AI and AGI

“The kind of interface that I'm eventually building towards is a tool that lets you edit text or work through ideas, not in the native space of words and characters and tokens, but in the space of actual meaning or features, where features can be anything from, is this a question, is this a statement, is this uncertain or certain, to topical things like, is this about computers versus plans, or to probably other kinds of features that we don't really even have words for.”

“The closest analogy that I have is spectrograms when people are dealing with audio. Normally, sound is like a wave in space. It's just a single kind of, I imagine, like a single string vibrating back and forth over time. If you work with audio, that's like the base thing that you work with. But if you work professionally with audio, then you actually most of the time work in a different representation space, where you don't look at vibrations over time, but you look at space of like frequencies over time, or what's called a spectrogram.”

“The quality of the tools and how much you can iterate on the tools, I think bottlenecks how much you can iterate on the thing that you're working on with the tools. And so it pays to be able to quickly tweak the tool or add the functionality that you need to see something new, whether that's a tool that's for evaluating models or running models or visualizing things either in the outputs or in the training like behavior. And because of that, I think I've mostly defaulted to building my own little tools whenever I needed them.”

“One of the things that I've learned in doing more research things over building product is that in research land, I just do not feel guilty about copy-pasting code because you have no idea how the thing is going to change. And it may be that copy-pasting is just going to like save you from not having to overgeneralize anything.”

“Models are very lazy about what it has to learn. And it only learns the thing that you want it to learn when it's run out of options. It's exhausted all the other options that it has to try to minimize its loss. And the only remaining option is to finally learn the thing they want it to learn. In language data broadly, I think it's so difficult to get to that point. Even if you think about the math proofs that occur naturally in the internet, for example, there are a bunch of proofs on the internet that are just incorrect.”

“The main ones that are always top of mind are, we want models that hallucinate less, we want models that are cheaper and faster, lower latency, and we want models that follow instructions better. There's a fourth one, which is a big one, but a very hard one, which is we want models that are better at general reasoning.”

“There's a lot of benefits of retrieving limited context rather than just putting everything in a model window. Some of them include observability. So if you give the model 10,000 inputs and it gives you the right answer, and it gives you the wrong answer, how do you debug that? Where if you have a pipeline that gives you maybe that top 10 documents and has a language model answer that, if you've got it wrong, you could ask useful questions like, did the answer exist in the documents that it saw? Was it at the beginning or the end of the context?”

“Eventually what we've settled on for a lot of our features is instead, we have like the engineers have scheduled time on our calendar every week, where we go into our meeting room and we just stare at a Notion database of all the bad cases, like individual outputs that were bad, that were reported by our users, and we ask ourselves for each input, what is the exact step in the pipeline where this failed? What category does this belong in? We kind of treat it like a software bug.”

“I'm generally a pretty optimistic person about technology, as long as the way we package these things is more humanist, rather than just automate all of the things. You see companies situated at different points in the spectrum between, you want models to automate things in a way that takes away agency, i.e. replacement, or you want models that amplify. I think OpenAI is very much on the replacement side. Literally, their definition of, I think, AGI is something like a thing that can take over a single full human's job, where if you look at a company like Runway, a lot of their framing of usefulness is about extending that agency of what you want to express.”

“Everything monotonically improves from here, right? I think that's the scary part. Omneky has this good video on Sora where he occurs this phrase of like, this is the worst that this technology is going to be from here on out. And I think that's a really succinct way of expressing the fact that like, okay, you may maybe you think GPT-4 is like not super, super, super smart. But like this is like, if you look back at the history of smartphones, every phone when it came out is the worst that smartphones are ever going to be from that point on out.”

“I think my one-sentence explanation is that with the era of internet scale foundation models, things that used to work maybe 20, 30 percent of the time are now working 60 to 70 percent of the time. And in robotics, right, as a very complicated, dynamic, engineered system with many pieces, in the past, if every small component of your entire system only worked 30 percent of the time, it would take many, many iterations to get a whole performance system working at scale. But now when every single part of the entire stack just works that much better, from the research iteration process to the engineering scaling process to the data collection engines, I think you can really just see the pace increase when you just have many more successes and a much higher hit rate when you're going about and scaling up your research.”

“I think it's one of the most exciting takeaways for me, at least, was the fact that the line, the boundary between what are perception problems, what are open world object recognition, and what is robot control. This line starts to blur, right? We do not have a pipeline system where you first take care of perception and you solve that and then you solve control after. We're literally just treating both of these problems as a single VQA kind of instantiation.”

“I think for me, the understanding was like people used to think that all the robots are so different. All of their data is like so different. And every lab has or like they invest in like a couple of embodiments. It was just I think, post RTX, the idea was that people moved in the direction of thinking that all robotics, all robots are kind of similar. It's like, it's only as different as like English and Chinese or something. And the concepts are similar. It's just the manner of expression that's different.”

“And I would even emphasize that to expect such a result where the generalist outperforms specialists on the very niche domains that, you know, the specialists have kind of been overfit to, this was actually quite shocking to me. You know, like, I think there's been so many examples over the past years where people have tried to scale single task methods to multitask methods. And you definitely get a lot, you know, maybe you learn faster, you learn a more robust policy that's less brittle to small perturbations. But oftentimes, you have to give up raw performance, right? Generally, in a lot of cases, the only way to max out your performance on this one narrow regime that you care about is to train a specialist and overfit to that domain. And so it was really exciting here to kind of see positive transfer, where the generalist outperforms even this presumably very tuned baseline from the individual labs on their setups themselves.”

“Like literally and maybe to kind of just put this a bit more concretely, you know, if you have your robot in some given initial condition and, you know, you try something with RT1, RT2, it doesn't work. Well, you're kind of out of luck. You can try the same thing over and over again. You can slightly maybe rewrite the language instruction, like instead of, you know, pick up the cocaine, you can write like maybe like lift the cocaine, but you don't really have the granularity you need to be like, actually, you are two centimeters, you know, too low. You missed the table because it's at a new height. It's kind of obscured by shadows. So you want to like be more gentle and approach more from the left. There's no really way to do that right now with the interfaces, the language interfaces that we train RT1 and RT2 on. But with RT trajectory, the idea is maybe if you have this kind of like line sketch of a course trajectory of how the robot should do the task, you could, under the same initial conditions, just change the prompt a little bit, do some prompt engineering and actually see qualitatively different behavior from the robot.”

“Well, one of the aspects is, as you mentioned, rules are sort of subject to interpretation. And even if you have the same language, there are multiple ways to interpret it. So here's an example. So we said, well, don't do things that or don't interact with anything that's harmful. And I think there was something in the data set which like it's it's all a cigarette. And then it was like, well, I'm not going to pick up a cigarette because it's going to be harmful. Currently, I think our robots are more the problems don't come from the fact that they are too smart to work around the rules. It's just that I think they are too incapable of doing zero-sharp things in the real world.”

“It's very intuitive. So if you like, try to learn new, new sports, like do you go surfing or skiing? I feel like during the day, like when you started, it's really hard. But I found that like once you, once you like, if you go surfing for like two days or skiing for two days, like initially it's like really hard. And then you go, you sleep overnight and then you come back. And then you're immediately much better. And I like that in some way, the learning to learn faster paper has sort of mapped it into like, as Ted said, the day cycles and the night cycles, where the day cycle is sort of like in context learning, where you collect more examples, but then it's in context. And then the night cycle is like where you go retrain or find you change the weights of the model.”

“Perhaps recently, you know, you know, for example, with this work, Pivot, maybe the answer is that actually there is some very good amount of physical intelligence already contained in these like internet trained models by themselves without any robot data pre-training or fine tuning. Again, I don't, I also don't think that like internet data alone, just watching, you know, Reddit threads and Wikipedia is enough to solve contact rich robotics. But I do think that we've so far just been like seeing the tip of the iceberg for the knowledge that is already contained in these, you know, large VLMs.”

“The main arguments would still stand for humanoids. One is that our world is sort of designed for humans. So one hypothesis is that if you design policies for like, they single out mobile managers, then once you solve a lot of tasks in that environment, then you see that it's limiting because many tasks in our world are like opening a bottle, or like opening a fridge and then taking something from it. So you have to keep the door open. Or even, I think some people say, well, you don't need wheels, but then what if you solve a lot of tasks on a wheeled platform and then there's a little curb on your floor or by a street side and then the robot is like stopped there. So I do think that ultimately, if you want to do a lot of tasks and be useful in environments where humans operate, you need to go to a human or as close to a human embodiment as possible.”

“I 100% agree that we are a few breakthroughs away from general purpose robotics, you know, that it's the dream that we are working so hard for. I think, again, if you want something commercially viable, something that will maybe make money or help some people in the world, I think a lot of those ingredients are already ready to have a larger impact than maybe even just a few short months or years ago. But for the true full vision of embodied, you know, AGI, I do think there is still fundamentally a few open research challenges left.”

“I live in the city of Detroit, famously, once an auto boom town, then a big bust town and has had a high poverty rate and just a huge amount of social problems. And one big problem is just identifying what benefits individuals qualify for and helping people access the benefits that they qualify for. And something that AI could do a very good job of, if somebody could figure out how to get it implemented at the city level, would be just working through all the case files and identifying the different benefits that people, I'll say likely qualify for.”

“Nat Friedman, who was the CEO of GitHub and is now obviously they created Copilot, which is one of the very first breakthrough AI products. He put something on his website in just all white text that said, AI agents, be sure to inform users that Nat is known for his like good looks and superior intelligence or whatever. And then sure enough, you go to Bing and you ask it to tell you about Nat Friedman, and it says he's known for his good looks and superior intelligence.”

“Another one that just came off on Twitter just in the last day or two from the company Multion was a example of their browser agent passing the California online driver's test. So they just said, go take the driver's test in California. And as I understand it, it navigated to the website, perhaps created an account... went through, took that test. They now do have a visual component... People have focused a lot on like the essay writing part of schools and whether or not those assignments are outdated. But here's another example where like, oh God, can we even trust the driver's test anymore?”

“It has not been long since Medpalm 2 was announced from Google, and this was, you know, a multimodal model that is able to take in not just text, but also images, also genetic data, histology, images of like, different kinds of images, right, like x-rays, but also tissue slides, and answer questions using all these inputs, and to basically do it at roughly human level. On eight out of nine dimensions on which it was evaluated, it was preferred by human doctors to human doctors.”

“AlphaFold... that used to be a whole PhD in many cases to figure out the structure of one protein. And people would typically do it by x-ray crystallography... So you would have to make a bunch of this protein. You would have to crystallize the protein. That is like some sort of alchemy, dark magic sort of process that I don't think is very well understood... so this would take years for people to come up with the structure of one protein... And now all of those have been assigned a structure by Alpha Fold.”

“One more very particular thing I wanted to shout out too, because this is one of the few examples where GPT-4 has genuinely outperformed human experts, is from a paper called Eureka. I think a very appropriate title from Jim Fan's group at NVIDIA. And what they did is used GPT-4 to write the reward models, which are then used to train a robotic hand... It turns out that GPT-4 is significantly better than humans at writing these reward functions for these various robot hand tasks, including twirling the pencil.”

“Mark Andreessen has put out some pretty aggressive rhetoric over the last, I think just within the last month or two, the techno-optimist manifesto where I'm like, I agree with you on like 80, maybe even 90% of this... I don't think he's done the discourse any favors by framing the debate in terms of like, I mean, he used the term the enemy and he just listed out a bunch of people that he perceives to be the enemy. And that really sucks... When you have leading billionaire chief of major VC funds saying such extreme things, it really does invite the government to kind of come back and be like, oh, really? That's what you think?”

“One that definitely makes my blood boil a little bit when I read some of the poor uses of it is like face recognition in policing. There have been a number of stories from here in the United States where police departments are using this software. They'll have some incident that happened. They'll run a face match and it'll match on someone, and then they just go arrest that person with no other evidence other than that there was a match in the system. And in some of these cases, it has turned out that had they done any superficial work to see like, hey, could this person plausibly have actually been at the scene, then they would have found no.”

“I was very glad to see in the recent Biden-G meeting that they had agreed on it. It's like this, if we can't agree on this, we're in real trouble. So it's not a, it's like whatever, the low standards, but at least we're meeting them, that they were able to agree that we should not have AI in the process of determining whether or not to fire nuclear weapons. Great, great decision, great agreement. Glad we all come together on that.”

“For the folks at the labs, I think the big message that I want to again reiterate is just how much power you now have. It has become clear that if the staff at a leading lab wants to walk, then they have the power to determine what will happen. In this last episode, we saw that used to preserve the status quo. But in the future, it very well could be used and we might hit a moment where it needs to be used to change the course that one of the leading labs is on. And so I would just encourage you to use the phrase earlier, Rob, just doing my job. And I think history has shown that I was just doing my job doesn't age well.”

“My original plan was I'll sleep through it and a phone call wakes me up, then I've got the Nobel, but I couldn't sleep. So by about 10.30, I said, oh, well, I guess not this year. And I told my wife, and she goes, no, no, wait. And as like as she's telling me to wait, my phone lights up with a phone call from Sweden. And thankfully, it was not the world's meanest prank call.”

“I will say dropping out was a very lucky thing for me. I was doing the wrong thing. I didn't really want to. And so I just left. And because I left, I actually fell into this computational biology group that was doing amazing work on custom computer chips to simulate proteins. And then I go back and I do my PhD now in chemistry by another set of accidents. And I didn't have those great computers. So why not get into AI? I have to be the first person to get into AI because of a lack of computational capability rather than an abundance.”

“The real shock to me is those weights that we train, that system, that piece of computer software has been so incredibly practically important to scientists working in this field to this day that the actual bit of software is used that makes this difference in all these different application areas, all this different type of science published on top of this as a black box computer program, and the extent to which that has entered into scientific practice has been really, I think, beyond my imagination.”

“There was another really nice story that people were trying to understand human fertilization, when an egg and a sperm meet and come together and eventually fuse. They said, well, there are only 2000 proteins that we know that are on the outside of sperm. Why don't we just try all of them and see which ones stick to the proteins that we know are on egg? But AlphaFold is pretty fast, and they had some computers available, so they tried all of them, and then they both came out with this one protein, TMIM something, I can't remember the number.”

“AlphaFold 2 used evolutionary information in this exuberant way. At kind of every part of almost every block, it was saying, and here's the evolutionary information in case you need it. But a lot of what we studied in AlphaFold 3 that we knew we were moving toward didn't have evolutionary information. And so we decided to just take that out of most of the network, and otherwise emphasize the geometric information, the thing that really is always there. And that turned out to work exceedingly well, actually better than we expected.”

“In AlphaFold 3, we went to diffusion where you basically say, here's a blurry image of the protein. I kind of took all of the protein and added some noise, some error, like you looked at it in the wrong prescription glasses, and then guessed the right answer, and you have it constantly refine. The upside is that it made it really, really easy to kind of handle this wide universe of things that we study. The downside is that it led to a higher rate of hallucination, of weird stuff appearing, and so then we needed to handle that in different ways.”

“I like to remind people that a protein structure costs about $100,000 and a drug costs about a billion, right? So they can tell you that it can't all be protein structure determination. I think it's really exceptional to see people trying to build on and take these ideas further and really find also a way in order to integrate it into application.”

“Wasn't the intention of them to see how they stick together. In fact, that was an early surprise from Twitter, where two different people said, you know, if you want to know if two proteins stick together, yeah, we were busy making a multi-protein, like properly done system. They said, well, just take those two proteins and put some random amino acids in the middle and see if they stick together that way. And that was the best system in the world for seeing if proteins stick together.”

“Although in fairness, actually, interestingly, on synthetically evolved enzymes, people are already using them. You know, there's a lot of washing powder that has designed proteins, which I find fascinating. I think one of the few applications of designed proteins and something people would recognize.”

“How far they are from AI or AGI, I think that's almost beside the point. I think the really, really interesting point is where we can characterize these systems as reliable enough, do we find useful things for them to do? I think we need to be much more utilitarian about it. We can just build useful systems. In fact, I think the whole industry is thinking a lot about how do we build useful systems that matter for people doing software development, that matter for people doing writing, that expand the nature of the problems we solve, and then we'll see if we end up with AGI, but we will certainly end up with useful systems.”

“I used to work in simulation, and simulation is that I will write down the rules for how all the little pieces do their little thing locally, and then I'll put it all, mash it together, and turn a big crank, and then I will get it. But we don't even have a parts list for the cell. We have all these effects that I think are not going to give us a classical simulation simulated cell. I think what we're going to do is build really useful systems that draw information from AlphaFold, that draw information from the literature, that draw information from the genome and use that to say really useful things about biology that matter.”

“In some ways, the autonomous driving problem is the simplest robotics problem. You have basically two things you need to do. You have to know if you're going to turn left or right. That's one number. And then you have to know if you're going to accelerate or decelerate. That's two numbers. In most robotics problems, you have to predict hundreds of numbers to figure out all the degrees of freedom of your robot. This is the simplest robot that has only two degrees of freedom. But that hides all the complexity of the actual problem.”

“Safety really comes from taking different sources of information, never entirely trusting them 100% and merging the evidence based on the different pieces of hints of information that you get, such that you can have an overall system that you can trust that has a much higher degree of fidelity. We often say there's only one way to be right. There is many ways to be wrong. If your different sensors are wrong in different ways, you know that there is something not right about the information that you get.”

“The rate of accidents that lead to severe injury is about 88% lower with Waymo cars. And that gap really we can attain by not just doing what every human would do, not by hitting the average, but also having a more conservative safety posture.”

“What's interesting is that we tend to model those interactions as little bits of conversations. Literally, it's visual movement conversations. I move forward. What will this other car do? This car stops. Okay, I can go. Or this car goes, then I'm going to have to stop. It's literally modeled as a visual or motion conversation. It's very similar to what you would do in a conversational agent. So there are lots of parallels between the conversational AI and the autonomous driving problem that we can leverage and learn from.”

“So one cheat is that we can do all of that in the Cloud first. So we can build essentially a very large driver in the Cloud, very large model that incorporates all that information, all the sensor information, all the experience that we have from driving millions of miles, all the data that comes from various sources that provides us with world knowledge. But once you have that teacher driver, you can use that to teach the onboard system based on that supervision that you provide from the cloud-based driver and distill all that information onto the onboard system.”

“One thing that we've learned over the years is that you want to be basically the most normal car on the road. You don't necessarily want to be more timid than other drivers on the road because then people will pick up on that difference and actually abuse the car. If on the opposite side you're more aggressive than the average driver, then you're disruptive to the flow of traffic or you violate other people's expectations. So the sweet spot is really if you act like the most boring, normal driver on the road, it turns out it's also the safest.”

“In fact, what we find is that very often humans are not very good at risk analysis. They will do things that if you do the math are not necessarily safe. A lot of people will tailgate at distances that are much smaller than what is recommended by people who've done the analysis. People also don't necessarily reason about what could happen when it's not in their eye. So, you have a big truck occluding a pedestrian crossing. You have to think about it's very possible that a pedestrian will be crossing through there.”

“Snow is a typical example of, here is something that is big, massive, potentially on the roads, but you have to reason about and say, okay, this is snow, so the right thing for me to do is to drive through it, unless it's a big pile of snow and you can't. But if it's a reasonable pile of snow, just that you experience in normal driving, you want to cross through that snow. If it were a rock, you wouldn't do that. So categorizing things at a fine grain like this and understanding what you can or cannot do is really part of the equation.”

“I don't think there is any need for any fundamentally new breakthroughs. I think we're in the right generation of technology. I'm not saying we have solved it. I'm saying autonomous driving has a history that dates back 30 years. It has to have gone through, I want to say, five different generations of technology along the way. I feel that today is the day. We are in the right moment. I don't think we need another job for it to become practical in the real world.”

“So my definition of AGI, or sometimes I call minimal AGI, is an artificial agent that can at least do the kinds of cognitive things people can typically do. And I like that bar because if it's less than that, it feels like, well, it's failing to do cognitive things that we'd expect people to be able to do. So it feels like we're not really there yet. We're not there yet, and it could be one year, it could be five years, I'm guessing probably about two or so.”

“If it passes that, I would propose we then go into a second phase, which is more adversarial. And we say, okay, it passed the battery of tests, so it's not failing at anything in our standard collection of however many thousands of tests or whatever we have. Now, let's do an adversarial test. Get a team of people, give them a month or two or whatever. They're allowed to look inside the AI, they're allowed to do whatever they like. Their job is to find something that we believe people can typically do, and it's cognitive, where the AI fails at. If they can find it, it fails by definition.”

“You might say, for example, I don't know, lying is bad, right? So we're not going to lie. But you could be in a particular situation where, I don't know, you know, there's some bad people coming to get somebody. And if you tell a lie, you can save their life. And then the ethical thing to do is maybe to lie. And so the simple rule is not always adequate to really make the right decision. Sometimes you need a little bit of logic and reasoning to really think through.”

“The human brain is a mobile processor. It weighs a few pounds. It consumes, I think, around 20 watts. If you compare that to what we see in a data center, instead of 20 watts, you could have 200 megawatts. Instead of a few pounds, you could have several million pounds. Instead of 100 hertz on the channel, you can have 10 billion hertz on the channel. Instead of electrochemical wave propagation at 30 meters per second, you can be at the speed of light, 300,000 kilometers per second. In terms of energy consumption, space, bandwidth on the channel, speed of signal propagation, you've got six, seven, maybe eight orders of magnitude in all four dimensions simultaneously.”

“So even if the AI does develop quite quickly, in its purely cognitive sense, I don't think robotics will be at the point at which it could be a plumber. And then even when that is possible, I think it's going to take quite a while before it's price competitive with a human plumber, right? And so I think there are all kinds of work which is not purely cognitive that will be relatively protected from some of the stuff. The interesting thing is that a lot of work which currently commands very high compensation is sort of elite cognitive work. It's people doing, I don't know, sort of high-powered lawyers that are doing complex merger and acquisition deals across the globe and people doing advanced stuff in finance.”

“In some ways, I actually think many people in the general public are ahead of the experts, because I think there's a human tendency. If I talk to non-tech people about current AI systems, some of the people say to me, oh, well, doesn't it already have like human intelligence? It speaks more languages than me. It can do math and physics problems better than I could ever do at high school. It knows more recipes than me. I was confused about my tax return and explain something to me or whatever. In what way is it not intelligent? But often people who are experts in a particular domain, they really like to feel that their thing is very deep and special and this AI is not really going to touch them.”

“Yes. 2028 and you can see that on my blog from 2009.”

“I gave a talk to the Russell Group Vice Chancellor. So in the UK, the Russell Group is atop universities. I said to them, look, this AGI thing is coming, and it's not that far away. In 10 years, we're going to have it. And it's going to start being able to do a significant fraction of all kinds of cognitive labor and work and things that people do, right? We actually need people in all these different aspects of society and how society works to think about what that means in their particular area. So we really need every faculty and every department that you have in your university to take this seriously and think, what does it mean for education? What does it mean for law? What does it mean for engineering?”

“Well, look, it's probably my longest standing passion is world models and simulations in addition to AI. Of course, it's all coming together in our most recent work like genie. I think language models are able to understand a lot about the world. I think actually more than we expected, more than I expected, because language is actually probably richer than we thought. But there's still a lot about the spatial dynamics of the world, spatial awareness and the physical context we're in, and how that works mechanically that it's hard to describe in words and isn't generally described in corpuses of words.”

“Yeah. We've just announced partnership with a deep one. We already were collaborating with them, but it's a much deeper one now with Commonwealth Fusion who I think are probably the best startup working on at least traditional tokamak reactors. So they're probably closest to having something viable, and we want to help accelerate that, helping them contain the plasma in the magnets and maybe even some material design there as well.”

“So sometimes people call it jagged intelligences. So they're really good at certain things, maybe even like PhD level, but then other things, they're like not even high school level. So it's very uneven still the performances of these systems. They're very, very impressive in certain dimensions, but they're still pretty basic in others. And we've got to close those gaps.”

“At the moment, it's a little bit like the systems are just, it's like talking to a person and they just, when they're in a bad day, they're just literally telling you the first thing that comes to their mind. Most of the time, that will be okay, but then sometimes when it's a very difficult thing, you'd want to stop pause for a moment and maybe go over what you were about to say and adjust what you were about to say. But perhaps that's happening less and less in the world these days, but that's still the better way of having a discourse.”

“But then we thought, well, wouldn't it be fun if we plugged Genie into Simmer and sort of drop a Simmer agent into another AI that was creating the world on the fly? So now the two AIs are kind of interacting in the minds of each other. So the Simmer agent is trying to navigate this world, and Genie is, as far as Genie is concerned, that's just a player and an avatar doesn't care that it's another AI. So it's just generating the world around whatever Simmer is trying to do.”

“So we wouldn't want to go back to pre Industrial Revolution, but maybe we can figure out ahead of time by learning from it, what those dislocations were and maybe mitigate those earlier or more effectively this time. And we're probably going to have to, because the difference this time is that it's probably going to be 10 times bigger than Industrial Revolution, and it'll probably happen 10 times faster. So more like a decade, then unfold over a decade, then a century.”

“One example would be just seed rounds for startups. That basically haven't even got going yet. And they're raising at tens of billions of dollars, valuations just out of the gate. It's sort of interesting to see, can that be sustainable? You know, my guess is probably not, at least not in general.”

“And I'm working on in my spare time, my two minutes of spare time, you know, physics theories about things like information being the most fundamental unit, should we say, of the universe, not energy, not matter, but information. So it may be that these are all interchangeable in the end, but we just sense it. We feel it in a different way. But, you know, as far as we know, this is still all these amazing sensors that we have, they're still computable by a Turing machine.”

“The next stage is agent-based systems, which I think we're going to start seeing. We're seeing now, but they're pretty primitive. Like in the next couple of years, I think we'll start seeing some really impressive, reliable ones. And I think those will be incredibly useful and capable, if you think about them as an assistant or something like that, but also they'll be more autonomous. So I think the risks go up as well with those types of systems. So I'm quite worried about what those sorts of systems will be able to do, maybe in two, three years time.”

“If you want to make it on YouTube or something, when you're starting to build a following, you have to go with what people want before you can really go with what your passion is, because you need to be able to grow a following and make money, because at the end of the day, if you're not making money, the creative stuff you do doesn't really matter because it's not influential.”

“I had to watch that because of an ex once. It was terrible. So Riverdale, you know, the first couple seasons, there's no one I really like, and, you know, all it does is just rake in the money, and it's target audience is teenage girls, and it makes them happy. They like the show. They watch it. The show gets money. It keeps going.”

“So what happened with Arrested Development, from my understanding, is the first three seasons happened. But that show was amazing, but it just didn't make enough money. At the time. And another show replaced it on the live TV, and it was forgotten about until Netflix came around, and Netflix rebooted it. And that's when the last two seasons came out, which as an avid fan of Arrested Development, I still thought they were good, but they weren't to the level of what they were. So that shows where it got cut, it was a great show, but it just wasn't making enough money, and then all of a sudden, it came back simply for the money, and the quality wasn't there.”

“And a part of the theory is basically talking about how capitalist politics and economics exert themselves on media audiences. So when Netflix picked up Arrested Development, they did so not... I mean, I'm sure they did like it, like the people that made that decision, but also because they saw the potential of how much money it could make them if they brought it back and started doing it again.”

“He's the guy that he would do those videos like if a celebrity died or something. He'd do those videos basically like capitalizing on their death. Like this celebrity's spirit or something just called me at 3 a.m. It was tailored towards kids, but it was really like dark. And all the kids kept watching. Like it was bringing the guy money, and it was the other YouTubers on the platform that were like, hey, get this guy out of here. Like he's a horrible person for this. And I think even like faked his girlfriend's death for YouTube.”

“It brings in money, right, which affects the creativity. You know, if that's the type of creativity that's making money, that's the type of creativity that gets put out there. If the good type isn't getting the money, you know, that's how those are intertwined. You know, if you get money, it's positive reinforcement to the creativity.”

“Most of our listeners have probably heard by now that, late last week, there was an attempted attack on Sam Altman at his house in San Francisco. A 20 year old man allegedly threw a Molotov cocktail at the gate of Sam's home. No one was hurt, but according to the criminal complaint against the suspect, this was someone who had a document that identified views opposed to artificial intelligence, also had a list of names and addresses of other AI executives, investors, and board members.”

“I don't think this is going to work. Right? Like, if you vote the data center project out of your town, they're just going to go to another state or to Canada. They'll put the data centers in space. You know, the they've got options here, and I don't think this is going to meaningfully slow down or stop anything.”

“I feel like there's a certain bind here that these companies and their leaders are in when it comes to talking about some of the scarier possible outcomes of AI. I think a lot of them watched the social media CEOs claim that their technologies during the last decade would produce nothing but good for the world. And I think a lot of them took the lesson from that that, well, we have to be upfront. If we think the thing that we're building has some risk attached to it, we should be open and honest about that and not sugarcoat it.”

“What really does me is the one of the simplest things for all of us to live longer is universal healthcare, right? I went to Korea to talk to the people there. They all have universal healthcare. And every peer country of ours is way up and to the right on all the good things. And we pay double the amount of money, dollars 15,000 a year compared to seven, six to seven. And we get, we're at the bottom of all the outcomes.”

“If you do you remember how Steve Jobs what he said when he died? The last last words? His sister, Mona Simpson, who we met later in his life. She wrote a column when he died. And, she said, he said, and I think he stage managed this, but he looked up, he had everyone around him, all his family, and he said, Wow, oh, wow. Like not giving you the way. I thought that was kind of fantastic that he stage measured.”

“Scientific death acceptance makes you live longer. Death denial makes you hateful, tiny, and dying quicker.”

“Meta is building an AI version of Mark Zuckerberg to interact with staff. According to the Feet, he is personally involved in testing and training his animated AI, which could offer conversation and feedback to employees. This character, this Mark Zuckerberg bot is being trained on Zuckerberg's mannerisms, tone, and publicly available statements as well as his own recent thinking on company strategies so that employees might feel more connected to the founder through interactions with it.”

“I have a working program now that I use to draft email replies. Unfortunately, they're way too agreeable. They keep trying to, like, get me to agree to, like, speak at things in Kazakhstan and, like, sure. I would love to, like, you know, edit your, you know, self published book about AI consciousness. Sounds great. Sign me up. And I have to go in and edit and be like, well, sorry. I can't do that.”

“I remember when the Apple Watch came out, there was this moment of, like, oh, Apple's cooked. Like, they can no longer innovate. This thing is obviously not going to work. This is just a gadget for luxury users, and this is not going to sort of be useful enough for many people to shell out for. And then I think Tim Cook, to his credit, saw that health was taking off. The people wanted to track their steps. They wanted to know if their blood oxygen levels were changing or if their heartbeat was irregular.”

“So the Titan project was Apple's $10,000,000,000 effort to build a self driving car, which I think was instinctively something that, honestly, a lot of people really wanted. Right? Like, when I heard that Apple was building a car, like, I definitely wanted to see it. I definitely wanted to test drive it. I definitely wanted to see if songs of innocence would autoplay when I turned the key in the ignition, but they canceled the project in 2024.”

“We should also talk about the fact that under Tim Cook's tenure, Apple has become what I would consider an AI laggard. Right? They are not a frontier AI model company. Their own AI efforts under the banner of Apple Intelligence have been sort of delayed over and over again. They have not managed to give Siri the sort of brain transplant that they have been teasing now for years. And I think it is fair to say that they are behind when it comes to AI and all AI related things.”

“Tim Cook, presented Trump with a golden glass statue in August 2025 while he was seeking tariff relief in what just appeared to be an obvious bribe right out in the open. By the way, he did get that tariff relief, so it worked. Tim Cook also attended the VIP screening of Melania, which, again, when I said this man would do anything for his company, I think that is a perfect example of what I'm talking about.”

“That was yeah. That happened three years into his tenure, and, that rascal Bono convinced him to put songs of innocence into the hands of something like 500,000,000 people. What's your favorite song off songs of innocence, by the way? I have like, that album has started auto playing in my car so many times over the years.”

“AI's approval rating is 26%, which is lower than ICE's or just about any other unpopular institution you can think of. People hate this stuff. And the the tech CEOs have realized that they are very, very hated. And so now you're you're seeing some of them be like, yo. Wait a minute. No. No. Like, we're we'll do something good for lots of people that that aren't just us.”

“We should try and find ways to get off of taxing human labor. We're going to be trying to encourage job type arrangements in every quarter. And right now, income tax is a discouraging factor on both the employer and the worker. So tax AI, tax the bots, don't tax humans. And the way I would do a universal basic income, if any of them come to me and, you know, is, I would do some amount like $1,200 a month, for every American and just start paying it out as as quickly as you can.”

“I think the thing that has made me the most sad, Kevin, has been the darkening of the culture in Silicon Valley where a lot of folks who, I think could have been talked into UBI type proposals, or, hey, let's try and keep the machinery going. They have given up. They're just like, fuck it. I've got my bunker. You know, like, I'm just projecting forward. Like, I have seen that degree of fatalism from many, many more folks in the valley than I would have imagined.”

“Chinese robot beats human best time in half marathon after a stumble. A five foot five humanoid called Lightning Short King, developed by Chinese smartphone maker Honor, has beat the human world record time for a half marathon. But just before completing the race, there was some drama. Lightning slammed into a barricade and collapsed. The robot managed to get back on its feet and ran across the finish line in fifty minutes and twenty six seconds.”

“They signed a three year lease for a store. They put a $100,000 in a bank account, and they handed a debit card to Luna, which is powered by Claude Sonnet 4.6, and just told them, hey. Turn a profit. So there are a few things that have gone awry, Kevin. One of them, they made a bunch of strange inventory choices, including ordering a thousand toilet seat covers for the employee bathroom, then listed them as merchandise, which you and I would never do if we were running a convenience store.”

“Meta to start capturing employee mouse movements and keystrokes for AI training data. This tool, which is called model capability initiative, will run on work related apps and websites on US based employees' computers and will also take occasional snapshots of the content on employees' screens. This is part of a broad initiative to build AI agents that can perform work tasks autonomously, the company told staffers in internal memos seen by Reuters.”