there’s a lot of dystopian thinking in the world right now either you Embrace progress and abundance and and growth or how tightly can we embed the machine learning algorithms into the physics of electrons the tools that you’re about to inherit are tools that can boost intellect not just thousands but billions of fold potential if people have a mindset that they want to embrace technological change they want to figure out how to best augment themselves augment their businesses with AI they will have a place in the future welcome to moonshots Peter D mandis here I’m about to have a conversation with Gom Verdon he is the founder of the accelerationist movement effective accelerationism eak he’s also the founder and CEO of extropy AI it’s a new form of computation uh thermodynamic
[00:01:02] Computing different from digital different than Quantum uh he spent 3 years at Google working with Sergey Bren he’s a Quantum physicist a brilliant individual we’re going to go deep into why is it important to have an accelerationist abundance mindset and what is the future of AI Computing when the cost the thermodynamic efficiency is something that is hundreds of thousands thousands of times cheaper and we make this ubiquitous throughout the Universe his mission is to increase the amount of intelligence per watt and the amount of intelligence in the universe all right if you love conversations like this please subscribe please upvote this help me bring incredible moonshot Engineers like Gom Verdon who goes by Gil to the moonshots podcast all right let’s jump in Gil welcome to moonshots it’s a pleasure to have you here buddy thanks
[00:02:00] thanks for having me super excited to be here yeah we’ve seen each other twice in the same uh same quarter first off on the stage of abundance 360 this past March and then you joined us at the xprize Deep Tech Quantum trip in up in the Bay Area and uh yeah it was fun that was a lot of fun yeah met some brilliant folks there always amazed by the quality of the X prise community so I’m super happy to be here thank you so today we’re going to talk about two of my favorite subjects uh the first subject is the whole abundance acceleration movement uh and just to give people an understanding of how fast things are changing and why that’s a good thing right because so many people are fearful of the speed of change and want to put on the brakes mostly I would almost say governments are like if you don’t understand it the answer is stop slow down Y and so yeah why is that a bad idea and can it actually be slowed down
[00:03:01] and stopped the other is what you’re building which is a new class of computational uh Hardware uh to enable this acceleration movement so we’re going to jump into both um if you don’t mind let’s start with what is your moonshot if you you know guon Verdon have a moonshot uh that you want to make happen the next decade what what is it what’s the equivalent to elon’s going to Mars for you that’s a great question I think having a processor that is brain scale uh in terms of numbers of parameters and and model capability but that is far more energy efficient I think that’s my more energy efficient than the brain yes yes the the brain’s pretty damn energy efficient that’s right and why is that and and that’s what we’re trying to reverse engineer interesting so I just to put a number on it uh for folks you know 100 billion neurons 100 trillion synaptic connections running on what how
[00:04:00] many watts do you tens of Watts like I think like 14 to 20 watts of of energy but the equivalent for a gp4 system would be what tens or hundreds of millions of times less efficient yeah it’s really but you want to be more efficient than that more efficient than the brain that’s what we’re aiming for yeah amazing um you know uh I in listening to you and I’ve studied your work and I’m just I’m fast fated by it uh I wrote down that your mission would be you know the ultimate substrate for AI compute to build the ultimate AI Computing machine and also to propagate as much intelligence per watt in the universe yes is that fair to say what does that mean yeah I would say you know there there’s kind of two two dual missions that come together towards one
[00:05:00] greater Mission the broader mission is to scale intelligence throughout the Universe Scale the total amount of intelligence in our in our corner of the cosmos um but to do that you have to increase you know how much energy we produce right going up what is called the cev scale and we’ll talk we’ll talk about cev yes sure it’s a way to measure how much energy we produce and consume um and so so that’s kind of the the core cause area of eak is to argue for um uh policies that will help us scale our our energetic consumption and and growth of civilization and then with ext Tropic our goal is to get more intelligence per one um and uh that is ultimately a race to the bottom right it’s how tightly can we embed the machine learning algorithms into the physics of electrons right I love it I love we’re going to get into all of that and I want to talk about the accelerationist movement and talk about
[00:06:00] xtropicalovex I think um you know the original Manifesto I wrote U around the same time I was founding ex Tropic I was going through bit of a lull just P you know founding paperwork right you know you’re you’re waiting on on on some some lawyers and so on and I had I was like okay this is probably the last two weeks of vacation I have for the rest of my life once this gets going and so let me let me try my hand at uh philosophy right instead of just doing science and math and algorithms which you know did for more or less 10 years before that uh
[00:07:02] what if I just like tried to apply my sort of physics mindset to understanding the rest of the world and and write up write something up pretty quickly I’ve been having I was having conversations late at night with other technologists sort of online there’s sort of communities where people have Anonymous accounts right uh because they’re employed by XYZ and they don’t want their opinions to reflect that of their employer and they want to have the freedom to experiment with their thoughts right and have candid conversations of like hey where is civilization going where is society going where’s this all going you know and we would have these conversations late at night and and essentially we decided to write something up and uh you know I added my own twist uh to it and uh you know put out the manifesto it went viral at first it was kind of uh dismissed and then it just kept compounding kept compounding and now it’s kind of you know uh truly like a a counter narrative to to the the culture
[00:08:01] of sort of um AI doom and and and overregulation and safetyism that we see today I mean there’s a there’s a lot of dystopian thinking in the world right now right there’s a lot of fear and I I remind people that our default mindset is that of fear and scarcity yeah right fear and scarcity evolved in the savannas of Africa 100,000 years ago and it saved our lives back then and today um it doesn’t contribute it’s not valuable in the world we live in um EAC stands for Yak for Effective acceleration and your Manifesto if you going to summarize it would be what the idea is to understand the process that got us here the process of progress itself of advancement of civilization uh of inspiring ourselves from well trying to understand it from a physical standpoint right like there’s
[00:09:01] been quite a bit of work in the physics of life right like understanding how did life assemble and become so complex right so the science of complex self-organizing systems that have energetic constraints so out of equilibrium thermodynamics and taking inspiration from those ideas and applying them at civilization scale to have a prediction of where are we going how do we reached how do how do we re reach sorry rewind how do we reach the better futures um ahead and how do we maintain a sort of robust advancement of progress towards this greater grander future and essentially what I saw was that we need to maintain variance and dynamism as a core uh value for us to be malleable and adaptive to whatever challenges come our way rather than trying to freeze everything slow down and panic actually what we want is actually more malleability more dynamism
[00:10:00] so important so the malleability adaptability the agility I would say is uh if you don’t have that when you get hit by a disruptive Force he can destroy you right the analogy I use and your you’re free to use it is you know 66 million years ago when an asteroid struck the Earth the dinosaurs that were slow and lumbering were not malleable they were not adaptive and they died but it was the furry mammals that right flittered around jiggled around to use the free electron analogy that ended up becoming dominant and so that malleability and Agility today comes from from what from millions of entrepreneurs trying millions of ideas there I I I think like every uh potential parameter space of how we organize ourselves what are our cultures uh how how we do things which Technologies we’re pursuing where we live how we live every every possible
[00:11:00] space should have some amount of variance to it and we should allow the freedom to explore and and and dynamically optimize ourselves because for example you know the dinosaur example if you didn’t have genetic variants at the time if we were all you know being dinosaurs we would have been wiped out right but because we had variance we were robust to a change in in in in the landscape right and so I would say right now you know there is a sort of weird Trend in in the past uh I don’t know decade or so with the arrival of the internet that there’s a trend towards over centralization and top- down control of of culture amongst other things right and and now it’s that sort of uh centralization around a mon culture is is trying to uh also potentially control a core technology like AI right so if AI is in the hands of very few it’s not a lot of variance in How We Do AI right there’s going to be a couple prescriptions dominant models dominant players exactly and and
[00:12:00] and their uh tactics for how to you know align the AIS are going to be the only options we have and and to me that doesn’t seem robust because there’s there’s too much uncertainty right now and and in times of uncertainty you want to have variance in how you do things so you’re hedging your bets right yeah there’s a great biological analogy you really want genetic variants so when new think enters the ecosystem it might kill a large percentage but a few of the variations will be resistant survive so we’re talking about human survival in one sense here as an underlying optimization function mhm and and but we’re also talking about sort of how do we keep a a varied culture different subcultures will have different opinions about how to embrace technology or reject it and really in the end each subculture is going to be tested you might have people that want to merge with the machine people that don’t want anything to do with it some people that fully embrace it and I I think all paths will be explored but in
[00:13:00] the end whoever like the message of eak is you know there’s a tendency of the universe towards growth and it will adapt and reconfigure everything uh that is alive towards uh this growth and whether you align yourself with this growth or not is your choice but that choice ultimately has consequences as to whether or not you’re influence or you are part of that those likely Futures right and and and that comes from some very esoteric equations from thermodynamics but um essentially it’s a message of hey um either you Embrace progress and abundance and and growth or you know may you’re you’re probably not going to be you’re going to miss the boat in a sense in fact at the abundance Summit this year um Alexander wezner gross was there was talking about uh AI is coming on strong um it is going to you know reach what whatever we call AGI and then digital super intelligence
[00:14:02] there’s no there’s no barrier that says AI only goes towards you know an IQ of humans it it blasts through and continues that infinitum especially if the hardware You’re Building comes into existence or when it comes into existence I should say um and the question is does Humanity couple with it yeah or do we decouple and that’s really a lot of the interesting conversation um i’ like to talk about that I’d also like to talk about what people’s fears are one second because again so the coupling with AI means that we get a chance to uh to accelerate alongside it enabled by it it’s almost like when life began and we had these procaryotic life forms and they absorbed mitochondria yeah and became a eukaryotic life form was able then to utilize the the free energy of oxygen uh for oxy otion to grow more rapidly yeah
[00:15:02] so today we have our phones and our devices are kind of like our mitochondria to some extent which I definitely want to implant in my head they’re they’re intellectual powerhouses of our of our system right and uh in a sense we’re already pretty augmented we kind of feel naked and incomplete with our our phones and uh you know over time we’re going to have wearables that share our perceptions share our experiences have priors of our actions based on the state of the world and on previous history of what they’ve seen and that’s a sort of exogenous neural augmentations neural augmentation of ourselves of course there’s our friends at neuralink that are working on the full merge with even higher bandwidth but even without that sort of uh invasive approach to to merging with the AI I think we’re already most people are merging without realizing it right sure um and so you know what does the human of the future look like well I think it’s one that really harnesses is all these tools to
[00:16:00] of of cognitive leverage right to to augment uh itself right and I think that the humans that maybe dismiss technology or don’t embrace it those are the people that are going to maybe be in trouble or relative disadvantage and so I think so far the beauty is that you know you know we’re both CEOs right we both have like standard issue iPhones or Androids right um and it’s the same as anyone else right everybody everybody has access to to the same technology and it’s ubiquitously accessible and cheap and that’s the beauty of capitalism right um I think that hopefully uh Ai and neural augmentations can be uh cheap enough and ubiquitous enough so that everybody can own their own uh AI the AI that is an extension of themselves and that they have control over it uh because I think if we only allow for AI augmentations that are controlled by Central parties we’re kind of losing a sense of self there uh we’re we’re kind of delegating
[00:17:01] to this sort of uh I’ve always imagined uh for the longest time an AI software shell the closest thing is Jarvis uh from Iron Man but an AI that is my I used to call my AI uh Jamie joint anthom meano interface that was my AI was able to interface with everything in the world I could step into an F35 fighter not know how to fly it but Jamie knows how to fly it and you know I can say you know move that image here or there or how you know the technology that you’re enabling and I don’t want to go there yet but because it has the ability to operate potentially at room temperature and with very low wattage it also feels like a technology that could be incorporated into me yeah a lot more than any of the other AI technology yeah uh do you imagine a future in which I have become a cyborg with with the those AI
[00:18:00] implants yeah I I would say you know at least one of my goals is not just you know uh to augment humans ex exogenously but potentially integrate these devices um into our bodies obviously that’s very Moon shoty speaking you know sort of thinking but at the same time within a certain thermal budget right which is a a bottleneck today for for implants in our brains you know our goal is within a certain thermal but to be you know the most performant neural information processor out there right and we’re going to keep iterating to to maintain our our lead there so and I I I love it you’re again we’ll come back to this in detail but the power and uh and temperature and efficiency Vision you have um because it’s orders of magnitude different than what exists today enables integration to the humans I about the fear there’s a lot of fear out there yeah um and the fear dominates the
[00:19:02] conversation you know I blame the crisis News Network and the news for basically broadcasting every negative piece of information on the planet um have you been as as you have been uh leading this conversation um on the accelerationist movement and you’ve had folks like uh Mark Andre uh who’s sort of come in as well into that conversation what’s been the feedback from society have you gotten a lot of push back uh yeah I mean it’s it’s it’s very polarizing right like there’s some people that are uh you know positive some abundance mind mindset they’re like yes we think technology will help us uh conquer our problems and and and help us tackle any issues and there’s the people that uh you know think technology maybe is is negative or they’ve seen how it
[00:20:00] impacted their lives and maybe maybe they want less technology right they’re kind of it’s kind of techn progressive versus techn regressive right it’s kind of a new it’s a new axis of of of polarization of opinions and uh clearly from my experience online it’s been very Pol polarizing we’ve had quite a few fans we have quite a few uh opponents but you know I welcome uh discussions right the whole point is to have discussions about uh how fast we want to go right but if it was only one a one-sided discussion beforehand about slow things down centralize you know uh let’s regulate regulate um then we were going to head towards that without any sort of opposition whereas now I I feel like we kind of brought balance to this force of sort of novelty seeking uh you know favoring entropy versus sort of higher uh you know more order more constraints uh and there’s kind of this uh there’s
[00:21:00] kind of this thing that happens in complex systems where the optimality is that criticality the balance between Order and Chaos between energy minimization and entropy is is where you want your complex system to be because that’s where it’s it’s most performant so I don’t think people realize there’s no onoff switch on this technology yeah and I don’t think there’s a velocity switch either I think it is um I often asked myself the question if you’d gone back in time and said Einstein listen stop thinking about this it’s going to lead to the atomic bomb whether or not he would have been able to stop thinking about it and if he did the next person would have taken it over and and moved it forward so you know I I tell people there’s no there’s no slowing it down and if you believe that which I do then the question is what do you do and I think it’s guiding it that is the only real option we have correct and I I would say that um you know the the
[00:22:01] market itself right um is a very powerful aligning Force right if you have a product that is not of positive utility to us we don’t buy it it runs out of whatever company makes that product runs out of capital and the product dies off right there’s a selective pressure on the space of products and right now because uh L AI models are products right you have model as a service companies like opening ey anthropic eventually XI um this competition of for users and ultimately of capital to you know fuel the gpus that keep these systems alive um uh this competition induces a certain selective pressure and models that are not aligned that don’t do what you ask it that are hard to interpret hard to read actually don’t do well in the market and in a sense it’s it’s a much more careful sort of gentle guide towards systems that are
[00:23:00] aligned compared to um sort of centralized regulation like this is how much compute you’re allowed to use for a model nothing more and so on I I I think that’s going to be net negative uh overall so in a sense people have um a vote uh in in the system they they can vote with their dollars they can vote with their usage their API calls of which systems they like and that’s going to steer the market sort of evolutionarily in the space of potential neural Nets towards more of uh models of that kind right but Gil what happens when someone comes to you and say listen um I get this I like it I love the abundance future but let’s be serious uh this is super powerful technology right um uh Claude 3 is already at 101 IQ it’s more intelligent than humans and we’re giving infants nuclear weapons to play with and these
[00:24:00] things are going to um ultimately destroy Society um and there’s a small chance we survive but this is way too powerful and we need some level of control we need some level of centralization just to make sure things don’t go off the rails how do you respond to that I would say that I’m I’m more weary of the dangers of centralization than giving everyone you know access to to neural augmentation I I think the I like like you said I I don’t think there’s going back right I don’t think we can go back to not knowing about this technology there’s too much upside on the table to creating it so it’s it’s a it is an arms race like never before yeah yeah and so for me it’s like how do we guide this acceleration towards the positive future to me I think if only a few people or parties have control over the only AI that are legally allowed um
[00:25:02] we’re going to have a lot of problems because that’s going to create a sort of gradient of power right people with but we have duopolies now with in cellular phone networks in cell phones and computers and so forth is it how is this different from those duopolies I think if we’re going to truly augment ourselves our own intelligence with AIS I think in order to maintain the benefits of having individuality right individuality we celebrate individuality in our society at least in the west and it’s our greatest strength because this variance everybody brings something different to the table and we’re searching over all sorts of spaces of science art culture and so on and we find new Optima something original that then gets spread throughout the network and is of massive benefit to everyone and if we only have centralized models right a few models that are trained for everyone they’re amortized so so so there’s one
[00:26:01] model for everyone we lose the benefits of having sort of individuality and so to me my quest both on eak and xtropicalovex maybe you have 20 20 agents that work for you right yeah Peter three of 10 is going to hang out with you yeah I think that’s the future I think that um people you know will it’s kind of like having you know employees you you know management is prompt engineering to some extent you can prompt different agents to do tasks for you maybe you fire them up and then boot them down when you’re you’re done and I think that gives us a lot of intellectual and operational leverage and I think people tend to think too much much about the economy
[00:27:00] has this Zero Sum system where like if AI have take some of the jobs there’s going to be less jobs for us but we all know that’s not how the world works right like if if if we’re able to do more at a certain cost we’re going to try to aim higher and and and you know there’s plenty of room to grow uh out there that’s a really important point and and you know when I think about what do I worry about um and I’m I’m definitely the guy who says you know the glass is not half full it’s overflowing yes but I do think about if AIS are doing all of the work and if I can write a book at the snap of a finger and start a company at the snap of a finger and what’s challenging and that we humans need challenges there’s a great uh paper I read recently um uh it’s called Universe 25 if people on Google it and so the the studies were
[00:28:00] done in the 60s in which a large open space was created for field mice and it had all of the room and the nests and the food and they had no struggles at all and the mice you know breeding pairs were put into this and they grew and they grew and they grew and then after a couple of generations it basically died off because there was no struggle in there and and so one of the things that I think about is is that humanity is going to have to uplevel yeah um our Ambitions and our struggles M uh and I’m excited about that but people need to have you know the ability to have a massive transformative purpose to have a moonshot so let’s talk about the kardashev um uh you know scale here so a a Russian cosmologist astronomer right um comes up with this idea so explain
[00:29:00] what it is yeah it it’s a sort of Milestone system to keep track of the progress of growth of civilization um there’s originally there was uh three types or three big milestones and then Carl San found a way to interpolate between the Milestones so we have a continuous uh scale there um but the original scale was uh in type one a type one cev’s uh scale civilization uh would would produce as much energy as is incident onto earth from the sun right so if you take the sun we occupy a certain amount of solid angle certain amount of the sun’s Rays hits us that’s a certain amount of power and by the way uh the numbers that I remember because I I speak about this when talking about energy abundance is that today I think there’s 8,000 times more energy that hits the surface of the Earth that than we consume as a species in a year we’re still we’re still really early we’re still below type one yeah well below
[00:30:00] type one um type two would be uh having the equivalent of the energetic production yeah Dyson Sphere that that captures everything being emitted by our son and type three would be the entire galaxy right and so I think in general if you set um this guy was really ambitious back then he didn’t start with like a type one as a fireplace or you know we still type zero right now yeah we’re still at the beginning of this but but I think this is you know in our moon shots conversation in our abundance mindset when people think about well what am I going to do you know it’s like you’ve got to point your your vision yeah you know 90 degrees up and start talking about you know how do we achieve uh Transcendence in our solar system and in our galaxy yeah I think a lot of our goals people’s goals are to anthropos Centric or they want to do Rel relative
[00:31:00] to others and now that AI comes in and kind of breaks this sort of Zero Sum competition between humans right we got to set our sites on a non-anthropocentric goal right and we have a goal prescribed by the universe in a sense which is to grow yes uh because any life form seeks free energy and looks to grow and the point is that if we have AIS uh that help us and extend our intelligence we should tack tackle harder things and there’s an near infinite scale of harder things to tackle because unlocking that next scale of civilization there’s tons of challenges to to achieve that and so that’s the sort of mindset uh I bring to the table and and and frankly you know my whole career was trying to tackle you know how to leverage AI to understand the physical world I haven’t been trying to let’s say automate humans uh I’ve been trying to engineer matter understand chemistry at the base level understand the physics of the world so
[00:32:01] that we better perceive predict and control it which is kind of a you know core based technology for us to unlock all sorts of other Technologies right and is when someone hears this conversation they say well my God I can’t think about that I don’t know how to I’m not Elon to build you know Starships or Gil to build quantum computers and and such but the reality is the tools that you’re about to inherit are tools that can boost intellect you know not just thousands but billions of fold potentially well we’ll see how it shakes out I I think that the current approaches where we train on human generated output right the internet is broadly at least right now uh I’m sure in a couple years won’t be the case but generated by humans yes right and we’re kind of distilling a mixture model of all human intelligences right you could think of the LM trying to distill a mixture across the opp of all our brains
[00:33:00] right and so at least to me it seems like it would saturate to something nearing you know typical human intelligence um until well until it’s embodied and then can interact with the environment and get its own samples and and query the environment in a way that you know isn’t bottlenecked by what was generated previously by a human I me one of the conversations we had on the Abundant stage was uh the excitement about AI helping us decipher and deeply understand physics and math and biology and chemistry um in ways that we can’t fathom right now I mean you do believe that don’t you yeah I do I do think it’s possible I do think it’s much harder than distilling human intelligence I think uh understanding biology and chemistry is orders of is going to take orders of magnitude more computation and so the computers were building yes they’ll be able to run uh models that
[00:34:01] are anthropomorphic right like llms and so on train on on human data um but ultimately it’s machines that are going to help us grock the physical world um and there’s this beautiful Theory by um by Steven Wolfram on on complex and self-organizing systems and his prediction is that certain uh systems in nature are irreducible you can’t you can’t get a you know tldr you can’t compress right the gist of it to something very simple you actually don’t have a choice but to go through the highly complex computation to predict what’s going to emerge as a behavior at a different scale and so nature nature is very hard to predict uh at all scales and I don’t actually believe that you know there will be one God AI model that will emerge overnight immediately understand all of physics and uh you know create uh Nanobots that eat the Earth which some people people believe in but at least fundamentally you know from my own
[00:35:01] experience studying complexity in in Quantum systems and and Quantum machine learning uh and and then you know from wolfram’s Theory it seems that there’s a fundamental complexity in nature where we’re going to have to scale our computation of intelligence proportionally to the complexity of the systems we’re trying to understand and there won’t be like an overnight sort of uh runaway um intelligence explosion like that it’s going to be consistent exponential progress and to me there’s much higher likelihood that we shoot ourselves in the foot and stop this beautiful process of exponential progress uh then there is you know us you know giving the keys to uh Singularity thus thus the Boomer versus Doomer point of view yeah that’s right I mean because the majority of people feel just the opposite that this is uncontrollable this is a reaction that has no bounds and therefore uh is dangerous and and I I hear you saying
[00:36:01] this is a precious flame that we have to be careful we don’t blow out yes yeah it’s a it’s a different narrative than what you’ve hear you’ve heard you typically hear people have sci-fi based priors right like what what are their priors on on what the future holds they’ve seen a lot of sci-fi movies I blame Hollywood I really do yeah I mean I mean the only film that I I think has done a good job here is her you know where the AI gets bored and leaves yeah that’s actually accurate uh but um you know in my case I think once we’ve created AIS that are you know of similar intellect to us we’re going to learn how to interact with them we’re going to learn how to employ them in a in a positive fashion uh there’s going to be a big adjustment there but once we do so we’re going to have way more challenges for us to scale civilization to the stars and there’s plenty of challenges left so there will always be more work to do right and and
[00:37:02] we’re going to put our most capable systems on the most complex and difficult tasks and there’s still going to be work left of all kinds and so I think if people have a mindset that they want to embrace technological change they want to figure out how to best augment themselves augment their businesses with AI like they will have a place in the future those that want to you know stay away from it then you know I mean they could go back to the we have the Amish we have the Amish example right the and and the the challenge becomes you know people who feel this way I’m like okay just for just a week go without your phone your TV you know your car all the technology don’t buy food in the supermarket go find a cow and look at yourself go plant your and the reality is um what makes the the leveling the playing field for Humanity is the poorest and the wealthiest all
[00:38:01] have 24 hours in a day 7 days in a week 365 in a year it’s how you use your time yeah that differentiates you so my ability to have ultimately hundreds of of extraordinary agents that can do do my the things that I desire and bring back the answers for me is a massive uh Force multiplier for productivity yes I think I think people should should think more like that like a capital cater like a manager as the way we’re going to merge with AIS um and it’s going to allow them to do much more I think more people should be entrepreneurial more people should think hey what opportunities do I see in the world now that we have these AIS that are on the verge of human like intelligence what should I aim to build how will I direct capital to unlock more value right um and if everybody shifts to that sort of mindset I think the fears about what are we going to do in the future uh will fade right everybody
[00:39:00] I want to take a short break from our episode to talk about a company that’s very important to me and could actually save your life or the life of someone that you love company is called Fountain life and it’s a company I started years ago with Tony Robbins and a group of very talented Physicians you know most of us don’t actually know what’s going on inside our body we’re all Optimist until that day when you have a pain in your side you go to the physician or the emergency room and they say listen I’m sorry to tell you this but you have this stage three or four going on and you know it didn’t start that morning it probably was a problem that’s been going on for some time but because we never look we don’t find out so what we built at Fountain life was the world’s most advanced diagnostic Centers we have four across the us today and we’re building 20 around the world these centers give you a full body MRI a brain a brain vasculature an AI enabled coronary CT looking for soft plaque a dexa scan a
[00:40:02] Grail blood cancer test a full executive blood workup it’s the most advanced workup you’ll ever receive 150 gab of data that then go to our AIS and our physicians to find any disease at the very beginning when it’s solvable you’re going to find out eventually might as well find out when you can take action Fountain life also has an entire side of Therapeutics we look around the world for the most Advanced Therapeutics that can add 10 20 healthy years to your life and we provide them to you at our centers so if this is of interest to you please go and check it out go to fountainlife decomp Peter when Tony and I wrote Our New York Times bestseller life force we had 30,000 people reached out to us for Fountain life memberships if you go to Fountain life.com back/ Peter will put you to the top of the list really it’s something that is um for me
[00:41:01] one of the most important things I offer my entire family the CEOs of my companies my friends it’s a chance to really add decades onto our healthy lifespans go to Fountain life.com back/ Peter it’s one of the most important things I can offer to you as one of my listeners all right let’s go back to our episode so a lot of folks listening are entrepreneurs and they’re looking at moonshots they’re looking to do something big and bold and significant I like I jokingly said not another photo sharing app you know uh how do you what’s your advice for entrepreneurs today uh looking over the decade ahead yeah I would say um you know everything that’s like white color work or software you know doesn’t necessarily have everything that has pre-existing uh abund abant data sets might not have too much of a mode right if if intelligence
[00:42:02] becomes more abundant and if the current systems we have don’t necessarily generalize too well but they’re really good at interpolating across data points that are pre-existent maybe stay away from things that are typical right and go towards the atypical do something that’s never unique data sets yes right so something that’s like surprising contrarian and so on you know yes that gets people to judge you that like okay this sounds like a crazy idea but actually everything that is typical is going to have plenty of AIS that can do those tasks right and so to me I think we’re seeing a sort of deep Tech Renaissance uh and even I think this narrative is floating amongst the Venture community that actually deep Tech you know the world of atoms is where the hard problems are and where AI won’t be able to follow you yet right and I think there’s a a fundamental reason why that is I think the physical world is really hard and even uh even with all the help of uh White Collar AIS
[00:43:02] that we can muster there’s going to be a bottleneck to creating things in the world of atoms so build companies that are uh doing hard things in the world of atoms and you will do very well in the future that is my advice uh I want to dive into your startup again there’s a lot of folks who are uh super excited and all they think about right now is how can I get access to h100 networks and how do I start coding for this and and if if you’re able to pull off what you’re building right now you will disrupt those that capability I don’t want I I can’t put orders of magnitude on it massively yeah um but let’s go back to your history that led you here you were at University of waterl yeah in studying quantum physics Quantum yeah quantum gravity Quantum information uh during my masters
[00:44:02] and then over time I realized that actually to better understand the physics of the world it wasn’t going to be a couple mathematicians in a room on on a Blackboard to solve the The Theory of Everything But it’s probably going to be some form of computation and AI that would solve it and so to me uh that Journey led me to be a pioneer of a field called Quantum deep learning um wrote some of the first Al alms in the space and you got you got recruited out of school didn’t you yeah yeah that’s right uh basically first year of PhD uh I met um Hartman Nan who now leads to goog heart well yes Google Quantum IAB and uh uh essentially you know we were on the same wavelength right what did he say to you um in his in his German accent I I won’t imitate his accent but uh you know I think we met at Nasa I gave I gave a first talk after writing a a very large paper on how to to do deep learning on quantum computers and he was like come give a talk in in in Venice
[00:45:01] Beach uh not too far from here and and talk to our scientists and so uh we you know brought brought my co-author Michael at the time we did and they asked us hey you know try to build a prototype for what a tensorflow which is for Quantum Computing would look like tensorflow is Google’s core machine learning framework right or at least used to be um and uh we hacked it together they liked it and basically onboarded uh the whole team uh on they gave you a offer you could not refuse yeah yeah exactly and and frankly you know uh waterl is great it’s a great school but you know it is in the middle of nowhere in Canada and and to me to move to California seemed like the right um you know opportunity I should I should take and uh you know just went for it and haven’t looked back really um and so it’s been it’s been a it’s been a ride yeah uh and then you actually spent some time working closely with Sergey Bren yeah so uh after we you know built tensor L Quantum that there’s a team that was forming around Sergey working
[00:46:00] on Quantum Technologies uh and Ai and physics and AI more broadly uh and and to me I was sort of getting a bit uh impatient with the timelines with the comput the Computing stack and I saw that there were opportunities and Quantum Communications and sensing that were maybe shorter term and so I wanted to try my hand at that MH and to me it was a a completion of sort of the vision of understanding the world that at a quantum mechanical level because if even if you have the algorithms running on quantum computers that can understand Quantum data and learn AI representations of them how do you acquire Quantum data and how do you transmit it and so that’s what I worked on uh so I worked on Quantum analog digital conversion the US Quantum Internet and so to me it was completing the stack for us to be able to perceive and predict and eventually control our world at a quantum mechanical level which to me is kind of a very deep node in the tech tree let’s say it’s a civilizational technology that’s really important but during that time in
[00:47:00] Quantum Computing I realized that actually there was going to be different nodes of our our our Tech tree that need development imminently that use a different kind of physics that’s not quantum mechanical physics and that would be much more useful for for Gen AI as I was seeing sort of jvi workloads eat more and more of the compute internally at Google so we’ve got classical digital computers right now the classical CPU from Intel and such and then Nvidia I think very luckily fell upon uh the opportunity with gpus most people hopefully know gpus graphical processor units were originally created for video games for graphics yeah for graphics and then they just happened to get a market in Bitcoin mining yeah right and then all of a sudden here comes the whole generative AI world and and Nvidia becomes A2 trillion doll company yeah like that’s a lot of good luck that’s a lot of good luck turns out uh Matrix multiplications which gpus excel at are
[00:48:01] very useful for all sorts of uh different applications including AI but as as you mentioned gpus weren’t designed from the groundup from first principles to be AI processors right it’s kind of a co-evolution between the hardware and the algorithms right the algorithms that ran on gpus like modern deep learning tended to do well because gpus already existed and then both kind of fed off each other right so we’re trying to create an evolutionary for in the space of Hardware it’s going to engender evolutionary Forks in the space of algorithms and they’re going to co-evolve that’s why we’re a full stack company and we co-design the algorithms so let me uh let me read uh something here um that sort of describes what you’re doing sure and see how this how this hits um we’re building the ultimate substrate for AI compute looking to hit the limits of physics in terms of Energy Efficiency and speed of AI embedding AI algorithms into the physics of electrons
[00:49:02] dancing around and we’re doing this by building a full stack of hardware and software Reinventing at first principles how to how to create generative AI MH um you call it thermodynamic Computing MH probabilistic Computing and this is a third branch of computing isn’t it yeah it seems like it cuz today we we have the determinist to computers right your transistors are definitely on or definitely one or zero one or zero it’s one or the other right and you definitely know which one it is right uh and then you have a quantum computer which has super positions of ones and zero it’s 0 plus one0 minus one and everything in between everything in between complex numbers you can make those interfere with one another but actually having a computer that you’re unsure of the state of the computer and it’s problemistic it’s Z one or something in between but um you’re not not sure exactly the state of the computer is actually much more energy
[00:50:00] efficient because knowledge costs energy there’s this old uh tale of Maxwell’s demon I don’t know if you’re familiar with bringing back Fain memories go ahead yeah yeah so so you know Maxwell’s demon uh you know tells us that actually it’s it’s a thought experiment that examines the energetic cost of of knowledge right um and I I guess I could I could go into it uh but yeah Maxwell’s demon essentially you can imagine having a box with a a partition in the middle right uh and you have one side of the box has a bunch of red particles and the right side of the box has a bunch of blue particles and you have a trapo in the Middle with a little demon right and you know if you if you keep the trapo open you you wait a long time you know the balls cross and on average you get a mixed thing where you have red and blue balls in both partitions now where the demon comes in would be you can actually reverse this process of going to a
[00:51:02] higher entropy state right which would violate the laws of thermodynamics by having the demon look if a if a ball of a certain color comes in opens the door if a ball of the right color comes in the other side opens the door and can filter and again separate out into red and blue partition which seems to violate the second law of the ramics that states that so therefore there is a there must be a cost to that observation there’s an energetic cost exactly so what we see is that a lot of the cost of running a computer comes in when you’re trying to maintain its determinism uhuh right and it turns out that you don’t need to always be maintaining determinism when you’re running AI algorithms because AI algorithms are natively probalistic MH and so having AI run on a digital deterministic programs super inefficient that is emulating probalistic programs yeah super inefficient so why not run probis programs on a problemistic computer and so just from that we have just an
[00:52:01] efficiency and a tightness to the embedding of the algorithms into the physics of the hardware that’s really hard to achieve on digital computers but not only that we’re actually catering to the the the imminent what would be otherwise problems uh uh of of of uh transistor based Computing right um because as you scale down transistors and you try to make them more energy efficient uh unfortunately the fact that transistors are made of matter and they’re jiggling causes your transistors to misfire and get hot right get they get hot and and sometimes they they say things they don’t don’t want to say no but like they’re they’re they’re misfiring um and they become effectively stochastic um and instead of uh trying to filter that noise and filter uh uh that stochasticity and and make it deterministic again right through error correction similar to how we have to filter out the stochasticity and Quantum Computing with Quantum a correction um instead we embrace the noise and use it
[00:53:02] as part of the algorithm and it’s part of our models of the hardware and it’s part of the algorithm and so it’s a very different way of thinking uh it’s very challenging because we have to rebuild the whole stack to go with it it’s very ambitious but to us it’s a necessary and clear from first principles evolutionary step in Computing and it’s one that we think is inevitable uh we’re just the ones that are boldly going for it right now yeah and you describe thermodynamic Computing as being in particularly valuable at describing chemistry and biology because we’re talking about thermodynamic systems there yeah so so um you know chemistry has some Quantum effects uh in there mixed in but uh biology proteins molecular Dynamics at the mezos scales right they’re bouncing yeah they’re bouncing around they they’re around and that’s very tough to simulate right because we have to embed that stochastic
[00:54:01] process into uh digital computers right and and it’s it’s a process that these fluctuations happen on very small time scales so you can’t just fast forward the movie Very efficiently and so for us we’re looking to embed the physics of protein folding eventually not not initially but protein folding which happens on a certain time scale because you have big proteins and they’re jittering your about into the Jitters of electrons that we control how they dance and electrons because they’re much lighter they Jitter much faster so you get a speed up by embedding the Dynamics of proteins into the Dynamics of electrons and that’s it there’s no you know it’s just a it’s very analogous the physics of the mesoscales of matter to the native physics of the hardware and so we get a we get a speed up there but we’re we’re going to work on quantifying exactly which what sort of speed up we expect right now it’s an intuition similar to fineman’s intuition about why
[00:55:01] would you build a quantum computer where well there are quantum mechanical systems you want to simulate and it turns out that yes in fact you get a significant speed up running Quantum simulations on quantum computers versus classical right so so um you had this Insight while you were at Google and this was what drove what no liated you or actually before I started my career in Quantum machine learning I wrote down the equations for our very first chip while I was a waterl uh and I thought this idea was crazy it was too original you know I was I was straight out of M my masters in theoretical physics I had just learned machine learning you wrote it down in one of these notebooks over here yeah just a notebook yeah crazy IDE yeah there you go um and I thought the idea was too crazy you know I I was still I was wrapping up my masters at the time and I wanted to understand and go through the exercise of inventing a bunch of algorithms in physics based AI more generally MH before I went for it
[00:56:02] right so I built up you know years of credibility shipping a lot of papers and products and going to Google and so on to have the experience to do this moonshot at the end the day you got to find what is your best idea what is the most impactful idea uh that you can work on for the rest of your life what are you like willing to die for yeah this is we call that a massive transformative purpose and your moonshot right yes it’s like and you know I wrote down uh again what I hear as your MTP which is maximizing the amount of intelligence in the universe yes and along those lines intelligence per watt like maximizing that and if you can do that it’s upleveling m it’s upleveling everything yeah it’s the highest it’s a very it’s a point of very high leverage right and obviously not everyone has to create a tech technologies that are as impactful as that but at the same time if
[00:57:00] everybody thinks about which technology they can build which Technologies do they have the unique skill set for that they can that they think would truly impact the world in massively positive way if everybody goes and does their moonshot they’re thinking about I think the world would be a much better place right and that’s kind of the ethos of the acceleration Community I know it’s the ethos of your community uh which really you know uh was been around for much longer than we have and I think a lot of people sometimes just need a push to it’s okay to be ambitious it’s okay to go for it it’s okay to take risks you know you’ll have a supportive Community go for it it’s actually like if you achieve what you’re looking to achieve we’re all going to benefit so we’re going to support you and I think having a supportive Community is is so important um do this this very simple concept of maximizing in Ence per watt and in the universe seems like a fundamental yeah it it
[00:58:03] seems like uh what life would always Trend towards yes and given the fact that we humans are only some 4 and a half billion years in a 13.8 billion year G universe as we know it um I’m wondering where all the intelligence is you know I don’t want to get into F’s Paradox of why aliens aren’t here right but I have to imagine there is a maximization principle for intelligence the universe and I always thought about Intelligence being the counterveiling force to entropy like entropy increases um the counterveiling force for that would be would be intelligence increasing as well does that make sense yeah actually um you know there there are theories for example Carl friston someone you should talk to
[00:59:00] Absolute brilliant uh scientist his theory of intelligence is that we seek to minimize surprisal right and entropy is expected surprisal sure right um and so intelligence is trying to model the world to have the Min a minimization of surprise and it turns out that for for biological systems if you can predict your environment really well you’re in a position to extract more free energy and cons it in a clever fashion that’s thermodynamically optimal so now I I guess my theory which you know I’ve only Loosely kind of had time to put my thoughts on paper through the manifesto and some tweets but something I’d like to formalize in the coming years on the side is you know how how did Intelligence potentially evolve as a byproduct of this concept of thermodynamic dissipate adaptation which is a a concept from a professor named Jeremy England from MIT that posits that uh systems that are uh complex and and
[01:00:03] uh uh subject to the laws of thermodynamics self-organize in order to dissipate more heat over a long time scale not instantly not overnight to to to acquire more energy and then dissipate yes yes essentially more precisely the theory says that trajectories of of States over time um the ratio of of likelihoods of two different histories two different trajectories um scales exponentially with how much free energy was dissipated so F so paths toward the future where we’ve dissipated more heat are exponentially more likely and so there’s actually this probalistic bias of the universe towards growth yeah right and that’s what that principle at least According to some theories is what led to life self-organizing and creating the complex systems that we are today and to me that is the process that led to all the creation of all the wonderful things
[01:01:01] we see today and it’s almost like sacred in my book we should keep that process going we don’t know what upside we’re leaving on the table if we were to stop it or or decelerate and and obliterate ourselves right is there is there any way to know given this basic theorem whether a tendency towards a recurrent simulation would be the end result so recurrent simulation well meaning you know we are a simulation nth generation simulation that keeps on um re instantiating yeah I mean I I I’ve studied simulation quite a bit right my my job was to figure out how to simulate uh I’ve simulated uh you know early universes for fun on on quantum computers as a hobby and sold it as art in the past right but the there’s a certain um uh density to information if you will um and and and my work on on Quantum Internet was actually studying how
[01:02:01] densely you could pack Quantum information in various substrates and in a sense this um assumption that you can have a simulation within a simulation within a simulation really doesn’t hold because at the end there’s a base reality and in that embedding Universe you have a maximal uh information density for your computer um if the laws of physics in that Universe are anywhere close to ours um and so the assumption that we live in a simulation from from from this uh assumption that you can embed a simulation within a simulation um yeah only go so far only goes so far it doesn’t really hold yeah so to me I don’t think we live in a simulation and I’m happy to chat with anybody who thinks we do uh and show them the mathematics of how hard it is one thing we can test is whether we live in a Quantum simulation or not right um because I think Google’s quantum computers amongst others are reaching the number of cubits where there wouldn’t be enough atoms in the
[01:03:00] observable universe to to emulate the quantum computer with a classical computer and so we can at least rule that out uh and and if if uh one of the quantum computers show that uh I’ll be very satisfied with that I think that’s a very interesting thing to answer so you have this idea of a thermodynamic computer mhm and uh when did you pick it up again uh I picked it up you know I I left my career in in Quantum Computing Quantum machine learning took a couple months to you know gather my thoughts and I I I went for it basically uh summer 2022 and um just got going and got the company going and now it’s been going for almost two years you call it extr Tropic Ai and I’ve seen some uh some great video of your your Fab uh and your uh your C your cryo Hangout spots you know so uh your goal here is not something that’s operating
[01:04:00] at cryogenic temperatures it’s something that is operating at room temperature that’s right and being built on the Silicon fat yeah so so you know when you prototype things you start with the biggest most macroscopic prototype you can because it’s simpler it’s easier to get going uh and you can probe it and understand it better in our case we couldn’t do a breadboard prototype you know like you would do with other electrical circuits because we want to operate it in the regime of ultra low power and the right ratio of power of noise where we have the right properties of of the electron physics so for us the first prototype is the most macroscopic will make of a thermodynamic computer but we had to super cool it because that’s how the physics works out uh essentially uh we did a super connecting prototype it’s our first prototype but our next chips are going to be uh in Silicon and we’re really excited about you talk about embedding physics uh and embedding the algorithms
[01:05:00] yeah uh explain what that means I mean my whole career was figuring that out how to embed quantum physics into a quantum mechanical computer how to embed AI algorithms into quantum mechanical physics right and so for us it’s very similar sort of mindset um there’s ways to compile things into Primitives that then get compiled to Primitives of the hardware physics right uh in Quantum Computing they’re called Quantum Gates right and we have something similar um in terms of Frameworks that that we have internally and we’re looking forward to putting out a lot of the details uh on how to compile for uh any sort of algorithm to a thermodynamic computer and and frankly part of my goal is to uh partially open source the concept of a thermic computer so a broader Community can join us on this Quest you know we’re one effort for this but it’s it’s too important of a technology to to keep it on a shelf for for a few more years we
[01:06:01] don’t have time I mean the the potential for this and if you can describe the potential for this in the gener of AI World cuz today there is if we’re projecting the growth of generative AI yeah are we running out of chips or energy first bit of both right uh I I I think energy is going to be a big bottleneck cuz chips are reacting to to the market right now there might be an over production of chips I doubt it but it’s it’s going to be up there I what’s what’s the state of of the best chip today is it h200 what is what’s out there the best chips are manufactured in Taiwan by tsmc right with um machines from asml which is an supplier of lithography machines and those are Exquisite machines but um you know our our goal is to have a different way to embed the problem into uh the devices and for us to not depend on the same processes that everyone else depends on
[01:07:01] which we feel is important hedge because otherwise Taiwan is a very sensitive uh area and the world supply chain depends on it and so if we could you know forego our Reliance on on Taiwan to make the most Cutting Edge chips for AI that would I think put everyone at ease and be a net benefit did you see the movie Oppenheimer if you did did you know that besides building the atomic IC bomb at Los Alamos National Labs that they spent billions on biod defense weapons the ability to accurately detect viruses and microbes by reading their RNA well a company called viome exclusively licensed the technology from Los alos labs to build a platform that can measure your microbiome and the RNA in your blood now viome has a product that I’ve personally used for years called full body intelligence which collects a few drops of your blood spit and stool and can tell you so much about your health they’ve tested over 700,000 individuals and used their AI models to
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[01:09:01] Peter so the exoic chips in success and I’m curious what your time frame is would be how people would stand up their their gener of AI large language models on their every place yeah so at at first it’s going to be you know application specific devices it’s going to be smallish devices with not that many neurons right but they’re going to be very fast and very energy efficient um there’s all sorts of applications for that at the edge but over time yes as the chips grow in more and more of the program uh becomes part of the physics of the chip or they become thermodynamic programs then eventually we could run the whole program on the chip because for us most of the energy and time is actually consumed by the computers that have to interface with the chip and the chip itself um which is really weird think about um but yes over time we want to you know tackle the broader gener of
[01:10:00] AI Market it may seem far off at the m at the moment because we’re just building a couple building blocks but given that we’re using a lot of the existing Supply chains uh for semiconductors and how semiconductors have a very mature set of tools you know semiconductors are the things we can manufacture at scale the most reliably right and so if we rely if we use a lot of the knowhow there we can actually scale this technology much faster than previous attempts at novel Computing technology can give folks listening an understanding of the potential efficiencies in terms of power speed cost these these things how do you think about this because it’s pretty staggering it’s it’s um you know at a fundamental level like the the the the Primitive itself um of you know simulating the physics of this device uh if you were to just sample it right if you somehow embed your algorithm directly in the physics of the device uh
[01:11:01] for example Monte Carlo algorithm fits very nicely into the physics of the device a Monte Carlo algorithm usually you could program it on a computer a CPU or GPU you get maybe a th samples per second um you know this chip does samples on the you know 1 to 10 Pico second time scale depending on the on the land Escape but Pico seconds is below it’s thousand times below a nanc mhm um and so it’s it’s it’s really fast right and the speed up you can expect depends on the algorithm and how well it fits on the device so it’s hard to give like one number so help me understand what this looks like in the world ahead uh these xtropicalovex are they the base for AI everywhere at some point I mean that in
[01:12:00] success that’s part of the goal right that yeah that that is Ultimate success and it’s certainly possible we’re we’re starting with something humble you know just put on some Edge devices but over time yeah it would be the most performant chip not only for the cloud but the edge as well and um you know personally I would love to uh wear that wear some chips uh or or gadgets uh powered by our chips someday at scale of course um and uh you know I I think intelligence will be embedded in in Far More systems than we’re used to if we achieve our mission you know that’s on a you know 15 to 20 year time scale right like we have so much to do until then um but I think it’s it’s certainly possible and and to me it’s it’s going to be much faster in scaling than than Quantum Computing by by quite a bit and um so let’s go there one second in terms of um you know Kur and I had a conversation and he says listen we’re going to see as much change
[01:13:00] in the next decade as we’ve seen the last century M right in a very steep segment of the curve yeah and do you imagine I mean I can imagine a world where everything is intelligent we intelligence is embedded in every aspect of our lives and AI is is is everywhere yeah I I I think that makes you think people are you think going to be able to adapt to this speed of change I I I I think so I think if people maintain an open mind and you know the speed at which we can adapt you know if you look at the fundamental theory of natural selection from uh Fisher um the speed at which you can adapt a system is sort of uh bounded by or proportional to its variance um so having variance and how we do things helps us adapt quickly because if you try different things you get a better sense of what’s better you’re not locked into a local minimum maximum exactly
[01:14:00] right and you get out of the local minimum and that’s basically the message we’re trying to tell people is like I know right now if if you’re at the precipice of a lot of change and you know our first reaction is to stop and try to freeze things but that’s exactly how you become fragile to be anti fragile you need to be constantly adapting and high variance and malleable um and and uh you know if if if you this is you you know this is a theory of complex systems if you have systems that are malleable and flexible they can adapt if they’re too stiff they break and they have catastrophic failure which is what we’re trying to avoid um and so part of the message of acceleration is for us to be robust and adaptive to whatever is to come we cannot predict the future you provably cannot predict the future if you could there would be a couple people on Wall Street making all the money right now right and and clearly they they can’t right you can’t reduce the markets to a simple model and so you can predict the future all you can do is prepare for it and and and
[01:15:00] maintain uh dynamism and adaptability for whatever is to come and that’s what we’re we’re arguing for what’s next you’re uh you’ve got a Fab you’re going to be demonstrating silicon your chips on Silicon uh is this going to be a partnership with an Intel or Nvidia or you going to go it yourself what do you think at least right now we’re we’re looking at partnering with the traditional Fabs directly ourselves right um and so I I think a lot of the uh ways that uh the traditional players in digital uh Computing um you know the the ways they’ve been creating computers and programming them won’t necessarily car carry over to our systems and so yeah you’ve had to build a full Hardware software stack for this yeah and and I mean we’re still very much actively building it uh you know we’re looking to get our first prototypes um um you know in the coming year or so on the on the Silicon side are you hiring moonshot
[01:16:00] Engineers definitely I mean I think um anybody who is kind of tired by the old way of doing things maybe an electrical engineer that’s been in their career for a while wants to go for S something Cutting Edge and and and very ambitious should consider joining us I think there’s a lot of people in machine learning as well they’re getting Jaded by sort of the the monoculture around llms and and and Transformers today and just training big models on data there’s not a lot of artistry to it um what we offer is basically a big mathematical challenge to to figure out the new software stack and algorithms and architectures for for this new substrate and so we’ve been able to attract some really top talent there so anybody who needs a really strong intellectual Challenge and has a lot of uh AI experience should consider joining us and help us Pioneer this this new paradigm xtropicalovex well buddy thank you for your your passion you love what you do you and and
[01:17:00] you you know I think uh the importance of having an accelerationist mindset a a mindset for uh uh exploring and creating intelligence and being able to solve problems um you know I think mindset is the single most important asset we humans have and thank you for your mindset thank you so much thanks for having me and thanks for you know pioneering the way as a techno Optimus pleasure great to be here thank you [Music]