The Jordan B. Peterson Podcast - 563. “Something Non-Human Has Been Here A Long Time” ｜ Dr. Garry Nolan

00:00:00.000 I'm a professor in the Department of Pathology at Stanford.

00:00:03.800 It's pretty obvious that you have a multitude of abilities and a stellar track record.

00:00:10.300 You started to become interested in unidentified aerial phenomena.

00:00:14.860 Somebody representing the CIA and an aerospace company showing up in my office at Stanford

00:00:19.660 showed me their credentials and said, we need your help looking at patients who had harm done to them.

00:00:26.680 And then a small subset of them said that they'd been in proximity to things that you would call a UFO.

00:00:32.880 I thought it was a joke at the beginning.

00:00:35.080 Let us know, if you would, what the hell you think is going on.

00:00:39.040 That there's something non-human here, and it's been here for a long time. 0.99

00:00:42.240 Well, I imagine it's put a bit of a bump into your life.

00:00:45.580 I mean, maybe one that's mostly interesting, but still, to call it strange is to barely scrape the surface.

00:00:51.360 If something is here, it's likely been here longer than humans have even been civilized.

00:00:56.680 Dr. Gary Nolan is an immunologist, academic inventor, and biotech entrepreneur, serial biotech entrepreneur.

00:01:19.680 He's a professor at Stanford University School of Medicine, and, somewhat surprisingly, a ufologist.

00:01:27.400 We talked about his career, his research interests, the rise of AI, and his interest in unidentified aerial phenomena.

00:01:36.540 So, Dr. Nolan, Gary, before we get to the heart of the matter with regards to your interest in unidentified aerial phenomena, more commonly known as UFOs,

00:01:50.160 let's talk a little bit about you so that we can situate you in the minds of our readers.

00:01:55.780 So, you have a remarkable research background and a technology background.

00:02:03.380 Clue us in a bit and tell us who you are.

00:02:06.240 So, I'm a professor in the Department of Pathology.

00:02:09.820 I hold the Ratchford and Carlotta A. Harris Endowed Professorship, and the major focus of my lab's research, frankly, over the last 30 years since I've been at Stanford,

00:02:24.180 has been on the immune system and creating technologies that allow us to collect more and more data about the immune cells and or cancer cells that we're interested in.

00:02:35.400 And so, that's led me from the development of retroviral techniques for gene delivery and gene therapy.

00:02:42.680 So, all the retroviruses and lentiviruses that are used in the world today for gene therapy were developed based on a technique that I came up with called the 293 T-cell technique.

00:02:54.980 And, frankly, that's old technology to me, but it still generates a nice royalty stream.

00:03:01.660 So, and then from there, it's about measuring more and more what we call parameters per cell, which are events that relate, we think, to the biology of the cells.

00:03:14.360 And so, we've created and spun out, I don't know, probably at least half a dozen companies on that side of things alone.

00:03:21.560 Lately, we've been moving into artificial intelligence.

00:03:25.760 We've started and spun out two companies there.

00:03:28.440 And now, I'm actually moving into atomic imaging because I sort of feel like that's the next level down of information that I need to get at to understand gene function.

00:03:40.020 So, I've raised the money to create a whole new kind of instrument that can measure things at the atomic level.

00:03:45.900 Tell me about that a little bit.

00:03:48.520 I mean, that's a lecture on microscope territory.

00:03:51.460 You have a new technology that you're – I know that's an old technology now.

00:03:56.520 It's a fusion of two technologies, something called atomic probe tomography and field ion microscopy.

00:04:03.180 And it's a way to bring the two together because previously they couldn't sort of exist in the same machine.

00:04:10.560 So, by bringing them together, we can go another order of magnitude lower.

00:04:16.120 We can get down to what's called sub-angstrom.

00:04:18.280 Like, the bond length between two atoms is in the sub-angstrom realm.

00:04:22.920 But this technology that we've developed not only can see down at that level, but can also determine what kind of bond structure we have locally.

00:04:33.180 And that has a range of applications all the way from biology through to metals, alloys, nanotechnology, et cetera.

00:04:39.680 And actually, the instrument is already half-built down at a lab here in Cupertino that we've set up.

00:04:47.400 So, we're excited about that.

00:04:49.260 So, how many companies have you started or been involved in starting?

00:04:54.420 About a dozen.

00:04:56.680 And we've had about eight successful exits so far.

00:05:02.040 Hmm.

00:05:02.800 Well, that's a pretty good track record, all things considered.

00:05:06.440 All things considered.

00:05:07.220 One complete failure, but that's okay.

00:05:09.440 One failure out of that many isn't so bad.

00:05:12.980 Yeah.

00:05:13.220 Well, if you don't fail, maybe you're not trying enough diverse things.

00:05:17.240 I mean, that seems to be particularly true on the entrepreneurial side, right?

00:05:20.460 It's very difficult to invent something and then equally difficult to make it profitable or maybe perhaps more.

00:05:27.500 Right.

00:05:27.980 So, on the medical side, tell me a little bit more about your research into viruses.

00:05:34.940 So, our research into viruses was, well, first of all, the retroviruses.

00:05:40.780 I got involved with HIV research back in the day.

00:05:43.940 And that was mostly trying to understand what turned the virus on and off in the immune system.

00:05:50.080 So, I was involved with what was called the cloning and characterization of what are called transcription factors that turned the virus on and off.

00:05:59.420 And I actually cloned it in David Baltimore's lab at MIT when I was a postdoc there.

00:06:03.860 David won the Nobel Prize, actually, for reverse transcriptase.

00:06:06.640 Very famous man, obviously.

00:06:08.880 So, but when I came to Stanford and using the technologies that we developed, we did everything from Ebola research to Zika to whatever the current manifestation of whatever the plague was that people were worried about.

00:06:25.820 And we even actually saw the first COVID lungs from, unfortunately, deceased patients.

00:06:31.740 And that's what really, that actually scared the bejesus out of me when I realized that we're dealing with something extremely serious with COVID, at least in some people.

00:06:41.820 But most of my research these days is in cancer research and looking at how the immune system interfaces with the tumor and trying to learn about the signals that the usually tumor manifests to turn off the immune system.

00:07:00.900 Or to disrupt the function of the immune system.

00:07:03.940 And so, that required the development of new instruments to see things at a level previously people were incapable of, but then also to develop the algorithms to understand the complexity.

00:07:15.800 Because you've got thousands of cells in a complex dance with the cancer and trying to figure out what that means took a lot of algorithmic effort.

00:07:26.140 So, we're a computational lab as well as a wet lab, as we call it.

00:07:30.900 Hmm.

00:07:31.720 Do you have an engineering background?

00:07:34.140 No, but I've always been a tinkerer.

00:07:38.020 And so, you know, I mean, I think what makes a good scientist is knowing what you don't know and knowing who to bring in to help you create what it is that you want and being able to explain it to them in a way that gets them interested.

00:07:53.980 And that talent, frankly, translates very well in the entrepreneurial side of things.

00:08:00.640 When you're talking to venture capitalists, you can convince them that the biology is interesting, that it's doable, that here's the kind of people I need.

00:08:10.060 And I always use this term inevitable, that it's inevitable.

00:08:14.200 This is something that's coming, so it really is up to the early bird that gets the worm.

00:08:20.760 If you can see that it's something that will happen and has to happen and you have a solution for it, it might not be currently the best solution, but it's a solution.

00:08:30.920 Get to it first and own the market.

00:08:32.580 And your degrees, in what areas were your degrees awarded?

00:08:39.040 All genetics, but one way to think about genetics is genetics is actually programming.

00:08:44.280 I'm actually pretty good as a programmer as well, but genetics itself is software.

00:08:49.380 And so if you think about genetics as software, it was very easy, again, for me to be both a programmer and a geneticist at the same time.

00:08:59.800 So I've always been good at math.

00:09:01.260 I've been good at the, I'm more, I would say, good at the intuition of how biology works.

00:09:08.680 And intuition plays a larger part, frankly, in science than people would like to admit.

00:09:15.700 Yeah, you know, one of the things that's always struck me as peculiar about scientific research papers is that the introductions are always a lie.

00:09:25.140 You know, it's so interesting because I thought about this for a long time.

00:09:31.920 It really struck me when I was first in graduate school because when you write a scientific research paper, you present the situation as if all the background reading that you did produced an incremental transformation in your thinking such that you generated a hypothesis.

00:09:53.020 And that's almost never the case.

00:09:55.460 Usually what happens is that people have an intuition that's derived from some pattern recognition and then they backfill it and make it look like it's algorithmic.

00:10:06.860 And then the other thing that's so bloody peculiar about that is that there's almost no discussion in graduate school training.

00:10:14.460 Maybe this was different where you went on hypothesis generation itself.

00:10:19.460 It's as if scientists swallow the idea that you're creating your hypotheses in this algorithmic manner as a consequence of grinding through the research.

00:10:29.460 I know I had a student at Harvard, Shelley Carson, who worked on creativity, and she would make very large leaps with regard to her hypothesis, and then it would take her, you know, a few months to backfill so that she could bring people along to explain the justification.

00:10:51.220 But that certainly wasn't the method by which it was derived.

00:10:54.220 You strike me as a peculiarly creative person for a scientist, by the way, I mean, that might seem a perverse characterization, but we also studied predictors of scientific prowess and openness, which is the trait marker for creativity, was actually slightly negatively correlated with scientific productivity, at least at the graduate school and then early career level.

00:11:20.220 But you've got a very wide range of abilities, but you've got a very wide range of abilities, and then you've got this both an entrepreneurial and a managerial twist.

00:11:30.600 Is that a fair characterization?

00:11:32.900 Because that's also a rare combination.

00:11:35.280 I think what, I mean, it's hard to talk about yourself as if you don't sound like, not to sound like a narcissist, but I think if you can marry creativity with practicality,

00:11:48.220 that's the magic mix, at least for me, is I'm very good at rapidly iterating all the possible reasons why something can be the case, and then rank ordering them very quickly, coming up with at least two cutoffs.

00:12:06.520 One is, yeah, this is possible, but it's very unlikely that magic dwarves run the universe, right, everything below that level. 0.94

00:12:15.920 But then above that level, there's two, there's one more cutoff.

00:12:19.420 One is possible, but impractical or perhaps not easy to prove.

00:12:25.380 And then above that is the provable.

00:12:27.340 And so if you can rapidly rank order and then come up with where something sits, then you can immediately turn and tell a student, yeah, you should do this.

00:12:37.840 Or, yeah, you probably shouldn't do that because here's the reasons why there are so many other things that it could be that you can't prove or disprove.

00:12:46.140 So let's not go down that road because it's a rabbit hole.

00:12:49.800 So I think marrying creativity with practicality and being able to see, and frankly, what I call reverse engineer the future, you know, it's like you can see what it is, and then you know I need to do this first, and then I need to do that.

00:13:06.340 And once I've done each of these steps, those are milestones that give you confidence to take the next step.

00:13:12.520 And then because if I think like that, then that actually helps with talking to venture capitalists because they can then follow the path that you've just laid out for them.

00:13:23.760 And when did you start your first company?

00:13:27.580 Soon after getting to Stanford, actually.

00:13:30.100 It was around 1994.

00:13:33.380 But I had already learned a lot from my mentors, Len and Lee Herzenberg.

00:13:37.920 So I was lucky having come to Stanford to end up in their lab because Len and Lee, who were frankly hippies, you know, they had the two of the three biggest patents at Stanford.

00:13:54.060 One was for something called the flow cytometer, which brought in hundreds of millions of dollars.

00:13:59.940 And perhaps even more important were the monoclonal antibodies, what are called humanized antibodies.

00:14:06.040 So by making monoclonals that could be injected into humans without raising an allergic reaction or a strong immune response, almost all of the injected antibodies today are based on those original technologies.

00:14:21.340 And Len was just a natural entrepreneur.

00:14:26.720 He never started any companies, but he knew how to license them.

00:14:30.320 So he would always bring me into his office when he was negotiating with the pharma companies, and he would give me the contracts to read because I was one of his favorite students.

00:14:42.480 So he's like, okay, I'm not going to waste my time by giving this guy something because he'll actually understand it and pick it up.

00:14:49.080 And he introduced me to the best patent attorneys of the day.

00:14:52.380 And so I learned from them what it was all about.

00:14:54.960 So it was, you know, much of what I would love to say is mine is just a rewrite of what I learned from Len and Lee.

00:15:05.500 Right.

00:15:06.100 So you were very favored in your mentoring.

00:15:09.300 Yes.

00:15:09.820 I got lucky.

00:15:11.640 But I was also—

00:15:12.340 Yeah, well, that's a good deal.

00:15:13.380 I rotated in Stan Cohen's lab, and Stan, of course, had the Cohen-Boyer patents.

00:15:20.100 So Boyer, Herb Boyer, started Genentech.

00:15:23.540 Stan Cohen had the other of the three biggest—and they were all in the Department of Genetics.

00:15:28.680 And those were the patents for genetic engineering.

00:15:33.320 So it was sort of an environment that led you to think about practical applications.

00:15:40.340 Now, when I started my company as an assistant professor, I got a heck of a lot of pushback from senior scientists saying,

00:15:50.840 Gary, it's too early.

00:15:52.720 You're, you know, you're going to—you shouldn't dirty your hands with this yet or now, frankly.

00:15:59.820 They didn't even want me getting involved at all.

00:16:01.720 And it was funny because a lot of them, you know, 10 or so years later, were back in my office asking for my advice on how they could start a company.

00:16:10.960 Right, right.

00:16:11.940 Well, it's a rarer pathway to be scientifically productive and to make your talents manifested in multiple directions and to be an entrepreneur.

00:16:27.040 I mean, that's—the research that I referred to looking at predictors of scientific productivity showed that the best predictor, apart from IQ, obviously,

00:16:38.520 which is always the best predictor of virtually anything complex by a lot, was conscientiousness, right?

00:16:45.360 Just sheer diligence and that there's a certain kind of narrow focus that goes along with conscientiousness, too.

00:16:52.060 And so, it is reasonable advice if you're talking to someone whose primary talents are diligence and industriousness for them to focus intently on one area so that they can establish themselves.

00:17:05.340 But that obviously wasn't the appropriate pathway for you.

00:17:10.140 And so—but that—and it's also complicated and difficult to start a company as well as a research lab and to teach and all of that.

00:17:19.860 So, as generic advice, it probably wasn't too bad, but it didn't seem to hold in your case.

00:17:26.100 How many patents do you have?

00:17:28.480 I think somewhere between 50 and 60 at this point.

00:17:32.840 Aha.

00:17:33.340 And research articles?

00:17:35.420 Over 350.

00:17:38.360 Right.

00:17:38.840 Okay.

00:17:39.200 Well, so for everybody watching and listening, I mean, obviously, that's a tremendous number of patents because actually one patent is a lot of patents.

00:17:46.760 And so, 60 is a tremendous number.

00:17:50.820 And on the research side, you can do a rule of thumb calculation, and not everybody agrees with this, but three research papers properly packaged make a pretty nice PhD thesis.

00:18:03.380 So, 300 is roughly equivalent to 100 PhDs.

00:18:06.960 And that's a lot of PhDs, and I think that's a reasonable way of looking at it, not least because most PhDs end up with either zero or one publications.

00:18:17.700 So, the three publication rule of thumb isn't a bad one.

00:18:21.720 What do you think of that characterization?

00:18:23.860 Yeah, I think it's good.

00:18:25.080 I think the better way to do it is how often are you cited.

00:18:29.400 So, you can publish and never be cited.

00:18:32.980 So, at this point, I think I'm at about 89,000-something citations.

00:18:39.040 So, that puts you in the top whatever percent.

00:18:42.020 And a lot of those, frankly, were the retroviruses because people use the retroviruses.

00:18:49.100 And then a lot of it are the technologies that I've developed because for the immune system measuring technologies, whether it's something called Cytoff that I co-developed with this guy at the University of Toronto.

00:19:00.340 His name was Scott Tanner.

00:19:02.580 He invented the machine, but I showed how it could be used for immunology or the Codex or the MIBI or Phosphoflow or now the split pool synthesis technology for single-cell analysis were all things that sort of just, like, came to me.

00:19:20.820 It's amazing that some people think that science is this methodical step-by-step, whereas more often than not, it's you pose the question in a way that sort of sets your subconscious to work.

00:19:37.020 But then you lay out in front of you all of the necessary raw material and you say, somewhere in this morass is the answer.

00:19:45.760 And then at a lecture, out of nowhere, it suddenly just appears, you know, in your head, fully formed.

00:19:55.000 It's almost as if your subconscious was busy working and it finally said, oh, I'm done.

00:20:00.920 Here it is.

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00:21:16.780 Yeah, well, your thoughts and revelations, let's say, as well as your perceptions, are extremely influenced by your goal.

00:21:28.480 And so, if you set the right question, you establish the quest, and your thoughts are orienting mechanisms.

00:21:36.820 They're going to be working on the pathway to that goal.

00:21:40.000 And they do deliver the goods, just like when you're walking down the street and you orient towards a goal, you can see the way to walk.

00:21:48.360 I mean, it's analogous to that.

00:21:50.600 And we're not shocked that our perceptions are delivered to us.

00:21:59.000 No, it's not like we effortfully construct them.

00:22:01.780 They make themselves manifest in our consciousness.

00:22:04.780 And if thought is an abstracted equivalent of perception, which is at least one of the things it is,

00:22:11.460 it's not that surprising that once you set your mind to the task that you're, what would you say,

00:22:19.660 that the spirit of revelation visits you in the appropriate manner.

00:22:23.980 That's especially true if it's a genuine question, you know, if you're really interested in it.

00:22:30.780 So, how, could you describe your typical day?

00:22:34.680 Like, how many hours a day do you work, and how do you set up your day?

00:22:39.760 Like, I probably work 14, 15 hours a day.

00:22:45.720 I get up, I feed the dogs because they're very demanding.

00:22:50.600 I sit down, I start on email, and then I look at my task list.

00:22:56.280 And usually these days, it's a lot of editing.

00:22:59.680 And luckily, large language models have come along and help with that.

00:23:03.760 In fact, it's actually almost fun to write grants now.

00:23:07.740 And that's a, you know, that should shock it.

00:23:10.760 Wow, that's something to say.

00:23:12.040 That's for sure.

00:23:13.080 Because I figured out how to use large language models to write grants.

00:23:16.500 And so now, and what's interesting is that you really only need to give it like five or six sentences of the basic idea,

00:23:25.200 the way I've constructed this large language model version.

00:23:28.240 And the rest of it gets automatically produced, which is actually kind of sad.

00:23:34.200 Because what it means is the majority of it is rote.

00:23:38.220 The majority of what we write is similar to what you were saying about papers.

00:23:42.000 The majority of that is rote, and it's just there for the convenience of the reviewer.

00:23:47.380 But the central idea is only a few sentences.

00:23:52.720 Right, right.

00:23:53.520 Now, do you have your own large language model?

00:23:56.300 And how do you stop them from lying to you and producing false, like, hallucinations and citing papers that don't exist?

00:24:04.760 Yeah, we, you know, we use pretty sophisticated versions.

00:24:08.400 We don't have our own LLM, but we have our own chain of thought layer that sits on top of these for the work that we do with the large language models.

00:24:18.900 We have, we use OpenAI or Anthropic or Gemini, you name it.

00:24:25.180 And then we have a layer sitting on top.

00:24:28.820 Oh, yeah.

00:24:29.380 And is that layer trained on your work?

00:24:31.840 Yes.

00:24:32.480 Or on relevant work?

00:24:33.640 Relevant work.

00:24:34.260 I see.

00:24:34.700 I have one of those as well that I trained on my books and some other material.

00:24:39.840 Right.

00:24:40.320 Yeah, and it's a very weird thing to use.

00:24:42.580 I don't know if you have the same experience, but in this system we trained thinks like I think, but it can also think up things that I haven't thought up, which is, I guess what's happening as far as I'm concerned is that in the statistical encoding of my linguistic knowledge,

00:25:04.240 is all sorts of latent information, right?

00:25:06.500 I mean, I mean, there's relationships in my patterns of thought that I haven't explored, obviously.

00:25:11.500 And they're probably near infinite in scope.

00:25:16.500 I mean, I would say that's the case for everyone, but because there's just so much information that's encoded.

00:25:22.020 And so does your system refer to the material that you've trained it on first and then to the large language model that's general after that?

00:25:30.040 I mean, it's in the context window.

00:25:32.480 It starts with it, but I think the value is because it has lowered barriers, which is really what you're talking about.

00:25:40.860 The barriers are lower to finding analogous or metaphors of what it is that you've said in other ways of thinking.

00:25:48.680 I mean, much of my work, the inventions that we've made, we're taking the metaphor approach of finding somebody else's technology that works in something else, completely unrelated to biology, and showing how it could be applied to biology, right?

00:26:05.300 And so, you know, ideas very often, the best ideas are saying, or the best teachers are people are saying, this is something like this.

00:26:12.860 Think of it like this, and then giving a metaphor or an anecdote that explains the idea.

00:26:19.740 And so the large language models are just metaphors on steroids.

00:26:23.500 Depending on how you set the heat, it can find things for you and solutions for you that you probably could have thought of,

00:26:32.600 but it did the legwork.

00:26:36.560 So for me, for instance, on the atomic imaging idea, you know, I said, okay, well, here's what I'm doing.

00:26:42.940 Help me write the patent on it, and it helped me start the patent that I gave to the patent attorneys who wondered what lawyer I'd used to write this because it was already pretty good.

00:26:52.760 But I said, find me five other ideas that might also do the same thing.

00:26:59.640 And surprisingly, it came up with ideas.

00:27:01.660 Now, they were impractical, but it came up with ideas that were, you know, were like, oh, that's pretty cool.

00:27:08.240 I wish I knew about this area of physics.

00:27:11.840 So, you know, it's actually there was a study just done out of Stanford just last fall that showed that large language models can be as creative as humans,

00:27:22.260 if not more creative, as scored by humans, just less practical.

00:27:29.300 Yeah, I wonder what the bound is on practice.

00:27:32.720 Like, do you suppose, I've talked to some computer engineers, including my brother-in-law, who's quite a genius.

00:27:40.460 And one of the things that he is prognosticating, and not only him, is that we have these, obviously, we have large language models that are assessing the statistical relationship between words at multiple levels of resolution

00:27:55.820 and can do this remarkable thinking, for lack of a better word, because it sure looks a lot like thinking to me.

00:28:02.720 But, you know, human beings, we seem to be able to do that with images as well, right?

00:28:08.860 And also with movement, like embodied movement.

00:28:12.320 And my guess is the practicality constraint is probably something like the referencing of the semantic system to the domain of image and movement, right?

00:28:24.160 Will this, because just because it's coded hypothetically in the linguistic corpus doesn't mean that it's in keeping with the way the world makes itself manifest.

00:28:34.800 And humans have three different memory systems, at least, right?

00:28:38.360 We've got semantic and episodic and procedural.

00:28:41.040 And my suspicions are that when we're looking for practicality, that we assess the joint contributions of all of those different ways of representing information.

00:28:51.720 And the large language models can't quite do that yet, but they will soon.

00:28:56.440 I mean, it's got to be the case, right?

00:28:58.160 Because someone like Elon Musk, for example, he has this immense corpus of real-world data.

00:29:03.480 And it's got to just be a matter of time before that's integrated with the large language models.

00:29:08.780 Right.

00:29:09.000 Well, actually, you know, there's a part of your brain that does a lot about what you're talking about.

00:29:13.020 And it's called the basal ganglia and the caudate potamen, which is actually where intuition happens.

00:29:20.180 So there's a game, a Japanese game of chess, which is sort of a limited form of what we think of as chess.

00:29:28.620 And so they were doing basically reads of people's brains while they made these moves.

00:29:37.160 And especially when they made like what would be considered a genius move.

00:29:41.040 And the area of the brain that lights up is the head of the caudate and the potamen, which is – so the basal ganglia is actually what part of the brain tells you where your body is in 3D space, what your memories are, et cetera.

00:29:56.080 It's all subconscious, subservient to your executive function.

00:29:59.380 So when you make a decision to do something, that gets sent to the basal ganglia, which determines whether or not you can actually do it and whether you want to do it.

00:30:09.260 Like if you're walking across a room, how do I walk?

00:30:12.000 All those subconscious decisions are all done in the basal ganglia.

00:30:16.740 But as it turns out, as humans have evolved, that has then been sort of taken over to be used as our decision-making system.

00:30:27.040 Our intuition system works through the basal ganglia.

00:30:30.680 So all those ideas that you just talked about, where it actually finally comes to, is this practical or not, the basal ganglia is part of that process, a central place for that process.

00:30:44.820 So is that an embodiment constraint essentially?

00:30:48.940 Sorry, what do you mean by that?

00:30:51.080 Well, some things you can act out and some things you can't.

00:30:55.760 Yeah, but it also, yeah, it also appears to be used in the abstract sense now.

00:31:01.880 Like, is this the right move to make in a chess game, which is kind of, you know, abstract reasoning.

00:31:11.160 And we actually did a study on it.

00:31:14.600 I mean, believe it or not, we came to this area of the brain because of some of my UAP stuff.

00:31:20.160 Um, and we did a study with a group at Harvard and found, in fact, that, uh, the size of this area of the brain, uh, correlated directly with intelligence.

00:31:33.700 Um, and so.

00:31:35.060 Oh, really?

00:31:35.680 Yeah.

00:31:35.880 Which, which part exactly?

00:31:37.600 Was that the caudate?

00:31:38.440 That was the caudate.

00:31:39.540 Yeah.

00:31:40.020 But we were.

00:31:41.060 How high was the correlation?

00:31:42.940 Do you remember?

00:31:43.800 You know.

00:31:44.280 What was the magnitude of the correlation was?

00:31:45.900 I can't remember.

00:31:46.560 But we have three papers.

00:31:47.900 Okay.

00:31:48.340 Three papers on it that we published.

00:31:50.880 So it's interesting stuff.

00:31:52.380 When were they published?

00:31:53.620 In the last three or four years.

00:31:56.300 Oh, yeah.

00:31:56.820 Okay.

00:31:57.160 So I haven't come across those.

00:31:58.460 I'm very interested in the neurological determinants of intelligence.

00:32:02.500 But there was a, there was a guy in literally, I think, the year 2000 from Harvard, who, through his own sort of best guesses or whatever, who had proposed before anybody actually found it, that, I think his name was Hoffman.

00:32:19.100 And he's now at UCLA.

00:32:21.120 He's a professor of neurology there.

00:32:23.820 Had proposed that the caudate and the basal ganglia were going to be involved in intuition.

00:32:30.380 I didn't read his whole paper on it.

00:32:32.400 But he was already a postulate when he was like a postdoc.

00:32:36.100 I wonder what the, what do you think the connection is?

00:32:38.940 I mean, when you think of intuition, you tend to think, at least I tend to think of pattern recognition, let's say.

00:32:46.540 What, what do you suppose the connection is between pattern recognition and, and, and the caudate and, and it's, and it's, and its relationship to, to motoric movement?

00:32:58.220 It's a, it's making a decision with sparse data.

00:33:01.020 It's, it's, it's the, it's the instantaneous decision to leap when there's, when there's movement, like the leopard's about to jump out of the tree at you.

00:33:13.380 And it's the movement, but, you know, in, in the military.

00:33:18.560 Right. So that's, that's, that's having to, well, I would imagine someone on a playing field, you know, in a hockey game or a soccer field.

00:33:25.300 Obviously, they're tracking many moving objects simultaneously and abstracting out something like a meaningful pattern.

00:33:33.800 Right.

00:33:34.100 Which direction is this going?

00:33:35.840 And then they're modulating their reactions in consequence of reading the field.

00:33:41.040 And the great athletes, the great team athletes are particularly good at that.

00:33:45.080 Wayne Gretzky was particularly good at that in hockey.

00:33:47.580 And so, and so, okay, so the pattern recognition would be something like, you can imagine that being also crucial in a hunt.

00:33:56.440 Right.

00:33:56.940 Because you're going to want to know where the animal is going to go.

00:33:59.740 And, and with your pack, you, you have to orchestrate your movements and you have to do that together.

00:34:05.700 There's something almost musical about that.

00:34:07.580 Like lions can do that and pack animals.

00:34:09.660 And so, oh yeah, I see.

00:34:11.380 And that, and that would be focused on a goal, the hunting arrangement.

00:34:15.540 And that would require extremely fast reflexes.

00:34:18.940 So it's, the intuition in that regard is a very complex form of reflex in a sense.

00:34:24.020 Yes, exactly.

00:34:25.080 So it's, humans seem to have evolved a way to use a pre-existing system in the basal ganglia that was really just there for motor movement and making subconscious decisions.

00:34:35.680 And they've, they've, they've layered over it and they've put an abstraction layer on top of that so that we can now use it in for mathematical principles and, and other ideas.

00:34:46.600 It is what provides that aha moment.

00:34:50.520 And, and I've learned actually to, to see when the aha moment comes, it's almost like a form of color.

00:35:00.380 It's like when the next time you get an aha moment, you, if you can try to capture that it, when it happens and realize that it was a different kind of input than what a methodological moment is, where you basically, you've added it up and you've gotten the number by just simple addition, as opposed to that aha moment.

00:35:24.920 And I've learned to recognize and pay attention to the aha moment, not that it's always true, but that it came from an intuition because, you know, it's, it, it very often you can get it and then just dismiss it because it was just an intuition as opposed to something that you figured out.

00:35:45.940 So, listening to those aha moments, and I, I, I, I'm telling you, I see it as a color.

00:35:52.320 When it happens, I, I, I recognize it as a different kind of thought.

00:35:56.860 It's not like it's being given to me magically or anything like that.

00:35:59.280 I know a lot of people would like to think that that's what it is, but.

00:36:02.280 Well, I don't know.

00:36:02.800 There's something kind of magical about it.

00:36:04.780 It, it, like, thought has this revelatory quality, as you pointed out.

00:36:09.280 You can set your sights on something and then the pathway there, the, the mechanism that delivers you there is delivered to you, you know, so there is a magic about it.

00:36:20.580 You know, I'm going to, people are going to laugh at me for this because I always do it, but I'm going to do it anyways.

00:36:26.180 That, I've been studying Old Testament literature a lot for a long time.

00:36:34.960 And I'm interested, I'm bringing this up because of something you said about the basal ganglia, too, developing an abstraction layer.

00:36:43.440 You know, part of that abstraction layer is no doubt our ability to tell stories because stories are verbal representations of action patterns.

00:36:52.920 And so, so the, the burning bush episode in, in Exodus, that's an intuition episode.

00:36:59.960 You know, and Moses takes his intuition seriously enough to deviate from the, from the, from his normative path.

00:37:09.460 And then he delves deeply into the source of the intuition.

00:37:13.740 And that's what transforms him into a leader, right?

00:37:16.940 He gets to the bottom of something, down a rabbit hole to the bottom of something.

00:37:20.760 And so it is a, it is a, it is a, it is a narrative representation of not only of intuition, but of the willingness to attend to it and to, and to delve into it deeply, right?

00:37:35.480 So, okay, so we should switch topics here.

00:37:40.220 I wanted to go over your background with you to establish for everybody listening who you are.

00:37:46.800 And it's pretty obvious that you have a multitude of abilities and a stellar track record that's continuing.

00:37:56.660 And so that sets the foundation for our next discussion.

00:38:01.860 You started to become interested, and I would like to know the story, in unidentified aerial phenomena.

00:38:10.060 And that's definitely a lateral move from your other interests.

00:38:13.900 And so I'm very curious about all of that.

00:38:17.920 I guess what I'd like to start with is why the interest and, and why take the risk to pursue it as well?

00:38:26.140 Because you have a lot to lose, let's say, on the reputational front.

00:38:29.300 And it's clear you're a very creative person.

00:38:31.200 So I'm sure your interests go everywhere.

00:38:33.760 But tell us how it is that you became interested in this and why you decided to pursue it with some degree of seriousness.

00:38:42.440 So, I mean, there's a couple of origin stories to it.

00:38:48.580 But I think the most, the easiest to start with is with the Atacama mummy, right?

00:38:54.660 The small mummy that people had been promoting as being an alien, right?

00:39:00.480 The mummy that was found in Atacama, Chile.

00:39:04.380 And-

00:39:04.500 Right, that was a couple of years ago, not too long ago.

00:39:07.120 Oh, actually, it was, no, it was 12 years, it was-

00:39:09.900 Oh, was it 12 years ago?

00:39:10.840 Yeah, yeah, actually, already.

00:39:11.880 Yeah, yeah, it was a long, long time ago.

00:39:13.580 I mean, that was, and so I had seen it on YouTube.

00:39:17.860 I reached out to the people who were, let's say, marketing it.

00:39:21.360 And I said, hey, I can figure this out for you.

00:39:24.420 I can tell you what it is.

00:39:25.820 And so we arranged to get a small piece of the body, a rib.

00:39:33.700 I wanted the rib because I wanted the bone marrow from within the rib because I felt that that would be the place best protected from bacterial contamination.

00:39:41.760 And the long and the short of it was that we showed that it was a human baby, probably, well, it was probably preterm birth, but that we found a number of mutations in the genome that could explain what it looked like and why it looked the way it did.

00:40:00.420 And so, you know, when a movie came out regarding that circa 2012 or so, it was like sending up, you know, a flare to two sides of the world.

00:40:16.440 One, the people who didn't like that I was debunking the alien.

00:40:21.180 So I became an instant symbol of dislike for the UFO community, which is interesting, you know, paradoxically these days.

00:40:32.880 But then it also was a flare to scientists, as it turned out, as well, the intelligence community, that here's a guy willing to look at things and just call them as he sees it.

00:40:44.820 And so that led, as it turned out, to somebody representing the CIA and an aerospace company showing up at my office at Stanford, literally unannounced, showed me their credentials and said, we need your help looking at patients who've had harm done to them.

00:41:05.580 And I was like, well, what kind of harm?

00:41:07.540 And then they laid out the data, literally like MRIs and x-rays of internal scarring of—

00:41:16.300 Are these people who reported abductions?

00:41:19.080 No, no, no.

00:41:20.420 Oh, no. Oh, sorry. Okay.

00:41:22.280 These were—

00:41:22.960 I'm off on a wrong tangent.

00:41:24.140 These are intelligence agents, diplomatic corps, military personnel, et cetera, all who said that they were hearing buzzing in their ears or, you know, and then a small subset of them said that they'd been in proximity to things that you would call a UFO.

00:41:42.360 So, I thought it was a joke at the beginning, especially when they mentioned the UFO stuff, because I had no intention at the time of going back and doing more alien research after the Atacama mummy escapade.

00:42:00.220 And so, they had come to me.

00:42:03.460 I mean, why come to me?

00:42:04.480 Well, one, I was willing to talk to people about this stuff.

00:42:08.660 But two, they wanted to do blood analysis of the individuals who'd been harmed as part of a complete medical workup.

00:42:16.860 And so, they'd asked around, and they said, well, who does the best blood analysis?

00:42:20.220 Oh, you need to go talk to this guy, Nolan, at Stanford.

00:42:23.080 He has this thing called Cytoff that can do the deepest analysis of blood that, you know, currently today and still.

00:42:29.060 So, basically, over the course of two or three years on working with this group and on these patients, it turned out that these were actually the first of the Havana syndrome patients.

00:42:43.660 I'm sure you've heard of Havana syndrome.

00:42:46.320 Review that for everyone.

00:42:47.980 So, Havana syndrome was something that basically came out around 2015, 2016.

00:42:55.200 And it was called Havana because it was the diplomatic core individuals in our government who were getting headaches or having to be sent home.

00:43:07.520 And it turned out that it's probably a kind of microwave technology being used by some of our adversaries.

00:43:13.600 It's 100% real.

00:43:15.240 You know, some people in the CIA tried to debunk it, but now there's a whole, like, set of paperwork put out by the Department of Health and Human Services on anomalous, what's now called anomalous health incidents, where Havana syndrome and all of the sets of associated symptoms are all listed.

00:43:37.240 And there's a path now for people who think that they have it to go follow it up, you know, appropriately with the Veterans Administration or what have you.

00:43:48.900 But, you know, in the three years—

00:43:50.740 Okay, so let me get this straight.

00:43:52.560 So, you had someone from the CIA show up to your office, and he had a list of people who had medical problems.

00:44:00.600 And some of those medical problems were a consequence of people coming into contact with—contact with, what, technology that is mysterious?

00:44:13.700 Is that the right way of thinking about it?

00:44:15.540 Yeah.

00:44:16.100 Well, I mean, they didn't know what the source of it is, but now we know that it was basically—I mean, it's an energy weapon, just a microwave weapon.

00:44:23.940 And just imagine you could focus the beam of your microwave in a very narrow path towards a person's head.

00:44:30.740 You'll bake the brain cells in their head. 0.61

00:44:33.720 So, I mean, there's nothing magical about it.

00:44:35.960 We have them.

00:44:36.720 Everybody knows that these things exist.

00:44:39.480 At the time, when we were working on it, we were calling it interference syndrome.

00:44:44.140 You call something a syndrome when you don't know the exact cause, but it can have a variety of manifestations.

00:44:49.760 And so, what we had done was we had matched the symptoms to what are called the international diagnostic codes so that we had the ability to say, oh, it's this, and it's this, and it's this.

00:45:01.620 And if you have 10 of 15 of these, you have interference syndrome.

00:45:07.360 So, at the same time, somebody was figuring out what Havana syndrome was.

00:45:11.020 And it turned out that our set of symptomologies matched perfectly with the Havana syndrome ones for most of our patients.

00:45:19.300 We were able to hand all of that over to the U.S. government, and I've worked with Senate staff and others on that, and that's something I can't talk much about.

00:45:30.420 But what remained, and this is what's good about how science is done, once you've characterized something and you find it uninteresting, not that it's uninteresting that these patients are being harmed, but I could hand it off to somebody else who would then take care of it as a national security concern.

00:45:46.220 What was left on the table were the oddities, and those were now the people who had gotten close to UAP, they claimed, at least some of them.

00:45:57.400 And they had, as it turned out, slightly different symptomologies.

00:46:02.720 Some of those were more likely to have erythemas or scarring on the skin as opposed to internally or manifestations on the back of their neck of some kind of irradiative damage of some kind.

00:46:19.900 Now, there—

00:46:20.960 And there was a pattern to this.

00:46:22.300 Yes, there was, yes.

00:46:23.320 And the pattern was always anecdotal, unfortunately, in that they had a story that you at face—

00:46:30.640 Right, but I mean the symptom pattern was stable.

00:46:33.820 And how many people, how many individuals approximately, like what kind of sample pool were you assessing?

00:46:40.140 Now you're down to about five or six people because of the original—

00:46:44.320 Okay, so it's a small number of people.

00:46:45.460 Of the original hundred that we started with, 90 or so, it turned out, were what we could think of as Havana syndrome.

00:46:51.980 The remaining were what were interesting.

00:46:54.840 And, you know, but sort of back to, let's say, my career.

00:46:58.620 My career has always been—I've always been good at seeing the data point off the curve and realizing that it's not noise, or at least asking the question, how did that data point get there?

00:47:15.040 And not just, you know, going with what's sitting on the line, but understanding why the data point off the curve is important.

00:47:25.780 And then being able to quickly, again, back to that, iterate the possibilities, say, ah, well, it's—if we know that it's not a problem with the instrumentation, then it's an indication that we don't understand something.

00:47:44.900 And so that was where I was already starting to get introduced because of this UAP stuff, because of that we had these groups of individuals who said that they'd gotten harmed by UAP.

00:47:56.700 And we diligenced them to make sure that they didn't have some sort of psychological problem.

00:48:03.760 They had full psychological workups.

00:48:05.900 And we knew that these were people that we're, you know, we're trusting the nation's security with.

00:48:10.760 You know, it's kind of like, okay, well, it's an anecdote, it's a story, and now I've heard 50 stories like this by that point.

00:48:21.760 Right, right.

00:48:22.860 And it's like, well—

00:48:23.760 No, they say the plural of anecdote isn't data, but the plural of anecdote is definitely hypothesis.

00:48:29.560 Yes, right.

00:48:30.540 And so once you start to get that, I was like, okay, well, there seems to be something here.

00:48:35.120 And you raised a point, you know, ruin your career.

00:48:40.100 I literally was told by a senior official at the National Cancer Institute by around circa 2014, 2015, because I was just talking about this, just saying, isn't this an interesting idea?

00:48:51.640 You're going to ruin your career, Gary.

00:48:53.960 And I was just like, but it's—but the data's on the table.

00:48:59.180 It isn't ridiculous to ask the question.

00:49:03.380 But the fact that they were trying to push it off the table incensed me.

00:49:09.340 It was just like, that's not how a scientist thinks.

00:49:12.420 That is just your—and I said to him, I said, you sound more like a priest than a scientist.

00:49:18.660 Maybe you should give your PhD back.

00:49:21.180 Oh, and—

00:49:21.840 Well, there aren't that many scientists, you know.

00:49:24.080 There are a lot of people who act out the role of scientists, but that's not the same thing.

00:49:28.980 Yeah.

00:49:29.520 Right?

00:49:29.740 Scientists are very peculiar people when they're real.

00:49:32.380 So, and that's been sort of my approach to it.

00:49:38.440 It's like, how dare you tell me I can't ask the question?

00:49:41.160 Because there's more than enough evidence that there's something worth studying.

00:49:47.400 And people mix up evidence with proof.

00:49:50.720 You know, data sits in isolation and has no meaning whatsoever.

00:49:56.020 It only has meaning in the context of a hypothesis.

00:49:58.800 And, you know, so does the hypothesis and the data match to mean that it is perhaps evidence?

00:50:07.640 Evidence, just as in court, is not proof of anything.

00:50:10.580 That requires a jury to decide whether or not the evidence is sufficient to, you know, manifest guilt or not.

00:50:17.620 The same thing in a paper.

00:50:19.980 There's very few papers that you will ever read that ever say there is, at least in biology, this is a conclusion.

00:50:26.800 There's all kinds of weasel words that we as biologists use to give ourselves diplomatic egress just in case.

00:50:34.420 So, but, you know, when people like Neil deGrasse Tyson say there's no evidence, well, that's just a lack of understanding of what the difference between data and evidence is.

00:50:47.000 There's reams of evidence.

00:50:49.540 There's libraries full of evidence.

00:50:51.300 There's books I could throw, I could drown people in with evidence.

00:50:56.140 But that's not a conclusion.

00:51:00.020 That's not what we think of as scientists as proof.

00:51:03.280 Now, I have, I'm of personally two minds.

00:51:06.480 As far as I'm concerned, there's definitely something going on that appears to be not human.

00:51:14.060 That's just my person.

00:51:15.380 Okay, so, okay.

00:51:16.480 But that's different than science, right?

00:51:19.760 I'm trying.

00:51:20.180 Yeah, right, right, right.

00:51:22.020 Go for it.

00:51:22.480 Okay, so tell me, well, tell me a typical story, like the typical story pattern that characterized the testimony of these leftover individuals whose symptoms were troublesome but somewhat anomalous.

00:51:37.140 Like, what were they reporting?

00:51:39.240 And then you took it seriously because there had been psychological workups done on them, and there were a number of people reporting the same thing.

00:51:47.380 So, you know that something's up.

00:51:49.900 So, tell me a story, and then tell me what you started thinking about with regards to a potential cause.

00:51:57.140 Well, one was a guy by the name of John Burroughs and the Randall Shum Forrest case, where he literally got close to one.

00:52:07.420 That came down near our nuclear storage facilities there.

00:52:12.500 It's a very famous case.

00:52:13.760 And he came to me as part of this group of 10 remainders, and I was introduced to him to do the blood analysis and do the collection of the blood.

00:52:27.760 And then later, as it turned out, and here's an interesting thing, later, he developed a heart problem, and he couldn't get the Veterans Administration to open up his file so that he could get – he could prove that – or that it might have actually been originally caused at Randall Shum in England because his medical file was deemed top secret.

00:52:52.720 So, we literally had to go to Senator McCain in whose state this guy lived in Arizona and get him to write a letter to the Veterans Administration forcing them to open his file so that he could get insurance payment for his heart condition.

00:53:14.400 It's all on the record.

00:53:15.400 It's all on the record.

00:53:16.860 So, why does an individual who had a problem that he claims had been, you know, caused through some interaction way back when, why do you have to make his file top secret?

00:53:29.280 What's in it?

00:53:30.960 There was nothing in it, frankly.

00:53:32.540 It was just somebody had decided it needed to be top secret because things related to UFOs just need to be, you know, nobody talks about them, brush them under the table.

00:53:41.900 But we literally – and it's, again, it's public record.

00:53:46.600 And so, what did he experience?

00:53:48.740 He saw something.

00:53:50.220 He came close to something, something that was about five feet across on the ground.

00:53:55.600 And I don't know.

00:53:57.320 I mean, I wasn't there.

00:53:58.040 I'm just relaying the story.

00:53:59.920 Right, right, right.

00:54:01.300 And was that a – what's the typical pattern of encounter?

00:54:05.220 You know, I mean –

00:54:06.060 Is there a pattern of the phenomenon?

00:54:07.840 No, no, no.

00:54:08.460 There's not enough of a – this is the problem, is that –

00:54:11.880 You can't repeat harm.

00:54:14.680 You know, when harm happens, it's sort of incidental.

00:54:19.380 And so, you just have to deal with – and I think it's less about the harm.

00:54:23.160 So, I mean, I think we should move away from a discussion of the harm and just talk more about what it is that people are seeing.

00:54:33.100 And I'm talking about credible people, right?

00:54:36.440 What's the credible data that we can collect?

00:54:39.980 What's –

00:54:40.340 Okay.

00:54:40.600 So, it's a broader conversation on unidentified aerial phenomena.

00:54:45.100 That's – so, sure.

00:54:47.660 And I want to talk about your Sol Foundation as well and also the fact that you've analyzed materials with unusual properties.

00:54:57.580 So, if we can tangle all that together, that would be good.

00:55:00.780 Yeah.

00:55:00.960 So, the reason why we started the Sol Foundation – and it was me, Peter Scafish, and David Grush.

00:55:07.740 David Grush was the gentleman who testified in front of Congress about what he claims were the reverse engineering programs.

00:55:14.480 And the principal reason for starting the Sol Foundation was to enable, let's say, a picket fence within which people of reasonable intelligence or academics, who don't always have reasonable intelligence, but could have a conversation and not be laughed out of the room.

00:55:34.160 So, to be able to say, here's a hypothesis, and here's the data I have.

00:55:39.020 Do you think my hypothesis matches, or do you have another idea?

00:55:42.620 But the spectrum of things about which we wanted to be able to talk about were everything from religion all the way through to material science on my side.

00:55:53.060 So, we have Peter Scafish, who's an anthropologist, and a – what is the other one?

00:56:02.320 Well, he's an – let's call him an anthropologist.

00:56:04.160 And so, he's interested in people's stories, right?

00:56:08.680 What are so-called experiencers?

00:56:10.560 What's the pattern of the experiencers?

00:56:13.980 And what kind of, let's say, trauma might they undergo?

00:56:18.020 Not only because of the experience itself, but the trauma of not being able to talk to your friends and or family about what it is that you think that you saw.

00:56:28.220 Because of the stigmas associated with talking about this and not wanting to be, you know, considered crazy.

00:56:34.440 And then – so, he's collecting and writing papers on that.

00:56:39.820 We have a focus on religion.

00:56:41.720 We had somebody from the Catholic hierarchy write a paper on that for us.

00:56:48.240 Two, on the more, you know, extreme science side, the hard science side, the materials analysis that I do.

00:56:58.940 And part of it, again, was to say, okay, let's have this conversation.

00:57:05.600 Let's – we had our first foundation meeting, I mean, big convention at Stanford where we had about 200 or 300 people there who'd come from all over the world.

00:57:19.400 What year was that?

00:57:20.340 That was three years ago now.

00:57:22.740 We've had one each year.

00:57:25.420 And the funny story there was about two weeks before we were to have the meeting, I started getting these pings from administrators around Stanford that there might be a problem.

00:57:37.760 And I was like, oh, God, you can't do this to me.

00:57:40.420 Everybody's invited.

00:57:41.360 The plane tickets are paid for, you know, et cetera.

00:57:45.100 What's going on?

00:57:45.980 And I managed to trace down who it was at Stanford that was sort of causing the trouble.

00:57:52.580 It turns out it was the branding office at Stanford.

00:57:55.880 And that they had a problem with that Stanford's name wasn't first.

00:58:01.080 That we had put Sol Foundation first and not Stanford.

00:58:04.060 And they wanted it Stanford, you know, and the Nolan Laboratory, not the Sol Foundation.

00:58:09.340 So Stanford was more than willing to, you know, to be upfront about it.

00:58:14.000 They were, you know, open about it.

00:58:15.980 In fact, the Alumni Association had me give, at the last homecoming, a big talk to probably about 200 people about it because of the interest level.

00:58:31.220 So there's been no problem on that front.

00:58:34.160 But then I then got interested in the materials because, again, through the connections that I had made, I came to know a gentleman by the name of Jacques Vallée.

00:58:46.880 Jacques Vallée is probably one of the most famous, let's call them, ufologists ever in terms of, like, his scientific prowess.

00:58:55.000 He was involved in the early days of the internet.

00:58:58.140 He was an astronomer.

00:58:59.200 He's a venture capitalist in the Bay Area.

00:59:01.580 And he's heretical in the sense that he didn't believe that whatever this was was necessarily extraterrestrial.

00:59:11.140 But it was some other kind of manifestation of either the human psyche or something more beyond, something almost, you know, paranormal in its capabilities.

00:59:28.880 So it was interesting to listen to this, but I was more interested in, you know, okay, well, what can I teach another scientist?

00:59:38.600 How can I convince another scientist?

00:59:40.260 So it turns out Jacques had a number of materials, metals and or objects that had been associated with landings of alleged UAP or UFOs.

00:59:53.220 And so I said, okay, well, give me some of them.

00:59:58.360 I need only tiny amounts and we can do pretty traditional analysis on it.

01:00:04.260 And so one of the things that I got a hold of, we showed recently to be, that was from a beach in Ubatuba, Brazil, that a fisherman had seen this object drop from some other, from this UFO.

01:00:23.160 And it was, it shattered and he picked up some pieces of it and it made its way through what I would consider to be a reasonable chain of custody.

01:00:32.520 And we measured it and we measured it and it was 99.999% silicon.

01:00:39.980 Okay, that's not hard to make today, but it's not something in the late 1950s or early 1960s, you drop giant pieces of all over a beach in Ubatuba, Mexico.

01:00:54.960 So it's, whatever that was, it was clearly an object of industrial purpose, right?

01:01:05.260 There's no 99.999% silicon anywhere on planet Earth.

01:01:10.000 It's all contaminated.

01:01:11.820 And I actually have atomic, I have an atomic map of one of these pieces that we developed, that we did with atomic probe tomography.

01:01:20.200 What was fascinating was that one of the two chains of custody that I obtained also had magnesium ratios that were not what you would expect from Earth.

01:01:36.000 They were different than the standard magnesium ratio.

01:01:39.060 So magnesium has three isotopes, 24, 25, and 26.

01:01:44.660 24 is like, let's just say, rounded up to 80%, and the other two are 9 and 11%.

01:01:53.580 Whereas the, one of the two chains of custody, the magnesium ratios were just higgledy-piggledy all over the map.

01:02:04.180 They had nothing, they didn't look anything like what you expect to find from a piece of silicon on Earth.

01:02:12.060 Anywhere you look on Earth, you're going to find silicon, sorry, the magnesium at the 80, 11, and 9 ratio.

01:02:21.540 Whereas this, one of these pieces was wrong.

01:02:25.680 That doesn't prove that it's a UFO.

01:02:29.780 It just proves that it's of some kind of manufacturing purpose.

01:02:37.000 So that's one.

01:02:38.100 We're actually writing the paper up on that one.

01:02:40.680 I published a peer-reviewed paper on another thing, another object from what's called Council Bluffs, Iowa, where, again, there were multiple witnesses.

01:02:51.520 In this case, even the police, had seen an object, and it seemed to drop something.

01:02:58.180 And when the people arrived, they thought actually it was a plane crash.

01:03:01.200 When they arrived, they found about 30 pounds of molten metal in the middle of a frozen field.

01:03:08.560 And I have the original Polaroids.

01:03:12.440 And so I just did an analysis of it.

01:03:14.580 And the long and the short of the analysis was there was nothing wrong with the isotope ratios, but it was a mixture of metals that nobody would normally put together.

01:03:24.460 It was not fully mixed.

01:03:26.680 It was only partially mixed.

01:03:28.660 So it's kind of like if you were to take chocolate, vanilla, and strawberry ice cream and partially melt them and just kind of turn your spoon a couple of times around.

01:03:37.120 Depending on where you looked, you'd find different ratios of chocolate, vanilla, and strawberry, as opposed to if you were to put it in a blender, everywhere you looked, it would look the same.

01:03:47.900 So what I found in the metals was that it was incompletely mixed.

01:03:52.820 Okay, so who would drop 30 pounds of incompletely mixed iron, titanium, and aluminum in the middle of a field for no good reason from something that looks like a UFO?

01:04:07.120 So all the conventional explanations that it was thermite, it's not thermite because there's no aluminum hydroxide, I checked.

01:04:16.820 You know, to carry that much molten metal requires, at that temperature, a cauldron that would be like half a ton to the middle of a field.

01:04:26.540 You're not going to put it in a plane.

01:04:28.640 So what is it?

01:04:30.420 Unexplained.

01:04:31.020 But the reason for doing it, and actually there's somebody who it looks like is going to give me sort of free money to analyze more of these things,

01:04:42.020 is not to prove that they're from UAP, but it's to do the right kind of analysis on the materials

01:04:50.600 so that I can get it out there and publish it with no conclusions, just here's the data and here's the story,

01:04:57.800 and here's the analysis as complete as we can do at this time.

01:05:00.980 Because maybe somebody else will look at it three years from now or some other enterprising student and go,

01:05:05.640 ah, that's how you would, if you released this, this would be the engine control for, I don't know, anti-gravity or something.

01:05:18.180 So it's, you know, it's part of that thing of like you come up with an intuitive idea because you've spread all of the data in front of you.

01:05:28.440 Well, if you don't have the data, you can't come up with the solution.

01:05:32.800 But if I can get the data out to as many people, maybe somebody else will come up with the hypothesis that unifies the story.

01:05:41.140 So it's part of like the, I mean, I think of it as the open source data approach or the open science where you get the data out for everybody

01:05:50.480 because somebody paid for it, so maybe you shouldn't keep it in your, you know, in your desktop drawer or these days in a folder on your computer.

01:05:59.840 Get the data out there so that other people can use it.

01:06:03.620 Does that make sense?

01:06:04.240 Okay, so, so far it makes sense.

01:06:07.760 I've got more questions.

01:06:09.060 So you started by assessing the medical problems of a small subset of people whose symptoms didn't fit the pattern,

01:06:19.040 but whose self-reported stories had their own characteristic and that their symptoms had their own identifiable characteristics.

01:06:28.780 Now, I'm not sure how you got from that to the soul foundation.

01:06:33.540 Now, my understanding is that because you had worked on that hypothetical alien corpse and debunked that,

01:06:45.740 and then you got involved with the CIA project, that more of these stories were coming your way?

01:06:51.160 Yes.

01:06:51.600 Is that?

01:06:52.440 Yes.

01:06:52.860 Okay, and so what other kinds of stories and tell us about the foundation itself and who's involved?

01:06:59.620 And then I'm also extremely curious about your conclusions.

01:07:04.420 I mean, I'm sitting here thinking, you're obviously studying anomalous phenomena.

01:07:11.780 Why would you make the, or have you even, derive the inference that, apart from the isotopes,

01:07:22.120 why would you derive the conclusion that extraterrestrial origin is the most likely culprit?

01:07:27.460 No, I never said that.

01:07:28.340 Culprit, or, okay, fine, fine, fair enough, fair enough, you didn't.

01:07:32.340 And so, well, that's exactly why I'm posing the question.

01:07:34.640 I'm not trying to corner you with that.

01:07:36.360 I want to know.

01:07:37.560 Like, you're studying anomalous phenomena.

01:07:40.720 You know of Charles Fort, by the way?

01:07:42.480 Oh, very well, yeah, yeah.

01:07:44.440 Yes, okay, okay, okay, yeah.

01:07:46.660 Did you ever watch Magnolia?

01:07:49.140 No.

01:07:49.760 The movie?

01:07:50.340 No.

01:07:50.940 Oh, Magnolia is a great movie, by the way, and it's about Charles.

01:07:54.780 Okay.

01:07:55.340 It has a sub-theme of Charles Fort.

01:07:58.120 So, if you're interested in Charles Fort, Magnolia is very much worth watching.

01:08:02.520 It's a great movie, also, beautifully put together musically.

01:08:05.600 And, of course, Charles Fort studied anomalous phenomena his whole life, and Magnolia happens

01:08:11.460 to be about that.

01:08:12.260 But, okay, so you're studying anomalies.

01:08:14.180 Lay out the realm of hypotheses, because there's military experimentation.

01:08:20.620 I mean, there's all sorts of obvious competing hypotheses.

01:08:23.460 So, tell me what you've gone through, more about your foundation, and what you've concluded.

01:08:30.300 So, the principal reason for starting the Sol Foundation was that I was, because of, let's

01:08:38.380 say, my public persona about this, more and more scientists were coming to me and saying,

01:08:46.500 hey, I want to help.

01:08:48.280 How can I do it?

01:08:49.140 And then a common friend of Peter Scafisch and I, along with David Grush, who I had met

01:08:58.140 through all of these events.

01:09:00.520 And David, again, was the guy who sat in front of Congress and testified about the alleged

01:09:05.140 reverse engineering programs of which he was aware.

01:09:07.740 And I'd met with Dave and spoken with him, you know, very deeply and watched every element

01:09:14.400 of his body language that I possibly could to see, you know, look for evidence of being,

01:09:21.200 of misconstruing him in some way.

01:09:24.520 And as far as I could tell, he's telling, at least as far as he's concerned, the truth

01:09:28.260 about what he knows.

01:09:30.200 And I said, okay, well, we need a more formalized way to approach this.

01:09:35.260 And so what do you do as a scientist in a new area?

01:09:38.980 You start a society, more or less, or you start a foundation that becomes the lead foundation

01:09:45.600 for other groups to come together.

01:09:47.940 And the Sol Foundation pretty much has established itself as a nonpartisan umbrella group through

01:09:56.840 which the many individuals who are interested in UAP and talking about it, you know, in a

01:10:03.200 professional manner, can come together.

01:10:06.180 And our next, actually, event is going to be historic.

01:10:08.700 It's going to be in Italy.

01:10:11.360 And we've got people from the European Parliament.

01:10:14.480 We've got a number of former, let's say, U.S. officials who will be there to talk about

01:10:22.280 these matters.

01:10:23.740 And again, it's, I don't expect a revelation.

01:10:29.020 I expect just from this, people to come and know that there's a place where they won't

01:10:37.220 be laughed at, but they can share and maybe give ideas.

01:10:40.960 And one of the sets of ideas of what's going on right now is there's a big movement for what's

01:10:45.880 called the UAP Disclosure Act that, for your listeners, for the last two years, Senator

01:10:53.360 Rounds and Senator Schumer, supported by multiple representatives on both sides of the aisle,

01:11:01.440 have put forward a part of the bill that goes into the Defense Department bill, 60 pages of

01:11:09.400 which talks about the reverse engineering programs and extraterrestrial or not, let's

01:11:15.180 say, not even extraterrestrial, non-human intelligence.

01:11:19.260 And that for, you know, the next five to 10 years, there will be an oversight group which

01:11:25.660 will collect and gather all of this information for potential benefit of humanity.

01:11:32.720 Now, you just asked me about ruining my career.

01:11:36.940 Would Senator Schumer, the head of the Democratic Party, and Senator Rounds, an important figure

01:11:44.920 on the Republican side, come out and make any of these kinds of statements or allow for their

01:11:51.340 offices to be the vehicles through which such a bill would manifest itself if they felt that

01:11:57.140 they were going to be derided on the floor of the Senate?

01:12:02.440 Probably not.

01:12:03.440 And so there's Marco Rubio has come out openly and talked about this.

01:12:11.180 He's now our Secretary of State.

01:12:13.620 There's 20 minutes of part of a film that he's in where he's openly talking about the fact

01:12:20.100 that there are these objects moving in ways that we don't know.

01:12:25.780 I was speaking with your producer prior to your getting to the set.

01:12:29.380 The Sol Foundation, one of our purposes, we put together press kits of like 15 different

01:12:37.100 snippets from former heads of the CIA, the DIA, NSA, President Obama, et cetera, all saying

01:12:44.920 there's something that we don't understand and is moving in ways in our atmosphere that

01:12:50.140 we can't explain.

01:12:52.280 And it appears to be technology.

01:12:53.660 Now, they'd like you to think that it's something out of Lockheed, perhaps, but these things were

01:13:02.380 being seen before Lockheed existed, right?

01:13:05.880 They were seen in World War II.

01:13:07.460 They were seen subsequent to World War II, long before we had any capabilities.

01:13:12.180 So what is it?

01:13:13.280 I don't care if it's human or not.

01:13:17.260 I just want to have reproducible findings.

01:13:20.460 And yet, somehow, for some reason, the government won't release the information that it has.

01:13:26.520 I mean, just recently, there was a Freedom of Information Act release of the so-called

01:13:32.960 Mosul Orb, M-O-U-M-O-S-U-L, Mosul, Iraq.

01:13:38.660 And a solid silver ball that Arrow, which is the anomaly resolution office of the Department

01:13:50.600 of Defense, came up and said, yeah, we see lots of these things.

01:13:53.960 The former assistant director of Arrow, which is the office programmed and set up by the

01:14:02.240 DOD to collect the kind of information around these anomalies, openly stated just three weeks

01:14:10.780 ago on a podcast that, yeah, we have videos of these black triangles that move in ways

01:14:18.380 that we don't understand.

01:14:19.940 Okay, if it's our technology and we can move in ways like that, why are planes still crashing

01:14:28.360 at Reagan Airport, right?

01:14:31.720 Why are we letting, you know, airplanes use fuel when we have some other kind of technology

01:14:39.140 that can move the way that these things can and is being kept a secret?

01:14:43.380 Is that just for defense?

01:14:45.120 So you talked about black triangles and silver orbs.

01:14:48.860 Can you go into a little bit more detail?

01:14:50.460 Like what, I'd like to know the central phenomena.

01:14:53.920 What, where do you think most of the signal resides with regard to these anomalous sightings?

01:15:00.120 What's the pattern?

01:15:01.080 The pattern, the best pattern are what it is that the military sees.

01:15:05.640 And those are the ones that where I have a focus and where actually I'm involved with

01:15:11.060 another group that's funded privately called Skywatcher.

01:15:17.160 And what we're doing is we've been setting up sensor systems in what we call cleared areas

01:15:24.340 where we know that there's no overflight.

01:15:26.520 And we do sometimes work in concert with the FAA and others to make sure where we're setting

01:15:33.220 up for repeatable measurements and sensor systems to see things.

01:15:37.200 And we're seeing stuff that doesn't make sense.

01:15:41.080 And so we're not coming to conclusions, but we're collecting the data.

01:15:45.980 And because I'm a scientist, I'm like their principal advisor to this group.

01:15:52.000 And we're setting up and doing the kinds of measurements that I think are necessary because

01:15:56.880 I'm not going to wait for the government.

01:15:58.800 You know, I'm not going to wait for daddy government to tell me what's right.

01:16:01.740 I'm just going to, I'm just, I'm a scientist.

01:16:03.060 I'm going to go out and do it myself.

01:16:05.280 And so that's what we've done.

01:16:07.200 And we've raised significant funds.

01:16:08.640 I mean, and you can go look up Skywatcher on the internet and what it is that we're doing.

01:16:14.440 And, and part of what we're doing is it's, it's, it's two purposes.

01:16:18.800 One is, it's basically aerial surveillance, partially just for drones, because we've seen

01:16:24.200 what drones can do in wars.

01:16:26.360 So, and we knew, and you know about the drone incidents in New Jersey, right?

01:16:30.520 And all of the hubbub that that caused.

01:16:33.100 Well, we were actually there.

01:16:34.660 We were actually measuring things.

01:16:36.820 What was that?

01:16:37.800 What was that?

01:16:38.640 Some of them were simply drones.

01:16:41.540 Some of them though, were moving in ways that would be hard to explain by drones, but all

01:16:47.380 the stuff that we observed close to shore was clearly human activity.

01:16:53.360 But so we're setting up, we're setting up Skywatcher as sort of a dual purpose.

01:16:58.760 One is to work with the government to hopefully, or defense contractors, or anybody who wants

01:17:06.240 to pay for our services to collect aerial data basically as rapidly as possible.

01:17:14.880 Because often you can't collect the data when there's an anomaly that shows up, I mean, our principal goal is protection of the United States.

01:17:29.460 But if in so doing, we happen to collect other information about some, let's say, anomalous objects, we will have the tracking data necessary to say, hey, well, this is, we don't understand this.

01:17:42.200 And it's important to know because if it isn't a human adversary who have capabilities that we don't appreciate, even if somewhere in Area 51, they have something that does that.

01:17:58.460 It's good for, I think, our military, the more public aspect of our military to know that these objects do exist and report them when you see it, because it might be the Chinese, or the Russians, or the Iranians, right?

01:18:13.880 You want to know this because if you ignore it, you could be ignoring the data point off the line that is important to know about.

01:18:25.280 So, always back to that, don't ignore the anomalies.

01:18:30.260 Because anomalies, just about every single Nobel Prize that was ever awarded in physics and chemistry or biology is because somebody paid attention to the anomaly.

01:18:40.140 Right, right, right.

01:18:41.760 Yes.

01:18:42.160 So, tell me about the patterns of anomalous activity that characterize, that define something as an unidentified aerial phenomena.

01:18:56.160 And then tell me what you've concluded as a consequence of your investigations.

01:19:04.200 And where you, yeah, let's do that.

01:19:06.440 So, there are, let's say, five characteristics of something that you would think of as an anomaly.

01:19:15.960 One is instantaneous acceleration and deceleration, right?

01:19:20.440 There's very few things that we know of that can go from zero to 5,000 miles an hour and then stop on a dime without squishing everybody on the inside, you know, sending them through the windshield.

01:19:30.320 So, when you see these things go from, in the case of the, I think it was the Nimitz or the Eisenhower, it goes from sea level to space in less than a second.

01:19:43.740 And they have the radar trackings of those things.

01:19:45.680 And now imagine the size of the object, let's say it weighs a ton, to instantaneously accelerate and decelerate at that level, it takes more than the, would take the energy of more than the nuclear output of the United States for a year.

01:20:02.740 Okay, so, where did you get that energy, first of all?

01:20:07.180 So, instantaneous acceleration and deceleration.

01:20:11.540 So, seeing things that do zigzags across the sky means that somebody or something has control, if it's going fast enough, and it's not doing an arc.

01:20:23.380 It means something has control of momentum and inertia.

01:20:27.580 You can, they can negate momentum and inertia.

01:20:30.560 So, that's an observation seen hundreds, if not thousands of times by pilots all over the world.

01:20:38.780 So, what does that mean about our understanding of physics, first of all?

01:20:43.160 So, that's one thing.

01:20:44.320 The other is no apparent flight services and no apparent exhaust.

01:20:50.280 So, no energy output.

01:20:51.800 So, you're moving and doing these things, and yet if you look at them with FLIR, which is a kind of infrared, you don't see any hotspots.

01:21:01.120 If you were to look at a jet, all you would see is the plume from the jet.

01:21:09.040 So, no flight surfaces, meaning, you know, basically, Bernoulli's principle is not at play here, right?

01:21:17.420 Which is basically how the wings work and lift.

01:21:19.900 So, Bernoulli's principle is not at play.

01:21:23.800 So, you're moving without a flight surface and without an apparent mode of inertia.

01:21:33.100 You're not putting something out so that you can move forward.

01:21:35.760 And then, the other one is what you would think of as what's called transmedium travel, meaning something that can go from the water to the air and then back again or to the air and to space.

01:21:51.240 We have nothing that can do something like that.

01:21:54.360 But recently, there have now been drones made and talked about openly, and actually, these are U.S. drones just shown on a – I saw on a military video recently where they can go – drones can be underwater, travel, and then come out of the water and go do the attack.

01:22:12.420 But that's only been developed in the last few years, not something from 50, 60 years ago.

01:22:18.600 So, those are the kinds of things that people see.

01:22:21.260 And again, it's – you know, you ask me what I think of as real.

01:22:27.680 Those anecdotes are, to me, stories, and why I get interested in the medical or the material side is it's something I can repeat.

01:22:39.240 I can't repeat these pilot observations, but I can repeat experiments on materials or experiments on – not experiments on human, but reading the humans who have been harmed.

01:22:51.600 Now, the thing about Skywatcher is that we, at least in a limited sense, have a signal that can be released that sometimes it seems to attract these objects.

01:23:10.520 And so, that's where the repeatability attempt is coming in.

01:23:14.120 Explain that a bit more.

01:23:15.320 So, there's a – because it's a company and I'm not the official spokesperson for it, and this is public information that's out there, is that there's a signal that an individual as part of Skywatcher had determined when he was working with the military not as – it wasn't his purpose to develop it.

01:23:38.820 So, he didn't take anything out, it was sort of a – he noticed something, and then he refined the technique, and now he knows that he has, let's say, an electromagnetic sequence that he can release that somehow seems to have these things, objects, show up.

01:23:58.160 And I was there when it happened.

01:24:00.020 We go out on these week-long events in, like, the middle of nowhere, and stuff shows up.

01:24:13.620 And, you know, some of it's been on – some of it's, you know, you can go find it on Twitter.

01:24:20.600 But the stuff that's on Twitter isn't good enough, in my opinion.

01:24:24.180 I'm more interested in the data that we're more recently collecting with better cameras and better sensor systems.

01:24:31.240 Because the idea is just to do the science.

01:24:32.960 So, I think what we'll do on the Daily Wire side, because we have to wrap this up in relatively short order, I want to close here by asking you what you've concluded provisionally as an explanation for this, like what hypothesis you're nursing.

01:24:54.020 And then, on the Daily Wire side, for everybody watching and listening, I'd like to ask you more about stories, about what you've seen, for example, when you've been on these Skywatch expeditions, and what the Skywatch program is reporting.

01:25:10.040 And then also to delve a bit more into the political, you talked about the – Schumer and – I don't remember the other senators.

01:25:20.360 Senator Rounds.

01:25:21.100 Rounds, this bipartisan proposal to declassify and make public –

01:25:28.760 Some of the information.

01:25:29.980 Narratives of sightings from pilots in particular.

01:25:33.580 Okay, so we'll delve into that more on the Daily Wire side for everybody watching and listening, so you can join us there for an additional half an hour.

01:25:40.820 To close up here, I think it would be useful for you to let us know, if you would, what the hell you think is going on.

01:25:51.700 And what this has done to you, too, this – I mean, this has got to kind of come out of the left field, so to speak, in a severe way.

01:26:01.580 And so, I imagine it's put a bit of a bump into your life.

01:26:05.300 I mean, maybe one that's mostly interesting, but still, you know, it's – to call it strange is to barely scrape the surface.

01:26:11.980 So, what are – what are you – what do you make of this?

01:26:16.240 That there's something non-human here, and it's been here for a long time, is my provisional conclusion.

01:26:22.480 And, you know, the question is not – that people should ask is not, is there something here?

01:26:31.600 You have to ask the question first, can there something be here?

01:26:36.560 And the short answer is, of course there can, because the universe is 14 billion years old.

01:26:41.700 You could have gotten from one side of the galaxy to the other in our galaxy in Elon Musk's Tesla if it were traveling at 10,000 miles an hour.

01:26:53.260 Right?

01:26:53.680 But what got on in the first place doesn't mean the same thing as what gets off on the other side.

01:26:59.700 So, yes, the short answer is something can be here.

01:27:04.920 What it is, I'm not 100 percent sure, and I feel very uncomfortable with the sightings of biological beings.

01:27:12.760 If only because they just look a little too much like us. 0.88

01:27:19.820 And I just can't see from a genetics point of view how – why the human form is so – or even, you know, two legs and two arms is necessarily biologically the most, you know, successful shape.

01:27:33.780 So, I think there's something here.

01:27:37.120 I think the data, the evidence of the hypothesis, there's more than enough evidence to say that it's worth investigating.

01:27:45.720 So, I would ask my colleagues to just hold their, you know, hold their sarcasm for a while.

01:27:54.080 Because how do you deny thousands of reports like this?

01:27:59.780 And, you know, I don't want to sound conspiratorial, but, I mean, I did get a phone call from somebody representing the White House because I was talking about something that they felt was a little too on the edge.

01:28:14.760 They said, you need to just shut up, Gary.

01:28:18.260 I mean, I'm just telling you.

01:28:20.500 I mean, I've briefed Canadian Parliament.

01:28:25.360 I went to your Parliament in Toronto, and I briefed all three parties on it.

01:28:32.220 I only didn't – the only ones who didn't want to hear anything were the separatists.

01:28:38.040 So, interestingly.

01:28:39.660 But, no, I spent two days there on that.

01:28:43.020 And we've briefed the European Parliament as well on it.

01:28:47.660 And they're aware of what their own – some of their own military are talking about.

01:28:53.240 So, that – I conclude that there's definitely something here.

01:28:57.820 But I think the more interesting conclusion is if they are – if something is here, it's likely been here longer than humans have even been civilized.

01:29:09.100 So, it really opens the question – and actually, it's something that I think Charles Fort actually said – is, you know, Earth is probably somebody else's property.

01:29:21.120 Well, that's a hell of a place to end.

01:29:25.120 So, I think we will end there.

01:29:26.800 For everybody watching and listening, we're going to continue our investigation on the narrative side and the political side behind the paywall at Daily Wire.

01:29:36.120 And so, if you want to join us there for an additional half an hour, that would be good.

01:29:40.280 Thank you very much, Dr. Nolan.

01:29:42.280 That was interesting, to say the least.

01:29:47.280 It's very difficult to know what to make of it, obviously.

01:29:51.860 You have an incredibly credible background and a very wide-ranging mind.

01:29:56.840 And it's very fascinating to see your reaction to this set of circumstances that have come your way.

01:30:07.980 And thank you for sharing out what you've learned with us.

01:30:12.280 And to everybody watching and listening, thank you very much for your time and attention.

01:30:17.080 We'll continue on the Daily Wire side.

01:30:19.320 Join us there.

01:30:19.980 We'll see you next time.

01:30:28.020 Bye-bye.

01:30:29.140 Bye-bye.

01:30:30.780 Bye-bye.

The Jordan B. Peterson Podcast - July 17, 2025

563. “Something Non-Human Has Been Here A Long Time” ｜ Dr. Garry Nolan

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