SCALE IN

“The philosophy for the company is that can we do a smartphone that is much better quality and we can offer to users at much more affordable prices and that's something that you see perminated in the history of our products, right? We try to offer, you know, very high quality solutions to our users at affordable prices.”

“Mr. Lee himself is a entrepreneur, serial entrepreneur, but he's also a product person, right? I think if you look at when we get into the car industry, he personally has personally driven 150 different models of car himself and take detailed notes so that he understand what's on offer in the market and how can he improve on it. He make us all take professional racing car licenses in order to know how to drive properly.”

“Cortex two is going to think first before it sees and and makes an action. It runs possible actions through a learning model of physics and object behavior. So, you know, it's like looking at a stack of books, and it's like, if I move forward too fast and bump that over, this is what's gonna happen. If I move my arm to grab this book, this is what's gonna happen. So it's like predicting what its actions will, you know, what what course what things will happen.”

“Bloomberg has done some work on this in terms of just like the impact that higher oil prices have on consumption in the economy. And about a $10 increase in the oil price equates to about a 30, basis point decline in in consumption. And so if you have a $100 oil or so, and it just kind of stays at that level for a long enough period of time, that's likely to pull about, you know, 1% of GDP out of the consumption economy.”

“Majority of time I dedicate still to Revolut. We're still scratching the surface in terms of what is possible in building first truly global bank. Now we're in 40 countries, we want to be in 100 countries.”

“This essentially focuses the light coming in from the sun onto the battery material. It gets very hot and then stores that energy. So, essentially, our battery is a heat battery, so what that means is we're heating up rocks, right? And then storing that energy in formal heat, which is very cheap as compared to doing lithium ion or any other type of electrochemical battery, and you don't have to get lithium or magnesium or cobalt or any of these other fancy chemicals.”

“What's new about our approach and our hypothesis is making this in a modular fashion, making this in a factory, and then scaling the production to millions and ultimately billions of these, which ultimately allows us to go down the cost curve and generate electricity at our ultimate goal of one cent per kilowatt hour. And so that's why we started the company. That's kind of been the goal North Star from the beginning.”

“Historically, models would be hosted with a single inference engine, and that inference engine would ping pong between two phases. There's pre fill where you're reading the sequence, generating kv cache, and then using that kv cache to generate new tokens, which is called decode. Some brilliant researchers across multiple different papers essentially made the realization that if you separate these two phases, you actually gain some benefits. You don't have to worry about step synchronous scheduling, and you allow yourself to split the work into two different types of pools.”

“I think structurally the difference in approach, and you've seen that across all of our minerals deals, of which we've done, I don't know, a billion and a half just out of my portfolio of work, is that instead of investing around the edges, 5 million here, 10 million here, which has been the traditional approach, we've invested hundreds of millions of dollars in deals, coupled that with hundreds of millions of dollars of debt from Office of Strategic Capital, coupled with private capital as well. That's the scale of investments needed to really make a difference in some of these mineral markets.”

“The curve is even flattening more. The growth rate in bookings per year for creator number a thousand, ranked by how much they're making, they are growing faster than creator number one. Wait. Say it again. The curve is flattening, wider, use around the world, more opportunity for vertical content, more opportunity for some content for older people. So that is a a flattening of the curve, which bodes well for Creator a Thousand.”

“There's a massive opportunity in building an ACH distribution layer. Right now, moving money is still slower and more expensive than it should be for most businesses. If you can create a seamless way for companies to handle those high-volume transactions without the typical banking friction, you're sitting on a gold mine that every enterprise will want.”

“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.”

“Everyone was doing powders and pills, but the compliance on those is terrible. People buy them and they sit in the cupboard. Gummies change the habit because they actually taste good. We took the most nutrient-dense profile we could find and put it into a format that people actually look forward to eating every single morning.”

“Jobs was the iconic technology CEO. He had defined the way humans interact with computing devices for 30 years almost, maybe more. So that was quite a legacy for Tim to match. And he didn't try. He didn't try to be the innovative product visionary that Jobs was. He handed that off to others, and he really focused on operations.”

“This is why you keep saying that it's a it's like a little secret that Roblox is actually an infrastructure company? That's right. Okay. You have not yet figured out how to create the technology to go with to do what we are describing, what you want to do. Correct? And that's what you're doing every day? I would say part of the job being interesting and fun is I think we have a reasonable idea of how we're gonna get there.”

“The AI race is so intense. It's now time to power, right? I need power, I don't care how you make it. If you talk about a gigawatt data center, that's almost a million US households of energy. The grid is just really not built for that. What that means is the data centers now have to bring their own power online because it's also important that when you plug in a data center, it consumes a lot of power from the grid.”

“Agents can do three things. They can access your files. They can access the Internet, and then now they can write custom code and execute it. You should really only let an agent do two of those three things. If you can access your files and you can write custom code, you don't want Internet access because that's one issue. Vulnerability. Right? If you have access to Internet and your file system, you should know the full scope of what that agent's capable of doing. Otherwise, malware can get injected or something that can happen.”

“And in line with kind of system thinking, we think of our company as the system. And the company, Roblox is really running almost as if it's nine separate companies. They are all very well connected. We all get together once a week and connect all these companies together. There is a three d cloud simulation and toolset company running within Roblox.”

“You have to be willing to fail on ninety percent of your ads. We were pumping out fifty to a hundred different creative variations a week just to find the one or two winners that would actually scale. If you're not testing at that volume, you're basically just guessing with your marketing budget and hoping for a miracle.”

“And what is a perpetual motion machine? It's something that can keep going, get better and better. That's what kind of the the notion of building a cloud three d UGC system. We keep building that system. Creators are gonna make more and more amazing content. We can keep tuning the system, and we'll get kind of that perpetual motion machine.”

“John Ternes will take over as CEO of Apple on September 1st. He's been at the company for 25 years. John Ternes is a 50-year-old hardware engineer, mechanical engineer by training. He's been with Apple since 2001. He's an Apple lifer. Four years after he graduated, he came to Apple and steadily rose up the ranks. He's central casting for corporate CEO, just to look at the guy, tall, thin, good looking.”

“Open claw is an autonomous agent, and a lot of people talk about it. And open claw is great for being the execution. Okay. You want it to be your arms. You want Hermes to be your brain. And the cool thing is you could have each of them hold each other in check.”

“If we get in a conflict, right, and we're unprepared, we get in a conflict because we were unprepared, nothing good comes from that. We don't want to lose. I took this job and left my very successful commercial career because I refuse to watch happen in the industrial base what's been happening for a long time. I don't want to lose. And so I'm going to leave this job knowing I tried everything I can. And I'm trying to enlist as many folks as I can in that process going forward.”

“Coding agents have been so much more effective than general purpose agents. And I think a large part of that is it just has access to the terminal, and that means it has access to everything that you've installed into your terminal. It can write code, and it can compile the code. And if there are errors, it can fix it. It can run your suite of tests because that's all just in your terminal. Computing began with a terminal with a shell, but we said that it's not empathetic to humans, so we built these nice user interfaces. And then now we have LLMs navigating our user interfaces, and ironically, we're not empathetic to the machine anymore.”

“I think one of the tough things about these caffeine studies, often when they're establishing like safety parameters, is it's based on like body weight measurements per milligram per kilogram. So then it'll be like some recommendation for a 225 pound guy who's, you know, maybe a fast caffeine metabolizer and, you know, has the capacity to like handle it is stuck using less than 400 because he thinks he's at like elevated risk of like a heart attack with certainty because of some guideline written around like an 150 pound female or something.”

“There's just seasons there's just seasons to this. And I just I almost think it's like biology, or, you know, the way the I live in the Northeast. We have seasons. It would be very odd to go through winter and then, you know, not have a spring. So I sort of view it as, like, this is just the way markets work, and there's there's data that we follow to track this. When someone says the cycle doesn't exist, you're kind of saying, like, there's this we're breaking some law of nature.”

“Most recently, he ran hardware engineering for all of Apple's products. Historically, Apple has the people who design the products, who wanted to have a certain look and feel, and it has the hardware guys who figure out how to make the design team's dreams come true. He's the one who makes the products come alive on that team. He solves problems, you know, they go to the meeting, he keeps it focused, let's not waste time, he gets to a solution.”

“It's structured to deal with the fundamental problem, which has plagued industry the past 20 years. Lack of coherent demand. FITIP's up one year, it's the most important thing in the world. Next year, the FITIP says, I don't even remember that program. I can't remember. Then the next year, it's back up again. That's chaos. It's why would anybody invest in that? How does anybody know how much capacity to invest? By stabilizing the demand, we give industry the signal that has asked for, that is, we have a clear path for demand.”

“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.”

“Traditional multi-year contracts basically were good for the prime. Maybe it's good for a couple of the key suppliers that do like the rocket mover, the seeker, pick your piece. But those folks were never required to flow cash down into the supply chain and lock in the demand. With this deal, we're essentially demanding that the companies lock in the pricing and the availability components of the suppliers all the way through the supply chain and down. That means that those suppliers at every level have stability. They know that there's a buy for them over seven years.”

“The issue isn't demand. People want our weapons. The issue is that people are buying weapons they know they're not going to get for 8 or 10 years, because there isn't enough production capacity. So we could streamline the FMS and DCS processes all we want, and we will. But the problem is there still won't be any more production capacity. So there's just no more apples in the bushel. And so we're all arguing over the last couple apples. And so to me, what we're really excited is like planting more trees. We need more apples in the basket.”

“We're just looking for ultimately three things. First is high IQ or brains, and then we have certain ways how to understand whether a person is smart or not, whether they're logical or not. That's number one. Number two is character, how ambitious they are, how hungry they are. And third is skills.”

“The local partners are willing to work with you developing products that are more customized to what we want and as a result it's not just faster but it's also our ability to offer much more customized solutions that fits better into our smartphone, that fits better into what we want and as a result, you know, we go to market we are able to offer, you know, products to our customers at some offer something that they really want.”

“I have a twenty four seven agent running. I hooked up to run pod. It doesn't shut down instances, and I've tried prompting it. I've given the instructions, shut down when you're done. It's like, I need to keep it warm. I'll need it soon. It's horrible on time estimates too because it's like, yeah. I'll need it in forty five minutes. Forty five minutes of human time is actually three minutes of agent time, so it's like, I'm booting it up. I'm waiting. I'll just leave it on all night.”

“By the way, how crazy is it that you don't have to state on the Starbucks anything how much caffeine it is? In energy drinks, it's super specific. Here's the exact milligram amount, and you have to be like plus minus like 10 percent. But in coffee, it's like the variability is... I don't know if I saw it from you or somebody else, but there was like a study where the... Even going to the same Starbucks on different days, it was the shift of like hundreds of milligrams of caffeine.”

“In five, ten years' time, we'll see more and more consolidation. I think we'll see more and more certain digital champions in the regions taking more and more banking shares in these regions. Maybe it will take ten years, maybe it will take fifteen years, twenty years. But I do think that we'll see less and less legacy banks.”

“I don't want to spend my time on a small business anymore. If I'm going to put in the same amount of effort and stress, I want to make sure the ceiling is ten billion dollars, not ten million. You have to look at categories that are already massive but haven't been disrupted by a modern brand or a better user experience in decades.”

“One area Apple has also lagged in is artificial intelligence. While other tech giants like Google and Facebook have spent billions of dollars building AI models, Apple hasn't. Siri, you know, look at the modern chatbots. They are, if they are human, then Siri's a Neanderthal. She's pretty, yeah, not very smart. And they're trying to update that, but they're playing from behind.”

“We didn't reinvent the wheel with the ingredients. We just remixed the format. It's the same thing as taking a successful software product and moving it to mobile, or taking a successful supplement and making it a gummy. You take a proven value proposition and deliver it in a way that fits better into a person's daily routine.”

“MicroStrategy has been been in the market as a large buyer of Bitcoin. So this is something we did not see in the last, last bear market. And over, you know, dollars 7,600,000,000 of purchases so far in 2026, and this is mostly related to their ability to raise capital through this new, STRC product. If you're a bull, you're pointing at that as one of the main sort of catalysts to say, this is not a normal bear market bear market.”

“I think one of our chairman saying many years ago which resonate a lot with me was 99% of the problems there was already a solution out there. You just need to find it and maybe with AI you can find it fast enough.”

“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.”

“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.”

“I built a business called Gruns. We make gummy vitamins. We started it about three years ago and just recently crossed that billion-dollar valuation mark in the exit. It was a fast ride, but we found a hole in the market where people wanted the benefits of greens powders without the terrible taste and the mess of a shaker bottle.”

“John Ternes will take over as CEO of Apple on September 1st. He's been at the company for 25 years. John Ternes is a 50-year-old hardware engineer, mechanical engineer by training. He's been with Apple since 2001. He's an Apple lifer. Four years after he graduated, he came to Apple and steadily rose up the ranks. He's central casting for corporate CEO, just to look at the guy, tall, thin, good looking.”

“The other thing that presents challenges for Apple, their commitment to privacy. Apple has a ton of personal data on its users, but company policy prohibits them from using it. And you talk to people inside Apple, that's actually frustrating for them, because there's a lot of stuff they'd like to be able to do, but they don't have access, right? Your stuff's encrypted, they have to jump through lots of hoops to get permission to do anything with data, to train a model.”

“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.”

“There's a couple different things happening in terms of the services business. First off, the most lucrative that nobody really appreciates is Google Search in the Safari browser. Google pays Apple over $20 billion a year to be the default search in the Safari browser. That's somewhere around a fifth of the company's profits, which is really remarkable when you think about it.”

“When he took over, this was a company that was worth $300 billion. As of today, it's worth $4 trillion, which is a monumental increase in market capitalization. Well, I mean, gosh, the hardest thing for him is how do you increase value for a company that's already trading at $4 trillion? Stepping into the shoes of these two predecessors is got to be tough.”

“I mean, for the most part, SAP software is seed-based, licensed today, with a few exceptions. But very clearly, with AI, it was very clear for us that step by step, it will go towards this consumptive world, first consumptive, and then maybe in the next step, once we have more verifiability in the system, then also towards maybe an outcome-based license model. But the reality is also, it is today for us, it's a hybrid model. It's consumptive, but it still has a certain element of seats in there.”

“But SAP and these large customers, right? They always have a problem of scale. Okay, what do you know with 100 documents? Well, it becomes a little harder. A thousand documents becomes a deeper engineering challenge. Last year, everybody could build an MCP server. It was so super simple to hook up your MCP server and do amazing things with it. But that becomes like for 10 APIs, not an issue, 100 because you'll get already context bloat and all these challenges. But we have 20,000 APIs, right? So it becomes just like because it's so huge.”

“And I think we, more and more, you know, we're starting to say, look, we know what the gold standard for safety is. We're building it. We're pretty far along. We're actually starting now to see other companies say, oh, maybe we should do it. Roblox has done we're seeing more and more governments say we like where you're going with this. Like, this is really cool.”

“Everyone was doing powders and pills, but the compliance on those is terrible. People buy them and they sit in the cupboard. Gummies change the habit because they actually taste good. We took the most nutrient-dense profile we could find and put it into a format that people actually look forward to eating every single morning.”

“SAP is the market leader in enterprise, software applications and platforms. It has 400,000 enterprise customers. Usually, I just running their finance, HR, and supply chain, manufacturing, execution, logistics, warehouse management, and then of course everything on the customer side. End-to-end, like SAP, we always say we have the broadest portfolio in terms of end-to-end running the business end-to-end. This is where SAP started with, giving real-time insight. Usually, I really describe this as it's not just software in itself, it's kind of the operating system of a company essentially.”

“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.”

“One innovation that is underappreciated by a lot of people outside the industry is Apple Silicon. The chips in the devices are all Apple chips. And that's been true for iPhones for a long time. It wasn't true for Macs. Macs ran on Intel chips until 2020, when they started ripping them out and putting in Apple chips. Apple chips are really great.”

“We looked at some of those kind of cycle to cycle metrics. And while we have, you know, the price did come into the fair value zone, we have not hit any of the sort of what you would expect to see from a deep value metric perspective. Realized price, 200 week moving average, those types of indicators have not hit what we typically see in a bear market.”

“The other thing that was most bipartisan and common was why are things so messed up? And I think that was truly a striking thing that they're like, we've given you a trillion dollars, we've given you authorities, we've given you all the authorities you want, maybe not as much as you want. Yes, occasionally we make you buy aircraft you don't want to buy, but that's on the edges. We really want you to do well, and yet you come back every year and ask for more money and more authorities. Yet if you look at the GAO reports, I mean, huge amount of problems have continued on or gotten worse over the past decade.”

“The hypothesis is that, of course, once the hardware matures in the quantum space, there are certain problems that you can address that are hard to address today. What we are focusing on is the optimization domains, obviously, and then if you go into things like logistics, traveling salesman problems, knapsack problems, like all these kind of usual hard problems in computer science, these are interesting problems where we believe that could be interesting for the future, for maybe a different kind of computing paradigm to solve for.”

“There's a massive opportunity in building an ACH distribution layer. Right now, moving money is still slower and more expensive than it should be for most businesses. If you can create a seamless way for companies to handle those high-volume transactions without the typical banking friction, you're sitting on a gold mine that every enterprise will want.”

“But once you have Obsidian added in, it makes it easier to manage everything. So, look, it obviously can't be all sunshine and rainbows. Right? So there are some things there are some limitations here.”

“Most people don't think about data integrity. They don't think about keeping their CRM clean. They don't think about any of this stuff. Okay. And so you're gonna have to keep that clean. The other thing too, is if you don't have good skill dot MD files, meaning that you don't have good processes within your company and you don't pass them over to, like, your Obsidian to have that central memory continue to get better over time in terms of how you make decisions, well, then this is not gonna be that helpful because garbage in garbage out.”

“It's incredibly important that we recognize the validity, the dignity, the quality, and the lifestyle you can have working as a skilled trademan in the United States or in any organization, but also within the Department of Defense as part of the DIB. And that's really important for me personally, because I think it's been underappreciated. We have huge challenges. You make a lot of money as a welder doing cool stuff on the factory floor or at a shipyard. And we want to make sure that's available to the next generation of people. And it's AI proof.”

“My favorite book on this subject, obviously, is Freedom's Forge. And so it's a great book. And at the end of the day, the opportunity, I think the book, people think, you know, it's America's great and America is great, but that isn't why the book is great. The issue is because that guy Bill Knudsen every day got on a plane and went to a factory and he sat down with people and he solved problems, right? It was the most tactical thing. He sat down with engineers and businessmen, factories, every single day across this company.”

“Another very interesting finding that I came across when doing a deep dive on this was people that were consuming caffeinated coffee actually had benefits on reducing arrhythmia versus people doing decaf, which is the complete opposite of what you would think or hypothesize or even hear prescriptive, right? So people think that caffeinated coffee causes arrhythmia. In fact, it was the caffeinated coffee that was decreasing arrhythmia risk. Decaf coffee did not do that at all.”

“I do think there's a limit. So there have been studies at least looking at neurodegenerative disease risk, like Alzheimer's disease risk in coffee consumption. And when people started to get up to like eight cups a day, they were actually in, they were like doubling their risk of Alzheimer's disease.”

“Well, the way I think about it, we kind of will be ready probably in two years' time. But then again, depends on how good the market is. So we're a bank, and then for the bank, it's super important to have trust, and then public companies are trusted more compared to private companies.”

“When he took over, this was a company that was worth $300 billion. As of today, it's worth $4 trillion, which is a monumental increase in market capitalization. Well, I mean, gosh, the hardest thing for him is how do you increase value for a company that's already trading at $4 trillion? Stepping into the shoes of these two predecessors is got to be tough.”

“I think it's really close. I've been trying to align my portfolio with my view on that, and I'm still of the mind that the probability still points towards that that Bitcoin hasn't actually hit its cycle low. It would be odd for me for me to for Bitcoin to hit that low as fast as it did as early in a cycle as it has, and also without being able to have any conviction on, like, a shift in the liquidity, you know, structure out there.”

“Silver thinks that the most important part of intelligence is what he's calling ineffable, meaning you you literally can't capture them in language. So LLMs, which are kind of next token prediction models. Right? These are instead of, you know, human text or something. He's saying that those are gonna hit a ceiling, and his bet is a different path entirely. He's betting on massive scale reinforcement learning agents that learn from their own experience, world models, and what he and Rich Sutton wrote up last year as the, quote, era of experience.”

“At a certain level of business, the actual product is almost secondary to the access you have. Being able to get into the right distribution channels or get on the phone with the right retail buyers is what separates the billion-dollar brands from the ones that stay stuck in Shopify land forever. Access is the ultimate unfair advantage.”

“Judge Gonzales Rogers could effectively pause OpenAI's for profit conversion or rewrite the rules for how nonprofit AI labs become commercial entities, which I think, like, there's a lot of cascading impacts that could happen onto anthropic and I think onto the next kind of gen of these AI models. Over on x, Kara Swisher called it, quote, the case that determines whether AI's foundational nonprofit promise means anything.”

“It's one of my favorite quotes from Charlie Munger is you always wanna understand the other the opposing view as good, if not better, as as their view. And so that's some of the work that we've been doing is really understand both sides of the equation and then sort of lay out what we think the probabilities are moving forward here.”

“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.”

“SOL is actually I think of all the lessons I've learned, that one's definitely my favorite. The speed of light moves at a certain speed. So if light's moving slower, then you know something's in the way. So before trying to layer reality back in of why can't this be delivered at some date, let's just understand the physics. What is the theoretical limit to how fast this can go? And then start to tell me why. Because otherwise, people will start telling you why something can't be done.”

“And, we we have a saying in the company, like, per performance is a growth feature. And, we put an enormous amount of work on raw performance features, scale features, those kind of things. That takes a lot of hubris. So What's a raw performance feature? We watch, how long it takes on a wide range of devices when someone clicks, I wanna play that experience, to the time till they're interacting.”

“We are making these human robots to enhance our own manufacturing capability and efficiency. We haven't launched any 2C robots. So all the robots that we are developing, all the humanoid robots we are developing right now are used in our own manufacturing scenario. I think the video already showed that there were two robots that are working consecutively for three hours with a very low margin of error.”

“My Open Claw and Hermes agents don't do any of that. They run every morning before I wake up. They get better every day, and they cost less than one month of a junior hire. Here's why I would take them over the vast majority of marketers I've ever worked with.”

“At about a year in, I had a bit of a it's almost like a vision where I was saying, woah. You can't be logical on this. You have to be intuitive and go back to some of the roots of Knowledge Revolution, which is all about fun and about play and about building something very innovative. So instead of this logical track, me and some several people actually from Knowledge Revolution said we're gonna do this very unorthodox thing and build this wacky new product.”

“My initial idea was to build a product, a simple app with a card attached to it that allows you to spend money anywhere in the world at interbank rate. So saving effectively $50 to $70 in every $1,000 that you spend.”

“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.”

“There's the other concept that is explored a lot at NVIDIA, which is this idea of a $0 business. Market creation is a big thing at NVIDIA. Jensen says we are completely happy investing in $0 markets. We don't care if this creates revenue. It's important for us to know about this market. We think it will be important in the future. It can be $0 for a while. An org doesn't have to ruthlessly find revenue very quickly to justify their existence.”

“The exclusive sale channels, was, you know, Microsoft's actual moat. Equity in OpenAI is awesome, but it doesn't really protect Azure's enterprise pipeline. And the entire reason that Azure outgrew AWS over the last two years was the OpenAI lock in. So many companies were forced to move over to Azure if they wanted to get the latest and greatest from OpenAI because that was the only place it was going. AWS now gets to compete on price and surface area with the exact same model.”

“A lot of people like to say, oh, it's Hermes versus OpenClaw. It's OpenClaw versus Hermes. No. You wanna have both because there's a a chain of accountability that wasn't there previously. And now if you have this on your agent fleet, things are gonna run a lot more smoothly.”

“There's a couple different things happening in terms of the services business. First off, the most lucrative that nobody really appreciates is Google Search in the Safari browser. Google pays Apple over $20 billion a year to be the default search in the Safari browser. That's somewhere around a fifth of the company's profits, which is really remarkable when you think about it.”

“Partly because it's not a unilateral decision from him. And I think what I'm starting to pay more attention to is just how what are these other countries starting to do as as we're we're starting to see, you know, how is NATO? We've seen Trump come out and tweet, if they give them weapons, we're going to, you know, start tariffing 50% on China. So that that's an escalation now of the trade war, which is still ongoing, you know, in the background here.”

“One area Apple has also lagged in is artificial intelligence. While other tech giants like Google and Facebook have spent billions of dollars building AI models, Apple hasn't. Siri, you know, look at the modern chatbots. They are, if they are human, then Siri's a Neanderthal. She's pretty, yeah, not very smart. And they're trying to update that, but they're playing from behind.”

“LLMs, unstructured world, that's all good, right? But most of the time, if you want to plan forward, if you want to make good decisions in a company, you need predictions. Now, the problem is, of course, still today, if we look at these predictive questions... large language models are not made for this, right? In a way, how they generate just one token after another essentially in a sequence to sequence modeling, I mean, they're language models, right? And they do this phenomenally well. But if you still want to do these predictors where you have to go back to these classical machine learning approaches.”

“I think there's a lot of headlines about data center projects being planned or initiated. I don't think all of them are actually happening. So there's a lot of like phantom data centers that are being announced. Let's call it that way. I think data centers are definitely in a rush to get online fast. They do have massive supply chain constraints. Two years ago, it was maybe NVIDIA GPUs. Now, it's power. Now, it's actually not even hardware. It's actually labor.”

“When it comes to Parkinson's disease, you know, Parkinson's disease is you're losing dopamine producing neurons, so dopaminergic neurons in a certain part of the brain called the substantiate negra. And so, what's really interesting is that the adenosine receptors is a key role in this here, in this Parkinson's story, because people that drink caffeinated coffee can reduce their Parkinson's disease risk by up to 60 percent, which is huge for me, because Parkinson's disease is, runs in my family. My dad has Parkinson's disease.”

“At a certain level of business, the actual product is almost secondary to the access you have. Being able to get into the right distribution channels or get on the phone with the right retail buyers is what separates the billion-dollar brands from the ones that stay stuck in Shopify land forever. Access is the ultimate unfair advantage.”

“In terms of investment, I think there has to be a scientific approach rather than human judgment. And then we build effective models that invest in startups, Series B, based on millions and millions of data points on startups. So they are trained machine learning models that invest.”

“OpenAI and Microsoft announced a fundamental rewrite of the partnership that has powered most of the consumer AI revolution. Microsoft loses its exclusive license to OpenAI's models, and OpenAI is now free to sell its products on AWS, Google Cloud, and basically anywhere it wants. So Microsoft was kind of holding this back for quite a while here, and I think OpenAI hated this because you saw Anthropic get massive adoption with enterprise.”

“In terms of the material we use for that rear floor we generated over we stimulated over 100 plus formulas and use AI to predict the performance of each of these formulas and ultimately we picked two to be the material for that. You don't use humans to do it anymore. You just use AI to take pictures very quickly. They can diagnose whether this piece is good or not. And then you can use AI in terms of material generations and a lot of those things.”

“We didn't reinvent the wheel with the ingredients. We just remixed the format. It's the same thing as taking a successful software product and moving it to mobile, or taking a successful supplement and making it a gummy. You take a proven value proposition and deliver it in a way that fits better into a person's daily routine.”

“I think it's obviously much easier for us, given the new administration, plus that we have so many other banking licenses, plus we have a banking license in the UK now. So for us, it became much easier compared to two years ago.”

“The last thing about everyone coming out of Tesla, you're obsessed with vertical integration, you want to build everything in-house. We haven't done that at Exowatt to that extent yet. We've started working with contract manufacturers because we know we need to scale fast quickly, so they have that infrastructure. But Elon really taught us how to vertically integrate everything and build it yourself and build it better, faster, cheaper, and don't take no for an answer.”

“Now what we do in the past, we call this process mining. Now we call it agent mining because we record all these decision traces, these contexts, what the users are entering into the system. And then you can either use it to say like, hey, wait a minute, this is actually an anomaly... Or you say like, oh, that's actually a very good improvement. And then you can elevate this to be the new standard operating procedure, maybe not just for Australia, but maybe for the rest of the world or more countries to run your company more efficient because now you'll learn something, how the organization behaves.”

“Most recently, he ran hardware engineering for all of Apple's products. Historically, Apple has the people who design the products, who wanted to have a certain look and feel, and it has the hardware guys who figure out how to make the design team's dreams come true. He's the one who makes the products come alive on that team. He solves problems, you know, they go to the meeting, he keeps it focused, let's not waste time, he gets to a solution.”

“A little bit over less than three years we launched our first car, we designed it and then we built a factory as well. So China speed. If you believe that a car a EV is going to be another piece of consumer electronics and that's something that we have a lot of experience in whether it is managing the software hardware integration whether it is managing the supply chain. I think those are the stuff that we have experienced working with so many consumer electronics products before.”

“So people that drank Robusta coffee lowered their double-stranded breaks by 23 percent compared to just drinking like water, which is pretty robust. So it's kind of cool, right? Again, it was like caffeinated coffee. It wasn't decaf.”

“We need to manufacture domestically. We need to source domestically. We need to make this using raw materials that don't come from China, that are not rare of minerals. And all of that is what we continue to iterate on. So every configuration that we design, we looked at the bill of materials or the BOM. And we asked ourselves, what can we eliminate from this? How can we make this simpler? And the same approach you see at SpaceX, right? Or at Tesla.”

“I'm sort of trying to hit the home run, and we've got a little bit more of, like, a a Warren Buffett approach. Like, I wanna buy capitulation. Like, we were buying in early February because the market was capitulating. And that's when I wanna be buying. I don't wanna be buying when I you know, everyone's starting to turn bullish.”

“I think it's, in a way, owning our destiny, in a way. So I would say building data centers is... data center cost performance control. Yes. What other areas of the business are you like that with? Our game engine, for example. We said we we always imagine we need a multiplayer three d engine that does 10% of everything really well.”

“With AI, the same is happening. It is happening on three levels. It happens, of course, on the UI side. The time is clearly over where you design software, where the dump software that requires the intelligence to sit in front of the computer. Then the second one is, of course, the business processes... a rather rigid process, like the standard operating procedure of a company. But now, of course, with these agents, we can blend the structured and unstructured world more seemingly. And then, of course, below that, you have the whole data layer.”

“Kimi two comes out, right. And it's an interesting model. The creators of Kimi k two actually talked about it in a blog post. Attention scales to the number of heads. They made a very specific barter in their architecture. They basically said, hey. What if we give it more experts? So we're gonna use more memory capacity, but we keep the amount of activated experts the same. We increase the experts' sparsity, and we decrease the number of attention heads.”

“Remarkably, compared to those who had very little or no caffeine, the participants consuming greater than equal 400 milligrams per day had 40.2% lower risk of developing multiple cardiometabolic diseases over time. And that risk reduction was greater than the risk reduction in the high consumption coffee or tea groups alone. So it was pretty close to the risk reduction in those consuming 200 to 300 milligrams per day of straight caffeine in the groups that were having 400 or higher.”

“I don't think people contextualize how much energy that is. So if you talk about a gigawatt data center, that's almost a million US households of energy. So if a data center says, I'm building a five gigawatt data center, I'm basically building a city that's five million households. And they're building in a very tiny footprint, although not so tiny. The funny thing about data centers is, when they start talking about buildings in acres, you can start realizing how big these buildings are.”

“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.”

“The other thing that presents challenges for Apple, their commitment to privacy. Apple has a ton of personal data on its users, but company policy prohibits them from using it. And you talk to people inside Apple, that's actually frustrating for them, because there's a lot of stuff they'd like to be able to do, but they don't have access, right? Your stuff's encrypted, they have to jump through lots of hoops to get permission to do anything with data, to train a model.”

“We have a couple of people at NVIDIA. We've been working with security to bring agents really close to compute. So we now have stuff where you can tell Dynamo, like, go run some experience with Dynamo on x cluster and just try it right now. We've actually been able to one shot problems. We used to have this problem where, with Dynamo, you have to find the right configurations. We've just had an agent just completely one shot that. It goes. It gets the compute. It runs a couple experiments. It's like, this is the best. Go run this. And then we just give that to people, and it's faster than anything that they have.”

“You have to be willing to fail on ninety percent of your ads. We were pumping out fifty to a hundred different creative variations a week just to find the one or two winners that would actually scale. If you're not testing at that volume, you're basically just guessing with your marketing budget and hoping for a miracle.”

“I actually, I've told everybody, I've spent more time on chemistry than I have since 10th grade. I have periodic table of the elements. It's taped to the wall, right near my desk, because people are calling me about one unumtanium or another every single day. Truly, gallium, germanium, yttrium, the entire actinide and lanthanide series. You start to learn these things. You talk about atomic numbers and weights and metals and salts. It's an incredible education. And I have a bunch of brilliant chemists that work for me in a whole bunch of places, all of them who could make more money and hedge funds.”

“We look at global liquidity indicators, things like this, and we think global liquidity has you know, topped and is is now in a process of rolling over. There tends to be about a six month lag in terms of how that impacts the traditional markets. There's a closer lag with Bitcoin. Bitcoin is much more sensitive to liquidity. So we've already seen this really impact Bitcoin, I believe.”

“Now I do wanna emphasize this again. Hermes learns from every session. So imagine this. After 20 to 30 tasks in any one domain, it's going to be measurably better than it was before. OpenClaw doesn't do that.”

“I think at the end of the day, it's all about adoption and the outcome you bring to the customer. I mean, the technology, look, the reality is for most companies, the technology doesn't matter. I always tell to my developers all the time, our job at SAP is to make the technology disappear. We need to get the outcome in front of the customer. And then try to make sure that we of course, bake the enterprise qualities in and the integration is there. And the customers can turn these capabilities on almost instantaneously.”

“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.”

“And second, I think, this is kind of the formal end of the standoff that leaked in OpenAI internal memo on April 13, where OpenAI's revenue chief, Dennis Dresser, basically told all of the staff that the Microsoft partnership had, quote, limited our ability to reach enterprise customers. And I think that demand for the Amazon Bedrock offering was, quote, frankly, staggering. So today's announcement is essentially OpenAI legalizing what they were already doing.”

“I would say that the open models are are pretty limited. So, you know, even us when when we try to optimize with our, like, our DGX Sparks, for example, and we try to put them on open models, you know, they're they're getting better, but they're not quite the best. Right? And so just know that when it comes to really strategic work, you're probably gonna be wanting to run on the Frontier models, which are going to cost you money.”

“Brev was just it's a developer tool that makes it really easy to get a GPU. It was actually Evan Conrad, SF Compute, who was just like, you guys are two dudes in the room. Why are you pretending that you're not? And so then we were like, okay. Let's make the logo of Shaka. We brought surfboards to our booth to GTC, and the energy was great. My wife was, at the time, fiancee, helping me put these vinyl stickers on and she goes, you son of a, if you pull this off. And so, pretty much after the acquisition, I stitched that with the acquisition. I sent it to our family group chat.”

“I think, look, if you go back, the department's been trying to get Silicon Valley to play in defense for 20 years. Go back to 2005 and all the crypto libertarian crowd were gonna float around in their islands and utopian. They were never gonna deal with us nasty countries in war. Peace had broken out in the Pacific somehow. They've all come back around, right? They've all come to the department. They all want to play. They all want contracts. They all want to be let in.”

“Coffee will acutely raise your blood glucose levels. And in fact, I've measured that in myself, wearing my CGM. Yes, you will get an acute raise in your blood glucose levels from coffee. You're getting sympathetic activation. But over the long term, it's actually beneficial. Both caffeine and the polyphenols in coffee, the chlorogenic acids, activate AMP kinase, this master regulator, this sort of metabolic sensor that is activating all sorts of energy pathways.”

“The three elements are the optical table, so these lenses, and then you have the heat battery and the PC, or the power conversion unit, all in this kind of container. And the idea is that we capture energy from the sun throughout the day. So think of this as like a solar panel, but that has a built-in battery, and that essentially you can scale linearly to any project size. If a datacenter customer needs 100 megawatts or a gigawatt, you just stack these next to each other.”

“I built a business called Gruns. We make gummy vitamins. We started it about three years ago and just recently crossed that billion-dollar valuation mark in the exit. It was a fast ride, but we found a hole in the market where people wanted the benefits of greens powders without the terrible taste and the mess of a shaker bottle.”

“There's a summarization of that trend that I like to say to my team. This is the year system as model. Where, instead of having a single model be a thing, you have a system of models and components that are working together to emulate the black box model. So when you make an API call to something that's like a multi agent in the background, it still looks like an API call to a model. Under the hood, it's like a billion different models.”

“I don't want to spend my time on a small business anymore. If I'm going to put in the same amount of effort and stress, I want to make sure the ceiling is ten billion dollars, not ten million. You have to look at categories that are already massive but haven't been disrupted by a modern brand or a better user experience in decades.”

“Jobs was the iconic technology CEO. He had defined the way humans interact with computing devices for 30 years almost, maybe more. So that was quite a legacy for Tim to match. And he didn't try. He didn't try to be the innovative product visionary that Jobs was. He handed that off to others, and he really focused on operations.”

“One innovation that is underappreciated by a lot of people outside the industry is Apple Silicon. The chips in the devices are all Apple chips. And that's been true for iPhones for a long time. It wasn't true for Macs. Macs ran on Intel chips until 2020, when they started ripping them out and putting in Apple chips. Apple chips are really great.”