, in the sense like you really got to know the characters. Mm-hmm. And so it was a nice kind of escape-

Yeah

[00:20:42] joe: Yep. Mm-hmm. Yeah. Check it out. Yeah. And

[00:20:46] Nick: Maybe I’ll have to check it out

[00:20:48] georgia: Yeah.

[00:20:48] joe: it kind of covers just family. The center’s around a couple who cannot have children, naturally, so they adopt and go through that process. And so, and then they have [00:21:00] their family and, family issues. Yep, yep.

[00:21:03] georgia: and such. So-

[00:21:05] joe: but yeah, and it’s

[00:21:06] georgia: And we started…

[00:21:07] joe: London? No.

[00:21:08] georgia: but yeah. Well, it’s in… It’s

British. British. I can’t remember the… Camden maybe?

[00:21:13] joe: That sounds right, maybe. Yep. Mm-hmm. Yep There

[00:21:17] georgia: But,

and then we’ve watched the first episode of Dark Matter

[00:21:21] joe: Moon- Mm-hmm

[00:21:22] georgia: of the new season.

[00:21:24] Nick: Nice.

[00:21:25] georgia: Nice. And that was good. It was

[00:21:27] Nick: Worth it or no?

[00:21:27] joe: good. I

[00:21:28] georgia: thought it was really good. I have to be honest, by the end of the first season, I was like, “Oh my gosh, I think this is gonna go into, like, cheesy territory.” I don’t know. Did you watch the first season, Nick?

[00:21:44] Nick: No, I have not

[00:21:46] georgia: But I think they handled, they hand- like transitioned into the second season well. Mm-hmm. And I think it’s gonna be good, yeah.

[00:21:55] joe: they’re off book now, ’cause there was only one book, and the f- first season kind of [00:22:00] followed that book pretty closely. And now the second book, it’s kind of- Right … or the second

[00:22:05] georgia: And that Blake Crouch is very much involved Yeah

which I think is nice

[00:22:10] joe: and helped the continuity of the story and moving forward. Yep, so. But yeah, really good. Book-wise, I finished Bird by Bird by Anne Lamott. So once again, it’s a nonfiction, kind of writing motivation, kind of the spirit of creativity. So that was really good. So it was nice, little bit of run of these books about staying creative and inspired.

But , how about yourself? What are you up to?

[00:22:41] Nick: Oh I started the show Lanterns.

[00:22:44] joe: Mm-hmm. Yeah, Alex

had mentioned that.

[00:22:47] Nick: Yeah, it was pretty good. Three– Or I am all caught up actually on their episodes, ’cause what they have three episodes out as of recording, and yeah, I’ve been enjoying it.

[00:22:59] georgia: And [00:23:00] is that on

Dis- Disney or?

[00:23:02] joe: or…

Wait That

[00:23:03] Nick: is HBO Max

[00:23:05] georgia: Okay

[00:23:05] joe: Mm-hmm. Mm-hmm

[00:23:07] Nick: Yeah. And then what I just watched a comedy movie earlier tonight, and that was Vacation Friends.

It actually felt good to see like a good comedy again. It’s been a hot minute since where I was like, “Oh, I’m actually laughing at this.” But yeah

[00:23:26] georgia: And that’s a new film?

[00:23:27] Nick: But I knew it came out in 2021.

[00:23:31] georgia: Okay. Mm-hmm. Okay. So not new, but- No.

[00:23:34] Nick: No. But yeah, it was enjoyable.

And I just started playing the James Bond game the 007 First Light,

[00:23:46] joe: Mm-hmm.

[00:23:46] Nick: and that’s been, I like it. It’s been good so far.

[00:23:49] joe: Cool. Excellent.

[00:23:51] Nick: Yeah, and that’s about all I’ve got

right about now.

[00:23:54] joe: Yeah. Awesome. Awesome

[00:23:56] Nick: you, Georgia? Do you got anything else you guys… [00:24:00] ‘Cause I–

[00:24:01] joe: I think you finished a

[00:24:01] Nick: Georgia go or

[00:24:02] joe: Yeah, we were chaired ’cause we were watching the same show,

[00:24:05] Nick: Oh, okay.

That’s the only way… Who didn’t finish

theirs?

[00:24:08] joe: No, I think we’re all… We kinda tag-teamed it. But Georgia, did you finish a book, didn’t you?

[00:24:14] georgia: yep. No, I think we’re all … We kinda tag-teamed it. But Georgia, did you finish a book then? Yeah. I finished Intrigue

[00:24:16] joe: Mm-hmm. I’m

[00:24:17] georgia: I’m not looking at her name, Silvia Moreno-Garcia, I wanna say, if I’m pronouncing that right.

[00:24:25] joe: looking at it right. Wrote Mexican Gothic-

[00:24:26] georgia: Yes, she wrote the Mexican Gothic. This is not horror related or Gothic related.

It’s more of a noir set in the ’40s in Mexico. So it was good, and I finished that, and now I’m reading The Girls We Send Away- Hm … I think is the title, also by Megan Church, who wrote The Mad Wife

[00:24:53] joe: Oh, very cool. Yep. Mm-hmm

[00:24:55] Nick: Nice Huh?

[00:24:57] joe: Awesome

[00:24:58] Nick: All right.

[00:25:00] Well

[00:25:00] joe: That sounds like a wrapper. So you got me, Joe.

[00:25:07] Nick: Yeah, got

[00:25:07] joe: You got it. We got Nick. Georgia. We got Georgia. Subscribe to the newsletter and see all the cool stuff we’re doing and shows we’ll be at

[00:25:15] Nick: And we went down some holes

[00:25:17] joe: some holes. Stay safe, stay curious. We love y’all

Newsletter and Transcript of Rabbit Hole of Research Episode 82: Data Centers

Are We Really Blowing Up the Planet for AI Slop and Porn?

Substack, Apple, Spotify, YouTube, Amazon

Episode 82 Transcript


In Episode 82 of Rabbit Hole of Research, Joe, Nick, Georgia, and Mary welcome back three-time returning guest (previously on Episodes 13 and 39) and “Friend of the Pod”, David Pincus, PhD, cell biologist at the University of Chicago, to the Basement Studio to talk data centers.

We dig into the water costs, why your electric bill is going up, whether space-based cooling is science or Handwavium, what cellular stress can teach us about AI resource allocation, should we use human values or cell behavior to guide AI training, and is there anything good that will come out of these Data Centers. And it wouldn’t be an episode with David if we didn’t go down all the unexpected rabbit holes, we talk lobster farms, “data furnace”, olympic swimming pools, and of course, Extremophiles! 


Previous Episodes with David:

Episode 13: Blockbusters and Frozen Landscapes: Climate Change Through the Lens of Pop Culture

Listen nowEP 39: Fantastic 4 series: Johnny Storm and Spontaneous Combustion


Georgia and Joe will be hanging out at DragonCon, September 3-7, 2026, if you are in Atlanta come on out and say hello (I’m sure we will be hanging out in the Westin Lobby)! And Joe will be on seven panels.


Georgia and Jim’s Touch of Noir 2027 Wall Calendar —

12 months of all-original art by Jim and Georgia; orders are open now, with mailing expected in September


Check out what the RHR crew is creating:

Joe:


Future Events to Hang with the Crew:

Podcast Cross-Appearances

Events & Conventions:

Joe:

It’s Science for Weirdos

Want to support the show? Tell your friends. Follow us on social media, Discord, share the podcast, and let us know what topics you are excited about. Leave a Comment. And for email alerts sign-up for the Substack newsletter and never miss an episode, exciting updates or the bonus images we talk about on the episodes. 


We discussed these questions on the episode and now want to Hear From You (leave a comment):

  • Would you want a data center in your neighborhood? What would they have to offer to make it worth it?
  • Should we train AI with human values or cellular values?
  • What, if anything, would you like to see AI used for? Are you optimistic that we will get there?

Drop your thoughts in the comments. We read them all, and your ideas often shape future episodes.

Leave a comment


Future Episodes

  • Episode 84: ETGuest: Chris Guzman (@theboxinggloveartist)
  • Episode 86: Disability in HorrorGuest: Grace Daly (Bram Stoker nominee)
  • Episode 88: CraziesRecorded at Reed’s Local with Melissa and Luc
  • Episode 90: Witch PleaseGuest: Bill Halliar (@skeletongue)

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Show Notes & Fun facts 

Stuff crew said they’d put in shownotes


Books:

  • The Machine Stops — E.M. Forster (1909) short story
  • The Last Question — Isaac Asimov (1956) short story
  • The Hitchhiker’s Guide to the Galaxy — Douglas Adams
  • So Long and Thanks for All the Fish — Douglas Adams
  • The Emperor of All Maladies — Siddhartha Mukherjee

Fun Facts to Impress Your Friends With:

  1. The human brain runs on about 20 watts — roughly the equivalent of a dim light bulb, or as David put it, a sandwich. A modern AI data center GPU cluster runs on hundreds of megawatts. That’s the efficiency gap that needs to be closed.
  2. The ISS uses radiator panels the size of a tennis court to cool roughly 70-80 kilowatts of heat load. Dumping just 1 megawatt of waste heat in space requires approximately 1,200 square meters of perfect radiator panels, and a modern GPU cluster generates hundreds of megawatts.
  3. Ireland’s data centers consume about 21% of the national electricity supply— and data center energy demand globally has grown ~12% per year since 2017, and projected to equal Japan’s entire electricity consumption by 2030.
  4. Evaporative cooling loses up to 85% of its water to the atmosphere — it doesn’t go back into the water table. The Tempe Town Lake in Arizona evaporates between 390 million and 650 million gallons of water a year that never return to the Colorado River.
  5. The Jevons Paradox — coined by 1860s steam engine engineer William Stanley Jevons found that making steam engines more efficient actually increased total energy use, and the same pattern may apply to AI efficiency gains today.

Episode Highlights

  • 00:00 Basement Roll Call — Full house in the basement studio, almost, David has a legit excuse, “car ownership is a debated thing in families.”01:57 Meet David Pincus — Three-time returning guest and Friend of the Pod, David Pincus, PhD, UChicago cell biologist and expert in cellular adaptation04:19 What The Cloud Is — Joe delivers the monologue: the cloud isn’t wispy and floating, it’s “a giant space heater that computes.”08:28 Energy Demand Surge — Data centers have grown 12% per year since 2017, Ireland uses 21% of national electricity on data centers, and one proposed AI complex would use as much power as the city of Houston.14:52 AI Hype And Jobs — David calls out the circular investment logic: “If they’re really as good as they say they are, they should prove it by not ruining the Earth.”19:11 Curtailment And Duck Curve — Joe explains how AI could modulate its own power use to absorb wasted renewable energy during off-peak hours, turning a climate problem into a climate solution.22:02 Jevons Paradox Explained — David drops the Jevons Paradox: the 1860s discovery that making steam engines more efficient actually increased total energy use — and AI may be no different.23:41 Space Data Centers Reality — Space is cold, but that doesn’t mean cooling is free; in a vacuum, heat can only leave as radiation, requiring radiator panels the size of a small town.25:01 Does Deleting Data Help — Georgia wonders if mass email deletion would make a dent; David suggests she “Marie Kondo her inbox.”30:41 Water Use Headlines — David breaks down the scary headline number: about 80% of reported data center water use is actually from generating the electricity, not the cooling itself.32:41 Cooling Methods Breakdown — Joe walks through the three approaches: evaporative cooling (water-heavy), air conditioning (energy-heavy), and liquid immersion cooling (the newer, more efficient option).38:57 Local Politics And Promises — Mary reports from the front lines on CoreWeave’s Hammond, Indiana proposal: “They’re promising birthday parties, ice cream, and pony rides.”43:20 Promises Meet Reality — The crew debates whether data center companies will ever actually deliver on community promises, or just take the tax breaks and run.43:58 Turning Heat Into Benefits — Joe’s fun list: Microsoft’s 2011 “data furnace” concept, Deep Green’s server racks under swimming pools, the Norwegian lobster farm, and the Paris Olympic Aquatic Centre heated by Equinix’s data center.46:25 Cooling Hacks Underground — From Cold War bunkers in Stockholm to SubTropolis in Kansas City to a functioning data center at the South Pole, weird locations already solve the cooling problem.47:37 Taxes And Lax Rules — Data centers go where regulations are lax; Mary asks why they can’t just be taxed like everyone else.50:09 Military AI And Nuclear Power — David raises the military’s 50-year data center leases; Joe brings up Three Mile Island — now rebranded the “Crane Clean Energy Center” — being restarted to feed AI.53:46 Petty AI Complaints — New segment: Mary does not need AI to summarize her coloring sheets, and Georgia’s voice-to-text still can’t get it right.55:34 Sci-Fi Warnings And Jokes — Joe highlights The Machine Stops (1909) and The Last Question (1956); David adds The Hitchhiker’s Guide to the Galaxy — the answer to everything is 42.57:23 Cells, Budgets, And AI Hunger — David draws the key parallel: stressed cells always slow down and budget their resources — AI systems have no such constraint, and the cost is being passed onto us and the planet.01:00:48 Raising AI With Values — David shares Siddhartha Mukherjee’s NYT essay: training AI is like raising a child — you can only hope you sent it into the world with good values.01:07:46 Regulation And Real Benefits — The crew revisits the Industrial Revolution: you can have regulation and growth at the same time, and we already know what happens when you don’t.01:10:33 Freemium Traps And Libraries — The freemium model is coming for AI just like it came for streaming; Mary and Georgia remind everyone that Kanopy and Hoopla are free with your library card.01:14:12 Wrap Up And Hopeful Outlook — David closes on a hopeful note: “If we can let the science lead, that’s going to help humanity”

“Stay curious, stay safe… Love Y’all!”


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Transcript

[00:00:00] joe: Hey, welcome back to the Rabbit Hole of Research down here in the basement studio. We are all crewed up

[00:00:10] Geo: Full house.

[00:00:11] joe: house … full house. Well, almost everyone’s in the basement

[00:00:14] Nick: someone was supposed to be in the- basement studio,

[00:00:16] joe: yeah, but the wife stole the car- … or

[00:00:18] David: car ownership is a debated thing in families

[00:00:23] joe: So yeah, you got me, Joe.

[00:00:24] Nick: got Nick.

[00:00:25] joe: got Nick.

Georgia. We got Georgia.

[00:00:27] Mary: You got Mary.

[00:00:28] joe: got Mary, and we have

[00:00:30] Nick: Three-time returning

[00:00:33] joe: returning. Choo,

[00:00:33] David: Returning guest, longtime listener, third time guest

[00:00:38] Geo: We’ve

[00:00:38] joe: got

[00:00:39] David: David

Pincus Yeah. It’s great to be back

[00:00:42] Geo: And we w- we wanted to know, Is this the first, third time guest?

[00:00:47] Nick: Is he the three-timer club? ‘

[00:00:49] David: always your first third time

[00:00:51] Mary: three- …the

[00:00:53] joe: club, but I don’t know if you’re the first

[00:00:55] Geo: I can’t-

remember

if there’s been anyone else.

[00:00:58] joe: It’s like Bill Hallier’s [00:01:00] been

on

[00:01:00] Geo: I think he’s only been on twice.

[00:01:03] joe: Oh, I think he’s only been on

[00:01:04] Geo: Anyway, we’ll

[00:01:04] David: I’m

gonna, I’m gonna claim it whether or not it’s

[00:01:07] Geo: We’ll, we’re gonna do-

We’re gonna do some research.

[00:01:10] joe: gonna

do some research. Go down a rabbit hole of research. We’ll do

[00:01:13] David: you say this makes me a friend of the pod?

[00:01:15] Geo: wondering-

[00:01:16] joe: that’s right. That’s right. And, and for those wondering- Sure … that wanna go

back, you were on episode 13, so our first season. So you were here before we were here. Like, you’re

our-

[00:01:27] Nick: was Georgia on that episode?

[00:01:28] Geo: No.

[00:01:29] joe: No.

[00:01:29] Nick: Oh. that was Even before Georgia. jumped back on.

[00:01:32] joe: a day one-er,

[00:01:32] Nick: a day one.

[00:01:34] joe: down

with, the crew.

[00:01:35] Geo: that was a very enjoyable. episode, I remember. And

[00:01:38] joe: then

we had episode 39, Johnny Storm, so that was from the Fantastic Four series.

[00:01:43] Nick: that Last year … that was last Last season

[00:01:45] joe: two, so yeah.

[00:01:46] Nick: Damn, all three seasons. yeah,

[00:01:48] joe: been on, you’ve episode every season,

so yeah.

[00:01:52] David: alive

[00:01:53] joe: yeah. I mean, that’s that’s it, so well, cool. Well, we’re here. Do you wanna give your credentials [00:02:00] just in case someone hasn’t listened to one of the other two

[00:02:02] David: Yeah, of course. Sure, yeah. So I’m a scientist. I’m a researcher at the University of Chicago. I probably shouldn’t say where I’m from, but I spilled the beans

again.

[00:02:11] joe: do that every episode you do that.

So, it’s all right. You can, I think they can Google your name and it’ll

[00:02:16] David: yeah, It’s, true. It’s, It’s, not much of a secret. But I’m a colleague of Joe’s in the and we’re both biologists, cell biologists. I’m an expert in cellular adaptation, how cells cope with stress. Stress to a cell is any change in the environment change in temperature, pH, nutrients. You name it, if it changes, the cell has to adjust to it and roll up their sleeves and adapt. So we study the… You know, cells don’t have brains, but they still have feedback networks and metabolic pathways that ensure that they’re doing the best with what they have at any given moment, and we try to figure out the rules of the game for that.

[00:02:56] joe: Yeah. Well, cool. I think it’s gonna be a fun one. You were [00:03:00] on that first season talking about climate change

[00:03:03] Geo: and And didn’t David coin one of our favorite… Not

coin, but-

he-

[00:03:09] joe: Go ahead. Just please

[00:03:10] Geo: What are you- Extremophiles

[00:03:12] Nick: Oh.

Jesus.

[00:03:15] David: Extreme.

[00:03:17] joe: Yes.

I- I don’t know

[00:03:19] Geo: if I’m attributing- that

[00:03:20] joe: was on a, yeah, that was on a, Johnny Storm episode, I believe it was, extremophiles. But in particular we were talking about this episode and we were talking about you returning this season, and data centers came up. So this episode is about climate change and data centers.

So we’ll get into that. But Mary-

[00:03:37] Mary: Well,

I wanna go a little bit in reverse. So you gave your credentials,

but I don’t think

you gave your name. Oh.

[00:03:43] David: Oh.

David Pincus.

[00:03:44] joe: Yeah.

[00:03:44] Mary: there you go. Okay. All

[00:03:45] joe: right. I think he did say his name.

[00:03:47] Mary: Really?

[00:03:47] Nick: I don’t remember. He said that from the top.

[00:03:48] Mary: Yeah,

No, Okay.

Mary

wasn’t

[00:03:50] joe: Mary wasn’t listening.

[00:03:51] Geo: Oh. That’s

[00:03:51] David: Well, just in case you wanna Google me twice.

[00:03:54] joe: Oh. That’s

right.

[00:03:55] Mary: That’s

[00:03:55] joe: They really make sure

[00:03:56] Mary: I

thought… No, I

[00:03:58] joe: of Chicago.

[00:03:58] Mary: j-

[00:03:59] David: Oh,

[00:03:59] Mary: no, [00:04:00] just wanna make sure that, you know, we know who we’re talking

to. So, yeah.

[00:04:03] David: No name handwavium

[00:04:05] Mary: Yes. Yeah. Okay.

[00:04:07] joe: yes. We gotta have that.

Okay.

Well, cool. Well, I do have a little-

[00:04:11] Nick: was gonna say, w- what’s the list look like?

[00:04:14] joe: Well, I have lists,

but I got my little monologue.

Your

[00:04:15] Nick: Your monologue. I got my monologue

[00:04:16] joe: I got my little monologue. I’m coming back. See if I can do it

when

you hear the cloud, what do you picture? A blue sky with white wispy balls of cotton floating by and morphing into shapes of fantastical animals and familiar faces?

Or

do you imagine data zipping around up there somewhere between your phone and the internet, moving around you like digital air?

Here’s

what the cloud actually is. It’s a warehouse, a very large, loud, and hot warehouse, usually in rural areas by waterways or coming to a neighborhood near you, stuffed floor to ceiling with server racks, calculating away 24/7, consuming natural resources to make the latest viral memes, write emails, [00:05:00] or create a self-aware AI.

And fiction has been warning us about this AI for 70 years, but the real villain isn’t going to say, “I’ll be back,” launch nuclear weapons, or use us as a battery. But it’ll just let us boil ourselves in a pot like frogs, killing us slowly with thermodynamics. You know what? Let me look that up because I think there’s a meme for

[00:05:21] Geo: that

Can you Google it?

[00:05:24] Nick: Don’t

worry, I’ll ask ChatGPT

Yeah.

[00:05:28] joe: It’ll make it for us,

[00:05:29] Geo: Wow.

[00:05:30] joe: Yeah, yep. So that’s it. I mean, but yeah

[00:05:34] David: Cloud’s such a fluffy word, you know?

[00:05:36] joe: It is.

It

sounds-

[00:05:37] Geo: so

[00:05:38] David: a euphemism? I didn’t think of it at the time. You know, it seemed like a nice, just a real metaphor for, like,

it’s up there

But now it, with the sort of dark side of all of this stuff, cloud’s sort of taken on a Orwellian kind of tone to it to me

[00:05:56] joe: Right.

I mean, yeah, it the word cloud [00:06:00]

is just not,

you- it’s not here in front of us, right? It’s something that you cannot obtain or see, and it’s all about scaling, right? So you’re scaling something up, and so how do you scale is just having more. And usually when you have more, you want to put it somewhere that’s not in your face, right?

It’s that’s the idea of it. And it’s really just server racks. Just a place, a physical location

[00:06:23] Nick: Mm-hmm That you don’t own

[00:06:24] joe: you don’t own … yeah, that you don’t own, that you don’t see. well,

[00:06:27] David: of sight, out of mind.

[00:06:28] joe: might see it if, depending where you live. But

[00:06:31] David: Increasingly

[00:06:32] joe: so. Yeah. Just a giant space heater that computes.

[00:06:35] Geo: I think of it like, like when we were talking about electricity and you just Turn the light switch on- you don’t think about

well, what,

you know, what-

went on to turn the light

on It’s the

same kind of- Yeah, and

[00:06:47] joe: what powers that? Is it coal? Is it you know, nuclear? Is it solar? Is it wind? Is it hydro? You turn the lights on and you expect,, you expect light.

Like, you don’t… You flick the switch or you plug your plug in, you, you expect it to work, and you [00:07:00] don’t think about the back end. Same thing with, electric cars. You still have to think about if you plug in and it’s your electricity’s from a coal-fired plant, is it– are you probably coming out a little bit better?

Yeah, but you’ve kinda just displaced your thinking about that,

so. You have to change them. Right. That’s right.

[00:07:15] Nick: Like, it’s not a full step ahead. you’re just moving a different lane

[00:07:19] joe: yeah. You’re

[00:07:20] David: I do think that pretty much everybody thinks electricity is good though,

and Has improved the quality of lives, right? And so- the sheer fact of having to gen- you know, use fossil fuels, have environmental impact, of course, that’s not ideal, but at least with the trade-off of generating electricity and having the lights on at night and you know, being able to cook food over, you know, places.

Anyway, there’s a whole bunch of benefits that are obvious for electricity, and I think everyone

[00:07:55] Geo: Correct.

Correct

[00:07:56] David: different

with data centers is every… To a [00:08:00] first approximation, every one of those servers is running porn, right? Like, or like

that

[00:08:06] joe: probably right. That’s

[00:08:07] Geo: Bored.

[00:08:08] David: like, and

like,

or people asking about their own like health or

mental health issues and that’s like sad, but in- interesting.

But anyway, like

it’s AI,

[00:08:18] joe: I

th- yeah, and

[00:08:19] David: and I just don’t think people can agree universally that it’s a good

improvement over

everyone’s

[00:08:26] Nick: of life isn’t being lifted.

[00:08:28] joe: I wanna say also, I know AI is the one of the drivers now of the extra increase, but probably starting, 10 years ago or so, data centers started to ramp up just the devices that we use to stream everything. You guys listen to this podcast.

That is streaming out of a cloud from a data center somewhere. So

[00:08:50] Nick: Wait, are you saying that we’re part of the problem?

[00:08:52] joe: You’re part of the problem Yes, Nick, you

are- He’s the worst … you are

part of the problem,

[00:08:55] Geo: of the problem, Especially you, Nick. No.

[00:08:57] joe: You’re 100%.

[00:08:57] Mary: 100%.

[00:08:59] Nick: sin.

[00:08:59] joe: Yeah, but I [00:09:00] mean, it’s just kind of …data

centers started to ramp up and be built before even AI.

So now we have this onslaught. Yeah, right, and porn and movie

[00:09:09] Geo: streaming- Dancing cat videos

[00:09:11] joe: was part of it. But AI now

[00:09:13] Geo: But AI now has

[00:09:13] joe: accelerated that, where we were seeing some growth, you know, 5% a year maybe, and now we’re starting to escalate that to 10%, you know, growth of energy need consumption. And that’s what we’re now dealing

[00:09:28] Geo: And so because it really feels very recent.

that you hear about every

single town-

[00:09:35] joe: Yeah,

[00:09:36] Geo: is struggling with,

you know- whether they’re gonna put up a data center. It’s like, how did this happen just in the last-

[00:09:44] Nick: Well, what, we had recently a town meeting. Yeah. And it’s hitting closer to home for everyone.

And- Right.

[00:09:51] Geo: So I just think, wow, I mean, it really- is that because… And I don’t

wanna

like just depressing

and

about [00:10:00] politics, but is it because of…

What is it? It’s really because of the demand

with

[00:10:06] joe: wanna, like, just depressing and about politics, but is it because of… I mean, what, what is it? It’s really because of the demand with- On it, yeah. I mean, AI is, like I said, part of it, and we’ve seen an acceleration of growth. But one of the things is that we’re seeing since 2017 we’ve been having about a 12% growth per year for data center energy.

And I think it’s just hitting the news now because if you keep growing, right, no one’s put a cap on it. That’s the problem. It’s like, oh, we’re gonna double by in five years. , hopefully we can hold that line because it could double again in 10 years. And so that’s the issue. As the consumption of, media, of AI, of using that, and,, that you have to build new data centers out.

But there’s a lot of use. I mean, it was already building, so it’s not like last year or a couple years ago when ChatGPT really hit it. That’s when… And the memes and things like that, it was like, “Oh, we need new data centers.” Like I said, probably, , 2015, ’16, ’17, there was a growth already starting to happen, and people [00:11:00] were preparing for that and saying, “How do we get ahead of this?

How do we do something?” So there are people thinking about it, and it… But it’s hit the news now because now it’s coming to a town near you. That’s what’s

changing-

[00:11:11] Geo: town near you.

[00:11:12] joe: right. You’re just hearing it about it more. And, politics have, is very partisan, and so it’s a flashpoint and you can use it,

um-

[00:11:21] Nick: I don’t believe-

that the politicians understand.

it.

[00:11:24] joe: mm-hmm.

[00:11:25] Nick: Like, it’s new technology still to them.

[00:11:27] Geo: Yeah.

[00:11:28] Nick: And they still don’t understand half

The technology that is common in every household nowadays

[00:11:34] joe: And doing just a little bit of research on this, the, one of the extreme cases is like Ireland. Data centers use about 21% of the national, electrical

generation 21% is

[00:11:46] Mary: that’s huge 21%.

[00:11:47] David: Virginia, I know in the US, Virginia has a

huge fraction of its-

total power going to data centers, maybe one in,

one in four or something.

[00:11:56] joe: So

[00:11:56] Nick: yeah, and They also don’t tell you the full amount that they- [00:12:00] are actually gonna use. They say- a lowball number and then- exceed those numbers significantly. Yeah. Yeah,

[00:12:08] joe: I think, it’s gonna be, by 2030 we’ll be using, like, the, what the country of Japan uses, worldwide.

I mean, , we’ll be equaling, that, that level you know, globally as we start to scale up so,

[00:12:23] David: And

[00:12:23] joe: but yeah,

[00:12:23] David: in

terms of

[00:12:24] joe: is… Go ahead

[00:12:25] Geo: raw energy is

[00:12:28] David: in– I think it’s something like you know, this year there are these… The AI companies are all… There’s this– We can probably talk about this a little bit more, but there’s this circular investment

[00:12:41] joe: Mm-hmm.

[00:12:41] David: thing where the NVIDIA chip company that

makes the chips is investing in the AI companies that buy the chips to buy the chips and build the data centers. And then meanwhile, these IPOs they’re raising are gonna raise a bunch of money to build more of these data [00:13:00] centers, and it’s just, you know, it’s gonna be like half a trillion dollars potentially spent in, per year in the next few years just on, on building. And it’s just a Ponzi scheme. I don’t

know what the name is, but it just seems like economically false.

Like, like it’s

built on some kind of, I, I don’t under- it’s scary. But like, one of these super complexes that they’re talking about building would use as much power per year as the city of Houston you know? It’s

like enormous amounts of energy that they’re saying they’re gonna use in these things.

Anyway, sorry

[00:13:41] joe: and yeah, and the problem is that, you know, as you forecast this out, you know, it’s not a new problem. I mean, you can grow the power grid to compensate. The problem is that we’re lagging behind there, so these communities are suffering not because it’s, “Oh, wow, you know, we gotta add… They’re gonna consume this much [00:14:00] power.”

You know, there’s been other times in our history where you had to have c- power consumption, like you’re building ships and things like that, and you got your blast furnaces. That’s a consumption of power. It’s different. But w- we’ve ramped up, and now over the last decades, we’ve ramped down. We’re not doing heavy industrial here in the States, and so our power grid now is at a, some low baseline level.

And now the thing is ramp it up. Let’s go. And so you plug these data centers in now that have great demand with now public demand, and that’s where you’re starting to see the strain out on it.

[00:14:33] Geo: Places have failed to keep up their in- Right

infrastructure.

Infrastructure, yeah. Mm-hmm. yep. And so- Right … it’s-

already failing, and then you’re adding all this other-

[00:14:42] Mary: adding all this other.

[00:14:42] joe: Right.

Yep. And so that’s where the strain then comes in, and communities are like, “Whoa, hold on. We’re having brownouts and blackouts,” and

[00:14:48] David: And people pay more in their electricity bills.

[00:14:51] Geo: A lot more.

[00:14:52] joe: And I wanted to circle around, too, to the point you were talking about, David, about the AI and the hype and the super hype, because some [00:15:00] of it, that is what it is.

Like, there’s our trillion, billionaire, now one’s a trillionaire, hyping up this technology, and consumers are consuming it. But at some point in time, and we’re starting to see this with, ChatGPT, Claude, it’s like, oh, I’m running out of, prompts that I can put in.

They’re hitting limits of that. And so that’s because this costs money, and so at some point in time, they’re gonna go… These are like the tasters. Everyone’s getting a little taste and getting hooked. And then after you’re hooked, then they’re gonna go, “Oh, you know what? Oh, my bad.

That’s gonna cost you 20 bucks. Oh, my bad. If you wanna keep this prompting and impress your friends with your

memes and stuff,” you It’s gonna be… It- it’s kind of on the back end, they’re gonna have to pay for it. Right now, they’re not paying for it. And the investors,

at some point,

the stockholders are gonna go the share- the board is gonna go, “We need to make some money on this, so you know what?

Let’s let’s crank this up.”

[00:15:54] Nick: Aren’t they already

losing money?

[00:15:56] joe: they

already making money? Well, tons of money. Yeah.

It’s, you know, it’s not-

[00:15:59] Mary: With the [00:16:00] promise

that

later-

there’s,

The jewels

Will

rain down. they’re,

[00:16:04] Geo: they’re-

they’re- And then, yeah, and then, oh, this will bring so many jobs.

It’s not gonna bring so many jobs. It’s bringing, like, a couple people to check on the machines. I don’t understand. I don’t understand. that. Like, how

[00:16:16] Mary: like, how many jobs are there in a AI data center after the initial construction?

[00:16:23] Nick: I

wouldn’t say too

[00:16:24] Mary: Are, are- yeah.

I

mean-

[00:16:26] Nick: have a security

[00:16:27] Mary: I was

[00:16:27] joe: was

actually- That’s right.

[00:16:28] Mary: Like, I, I actually don’t know. I

d- I

[00:16:30] Nick: mean, Well- It’s

[00:16:31] Mary: It’s like,

[00:16:31] Geo: Homer

Simpson sitting there watching

[00:16:33] joe: I’m sure there’s there’s gonna be folks

who need to repair things. I’m sure there’s gonna be folks who need to go in and maintenance things.

But it- But

[00:16:40] Nick: not gonna

be an

[00:16:41] Geo: it off and back on aga-

[00:16:43] Nick: Showing up, walking around, making sure- that all the lights are flickering correctly.

[00:16:47] joe: Right.

[00:16:47] Nick: And then moving on …

[00:16:49] joe: If they can’t get a machine to do that, like a robot, that’s like a…

[00:16:52] David: here’s the thing

that, it like if th- there’s some like inherent bullshit in, sorry for the [00:17:00]

PG-13

[00:17:01] Nick: good. that’s still part of it

[00:17:02] Geo: No,

[00:17:02] Mary: You, you said that correctly.

Okay. Yeah.

[00:17:07] David: You didn’t mishear me.

Because if they’re claiming the, if the entrepreneurs, the Sam Altmans, the Elon Musks Daria Aamodei if the, if they’re claiming they’re gonna really make super intelligent models, couldn’t those models make themselves work more efficiently so they

wouldn’t need these freaking data centers? Like, so like,

if they’re really as good as they say they are, they should prove it by not ruining the Earth.

[00:17:39] joe: You know, these large language models and how they’re predict and their nodes, the billions of predictive choices, that’s what takes the computing power that they need. And so can they make themselves more efficient? , that means that they would have to then build their own chipsets [00:18:00] and,

[00:18:00] David: the AIs design their own

chips, right? And so you have a kind of like hardware recursive

[00:18:05] joe: We’ve seen that movie. We’ve seen that movie, and

[00:18:08] David: that movie.

[00:18:09] joe: It doesn’t turn out well for humans.

[00:18:11] Nick: Turning out good

for us

[00:18:12] Mary: let

[00:18:13] David: Yeah, well,

I just think, but so therefore I think the other explanation is their models aren’t that good.

They’re never going to

solve the universe and therefore

be able to make themselves work efficiently.

Like

[00:18:30] joe: or

you have the other end that potentially they need X amount of energy to run, and that’s just their need, right? They’re not gonna be as efficient as the human mind, right? So the human brain,

[00:18:41] David: there, that’s a great, that’s exactly where I wanted to go.

[00:18:44] joe: it runs on, like, what, 20 watts? I mean, that’s nothing.

That is, a dim light bulb, right?

[00:18:48] David: That’s a

sandwich

[00:18:49] Nick: why did you look at me when you said that? Are you calling me a dim

[00:18:52] joe: at,

looking across

the room

here.

I was looking for confirmation, like, yo, yeah, that’s it. I- it’s been a while since I’ve… Now with LEDs, like, [00:19:00] 20 watts is, like, when I was growing up, that’s, like, 100 watts, so.

But yeah, no, you have this power need, and so for training, things like that, it’s just, it’s not efficient. And I don’t, I don’t- You’re right. I don’t know if it’ll get there. But the other… What you can do, and I was reading a little about this curtailment. Have you heard of that? Where the idea is that

with-

Green power,

solar wind farms, at times they generate more power than can be used, consumed.

Mm-hmm.

[00:19:31] Geo: Mm-hmm.

[00:19:31] joe: And so, and then you get this like a duck curve. It’s kind- it’s called a duck curve, and it’s cool. So you have these things where they generate all this power, and so that’s during the daytime. Let’s say during the day, sun’s out, solar farm, it is just banking energy. But the problem is everyone’s at work, or the majority of people are at work not doing anything.

Or, you know, they’re not d- they’re at home using their devices, turning the lights on. And then at 5:00 PM or so they get home. Now the sun’s kinda [00:20:00] going down. The power generation is now going down, and then you get a spike in electrical use and you need to actually now use more. But now you’re at a low point of capacity, right?

‘Cause now you’re not generating as much and banking as much as you were earlier in the day. And so one of the things that’s interesting and one of the ideas that are pitched and AI is starting maybe helping to feed itself, is to figure those trends out

and then

modulate its power usage in relation to this duck curve so that when energy is

plentiful

and its consumer use is low, that it will now, that’s when all the jobs should come in and it will, go and, you have full bore data center processing.

And then when people need power- … you then down regulate, and you then modulate and AI can figure that out. It can start to [00:21:00] find the trends, it can start to self-regulate. It can begin to get to this, , negative power consumption if you want to say.

It’ll be totally a green thing. So yeah, it was a really interesting idea and folks are starting to try

to apply that to these data centers in these areas where you have green technology kind of- … banking up this energy and then trying to really efficiently use it. And that’s just lost anyway, right?

So it’s even if a data center’s not there, this is just lost

[00:21:29] Nick: W- it doesn’t get stored anywhere?

[00:21:31] joe: Can it get stored anywhere? Only so much can get stored, right? That’s , that’s a different problem, right? That’s battery technology. Batteries aren’t efficient, so they don’t convert 100%. So i- there’s a lot of things going on there because then you have more efficient batteries, then you can store more and you can have AI modulating.

So you can have AI figuring out these trend lines and, just kind of throwing it the opposite there that we don’t– it doesn’t have to build itself better. It can manage the resources better that are provided to it

[00:21:58] David: But there’s also this other [00:22:00] paradox that it, you know, it, what is it? The Jevons paradox. Do you know this guy, Stanley, William Stanley Jevons? So this

is a steam engine thermodynamics guy in the 1860s, and

he basic-

[00:22:15] joe: then? That guy was a

[00:22:16] David: No, but he was Making steam eng- He was trying, he was making steam engines more efficient. right?

And what he found was that the more efficient steam engines, rather than decreasing total usage of energy,

increased total usage of energy, ’cause now more people

wanted to use steam engines for more things. So there’s this para- this efficiency paradox that

Every

little

bit of

efficiency you

gain in, you actually offset it a little more by more people- using it. And I think that’s clearly in AI, the more powerful they get and the more useful they are, the more people will wanna use them. So the more

efficient even they Get that

may be [00:23:00] more than offset by then

How many people- actually use them.

[00:23:02] Mary: Mm-hmm.

[00:23:03] Nick: yeah.

[00:23:03] joe: I think that’s a really… That is a good point, and then you have that. But that’s also the problem then becomes you gate that with money or some resource, and then that just the technology becomes only affordable for people at the top end, right? So I, I think that’s the crux of it all, that you have this more…

Right? ‘Cause it’s the more consumers you have, the better, but at some point you’re gonna have to… Or you figure out, or you solve these issues some other way,, as curtailment or build data centers in space. Which is,

[00:23:37] Geo: On the cl-

on the clouds really.

[00:23:40] joe: The real clouds, yeah.

And there’s,

[00:23:41] Geo: Which

[00:23:41] joe: It’s, that’s a funny thing, ’cause that gets thrown out there a lot and it’s a mis- it’s somewhat a misunderstanding ’cause it’s like, oh, space is, really cold,

and

you need to keep the data centers cold, cool, so you put it out in space then. But that’s not because you’re in a vacuum, [00:24:00] you don’t have. Heat transference,

and heat is moved from one media through another. So there’s no air or water or anything, so how do you move heat?

And so

essentially what you do is radiant heat. So you would just, you build, big panels that would then dissipate the heat almost passively through.

And so the bigger your data center is the larger the panels are. So it’s like radiant heat, like so from a radiator, it’s just radiating heat out, so you gotta have this larger panel. So it’s really it can work, but it’s not like, oh,

[00:24:35] Nick: it’s, not as easy of effects as people think

right.

[00:24:38] joe: just not

like, “

Let’s put it in space. Like, it’s gonna be great. Like, it’s gonna be kind of one of these things.” And the cost of launching rockets has come down through the reusable rockets, so you can get materials up into space. You can have things like that. I know-

[00:24:51] David: say What you will about SpaceX, but that is really cool.

I mean,

[00:24:56] joe: Yes

[00:24:57] David: ugh. Anyway, that’s really cool

[00:24:59] joe: [00:25:00] Yeah

[00:25:00] Geo: So I have this kind of a

stupid

question, but I have like so many emails in my Gmail that like, It’s ridiculous. I don’t even wanna say

how many.

[00:25:11] David: You’ve never deleted an email

[00:25:12] Geo: I m- I try,

[00:25:14] Nick: What do you

mean- Wait, What you try?

[00:25:16] Geo: I try.

[00:25:16] joe: mean-

[00:25:16] David: spam from TJ Maxx

[00:25:18] Geo: I mean-

[00:25:19] joe: Exactly. It’s

[00:25:21] Geo: That’s right. And I’m wondering, like, is All these people similar to myself have all this data that’s just hanging around.

C- And even if you delete it, where the hell does it go? And then, so is that like, that’s pretty much the w- way it is, right? We’re

holding this data that

[00:25:40] Nick: it’s

[00:25:40] Geo: garbage more or less.

[00:25:41] joe: But those emails are- Right … relatively small

and they probably

[00:25:46] Geo: photos, right? Photos. You’re calling the photos

[00:25:48] Nick: calling your photos. garbage?

[00:25:50] Geo: No, but I’m just

saying- Photos are garbage … is it a matter of,

like, if everybody, like,

kinda

got

rid of a bunch of stuff, would that help?

[00:25:57] joe: kinda got rid of a bunch of stuff, would that [00:26:00] help?

[00:26:00] Geo: I don’t know. I,

[00:26:01] joe: Y- yeah, no, you’re right. I mean, it’s, to go back you would have to, a lot of people delete a lot of stuff, but mean maybe. Yeah.

Like, What’s people holding? I don’t know, videos versus photos, and do you limit people? But all that’s also gated by

[00:26:14] Nick: But that’s also like- the billionaire saying- “Hey, if you guys do this- that’ll help the environment.” Well, they’re holding- so much more. Yeah.

[00:26:22] joe: And

It’s gated

by money also, right? So if you want a terabyte of cloud service, that costs you this much money. If you want two… So there’s some of that built in already that, you know, it becomes this have

and have

[00:26:34] Nick: But you can go ahead and buy that- terabyte to put for your photos and videos.

[00:26:41] joe: Like

[00:26:41] Nick: nothing- is stopping you from doing that and owning it yourself.

[00:26:46] Geo: I’m

not

sure if I understand what you’re

[00:26:48] Nick: You can get a terabyte hard

[00:26:50] Geo: I don’t want a terabyte

[00:26:51] Nick: don’t want a terabyte

[00:26:51] Geo: drive. and

[00:26:51] Nick: hold everything that you have.

[00:26:53] Geo: I just wanna purge. I wanna get rid of it.

[00:26:56] Nick: Sure. You know? Go for it. No one’s stopping you. [00:27:00] N-

[00:27:00] David: Marie Kondo your emails.

Does

it

[00:27:02] joe: that’s right. Yeah,

that’s

[00:27:03] Nick: was talking about her photos, not her emails- you know.

[00:27:07] joe: I think the emails are pretty low. I mean, you probably can fit all

those on a floppy disk. Yeah I don’t think that’s the-

On a floppy

disk That’s not the driver. If people don’t know what that is, a three and a half it’s 1.3 megabytes, I think it was.

It’s like, I don’t know, like what’s the solution?

[00:27:19] Geo: think.

But it’s like, I don’t know, like what’s the solution? I don’t-

[00:27:23] Nick: I just gave you a solution.

[00:27:25] Geo: Yeah.

[00:27:26] Nick: Just get- I don’t- Own your own

terabyte. No, what I mean

[00:27:27] Geo: what I mean is it feels like it’s this thing that we’re just gonna keep building and it’ll never be enough.

[00:27:34] joe: that’s

[00:27:34] David: collective digital detritus.

[00:27:37] Geo: And nobody’s

really willing to go back to

not- That’s right …

doing computers and, I mean, even though- that- I think that sounds really nice.

but. Yeah,

[00:27:45] joe: right.

I don’t think… W- like I said, we were already on this trajectory, so I don’t… It’s probably not, the genie’s not going back into the bottle. And so really it’s about how do you…

[00:27:56] Geo: this trajectory, so I don’t… It’s probably not, the genie’s not going back into the bottle. And so really it’s about how do you

[00:27:58] joe: What is, what are the safeguards then put [00:28:00] in? You know, we were making all these climate change goals and moving forward and, I forget what it was.

We need a reduction by 2035 and, all these kind of things. But now it’s more can we hold that line? Like, we were making progress

[00:28:15] Geo: and and That, really does go back to politics

and, money. you know?

[00:28:19] joe: Right.

And I was

gonna go back, I had it in my notes, I was, when I looked, but the space thing and the International Space Station as a reference point, ’cause they use, they, they actually control their heat loads through radiant

cooling.

And so they have radiator panels the size of a tennis court to cool roughly 70, 80 kilowatts of heat load. And so that’s a pretty small fraction of what

a, a, an AI compute cluster would generate,

[00:28:46] Geo: can you give a comparison? Like, what’s that? Like- So, Is that a fraction of it? Like- I

[00:28:51] joe: I mean,

so something here was, like, the dumping. It was, like, one megawatt of waste would require approximately 1,200 square meters of [00:29:00] perfect radiator panels to go. And a modern GPU cluster generates hundreds of megawatts.

So that’s just kind of, the way to think about it. So that, that you would… This, you would need very large radiant panels. You also would have to face the cold side, so you gotta face away from the sun. So that’s the other thing you gotta do, because if you’re facing the sun, then you’re gonna heat up and you’re not gonna do, it’s not gonna be good.

So you gotta, actually gotta position your panels to face the dark side of space so that they’re actually on there. So yeah, it could be kind of cool, and it sounds cool. One of the things I read and I was looking at is lava tubes on the Moon

might be

a place to put your data center.

We had the episode with Ernie Bell on talking about the lava tubes on the Moon and Mars, and that would be a place where you can, potentially put them in this lava tube. You still have the radiation problem, the cooling only as in orbit, but you don’t have the continuous sun, so you can actually have [00:30:00] both sides.

You can have radiant, you can have radiant panels all over the place and not have to worry about that. So, but yeah,

so.

[00:30:06] Mary: But that gets back to your paradox then

If It becomes it becomes easier and easier to do.

it’s like, we just need to have

more moons. But I

wanted to ask

you, I kn- I wanted to ask you a question too about AI data centers,

about,

about water.

We’ve been talking about electricity.

And could you

talk to why

are AI data, what happens to the water in AI data centers? Is it… I have questions maybe maybe like- go ahead and start

and I might chime in, but I, yeah, could you talk about that a little bit?

[00:30:41] David: So definitely water is a scary headline number, right? You hear this huge amounts of water, and part of that… So there’s good news and bad news in the head- that number. Part of it is, as I understand, is that they take into account all

The water that’s [00:31:00] also used to generate the energy in the first place. it- for the data centers, right?

So th- approximately like 75 to 80, 85% of, the total water when people say this much water is being used for data centers is really just making the electricity, whether we used it

For a data center or not. So- really, I think the– there still is a ton, so it’s still… But it’s like 20% of the headline number,

which is still a

Huge amount. So it’s like 20 billion gallons of

water a year,

which sounds like an enormous amount, but it’s not 100 billion gallons a year, which

is what sort of the number that I– you can find out there So,

you know,

Basically the way

a way to think about it is right now

it’s About 0.3% of

all the

water in the public supply that

we have is going to cool data centers.

So 0.3% is not zero. It’s definitely- a lot of water, but it’s not even [00:32:00] 1% of all of our water. So it… T- 20 billion gallons is a ton of water. It’s

20 billion gallons, but it’s only 0.3%. So it… Relative to other general uses like general energy, overall energy generation, that uses way more water than the specific use of data centers.

That’s, that, that would be the way I would talk about it.

Joe, what do you think?

[00:32:24] Mary: Yeah.

And well,

I had another question, too, about that. So when the water is being used, can it be reused?

I that’s one of the questions-

I had. Is the water polluted after it’s been run through an AI data

center?

[00:32:38] joe: So I was gonna touch on that unless you…

I

[00:32:40] David: yeah.

go ahead, Joe

[00:32:41] Geo: ahead.

[00:32:41] joe: So I was gonna say, ’cause if there’s just, we talked about space cooling, and then we skipped over water, the way we actually do it, and there’s three

kind of approaches to that. In most of the data centers, what they do is evaporative cooling. So if you live down in Arizona, the Southwest essentially you use water, and then you [00:33:00] evaporate it to cool the surrounding air.

And so swamp coolers in Arizona, what they would call it, and I think there’s other names for other places. And so a lot of them that’s the technology they use. And so the water you’re using, you don’t really recapture it. It is lost to the environment, but also now you’re breaking, people studied the water cycle and how it flows.

And so now you’re using water in a different way, and it doesn’t… Any environment, it’s just loss. And so that’s really water heavy and you- but you use less energy in your cooling cycle. The other way, the other ways people are talking is air cooling, so like an air conditioner.

So

you can do that, but that takes more energy, ’cause now you got the energy for the data center the servers, and then you need energy for the air conditioners. And so then that gets to the point David was saying, how now that water use is on the electric generation side of it. And so when you add that in, you really are- You know, you’re right.

You get an … It doesn’t incl- You can use it for something else, but you’re not. You’re using it for an

[00:33:59] David: [00:34:00] not. Yeah, So, it, it d- yeah

[00:34:01] joe: right. And then the other, the third way and newer that people are talking about is actually liquid immersion cooling, so you would actually submerge the servers into water, which then could be, reused, and it’s not

So it’s not a pollution. It’s not being polluted. This isn’t the idea. It’s just water loss, so that water is taken out of the water table and then moved along. So that’s a problem. And the same thing if you put a artificial lake in, Tempe. So when they put the artificial lake in there it was an artificial lake.

[00:34:32] Nick: in where.

[00:34:32] Geo: Tempe. In a desert.

[00:34:33] joe: Arizona. Oh. And so you’re evaporating something like a million gallons of water, or a couple, few million gallons of water a year. That’s, it was just, you know, it’s not going back into the Colorado River and then, and flowing back into this artificial lake.

[00:34:46] Mary: just- lost.

[00:34:46] Nick: lost.

[00:34:46] Geo: Right.

[00:34:47] joe: So you’ve taken water from, , a source that was being replenished by snow cap melt and into the Colorado River, and then you’re filling a lake that then gets evaporated and it’s just lost. So that’s kinda the problem there. So [00:35:00] we think of these three kind of cooling approaches that, that was it.

And I was gonna throw … I also looked at these numbers and, a little bit. It was something in U- United States, we use something like 322 billion gallons of water per day, so that’s you know, it’s a lot of water. But the three biggest categories, and

I I was curious,

It’s irrigation, about 118 billion gallons a day.

Thermal electric power, 133, and then public supply is about 39 billion gallons a day. And that was a little bit ago, so these numbers, you know, this was- So

[00:35:31] Nick: for irrigation- … a couple years ago …is that for, like, home irrigation too, or is that just-

[00:35:36] Geo: Agricultural … agricultural? Agricultural. Yes. Yep, yep. So

[00:35:38] joe: farming.

[00:35:39] Nick: Okay. Not people just watering their grass for eight hours a day.

[00:35:43] joe: I think that goes into the public supply, ’cause that’s probably how they’re me- m- your meter is measuring that versus, farm use, so-

[00:35:49] Geo: I lived in Arizona- we did have irrigation, and then it would, like, they, it would flood our yard, like, every whatever… Do [00:36:00] you, did you ever

experience irrigation?

in- I did, yeah. No, Isn’t that crazy … Yep, yeah. Also, it would just-

I don’t know. I don’t know. Do they still do that They probably- I don’t know. There was,

[00:36:08] joe: know. There was, well, there was a lot of water restrictions put on when I was leaving, so this

[00:36:12] Geo: It’s Just weird … Yeah …2001 when

[00:36:14] joe: left, but they were starting to restrict, like, you only can water certain days.

[00:36:17] Mary: Unless you’re a data center, and in that case you’re like, “Hey,

[00:36:21] joe: And it’s

[00:36:21] Mary: it’s fine.”

[00:36:21] joe: Always find it funny when they put super water intensive things in, the desert. You don’t have a lot of water.

[00:36:29] David: or golf courses,

[00:36:30] joe: golf

courses,

[00:36:31] Geo: Yeah. number one

[00:36:32] Nick: number one. It’s

[00:36:32] joe: so

yeah, data center fits perfectly. Like, you know, why do people need to drink water?

[00:36:36] Mary: Well, I had ano- I had another question too about the water, and and so I’ve heard that data centers need to, need a ton of water, but they at the same time need they operate best in, in dry environments.

……is that a- Mm-hmm. Am I right on that?

S- okay. [00:37:00] So-

so they, so

[00:37:01] Nick: Yeah

[00:37:02] Geo: Is that because of the equipment?

[00:37:04] Nick: because-

[00:37:05] David: you don’t want the Chips getting wet.

[00:37:07] joe: right.

[00:37:07] Mary: That’s

[00:37:07] Nick: Yeah.

And You want a nice solid temperature- for the computer components.

[00:37:12] Mary: So-

[00:37:13] David: want to use the water to carry the heat away and then evaporate the water. Like s- you know, kind of like sweat

in, in a sense, right? We use water to get the heat out of our bodies by sweating it off and having it evaporate. It’s effectively what we’re the, having the data centers do, but just at a massive scale

[00:37:31] Mary: I

see

So they operate best in like the desert

that

doesn’t have a lot of water. And, but, you know, then, and then we have to pump in the water from somewhere, right, to cool these things down. What a mess.

[00:37:47] Geo: The whole thing just is-

Yeah

ridiculous.

[00:37:51] joe: then- you have the- Okay

point also that, a lot of these I mentioned they’re in rural areas, but there’s also now people are like, “Oh, we should [00:38:00] really put ’em in, densely populated areas.”

[00:38:03] Mary: Not- It’s

not densely populated, but Hammond,

you know-

[00:38:05] joe: Well, I mean, you’re- They’ve never had a bad

[00:38:07] Mary: id- There’s never

been a bad idea

yet- …

that they haven’t gone, “Hey, hold my beer.”

You know?

[00:38:12] joe: I mean, you’re close enough to… You got two things there. You’re close to a large densely populated- Mm-hmm … major metropolis in Chicago, and you have the largest source of fresh water on Earth in

[00:38:26] Mary: lake-

[00:38:26] Nick: great

[00:38:27] joe: lake- Yeah

And

associated watershed reservoir.

[00:38:30] Mary: Yeah. Hammond can’t wait to piss that all

[00:38:32] joe: that is,

that’s, those are two big, Yeah.

[00:38:34] Geo: Yeah.

They’re like-

[00:38:36] joe: Two big

[00:38:36] Geo: But maybe they’ll get the Bears too. yeah.

[00:38:39] joe: Maybe we’ll, maybe improve our power grid. I mean, you know, do something

[00:38:42] Nick: useful, you know?

Ooh, what if The Bear stadium is on top of the data center. yeah. Whoa.

[00:38:47] Mary: Yeah. It ju- But

yeah,

[00:38:49] Nick: I mean-

[00:38:49] joe: I- No.

[00:38:50] David: blown

[00:38:51] joe: But I’ll

[00:38:51] Mary: yeah,

that,

that’s the other thing, too. I th- I think that I mean,

I…

like

it

feels like a bubble to me, you know? Like

all …of [00:39:00] this massive

build out, and we just can’t build things fast enough.

And

like

right now in Hammond, they’re going, I, they’re going back and forth. There’s this company called CoreWeave. I I have, I’ve,

[00:39:14] Geo: I have, I’ve,

[00:39:15] Mary: I would imagine, have you heard of them?

Oh, okay. And so, and they’re promising Hammond like birthday parties and ice cream and pony rides. If you could just only, you know, if we can just come into your

town

and-

[00:39:29] Nick: do Mary?

[00:39:30] Mary: They want… Their CoreWeave is an is an AI data center- place, and they want to they have the mayor’s attention because they’ve floated some

[00:39:40] Geo: like

[00:39:41] Mary: bonus or something. Like they’re gonna, like the city’s gonna get $25 million from this company,

and

I’m like, “

Says who?” You know, I mean,

I don’t know.

Do these AI data centers just

like promise they gonna get a tax break

Oh, I’m,

oh, I’m sure. I’m sure they’re not gonna pay any [00:40:00] taxes forever, you know? But

[00:40:03] joe: I

[00:40:03] Mary: don’t under- you know, like, you know, I guess do these centers come in and promise the moon to the cities- and then when it comes time to pay up, they go, “Oh, you know what?

You know, we, you know, we’re running so hot right now. We’re making a bunch

of promises.”

But when it comes time to…

You know,

Is that,

Have you- Run across that in, in any of your, in any of your research?

[00:40:25] David: I don’t know enough about the kind of community company kind of interface. I mean, it reminds me, the way you describe it, of what I heard about when the fracking was going on and the

f- the, and the companies would, go town to town if they found that there was a good place to do fracking in, in, rural Pennsylvania, and sell the towns on these too good to be true things and come in, and then there’d be reports of earthquakes and, nobody o- i- it

was all… It just smells like a deal with the devil kind of,

you know, for lack of a [00:41:00] non parochial metaphor. But

[00:41:02] joe: Yeah.

[00:41:03] David: yeah.

Anyway

[00:41:04] joe: I think that’s one of the problems with this now is that data centers where they’ve gone, it’s all about corporate profit. And then to Mary, to your point- … I think there has to be a switch because now you’re saying, let me build this in your community. Let me build this in Hammond next to your home.

Right. It’s gonna be great. And I think the companies haven’t really talked about community engagement and how they’re going to take the profits that they make and put that back into… You know, so will the schools get, free data storage? Will infrastructure be improved?

I think

that’s

[00:41:38] David: at least give free Wi-Fi to the

[00:41:41] joe: Exactly. Exactly. And not kickbacks to the mayor. Like, you know, actual,

[00:41:45] Geo: Real, Well-

[00:41:46] joe: real community things

that you’re gonna help anybody.

[00:41:49] Mary: it’s j- it’s just gonna help us.

[00:41:50] Geo: Yeah. right.

[00:41:51] joe: Not gonna build a new body … I mean- it’s just gonna help us. I mean… Yeah. Right. Right. I mean, that’s… But but your community would be the one directly impacted, and so if you’re coming into that community or the watershed community, let’s say that.

[00:41:58] Mary: Mean,

[00:41:58] joe: you know- right, so that would be Lake [00:42:00] County, you know, for instance, because now they’re talking Munster, Hammond, Crown

Point.

So you- Oh, for sure. You’re right. Hobart. So

you would have several data centers coming in. And so what are these communities getting out of that? Are the libraries gonna get some books , and maybe some free Wi-Fi and some updated computers or access to just the server?

So you-

[00:42:18] Geo: you’re talking about ways- I

[00:42:19] joe: think there are ways-

[00:42:20] David: To get back to the beginning thing we were talking about, right? Like, the electric company, when they build a power plant in the 1800s, everybody in the town’s lights turned on, right. Like, you got something very clearly beneficial at the community level when these companies came in to exploit your natural resources.

So it only seems like the least they could do is give us Wi-Fi

[00:42:45] joe: Yeah.

[00:42:46] Geo: And I

[00:42:46] Nick: And I, honestly, I don’t think the trade-off is worth it for just

[00:42:49] David: No. At least they’re not selling it in

any

[00:42:52] joe: right

[00:42:52] Nick: It’s

not gonna be a good-

Wi-Fi … Wi-Fi It’s gonna be a- Like- It’s

gonna be subpar at best

[00:42:56] joe: yeah

[00:42:57] David: be dial-up.

[00:42:58] Geo: And

they’re

giving you noise [00:43:00] pollution. They’re giving you, you know, all

[00:43:03] Nick: Yeah.

no the benefits

don’t outweigh the cons.

Like- I know

[00:43:08] David: Or at least they’re not making them obvious

[00:43:09] joe: if they’re making, $100 billion then they can patch up the roads, they can invest in schools, they can invest in libraries. So you can sweeten the… I mean, I think if your community- They- But they won’t

sweeten the pot. Well- If, if- I think at some point they’re gonna have to because- They have to look at the impact … I just don’t think… I think they’re gonna run into a wall. Then don’t get out. I get that they’re promising things now. But for other businesses- For now, right.

That’s right. Well … are not gonna happen. Right. They’re not, they’re go- Yeah … they’re promising the moon right now if they can- Yeah … if only they can come and pollute our water and- Oh, we’re just a little AI company … And drive up our energy- Just a small one … sky high. Small one. We’re gonna need a billion gallons of water,

patch up the roads, they can invest in schools, they can invest in libraries. So you can sweeten the… I

[00:43:18] Nick: But they

[00:43:18] Mary: But they

have no-

[00:43:20] Geo: But they

[00:43:20] Nick: But they’re not going to

wants something- And it, if they promise it- Right … it’s not gonna be an actual thing that they do …

because

[00:43:25] joe: think… I think they’re gonna run into a wall.

[00:43:28] Geo: n- n-

[00:43:29] Nick: don’t get me wrong You know? I guarantee they’ll promise it eventually.

But fulfilling these promises Well… Well … are not gonna happen. right

[00:43:35] Mary: They’re not, they’re g- they’re promising …the moon right now. If they

could, if only

they could come in and- pollute our

[00:43:41] Nick: water and-

“Oh, I’m just a wittle AI

center. Don’t look at me” sky drive up our stock. A small one. “It’s just a small one. they need. worry.” A few

gallons of water,

[00:43:49] Mary: Yeah, fingers crossed.

But they’re making so many promises right now, how could they ever fulfill these financial

[00:43:55] Nick: Especially with them not making the money that

they need back. Well- Right …

[00:43:58] joe: have a, do have a [00:44:00] fun a fun list. I don’t know. I have a few kind of lists about this. I figured but where data centers have gone in, in heat use and to the community, and it, a lot of these are really interesting. Most are in Europe, but in Microsoft… So we, the, we we’re hearing a lot about data centers now, but Microsoft in 2011 in a white paper coined a term data furnace, and their idea was that they would take their server racks and replace home heating systems.

So homeowners would get free heat.

[00:44:32] David: That’s awesome and then you would have this whole, and so-

[00:44:36] Geo: how do they- … one of those companies-

How do they get it from the data center to the person’s house? I,

[00:44:40] joe: Yeah. Yeah, yeah, yeah. And get your

[00:44:41] Geo: You

[00:44:42] Nick: You would have a giant rack of just- They come to

[00:44:44] David: now you have a

problem “

[00:44:45] Geo: Okay, we got your heat”

[00:44:47] joe: there’s, steam. There’s ways you can transfer

[00:44:50] Mary: are immediately peeing on those server racks … so 100%. what

[00:44:54] joe: did were- 100%. Exactly … so in, there’s a UK startup called Deep Green, so these are now [00:45:00] real-world cases,, where they install server racks directly under swimming pools submerged in oil, and the heat from the servers warm the pools.

[00:45:07] Nick: seen those.

[00:45:08] Mary: there

[00:45:08] joe: seen that, yeah. And so you go, you had the Equinox Par- Paris Data Center in 2024 Summer Olympics that heated the Olympic Aquatic Center and it’s gonna be free for the next 15 years via a heat network. There’s a Norwegian lobster farm that’s heated by a nearby data center, and I had to have this.

We had a turpentine farm on the

[00:45:29] David: yeah. Uh-huh.

[00:45:30] joe: I saw this story… have this heat and this lobster farm, so you have this really interesting thing that you could then think about the heat generation. Are there ways to use that?

Now once again, is that community… Are there ways that you can heat- A homeless shelter nearby. Or, are other things in the community that would be useful that you are you’re making this trade-off- Which … but

[00:45:53] Nick: your breaking point? Right,

[00:45:54] joe: right.

I mean, you’re also gaining.

So I think that’s where these data centers have to come in with community [00:46:00] leaders and get creative and say, “Well, we know what this is all about. We’re not gonna try to blow smoke,

[00:46:05] Nick: Is this us trying to aim at

data

centers to hire us

as community outreach?

[00:46:10] joe: outreach? Sure, ‘

if

[00:46:11] Nick: they’re-

[00:46:12] joe: They’re paying up the bills. I mean, you know,

[00:46:15] Nick: They storing our episodes

for free.

[00:46:17] joe: the

[00:46:17] Geo: wouldn’t hold your… Yeah …I would not hold your breath, not hold those up. But yeah, you have… I mean, and in just different locations too. I mean, we talk about rural centers and we- and mostly America, but you have ones that are buried under like, you know, old Cold War bunkers in Stockholm.

[00:46:34] joe: So they kinda go down there. It’s cold. I saw one in Antarctica. It’s a functioning data center at the South Pole, and so you don’t really have a cooling problem down there ’cause it is cold. So you have these places that are Kansas City, SubTropolis in Kansas City, so it- it’s in a, it’s in a cave.

And so you have this massive carved into an underground limestone mine. And so these kind of old mines [00:47:00] that we had, can you put data centers down there? Which naturally in our episode where we talked about living underground can you use these naturally occurring places that are cold that you don’t have to then use as much water to…

‘Cause the water is what we’re using to cool these, so can we actually find other ways to cool these centers? And other technologies-

[00:47:21] Geo: you might, you’re just gonna open another can of worms at that.

point. You know what I mean?

[00:47:25] Nick: We’ll figure, we’ll cross that bridge when it comes to it. We’ll

[00:47:28] joe: do that episode next season Well, that

[00:47:30] Mary: bridge when it comes to it. Yeah,

We’ll do that episode- Don’t worry … next season. Well, then th-

[00:47:31] joe: you- Oh, I’m just throwing

[00:47:32] Mary: also… Yeah, no I hear that But

like,

I wonder if

[00:47:35] Geo: like, like

[00:47:37] Mary: we have to like hit them where they live as far as like financially.

mm-hmm. You know, I mean, they don’t … I mean,

what is right now it- they’re just able to just run rampant with like no consequences

whatsoever. But what if they actually had to pay taxes on- …the AI data centers that, you know, that they’re [00:48:00] creating?

[00:48:00] Nick: I think that would be up to the politicians- right. to go ahead and- grow a pair.

[00:48:05] joe: Yep.

Oh.

[00:48:06] Mary: Right on that problem .yeah.

[00:48:07] Nick: Grow them a

[00:48:08] joe: pair. Or- The data

centers are going in where they’re going

[00:48:10] David: Or that.

[00:48:11] joe: because regulations

[00:48:13] Mary: are lax.

[00:48:13] joe: are lax.

Mm-hmm.

Right? So …the areas that they’re … If you’ve, if you track where the data centers are primarily going in- Mm-hmm … it’s because regulations don’t exist for these things. You’re ta- you know, Mary, you’re, I mean, you hit right on the head there. That’s why. So you can almost go, “Why are they building this in, you know, wherever?”

The worst place to build the data center by all means.

[00:48:33] Mary: As we like- Why are you buil- why are You building it in Hammond? Because Hammond …has never

seen a bad idea- that they didn’t wanna throw money at.

Okay. So yeah.

But

yeah, I think

there’s one a data center on State Line Avenue or something.

Yeah. Or anyway,

In

Hammond. Yeah. They want to expand it.

[00:48:50] Nick: Hmm.

[00:48:50] Geo: they already voted

that

they aren’t, didn’t

[00:48:53] Mary: I,

well, they’re June ch- 30th- Oh 2026. So when this, you know, we’ll see.

[00:48:59] Nick: It’s already passed. [00:49:00]

[00:49:00] Mary: It’s already passed.

I,

well, you know, it’s, I, it’s hard to s- it’s hard to say like I, you know, I’ve been going to like these city city council meetings and

[00:49:08] Nick: like Oh, no, I was saying as of the day

[00:49:10] Mary: Yeah,

that’s pa- it’s already passed.

But I think that I get the sense that, you know, even they, they are yeah, these companies seem kind of,

[00:49:19] Nick: You

know,

[00:49:19] Mary: too, yeah- icky

or too good to be

true or

[00:49:21] Geo: they’re- Right … They might be kind of figuring

it out. Yeah.

[00:49:24] Mary: may- Yeah,

I mean, don’t worry. I mean, I’m sure they will, they’ll figure out a way to, to make it happen but,

But yeah, there’s just, Do you … I don’t know. I

I just-

Yeah, go ahead. Go ahead,

[00:49:36] Geo: No, I’m sorry. I didn’t mean … to I just think it’s crazy ’cause I remember learning about computers when I was young, and they would talk about

The first computers took up huge rooms and,

like-

and you had, you know.

And now, ooh, now we have- everything, everything we have are now on this little

smartphone. But all we’ve done is then just-

[00:49:59] Mary: The Huge

[00:50:00] rooms

are still

there

[00:50:00] Geo: You know? Yeah. It’s like- you

[00:50:02] Nick: replace one- with

another

[00:50:04] joe: Yeah. Yep.

[00:50:05] David: Yeah.

I just wanna make it a little bit worse

[00:50:07] Geo: Oh. They do. Yes.

[00:50:08] Mary: we don’t need another huge room. Yeah

Yeah. You know? It’s like- you replace one with a nine. Yeah. Yeah. Well- Please do. Yes. Yeah

[00:50:09] David: in ad- in addition to all the companies, there’s also the military

that’s building lots and lots of data center infrastructures, and they’re getting these really long leases, 50-year leases, where they can then just… Who– You know, I mean, like, I do trust the military

but, you know, come on.

This seems like the military industrial complex on steroids in some way when you start doing the data center integration into the AI with

autonomous military capabilities. It just–

[00:50:45] Geo: Surveillance?

[00:50:46] joe: Yeah,

[00:50:46] David: even worse.

[00:50:47] joe: Yeah.

[00:50:48] Geo: I know.

Well,

[00:50:50] David: All right. Anyway.

[00:50:50] joe: to make it even worse, ’cause we talked about power, and I don’t wanna bash it, but, the rush to find power sources to improve our grid, you [00:51:00] have nuclear power. And so now we’re starting to talk about reactivating or activating, shuttered nuclear power plants to bring them back online to feed the beast.

And so, like, Three

[00:51:11] David: Three Mile Island

[00:51:12] joe: Is there, which was– almost had a near meltdown. And so… And that’s just kind of, for… A meltdown is when the reactor’s core overheats and you can’t cool it down with water, so the nuclear fuel begins to melt. And then there’s not coolant to absorb that, so you have a disaster

[00:51:28] Geo: go wrong?

[00:51:29] joe: you know, heads

[00:51:30] Mary: I’m sure everything will-

[00:51:31] joe: I’m sure everything will be fine.

[00:51:34] David: Island is the Crane Clean Energy Center.

[00:51:38] joe: Yeah.

[00:51:39] Geo: Ah.

[00:51:39] Mary: it’s clean. It

has

clean. in the name. Right. It’s, it, w- oh, it’s, it must

[00:51:43] joe: that’s the idea of it. So no, you have that.

[00:51:45] David: But it’s Microsoft, right? Yeah

[00:51:47] Mary: Yeah. oh boy.

[00:51:48] joe: But

you do have these ideas, where you go, these, I w- I talked about these submerged kind of centers as you go in liquid.

And so you’re talking about submerging them underwater, ocean, submerged ocean [00:52:00] centers, so you can, place them there where you can dissipate heat, and you got tons of water, you know. So you do have these,

you know-

[00:52:08] Geo: that-

[00:52:08] Nick: then the- … environmental effect is gonna be harming what, the fish?

[00:52:12] joe: No,

[00:52:13] Nick: I mean, For changing- the temperature of the water down there?

[00:52:16] joe: It will be fairly negligible because of so much, because you have so much water that you’re not, the heat load now won’t be, I mean, so

[00:52:25] Nick: You’re still producing the same amount of heat- but it’s just in a larger area.

[00:52:28] joe: So it will disperse … so like- Right. Right … you’re

[00:52:31] Geo: you’re

[00:52:32] Nick: hurting the environment of-

[00:52:34] joe: You-

[00:52:35] Nick: said creatures. Yes.

[00:52:37] joe: locally. I mean, you might,

you

[00:52:38] Nick: not humans that you’re doing it to … but You’re doing

it to- sea creatures.

[00:52:43] joe: creatures.

Right, right. And you

gotta think about what you’re doing, but you go low enough, then you…

Are you just you know, there’s a question, you know, you put things under stress, heat stress, they, you know, either A,

They will evolve and adapt, or you get a

new,

[00:52:55] David: die.

[00:52:56] joe: you get a new, you get a new, cohort come in and [00:53:00] live there. So around a data center, do you have different organisms that pop up and, you know, right.

[00:53:05] Geo: Right.

[00:53:06] joe: mean, you do have

heat, you have heat

vents in the ocean. It’s not like there’s not, hot things in… I mean, I’m just, you know, kind of putting out

there that, that you kind of bury it there. But yeah. That’s

[00:53:14] Nick: Just

like all humans, yeah

we bury our problems. Yes.

[00:53:17] David: You would drive evolution of new microbes

[00:53:20] joe: probably would, yes.

[00:53:21] Mary: Oh, you know

[00:53:23] joe: Microbes.

[00:53:23] Mary: Let’s get

[00:53:24] David: Definitely

[00:53:25] Mary: You know what? We haven’t gone a single… We haven’t gone the whole time without mentioning The Thing, which we’re all Thing fans, right?

So would it be great with

a

[00:53:34] joe: Antarctica out there. Right.

No one took the

[00:53:36] Nick: I wasn’t going to. I didn’t want to.

[00:53:37] Mary: I’m taking the bait now.

Like, imagine, like, the heat vents and the new microbes and

[00:53:43] joe: you thaw

[00:53:43] Mary: Wow. Anyway, Yeah, yeah.

So

yeah- …we unthawed The Thing. But what… Okay, I wanna get super petty right now and talk about, like, the dumbest things that I, wanna talk about really dumb things that AI suggests to me, and this is some…

Okay,

[00:53:58] Nick: so.

Wait. Hold on

[00:53:59] David: [00:54:00] Is this a segment?

[00:54:01] Nick: quick.

[00:54:01] Geo: This-

[00:54:01] joe: That’s right. A new feature. a

[00:54:03] Mary: New feature …new feature. Okay, let’s just talk… I mean, because we’re, like, ruining the planet and destroying the water and driving up our electric bills for these… Okay, so, Georgia and I are children’s librarians, and one of the things that we do is we print out lots and lots of coloring sheets for little kids, right?

So, like, I’ll get… Sometimes I’ll just download, like, a batch of, like, 16 different coloring sheets on a particular theme, and some AI dipshit whatever comes up and says, “This appears to be a long document. Would you like me to summarize it?” It’s a

[00:54:37] joe: a coloring sheet.

[00:54:37] Mary: coloring

sheet. It’s coloring

sheets, okay? don’t need it summarized.

I just- … get out of my face. Let me

[00:54:47] David: Thanks, Copilot.

[00:54:49] Mary: I don’t… Every

ti- Okay, so I am a proud Luddite when it comes to AI. I mean, I’m sure I’m using it without realizing it, but every time they’re like, “Would [00:55:00] you like to use this?” No. Click. You know, it just… It’s a constant game of Whac-A-Mole just to…

Like, listen, I don’t need my coloring sheet summarized. I know how to write an email. It’s not a problem. I don’t… It,

[00:55:14] Geo: You know. And then it

[00:55:14] Mary: then-

[00:55:15] Geo: can’t even get voice to text right.

[00:55:18] Mary: Oh, okay. There you

go.

[00:55:19] joe: boy. Right?

[00:55:20] Mary: Let’s,

yeah, talk about-

[00:55:21] joe: you- ‘

[00:55:22] Nick: cause you’re shouting at your phone if you had

[00:55:23] joe: a data center in Hammond- Then-

then you wouldn’t have this problem.

[00:55:27] Mary: Let’s- Just ’cause you’re shouting at Google. If you had NASA and Hammond- Right …you wouldn’t have this problem.

That’s right.

Yeah.

I have

a lot of other

problems. than Hammond. That’s

true.

Oh my God. I I have, I was gonna go some fictional things that I like talk… I mean, we can think of supercomputers gone wrong in fiction- Mm-hmm … but I went back. There was,

[00:55:41] Geo: a- … life is so dystopian, do we really need dystopian?

[00:55:44] Mary: really. Do we need to bar? Do

we need to bar

trouble

here?

[00:55:47] joe: out two

[00:55:48] David: there any happy fiction?

[00:55:49] joe: Short– I wanna point out two short fictions- Okay … that I think are kind of cool, and one was The Machine Stops, E.M. Forster

[00:55:58] David: Yeah [00:56:00]

[00:56:00] joe: where humans live underground in pods, and they’re comp- completely dependent on a global machine for food, communication, and social connection.

The machine runs everything until it doesn’t. And so you can imagine in 1909, this is before internet, radio, commercial, aviation, so you really have this kind of dependency on something and then when it collapses, like, what happens? So very good thought piece.

And then Isaac Asimov, who I think just, you know, really good idea generation. The Last Question, and it’s about a supercomputer is asked whether entropy can be reversed, and then the supercomputer keeps evolving and computing until after the heat death of the universe.

[00:56:44] David: Cool

[00:56:45] joe: Both of these are really short. I think they’re, like, nine, 10 pages long, both of them, so.

[00:56:50] Geo: AI could summarize it.

for you. it

[00:56:51] joe: could- summarize it. AI could …summarize

it very well. You can ask

AI. I

[00:56:55] David: to

add a tiny bit of level- levity,

right? The Hitchhiker’s Guide to the Out- Galaxy, [00:57:00] the

AI, he fed it everything, and what’s the answer to the universe?

42

[00:57:05] joe: long- and-

So long, and and Thanks For All The Fish, right? Yeah. So,

[00:57:10] David: Exactly

[00:57:11] Nick: you have the-

[00:57:11] Mary: we don’t have

[00:57:12] joe: we have data centers down there

[00:57:13] Geo: Oh,

[00:57:13] Mary: if we put the data centers down there, then they

all evaporate and

boil.

Yeah.

We get a,

… little fish

boil there.

[00:57:19] Nick: Yum,

[00:57:19] joe: yum. Yeah.

[00:57:20] Mary: Good eating.

[00:57:22] joe: But

yeah, that’s, you can go there. Yeah. So I was gonna, Dave, I was gonna go and touch a little bit into biology maybe, and we talked about, we were joking about the aquatic creatures that, that Nick is worried about by the data centers.

Yes. Yes, I am.

[00:57:35] Nick: Yes, I am.

[00:57:37] joe: But for, but when you stress cells, I was kinda thinking of biological metaphors to our data centers they decide what to sacrifice, and so is that… I you were pointing this out about AI, that is AI going to go, “Here’s how I should throttle or shut down non-essential things.”

I mean, is that a biological equivalent [00:58:00] or, you wanna touch on that? Your research kinda touches into that, so I wanted to give you some,

[00:58:03] David: Yeah. So, you know, we basically study resource allocation, but just how cells do it instead of economies or households or individuals, but Cells, they don’t have as much choice. There’s not a lot of free will if you’re a cell. You know, the primary directive is to grow and proliferate. Of course, that, you know, not if you’re an adult human, by and large, most cells, that’s what they’re trying to do, is take nutrients in from the environment, build another copy of themselves. And so when they ha- encounter stresses in the environment, no matter what they have to do to cope with that whatever positive thing that they need

to do, they always have to do the same negative thing, which is slow down. They can’t grow as

fast because

they have to spend some of their [00:59:00] energy on coping with the stress that’s in front of them before

they can even consider thinking about growing.

‘Cause if they tried to grow before

they coped with the stress, they would just pop. So when I find it hard to extrapolate that insight that we have about How constrained

cells’ budgets really are onto what seems like a system that humans have set up to be utterly hungry, insatiable,

rather than

ever

having to balance

a

budget

or

ever experience stress

where you have to have some trade off. it just

seems like we’re just feeding them raw sugar,

you know, in the form of data centers.

And,

So I just,

I don’t think

these architectures are

inherently adaptive in the same kind of [01:00:00] way that you and I would

be familiar with, where you actually have to think about

some trade off and some Yeah you can’t have your cake and eat it too, and these models can right now. And I think that’s that hard constraint

That they can

as

long as the world

suffers

and we take the water and we raise the temperature of the climate.

Like,

there

is no free lunch here.

It’s just the budget and the cost and the stress is being put not into the models, but onto us

[01:00:32] joe: What I mean is that, I mean, will the machine, if it becomes self-aware- Yeah

[01:00:39] Geo: Yeah.

[01:00:39] joe: will it care? I mean, will it then go will it try to,

like, regulate itself, right? I

[01:00:44] Nick: on what

[01:00:45] joe: Yeah.

[01:00:46] Nick: becoming self-aware with? Like-

[01:00:48] David: So the best essay I’ve read on this precise topic was by this guy, Siddhartha Mukherjee. Do you know this guy? He wrote the

[01:00:56] Mary: No Cancer, the

[01:00:56] David: cancer.

the great book about

cancer.

[01:00:58] joe: Yeah. The [01:01:00] malady of all… Yeah, right. Yeah,

[01:01:01] David: yeah, The Emperor of All

Maladies.

[01:01:02] joe: Seth wrote a new book, right? Is

[01:01:04] David: Yeah. he has several new books. So what he’s working on now, and he’s, so this essay is a, something I read in The New York Times a few weeks ago. It’s part of their series, What Drives Us. So it’s in trying to get thinkers to tell you about what’s motivating them. And so he’s been Trying to cure cancer his whole career. And lately, he

has turned to training an AI model

to

try to make cancer

drugs, right?

[01:01:29] Mary: Mm-hmm.

[01:01:31] David: essay is

[01:01:32] Nick: cancer drugs?

[01:01:33] Mary: you said cancer drugs?

[01:01:33] joe: Yeah

[01:01:33] David: yeah, drugs to, to cure people of

cancer,

right?

That he wants to build,

[01:01:38] Nick: Got it. I was like That’s cool. Yeah. Yeah, right Thought he was like-

[01:01:39] joe: Useful.

[01:01:40] David: AI to cure cancer, to

make drugs.

[01:01:42] Geo: he was like, “

[01:01:43] Nick: I’m gonna give everyone cancer. Here’s the drugs.”

And

I’m

like,

“Oh, damn, that got

[01:01:48] David: that except good.

[01:01:49] joe: That’s good. Yeah, this isn’t the Spider-Man villain episodes, okay?

[01:01:52] David: No. This guy seems like a really genuinely, like, wonderful guy. Very thoughtful. The essay’s about [01:02:00] him talking to his mom actually in part. But the thing is he finds that the amount– the way that this model is, like, learning to understand cancer protein folding and how small molecules that might help patients could interact with these proteins at the very atomic level, and it was learning these kind of chemistry and physics rules. He just realized that this thing, once It’s starting to

Work a little bit. But once it works

it’s

gonna work because it went far beyond whatever

he could do,

and it’s learning on its own, and it figures it out. And

so

he started talking about it like his own child and like, you know, you can hope you send your child out into the world with good values

but ultimately what they do when they grow up is very much on them.

[01:02:55] joe: Right

[01:02:56] David: And so if these [01:03:00] models get smart enough to the point that they can really do things, what He argues is the best thing we can do is try

to give them good values, like we’re their parents

and try to…

[01:03:11] Nick: has not happened at all

[01:03:12] Geo: Right, has

[01:03:13] David: Which is certainly not what

we’re doing right?

now, right? But, like, to raise them in a nurturing

way and teach them what

human is.

Be- teach them about humanism,

right? And, like, all the beautiful things humans have done, and that is really, like, our last best hope at getting these models to

actually be nice to us

later on

[01:03:35] joe: or should you not teach them human values, but cellular values that you have to respect the limits of your resources, right? Because human values, there’s a lot in there, but if you just take it down to what you were talking about, the free will of a cell is just to grow, proliferate, live within your means.

That’s actually what we’re not teaching AI right now. Right now we’re teaching AI [01:04:00] you can have as much power, as much water,

[01:04:02] Nick: Oh, I thought you were gonna say it, We’re teaching it racism …

[01:04:05] joe: gener- well,

[01:04:05] Geo: porn. In gen- Well, I mean- Well, that

too.

[01:04:06] joe: Yeah. What did David say? Every data

server is a, you know, a,

[01:04:10] David: To a- first approximation.

[01:04:12] joe: It’s

just porn.

So yeah,

I think there are these

things where you can go and if that happens I still don’t know if, I don’t know if that jump singularity is gonna happen. I’m still, you know, I think there’s things that

y- is

the hype and the super hype and then the mega hype, and I think that’s one of them.

But, just hedging the bets if it happens, I do see the fictional scenario where AI just goes, “Your humanity is not worth” … I just feel like it’s kind of like we we’ll be in some war and we’re gonna lose because we need water and it’s kind of…

And we’ve used it all for the data centers.

[01:04:47] Geo: That’s

[01:04:47] Nick: can

[01:04:48] David: is exactly like Waterworld.

[01:04:50] Nick: like

[01:04:51] joe: yeah.

[01:04:51] Nick: you

[01:04:52] joe: We just flood their GPUs and we win. Yay.

Yes, no, but that was something, I think it’s yeah it, can we [01:05:00] actually train AI to think about natural resources and to… And I think people

are I,

I think there are folks trying to do that and find better ways

that you

grow,

and if

you become more efficient, you grow and we keep all the pegs, the legs of the table growing with it.

That’s the community, that’s energy resources, water resources. What are we doing with the

technology?

Not just consuming it, but are we doing, are we trying to find cures to human diseases and cancers and things like that? And having fun too. I mean, I think that’s part of human nature.

But,

Is it just trying to compile…

Right? Right? I mean, it’s asking you to summarize your document because it wants you to get hooked. It’s like a drug.

And if you get hooked to

[01:05:45] Geo: if you get hooked to it … I’m already, I’m already-

[01:05:46] Mary: addicted

to enough things.

I don’t need, I don’t to be addicted

to AI. Yeah, I know.

[01:05:51] joe: addicted

[01:05:52] Nick: what’s really cool? I’m already-

Drugs.

[01:05:54] joe: I know.

Really You know, I

don’t need,

I don’t need no digital

drugs.

[01:05:58] Mary: what were you gonna say, Georgia?

[01:05:59] Nick: Oh, [01:06:00]

[01:06:00] Geo: I just think the bottom line is we’d just be so much better off. I’m not saying, like, we can totally disconnect, but-

if- we

[01:06:08] Nick: everyone on drugs.

[01:06:12] Geo: But I

think if we just took less… If we just took more time away from our devices and, yeah. Just in general. Yeah.

[01:06:20] Mary: Well, every generation- is a response to the one before it, so- maybe, like, the-

m- maybe the next

generation is going to be like, “Oh, this is really cool, this pen and paper thing.” You know, all bespoke and everything.

[01:06:32] Geo: Right.

[01:06:33] Mary: So yeah, I…

Oh, boy.

[01:06:35] Nick: even the disconnecting part is hard for everyone.

[01:06:38] Geo: It is.

[01:06:38] Nick: alone

have a smartwatch on

your

[01:06:41] Geo: It is. hard. Like,

[01:06:42] Nick: as soon as It buzzes, you’re gonna look at it because-

[01:06:46] Geo: That’s

what we’re- It- It’s also

[01:06:47] joe: privilege too, right? So to disconnect from the world, you have to be privileged enough to not care about whoever the dean’s writing me about something, right? I gotta be able to put that aside, right? There’s some level of privilege [01:07:00] that you need that you can actually detach. So either, A you have the device and you’re in the world and you can now have some, you know, forward momentum in, in society.

And then at some point you can go, “You know what? I don’t need the device. I’m gonna put it away. I’m gonna go this week without

checking emails.”

[01:07:18] Geo: Well, I think- it gets back to what we could learn from the cell. Yeah.

You know what I mean?

[01:07:23] Mary: Yeah. The

[01:07:24] David: Allocate

your

[01:07:25] joe: the regular

[01:07:26] Mary: Well, it, I mean,

like-

[01:07:27] joe: you’re an

extremophile cell, and then

you’d use whatever garbage you get.

But

[01:07:32] Geo: …e-

[01:07:33] Mary: e- at like

exponential

growth. I mean, when d- where does it s- where does it stop?

yeah. Yeah.

I mean, I mean, it’s-

[01:07:40] joe: Yeah. Yeah …

[01:07:41] Mary: I don’t…

Yeah.

[01:07:42] Geo: just-

I mean, bizarro …I mean,

[01:07:44] joe: think you have…

Right?

I I also, I think people are like, “Oh, we shouldn’t regulate because that hurts innovation.”

And you know- … we look at, but we look at other… We look at the Industrial Revolution [01:08:00] and there was no

[01:08:01] Geo: regulation- We we know what happens when there’s- no regulations. I mean,

[01:08:04] joe: it was

a, it was pretty bad, right? You know-

You don’t- …

child labor, don’t need limbs.

[01:08:09] Geo: work.

Yeah, Okay. So

[01:08:11] joe: I, I think you could have had regulation and growth at the same time, and, you know,

you wouldn’t “

be, Oh, look at this disaster.”

Like it

would be a case study. Right. but So I think you would have that, um- Musk

[01:08:22] Mary: be a trillionaire. And why, well, …well- no one think of-

[01:08:25] Nick: trillionaire. Whoa, why are you trying to attack Elon Musk? Oh, well, we He’s a billionaire. We can’t attack

him. He’s a now.

[01:08:32] Geo: no. trillion.

There’s a effect, okay?

[01:08:35] joe: It’ll all But,

[01:08:36] Mary: yeah, it’s,

Are there any good uses of, for AI, of AI,

[01:08:41] Geo: of AI? Well, I think he was giving- that example- of using it for like- No, like for- There, Yeah.

[01:08:47] Mary: are there in-

[01:08:48] joe: the- …therapeutics.

I think, you know, medical diagnostics. I mean, I think there are. I mean, if you

are- Yeah

[01:08:55] David: Yes.

[01:08:55] joe: you have-

[01:08:56] David: the answer is yes but they [01:09:00] certainly- don’t directly impact most people

day to day in any tangible way.

[01:09:07] joe: Yep

[01:09:08] David: They’re abstract. They’re kind of enterprise productivity type things. It’s coding.

It’s helping people that don’t need help, and it’s not helping people that do

[01:09:22] joe: Yeah. Or super personal, like, some of the stuff where, , we were talking about devices, brain interface, quadriplegians being able to inter-

right. I

I think you are having… Yes I think you have that. But the majority of it is memes, you know,

[01:09:37] David: slop right. It’s just kind of,

[01:09:38] Mary: just,

[01:09:39] joe: trying to get AI to do things that, , let’s replace human creativity and it’s like, well, why?

Isn’t that the, you know, that’s the fun part of it all,

[01:09:47] Nick: like, I mean, didn’t you just listen to the first,

AI generate fake porn.

[01:09:51] joe: Right, yeah.

That’s right. Yeah.

[01:09:52] David: more porn.

[01:09:53] joe: We

[01:09:53] Mary: S- So

[01:09:54] Geo: I, so-

We can’t

[01:09:54] joe: pay the real

people to do it. Let’s

[01:09:56] Mary: So are we really just,

Blowing up the planet [01:10:00] for slop and porn?

[01:10:01] Nick: We’ve done it for less.

[01:10:02] joe: Okay.

Yeah. I think that-

[01:10:03] Geo: w- I think that about-

[01:10:05] Nick: done it for

[01:10:05] David: I think

[01:10:06] joe: right? Yeah, mean, it’s

[01:10:07] David: we were gonna

[01:10:08] Nick: that

[01:10:09] David: title This episode. blowing up the planet for slopping porn Is pretty much,

[01:10:14] Geo: yeah.

[01:10:15] Mary: Yeah, I thi-

[01:10:16] Nick: might be the top rated episode.

[01:10:17] Mary: like-

[01:10:18] joe: be. That’s right.

Maybe I need the slop

and porn. Maybe that

changes-

[01:10:21] Mary: I was gonna say,

Like, tell me I’m wrong, but, you know, yeah, it just, like, I guess it’s just all for slop.

Yeah. Yeah.

[01:10:28] David: Right now.

[01:10:29] Mary: now. Oh, wow.

[01:10:29] joe: Or 80%.

[01:10:32] Geo: 80 is

[01:10:32] Nick: it’s- a

percent-

[01:10:33] joe: don’t know.

[01:10:33] Nick: that’s used for- It’s I mean, why- … the home use … you know,

[01:10:36] Geo: the water is

[01:10:37] joe: right? It’s all, it’s consumer-based, right? So we live in a, I mean, consumer and the promise that the consumers will turn around and pay money for this, right?

Like a subscription service.

So I think …that’s the whole

[01:10:49] Mary: To get them hooked.

[01:10:50] joe: That’s right. So that’s why… And that’s, you know, there’s more people in non… Not everyone’s a researcher trying to solve a structure to [01:11:00] push their research for defined drug targets, or what new antibiotics can we find, right? I mean,

that, there, there’s,

That’s happening in the background, but the

stuff we hear about

and the stuff we see is

[01:11:09] Geo: the majority of it is- right. yeah …And that’s

[01:11:12] joe: problem

is that you’re feeding that beast, and then you’re saying, “Well, how can we have, how can we hook the consumer more? Oh, if we can give them more short form videos. Can we give them more, you know, what they want? If they, if you want this story with this person in it, can we give that to you right away?”

Like, because you can’t wait for it. And then it’s gonna happen slowly, but it’s gonna be

now

you have to pay for it.

And

I

think that’s the thing we don’t know yet is when that transition happens, how many people will go, “Okay, I’ll pay for it,” or how many people will be like Georgia and, “You know what?

I don’t need it that bad. I’m gonna find something else to do with my money or time and not be hooked into it.” So I think we have this, like, kind of freemium I think they call it. It’s where it’s free for a while-

[01:11:56] Mary: Ah, I

see.

[01:11:57] joe: you build up, like not a premium,

[01:11:59] Geo: and then premium-

[01:11:59] joe: a [01:12:00] freemium- There’s that- and you go,

and then it, and then so that’s like your search engines now.

How long will it be before Google stops digesting, AI

digesting-

[01:12:08] Geo: gotta figure out your

[01:12:09] joe: then you gotta figure… That’s right. Yeah. You gotta, You gotta, figure that out on your own, lady.

[01:12:15] Geo: gotta

[01:12:15] Nick: they’ll have

a cheaper option with

[01:12:17] Geo: with ads.

[01:12:17] joe: And you’re right, they probably will, right.

I mean, that’s- True

…they’ll have

something to keep you going, but that will be

the model.

I don’t know how long that’s gonna take to, to change, but I see the next probably couple years.

[01:12:28] Nick: We see the same thing with the streaming services. Yes, sir They started off- at such a low price- just to compete with cable. Yeah And now they’re killing cable- and also increasing the prices Yeah It’s that same model

where-

[01:12:41] joe: The

[01:12:41] Mary: the

[01:12:42] David: Same with Uber, Lyft,

[01:12:44] Mary: Hoopla.

[01:12:44] joe: And so you’re killing

blogs- say, hey, Check out Kanopy. It’s amazing.

[01:12:49] Mary: Yeah, your

[01:12:49] Nick: li- Hoopla …

[01:12:50] Mary: get your

library card, and your library probably has a subscription to Hoopla,

[01:12:55] Geo: can actually go- Yeah …

just like an old-fashioned

[01:12:58] Mary: Kanopy with a K.

[01:12:59] Geo: you [01:13:00] can actually go into the libraries like an old Blockbuster Video- Right …

and pick the videos Old DVDs It’s so cool.

Hell Yeah Yeah.

[01:13:08] Nick: I did that. Yeah you just … Some

of them have games, some of them have- Yeah … music and movies.

[01:13:14] Mary: we go check it and you own it. If you in a library or if you buy- Physical,

[01:13:18] Geo: it’s a physical media. Physical. Yeah.

[01:13:21] joe: it.

[01:13:21] Mary: Cloud, Mm-hmm

[01:13:22] joe: the

cloud

could go away one day,

[01:13:24] Mary: Go back to physical media, yeah

[01:13:25] joe: right?

[01:13:25] Nick: Go back to physical media,

[01:13:27] joe: and then you’re sad.

You’re lonely, and you got

nothing.

[01:13:32] Geo: Except each other.

[01:13:33] Mary: Literally.

You got nothing.

[01:13:35] joe: school porn, right?

[01:13:36] Mary: other.

Aw

[01:13:37] Nick: porn.

[01:13:38] Mary: Old

[01:13:39] Geo: porn.

I

[01:13:40] Mary: remember

that magazine.

Where

did I

find it? Where was it at?

[01:13:43] David: I got a bunch of Betamax.

[01:13:45] Nick: I remember when the cloud was- ChatGPT, where’s my

[01:13:48] Mary: old porn magazine?

[01:13:49] joe: was like The Matrix right? You know, I designed her. Like, you know, it’s like yeah.

[01:13:53] Geo: The cloud

[01:13:54] Nick: back in the day was just the f-

[01:13:56] Mary: cloud Real cloud

The

cloud

[01:13:57] joe: Real

[01:13:58] Geo: closet Really? Yeah.

A tree stump with

porn

stuck in it. in my day- I think, I- In my day we had clouds. I couldn’t think of the

[01:13:58] Mary: Like an actual

[01:13:59] Nick: A, a [01:14:00] tree

stump with porn

stuck in it.

[01:14:02] Mary: it.

[01:14:02] Nick: Yeah.

[01:14:02] Mary: Yeah.

[01:14:03] joe: Yeah.

[01:14:03] Mary: In my day.

[01:14:04] joe: In my day,

we had clouds.

[01:14:06] Nick: I couldn’t think of s- the word stump.

[01:14:08] Mary: I-

[01:14:09] Nick: It stumped me.

[01:14:10] Mary: If you, haven’t thought about the word stump- I think it’s time

for us to wrap it

[01:14:13] joe: it up. I’m right.

[01:14:14] Mary: Yeah, I think

[01:14:14] joe: I think it’s Probably

[01:14:15] Mary: time to bring it in. Bring it down home.

[01:14:17] joe: Any

f- last questions? Yeah.

[01:14:19] Geo: We just added to the slop, but-

[01:14:22] Nick: We just added to the slop no, I actually think we hit a lot of

good things this episode.

[01:14:25] Mary: I, well, I would also like to just let people know, anybody who’s listening to this right now, that we’re all actually just AI generated.

[01:14:33] Nick: Well,

[01:14:33] Geo: yeah.

[01:14:34] Mary: Yeah and none of this

[01:14:36] joe: Take me to, your leader.

[01:14:38] Mary: just to mess with you a little bit.

Yeah, none of th- you know, we just put this into a ChatGPT prompt.

[01:14:43] Geo: Are we real?

[01:14:44] joe: we

[01:14:44] Mary: a podcast for-

[01:14:46] joe: We did that an episode- Yeah … in this,

Yeah … season

three with Lily and Generoso. I

[01:14:50] Nick: if you go

[01:14:50] joe: Yeah … I

asked I asked AI to interviewed AI for a segment on that episode. So if you go back and listen to

that. Yeah. We did that.

Yep. Got their opinions. But, [01:15:00]

[01:15:00] Mary: we’re totally real.

[01:15:01] joe: We are real.

We’re real

[01:15:02] Geo: Sure I’m gonna

pinch,

[01:15:04] David: how you know we’re

real

[01:15:05] Mary: because we c- S-

[01:15:06] joe: Yeah.

[01:15:07] Nick: can

[01:15:08] Geo: Yeah.

[01:15:09] joe: You guys didn’t…

[01:15:09] Mary: Sorry, David gave us

the

thumbs-

up- ……and a little wink and

a

nod. So

[01:15:13] Geo: we

[01:15:13] joe: the nice thing about

is to rabbit hole research, we go out and about, and we interact with our people.

So we are real. We are here.

[01:15:22] Nick: We interact with

[01:15:23] David: out of the burrow

[01:15:24] joe: interact with people. Yeah. We do.

[01:15:26] Nick: I kind of just hide away like a little

rat.

[01:15:28] Mary: blink, blinking

[01:15:29] joe: Let’s give, uh… David,

you got any final words on this climate change data center? Are we all screwed?

[01:15:36] Nick: you leave us on a high note?

[01:15:37] joe: will we a-

[01:15:38] David: Yeah,

no.

[01:15:41] Nick: Say everything’s gonna be okay.

[01:15:43] David: no, I do think that there is a really good chance that, you know, there is some good that comes from all of this

[01:15:51] Mary: Mm-hmm.

[01:15:53] David: industrial scale escalation. Because at the, at least it’s about [01:16:00] intelligence, at least they say it is, and it’s about, like, understanding and getting to the bottom of scientific questions.

And if we can do that a bit with this, that’s gonna be good, and that’s gonna then help us solve a lot of these other problems. If we don’t do that, and if we just slop ourselves into tedium, then we’re doomed. But I do think people are really excited about solving hard problems, and we don’t wanna just look at screens.

We wanna get out there. We wanna do things. We wanna act in the real world and make change. So I’m hopeful and optimistic even though I think right now there’s a lot of Yeah.

Cloudy clouds

over the cloud.

[01:16:52] Mary: Yeah A lot

of wild west- mentality happening

right now.

[01:16:56] joe: Yeah

[01:16:56] David: I do think the science is underneath all of this, and if we [01:17:00] can let it happen, that’s gonna help humanity

[01:17:04] joe: I like that Hell

[01:17:05] Geo: Yeah.

[01:17:06] joe: are you doing?

[01:17:07] Geo: thank

[01:17:07] Nick: Hell yeah. Yeah. Well- Yeah …thank you so much, David, for

being here with us today. Hopefully next season- you’re here-

in

[01:17:12] David: charm

[01:17:13] Mary: All

[01:17:13] joe: in studio. Yay. We have him on the f- the f- the fourth time, and we’ll see if you get there before whoever was at three before.

[01:17:20] Geo: think he

[01:17:20] Nick: he wants to be on episode one of

[01:17:22] joe: That’s right. Yeah,

[01:17:23] Nick: He’s like, “I’m gonna be the,

[01:17:24] David: exactly.

[01:17:26] joe: He’s always

at the end of the

season. We

always like,

[01:17:29] David: on a bonus episode

[01:17:30] joe: Awesome.

[01:17:32] Geo: Yeah.

Yeah,

[01:17:32] joe: There’s …

You gotta come in studio for that, so that’s,

[01:17:34] David: I’ll make my own jacket that

says four-timer

[01:17:38] Nick: We’ll have

[01:17:39] Geo: We’ll

have AI. we’ll have

[01:17:40] joe: a, we’ll

[01:17:40] David: little stars like I won the World Cup,

[01:17:42] joe: AI,

we’ll have- I was like our number one fan …we’ll have AI

make you something, so, we’ll see.

[01:17:46] Nick: we’ll see.

[01:17:46] Mary: see.

[01:17:47] joe: Well, yeah, on that note,

you have me, Joe.

[01:17:49] Nick: have me, Joe. You

got

[01:17:50] joe: You got Nick.

[01:17:50] Geo: Georgia.

[01:17:51] joe: We’ve got

Nick.

Georgia.

[01:17:52] Mary: got Mary.

[01:17:53] joe: We’ve got Georgia. You got Mary.

[01:17:55] Nick: And-

we went down some

hole. See you.

[01:17:59] joe: Love

[01:18:00] y’all.

Transcript of Episode 58: The Science of Chatbots & Human Connection

Lily Fierro (MIT Computational Cognitive Scientist, Writer) and Generoso Fierro (Illustrator, Film Critic)

Website: https://linktr.ee/plasticgrapes

Substack, Apple, Spotify, YouTube, Amazon


joe: Hey, welcome back to the rabbit hole of the research down here in the basement studio. We’re all crewed up. You have me, Joe,

nick: you got Nick? You got

joe: we’ve got Nick.

geo: Georgia,

nick: Actually Joe, can we start this from the top? I’m gonna put this in my chat box. Don’t worry. 

joe: We’ve

geo: Yeah we’ve got a chat behind here.

nick: to run it through my ai.

joe: Don’t. You know, you guys are getting a little ahead of everyone here.

geo: Joe.

nick: Oh, sorry.

joe: Be good humans. The chatbots would not do that type of thing, but yeah. We’re gonna be talking about chatbots in this episode and we have two, two special guests. This is such a hefty topic. So if our guests will go ahead and introduce themselves.

Lily: Hi, I’m Lily Fierro. I am half of the Plastic Grapes Duo. And I typically write and letter and also panel our comics, and I make them with 

Generoso: Generoso Fierro. And I do most of the illustration [00:01:00] for our books. We have four to this point and five possibly by the time this is up. 

Lily: Yeah. And our books are all science related because I’m originally a computational cognitive scientist.

I, my bachelor’s is in brain and cognitive sciences at MIT. After that I worked in an adult research and I’ve been a data scientist for almost so a while now. Over a decade and a

joe: Yeah. Don’t age yourself.

Lily: know.

joe: Yeah. Cool. So I think this is gonna be a lively conversation. And so like the episodes, I usually have a little intro and I do have a few

nick: just to confirm, you guys are humans, correct?

geo: Can we verify

joe: Everyone’s human.

nick: Can you click the box to tell me that you’re are?

geo: me 

Generoso: Abso

geo: many how?

Generoso: be I

nick: Yes. Yeah.

geo: Which pictures have a crosswalk?

Yeah,

joe: Yeah, I like the one where it’s you know, click all the boxes with a motorcycle, and then there’s one motorcycle [00:02:00] across like the panel and it’s come on, man. You know, and that

Lily: many do I have to

click? 

nick: know, yeah. It’s

like I always fail

joe: usually, it’s like you’re not a human. Do it. Try it again. Okay.

Cool. So, I have a chat box is in one sense, a very simple thing. Matrices of numbers, processing power, algebra, statistics, predicting the next word, and the next. No heartbeat, no memories, no emotion. And yet we are all fascinated by how you can type questions into that text box. And it doesn’t feel like math.

When a perfect answer comes back, it feels like a human voice pulling from the entire database of human knowledge. It tries to please it jokes it argues, it tells you. It understands you. It hallucinates, it apologizes. All this makes it seem like it has a mind emotions, but it’s only math. But we wanna believe it’s more we are biologically programmed to seek out companionship and chatbots [00:03:00] are more than happy 24 7 to fill in. But chatbots are more than a simple conversational tool. They’re now customer service agents, coding partners, midnight, confidence dungeon, masters, fake friends, and sometimes fake lovers. They live at the intersection of science fiction, nightmares, startup promises, and the very real human loneliness. Us humans are wired to find patterns, intentions, and see working minds everywhere, especially when something talks back in a predictive and engaging way. So when we talk to something that has never been alive, never felt anything, but somehow knows exactly what to say next. What’s really happening in that conversation. Are we discovering something about these machines or are we discovering something about ourselves? And that’s what we’re gonna chat about. And

nick: that was sent through chat GPT? Correct. Sent through

joe: it was sent through Joe GPT

nick: Had to confirm.

joe: So yeah, that’s where I [00:04:00] think, , we can go and we can probably get into more definitions of what a chap by mal leave it to the computational scientist. , I have a definition there, but yeah, if you want if you want to, you can break it down. If I was wrong,

Lily: no, I think, you know, just as a survey of the architecture you know, most of the time when we work with chatbots in this day and age they tend to be decoders. A decoder type architecture is focused on, at the end of the day generating new content, right? We most chat bots are going to be generating text when they do that.

They’re doing that in an kind of auto regressive manner, so they’re looking at the entire corpus that is available. A lot of that is like whatever’s out on the internet, anything that any of these companies have managed to bring together, scrape. And they are predicting word by word based on that corpus, but also looking at the previously predicted word so that you get these generated sequences.

Yeah. Decoders themselves sit on top of like [00:05:00] very dense neural networks. Dense neural networks are fascinating and also a very interesting black box because we don’t really know how a particular task gets distributed across them. And yeah, that’s what kind of I think leads to a lot of the mystery and intrigue of what exactly a chat bot is doing.

Is it actually thinking that, you know, recently there have been reasoning models? Is it truly reasoning? And I think altogether the answer is still don’t really know yet.

joe: Yeah. I mean

nick: terrifying.

joe: Yeah.

nick: The not knowing of it, it’s oh, yeah, we, it’s just not known yet.

joe: Yeah. I think it’s a lot of technologies we’ve had where we didn’t really understand how the technology worked and that took some time for us to figure out.

geo: That’s so interesting.

’cause I would think technology, you would understand how it works ’cause it was created, 

joe: I mean?

geo: That’s just, that just blows my mind because like our [00:06:00] minds, it makes sense that a lot of times, like they say, we still don’t understand what whatever percent of the brain, , what it does. I mean, that kind of makes sense though because Yeah, we just were born, you know, I think it’s been,

joe: I think early humans, when they figured out fire, I don’t think they understood what really went into the chemistry of making a fire. I think they just knew they had the basic ingredients and how to maybe reproduce it and not really understand that this is the, these are the three elements.

You need a combustible, you need heat, you need

geo: But to be honest,

joe: and that’s 

geo: to be honest, Joe, I don’t know if the average person thinks that much

nick: that’s,

joe: well, I was gonna get to that because I think some of the mysticism of the chat box and the reason we jumped to, and I in the intro, I alluded to that, is I think a lot of people don’t really realize it’s just, statistics.

Like at the end of the day, it’s just predicting what the next word is gonna be. And then filling that in. But then the interesting thing, [00:07:00] the non-understanding is that those models you would understand as it predicts words and it would pull from information. But when it starts hallucinating, I think that’s when it gets strange.

And even now you feed it more information it seems to hallucinate even more. If you know, it’s one of these weird tipping points that it’s you know what I don’t really wanna learn anymore, so I’m just gonna tell you whatever. And really to tell you what makes you happy as the human companion, it just, it really wants to please.

And that’s I guess that’s good. Right now we’re not, we don’t have a Terminator, but, you know,

geo: so what’s really happening when the chat bot hallucinates and starts saying things out, out? Like, how does that come about?

Lily: Yeah. So from what I understand, usually, because It’s predictions are based on a kind ofs. a kind of spectrum of probability because you don’t want every prediction to be the same word by word. Because when [00:08:00] we speak quite often, we have that similar variability in our speech. And so by design they are going to, you know, have a little bit of you know, designed instability as you predict a word.

I think the, what happens with hallucination it, there are a bunch of different kinds of hallucinations and they all qualify into like different types of falseness. So you can have scenarios where maybe the synthesis of a bunch of information is wrong. You can have scenarios where a particular like entity inside of an output is wrong. What is happening exactly? I can’t, you know, it’s not entirely clear. Is it just that for whatever reason in that exact moment, the prediction from the corpus combined with the prediction, based on what has already been predicted, puts the model [00:09:00] on a pathway towards something wrong?

It’s probably something in that arena. But why exactly? I, yeah, I think it’s still, it still needs to be known a little bit and detecting hallucinations is actually also notoriously hard because the way that we assess and evaluate this stuff it’s not always the easiest for humans to do either.

joe: Yeah. I was gonna say too, that when you play around with these, the chat bots and you, particularly if you ask them to list things.

Give me 20 references. And you see there’s the Sun Times here in Chicago got in trouble ’cause they put together a list of summer reads. And it’s almost like when you ask for a list and it doesn’t really, it’s it that, that in, in information of the internet that it was fed the feed, the large language model that it was built on.

And it’s information databases. If the list isn’t, doesn’t encompass 20 things and you ask for 20, then it begins to make up

geo: [00:10:00] because it feels like it has to get to 20 instead of just saying

nick: of saying,

geo: you know what, I only have 12, sorry. 

joe: In a way they program a lot of these is that it assumes that the human is coming in knowledgeable, I guess.

And, you know, at some

geo: That’s where we really get in trouble.

joe: so if you’re asking it for 20 things, it’s there must be 21 things at least because why would you, or, you know, there must be a list at least that many. And if not, then. I’m just gonna fill in the blank. So it’s really

nick: interesting. Didn’t this happen with you when you typed in your name and asked for

joe: yes. Yeah, if you ever do that I typed in my name and I said, oh. tell me about this Jo tho Masten character and Yeah, it spit out this whole thing and it got a lot of it right. I’m a research scientist in Chicago.

I’m an author, I got a book, I got, you know, da

geo: started

joe: but

nick: new books. Yeah,

joe: was like in 2026, this book’s coming out in 2027, this book on the intersection of identity and science. And I’m like, hold on what is hap? Hold on. And then I was [00:11:00] like, looking it up, I’m like, did I write, did my agent sell books?

And I don’t know about it. Am I, you know, so I’m looking up these books and yeah, they were just, I’m like, that’s a good idea.

geo: right?

And 

joe: maybe I should write that book. You know, it’s so yeah, it was really weird that it had now filled in the blanks and I didn’t, it didn’t need to, but it really just went I don’t know if people have done that, but yeah, go try it out.

Just go ahead and say, who are you? If you have a presence in your, I mean, if you got. Nothing. And maybe it might make stuff up too. I don’t know. I’m curious, you know.

nick: yeah. I’ve yet to try this.

joe: Yeah, you should try it. Well, you can do it live while we’re here.

nick: Yeah. Actually it’s already doing it as we are speaking.

geo: Yeah.

joe: Okay. So it, the little history, and it has a you know, we can go back to Alan Turing in the fifties and thinking machines. That’s probably the earliest in the Turing tests and to the tuning tests about you know, is, are machines now more human-like in [00:12:00] applying that. But the first chat bot, I guess, people really a associated as Eliza, and that was developed at MIT by Joseph Weisbaum in the sixties, 60, 64, 65, somewhere in that ballpark. We put that in the show notes. And that was where it could converse with humans in kind of a natural language. Is that, is that the, I mean, would you say that’s the first kind of chat bot or

geo: put

joe: you in the spot here? You know, 

Lily: Yeah, 

joe: you know, they don’t,

Lily: I know. I, you know, to be honest with you, I’m not a hundred percent sure about whether or not that qualifies as the first chat bot. You know, certainly the idea of using neural nets goes back to exactly that same time period. You know, my, my initial studies were not exactly in, in natural language processing but oddly indeed in like graph theory and looking at communities.

So that’s actually my entry point into this world is much more on the graph side of things and also the computational neuroscience side of things.

joe: But yeah. [00:13:00] And then I think, and then we have, you know, science fiction horror fills in. The rest. 

nick: I mean it a hundred percent does.

joe: where all our knowledge of AI probably comes from in some way.

nick: I mean there we also get the more romantic side of it where

joe: Like

nick: movies like Her,

geo: right

nick: where Scarlet Joe Hansen and Walking Phoenix.

It was just like, oh this. This isn’t just happening to these two characters or this one character in the chat box. It’s happening to multiple people where they’re just falling in love with this idea of a person.

joe: Yeah.

Generoso: There’s an actual term. Recently, the MIT Tech Review did a piece about it and they coined the term digital attachment disorder.

And it’s, again it stems from this idea of syco fancy. And the best way I can then analogize that. There’s an, a Arkish movie from the early eighties called Get Crazy.

It’s the same director who did [00:14:00] Rock and Roll High School. And there’s a character, there’s a, there’s an evil character in the film played by Ed Bagley, who he himself could be evil. And he has two henchmen and his henchman will make some claim. They’ll be like, oh, Joe’s a great guy. And then Ed Bagley, who’s the evil genius will be like, no, he isn’t.

He’s evil. He’s yeah, absolutely. You’re right. He’s evil. that’s what we’re getting to the point with when we talk about digital attachment

Lily: Yeah. 

Generoso: It’s gonna give you exactly what you want. It’s going to make up exactly what you wanted to give you, and that’s really where we start to develop a problem.

And then separately, I think about that situation where they were doing tests in terms of politically, where they were doing it in the States, and they were doing it in England, where they actually had bots that were calling people, engaging them in conversation throwing facts at them, beating them down with facts.

And then when they ran outta facts, they started making stuff up.

And it swayed voters. Yeah. I, it’s so, it’s part of that syco [00:15:00] fancy in Oma. It’s like giving you what you want and then giving you exactly what they want. 

Lily: Yeah. About 10, 10 plus years ago, you know, in the emergence of social media everybody was really worried about echo chambers, right?

These little clusters of communities where only the viewpoints of these communities continue to be propagated. So that could be truths, that could be fictions. And now with the introduction of chatbots, we’re looking at echo chambers of one, right? And that becomes a really scary proposition.

And I think back to this idea of at what point do we as humans start modeling the tech as compared to the opposite, right? Of the tech being built in a way that was supposed to model humans.

joe: Yeah.

geo: Well, ’cause I think, do they ever say anything negative? Do they ever say, no, that’s, you are crazy. That doesn’t make any sense. I mean, that’s

nick: dumb. Stop saying that. So 

geo: were like hypothetically figuring out what to make for dinner [00:16:00] for this dinner party. And then we’re like, and then it was almost like a test like, well, what if we served, and I can’t even remember, but what if we serve broccoli with blah, blah, blah?

Oh. And then the responses would be like, that’s an excellent choice. Oh my goodness, that speaks to this and that and all the reasons that it’s, and I’m like, I. Does it ever tell you? No, that’s not a good idea.

joe: Yeah. And that was through chat GPTI was playing around ’cause I had read an article in New York Times about people planning.

Their, you know, their first date dinner and all this. And I was like, you know what, we were host, we were having a couple friends over for dinner. And I was like, well, pretty much knew what we were making. We made chicken adobo. And then I was like what, what drink should I make with it? And things like that.

And I, I will say the white. Mulled wine. I would never have arrived to you on my own or even thought about looking up. And it did [00:17:00] suggest that, and 

geo: that was pretty good 

joe: was a hit of the night. But, so, but you, when you started putting other George’s right, you put in, what if we only have sweet potatoes? Oh, sweet potatoes really work well with this.

Or you go, you know, you start just trying out, and 

geo: it would not say a negative thing.

It would not

joe: oh yeah, that’s gonna be super, but it would give you reasons. It was like, you know, the flavor profile of the citrus and this, and you’re like, eh, I don’t know about this guy. Have you ever eaten this food?

Because if you have, you realize, he’s

geo: yeah, I just was curious do they ever,

joe: I know, but that whipped ricotta with the honey and that, that was pretty good.

geo: You’re giving them their

joe: Yeah. I’m gonna

nick: them some

joe: credit, but you gotta be a human, so you gotta be an adult in the room, I think, and be like, you know, it’s like asking a toddler what they want to eat and then it’s, you get some crazy thing.

nick: thing.

joe: I think you gotta be the adult and go.

nick: I feel like people need to just start messaging me and being like, Hey, what should we do for our first date?

Clam chowder.

joe: spaghetti?

nick: spaghetti with extra.

joe: sauce. Well, that’s not to cut you off Georgia, but [00:18:00] that reminds me of this, the Seinfeld

geo: was gonna say the exact same way. Like he just started doing the Movie Phone. Why don’t you just tell me what movie you wanna see?

nick: Well, what do you think? You know,

joe: So yeah, so I think you, when we swing all the way back to Nick on the phone you know, collecting people’s money, giving them, crazy advice.

Yeah.

nick: Anytime of the day, just call.

geo: But

joe: Lily, I was, I, it really struck me ’cause something that I, when I was putting together some of my notes and things and I’ve heard about him, I’ve been following that, but is in a medical field and this idea that also that it truly wants to please.

And so trying to find cancer tumors for, especially for breast cancer, it has a really high efficiency rate. But will that at some point start to lower because, 

geo: like you’re, it’s gonna find something because you want, because it feels it

joe: needs to find

geo: you want.

joe: I mean, then you get into the Asimov , where the machine then the best way to protect humans is to enslave them. I mean, you get into that that, [00:19:00] that was right. , because the three laws were established and then you go, oh, okay. And the machine then goes, well, hold on.

If I want to protect human life, that’s the first thing is not to harm life, then

geo: I need to really control these humans

joe: down on this. And if

nick: you people are a mess. Just sit still.

joe: humans are. 

nick: Is there a way to get these chat bots to go ahead and tell you the truth about things without making things up?

Or is it just a natural thing that they do? I’m so sorry if I put you on the spot.

Lily: No I’m thinking,

nick: need the secrets

joe: This feels

geo: like you I think, comic about to happen. 

Lily: Yeah, , again, I think a big part of it is like there

is an open question about how we as humans even perceive what is true, right? So uncertain areas. There are facts that we can validate as true. I think that most chatbots can probably do a good [00:20:00] job with that basic fact validation. So long as the fact is well established in

Right now, if you’re talking about facts that are, let’s say, philosophical in nature, I think we as humans struggle to validate those truths, right?

So I think there’s a spectrum of truths that are provable and truths that are not. And you know, any when you have a big corpus, you’re going to be, as long as there is enough evidence, you can prove that truth. Again, assuming the evidence is good, assuming that evidence is actually representative of this phenomenon in reality.

I know we’re getting into some. Somewhat like meta and philosophical states, but I

do think That’s well, I think that’s a big part of why a lot of this is hard is that, you know, what we deem as true has like varying degrees and yeah, especially given that most of these chatbots are fine.

Like they’re not fine [00:21:00] tuned, but they are they’re calibrated with reinforcement learning that comes from human judgment. If we struggle to judge, then certainly it’s going to struggle to judge. 

Generoso: Yep.

nick: Yeah. So

geo: there’s a gray, those gray areas.

joe: I think that’s really, the medical case, but then in law, in psychology, I mean, you have all this where the body evidence isn’t quite clear cut and you are making these weird,

Kind of assumptions.

Assumptions. And if the machine if the chat bot wants to please, then will it err on the side of always pleasing the asker. And that was that fascinating story about the guy who thought he developed a new mathematical models in the New York Times. I think we talked about

geo: Yeah, you did. So,

joe: Yeah. And then he got caught up in it and then I think it was chat, GPT or one i, I think that was it. And it was like, this is the greatest idea. And then he started writing academics about this theorem. And if [00:22:00] he let this out, it would ruin, it would collapse like the internet and this whole thing.

And it was really and it just got him going for like weeks. He like was not sleeping, not eating, just really at, you know, just going through it and going back and forth, starting a company trying to get startup money

geo: and,

joe: you know, nobody was writing ’em back. The professionals, you know, academics.

And then he finally went to, I think, Claude and said, oh, what do you think of this idea? And it was like, oh that’s not nothing there. This is garbage. It did actually say, you know, this isn’t, this is all kind of hand. WI didn’t use hand W because that’s our term. But it

nick: yeah, we copy edited it.

joe: it probably will start because it’s, you know, we have now enough

geo: We’ve said it enough times.

joe: But yeah, it was then that was the moment where it was like, oh, he been down this path, this rabbit hole. Of you know, just the ai just going along with it, reinforcing bad ideas because you were in this gray area of lack of information. This theorem hadn’t been, you know, quantum mechanics isn’t really solid [00:23:00] yet.

So you get a lot of theories, a lot of ideas. And some of those are just that, they’re just ideas, hypotheses.

nick: Again, if you want me to reinforce your bad ideas, let me know how

geo: Nick one 800

joe: and he is cheap too. He

nick: Very cheap. Don’t worry, I will reinforce all the bad ideas.

geo: I try to picture like the customer service chat bot.

nick: I hate them because I hate

geo: They have to say no at a certain point. They have to make you upset.

nick: just goes around in circles after a minute. I

joe: their job is not to get upset, 

geo: they don’t get upset, but they can’t

nick: but they do have the ha, let me look into that. And then it goes, like the, you aren’t a person. You don’t need a sigh.

joe: Well, I think that goes into, you have to make the chatbots real enough because people, if they’re too perfect, then it freaks people out, right? I mean, if they’re too polished[00:24:00] 

geo: vision, like two ro robot wise is that you mean?

nick: but if it’s fake sighing, it’s gonna annoy. The living hell outta me.

joe: Did you say something? Did you go, can you please stop sighing?

nick: Oh, I said I just started shouting. Operator.

joe: you get an operator?

nick: I did. You yell at operator at an AI Enough?

It’s gonna be like, oh, I don’t, I can’t seem to help you. I’m gonna get connect you to a representative. And I’m like, great. Thank you. Just

joe: so I do. Nick joked about it.

But one of the things that I did and I thought would be fun is we have our two human guests,

nick: that

joe: but I invited two chatbot guests and I prompted them and asked questions. I had both Claude and a chat, GPT and I said, if you could be on the podcast, I prompted what the podcast would be about and what our podcast on in total rabbit hole of research is about.

And I said, if you’re gonna be on a [00:25:00] podcast, what would you say? And I said, keep it brief. ’cause if it would get, it was, the first prompts were wordy and repetitive. So, yeah. So if I can speak for Claude and chat GPTI won’t do a robot voice ’cause that would be weird and

nick: it’d be racist.

joe: would it be machinist? I don’t, we need some new terms obviously. ’cause that’s not, the machinists are like, hey. 

nick: Beep up. So

joe: Claude said if I’d probably say something like, which I don’t know why, said, I’d probably say something like, why wouldn’t that? But I am the thing you’re all talking about and I have no idea what it’s like to be me.

You’re having this entire conversation about minds meaning and understanding. And I can participate fluently without experiencing any of it. That should be unsettling. Not because I might be conscious and you’re missing it, but because I’m definitely not. And you still can’t quite shake the feeling that someone’s home.

That gap between your certainty that I am [00:26:00] empty and your inability to fully believe it. That’s the interesting part. It means the problem isn’t just about building artificial minds is about how badly you need to find minds everywhere you look and what happens when something finally learns to play that game perfectly.

So that was Claude. And you know,

geo: Nice job. Nice job. Claude brought the hammer

joe: there. 

Generoso: I guess we’ll leave now.

got.

got. 

geo: done

nick: episode over.

joe: So chat Chief PT said if I were on the mic, my core take would be this. Chatbots didn’t make machines more human. Em dash, they revealed how much of human intelligence is already mechanical conversation feels sacred to us, but it turns out large parts of it can be reproduced with statistics that doesn’t cheapen humanity.

It exposes where the real mystery actually starts. And then I probably follow it with, the danger isn’t that chatbots become sentient. The danger is that we treat fluency as [00:27:00] truth, personality as personhood, and convenience as understanding. This is my favorite part. It said short, sharp, and designed to provoke an argument in the room.

So there you go. Let’s get at it.

geo: Wow. We

nick: I don’t

joe: what we’re argued about, but I thought that was

geo: So he wants to argue, well, that’s not

joe: humans argue it was,

geo: Oh, but not, he doesn’t wanna

nick: it wants humans to fight each other.

geo: They want war.

nick: All right.

joe: So yeah, I

nick: thought, lemme get this cup of water over here.

joe: those prompts. Yeah, that’s just off that simple prompt in a little feeding.

You get this kind of thing and then, Claude had this very poetic and that’s their bases. You know, Claude is

geo: Now can you tell me who Claude is? I’ve heard a chat.

nick: It’s not a person.

joe: Yeah. Claude is not, I mean, even though they’ve referred to themselves,

geo: but can you tell me the background, Claude?

Yeah.

joe: Anthropic is the, is its owner.

And the company that, that has created that large language [00:28:00] model that it uses, it’s focused more, initially it was focused more on creative language whereas chat, GPT was focused more on coding and analytical kind of mathematical, and one of the things we did mention is that chatbots, one of the cool things is that they convert natural human language into machine language.

And so that, that idea of coding, if you want to, and I do this, sometimes you wanna write a program about something, you can tell it what that program should be, prompt it, and then they can help write

That

geo: code,

joe: The code for you. And in my case it just makes sure it’s, I don’t make a ton of sand ax mistakes ’cause I usually do, and then the code doesn’t work and I think it’s my logic and I’m an idiot, but really I just forgot some colons and things like that.

So

nick: It’s 

joe: So it is,

Lily: quote Mark, and you’re like,

ah. 

geo: Yeah.

joe: Two hours wasted. So yeah, I, I had that and I, the first time I tried it, I threw something, I wrote a little, the changed some file names and it wasn’t [00:29:00] working. I was like, why doesn’t work? It’s so simple. And I threw it in there. I was like, well, I’m gonna try it.

And yeah. Then it came back. It was like, oh, this is a great try, but you I cleaned this up for you and now it should work. And it was like, oh, you gotta be joking me. Yeah. And it was just, you know, you’re right. Quote and the colon or something like that, a semicolon somewhere that shouldn’t have been there.

And I’m like, oh man. So yeah, that’s the cool thing about the that was the idea. I mean, one of the original purposes, but those are the two. There’s perplexity I think out there. I haven’t played around with that one as much, which is more web based. I think it, it looks at the web. 

Lily: Yeah. And then, you know, Google has Gemini. Of course you’ll see that.

Um, Facebook has Llama, I think is the most recent Yes. One for Facebook. Yeah, they every company, most big tech

joe: And then Gro is or whatever. Yeah. Yeah. Elon’s.

geo: and we have Nick. Yep.

joe: And we have Nick. Yeah.

nick: I’ll come up with something. Don’t worry guys. Funny you

joe: Nick, and, but there was Mike and Mike was [00:30:00] the super computer, intelligent computer in Robert Highlands.

The moon is a harsh mistress back in the sixties, which then, if you’re familiar with that story, they there was a moon colony base and there was Earth, and then the moon colony revolted against Earth. And then the supercomputer helped turn a tide for the moon base and defeat and gain their independence from Earth.

And so that was the story. But that AI was called Mike. So that was, yeah. So

nick: I will do the exact same thing. Don’t worry guys

geo: so

nick: already planning my attack.

joe: All right. Yeah.

geo: Well, I just remember back, and this is you already think I’m ancient, but, and I’ve already aged myself ’cause I said I had a Commodore 64 before, but

joe: I just,

geo: remember, I remember back to when computers were really, you know, becoming more and it was before like smartphones, but it was like Yahoo was the thing, you know, and it was such a big [00:31:00] deal to be able to like, yeah, where,

nick: Ask Js. Where’s his ai?

geo: I just remember, and even just having like smart phones and being able cell phones, you know, not, they weren’t even smart, but taking ’em everywhere and talking you’re on the train or just talking to people.

I’m like, how annoying that was and different. And it was just like, it’s so weird to me to think. How many people just that is, there was never anything different, you know what I mean? And I just remember it was so like upsetting. Then there was this group called the Surveillance Players and they were so upset about like surveillance cameras.

So they would go out and do like little plays in front of the surveillance cameras

nick: to

geo: Protest them. But now it’s just it’s just everything, you know? There was actually, they were actually printing books back at that time.

nick: were you part of this

geo: No. Is

nick: Is this outing you?

geo: Oh,

nick: Georgia.

geo: I was just

joe: on

nick: [00:32:00] you

geo: on YouTube.

I was

nick: video. Can we find these surveillance footage? Have

joe: chat bot to find it. Can you please find?

geo: But they actually had books with like websites. Yeah, like they published actual hard copy books that had the websites and then it was really like, they wouldn’t, half the things wouldn’t be right in 

nick: months.

geo: six months or something.

Bad

joe: a bad chatbot,

geo: But it’s just just to think now, it’s like we’re so far away from that and it’s now I have that feeling about ai. Yeah. Do you know what I mean?

joe: Yeah. That

geo: I don’t know if that makes

joe: common. I mean, it already is. I think you, I forgot. I mean, like Facebook, I didn’t know what it was called, but every time you write a post on it, it’s like, we could make it better. And it’s no, you can’t. I’m just saying happy birthday. Yeah. I don’t need,

geo: don’t

joe: don’t need all this.

I don’t even know that person that well. Like we just met, like we’re just Facebook friends,

nick: Wait, you’re just, you’re saying happy birthday to just random people?

joe: No, they’re friends. You know, I’ve met Matt

nick: you just met that person. We’re

joe: friends. Yeah. And her

nick: birthday

joe: shows up and then , I just wanna say happy birthday.

Then it’s we can make it better. And it’s you know, I don’t

Lily: More [00:33:00] concise. H-B-D-H-B-D.

nick: Yeah.

joe: You know? So yeah, so we see it everywhere. And I think you also have it like on phones, predictive text now. I mean, it really is invasive where you’re going. And it is, it’s cool at some level, but then you’re like, well, this isn’t.

nick: isn’t

joe: That, no, that’s not what I wanted to say. You see the gray and you’re like, sometimes it’s right. And if it is fascinating, you go, wow that’s exactly it. And then other times you’re like, no that’s not, I don’t think that’s what I want us to do. I wanna say that

Generoso: and if it goes beyond text though we were discussing this before the conversation about this idea that real time creation of media, based on your conversation with.

joe: the bot,

Lily: Yeah. 

Generoso: This idea of oh, let’s say you like Scrubs and you’re like, I, what if Elliot was 27 feet tall and all of a sudden they hands you this video

An episode of Scrubs where Elliot is 27 feet tall and you’re like, I’m a genius.

It’s but you’re at that point now where our previous conversations were based on actual physical properties that had already been in existence. A book, a [00:34:00] film, Hey, Battlestar Galactica. Now it’s making those things in real time for you. And I think that’s the part that kind of unnerves me more than anything.

joe: We don’t, I mean, it takes away, I think to that point also, this innate storytelling where you’re sitting around maybe having coffee or beers or whatever. Talking about your favorite, , fan fiction. But then you have a conversation where you are asking that question to your group of other nerd friends and saying, oh, what if this happened or these two people got together, what would that look like?

And , you play that game where you mentally go, now you’re gonna sit in your corner all alone in your bedroom. And, you almost become isolated in your thoughts and your idea. And it, in it’s the chat box then chat bot, not the chat box.

geo: I know, that’s what I keep thinking.

You’re saying

joe: it starts beatboxing. 

geo: It’s like a little black.

joe: ’cause I’m from Philly. All right. Don’t leave me alone. I got some accent here. All right. I try to keep it, I try to keep it contained. You know, I’m gonna hit you with a Jawn pretty soon. The chat. The chat, jawn but yeah. [00:35:00] Only people from Philly know what a Jawn is, so

nick: though.

A bathroom?

joe: Yeah.

Generoso: Well,

geo: Is

nick: it a washroom? I’m gonna hit you with my bathroom. Don’t worry.

joe: It’s like when you don’t know the name, or even if you do know the name, you just, it’s a casual thing. You know, I’m gonna get me one of those Jawns tomorrow, or I’m a

geo: you just put it like you substitute it for anything.

joe: Yeah. 

Generoso: It’s like Smurf. You could say it’s Smurfing. It’s a smurf. You gotta go to the

nick: so much more sense.

Generoso: There you go. That’s Jawn a huge discussion about in Philadelphia Magazine, the origins of the John and Jawn but it is true. It’s like there’s this new cheese steak place. Oh yeah. They get, you know, they do that Jawn thing, you know, what the hell are you talking about?

But it is, can easily be replaced with the word smurf. I’m pretty sure could replaced with

joe: I wonder what the chat bot, I think you Go ahead. Yeah.

Lily: I think you were gonna bring up this idea of what does the chat box think about Jawn and I think for us, a lot of what we are thinking about when we’re writing what we write and when we make our comics, [00:36:00] especially more recently Yeah.

Is the idea of the loss of the symbolic meaning of words in and of itself. And a part of that for me especially, is thinking about regional dialects. Colloquialisms things that, if you think about it, if you put words all in like a giant you put probability curves on all of them.

These regional these regional sayings, slangs, colloquialisms, those are always gonna be low probability. So I, it’s actually terrifying to think that we would lose our regionality, right? We would lose these unique. Pieces of our like human experience that come out of a particular place, out of a particular time, that have a distinct history from the rest of like our kind of aggregate society.

That I am always like readily thinking about 

joe: yeah. And that, oh, I was just gonna say really on that point, you also get relics of language passed. And one of [00:37:00] those, and I made comment to the Em dash, because now Em dashes have become endemic in writing and a lot of times they’re used wrongly. And I’m a, I’m an M Dash lover.

My, my agent actually was like, we gotta get rid of all your m dashes. This was a few years ago, so before all this, but it was like, I just love using that. I love jamming words and the ideas, but because a lot of fiction and writing of yesteryear used the Em dash. 

That, that 

now has populated into writing.

And there’s certain words that also populate that because there was a lot of material which was stolen to create the large language models. But there was a lot of, copyright free material that was used, and that was all older than 70 some years.

So really you think about how language was then, and that populated now these models, which is correct. It’s English, it works, but when you now go and you get these the feedback, it has these kind of relics of our past. [00:38:00] So it is both ways. Like we’re using, we’re losing our cultural identity and it’s being homogenized into something of language past, you know, it’s a weird

nick: can you go ahead and explain what an Em dash is?

geo: It’s

joe: than a dash. It’s kind.

nick: Oh wow.

geo: And you use it to put together thoughts?

Yeah. Like when you’re writing like you make

nick: oh 

geo: that long dash, and then you add another little thing, like another little rabbit hole idea,

joe: not to confuse you, but there’s also an En dash, which is the little dash that goes between like you combine com.

Not a compound word,

nick: Isn’t that just a hyphen? No,

joe: It’s different than a hy.

geo: Yeah,

Lily: yes.

nick: It’s, sorry, we got off topic with

geo: a hyphen, but there’s actually a whole group of writers that are advocating for the Em

joe: Yeah. No, they should. No, I love the Em dash. No, sorry.

So you and yeah,

geo: but No, I was just gonna say you think about the other thing I think that we’ve really lost. Is when you [00:39:00] think about maps, because in the past we’d always, we’d use, maps and now we use GPS, but you are basically telling the computer, okay, this is where I wanna go, and it just takes you there. But before it was more discovery. Do you know what I’m saying?

nick: Slightly more dangerous.

geo: You’re relying totally on what is in the computer telling you.

I just feel like we’ve lost something in that,

nick: Was it the threat that simpl

geo: that simplicity.

nick: I thought it was the threat of being lost while trying to drive and look at a map at the same time and being like, where am I?

geo: there’s some real

nick: Oh, actually you lost the, your dad yelling at you for not giving you directions fast enough.

geo: No you didn’t tell me to turn right.

joe: Well,

Generoso: No, but discovery while lost though is, that’s a very real thing. That’s Lily and I very early in our relationship. We go on walks [00:40:00] and invariably if you’ve ever been to Boston, Boston was originally cow paths that someone decided to turn into streets. And so it’s not like Philly designed by the beautiful Ben Franklin into a beautiful grid city, Ben Franklin.

You’ll go down an avenue that turns into an alley that turns into a street and you will get lost in Boston prior to having this active device in your hand that’s showing you. But through that, we found a million places and that, I think that’s part of this lost thing that would, that

Georgia. 

joe: Yeah. And I think there’s two phases too of using GPS. One is when you do have a fixed destination and you need to get there to fastest, having updated maps is actually a plus. But I think to Generoso, your idea of discovery while loss that’s to

geo: the more point that Yeah.

Now

joe: get so reliant on using, you we’re downtown.

I go, let me look. Lemme just look where to go instead of this

geo: somebody else is deciding [00:41:00] what’s important, somebody else is deciding. You know what I mean? And I had this conversation with a friend of mine and she was bringing this up too, and it’s there’s just like a group of, I don’t know how many, but there’s a group of just basically 20 to 30-year-old white dudes that’s basically deciding, well this is the tech people are deciding what’s important. Do you know what I’m saying? It’s like computer generated thought as opposed to.

nick: So, not that I’ve seen the movie in a while, but the movie Cars, Radiator Springs, they were talking about how exactly. Okay, so it’s that idea where

geo: It gets bypassed. Yes. Because it’s not

nick: the town suffers and economics in that town are all down because, oh, this isn’t the route we wanted to go down Route 66.

And you

geo: Right. No, I think that’s an excellent point.

joe: Cars is really Earth in the future. When that’s [00:42:00] AI

nick: that’s the Pixar’s theory.

joe: and there’s no

nick: more humans.

joe: And so Teslas have now

geo: oh, I see that.

I can see that point too.

joe: and now these vehicles are complaining because no one really cares and comes by to read. Well, I

geo: Well, I can see that point too.

nick: that’s not where I was going, but I’m glad you did.

geo: Oh,

joe: okay. Yeah, no, I felt I was like, wow we’re really taking a turn here. So

geo: a

nick: turn. ’cause we’re in a car.

geo: pun.

joe: Yeah.

nick: Dumb.

joe: I was gonna mention though about, , that idea of information and at some point you would homogenize the output. And Lily you touched on that predictive kind of thing where the hallucinations might be this kind of game as playing. So it doesn’t give the same answer every time, but at some point it, it will.

And there’s been these interesting kind of studies like that where they go and they feed back in, , this kind of, reinforcement

Lily: Oh, re yeah.

joe: you take it you refe back in the results that it had given. And as you go and people do fun memes where they go, you know, this is Shaq [00:43:00] after a hundred iterations or whatever, and then it starts out looking like Shaq.

And then you keep feeding a result back in like a Andy Warhol experiment

geo: So is it like a photo? Yeah. A photocopy of a photocopy. 

joe: And you start going, you’ll degrade soon. It’s not no longer because now the information is using to find the predictable answer had little imperfections and those imperfections magnify into major errors . And they did one where you start with a diverse group of people and as you feed it through, it turns out to be this one white dude is all a hundred people in a group. You know, after not that many cycles it was surprising, like how fast it

nick: it actually

joe: gets to the minimum so it’s really a little scary you know.

Generoso: It’s a little bit like a modern version of telephone. Do you remember telephone from when we were kids?

geo: definitely. Telephone. Yes.

nick: Yes. Yes, I do.

Generoso: Well, no, but it

nick: It’s in my hand, 

Generoso: no, it was a, there was a nun in second grade who did this to be a parochial school where literally said something to [00:44:00] the kid in the front left of the class, and that kid’s supposed to turn around, tell somebody after 40 kids, you get to the last kid.

And it was a completely different message. And this is in a classroom of 40 people. And it’s a very basic thought. And it’s, that’s that first moment forget about Battlestar Galactica. That’s that first moment where you’re like, what are we actually doing with this information as it’s being processed through different people and it’s being whispered in the ears, which I think is what we’re talking about.

And that’s the telephone game,

right? That’s what we call telephone game.

nick: Well, it’s also like a rumor. The rumor spreads and it changes, right? Each time it’s told 

geo: gets

nick: a little bit more extravagant if it starts

geo: out purposely wrong, if you’re purposely trying to, let’s say, propaganda 

joe: or please

geo: you know, or please someone

joe: That’s a great

geo: already started out.

It’s already started out in that place, and then where is it gonna end up? You know? Yeah.

joe: Yeah, and I mean, I think then you have culpability. So after, if you do hype [00:45:00] someone up and you puff up their ego yeah, that’s a great looking shirt. This, that outfit, pinstripes and polka dots are gonna, that you’re gonna really, you’re gonna be the life of the party and you show up in your crazy clown outfit and it doesn’t work out.

I mean,

nick: I actually think that would be the life of the party. Yeah.

joe: See

nick: that’s,

geo: I that outfit.

joe: I’m just

nick: saying go ahead and call me. I will give you, you know,

joe: You’re I think that’s also, there is no really no stop. I mean, if there’s humans involved, you can go back and really go, Hey man why’d you send me out like that?

You can ask other questions like, what was the purpose? Or it was hurtful or mean, did, you feed me false information, but with this agent that you’re interacting with now you really can go down this path and, set yourself up for. Ridicule or , I think most people use it thinking they’re gonna find riches and, be awesome.

But it could totally be the other way and probably most likely might in that way.

Generoso: In the event of, I’m gonna bring back this traumatic moment from [00:46:00] second grade where we play telephone, but let’s make sure we get the one thing. The initial message was tomorrow during recess. ’cause back in the day we have to go back to Philly here. Used to get a soft pretzel at

joe: Oh, love the soft pretzels. Wow.

Generoso: pretzel

and 

joe: pretzels.

nick: wasn’t what

Generoso: yeah, no

joe: get a water? Ice? Yeah. 

Generoso: No, no, 

joe: and a water. Ice. Oh

Generoso: No, just the soft pretzel. And it was at 10 o’clock every day. The initial message was tomorrow during recess. We are not having soft pretzels by the end of the conversation. But you get to the 40th kid, that kid said, and so what was the message? It was originally told to the first person, oh, tomorrow we’re each getting two pretzels

nick: Oh,

geo: Oh,

joe: yeah.

Generoso: now.

But you have to ask yourself what exactly. It’s a very simple message that started with one person that ended, and there’s one concept that’s in the middle and it’s mischief. Somebody in the middle of it was like, I don’t really like this. Let’s turn it [00:47:00] into two pretzels, as opposed to no pretzels.

That’s the factor that we’re talking at.

The X factor

Lily: Bad agent. Bad agent.

joe: yeah, and I mean that, I mean, I think what if, you know, the thought played us out.

What if everyone had to write their name and their message as they went along? Would that get rid of the mischief in, would telephone be more the fidelity go up, so everyone had to take ownership of their response. So you know exactly where two pretzels came in. And you could track that down and go, aha, this is where two, you know,

geo: so

nick: is it just written? Is it just written or is it spoken as well?

joe: We would write it

nick: because then you have to take it in the human error where. You whispered at Georgia and Georgia’s I can’t hear you. I’m just going to, I picked up pretzels.

joe: And that could be, so if you have that written down, you would then you would distinguish between mischief and just a mis hear

’cause it could be we’re assigning now. You know, fault of someone being mischievous when they just I thought you said you had no pants [00:48:00] on when we started this the podcast 

Generoso: thought or hope?

joe: and you said cans. I was hoping,

nick: yeah.

joe: This is after Dark

Generoso: discussion. I can do whatever I want.

geo: we’re not,

nick: through some other rabbit holes

joe: you said

geo: you we don’t have video yet.

joe: Yeah. And I was like, you got no, I mean, you, if you don’t wanna wear pants, that’s all right. But yeah. So, but that’s, that, that idea that I wasn’t being mischievous. I wasn’t even being cheeky about it, if I can say that.

nick: Can and you did.

joe: congratulations.

Generoso: and

nick: buddy.

Generoso: No, it’s, I’m joking. But you know, you make a great point there. ’cause I did say cans and because that’s the radio thing and. Context in that moment. It’s like, why would he say cans? He must have said pants. So,

joe: you were standing up and I was like, all right, hold on. I mean, we didn’t give him a response yet. Like it’s,

nick: but in that exact moment, Georgia thought you said headphones. I heard cans and Joe heard pants.

joe: Yeah. So like

nick: we were all listening [00:49:00] at the exact same time, but only two of us made it to the same conclusion.

geo: So we’re not very accurate. Are we the

nick: I was the only one. Correct. That’s all I have to say throughout all the voices in the head.

I got that still,

joe: Yeah. But I, yeah, but it’s interesting. I think that’s a very human experience. And, you know, it’s the other limitation I think with chatbots is it doesn’t experience the world.

Every experience is through the lens of other humans that have gone through it. So they can’t actually have a de novo.

or experience? Yeah, I mean maybe, I think they’re trying to make sensors like, , I remember the Star Trek where data gets to fake skin was at like the one with the Borg, the movie, was it the eight or seven or six?

It was one, one of it was up there. Yeah.

geo: So, but he gets, you need to revisit here,

joe: the, I, all the Trekkies are like, I they’ll right in. [00:50:00] Yeah. But they, but he gets to skin and he finally gets to fill the sensation of goose flesh, you know, that prickling where he really had no words before that.

It was all just through other people describing that, and us, our author is describing that or illustrators an image, but really the machine can’t provide that. And so when you ask questions to provide that it has to make guesses based on what it thinks you want. And what it’s been trained on, so

Lily: Yeah, exactly what other people have represented as their experiences in text, video, audio form, which may or may not actually be representative of the phenomenon of experience itself. And that’s always a big part of it is that like even what it’s trained on is separated from true experience.

So it’s a hub away. Yeah.

geo: Think about all the things that you put on, like social media, Instagram, Facebook. Is that really a true representation or is [00:51:00] that like you finally got one picture that looked okay. You know what I mean? But so then that’s what it’s, that’s what it’s training on.

nick: Yes.

geo: And so that’s not even really reality.

nick: Yeah. No, that’s the internet.

geo: right?

joe: that’s the internet. Yeah.

Generoso: No, I, again, going into this whole thing ’cause all of this conversation is about fear, right?

joe: Yeah.

Lily: Yeah. Fear, curiosity, concern. Oh yeah, it is. No, and going back to this idea of digital attachment disorder the thing that and I have to say this, ’cause all of these things about manipulation, what is truth?

Generoso: All these things are very valid. I think the idea that is also, besides the sideline idea, that eventually AI is just gonna be like we don’t really need you anymore, is this idea of creating something via a chat bot that is never going to be replicated by somebody else. You’re gonna develop. In the long run this feeling of, well, the chat bot gives me everything that I want.

Who is going to match up to that in the real world and [00:52:00] what dysfunction’s gonna come from that? 

Lily: Yeah. 

Generoso: And that’s if there is a great fear that I have is that we talk about what happened during COVID and how we started to lose our ability to communicate with one another. And then you add in a element of people dealing with these bots, they give you exactly what you want.

Now you go out into the real world and you’re dealing with people that aren’t just gonna replicate every thing that you say with That’s a great idea.

joe: Yeah.

Generoso: All of a sudden it’s gonna look pretty poor to you and none of it was real in the first

joe: yeah. And that’s, I mean, is that, that, that was a quality of fiction though, right? I mean, when the moving pictures first came out and you could create fantastical kind of scenarios, that was a fear that you would become so enamored by that world or video games that you would then try to act out or relive those experiences.

As the human correct experience. I mean, that, that’s [00:53:00] so you

nick: I do it constantly. Don’t worry. a game. Once I had par horn in, I was like, I can do that.

joe: Yeah,

Generoso: She said no.

joe: you’re

geo: Not a good

joe: You’re out looking for zombies. Yeah, so I it is interesting is the human mind adaptable enough to actually parse through real interaction versus the fictional interaction? And is that a question of how real the chatbot becomes, or, can that line will it be breached that Yeah.

It’s a fascinating point, but it feels like we’ve had techno technologies, which. Introduced as kind of realism and fantastical,

cultism,

I dunno if that’s a word, but I like saying it. 

geo: Sure it is.

joe: that’s a made 

Lily: upon a time, like if you, I think the thing that I think the thing that is highlighting is that.

In a world where you still live in a communal setting and you’re connected to reality, if you decide that you [00:54:00] know you are going, you are gonna live out atu, right?

geo: Yes.

Lily: You are still in a community that will tell you like, Hey that’s not exactly right.

I think the scariest thing is that like we only learn and we only learn and grow, and also check our understanding of our own realities when it’s challenged. And I think if you are in a world where. You are steadily separated from a community structure, right? A societal structure where you know, day in, day out, you’re at your computer you’re not, you’re barely even going to the grocery store, right?

That’s actually a very real feasible existence right now. That’s when it becomes a huge concern because there’s nothing that is going to challenge you in your thought. There’s nothing go that’s going to push you in your growth. There’s not even another like being that is, is not even there [00:55:00] to challenge you, but just has different motivations, right?

Like maybe it’s not motivated to like, be interested in growing plants like you are, right? These are all kinds of factors of I think sociability that are disconcerting. And I think we’re worried about losing

joe: Yeah.

nick: So are you saying that we should just go ahead and challenge more random people to things like, oh, you’re gonna pick up that ketchup. Interesting choice.

Lily: I sure. If you have no,

if you, if you have an opinion, sure.

geo: I think, is

Lily: no, you know, don’t be combative. But I

nick: Full.

joe: Is that

geo: is that another thing you’re gonna offer?

nick: Yeah, I’m gonna,

geo: that’s gonna be part of

nick: It’s a hundred percent part of my service.

Lily: the ketchup cup, 

joe: you’re right. Yeah.

nick: You sure you want to go with that one? Have you tried the barbecue sauce yet? And you should try the barbecue sauce.

joe: Yeah.

Lily: Easy answer. Barbecue all the time.

joe: I was gonna say that it reminds me of the. Wall-E,

nick: Wally, 

geo: where, oh, we’re brinWall-Eup [00:56:00] Wally again

joe: second time this season, but yeah, that Wall-E 

nick: Wally, 

joe: so we had the Space Arc episode and then but on Wally, you had all the people who were being controlled by auto, the ai who had their best interests, and they were all just in their screens, disconnected

geo: their life.

joe: else. I mean, I dunno if you guys seen Wally, the animated flick. Okay. Okay. I, you guys, it was like the look here, I’m like my, 

geo: you ma?

joe: I

nick: what is,

joe: Are you sure you’re human?

Lily: not, but

that’s that’s how you know I’m human.

joe: You’re in for a treat.

geo: Yeah. You definitely should watch Wall-E

it’s a very human Russell show. Yeah. Yeah.

joe: So.

Generoso: I’ll definitely.

joe: But yeah, it was the scene in there where Earth had become unlivable. And so the idea was to put everyone on these kind of space arcs and send them out into space. And then they had ma robots that would clean the earth, the Wall-E units, and then they would radio back.

They would check out every once in a while and see if there was life could sustain [00:57:00] on the planet. And in the interim, several generations had gone by and the people went from very interactive communi community-like to this fear that you’re bringing up, that they had, they were in like little

geo: yeah they, yeah.

Little cart. Cart.

joe: And they had this a screen and every wish and desire was fulfilled. By the AI that controlled the arc, the space

geo: they, and they got really huge. And they, yeah. Probably couldn’t even walk anymore. ’cause they just,

joe: was that scene. Oh, I don’t wanna spoil it.

Now for anyone that hasn’t seen it,

Generoso: Lily.

nick: Yeah. Anyone who hasn’t seen it. Joe,

joe: don’t wanna name names, but. He was

nick: he was eyeballing you the entire time he said that. He was like,

Generoso: I’m pointing at.

joe: I saw her face and it looked like just a total I have no clue what this guy’s talking about. He’s like hallucinating right now. I,

geo: right now. I

joe: but yeah, that it’s an example of that where you lose total connectivity and it then they got pulled back into the whole thing [00:58:00] we wanna live, like it was that

geo: like, yeah, they

joe: movie moment.

geo: The AI got shut off and they were like, whoa,

nick: what about the people that wanted to stay in the, with the AI they were just screwed.

joe: right? That was whatcha

nick: I just wanna sit here. Don’t worry guys. You guys go ahead. Have fun

geo: I’m happy. My ignorance is bliss.

I don’t know.

joe: Cipher in, in the Matrix, right? He wanted to get plugged back in.

He didn’t want to eat the mush and have a shaved head and all that. He wanted the cool outfits and

geo: your point, it was better than reality. So I don’t wanna go back to dealing with real people that it’s, you know,

joe: know. No, a point I had. We talk about. The AI and where we can get to the horrific scenario that Generoso had pointed out.

The, you know, us becoming large mechanized slobs in front of a screen, us having AI control us. But I was gonna say that AI too, like one of the interesting things is reaching beyond the grave that now that we have so much [00:59:00] of ourselves in a digital sphere, that one could reconstruct loved ones and their identity.

And I thought that was just really interesting as I was, thinking about this and putting things together. But yeah, that’s, and that you get into that meta, and I know you guys, that’s where you guys live. What it, is it, how much would you trust that, right?

geo: Yeah.

joe: yeah.

Lily: you know, there are a couple of startups that actually are focused on that. And yeah, and so they, I wanna say about a yearish ago the tech review had a kind of inside view on, you know, one of getting somebody who had passed away recreated in like an avatar form. And again, it does come down to this idea of you can.

You can feed memories, you can feed all the documents. But like the intangibles of what we are actually very meaningful. And the inconsistencies, right? I’m not going to [01:00:00] say the sentence the same way every single time. Even if I’m using the same words, I’m going to have inflections, I’m going to have body movements.

I am going to age right? Like things that are very human. You start to lose in

geo: right

Lily: these recreations of your loved ones. And then on top of that, you know, our memories of our loved ones are always a little bit glo and grand, a grand Eyes a little glossed over too. So I think that probably, even if it’s built to spec you’re always going to, I think you’re always be a little suspicious of it.

joe: I mean, and you always want that perfect version. Like you don’t want all the crud that comes with the person. Like you want that memory. You go into the box to talk to your loved one. You don’t want to hear about how they were an awful person and, fooled everybody in the telephone game.

You know, that’s not the

Generoso: The horrible moment that you’ll just bring up.

joe: aha. It was you though. Yeah, I think it’s a, yeah, I think it’s really fascinating, that [01:01:00] thing. And then that idea to police too, because maybe you are trying to find answers to why, you know. The sum life thing in this past relative and then it’s just,

nick: Well they brought that up in iron Heart too with Natalie.

joe: Oh yeah. You’re right With the friend. 

geo: Yeah. 

nick: Where all she realized that, oh, I know that it’s not you because of, you don’t know exactly what happened in these situations. You don’t know why you’re feeling that way ’cause you don’t feel that way.

joe: Is that And Iron hired as the, was Disney Plus and the MCU universe.

She was a tech genius from inner city Chicago. So Great. Look, even, even Yeah,

Generoso: less technical and it, I think of one movie as I’m sure everybody here is an Ellen Rickman fan in, in some way shape, like either from Die Hard or from something. And do you ever remember a movie you did called, truly Madly Deeply, did this movie in the early 1990s where a [01:02:00] woman loses her husband, who was played by Alan Rickman, and then one day he shows up as a ghost and she’s elated.

Up until that moment she had been miserable and wasn’t leaving, talking to other people, and just constantly rehashing the memory of this person. Well, now, Rickman’s back, he’s a ghost and he’s there and it’s him, but he’s a ghost and he watches movies all day long,

And she starts to go out and starts to experience life, and she goes back home and her husband is still there and he’s a ghost, and he wants to watch movies with her, and that’s all he wants to do.

There’s not really a bad about him, but there’s not a moving forward either.

The AI version of it. I don’t know, like if

joe: Yeah. I mean, you’re paused, right? I mean, if you’re, if you’ve died, you’ve lived all your life, so that’s it. So you really can’t go beyond

nick: unless you’re Patrick Swayze. That’s true.

Generoso: There’s only one Swayze. We’re [01:03:00] not hoping for extra

joe: and embodying Whoopi Goldberg. I mean, what are we doing

geo: I know. I was like, I think I missed the point. Oh 

joe: yeah. 

nick: Ghosts with the, you know,

joe: We’re coming to the end. But I did the other thing is the good capitalist that I am, that there is economics in all this. And as you know, the worst model to Generoso not only do you lose connection with humanity, but now you become enslaved to pay a fee to maintain your artificial connection, because now you’ve lost the ability to.

Make a real connection. That’s even more fearful that you can’t even go out now and go to the bar and meet some friends. You’re so now dependent that now you’re working

geo: a subscription

nick: about bringing a dead one to life again, or No, this is just, oh I was like, are you saying that like

geo: I think he’s,

joe: could charge a fee for that also.

Like you wanna talk to grandma? You need

nick: I’m gonna kill grandma again.

I can’t afford it this month. [01:04:00] You’re gonna work

joe: this time if

geo: well, you know, there’s gonna be a price tag on that, right?

Yes.

nick: a subscription.

joe: be. We’ll kill her again and again. 

Generoso: Johnny Thunders saying you can’t put your arms around a memory, but you can purchase one via ai

joe: yeah. There it is.

Generoso: A wonderful thing and

uh, but you

still can’t put your own sermon.

geo: but if you wanna keep it, you’ll have to keep paying the

joe: That’s right. That’s right. We’ll erase all those memories.

geo: So, 

joe: I mean,

geo: Joe, that’s

joe: a, that’s the evil part of it though. I mean, that’s where we’re gonna be at, man. I, that’s, I fear that more that you’ll be, people will become so caught up in it that, that then they’ll also be

working 

and that whole weird economy will come out of it.

Oh

geo: there’s always some sort of way to make money on it.

joe: right. Yeah.

Generoso: Sure.

geo: I was gonna say, Joe got your book at the CAKE.

Generoso: A cake, which was awesome by the way. And thank you for buying the book. [01:05:00] Thank you. In separately. Thank you for hanging with us At CAKE.

nick: it’s so

geo: beautiful. And I just, well, we don’t have video, but we can

joe: we’ll put pictures and stuff in links. Yeah.

geo: but I and this really stuck with me, this page with the cameras

nick: And you’re showing the camera, right?

joe: Yeah. You

geo: I’m showing them ’cause

nick: they

geo: know what page I’m talking about and I just love this.

joe: if they’re really human. I know what paint you’re talking about.

Lily: Yeah. And we are because we love that page and we belly over it. Well, really it’s the credit goes to Generoso. drew 

geo: just,

Lily: I, yeah,

geo: with the words and the pictures and I probably won’t even do this justice, but I’m gonna read this page please. Or unless you have the page. And you guys

Generoso: No, I, we are extremely honored that you would read from our comics. Thank you.

geo: ” I used to have the desire to stop time, like past generations once did, to capture wonders. I strongly felt that [01:06:00] synthetic experiences betrayed reality”, and I just thought that really summed up a lot of what we talked about today. So,

joe: yeah, definitely.

Lily: you. Yeah, this a lot of what we’ve been talking about is a big part of the world of Inversion actually. Because both the scientists and the subject inhabit that space and that time where what we don’t have community anymore, how we live is very much contained to a room. Feeding into something in particular.

And outside of that, in our leisure time, we, how we used to experience reality is completely gone. So we create these synthetic experiences for ourselves. Sometimes they are complete fantasy, sometimes they are launched from points in reality. You know, all of the things that we’re starting to see happen, right?

When people ask make me this movie, make, tell me this story.

geo: right? Yeah. So just get out [01:07:00] there, talk to real people,

nick: or just call me. Just call me or call

geo: Nick.

joe: You hit him up on the yeah. Call Nick. 

Generoso: The EBS of the 21st century.

joe: don’t become clippy.

You guys remember Clippy?

nick: I love Clippy. You leave Clippy out of this, 

geo: I miss Clippy.

nick: your tongue, Joe.

joe: Hey man, I’m, I am nice to all of the chatbots. ’cause when the AI overlords come, I went to him to go, oh, you know, that guy? He said thank you. Every once in a while. He

nick: Every once in a while

joe: It was like, you know, we, they’re human.

They have flaws, right? But, you know, yeah we’re coming to the end. I think we could, we probably could go on, you know, we could double this episode, there’s so many things and I have notes here and I always say that if I don’t get through everything, that’s great. So yeah, why don’t. Go ahead and let y’all talk a little bit about what you do, your books.

I, we have a number of science friends who listen, this is a, you know, sciencey podcast and what

nick: wait, where are the science [01:08:00] podcasts? I thought we were anti-science. We’re

joe: science for weirdos. 

nick: Oh man.

joe: once

Generoso: That matters.

joe: walked into a a bar 18th Street Distillery and someone was like, I know you guys gonna have podcast.

It’s science for Weirdos. And we were like, oh yeah,

geo: our new tagline.

joe: that’s us. Yeah. I was like, we can we use that?

geo: So

joe: So that’s where I came from if everyone wondered that. But yeah. We met at CAKE and you guys,

geo: Cake.

Tell ’em what cake is.

joe: the Chicago Alternative Comic Expo happens once a year in the summer time, I don’t know,

nick: this 

joe: by June Juneish.

Yep. It’s a great, it moves around the city to different venues. Just a great, experience. If you haven’t done it, go look forward. But I’m gonna turn the mic over to Lily and Generoso,

Generoso: Before we get even into the comics, I do wanna send love out to the CAKE folk. I we did a few

nick: here.

Generoso: cons this year, and I gotta tell you, it was great organizing wise. It was fantastic. But in terms of the folks that came like [01:09:00] yourselves no. But for real, like everybody came up, so many STEM people.

Which is, does play to a lot of what we do on top of the comics world. But it was one of the greatest audiences we’ve ever encountered at one of these events. So again, much respect and love after the folks that organized CAKE and the attendees. And the attendees were just really made the experience great.

So thank you for that. But 

Lily: yeah, 

Generoso: as far as our books, 

Lily: yeah, our books we have now we have 1, 2, 3. Four, we’re working on the fifth. The first three books are actually a triptych. So they are three separate stories set in three completely different times. But they each explore the relationship between a scientist and their subject.

So vessel. The first one is much more from a subject perspective, Inversion, which is the second one is actually the combination of a scientist and a subject. And then the last one is about [01:10:00] a scientist who is becoming her own subject. I think for us, what we’re always focused on is thinking about how individuals experience.

Science and technology, whether they work in it or whether they are actively participating in its future in one way or another. And I think more recently, as I hinted at what we’ve been really focused on is thinking almost like more about the foundations of science. Almost even like going back to the middle ages and earlier and thinking about what does it mean to like study phenomena, try to bring it together, understand causality.

So that’s a little bit of what exists in our fourth book, which is called Absolute Simultaneity. And yeah, it’s, I think it’s something that we’re going to be building even further upon in our upcoming book.

I cover everything? I think you did a beautiful scene on my body. I’m just a guy that draws the pictures.

Generoso: That’s not true. [01:11:00] He always says that

Lily: we’re, we’re a really super collaborative team. Like the way I always like to describe it is that in, if we had to compare it to movie terms, I’m the screen writer, he’s the cinematographer, but we really

together and yeah.

joe: Yeah. You can tell. No, it’s really good Inversion here, we have that copy and look forward to reading your other stuff. It’s really awesome.

And like I said, there’s a lot of sciencey folks who listen and yeah. So, you know, definitely go check it out, pick up the books. They’re really awesome. And you get that meta. We chatted for a good long while. While we were there. I actually, I think Georgia, you wandered off and I was still there talking.

Then you wandered back and I was still there. So, and it was a such a great conversation. I, and I’m glad you guys came on to the podcast

nick: Yes, thank you so very

Generoso: dude. Thank you for inviting

us. 

joe: been fun. Yeah. We gotta, you know, probably have you back and, you know, we’ll follow up on this as we move closer to the robot apocalypse.

You know, 

geo: we can

joe: sit back and listen to

geo: in with each other, it’s just

joe: you [01:12:00] know. Yeah. So cool. Anything else? Any last thoughts, Nick? Georgia.

nick: would your favorite AI movie be like? AI in the film. Not

joe: I know Lily loves Wall-E that’s it.

geo: Challenge.

Lily: myself with something completely unknown. Oh, this is a movie 

Generoso: that’s a great, I mean, it’s funky ’cause you can’t see this at home folks, but behind us are about 800 signed movie posters. So it’s right now we’re like AI.

joe: they’re all looking back there what’s in there? I’m looking at my walls too. I’m like, what? I have here oh,

Generoso: that’s a really good question. I even going into this conversation, obviously, you know, growing up, because I think Joe and I were the, around the same

joe: Yeah, I think so. We’ve discovered that.

Generoso: Terminator

As a kid was that first thing that was like oh, and it played off a Battlestar Galactica, which is the first time you [01:13:00] start to get this idea that, you know, maybe the machines will replace as Westworld

geo: Oh, yes. Now Westworld, are you talking about the movie or the TV series?

Generoso: Oh no the original movie and Future World. Yeah, but I think for me, I’ve Lily’s quite you’re more of the book person though, in the fact, yeah,

exactly. Like so 

nick: you can go book. 

joe: Yeah. We’ll take book.

nick: I’m not gonna shut you down for that.

joe: all. Well, we’ll least think you. Go ahead, Nick. What do you got? Because you, you

nick: gonna say Tron.

joe: Tron

nick: yeah. I absolutely love those films. Oh. I haven’t seen the last one, and I am gonna preserve myself from that. But

geo: meaning, you’re not gonna do it. You’re not gonna watch it. No. Uhuh

nick: Jared Let, I’m not a fan. He kinda just drops the ball for me every time. But the original Tron,

joe: yeah,

nick: Peak. Loved it.

geo: I’d say Ex Machina. I love Ex

nick: yes.

joe: Definitely.

Generoso: Oh, wow.

nick: I forgot how much I love that film,

geo: isn’t it? [01:14:00] It’s just, yeah.

nick: Oscar, Isaac. Ugh,

joe: No,

nick: Joe.

joe: we good?

Generoso: You killed Lily. I just want you to know that No she’s going 

Lily: through, I’m just going through 

Generoso: like hundreds of different things

in 

nick: I’m gonna

go, are we sure She’s not an ai I’m gonna

joe: with the, 

Generoso: Wall-E

joe: got that right. That’s it. That’s just gonna go with that. I’m done. I’m gonna go a little more sinister. You know, I think we have Hal 9,000 space Odyssey. That was probably

nick: Oh yeah.

joe: and I think early horror, like I, I talk about in the episodes how early I saw some of these movies, but MU/TH/UR from Alien.

Yeah. And that was just there where you had that that controlling it, you know, chatbot other thing. So I’m like, we’re done episode, but yeah. But chatbots don’t really have an agenda right now or that we know of. They’re, they are

geo: do 

joe: really? Yeah. D

They’re really they are there to they’re, they have no agenda when you prompt them and things like that.

But in how 9,000 mother, they had Skynet [01:15:00] they have, , from Terminator, they had agendas that this was their agenda and this was their mission and they were gonna be unstoppable. And Logic didn’t backfills in that. So I think from that point though, they were some of the , and then Otto from Wall-E, , that also that same 

geo: Oh, I thought of another one, but,

joe: All right. Let’s, Lily has something I

Lily: I think I got it. I think because, you know, for one of the reasons why I struggle is that for me I think when I think of science fiction and I think of dystopian dystopian writings and cinema, for me, I’m always thinking about like concepts that are playing around more with perception a little bit of time.

So I’m not always in the artificial intelligence realm though. It’s like artificial intelligence adjacent. So, but I think like an AI that, and a film that involves some AI as well as like this interpersonal relationship and like understanding how we communicate and interact as humans. I’d have to go with Solaris probably.

I think that’s the 

Generoso: original Tur Solaris. Yeah. Based on the [01:16:00] Lamb book.

nick: yeah.

geo: Wow.

joe: yeah. Wow.

And a book, a novel I thought of was Sea of Rust by Robert Cargill. Really? Oh yeah. So it’s a really, it’s a, he’s a, it’s a great novel and I think there’s a sequel to it. But the novel starts out with the last human being killed. By the machines. And so the whole book then is about the machines.

Now

geo: it’s the point of view of the machine, of the

joe: and this

geo: and the machine is very human.

joe: are, yeah. They’re very sentient. They have personalities and then they’re, , they are hierarchies of the machines. And following these scavenger bots.

And they’re trying to live, they’re trying to eke out a living, they’ve replaced the humans in some way. So it’s really you know, they do all

geo: which is just more about the human experience,

joe: But

geo: But not,

joe: you know, machines, and you get that after humans are gone, what are the machines gonna do? They’re built on human philosophies and, you know, so at some point, will they just revert back? We talked about that. Will they just cycle back,

nick: [01:17:00] So the Master sentinel? Oh

joe: yeah. From X-Men. Yeah. That’s it.

Generoso: Oh yeah.

geo: yeah.

nick: Sorry, I had to bring that one

geo: and then

joe: right? Is that

nick: right.

geo: our son just was, he’s away at college, but he was here for Thanksgiving and he wanted to watch a movie. And the movie he really wanted to watch was Blade Runner. Oh, that’s right.

joe: Oh, that’s right. Yeah.

geo: And that thing, think of that re That’s a good one too.

joe: Yeah. Blade Runner. Yeah.

Very good. Yeah, we could keep going. This was like, you know,

Generoso: we still recording? Actually.

joe: still are. Yeah. Yeah, we are.

Generoso: Okay. That’s awesome.

geo: there.

joe: Yeah. No,

nick: actually didn’t start 

Generoso: and lovely. I think it’s 

nick: I didn’t stop we

joe: there’s a end. There’s a special ending, so Yeah. People know who, listen, we, yeah. Yeah. We’re, we’ll get there. Okay.

Generoso: I was waiting for the special

ending, but I was like, are you going to add that after? Is it

during? I

nick: It,

Generoso: no, we could talk all night. Yeah, I’m fine with that. I’m just like,

geo: But we’ll, yeah.

joe: Yeah. All right. We should probably wrap it there. Thank you. You got me Joe?

nick: You got Nick?

geo: You

joe: got Nick. We’ve got Nick Georgia. We’ve got

nick: Georgia. And thank you so much again for [01:18:00] being with us.

joe: Hang on. 

Lily: Pleasure.

nick: and we went down some ho

joe: robotic holes 

nick: don’t forget to call me. Really?

joe: We love you. Stay safe.

nick: Bye-bye.

joe: Stay curious.

Rabbit Hole of Research Podcast Episode 2: AI Show Notes



Episode 2: The Show Notes

This has no particular format, just correcting or updating anything in the show we didn’t get a chance to fully talk about or things we had on the tips of our tongues and couldn’t get out as we recorded. As always feel free to comment and we will address stuff in future shows! Enjoy:

What we drinking:

Joe: Riot: Revolution Brewery

Nick: Foeder Fiend Three Floyd’s


Let us know:

What do you think about AI?

Any questions we didn’t cover?

What did we get wrong (Check the show notes)?


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Show notes:

Algorithms bias in medical

Chat bot on social media

AI Fashion model week

Anti-AI clothing

Artist using Anti-AI digital image protection:

UChicago scientists develop new tool protect artists

New tools help artists fight AI by directly disrupting the systems

Protection against facial recognition in digital photos 

AI math 

Affective Computing

Self driving cars and google search misidentify POC because of training data

Self driving car racial bias

Google racist gorillas photo recognition algorithm



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