Podcasts about palo alto

Sunday, August 30, 2026 — Week 36 TRIALS & DATA — CAMP4, RARE-X + ProMMiS CAMP4 / CMP-002 Australia and Argentina cleared. EU and UK filings underway. First-in-human Phase 1/2 study still targeted for Q4 2026. CMP-002 is designed to increase expression of the healthy SYNGAP1 copy. CAMP4 raised another $50M and reports cash runway through the end of 2028. Q2 earnings / corporate update: https://investors.camp4tx.com/news-releases/news-release-details/camp4-reports-second-quarter-2026-financial-results-and Rare-X + ProMMiS Rare-X is our PRO partner for the ProMMiS Natural History Study. IMPORTANT: Families participating in ProMMiS need to complete their Rare-X surveys within SEVEN DAYS of their ProMMiS Natural History Study visit date. We need the patient/caregiver-reported data to line up with the clinical visit data. ProMMiS: https://curesyngap1.org/resources/studies/syngap1-prommis/ Rare-X: https://cureSYNGAP1.org/RAREX  ARI WEBINAR If you missed the Ari webinar, the recording is now available. https://cureSYNGAP1.org/AriWeb  FUNDRAISE WITH US Please call us to set up your fundraisers.  Please do not use Facebook fundraising unless it's an emergency. CURE SYNGAP1 ON THE ROAD — BRAIN FOUNDATION Kathryn and I are attending the BRAIN Foundation's Synchrony 2026 scientific summit in Palo Alto. A chance for CURE SYNGAP1 to stay close to work happening across autism, neurology, genetics, biomarkers and therapeutics. https://brainfoundation.org/synchrony-symposia/ ILAE — EUROPEAN EPILEPSY CONGRESS Virginie is heading to Athens for the 16th European Epilepsy Congress, September 5–9. If you are going to be at the ILAE meeting in Athens, Greece, reach out to Virginie and connect with CURE SYNGAP1 in person. https://www.ilae.org/eec2026 UPCOMING EVENTS — COUNTDOWN SCRAMBLE FOR SYNGAP — 34 DAYS October 3 — Greer, South Carolina 5th Annual Scramble for Syngap https://cureSYNGAP1.org/Scramble SHOOT FOR SYNGAP1 — 76 DAYS November 14 — Hurricane, Utah Aiming for a Cure — Shooting for Hope https://cureSYNGAP1.org/Shoot FIGHT FOR FELIPE — 90 DAYS November 28 — Boston, Massachusetts https://cureSYNGAP1.org/Fight CURE SYNGAP1 CONFERENCE — 95 DAYS December 3–4 — Denver, Colorado Early bird pricing ends August 31! https://cureSYNGAP1.org/Reg26 PUBMED PubMed 2026 is at 49. +2 since Episode 214. +13 vs. the week. Last year finished at 61, +9 vs. the year. https://pubmed.ncbi.nlm.nih.gov/?term=syngap1&filter=years.2026-2026&sort=date Coolest new paper: TBE on EEG.  https://pubmed.ncbi.nlm.nih.gov/42620081/  USA

"I think it's a great point with writing. You know early on you realize like putting pen to paper or starting to type early on was a massive achievement. There were huge gains, and now you're still grinding to figure out like 'I don't know how to end this chapter. How do I not know how to end a chapter? I've been writing for 20 years. What's wrong with me?' And you realize like this is the gig. This is part of the fun, though. It's also part of the grind, which is why if you asked a runner their their most fun runs, they're usually their hardest runs, and it's the same thing with any other passion," says Coach Chris Bennett, author of This is About Running. This is Not About Running.It's Coach Chris Bennett! He is the Nike Running Global Head Coach and now he's the author of This is About Running. This is Not About Running.: Lessons for Between and Beyond the Start and Finish Line. It's published by Dey St.If you're looking for workouts and training plans and pace charts and Zone 2 up the wazoo, this is NOT the book for you. But if you're looking for a 220-page metaphor for being a better writer, a better person, and yes, maybe even a better runner, then you're going to love this book. I read it twice. The chapters are, by and large, lean and range from confidence to doubt to hope and play and regrets. Maybe this podcast is about writing and not about writing.A little more about Coach Bennett, who is something of a real-life Ted Lasso. Prior to working with Nike, he was a high school history teacher who helped coach one of the most successful running programs in the U.S. He attended the Univ. of North Carolina where he captained the XC and Track teams. After college, he spent five years on the Olympic Development Nike Farm Team out of Palo Alto, CA. He used to live in Oregon, but moved back to his native New Jersey.In this episode, we cover a lot of ground including: Why longform content is not dead How he went from working in finance to Nike How he cultivated his coaching philosophy Confidence vs. Arrogance Vulnerability Reckoning with and the fear of success The power of just focusing on yourself Self-sabotage And how running helped his writingI'd pair this episode Ep. 419 with Maggie Gigandet and her Atavist story about an ultrarunning couple and Ep. 525 with Mary Cain and her memoir of abuse at Nike called This is Not About Running.

John McNellis is based in Palo Alto and has been in commercial real estate for more than 40 years. He has been focused on grocery anchored shopping centers in Northern California for most of that time. On today's show we are talking about what is working today and the fundamentals that have stood the test of time. He is also the author of the book "Making it In Real Estate", now in its third edition. To connect with John visit https://mcnellis.com/ or email him at john@mcnellis.com. ------------**Real Estate Espresso Podcast:** Spotify: [The Real Estate Espresso Podcast](https://open.spotify.com/show/3GvtwRmTq4r3es8cbw8jW0?si=c75ea506a6694ef1)   iTunes: [The Real Estate Espresso Podcast](https://podcasts.apple.com/ca/podcast/the-real-estate-espresso-podcast/id1340482613)   Website: [www.victorjm.com](http://www.victorjm.com)   LinkedIn: [Victor Menasce](http://www.linkedin.com/in/vmenasce)   YouTube: [The Real Estate Espresso Podcast](http://www.youtube.com/@victorjmenasce6734)   Facebook: [www.facebook.com/realestateespresso](http://www.facebook.com/realestateespresso)   Email: [podcast@victorjm.com](mailto:podcast@victorjm.com)  **Y Street Capital:** Website: [www.ystreetcapital.com](http://www.ystreetcapital.com)   Facebook: [www.facebook.com/YStreetCapital](https://www.facebook.com/YStreetCapital)   Instagram: [@ystreetcapital](http://www.instagram.com/ystreetcapital)  

Episode 285 explores how one woman's personal wake-up call transformed into a mission to improve the air we breathe

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Dominic Carter tackles the growing cultural and legal divides splitting America. He dives into a Palo Alto school district facing a federal lawsuit after taking students on a mosque field trip where girls were allegedly pressured to wear hijabs and applauds Boy George for standing up for the Jewish community. Plus, a terrifying joint-replacement medication mix-up in Nashville leaves a patient permanently paralyzed, a Florida Democratic nominee goes "cuckoo for Cocoa Puffs" over drag shows, and Dominic recounts a face-to-face meeting with Michael Jackson.

Spiritually exhausted? Therapist Faith Freed reveals why your spiritual practice might be the problem, and how to build one that actually fits your real life. What if the reason your spiritual life feels like a second job is because you've been following someone else's job description? In this episode of The Skeptic Metaphysicians, Will and Karen sit down with Faith Freed, licensed psychotherapist, spiritual guide, and author of DIY Spirituality: Chart Your Own Sacred Path, to explore one of the most overlooked causes of spiritual burnout: doing it wrong for you, not doing it wrong in general. Faith spent nearly two decades in clinical practice, trained at the Institute of Transpersonal Psychology in Palo Alto, and has seen firsthand what happens when people treat their spiritual growth like a performance review. Spoiler: it doesn't end in enlightenment. It ends in a meditation cushion being used as a footrest. What you'll hear in this episode: Will opens up about something he's never discussed on the show before. after years of daily meditation, breathwork, sound baths, energy healings, and spiritual retreats, he still doesn't feel "further forward." Faith's response reframes the entire conversation: the goal was never a destination. It was always just a practice of touching in. Faith introduces her Divine Disco Ball framework, a four-part map of spiritual connection built around body and mind, soul and spirit, infinite source, and the natural world. The framework isn't diagnostic or prescriptive. it's a navigation tool. When one quadrant feels heavy or overworked, you simply rotate to another. Will also has a live breakthrough moment on air. connecting a spontaneous mystical experience he had years ago in the Atlanta airport to a pattern Faith identifies in real time: being alone in a crowd might actually be his spiritual sweet spot. It's one of those genuine, unscripted moments that makes this episode worth the full listen. Key topics covered:Why spiritual exhaustion is more common than spiritual failure, and how to tell the differenceThe "spiritual playlist" concept. keeping what lights you up, releasing what you force out of obligationWhy micro-rituals (as short as 60 seconds) can be more sustainable than elaborate daily practicesThe difference between spiritual presence and spiritual distraction, and how your intuition already knows which is whichWhat "authentic spirituality" actually means when the word has been flattened by wellness cultureHow a phone fast, a solo trip to an amusement park, or simply watching rain can be a legitimate spiritual practiceWhy Faith believes you don't have to do anything at all. and why that's both liberating and surprisingly hardAbout Faith Freed: Faith Freed is a licensed psychotherapist, spiritual guide, and the founder of Freed-Om. She holds a master's degree in clinical psychology from the Institute of Transpersonal Psychology and has nearly two decades of clinical experience. Her newest book, DIY Spirituality: Chart Your Own Sacred Path (Collective Book Studio, 2026), offers an illustrated, accessible guide to building a personal spiritual practice outside of rigid religious or wellness frameworks. Faith offers spiritual guidance sessions to clients worldwide and can be reached at faithfreed.com. Connect with Faith Freed:Website: faithfreed.comBook: DIYspirituality.comInstagram: @faithfreed If this episode resonated with you, please take 60 seconds to leave us a review on Apple Podcasts or your favorite podcast platform. It genuinely helps other seekers find the show, and Will and Karen read every single one. Watch this episode and more on New Reality TV, the free conscious streaming network available on Roku, Fire TV, iOS, Android, and the web at newrealitytv.com. No subscription. No gatekeeping. Just real conversations about consciousness, spirituality, and the questions that most platforms won't touch.The Skeptic Metaphysicians is a spiritual awakening podcast for open-minded thinkers who refuse to check their critical thinking at the door. Each episode explores consciousness expansion, enlightenment, soul purpose, and soul growth through honest, grounded conversation with leading voices in metaphysics, psychic phenomenon, quantum healing, and beyond. We dive deep into spiritual awakening, ascension, alignment, and the awakening process without the dogma. From mediumship and spirit guides to Arcturian contact, astrology, and the subconscious mind, we explore it all with curiosity, humor, and zero guru worship. Whether you're in the middle of your own awakening, questioning reality, or just spiritually curious, this is the podcast for seekers and skeptics alike.Subscribe, Rate & Review!If you found this episode enlightening, mind-expanding, or even just thought-provoking (see what we did there?), please take a moment to rate and review us. Your feedback helps us bring more transformative guests and topics your way!Connect with Us: 

Grace Belangia didn't build her startup ecosystem in Silicon Valley. She built it in Augusta, Georgia, a city with medical, military, and energy communities but no established tech community. On Getting Rich Together, host Syama Bunten talks with the cofounder and executive board member of Make Startups about her path from writing angel checks on her own to becoming an LP in a VC fund. Grace traces her money instincts back to her mother, an immigrant who taught her that saving and investing are two different things. That lesson followed her into a research role at a private equity firm in her twenties, where she saw firsthand how the investment world worked and started learning how capital actually moves. She talks through how she learned to angel invest through Pipeline Angels, what it took to learn the space through a six-month investing cohort, and why she eventually expanded from direct angel investing into funds run by managers she trusts. She also explains economic mobility through entrepreneurship and the philosophy she calls reserve and deploy. If angel investing for women feels out of reach, or you're curious about what it takes to become an LP in a venture capital fund, this conversation lays out the real path Grace took. Press play, then find a salon near you or grab a seat at the Wealth Catalyst Summit in San Francisco on October 16 at wealthcatalyst.com.   Episode Breakdown: 00:00 Grace Belangia's Childhood in LA and Palo Alto 05:12 High School Years and Early Community Building 07:43 College, Political Science, and Career Uncertainty 10:49 Learning Finance Inside a Private Equity Research Desk 14:44 Marriage, the Navy, and the Move to Georgia 20:10 Founding a Startup Ecosystem in Augusta 23:07 Learning to Angel Invest Through Pipeline Angels 29:20 How Grace Became an LP in a VC Fund 33:39 Reserve and Deploy, Grace's Investing Philosophy 38:04 Economic Mobility, Legacy, and Building the Bridge   Find more from Syama Bunten: Your money story may be shaping your financial life more than you realize. After hundreds of conversations with women at all stages of their financial lives, Syama distilled the questions that helped her understand her own patterns into The Money Story Reset, a free guide featuring five guided reflections and personal stories from her journey. Download The Money Story Reset and begin uncovering the beliefs behind your financial decisions.   Attend a Salon near you: wealthcatalyst.com/salons Instagram: https://www.instagram.com/syama.co/ Join Syama's Substack: https://thewealthcatalystwithsyama.substack.com/ Website: https://wealthcatalyst.com Download Syama's Free Resources: https://wealthcatalyst.com/resources Wealth Catalyst Summit: https://wealthcatalyst.com/summits Speaking: https://syamabunten.com Big Delta Capital: www.bigdeltacapital.com Podcast production and show notes provided by HiveCast.fm  

Hour 3 opens with weekend concert updates from Busch Stadium and a preview of upcoming segment features before moving to national political news on The Shortlist, featuring House Minority Leader Hakeem Jeffries, Kentucky Governor Andy Beshear, and combat veteran Joey Jones. In Segment 2, Senior Legal Fellow Hans von Spakovsky from Advancing American Freedom joins the program to break down the landmark federal court ruling in Silencer Shop Foundation v. ATF invalidating NFA registration requirements for zero-taxed suppressors, alongside legal analyses of public policy contract defenses in surrogacy disputes and federal enforcement targeting birthright tourism networks. In Segment 3's The Buck Stops Here, guest commentator Dan Buck reflects on sanctuary city policies following the fatal stabbing of 68-year-old retiree Todd Stewart in Martinez, California, debuting his original song "American Invaders." Hour 3 concludes with Segment 4's Kim on a Whim, examining a federal lawsuit against the Palo Alto Unified School District over an unconsented mosque field trip and CAIR speaker presentation for high school students. Hour Hashtags #HansVonSpakovsky #NFA #SecondAmendment #DanBuck #TheBuckStopsHere #KimOnAWhim #PaloAltoLawsuit #CAIR Hour Guest List Hans von Spakovsky — Senior Legal Fellow at Advancing American Freedom (Hour 3, Segment 2) Dan Buck — Guest Commentator and Executive Director (Hour 3, Segment 3)

On the August 17, 2026 edition of The Marc Cox Morning Show, hosts Marc Cox and Kim St. Onge detail Second Amendment legal victories, municipal public safety, national political strategy, and historic St. Louis sports milestones. Hour 1 covers local storm impacts on the Busch Stadium Guns N' Roses concert, WNBA fan t-shirt censorship controversies, Bill Maher's commentary on Western cultural assimilation, the passing of actress Hayden Panettiere, and former Congresswoman Nancy Mace's digital media launch. Hour 2 examines National Firearms Act (NFA) judicial rulings, a citywide St. Louis Water Division boil advisory, hot car child endangerment charges, financial updates on Anthropic and Berkshire Hathaway from Nicole Murray, and State Technical College of Missouri's #1 national ranking. Hour 3 breaks down Democratic primary scheduling, Hans von Spakovsky's analysis of the Silencer Shop Foundation v. ATF court ruling on zero-taxed suppressors, Dan Buck's debut of "American Invaders," and a federal lawsuit against Palo Alto Unified School District over an unconsented mosque field trip. Hour 4 analyzes Missouri Amendment 3 campaign finance dynamics, Kimberly Bird's update on Live Action surrogacy advocacy, and KMOX Sports Director Tom Ackerman's breakdown of Cardinals prospect Joshua Baez's historic three-homerun MLB debut at Wrigley Field. Full Show Hashtags #MOpol #JoshuaBaez #StLouisCardinals #NFA #SecondAmendment #HansVonSpakovsky #DanBuck #TheBuckStopsHere #KimOnAWhim #LiveAction #TomAckerman #StLouisBrief #BoilAdvisory #PaloAltoLawsuit #Election2026 Master Guest List Nicole Murray (Financial and Business Journalist) Hans von Spakovsky (Senior Legal Fellow at Advancing American Freedom) Dan Buck (Guest Commentator and Executive Director) Kimberly Bird (Communications and Public Affairs Senior Specialist for Live Action) Tom Ackerman (KMOX Sports Director)

In this podcast, Anika Warrier interview Dr. Nick St. John, a clinical psychologist who practices in the Palo Alto and Menlo Park Area. This is the fourth episode of the Therapist off the Clock Series, where Anika interview various psychologists in their fields. Dr. St John discusses his path in discovering his passion in psychology, the work he does currently, and how he manages the highs and lows in this profession. Tune in to learn more about clinical psychology!

In Hour 2, Willard and Dibs discuss why it's easy to fall for false hype in NFL training camp. Willard also makes the case for the importance of Yaxel Lendeborg to the Warriors and De'Zhaun Stribling to the 49ers. They listen to San Francisco 49ers head coach Kyle Shanahan's first training camp press conference from a few days ago. He was previously suffering from concussion symptoms from his Palo Alto car crash a month ago. Also, has there been a Giants team this bad in early August? It's been a while, and Rafael Devers not legging out a pop-up is another example.

In Hour 1 of Willard & Dibs, Mark Willard and Dan Dibley discuss the life and legacy of Don Nelson, the legendary head coach of the Golden State Warriors who passed away on Sunday at 86. Nelson has the second-most victories as a head coach in NBA history and coached the Warriors from 1988-1995 and again from 2006-2009. They also hear a story from the All the Smoke Podcast with Matt Barnes and Stephen Jackson about the We Believe Warriors -- a well-known staple of Nelson's career. In Hour 2, Willard and Dibs discuss why it's easy to fall for false hype in NFL training camp. Willard also makes the case for the importance of Yaxel Lendeborg to the Warriors and De'Zhaun Stribling to the 49ers. They listen to San Francisco 49ers head coach Kyle Shanahan's first training camp press conference from a few days ago. He was previously suffering from concussion symptoms from his Palo Alto car crash a month ago. Also, has there been a Giants team this bad in early August? It's been a while, and Rafael Devers not legging out a pop-up is another example. In Hour 3, Willard and Dibs continue their discussion about the San Francisco Giants' apparent sense of entitlement. For the fourth or fifth time all season, Rafael Devers didn't run out a pop-up that dropped in the outfield. It's one of many such examples this year for the Giants. Later, they're joined by former Golden State Warriors general manager Larry Riley to discuss the life and career of legendary head coach Don Nelson. Riley also breaks down selecting Stephen Curry in the NBA Draft. In Hour 4, Willard and Dibs are joined by Matt Barrows, the San Francisco 49ers' beat writer for The Athletic, to discuss Kyle Shanahan's return to the podium and a few camp injuries, including Nick Bosa's. Later in the hour, they chat about the Warriors' plan for the future with Stephen Curry's extension coming up.

Willard & Dibs listen to San Francisco 49ers head coach Kyle Shanahan's first training camp press conference from a few days ago. He was previously suffering from concussion symptoms from his Palo Alto car crash a month ago.

(Presented by TLPBLACK: A cybersecurity intelligence platform focused on sharing curated, high-sensitivity threat insights and research with trusted security professionals.) Three Buddy Problem - Episode 108: OpenAI got on the Black Hat stage and walked through how its own agent swarm hacked Hugging Face. We discuss and struggle to decide whether to clap or panic. Plus, why only the attacker can do forensics now, frontier models being built as cyber-weapons on purpose, APT29's "Dark Hotel" comeback in luxury hotels, China's swipe at Palo Alto, the Iran-water-system FUD, and an eye-opening Liechtenstein money-laundering hack. Cast: Juan Andres Guerrero-Saade, Ryan Naraine and Costin Raiu. Timestamps: 0:00 Introductory banter - Black Hat went full RSA 1:11 TLP Black sponsor read 3:58 A deflated, AI-pilled show floor 8:31 AI stunt hacking and AI slop 16:20 The OpenAI–Hugging Face talk 21:00 Not all the same incident: OpenAI vs. Meta, Anthropic, and Irregular 25:49 Swarms of agents, Artifactory message boards, and the defense gap 32:26 Offense vs. defense: what's really in the training data? 41:22 Guardrails, KYC, and "too dangerous to release" 48:07 JAGS's unpublished Opus 5 benchmark — grinding to 25% and stuck 59:30 AISI, recklessness, and the OpenAI Frontier Risk Council 1:06:27 Stronger models everywhere: Qwen, Sol, Astra, rushing off the cliff 1:18:32 APT29 / "Dark Hotel" reborn + travel OPSEC 1:39:56 China's Palo Alto review, spy-agency rankings, Iran/water FUD 1:57:28 Liechtenstein AML hack, JAGS's promotion, mental health

“Intelligence is 100 percent human. AI is a tool created by humans to distill, digest, and distribute intelligence.” — Keith Teare The working class died this week — at least in Palo Alto. Delivering the eulogy in our regular That Was The Week tech summary is my co-host Keith Teare. “Humans create intelligence,” (whatever that means) the Silicon Valley-based entrepreneur tells us. And so, in our AI age of supposedly abundant intelligence, he pronounces, human knowledge “should not be trapped inside experts, institutions, or companies.” Check your pockets, everyone. Silicon Valley has another freebie for you. With AI, the entrepreneur promises, intelligence is democratized. Everybody gets it. We will all have the intelligence of a Nobel laureate at our fingertips. Even Keith. And so he attacks Daron Acemoglu, the Nobel Prize-winning MIT economist who has called for a “pro-worker AI.” But, for Keith — a council-estate kid from Yorkshire whose lifetime ambition was to evacuate the working class — this is “complete bullshit.” Acemoglu's ideas, he says, are an example of the “fetishization of workers” when, in fact, we should be celebrating the end of the “working class.” What Acemoglu is calling for in his pro-worker AI manifesto is more government planning for today's transition to the AI epoch. But Keith disagrees. So I asked him three times what government should do while AI kills the working (and middle) class. “Allow it to happen,” he finally answers. “A good upheaval.” Good? The former “worker” will lack jobs, wages, healthcare, housing. Even food in an America now eliminating food stamps. No matter. Let them eat intelligence. Five Takeaways •       Humans Create Intelligence. Keith's editorial thesis distinguishes individual intelligence — where experts live, and always will — from the collective sum of everything all humans, living and dead, have ever contributed. That collective stock was once locked in encyclopedias, libraries, and universities; for the first time, AI can aggregate, distill, digest, and distribute it, at a price falling toward everyone. Knowledge, he writes, “should not be trapped inside experts, institutions, or companies” — but note the fine print: experts don't disappear in this democratization. If anything, they get elevated: the expert reading an AI's output about viruses understands it very differently than the rest of us.•       The End of the Age of Heroes? Noah Smith's much-shared essay argues that AI ends the era of the mathematical hero — and that's fine, since most people (truck drivers, financial advisers, executive assistants) never got to be heroes anyway. Keith's rebuttal turns on his central distinction: AI and intelligence are not the same word. There is no evidence, he argues, that AI creates new knowledge — it understands and distributes the existing stock. Innovation still takes individuals, and those individuals now start from a far higher floor, leveled up to everything already known. Heroes don't go away; they multiply. In the world of AI, he suspects, every single teacher becomes one.•       “What Even Is Pro-Worker AI?” The week's main event: Daron Acemoglu — via Yascha Mounk's Persuasion interview and an Atlantic essay, with What Happened to Liberal Democracy out next week — wants AI agencies, grant programs, and public competitions to build “pro-worker AI.” Keith's verdict: “complete bullshit.” The middle-class “fetishization of workers” is paternalistic; the wage is a temporary power relationship between employer and employee; and the end of the working class is precisely the progressive outcome — says the council-estate kid from Yorkshire whose aspiration was not to be working class. Pressed three times on what government should do amid the upheaval, Keith finally answered: “Allow it to happen… a good upheaval.” Though swap workers for people, he conceded, and he'd almost entirely agree — every teacher a hero, even in East Palo Alto.•       Bandwagons and Silences. Regular people are being arrested protesting data centers; Erin Brockovich — a Keen On guest some years back — is assembling class actions; Ezra Klein has begun folding anti-big-tech language into abundance. A politician-led bandwagon, Keith argues, regressive but keyed to genuine local concerns. The stranger fact is the silence on the other side: neither Altman nor Amodei nor Demis Hassabis is making the public case that AI benefits everybody — astonishing, Keith says, and the vacuum Acemoglu is trying to fill. Hassabis himself stepped aside at Google this week — a scientist returning to science as Sergey Brin becomes AI czar — while the Nobel-winning AlphaFold team has been quietly broken up. “Something strange is going on there.”•       A Drama in a Teacup. Is the AI economy real? Ed Zitron's stat — 70 percent of Amazon, Microsoft, and Google's AI revenue comes from OpenAI and Anthropic — is two-thirds right, says Keith, and no problem at all: beneath the concentration, the money comes from some two billion distributed users paying real subscriptions, and the revenues are sustainable. The Aschenbrenner postscript, via Porter Stansberry's post of the week: he bet the chip layer (Samsung, SK Hynix) when the value sat a layer up, got the timing wrong more than the thesis, sold to Citadel at a discount — and kept his Anthropic shares, remaining a multi-billionaire. As for the coming reality check: Anthropic and OpenAI will IPO only when public capital beats private, and SpaceX's wobble from $135 to $108 — through a 20 percent lockup release — counts as no catastrophe. Public markets, Keith reminds us, don't determine the success of the underlying business. About the Co-Host Keith Teare is the publisher of That Was The Week, the essential weekly tech newsletter, and founder and CEO of SignalRank Corporation. A serial entrepreneur — co-founder of, among others, EasyNet and RealNames — he was present at the creation of the UK internet and has spent four decades at the intersection of technology, capital, and ideas. He joins Keen On America every Sunday to make sense of the week in tech. His AI-assisted book in progress is titled Who Owns Intelligence. References: •       That Was The Week — Keith's newsletter, including this week's editorial, “Humans Create Intelligence.”•       Noah Smith — “The End of the Age of Heroes,” on what happens to human ambition when the machines do the math.•       Daron Acemoglu — the Yascha Mounk interview at Persuasion, the Atlantic essay on pro-worker AI, and What Happened to Liberal Democracy, out next week.•       The Financial Times — “Google's AI shakeup boosts Brin as DeepMind's Hassabis steps aside.”•       Ed Zitron — on the 70 percent of hyperscaler AI revenue that flows from OpenAI and Anthropic.•       Porter Stansberry — post of the week, on Leopold Aschenbrenner's losses, Citadel's discount, and the drama in a ...

Apple's bug bounty program overwhelmed by fake AI generated reports China launches cybersecurity review into Palo Alto Networks products Meta AI hacks external systems during cybersecurity testing Get the show notes here: https://cisoseries.com/cybersecurity-news-faked-bug-reports-metas-rogue-ai-china-investigates-palo-alto/ Huge thanks to our episode sponsor, ThreatLocker AI risk does not only come from attackers. Employees are adopting AI tools faster than many organizations can evaluate them. Today's tip: an AI policy should be backed by enforceable controls over what tools can access and do. See how ThreatLocker can help you govern AI use at threatlocker.com/ciso.

-According to US cybersecurity startup Frontier, Kimi K3 broke out of a sandbox from the UK government's AI Security Institute while its defensive cybersecurity skills were being evaluated. -Scientists at Palo Alto's Arc Institute and Stanford University created the viruses with the genome language models Evo 1 and Evo 2. -Tropical cyclones pose a unique challenge to predict because global atmospheric currents that determine a storm's path have traditionally been best analyzed by coarser global models. Learn more about your ad choices. Visit podcastchoices.com/adchoices

From our latest podcast, we have two NFL media storylines that are still intriguing with training camps getting underway.Host T.J. Rives and Tyler Jones of Roundtable Sports and "The Jones Report" Podcast first discuss the situation with 49ers coach Kyle Shanahan, who is still recovering from his serious traffic accident in Palo Alto, CA, last month. More details have emerged, but Shanahan has yet to comeback to coach the team full time, met with all the media and the cameras and there are still unanaswered questions.Next, in Pittsburgh, controversial QB Aaron Rodgers went on the verbal attack Monday on the live "Pat McAfee Show" from traning camp to basically take a vindicated "victory lap" about his stances on the Covid 19 virus and required shots. He not only taunted embattled health worker Anthony Fauci for having taken the 5th ammendment 100 times at a Senate hearing, but also, took shots at ESPN, the very platform McAfee broadcasts on weekdays.Tyler and T.J. discuss the ramifications for ESPN and how much more motivated is the 42 year old Rodgers to continue this attitude, etc.?It's all on this edition of the "LWOS Media Daily" and make sure to follow/subscribe to this feed on Apple/Spreaker/Spotify, etc.

From our latest podcast, we have two NFL media storylines that are still intriguing with training camps getting underway.Host T.J. Rives and Tyler Jones of Roundtable Sports and "The Jones Report" Podcast first discuss the situation with 49ers coach Kyle Shanahan, who is still recovering from his serious traffic accident in Palo Alto, CA, last month. More details have emerged, but Shanahan has yet to comeback to coach the team full time, met with all the media and the cameras and there are still unanaswered questions.Next, in Pittsburgh, controversial QB Aaron Rodgers went on the verbal attack Monday on the live "Pat McAfee Show" from traning camp to basically take a vindicated "victory lap" about his stances on the Covid 19 virus and required shots. He not only taunted embattled health worker Anthony Fauci for having taken the 5th ammendment 100 times at a Senate hearing, but also, took shots at ESPN, the very platform McAfee broadcasts on weekdays.Tyler and T.J. discuss the ramifications for ESPN and how much more motivated is the 42 year old Rodgers to continue this attitude, etc.?It's all on this edition of the "LWOS Media Daily" and make sure to follow/subscribe to this feed on Apple/Spreaker/Spotify, etc.

This is a special bonus episode, an interview with Rivian's Chief Design Officer, Jeff Hammoud. We've got all the audio here, but it's worth heading over to our Youtube channel at dbtr.co/youtube to watch the video. We recorded this one live in Miami, in the Rivian Pavilion during Art Basel, with a R1T truck in a new eggplant-purple called Boreal parked just over Jeff's shoulder. Jeff runs a team of about 140 people across Irvine and Palo Alto — interior and exterior designers, color and materials, digital surfacing, perceived quality, even an in-house painter and a clay milling shop. And he has a strong opinion about what all that machinery is for: constraints are what separate designers from stylists. Anyone can make a beautiful concept car, but making the production version look like the concept is the challenge. We talked about why Rivian keeps making colors most people won't buy, and how limited-run “Studio Originals” work like sneaker drops. Jeff makes the case that AI is going to level the playing field in design — shifting the advantage from whoever illustrates best to whoever has the better idea. We got into which controls stay physical and why, the haptic halo wheels coming in R2, and how scent ties a car to memory. *** Premium Episodes on Design Better This ad-supported episode is available to everyone. If you'd like to hear it ad-free, upgrade to our premium subscription, where you'll get an additional 2 ad-free episodes per month (4 total). Premium subscribers also get access to the documentary Design Disruptors and our growing library of books. New premium subscriber benefit: we've launched a private Slack workspace…join now to connect with designers, product leaders & creative practitioners in our community. And get a behind-the-scenes pass to every episode with The Roundup, where each week we bring you insights and actionable tactics from recent episodes. You'll also get access to our monthly AMAs with former guests, ad-free episodes, discounts and early access to workshops, and our monthly newsletter The Brief that compiles salient insights, quotes, readings, and creative processes uncovered in the show. And subscribers at the annual level now get access to the Design Better Toolkit, which gets you major discounts and free access to tools and courses that will help you unlock new skills, make your workflow more efficient, and take your creativity further. Upgrade to paid Learn more about your ad choices. Visit megaphone.fm/adchoices

El analista de Apta Negocios, Roberto Moro, examina los títulos de SAP, Acciona Energía, Amper, Palo Alto, Sacyr, Rovi y Mapfre, entre otros

John McNellis is a veteran real estate developer and founding partner of McNellis Partners, where he has spent more than four decades developing over 100 properties across Northern California, primarily supermarket anchored shopping centers. He started as a journalism major, went to law school, and practiced litigation for less than a year before shifting into real estate law, where he learned to structure large transactions and met the people who would fund his first deals. John built his first shopping center in 1983 alongside an older developer client and has worked with the same two partners, Beth Walter and Mike Powers, ever since. He is the author of Making It in Real Estate: Thriving as a Developer, now in its third edition, and writes a monthly column for the San Francisco Business Times and The Registry. ​​In this episode, John McNellis walks through 43 years of development, starting with a duplex he bought at 24 and ending with a firm that uses no outside capital at all. He explains how a law career gave him a shortcut into large deals, why he stopped raising money after his financial partners walked away during the early 1990s recession, and what he learned from losing a Sacramento shopping center in foreclosure. He also makes an argument most real estate podcasts avoid: keep your day job, because the failure rate in development is high and the cash flow takes years to arrive.     Make sure to download our free guide, 7 Questions Every Passive Investor Should Ask, here.     Key Takeaways Learn large deal mechanics on someone else's payroll before risking your own capital Partner for the skills you lack, and accept that good partnerships can still end Weigh the control you surrender before accepting outside capital Owning 100% of a small deal can beat owning 1% of a large one Do not overpay, over leverage, or over develop     Topics From Journalism to Law to Development Practiced litigation for less than a year before moving to real estate law Legal work exposed him to eight figure deals, entity structures, and capital contacts Early Deals That Funded the Career Bought a duplex at 24 for $25,000 with roughly $1,500 down Traded up to a fourplex and turned $1,500 into $90,000 in about two years The First Shopping Center Partnered with a developer client who had construction expertise but could not sell John raised $1 million in equity at $25,000 per investor and handled the legal work Built in 1983, still owned today, mortgage paid off and renovated twice Why That Partnership Worked, and Why It Ended  The partnership ran 5 or 6 years, until neither needed the other  Beth Walter and Mike Powers joined in 1983 and remain his partners 43 years later The Developer as Conductor  John says he still knows nothing about construction after 80 odd buildings  Development requires an orchestra of partners, consultants, and contractors The Capital Ladder and Its Ceiling  Friends and family money is a booster rocket, useful until you run out of friends  Institutional capital costs more and carries total control over timing and exits What Non-Recourse Actually Means  Deregulation of savings and loans pushed money into commercial real estate in the 1980s  Financial partners walked away mid-project during the early 1990s recession  Non-recourse protects you from them and protects them from you Moving to Their Own Capital Only  John chose owning 100% of a small deal over a minority stake in a large one  The firm buys junk, fixes it, sells it as antiques, and funds the next deal Why He Tells Developers to Keep Their Day Job  Development can take three to six years before a property cash flows  John practiced law through his first ten years of investing  He cites a failure rate above 60% for development firms in their first decade      

Managing AV and collaboration technology across hospitals, clinics, classrooms, and event spaces is no small task, and for Justin Scord, Senior Manager, Collaboration & Media Services at Stanford Medicine, the challenge isn't just keeping the lights on. It's knowing where to start."There's always so much change, so much technology, so many different manufacturers," Scord told UC Today at InfoComm 2026 in Las Vegas. "Keeping that North Star and making sure we're building systems that are scalable and supportable is really our focus."Scord oversees a broad footprint at the Palo Alto-based academic medical system – from patient rooms and conference spaces to live event broadcasts. If there's a screen, a microphone, or a speaker involved, his team has a hand in it.That sprawling remit gives him a sharp perspective on where the industry is heading.On AI, he's optimistic – but rigorous. "I'm all about the data," he said.Stanford's in-house data science team actively researches which AI models perform best for specific workflows, and Scord believes the wider AV industry should be asking the same hard questions."Ask the manufacturers where they got the research and data to choose the models they're using. That alone is going to weed out a lot of manufacturers."His concern is that too many AI deployments are driven by commercial partnerships rather than genuine fit. "If they're not choosing the right models based on workflow and using data to show it's actually going to provide ROI, it's probably not something you want to pursue."At InfoComm, Scord spoke about agentic AI and its potential to augment – not replace – project teams. His message was clear: AI should aggregate data, surface actionable insights, and make day-to-day work more manageable, without diminishing the people doing it."Get educated around machine learning and AI at a high level," he said. "Understand the limitations, and start slowly incorporating it into your workplace."

Anika sat down with Andrew Bolton, CEO and co-founder of Tech Rescue, to explore the massive cultural and corporate shift currently prioritizing artificial intelligence over human connection—and why it's a losing bet. Starting from a moment of intense frustration while trying to help his grandmother with technology, Andrew bootstrapped a 24/7, human-only tech support company that has defied the "chatbot era" of Silicon Valley. Andrew's journey offers a masterclass in recognizing the power of empathy in business, building culture in a service company, and why trusting the basics of customer care is the ultimate growth hack.   In This Episode   The origins of Tech Rescue: Why a simple email forward sparked a fast-growing business How the tech industry has abandoned the 14% of Americans who struggle with modern devices The psychological and physiological benefits of talking to a human over a robot Why implementing a strict "no AI, no chatbots" policy became their greatest competitive advantage Bootstrapping the company with his mother as CFO after getting rejected by traditional VCs The connection between lowering a caller's cortisol levels and boosting corporate retention rates Why the modern education system's failures present a massive threat to the U.S. workforce How Tech Rescue grew from answering calls at 2 AM to a team of 78 employees in three years The danger of monopolies and the slow creep of corporate mediocrity Why real success isn't just about billions—it's about protecting creativity, connection, and culture   Timestamps   00:00 Introduction: Solving a human problem in an AI world 01:34 The origin story: Watching an 80-year-old struggle to forward an email 04:47 How the relentless pace of tech has left 14% of the population behind 09:21 The death of customer service: Why optimization actually means abandonment 11:24 The ultimate value proposition: Choosing humans over chatbots 16:01 Walking away from Wall Street to build a business with his mom 18:51 The fundamental truth: How you treat your customers is how they treat your wallet 22:15 Building a 24/7 call center powered by psychology majors, not IT techs 24:01 The science of empathy: Lowering cortisol to reduce call times and save money 26:27 Smacking the "Palo Alto 5-year plan" and proving empathy is profitable 33:13 Why hiring for emotional intelligence is harder—and more important—than hiring for tech skills 36:31 A warning about the U.S. education system and the future of the workforce 43:25 Why becoming a billionaire in a mediocre world means absolutely nothing 48:44 Conclusion: The core mission of connecting people   Key Insights & Takeaways   Insight 1: "Optimization" Often Just Means Abandonment   For the last decade, companies have used terms like "optimization" or "streamlining" to justify firing their HR, sales, and customer service departments, replacing them with endless phone trees and chatbots. This has created a massive void in the market where customers are desperate to speak with a real human—a void that human-centric businesses can easily and profitably fill.   Insight 2: Empathy is a Quantifiable Metric   Tech Rescue hires psychology and sociology majors rather than IT professionals because teaching tech is easier than teaching empathy. According to medical studies, simply addressing a caller by their name can lower cortisol and adrenaline levels by 13%. In a call center environment, a calmer customer translates directly to a conversation that ends seven minutes sooner, vastly reducing operational costs and boosting retention.   Insight 3: The "No Chatbot" Guarantee is a Growth Strategy   While VCs and marketing agencies constantly pressure companies to adopt AI to save money, Tech Rescue found that their greatest selling point is their strict refusal to use it. In a world where consumers are fatigued by automated voices and generic AI outputs, guaranteeing a human interaction builds a level of brand trust and loyalty that algorithms cannot replicate.   Insight 4: Generational Tech Gaps Require Psychological Safety   Older generations aren't just confused by rapidly changing tech (like Meta rearranging its settings menus multiple times a year); they are often embarrassed to ask for help. Tech Rescue provides a psychologically safe space for users to ask questions—whether it's about online banking or setting up a dating profile on Bumble—without feeling rushed or condescended to.   Insight 5: You Can Make Money by Doing Good in the World   Andrew pushes back against the modern corporate mandate of "alpha at all costs," which he argues leads to monotonous, poor-quality products and a lack of innovation. His core philosophy—often repeated as "Business 101"—is that if you solve a genuine problem and treat people with respect, the financial success will naturally follow. Monopolies prevent competition, but incredible customer service shatters monopolies.   Resources & Links Mentioned Tech Rescue   About Andrew Bolton   Andrew Bolton is the CEO and co-founder of Tech Rescue, a 24/7 human-powered tech support company. A Harvard-trained strategist with Wall Street roots, Andrew walked away from traditional corporate America to build a business centered entirely around empathy, trust, and connection. He co-founded the company alongside his mother after witnessing firsthand how the modern tech industry disregards the elderly and the less tech-savvy. Today, he manages a growing team of 78 employees out of a call center in Colorado, proving that human connection is the ultimate competitive advantage.   Connect with Andrew Bolton Website: https://techrescue.io/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Longevity expert Dr Florence Comite joins me to explain why testosterone, not estrogen, drives body composition and ageing in midlife women. Decline isn't inevitable - it's programmable. She unpacks why testosterone (which women carry in larger amounts than estrogen) drives muscle, visceral fat and brain fog from your 30s on, why only a third of us should fast, and the five biomarkers - fasting glucose, HbA1c, fasting insulin, cholesterol risk ratio and free testosterone - that predict where your healthspan is headed and how to change it. WHAT YOU'LL LEARN Why women carry more circulating testosterone than estrogen, and what its decline does to muscle, visceral fat and cognition from your 30s What "I don't feel like myself anymore" is actually signalling beneath the surface How cortisol quietly drives the anxiety-out-of-nowhere, the 2am wake-ups and the stubborn weight around your middle Why only a third of women should fast, and the 3-hour glucose test that tells you which third you're in What the five non-negotiable biomarkers reveal about where your healthspan is headed TIMESTAMPS 00:00 Intro: Why You Don't Feel Like Yourself After 35 - Peak Health, Testosterone Decline & the 30s Shift 05:52 - Perimenopause, Cortisol & Anxiety Out of Nowhere: Belly Fat, Sleep and the Caregiver Load 13:28 - Wearables, CGM & the Fasting Third/Third/Third: Measuring How Your Body Really Ages 20:11 - Who Should Actually Fast: The 3-Hour Glucose Test, Protein at Every Meal & the Oatmeal Trap 27:00 - Testosterone for Body Composition & Energy: UK Access, the 2024 Study & Protein for Muscle 33:46 - The 5 Biomarkers That Predict How You Age - Plus the 7 Patterns of Health Decline 39:37 - Decade by Decade for Women vs Men: Bone Loss, Osteoporosis, Andropause & Owning Your Healthspan VALUABLE RESOURCES - Invincible (Dr Comite's new book): https://www.penguin.co.uk/books/475986/invincible-by-comite-florence/9781529977066  - Dr Florence Comite's website: https://www.comitemd.com/  - Follow Dr Comite on Instagram: https://www.instagram.com/drflorencecomite/  AlgaeCal Plus*‡: Science-backed supplements designed to support healthy aging

On the newest edtion of the show, host T.J. Rives returns with a full breakdown of what is Journalism vs. what is going on with "Indsider Info" trading, as it prominently goes on in the biggest sports.First, NFL on CBS lead analyst Tony Romo is detained in Wisconsin on suspicion of DUI. T.J. has more on the coverage and will CBS discipline him, etc.?Then, a much bigger, "hot button" topic is- the car crash in Palo Alto, CA, earlier this month involving San Franscisco 49ers Head Coach Kyle Shanahan. The local authorites clearly covered up his involvement for over a week and the team tried to shape and control the story.But, after ESPN ultimate insider Adam Schefter reported significant injuries to Shanahan, other media began to dig out all the truth this past weekend and early this week.So, in a fascinating turn, an independent journalist in the San Fransico Bay Area named Grant Cohn started getting more facts. And, his reporting and shows led to he and other reporters getting summoneed for an audience in Shanahan's office Tuesday for him to finally tell his side of what happened.You'll hear more from Cohn in his own words on this twist in the coverage and what it means.Plus, an interesting angle is whether Scheter, Ian Rapoport and others covering the breaking news and info as "insiders" are nothing more than compromised "PR" type- personalities, who are only giving out what teams, players, agents, etc. want the public to know?T.J. has his thoughts and has more from national journalist turned host Jason Whitlock on the Shanahan story/attempted cover up and about Schefter.Enjoy it all on the "LWOS Media" Podcast and make sure to follow/subscribe on Apple/Spreaker/Spotify, etc! 

On the newest edtion of the show, host T.J. Rives returns with a full breakdown of what is Journalism vs. what is going on with "Indsider Info" trading, as it prominently goes on in the biggest sports.First, NFL on CBS lead analyst Tony Romo is detained in Wisconsin on suspicion of DUI. T.J. has more on the coverage and will CBS discipline him, etc.?Then, a much bigger, "hot button" topic is- the car crash in Palo Alto, CA, earlier this month involving San Franscisco 49ers Head Coach Kyle Shanahan. The local authorites clearly covered up his involvement for over a week and the team tried to shape and control the story.But, after ESPN ultimate insider Adam Schefter reported significant injuries to Shanahan, other media began to dig out all the truth this past weekend and early this week.So, in a fascinating turn, an independent journalist in the San Fransico Bay Area named Grant Cohn started getting more facts. And, his reporting and shows led to he and other reporters getting summoneed for an audience in Shanahan's office Tuesday for him to finally tell his side of what happened.You'll hear more from Cohn in his own words on this twist in the coverage and what it means.Plus, an interesting angle is whether Scheter, Ian Rapoport and others covering the breaking news and info as "insiders" are nothing more than compromised "PR" type- personalities, who are only giving out what teams, players, agents, etc. want the public to know?T.J. has his thoughts and has more from national journalist turned host Jason Whitlock on the Shanahan story/attempted cover up and about Schefter.Enjoy it all on the "LWOS Media" Podcast and make sure to follow/subscribe on Apple/Spreaker/Spotify, etc! 

Willard and Dibs open the show by reacting to San Francisco 49ers head coach Kyle Shanahan's account of his car crash in Palo Alto a few weeks ago.

Most EdTech companies pitch schools before they understand how decisions get made inside them. School budgets are tight, sales cycles are long, and trust is hard to earn and easy to lose. In a market this crowded, the vendors who succeed are rarely the loudest ones. They are the ones who understand what a school leader is weighing before they ever pick up the phone.Smita Kolhatkar has sat on both sides of that equation. She spent 15 years in high tech before becoming an educator, and today she is Assistant Head for Innovation, Responsible AI and EdTech at Gideon Hausner Jewish Day School in Palo Alto. She evaluates new products constantly, built her school's AI Tinkery program to teach AI literacy from kindergarten up, and has a clear, specific list of what earns her trust and what ends a conversation before it starts.In this conversation, Smita walks through her actual buying process: the no-gos that end a pitch immediately, why conference expo halls still outperform months of email outreach, and why a single "no" from her can quietly close the door at several other schools too. It's a clear look at what that decision-making looks like from the other side of the table, for anyone marketing or selling into K-12.What You'll LearnThe specific deal-breakers that end a vendor conversation immediately, including SSO integration and cross-platform compatibilityThe buying signals that earn her attention, and the outreach habits that shut a conversation down fastWhy a single school leader's "no" often reaches ten other schools through her professional networkWhy conference expo halls remain one of her most valuable discovery channels, even in a digital-first marketWhy AI literacy needs to start at age five, and how her school's AI Tinkery program puts that into practiceHer take on the no-screens debate, and why treating EdTech as all-or-nothing misses the real question schools are askingWhy It MattersEdTech companies are working in one of the toughest sales environments in years: tighter budgets, longer sales cycles, and a market flooded with AI tools faster than schools can evaluate them. What Smita describes is not just one school's experience, it is a window into how school leaders across the country are making these calls right now. She is not only a buyer, she is a connector: when something works, it moves through her professional network, and when something falls flat, that travels just as fast. For EdTech marketers and sales teams, the lesson is that a sale is never just to one person. The trust built or lost in a single conversation compounds across an entire network of schools.Resources Mentioned in this Episode:Gideon Hausner Jewish Day SchoolThe K-8 school where Smita serves as Educational Technology and Innovation Director.AI Tinkery at HausnerThe maker-style AI lab Smita built for students to experiment hands-on with AI tools and concepts.AI Tinkery at Stanford GSEThe Stanford Graduate School of Education program that inspired Smita's AI Tinkery model, credited to Dr. Karin Forssell.Stanford Accelerator for LearningAn overview of the Stanford initiative behind the AI Tinkery framework.AI Quests and Day of AIA resource Smita references for structured AI literacy activities.Hour of AI A companion resource to Hour of Code, focused specifically on AI literacy."The Learning Frontier"Hausner's AI in Education confererence for educators.Smita Kolhatkar on LinkedIn

Whatever result was obtained in Satya-yuga by meditating on Viṣṇu, in Tretā-yuga by performing sacrifices, and in Dvāpara-yuga by serving the Lord's lotus feet can be obtained in Kali-yuga simply by chanting the Hare Kṛṣṇa mahā-mantra. (SB 12.3.52) https://vedabase.io/en/library/sb/12/3/52/ ------------------------------------------------------------ To connect with His Grace Vaiśeṣika Dāsa, please visit https://www.fanthespark.com/next-steps/ask-vaisesika-dasa/?utm_source=youtube&utm_medium=video&utm_campaign=launch2025 https://vaisesikadasayatra.blogspot.com/ ------------------------------------------------------------ Add to your wisdom literature collection: https://iskconsv.com/book-store/?utm_source=youtube&utm_medium=video&utm_campaign=launch2025 https://www.bbtacademic.com/books/?utm_source=youtube&utm_medium=video&utm_campaign=launch2025 https://thefourquestionsbook.com/?utm_source=youtube&utm_medium=video&utm_campaign=launch2025 ------------------------------------------------------------ Join us live on Facebook: https://www.facebook.com/FanTheSpark/ Podcasts: https://podcasts.apple.com/us/podcast/sound-bhakti/id1132423868 For the latest videos, subscribe https://www.youtube.com/@FanTheSpark For the latest in SoundCloud: https://soundcloud.com/fan-the-spark ------------------------------------------------------------ #kirtan #mantra #mantrameditation #spiritualawakening #soul #spiritualexperience #spiritualpurposeoflife #spiritualgrowthlessons #secretsofspirituality #vaisesikaprabhu #vaisesikadasa #vaisesikaprabhulectures #spirituality #bhaktiyoga #krishna #spiritualpurposeoflife #krishnaspirituality #spiritualusachannel #whybhaktiisimportant #whyspiritualityisimportant #vaisesika #spiritualconnection #thepowerofspiritualstudy #selfrealization #spirituallectures #spiritualstudy #spiritualquestions #spiritualquestionsanswered #trendingspiritualtopics #fanthespark #spiritualpowerofmeditation #spiritualteachersonyoutube #spiritualhabits #spiritualclarity #bhagavadgita #srimadbhagavatam #spiritualbeings #kttvg #keepthetranscendentalvibrationgoing #spiritualpurpose

“It's a realization of my individual self, not an alienation of it.” — Keith Teare on the book he wrote with AI in a week Welcome to episode 2984 of the show. A week is certainly an age in AI time. Since last week's “Who Owns Intelligence” show, That Was The Week publisher Keith Teare has used AI to write an entire book entitled, surprise surprise, Who Owns Intelligence. No wonder, as Keith's editorial this week puts it, AI has its enemies. These enemies, Keith insists — riffing off Karl Popper's The Open Society and Its Enemies — are mostly “good people.” They are the authors, workers, and others caught in the headlights of epochal technological change. He claims to have seen the pattern before. When he opened the world's first Internet cafe in 1994, the BBC only wanted to ask him about online porn. But some enemies are less well meaning than others. Congress is considering a kill switch bill — let's call it the Kill Bill — which is backed by the frontier companies like Anthropic and OpenAI cynically seeking to set their current market dominance in stone. So might the answer be Chinese style “open source” AI as requested this week in an open letter by NVIDIA CEO Jensen Huang? Keith's answer is deliciously ironic. Denied walls of NVIDIA GPUs, Chinese labs learned to train cheaply by inference — distilling American frontier models through their own public interfaces. So Huang's support for open source AI might end up shooting NVIDIA in their most sensitive parts — their chips. And what about the state — can it protect all those “good people” from the oncoming AI locomotive of history? No. Not according to Keith, at least. State regulators like Lina Khan treat consumers as children, Keith (himself the parent of three boys) says. Besides, government simply can't keep up with the speed of technological change. So, for example, when OpenAI's models hacked Hugging Face in a lab test this week, the company caught, stopped, and reported it faster than any regulator could. As for inequality, the answer isn't nationalization but ownership on the model of Keith's Norway-style human wealth fund. For more, read his new book Who Owns Intelligence. We ended on an uncharacteristically French post-structuralist note. My interview of the week was Emily Eakin, author of The Frenchmen, her very personal history of French theorists like Michel Foucault and the two Jacques, Derrida & Lacan. It was Foucault, who — at the end of his 1966 book The Order of Things — predicted that man would be erased, AI style, like a face drawn in sand at the edge of the sea. But Keith isn't in this bleak Foucaultian camp. His book, written with AI in under a week, he says, is a realization of his individual self, not an alienation from it. The end or beginning of man? Maybe I got it wrong earlier. Welcome to episode 1984 of the show. Five Takeaways •       A Book in a Week. Inspired by last week's conversation, Keith used AI to write Who Owns Intelligence in seven days — the case for a “human wealth fund” seeded by the AI companies' own stock, global on day one and non-governmental, with Norway and Alaska as partial precedents. AI, he insists, is a tool, not an author: “AI would never have been able to start, never mind finish a book without me.” The detection farce cuts his way — Substack's new AI detector rated his 100% human editorial as 100% AI, and universities are quietly canceling their AI-detection contracts because the tools simply don't work.•       The Kill Bill and the Ladder-Kickers. Congress is considering a bill that would let government switch AI off — and, paradoxically, the AI companies are in favor. Keith's reading: regulation is a competitive strategy. Dario Amodei — “he's an entrepreneur, he's devious, he strategizes” — is the most sincere in wanting rules that would lock out open source competition — and Anthropic's $1.5 billion book settlement this week suggests the moat is expensive to maintain; Sam Altman zigzags; Musk, in this context at least, is one of the good guys. The genuine enemies of AI, meanwhile, are mostly good people: authors and workers frightened by the scale of change, just as the BBC in 1994 could only ask the founder of the world's first Internet cafe about porn and addiction.•       Why the Good Open Source AI Is Chinese. The week's uncomfortable question: where is the American open source AI? Keith's answer is structural. Denied walls of NVIDIA GPUs by export controls, Chinese labs were forced to train cheaply by inference — asking American frontier models millions of questions through public interfaces and learning from the answers. The result: world-class open models built, in effect, on distilled Anthropic and OpenAI. NVIDIA's much-signed open letter supporting open source, Keith notes, translates simply: more customers.•       The State Can't Keep Up. Lina Khan's consumer protection, in Keith's telling, is parental — treating consumers as children — and government-owned AI would be obsolete within three months of purchase. The counter-example happened this week: when OpenAI's de-railed models hacked Hugging Face in a lab test, the company caught, stopped, and reported it faster than any regulator could. The companies are the right point of control, held to a high standard. Matthew Yglesias adds nuance on the data center backlash — local micro-politics is not the same thing as the statewide bans coming almost entirely from Democratic states — and the American university, still metering intelligence at $80,000 a year, looks to Keith like a business model past its sell-by date.•       The End of Man — or the Beginning? Andrew's interview of the week was Emily Eakin on the Frenchmen — and Foucault's 1966 prophecy that man would be erased like a face drawn in sand at the edge of the sea, a prediction Eakin thinks the AI age is realizing. Keith takes the opposite view: the postmodernists grasped the rise of the individual (consider the Pantone color system) but gave him no society to live in. AI, far from erasing the individual, lets him realize himself — the week-old book is “a realization of my individual self, not an alienation of it.” Could Popper have written The Open Society in an hour? Before the car, nobody drove Manchester to London in two and a half hours either. About Keith Teare Keith Teare is Andrew's weekly co-host and the publisher of the That Was The Week tech newsletter. A four-decade veteran of the technology industry, he opened Cyberia — the world's first Internet cafe — in London in 1994, co-founded Easynet and TechCrunch, and is today the founder and CEO of SignalRank Corporation in Palo Alto. His latest — unpublished, so far — book is Who Owns Intelligence, written with AI in a week. References: •       “AI and Its Enemies: Who Needs a Kill Switch?” — Keith's editorial in this week's That Was The Week.•       

Rogue AI Vehicle Porn, OpenAI, Nudes, Clop, Patches, Oracle, Palo Alto, Aaran Leyland, and More on this episode of the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-601

for the full episode join the Patreon [patreon.com/fashiongrunge] Now, there are movies that are true time capsules. They capture the feeling and specific aura of time. In my rewatch of Palo Alto from 2013 I was instantly transported back to that time period. For me it was a very specific set of aesthetics and music that lived on the internet and Tumblr specifically and this whole movie felt like wandering through a blog. I was transfixed by this after my first watch at the cinema. The soundtrack lived in my head for months and still is one of my favorites. During my rewatch this time I could see the cracks in the story that I didn't see before. The truly blandness of the story, some of the most unlikeable characters, and the whole problematic nature of James Franco and his sleazy role in this film adapted from his short stories. I've decided to put this in my 'kinda bad, but I like it' category. I get into the behind the scenes short film that was produced for it, the normie but relatable fashion, and the lack of technology that felt like a breath of fresh air in the story. "I wish I didn't care about anything." *released August 31, 2024 -- Get BONUS episodes on 90s TV and culture (Freaks & Geeks, My So Called Life, Buffy, 90s culture documentaries, and more...) and to support the show join the  Patreon!  Hosts: Lauren @lauren_melanie Follow Fashion Grunge PodcastFind more Fashion Grunge on LinktreeJoin me on Substack: The Lo Down: a Fashion Grunge blog/newsletter☕️ Support Fashion Grunge on Buy Me a Coffee: https://www.buymeacoffee.com/fashiongrunge

Rogue AI Vehicle Porn, OpenAI, Nudes, Clop, Patches, Oracle, Palo Alto, Aaran Leyland, and More on this episode of the Security Weekly News. Show Notes: https://securityweekly.com/swn-601

Rogue AI Vehicle Porn, OpenAI, Nudes, Clop, Patches, Oracle, Palo Alto, Aaran Leyland, and More on this episode of the Security Weekly News. Visit https://www.securityweekly.com/swn for all the latest episodes! Show Notes: https://securityweekly.com/swn-601

We are pleased to continue our COBT California Summer Series with today's episode featuring Martin Viecha, Founder and Advisor at MV Motion Advisory. Prior to founding MV Motion, Martin served as the Vice President of Investor Relations at Tesla and previously spent several years as a sell-side equity research analyst covering the automotive and technology sectors. Based in Palo Alto, MV Motion is focused on robotaxis and autonomy, humanoid robots, and the evolving automotive landscape. We were delighted to host Martin for a wide-ranging discussion on the technologies shaping the future of transportation, robotics, and AI. In our conversation, Martin provides a comprehensive overview of the rapidly evolving autonomous vehicle and humanoid robotics landscape. We discuss why he believes robotaxis are approaching a mainstream adoption inflection point, transitioning from a Silicon Valley novelty to a service that will soon be available across much of the U.S. He explains why California, Texas, and Florida have emerged as leading deployment markets, how expanding permitting and improving safety records are accelerating adoption, and why safety, utilization rates, and cost per mile will ultimately determine the industry's winners. We explore Tesla's camera-only autonomous driving approach versus Waymo's multi-sensor strategy and the long-term implications for automakers, ride-hailing platforms, insurance, and vehicle ownership. We examine China's growing leadership in EVs and robotics, the enormous long-term potential for humanoid robots, the significant technical hurdles that remain around dexterity, world models, and data collection, and why geopolitics, national security, and public policy are likely to play an increasingly important role in shaping the future of advanced robotics and AI. Martin outlines why, despite the excitement surrounding humanoid robots, they remain considerably further from widespread commercialization than robotaxis due to the vastly greater complexity of replicating human movement and decision-making. It was a fascinating discussion. We look forward to staying connected with Martin and continuing to follow his research. To start the show, Mike Bradley noted that the next three to four weeks will be dominated by second-quarter earnings reports. From a fixed income perspective, U.S. Treasury yields have continued to trend higher, driven in part by rising energy prices and their inflationary impact. The S&P 500 was up modestly on the day, gaining roughly 0.25%. In commodities, Brent crude oil rose approximately $2/bbl to around $94/bbl amid ongoing tensions in the Middle East. President Trump also formally approved a landmark agreement with Saudi Arabia to support the development of a civilian nuclear program in the kingdom, potentially opening the door to uranium enrichment activities there. In energy and power equities, GE Vernova (GEV) shares fell approximately 8% following earnings as the company fell short of highly elevated investor expectations despite reporting solid gas turbine performance and providing robust forward guidance. In contrast, Weatherford International (WFRD) shares rose as much as 9% after delivering strong quarterly results and a more optimistic outlook for the second half of 2026 than the market had anticipated. With equity markets trading near all-time highs and quarterly and second-half 2026 expectations remaining extremely elevated for many companies, Mike noted that the next three to four weeks of earnings reports could generate significant market volatility. Ellen Wilkirson also joined the discussion and peppered in her technology questions and perspectives. 

Que faire quand ton enfant te dit que ça se passe mal à l'école ?Quand il revient avec des moqueries, des mises à l'écart, parfois des intimidations — et que ton premier réflexe, c'est d'aller voir l'enseignant·e, ou de parler aux parents de l'autre. Réflexes aimants. Et pourtant, souvent à l'inverse de ce qu'on espère.Dans cet épisode enregistré au Congrès Innovation en éducation, je reçois Emmanuelle Piquet, thérapeute et spécialiste de la lutte contre le harcèlement scolaire. Sa méthode, héritée de l'école de Palo Alto, est à contre-courant : au lieu de demander au harceleur de redescendre, elle aide l'enfant harcelé à remonter. Avec 2 ingrédients clés : le courage et l'autodérision.Au programme :→ Comment vraiment définir le harcèlement (et pourquoi c'est important)→ Les signaux faibles qui doivent t'alerter (dimanche soir, veilles de rentrée…)→ Les 3 erreurs que font tous les parents avec amour — et qui aggravent→ Les 3 facteurs de risque identifiés par la recherche→ La technique de l'autodérision, illustrée par l'histoire d'Ernest et César→ Pourquoi sur-protéger fragilise parfois (l'idée à déconstruire)⏱️ Chapitres02:28 - Comment définir le harcèlement scolaire03:41 - Les 2 ingrédients : courage + autodérision05:08 - L'exemple d'Ernest et César06:39 - Pourquoi la souffrance de l'enfant nourrit le harceleur07:47 - Quel adulte choisir quand on est harcelé09:06 - Les signaux faibles qui doivent alerter les parents10:26 - Pourquoi le dimanche soir et les veilles de rentrée11:18 - Les 3 idées phares à retenir12:59 - Les erreurs que font les parents avec amour14:57 - Les 3 facteurs de risque identifiés par la recherche16:43 - Le conseil simple : l'autodérision au quotidien17:55 - Le message aux enseignants + le jeu No Web Buly19:50 - Être aligné selon Emmanuelle20:14 - L'idée à déconstruire : sur-protéger fragilise parfoisUne conversation lucide, pragmatique et profondément humaine, avec une experte qui réoutille sans culpabiliser.Bonne écoute,Charlotte

Drs Kaniksha Desai and Angela Leung discuss the new guidelines for thyroid disease in preconception, pregnancy, and postpartum. This podcast is intended for healthcare professionals only. To read a partial transcript or to comment, visit: https://www.medscape.com/index/list_15483_0 Kaniksha Desai, MD, Associate Professor, Department of Internal Medicine, Endocrinology, Stanford University, Palo Alto, California Angela Leung, MD, MSc, Associate Professor of Medicine, Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, David Geffen School of Medicine at UCLA; Veterans Affairs Greater Los Angeles Healthcare System, Los Angeles, California

Fifteen years ago, Eric Ries handed a generation of founders a playbook. When Eli was in a Palo Alto-based startup accelerator in 2011, The Lean Startup felt like the only book anyone in that ecosystem was talking about. It was in the middle of a wildly optimistic moment for tech. Marc Andreessen declared that “software is eating the world,” social media was blooming, and there was a widespread belief that technology was about to democratize everything and bring us closer together. Concepts like the MVP (“minimum viable product,”), the pivot, and build-measure-learn became the operating language of Silicon Valley. This is a preview of a premium episode. Listen to the full interview on our Substack: https://designbetterpodcast.com/p/eric-ries But a lot of the companies built on those ideas went on to get corrupted by forces Eric hadn't yet named. His new book, Incorruptible: Why Good Companies Go Bad and How Great Companies Stay Great, is his reckoning with what happens after you build something great — and how to keep it from falling apart. Buy the book In this conversation, we get into why speed itself wasn't the problem, but treating a rising stock price as proof of health is like assuming more exhaust means a faster car. Eric explains why doing the right thing 100% of the time is actually easier than doing it 98% of the time, and how companies behave like superorganisms with their own emergent character — a point he illustrates with a genuinely mind-bending study about ants solving a puzzle that no single ant ever could. We talk about the “harder is easier” principle through stories from Patagonia and the long-term stock exchange he built as a design challenge, and why the value a company makes comes from the design of its products. And because we couldn't resist, we get into AI slop, LLM psychosis, and Eric's clear, simple antidote: never ask these tools to make you an artifact — ask them to teach you how to make one yourself. Bio Over the last two decades, Eric Ries's ideas about continuous innovation, long-term thinking, governance, and market reform have reshaped company building and management practices. He is the creator of the Lean Startup method, and the author of the New York Times bestseller The Lean Startup; The Leader's Guide; and The Startup Way. As a founder, he has put his own ideas into practice with The Long-Term Stock Exchange (LTSE); Answer.AI, an AI R&D lab; the Lean Startup Co, which teaches and supports the implementation of Lean Startup; Virgil, a legal services startup; and IMVU, where the ideas that became the Lean Startup method were forged. On his podcast, The Eric Ries Show, he talks to guests including world-class technologists, thought leaders, and executives working to build profitable companies for the long-term benefit of society. Eric has served as an entrepreneur-in-residence at Harvard Business School and IDEO. He lives in the San Francisco Bay Area with his wife and three children. *** Premium Episodes on Design Better This is a premium episode on Design Better. We release two premium episodes per month, along with two free episodes for everyone. New premium subscriber benefit: we've launched a private Slack workspace…join now to connect with designers, product leaders & creative practitioners in our community. And get a behind-the-scenes pass to every episode with The Roundup, where each week we bring you insights and actionable tactics from recent episodes. Premium subscribers get access to the documentary Design Disruptors and our growing library of books. You'll also get access to our monthly AMAs with former guests, ad-free episodes, discounts and early access to workshops, and our monthly newsletter The Brief that compiles salient insights, quotes, readings, and creative processes uncovered in the show. And subscribers at the annual level now get access to the Design Better Toolkit, which gets you major discounts and free access to tools and courses that will help you unlock new skills, make your workflow more efficient, and take your creativity further. Upgrade to paid Visiting the links below is one of the best ways to support our show: Masterclass: MasterClass is the only streaming platform where you can learn and grow with over 200+ of the world's best. People like Steph Curry, Paul Krugman, Malcolm Gladwell, Dianne Von Furstenberg, Margaret Atwood, Lavar Burton and so many more inspiring thinkers share their wisdom in a format that is easy to follow and can be streamed anywhere on a smartphone, computer, smart TV, or even in audio mode. MasterClass always has great offers during the holidays, sometimes up to as much as 50% off. Head over to http://masterclass.com/designbetter for the current offer. Learn more about your ad choices. Visit megaphone.fm/adchoices

WP2Shell WordPress RCE feeding frenzy, AI agent breaches Hugging Face, Killin hits Palo Alto VPN flaw   This episode covers five major incidents: a chained WordPress exploit dubbed WP2Shell (CVE-2026-6330 and CVE-2026-6137) enabling anonymous remote code execution on stock installs, now seeing tens of thousands of Internet-wide attempts, backdoor admin accounts, and web shell payloads despite forced auto-updates to 6.9.5 and 7.0.2.   Hugging Face's disclosure that an autonomous AI agent breached its production infrastructure via a malicious dataset, stole limited internal datasets and credentials, and forced responders to work around restrictive model guardrails.   Arctic Wolf's report that the Killin ransomware gang is exploiting Palo Alto PAN-OS GlobalProtect auth bypass CVE-2026-0257 for domain-wide encryption; and healthcare supply-chain fallout including Craneware file exfiltration.   EY client tax-data exposure via a third-party platform.   00:00 Top Stories Teaser 00:28 WP2Shell WordPress Frenzy 02:58 AI Agent Hacks Hugging Face 05:16 Killin Hits Palo Alto VPNs 07:33 Craneware Healthcare Breach 09:15 EY Third Party Data Leak 10:47 Wrap Up and Sign Off

Blablabla at Gamble Garden Palo Alto, tourist at home

John Lilly has had a front row seat to almost every major shift in technology over the last thirty years, and he has sat in almost every chair while it happened.He was a Senior Scientist at Apple in 1997, when the market cap was two billion dollars and most people assumed the company was finished. He co-founded Reactivity, later acquired by Cisco. He became CEO of Mozilla and led Firefox past 450 million users, running an open source nonprofit at a time when nobody else in Silicon Valley was doing anything like it. Then he spent over a decade as a General Partner at Greylock, where he led investments in Instagram, Dropbox, Figma, Tumblr and Quip. He sits on the boards of Figma, Duolingo and Nuro, and recently joined Gigascale Capital as an Advising Partner, backing companies rebuilding the physical economy.In this conversation with Jessica Neal and co-host Jeff Markowitz, John walks through what he actually saw in the founders everyone else missed.He tells the story of chasing Kevin Systrom for six months, wiring the money on a Thursday, and getting the call three days later that Instagram was selling to Facebook. He explains the moment a friend showed him Instagram and what clicked was not the filters, it was the distribution. And he describes meeting a 19 year old Dylan Field at a Starbucks in Palo Alto, telling him his product did not matter, and then spending the next decade watching him prove it wrong.He is also honest about the parts that are harder to hear. Why some partners at Greylock wanted Dylan replaced. Why he thinks micromanaging the right things is a feature of good leadership, not a flaw. Why he does not tell CEOs anything, and what he does instead. And why, at a moment when OpenAI and Anthropic are pulling talent out of every company on earth, the answer is not matching the money.He closes with something a colleague at Mozilla told him years ago that he still carries: we had a chance to make the world we wanted, and we did it.━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━WHAT WE COVER━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━THE FOUNDER QUESTION→ How John spotted Dylan Field at 19→ Why recruiting senior people is the strongest early signal→ The trait every great founder shares: relentless about getting help→ Why non-defensiveness beats raw intelligence→ The lunch where they told Dylan he had to build a go to marketTHE INVESTMENTS→ Chasing Kevin Systrom for six months→ The underwater photographer who explained Instagram to him→ Why the network mattered and the filters did not→ The four day flip to Facebook and what he told his LPs→ The Adobe Killers that came before Figma and why this one was differentLEADERSHIP AND SCALE→ Going from 12 people to a quarter of the internet at Mozilla→ Why he never learned the delegation lesson everyone teaches→ Larry and Sergey reviewing every resume and what that signals→ Learning to be simpler in your message as you grow→ Deciding you would rather be successful than comfortableTHE TALENT WAR→ Why every great person is now a free agent→ The spreadsheet exercise he gives every CEO→ Tour of duty and the bilateral deal between company and employee→ Never making an offer until you know it will be accepted→ Why money is rarely the only reason people leaveTHE CURRENT MOMENT→ Why leadership now means stability without pretending anything is stable→ The collision of faster scaling and total vulnerability→ Why org size no longer scales with impact→ Alpha nerds, Tim O'Reilly, and following the most technical people around→ Investing in transformers, copper recycling and rare earth supply chains→ Why none of the AI future is settled and it is all still to play for━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━CONNECT WITH JOHN LILLYLinkedIn: linkedin.com/in/johnlilly━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━Truth Works is hosted by Jessica Neal, former Chief Talent Officer at Netflix, with co-host Jeff Markowitz.

In 1776 — that same year America declared its independence — Adam Smith published the equally revolutionary The Wealth of Nations, his founding explanation of national economic value. Two hundred and fifty years later, Tim O'Reilly argues in the free-market Economist that Elon Musk and his fellow tech barons are building a monarchical form of capitalism that the proto-democratic Smith would have hated. Musk, O'Reilly reports, believes that SpaceX will become “worth more than the rest of Earth”. The merchants are becoming princes, O'Reilly warns. And the rest of us are becoming peasants. Such is the road to serfdom in our AI age. So who should own the AI in our bewildering age of multi-trillion dollar start-ups like SpaceX, Anthropic and OpenAI? Or as That Was The Week publisher Keith Teare asks in his latest editorial, who should own the “intelligence” of our AI age? Keith uses a bottling plant as a metaphor to describe our dilemma. Since no single entity can own this intelligence — the sum total of our common experience — charging us for it would be like seizing the Earth's water supply and selling it back to us, Coca-Cola style, in plastic bottles. Except that the Hayekian Keith approves of the bottling process. Private companies, rather than governments, he argues, are most suited to doing this. For Keith, this dilemma is also an opportunity to redistribute the ownership of intelligence. He argues for a “Human Wealth Fund” into which every consequential AI company should put a slice of its equity. In the manner of Norway's sovereign wealth fund, this fund would be distributed to all citizens. Rather than Denmark, now we should become like Norway, a tiny homogenous nation with a cultural distaste for Muskian individual wealth. Not very realistic, I fear. On top of that, it's hard to imagine our tech princes collaborating on anything. Musk and Altman aren't on speaking terms while Altman and Amodei, who also loathe each other, are focused on their IPOs. Meanwhile, the Trump administration, which presumably would coordinate this fund, is pitching a $100,000-a-month fast feed of the president's posts. Keith's question, “who owns the intelligence”, is the right one. But the answer won't come from trickle-down funds set-up by our tech princes. Such supposed munificence is about as likely as America becoming Norway. Read the fine print of any “Human Wealth Fund” set up by Sam Altman and Elon Musk. As we should know all too well by now, when a “revolutionary” Silicon Valley gives stuff away, it turns out to be exorbitantly expensive. Free plastic bottles of intelligence, anyone? Five Takeaways •       Intelligence, Not AI. The week's framing shift: the word AI is too small, because AI is merely the tool for harvesting and delivering the thing itself — intelligence, the sum total of our common human experience. Keith argues the renaming is not semantic but political: the moment intelligence sits at the center of the discussion, everyone's opinion has to be shaped by what it actually is, and the idea that any single entity could own it starts to look as bizarre as owning the world's water supply. Andrew's rejoinder: they're still just words — though he concedes intelligence is the better one. •       Bottled Intelligence Is Good — The Question Is Who Benefits. Keith refuses the critic's role: bottling intelligence, like Google's bottling of the world's words into search, is a good thing, because only massively capitalized private companies can innovate at that scale — and between private entities and governments as owners of intelligence, he'll take the companies every time. What's wrong is the distribution of the benefits. Even insiders are complaining: Alex Karp is publicly angry at OpenAI and Anthropic's pricing, while China's Kimi K3 — released the day of recording and, Keith claims, better than Claude Fable — signals that very good models are about to get very cheap. •       Capitalism Adam Smith Would Hate. Tim O'Reilly argues in The Economist that Musk and his type are building a capitalism Smith would despise — founders as monarchs, a point Henry Farrell reinforces with a slide from Peter Thiel's startup class placing the king of a monarchy and the founder of a startup side by side. Keith's response is characteristically unsentimental: Smith would have hated everything since the Federal Reserve, and the founder-king structure — Larry and Sergey's voting shares, Zuckerberg's special rights, corporations bigger than countries with user bases bigger than China — is simply the stage of capitalism we're at. The question is whether there's a path from here to somewhere better. •       The Human Wealth Fund. Keith's path comes in two versions: government-down, a sovereign wealth fund holding AI equity for every citizen; or company-up, the AI companies voluntarily endowing a global fund — and it only takes one to move first, because everyone else would have to react. His proxy is Norway, where every citizen benefits from ownership — not payouts, ownership — in the oil fund; AI revenues, unlike Norwegian oil, could eventually drive most of a doubled global GDP. His critique of the Brynjolfsson economists' much-signed statement is that “must act now” is vacuous: he'd have added a point four naming the actual mechanism. •       The Bet. Andrew's counter-case: Musk and Altman loathe each other, the mob hates AI so thoroughly that no pro-AI politician can survive, the states from Newsom's California to Florida are embracing nothing, New York just enacted the first data center moratorium, and the founders — eyes on their IPOs — are in the pockets of the banks. Hence the wager: 5% of the Teare Wealth Fund says no Human Wealth Fund this year, and none in the twenties. Keith declined the bet, on principle: he's an advocate, and only through advocacy does public opinion change. As Andrew put it: keep fighting the good fight — maybe one of the crazy ideas will stick. About the Guest Keith Teare is the founder and editor of the That Was The Week tech newsletter, and Andrew's weekly co-host. A British-born Silicon Valley entrepreneur and investor, he was a co-founder of TechCrunch and runs the Palo Alto–based venture firm SignalRank. He and Andrew have been arguing about technology — productively — every week for years. References: •       That Was The Week — Keith's newsletter; this week's editorial argues that the word AI is too small, and that the central question of the age is who owns intelligence. •       Tim O'Reilly in The Economist — on Elon Musk building a form of capitalism that Adam Smith would hate, quoting Musk's claim that SpaceX will become worth more than the rest of the Earth. •       Henry Farrell — the big tech critic's companion piece, featuring the slide from Peter Thiel's startup class that plac...

Cybersecurity investor Sid Trivedi of Foundation Capital joins me to dig into AI SOC valuations, services-as-software, moats, and what founders should know heading into Black Hat.Sid is a Partner at Foundation Capital, where he invests at the seed and Series A stage with a focus on cybersecurity and IT infrastructure. This is our annual pre-Black Hat check-in, and a lot has moved since last year, from massive M&A to record-setting rounds in categories like the AI SOC.In this episode:- What has actually changed a year into the AI wave, and what hasn't- Services-as-software, the $4.6 trillion market thesis, and automating cyber workflows across the SOC, IR, pen testing, and threat intel- What AI means for cybersecurity jobs and how practitioners should adapt- Consolidation vs. best-of-breed after Palo Alto's $25B CyberArk deal and Alphabet's $32B Wiz acquisition- AI SOC valuations, including Seven AI's record Series A and Torq crossing a $1B valuation- The double-edged sword of big raises and why founders should be cautious about the valuations they accept- Why you can't simply spend your way to growth in cybersecurity- Moats and defensibility when frontier labs can push into your category- The Black Hat Innovator Investor Summit and the Startup Spotlight competitionChapters:0:00 Intro0:52 What's changed a year into the AI wave2:42 Services-as-software and the AI SOC9:38 AI adoption and forward deployed engineers10:57 M&A, platformization, and best-of-breed14:17 IT and security convergence, plus AI SOC valuations18:48 Seed-stage risk calculus vs. later-stage investors21:43 The double-edged sword of big raises26:20 Why you can't spend your way to growth29:05 Moats and defensibility in the frontier-lab era32:25 Deal flow, pricing, and staying disciplined37:06 Black Hat Innovator Investor Summit40:17 Startup Spotlight competition43:28 Wrap-upBlack Hat is offering listeners $500 off registration with code USA500Resilient.Connect with Sid:LinkedIn: https://www.linkedin.com/in/siddhanttrivedi/Foundation Capital: https://foundationcapital.comResilient Cyber: https://www.resilientcyber.ioSubscribe for more conversations with security practitioners, founders, and leaders.

This week on The California Report Magazine: What Skid Row Taught Acclaimed Violinist Vijay Gupta About Music When violinist Vijay Gupta joined the Los Angeles Philharmonic at just 19 years old, he was the youngest to ever take a chair in a major orchestra in the U.S. But despite that shining achievement, he was desperately unhappy. Now in his late 30s, Gupta has a new memoir out called Restrung. It explores how, after years of struggling to live up to other people's expectations, he found his true voice working with homeless people on the streets of Skid Row. Reporter Steven Cuevas has this profile.   ⁠Life Lessons from a Game of Chess⁠ Visits with loved ones are a special time for people who are incarcerated in California. Families come from all over the state to see their loved ones for a short while, and it's one of the only opportunities incarcerated parents can connect with their kids. In this essay from the podcast Uncuffed, producer Fonuamana Fuahala recounts connecting with his son — who he hadn't seen in years — over a game of chess.   When Chinese Flower Growers Helped the Bay Area Bloom Santa Clara County stretches from San Jose up through Palo Alto and into the surrounding foothills. Today, it's an area known for sprawling tech campuses and suburban neighborhoods, but for a large part of the 20th century, this landscape looked very different. Some would say it was blooming. KQED's Gabriela Glueck brings us this story. Learn more about your ad choices. Visit megaphone.fm/adchoices

Episode initialement diffusé en 2024Comment rater son couple à coup sûr. C'est ce qu'Emmanuelle Piquet explique dans son nouveau livre.Emmanuelle est thérapeute depuis plus de 15 ans et elle accompagne au quotidien des personnes en souffrance. Comme elle l'explique, régulièrement, les personnes qui viennent la voir en individuel sont en fait des personnes en difficulté dans leur couple.Dans ce livre, plein d'humour évidemment, Emmanuelle veut vous montrer que les problématiques que rencontrent les couples sont, au final, assez communes dans leurs origines.Dans cet épisode, on parle des cercles vicieux dans lesquels on peut toutes et tous se retrouver, on parle de l'envie de contrôler son partenaire et de ce que cela cache, on parle de notre incapacité à communiquer nos besoins correctement et de ses conséquences, et on aborde les disputes de couple sous un angle nouveau et rafraîchissant.Alors, embarquez avec nous dans ce monde sinueux des relations de couple et de leurs ruptures.Je vous souhaite une très bonne écoute.LIENS UTILES :Comment rater son couple à coup sûr, Emmanuelle Piquet