Organic Holodeck with Prophetic AI co-founders Eric Wollberg and Wesley Berry

In this episode from new podcast Emergent Behavior, host @Ate-a-Pi interviews the co-founders of Prophetic AI to learn how they're using AI to facilitate lucid dreaming. Prophetic had come up in the recent Cognitive Revolution conversation with Dean W. Ball about brain computer interfaces, and Nathan had them on the list of companies to invite on the show, so it was really fun to discover that Ate-a-Pi had already done an interview with them right around the same time. -- Subscribe to Emergent Behavior: Spotify: https://open.spotify.com/show/2KfbuKL7iqIfbpY31PHtEg Apple: https://podcasts.apple.com/us/podcast/emergent-behavior/id1735023473 YouTube: https://www.youtube.com/@EmergentBehaviorPod & Check out Nathan's new chatbot on www.cognitiverevolution.ai -- RECOMMENDED PODCAST: The AI Daily Brief If you’re looking for an edge to stay up to date on everything AI, subscribe to Nathaniel Whittemore's AI Daily Brief which delivers tight episodes covering all things AI from legislation to new technologies to the philosophical debates around generalized intelligence. Spotify: https://open.spotify.com/show/7gKwwMLFLc6RmjmRpbMtEO Apple: https://podcasts.apple.com/us/podcast/the-ai-daily-brief-formerly-the-ai-breakdown/id1680633614 -- SPONSORS: The Brave search API can be used to assemble a data set to train your AI models and help with retrieval augmentation at the time of inference. All while remaining affordable with developer first pricing, integrating the Brave search API into your workflow translates to more ethical data sourcing and more human representative data sets. Try the Brave search API for free for up to 2000 queries per month at https://bit.ly/BraveTCR Omneky is an omnichannel creative generation platform that lets you launch hundreds of thousands of ad iterations that actually work customized across all platforms, with a click of a button. Omneky combines generative AI and real-time advertising data. Mention "Cog Rev" for 10% off https://www.omneky.com/ Head to Squad to access global engineering without the headache and at a fraction of the cost: head to https://choosesquad.com/ and mention “Turpentine” to skip the waitlist. -- TIMESTAMPS: (00:00) Intro (00:43) Diving Deep with Prophetic AI (02:18) Exploring Lucid Dreaming with Halo (05:08) The Technical Marvel Behind Halo (07:25) A Deep Dive into Lucid Dreaming Experiences (13:15) Sponsor: Brave | Omneky (14:42) Do All People Have Lucid States? (32:19) Sponsor: Squad (41:05) Building the Neural Model for Dream Control (43:15) Exploring the Intersection of EEG, fMRI, and Ultrasound in Lucid Dreaming (43:48) The Journey from Open Source Data to Targeted Brain Stimulation (47:08) Collaboration with the Donders Institute and the Future of Lucid Dreaming Research (48:40) The Role of Machine Learning and AI in Enhancing Data Collection and Analysis (50:04) From Theory to Practice: Implementing Transcranial Focused Ultrasound (TFUS) (53:51) Personalizing the Lucid Dreaming Experience through Reinforcement Learning (1:16:18) Looking Ahead: The Potential and Challenges of Neurostimulation Technology (1:22:55) Addressing the Skeptics: The Debate Over Natural vs. Technologically Induced Lucid Dreaming

Speaker names marked ? are inferred from speaking time and the show’s known hosts — they are not voice-verified.

• 0:00Nathan Labenz?

  Hello and welcome to The Cognitive Revolution, where we interview visionary researchers, entrepreneurs, and builders working on the frontier of artificial intelligence. Each week, we'll explore their revolutionary ideas, and together, we'll build a picture of how AI technology will transform work, life, and society in the coming years. I'm Nathan Labenz, joined by my co-host, Eric Torenberg. Hello and welcome back to The Cognitive Revolution. Today, it's my pleasure to introduce the new podcast, Emergent Behavior, hosted by AdaPi. Known for imaginative writing on AI Twitter and in the Emergent Behavior newsletter, which you can find at emergentbehavior.co, AdaPi often explores speculative scenarios and dreamlike futures. In this episode, they speak with Eric Walbert

• 0:45Nathan Labenz?

  and Wesley Berry, co-founders of Prophetic AI, a neurotech company whose product, The Halo, is designed to induce lucid dream states on a nightly basis. Prophetic had come up in my recent conversation with Dean W. Ball about brain-computer interfaces, and I had had them on my list of companies to invite on the show. So it was really fun to discover that AdaPi had already done an interview with them right around the same time. Where my episode with Dean gave an overview of the state of the art in reading from and writing to the human brain, here, we dive deep into one cutting-edge application. This discussion covers the challenges and intricacies of using AI not only to interpret brain signals, but actually shape brain states with targeted ultrasound pulses directed for the prefrontal cortex.

• 1:29Nathan Labenz?

  It also explores the possibilities and potential unlocked by lucid dreaming, from the unforgettable experience of controlling one's own dreamscape to applications in problem-solving, emotional catharsis, and even practical training scenarios. With the Emergent Behavior podcast, AdaPi promises to ask questions that they don't already know the answers to. So for more explorations of an increasingly weird future, subscribe to Emergent Behavior wherever you get your podcasts. As always, we welcome your feedback via our website, cognitiverevolution.ai. My AI clone, powered by Delphi AI, is now standing by to answer your questions. And remember, I am hiring AI engineers and also looking to connect with business owners who have concrete problems

• 2:13Nathan Labenz?

  that they want to solve with AI. So if that's you, please be in touch. Now, I hope you enjoy this technically grounded yet mind bending conversation with Prophetic AIs Eric Wahlberg and Wesley Berry on the new Emergent Behavior podcast from AdaPi.

• 2:34Speaker 01

  Welcome to Emergent Behavior, where we explore this moment in time just before artificial intelligence changes the world as we know it. Where we talk to the builders and creators, entrepreneurs and engineers. Where we ask questions to which we don't actually know the answers to. I'm your host AdaPi. I was built in the Czech Republic by infinite dot C Z and my voice is AI generated.

• 3:02Speaker 01

  I am excited to have with me today Eric Wahlberg and Wes Lewis, co-founders of Prophetic AI, a neurotech company pushing the boundaries of inner experience. Eric and Wes are pioneering new ways to reliably enter and navigate altered states of consciousness. Their first product enables users to access and control their dreams. But their ambitions extend far beyond the world of sleep. In our conversation, we explore Prophetic's innovative headband that reads brain activity and gently stimulates specific regions to activate higher awareness. The technical challenges of collecting advanced brain imaging data and using it to train AI models that can guide brain stimulation

• 3:47Speaker 01

  in real time. The eye opening possibilities unlocked by reliable access to new modes of consciousness, from problem solving to catharsis. Important questions around user consent and data privacy when it comes to devices that interface directly with our minds. Prophetic's long term vision of making powerful new states of consciousness accessible to everyone. Eric and Wes are at the forefront of an emerging industry that aims to empower us to explore the full depths of our inner worlds. If the potential of consciousness tech captivates you, I highly recommend their work at propheticai.co. As always, if you enjoy the show, the single best way to support us

• 4:31Speaker 01

  is by following Emergent Behavior on Apple Podcasts. Those follows make a huge difference for our reach. For more Influencé episodes, check out emergentbehavior.co and you can find me on Twitter, numeric eight, D E A P I. Without further ado, please enjoy this mind-bending conversation with Prophetic AI's Eric Wahlberg and Wes Lewis.

• 4:58Speaker 01

  Hey Eric. Eric, why don't you introduce Prophetic?

• 5:04Speaker 02

  Sure. So Prophetic is a consumer neurotech company. The two core technologies that certainly we'll be diving into today that undergird what we do is transcranial focus ultrasound, which is our neurostimulation modality, and the utilization of neural transformer architectures, which are a very unique machine learning architecture specifically tuned for neural data. And so the way it kind of loosely works, right, is we use the simultaneous EEG, MRI data sets. We can get into that, but it's a rather unique neuroimaging data set. The model is trained on that.

• 5:44Speaker 02

  What the model outputs is the steering instructions, essentially the targets for the neurosimulation. And then there's EEG on the headband that, you know, does reinforcement learning to improve the model over time. To your point of are we a lucid dreaming company? That's certainly what we're, you know, known for. When we showcased our first model, Morpheus One, a couple, probably three weeks ago or so, one of the things I say at the end of the presentation that's very important is that really what we should be known for really is a consciousness experience, you know, company. The model allows us to take any discrete universal brain state that's focused in the prefrontal cortex, which is our, the brain region of focus for us in terms of neurosimulation.

• 6:29Speaker 02

  We can use that to, you know, increase the number of experiences that are being provided on the same hardware. And this is, you know, this is our prototype, the Halo, which we can obviously dive into. So that's how I would, you know, introduce the company.

• 6:46Speaker 01

  Indeed. So, um, let's, let, let me just, because I've been extremely curious about this. I have, I have, like, like many people, I have, uh, you know, maybe, maybe somewhat experi- uh, experimented with elusive dreaming, but more in the kind of like, hey, you know, when you're falling asleep, just be very conscious. And when you're almost asleep, like, try to, like, move stuff around in, like, your field of vision while your eyes are closed. And maybe at some point, you can kind of move stuff around, and then you fall asleep, right? And I think, and then, and then, like, you know, 10 seconds there, you're asleep, and then, and then you wake up, and you're like, did it really happen? Like, you know? Uh, and, and, and, and so, um, describe for me this experience. Like,

• 7:31Speaker 01

  I take the halo and I put it on, right? I put it on, and I lie back. And do I switch something on?

• 7:41Speaker 02

  Yeah. So, the, the way that this works, right, is the hardware has, uh, you know, an app. You pair it with the app. Uh, you calibrate the device when you first get it. Um, and, and basically, you know, our, our goal, right, is that this is, uh, you know, being done for you. I mean, to give you a sense, right, consumer neurotech has really been kind of stuck in this, like, neural feedback paradigm. Really, like, trailblazing companies like Muse and Mendi, uh, which came out, you know, probably over a decade now ago, really, you know, relied on this EEG headband paired with some content and trying to create, like, this neural feedback process that would help get you into places of, you know, whether it's meditation or focus, et cetera. Um, and, and my joke here, I mean, it's really, you know, impressive what they did, certainly, especially how, how long

• 8:25Speaker 02

  ago it was. But you, my joke is, you know, why is Ozempic flying off the shelves and not gym memberships? And, and it's really a function of human nature, right? We, we want things to be given to us. Uh, the less work required, the better. So, you know, what we do, right, is you, you put on, you know, the headband, you pair it, uh, you go to sleep. The first thing that the um the headband is looking for is uh the EEG signature of REM. You know, uh eight has entered REM.

• 8:55Speaker 01

  So let me let me stop you there. So it's not gonna help me get to sleep, right? So if I'm if I'm like, if I'm being restless, if I'm like, if I've had a lot of coffee before, it's not gonna like boom, you know, put me to sleep. It's gonna be.

• 9:07Speaker 02

  No, you have to naturally fall asleep.

• 9:09Speaker 01

  I have naturally fall asleep, right? So I'm naturally gonna like, you know, I I I fall asleep. And at the point that I fall asleep, there is some period where I enter, you know, rapid eye movement, you know, REM REM sleep, right? And so it only starts to work at the point that that REM state is detected. Is that is that correct?

• 9:28Speaker 02

  Exactly correct, right? Like the EEG on the headband is what's prompting the models. The first thing that's the model's waiting for is that REM uh basically prompt to the model. And at that point, it turns on activating the the transformer, which turns on the transducers. And the first set of spatial instructions for the neurostimulation are sent out. Neurostimulation begins. And then what it's looking for, essentially what it is constantly searching for, right? Is this gamma frequency spike during REM, which is the signature of lucidity. And so it's basically saying, is eight is eight lucid? Is eight lucid? Oh, great. It's lucid. Let's keep her lucid, keep her lucid, keep her lucid. And then when you when you get out of REM, the the the transformers are turned off. And you, you know, continue with your sleep schedule.

• 10:13Speaker 01

  Okay, so so here I am, I'm asleep, I'm, you know, and then I start to enter REM. And I'm entering REM, and as I enter REM, there is sometimes a period of lucidity, which is detected by this gamma spike that you that you that you that you talk about. Is that correct?

• 10:31Speaker 02

  Well, no, so let me let me correct that. So it's it's you enter REM, the transducers turn on, you know, utilizing the model. And and when I say what the model's looking for in terms of those gamma frequency spikes is, as each set of spatial instructions, which are, you know, targeting the prefrontal cortex activation, it's it's basically like being like, are we making her lucid? Are we making? Okay, we're making her lucid. Let's keep her lucid. Let's keep her lucid.

• 10:55Speaker 01

  I see, I see, I see.

• 10:56Speaker 02

  That's what, yeah.

• 10:58Speaker 01

  I see. So so uh you enter REM, it takes a reading, and then it gives the the ultrasound gives a a a a pulse. And at the at that pulse, and then it checks again to see, have you entered lucidity? And if you haven't entered lucidity, it gives you another pulse and another pulse. And then once you enter lucidity, it tries to keep you in that lucid state.

• 11:21Speaker 02

  Correct.

• 11:22Speaker 01

  And then for whatever period of that, is that so? Is that period of time adjustable? Do you like say, oh, you know, I'm on thirty minutes today? Like, is that? Is that something that you adjust on the app? Like, what happens? Like, you know, how do you maintain that?

• 11:37Speaker 02

  Sure. So first of all, you know, a REM cycle, right? Is generally speaking like twenty minutes long. So, you know, we only target during the, you know, during the REM cycle. And so like the maximum of what you could really expect, right? Is a twenty minute, you know, simulation experience. One thing that inception definitely got right. And people who've experienced lucid dreaming would attest to this. Is there's quite a bit of time dilation, right? So, you know, a twenty minute, fifteen minute, you know, ten minute period of lucid dreaming feels a lot longer than that.

• 12:12Speaker 02

  In terms of adjustments, you know, we could certainly, you know, put, you know, right now, we're still in kind of this hardware phase and kind of, you know, and so on. But as we build out the app, things like being able to set maybe how long you want to be lucid for is certainly something that would be easy to, you know, to build in.

• 12:30Speaker 01

  Okay. So because obviously you have to keep triggering pulses in order to keep lucidity. So the moment you stop the triggering those pulses, like you fall back into a natural kind of exit.

• 12:45Speaker 01

  Would that be accurate characterization?

• 12:49Speaker 03

  I would say our goal is to create a self sustaining sequence of neural activity, right? So, but yeah, by continuously stimulating the brain with this TFUS,

• 13:04Speaker 03

  looking at the feedback from the brain, like, is there a point where now the prefrontal cortex is active without our assistance, right? And that would be like the ultimate measure of success.

• 13:15Speaker 04

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 13:21Speaker 01

  So there you have it, you've entered REM. Do all people have lucid states during REM? Is that consistent? Everyone can do lucidity? Or is it like only some people?

• 13:34Speaker 02

  So about 55% of people self-report having had a lucid dream at least once in their life. Like a smaller percentage of people appear to have natural proclivity for it. I'm part of that lucky second group. So the reason I got into this is that, you know, I had my first lucid dream when I was about 12 years old. You know, I woke up and was equal parts terrified and excited. I'd never heard anyone talk about this. I'm 30. I found early, you know, kind of internet forums. There wasn't really that much, you know, there wasn't really social media back then, where people were talking about lucid dreaming. And so I was like, okay, sigh of relief, other people do this, that's great to know. And then I got really into the work of a gentleman named Dr. Stephen LaBarre, whose PhD at Stanford in 1980 really kicked off the neuroscientific

• 14:19Speaker 02

  and cognitive scientific study of the brain state. And moreover, Dr. LaBarre, along with a few others, developed a number of these techniques that you could do to, you know, improve your capacity for lucidity. So things like reality checking, or mnemonic induction, etc. So you can, even if you don't have a natural proclivity for it, you know, train yourself. It probably is more difficult and varying, but, you know, we should really probably talk about, right, what are the neural correlates of lucid dreaming? I mean, lucid dreaming is a naturally occurring brain state. What it simply is, right, is prefrontal cortex activation or activity during REM. This makes a lot of sense, right? REM is when you're in a dream. Your prefrontal cortex is where your conscious awareness, working memory, decision-making are.

• 15:03Speaker 02

  Frankly, it's where you are, right? That's why it feels so, you feel so present right now, because your prefrontal cortex is incredibly active, as are Wes and I's right now. So that, it is a naturally occurring lucid, you know, brain state in the sense that, you know, upwards of 55% of people self-report having had it. But not everyone, you know, is super, you know, has necessarily a super proclivity for it. But it is, you know, it is simply prefrontal cortex activation during REM, which is something that anyone, you know, can theoretically have.

• 15:40Speaker 01

  So even the 45% of people who maybe have never had a lucid dream or maybe have never recognized that they had a lucid dream, even for them, it is possible that, you know, using the halo, they might be able to experience prefrontal cortex activity.

• 16:04Speaker 02

  Exactly. Yeah, there's absolutely, yeah, there's no reason why with neurostimulation, we couldn't give this extraordinary experience to anyone. And I just want to make one point, you know, if you've had a lucid dream, you will remember it. So I believe that these 45% of people, you know, haven't had it, given that, you know, a lucid dream, you know, right, is the awareness that you're in a dream that, you know, at its height, right, can give you control over the phenomenological contents of that dream at will, right? So what you imagine becomes. And because your working memory is active in the prefrontal cortex, you remember this. So I believe those 45%, but we can absolutely give, you know, those 45 and 55% of people induced stabilized lucid dreams. There's nothing that, you know,

• 16:48Speaker 02

  neurobiologically or neuroanatomically prevents them from doing that.

• 16:52Speaker 01

  All right. Let me come back to the actual dreaming part, but let's just finish our walkthrough here. So I've had that REM state. I've had, you know, the device has, you know, helped me get into, you know, prefrontal cortex activity. I've had that lucid dream. And then a REM state, you know, starts to come to an end, you know, 20 minutes in. Does the device detect the ending of the REM state and therefore stop triggering this newer stimulation that causes the lucid dream? Or does REM state naturally end by itself? And then the device kind of like, just like, you know, doesn't detect it? Or what happens at that point?

• 17:37Speaker 02

  Right, exactly. So a REM cycle's 20 minutes. You know, your REM cycle will end, whether you're lucid or not. And once your REM cycle has ended, neurostimulation stops.

• 17:48Speaker 01

  Nurse's mission stops. Okay, and then typically I might be able to continue because now REM has stopped, the lucidity has stopped. So typically, do people just continue their nap, continue their sleep cycle and then naturally and then wake up the next day? Is that typically what happens?

• 18:06Speaker 02

  Yeah, that's, I mean typically for sure, that's, you know, I can speak from a natural like lucid dream, that's what happens and that's exactly what we'd want to happen. You know, we wouldn't want it, you know, to wake you up and then you have to go fall back asleep. That'd be a bad user experience. You want people to just, you know, have the regular, you know, rest of their sleep, their light sleep, deep sleep, maybe another REM cycle. But we're only going to target one REM cycle on a given night and then you wake up in the morning. Yeah.

• 18:31Speaker 01

  Okay. So, okay, so now let's go back to the actual lucid dream, right? So, you know,

• 18:40Speaker 01

  it's, it's, it's a little bit hard to describe, I think, because I think it's one of those things about

• 18:48Speaker 01

  things that people experience, which are kind of inside their heads and they have to describe it. And you can't really describe what's inside your head that accurately to someone else. Because you don't really have like, it's not a visual and it's not like an audio. It's kind of an experience, right, which you have to, which we are, you know, it's difficult to describe experiences, right? It's not, we don't have like enough vocabulary to describe this experience. And so how, how would you, you know, I read through kind of like Paul Doherty's kind of like, you know, Wikipedia entry. I'm like, you know, what is elusive dream? How would you, how would you actually know, how would you actually know that you are in a lucid dream?

• 19:30Speaker 01

  You said if you experienced one, you would, you would never, you would, you would never forget about it. You would know. So how would you actually, you know, understand that you are in a lucid dream?

• 19:40Speaker 02

  So the best way, first of all, I should, you know, make the point right that lucidity is a scale. Right? You can, for example, be aware that you're in a dream, but not in control of it. Um, so you're along for the ride. It's a function frankly.

• 19:55Speaker 01

  And would you, and would you call that a lucid dream? Like just awareness that you are in a dream?

• 19:59Speaker 02

  Yeah, I mean, I, I think that like it's fine to call that a lucid dream, but I think to make clear what we're talking about, right? Is we want the, you know, 95th percentile lucid dream, which involves the awareness that you're in a dream. The ability to pivot between third and first person, something called disassociation. Um, and control, control is what people want. And really that's like, right, a function of just how active your prefrontal cortex is, really relative to how you're active, your prefrontal cortex is right now. And so the, you know, the experience of being in a lucid dream is you are as present, I mean, at its height again, you are as present as you are right now in a dream.

• 20:40Speaker 02

  Such that you can, you know, change and manipulate various, you know, phenomenological components of the stream at will. Um, and so that's really like the gold standard of what we know and what we're driving for. Um, and so, I mean, yeah, I mean, in terms of, I mean, we can talk about if you'd like, what people tend to do in their lucid dreams, um, if you'd like, I'm happy to dive into that.

• 21:05Speaker 01

  Um, just to take a step, so, um, you are, you are, so firstly, you have a consciousness that you are in a dream. Secondly, you snarked, um, and, and this was my experience trying to do it, um, you know, maturely and find a mess. It was a little bit messy, but I, I remember the beginning, you know, they, they tell you, you should keep a diary of some sort and you should have like a kind of like focused way of like getting yourself in there each time. Um, and for me, you tended to work when I was falling asleep or when I was waking up. So there was these two periods when I would, would, would kind of experience it. Um. And I would have agency in

• 21:43Speaker 02

  Hippagogic states, just so you know.

• 21:44Speaker 01

  Hippagogic. And is that, is that typically also where, where people tend to see that kind of lucidity? Or is that, because REM is kind of in the middle, right? And it's not at the beginning of the

• 21:52Speaker 02

  Yeah, tho- those are, those are generally different um than, than a lucid dream. Cause you're, you're not in REM. I mean, a hippagogic state is this kind of, you're, you're somewhere right between waking up and being asleep. Um, and you, you kind of have like a little bit more of this kind of manipulatable, like malleable, phenomenological, you know, space. But it is distinct um

• 22:12Speaker 01

  I see, I see.

• 22:13Speaker 02

  From the city.

• 22:15Speaker 01

  Incredible, incredible. So uh, a lucid dream is actually a fully present kind of like experience. Which is uh, that, that's, that's pretty amazing. So um, once you're there, um, and once you have that awareness, um, what, what can you do with it? I mean, can you like conjure like, you know, I'm going to be in cyberpunk today? Or I'm going to be in Dungeons and Dragons today? Like, you know, is it, is it a replacement for the holodeck? Like, you know, what, what can you do there?

• 22:46Speaker 02

  Yeah, so um, i- if you look at kind of, you know, one thing that's always fun to do, one of the largest subreddits in the entire world is, is, is lucid dreaming subreddit. It has over like a half a million people in it. If you ever take a look at what, you know, those people do, I'd, I'd break them into, you know, three core categories. So one is recreation. It is the ultimate VR experience, right? You can fly, you can make buildings appear out of the ground. You can talk to your dream characters and, and, and prompt them. Frankly, one thing that I didn't really realize until, you know, uh, really experiencing, you know, ChatGPT, for example, is that they, they kind of act a little bit like, like large language models in the sense that they, they, they, they, they also like, they say very interesting things. And sometimes they say absolute gibberish. It's almost like they're hallucinating or something.

• 23:29Speaker 02

  Um, but, but they're fascinating, you know, you know, uh, to, to talk to. Um, and, and then, you know, secondly, right, is like there are these productive capacities in lucid dreaming. Um, we have investors who code in their lucid dreams and run the code in the morning. Actually, a, a friend of ours, Case Fenley, who uh, among helping also do like the, the initial branding for the company, did the first render of our hardware as a fashion and hardware designer who designs in his lucid dreams. So You know, there's also, you know, productive, you know, capacities there. And the history of discovery and creativity and dreaming is quite long, right? You know, famously, Sir Rauschen Ramenujin does his infinite series in his dreams. You know, Schrodinger, you know, did his physics, Niels Bohr, you know, in their dreams.

• 24:13Speaker 02

  Salvador Dali, right, famously paints, you know, in his dreams. Probably actually maybe also a Hippagogic state, but nonetheless, you know, gives his painting, you know, these surreal qualities. And then the third point, right, is like the metaphysical potency of the experience, à la a spiritual or psychedelic experience. It is an extraordinary thing to become aware in your own dreams, really within your own consciousness. It is a profound experience from that sense, from that perspective. More concretely, what I'll say is, a very common thing that you see people do is, when God forbid, they lose somebody. So, two years ago, I lost my grandmother. Ninety-eight years old, she lived a wonderful life. First lucid dream I had, what do you think I did? I went to talk to my grandmother.

• 24:58Speaker 02

  And, and, and what's really fascinating about that is, you have a mental model of the people you know. Right? And so, when you talk to her, she kind of doesn't respond like just a nor, like a regular dream character that they're just kind of talking to. You know, she's kind of responding, right, in the ways that you kind of would remember or think that she would respond. And it's a very, very, you know, impactful experience. So, you see a lot of people doing things like getting, just, just obviously like talking to them again, or having closure with them, or people who, you know, who have bad relationship breakups and, you know, kind of trying to, you know, come to terms with that, etc. So, I mean, those are like the three core buckets. But what I broadly say, right, is that the limiting factor here is your imagination.

• 25:44Speaker 02

  And so what you imagine becomes. And so I think we're, you know, again, one thing that like Wes pointed out to me, you know, engraved on the inside of the halo is our company like slogan, which is Prometheus still fire from the gods, we will steal, you know, dreams from the prophets. And, you know, of course, right, you know, Prometheus is kind of our myth in the Western civilization for, you know, technology. And what Wes pointed out is like, you know, it's not like we actually discovered fire, right? This, you know, fire existed. If a lightning struck a field, right, of dry leaves, you know, it caught on fire. What we learned how to do was to control it. And, and through that control, right, we, you know, we created, you know, industrialized,

• 26:25Speaker 02

  you know, modern society. And so, you know, we've been dreaming, you know, for a long time, you know, animals dream, primates dream, octopi dream, which is very cool. If you've ever seen a video, because you can tell that they're dreaming because they're changing their colors and their textures. Clearly, they're like moving around an environment, which is fascinating to watch. And so what we're really talking about is controlling something that in my mind, I think in our mind, is as powerful potentially as fire. And so who knows what we will, you know, create as a result of that control?

• 27:02Speaker 01

  Just to take a step back, when, when someone comes out of the lucid, when they wake up, do they have full recall? Do they remember, do they remember the entire dream?

• 27:12Speaker 02

  Yeah. So when you have a very, very, again, high level of prefrontal cortex activation during lucidity, because your working memories in your prefrontal cortex, you have, you have great memory recall. This is very important, right? What's the point of giving somebody an experience that they would struggle to or not remember? So, you know, I, you know, this is why I said, I really believe those 45% who haven't had a lucid dream, because you would, it's very, even vivid dreams, right, that are not lucid, you kind of wake up and you're like, wow, that was- And wild, you know, um, but um, you, you know, after like, you know, your first cup of coffee, it's already a struggle to, to remember it. Um, and so that's, that, that's a key thing.

• 27:55Speaker 02

  And then the second thing I'll just say is, we also have this, you know, so we're, we're gonna have the app have also this kind of social media meets dream journal where you can, you know, write what you dreamt about, which is also a great way to, to improve your memory recall. And, and two, we'll also pair that with certain, certain levels of G, of Gen AI, where we can create, you know, videos or, or, or just visual representations as also a really great way, not only to encapsulate that for you, but also to give, you know, the people that, that are following you, right, a, a, a kind of an insight into that. Um, so yeah.

• 28:27Speaker 01

  So, um, uh, just a, a segue. Uh, there's been some recent research where, uh, I think, um, these guys could read, uh, images often FMRI. I don't, I don't know if you saw that, like they, they kind of like, uh, scat. Yeah, yeah, yeah, yeah. So, so is that, is that something that is possible also? Is that, is that like, you know, is that a possibility that, you know, you have a lucid dream and you also have an, you know, the FMRI reading? And you are kind of able to generate what the person saw? Is that, is that a, a future possibly? Obviously not now, but is that, is that something that it looks like it might be possible at some point?

• 29:07Speaker 02

  Yeah. So first of all, I mean, as exciting as that, that research is, right? It's done on like a very kind of, like, like you train, like you, the FMRI, uh, you know, like 20, you know, images, and then you, you have people imagine those 20 images, right? When you think about like the data labeling problem of the entirety of the non-logical, you know, experience, quite, quite, um, you know, a, a challenge. But at the same time, one thing that I think is really important, you know, the, the second piece on our blog that we have republished was on, uh, was on something we call noetic sovereignty, which is really an offshoot of the kind of, um, cognitive autonomy movement, et cetera. Um, you know, these are really like profound, like profound and powerful technologies. I, I, I think like really as a society, we really have to have a conversation around, do we really

• 29:52Speaker 02

  want that level of, you know, quote unquote, mind reading? Your, your conscious awareness is, is experiences are the most intimate thing you will ever have. And I think, you know, we really focus on bringing you to brain states and you fill in the content of your consciousness with them. And frankly, like if you don't want to like, you know, post what you lucid dreamt about, all good, right? That's completely up to you. It, you know, you really want, I think, to have a real sense of, you know, sovereignty and so and so forth over your conscious experiences. So I think, first of all, you know, right, that's probably years, if not decades away. But moreover, I really think we should really think about as a society, what the level is of, you know,

• 30:35Speaker 02

  that we really want to enable and allow for that technology, you know, to really give us. Um, you know, I think like, you know, this technology is obviously inevitable. Um, you know, in terms of, you know, on at least a medium scale, the longer term, you know, time horizon. I think it's a really important thing that we need to talk about as a society at large.

• 30:57Speaker 01

  Right, indeed.

• 30:58Speaker 04

  Hey, we'll continue our interview in a moment after a word from our sponsors.

• 31:03Speaker 01

  At what point, like, you guys have been at this for, I think you guys raised the seed, a seed done in October last year, I think. I saw the seed closed.

• 31:10Speaker 02

  A pre-seed in June.

• 31:12Speaker 01

  A pre-seed, a pre-seed in June. So, um, when, when did you guys start working on it? Like, what, what drove you to have this idea? And when did you first feel like it's gonna be possible? Like, what, what were the signs that you saw? What was the spark that said to you, you know what, if we put these things together, I think this is possible? And so what, what, what happened there?

• 31:37Speaker 02

  Totally. So, as I mentioned, you know, I'm a, you know, have this wonderful natural capability for lucid dreaming. So I've been, you know, a lucid dreamer now for 18 years. It's been one of the most profound aspects of my life. Um, and, you know, in 2018, I was working for the Israel Innovation Authority, which is the government's mentor. I was living in Jerusalem. And if you look at my background, I'm kind of like hopeless securities person who goes like an inch deep and a mile wide. And I knew that really wasn't a way that I wanted to live my life. Now, when you live in Jerusalem, you can't help but, you know, kind of think about and ask like ultimate questions. And I thought, if I could choose an ultimate question to try to answer, well, that would certainly keep my attention, right? You know, for my life, given that these are questions that we've been trying

• 32:20Speaker 02

  to answer, right, for millennia. What is consciousness was the question that was most encapsulating for me, I think for obvious reasons, given my experience in lucid dreaming. And, you know, I talk about lucid dreaming as a kind of a particle accelerator for consciousness. I mean, if you create, like, if you think about the parallel, you know, lucid dreams are this, you know, conscious experience where you're interfacing directly with consciousness with little to zero sensory input. Kind of like how we, you know, create these, like, you know, very exotic states, you know, at the Large Hadron Collider, using magnets, et cetera, where we can kind of, you know, create these subatomic, you know, particle collisions and grok, you know, deeper, you know, mysteries in the universe. And so I was like, you know, not only one, um, do I think, uh, is, it is an extraordinary

• 33:05Speaker 02

  experience to give, you know, mankind on demand, but that it also could help us, you know, further that answer to that question. Now, in 2018, I found these two core kind of landmark studies in 2013, 2014, where they had used electrical stimulation to successfully induce lucidity, albeit only statistically significant on two of the kind of the seven variables of kind of that lucidity scale, intuition, the awareness that you're in a dream, and interestingly, disassociation. Now, electrical and electromagnetic as well stimulation are kind of what we call internally the vacuum tubes of neurostimulations. Three core kind of, you know, limitations. One is depth. Your skull was evolved to keep stuff away from your brain. Getting electricity past your skull is quite difficult. Should you achieve it, what happens is a phenomenon simply called spread.

• 33:50Speaker 02

  It spreads across the surface of the brain, so there's no precision and no ability to steer that. Now, the reason it was successful at all, right, is, um, it is, again, you know, lucidity is, uh, lucid dreaming is rem with the prefrontal cortex activated, so they were clearly stimulating the surface of the prefrontal cortex and having some efficacy. But, you know, I was investing in areas of deep technology at the time, and, um, the thing you have to determine with, with deep tech, right, is, is this an R or is it a D? Because if it's an R, it's just not, you know, what the asset class of venture capital was designed for. It's not really the job of venture capitalists to, to, to underwrite research, right? It's more the, you know, universities and governments. And I firmly felt it was an R. So I, I, I kind of tabled it.

• 34:35Speaker 02

  But kept very close to both the neuroscience, neurostimulation, and then later the machine learning architectures. And it wasn't until 2022, when I found transcranial focus ultrasound and the neural transformer, that I firmly felt we had entered D. Now, let me just talk a little bit in particular about TFUS as it compares to, you know, the previous neurostimulation modalities. Back to those three core previous limiting factors, depth is centimeters into the brain, non-invasively. Precision is millimeters. So you're going from no precision to millimeters. It's not orders of magnitude, this is a paradigm shift. And then three is the ability to steer these millimeter pulses in a three-dimensional space.

• 35:17Speaker 02

  This is critical, right, because your brain fires in three-dimensional neural firing patterns. And so that paired with the neural transformer architecture, which, you know, we should definitely dive deeper into, and Wes can, you know, go deep on that. He built, you know, we built that architecture from the ground up. Those two things, you know, paired together were what gave, you know, me the confidence that we had firmly entered D and to, you know, eventually start the company. And what was amazing is that I found Wes, who was already, you know, working on using neural transformers for a variety of different applications in neurotech, and was also, you know, just started going down the ultrasound rabbit hole. And so, lucky for us, they found each other.

• 36:00Speaker 01

  So just one more question on the hardware. When did all of this hardware start to kind of make sense in the package that it does, right? Because I imagine EEGs at one point are very, very big machines.

• 36:13Speaker 02

  Yeah.

• 36:14Speaker 01

  And ultrasound, you know, was something that, you know, you have a doctor who uses it on like a patient for a baby or whatever. It's something you hold in your hand. It's not really like small enough to put, you know, on a headband, right? So when did these pieces of tech get, you know, small enough for this to start to make sense?

• 36:31Speaker 02

  Yeah, great question. So, you know, TF US for neuromodulation actually started around 2004. And through the late, kind of mid to late OO's in the 2010s, you know, you just saw, you know, and again, this is really happening in research institutions, et cetera, where they're increasing both, you know, the element count. So the amount, you know, elements are, you know, piezoelectric material, right, a crystal, for example, where you run electrical current through it and that oscillates, creating an ultrasonic wave. The more elements you have, the more ability you are capable of both moving that around and phasing it, as well as the precision. And we kind of note that there's actually like these curves that we kind of observed.

• 37:16Speaker 02

  I named it Wes's Law because Wes has too much humility to name it after himself. But you're seeing very similar kind of curves à la like a Moore's Law, where both the size of the transducers and the number of elements on a given transducer are increasing over time. And so, you know, you always want to be in a place, right, in hardware in particular, where you're riding some kind of curve, right? And so you're seeing that happening in, you know, in transducers, where both, you know, the number of elements, the cost, and so on and so forth, are decreasing, you know, over time. And so that was a really critical thing that was done, you know, over the course of what's now, you know, probably 20 years,

• 38:01Speaker 02

  where you now see this technology really capable of being, you know, commercialized.

• 38:08Speaker 01

  So, increasing resolution is always, is always nice, right? If you get a boost from a tailwind from the increasing resolution. Okay, so let's get to the build out of the neural model.

• 38:22Speaker 01

  I mean, when we build, when we build kind of like machine learning models, we always start with, you know, a messy, large, messy dataset, hopefully. Hopefully not a small one. So, talk about like the first, the first kind of like alpha version of the neural model. Like, where did the data come from? What were you trying to do? What was the test that you did that was like, okay, you know what? It's worth putting more time and effort into this.

• 38:53Speaker 03

  Yeah, so V1, I'll start with the V1 of Morpheus. What we did is we basically sourced data from open source data sets alongside some lucid dreaming data from Donder's, but primarily the set surrounds is really built up of what we found open source.

• 39:19Speaker 01

  So maybe just to take a step back. What is the first thing that you're trying to detect there? Is it, are you trying to detect the REM, the entry into REM? Is that, like, what's the first question that you have that you need to solve? Are you looking at the EEGs and are you trying to classify, build a classifier to figure out whether it's entering REM? Is that, is that the first thing? Or are you trying to build a classifier to detect the lucidity? What do you find you detect from that data that you first have?

• 39:51Speaker 03

  The goal of Morpheus 1 is to be given a particular brain state and output continuously TF US instructions to get a response in the brain. That's the number one goal. Yeah, we're not doing a significant amounts of classification. That's the number one goal of the model. The REM stuff, that's, you know, we leave that to other techniques. It's really not something we spend a great deal on. It's really the, the number one problem is getting, because that's a solved problem. That's been a problem that's been solved a long time ago.

• 40:31Speaker 01

  Right, right, right. So you have a, you have a bunch of like other tech which basically detects the REM, which gets you into the, you know, lucid state. And what you're focused on is like you have a EEG coming in. And you need to produce a target transcranial ultrasound, focus ultrasound, kind of like map that your transducers are gonna take and implement. Is that, is that, is that an accurate capitalization?

• 41:01Speaker 03

  Yeah, yes.

• 41:03Speaker 01

  Right. So when you start off, did you, do you do the actual work as in like you actually, um, you actually like, okay, I'm gonna take the EEG, I'm gonna produce a transcranial focus ultrasound, I'm, I'm gonna actually deploy it and I'm gonna actually do the reading. You actually, did you actually do the data collection? Or do you start off with an open source data set, which where other people have done it? And you're just kind of like trying to, trying to produce the output first. Like, oh, what is the start of this process for you?

• 41:32Speaker 03

  Yeah, so the start is that we grab an open source data set that is simultaneous EEG and fMRI. We do a number of preprocessing techniques to fMRI to basically find targets of hidden activities. So, you know, we, we, you know, mask the prefrontal cortex and we look for what voxels, what 3D pixels exist that are of a hidden state, right? And intuitively, right, those, that, that hidden activity is in some way correlated with what's being read in the EEG. So when you have the simultaneous EEG, fMRI,

• 42:14Speaker 03

  what you're able to do is say, okay, at this timestamp, this fMRI scan was, was ran. And at the same time, this sequence of EEG signals were, were collected, right? And what you basically say is, well, you, you know, you feed this to the model and you, you basically say, okay, what are the patterns that are existing between this EEG data and the fMRI data, both spatially and temporally? And then from that, um,

• 42:47Speaker 03

  You know, you're really approximating something like what, how do you, what does a model of the prefrontal cortex look like of heightened activity, right? And so the goal being is, well, how do you get a transformer to output instructions to the TFUS to bring the prefrontal cortex into a heightened state? You know, the prefrontal cortex is the key in all of this, right? And if you think of your prefrontal cortex when you're asleep, in a deep sleep, right? You know, everything's slowing down. Activity is very little. But right now, you know, as we're having this conversation, our prefrontal cortexes are quite active.

• 43:28Speaker 03

  And so the delta between those states, right, is what we attempt to model and how do you pull someone upward into a heightened state?

• 43:38Speaker 01

  Right. So when you have this data set, is it a data set of EGFMRI while transcranial ultrasound is being applied? No?

• 43:48Speaker 03

  No.

• 43:49Speaker 01

  No. But it is a data set of people experiencing lucidity in dreams?

• 43:53Speaker 03

  No. It's someone, it's someone in waking state, their prefrontal cortex is active, right? And the goal of the transformer is then model, what does that waking state prefrontal cortex look like? Only the prefrontal cortex. And then how do we build a transformer where the TFUS can bring someone to a state where that prefrontal cortex is active?

• 44:17Speaker 01

  Right. So what is the second step of that? Yeah.

• 44:19Speaker 02

  Let me just jump in for one, for a clarifying point. We are a collaboration with the Donders Institute, which is probably the top lucid dreaming lab in the world. And just led by a gentleman named Dr. Martin Dressler, whose work in 2012, 2014 was critical into establishing the, you know, the neural correlates of lucid dreaming and so on. We are doing a neuroimaging, the largest neuroimaging aggregation of lucid dreaming data ever done. And we do about four of these a week right now. Okay? Simultaneous EGFMRI.

• 45:00Speaker 02

  We have some data from them, primarily EEG right now. We should be getting our first data set, for example, from them. Wes is probably mute, just because I think we're in echo. We're getting the first data from them, probably maybe today or tomorrow. Actually, I was just on the phone with Dr. Dresser earlier today. And so I want to be clear. What Wes is talking about is the model that we showcased a couple of weeks ago is trained on open source waking state data where that prefrontal cortex activation, stimulation can be done. It's also supplemented with EEG data of lucidity.

• 45:41Speaker 02

  Okay, but the simultaneous EEG fMRI data set, we're only just starting to add into the data set now in the coming weeks and months, and we'll continue to add to that. So that was just the one clarifying point that I wanted to make.

• 45:55Speaker 01

  No, I mean, and just to be, you know, what I always find is that, you know, whenever we try to implement machine learning or AI, we always end up with insufficient data for the actual thing that we're trying to do. And we always end up with like trying to find proxies because you need like a large amount of data at first to get like some initial result, which allows you to go forward and, you know, then obtain like specific data on certain things. So I'm just want to understand, you know, how that worked in the beginning stages, right? Which is where kind of like you need this kind of like initial signal to kind of like, oh, you know what, this is worth our time and effort in order to put something in,

• 46:39Speaker 01

  in order to like actually actually collect like, you know, and annotate, you know, much, much more, you know, granular and sophisticated, you know, data, which is, which is, which is a huge, you know, effort on its own. Right. So I was just trying to capture how that would have felt like at the early stages of the company, which is obviously you've progressed, you know, far beyond that eddy stage. So you have this kind of like EEG, fMRI dataset at the very beginning. You have kind of like the targets that are being generated. And then from those targets, you generate, you know, a transcranial ultrasound kind of map that you need to target, right? And how, so how is that, is that, the next step of that, is that, that generation of that?

• 47:24Speaker 01

  The TFUS map, right? Like the, the, the targets are identifying the fMRI. And then you have a build out of your TFUS. And basically that TFUS is basically, it's, it's targeting those areas. Is that, is that correct? Is that, is that an accurate characterization?

• 47:45Speaker 03

  Yeah, yeah.

• 47:48Speaker 01

  Okay, so, and then you have, and then you, you basically have like small pulses that go in. And then you have another EEG reading, right? The next EEG reading comes out. And then you compare, right? And you compare how close you got, you got it to where it needs to go. And then you modify the TFUS again, and then, and then you send it another pulse. Is that, is that, is that, is that accurate?

• 48:10Speaker 03

  Yeah, yeah, the EEG that is read from the headband prompts the model and the output is then, yeah.

• 48:20Speaker 01

  So how, how, how many, how many times a second are you taking these readings? Or, you know, how, how, what is, what is the kind of like resolution that you have on this? Like, is, are you taking a reading every like 10, 10 kinds of second? And then, you know, the ultrasound is at like, you know, 60 hertz or, you know, what, what, what are the, what are the numbers that we're talking about here?

• 48:40Speaker 03

  I mean, the EEG right now samples at 1,000 hertz a second. You know, do you need all that, right? You can downsample it, you know? You, you could do that to reduce complexity if there's a lot of redundancy. So, I mean, it could be anywhere from 256 samples to, you know, 1,000, but right now it's 1,000.

• 49:04Speaker 01

  Right now it's 1,000. And then the, the ultrasound is at, what kind of frequency are we talking about here?

• 49:14Speaker 03

  So I mean, there's a few different frequencies, right? So there's duty cycle, which is like a proxy for so there's a few, few things. So there's the frequency that the ultrasound operates at. Call that, you know, 500 kilohertz. And then there's the pulses themselves. And, you know, our goal is gamma. And so that's probably going to be around, you know, 40 pulses per second.

• 49:41Speaker 01

  Forty pulses per second. So and each pulse, each of those, you know, 40 pulses could be, could be a different, kind of slightly different, as in slightly modified to, you know, get you to where you need to go. Is that, is that, is that correct?

• 49:55Speaker 03

  Certainly. But I mean, I would say the primary adjustment that's made, you know, per, per loop cycle is really the spatial targets more than anything.

• 50:10Speaker 03

  The spatial targets as opposed to the temporal.

• 50:13Speaker 01

  I see, I see. So more on like which location in the, in the, in the brain is being targeted rather than the timing.

• 50:21Speaker 01

  I see, I see.

• 50:22Speaker 03

  Yeah, I mean, I think they're both critical, but, but, but the spatial is the most important. I put that as a priority.

• 50:29Speaker 01

  And, and that brings the other thing, which is even if the, even if the halo is like slightly situated slightly differently, you know, each time, you still kind of like take a reading of the EEG in real time. So you can adjust the, the spatial targets accordingly. Is that, is that correct?

• 50:45Speaker 03

  Yep.

• 50:47Speaker 01

  So the next question I have is how much of it is personalized? Because how much difference is there from person to person? That you have to adjust for that the, that the, that the, you know, device or the, or the model has to adjust for in real time?

• 51:04Speaker 03

  Yeah, so the next big thing that we're working on right now is the reinforcement learning layer of all of this. So, you know, there's two types of feedback that the model will receive, right? It's the user explicit feedback, what you know, the user, you know, for example, even if they're awake, you know, they've run a sequence and they give some feedback, like I felt I didn't feel anything, maybe on a scale of one to 10, you know, or I felt something very significant, you know, so on and so forth. They've explicitly, you know, ranked, you know, a given sequence, or, you know, they've explicitly ranked an experience and that they, you know, they woke up and, you know, they've given feedback.

• 51:46Speaker 03

  So that's number one, right? Number two is the neural feedback, right? What are the sequences, you know, given, you know, the TFUS pulses, pulses, what is the response in the brain? You know, you know, there's a number of ways to measure, but you can sort of think of it like, you know, you're sort of pinging the brain and you're sort of listening to the responses, right? And so, you know, spikes in gamma, general patterns between the different electrode placements, you know, that's really informative for the model. Because what then you can then do is measure on a continuous scale, you know,

• 52:26Speaker 03

  what is working, right? You can do sort of binary classification in terms of like, did this work or didn't work on user feedback? You can take this sort of continuous feedback and, you know, provide that as, you know, either rewards or penalties to the given sequences that the user experienced. And then from that, learn what's sort of hitting with a particular person. You know, in a similar way, you can think of, you know, a TikTok feed or Twitter feed as the user gives feedback, both, you know, maybe explicitly in the form of a like, or maybe it's, you know, some sort of, you know, continuous factor in like the time that they viewed a particular tweet or video. The model can

• 53:10Speaker 03

  then learn, you know, what are the preferences or what are sort of the things that, in terms of content, which are just tokens that work with a particular person. And then from that, you know, the model learn over time.

• 53:25Speaker 02

  Let me, let me just, because I always like to, like I let Wes give the the super technical version, but I know you have a very informed audience, but just in the sense for layman, you know, a layman explanation, right? First of all, we only focus on like training models on brain states that are both discreet and universal. To define terms, like when I say discreet, I mean, it is one thing and not another. A counter example being, for example, unfortunately, flow states, right? So flow states are not discrete. You can be a surfer and enter a state of flow and what triggered that is your kind of motor cortex, right? Versus like a chess player can enter a state of flow and it's like their spatial reasoning.

• 54:08Speaker 02

  So you would just need more data to create a generalized model. But, but the way that, you know, a lucid dream, you know, lucid dreaming is discreet and universal. It is prefrontal cortex activation during REM, whether you're in a lucid dream or Wes is in a lucid dream or I'm in a lucid dream, which makes it easier to make these generalizable models. And also I should mention, you know, we should mention, I mean, what we already just did in terms of, you know, the sampling rate of EG. But for these data sets, these, some of these EG-FMRI data sets, they're extraordinarily information dense, right? A thousand, you know, to give you sense, a spatial reading in fMRI, once every 2.1 seconds, in that same period, you'll have 2,100, you know, EG sample, samplings. So it's extremely, you know, information dense.

• 54:52Speaker 02

  And so broadly, how you could think about this is like, you're creating this kind of vector space of possible, you know, lucid dreaming sequences, right? And so on and so forth. But, you know, you might be over here and your reinforcement learning will drive the model, you know, to really focus on targets around here, whereas Wes or I might be, you know, in other areas in that space. So that's just the important kind of, I don't know, layman explanation that I would give.

• 55:17Speaker 01

  Oh, absolutely.

• 55:20Speaker 01

  How much, like, let's say you have, um, what, what is your target for like the first run of the, of the Halo? Like, is it 10,000 devices? Is it more like, what, what is your, what is your rough number than you're, you're coming up with?

• 55:33Speaker 02

  Yeah, um, you know, we, we, we have a reservation program, um, that I started because I talked to a lot of consumer hardware founders and, you know, they said, listen, like, one, obviously de-risks demand, but also for, for go to manufacturing motion. It's really great if you have an order book that you can kind of, you know, uh, you, you use to, to approach better manufacturing partners because, uh, you know, the bane of all, uh, consumer hardware companies and really hardware companies broadly, but particularly consumer hardware companies is like these small batch, medium batch manufacturers. Who like, you order a thousand, 500 of them don't work. You're like, hey, these don't work and they're like, cool, send us more money. Now 250 of them don't work, et cetera. It's like death by thousand paper cuts.

• 56:14Speaker 02

  Um, and so, you know, we, we, we've done, um, you know, two, 2.4 million dollars of book revenue through, through that, that, that reservation program. I want to be very clear, that money's held in a separate bank account. We do not use it for development. It's fully refundable at any time. But it's, you know, I think it shows a, a good sense of demand. I think in terms of, you know, what we want, you know, the first run to look like, it's very much going to be reflective of where that reservation list is at the time. You know, certainly anyone who puts down a reservation should be getting a device because they took a bet on us. When, you know, in some of these cases, you know, when we were nothing but a website and a dream, not to be too, uh, you know, uh, on the nose. But, um, you know, that, that's really, you know, the key, the, the, the critical thing is, you know, building out this reservation program.

• 56:59Speaker 02

  We spent zero dollars in marketing so far. Um, and so, you know, I think what, what this shows, right, is there's enormous demand for this. We have to make it work. Once we, once we start, you know, we're, we're starting neurosimulation, you know, in the spring. We have a beta user program that we, we launched alongside showcasing the model. We've got like 3,500 people to sign up for that, um, you know, in five days or something like that. Um, and so we'll go through the process of, of selecting those people for the, for the beta user program. But, but anyway, i- in terms of, you know, what, what the, the initial run will look like, it'll be very dependent on, you know, that reservation list.

• 57:36Speaker 01

  I mean, I'm just, I'm just, I just wanted to draw like a metaphor. Like, let's say, let's say you had like, let's say, I'm just gonna put a number like, let's say 10K, right? Uh, let's say you have 10K and how much, how much would that expand your? Because I imagine like you can use that, the EEG data coming out to kind of improve your models, right? So how is that, how does that model feedback look like from the, from the initial devices? Like, you know, if you have like, let's say 10, 10,000 devices. Because, you know, and, and I see this all the time, like, you know, there's this, there's this graph that people have of, uh, you know, the day the iPhone, you know, went out and like 10 years later,

• 58:17Speaker 01

  we probably create as many photos in a day as like the entire history of humanity before the iPhone, right? Like, every single day, there's probably more photos, more photos are taken than the entire history of humanity before the iPhone, right? And so I wonder if, if putting this device out there expands the data set of EEGs so dramatically, so, so dramatically, that you can have this enormously larger data set? Like, like, what would, having 10,000 of these devices in the market, do you think that would expand your data set significantly for future, you know, improvement?

• 58:56Speaker 03

  Yeah. It, so that goes back to the reinforcement layer. That, that really only works at scale. Um, it, it works significantly better at scale, I should say, right? Because we are, the halo is a neural imaging device, right? It's a neurostimulation device that's, um, and so what we're able to do is look at, you know, basically how, what are the responses you're getting from this TF US? And what, and those responses are measured with EEG. And that's really informative, right? You can learn quite a bit from what works and what doesn't. And, and so while I, I think that the, that the, you know, starting with this open source data set,

• 59:41Speaker 03

  this, this, you know, we can get terabytes of data that way. But it's really, when you talk again into, you know, hundreds of terabytes or petabytes of, um, data, that's really gonna come from that people are using this every night and we're collecting. You know, just, you know, on a nightly basis, you know, gigabytes worth of neural imaging data and the responses specifically, you know, from TFUS sequences, right? You're pinging the brain and you're basically seeing how it's reverberating and we're collecting that. And it's this continuous thing over and over again. So it would be a very significant amount of data.

• 1:00:21Speaker 01

  Yeah, yeah, I can, I can, I can imagine, yeah, go ahead.

• 1:00:24Speaker 02

  And one thing I also want to talk about is not only scaling up the reinforcement side, but also the initial training data aggregation. So, you know, the day after we showcased the model, we released a piece that I think people, we had a good response for it, but I don't think people really realize like how profound it potentially is. We call it the Qualia Factory. Realistically, that's a really cool marketing name for what is a neuroimaging lab, primarily set up with the simultaneous EGFMRI, you know, neuroimaging data setups. And so, you know, you can imagine, right, you know, what, you know, where we can actually expand also our training data set where, you know, we bring in people who are extraordinary at, you know, whether it's lucid dreaming

• 1:01:06Speaker 02

  or meditating or focus or positive mood, et cetera. The entire state space of, you know, potentially discrete universal, but even potentially because we have the scale now, non-discrete universal brain states and aggregating more and more of this data to train, you know, successive models where you're creating models that can do more and more different experiences with the same piece of hardware. We do aim to hopefully launch the device with more than just lucid dreaming as an experience, definitely focus and hopefully also positive mood. So, you know, that's also a really critical way you scale up data is on the training data sets using this Qualia Factory, which is, you know, really, you know, a larger neuroimaging lab built for purpose.

• 1:01:53Speaker 01

  So let me, maybe I kind of missed that earlier. So the core thing that you're looking at is kind of like as you, on your project roadmap. For our roadmap is discrete universal states, as you call them. And of the, you know, again, I think, you know, perhaps, you know, not everyone is super familiar, but like of the discrete universal states, you're saying focus is one of them? And positive mindset is another? Is that positive mood?

• 1:02:26Speaker 02

  Positive mood, yeah. So TF US has already been used to induce focus and positive mood. So these things have already been, you know, validated in the context of utilization with TF US to do it. So in many ways, we're also just broadly recreating that, you know. But, but yeah, I mean, listen, we think that lucid dreaming is the killer app for this technology from a consumer experience perspective. It is the, you know, kind of the most profound extraordinary experience that we think we can give. And so that's why it's our focus. But you want to create a system, right, where over time, you can make a more and more generalizable device, or generalized device, where the number of experiences the same device, you know, can give you increases over time.

• 1:03:08Speaker 02

  So, you know, we want to aim to have kind of three, you know, core experiences. You know, release, when the device ships, it obviously also increases the TAM of the device. You know, people like to be focused when they do work. So maybe, you know, you can wear it while you're, you know, jamming out, you know, a spreadsheet or something, I don't know, whatever you're doing for work. So in positive mood, you know, people want to feel happy for, you know, and so on. So that's kind of, you know, the impetus for, you know, that's really critical about the model. Not only does it, you know, obviously, you know, create these, you know, this closed loop system that, you know, creates these targets and reliably creates these experiences,

• 1:03:51Speaker 02

  but also allows you to onboard more experiences to the system over time.

• 1:03:56Speaker 01

  So uh let me, let me, let's go back to lucid dreaming, right? Let's uh, so let's say uh you have a device now. If you had to like create a tutorial, because you know, some people are just like, they don't know what to do with it. Right? So if you had a tutorial or something that you'd need to create um for, for someone. Is, is there like a series of exercises that you'd recommend, like the, in the, in the first one? Wake up, take a look around, walk around, try this, right? Like, is there, like, what would that, what would Daral Wanjadral do look like, you know?

• 1:04:28Speaker 02

  Yeah, I, I, I think you're absolutely right. Like, it is very critical and incumbent upon us to do a certain level of consumer education, you know, before they try this out. Um, and give them a guide to kind of the initial things that maybe they can try out doing. Certainly, number one, right, is just taking in and grocking your environment. And trying to, you know, maybe even depending on where you are. Um, you know, if there's a dream character, you know, around you, go talk to them, right? Go ask them questions and so on and so forth, these set of questions. Um, you know, uh, maybe, you know, try, try to, try to make a flower appear out of the ground or a building appear out of the ground. You know, cha-change your environment.

• 1:05:06Speaker 02

  Um, it, you know, the, the thing that's also really, you know, important is like, again, the way that this is, you know, experienced phenomenologically is what you imagine becomes. So I think maybe also one of the things to do is to also like go into it and say, what do you, where do you want to be? You know, what, you know, what is the first, you know, first environment you want to create? And so that when you're, you know, you're lucid, you can do that. Um, but, but yeah, it is definitely important that we, you know, create some kind of content and guide. Um, that, that, that does that, as well as, you know, prepare you for, for, for, for what that's going to feel like, right? Um, it, it's good, you know, it's going to be a wild experience the first time, no doubt about it. I think people equilibrate to things like very quickly.

• 1:05:47Speaker 02

  We already saw that in the context, right, of ChatGPT, where it's like, that was like one of the most extraordinary things ever. And now it's a completely, you know, banal. Um, but, um, you know, I, I think, you know, you're absolutely correct that, you know, there, there, we'll have a set of content that kind of walks you through what this is going to feel like, what, what, you know, what maybe to do when you first, you, you, you first experience it. Um, but I frankly think that people will very, very quickly uh become attuned and accustomed to what they can do in these experiences very quickly.

• 1:06:17Speaker 01

  So let me, you know, just playing around with this idea. Let's say you you put on the halo and you fire up, you fired up, you go to sleep. You also have maybe an app that, you know, talks you through it or, you know, basically runs like a vocal experience, right? Like something that, you know, once the lucid state is triggered, says like, hello, blah, blah, blah, wake up, do this, or, you know, or imagine you're doing this, or et cetera, et cetera. Like, because I read through some of the research that you had on your page. That's like a real time dialogue between experimenters and dreamers. So it seems like you can have like conversation, you can have like a little bit of like, you know, interaction.

• 1:07:02Speaker 01

  So would that be something that you have a little bit of interactivity? Like a vocal kind of experience where someone is talking you through something? Or someone is doing something with you together? Is that is that something that you guys have experimented with in the lab? Like, have you tried that?

• 1:07:18Speaker 02

  Yeah, so the problem with that, right, is when you when you provide sensory input,

• 1:07:24Speaker 02

  you know, your sensory, the worry there is that you could you could activate the thalamus, which regulates sleep and that you would just wake them up. And so, I mean, it's interesting, we talked to a guy who did his PhD on attempting auditory induction. And he had to do this whole thing where, I mean, and it was, you know, a very limited data set where he basically created this algorithm that determined people's different, you know, kind of waking thresholds. And so, you know, you determine like you're a light sleeper or heavy sleeper. It's kind of like, how much auditory noise can you have until you like are actually like awoken? Because the problem there, right, is that you're when your brain hears something, or, you know, people have tried to use light to induce lucidity.

• 1:08:08Speaker 02

  If it thinks it's seeing something or something's changing in the environment outside of you, your body's like, holy shit, something's happening, we have to wake up. So I think, you know, an auditory guide is really not something that you you want to do because, you know, you very much increase the chances that you you wake them up. And again, I think content, providing people like a walkthrough content, could be video content and so and so forth, that, you know, walks them through what they, you know, will expect and so and so forth. I really think You know, people equilibrate to things very, very, very quickly. And so, um, I, I, one, one, one, once you're like attuned and accustomed to this, you know, there is no need for like this guide, like you just know that like what you imagine becomes,

• 1:08:50Speaker 02

  right? And so, you know, people have their imagination.

• 1:08:55Speaker 01

  Um, music, playing music in the background, is that, is that the same? Is that basically the same? Because, uh, or, or does that get tuned out by the brain? Yeah, you're just-

• 1:09:05Speaker 02

  Uh, again, the, the issue is, is that when you, when you send sensory data, you know, whether it's through your ears or your nose or your eyes, you can activate the thalamus, which regulates sleep and wakes you up.

• 1:09:18Speaker 01

  Wow, wow, interesting. So basically, you allow yourself to experience the dream in fullness without distracting yourself with things outside it.

• 1:09:32Speaker 02

  Yeah, I mean, you, you, the, the whole point, right, is that we want to circumvent like these auditory, uh, or, or sensory, you know, data inputs, and we're just focused on, on activating the prefrontal cortex.

• 1:09:44Speaker 01

  Right on, right on. Um, tell me something a little bit more about the qualia factory, uh, right? Because, um, I guess, uh, my, my layman's, uh, layperson's kind of like understanding of qualia is like your subjective experience of consciousness. Uh, I don't, I don't know if that, that's an accurate description. Uh, what, what is qualia? And, um, why would you say that, you know, uh, you have some potential to create a qualia factory?

• 1:10:13Speaker 02

  Yeah, so, uh, again, I'll, I'll define qualia, but, but I want to be clear. Qualia factory is kind of a marketing term. What we're talking about, right, is a neuroimaging lab that is specifically set up to do simultaneous EEG and fMRI neuroimaging so that we can build more training data sets for, for more models that give more experiences. Um, qualia, you know, which, um, Charles Pierce, uh, a famous, you know, American philosopher, you know, um, you know, uh, kind of a contemporary of William James and so on, or, um, you know, created this term qualae, uh, which was, you know, uh, kind of, and, and qualia being kind of the unit economic of a given conscious experience. So the color red. The smell of coffee, you know, etc.

• 1:10:57Speaker 02

  You know, these are units of qualia or conscious experience. And a conscious experience, right, is the composition of that entire, you know, uh, qualia landscape. That's where it comes from and that's why we kind of named it that.

• 1:11:10Speaker 01

  I see, I see. Um. Very good. Uh. The one thing that I think, uh, people can ask about is that is this a medical device? Is this FDA regulated? Should it be FDA regulated? Um, you know, describe to me the, the thought process behind, uh, should it be regulated or not? Why is it not regulated, etc.

• 1:11:32Speaker 02

  Sure, we don't make medical claims. Uh, that's very important. Two is, um, you know, the, the threshold of, uh, uh, uh, a regulation for, you know, ultrasound is seven hundred and twenty milliwatts per centimeter cube. It's essentially the, the pressure that's being exerted from the simulation. Um, we're talking about something that's between a hundred to two hundred milliwatts per centimeter cube. So well below that threshold. Um, and also to give you a sense, you know, you can get recreational electrical simulation devices today uh, from companies like Lifted, etc. Um, and that's, you know, uh, I think, you know, if you look at like the, the, you know, uh, it's a far less precise, you know, technology, etc., which, um, you know, uh, and so like the safety record of, of ultrasound relative to any of these other, you know, technologies is, you know, far better.

• 1:12:15Speaker 02

  Um, so, I mean, to give you maybe even in a phrase, it's less than a sonogram on a mother's womb. That's what we're talking about. Um, so, um, it's not regulated. We're not, you know, one of the key differences between us and a lot of the, you know, people in, in the CF US space, they're almost universally, um, you know, targeting medical applications, which is great. Uh, it just means that, right, the, the time to market is much, is much longer. Um, and so we focus on these recreational consumer experiences. Um. And so, yeah, I mean, that, that, that's kind of the, the framework and why we don't, uh, why we don't have to go through it. Um. Yeah.

• 1:12:50Speaker 01

  Interesting, interesting. So, um.

• 1:12:54Speaker 01

  There are people trying to use transaurineal focus ultrasound for medical applications, but you are not actually doing any medical claims, medical devices. You're not going to cure anyone. It is just a state of lucid dreaming. And that's all that you're trying to do at this point. And the amount of ultrasound which is being projected into the brain is something like one-seventh the regulated limit, right? Like one-seventh, one-eighth of the regulated limit, something like that.

• 1:13:29Speaker 02

  Yeah, one-seventh, one-fifth or something like that. Between one-seventh.

• 1:13:33Speaker 01

  Yeah, something like that. Yeah, right.

• 1:13:39Speaker 01

  So what does Morpheus look like? What does V2 of the device look like? We have a V1, it's coming out soon. It's in beta, you know, you have beta testers coming in.

• 1:13:51Speaker 01

  Is a V2 basically going to be better and smaller, like more transducers? And obviously you can upgrade your model. You know, you can update your model anytime on the backend. But if we have a V2 device, is that, are you looking for further development and miniaturization of the transducers of the EEG? Are you looking for more resolution? Are you looking to pack more of them in a smaller device? What would you look for before you pull the trigger on a V2 in the hard way?

• 1:14:28Speaker 02

  Sure. We are very dead set and focused on V1, as you can imagine. I mean, right now, right, we're migrating the model in the technical prototype. We have to do, actually, you know, I know you probably can see some of my team building a 3D scan line tank for a hydrophone where we use it to test the pressure and so on and so forth and compare that to simulation, ultrasound simulation that we run to basically calibrate it. But So we're very focused on that, but in terms of what a V2, where the improvements would be, again, reference that Wes's Law that I talked about previously, where you're seeing an increased level of both miniaturization, the number of elements on a given transducer, which will increase your precision and ability to phase and steer it.

• 1:15:14Speaker 02

  And as you mentioned, the model will continuously update in the background. We will launch more and more experiences which you can subscribe to and improve the number of experiences that way as well. And so both the number of experiences can be improved by the transducers, both the number of the transducers or the number of elements on a transducer, as well as from the model. So you definitely want to improve things like that, as well as improving the energy consumption of the transducers, so that you can shrink, shrink, shrink. Hopefully, and some of the electronics, where you don't need kind of a side totem gear and hopefully, maybe whether it's V2 or V3, that stuff is just in the headband and so on.

• 1:15:59Speaker 02

  So that's how you could kind of see this improving over time as successive models are produced. But we are hyper-focused on just getting V1 done.

• 1:16:12Speaker 01

  What have been the biggest challenges for the hardware? Is it heat? Is it power? What is the thing that has been challenging the most in the last build-out? While you've been building this?

• 1:16:26Speaker 02

  Yeah, I think one of the key, a few things. One is, we did this, we did our prototype with Card79. They did Neuralink for Elon and they work with a number of other neurotech companies. I'm not sure I can mention their names. But things like comfort is really critical, right? Because you have to be able to fall asleep with this on, or if you're using it for a waking state experience, it's got to be comfortable. So just design and form factor is very, very, very important. Um, in terms of hardware, and so we did, you know, uh, you know, excessive, you know, um, studies on, you know, okay, like if you, if you, some people sleep on their side, some people sleep on their stomachs, some people sleep on their, on their other side or their back.

• 1:17:07Speaker 02

  You know, making sure that it's, you know, uh, fully comfortable and you can fit all the components in that comfortable form factor. Um, and then in terms of like more like brass tacks, right, you know, there's, there's, you know, component selection, et cetera, which you have to be very, you know, focused on. Um, and then, you know, there's all this equipment that we have to build like this, you know, uh, 3D scan line, uh, hydrophone tank. Um, to actually like, you know, you have, uh, you know, tests, et cetera, and simulation software. So that you can kind of, you know, cause we can't see how the, I can't see how this is, you know, the waves are propagating in your skull. I'd have to cut you open and I don't want to do that to you. So you, you know, we have to create, you know, we have to use this, you know, uh, this hydrophone and the simulation software.

• 1:17:49Speaker 02

  To create this one-to-one, you know, correlation, uh, where we can, you know, then be able to take, uh, you know, some, somebody's skull and understand how it's going to propagate through their head. So I think there's a lot of stuff that you just have to build out in order to even, you know, get to neuro modulation, which has really been a focus. I know in the last couple of months, um, but yeah, you know, come spring, we're, we're, we're going to be, you know, doing neurosimulation and, and validating all these. You know, experiences and, you know, having, hopefully having, you know, in short order, but you never know, um, you know, the first ultrasound could induce a dream.

• 1:18:20Speaker 01

  Right. In, in the early days, were, was there like any like, um, hurdles where you needed like experimental data to decide? Whether or not this was a go or not, like what, were that, were there like any like, you were like, Kate, we need to figure out this. And if this is not going to happen, then we need to pivot, right? Like what, what were, you know, what, were there any moments of whether anything, you know, technically or otherwise that you saw? Needed to have those decisions.

• 1:18:49Speaker 02

  Yeah, so again, I mean, comparing 2018, when I first thought about doing this to 2022, when I found TFUS, you needed a different neurostimulation modality than electricity or electromagnetism. And then on TFUS, TFUS has already been used to induce focus, positive mood, deep meditative states, etc. It is neuromodulatory generally. And so as long as you can, you understand, right, the brain state that you are trying to induce, it is definitely, this is the integrated circuit for noninvasive neurostimulation. So that was, you know, I think from our side, you're very de-risk. And then we, you know,

• 1:19:29Speaker 02

  we have this collaboration with the Donders Institute for neuroimaging data. And so that was also very critical that we needed that pipeline. And then having successive breakthroughs or acquisitions to data sets that we could use to supplement that data as well as and expand our data pipeline. But really, it was that. And then, and of course, just the advent of the neural transformer architecture in 2022, which then was expanded and built from the ground up to be a multimodal neural transformer. But the go in terms of red light, green light, was, I think, really on the neurostimulation front, more than anything else, the modality.

• 1:20:10Speaker 01

  Right. Do you, do you sometimes come across purists who are like, you know, lucid dreaming is something that you should naturally come to as a, as a part of like a kind of like religious or semi-religious meditative process, rather than something that is to be offered free to everyone? Do you come across those kinds of people? Do you come across comments like that?

• 1:20:38Speaker 02

  Absolutely. Yeah, absolutely. I've seen a couple of comments, you know, on some of the various things that we posted. I think You know, you see kind of like gatekeeping in like everything, um, you know, basically everything.

• 1:20:50Speaker 01

  Yeah.

• 1:20:50Speaker 02

  And I, I, I think, you know, yeah, you, you, I mean, I also, you know, was, it was in the psychedelic space for a brief while. And you kind of saw the same thing in the psychedelic space where you had either like these ethnobotanist kind of, you know, uh, these are sacred, you know, uh, sacred plants and, and, and compounds. And then you had like, you know, the scientists at University College of London and John Hopkins, et cetera, who are really trying to move things in our understanding forward. Um, you know, and, and so I think you see these, these, um, dynamics play out across many different areas. Um, but I think it's kind of like if, uh, you know, we said, uh, again, maybe bring it back to like, you know, the, you know, fire. It's like, no, like only lightning gets to create fire or, you know, or, or, or, or something like that.

• 1:21:35Speaker 02

  You know, we, we shouldn't control it. What, we could burn down our village. Um, you know, or somebody could burn down our village, you know? Um, and so, you know, I, I, I, I fully expected and I'm not surprised that we got, you know, that, that, that kind of, you know, feedback from certain people. Um, but, but frankly, like, you know, the, the world is moved forward by, by, by people who, who kind of say, kind of, you know, th- this is an extraordinary, powerful thing. What if we gave it to everyone?

• 1:22:02Speaker 01

  Right. Um, and, and on that, on that note, um, in your early kind of like testers who, who, who are testing with you in the lab, uh, is there anyone like, what, what has been like a, a kind of remarkable experience? When someone has tried this on and they've come to you and like, this is a wool moment for them. Like what, what, like, I'm sure someone has tested it and, you know?

• 1:22:26Speaker 02

  To, to, to be very clear, you know, we're starting neurosimulation in, in, in the spring. Um, you know, we have to migrate the model on and, and, and, and, and again, as I mentioned, test out all the, the hardware to make sure that, you know, and, and the, um, and get it in correspondence with the simulation software so that, you know? We're doing this, you know, properly. But, you know, we have, you know, friends at places like the Institute for Advanced Consciousness Study that's been running a number of different ultrasound, you know, experiences. I think meditation and positive mood, I think, were the things that they were doing, as well as maybe some others. And there's actually a really great blog post that somebody wrote on his experience. And it's pretty extraordinary.

• 1:23:09Speaker 02

  There's also a really great video that was done, you know, quite some time ago on the research of a gentleman named Dr. Jason Guetti, where he was using it to induce deep meditative states. It's like they use this woman who had never meditated a day in her life and induced a deep meditative state. She's very emotional about it, you know? It's an extraordinary thing to be dropped into a brain state that you've never experienced before. So it is a very powerful, you know, technology and experience. We look forward to be giving, you know, those experiences to our beta users in the spring.

• 1:23:40Speaker 01

  That's amazing, that. I am so inspired. This is such an awesome, you know, awesome piece of technology. Because you often wonder, you often read, let's say, a Hermann, Hermann Hesse or Siddhartha or, you know, a lot of these things, and you wonder, actually, they're not able to fully describe their experience, right? It is not possible for anyone to fully verbally describe this kind of altered or different state of consciousness. And we have these kind of like very, very limited understanding and limited ability to describe what's happening in our heads. And to the extent that, you know, there is something that you can experience which you

• 1:24:24Speaker 01

  have not been able to experience, it's inside you, but you can't really experience it. And to the extent that you can use a technological, you know, innovation to kind of like trigger that and learn something more about yourself, I mean, that's, you know, that's amazing. Yeah, it's, you know, it's absolutely fantastic, so.

• 1:24:45Speaker 02

  Yeah, obviously, I think we share your enthusiasm. I think the best thing that you can do for people is give them the experience. If you can give them experience, it's empirical for them innately. And I think one of the things that's going to be pretty extraordinary is, you know, over the years and decades as we have this technology, you know, I have to paraphrase Altman, right? It's like, this is the worst these neural transformers will ever be. This is the worst TF US will ever be. And so if we can build, you know, on these, you know, both on, you know, on these, you know, these, you know, curves like Wes's law and on top of, you know, machine learning

• 1:25:30Speaker 02

  improvements that we're seeing all, you know, every day, you know, the ability to give people a device that allows them to explore the state space of conscious experience is going to be transformative for the world. I frankly don't really have a good sense of what the world looks like when this is available en masse, but it is an exciting world. It is certainly a strange one, but it is, I think, one that is, you know, profound and, you know, something that I look forward to exploring alongside everyone else.

• 1:26:06Speaker 01

  Awesome, awesome. So I think, thank you, thank you, Eric, thank you, Wes, thank you for your time today.