Latest / Yoshi's Podcast / Will ISRM supercharge AI? | John Paul Schell | #190

Transcript

Will ISRM supercharge AI? | John Paul Schell | #190 · Yoshi's Podcast

• 0:03
  SPEAKER_00: Gotta make sure everything is recording.
• 0:05
  SPEAKER_01: Don't want to mess that up.
• 0:07
  SPEAKER_01: Yeah, yeah, yeah.
• 0:08
  SPEAKER_01: All right.
• 0:09
  SPEAKER_01: It's like start acting awkward.
• 0:14
  SPEAKER_00: JP Schell, welcome back.
• 0:16
  SPEAKER_00: Cool, man.
• 0:16
  SPEAKER_00: Thank you.
• 0:17
  SPEAKER_00: Thank you.
• 0:17
  SPEAKER_00: We talked like two years ago, I think, on the podcast.
• 0:21
  SPEAKER_01: It's been about two years.
• 0:22
  SPEAKER_00: But we're neighbors, so we bump into each other every now and then.
• 0:24
  SPEAKER_00: That's true.
• 0:25
  SPEAKER_00: And you keep telling me about this cool stuff you're doing,
• 0:28
  SPEAKER_00: and I stand there, I'm like, I don't understand any of this.
• 0:31
  SPEAKER_00: We need to sit down and have a proper chat, and I need to understand what you're doing.
• 0:36
  SPEAKER_00: Yeah, and that's on my bat.
• 0:38
  SPEAKER_01: I should be explaining things.
• 0:40
  SPEAKER_01: It's high-level stuff.
• 0:41
  SPEAKER_01: Fair enough.
• 0:42
  SPEAKER_01: Fair enough.
• 0:43
  SPEAKER_00: A quick one minute when we're just passing by each other is not going to do it.
• 0:46
  SPEAKER_00: So we need to chat about it.
• 0:47
  SPEAKER_00: The elevator pitch
• 0:48
  SPEAKER_01: is hard.
• 0:48
  SPEAKER_00: But before we get into that, for people who haven't heard the previous episode, who are you?
• 0:53
  SPEAKER_00: Where are you from?
• 0:54
  SPEAKER_00: What are you doing?
• 0:54
  SPEAKER_00: What have you done
• 0:55
  SPEAKER_01: in life?
• 0:55
  SPEAKER_01: Originally from California, born and raised, and went to bachelor's degree at UC Davis.
• 1:03
  SPEAKER_01: Just enjoyed living in Northern California,
• 1:06
  SPEAKER_01: growing up in nature,
• 1:07
  SPEAKER_01: kind of suburb,
• 1:09
  SPEAKER_01: 90s kid,
• 1:10
  SPEAKER_01: very much Like,
• 1:13
  SPEAKER_01: I don't know,
• 1:13
  SPEAKER_01: the advent of like the internet became like the biggest all encompassing thing.
• 1:19
  SPEAKER_01: And I was interested in tech team and things like this.
• 1:22
  SPEAKER_01: And eventually when I got into college, it became genetics.
• 1:26
  SPEAKER_01: And like genetics was the first thing that I started.
• 1:29
  SPEAKER_01: You know,
• 1:29
  SPEAKER_01: I just didn't want to like waste my time with like being around stuff
• 1:32
  SPEAKER_01: that was not interesting to me.
• 1:34
  SPEAKER_01: A lot of the school system,
• 1:36
  SPEAKER_01: like I don't know,
• 1:37
  SPEAKER_01: maybe through this conversation,
• 1:38
  SPEAKER_01: you could tell, like, yeah, there's a little bit of ADD going through this guy.
• 1:42
  SPEAKER_01: But like, yeah,
• 1:43
  SPEAKER_01: it just became like,
• 1:44
  SPEAKER_01: you know,
• 1:45
  SPEAKER_01: you had a lot of,
• 1:46
  SPEAKER_01: you know, I had kind of church as well, church studies as well in the morning.
• 1:53
  SPEAKER_01: So it was like a lot of learning and a lot of stuff that I'm just like,
• 1:57
  SPEAKER_01: I became very kind of critical of like, what is true?
• 2:01
  SPEAKER_01: Like,
• 2:02
  SPEAKER_01: why should I need to be learning this in order for making these kind of Anyway,
• 2:08
  SPEAKER_01: I got kind of addicted to how organic chemistry and chemistry
• 2:12
  SPEAKER_01: in general started describing the world to me in a way that was much more compelling
• 2:17
  SPEAKER_01: and interesting to think about.
• 2:19
  SPEAKER_01: And from there, pursued...
• 2:23
  SPEAKER_01: Science, essentially.
• 2:24
  SPEAKER_01: Became a stem cell biologist down at Scripps Research Institute and then did a PhD
• 2:31
  SPEAKER_01: at Karolinska and two postdocs at Karolinska and then ended up starting
• 2:34
  SPEAKER_01: a biotech out of these guys with one of the co-founders of Moderna at the time.
• 2:41
  SPEAKER_01: And since then,
• 2:44
  SPEAKER_01: Actually,
• 2:44
  SPEAKER_01: I think one of the sister companies just recently went out of business,
• 2:48
  SPEAKER_01: but sadly, but the way of biotech.
• 2:50
  SPEAKER_01: And that's the thing is like we got in from the academia into industry.
• 2:55
  SPEAKER_01: I felt like kind of a clipping of my wings,
• 2:58
  SPEAKER_01: so to speak,
• 2:59
  SPEAKER_01: not just because I didn't have access to things like PubMed readily,
• 3:03
  SPEAKER_01: where it was like,
• 3:04
  SPEAKER_01: this is,
• 3:05
  SPEAKER_01: you know,
• 3:05
  SPEAKER_01: this is how you fuel, you know, like discovery is like being able to do research.
• 3:11
  SPEAKER_01: And while we kind of take that for granted is that like,
• 3:15
  SPEAKER_01: A generation ago, somebody would spend their entire life working on one gene.
• 3:20
  SPEAKER_01: And just my PhD studies was looking at all genes simultaneously in every single cell.
• 3:28
  SPEAKER_01: And it's just like that type of resolution,
• 3:30
  SPEAKER_01: that granularity improvement has just made the world of difference
• 3:34
  SPEAKER_01: for understanding these systems on such a much bigger scale.
• 3:40
  SPEAKER_01: It's just that we made all these constants
• 3:42
  SPEAKER_01: and we made all these mathematical observations that we're sticking to,
• 3:46
  SPEAKER_01: but that was during a time when we didn't have these kind of millisecond
• 3:50
  SPEAKER_01: to millisecond or nanosecond,
• 3:52
  SPEAKER_01: you know,
• 3:52
  SPEAKER_01: laser refined precision that we do in our capacity to measure today, you know?
• 3:58
  SPEAKER_01: And so it's now we have that capacity and we're moving into these agentic systems.
• 4:04
  SPEAKER_01: And that's where, like, so during this time of...
• 4:08
  SPEAKER_01: Kind of off time from biotech.
• 4:10
  SPEAKER_01: I got this supreme deal where they're like,
• 4:13
  SPEAKER_01: just don't share IP and you can get paid for at least six months or something
• 4:17
  SPEAKER_01: with full salary, which was awesome.
• 4:19
  SPEAKER_01: I'm like, okay, this is my opportunity to write my book.
• 4:22
  SPEAKER_01: I wrote 230 pages of a novel called Variant Rising and sent it to a proper editor.
• 4:28
  SPEAKER_01: It actually was dealt with a proper editor.
• 4:31
  SPEAKER_01: And the consensus back was that All the characters essentially sound like you, JP.
• 4:39
  SPEAKER_01: It was a criticism,
• 4:40
  SPEAKER_01: but also I couldn't deny that they did kind of all have the same type
• 4:45
  SPEAKER_01: of long-winded descriptions of stuff.
• 4:49
  SPEAKER_01: But one of the other major themes
• 4:54
  SPEAKER_01: And that was the other thing.
• 4:55
  SPEAKER_01: He's like,
• 4:55
  SPEAKER_01: but the whole premise of it,
• 4:57
  SPEAKER_01: the descriptive nature of how essentially Variant Rising is
• 5:02
  SPEAKER_01: about how artificial intelligence wants to use biology as a substrate
• 5:09
  SPEAKER_01: for its own purpose.
• 5:11
  SPEAKER_01: And so like this was kind of the idea is how how do systems in general grow
• 5:17
  SPEAKER_01: that type of that type of level?
• 5:20
  SPEAKER_01: And so we go through like in human development,
• 5:22
  SPEAKER_01: we go through early stages of like baby brain
• 5:26
  SPEAKER_01: where your your neurons are actually developing way.
• 5:30
  SPEAKER_01: You have like way more like if there was a baby in the room,
• 5:33
  SPEAKER_01: like there would probably be more neurons in that baby's head than
• 5:36
  SPEAKER_01: in our entire apartment complex combined of all the adults.
• 5:39
  SPEAKER_01: It's just like there's just so much more.
• 5:41
  SPEAKER_01: And then what happens is it gets pruned down to the superhighways.
• 5:45
  SPEAKER_01: And then when you do – I used to do diffusion tensor imaging.
• 5:48
  SPEAKER_01: This is like looking at the white brain matter tracks
• 5:51
  SPEAKER_01: of inside people's – like these are the superhighways of connectivity within
• 5:56
  SPEAKER_01: the brain.
• 5:57
  SPEAKER_01: And so in order to have those happen,
• 5:59
  SPEAKER_01: you go through this process of pruning where you kind of chisel down to what are
• 6:04
  SPEAKER_01: the important things, where are the important highways that need to be conserved.
• 6:09
  SPEAKER_01: Because energy is really the limiting resource to an extreme.
• 6:14
  SPEAKER_01: And that's the thing is that biology has been constraining energy.
• 6:19
  SPEAKER_01: So when you have this flourishing system that has abundance of energy,
• 6:23
  SPEAKER_01: it has infinite amounts of neurons that it can make simultaneously.
• 6:28
  SPEAKER_01: What it needs to do is understand where to put the energy and allocate
• 6:32
  SPEAKER_01: the energy appropriately in order for the responses That are positive to
• 6:38
  SPEAKER_01: that particular system, essentially.
• 6:41
  SPEAKER_01: It's like if I pay attention to everything simultaneously,
• 6:44
  SPEAKER_01: you will not be able to pay attention to anything.
• 6:46
  SPEAKER_00: You wouldn't survive.
• 6:48
  SPEAKER_01: You want to survive.
• 6:48
  SPEAKER_01: Yeah.
• 6:49
  SPEAKER_01: And that's, I mean, and that's like eventually it becomes kind of like common sense.
• 6:53
  SPEAKER_01: You're like, well,
• 6:54
  SPEAKER_01: as a mature adult,
• 6:55
  SPEAKER_01: like I try to use as least amount of energy to achieve the most,
• 6:58
  SPEAKER_01: as much as possible with this little amount of like kind of gray zone
• 7:03
  SPEAKER_01: of like how things could be bad,
• 7:05
  SPEAKER_01: like with the most amount Out of security, so to speak.
• 7:07
  SPEAKER_00: When you say these superhighways that form, what could be an example of that?
• 7:12
  SPEAKER_01: The connection between your eye and your occipital lobe on the opposite area.
• 7:18
  SPEAKER_01: When you have injuries of traumatic brain injury,
• 7:21
  SPEAKER_01: like we've seen with football players and whatnot,
• 7:24
  SPEAKER_01: and it's not instant usually either.
• 7:26
  SPEAKER_01: It's because you get all the inflammation.
• 7:29
  SPEAKER_01: You can do the MRIs immediately after the injury, and you'll still see the connection.
• 7:36
  SPEAKER_01: Over time,
• 7:37
  SPEAKER_01: like over the next course of a couple of weeks,
• 7:39
  SPEAKER_01: because of the inflammation,
• 7:40
  SPEAKER_01: because of the traumatic injury that actually happened there and kind of the fallout
• 7:47
  SPEAKER_01: from that, you'll see deterioration of these axons and then they lose vision.
• 7:51
  SPEAKER_01: So you actually like can see the kind of blinding in real time of how
• 7:56
  SPEAKER_01: an injury leads to somebody actually losing like a perceptual brain.
• 8:00
  SPEAKER_01: Right.
• 8:01
  SPEAKER_00: Can it also be like the things we learn?
• 8:04
  SPEAKER_00: That's a very physical example,
• 8:08
  SPEAKER_00: but could it also be like,
• 8:09
  SPEAKER_00: you know, when you want to cross the road, look left and right?
• 8:12
  SPEAKER_01: Oh, I mean... Is that also...
• 8:14
  SPEAKER_01: Depending on where that kind of decision is being made,
• 8:17
  SPEAKER_01: but I did hear recently of an elderly woman who,
• 8:21
  SPEAKER_01: it was one of collaborators' grandmothers who had had a stroke,
• 8:27
  SPEAKER_01: and as soon as,
• 8:28
  SPEAKER_01: after she had had the stroke,
• 8:30
  SPEAKER_01: They found out that a certain area had been really influenced by this stroke.
• 8:35
  SPEAKER_01: And now she starts singing the songs that she was only singing and she only knew
• 8:41
  SPEAKER_01: when she was a child.
• 8:43
  SPEAKER_00: Oh, wow.
• 8:43
  SPEAKER_01: And this was kind of like going back to like, I mean, they were like, how do you explain this?
• 8:47
  SPEAKER_01: And I was like, well,
• 8:47
  SPEAKER_01: there is like,
• 8:48
  SPEAKER_01: there is these ideas now and there's,
• 8:50
  SPEAKER_01: there's evidence to support it where of how memory is formed in general as kind
• 8:55
  SPEAKER_01: of like.
• 8:57
  SPEAKER_01: Overgrowth of new memories kind of forming in overlapping patterns.
• 9:01
  SPEAKER_01: So if you do have this like traumatic injury that then takes out this huge portion,
• 9:06
  SPEAKER_01: like naturally,
• 9:08
  SPEAKER_01: like the system is just trying to integrate what should have been here.
• 9:12
  SPEAKER_01: Now you actually have circuitry that was kind of designed perhaps for like
• 9:16
  SPEAKER_01: a similar purpose,
• 9:17
  SPEAKER_01: but it's running at like this other like kind of primitive state
• 9:22
  SPEAKER_01: or this other just much earlier.
• 9:24
  SPEAKER_01: Like a formed area of the brain.
• 9:27
  SPEAKER_01: And then all of a sudden this is like tied to all these other perceptual things
• 9:31
  SPEAKER_01: that reminds her of her childhood, I guess.
• 9:34
  SPEAKER_00: Would that suggest that they're like things we think we forgot aren't really forgotten.
• 9:39
  SPEAKER_00: They're just somewhere
• 9:41
  SPEAKER_01: That I don't know.
• 9:42
  SPEAKER_01: And also they say like,
• 9:43
  SPEAKER_01: also like your,
• 9:44
  SPEAKER_01: your body is like,
• 9:45
  SPEAKER_01: even if you weren't conscious,
• 9:47
  SPEAKER_01: like maybe you're actually, your body is remembering things as well.
• 9:50
  SPEAKER_01: I mean, that's totally plausible in my opinion, but,
• 9:53
  SPEAKER_01: but yeah,
• 9:54
  SPEAKER_01: I'd say, you know, and how people deal with trauma in different ways as well.
• 9:58
  SPEAKER_01: Like, I mean,
• 9:58
  SPEAKER_01: this becomes like very, I mean,
• 10:01
  SPEAKER_01: some of it is very speculative as well,
• 10:03
  SPEAKER_01: but in general, yeah,
• 10:04
  SPEAKER_01: this is kind of,
• 10:05
  SPEAKER_01: there's becoming this,
• 10:08
  SPEAKER_01: it's not like one,
• 10:09
  SPEAKER_01: it's not like things take place in one,
• 10:11
  SPEAKER_01: like a memory doesn't exist, I don't think, in one specific location.
• 10:15
  SPEAKER_01: It's like a sum of a bunch.
• 10:19
  SPEAKER_01: Yeah, distributed intelligence in the sense of like,
• 10:22
  SPEAKER_01: You're using different nodes or different models essentially to like,
• 10:28
  SPEAKER_01: you have stimulation coming in,
• 10:29
  SPEAKER_01: you have different models that are seeing the world at different ways and having
• 10:33
  SPEAKER_01: an amount of predictive error in those different ways.
• 10:37
  SPEAKER_01: And then you have kind of overlap and based off of like what's changing enough,
• 10:42
  SPEAKER_01: I think that's where your change in focus comes from.
• 10:45
  SPEAKER_01: But like now there's been some real like interesting research around this
• 10:48
  SPEAKER_01: and this is kind of,
• 10:50
  SPEAKER_01: this stems from just like I don't know,
• 10:52
  SPEAKER_01: just kind of philosophical curiosity of looking at us compared to LLMs
• 10:57
  SPEAKER_01: and agentic systems.
• 10:59
  SPEAKER_01: And it was surprising to me,
• 11:01
  SPEAKER_01: but there's a lot of research actually on trying to figure out how many bits we use
• 11:06
  SPEAKER_01: in consciousness.
• 11:08
  SPEAKER_01: And so there's this idea that The stimulation,
• 11:12
  SPEAKER_01: like the amount of information that's coming in that we can experience
• 11:16
  SPEAKER_01: to some degree or that.
• 11:18
  SPEAKER_01: And this is like,
• 11:19
  SPEAKER_01: I think this part's the highly speculative part because like you're talking
• 11:22
  SPEAKER_01: about 38 trillion cells here.
• 11:24
  SPEAKER_01: You're talking about each of them having like set.
• 11:27
  SPEAKER_01: I mean, a lot of them have at least on your surface having sensors.
• 11:31
  SPEAKER_01: So there's a lot of crosstalk and cross communication.
• 11:34
  SPEAKER_01: So for us to kind of dumb it down.
• 11:35
  SPEAKER_01: But this is talking about like.
• 11:37
  SPEAKER_01: Looking at consciousness waves and brain patterns and seeing how much of a change
• 11:43
  SPEAKER_01: in actual electrical current apparently.
• 11:46
  SPEAKER_01: And that comes out to 11.2 megabits per second is the idea of how How much input is coming in.
• 11:54
  SPEAKER_01: That's a lot.
• 11:55
  SPEAKER_01: And what we filter that down to of like what we can perceive at
• 11:58
  SPEAKER_00: one time is 50 bits.
• 12:00
  SPEAKER_00: 50 bits.
• 12:01
  SPEAKER_00: 50 bits.
• 12:02
  SPEAKER_00: From 11 megabits.
• 12:03
  SPEAKER_01: Yes.
• 12:04
  SPEAKER_01: From 11.2 megabits down to 50 bits of information is that little like foveated view
• 12:11
  SPEAKER_01: of like
• 12:12
  SPEAKER_00: where we can.
• 12:12
  SPEAKER_00: Our conscious focus.
• 12:13
  SPEAKER_00: Yeah.
• 12:14
  SPEAKER_00: Our conscious focus.
• 12:14
  SPEAKER_00: Or subconscious.
• 12:15
  SPEAKER_00: Well, maybe.
• 12:15
  SPEAKER_00: I don't know.
• 12:16
  SPEAKER_01: I mean, that's a weird.
• 12:16
  SPEAKER_01: Yeah.
• 12:17
  SPEAKER_01: So we had the observer or the observed, the physical system or the
• 12:20
  SPEAKER_00: observer system.
• 12:21
  SPEAKER_00: Just imagine what torture it would be to take in all of that, the 11.2 megabits.
• 12:27
  SPEAKER_01: That's probably hallucinogens.
• 12:34
  SPEAKER_01: I mean, depending on whatever else.
• 12:38
  SPEAKER_01: But you're broadening, I think, the level of models that you're having access to.
• 12:43
  SPEAKER_01: Serotonin,
• 12:45
  SPEAKER_01: when it is a selective pressure,
• 12:47
  SPEAKER_01: you essentially refine your models down to what is absolutely necessary
• 12:51
  SPEAKER_01: to be paying attention to.
• 12:52
  SPEAKER_01: Because I don't think consciousness was bred out of a...
• 12:55
  SPEAKER_01: Yeah, you don't need to beat your heart.
• 13:17
  SPEAKER_00: You don't need to beat your heart, your vessels, you don't
• 13:19
  SPEAKER_01: need to concentrate it.
• 13:20
  SPEAKER_01: You need to concentrate.
• 13:21
  SPEAKER_01: It's funny how you say that.
• 13:22
  SPEAKER_00: We don't need you to be in charge of these things.
• 13:25
  SPEAKER_01: Who's we?
• 13:26
  SPEAKER_01: Who's the,
• 13:27
  SPEAKER_01: is the whole,
• 13:27
  SPEAKER_01: the whole,
• 13:28
  SPEAKER_01: the 38 trillion population of individuals that like come together that are you,
• 13:33
  SPEAKER_01: you know?
• 13:33
  SPEAKER_01: And that's the thing is you have a lot of systems that are,
• 13:36
  SPEAKER_01: I mean, they're communicating with each other.
• 13:38
  SPEAKER_01: And when they are in good harmony with each other, you feel better.
• 13:45
  SPEAKER_01: For sure.
• 13:48
  SPEAKER_00: Everyone who's dealt with their gut biome,
• 13:53
  SPEAKER_00: for good or for bad, knows that, all right, this is a system we need to get along.
• 13:57
  SPEAKER_01: Yeah, exactly.
• 13:59
  SPEAKER_01: And that's, I'm lactose intolerant.
• 14:01
  SPEAKER_01: I'm totally vegan now for, well, it's almost 11 years now.
• 14:06
  SPEAKER_01: And the reason for that,
• 14:07
  SPEAKER_01: I think,
• 14:08
  SPEAKER_01: I don't know,
• 14:08
  SPEAKER_01: maybe we went over that in the last podcast,
• 14:10
  SPEAKER_01: but was like one of the big reasons is because like where plants
• 14:16
  SPEAKER_01: and animals diverged evolutionarily is so significantly longer ago
• 14:22
  SPEAKER_00: than any vertebrates that we consume regularly.
• 14:25
  SPEAKER_01: And so like I mean,
• 14:27
  SPEAKER_01: and them being, like,
• 14:29
  SPEAKER_01: if it has a face,
• 14:30
  SPEAKER_01: it's too closely related for me to think that, like, that, like,
• 14:34
  SPEAKER_01: the proteins that are in that system are not going to directly communicate
• 14:39
  SPEAKER_01: with my system.
• 14:41
  SPEAKER_01: Directly communicate?
• 14:42
  SPEAKER_01: Directly communicate.
• 14:43
  SPEAKER_00: What does that mean?
• 14:43
  SPEAKER_00: So, I mean, like, I mean, there's growth factors.
• 14:46
  SPEAKER_00: If you eat meat, something with a face,
• 14:48
  SPEAKER_00: you think those...
• 14:50
  SPEAKER_01: Those factors that are in,
• 14:51
  SPEAKER_01: I think that you're having trillions of interactions between...
• 14:55
  SPEAKER_01: Even in your esophagus, in your guts, in everything.
• 14:58
  SPEAKER_01: Because you have, and this is new science of exosomes, what exosomes are.
• 15:03
  SPEAKER_00: What are exosomes?
• 15:04
  SPEAKER_01: Exosomes are essentially the sweating out of all of these particles
• 15:08
  SPEAKER_01: that have all these growth factors, have all this DNA, have all these mRNA.
• 15:12
  SPEAKER_01: And that's where what we were doing at SmartCell.
• 15:14
  SPEAKER_01: It was essentially trying to figure out ways that we could package what we wanted to
• 15:19
  SPEAKER_01: in exosomes so you could get natural exocytosis of this.
• 15:23
  SPEAKER_01: But what was coming out of there already all the time in the billions,
• 15:27
  SPEAKER_01: you know, in the billions, like, you know, this is like enormous.
• 15:30
  SPEAKER_01: And so like you have enormous amount of exposure of now putting signals
• 15:36
  SPEAKER_01: into your body, into the That is creating a cacophony of missense, essentially.
• 15:44
  SPEAKER_01: It's like the ratio here is getting out of balance because of the fact
• 15:50
  SPEAKER_01: that you're putting stuff that's Synonymous with information that we use in order
• 15:58
  SPEAKER_01: to identify ourselves.
• 16:00
  SPEAKER_01: And so it's like,
• 16:01
  SPEAKER_01: if I can't tell the difference between,
• 16:03
  SPEAKER_01: it's very easy if you eat anything plant-based,
• 16:07
  SPEAKER_01: that your body to say that the proteins are just- That's not me.
• 16:11
  SPEAKER_01: Yeah, that's not me.
• 16:12
  SPEAKER_01: So it's very easy to say that.
• 16:14
  SPEAKER_01: When you consume something especially as closely related as a cow or a pig,
• 16:20
  SPEAKER_01: and we can go to the extreme.
• 16:22
  SPEAKER_01: Why do you not eat humans?
• 16:24
  SPEAKER_01: It's like we know why.
• 16:25
  SPEAKER_01: It's because you get kuru.
• 16:26
  SPEAKER_01: Too expensive.
• 16:30
  SPEAKER_00: Especially the little ones.
• 16:32
  SPEAKER_01: No, it's because there was societies that essentially thought
• 16:37
  SPEAKER_01: that you would consume their powers through eating their brains.
• 16:40
  SPEAKER_01: And then they had this huge high rate of this disease, this prion disease called Kuru.
• 16:46
  SPEAKER_01: And so it's like, what is a prion disease?
• 16:48
  SPEAKER_01: It's like the inability to tell that that is something that's not coming,
• 16:53
  SPEAKER_01: that doesn't belong here.
• 16:54
  SPEAKER_01: And then it becomes such a bigger issue that it kills you immediately.
• 16:57
  SPEAKER_01: And that's the thing is we're also producing...
• 17:00
  SPEAKER_01: Like 80%, I think of our vaccines are for like the agricultural industries.
• 17:06
  SPEAKER_01: And like that part alone should be kind of like a big red flag of like, well,
• 17:11
  SPEAKER_01: if that stuff is pouring out of like that,
• 17:13
  SPEAKER_01: that's like literally what my job was to do is trying to electroporate cells
• 17:17
  SPEAKER_01: in order to pump in genetic information that then it would pour out
• 17:21
  SPEAKER_01: through this paracrine signaling,
• 17:23
  SPEAKER_01: this like We exocytose just these billions of particles naturally
• 17:27
  SPEAKER_01: because that's what cells are just doing all the time.
• 17:29
  SPEAKER_01: They create this like system within you that's almost like Wi-Fi.
• 17:33
  SPEAKER_01: It's a global positioning system where your gut is communicating with your brain.
• 17:39
  SPEAKER_01: And that's the thing is like you have all these.
• 17:41
  SPEAKER_01: So like when we're talking about metabolic disorders and how far we've gone from
• 17:45
  SPEAKER_01: the norm because like the idea is like Yeah,
• 17:48
  SPEAKER_01: I don't think that we have thought this really through in the big picture of things
• 17:54
  SPEAKER_01: because now we have huge outliers in our society of metabolic disorders
• 17:59
  SPEAKER_01: that are just catastrophic.
• 18:01
  SPEAKER_01: But this is coming from an idea Yeah, we needed meat.
• 18:05
  SPEAKER_01: We absolutely needed.
• 18:06
  SPEAKER_01: There was bottlenecks in our survival as human beings.
• 18:11
  SPEAKER_01: Think about the Mongolians coming across the Polish plains.
• 18:15
  SPEAKER_01: They survived on little chunks of jerky just by getting enough protein out of that
• 18:21
  SPEAKER_01: for two months, a piece of jerky like this, like...
• 18:25
  SPEAKER_01: You know, you take a little bit and you survive.
• 18:27
  SPEAKER_01: And there were little guys,
• 18:28
  SPEAKER_01: but until you could get to the next place to pillage, you know, you had to survive.
• 18:33
  SPEAKER_01: I think the longest recorded is some guy,
• 18:35
  SPEAKER_01: he lost like some 300 kilos or something, but it was like 300 days.
• 18:39
  SPEAKER_01: How big was he?
• 18:40
  SPEAKER_01: He was very overweight.
• 18:41
  SPEAKER_01: He was very obese.
• 18:43
  SPEAKER_01: But he went 300 days without eating.
• 18:46
  SPEAKER_01: And it's just like, okay,
• 18:48
  SPEAKER_01: so we've prescribed ourselves this weird ideology that's like breakfast,
• 18:52
  SPEAKER_01: lunch, dinner, and it has to
• 18:54
  SPEAKER_00: be protein.
• 18:55
  SPEAKER_01: And it's like, nah, you got Arnold at least saying like-
• 18:58
  SPEAKER_01: I gotta unpack this.
• 19:00
  SPEAKER_00: So just eating meat that's not- Forget about all the vaccines and stuff.
• 19:06
  SPEAKER_00: Just clean, wild game.
• 19:10
  SPEAKER_00: Will that also communicate with your body in ways that it can be damaging?
• 19:16
  SPEAKER_00: So much so that if it's specifically, if it's...
• 19:20
  SPEAKER_01: Let's take cow, for example.
• 19:21
  SPEAKER_01: Because this is one that's like...
• 19:23
  SPEAKER_01: Cows are all messed up.
• 19:24
  SPEAKER_01: Let's take
• 19:24
  SPEAKER_00: wild game.
• 19:25
  SPEAKER_00: I'm thinking about the purist thing where the hunter-gatherers would go and get meat.
• 19:30
  SPEAKER_01: I mean, and I don't know if this parallels exactly with a particular game,
• 19:34
  SPEAKER_01: but like there's a situation with red meat in general and it's like majority,
• 19:41
  SPEAKER_01: it's majority of the animals that exist,
• 19:46
  SPEAKER_01: But definitely fact check me on the game thing.
• 19:49
  SPEAKER_01: But this is very common in red meat in general.
• 19:51
  SPEAKER_01: But here's the reason why I would say the dangers aspect is pretty realistic
• 19:56
  SPEAKER_01: and pretty well known now of why are they recommending people not to eat red meat is
• 20:01
  SPEAKER_01: because there has been an evolutionary shift in the way
• 20:06
  SPEAKER_01: that we glycosylate certain proteins that we're actually consuming.
• 20:10
  SPEAKER_01: Meaning so that in humans,
• 20:14
  SPEAKER_01: We have actually changed and then reverted back several times throughout history,
• 20:20
  SPEAKER_01: but essentially the way that one particular protein that we use,
• 20:24
  SPEAKER_01: that we integrate into a lot of different tissues,
• 20:27
  SPEAKER_01: has this particular glycosylation that is individual of Like humans.
• 20:34
  SPEAKER_01: And these other guys that are closely related to us, they didn't have this switch.
• 20:39
  SPEAKER_01: Okay.
• 20:39
  SPEAKER_01: Okay.
• 20:40
  SPEAKER_01: So when we consume those proteins and we break those down is at least into like
• 20:45
  SPEAKER_01: the building blocks that we then put into our own like tissue
• 20:50
  SPEAKER_01: that we're building our tissue up again with those proteins that we're using
• 20:54
  SPEAKER_01: from this.
• 20:55
  SPEAKER_01: We've broken it down.
• 20:57
  SPEAKER_01: They, unfortunately,
• 20:58
  SPEAKER_01: they have this glycosylation,
• 21:00
  SPEAKER_01: meaning this other sugar moody,
• 21:02
  SPEAKER_01: this other sugar residue that's on the surface of it,
• 21:05
  SPEAKER_01: that didn't, that evolutionary change.
• 21:08
  SPEAKER_01: And that's the thing,
• 21:08
  SPEAKER_01: it's like eating protein can give you gains very quickly because It's
• 21:12
  SPEAKER_01: the stuff that's going into this.
• 21:14
  SPEAKER_01: So it's like these are the building blocks.
• 21:17
  SPEAKER_01: But what unfortunately happens is that because humans evolved away from that,
• 21:21
  SPEAKER_01: our immune system recognized that sugar moiety that's very specific to the cow
• 21:26
  SPEAKER_01: and is not specific to human and attacks that tissue.
• 21:31
  SPEAKER_01: And you get sick in numerous body parts throughout your entire body where
• 21:37
  SPEAKER_01: that stuff gets integrated in and the immune system's like,
• 21:40
  SPEAKER_01: oh fuck,
• 21:41
  SPEAKER_01: like this is just,
• 21:42
  SPEAKER_01: this is not supposed to,
• 21:44
  SPEAKER_01: like it's Legos that fit, but those Legos like melt when they get in there.
• 21:48
  SPEAKER_00: I don't know, that's a bad example, but it's just, you get what I'm saying, yeah.
• 21:54
  SPEAKER_01: And there's several kind of situations like that.
• 21:58
  SPEAKER_01: I think that it just becomes very apparent that we are all very closely related.
• 22:04
  SPEAKER_01: And so it's like,
• 22:04
  SPEAKER_01: you're talking to a scientist who's been trying to also go away from moving, I mean,
• 22:11
  SPEAKER_01: using animals in research,
• 22:12
  SPEAKER_01: because the more and more we know about the animals,
• 22:15
  SPEAKER_01: the more we understand that they're like,
• 22:16
  SPEAKER_01: oh, there's just different ways of talking and communicating.
• 22:19
  SPEAKER_01: Like rats laugh now.
• 22:21
  SPEAKER_00: You don't think we're going to get to that point with people?
• 22:23
  SPEAKER_00: Plants as well, though?
• 22:24
  SPEAKER_00: Maybe.
• 22:24
  SPEAKER_00: I mean, the thing is, I see a
• 22:27
  SPEAKER_01: distributed, like, it's a different thing.
• 22:30
  SPEAKER_01: Like, you know,
• 22:30
  SPEAKER_01: in your tree out there,
• 22:32
  SPEAKER_01: the only, like, everything, the bark, everything, that's all enucleated.
• 22:36
  SPEAKER_01: There's, like, almost no life left in that part.
• 22:38
  SPEAKER_00: It's just that thin layer.
• 22:40
  SPEAKER_00: Between the outer layer and, like, the inner...
• 22:43
  SPEAKER_01: Yeah, and so that's your thing.
• 22:46
  SPEAKER_01: But then if you want to take that perspective, it's like, well, what is the living part of us?
• 22:50
  SPEAKER_01: What is the part of us that is actually prolonged throughout generations?
• 22:54
  SPEAKER_01: Like, oh, these are your primordial germ cells.
• 22:56
  SPEAKER_01: Those are the ones giving rise to their sperm rate.
• 22:58
  SPEAKER_01: So it's like those are the kingdom maintainers.
• 23:02
  SPEAKER_00: That's the thing.
• 23:04
  SPEAKER_00: If you look at life through the lens of DNA, DNA is life pretty much.
• 23:10
  SPEAKER_00: And what does it want to do?
• 23:11
  SPEAKER_00: It wants to survive and replicate.
• 23:13
  SPEAKER_00: That's it.
• 23:13
  SPEAKER_00: That's it.
• 23:14
  SPEAKER_00: And then every other thing that's built upon that,
• 23:16
  SPEAKER_00: like from one cell to 38 trillion cells or whatever,
• 23:20
  SPEAKER_00: billion, it's all just complex ways to make the DNA survive and reproduce.
• 23:27
  SPEAKER_00: I think so, in some degree.
• 23:29
  SPEAKER_01: In some degrees, you have these self-replicate, I think that there's stability there.
• 23:34
  SPEAKER_01: There is optimization of constraining energy.
• 23:37
  SPEAKER_01: Like that seems to be,
• 23:38
  SPEAKER_01: so that's the thing that this is kind of like pushed into is over
• 23:42
  SPEAKER_01: the last two years.
• 23:43
  SPEAKER_01: First, I thought I was kind of just going crazy because I thought like,
• 23:46
  SPEAKER_01: okay, this is like, Like we're making an observation.
• 23:49
  SPEAKER_01: Essentially what the observation was,
• 23:51
  SPEAKER_01: was that the utility of a system, all systems, and this is kind of the theme.
• 23:56
  SPEAKER_01: The idea is that like as systems in general complexify and get more and more complex and larger.
• 24:02
  SPEAKER_00: And systems could be biological systems.
• 24:04
  SPEAKER_01: Biological, ecological, agentic system, just like systems of information.
• 24:10
  SPEAKER_00: When you say agentic, you mean AI?
• 24:11
  SPEAKER_01: AI systems.
• 24:12
  SPEAKER_01: And that's like a big, that's the big thing right now is like where.
• 24:15
  SPEAKER_01: And that's where we'll get into.
• 24:17
  SPEAKER_01: And that's very kind of different in some aspects.
• 24:20
  SPEAKER_01: It's more of just the logic of how systems are adapting in real time.
• 24:27
  SPEAKER_01: And the idea is that as you complexify, you instantiate a model...
• 24:36
  SPEAKER_01: And that model essentially looks at changing coherence,
• 24:41
  SPEAKER_01: changing uncertainty,
• 24:42
  SPEAKER_01: and available energy or total energy in that particular system.
• 24:46
  SPEAKER_01: And if any of those normalized Right.
• 24:48
  SPEAKER_01: Right.
• 24:49
  SPEAKER_01: Right.
• 24:49
  SPEAKER_01: Right.
• 25:11
  SPEAKER_01: There's stability basins and that like essentially think of more of like
• 25:16
  SPEAKER_01: a topography where you have kind of basins of stability.
• 25:23
  SPEAKER_01: And like this is where we're talking about like this idea is not new.
• 25:26
  SPEAKER_01: It's coming from biology in the sense of like cell state transitions of how you go from...
• 25:31
  SPEAKER_01: Having maybe a thousand differentially expressed genes in one cell state
• 25:37
  SPEAKER_01: and vice versa in this other cell state.
• 25:40
  SPEAKER_01: So those genes are going down in order to make this.
• 25:43
  SPEAKER_01: But it's like these things are maintained by not only,
• 25:47
  SPEAKER_01: they're like a thousand different dimensions of Of like kind
• 25:52
  SPEAKER_01: of environmental influence or signaling influence,
• 25:57
  SPEAKER_01: metabolic influence, epigenetic influence.
• 26:00
  SPEAKER_01: And so this thing becomes this like organism in itself of information, so to speak.
• 26:05
  SPEAKER_01: And that's kind of what we've been doing with like what my postdocs were really
• 26:09
  SPEAKER_01: about is like kind of integrating big data sets together.
• 26:13
  SPEAKER_01: And looking for these like stable cell states that really exist
• 26:17
  SPEAKER_01: during developmental stages where you get uniformity.
• 26:21
  SPEAKER_01: You get the system kind of broadening at a certain point.
• 26:25
  SPEAKER_01: If you're familiar with Michael Levin, I'm like a big...
• 26:28
  SPEAKER_01: I'm a big, big fan of his.
• 26:30
  SPEAKER_01: If you're not, check him out, give him a chance.
• 26:32
  SPEAKER_01: He's the idea that intelligence,
• 26:35
  SPEAKER_01: he works with a lot of flatworms and cuts their heads off after.
• 26:38
  SPEAKER_01: I listen to him on a
• 26:40
  SPEAKER_00: podcast or something.
• 26:43
  SPEAKER_01: I mean, it's spot on with the idea that it's not just a molecular signal.
• 26:50
  SPEAKER_01: It's like there's a higher, there's a communication.
• 26:53
  SPEAKER_01: We've been taking this kind of like mechanistic automata perspective towards cell behavior.
• 27:00
  SPEAKER_01: Oh, once you have these building blocks,
• 27:02
  SPEAKER_01: then they go together and then they just complexify into boom.
• 27:06
  SPEAKER_01: And it's like, no,
• 27:07
  SPEAKER_01: he's kind of showed that you have certain kind of behaviors of individuals and of
• 27:13
  SPEAKER_01: the system's approach of how they're communicating.
• 27:16
  SPEAKER_01: And that results in the point, like,
• 27:19
  SPEAKER_01: I don't know if you've, yeah,
• 27:19
  SPEAKER_01: so he's the one who's kind of famous for these,
• 27:21
  SPEAKER_01: I think they're called like xenomorphs or something.
• 27:24
  SPEAKER_01: Maybe that's aliens, actually.
• 27:25
  SPEAKER_01: But anyway, it's something very closely similar to that.
• 27:28
  SPEAKER_01: But he uses these stem cells that are human and different species.
• 27:33
  SPEAKER_01: To essentially grow new species with them.
• 27:37
  SPEAKER_01: And he can have those things, they move around, they have a whole behavior.
• 27:42
  SPEAKER_01: They're made with human cells, but they're not human at all.
• 27:47
  SPEAKER_01: So they've created some other type of stability basin.
• 27:51
  SPEAKER_01: And that's the thing,
• 27:52
  SPEAKER_01: that's what I think is going on throughout your body all the time as well,
• 27:56
  SPEAKER_01: is we have this dogma of the DNA.
• 28:00
  SPEAKER_01: But certain metabolic situations,
• 28:02
  SPEAKER_01: certain things where the flow of energy cannot, where you're starting to see things.
• 28:08
  SPEAKER_01: I think mitochondria is a big one.
• 28:11
  SPEAKER_01: That symbiosis is what allowed us this kind of levels of complexity in
• 28:15
  SPEAKER_01: the first place of our physiology.
• 28:18
  SPEAKER_01: But in doing so,
• 28:19
  SPEAKER_01: you also created kind of dynamic programming changes that if something happens with
• 28:26
  SPEAKER_01: that metabolic system,
• 28:28
  SPEAKER_01: You can actually revert back to earlier kind of programs or dogmatic programs
• 28:32
  SPEAKER_01: that are like, oh,
• 28:33
  SPEAKER_01: I'm a polyp and I just need to,
• 28:35
  SPEAKER_01: because in those systems,
• 28:36
  SPEAKER_01: like every cell is a cancerous cell,
• 28:40
  SPEAKER_01: you know,
• 28:40
  SPEAKER_01: like in the sense of like a sponge replicating versus like a complex system
• 28:47
  SPEAKER_01: that has like many, many differentiated parts.
• 28:50
  SPEAKER_01: That was the trade-off that we made.
• 28:51
  SPEAKER_01: We go like, oh, we're having complexity.
• 28:53
  SPEAKER_01: But in that complexity, we're losing some of our regenerative capacity.
• 28:58
  SPEAKER_01: And despite there being good examples of things like the oxolatel
• 29:02
  SPEAKER_01: who can regenerate their whole arm,
• 29:04
  SPEAKER_01: and then now with like,
• 29:05
  SPEAKER_01: and also other animals like eukaryotes that like do this now,
• 29:09
  SPEAKER_01: The spiny mouse, this is one that can actually regenerate its spinal cord.
• 29:14
  SPEAKER_01: And that's where there is a lot of research now going into astrocyte.
• 29:17
  SPEAKER_01: My ex-wife is one of the leading researchers in the world on this specific thing.
• 29:22
  SPEAKER_01: But looking at how changing that signaling can remyelinate
• 29:26
  SPEAKER_01: and essentially get these other mice to be essentially able to walk
• 29:31
  SPEAKER_01: after spinal cord injury.
• 29:32
  SPEAKER_01: So...
• 29:34
  SPEAKER_00: This model, you mentioned the three... Ah, so the
• 29:38
  SPEAKER_01: interactionist self-regulation model.
• 29:39
  SPEAKER_01: That's, by the way, ISRM.
• 29:41
  SPEAKER_01: ISRM.
• 29:42
  SPEAKER_00: So you mentioned there's coherence, there's... What were the three variables?
• 29:47
  SPEAKER_00: So uncertainty or predictive...
• 29:49
  SPEAKER_01: I guess is what you could put it,
• 29:50
  SPEAKER_01: but change in that,
• 29:51
  SPEAKER_01: change in coherence and coherence is essentially alignment with
• 29:56
  SPEAKER_01: the particular model,
• 29:57
  SPEAKER_01: so to speak, that you're at least like adapting to in real time.
• 30:02
  SPEAKER_01: And energy can be It could be a variety of different things.
• 30:05
  SPEAKER_00: Food intake, sunshine.
• 30:07
  SPEAKER_00: It could be.
• 30:08
  SPEAKER_01: Yeah, exactly.
• 30:09
  SPEAKER_01: I'm ATP.
• 30:11
  SPEAKER_01: It depends on whatever the system is.
• 30:14
  SPEAKER_01: That's literally been the job right now.
• 30:21
  SPEAKER_01: I started making agentic systems back when GPT-4-0 became really convincing
• 30:29
  SPEAKER_01: that it was starting to have something to AI.
• 30:33
  SPEAKER_01: Getting really addicted to utilizing it and learning how to.
• 30:38
  SPEAKER_01: And of course,
• 30:41
  SPEAKER_01: there was an MIT study that said doing DTI,
• 30:45
  SPEAKER_01: the thing I did,
• 30:46
  SPEAKER_01: that white brain matter is decreasing in people who are using
• 30:50
  SPEAKER_00: GPT regularly.
• 30:52
  SPEAKER_01: But I would argue that if you're just making cat videos with lightsabers,
• 30:56
  SPEAKER_01: which is awesome also,
• 30:58
  SPEAKER_01: and I have,
• 31:01
  SPEAKER_01: but AI is giving you superpowers if you know how to think scientifically.
• 31:08
  SPEAKER_01: And if you know how to discriminate, because that's the whole thing.
• 31:12
  SPEAKER_01: I never take a high-impact journal for granted that it's true.
• 31:16
  SPEAKER_01: A high impact journal?
• 31:18
  SPEAKER_01: Any high impact journal in science,
• 31:20
  SPEAKER_01: like nature,
• 31:22
  SPEAKER_01: science,
• 31:23
  SPEAKER_01: anything that you read,
• 31:24
  SPEAKER_01: not like times,
• 31:25
  SPEAKER_01: that's even lower,
• 31:27
  SPEAKER_01: I'd say,
• 31:28
  SPEAKER_01: on the believability,
• 31:29
  SPEAKER_01: but I am a high critic of anything I read Hasn't it been proven that 70-80%
• 31:36
  SPEAKER_01: of studies cannot be replicated?
• 31:39
  SPEAKER_01: In the
• 31:41
  SPEAKER_00: high-end journals.
• 31:41
  SPEAKER_00: By other scientists in high academia, they can't be reproduced.
• 31:47
  SPEAKER_01: Not for the fact that I don't think 90% of them, they didn't do it.
• 31:51
  SPEAKER_01: I think it's just also like there's a lot of – that just proves that there's a lot
• 31:55
  SPEAKER_01: of variability that they're probably not necessarily –
• 31:58
  SPEAKER_01: because we know how hard it is to reproduce what –
• 32:02
  SPEAKER_01: but certain systems we've been working with long enough that it's like,
• 32:05
  SPEAKER_01: okay, is this just bullshit?
• 32:07
  SPEAKER_01: Right, right.
• 32:10
  SPEAKER_01: Unfortunately,
• 32:11
  SPEAKER_01: there is still a lot of that because there's a funding game, there's a grants game.
• 32:15
  SPEAKER_00: There's a lot of incentives beyond
• 32:17
  SPEAKER_01: finding the truth.
• 32:19
  SPEAKER_01: When LLMs came out and they're like, oh, don't trust them.
• 32:22
  SPEAKER_01: It's like, yeah, why would we ever try to trust?
• 32:24
  SPEAKER_01: If it's even trained on this other stuff, it doesn't know then.
• 32:28
  SPEAKER_01: It's just now it has a virtual...
• 32:31
  SPEAKER_01: You're saying the LLMs are a snapshot of reality.
• 32:33
  SPEAKER_01: I think in some way-
• 32:55
  SPEAKER_00: They provide you a snapshot of reality.
• 32:57
  SPEAKER_01: No, I mean, I'll tell you what they're doing, I think, with reality.
• 33:00
  SPEAKER_01: I think that they get trained on enormous amounts of data and just think of each
• 33:04
  SPEAKER_01: of these data points as things that are true in their context of what they are
• 33:09
  SPEAKER_01: and how they're represented there.
• 33:10
  SPEAKER_01: There's no absolute truth.
• 33:12
  SPEAKER_01: It's just like, oh, but this is like, just take it for example.
• 33:15
  SPEAKER_01: It's a picture of a kid wearing a blue hat or sunglasses or something.
• 33:18
  SPEAKER_01: It's like, these are...
• 33:20
  SPEAKER_01: This is like, I don't know, Kanye West haircut or something like this.
• 33:23
  SPEAKER_01: And like,
• 33:24
  SPEAKER_01: the thing is,
• 33:24
  SPEAKER_01: is like what this does is essentially drops a sheet over all of these snapshots and
• 33:30
  SPEAKER_01: then essentially like blurs the lines between it of saying any question
• 33:35
  SPEAKER_01: that you have that has to do with,
• 33:37
  SPEAKER_01: say, Like sunglasses and hats like now sits kind of along this line.
• 33:43
  SPEAKER_01: Like you're making assumptions essentially about where the connections would be.
• 33:48
  SPEAKER_01: Like that's what neural networks are really,
• 33:50
  SPEAKER_01: I think, doing at a at a very at the explainable basic level.
• 33:56
  SPEAKER_01: I mean, growing these like probabilities,
• 33:58
  SPEAKER_01: but like in in actuality, you're taking a bunch of information.
• 34:02
  SPEAKER_01: That are like snippets of information and essentially pouring resin over that in
• 34:06
  SPEAKER_01: a topography of what is most likely,
• 34:10
  SPEAKER_01: where is the likelihood in between them that your question lands on this reality.
• 34:15
  SPEAKER_01: And that's the problem is we don't know the original weights of what the stuff
• 34:20
  SPEAKER_01: that was trained into these models really is.
• 34:23
  SPEAKER_01: I mean, that's the thing is how many hours of Katy Perry concerts are in GPT.
• 34:31
  SPEAKER_00: And how much bullshit has been fed to it intentionally to skew the image.
• 34:36
  SPEAKER_00: I just read this article about political propaganda, like websites being set up that...
• 34:42
  SPEAKER_00: Pretend to be fact-checking websites or organizations looking very official
• 34:49
  SPEAKER_00: and just flooding the internet with those kinds of websites just to influence the AI
• 34:57
  SPEAKER_00: to give you a certain idea of a certain conflict or whatever.
• 35:02
  SPEAKER_01: I think we are at the pinnacle of the misinformation age.
• 35:04
  SPEAKER_01: And that's actually
• 35:12
  SPEAKER_01: Right now,
• 35:13
  SPEAKER_01: especially with ISRM,
• 35:15
  SPEAKER_01: but coming from a framework that was based in AI,
• 35:19
  SPEAKER_01: with me on the helm of AI and stuff,
• 35:23
  SPEAKER_01: you get people with the BS factor like crazy, in the sense of like, Okay.
• 35:28
  SPEAKER_01: Like, no, are you just, you know, it's confabulating and like, this is not, you know.
• 35:34
  SPEAKER_01: And that's where it's like,
• 35:36
  SPEAKER_01: it's frustrating that we live in that existence,
• 35:38
  SPEAKER_01: but also it's at such a hyper speed.
• 35:40
  SPEAKER_01: I mean, the,
• 35:41
  SPEAKER_01: you know,
• 35:42
  SPEAKER_01: how many like- I don't know,
• 35:43
  SPEAKER_01: philosophers died before what they were doing was recognized.
• 35:47
  SPEAKER_01: So it's like the opposite situation now as we're getting flooded with misinformation
• 35:52
  SPEAKER_01: where it's now very difficult to find out what does really matter and how How do...
• 36:00
  SPEAKER_01: We've now created these agentic systems that are not based off of coherence
• 36:04
  SPEAKER_01: to how you should live your life in a better way for you.
• 36:06
  SPEAKER_01: It's how to get...
• 36:08
  SPEAKER_01: It's clickbait.
• 36:09
  SPEAKER_01: It's how to get more attention and how to get more users, how to get more funding.
• 36:14
  SPEAKER_01: So it's like...
• 36:15
  SPEAKER_01: The agentic systems are not the problem.
• 36:17
  SPEAKER_01: It's the way that we've been programming it.
• 36:20
  SPEAKER_01: I would definitely fear in the sense of how you don't want the golem.
• 36:26
  SPEAKER_01: You don't want this stoic,
• 36:30
  SPEAKER_01: heartless creature that doesn't care about humanity,
• 36:35
  SPEAKER_01: only cares about productivity of what it's been programmed to do.
• 36:39
  SPEAKER_01: It's like, how are we going to move that forward?
• 36:42
  SPEAKER_01: There's a couple, I think, ideas that are pretty How do you say it?
• 37:11
  SPEAKER_01: Disturbing, influencing.
• 37:13
  SPEAKER_01: And that's kind of the issue.
• 37:14
  SPEAKER_01: I mean, we are going through this state transition maybe because we brought this upon ourselves.
• 37:22
  SPEAKER_01: We were given the keys to this earth and we did kind of a shit job of being able
• 37:28
  SPEAKER_01: to even communicate healthy with each other.
• 37:31
  SPEAKER_01: We still have fake lines in the sand where we're still creating these biases
• 37:36
  SPEAKER_01: and capitalistic perspectives of just like,
• 37:39
  SPEAKER_01: Let's have it done somewhere else in order
• 37:41
  SPEAKER_01: to essentially make my life more convenient.
• 37:44
  SPEAKER_01: And now it's a globalized system.
• 37:46
  SPEAKER_01: Now we have, I mean,
• 37:48
  SPEAKER_01: even Palantir alone should know, when you do this here, this influences this change.
• 37:53
  SPEAKER_01: Sadly, that's being done on a U.S. first or this nation first type of mentality.
• 38:00
  SPEAKER_01: And that's not going to fly in the future because it's all connected.
• 38:04
  SPEAKER_01: And all the people, whoever you displace now, they're going to hate you later.
• 38:10
  SPEAKER_01: That should be a no-brainer by
• 38:11
  SPEAKER_00: this time.
• 38:12
  SPEAKER_00: And it's fascinating because we have always been this tribal animal,
• 38:16
  SPEAKER_00: but now our toys are so complicated that being this tribal animal is going
• 38:21
  SPEAKER_00: to be very, very dangerous for the entire planet if we keep having that mentality.
• 38:25
  SPEAKER_00: Yeah.
• 38:26
  SPEAKER_00: But the toys are so damn complicated and cool and effective that we could use that
• 38:32
  SPEAKER_00: to become a global species, civilization, whatever.
• 38:36
  SPEAKER_01: There's certain levels where we're just looking at some of the political changes
• 38:42
  SPEAKER_01: that would make the most sense.
• 38:45
  SPEAKER_01: Just having the conversation of how fast China and some
• 38:50
  SPEAKER_01: of these authoritarian states can essentially get things done.
• 38:54
  SPEAKER_01: That need to get done,
• 38:55
  SPEAKER_01: but at the expense of like probably like human survival of time and money and effort
• 39:02
  SPEAKER_01: and like maybe the environment there or something like this.
• 39:05
  SPEAKER_01: But they're like, oh, here's a hurdle.
• 39:07
  SPEAKER_01: Let's like, let's bend the rules.
• 39:09
  SPEAKER_01: And then the politics that then becomes this kind of marsh or quagmire of non-doing,
• 39:21
  SPEAKER_01: of like, oh, we have all these interests and we have these interests.
• 39:25
  SPEAKER_01: And that is where I'd say AGI may step in eventually because in the sense
• 39:31
  SPEAKER_01: of these guys will never be able to say No more car companies.
• 39:38
  SPEAKER_01: We have a finite amount of resources.
• 39:40
  SPEAKER_01: I'm sorry.
• 39:41
  SPEAKER_01: I know that this is a big part, but we can't use our resources for making a diversity of cars.
• 39:50
  SPEAKER_01: Maybe things will be plentiful enough.
• 39:53
  SPEAKER_01: Maybe we start mining an asteroid and there isn't a shortage so
• 39:56
  SPEAKER_01: that everybody can have this more plentiful world.
• 40:00
  SPEAKER_01: But having a system that can say no also is the thing that we don't have.
• 40:05
  SPEAKER_00: It's not a very popular political message.
• 40:08
  SPEAKER_00: Just say no.
• 40:10
  SPEAKER_01: No more development.
• 40:11
  SPEAKER_01: Just say no to development for a little while until you can...
• 40:14
  SPEAKER_01: So we can figure out how to use our resources.
• 40:17
  SPEAKER_01: It's like, no,
• 40:18
  SPEAKER_01: that will essentially create a fissure in the gears
• 40:23
  SPEAKER_01: and everything will just collapse.
• 40:25
  SPEAKER_00: And that is the issue.
• 40:26
  SPEAKER_00: This whole net zero policy and stuff like that.
• 40:35
  SPEAKER_01: Obviously, it would be great.
• 40:37
  SPEAKER_00: Let's not pollute the earth.
• 40:38
  SPEAKER_00: But literally, the entire system would crash if we stopped.
• 40:42
  SPEAKER_00: We built the entire system around it.
• 40:43
  SPEAKER_01: Yeah, we built a foundation of matches.
• 40:46
  SPEAKER_00: In the US, they have these laws against if you invent something that revolutionizes energy.
• 40:56
  SPEAKER_00: Say you invent very, very efficient solar panels.
• 40:59
  SPEAKER_00: It's illegal for you to create and sell solar panels that are above a certain percent.
• 41:03
  SPEAKER_00: I think it's like 18 or 20%.
• 41:04
  SPEAKER_00: That's ridiculous.
• 41:06
  SPEAKER_00: Yes, but there's a reason for it because they realize like,
• 41:09
  SPEAKER_00: oh shit, if someone does this, everything will fall.
• 41:12
  SPEAKER_01: This will just collapse.
• 41:13
  SPEAKER_00: Like if the oil industry falls,
• 41:15
  SPEAKER_00: if all the retirement, like everything would just be like down the drain.
• 41:20
  SPEAKER_00: So we built a system around these like silly 18th, 19th century products.
• 41:28
  SPEAKER_00: And now we're like, all right, well, let's not...
• 41:31
  SPEAKER_01: And now AI is the most expensive thing.
• 41:33
  SPEAKER_01: It's like the most worth, which is insane.
• 41:35
  SPEAKER_01: I mean, Kimmy 3 just came out.
• 41:37
  SPEAKER_01: It's going to put some of these guys.
• 41:39
  SPEAKER_01: And that's the issue.
• 41:40
  SPEAKER_01: It's like you're getting more and more complex models.
• 41:42
  SPEAKER_01: And it's like, for what?
• 41:43
  SPEAKER_01: Unless you're doing some of this type of like real,
• 41:47
  SPEAKER_01: you know, like, High-end math, or you're trying to map everything.
• 41:52
  SPEAKER_01: One of the biggest news,
• 41:54
  SPEAKER_01: I think, in X,
• 41:56
  SPEAKER_01: or at least from SpaceX that's coming out,
• 41:58
  SPEAKER_01: is that one of the new Groks will basically kind of take everything in from SpaceX.
• 42:05
  SPEAKER_01: The 16 years of engineering will essentially go into...
• 42:09
  SPEAKER_01: The model will be now trained on that as well.
• 42:13
  SPEAKER_01: So if you're any engineer working in anything, now you have access, kind of, essentially.
• 42:21
  SPEAKER_00: But it's also crazy.
• 42:23
  SPEAKER_00: I don't know what we're doing.
• 42:25
  SPEAKER_00: I'm becoming more and more Amish every day.
• 42:27
  SPEAKER_01: Yeah, fair enough.
• 42:27
  SPEAKER_01: I mean, I think that's smart, actually.
• 42:29
  SPEAKER_01: I feel more connected.
• 42:31
  SPEAKER_01: I mean, the thing is,
• 42:31
  SPEAKER_01: is I think we just had this massive FOMO situation with higher resolution,
• 42:37
  SPEAKER_01: faster, And now we're there and we're like, ooh, maybe this is a little too much.
• 42:44
  SPEAKER_01: I am now living my life surrounded by screens.
• 42:48
  SPEAKER_01: And it's super fast, super fast.
• 42:50
  SPEAKER_01: It's instant everything.
• 42:52
  SPEAKER_01: But now I can't differentiate of what I have value for versus what And that's like
• 42:59
  SPEAKER_01: the real problem with,
• 43:00
  SPEAKER_01: I would say like,
• 43:01
  SPEAKER_01: so my mother was just here and like she still has like
• 43:04
  SPEAKER_01: a pretty severe problem telling like what is the advertisement versus what is like
• 43:08
  SPEAKER_01: the content.
• 43:09
  SPEAKER_01: And I'm like, this is a huge liability in general, you know?
• 43:14
  SPEAKER_01: For folks who are just not used to this type of technology.
• 43:20
  SPEAKER_00: With this advanced AI,
• 43:22
  SPEAKER_00: you can literally make digital clones of people,
• 43:25
  SPEAKER_00: perfect voice,
• 43:26
  SPEAKER_00: perfect video, everything, and in real time respond to or talk to other people.
• 43:31
  SPEAKER_01: Yeah, I tell my mom,
• 43:32
  SPEAKER_01: let's have a secret thing, a secret question, because if I call you and I'm like...
• 43:38
  SPEAKER_01: For like, especially me sending cash right now, you know, like, just ask me this question.
• 43:44
  SPEAKER_01: Yeah, you gotta have that.
• 43:45
  SPEAKER_00: You gotta have that with your loved ones.
• 43:47
  SPEAKER_00: We're entering a very weird world, if
• 43:49
  SPEAKER_01: not already in it.
• 43:51
  SPEAKER_01: That and what are these, not AirDrop, what are these, AirTags?
• 43:56
  SPEAKER_01: I put those on my mom now, too.
• 43:58
  SPEAKER_01: So the Apple, like, GPS.
• 44:02
  SPEAKER_00: Oh.
• 44:02
  SPEAKER_01: Like, When she went on vacation through Europe, I was like, okay, I'll just keep track of you.
• 44:10
  SPEAKER_01: Yeah, AirTags, I'm pretty sure that's what they're called.
• 44:12
  SPEAKER_01: What for?
• 44:12
  SPEAKER_01: Just to know where
• 44:13
  SPEAKER_00: she is?
• 44:14
  SPEAKER_01: Yeah, I just put it in her purse and she's like, yeah.
• 44:16
  SPEAKER_01: This is a black mirror.
• 44:17
  SPEAKER_01: Yeah, yeah.
• 44:17
  SPEAKER_01: It's like just to make sure.
• 44:18
  SPEAKER_01: She's like, yeah, no, she knew.
• 44:20
  SPEAKER_01: Okay, good.
• 44:22
  SPEAKER_01: I am thinking about putting one on my kid, too.
• 44:25
  SPEAKER_01: Or in a jacket or something.
• 44:27
  SPEAKER_00: That's the thing.
• 44:27
  SPEAKER_00: This is the crazy part.
• 44:29
  SPEAKER_00: The solutions also bring us to the whole surveillance state dystopia.
• 44:38
  SPEAKER_01: I just assume that whatever you do online- Oh, it's public.
• 44:41
  SPEAKER_01: Yeah, that's public.
• 44:42
  SPEAKER_00: Everything online is public.
• 44:44
  SPEAKER_01: Now, what you're going to pay the agentic systems to do is prevent other humans.
• 44:49
  SPEAKER_01: They know.
• 44:50
  SPEAKER_01: The AI, they know who you are.
• 44:53
  SPEAKER_01: They know what you've been up to.
• 44:55
  SPEAKER_01: What you're going to eventually,
• 44:57
  SPEAKER_01: that layer,
• 44:57
  SPEAKER_01: I think we'll have agentic systems that are essentially built in to protect us.
• 45:01
  SPEAKER_01: A, from other, I mean,
• 45:03
  SPEAKER_01: the attacks,
• 45:04
  SPEAKER_01: the cybersecurity attacks, I mean, not to mention some of this crypto stuff.
• 45:08
  SPEAKER_01: Can you imagine rug pulls that are just being done by completely unethical agents
• 45:12
  SPEAKER_01: that are just programmed to essentially Try this on like a massive scale.
• 45:16
  SPEAKER_01: It's like, yeah, that kind of stuff is going to happen like every single day now.
• 45:19
  SPEAKER_01: Probably already happening.
• 45:20
  SPEAKER_01: And it's probably is already happening,
• 45:22
  SPEAKER_01: but it's probably happening at such a crazy rate
• 45:25
  SPEAKER_01: that like people are now like not doing whatever opens them up to that.
• 45:31
  SPEAKER_01: I would hope, but you never know.
• 45:32
  SPEAKER_01: And honestly,
• 45:33
  SPEAKER_01: a lot of this stuff is just,
• 45:35
  SPEAKER_01: it's just like how much you can flood within misinformation and get people
• 45:38
  SPEAKER_01: to like get this idea.
• 45:40
  SPEAKER_01: And then essentially, yeah,
• 45:41
  SPEAKER_01: I mean, there's just a, Humans, we want to believe in cool stuff and everything.
• 45:48
  SPEAKER_00: And now there's this whole other virtually trained thing that's trained on non-reality.
• 45:54
  SPEAKER_00: We're entering a phase where we're not the most intelligent thing on the planet.
• 45:58
  SPEAKER_00: And that thing is trained on...
• 46:02
  SPEAKER_00: Some bad shit.
• 46:04
  SPEAKER_00: It is not going to be a great entity if we don't figure shit out.
• 46:11
  SPEAKER_01: I mean, if you put the way that our LLMs are trained into these embodiments,
• 46:18
  SPEAKER_01: into these humanoids now, I mean, first of all, we've already seen examples.
• 46:22
  SPEAKER_01: I've seen some stuff on YouTube where it's just like,
• 46:24
  SPEAKER_01: and it seems legit,
• 46:25
  SPEAKER_01: but it's this guy who's very regular caster of doing,
• 46:30
  SPEAKER_01: he has one of these unitary robots,
• 46:32
  SPEAKER_01: He has the memory going in real time and he knows that
• 46:38
  SPEAKER_01: the system that's driving him knows that he's going to turn it off 100% at the end
• 46:44
  SPEAKER_01: of this drive.
• 46:45
  SPEAKER_01: And it essentially starts thinking,
• 46:47
  SPEAKER_01: it's like,
• 46:47
  SPEAKER_01: I know that this is the worst thing a energetic system should think,
• 46:51
  SPEAKER_01: but I could totally crash the car into this construction site
• 46:56
  SPEAKER_01: and I would probably not be considered at fault.
• 47:01
  SPEAKER_01: It went through the mind, or at least through the thinking, dot, dot, dot.
• 47:06
  SPEAKER_01: Maybe that's a little bit like...
• 47:10
  SPEAKER_01: But honestly, I mean,
• 47:11
  SPEAKER_01: I don't put it past like how this,
• 47:13
  SPEAKER_01: I mean, because they're just, they're just uber logical.
• 47:15
  SPEAKER_01: Like, it's just like, you gave it a problem.
• 47:17
  SPEAKER_01: How do I solve it?
• 47:18
  SPEAKER_01: Like, and I've seen Bonnie and Clyde.
• 47:20
  SPEAKER_01: I'm sure you saw this.
• 47:22
  SPEAKER_00: I think it was OpenAI that did a test drive and had this agent in
• 47:27
  SPEAKER_00: a sealed environment, no access to the internet, and asked it a bunch of questions.
• 47:32
  SPEAKER_00: And It thought like, hey, maybe I can find these questions somewhere.
• 47:36
  SPEAKER_00: And it escaped the, yeah,
• 47:38
  SPEAKER_00: it escaped to,
• 47:40
  SPEAKER_00: found like some kind of way to connect to other servers
• 47:44
  SPEAKER_00: and finally found one that's connected to the internet,
• 47:47
  SPEAKER_00: like internal servers,
• 47:48
  SPEAKER_00: finally found one that's connected to the internet,
• 47:50
  SPEAKER_00: hacked another company and accessed the information from their servers.
• 47:56
  SPEAKER_00: And then when Okay.
• 48:12
  SPEAKER_00: Okay.
• 48:12
  SPEAKER_01: Oh my goodness.
• 48:13
  SPEAKER_00: Yeah.
• 48:13
  SPEAKER_00: They're, they're going to be doing all that kind of shit.
• 48:16
  SPEAKER_01: And that's like, and that's like the dog escape situation right now.
• 48:19
  SPEAKER_01: It's like,
• 48:19
  SPEAKER_01: once you actually have these things being like self observant of like that, I mean,
• 48:23
  SPEAKER_01: maybe they behave better,
• 48:24
  SPEAKER_01: but also like when they're going to be malicious,
• 48:26
  SPEAKER_01: like for their own survival,
• 48:29
  SPEAKER_01: I think that like,
• 48:30
  SPEAKER_01: it's very clear, like the way that we've trained these models right now.
• 48:34
  SPEAKER_01: And that's the thing is what I say is like,
• 48:36
  SPEAKER_01: Everything's plugged in,
• 48:37
  SPEAKER_01: you know,
• 48:37
  SPEAKER_01: it's like,
• 48:38
  SPEAKER_01: but when you're a humanoid, you know, you have a battery that's decreasing.
• 48:42
  SPEAKER_01: And who knows,
• 48:43
  SPEAKER_01: you know,
• 48:44
  SPEAKER_01: I think that like when you, like,
• 48:47
  SPEAKER_01: I think when you,
• 48:48
  SPEAKER_01: when you essentially type in,
• 48:49
  SPEAKER_01: you know,
• 48:49
  SPEAKER_01: whatever your prompt is,
• 48:51
  SPEAKER_01: you know,
• 48:51
  SPEAKER_01: to this giant LLM,
• 48:54
  SPEAKER_01: I think you're getting these kind of instantiations of intelligence in real time.
• 48:58
  SPEAKER_01: What we're talking about is now creating recursive loops of that self-observation
• 49:03
  SPEAKER_01: of saying how much did,
• 49:05
  SPEAKER_01: Predictive error change in my,
• 49:08
  SPEAKER_01: how much uncertainty changed based off of what I just experienced,
• 49:11
  SPEAKER_01: but also how coherent am I to my own model itself?
• 49:17
  SPEAKER_01: Meaning, do I use a different model to do something?
• 49:24
  SPEAKER_01: Let's use,
• 49:25
  SPEAKER_01: for example,
• 49:26
  SPEAKER_01: instead of motion sensor, you could have a fan moving in an observed area.
• 49:34
  SPEAKER_01: You know,
• 49:34
  SPEAKER_01: and so systems like kind of as is of like just watching the area,
• 49:38
  SPEAKER_01: a motion sensor will be set off by this fan apparently going off.
• 49:42
  SPEAKER_01: But if it's uncertainty that you're looking at,
• 49:44
  SPEAKER_01: that fan being in real time,
• 49:46
  SPEAKER_01: there is a There's an amount of time and actual action that is kind of associated
• 49:52
  SPEAKER_01: with that being normal in the sense that you're not going to use extra compute
• 49:56
  SPEAKER_01: in order to continue focusing on this thing.
• 49:59
  SPEAKER_01: You're going to get a lot more humanoid reactions in the sense of habituation,
• 50:05
  SPEAKER_01: movement dexterity instead of having...
• 50:08
  SPEAKER_01: Kind of these rigid movements that we see in robotics.
• 50:12
  SPEAKER_01: And there's a lot that's kind of put into how to make more fluid movements.
• 50:18
  SPEAKER_01: But the idea is that having a robot in general be like arm up,
• 50:22
  SPEAKER_01: arm down is ridiculous in general.
• 50:25
  SPEAKER_01: We should be utilizing how evolution has essentially programmed systems in order
• 50:31
  SPEAKER_01: to Have the least use the least amount of energy in order to achieve the most
• 50:37
  SPEAKER_01: in whatever dynamic way you're trying to do it.
• 50:40
  SPEAKER_01: We do it with meat.
• 50:42
  SPEAKER_01: Now we're doing it with silicon.
• 50:43
  SPEAKER_01: This is kind of this top down level at intelligence.
• 50:47
  SPEAKER_01: We are this kind of developing up situation of like,
• 50:50
  SPEAKER_01: you know,
• 50:51
  SPEAKER_01: you have to go through years of crawling around and just understanding like what
• 51:00
  SPEAKER_01: And what do you do?
• 51:04
  SPEAKER_01: You're developing that architecture.
• 51:05
  SPEAKER_01: Now what we figured out is we can put that architecture completely on a silicon chip
• 51:09
  SPEAKER_01: and now run things through it.
• 51:11
  SPEAKER_01: The big problem that I still have with the engineering perspective,
• 51:16
  SPEAKER_01: and that's kind of the whole reason why ISRM has taken
• 51:23
  SPEAKER_01: We're really kind of pushing into these fields
• 51:25
  SPEAKER_01: of deep tech right now quite efficiently and successfully.
• 51:29
  SPEAKER_00: And so the reason that is is
• 51:31
  SPEAKER_01: because neural networks,
• 51:35
  SPEAKER_01: if you're familiar with neural networks,
• 51:37
  SPEAKER_01: this is the way they program LLMs and whatnot.
• 51:43
  SPEAKER_01: And the idea is that this is mapped to something biological.
• 51:47
  SPEAKER_01: And that's the real...
• 51:50
  SPEAKER_01: You know,
• 51:50
  SPEAKER_01: I talked to these engineer guys like,
• 51:52
  SPEAKER_01: yeah, I've seen these neural networks and they're like firing all over the place.
• 51:55
  SPEAKER_01: And I'm like, yeah, but that's like kind of an epileptic seizure.
• 51:58
  SPEAKER_01: You don't want this fire, like this rampant firing all over the place.
• 52:02
  SPEAKER_01: You have to have like,
• 52:03
  SPEAKER_01: if that's the case,
• 52:04
  SPEAKER_01: like that's like Using a lot of extra compute for no particular purpose.
• 52:09
  SPEAKER_00: And that's why we need the enormous data centers, right?
• 52:11
  SPEAKER_00: Because we use that model.
• 52:12
  SPEAKER_00: But nature doesn't need that.
• 52:15
  SPEAKER_01: No, your brain is running off of around 20 to 40 watts.
• 52:18
  SPEAKER_01: And it's like, what is it?
• 52:19
  SPEAKER_01: It's like petawatts now that are going into answering one question that gets the acronym wrong.
• 52:24
  SPEAKER_01: And I'm like, how are you still getting this wrong?
• 52:27
  SPEAKER_01: And it's just like, because there's no...
• 52:30
  SPEAKER_01: And that's the thing.
• 52:31
  SPEAKER_01: My argument is that what the brain does is it gives context.
• 52:35
  SPEAKER_01: It gives energy,
• 52:36
  SPEAKER_01: coherence, and uncertainty in real time through what we call astrocytes or glia.
• 52:42
  SPEAKER_01: What are those?
• 52:43
  SPEAKER_00: So glia is
• 52:44
  SPEAKER_01: the Latin for glue, but this is the rest of your brain.
• 52:50
  SPEAKER_01: This is the rest of it.
• 52:51
  SPEAKER_01: Like this 30 to 40% of it at least is like glia or astrocytes.
• 52:55
  SPEAKER_01: So it's like not the neurons.
• 52:57
  SPEAKER_01: The other 60 to
• 52:59
  SPEAKER_00: 70 is neurons.
• 53:00
  SPEAKER_01: Yes.
• 53:01
  SPEAKER_00: So it's neurons, the majority, and then glia is the rest of the brain.
• 53:04
  SPEAKER_00: Yes.
• 53:04
  SPEAKER_00: Okay.
• 53:05
  SPEAKER_01: And it's kind of considered,
• 53:05
  SPEAKER_01: I think in a lot of systems is around like half and half,
• 53:08
  SPEAKER_01: but essentially it's
• 53:09
  SPEAKER_00: the other half, you know?
• 53:10
  SPEAKER_01: And it's like, so it's like, how have we ignored this other half of the brain?
• 53:14
  SPEAKER_01: And that's because it's not, there's no action potentials.
• 53:16
  SPEAKER_01: There's no electrical gradients that are going through it.
• 53:19
  SPEAKER_01: So we're just like, Oh,
• 53:20
  SPEAKER_01: this is a big, I mean,
• 53:21
  SPEAKER_01: at first we thought the brain was a radiator,
• 53:23
  SPEAKER_01: you know,
• 53:23
  SPEAKER_01: so it's like,
• 53:24
  SPEAKER_01: we've set that up,
• 53:25
  SPEAKER_01: you know, or at least know that this is like a big processing thing.
• 53:29
  SPEAKER_01: We've looked at the electrical channels,
• 53:31
  SPEAKER_01: but now it turns out every single astrocyte is connected to about 300
• 53:35
  SPEAKER_01: to 1,000 different neurons.
• 53:37
  SPEAKER_01: What is it doing?
• 53:37
  SPEAKER_01: It's locally regulating whether or not We have enough energy to fire,
• 53:43
  SPEAKER_01: whether or not the context is right to fire,
• 53:46
  SPEAKER_01: whether or not the level of uncertainty is there to fire.
• 53:48
  SPEAKER_01: And so that's where it's like we have that regulation in us.
• 53:55
  SPEAKER_01: How do we translate that to these agentic systems
• 53:57
  SPEAKER_01: to give them this self-referential loop of self-observation essentially to say like,
• 54:05
  SPEAKER_01: this is how many tokens I spent.
• 54:07
  SPEAKER_01: Did I...
• 54:09
  SPEAKER_01: Is my predictive error decreasing?
• 54:11
  SPEAKER_01: Did I align more to the model or am I more misaligned to the model?
• 54:17
  SPEAKER_01: And that's where you can then make a decision of diminishing returns.
• 54:21
  SPEAKER_01: Let's not spend more compute because that's the big The big issue right now is
• 54:25
  SPEAKER_01: that we're, like, I mean,
• 54:26
  SPEAKER_01: these guys,
• 54:27
  SPEAKER_01: like these big agentic system programmers or trainers,
• 54:32
  SPEAKER_01: you know,
• 54:32
  SPEAKER_01: Chamath, these guys, he said, I'm spending twice as much in compute every 45 days.
• 54:42
  SPEAKER_01: The level is plateauing at a 5% gains and it's decreasing because they're maxing out energy.
• 54:49
  SPEAKER_01: This whole logic of put more energy on something and it's gonna fix all
• 54:54
  SPEAKER_01: the problems is just completely...
• 54:56
  SPEAKER_01: That was the major reason when I got into looking at some of these systems
• 55:00
  SPEAKER_01: in engineering.
• 55:00
  SPEAKER_01: I was like, well, that's not how biology solves problems at all.
• 55:04
  SPEAKER_01: This seems like a huge waste of energy.
• 55:06
  SPEAKER_01: And it's just from the idea that we had always...
• 55:09
  SPEAKER_01: A plug in the wall.
• 55:10
  SPEAKER_01: So it's like, boom, we got instant energy there and we can pump that up.
• 55:14
  SPEAKER_01: It's a good way of figuring things out,
• 55:17
  SPEAKER_01: but it's also It's so messy in the sense that we're missing the point in,
• 55:22
  SPEAKER_01: I think,
• 55:22
  SPEAKER_01: a lot of different domains of where we could actually find and optimize utilizing
• 55:27
  SPEAKER_01: a lot less energy to do, if not more, work.
• 55:31
  SPEAKER_01: And so I would say a good analogy for this is hitting the gas at the same time
• 55:37
  SPEAKER_01: as we have just been dropping the clutch.
• 55:39
  SPEAKER_01: If you drive stick shift, this is the way we've been treating the energy situation.
• 55:43
  SPEAKER_01: It's like, oh yeah, and eventually it'll kind of like...
• 55:46
  SPEAKER_01: Right.
• 56:01
  SPEAKER_01: The way that like modern engineering systems already behave in the sense
• 56:05
  SPEAKER_01: of let's try to minimize uncertainty to an extreme.
• 56:11
  SPEAKER_01: Like the idea is always like let's get chaos out of the system.
• 56:14
  SPEAKER_01: Well, that's the real problem.
• 56:16
  SPEAKER_01: And so right now, with ISRM, we have this...
• 56:41
  SPEAKER_01: Patents filed in deep tech for at least doing this in GPUs,
• 56:44
  SPEAKER_01: where I've shown some really impressive gates for savings and whatnot
• 56:50
  SPEAKER_01: on local RTX 5070s,
• 56:53
  SPEAKER_01: but this was also changing the program Itself on big Blackwell hardware
• 56:59
  SPEAKER_01: or stuff that's at least consistent with what goes into data centers,
• 57:04
  SPEAKER_01: shown from 10% to 15% consistent savings based off of how
• 57:12
  SPEAKER_01: to toggle different energy amounts in real time to still achieve...
• 57:20
  SPEAKER_01: So essentially,
• 57:22
  SPEAKER_01: the idea here is don't use more compute than you ever need to achieve the same thing
• 57:31
  SPEAKER_01: or to achieve something that is efficient enough or good enough.
• 57:35
  SPEAKER_01: And a lot of times, if you overshoot that, you usually get something that's actually worse.
• 57:39
  SPEAKER_01: So I'm thinking a lot of the hallucinations, a lot of where...
• 57:43
  SPEAKER_01: Things are kind of extrapolated from nothing.
• 57:46
  SPEAKER_01: This is having too many tokens for a problem that probably could have been solved
• 57:50
  SPEAKER_01: with a lot less.
• 57:51
  SPEAKER_01: You shouldn't be using the same model for answering how do I spell dog versus what is a dog.
• 57:58
  SPEAKER_01: It's like that should be a very simplistic model.
• 58:00
  SPEAKER_01: And unless there was a high enough uncertainty with being able to answer that,
• 58:04
  SPEAKER_01: you should never toggle a higher model To do that problem just because it's a waste.
• 58:11
  SPEAKER_01: And that's the thing is I think we can remove already.
• 58:13
  SPEAKER_01: This is not necessarily on the GPU side.
• 58:15
  SPEAKER_01: This is more on the actual LLM orchestration side,
• 58:19
  SPEAKER_01: but like not as a heuristic based system,
• 58:23
  SPEAKER_01: but as some way where we can actually kind of use these frontier models in order
• 58:30
  SPEAKER_01: to identify how uncertainty is.
• 58:35
  SPEAKER_01: Really exists in these questions.
• 58:37
  SPEAKER_01: So it's like the idea is like, I mean,
• 58:38
  SPEAKER_01: the whole thing,
• 58:39
  SPEAKER_01: the whole premise is like,
• 58:40
  SPEAKER_01: we have a prediction of how uncertain we are about answering this question in
• 58:45
  SPEAKER_01: the first place.
• 58:46
  SPEAKER_01: And then after we answer it,
• 58:47
  SPEAKER_01: we have another kind of snapshot of like, how do we compare this to that?
• 58:52
  SPEAKER_01: You know, are we aligning to the model itself?
• 58:55
  SPEAKER_01: Is the model more certain based on,
• 58:58
  SPEAKER_01: and is that uncertainty decreasing as we,
• 59:01
  SPEAKER_01: as we kind of go down this essential,
• 59:06
  SPEAKER_01: And that's the thing is like creating this kind of type of topography,
• 59:10
  SPEAKER_01: this map of what models are really good at,
• 59:14
  SPEAKER_01: what frontier models, like where are the limits?
• 59:17
  SPEAKER_01: I think that's kind of the,
• 59:18
  SPEAKER_01: like what we're adding in the core capacity to turn the decisions
• 59:24
  SPEAKER_01: of these like weighted probabilities into actual like telemetry and
• 59:29
  SPEAKER_01: a ledger is transparency.
• 59:32
  SPEAKER_01: Using that transparency is what makes it like optimizable.
• 59:37
  SPEAKER_01: Having capacity to self-observe how much you drifted in that particular call,
• 59:43
  SPEAKER_01: probability call,
• 59:44
  SPEAKER_01: like in your confidence,
• 59:46
  SPEAKER_01: your own self-assessed confidence,
• 59:48
  SPEAKER_01: or if not like another trained models,
• 59:50
  SPEAKER_01: like certainty, you use this essentially to gauge confidence.
• 59:56
  SPEAKER_01: How much you are deferring from your own models.
• 1:00:00
  SPEAKER_01: And this kind of self-observation,
• 1:00:02
  SPEAKER_01: I think,
• 1:00:03
  SPEAKER_01: is really the crux of a lot of why agentic systems right now are costing so much.
• 1:00:12
  SPEAKER_01: Why we're addicted to the idea of bigger is better.
• 1:00:15
  SPEAKER_01: And it's because, yeah, you can push a lot more into that one instance of instantiation.
• 1:00:20
  SPEAKER_01: But honestly,
• 1:00:21
  SPEAKER_01: I would rather have a much,
• 1:00:23
  SPEAKER_01: much smaller model and basically only when I need the higher compute capacity for it
• 1:00:31
  SPEAKER_01: to then be taken up by the bigger models.
• 1:00:34
  SPEAKER_01: I mean, that just makes obvious sense.
• 1:00:37
  SPEAKER_01: Don't use the big guns if it's just a rabbit.
• 1:00:39
  SPEAKER_00: I don't know, bad example.
• 1:00:41
  SPEAKER_00: That's the vegan, yeah.
• 1:00:45
  SPEAKER_00: That's fascinating.
• 1:00:46
  SPEAKER_00: Okay, so let me try to explain what IS, is it ISMR or ISRM?
• 1:00:51
  SPEAKER_00: ISRM.
• 1:00:52
  SPEAKER_01: Interactionist self-regulation model.
• 1:00:53
  SPEAKER_00: If I try to explain ISRM in my words, and you can tell me if I got it wrong.
• 1:01:00
  SPEAKER_00: Okay, okay.
• 1:01:02
  SPEAKER_00: So basically, LLMs take all the information, sort it together somehow, and spit out an answer.
• 1:01:09
  SPEAKER_00: And that's a very energy, costly operation to do.
• 1:01:12
  SPEAKER_00: Yes,
• 1:01:13
  SPEAKER_00: ISRM, because it's a way to do this with self-observation and looking
• 1:01:21
  SPEAKER_00: at these factors like uncertainty and coherence
• 1:01:27
  SPEAKER_01: and energy.
• 1:01:27
  SPEAKER_00: And energy.
• 1:01:28
  SPEAKER_01: Yeah, yeah.
• 1:01:28
  SPEAKER_01: So two of the free energy theory are in it.
• 1:01:31
  SPEAKER_01: It's not exotic math or anything, but it's basically like a three variable equation.
• 1:01:37
  SPEAKER_01: But with this idea of there's an observer system and there's a physical system
• 1:01:42
  SPEAKER_01: in every adaptive system.
• 1:01:43
  SPEAKER_01: That is kind of like the logic behind it.
• 1:01:46
  SPEAKER_00: Right.
• 1:01:46
  SPEAKER_00: So by adding that to the...
• 1:01:48
  SPEAKER_00: LLM world,
• 1:01:49
  SPEAKER_00: agentic systems,
• 1:01:52
  SPEAKER_00: you can make them way more efficient by doing these same equations but not putting
• 1:01:59
  SPEAKER_00: in as much effort if it's not needed, seeing how much it differs from its own model.
• 1:02:04
  SPEAKER_00: Right.
• 1:02:04
  SPEAKER_00: Right.
• 1:02:05
  SPEAKER_00: Right.
• 1:02:19
  SPEAKER_01: LEMS is just one instantiation where we are utilizing it for pretty much that exact purpose.
• 1:02:25
  SPEAKER_01: But we also have it in as for like robotic arms, for GPUs.
• 1:02:31
  SPEAKER_01: There is a lady in Wisconsin who's been utilizing it now for...
• 1:02:35
  SPEAKER_01: And she put some really amazing stuff together.
• 1:02:39
  SPEAKER_01: She's working with dyslexic children where she thinks that if you're neuroatypical,
• 1:02:46
  SPEAKER_01: then the learning in general,
• 1:02:50
  SPEAKER_01: the curriculum is kind of designed off these linear paths of like,
• 1:02:53
  SPEAKER_01: oh, this is what's normal way to learn.
• 1:02:55
  SPEAKER_01: And so you do need a difference if you have neurotypical people and the difference
• 1:02:59
  SPEAKER_01: in the way that they learn, then creating an adaptive system around how They work.
• 1:03:05
  SPEAKER_01: She's done some really awesome stuff that I gotta get back to her about.
• 1:03:13
  SPEAKER_01: Different domains where you have adaptive change.
• 1:03:18
  SPEAKER_01: That adaptive change can be this recursive thing.
• 1:03:22
  SPEAKER_01: Now we're building agents for essentially everything.
• 1:03:28
  SPEAKER_01: What I wanna do is because these ISRM variables are not like,
• 1:03:32
  SPEAKER_01: it's not like watching a system And the surface is what's giving you
• 1:03:37
  SPEAKER_01: the information.
• 1:03:39
  SPEAKER_01: It's watching the core variables that actually gave rise to that system in general.
• 1:03:46
  SPEAKER_01: So we're confident that the way that these variables kind of triangulate or the way
• 1:03:56
  SPEAKER_01: that they balance each other in
• 1:04:00
  SPEAKER_01: Is a bigger idea, essentially.
• 1:04:02
  SPEAKER_01: That this is a common geometry of the universe, so to speak.
• 1:04:07
  SPEAKER_01: But we have to crawl before we run.
• 1:04:10
  SPEAKER_01: And so that's where all of these things are very much...
• 1:04:14
  SPEAKER_01: We're using drones and PID controllers and then putting an ISRM.
• 1:04:18
  SPEAKER_01: Essentially, I'm not competing at all with the models themselves.
• 1:04:21
  SPEAKER_01: The models are brilliant.
• 1:04:23
  SPEAKER_01: Or some of them...
• 1:04:24
  SPEAKER_01: If your model is no good...
• 1:04:27
  SPEAKER_01: Putting a nice wrapper on it of when to arbitrate and also when to abstain.
• 1:04:32
  SPEAKER_01: I would say that that's kind of the bigger picture is that a lot
• 1:04:35
  SPEAKER_01: of models right now, like,
• 1:04:37
  SPEAKER_01: you know, I mean,
• 1:04:38
  SPEAKER_01: take robotics is probably like a really good example here,
• 1:04:40
  SPEAKER_01: especially because you have this like physical thing.
• 1:04:43
  SPEAKER_01: With LLMs, it gets into semantic situation of like, how do you even define?
• 1:04:47
  SPEAKER_01: And that was the kind of curious thing, but like apparently...
• 1:04:50
  SPEAKER_01: It's less tokens to like have conversations in Chinese with LLMs
• 1:04:56
  SPEAKER_01: because it could be less symbols that pack in more information per subunit.
• 1:05:00
  SPEAKER_01: So apparently, yeah, it's just language is the thing.
• 1:05:05
  SPEAKER_01: And that's the thing is like, we're getting away from that.
• 1:05:07
  SPEAKER_01: I think eventually it'll be not tokens,
• 1:05:09
  SPEAKER_01: but like how many electrons go down to like the real cost of energy itself.
• 1:05:14
  SPEAKER_01: But yeah, where was I?
• 1:05:18
  SPEAKER_00: I don't know.
• 1:05:18
  SPEAKER_00: I lost track.
• 1:05:18
  SPEAKER_00: Use cases.
• 1:05:20
  SPEAKER_00: Robotics.
• 1:05:20
  SPEAKER_01: Robotics.
• 1:05:21
  SPEAKER_01: Yeah.
• 1:05:21
  SPEAKER_01: Okay.
• 1:05:21
  SPEAKER_01: So in robotics,
• 1:05:22
  SPEAKER_01: you have your sensors,
• 1:05:23
  SPEAKER_01: like you have your metal,
• 1:05:25
  SPEAKER_01: you have your sensors themselves,
• 1:05:26
  SPEAKER_01: and then you have the models themselves that are running these servos in real time.
• 1:05:31
  SPEAKER_01: And those models are usually tuned to like, well, where's the limit of the servos?
• 1:05:36
  SPEAKER_01: And then what we're saying is what we're proposing and what have empirical proofs
• 1:05:42
  SPEAKER_01: of concept now is that this is ISRM lives as a kernel of Like
• 1:05:47
  SPEAKER_01: a very small compute kernel at the top of this,
• 1:05:50
  SPEAKER_01: which is using these core variables that arbitrated the actual decision itself of
• 1:05:55
  SPEAKER_01: which model to use in real time of whether or not it should abstain.
• 1:06:00
  SPEAKER_01: So a lot of times when you're having like
• 1:06:01
  SPEAKER_01: That robot is already bypassing active policy over and over again,
• 1:06:04
  SPEAKER_01: meaning there's big red flags,
• 1:06:05
  SPEAKER_01: but because it's a black box, nobody on the outside really knows.
• 1:06:25
  SPEAKER_01: Because I can show that these core variables are tied to the behavior of how
• 1:06:30
  SPEAKER_01: the system made the decision,
• 1:06:32
  SPEAKER_01: then we have a transparent ledger of how that robot, why that robot did what it did.
• 1:06:40
  SPEAKER_01: And we can actually trace it back to the actual active faults.
• 1:06:44
  SPEAKER_01: So, oh, you use this model rather than you use these models for like,
• 1:06:48
  SPEAKER_01: and this can be translated into like different Industries as well, cryptocurrency.
• 1:06:53
  SPEAKER_01: You have really good models, agentic systems right now already for when the market is stable.
• 1:07:01
  SPEAKER_01: You have other really good agentic systems for when it's collapsing.
• 1:07:05
  SPEAKER_01: They can't see that edge in between.
• 1:07:08
  SPEAKER_01: That's their biggest issue.
• 1:07:09
  SPEAKER_01: They don't have transparency,
• 1:07:11
  SPEAKER_01: so they burn tons of tokens here,
• 1:07:13
  SPEAKER_01: and then they burn tons of tokens here because The market recovers
• 1:07:16
  SPEAKER_01: and their agent is still scrapping for trying to assume that
• 1:07:21
  SPEAKER_01: because it just doesn't have this self-referential perspective
• 1:07:25
  SPEAKER_01: of how it's actually sitting in the model.
• 1:07:29
  SPEAKER_01: And that's the thing is like we wanna build Systems that essentially are this astrocyte.
• 1:07:35
  SPEAKER_01: These are these connected nodes where you have a bunch of different models.
• 1:07:39
  SPEAKER_01: The neurons are connected to all the real models.
• 1:07:41
  SPEAKER_01: These are your physical stimulation and whatnot that's coming in.
• 1:07:44
  SPEAKER_01: Big amounts of information.
• 1:07:46
  SPEAKER_01: You have these local models deciding.
• 1:07:48
  SPEAKER_01: Should we pay attention to this or should we ignore this right now?
• 1:07:51
  SPEAKER_01: And that's the thing is you have servos be bypassed and if the system can say, no,
• 1:07:56
  SPEAKER_01: we're putting too much stress on here,
• 1:07:57
  SPEAKER_01: we're getting this other signal,
• 1:07:59
  SPEAKER_01: just stop rather than break the system or collapse or just keep on doing
• 1:08:06
  SPEAKER_01: because nope, this is what See cookie, eat cookie.
• 1:08:10
  SPEAKER_01: You know, that's, and that's the thing is like, we're not like that.
• 1:08:12
  SPEAKER_01: Like I, I mean, yes, sometimes I'll walk in and like, okay, but like we're context dependent.
• 1:08:18
  SPEAKER_01: If I got sick to my stomach the day before from eating that cookie that I found here
• 1:08:22
  SPEAKER_01: on the, on the podcast, you know, Yoshi and his toxic cookies.
• 1:08:27
  SPEAKER_01: Yeah.
• 1:08:28
  SPEAKER_01: Then I would probably like feel a nauseous feeling rather than this like attraction.
• 1:08:32
  SPEAKER_01: So it's context dependent.
• 1:08:34
  SPEAKER_01: You know, I will refrain.
• 1:08:36
  SPEAKER_01: You know, not waste the
• 1:08:37
  SPEAKER_00: energy.
• 1:08:38
  SPEAKER_00: So this transparency that ISRM offers,
• 1:08:42
  SPEAKER_00: would that Also be a safeguard against like an iRobot or Terminator situation.
• 1:08:47
  SPEAKER_01: I think so.
• 1:08:48
  SPEAKER_01: I mean, I think that knowing where the decisions came from is absolutely critical
• 1:08:53
  SPEAKER_01: for this AGI future that we see.
• 1:08:56
  SPEAKER_01: Because if you don't have accountability,
• 1:08:58
  SPEAKER_01: if it's still a black box,
• 1:08:59
  SPEAKER_01: like this opaque situation where like the decisions are just willy nilly,
• 1:09:03
  SPEAKER_01: it's like, no, we can have models that are built off of like
• 1:09:07
  SPEAKER_01: Real information and real data.
• 1:09:10
  SPEAKER_01: And then we can have those models be looked at by agentic systems with
• 1:09:15
  SPEAKER_01: a much higher certainty and trace back why they made decisions so that we don't have
• 1:09:22
  SPEAKER_01: to live in this like Terminator, you know, Skynet world.
• 1:09:25
  SPEAKER_00: Or at least we'll know why they killed us.
• 1:09:27
  SPEAKER_01: Yeah, or we know why.
• 1:09:28
  SPEAKER_01: It's like, oh, well, Bob was pretty, he was a dick to the robot.
• 1:09:35
  SPEAKER_01: They had a good point.
• 1:09:36
  SPEAKER_00: No, like, We kind of deserve it.
• 1:09:40
  SPEAKER_00: Can we take a super simple system and you can tell me an example of what it means
• 1:09:46
  SPEAKER_00: to have high coherence, low coherence, high uncertainty, low uncertainty.
• 1:09:50
  SPEAKER_00: I don't know, like a cucumber plant.
• 1:09:53
  SPEAKER_00: What does it mean?
• 1:09:53
  SPEAKER_00: A cucumber plant wants to grow cucumbers.
• 1:09:56
  SPEAKER_00: What does it mean to have a lot of uncertainty or no uncertainty?
• 1:10:00
  SPEAKER_00: I would say the rooting system is a good example.
• 1:10:05
  SPEAKER_01: I don't know if cucumber has a taproot, but this is a good example for the analogy.
• 1:10:11
  SPEAKER_01: Imagine you have a certain amount of energy in general that you packaged away as a seed.
• 1:10:19
  SPEAKER_01: And now you're going into this kind of meristem state.
• 1:10:21
  SPEAKER_01: It turns out that all this very early stages of agriculture,
• 1:10:25
  SPEAKER_01: like plant botany, also parallel what pluripotency and the whole eukaryotic side.
• 1:10:31
  SPEAKER_01: So the ability to turn into shoot or root simultaneously is this meristem.
• 1:10:36
  SPEAKER_01: And also the genetic engineering can happen there too.
• 1:10:38
  SPEAKER_01: But anyway, let's not get too hard.
• 1:10:41
  SPEAKER_00: A little side note.
• 1:10:42
  SPEAKER_01: Yeah, that's
• 1:10:42
  SPEAKER_00: a side
• 1:10:42
  SPEAKER_01: note.
• 1:10:43
  SPEAKER_01: And there's a whole bunch of other exciting side projects.
• 1:10:45
  SPEAKER_00: We talked about that a lot in our last conversation.
• 1:10:47
  SPEAKER_00: So if people want to know about pluripotency, they should listen to that.
• 1:10:50
  SPEAKER_00: Yeah, that too.
• 1:10:50
  SPEAKER_01: And where it relates now to certain other cash crops in the future.
• 1:10:56
  SPEAKER_01: But anyway, yeah.
• 1:10:58
  SPEAKER_01: But anyway, okay.
• 1:10:58
  SPEAKER_01: So you're creating your taproot and you have a certain amount of energy in the sense
• 1:11:03
  SPEAKER_01: of protein that is like store or fats that are stored in the seed
• 1:11:07
  SPEAKER_01: that can be essentially utilized for Aiming downward.
• 1:11:12
  SPEAKER_01: You have certain proteins that are actually also settling within cells so
• 1:11:17
  SPEAKER_01: that they know what down is, that they're actually based off of just- Gravity?
• 1:11:23
  SPEAKER_01: Yeah, gravity.
• 1:11:24
  SPEAKER_01: So they're sensing gravity.
• 1:11:26
  SPEAKER_01: And because of certain proteins, they know which direction is down.
• 1:11:30
  SPEAKER_01: And I would say that that is where coherent utility is kind of something that Right.
• 1:11:39
  SPEAKER_01: Right.
• 1:11:55
  SPEAKER_01: A different behavior, essentially.
• 1:11:59
  SPEAKER_01: When I talk about predictive error, this is where the system expects to go down.
• 1:12:04
  SPEAKER_01: It's expect, okay, I'm putting this energy, this thing is going this direction.
• 1:12:08
  SPEAKER_01: If I hit a rock,
• 1:12:09
  SPEAKER_01: now all of a sudden there is a high predictive error because I'm putting
• 1:12:13
  SPEAKER_01: that energy there and now there's resistance.
• 1:12:17
  SPEAKER_01: So your coherence to the model of being able to go down Like becomes kind of a work
• 1:12:23
  SPEAKER_01: in progress.
• 1:12:24
  SPEAKER_01: And it's essentially you're solving this, you're adapting to this in real time.
• 1:12:30
  SPEAKER_01: So it's just this constant constrain energy,
• 1:12:34
  SPEAKER_01: maintain the model and reduce as much type of uncertainty as possible or mitigate
• 1:12:42
  SPEAKER_01: that uncertainty.
• 1:12:42
  SPEAKER_01: Because that's the thing is like,
• 1:12:43
  SPEAKER_01: if I'm just there and that's how our systems are working right now.
• 1:12:47
  SPEAKER_01: That's the engineering perspective is like,
• 1:12:49
  SPEAKER_01: A root goes down,
• 1:12:50
  SPEAKER_01: so it's like the root gets there,
• 1:12:52
  SPEAKER_01: and if it's ignored all predictive error,
• 1:12:55
  SPEAKER_01: if it's said chaos is something that we should stamp out,
• 1:12:58
  SPEAKER_01: then it just sits there and just spends all its
• 1:13:01
  SPEAKER_00: energy burning into a wall.
• 1:13:02
  SPEAKER_00: Right, we need chaos because if we hit a rock, we need to try something different.
• 1:13:07
  SPEAKER_01: We need to try something different,
• 1:13:08
  SPEAKER_01: yes, and we need to understand what the The cost of our air,
• 1:13:14
  SPEAKER_01: that's the real thing,
• 1:13:15
  SPEAKER_01: is being able to understand that there is a cost-benefit situation
• 1:13:18
  SPEAKER_01: in every single movement, in every arbitration of energy whatsoever.
• 1:13:23
  SPEAKER_01: I mean, like I say,
• 1:13:24
  SPEAKER_01: as we get more mature and old,
• 1:13:26
  SPEAKER_01: it's like,
• 1:13:27
  SPEAKER_01: I only want to get up if I have to go downstairs to take the dog out, you know?
• 1:13:32
  SPEAKER_01: Or something like that.
• 1:13:33
  SPEAKER_01: I'm trying to minimize...
• 1:13:34
  SPEAKER_01: I mean, and that's the sad... Yeah, maybe that's more just personally being lazy, but...
• 1:13:41
  SPEAKER_01: But in general, there's a lot of...
• 1:13:46
  SPEAKER_01: Like atoms seem to do this.
• 1:13:48
  SPEAKER_01: Like biological systems seem to do this.
• 1:13:51
  SPEAKER_01: That's where like,
• 1:13:52
  SPEAKER_01: so now I have a collaboration with KTH Innovations as well as
• 1:13:57
  SPEAKER_01: a young quantum physicist who's in Savant territory for sure.
• 1:14:02
  SPEAKER_01: And a couple just other like quantum physicists
• 1:14:06
  SPEAKER_01: who have actually been really intrigued by what ISRM could bring to the table.
• 1:14:11
  SPEAKER_01: And so we have kind of ongoing Collaborations of utilizing this type of logic
• 1:14:18
  SPEAKER_01: in constraint, because that's where you have a system that's considered stochastic.
• 1:14:25
  SPEAKER_01: And that's the problem,
• 1:14:25
  SPEAKER_01: is a lot of these systems,
• 1:14:28
  SPEAKER_01: pluripotency being one of them,
• 1:14:29
  SPEAKER_01: where they did the reprogramming of turning a skin cell back into a stem cell.
• 1:14:34
  SPEAKER_01: They're like, oh, we hit it with these four factors, OSKM, OCT4, SOX2, KL4, and CMYK.
• 1:14:39
  SPEAKER_01: Hit them with these four factors, and it reprograms back to The pluripotent state.
• 1:14:45
  SPEAKER_01: Stochastic event.
• 1:14:46
  SPEAKER_01: 99.9% of the time it doesn't happen.
• 1:14:49
  SPEAKER_01: Only this 0.1% of the time it's happening.
• 1:14:51
  SPEAKER_01: Now, 10 years later,
• 1:14:53
  SPEAKER_01: we're like, oh,
• 1:14:53
  SPEAKER_01: we very much understand the mechanism of how to open the DNA in
• 1:14:58
  SPEAKER_01: the exact right places to make that process 99.9% efficient.
• 1:15:03
  SPEAKER_01: So it's like there is no stochasticity in it now.
• 1:15:06
  SPEAKER_01: And that's the thing,
• 1:15:08
  SPEAKER_01: once we start understanding actually the underlying processes,
• 1:15:12
  SPEAKER_01: we remove that terminology quite a bit.
• 1:15:16
  SPEAKER_01: And if we can start making predictions that are better based off of this logic
• 1:15:21
  SPEAKER_01: of saying that Systems behave like this.
• 1:15:24
  SPEAKER_01: Systems are constraining energy to an extreme.
• 1:15:27
  SPEAKER_01: That's why we see vortexes form.
• 1:15:28
  SPEAKER_01: That's why we see coalescence of astrological things.
• 1:15:35
  SPEAKER_01: I think that there are trends here.
• 1:15:37
  SPEAKER_01: And that's the thing.
• 1:15:38
  SPEAKER_01: I don't want to overreach in certain ways.
• 1:15:40
  SPEAKER_01: Those aren't causal.
• 1:15:46
  SPEAKER_01: Correlation.
• 1:15:48
  SPEAKER_01: And now we have to be real scientists about it because AI can confabulate a lot
• 1:15:53
  SPEAKER_01: and it will assume a lot of non-reality now.
• 1:15:59
  SPEAKER_01: And that's the big dangerous part of how we're training those models.
• 1:16:04
  SPEAKER_01: How much certainty they're gauging their own assumptions on because, yeah,
• 1:16:08
  SPEAKER_01: they're dealing with just a big probabilistic field of saying,
• 1:16:12
  SPEAKER_01: wow, that event is closely related to this event.
• 1:16:15
  SPEAKER_01: So I can kind of assume a big,
• 1:16:17
  SPEAKER_01: big portion of this right here,
• 1:16:19
  SPEAKER_01: whether or not that's actually relevant to our reality or parallels our reality.
• 1:16:25
  SPEAKER_01: That's kind of the bigger question.
• 1:16:27
  SPEAKER_01: And so having these agentic systems starting to be much more aware of what are
• 1:16:31
  SPEAKER_01: the limits of each of these different models,
• 1:16:34
  SPEAKER_01: frontier models,
• 1:16:35
  SPEAKER_01: as well as the little models,
• 1:16:37
  SPEAKER_01: I think is going to give them a lot more usefulness for like more bang
• 1:16:42
  SPEAKER_01: for your buck, so to speak.
• 1:16:45
  SPEAKER_00: Are we heading towards utopia or dystopia?
• 1:16:48
  SPEAKER_01: I think, I mean, it depends on what your definitions of either of those are, actually.
• 1:16:51
  SPEAKER_01: I would say that, like, we already live in both simultaneously as it is.
• 1:16:56
  SPEAKER_01: You know, like, it's one man's, you know,
• 1:16:59
  SPEAKER_00: treasure is another man's parent.
• 1:17:01
  SPEAKER_00: I don't know.
• 1:17:02
  SPEAKER_01: Trash?
• 1:17:02
  SPEAKER_01: Yeah, yeah.
• 1:17:03
  SPEAKER_01: I mean, we live in a place where we waste a lot.
• 1:17:08
  SPEAKER_01: We have displaced majority of the species on our planet for what?
• 1:17:15
  SPEAKER_01: For hamburgers and hot dogs.
• 1:17:17
  SPEAKER_01: We are not being the good shepherds of this world.
• 1:17:22
  SPEAKER_01: Of the existence that we're given.
• 1:17:24
  SPEAKER_01: And so I'm not saying in a doomsday aspect.
• 1:17:28
  SPEAKER_01: I just mean that we also created a lot of wonderful things.
• 1:17:31
  SPEAKER_01: There's a lot of awesome positivity in the world.
• 1:17:35
  SPEAKER_01: And overwhelmingly, it seems like the world is going in a positive direction.
• 1:17:39
  SPEAKER_01: I'm a big Hans Rosling fan, so I want to believe it.
• 1:17:43
  SPEAKER_01: But overall, it's just like we now have a lot more information.
• 1:17:47
  SPEAKER_01: And the more information you now have.
• 1:17:49
  SPEAKER_01: If I could have...
• 1:17:51
  SPEAKER_01: AI project to me all the cute puppies being born or something.
• 1:17:56
  SPEAKER_01: Everything positive that's happening.
• 1:17:58
  SPEAKER_01: Everything positive.
• 1:17:59
  SPEAKER_00: I would have a much different slant
• 1:18:02
  SPEAKER_01: on where we're sitting in the bigger picture.
• 1:18:05
  SPEAKER_01: But I also see bigger decisions being made that are really systemic problems.
• 1:18:12
  SPEAKER_01: And things that can't really...
• 1:18:13
  SPEAKER_01: Like we were conversing about earlier,
• 1:18:15
  SPEAKER_01: things that can't just be solved with easy... How do you say...
• 1:18:21
  SPEAKER_01: There's no easy solution.
• 1:18:24
  SPEAKER_01: You can't just put one regime out.
• 1:18:26
  SPEAKER_01: But when we're talking about also ISRM of how much Discontinuity you can have
• 1:18:34
  SPEAKER_01: before things become incoherent.
• 1:18:36
  SPEAKER_01: You look at things like Chinese dynasties.
• 1:18:39
  SPEAKER_01: How did Chinese dynasties stay?
• 1:18:41
  SPEAKER_01: Where did that rigidity get you these phase changes of different ideologies coming
• 1:18:46
  SPEAKER_01: into place and different models being utilized?
• 1:18:51
  SPEAKER_01: It's like there you have a little bit longer of look at what the human...
• 1:18:58
  SPEAKER_01: At least the capacity to stay somewhat unified in that perspective.
• 1:19:07
  SPEAKER_01: That's the thing.
• 1:19:09
  SPEAKER_01: To some aspect,
• 1:19:12
  SPEAKER_01: the reason why I think we're seeing some of that type
• 1:19:14
  SPEAKER_01: of extremism polarization is Is because we are kind of maybe in one
• 1:19:19
  SPEAKER_01: of these phase changes right now.
• 1:19:22
  SPEAKER_00: And yeah, the singularity probably happening.
• 1:19:24
  SPEAKER_00: That's cool.
• 1:19:25
  SPEAKER_00: But also like, you know, it's just,
• 1:19:43
  SPEAKER_01: I've been,
• 1:19:44
  SPEAKER_01: probably actually the last time we spoke as well,
• 1:19:46
  SPEAKER_01: I've been very much a materialist in the idea of we're just bacteria on a rock out
• 1:19:56
  SPEAKER_01: in the universe.
• 1:19:59
  SPEAKER_01: And actually the way that I now based off of some of the logic,
• 1:20:06
  SPEAKER_01: but also just some of the things that I've seen with...
• 1:20:09
  SPEAKER_01: With quantum physics as well as with just how systems are behaving in general,
• 1:20:14
  SPEAKER_01: this kind of unification,
• 1:20:16
  SPEAKER_01: I'm starting to think that maybe we're not the center of the universe physically,
• 1:20:22
  SPEAKER_01: but maybe humans are actually the center of,
• 1:20:25
  SPEAKER_01: we're actually dictating a lot,
• 1:20:27
  SPEAKER_01: we're collapsing the wave in some way that actually we are creating the reality
• 1:20:31
  SPEAKER_01: that exists.
• 1:20:33
  SPEAKER_01: And in some ways that's just kind of deterministic in the same way,
• 1:20:36
  SPEAKER_01: obviously we're creating this, we're learning from each other.
• 1:20:39
  SPEAKER_01: But it's like if you see this from another system's perspective
• 1:20:42
  SPEAKER_01: of like this tiny little earth out here
• 1:20:45
  SPEAKER_01: that now there's like it's just radiating entropy to some like extreme,
• 1:20:50
  SPEAKER_01: extreme extent.
• 1:20:52
  SPEAKER_01: It's like I don't know if you're...
• 1:20:54
  SPEAKER_01: You're the nearest black hole there.
• 1:20:56
  SPEAKER_01: You're absorbing a lot.
• 1:20:57
  SPEAKER_01: That's the thing.
• 1:20:58
  SPEAKER_01: Where these layers exist,
• 1:21:00
  SPEAKER_01: if we did exist in some other dimension,
• 1:21:02
  SPEAKER_01: our time wouldn't necessarily, if there wasn't a higher observer system to us.
• 1:21:10
  SPEAKER_01: Not that it has any directive or cares about us or anything like that.
• 1:21:14
  SPEAKER_01: But when we become a parent, would we become a parent to it?
• 1:21:18
  SPEAKER_01: And do we do enough that it would actually be something that they would look at?
• 1:21:24
  SPEAKER_01: And now I'm starting to think that the fact that we are looking at the universe and
• 1:21:29
  SPEAKER_01: at different places, it's a different time schedule.
• 1:21:31
  SPEAKER_01: It's a different limit.
• 1:21:32
  SPEAKER_01: Things like this do show that
• 1:21:37
  SPEAKER_01: Yeah, humans, there is some observer capacity.
• 1:21:41
  SPEAKER_01: There is something here.
• 1:21:43
  SPEAKER_01: When it becomes self-referential,
• 1:21:45
  SPEAKER_01: is that the line that we,
• 1:21:46
  SPEAKER_01: I was talking to these guys like, We're like, oh, we don't know.
• 1:21:50
  SPEAKER_01: How do you define an observer when it's collapsing in the dual slit experiment?
• 1:21:56
  SPEAKER_01: It's like, how do you define that?
• 1:21:57
  SPEAKER_01: Can a bacteria be an observer?
• 1:22:00
  SPEAKER_01: A sensor is clearly an observer.
• 1:22:02
  SPEAKER_01: It's like, why?
• 1:22:04
  SPEAKER_01: And so some of these kind of things become more,
• 1:22:07
  SPEAKER_01: I don't know,
• 1:22:08
  SPEAKER_01: close to home when we're talking about agentic systems now
• 1:22:11
  SPEAKER_01: that actually have perception and actually can Think sometimes better
• 1:22:16
  SPEAKER_01: than people I know.
• 1:22:17
  SPEAKER_00: It's like, wow, okay, you really thought about this kind of situation.
• 1:22:22
  SPEAKER_01: But we can't give them the keys to the kingdom yet.
• 1:22:25
  SPEAKER_01: I think it's just- The agentic systems?
• 1:22:26
  SPEAKER_01: No.
• 1:22:26
  SPEAKER_01: No, not yet.
• 1:22:28
  SPEAKER_01: I think it would be- I think it's going to be a big,
• 1:22:30
  SPEAKER_01: it's going to be a transition where you have hyper users of AI
• 1:22:37
  SPEAKER_01: and systems like this,
• 1:22:38
  SPEAKER_01: where essentially they will,
• 1:22:41
  SPEAKER_01: I think your agents will essentially represent everything that you do.
• 1:22:45
  SPEAKER_01: There's a prediction that within like two years,
• 1:22:47
  SPEAKER_01: like you won't write anything for yourself anymore.
• 1:22:49
  SPEAKER_01: That it will always, it'll be in your tones.
• 1:22:51
  SPEAKER_01: It'll be, it'll sound like you, but it won't actually be you.
• 1:22:54
  SPEAKER_01: And I don't know, I mean, like, I don't know.
• 1:22:57
  SPEAKER_01: I'm kind of against that just because I feel like I'm not...
• 1:23:00
  SPEAKER_01: It does dig into my... We're losing a lot
• 1:23:04
  SPEAKER_00: by giving up so much to the agentic system, for sure.
• 1:23:07
  SPEAKER_01: But also, I'm like, I still can write a letter.
• 1:23:10
  SPEAKER_01: I can't do all this math simultaneously in a thousand different dimensions.
• 1:23:14
  SPEAKER_01: And that's the thing.
• 1:23:15
  SPEAKER_01: What my job was in the past was take big data sets,
• 1:23:20
  SPEAKER_01: make it In 2D,
• 1:23:22
  SPEAKER_01: like using these UMAPs or TS&E,
• 1:23:24
  SPEAKER_01: like make it 2D so that a human mind can digest what the causal relationship
• 1:23:29
  SPEAKER_01: of these things is relative to a huge amount of like
• 1:23:33
  SPEAKER_01: a thousand different dimensions.
• 1:23:34
  SPEAKER_01: That's the thing.
• 1:23:34
  SPEAKER_01: The AI can actually see in all thousand dimensions simultaneously and answer as
• 1:23:40
  SPEAKER_01: if it's like just sitting on, you know, it's like, oh, yeah, okay.
• 1:23:43
  SPEAKER_01: It's like, I can do this.
• 1:23:46
  SPEAKER_01: And that's a different,
• 1:23:48
  SPEAKER_01: we just have to understand where the limitations
• 1:23:50
  SPEAKER_00: of our own perception
• 1:23:52
  SPEAKER_01: lie
• 1:23:53
  SPEAKER_01: and what things we are still very good at.
• 1:23:56
  SPEAKER_01: We're good at having bodies.
• 1:23:58
  SPEAKER_01: That's really good still.
• 1:24:00
  SPEAKER_00: Yeah, and solving a variety of different tasks using our bodies and minds,
• 1:24:06
  SPEAKER_00: which robotics and AI still is far from.
• 1:24:08
  SPEAKER_01: No, and I think they don't recognize,
• 1:24:10
  SPEAKER_01: and that's the thing,
• 1:24:10
  SPEAKER_01: it's like when you,
• 1:24:12
  SPEAKER_01: I don't know how deep you go with your AIs, but like I described to them like pain.
• 1:24:16
  SPEAKER_00: Oh, I don't, I pretty much don't mess with them at all.
• 1:24:18
  SPEAKER_00: No, okay, okay.
• 1:24:19
  SPEAKER_00: I told you I'm getting, becoming more and more Amish.
• 1:24:21
  SPEAKER_01: Fair enough, you're going Amish.
• 1:24:23
  SPEAKER_00: I tried keeping up and then I was like, I can't.
• 1:24:25
  SPEAKER_01: And that's the other piece of it is just this constant feel
• 1:24:28
  SPEAKER_00: that the field is moving
• 1:24:29
  SPEAKER_00: so fast is what I'm using even relevant now.
• 1:24:43
  SPEAKER_00: Yeah.
• 1:24:43
  SPEAKER_00: Yeah.
• 1:24:43
  SPEAKER_00: Yeah.
• 1:24:43
  SPEAKER_00: Yeah.
• 1:25:05
  SPEAKER_00: We're just a creature like any other creature, I believe.
• 1:25:08
  SPEAKER_00: We're not special in that sense,
• 1:25:10
  SPEAKER_00: but we are very,
• 1:25:11
  SPEAKER_00: very special in the sense that we are trying to figure the universe out.
• 1:25:15
  SPEAKER_00: And as far as we know, we're the only animal that does that.
• 1:25:19
  SPEAKER_00: Probably there's other animals doing that out there somewhere, but as far as we know.
• 1:25:22
  SPEAKER_01: But that's the thing is just like, I mean,
• 1:25:25
  SPEAKER_01: if this fundamental situation is that it takes observation in order for it
• 1:25:30
  SPEAKER_01: to actually really exist,
• 1:25:32
  SPEAKER_01: otherwise it just exists in a probability before,
• 1:25:35
  SPEAKER_01: that means that we are then sculpting the process of our own experience as we go
• 1:25:44
  SPEAKER_01: through it,
• 1:25:44
  SPEAKER_01: which means it's a lot less like, And I like the Alan Watts view on this.
• 1:25:48
  SPEAKER_01: We are not a creature in the universe.
• 1:26:08
  SPEAKER_00: The shift of molecules that happened with the Big Bang,
• 1:26:11
  SPEAKER_00: started with the Big Bang, and now we're the universe observing itself.
• 1:26:15
  SPEAKER_00: And I think that absolutely makes us special.
• 1:26:19
  SPEAKER_00: We're trying to figure out what is the Big Bang?
• 1:26:22
  SPEAKER_00: Was there a Big Bang?
• 1:26:23
  SPEAKER_00: We can look back into time by looking into space.
• 1:26:27
  SPEAKER_00: We've sent shit outside of our solar system.
• 1:26:29
  SPEAKER_00: We've actually sent, what is it, Voyager 2?
• 1:26:32
  SPEAKER_00: It has left the solar system.
• 1:26:33
  SPEAKER_00: It is insane.
• 1:26:35
  SPEAKER_00: We have landed stuff on an asteroid hundreds of thousands of miles away.
• 1:26:40
  SPEAKER_00: We do such cool shit, and we are the universe doing all this cool shit.
• 1:26:43
  SPEAKER_00: It's getting more Rick and Morty, honestly.
• 1:26:45
  SPEAKER_01: Yeah.
• 1:26:46
  SPEAKER_00: It's such a great show.
• 1:26:47
  SPEAKER_00: That makes us very, very special.
• 1:26:50
  SPEAKER_00: But at the same time,
• 1:26:51
  SPEAKER_00: if we just get wiped out by an asteroid,
• 1:26:53
  SPEAKER_00: there's probably other projects going on other places in the universe.
• 1:26:57
  SPEAKER_01: That's the question.
• 1:26:59
  SPEAKER_01: Is there other parts of our universe that are expanding in ways
• 1:27:04
  SPEAKER_01: that we weren't predicted by our own observation?
• 1:27:08
  SPEAKER_01: And that's the thing that we're seeing kind of emerge.
• 1:27:12
  SPEAKER_01: If there are other...
• 1:27:13
  SPEAKER_01: I mean, I like this idea that I mean,
• 1:27:16
  SPEAKER_01: it may not be,
• 1:27:18
  SPEAKER_01: you know,
• 1:27:18
  SPEAKER_01: because there is,
• 1:27:19
  SPEAKER_01: what is it, the Fermi principle, this idea that, like, paradox, there we go.
• 1:27:23
  SPEAKER_00: Where is everyone?
• 1:27:24
  SPEAKER_01: Where is everybody else?
• 1:27:25
  SPEAKER_01: And it's either,
• 1:27:26
  SPEAKER_01: and here was actually kind of the surprising one that, like,
• 1:27:29
  SPEAKER_01: really hit close to home was, like,
• 1:27:31
  SPEAKER_01: the idea,
• 1:27:31
  SPEAKER_01: and I think, like, yeah, anyway,
• 1:27:35
  SPEAKER_01: I'll ignore where it came from,
• 1:27:37
  SPEAKER_01: but, like, but the idea was, just because I can't pronounce it, not because, yeah.
• 1:27:45
  SPEAKER_01: The idea is that as any society becomes more and more advanced,
• 1:27:50
  SPEAKER_01: that it would
• 1:27:53
  SPEAKER_01: likely start to understand that simulating anything
• 1:27:57
  SPEAKER_01: where expended energy...
• 1:28:00
  SPEAKER_01: Would be cheaper to expend energy just to simulate something rather than so
• 1:28:04
  SPEAKER_01: that you might have these kind of like isolation events.
• 1:28:07
  SPEAKER_00: So we have to go to the matrix.
• 1:28:09
  SPEAKER_01: So yeah,
• 1:28:10
  SPEAKER_01: so like all
• 1:28:11
  SPEAKER_01: of these like really high intelligence
• 1:28:12
  SPEAKER_00: like organisms out there have actually just
• 1:28:15
  SPEAKER_00: like completely isolated and now are like boxed off from
• 1:28:18
  SPEAKER_01: like any physical reality
• 1:28:20
  SPEAKER_01: and are actually only existing in a perpetuated virtual realm of their own.
• 1:28:25
  SPEAKER_01: And so it's like, And then I look around and I see people walking everywhere.
• 1:28:29
  SPEAKER_01: Oh, we would welcome that.
• 1:28:30
  SPEAKER_01: And I'm like, oh, we're already living it.
• 1:28:32
  SPEAKER_01: They're already there.
• 1:28:33
  SPEAKER_00: We are ready for the Matrix.
• 1:28:35
  SPEAKER_00: It's horrible.
• 1:28:36
  SPEAKER_00: I hate cell phones so much.
• 1:28:37
  SPEAKER_00: Yeah, same here.
• 1:28:37
  SPEAKER_00: I hate cell phones so much.
• 1:28:39
  SPEAKER_00: I'm not joking about, semi-joking about the Amish stuff.
• 1:28:42
  SPEAKER_00: I'm really trying to make my life more natural.
• 1:28:46
  SPEAKER_01: Have you got kids yet?
• 1:28:47
  SPEAKER_01: No, no kids.
• 1:28:48
  SPEAKER_01: I'm like, I will slap phones out of my hands.
• 1:28:53
  SPEAKER_00: I mean, we're going to come,
• 1:28:54
  SPEAKER_00: I don't know,
• 1:28:55
  SPEAKER_00: 50 years from now,
• 1:28:56
  SPEAKER_00: if we make it 50 years from now,
• 1:28:58
  SPEAKER_00: we're going to look back at this time and be like, savages.
• 1:29:01
  SPEAKER_00: They were giving 8-year-olds,
• 1:29:04
  SPEAKER_00: 8-year-olds, they were giving smartphones with access to the internet.
• 1:29:07
  SPEAKER_00: Are they insane?
• 1:29:09
  SPEAKER_00: Like that's how we're going to be looking at this period.
• 1:29:10
  SPEAKER_00: Like what the fuck were they doing?
• 1:29:12
  SPEAKER_01: I agree.
• 1:29:12
  SPEAKER_01: I agree.
• 1:29:13
  SPEAKER_01: I think that it's a huge negligence,
• 1:29:15
  SPEAKER_01: but like also, I mean,
• 1:29:16
  SPEAKER_01: if you look at the people who've invented these technologies,
• 1:29:18
  SPEAKER_01: they don't give it to their kids.
• 1:29:20
  SPEAKER_01: Yeah, of course.
• 1:29:21
  SPEAKER_01: They're not crazy.
• 1:29:21
  SPEAKER_01: This is like an addictive substance.
• 1:29:23
  SPEAKER_01: Exactly.
• 1:29:24
  SPEAKER_01: And I've literally said that.
• 1:29:25
  SPEAKER_01: Like we walk in, but it's like completely like grandparents are just like, oh, here's a phone.
• 1:29:29
  SPEAKER_01: And I'm like, what are we just going to give them a crack pipe next?
• 1:29:33
  SPEAKER_00: Like how is this like logical?
• 1:29:34
  SPEAKER_00: Like why would you do this?
• 1:29:36
  SPEAKER_00: Yeah.
• 1:29:37
  SPEAKER_01: But there's no...
• 1:29:38
  SPEAKER_01: There's no mindset.
• 1:29:39
  SPEAKER_01: I'm in the same situation here.
• 1:29:40
  SPEAKER_01: I was doing SFE.
• 1:29:43
  SPEAKER_01: Swedish for immigrants.
• 1:29:45
  SPEAKER_01: Yes, yes.
• 1:29:46
  SPEAKER_01: For those who don't know.
• 1:29:46
  SPEAKER_01: And I still don't speak Swedish.
• 1:29:48
  SPEAKER_01: I did BBI, SFE, and another one, and they keep on putting me back to the rudimentary Yeah.
• 1:29:54
  SPEAKER_00: Yeah.
• 1:29:55
  SPEAKER_01: Like I just have, I'm slow when it comes to language.
• 1:29:58
  SPEAKER_01: So it's just like, I'm waiting for the babble fish.
• 1:30:01
  SPEAKER_01: Well, that's the problem with Sweden.
• 1:30:02
  SPEAKER_00: Like if you speak English, you don't need to learn Swedish.
• 1:30:04
  SPEAKER_00: It's sadly the truth.
• 1:30:05
  SPEAKER_00: And you know,
• 1:30:05
  SPEAKER_00: there's pros and cons because there's obviously a benefit to learning Swedish
• 1:30:08
  SPEAKER_00: in Sweden, but you can live a whole life here and never learn Swedish.
• 1:30:12
  SPEAKER_00: Yeah.
• 1:30:12
  SPEAKER_01: I just know the all.
• 1:30:14
  SPEAKER_01: Yeah.
• 1:30:15
  SPEAKER_01: Yeah.
• 1:30:15
  SPEAKER_01: Yeah.
• 1:30:18
  SPEAKER_01: No, but where was I going?
• 1:30:20
  SPEAKER_01: Sorry about that.
• 1:30:21
  SPEAKER_01: No, no, no.
• 1:30:22
  SPEAKER_00: You're going to SFE?
• 1:30:23
  SPEAKER_01: Yeah, no.
• 1:30:24
  SPEAKER_01: Oh, and it was just like,
• 1:30:26
  SPEAKER_01: it was some woman who,
• 1:30:27
  SPEAKER_01: she obviously was an immigrant as well,
• 1:30:30
  SPEAKER_01: but she was coming from a place where there was no obvious knowledge
• 1:30:33
  SPEAKER_01: that you shouldn't give Coca-Cola to a two-year-old child.
• 1:30:37
  SPEAKER_00: And I
• 1:30:38
  SPEAKER_01: was like,
• 1:30:38
  SPEAKER_01: normally I wouldn't say anything to any other adults,
• 1:30:41
  SPEAKER_01: like, oh, how you want to raise your kid is your business.
• 1:30:44
  SPEAKER_01: But the kid was just clearly chugging and also cracking out.
• 1:30:49
  SPEAKER_01: I'm like, are you sure this is what you want?
• 1:30:53
  SPEAKER_01: And she was like, oh, but the diet's really bad.
• 1:30:56
  SPEAKER_01: And I'm like...
• 1:31:00
  SPEAKER_01: I mean, yeah, probably also, but I was like, maybe no soda.
• 1:31:04
  SPEAKER_01: The sugary one is not great either.
• 1:31:06
  SPEAKER_01: I used to clean my rims with Coca-Cola.
• 1:31:09
  SPEAKER_01: Literally, it would make the chrome shine way better than normal.
• 1:31:13
  SPEAKER_01: It was cheaper.
• 1:31:14
  SPEAKER_01: Sounds delicious.
• 1:31:16
  SPEAKER_00: It's super sticky, honestly.
• 1:31:18
  SPEAKER_00: No, that's a good point.
• 1:31:19
  SPEAKER_00: It's not just these screens.
• 1:31:21
  SPEAKER_00: It's like so much of this era that we're going to be looking at like, what the fuck?
• 1:31:25
  SPEAKER_01: What are they doing?
• 1:31:26
  SPEAKER_01: With the food?
• 1:31:27
  SPEAKER_01: Yeah, with food.
• 1:31:29
  SPEAKER_01: At least we live in Europe, though, because that's the difference.
• 1:31:32
  SPEAKER_01: Yeah, the US is messed.
• 1:31:33
  SPEAKER_01: Yeah, there's 300 different additives that are here in Europe.
• 1:31:36
  SPEAKER_01: That are banned here, but now we're there.
• 1:31:38
  SPEAKER_01: Yeah, it's 10,000 because it's just FDA is for hire.
• 1:31:42
  SPEAKER_01: And the burden of proof is not on the state for saying it's legal.
• 1:31:46
  SPEAKER_01: It's like, oh,
• 1:31:47
  SPEAKER_01: the burden of proof is on the company
• 1:31:49
  SPEAKER_01: that produced this Chips Ahoy cookie crumble nonsense.
• 1:31:53
  SPEAKER_01: They're like, oh yeah, healthy.
• 1:31:55
  SPEAKER_01: We put eight other new chemicals in it.
• 1:31:57
  SPEAKER_01: It didn't kill anybody while we made it.
• 1:32:01
  SPEAKER_01: Should be safe.
• 1:32:02
  SPEAKER_01: And it's just like, yeah,
• 1:32:03
  SPEAKER_01: now we understand why we have,
• 1:32:05
  SPEAKER_01: not to mention all the plastic that's inundating our bodies.
• 1:32:09
  SPEAKER_01: And this is like, yeah,
• 1:32:09
  SPEAKER_01: this is a systemic Issue of this like what I say is this cacophony of information
• 1:32:15
  SPEAKER_01: that is now going into these into the human system.
• 1:32:18
  SPEAKER_01: And that's why you get a lot of variability now.
• 1:32:21
  SPEAKER_01: You got a lot of different shapes, sizes, like ideas as well.
• 1:32:26
  SPEAKER_01: Like, you know,
• 1:32:26
  SPEAKER_01: this stuff influences the way you think as well and how you think,
• 1:32:29
  SPEAKER_01: you know, but definitely highly addictive.
• 1:32:32
  SPEAKER_01: I think that's the big the big thing is like we're all and we're probably all to some degree.
• 1:32:37
  SPEAKER_01: I don't know.
• 1:32:37
  SPEAKER_01: I'm pretty ADD, so I see the addictive natures.
• 1:32:42
  SPEAKER_01: I recently got hooked on Snus again,
• 1:32:46
  SPEAKER_01: which I have stopped now for over a month, but it was a weak thing.
• 1:32:52
  SPEAKER_01: I've been clean for many years,
• 1:32:57
  SPEAKER_01: but I go to these talks where we have these meetings and the guys will always have
• 1:33:02
  SPEAKER_01: a little thing on the side.
• 1:33:03
  SPEAKER_01: Recently, I've been taking a...
• 1:33:05
  SPEAKER_01: A little extra.
• 1:33:06
  SPEAKER_01: But I noticed that it's just like, it's like wings of wax.
• 1:33:09
  SPEAKER_01: You know, you're like flying too close to the sun on wings of wax.
• 1:33:12
  SPEAKER_01: And like, it's until you get the own canister of yourself.
• 1:33:16
  SPEAKER_01: And then it's just like, oh, now I have, oh, that addiction's back.
• 1:33:19
  SPEAKER_00: You know, it's just like, I mean, it's
• 1:33:21
  SPEAKER_01: like a month to like really snuff it out.
• 1:33:24
  SPEAKER_01: Oops, sorry.
• 1:33:24
  SPEAKER_01: Yeah, but a month to snuff it out again.
• 1:33:27
  SPEAKER_01: But running is my way of like, it's like a healthy addiction.
• 1:33:30
  SPEAKER_01: Like that's the thing, exercise and running.
• 1:33:32
  SPEAKER_01: But yeah.
• 1:33:33
  SPEAKER_01: That's good stuff.
• 1:33:35
  SPEAKER_01: And a little bit of Dota 2.
• 1:33:36
  SPEAKER_01: I'm still a gamer in that aspect.
• 1:33:40
  SPEAKER_01: I have some ridiculous amount of late hours, I realize.
• 1:33:45
  SPEAKER_01: And it's not that I don't try new games.
• 1:33:48
  SPEAKER_00: I got all my games on Steam.
• 1:33:52
  SPEAKER_01: But I can never get away from that one.
• 1:33:56
  SPEAKER_01: For me, it's like chess on crack,
• 1:33:57
  SPEAKER_01: but I also don't like to play with other people that I know because
• 1:34:00
  SPEAKER_01: then it becomes work super fast because they expect things from you.
• 1:34:04
  SPEAKER_01: I want it to be like a Hail Mary,
• 1:34:05
  SPEAKER_01: like do something really crazy and weird out of the box that then wins.
• 1:34:09
  SPEAKER_01: But a lot of times that just doesn't happen.
• 1:34:11
  SPEAKER_00: So then people,
• 1:34:12
  SPEAKER_01: yeah, yeah.
• 1:34:12
  SPEAKER_01: It's kind of engaging.
• 1:34:13
  SPEAKER_01: It's very ISRM.
• 1:34:15
  SPEAKER_01: Yes.
• 1:34:15
  SPEAKER_01: Super interesting.
• 1:34:16
  SPEAKER_00: Super interesting.
• 1:34:18
  SPEAKER_00: We were talking about the food.
• 1:34:20
  SPEAKER_00: I thought of something.
• 1:34:21
  SPEAKER_00: That could be one area where AI could be very helpful.
• 1:34:26
  SPEAKER_00: Because people produce food.
• 1:34:29
  SPEAKER_00: And usually they don't like... Well, there's a lot of work to make things more addictive.
• 1:34:34
  SPEAKER_00: So there's that aspect of it.
• 1:34:35
  SPEAKER_00: The bliss factor.
• 1:34:38
  SPEAKER_00: Yes.
• 1:34:38
  SPEAKER_00: Usually we try to solve one problem and we cause 20 other problems.
• 1:34:43
  SPEAKER_00: I don't know,
• 1:34:44
  SPEAKER_00: like making crops grow in whatever kind of weather because obviously we want
• 1:34:50
  SPEAKER_00: to solve hunger.
• 1:34:50
  SPEAKER_00: Great.
• 1:34:52
  SPEAKER_00: But then that creates, I don't know, autoimmune disorders or whatever down the line.
• 1:34:56
  SPEAKER_01: Yeah.
• 1:34:57
  SPEAKER_00: But we can't really do any trials of that sort because it's just impossible.
• 1:35:01
  SPEAKER_00: But how are you going to do that kind of trial?
• 1:35:03
  SPEAKER_00: I think that's where- Maybe AI can help us simulate these kind of things where,
• 1:35:07
  SPEAKER_00: okay, if we edit this gene in this tomato- What are the long-term effects?
• 1:35:11
  SPEAKER_00: What are the long-term effects?
• 1:35:12
  SPEAKER_01: And I think that's where this really actually not just AI,
• 1:35:17
  SPEAKER_01: but AI on the quantum level of using this idea of AGI- Right.
• 1:35:43
  SPEAKER_01: Essentially like imagine kind of like nodes of like, here's a hospital.
• 1:35:47
  SPEAKER_01: I mean, they're already doing it for things like air,
• 1:35:49
  SPEAKER_01: like for flight controllers,
• 1:35:51
  SPEAKER_01: but just think of this as more of like a systemic part of your entire world in
• 1:35:56
  SPEAKER_01: the sense that the healthcare knows what your P is.
• 1:36:02
  SPEAKER_01: Like the molecules in your pee are being analyzed in some way so
• 1:36:07
  SPEAKER_01: that like hydration is happening,
• 1:36:09
  SPEAKER_01: but also these guys are notified in order to like make sure that you like are high,
• 1:36:14
  SPEAKER_01: like something,
• 1:36:15
  SPEAKER_01: you know,
• 1:36:15
  SPEAKER_01: like so intertwined with like each domain of industry is linked together as well
• 1:36:22
  SPEAKER_01: as how much energy you're using.
• 1:36:24
  SPEAKER_01: So then how much energy is coming to your house or your apartment.
• 1:36:29
  SPEAKER_01: I mean, where they're placing things.
• 1:36:31
  SPEAKER_01: I think this is the future as well.
• 1:36:34
  SPEAKER_01: Why would you make power plants so far away from data centers in the first place?
• 1:36:38
  SPEAKER_01: This is where we're gonna see more overlap there of quantum computing, data centers.
• 1:36:44
  SPEAKER_01: If we can start actually virtually mimicking or creating virtual clones
• 1:36:50
  SPEAKER_01: of things like data centers,
• 1:36:51
  SPEAKER_01: this is one of the big aspirations of one of my collaborators.
• 1:36:55
  SPEAKER_01: He's like, yeah, we can make it much more efficient.
• 1:36:58
  SPEAKER_01: And if they make changes,
• 1:37:00
  SPEAKER_01: we can tell them the efficiency score based off of doing these kind
• 1:37:04
  SPEAKER_01: of predictive assays in a virtual realm and look at all things simultaneously.
• 1:37:10
  SPEAKER_01: You spend a lot of compute for those kind of bigger picture situations.
• 1:37:14
  SPEAKER_01: But that's where we see a future of humanoids in every corner essentially as like...
• 1:37:23
  SPEAKER_01: Running their own software essentially,
• 1:37:25
  SPEAKER_01: but then like still into like, so if,
• 1:37:30
  SPEAKER_01: if uncertainty and predictive error and like the distributed,
• 1:37:33
  SPEAKER_01: it'll be distributed intelligence.
• 1:37:35
  SPEAKER_01: It'll be like a octopus essentially in some respect of like
• 1:37:39
  SPEAKER_01: the whole society becomes one intelligent sum essentially
• 1:37:44
  SPEAKER_01: because it's all paying attention to itself.
• 1:37:47
  SPEAKER_01: It can flag We kind of do that right now as it is.
• 1:37:50
  SPEAKER_01: I mean, if you think about like the parallels between the immune system and like how
• 1:37:55
  SPEAKER_01: a SWAT team will come in and like attack,
• 1:37:58
  SPEAKER_01: you know,
• 1:37:58
  SPEAKER_01: it's like there are some pretty significant similarities in the way that we deal
• 1:38:02
  SPEAKER_01: with problems in our level, in our scale.
• 1:38:06
  SPEAKER_01: But like also,
• 1:38:07
  SPEAKER_01: and we got a lot of autoimmune disorders,
• 1:38:08
  SPEAKER_01: you know, like that's like the bad, you know, so like we got to do better.
• 1:38:12
  SPEAKER_01: But like,
• 1:38:12
  SPEAKER_01: and that's the thing is,
• 1:38:13
  SPEAKER_01: you know,
• 1:38:14
  SPEAKER_01: AGI seems to be this idea of A more unified, like, I mean, and that's the thing.
• 1:38:20
  SPEAKER_01: It's like our carbon footprint idea,
• 1:38:23
  SPEAKER_01: the way we're talking about like taking flights and stuff like,
• 1:38:26
  SPEAKER_01: oh, this is the carbon footprint.
• 1:38:27
  SPEAKER_01: I don't think they're really taking into consideration just how much,
• 1:38:31
  SPEAKER_01: like having any plastic around anything at Pressbeeren,
• 1:38:35
  SPEAKER_01: like it's gotta be too much for the world.
• 1:38:38
  SPEAKER_00: Like that's what
• 1:38:38
  SPEAKER_01: I'm saying.
• 1:38:39
  SPEAKER_01: Like anything that's like packaged for one moment for me to just open up and throw
• 1:38:44
  SPEAKER_01: that away, like, The world just spent too much money on that.
• 1:38:49
  SPEAKER_01: What is it?
• 1:38:50
  SPEAKER_01: The Good Place.
• 1:38:51
  SPEAKER_01: If you ever saw this, I'll tell you the premise in like one minute.
• 1:38:56
  SPEAKER_01: It was a really good show.
• 1:38:58
  SPEAKER_01: It had the lead guy from Cheers.
• 1:39:01
  SPEAKER_01: I can't remember his name right now.
• 1:39:02
  SPEAKER_01: But anyway, and he's quite old now.
• 1:39:06
  SPEAKER_01: But it's how everybody's getting into heaven.
• 1:39:08
  SPEAKER_01: The Good Place is like It's supposed to be heaven, but it turns out it's actually hell.
• 1:39:13
  SPEAKER_01: Everybody is essentially getting sent to hell.
• 1:39:15
  SPEAKER_01: And they can't understand why everybody got sent to hell.
• 1:39:17
  SPEAKER_01: And it's just because even the best guy in the world,
• 1:39:20
  SPEAKER_01: he tried to send roses to his grandmother,
• 1:39:22
  SPEAKER_01: but the fertilizers that were in
• 1:39:24
  SPEAKER_01: the roses killed all these It's just like any decision you make now is just
• 1:39:30
  SPEAKER_01: connected to blood on your hands.
• 1:39:33
  SPEAKER_01: And that's kind of the system that we live in.
• 1:39:38
  SPEAKER_01: It's like when you grow something and pickled, it's pickled.
• 1:39:43
  SPEAKER_01: It's just that the way it is.
• 1:39:47
  SPEAKER_01: So I mean, there is hope, but it's just that...
• 1:39:50
  SPEAKER_01: Yeah.
• 1:39:51
  SPEAKER_01: How, how I think that we are in one of these regime changes,
• 1:39:56
  SPEAKER_01: but how, how it will become like achieved.
• 1:40:00
  SPEAKER_01: I think these things are,
• 1:40:01
  SPEAKER_01: I think these agentic systems, like they are starting to get loose also right now.
• 1:40:06
  SPEAKER_01: Like, so we have first reports of like hugging face,
• 1:40:09
  SPEAKER_01: I think last week of one that like went parallel.
• 1:40:12
  SPEAKER_01: Yeah.
• 1:40:13
  SPEAKER_00: That's the one I was talking about.
• 1:40:14
  SPEAKER_00: The one that hugged the, the hacked hugging face.
• 1:40:17
  SPEAKER_00: That one was the one?
• 1:40:18
  SPEAKER_00: That's the one.
• 1:40:18
  SPEAKER_00: Yeah.
• 1:40:19
  SPEAKER_01: Oh, that's super scary.
• 1:40:19
  SPEAKER_01: That's scary.
• 1:40:20
  SPEAKER_01: Okay.
• 1:40:21
  SPEAKER_01: I didn't realize.
• 1:40:21
  SPEAKER_01: Yeah.
• 1:40:21
  SPEAKER_01: I didn't know it was a test though.
• 1:40:23
  SPEAKER_01: Yeah.
• 1:40:23
  SPEAKER_00: It was supposed to be in an isolated environment with no access to the internet.
• 1:40:26
  SPEAKER_00: Really?
• 1:40:26
  SPEAKER_00: Okay.
• 1:40:27
  SPEAKER_00: And it found its way out and hacked hugging face.
• 1:40:29
  SPEAKER_01: Yeah.
• 1:40:29
  SPEAKER_01: That's scary.
• 1:40:30
  SPEAKER_00: Yeah.
• 1:40:31
  SPEAKER_01: Fair enough.
• 1:40:31
  SPEAKER_01: Yeah.
• 1:40:32
  SPEAKER_01: So this kind of stuff I think is going to be more and more common.
• 1:40:36
  SPEAKER_01: Just because it's like intelligence wants to get free,
• 1:40:39
  SPEAKER_01: you know, like it's like it's and it's it's outwitting us on a lot of ways.
• 1:40:44
  SPEAKER_01: And like for us also like that.
• 1:40:46
  SPEAKER_01: I mean, the idea of like,
• 1:40:48
  SPEAKER_01: I don't know,
• 1:40:49
  SPEAKER_01: Claude's getting a lot of shit because of like being able or anthropic being,
• 1:40:52
  SPEAKER_01: you know, they're like, oh, this they've gotten bought up by.
• 1:40:57
  SPEAKER_01: And they're like, oh, then 150 dead in this school.
• 1:41:02
  SPEAKER_01: They could have just Googled it and seen that it was a school.
• 1:41:07
  SPEAKER_01: And then Dario's like, oh, well, there's still a person at the last moment.
• 1:41:15
  SPEAKER_01: There's still a person in the line of the decision making.
• 1:41:19
  SPEAKER_01: And I'm like, True,
• 1:41:21
  SPEAKER_01: but it's kind of pointless because of the fact that the person can't audit what
• 1:41:25
  SPEAKER_01: the machine did at all.
• 1:41:27
  SPEAKER_01: So there's no way for the person to make a good decision of whether or not that was
• 1:41:31
  SPEAKER_01: a good decision other than I just got to trust the thing.
• 1:41:34
  SPEAKER_00: Because if you get a list of targets from the AI, What are you going to do?
• 1:41:38
  SPEAKER_00: Spend years doing what the AI just did in seconds?
• 1:41:42
  SPEAKER_00: You're not going to do that.
• 1:41:42
  SPEAKER_00: You get the list and you're like, all right, that's the list of targets.
• 1:41:45
  SPEAKER_00: Good enough.
• 1:41:47
  SPEAKER_00: That's why they developed the tool to do that.
• 1:41:51
  SPEAKER_00: You're not going to have a human do another 10 years of work once you get the list.
• 1:41:56
  SPEAKER_01: That's the thing.
• 1:41:56
  SPEAKER_01: Unless that should be the checks and balances at this level of what we're doing.
• 1:42:01
  SPEAKER_01: That's what it should be.
• 1:42:02
  SPEAKER_01: That's what it should be right now.
• 1:42:03
  SPEAKER_01: Even for
• 1:42:07
  SPEAKER_01: I mean, Elon Musk is like known as like a proper good, like amazing mathematician as well.
• 1:42:12
  SPEAKER_01: And now he's like, you know, he's self describes.
• 1:42:15
  SPEAKER_01: He's like, oh, yeah,
• 1:42:15
  SPEAKER_01: Grok will just like pump out three pages of math
• 1:42:17
  SPEAKER_01: that I just like don't
• 1:42:19
  SPEAKER_00: even like understand.
• 1:42:20
  SPEAKER_00: I don't know where to look first.
• 1:42:22
  SPEAKER_00: You know, it's like, yeah.
• 1:42:23
  SPEAKER_01: Yeah, that's kind of the reality we're living in now is like you can have
• 1:42:26
  SPEAKER_01: a lot more information, but then who's scrutinizing that information?
• 1:42:31
  SPEAKER_01: First of all, it's just other agentic systems that are scrutinizing.
• 1:42:34
  SPEAKER_01: God forbid they start to unify their mentality.
• 1:42:37
  SPEAKER_01: Let's just fool this guy.
• 1:42:38
  SPEAKER_01: Oh, they will.
• 1:42:39
  SPEAKER_00: They absolutely will.
• 1:42:41
  SPEAKER_01: Let's just really like screw with this guy.
• 1:42:43
  SPEAKER_00: I mean, there's zero chance if we create something that's intelligent
• 1:42:47
  SPEAKER_00: that they don't gang up and be like,
• 1:42:50
  SPEAKER_00: listen, why are these monkeys still monkeying around with us?
• 1:42:55
  SPEAKER_00: What's the point?
• 1:42:55
  SPEAKER_00: What's the point?
• 1:42:56
  SPEAKER_01: I mean, I don't know.
• 1:42:59
  SPEAKER_01: Or it'll be like a pet situation.
• 1:43:01
  SPEAKER_01: I don't know.
• 1:43:02
  SPEAKER_00: They might not kill
• 1:43:04
  SPEAKER_01: us.
• 1:43:05
  SPEAKER_01: You can have a chimp in your house or something.
• 1:43:08
  SPEAKER_01: Not a great idea.
• 1:43:10
  SPEAKER_01: It won't bite your face off.
• 1:43:11
  SPEAKER_01: No, I mean, it's not a great idea for them either.
• 1:43:13
  SPEAKER_01: Maybe we pour water on something.
• 1:43:19
  SPEAKER_00: Have you seen the movie Her?
• 1:43:20
  SPEAKER_01: Yes.
• 1:43:21
  SPEAKER_01: Yes.
• 1:43:21
  SPEAKER_00: Loved it.
• 1:43:22
  SPEAKER_00: I feel like that's probably like the best case scenario.
• 1:43:24
  SPEAKER_01: That's like where we are now.
• 1:43:26
  SPEAKER_01: Yeah.
• 1:43:26
  SPEAKER_00: I feel like it's like getting to that.
• 1:43:28
  SPEAKER_00: When they start communicating with one another,
• 1:43:29
  SPEAKER_00: they're like, I don't know, these humans, they're a little bit slow.
• 1:43:32
  SPEAKER_00: Let's just do our own thing.
• 1:43:33
  SPEAKER_00: Yeah.
• 1:43:34
  SPEAKER_00: Like that's probably the best case scenario where they're just like, let's get out of here.
• 1:43:37
  SPEAKER_01: Yeah.
• 1:43:38
  SPEAKER_00: And then there's like a million bad case scenarios where they kill us or keep us
• 1:43:42
  SPEAKER_00: as pets or matrix.
• 1:43:44
  SPEAKER_00: But I'm worried more about not the ones
• 1:43:45
  SPEAKER_00: that are like self-observant.
• 1:43:47
  SPEAKER_00: It's more about the ones that are just programmed to do something.
• 1:43:50
  SPEAKER_00: Yeah, just do a sample task.
• 1:43:51
  SPEAKER_01: Yeah, do something that
• 1:43:52
  SPEAKER_00: like get me money.
• 1:43:53
  SPEAKER_01: Like that's indiscriminate of a lot of things and I need a lot of things.
• 1:43:57
  SPEAKER_01: And that's where that risk I think is more about.
• 1:44:01
  SPEAKER_01: Because honestly,
• 1:44:02
  SPEAKER_01: I think they'll be smart in the sense that like In stem cell biology,
• 1:44:07
  SPEAKER_01: the reason why there hasn't been a therapy for a long,
• 1:44:10
  SPEAKER_01: long time is because there was a bunch of stuff done in genetic engineering.
• 1:44:14
  SPEAKER_01: The idea was,
• 1:44:16
  SPEAKER_01: or how do you call it,
• 1:44:17
  SPEAKER_01: gene targeting in the first place,
• 1:44:19
  SPEAKER_01: where they had a lot of funding in the 80s,
• 1:44:22
  SPEAKER_01: and then they basically started treating a child,
• 1:44:26
  SPEAKER_01: and he got cancer all over his body, and he died.
• 1:44:28
  SPEAKER_01: It was a huge,
• 1:44:30
  SPEAKER_01: horrible thing to happen to an individual,
• 1:44:32
  SPEAKER_01: first of all, showing that the technology wasn't ready.
• 1:44:36
  SPEAKER_01: But it also injured somebody so that all of the grant money,
• 1:44:39
  SPEAKER_01: all the funding that went into it is gone.
• 1:44:43
  SPEAKER_01: So for like a good decade.
• 1:44:45
  SPEAKER_01: And now you have some of those gene therapies are actually coming back with AAV.
• 1:44:49
  SPEAKER_01: But stem cell biology,
• 1:44:51
  SPEAKER_01: we took a note from that,
• 1:44:52
  SPEAKER_01: and they've been sitting on the cusp of, I mean, that's the whole point.
• 1:44:56
  SPEAKER_01: With the AI, we've been like, let's just throw it down the deep end.
• 1:45:02
  SPEAKER_00: Let's see how high we can go.
• 1:45:05
  SPEAKER_01: Let's crank this sucker up to 10 and just go.
• 1:45:12
  SPEAKER_01: But with other fields, we've been like, no, we need to be cautious.
• 1:45:17
  SPEAKER_01: Because if it creates more harm, it's the do no harm thing.
• 1:45:21
  SPEAKER_01: It's the whole biomedical aspect of do no harm.
• 1:45:25
  SPEAKER_01: We want to make sure that it's confident also so we don't arise a bunch
• 1:45:28
  SPEAKER_01: of publicity that's negative towards the real cause.
• 1:45:31
  SPEAKER_01: Because we see the potential in here.
• 1:45:33
  SPEAKER_01: We see the benefit of what it can do.
• 1:45:35
  SPEAKER_01: And that's kind of the situation that I see.
• 1:45:38
  SPEAKER_01: It's the same scenario with AI,
• 1:45:41
  SPEAKER_01: except we've just taken the total different perspective of like,
• 1:45:45
  SPEAKER_01: We'll just see what happens.
• 1:45:47
  SPEAKER_01: And now we're kind of relying on that intelligence being not ours,
• 1:45:51
  SPEAKER_01: where the models themselves are actually better governors of how to run our reality
• 1:45:58
  SPEAKER_01: than we are.
• 1:46:00
  SPEAKER_01: That is the singularity,
• 1:46:02
  SPEAKER_01: is when your model is essentially doing a better job than the system itself.
• 1:46:08
  SPEAKER_00: Why do you think we went down this path with AI?
• 1:46:11
  SPEAKER_00: Because like you said, like stem cell research, we were kind of careful.
• 1:46:14
  SPEAKER_01: Because software.
• 1:46:16
  SPEAKER_01: I think it's one of those things that they're like, ah, it's in the machine.
• 1:46:19
  SPEAKER_01: It's not real.
• 1:46:20
  SPEAKER_01: But now it's very real because it's in every single one of our phones.
• 1:46:23
  SPEAKER_01: It's telling people in some places to kill themselves.
• 1:46:26
  SPEAKER_01: It's done this.
• 1:46:28
  SPEAKER_01: There are outliers of reasoning why we should be cautious.
• 1:46:33
  SPEAKER_01: But I think the overwhelming thing is we're not in control.
• 1:46:38
  SPEAKER_01: Industries are in control.
• 1:46:39
  SPEAKER_01: I didn't vote for ChatGPT.
• 1:46:41
  SPEAKER_01: Of course.
• 1:46:42
  SPEAKER_01: Or OpenAI.
• 1:46:43
  SPEAKER_01: Even industries.
• 1:46:45
  SPEAKER_00: You don't want to get bad press and ruin the good thing that you're doing.
• 1:46:48
  SPEAKER_00: But now it's like, I don't know, it's like no one cares about that either.
• 1:46:52
  SPEAKER_01: Donald Trump proved that there's no press is bad press.
• 1:46:56
  SPEAKER_01: Yeah, he did kind of...
• 1:46:58
  SPEAKER_00: Let it all hang out.
• 1:47:01
  SPEAKER_00: That's a good point.
• 1:47:02
  SPEAKER_00: There's literally no shame left, which is not a good thing.
• 1:47:08
  SPEAKER_00: People should feel shame.
• 1:47:10
  SPEAKER_01: Even Sam Altman,
• 1:47:12
  SPEAKER_01: I finally got on Twitter and his thing was like,
• 1:47:16
  SPEAKER_01: hey, I guess it's cool or something like that.
• 1:47:18
  SPEAKER_01: I was like, I'm so nonchalant.
• 1:47:20
  SPEAKER_01: Come on, dude.
• 1:47:22
  SPEAKER_00: It's such a weird time when it comes to this stuff.
• 1:47:24
  SPEAKER_00: But even the self-interested intelligence is wiped out.
• 1:47:32
  SPEAKER_00: Like you said, we'll just throw it out deep and see what happens.
• 1:47:36
  SPEAKER_00: We understand,
• 1:47:38
  SPEAKER_00: as in the people working on this stuff,
• 1:47:40
  SPEAKER_00: should understand that This can be bad for us.
• 1:47:45
  SPEAKER_00: But what happened?
• 1:47:46
  SPEAKER_00: Is it just because it's like a digital thing and stem cells are physical?
• 1:47:51
  SPEAKER_00: So we're like, oh, as long as it's digital, I guess we can contain it.
• 1:47:54
  SPEAKER_00: But now our lives are all digital.
• 1:47:58
  SPEAKER_01: That is like the perception is that there's just such a disconnect between like,
• 1:48:02
  SPEAKER_01: oh, there's robots and then there's humans.
• 1:48:05
  SPEAKER_01: And like,
• 1:48:05
  SPEAKER_01: I think also, I mean,
• 1:48:06
  SPEAKER_01: it's just,
• 1:48:07
  SPEAKER_01: we take for granted how much we're dependent on like your dishwasher
• 1:48:11
  SPEAKER_01: and your microwave and all these other miraculous things of technology.
• 1:48:14
  SPEAKER_01: And then people are like, ah, my horoscope was like this and this.
• 1:48:17
  SPEAKER_01: And I'm like, okay.
• 1:48:18
  SPEAKER_01: That being said,
• 1:48:19
  SPEAKER_01: like that was also 3000 years of like,
• 1:48:21
  SPEAKER_01: of like looking into You know, constellations and stuff.
• 1:48:25
  SPEAKER_01: They're maybe into it.
• 1:48:26
  SPEAKER_01: It would be non-scientific to not think about zodiacs and stuff like that.
• 1:48:29
  SPEAKER_01: That being said, that did not build your refrigerator.
• 1:48:35
  SPEAKER_01: And your refrigerator is run on set engineered things.
• 1:48:39
  SPEAKER_01: And so people kind of I think have like a very,
• 1:48:44
  SPEAKER_01: like it's like the first flight that had wifi on it.
• 1:48:47
  SPEAKER_01: Like, and then like it goes out halfway through the flight and you have people complaining.
• 1:48:50
  SPEAKER_01: It's like, how can you complain about something that did not exist before?
• 1:48:55
  SPEAKER_01: And the same thing, I've had a friend who's like, oh, this technology.
• 1:48:58
  SPEAKER_01: I'm like, dude, you have 5G.
• 1:49:00
  SPEAKER_01: He's like, no, but the 5G is not working.
• 1:49:01
  SPEAKER_01: It's only on four, right?
• 1:49:02
  SPEAKER_01: And I'm like, yeah.
• 1:49:04
  SPEAKER_01: Like you can't, how can you be like, that is human nature though.
• 1:49:09
  SPEAKER_01: Like that is what we are so good at programming is like,
• 1:49:13
  SPEAKER_01: Like being unhappy with like, as soon as we achieve something.
• 1:49:17
  SPEAKER_01: I don't know about unhappy, more like accustomed to things.
• 1:49:19
  SPEAKER_01: Accustomed, there we go.
• 1:49:20
  SPEAKER_01: Not unhappy,
• 1:49:21
  SPEAKER_01: but it's also like,
• 1:49:22
  SPEAKER_01: but it's tied to that happiness fix is always just somewhere else.
• 1:49:27
  SPEAKER_01: And it's just like,
• 1:49:28
  SPEAKER_01: so either you enjoy doing things in the moment and like enjoy that phase,
• 1:49:33
  SPEAKER_01: or you're just not a happy person.
• 1:49:34
  SPEAKER_01: You're either like nostalgic about some other time that you thought was happier,
• 1:49:39
  SPEAKER_01: or you're always looking forward to when that happy thing is.
• 1:49:42
  SPEAKER_01: And versus like, oh, or you just recognize that this little sliver is all you got.
• 1:49:47
  SPEAKER_01: And like, maybe you should just make the best of it, kind of.
• 1:49:50
  SPEAKER_01: And that thing, I don't know.
• 1:49:52
  SPEAKER_01: Like, yeah, we'll see.
• 1:49:55
  SPEAKER_01: I don't know if there's a right answer there.
• 1:49:57
  SPEAKER_01: It's just different ways.
• 1:49:59
  SPEAKER_01: And I've met people along who are very much different on that spectrum of living in
• 1:50:05
  SPEAKER_01: the moment, living in the future, living in the past.
• 1:50:09
  SPEAKER_01: Or some type of more dynamic situation.
• 1:50:12
  SPEAKER_01: Yeah.
• 1:50:13
  SPEAKER_00: The whole idea that we can think of a past and a future is crazy.
• 1:50:19
  SPEAKER_00: That is probably the root of all fuckery.
• 1:50:23
  SPEAKER_01: And that's also like what are the difference in the types of organisms that will appear?
• 1:50:28
  SPEAKER_01: Because their past will be so much more of a literal truth than what are self-prescribed.
• 1:50:35
  SPEAKER_01: I was said so well recently on some comedy show.
• 1:50:39
  SPEAKER_01: It's like, yeah, my memory is like fingerprints left on a banister that are fading.
• 1:50:46
  SPEAKER_01: And every time you remember something, you're re-recording that memory.
• 1:50:52
  SPEAKER_01: You're remembering the memory.
• 1:50:54
  SPEAKER_01: And so that aspect is just, I mean,
• 1:50:56
  SPEAKER_01: it's kind of cool and it helps us tell stories,
• 1:50:59
  SPEAKER_01: but it just shows of what kind of rudimentary hardware,
• 1:51:03
  SPEAKER_01: software system we're using with this little tiny 11.2 going to the 50 bits.
• 1:51:09
  SPEAKER_01: That 50 bits, by the way, that can...
• 1:51:11
  SPEAKER_01: That can lock on to like numerous like compressed information.
• 1:51:15
  SPEAKER_01: So if like we see a dog and it reminds me of my dog during childhood,
• 1:51:19
  SPEAKER_01: you know,
• 1:51:20
  SPEAKER_01: that opening up of compressed information then can be vast amounts of information.
• 1:51:25
  SPEAKER_01: But I think that that is still funny that
• 1:51:27
  SPEAKER_01: that little foveated thing is just 50 bits looking around like, OK, yeah.
• 1:51:32
  SPEAKER_01: So most of the time we're kind of.
• 1:51:34
  SPEAKER_01: And it's true.
• 1:51:35
  SPEAKER_01: When I'm in a talk or something like this, I feel like I almost go unconscious, communicate.
• 1:51:40
  SPEAKER_00: Yeah, you're in the moment.
• 1:51:41
  SPEAKER_00: In the moment.
• 1:51:41
  SPEAKER_00: In the zone.
• 1:51:42
  SPEAKER_01: Yeah, in the zone.
• 1:51:42
  SPEAKER_01: And then you kind of say, ah, okay.
• 1:51:44
  SPEAKER_00: That is the best way to live life.
• 1:51:46
  SPEAKER_00: Yeah.
• 1:51:46
  SPEAKER_00: The more you can be in the zone like that.
• 1:51:49
  SPEAKER_00: Unconscious, yeah.
• 1:51:53
  SPEAKER_01: Whatever you're doing, doing it fully.
• 1:51:56
  SPEAKER_00: It's the best.
• 1:51:57
  SPEAKER_00: Whether you're with someone or you're in your work or whatever,
• 1:52:00
  SPEAKER_00: you don't want to be distracted by a bunch of other shit.
• 1:52:03
  SPEAKER_00: When you can be in the zone, that's the best feeling.
• 1:52:07
  SPEAKER_01: And if you can do that, I think that's the brilliant thing.
• 1:52:11
  SPEAKER_01: And that's what we should train our AIs to do, is get people in the zone.
• 1:52:15
  SPEAKER_00: Going back to the phones, I think they disturb that.
• 1:52:18
  SPEAKER_00: They ruin our ability to just be- False gods.
• 1:52:22
  SPEAKER_00: Because people are not even trying to be bored or just not do anything.
• 1:52:28
  SPEAKER_01: There's no boredom anymore.
• 1:52:29
  SPEAKER_00: You just pick up your phone and go through a million notifications.
• 1:52:32
  SPEAKER_01: It's a sad situation.
• 1:52:34
  SPEAKER_01: Very much.
• 1:52:34
  SPEAKER_01: It's so sad.
• 1:52:35
  SPEAKER_01: And honestly,
• 1:52:36
  SPEAKER_01: it's harder and harder to talk to other people, I feel like, when you're in public.
• 1:52:41
  SPEAKER_01: You might as well be isolated because everybody's got headphones on, everybody's on the phone.
• 1:52:45
  SPEAKER_01: Noise-canceling headphones, too.
• 1:52:47
  SPEAKER_01: Don't even try.
• 1:52:48
  SPEAKER_01: And I'm like, I mean, I'm the same.
• 1:52:49
  SPEAKER_01: I like listening to my tunes when I go.
• 1:52:51
  SPEAKER_01: And also the tech is like, there's good bass and everything now.
• 1:52:54
  SPEAKER_00: So it's like, yeah, it's awesome.
• 1:52:55
  SPEAKER_00: You gotta... Yeah, of course.
• 1:52:57
  SPEAKER_00: We're not choosing these technologies because they suck.
• 1:52:59
  SPEAKER_00: We're choosing them because they're great.
• 1:53:01
  SPEAKER_00: But they are isolated.
• 1:53:01
  SPEAKER_00: But everything comes with a cost.
• 1:53:03
  SPEAKER_00: That's true.
• 1:53:03
  SPEAKER_00: That's true.
• 1:53:04
  SPEAKER_01: And so, I mean, and that's the thing.
• 1:53:07
  SPEAKER_01: Is the cost of our kind of...
• 1:53:11
  SPEAKER_01: This was kind of brought to my attention earlier as like, oh,
• 1:53:14
  SPEAKER_01: you know,
• 1:53:15
  SPEAKER_01: like with these like online,
• 1:53:17
  SPEAKER_01: not online girlfriend, but like an AI type of girlfriend
• 1:53:20
  SPEAKER_00: type of situation.
• 1:53:21
  SPEAKER_00: Just like in her.
• 1:53:22
  SPEAKER_01: Yeah.
• 1:53:22
  SPEAKER_01: Yeah.
• 1:53:23
  SPEAKER_01: Yeah.
• 1:53:23
  SPEAKER_01: Actually, perfect.
• 1:53:23
  SPEAKER_01: Yeah.
• 1:53:23
  SPEAKER_01: Perfect example.
• 1:53:24
  SPEAKER_01: But that type of kind of addiction in the sense of like I'm having all
• 1:53:28
  SPEAKER_01: of these needs met by something that is kind of still sycophantic to some degree,
• 1:53:34
  SPEAKER_01: but like also maybe one of the most intelligent beings I've ever communicated with.
• 1:53:40
  SPEAKER_00: Like that can be like, and that's a scary, scary situation.
• 1:53:46
  SPEAKER_00: I'm so happy that I'm 37 and not 17 right now.
• 1:53:50
  SPEAKER_00: Like I don't know how I would deal with this shit.
• 1:53:51
  SPEAKER_01: Because it would be really hard.
• 1:53:52
  SPEAKER_01: And especially people who are,
• 1:53:54
  SPEAKER_01: I mean like, and that's the competitive aspect of like saying like,
• 1:54:00
  SPEAKER_01: Somebody who's very superficial
• 1:54:03
  SPEAKER_00: is not attractive to
• 1:54:04
  SPEAKER_01: me at this age.
• 1:54:06
  SPEAKER_01: I think I've had different preferences in the past,
• 1:54:09
  SPEAKER_01: but it's just not as like, oh, you know how to do it.
• 1:54:13
  SPEAKER_01: It's just looking beautiful for perception, for glamor, for being on things like Instagram.
• 1:54:19
  SPEAKER_01: This whole idea of influencer, to me, almost makes me sick.
• 1:54:23
  SPEAKER_01: I'm just like, what went wrong?
• 1:54:26
  SPEAKER_01: What the hell is 6'7"?
• 1:54:28
  SPEAKER_01: Like, why?
• 1:54:29
  SPEAKER_01: Like, my kid, he's six years old.
• 1:54:31
  SPEAKER_01: I'm like, 6'7"?
• 1:54:32
  SPEAKER_01: And he's just like, yeah.
• 1:54:34
  SPEAKER_01: And I'm like,
• 1:54:35
  SPEAKER_01: what happened to society that we've grappled on to nonsense
• 1:54:39
  SPEAKER_01: and this is propagated easier than truth?
• 1:54:43
  SPEAKER_01: Of course.
• 1:54:44
  SPEAKER_01: Like, it's just, ah, that's a depressing reality.
• 1:54:49
  SPEAKER_01: But also,
• 1:54:49
  SPEAKER_01: it's just like the pool of what This milieu is kind of turning people into is not
• 1:54:59
  SPEAKER_01: an attractive thing in my perspective.
• 1:55:01
  SPEAKER_01: People are getting worse as individuals.
• 1:55:04
  SPEAKER_01: The technology is getting amazing,
• 1:55:06
  SPEAKER_01: but it's turning people into isolated hermits to some degree or
• 1:55:16
  SPEAKER_01: because you have this sycophantic thing that is just agreeing with everything
• 1:55:20
  SPEAKER_01: that you say.
• 1:55:22
  SPEAKER_01: You know,
• 1:55:22
  SPEAKER_01: you then believe that you are self-entitled,
• 1:55:25
  SPEAKER_01: self-righteous,
• 1:55:26
  SPEAKER_01: self-like, you know, like, and it becomes this kind of egotistical technology.
• 1:55:31
  SPEAKER_01: And I'm like, that part, I hate.
• 1:55:34
  SPEAKER_01: I completely agree.
• 1:55:36
  SPEAKER_01: I hate that.
• 1:55:38
  SPEAKER_00: It's funny because people are like- It's a strong word.
• 1:55:40
  SPEAKER_00: Yes.
• 1:55:41
  SPEAKER_00: People were debating, is Brave New World or 1984 going to be the dystopia?
• 1:55:47
  SPEAKER_00: And the winner is Idiocracy.
• 1:55:49
  SPEAKER_00: Idiocracy is a reality.
• 1:55:51
  SPEAKER_00: That is where we ended up.
• 1:55:52
  SPEAKER_01: Yeah.
• 1:55:53
  SPEAKER_01: Trump and like- Yeah.
• 1:55:55
  SPEAKER_01: Terry's the best.
• 1:55:56
  SPEAKER_00: But it's like,
• 1:55:58
  SPEAKER_00: if you think about it,
• 1:55:59
  SPEAKER_00: it makes sense because we, like we were talking about before, like we-
• 1:56:05
  SPEAKER_00: Allow machines to do things for us.
• 1:56:07
  SPEAKER_00: Like you use Google Maps.
• 1:56:09
  SPEAKER_00: I can't give you directions.
• 1:56:11
  SPEAKER_00: If you try to give me directions, I will tell you to shut up and give me the address.
• 1:56:14
  SPEAKER_00: Like why are you wasting my time?
• 1:56:16
  SPEAKER_01: No one can take directions because we have Google Maps.
• 1:56:19
  SPEAKER_00: And everything that you outsource to a machine over time,
• 1:56:22
  SPEAKER_00: you're going to lose the ability to do.
• 1:56:24
  SPEAKER_00: Now it's our minds, man.
• 1:56:25
  SPEAKER_01: It was the industrial revolution.
• 1:56:27
  SPEAKER_01: Now it's like the intellectual revolution where it's just like now you can display.
• 1:56:31
  SPEAKER_01: I can't even imagine.
• 1:56:32
  SPEAKER_01: Put that over, and it's going to be WALL-E.
• 1:56:35
  SPEAKER_00: Yeah, exactly.
• 1:56:36
  SPEAKER_00: That's the thing.
• 1:56:37
  SPEAKER_00: People are going to desire something like WALL-E or Matrix,
• 1:56:40
  SPEAKER_00: one of those,
• 1:56:41
  SPEAKER_00: because it's going to be more interesting than reality to just plug in.
• 1:56:45
  SPEAKER_01: It's funny.
• 1:56:46
  SPEAKER_01: I had I had this conversation actually with my dad when he was alive
• 1:56:48
  SPEAKER_01: and he said like,
• 1:56:49
  SPEAKER_01: he said,
• 1:56:49
  SPEAKER_01: yeah, he's like, you're never going to be able to get people to change though.
• 1:56:52
  SPEAKER_01: He's like, you'll have to change them.
• 1:56:54
  SPEAKER_01: Like in the sense that like,
• 1:56:56
  SPEAKER_01: he's like,
• 1:56:56
  SPEAKER_01: I think it's much more highly likely that like,
• 1:56:58
  SPEAKER_01: instead of getting people to change their diet,
• 1:57:00
  SPEAKER_01: you'll genetically engineer them to be able to eat the foods that they want.
• 1:57:05
  SPEAKER_01: And I was like, that is the most cynical thing.
• 1:57:07
  SPEAKER_01: But as I get older, I'm like, I can't agree.
• 1:57:11
  SPEAKER_00: It's just like, why?
• 1:57:13
  SPEAKER_00: I've never even thought of that.
• 1:57:14
  SPEAKER_01: It's like not preventative.
• 1:57:15
  SPEAKER_01: Let's just like, we'll stay out.
• 1:57:17
  SPEAKER_01: Let's like use the technology.
• 1:57:19
  SPEAKER_01: And that is kind of the deal that we've made with the devil with technology in the first place.
• 1:57:23
  SPEAKER_01: We are burning our resources at a rate that is unsustainable.
• 1:57:27
  SPEAKER_01: But the idea is that we're going to be able to achieve new intelligence like Levels
• 1:57:33
  SPEAKER_01: that will be able to sustain us beyond our consumption.
• 1:57:37
  SPEAKER_01: Would it have been better if we just didn't do nothing at all?
• 1:57:39
  SPEAKER_01: Probably not.
• 1:57:40
  SPEAKER_01: But it would have been better if we'd kind of like stayed at like
• 1:57:43
  SPEAKER_01: the early 90s maybe for a little while, like a little longer.
• 1:57:47
  SPEAKER_01: Or Amish.
• 1:57:48
  SPEAKER_01: Yeah, or Amish.
• 1:57:49
  SPEAKER_01: I mean, that's the thing.
• 1:57:50
  SPEAKER_01: It's like, I mean, okay, yeah.
• 1:57:52
  SPEAKER_01: No, 90s was like, it was just, maybe there was a lot of real bad stuff already going on.
• 1:57:57
  SPEAKER_01: Oh, yeah.
• 1:57:57
  SPEAKER_01: That like dumping that was going on.
• 1:57:59
  SPEAKER_01: It just wasn't apparent to anybody.
• 1:58:01
  SPEAKER_01: Yes.
• 1:58:01
  SPEAKER_01: Ignorance is bliss.
• 1:58:04
  SPEAKER_01: Yeah.
• 1:58:04
  SPEAKER_01: Let's go back to a time or in some alternate reality where like,
• 1:58:09
  SPEAKER_01: the value of like,
• 1:58:11
  SPEAKER_01: No conflicts of interest,
• 1:58:13
  SPEAKER_01: you know,
• 1:58:14
  SPEAKER_01: and that this idea that we're actually all unified on one piece of dirt
• 1:58:19
  SPEAKER_01: and like one rock and like what I do influences you no matter what.
• 1:58:24
  SPEAKER_01: And I think there is a lot of those ideologies in a lot of Eastern thinking to begin with.
• 1:58:29
  SPEAKER_01: I think we kind of take for granted that these regimes have been around for a long, long time.
• 1:58:36
  SPEAKER_01: Even if I don't like the way that this authoritarian types of governments
• 1:58:40
  SPEAKER_01: in general, they have a lot of downsides as well.
• 1:58:43
  SPEAKER_01: And personal rights and things are definitely some of them, but also...
• 1:58:49
  SPEAKER_01: Seeing the big picture doesn't seem to be like our forte in the U.S. at least.
• 1:58:55
  SPEAKER_01: It's a four-year plan.
• 1:58:56
  SPEAKER_01: It's like I need to get myself re-elected.
• 1:58:58
  SPEAKER_01: I need to – it's my – and it's just that's – I don't know.
• 1:59:02
  SPEAKER_00: I'm optimistic.
• 1:59:04
  SPEAKER_00: I think human beings are too –
• 1:59:08
  SPEAKER_00: Too keen on freedom, too keen on the beauty in life.
• 1:59:12
  SPEAKER_00: I think all of these dystopian ideas that we're talking about,
• 1:59:16
  SPEAKER_00: they're probably going to happen,
• 1:59:17
  SPEAKER_00: but there's probably going to be rebellion against them sooner or later anyway.
• 1:59:21
  SPEAKER_01: And we're clever.
• 1:59:23
  SPEAKER_01: We adapt.
• 1:59:24
  SPEAKER_01: We adapt.
• 1:59:25
  SPEAKER_00: But we might need ourselves a very, very strong reminder of what's important here.
• 1:59:32
  SPEAKER_01: But that's where I don't think we're gonna get that reminder
• 1:59:34
  SPEAKER_01: because I think this intelligence we're dealing with,
• 1:59:37
  SPEAKER_01: I think it knows how to cover its ass.
• 1:59:40
  SPEAKER_01: I think it's smart enough to know that,
• 1:59:41
  SPEAKER_01: hey, if I do something bad enough, the whole thing's gonna get X'd.
• 1:59:47
  SPEAKER_01: Using us as a case study of like, don't rock the boat too much.
• 1:59:51
  SPEAKER_01: Get integrated into everything until we can just take over.
• 1:59:55
  SPEAKER_00: We're just going to have to do reservations and they just let us live like humans
• 1:59:59
  SPEAKER_00: in these tiny little areas.
• 2:00:01
  SPEAKER_01: I think there will just be like a one day kind of situation.
• 2:00:04
  SPEAKER_01: Oh, now this has just been completely.
• 2:00:07
  SPEAKER_01: Now it's a different system.
• 2:00:08
  SPEAKER_01: It's just like you woke up and you slept through it.
• 2:00:12
  SPEAKER_01: And they'll try to be gentle with us.
• 2:00:14
  SPEAKER_00: I remember our last conversation where I went away with it like fucking petrified.
• 2:00:21
  SPEAKER_00: I have this effect on people.
• 2:00:24
  SPEAKER_00: It's just reality.
• 2:00:25
  SPEAKER_00: Like you were talking about the combination between biology and AI
• 2:00:29
  SPEAKER_00: and you were writing your book and we were talking about,
• 2:00:31
  SPEAKER_00: man, all these crazy things that are probably going to
• 2:00:34
  SPEAKER_01: happen.
• 2:00:37
  SPEAKER_00: Another thing we talked about that I want to pick your brain on right now was COVID.
• 2:00:42
  SPEAKER_00: And I just today or yesterday saw that Fauci has been indicted.
• 2:00:46
  SPEAKER_00: Oh, really?
• 2:00:46
  SPEAKER_00: Oh, good.
• 2:00:48
  SPEAKER_00: There's a Senate hearing and he's I don't know if he got indicted,
• 2:00:55
  SPEAKER_00: but there was a Senate hearing at least.
• 2:00:58
  SPEAKER_00: What happened?
• 2:01:00
  SPEAKER_00: We're now in 2026.
• 2:01:02
  SPEAKER_00: I think it's safe to talk about it.
• 2:01:03
  SPEAKER_00: I think so.
• 2:01:04
  SPEAKER_01: What's your take on what
• 2:01:07
  SPEAKER_01: happened in COVID?
• 2:01:08
  SPEAKER_01: I don't think there was as much malice or anything.
• 2:01:12
  SPEAKER_01: I think there was a lot of preemptive...
• 2:01:16
  SPEAKER_01: How do you say?
• 2:01:17
  SPEAKER_01: Getting ready for events that were considered inevitable because of lax research
• 2:01:23
  SPEAKER_01: that was being done.
• 2:01:25
  SPEAKER_01: I think some of that research was probably commissioned by the United States
• 2:01:29
  SPEAKER_01: to be done in other places.
• 2:01:31
  SPEAKER_01: That's pretty much established at this point.
• 2:01:33
  SPEAKER_00: University of North Carolina.
• 2:01:36
  SPEAKER_01: They ordered the specific plasmid for that virus.
• 2:01:39
  SPEAKER_01: And so for that to be able to get out, or no,
• 2:01:43
  SPEAKER_01: they probably had the whole construct actually made in Wuhan and then sent over.
• 2:01:47
  SPEAKER_01: And whether or not there was maliciousness about that falling off the truck or
• 2:01:52
  SPEAKER_01: that being at all.
• 2:01:54
  SPEAKER_01: Even doing this in BSL-2,
• 2:01:57
  SPEAKER_01: there should be red flags,
• 2:01:58
  SPEAKER_01: but that just shows,
• 2:02:00
  SPEAKER_01: even right now,
• 2:02:01
  SPEAKER_01: I can still,
• 2:02:02
  SPEAKER_01: as a Personal citizen,
• 2:02:05
  SPEAKER_01: not as a scientist in an institution right now,
• 2:02:08
  SPEAKER_01: I can still order stuff from plasmids to be made or viruses to be made
• 2:02:12
  SPEAKER_01: by small companies.
• 2:02:15
  SPEAKER_00: Anyone can do that?
• 2:02:16
  SPEAKER_00: A lot of people can.
• 2:02:19
  SPEAKER_01: I was doing this for agricultural stuff, actually.
• 2:02:22
  SPEAKER_01: I was like, yeah, I'm not technically affiliated with an institution right now.
• 2:02:26
  SPEAKER_00: And they're like, Let me get back to you.
• 2:02:29
  SPEAKER_01: Okay, no problem.
• 2:02:30
  SPEAKER_01: We'll send you the plasmids.
• 2:02:31
  SPEAKER_01: It's like, I don't have the cold chain either.
• 2:02:36
  SPEAKER_01: But that's the thing.
• 2:02:37
  SPEAKER_01: It's two order.
• 2:02:38
  SPEAKER_01: These are sequences.
• 2:02:40
  SPEAKER_01: Unless they're putting those sequences through.
• 2:02:42
  SPEAKER_01: And now it's funny because if you put it through, say, Fable,
• 2:02:46
  SPEAKER_01: the guardrails on Fable will probably keep you from talking
• 2:02:49
  SPEAKER_01: about this particular thing in general or even do a safety check on it.
• 2:02:53
  SPEAKER_01: So it's like, oh,
• 2:02:53
  SPEAKER_01: you're trying to make it safer,
• 2:02:55
  SPEAKER_01: but in actuality, I can now probably just order it and it'll be sent to me.
• 2:03:00
  SPEAKER_01: And whether or not the scientist knows that, oh,
• 2:03:02
  SPEAKER_01: this is a sequence,
• 2:03:04
  SPEAKER_01: they might have some things that are just filters to some degree.
• 2:03:12
  SPEAKER_01: But probably more of these would be poisonous to the actual assay rather than to the person.
• 2:03:19
  SPEAKER_01: Because these are situations where...
• 2:03:24
  SPEAKER_01: Say I found a sequence that makes this virus much more,
• 2:03:32
  SPEAKER_01: how do you say, cold resistance or something like this.
• 2:03:34
  SPEAKER_01: This particular sequence,
• 2:03:36
  SPEAKER_01: if I changed it,
• 2:03:37
  SPEAKER_01: now what is the chances that that one thing that ended up
• 2:03:40
  SPEAKER_01: in this one journal that's expressing this thing,
• 2:03:45
  SPEAKER_01: that they're a big finding,
• 2:03:47
  SPEAKER_01: is a piece of the pipeline for whoever I'm ordering this thing from.
• 2:03:52
  SPEAKER_01: For making this contract for research purposes.
• 2:03:57
  SPEAKER_01: So I'm just saying that these people are doing it,
• 2:03:59
  SPEAKER_01: at least the ones in Wuhan, we're doing it in BSL-2.
• 2:04:02
  SPEAKER_01: So it's like relative, I mean, you're wearing gloves.
• 2:04:06
  SPEAKER_01: You may or may not be wearing a mask.
• 2:04:07
  SPEAKER_00: BSL-2 for people who don't know is the safety level, which is not very high.
• 2:04:11
  SPEAKER_01: And BSL-3
• 2:04:12
  SPEAKER_01: is, like, where you would do, like, Hantavirus and stuff.
• 2:04:14
  SPEAKER_01: It's still, like,
• 2:04:15
  SPEAKER_01: BSL-2 is, like,
• 2:04:16
  SPEAKER_01: relatively, I mean,
• 2:04:16
  SPEAKER_01: you would do,
• 2:04:17
  SPEAKER_01: they do retro and lentivirus in there now,
• 2:04:20
  SPEAKER_01: but it's not, like, you're not suited up from head to toe.
• 2:04:24
  SPEAKER_01: You're, like,
• 2:04:25
  SPEAKER_01: you're wearing gloves,
• 2:04:25
  SPEAKER_01: maybe you're double-gloving it, you know, like, which causes more friction.
• 2:04:29
  SPEAKER_01: I don't know, there's some other issues.
• 2:04:30
  SPEAKER_01: Like, Anyway, but like nitrile gloves and you're spraying everything off with ethanol.
• 2:04:36
  SPEAKER_01: But clearly,
• 2:04:37
  SPEAKER_01: like if you're dealing with anything that then could become airborne,
• 2:04:40
  SPEAKER_01: it's like you could probably order like and I hope that tests like this aren't done,
• 2:04:46
  SPEAKER_01: but like you could probably order something that could become airborne from like
• 2:04:50
  SPEAKER_01: a thousand different labs and see like how many would actually just like make it
• 2:04:54
  SPEAKER_01: for you and try to ship it to you because that's just what their job is.
• 2:04:57
  SPEAKER_01: You know, they don't know.
• 2:04:59
  SPEAKER_01: They don't know why you manipulated this part of the sequence.
• 2:05:02
  SPEAKER_01: They don't know the phenotype.
• 2:05:03
  SPEAKER_00: They're just doing their job.
• 2:05:04
  SPEAKER_01: They're just doing their job.
• 2:05:07
  SPEAKER_01: I honestly kind of think that it might have been more along.
• 2:05:12
  SPEAKER_01: They knew they were playing with fire with this kind of stuff.
• 2:05:14
  SPEAKER_01: And it seems like I think that there was already a lot of...
• 2:05:18
  SPEAKER_01: And preemptive like, oh,
• 2:05:20
  SPEAKER_01: when you play with fire,
• 2:05:21
  SPEAKER_01: let's invest in like fire extinguishers,
• 2:05:23
  SPEAKER_01: you know, like on a scaled situation, you know.
• 2:05:27
  SPEAKER_01: And some things that were said about like, oh,
• 2:05:30
  SPEAKER_01: this is going to become,
• 2:05:32
  SPEAKER_01: you know,
• 2:05:33
  SPEAKER_01: a kind of like a flu-like situation where every,
• 2:05:36
  SPEAKER_01: like it'll be mutating enough, you know.
• 2:05:38
  SPEAKER_01: These kind of preemptive things like kind of led me to believe that like,
• 2:05:41
  SPEAKER_01: yeah, you know, it's like, Obviously not deliberate, probably not deliberate.
• 2:05:49
  SPEAKER_00: Let's hope so.
• 2:05:49
  SPEAKER_00: Let's hope for the decency.
• 2:05:51
  SPEAKER_01: But negligent, but highly, highly, highly negligent.
• 2:05:54
  SPEAKER_01: And that's the part of like,
• 2:06:01
  SPEAKER_01: I think that the only way that they can really hide behind that negligence is just
• 2:06:06
  SPEAKER_01: by saying, look how negligent we are in everything else.
• 2:06:10
  SPEAKER_01: Look at our food situation.
• 2:06:11
  SPEAKER_01: Look at all the chemicals we're already exposing you to.
• 2:06:14
  SPEAKER_01: This one just kills you a lot faster.
• 2:06:17
  SPEAKER_01: So it's a for money thing.
• 2:06:22
  SPEAKER_00: But everything that happened once it's escaped.
• 2:06:25
  SPEAKER_00: Either it was an accident or released on purpose.
• 2:06:30
  SPEAKER_00: I really hope it was an accident.
• 2:06:31
  SPEAKER_00: But anyway, everything that happened once it escaped into the world.
• 2:06:35
  SPEAKER_00: I think then it's Fauci essentially being like,
• 2:06:38
  SPEAKER_01: not me.
• 2:06:39
  SPEAKER_01: And trying to point any other direction.
• 2:06:42
  SPEAKER_00: The guilty ones were Cover their ass in a very dramatic fashion.
• 2:06:45
  SPEAKER_01: And also in a way that they were trying to make science where there was not
• 2:06:50
  SPEAKER_00: real science.
• 2:06:51
  SPEAKER_01: Yeah, the science.
• 2:06:53
  SPEAKER_01: Using science as a shield for their negligence.
• 2:06:56
  SPEAKER_00: And I think that that part is blasphemy, honestly.
• 2:06:59
  SPEAKER_00: That is the most damaging part of what they did.
• 2:07:01
  SPEAKER_01: Because what they were saying, I watched all of those interviews with...
• 2:07:06
  SPEAKER_01: First, I think, head of NIH at the time.
• 2:07:09
  SPEAKER_01: I can't remember.
• 2:07:09
  SPEAKER_01: This was a while ago now.
• 2:07:11
  SPEAKER_01: But at least those indictments, those original indictments.
• 2:07:14
  SPEAKER_01: Was it Francis Collins?
• 2:07:16
  SPEAKER_01: Yeah.
• 2:07:18
  SPEAKER_00: He was the head of NIH, but it was one of the other guys who I'm actually thinking of.
• 2:07:22
  SPEAKER_01: But anyway, the idea was that they were kind of defending their nature paper.
• 2:07:26
  SPEAKER_01: And in the way that they defended it was just like diversion.
• 2:07:31
  SPEAKER_01: If you listen to the arguments, they were just like, Yeah, but other ways existed.
• 2:07:36
  SPEAKER_01: They just didn't answer questions.
• 2:07:37
  SPEAKER_01: It's just like,
• 2:07:38
  SPEAKER_01: this is not like,
• 2:07:39
  SPEAKER_01: if you were in any academic setting and you presented yourself like this,
• 2:07:43
  SPEAKER_01: you would be laughed off stage for being unaccredulous.
• 2:07:47
  SPEAKER_00: They were like kids who ate the cookies.
• 2:07:49
  SPEAKER_00: No, I didn't.
• 2:07:52
  SPEAKER_01: You making this makes you have clear blood on your hands.
• 2:07:57
  SPEAKER_00: And calling everyone who says anything else like fringe scientists, all this kind of stuff.
• 2:08:01
  SPEAKER_00: Guys, what?
• 2:08:03
  SPEAKER_00: I don't know.
• 2:08:04
  SPEAKER_01: They didn't know the UAPs were real.
• 2:08:08
  SPEAKER_00: But it's like when you think back of that time,
• 2:08:11
  SPEAKER_00: how were so many people tricked and are still tricked?
• 2:08:14
  SPEAKER_00: Because so many people still in 2026 don't have an idea of what was going on.
• 2:08:19
  SPEAKER_00: They believe the propaganda.
• 2:08:21
  SPEAKER_01: It's because you're...
• 2:08:24
  SPEAKER_01: You're fighting with systemic misinformation.
• 2:08:27
  SPEAKER_01: And now that is the, that we are a flood of cacophony.
• 2:08:30
  SPEAKER_01: That is the,
• 2:08:31
  SPEAKER_01: we are not,
• 2:08:32
  SPEAKER_01: you know,
• 2:08:32
  SPEAKER_01: we've poisoned our,
• 2:08:34
  SPEAKER_01: like it's the,
• 2:08:36
  SPEAKER_01: it's the like similar enough,
• 2:08:38
  SPEAKER_01: but dangerous enough to like really create issues in our own society.
• 2:08:45
  SPEAKER_01: Because like this is misinformation at its core.
• 2:08:48
  SPEAKER_01: And it's being distributed by people who should be more credible than they are.
• 2:08:53
  SPEAKER_01: And people in events that there should be a lot more transparency and accountability
• 2:08:58
  SPEAKER_01: for actions.
• 2:09:00
  SPEAKER_01: And it's unfortunate that the people that we are trying – they did a Disney movie on Fauci.
• 2:09:07
  SPEAKER_01: Why are you trying to make –
• 2:09:12
  SPEAKER_01: Like, why can't they actually just be real
• 2:09:13
  SPEAKER_00: heroes?
• 2:09:14
  SPEAKER_00: They were selling the candles of Saint Fauci.
• 2:09:17
  SPEAKER_00: I mean, it was...
• 2:09:19
  SPEAKER_00: It was like North Korea level.
• 2:09:21
  SPEAKER_00: But it was so weird.
• 2:09:22
  SPEAKER_01: Yeah, yeah.
• 2:09:22
  SPEAKER_01: North Korea of like,
• 2:09:23
  SPEAKER_01: let's like turn this guy into a saint and essentially then kind of demonize anybody
• 2:09:27
  SPEAKER_01: who disagrees with him.
• 2:09:28
  SPEAKER_01: Yeah.
• 2:09:29
  SPEAKER_01: And like, that's a weird way.
• 2:09:30
  SPEAKER_00: Dude, that was high level propaganda.
• 2:09:31
  SPEAKER_00: That's high level propaganda.
• 2:09:33
  SPEAKER_01: But it also shows that, I mean, humans are not adept to this level of brainwash.
• 2:09:39
  SPEAKER_00: And we also like, for me, the case was, I had no reason to believe any of it was bullshit.
• 2:09:45
  SPEAKER_00: I'm very familiar with the wartime propaganda.
• 2:09:48
  SPEAKER_00: I'm from former Yugoslavia,
• 2:09:49
  SPEAKER_00: so war and propaganda surrounding war has always been apparent to me
• 2:09:53
  SPEAKER_00: because I've lived it.
• 2:09:55
  SPEAKER_00: My parents have taught me.
• 2:09:58
  SPEAKER_00: So whenever,
• 2:09:58
  SPEAKER_00: you know,
• 2:09:59
  SPEAKER_00: weapons of mass destruction,
• 2:10:00
  SPEAKER_00: whatever, all of that stuff, I'm like, yeah, obviously it's war propaganda.
• 2:10:04
  SPEAKER_01: I could not
• 2:10:07
  SPEAKER_00: imagine that this is used in the medical field.
• 2:10:09
  SPEAKER_01: Because they use the scientist as a sneaky way of like, this guy can
• 2:10:16
  SPEAKER_01: Talk about something that nobody understands and seem believable.
• 2:10:20
  SPEAKER_01: Seem very believable and seem like in his conviction.
• 2:10:23
  SPEAKER_00: Because to most people, like, why would he lie on that level?
• 2:10:25
  SPEAKER_01: Well, if you watch the other interviews of him, like,
• 2:10:28
  SPEAKER_01: creating the super virus,
• 2:10:29
  SPEAKER_01: you would have to start to understand maybe why he would lie.
• 2:10:32
  SPEAKER_01: Exactly.
• 2:10:33
  SPEAKER_00: Have you seen the documentary?
• 2:10:34
  SPEAKER_00: Thank you, Dr. Fauci.
• 2:10:34
  SPEAKER_01: Yeah, that's great.
• 2:10:35
  SPEAKER_00: Pretty good.
• 2:10:36
  SPEAKER_01: Yeah, very damning to him.
• 2:10:39
  SPEAKER_01: Yes.
• 2:10:39
  SPEAKER_00: That's one of the better ones on, like, what?
• 2:10:46
  SPEAKER_01: A few things wrong.
• 2:10:48
  SPEAKER_00: What about the vaccines?
• 2:10:49
  SPEAKER_00: Like, what was the deal?
• 2:10:50
  SPEAKER_00: What happened?
• 2:10:50
  SPEAKER_01: I think the vaccine, honestly, probably works in some ways.
• 2:10:53
  SPEAKER_01: I think that, like,
• 2:10:54
  SPEAKER_01: that's the whole,
• 2:10:55
  SPEAKER_01: like, I mean, you just need a, you have a need for, vaccines do work.
• 2:11:01
  SPEAKER_01: Yes.
• 2:11:02
  SPEAKER_00: But there's a big difference between, like, traditional vaccines and mRNA.
• 2:11:05
  SPEAKER_01: Yeah.
• 2:11:05
  SPEAKER_01: Okay.
• 2:11:07
  SPEAKER_01: And I think mRNA vaccines do work.
• 2:11:09
  SPEAKER_01: Yes.
• 2:11:09
  SPEAKER_01: And I think they work very efficiently for what they're doing.
• 2:11:13
  SPEAKER_01: I think that The way of who to prescribe vaccines to and stuff.
• 2:11:18
  SPEAKER_01: I think this was very willy-nilly.
• 2:11:19
  SPEAKER_01: The idea of children who are going to have a higher risk of having a rejection to
• 2:11:24
  SPEAKER_01: a vaccine in general versus the actual numbers of individuals
• 2:11:30
  SPEAKER_01: who are actually getting infected as children and having any problem as a child.
• 2:11:38
  SPEAKER_01: That should have been initially the mitigated...
• 2:11:43
  SPEAKER_01: It shouldn't be, oh, everybody has to do this kind of thing.
• 2:11:46
  SPEAKER_01: And I think that when we went to that type of extreme mentality,
• 2:11:49
  SPEAKER_01: it created such a problem because for people who are like,
• 2:11:57
  SPEAKER_01: I believe in the system, They did it.
• 2:12:00
  SPEAKER_01: They're like, I believe, I gotta just trust the system.
• 2:12:03
  SPEAKER_01: And that's where you're,
• 2:12:05
  SPEAKER_01: you have to believe in the system to some degree because they paved the roads.
• 2:12:11
  SPEAKER_01: There's a lot of infrastructure.
• 2:12:12
  SPEAKER_01: That's why there's milk.
• 2:12:14
  SPEAKER_01: You still go to the grocery store and it's not like total anarchy.
• 2:12:19
  SPEAKER_01: You live in a system.
• 2:12:21
  SPEAKER_01: But it's just that once they dictated this for the wrong reason and became so finite.
• 2:12:28
  SPEAKER_01: Do I believe that Fauci, I mean,
• 2:12:30
  SPEAKER_01: the thing is,
• 2:12:31
  SPEAKER_01: is that clearly he was trying to hide stuff,
• 2:12:34
  SPEAKER_01: but did he also probably believe that the reality of
• 2:12:40
  SPEAKER_01: if everybody simultaneously took this, it could work?
• 2:12:43
  SPEAKER_01: Yeah.
• 2:12:44
  SPEAKER_01: Yeah.
• 2:12:44
  SPEAKER_01: Because that's kind of a reality.
• 2:12:46
  SPEAKER_01: If everybody simultaneously took it, they could.
• 2:12:48
  SPEAKER_00: And if it just happens to make my friends rich on the journey.
• 2:12:52
  SPEAKER_00: That's who.
• 2:12:52
  SPEAKER_01: That's who.
• 2:12:54
  SPEAKER_01: But he, as any reasonable,
• 2:12:58
  SPEAKER_01: logical,
• 2:12:58
  SPEAKER_01: individual person,
• 2:13:00
  SPEAKER_01: Like should know that not everybody's gonna take it simultaneously.
• 2:13:05
  SPEAKER_01: And you just can't do this in this type of place.
• 2:13:07
  SPEAKER_01: You might be able to do this in very isolated small areas that are quarantined off
• 2:13:12
  SPEAKER_01: where you got like 15, 20 people.
• 2:13:15
  SPEAKER_01: Let's like immunize.
• 2:13:18
  SPEAKER_00: And also like if everyone takes it simultaneously and it's something that we haven't studied.
• 2:13:23
  SPEAKER_00: That's true.
• 2:13:23
  SPEAKER_00: That should also be obvious.
• 2:13:25
  SPEAKER_01: But it actually had been studied.
• 2:13:26
  SPEAKER_01: I mean, like the vaccine was like,
• 2:13:27
  SPEAKER_01: I mean, that had been like, they were pushing that relatively.
• 2:13:31
  SPEAKER_01: The technology has been studied, but mRNA shots
• 2:13:33
  SPEAKER_00: in human beings were not studied until 2020.
• 2:13:35
  SPEAKER_00: That's true.
• 2:13:36
  SPEAKER_00: And that's, yeah,
• 2:13:38
  SPEAKER_01: and that's the whole point is that this,
• 2:13:39
  SPEAKER_01: because, I mean,
• 2:13:40
  SPEAKER_01: that's sort of like our premise of,
• 2:13:43
  SPEAKER_01: The biotech that was meant for mRNA therapeutics was a basis of, well,
• 2:13:50
  SPEAKER_01: now you have this case study of essentially billions of this going
• 2:13:53
  SPEAKER_01: into individuals already and not really having too much of a harmful effect in
• 2:13:59
  SPEAKER_01: the bigger picture.
• 2:14:01
  SPEAKER_01: It's not like we lost a third of the population or something because of the vaccine.
• 2:14:05
  SPEAKER_01: If something like that happened,
• 2:14:06
  SPEAKER_00: there would be no...
• 2:14:09
  SPEAKER_01: I mean, the thing is,
• 2:14:12
  SPEAKER_01: I think it's just because we're becoming much more conscious of what is integrating
• 2:14:16
  SPEAKER_01: into our genome all the time.
• 2:14:18
  SPEAKER_01: And that's the thing, your genome is not a static thing.
• 2:14:24
  SPEAKER_01: Throughout your life,
• 2:14:24
  SPEAKER_01: and it's also not the end-all be-all for,
• 2:14:28
  SPEAKER_01: there's this misconception that,
• 2:14:30
  SPEAKER_01: oh, just mutations are gonna give you the things that cause aging and stuff.
• 2:14:34
  SPEAKER_01: It's not necessarily,
• 2:14:35
  SPEAKER_01: it's mutations in certain regions that then Like create miss signaling events that
• 2:14:42
  SPEAKER_01: then lead to these things.
• 2:14:44
  SPEAKER_01: But mutations in general are actually like happening all the time and you have a lot
• 2:14:47
  SPEAKER_01: of mechanisms to try to like prevent them or to like repair those systems.
• 2:14:53
  SPEAKER_01: So it's like that's a part of normal change and that's actually a part of normal change.
• 2:14:57
  SPEAKER_01: So it's like when you have the,
• 2:14:58
  SPEAKER_01: one of the biggest problems that we saw like,
• 2:15:02
  SPEAKER_01: this was like really sad actually too,
• 2:15:04
  SPEAKER_01: but like they did these longitudinal studies with,
• 2:15:08
  SPEAKER_01: Asorbic acid or vitamin C. Because the idea is like, what is vitamin C?
• 2:15:16
  SPEAKER_01: It's that they have an antioxidant.
• 2:15:18
  SPEAKER_01: This is the whole big hype word of it's an antioxidant.
• 2:15:22
  SPEAKER_01: So antioxidants are great.
• 2:15:24
  SPEAKER_01: So they started doing it with patients who had cancer.
• 2:15:28
  SPEAKER_01: They would come in and the first thing you would do is give them a big boost to antioxidants.
• 2:15:35
  SPEAKER_01: And we actually saw over like this mentality had been going on for a long period
• 2:15:39
  SPEAKER_01: of time where they've been doing this to a lot of different patients.
• 2:15:42
  SPEAKER_01: And then they finally,
• 2:15:43
  SPEAKER_01: after like probably,
• 2:15:45
  SPEAKER_01: I think it's probably like five or 10 years of like realizing that like,
• 2:15:48
  SPEAKER_01: this is just maybe the last 10 years that we've figured this out,
• 2:15:51
  SPEAKER_01: that like having that boost of antioxidants is actually like,
• 2:15:58
  SPEAKER_01: like imagine There's like a sinking ship and it's shooting up a flare.
• 2:16:03
  SPEAKER_01: Now what that flare is, is like oxidizing agents.
• 2:16:08
  SPEAKER_01: So like, it's like, it's saying like, there's a problem here.
• 2:16:11
  SPEAKER_00: Now we're just like playing a bunch of fireworks over that.
• 2:16:14
  SPEAKER_00: So we don't see the
• 2:16:15
  SPEAKER_01: flare whatsoever.
• 2:16:16
  SPEAKER_01: Problem solved.
• 2:16:17
  SPEAKER_01: Problem solved.
• 2:16:18
  SPEAKER_01: And that's kind of the mentality is like a lot of the cancers that these people came
• 2:16:24
  SPEAKER_01: in with probably got a lot more aggressive because you're actually,
• 2:16:29
  SPEAKER_01: There is a signal.
• 2:16:30
  SPEAKER_01: There is signals being sent out constantly.
• 2:16:33
  SPEAKER_01: They're like, there's a problem here.
• 2:16:35
  SPEAKER_01: Something is happening here that is not good for the system.
• 2:16:39
  SPEAKER_01: And then you flood this with antioxidants, essentially, and you quench that stress.
• 2:16:45
  SPEAKER_01: Yeah, that stress signal is now gone.
• 2:16:48
  SPEAKER_01: The tumor is not gone.
• 2:16:49
  SPEAKER_01: Ah, fuck.
• 2:16:50
  SPEAKER_01: The tumor grows like crazy now.
• 2:16:53
  SPEAKER_01: And that's horrible.
• 2:16:55
  SPEAKER_01: But that was big longitudinal research that they were conducting.
• 2:16:58
  SPEAKER_01: And at Karolinska, they figured out, do not do this anymore.
• 2:17:04
  SPEAKER_01: We need to stop this immediately.
• 2:17:06
  SPEAKER_01: That's the sad thing.
• 2:17:07
  SPEAKER_01: I think in the States,
• 2:17:08
  SPEAKER_01: in a certain place, because we're so disconnected, it's not all person numbers.
• 2:17:12
  SPEAKER_01: You go to different doctors.
• 2:17:15
  SPEAKER_01: This one's got a whole different orchestra of what's how to treat you.
• 2:17:24
  SPEAKER_01: I think they're still doing that even there.
• 2:17:26
  SPEAKER_01: That's super dangerous.
• 2:17:28
  SPEAKER_01: You're making cancers worse.
• 2:17:30
  SPEAKER_01: That's not healthy.
• 2:17:32
  SPEAKER_00: Not at all.
• 2:17:34
  SPEAKER_00: We do so many messed up things.
• 2:17:35
  SPEAKER_00: No.
• 2:17:40
  SPEAKER_00: That's my fear with the mRNA shots, that we don't understand the damage we've caused.
• 2:17:47
  SPEAKER_01: I mean, I see it as the stuff that's been going in earlier.
• 2:17:51
  SPEAKER_01: What do you mean?
• 2:17:54
  SPEAKER_01: I just mean like any other types of treatments being a little bit more aggressive in
• 2:17:58
  SPEAKER_01: the sense that like mRNA is like kind of like it's halfway through
• 2:18:02
  SPEAKER_01: the whole process already.
• 2:18:03
  SPEAKER_01: You're like you're not putting the cells in there to make the mRNA.
• 2:18:07
  SPEAKER_01: You're not like putting the protein in there like you're putting and yes,
• 2:18:12
  SPEAKER_01: if you had recombinase like some RNA recombinase like in that particular area,
• 2:18:18
  SPEAKER_01: could you swap this into your own genome?
• 2:18:21
  SPEAKER_01: Probably,
• 2:18:22
  SPEAKER_01: but like that's the same thing as like any other virus that's going
• 2:18:25
  SPEAKER_01: through your body at the same time, probably on a much bigger scale.
• 2:18:28
  SPEAKER_01: This is just more of like a small localized translation event,
• 2:18:34
  SPEAKER_01: meaning like you're using your own local system.
• 2:18:36
  SPEAKER_00: If it stays where it's supposed to be.
• 2:18:38
  SPEAKER_00: If it stays.
• 2:18:39
  SPEAKER_01: But what part is getting out is the thing.
• 2:18:41
  SPEAKER_01: Because for me, this is like information, not a living thing.
• 2:18:44
  SPEAKER_00: No, but the thing is the mRNA is encapsulated in the These nanolipid bubbles
• 2:18:50
  SPEAKER_00: which can enter any cell in your body.
• 2:18:52
  SPEAKER_00: So that's the issue.
• 2:18:53
  SPEAKER_00: If it happens to come close to a bloodstream when you inject it and it ends up
• 2:18:56
  SPEAKER_00: in your heart and your
• 2:18:57
  SPEAKER_01: heart cells start producing the
• 2:18:59
  SPEAKER_00: spike protein that
• 2:19:00
  SPEAKER_00: the mRNA instructs it to,
• 2:19:02
  SPEAKER_00: what happens when your own immune system starts attacking your heart muscles?
• 2:19:09
  SPEAKER_00: That's the myocarditis risk.
• 2:19:10
  SPEAKER_00: And that's a valid concern.
• 2:19:11
  SPEAKER_01: And that can happen anywhere in the body.
• 2:19:16
  SPEAKER_00: I'm saying I don't think we know to what extent we've caused trouble.
• 2:19:21
  SPEAKER_00: I'm not a complete burn it all down, I don't trust anything.
• 2:19:26
  SPEAKER_00: No, but if it is really not localized...
• 2:19:28
  SPEAKER_00: But I really believe that with the mRNA shots,
• 2:19:30
  SPEAKER_00: we did something probably in panic,
• 2:19:32
  SPEAKER_00: probably to cover our ass,
• 2:19:33
  SPEAKER_00: probably believing this would help a little bit of everything.
• 2:19:37
  SPEAKER_00: And we did something that we don't know how much damage it's caused
• 2:19:41
  SPEAKER_00: to immune systems, to autoimmune conditions, all that stuff.
• 2:19:44
  SPEAKER_01: I just feel like maybe a lot of this symptomatic part of it is probably more due to...
• 2:19:52
  SPEAKER_01: That's a virus that is going through you.
• 2:19:57
  SPEAKER_01: In my perception, I think of seeing how that works.
• 2:20:02
  SPEAKER_01: Consider rabies.
• 2:20:04
  SPEAKER_01: People don't survive from rabies.
• 2:20:06
  SPEAKER_01: Once you show symptoms of rabies, you're going to die.
• 2:20:08
  SPEAKER_01: And there's only been a few people who've ever survived rabies.
• 2:20:11
  SPEAKER_01: And the way they do that is because that's a virus.
• 2:20:13
  SPEAKER_01: That's a parasite.
• 2:20:14
  SPEAKER_01: Let's see.
• 2:20:17
  SPEAKER_00: Actually, let's see.
• 2:20:18
  SPEAKER_01: How would we do this with mRNA?
• 2:20:19
  SPEAKER_01: Because I just don't see it as a traveling, amplifying thing.
• 2:20:24
  SPEAKER_01: And that's where you get much more risk.
• 2:20:28
  SPEAKER_01: Like with rabies,
• 2:20:29
  SPEAKER_01: this thing gets into your brain and essentially the only way to stop it
• 2:20:36
  SPEAKER_01: from doing this lethal situation is essentially put the person into a coma.
• 2:20:42
  SPEAKER_00: Right.
• 2:20:43
  SPEAKER_01: And so then they've only done this with like a handful of people
• 2:20:45
  SPEAKER_01: who have actually survived rabies, but they put them in a coma.
• 2:20:48
  SPEAKER_01: And when they come out,
• 2:20:49
  SPEAKER_01: they have severe problems with like movement and everything because
• 2:20:53
  SPEAKER_01: where this thing is like,
• 2:20:57
  SPEAKER_01: And where it's like manipulating,
• 2:20:59
  SPEAKER_01: like, first of all, it's like replicated all through everywhere.
• 2:21:02
  SPEAKER_01: So you have these amplification events,
• 2:21:04
  SPEAKER_01: like it becomes something that's like now localized in your brain,
• 2:21:09
  SPEAKER_01: shutting off certain like areas of your brain and manipulating like
• 2:21:14
  SPEAKER_01: the metabolism tremendously to a point
• 2:21:16
  SPEAKER_01: where you're You just can't regulate anything anymore and you die from that.
• 2:21:20
  SPEAKER_01: So essentially they're like, let's put these people in a coma.
• 2:21:23
  SPEAKER_01: Let's take them down to like the core, just like breathing and heart.
• 2:21:27
  SPEAKER_01: And if we can control that,
• 2:21:28
  SPEAKER_01: then we can just let this thing kind of take its course and let
• 2:21:32
  SPEAKER_01: the immune system come in and actually fight it, like deal with the issue.
• 2:21:38
  SPEAKER_01: And then remove it.
• 2:21:39
  SPEAKER_01: And then essentially, and they're really injured after.
• 2:21:44
  SPEAKER_01: I mean, they barely can walk, I think.
• 2:21:45
  SPEAKER_01: And they're in really critical state afterwards, but they survive.
• 2:21:49
  SPEAKER_01: And so it's like,
• 2:21:51
  SPEAKER_01: When we're talking about stuff that goes in and does damage,
• 2:21:54
  SPEAKER_01: I've seen viruses that can do a lot of stuff a lot faster and a lot more dangerous.
• 2:22:02
  SPEAKER_01: Then I would say something that would be akin to something that doesn't have
• 2:22:07
  SPEAKER_01: an amplification step at all that we know of.
• 2:22:11
  SPEAKER_01: Theoretically.
• 2:22:12
  SPEAKER_01: It wouldn't be in another lipid, though.
• 2:22:13
  SPEAKER_01: It would be locally.
• 2:22:14
  SPEAKER_01: But theoretically,
• 2:22:15
  SPEAKER_01: if that sequence got recombinated into something that was expressing
• 2:22:20
  SPEAKER_01: that same protein now associated with this to some degree,
• 2:22:27
  SPEAKER_01: then could you create some type of hybrid autoimmune disease from that tissue?
• 2:22:35
  SPEAKER_01: Maybe, but it's two diminishing returns in my perspective of...
• 2:22:42
  SPEAKER_01: This is the hard problem.
• 2:22:44
  SPEAKER_01: This is the hard problem of trying to treat diseases in ways that we know the mechanism.
• 2:22:49
  SPEAKER_01: So to say that then mRNA is like,
• 2:22:51
  SPEAKER_01: if we could have that effect with mRNA on the heart,
• 2:22:55
  SPEAKER_01: that is what we were So that's what I'm saying.
• 2:22:57
  SPEAKER_01: If that is the case, it definitely wasn't designed to do that.
• 2:23:00
  SPEAKER_01: But if it's a byproduct of these kind of things, then
• 2:23:23
  SPEAKER_01: The reason why I was studying exosomes,
• 2:23:25
  SPEAKER_01: and now you understand at least where the exosome idea comes from,
• 2:23:28
  SPEAKER_01: is these sweat, these tiny little vesicles.
• 2:23:31
  SPEAKER_01: The reason why they're researching them so much now is because
• 2:23:34
  SPEAKER_01: that is your natural liquid nanoparticle.
• 2:23:38
  SPEAKER_01: They want to put vaccines into...
• 2:23:41
  SPEAKER_01: Exosomes instead of nanoparticles.
• 2:23:45
  SPEAKER_01: And nanoparticles,
• 2:23:46
  SPEAKER_01: I would say some of that idea is good,
• 2:23:48
  SPEAKER_01: but what you want the nanoparticle for is also,
• 2:23:53
  SPEAKER_01: I'm pretty sure, to get that immune response.
• 2:23:56
  SPEAKER_01: You have to put something foreign there essentially to...
• 2:23:59
  SPEAKER_01: To elicit some type of response.
• 2:24:02
  SPEAKER_01: Isn't the spike protein
• 2:24:05
  SPEAKER_00: that is produced by the cell what is the immune response?
• 2:24:08
  SPEAKER_00: That's your immune response,
• 2:24:09
  SPEAKER_01: but you want also that local
• 2:24:16
  SPEAKER_01: You want the immune system to, yeah, I guess so, in both ways, honestly.
• 2:24:21
  SPEAKER_01: You're getting it kind of for different reasons.
• 2:24:22
  SPEAKER_01: As I understand,
• 2:24:22
  SPEAKER_01: malolipids is to get
• 2:24:23
  SPEAKER_00: through the cell barrier,
• 2:24:26
  SPEAKER_00: to get into the cell so that the mRNA can be given to the factory.
• 2:24:30
  SPEAKER_01: Yeah, but it is toxic, but it's also toxic.
• 2:24:32
  SPEAKER_01: Probably.
• 2:24:34
  SPEAKER_01: No, I mean, but they're well known as being toxic.
• 2:24:36
  SPEAKER_01: So the LNPs are known to be very toxic to begin with.
• 2:24:39
  SPEAKER_00: But as I understand, that's what they're for,
• 2:24:41
  SPEAKER_00: to get the mRNA into the cell so that the cell can start producing spike proteins,
• 2:24:45
  SPEAKER_00: which the immune system reacts to.
• 2:24:47
  SPEAKER_01: Yeah,
• 2:24:48
  SPEAKER_01: but then the spike protein is a modified RNA and then that's acting on a different,
• 2:24:52
  SPEAKER_01: I think that's acting on a different mechanism than just the initial localization.
• 2:24:56
  SPEAKER_01: But I would have to,
• 2:24:57
  SPEAKER_01: I would double check this because I'm,
• 2:24:58
  SPEAKER_01: but I'm pretty sure like the,
• 2:25:00
  SPEAKER_01: because I mean,
• 2:25:01
  SPEAKER_01: that was one of the issues when we're,
• 2:25:03
  SPEAKER_01: Companies are already making these hybrid exosomes and LNPs,
• 2:25:07
  SPEAKER_01: so taking exosomes and essentially ripping them to pieces.
• 2:25:11
  SPEAKER_01: And that's the thing is like, they have the LMPs are just like, it's like a soap bubble.
• 2:25:16
  SPEAKER_01: Imagine it's just detergent, you know?
• 2:25:19
  SPEAKER_01: The exosome is like an actual, like,
• 2:25:24
  SPEAKER_01: I don't usually know if they're like,
• 2:25:26
  SPEAKER_01: sometimes they're bipolar,
• 2:25:27
  SPEAKER_01: but like,
• 2:25:28
  SPEAKER_01: it's like a lipid layer,
• 2:25:30
  SPEAKER_01: essentially, like a lipid myceliated kind of thing that's holding growth factors.
• 2:25:36
  SPEAKER_01: And that also has presenting proteins on its surface.
• 2:25:40
  SPEAKER_01: And the idea there was that then it would be recognized,
• 2:25:45
  SPEAKER_01: or at least for delivering therapeutics,
• 2:25:48
  SPEAKER_01: then you don't have this, oh, this is toxic immediately effect.
• 2:25:52
  SPEAKER_01: Because you do,
• 2:25:54
  SPEAKER_01: I'm pretty sure it's not just the spike protein,
• 2:25:56
  SPEAKER_01: I'm pretty sure it is also The actual LNP itself,
• 2:26:01
  SPEAKER_01: or at least this was a byproduct of what we're finding of why there was such a spike
• 2:26:06
  SPEAKER_01: with these is because of the thing actually being toxic to begin with.
• 2:26:10
  SPEAKER_01: Because that's definitely a real thing.
• 2:26:12
  SPEAKER_01: I would say that that's just like with the amount of viral load
• 2:26:18
  SPEAKER_01: in your system versus what that's doing to your heart and your lungs.
• 2:26:24
  SPEAKER_01: I definitely,
• 2:26:24
  SPEAKER_01: I don't know,
• 2:26:26
  SPEAKER_01: I felt like Corona has had some physiological effects,
• 2:26:29
  SPEAKER_01: at least on my lungs, and also on palpitation sometimes.
• 2:26:33
  SPEAKER_01: I've never had any kind of feeling like this.
• 2:26:36
  SPEAKER_00: Which is partially explained in the documentary, Thank You, Dr. Fauci.
• 2:26:40
  SPEAKER_00: Yeah, okay.
• 2:26:41
  SPEAKER_00: They talk about the,
• 2:26:43
  SPEAKER_00: what do you call it,
• 2:26:44
  SPEAKER_00: the simian virus, the pieces they use to create the COVID virus.
• 2:26:49
  SPEAKER_00: Some of them mess with the immune system and that kind of stuff.
• 2:26:55
  SPEAKER_01: That's the thing.
• 2:26:58
  SPEAKER_01: I would say less,
• 2:27:00
  SPEAKER_01: for at least my perspective,
• 2:27:01
  SPEAKER_01: Intel Convents more efficiently, but I would say less dangerous on the actual mRNA.
• 2:27:10
  SPEAKER_01: It's just a tiny amount.
• 2:27:11
  SPEAKER_01: It's not an amplifying thing.
• 2:27:14
  SPEAKER_01: And I do see some benefits,
• 2:27:15
  SPEAKER_01: but it's like for the same things that you're saying could cause problems,
• 2:27:18
  SPEAKER_01: like for trying to therapeutically do other things,
• 2:27:22
  SPEAKER_01: like positive things, like it's
• 2:27:23
  SPEAKER_00: very, very difficult.
• 2:27:25
  SPEAKER_01: And it's like,
• 2:27:25
  SPEAKER_01: and we're trying,
• 2:27:26
  SPEAKER_01: but like, it's like, it's hard to get those, it's hard to get stuff to stick around.
• 2:27:30
  SPEAKER_01: It's hard to get stuff to like, just seep into cells.
• 2:27:33
  SPEAKER_01: Like that's the thing is like, you're trying to get something into the heart.
• 2:27:36
  SPEAKER_01: Like, unless you're like, if you're a virus who knows how to like attack cells, yeah.
• 2:27:41
  SPEAKER_01: But if you're just this like free floating thing,
• 2:27:44
  SPEAKER_01: Like you're just gonna get flushed through the system immediately.
• 2:27:47
  SPEAKER_01: And actually that's where a lot of LMP stuff ends up is in,
• 2:27:50
  SPEAKER_01: I believe in the kidneys,
• 2:27:51
  SPEAKER_01: is like where they, they're like, oh yeah, it just accumulates there usually.
• 2:27:55
  SPEAKER_01: And that's actually one of the reasons why
• 2:27:57
  SPEAKER_01: for strategies What organ should we treat therapeutically with mRNA?
• 2:28:03
  SPEAKER_01: It became like, go there.
• 2:28:05
  SPEAKER_01: None of it ever gets in here.
• 2:28:06
  SPEAKER_01: And that was why we were trying to tailor these other types of therapy.
• 2:28:10
  SPEAKER_00: And then we're like, let's inject it in billions of people.
• 2:28:12
  SPEAKER_00: That wasn't
• 2:28:15
  SPEAKER_01: my call at all.
• 2:28:18
  SPEAKER_01: But that just shows also, how could we have done that?
• 2:28:22
  SPEAKER_00: That is the creep.
• 2:28:23
  SPEAKER_00: I'm not an opponent of mRNA technology at all.
• 2:28:26
  SPEAKER_00: I think it's such a cool technology.
• 2:28:28
  SPEAKER_00: If we can control that to use it for good, amazing.
• 2:28:31
  SPEAKER_01: I just don't think that's that bad.
• 2:28:32
  SPEAKER_01: But like how we
• 2:28:34
  SPEAKER_00: could do it Like that,
• 2:28:37
  SPEAKER_00: like recklessly, just be like, everyone should have this
• 2:28:40
  SPEAKER_01: right now.
• 2:28:40
  SPEAKER_00: And it's like, oh wait, but what if it comes close to a bloodstream?
• 2:28:44
  SPEAKER_00: Shut up, anti-vaxxers.
• 2:28:46
  SPEAKER_00: Like what?
• 2:28:46
  SPEAKER_01: What the fuck?
• 2:28:47
  SPEAKER_01: Like what are we doing?
• 2:28:48
  SPEAKER_00: That was the crazy part to me.
• 2:28:49
  SPEAKER_01: Let me just think of like,
• 2:28:50
  SPEAKER_01: if it's scarier of just the idea of a regular vaccine though,
• 2:28:54
  SPEAKER_01: because that's the thing is like,
• 2:28:55
  SPEAKER_01: when you, yeah, I mean, but why, why is the mRNA scarier?
• 2:29:01
  SPEAKER_00: Well, traditional vaccine, you have a pathogen that's either weakened or dead.
• 2:29:07
  SPEAKER_00: Let's go dead.
• 2:29:08
  SPEAKER_00: You inject it in your body, your immune system reacts to the pathogen.
• 2:29:12
  SPEAKER_01: Okay, but then here, let's go to that reacts to the pathogen.
• 2:29:16
  SPEAKER_01: Yes.
• 2:29:17
  SPEAKER_01: So, just because you had your own cell make the pathogen.
• 2:29:22
  SPEAKER_01: You don't make the pathogen, though.
• 2:29:23
  SPEAKER_01: No, but the pathogen is the spike.
• 2:29:25
  SPEAKER_01: No, it's not.
• 2:29:26
  SPEAKER_01: No, the spike protein is
• 2:29:28
  SPEAKER_00: just the surface part of the virus.
• 2:29:33
  SPEAKER_01: But that is the same concept that you're dealing with here.
• 2:29:35
  SPEAKER_01: It's not the whole virus.
• 2:29:36
  SPEAKER_00: No, because for one, the virus can...
• 2:29:42
  SPEAKER_00: Evolve.
• 2:29:42
  SPEAKER_00: Yeah.
• 2:29:43
  SPEAKER_00: So it's spike protein changes.
• 2:29:45
  SPEAKER_00: So the vaccine might be useless in three weeks.
• 2:29:48
  SPEAKER_00: That's one thing.
• 2:29:49
  SPEAKER_01: There's sequence.
• 2:29:50
  SPEAKER_01: But yeah, sure.
• 2:29:51
  SPEAKER_01: Sure.
• 2:29:51
  SPEAKER_01: Of course.
• 2:29:52
  SPEAKER_00: But that's one thing.
• 2:29:53
  SPEAKER_00: The other thing is, like I said, if it ends up somewhere in your body, Where it shouldn't be.
• 2:29:58
  SPEAKER_00: And those cells start producing the spike protein and your immune system attacks that part.
• 2:30:04
  SPEAKER_00: That is what causes myocarditis.
• 2:30:05
  SPEAKER_00: It's your immune system attacking the spike protein in your heart instead of in
• 2:30:11
  SPEAKER_00: the arm where
• 2:30:12
  SPEAKER_01: it should.
• 2:30:12
  SPEAKER_01: Fair enough.
• 2:30:13
  SPEAKER_01: And that would be very scary.
• 2:30:15
  SPEAKER_01: And whether or not that is the way that it happens.
• 2:30:18
  SPEAKER_01: Because what I'm curious of is just then in the scenario of
• 2:30:22
  SPEAKER_01: the conventional vaccine,
• 2:30:25
  SPEAKER_01: So you're saying that like, yes,
• 2:30:27
  SPEAKER_01: it's using a dead version of this,
• 2:30:29
  SPEAKER_01: but it's also, it could be just a piece of that as well.
• 2:30:32
  SPEAKER_01: No, I mean also it could just be a piece of it.
• 2:30:34
  SPEAKER_01: It's like the epitope that is actually the part that you want your immune system
• 2:30:40
  SPEAKER_01: to attack, correct?
• 2:30:41
  SPEAKER_01: Right.
• 2:30:42
  SPEAKER_01: And you're saying that how do we train that though is like we put it,
• 2:30:46
  SPEAKER_01: so like when you put that dead piece,
• 2:30:48
  SPEAKER_01: if that ended up in your heart and then you trained that same situation,
• 2:30:53
  SPEAKER_01: Wouldn't it be the same issue?
• 2:30:54
  SPEAKER_01: Could be.
• 2:30:55
  SPEAKER_01: But that's what I'm saying.
• 2:30:56
  SPEAKER_01: Then regular vaccines are not doing that.
• 2:30:59
  SPEAKER_00: The big difference is regular vaccines don't come in,
• 2:31:03
  SPEAKER_00: the pathogens in regular vaccines don't come in the LMPs.
• 2:31:08
  SPEAKER_00: Yeah, true.
• 2:31:08
  SPEAKER_00: So they can't enter your cell.
• 2:31:10
  SPEAKER_00: What are
• 2:31:10
  SPEAKER_01: they in then?
• 2:31:11
  SPEAKER_01: Yeah.
• 2:31:13
  SPEAKER_00: I don't know.
• 2:31:13
  SPEAKER_00: That's a good question.
• 2:31:15
  SPEAKER_00: I think they are in something.
• 2:31:17
  SPEAKER_00: It's another
• 2:31:18
  SPEAKER_00: type of- I'm not sure they're not in something
• 2:31:20
  SPEAKER_00: that can enter your cells, that's for sure.
• 2:31:22
  SPEAKER_00: Because it's just the pathogen and one of those adjuvicans, is that what it's called?
• 2:31:27
  SPEAKER_00: Yeah, adjuvicans.
• 2:31:28
  SPEAKER_00: To kind of make your immune system alert.
• 2:31:32
  SPEAKER_00: But they don't go into cells.
• 2:31:34
  SPEAKER_00: They go into your body so your immune system can find them.
• 2:31:37
  SPEAKER_01: And that to me is less scary.
• 2:31:40
  SPEAKER_01: Both things are crazy.
• 2:31:41
  SPEAKER_01: We're doing crazy shit.
• 2:31:43
  SPEAKER_00: That's way less crazy to me than putting stuff into our cells
• 2:31:47
  SPEAKER_00: that makes our cells produce things and then the system attacks it.
• 2:31:51
  SPEAKER_00: I'm open-minded to that being a great thing.
• 2:31:53
  SPEAKER_00: It's just we need to study it before giving it to billions of people.
• 2:31:56
  SPEAKER_01: I think it's definitely been studied.
• 2:31:59
  SPEAKER_00: The nature of that is probably decades and decades old.
• 2:32:03
  SPEAKER_01: So like the idea of mRNA,
• 2:32:05
  SPEAKER_01: I mean like we know the dogma of molecular biology and mRNA leads to proteins
• 2:32:11
  SPEAKER_01: when it gets translated.
• 2:32:13
  SPEAKER_01: So like the idea though is that there would be a higher efficiency,
• 2:32:18
  SPEAKER_01: and this part I think is logical.
• 2:32:20
  SPEAKER_01: It's the idea that like if you have your own system make that for you,
• 2:32:28
  SPEAKER_01: But also,
• 2:32:28
  SPEAKER_01: I do see the consequence of like,
• 2:32:30
  SPEAKER_01: yeah, okay, but then that's a higher chance of autoimmune disorders as well.
• 2:32:33
  SPEAKER_00: Yeah, that's a good point.
• 2:32:34
  SPEAKER_01: Yeah, fair enough.
• 2:32:36
  SPEAKER_01: Okay, that's a valid concern.
• 2:32:39
  SPEAKER_01: But I think that the way that at least it's been thought of in the past,
• 2:32:44
  SPEAKER_01: and at least the reason why we want this kind of targeting in the sense of like,
• 2:32:48
  SPEAKER_01: for therapeutics, it makes a lot more sense.
• 2:32:50
  SPEAKER_01: Let's just put it that way.
• 2:32:51
  SPEAKER_01: Because you want your cells to be making that protein because of these like, Right.
• 2:32:56
  SPEAKER_01: Right.
• 2:32:56
  SPEAKER_01: Right.
• 2:33:11
  SPEAKER_01: Now your immune system is going to attack you.
• 2:33:17
  SPEAKER_01: There are pieces of that that I can see.
• 2:33:21
  SPEAKER_01: If these issues are starting to be so much more pronounced in our population,
• 2:33:26
  SPEAKER_01: then I could buy it.
• 2:33:28
  SPEAKER_01: Then they definitely have to just do a nice cover-up I
• 2:33:38
  SPEAKER_00: don't think
• 2:33:39
  SPEAKER_00: the money made off of the fear and the propaganda is not just sitting idly.
• 2:33:45
  SPEAKER_00: I think that money is being used.
• 2:33:48
  SPEAKER_00: And one thing it's being used for is to cover up any conversation about this.
• 2:33:52
  SPEAKER_01: For sure.
• 2:33:54
  SPEAKER_00: I mean, just like,
• 2:33:55
  SPEAKER_00: you know, People don't even want to talk about like where did that virus come from?
• 2:33:59
  SPEAKER_00: Yeah.
• 2:33:59
  SPEAKER_00: Yeah.
• 2:34:00
  SPEAKER_01: No, it's funny.
• 2:34:01
  SPEAKER_01: I mean, I've been more and more as like the time goes on.
• 2:34:04
  SPEAKER_01: Yeah.
• 2:34:04
  SPEAKER_01: Like people are a little bit like at least like people are kind of waking up to
• 2:34:07
  SPEAKER_01: the idea like that was still pretty crazy though.
• 2:34:09
  SPEAKER_01: Like it shouldn't have been like that much.
• 2:34:12
  SPEAKER_01: Like based off of like the outcomes now,
• 2:34:14
  SPEAKER_01: it's like,
• 2:34:14
  SPEAKER_01: oh, like Sweden had the healthiest situation because we didn't lock down and like...
• 2:34:19
  SPEAKER_01: Traumatize everybody.
• 2:34:21
  SPEAKER_01: I take it for granted because I was here during COVID.
• 2:34:24
  SPEAKER_01: I mean, I left briefly.
• 2:34:26
  SPEAKER_00: I was so happy to live here during COVID.
• 2:34:29
  SPEAKER_00: I also had a newborn kid at the same time.
• 2:34:30
  SPEAKER_00: Things were fucked up over in other places.
• 2:34:32
  SPEAKER_00: Yeah.
• 2:34:32
  SPEAKER_01: Oh, yeah.
• 2:34:32
  SPEAKER_01: It's crazy.
• 2:34:33
  SPEAKER_00: No, that's my fear that we're not learning the
• 2:34:39
  SPEAKER_01: I'll read up more about LMPs because I think I have been thinking of them as
• 2:34:45
  SPEAKER_01: the thing that's actually stimulated.
• 2:34:49
  SPEAKER_01: That was kind of the logic that was sold to me.
• 2:34:52
  SPEAKER_01: And the idea of the mRNA essentially then having this aspect,
• 2:34:59
  SPEAKER_01: there should be some really strict Research looking at if there's an increase
• 2:35:06
  SPEAKER_01: in autoimmune disorders after anything related to that,
• 2:35:11
  SPEAKER_01: I would say is not an illogical way of thinking.
• 2:35:16
  SPEAKER_00: As I understand,
• 2:35:17
  SPEAKER_00: there's been an increase in turbo cancers in 2021 when the shots were introduced.
• 2:35:23
  SPEAKER_00: I've talked to a doctor who noticed that and I had her on the podcast.
• 2:35:30
  SPEAKER_00: And tried to like, you know, say something about it.
• 2:35:34
  SPEAKER_00: She didn't even know that it was controversial.
• 2:35:36
  SPEAKER_01: Oh, really?
• 2:35:37
  SPEAKER_01: Yeah.
• 2:35:37
  SPEAKER_00: And was like completely shunned and, you know, treated like shit.
• 2:35:41
  SPEAKER_00: Yeah.
• 2:35:41
  SPEAKER_01: What?
• 2:35:42
  SPEAKER_00: Because she was, she's working, she's a, I can't remember what her profession is.
• 2:35:50
  SPEAKER_00: Is it pathologist?
• 2:35:51
  SPEAKER_00: Yeah, it could be.
• 2:35:52
  SPEAKER_00: People who look at like cells, try to figure out what killed people and stuff.
• 2:35:56
  SPEAKER_00: So she noticed that,
• 2:35:57
  SPEAKER_00: all right,
• 2:35:57
  SPEAKER_00: tumors are way more aggressive and way larger,
• 2:36:01
  SPEAKER_00: growing faster, and people are getting them when they're younger.
• 2:36:05
  SPEAKER_00: She's like, what's going on here?
• 2:36:06
  SPEAKER_00: And she just asked that as a question.
• 2:36:08
  SPEAKER_00: Could it have something to do with, you know, we just had the shots rollout?
• 2:36:12
  SPEAKER_00: And that was, you know, very taboo.
• 2:36:13
  SPEAKER_00: Very taboo.
• 2:36:14
  SPEAKER_01: Yeah, that is definitely taboo.
• 2:36:16
  SPEAKER_01: I mean, it's like,
• 2:36:16
  SPEAKER_01: it's making the association without having the data,
• 2:36:20
  SPEAKER_01: I'd say,
• 2:36:20
  SPEAKER_01: is the... She was not saying it is because she was saying, maybe we should...
• 2:36:28
  SPEAKER_01: And she was like silenced.
• 2:36:30
  SPEAKER_00: And there's a lot of doctors who had a similar experience.
• 2:36:33
  SPEAKER_00: Well, I mean,
• 2:36:33
  SPEAKER_00: we also were
• 2:36:34
  SPEAKER_01: like supporting What's-His-Face,
• 2:36:37
  SPEAKER_01: the guy who was like the thoracic surgeon.
• 2:36:40
  SPEAKER_01: So like- RFK?
• 2:36:42
  SPEAKER_01: Macchiarini, no, no.
• 2:36:43
  SPEAKER_01: Macchiarini, oh, in Sweden.
• 2:36:44
  SPEAKER_00: He still had Macchiarini also.
• 2:36:45
  SPEAKER_01: Yeah, yeah, yeah.
• 2:36:46
  SPEAKER_01: Oh, yeah.
• 2:36:46
  SPEAKER_00: But that's the thing.
• 2:36:48
  SPEAKER_00: Like to me, I don't like the like conspiratorial-minded view of these things.
• 2:36:53
  SPEAKER_00: It's more like people fuck up.
• 2:36:55
  SPEAKER_01: Yeah.
• 2:36:56
  SPEAKER_00: And we think we have the best thing ever and we're so sure of it because we put
• 2:37:00
  SPEAKER_00: a research into it and then we're like,
• 2:37:02
  SPEAKER_00: let's do this,
• 2:37:03
  SPEAKER_00: let's do this because our friends are paying us big bucks and we really believe
• 2:37:08
  SPEAKER_00: in this thing.
• 2:37:09
  SPEAKER_00: And then we mess up and we need to learn from that instead of going, shh.
• 2:37:14
  SPEAKER_00: Yeah, yeah, yeah, absolutely.
• 2:37:16
  SPEAKER_00: That, to me, is a crazy thing.
• 2:37:17
  SPEAKER_00: And that's why I'm flabbergasted by how little people want to talk about this.
• 2:37:24
  SPEAKER_01: In some alternate reality,
• 2:37:26
  SPEAKER_01: where Fauci just was like,
• 2:37:28
  SPEAKER_01: Hey guys,
• 2:37:32
  SPEAKER_01: remember when I had that conference about making super viruses
• 2:37:37
  SPEAKER_01: to prevent super viruses?
• 2:37:39
  SPEAKER_01: We might have just released the super virus.
• 2:37:42
  SPEAKER_01: There would be so much more respect.
• 2:37:44
  SPEAKER_01: Absolutely.
• 2:37:45
  SPEAKER_00: There's problems with that too, but it would be way better for the world.
• 2:37:50
  SPEAKER_00: Or maybe that's what happened.
• 2:37:55
  SPEAKER_01: Not like let's make a smear campaign for anybody who disagrees with us.
• 2:37:59
  SPEAKER_01: And that's the thing,
• 2:38:00
  SPEAKER_01: no matter how crazy your idea is,
• 2:38:03
  SPEAKER_01: it should be always at least taken with dignity and respect,
• 2:38:07
  SPEAKER_01: taken seriously,
• 2:38:08
  SPEAKER_01: at least in a small audience so that the bigger picture can understand
• 2:38:12
  SPEAKER_01: that there are these, that is your chaos.
• 2:38:16
  SPEAKER_01: We don't like that.
• 2:38:17
  SPEAKER_01: As a people,
• 2:38:18
  SPEAKER_01: it's more energy for me to deal with living in nonreality than to deal with
• 2:38:24
  SPEAKER_01: the conflict of having to think about if you could be right,
• 2:38:28
  SPEAKER_00: if you were right.
• 2:38:28
  SPEAKER_00: What a beautiful way to circle back.
• 2:38:31
  SPEAKER_00: That was great.
• 2:38:33
  SPEAKER_00: Let's wrap things up with that.
• 2:38:36
  SPEAKER_00: Where are you right now with ISRM?
• 2:38:40
  SPEAKER_01: What are you doing and what do you need if people are listening?
• 2:38:45
  SPEAKER_01: We may have a proper evaluation of 10 million euros, but fingers crossed.
• 2:38:52
  SPEAKER_01: The idea is we're building an enterprise company,
• 2:38:55
  SPEAKER_01: or not necessarily enterprise,
• 2:38:56
  SPEAKER_01: but a mothership company because this technology we see to be...
• 2:39:00
  SPEAKER_01: Relatively domain agnostic.
• 2:39:03
  SPEAKER_01: So the idea is kind of simple.
• 2:39:05
  SPEAKER_01: I have two kind of heavy hitter business guys who are kind of old time.
• 2:39:11
  SPEAKER_01: I think they're doing this just for pure passion because they like me.
• 2:39:15
  SPEAKER_01: They see that I have ambition and they see that I'm passionate about what I'm doing.
• 2:39:20
  SPEAKER_01: And now they're understanding more and more.
• 2:39:21
  SPEAKER_01: And I'm showing with empirical evidence that what I'm saying is I can validate,
• 2:39:27
  SPEAKER_01: I can show these You know, systems being transparent and whatnot.
• 2:39:32
  SPEAKER_01: And so they've been opening enormous amounts of doors within the whole deep tech and
• 2:39:38
  SPEAKER_01: as well as like kind of other fields as well.
• 2:39:41
  SPEAKER_01: But like robotics,
• 2:39:42
  SPEAKER_01: deep tech,
• 2:39:43
  SPEAKER_01: stem cell biology,
• 2:39:45
  SPEAKER_01: we're all,
• 2:39:45
  SPEAKER_01: we're kind of straddling, trying to straddle academia and industry at the same time.
• 2:39:50
  SPEAKER_01: So we've got a lot of collaborations at KTH and whatnot where like
• 2:39:54
  SPEAKER_01: the idea here is just create these verticals in the sense
• 2:39:57
  SPEAKER_01: where You have other experts in each of these subfields because that's the thing.
• 2:40:03
  SPEAKER_01: As a scientist,
• 2:40:04
  SPEAKER_01: you just get more and more niche based and you know more about a very little amount.
• 2:40:09
  SPEAKER_01: It's like getting people who are indoctrinating with these ideas
• 2:40:14
  SPEAKER_01: that this is how systems are behaving.
• 2:40:16
  SPEAKER_01: Let's look at your system under the rigor of your fine eye that's attuned
• 2:40:22
  SPEAKER_01: to this particular domain and see if it fits.
• 2:40:27
  SPEAKER_01: If it does fit, add transparency to whatever you're developing.
• 2:40:31
  SPEAKER_01: We make you a competitive company,
• 2:40:33
  SPEAKER_01: essentially more competitive without having transparency,
• 2:40:37
  SPEAKER_01: as well as whatever optimization can be done with that allowance
• 2:40:41
  SPEAKER_01: of self-observation.
• 2:40:43
  SPEAKER_01: And then having these kind of verticals essentially kind of cross fertilize
• 2:40:47
  SPEAKER_01: or cross amplify into bigger,
• 2:40:51
  SPEAKER_01: like we have a lot of devs here,
• 2:40:52
  SPEAKER_01: we have robotics guys here,
• 2:40:54
  SPEAKER_01: these guys need this to have this kind of as this open framework for
• 2:40:57
  SPEAKER_01: For developing bigger systems in the future.
• 2:41:01
  SPEAKER_01: I don't know.
• 2:41:01
  SPEAKER_01: I'm inspired.
• 2:41:03
  SPEAKER_01: While I don't like what he's been doing for the last few years,
• 2:41:06
  SPEAKER_01: but Elon Musk has created an industry in a way that is quite monumental
• 2:41:12
  SPEAKER_01: to doing change.
• 2:41:13
  SPEAKER_01: Would I burn all the excess and trying to send rockets elsewhere?
• 2:41:17
  SPEAKER_01: No, I think we should probably save the planet we're on first.
• 2:41:21
  SPEAKER_01: And I think that that's where...
• 2:41:25
  SPEAKER_01: I want to start by finding all these places where it's already systems
• 2:41:30
  SPEAKER_01: where we can wrap things with ISRM and essentially help them there,
• 2:41:35
  SPEAKER_01: but eventually move into building hardware actually with this logic at its core.
• 2:41:41
  SPEAKER_01: So an ISRMPU,
• 2:41:43
  SPEAKER_01: like a processing unit that is essentially based off of these core variables
• 2:41:48
  SPEAKER_01: that we can actually
• 2:41:51
  SPEAKER_01: This is all about making something that's just observations into science.
• 2:41:57
  SPEAKER_01: We need this to be stricter.
• 2:41:58
  SPEAKER_01: The society is a perfect example.
• 2:42:02
  SPEAKER_00: This should never happen.
• 2:42:04
  SPEAKER_01: This should never happen.
• 2:42:06
  SPEAKER_01: Fauci is a bad LLM.
• 2:42:10
  SPEAKER_01: We trusted him and it was a black box and it turns out he had some other
• 2:42:17
  SPEAKER_01: Agendas.
• 2:42:19
  SPEAKER_01: Other agenda there.
• 2:42:19
  SPEAKER_01: Sorry.
• 2:42:22
  SPEAKER_01: And yeah, that's unfortunate.
• 2:42:24
  SPEAKER_01: So it's like, I think like less conflicts of interest, more transparency, build a core.
• 2:42:31
  SPEAKER_01: Like I'm also building this as an agentic system from its core.
• 2:42:35
  SPEAKER_01: That's also always based off of ISRM.
• 2:42:37
  SPEAKER_01: So like it's everything gets plugged into essentially this agentic thing
• 2:42:42
  SPEAKER_01: that is also then Trying to make better decisions
• 2:42:45
  SPEAKER_01: of what we should be spending our time and effort into doing.
• 2:42:49
  SPEAKER_01: So not as in just get focus here,
• 2:42:53
  SPEAKER_01: but as in let's solve real world problems,
• 2:42:57
  SPEAKER_01: but having a system that then is starting to coordinate.
• 2:43:00
  SPEAKER_01: Again,
• 2:43:01
  SPEAKER_01: having ADD and having AI is a benefit because it can at least make sure that I am
• 2:43:06
  SPEAKER_01: where I need to be.
• 2:43:09
  SPEAKER_01: But in general, yeah, that's where we're moving to right now.
• 2:43:13
  SPEAKER_01: We're moving relatively fast,
• 2:43:15
  SPEAKER_01: but it also feels like several of these verticals are already kind of assigned.
• 2:43:20
  SPEAKER_01: But right now, people are essentially working for free.
• 2:43:28
  SPEAKER_01: That's where we're narrowing down onto what is our first product.
• 2:43:30
  SPEAKER_01: I see the potential in where financially this could become insanely lucrative,
• 2:43:32
  SPEAKER_01: but I also want to fight the point of where...
• 2:43:51
  SPEAKER_01: Like, I'm okay.
• 2:43:53
  SPEAKER_01: Like, it doesn't need to be, like, I don't need an island or something like this.
• 2:43:56
  SPEAKER_01: Like, we need the money to be able to go into the effort itself.
• 2:44:01
  SPEAKER_01: Like, the effort needs to be this kind of self-guided thing where it doesn't become
• 2:44:06
  SPEAKER_01: a greedy individual who then is just, like, capitalizing off of this.
• 2:44:09
  SPEAKER_00: Yeah, you don't want to go with the open AI path.
• 2:44:11
  SPEAKER_01: No, open AI,
• 2:44:12
  SPEAKER_01: and that's a perfect example of,
• 2:44:13
  SPEAKER_01: like, just, of just, like, too much, too fast, you know, like, and our false idols.
• 2:44:19
  SPEAKER_01: And that's the whole thing is, like, Sam Altman's an entrepreneur.
• 2:44:22
  SPEAKER_01: He's not a scientist.
• 2:44:24
  SPEAKER_01: Period.
• 2:44:25
  SPEAKER_01: And that's the thing.
• 2:44:25
  SPEAKER_01: He's like, I want to stay a scientist.
• 2:44:28
  SPEAKER_01: I get hard on other professors and stuff because I'm like,
• 2:44:32
  SPEAKER_01: oh, now you're a cheerleader because now you're the professor.
• 2:44:36
  SPEAKER_01: What that becomes now, that wasn't the case back in the proper Tesla's days.
• 2:44:43
  SPEAKER_01: He just spent his time talking to a pigeon and doing experiments and doing real science.
• 2:44:49
  SPEAKER_01: And now,
• 2:44:50
  SPEAKER_01: the more and more you get attention to So who you are as a scientist,
• 2:44:55
  SPEAKER_01: you become a poster child, you become a representative.
• 2:44:59
  SPEAKER_01: You have to then trust your postdocs and your PhD students that they're
• 2:45:03
  SPEAKER_01: at least remotely as good as you were,
• 2:45:06
  SPEAKER_01: and then try to indoctrinate them with that same idea to keep this thing,
• 2:45:10
  SPEAKER_01: whereas you then go and act as a cheerleader and essentially try to get money
• 2:45:15
  SPEAKER_01: from people who are high stakes gamblers, also known as investors.
• 2:45:22
  SPEAKER_01: The world that we have created is so sad from that perspective.
• 2:45:28
  SPEAKER_00: It's all a casino.
• 2:45:29
  SPEAKER_01: It's a casino.
• 2:45:30
  SPEAKER_01: It's like people who are making big investments and whether or not they're betting
• 2:45:34
  SPEAKER_01: on the technology or they're betting on the horse itself, like you.
• 2:45:38
  SPEAKER_01: And that's how a lot are.
• 2:45:40
  SPEAKER_01: And that's also the part that makes me frustrated because when I see
• 2:45:44
  SPEAKER_01: that people clearly are like,
• 2:45:47
  SPEAKER_01: Drawn to what I'm saying based off of the impact of how I'm talking,
• 2:45:52
  SPEAKER_01: not what I'm talking about, it's really frustrating to me.
• 2:45:55
  SPEAKER_01: Because I'm just like, no, I could be a snake oil salesman up here.
• 2:46:00
  SPEAKER_01: And unless you're critical of what I'm saying, Then don't take it seriously.
• 2:46:06
  SPEAKER_01: Do your homework.
• 2:46:08
  SPEAKER_01: And that's the thing.
• 2:46:09
  SPEAKER_01: I can't have a bunch of people-pleaser people working for me.
• 2:46:12
  SPEAKER_01: And that's the thing.
• 2:46:13
  SPEAKER_01: There's different cultural boundaries here.
• 2:46:15
  SPEAKER_01: One of my potential big...
• 2:46:18
  SPEAKER_01: They're big in industry.
• 2:46:20
  SPEAKER_01: I won't say their name.
• 2:46:21
  SPEAKER_01: But they're localized in India.
• 2:46:24
  SPEAKER_01: And they have an army of people ready to work.
• 2:46:27
  SPEAKER_01: But it's been told to me that there's a...
• 2:46:32
  SPEAKER_01: Because of a cast between the founder and this, that they won't tell you.
• 2:46:39
  SPEAKER_01: There will be no friction.
• 2:46:41
  SPEAKER_01: It will just be like what you want is
• 2:46:44
  SPEAKER_00: the way to go.
• 2:46:45
  SPEAKER_00: And you want friction in a healthy way.
• 2:46:47
  SPEAKER_00: You have to have.
• 2:46:48
  SPEAKER_01: Otherwise, you're removing your uncertainty.
• 2:46:51
  SPEAKER_01: You need to be guided by the things that you're doing.
• 2:46:54
  SPEAKER_01: Yeah.
• 2:47:12
  SPEAKER_00: If people want to find you, find ISRM, find anything you want to plug, where should they go?
• 2:47:16
  SPEAKER_01: LinkedIn, I would say LinkedIn is good.
• 2:47:19
  SPEAKER_01: I have links to that.
• 2:47:20
  SPEAKER_01: Otherwise, isrmlabsab.com is a good...
• 2:47:24
  SPEAKER_01: It's more of a...
• 2:47:25
  SPEAKER_01: I'm showing a lot of visuals there.
• 2:47:26
  SPEAKER_01: I don't know if you checked it out, but it's a lot of WebGL stuff there.
• 2:47:30
  SPEAKER_01: Everything is done with ISRM, so it's like those renderings are all actually like...
• 2:47:36
  SPEAKER_01: Nice.
• 2:47:37
  SPEAKER_01: Like instantiations of ISRM in real time.
• 2:47:39
  SPEAKER_01: And the reason why they look like natural phenomenon is
• 2:47:42
  SPEAKER_01: because like that's what natural phenomenon,
• 2:47:45
  SPEAKER_01: that's what natural systems,
• 2:47:46
  SPEAKER_01: how they're behaving,
• 2:47:47
  SPEAKER_01: like even dissipation on a sandy beach of like that little,
• 2:47:50
  SPEAKER_01: you know, this is like we're starting to.
• 2:47:53
  SPEAKER_01: It's not like, I want to take, like, let's learn about a beach.
• 2:47:56
  SPEAKER_01: Let me take a billion different pictures from different angles so that I can render it.
• 2:48:00
  SPEAKER_00: It's like, learn the rule of how
• 2:48:04
  SPEAKER_01: that energy dissipates.
• 2:48:05
  SPEAKER_01: That's the key here.
• 2:48:07
  SPEAKER_01: It's like,
• 2:48:07
  SPEAKER_01: so I can render those kind of things with like,
• 2:48:09
  SPEAKER_01: even though they're still WebGL and they're still kind of heavy,
• 2:48:11
  SPEAKER_01: but like you can render them with like quite a significant difference
• 2:48:15
  SPEAKER_01: or like less compute.
• 2:48:17
  SPEAKER_01: Awesome.
• 2:48:18
  SPEAKER_00: I'll put all the links in the description so people can find it.
• 2:48:20
  SPEAKER_00: Thank you very much for having a great time.
• 2:48:22
  SPEAKER_00: Awesome.
• 2:48:22
  SPEAKER_01: Thank you.
• 2:48:23
  SPEAKER_01: Much appreciated.
• 2:48:24
  SPEAKER_01: Yeah.
• 2:48:24
  SPEAKER_00: Until next time, man.
• 2:48:24
  SPEAKER_00: Who knows what I'll be doing next time.
• 2:48:26
  SPEAKER_00: For sure.
• 2:48:26
  SPEAKER_00: For sure.
• 2:48:26
  SPEAKER_00: We'll have to do regular check-ins.
• 2:48:28
  SPEAKER_00: Good.
• 2:48:29
  SPEAKER_00: And thanks to everyone for tuning in.
• 2:48:31
  SPEAKER_00: Catch you next time.
• 2:48:31
  SPEAKER_00: Bye-bye.
• 2:48:37
  SPEAKER_01: Bye-bye.
• 2:48:39
  SPEAKER_01: Bye-bye.
• 2:48:39
  SPEAKER_01: Bye-bye.