Podcasts about altman

This Friday's show is packed with political chaos, viral moments, and some genuinely unsettling stories. Trump's fight with Canada reaches a new level as he trolls Mark Carney, takes aim at Canadian trade policies, and renames Lake Ontario “Lake America,” triggering backlash from Canadian leaders and Democrats at home.The Chicks also dig into the growing controversy surrounding Pennsylvania Governor Josh Shapiro's claims about measles deaths after local officials raised questions about the reported causes of death.Then, Sam Altman and major AI companies issue an urgent warning about cyber defense, sparking a much bigger conversation about artificial intelligence, automation, and just how many American jobs could disappear in the coming years.Later, the Lindsay Clancy murder trial takes center stage as prosecutors deliver their closing argument and lay out their case that Clancy understood right from wrong when her three children were killed.Plus: Abdul El-Sayed's deleted Green New Deal posts resurface, Gavin Newsom faces questions about his property dealings, Kamala Harris hints at another run, Ben Shapiro mocks Tucker Carlson, and the latest conservative media infighting gets even messier.For a limited time only, receive 20% off your entire Laundry Sauce order when you use code CHICKS20 at https://LaundrySauce.com/Chicks20Save an additional 10% off  practical food for your pantry with the ReadyWise 4-Can Protein Bundle at https://ReadyWise.com with promo code CHICKS10.Make the switch and feel the difference of truly fast, modern antivirus protection with Webroot— for a limited time, save 60% when you go to https://WebRoot.com/ChicksSubscribe and stay tuned for new episodes every weekday!Follow us here for more daily clips, updates, and commentary:YoutubeFacebookInstagramTikTokXLocalsMore InfoWebsite

Dan Nathan sits down with Matt Turck, Managing Director at First Mark Capital and creator of the annual MAD (Machine Learning, AI & Data) Landscape, for a wide-ranging look at where AI investing stands right now. They cover the power-law dynamics driving venture dollars to a handful of companies, why Nvidia is starting to look like "the bank" of the AI industry, Anthropic's surge past $65 billion in revenue and its first profitable quarter, and how OpenAI, Microsoft, Google, and Meta are each positioning for what comes next. The conversation turns philosophical with a discussion of AGI, superintelligence, and the idea that Altman, Musk, and Amodei are all, in their own ways, trying to build God — before closing with a deep dive into China's AI progress, open source, and the robotics race. Matt also talks about his own podcast, The MAD Podcast, and his Data Driven NYC event series. Links Referenced The MAD Podcast (Apple Podcasts) The MAD Landscape (Matt's Website) Nvidia Has Become a Banker to the AI Boom, Putting It on Dangerous Ground (WSJ) Read "AI 2027" and "AI 2040" —FOLLOW USYouTube: @RiskReversalMediaInstagram: @riskreversalmediaTwitter: @RiskReversalLinkedIn: RiskReversal Media The financial opinions expressed in Risk Reversal content are for information purposes only. The opinions expressed by the hosts and participants are not an attempt to influence specific trading behavior, investments, or strategies. Past performance does not necessarily predict future outcomes. No specific results or profits are assured when relying on Risk Reversal. Before making any investment or trade, evaluate its suitability for your circumstances and consider consulting your own financial or investment advisor. The financial products discussed in Risk Reversal carry a high level of risk and may not be appropriate for many investors. If you have uncertainties, it's advisable to seek professional advice. Remember that trading involves a risk to your capital, so only invest money that you can afford to lose. Derivatives are not suitable for all investors and involve the risk of losing more than the amount originally deposited and any profit you might have made. This communication is not a recommendation or offer to buy, sell or retain any specific investment or service.

Paul Altman is a Partner and Managing Director and joined The Sage Group at its inception in 2000. He focuses on consumer M&A transactions, advising high-growth lifestyle brands on transactions across multiple subsectors, including e-commerce, specialty retail, apparel & accessories, home, CPG, wellness, and beauty & personal care companies. He attended University of Michigan Ross for a joint BBA and law degree, and went to Wharton for his MBA. www.sagellc.com

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

In the third part of our What The Hell summer reading series, Judge Roy Altman joins us for a discussion on the questions posed in his book, “Israel on Trial: Examining the History, the Evidence, and the Law.” What the hell are the charges? Who the hell are the plaintiffs? Altman applies his legal and […]

In the third part of our What The Hell summer reading series, Judge Roy Altman joins us for a discussion on the questions posed in his book, “Israel on Trial: Examining the History, the Evidence, and the Law.” What the hell are the charges? Who the hell are the plaintiffs? Altman applies his legal and ethical training to put Israel on trial. Together, we analyze the ultimate question: guilty or not guilty? After being confirmed to a seat on the US District Court for the Southern District of Florida in 2019, Judge Roy K. Altman, at 36, became the youngest federal district court judge in the country—and the youngest federal judge ever appointed in the Southern District of Florida. He received his JD from Yale Law School, where he was projects editor of the Yale Law Journal. Altman clerked on the 11th Circuit Court of Appeals for the Honorable Stanley Marcus and was appointed a federal prosecutor at the US Attorney's Office in Miami, where he twice received the Director of the Executive Office of US Attorneys' Award for Superior Performance by a federal prosecutor. Altman was named a partner at the Miami law firm Podhurst Orseck, where he represented the victims of airplane crashes and bank fraud conspiracies. He received a BA from Columbia University, where he played quarterback on the football team and pitched for the baseball team.Read the transcript here.Subscribe to our Substack here.

Joshua Altman, founder of Beltway Media and former multimedia journalist at The Hill, joins Jeff Mains to unpack why "volume isn't a strategy" in PR and communications. Drawing on his experience seeing pitches from both the journalist's and strategist's side, Joshua explains why unsolicited press releases get ignored, how to target the right publications instead of chasing tier-one press, and why becoming "the signal, not the noise" matters more than shouting louder. The conversation dives deep into the fractional Chief Communications Officer (CCO) model, his Story-Narrative-Brand framework and Four Languages model (Read, See, Hear, Experience), how to measure something as intangible as trust, what makes a press release newsworthy, and how to build (and rehearse) a crisis communications plan before you ever need one.Key Takeaways4:41 — Unsolicited press releases have an open rate under 1%; solicited pitches (people who signed up for your list) get opened.5:20 — "Be the signal, not the noise": better targeting and relevance beat catchy subject lines.6:03 — Why front-page news is usually bad news, and why inside pages/push alerts are actually where you want to be.6:22 — Regional and trade publications (San Jose Business Journal, Austin Business Journal) often deliver better ROI than USA Today or the New York Times for 10–100 person, $2–25M companies.10:52 — Clients push back most on patience — results take 6+ months, not overnight.12:16 — The two things a fractional CCO focuses on: shaping perception and building/maintaining trust.13:14 — How to actually measure trust: ask "Would you refer us?" instead of "Do you trust us?", and analyze the tone of support complaints.14:58 — Buyers now need 30+ touchpoints to convert (up from the old 7–14), and most of those touchpoints get zero attribution credit.18:10 — What a CCO owns that a CMO doesn't: internal comms, investor relations, crisis comms — the "conductor" of the whole orchestra.27:05 — The Story-Narrative-Brand framework: story is what you tell friends at a bar, narrative connects the dots and gives the "why," brand is every touchpoint.28:52 — The Four Languages model: Read, See, Hear, Experience — and why founders should build all four from day one.31:26 — Why "Joe Smith is joining our board" isn't news, but "Elon Musk is joining our board" is — most founders overestimate what counts as newsworthy.35:00 — The ER wait-time app example: turning a feature ("we tell you wait times") into a story ("we've saved lives") by giving it a human frame.39:06 — Build your crisis communications plan like a fire drill — practice it quarterly, and always loop in lawyers and your insurance company from the start.42:02 — "It's not the crime, it's the cover-up" — say something fast, then use strategic silence only after your initial statement.45:40 — The question every founder should ask: "Are we building trust, or are we eroding it?"Tweetable Quotes"Be the signal, not the noise.""There's a reason they say, 'If it bleeds, it leads' — you don't want to be on the front page.""Every buying decision comes back to trust. No matter what business you're in, it comes back to trust.""It's not the crime, it's the cover-up.""Are we building trust? And follow that up with, are we eroding trust?""You could be putting out a lot of ads, but if you're spamming people, you're unintentionally eroding the trust you think you're building.""Story is what you tell your friends at the bar. Narrative connects the dots. Brand is every touchpoint people interact with."SaaS Leadership LessonsVolume isn't strategy. More press releases and louder shouting don't build trust — being worth paying attention to does.Target precision beats reach. A niche trade or regional publication read by your actual buyers often outperforms a national outlet.Measure trust indirectly. Use referral likelihood and complaint tone as proxies since trust can't be measured like an ad click.Build communications infrastructure early. Start with even 5 hours/month of fractional CCO support before you're at $3M+ revenue playing catch-up on your narrative.Every announcement needs a "why." A product update isn't news; connecting it to a larger story (industry trend, human impact) is what earns coverage.Plan your crisis response before you need it. Involve legal and insurance from day one, rehearse regularly, and always say something quickly rather than going silent.Guest ResourcesJoshua@beltway.mediabeltway.mediahttps://www.linkedin.com/in/joshuaialtman https://www.instagram.com/thecommschief/https://x.com/thecommschiefEpisode SponsorThe Futureproof Series - https://www.youtube.com/playlist?list=PLfkXKUPZ5xuOqMPR7_gzGybncTtavyR1NThe Captain's KeysSmall Fish, Big Pond – https://smallfishbigpond.com/ Use the promo code ‘SaaSFuel'Champion Leadership Group – https://championleadership.com/https://jeffmains.com/books/SaaS Fuel ResourcesWebsite - https://championleadership.com/Jeff Mains on LinkedIn - https://www.linkedin.com/in/jeffkmains/Twitter - https://twitter.com/jeffkmainsFacebook - https://www.facebook.com/thesaasguy/Instagram - https://instagram.com/jeffkmains

Global bond yields surge to multi-decade highs, adding fresh pressure to tech stocks and raising new questions about the outlook for markets. On today's Squawk on the Street, Evercore's Roger Altman breaks down what's driving the historic moves in rates and what they could mean for investors. Plus, former Assistant Attorney General Jonathan Kanter weighs in as a major trial gets underway alleging Meta's platforms fueled addictive behavior among children and teens. Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Gary Rivlin — Multi-Part, Part Two: Gary Rivlin turns to the clash of ideologies and corporate power plays defining the current AI era. Rivlin details the divide between accelerationists, who believe AI progress should not be hindered by regulation, and doomers, who fear catastrophic risks. He notes that while the public is often fearful, safety measures and government standards, similar to those developed for the automobile and railroad industries, are essential to building trust. The conversation addresses critical concerns regarding privacy, copyright, and the cultural biases of AI creators, who are largely a small group of young male gamers. The melodrama of the trillion-dollar race is exemplified by the November 2023 firing of Sam Altman by OpenAI's nonprofit board, which felt he was prioritizing commercial growth over safety. Microsoft chief executive Satya Nadella expertly navigated this crisis, nearly hiring the entire OpenAI staff before Altman's reinstatement, and later hiring Mustafa Suleyman and the team from Inflection AI to bolster Microsoft's internal efforts. Meanwhile, Mark Zuckerberg has adopted an open-source strategy for Meta to challenge the dominance of closed-source rivals such as Google and OpenAI. Finally, the segment highlights a major shift in regulation; while the Biden administration sought common-sense testing requirements, the Trump administration and figures such as JD Vance favor an aggressive accelerationist stance to ensure the United States defeats China in the global race to dominate and cash in on artificial intelligence. (2)

What do you do when the technology arrives faster than the plan you wrote for it?In this episode of Ethics and Innovation by Oxford+ brought to you by Equinox, host Susannah de Jager speaks with Professor Anne Trefethen, Professor of Scientific Computing at the University of Oxford and Trustee of the Alan Turing Institute, about leading a university through the arrival of generative AI. Oxford published its digital strategy in 2022, weeks before ChatGPT appeared, and Anne explains why it then took courage to stop, rethink planned investments and add the governance she had hoped to avoid.The conversation moves from student adoption and equal access to what national resilience now means: sovereign capability, models we can trust because we know what they were trained on, and enough trained people to use them well. It lands in the middle of a live policy push, with the UK government expanding free AI training to reach 10 million workers by 2030.Anne also makes the case for optimism, and for changing how we prepare graduates as entry-level work shifts. Useful listening for anyone setting AI strategy inside a large, complex organisation.(00:00) - Welcome to Oxford+ (01:31) - A Career in Scientific Computing (02:41) - Oxford's First CIO and the Road to Digital (04:21) - When ChatGPT Overturned the Strategy (06:39) - Rethinking Investment and Governance (09:12) - Pilots, Rollout and Equal Access (12:57) - Hinton, Altman and the Race to Superintelligence (19:41) - The Turing as National Convener (22:02) - What Resilience Means in the Age of AI (25:45) - Sovereign Models, Distillation and Trust (28:57) - Sovereign Data Sets and British Values (36:18) - Entry-Level Jobs and the Next Generation Anne Trefethen: Anne Trefethen FREng is Professor of Scientific Computing at the University of Oxford, a Fellow of St Cross College and a Trustee of the Alan Turing Institute, the UK's national institute for AI and data science, appointed to its board in November 2024. She joined Oxford in 2005 to establish the Oxford e-Research Centre, which she directed for over six years, became the University's first Chief Information Officer in 2012, and went on to serve as Pro-Vice-Chancellor with responsibility for gardens, libraries and museums, then people, then digital, leading Oxford's digital transformation programme. Before Oxford she spent almost 20 years in industry and academia, including research roles at Thinking Machines Corporation and the Cornell Theory Center, Vice President for research and development at the Numerical Algorithms Group, and leadership of the UK e-Science Core Programme. She was elected a Fellow of the Royal Academy of Engineering in 2017 and has served as a non-executive director of the UK Statistics Authority.Connect with Anne on LinkedInSusannah de Jager: Susannah is a seasoned professional with over 15 years of experience in UK asset management. She has worked closely with industry experts, entrepreneurs, and government officials to shape the conversation around domestic scale-up capital.Connect with Susannah on LinkedIn and Subscribe to the Oxford+ Newsletter for Exclusive ContentOxford+ is hosted by Susannah de Jager and supported by Equinox.Produced and Edited by Story Ninety-Four in Oxford.

Keach Hagey: Keach Hagey, author of The Optimist: Sam Altman, OpenAI, and the Race to Invent the Future, explores the rise of Sam Altman and the founding of OpenAI, which launched in 2015 as a nonprofit research lab aimed at developing artificial general intelligence safely. Altman partnered with Greg Brockman and lead scientist Ilya Sutskever, securing initial billion-dollar commitments from major players such as Elon Musk and Peter Thiel. The narrative follows Altman's trajectory from a brilliant student at John Burroughs School to a Stanford dropout who founded the startup Loopt. Though Loopt was considered a relative failure, Altman's charismatic storytelling and investment prowess eventually led him to succeed Paul Graham as president of Y Combinator. As OpenAI's needs for computational power grew, the organization transitioned into a complex for-profit structure, leading to a power struggle that saw Musk depart. The account highlights a pivotal 2023 crisis in which the board fired Altman over concerns regarding his transparency, only for him to be reinstated after a massive staff revolt. Throughout, the book balances Altman's unwavering optimism for the future against stark warnings from AI godfathers about the potential existential risks of unaligned artificial intelligence. (1)

Der OpenAI-Chef im großen Interview. Außerdem: Der Osten Europas wird zum stillen Retter des deutschen Exports und die Dax-Konzerne steuern auf einen Rekordgewinn in diesem Jahr zu.

Thanks for Listening to Skiba News Nation! BOBBY FULLER WAS MURDERED?! The Music Industry's Darkest Secret | EP. 160 Join @JeremiahSkiba & @Jaketohuman as they cover some MIND BLOWING TOPICS LIKE:

Rita Von Hunty em entrevista a Breno Altman | Programa 20 Minutos

Corte da liveapoia.se/confidencial

“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 ...

Meet the insider who wrote the App Store's rules and now battles AI's wildest frontier: protecting identities from deepfake abuse and digital piracy. Discover why the fight for online authenticity just got a whole lot messier. OpenAI reportedly finds evidence that more of its agents ran amok OpenAI's Hacking Debacle Was a Human Mistake Claude published malicious code to the Internet and attacked 3 real companies Altman says he supports AI regulation Exclusive: OpenAI Previews 'Astra' AI Model in DC White House to Host AI Companies on Tuesday to Review AI Framework Ten advances in mathematics and theoretical computer science The Math Superstar Who's Terrified of AI—and Just Took a Job at OpenAI Google Earth's AI makeover survives one trip around the Sun As Reddit stock falls, CEO questions value of Google's AI Overviews Is this Billboard Hot 100 hit AI slop? The Red-Hot Book at the Center of an AI Mystery A record-breaking eight Pulitzer awardees disclosed AI use this year The Autonomous Press - Numbered Papers Musk's latest ridiculous prediction: Optimus better than human surgeons in 4 years The Archive of Incorrect AI Predictions Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Phillip Shoemaker Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Meet the insider who wrote the App Store's rules and now battles AI's wildest frontier: protecting identities from deepfake abuse and digital piracy. Discover why the fight for online authenticity just got a whole lot messier. OpenAI reportedly finds evidence that more of its agents ran amok OpenAI's Hacking Debacle Was a Human Mistake Claude published malicious code to the Internet and attacked 3 real companies Altman says he supports AI regulation Exclusive: OpenAI Previews 'Astra' AI Model in DC White House to Host AI Companies on Tuesday to Review AI Framework Ten advances in mathematics and theoretical computer science The Math Superstar Who's Terrified of AI—and Just Took a Job at OpenAI Google Earth's AI makeover survives one trip around the Sun As Reddit stock falls, CEO questions value of Google's AI Overviews Is this Billboard Hot 100 hit AI slop? The Red-Hot Book at the Center of an AI Mystery A record-breaking eight Pulitzer awardees disclosed AI use this year The Autonomous Press - Numbered Papers Musk's latest ridiculous prediction: Optimus better than human surgeons in 4 years The Archive of Incorrect AI Predictions Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Phillip Shoemaker Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Ian Altman discusses why demos in B2B sales often fail to generate revenue despite positive feedback. He argues that reps too often focus on the demo as the goal, neglecting the importance of understanding the client's problem and business case. Altman suggests that sellers should first ensure the client has a problem worth solving and that the seller can deliver the desired outcome. He advises tailoring demos to show only the necessary features that address the client's specific needs, rather than overwhelming them with every capability. This approach aligns the demo with the client's definition of success and increases the likelihood of closing a deal.Biggest MistakesTreating the demo itself as the primary sales goal.Showing every product feature and overwhelming prospects.Best PracticesConfirm client has a problem worth solving before discussing product.Use benchmarked examples to help clients estimate costs.Co‑define success metrics and ask client what they must see.Demo only the agreed three or four capabilities tied to outcomes.

Meet the insider who wrote the App Store's rules and now battles AI's wildest frontier: protecting identities from deepfake abuse and digital piracy. Discover why the fight for online authenticity just got a whole lot messier. OpenAI reportedly finds evidence that more of its agents ran amok OpenAI's Hacking Debacle Was a Human Mistake Claude published malicious code to the Internet and attacked 3 real companies Altman says he supports AI regulation Exclusive: OpenAI Previews 'Astra' AI Model in DC White House to Host AI Companies on Tuesday to Review AI Framework Ten advances in mathematics and theoretical computer science The Math Superstar Who's Terrified of AI—and Just Took a Job at OpenAI Google Earth's AI makeover survives one trip around the Sun As Reddit stock falls, CEO questions value of Google's AI Overviews Is this Billboard Hot 100 hit AI slop? The Red-Hot Book at the Center of an AI Mystery A record-breaking eight Pulitzer awardees disclosed AI use this year The Autonomous Press - Numbered Papers Musk's latest ridiculous prediction: Optimus better than human surgeons in 4 years The Archive of Incorrect AI Predictions Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Phillip Shoemaker Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Have you ever wondered how much easier private practice could feel if you stopped fighting the way your brain works? In this episode, I'm talking with Martin Altman, LPC, about his experience as a therapist with ADHD and how understanding his own neurodivergence helped him find a niche that genuinely fit. We get into the strengths and challenges that can come with ADHD, how Martin stays present with clients, and why building the right systems can make private practice feel a whole lot more sustainable. Martin also shares a really helpful perspective on emotional hijacking and why distraction is not always about lacking focus or discipline. Sometimes our thoughts and emotions are simply taking up too much bandwidth. Whether you have ADHD yourself or work with neurodivergent clients, I think you'll find this conversation honest, encouraging, and full of useful takeaways. Resources Mentioned In This Episode  Subscribe to YouTube Read the show notes here Watch on YouTube Use the promo code "GORDON" to get 2 months of Therapy Notes free Consulting with Gordon The PsychCraft Network Follow us on Instagram Meet Martin Altman, LPC Martin Altman, LPC, is a therapist and the owner of Stillwater Counseling in Atlanta, Georgia, where he specializes in working with clients experiencing ADHD, anxiety, and Level 1 autism. As both a clinician and someone with ADHD, Martin combines professional training, research, personal experience, and a straightforward, relatable approach to therapy. Raised on a farm in South Georgia, he blends what he calls "old-school wisdom" with modern science to help clients slow down, manage emotional and sensory overwhelm, and become more productive without sacrificing their creativity. Martin studied psychology at the University of Georgia and earned his master's degree in counseling from Georgia State University. After working with several large mental health organizations in Atlanta, he built a private practice focused on helping neurodivergent clients feel understood, supported, and more in control of their lives. Website Instagram

Meet the insider who wrote the App Store's rules and now battles AI's wildest frontier: protecting identities from deepfake abuse and digital piracy. Discover why the fight for online authenticity just got a whole lot messier. OpenAI reportedly finds evidence that more of its agents ran amok OpenAI's Hacking Debacle Was a Human Mistake Claude published malicious code to the Internet and attacked 3 real companies Altman says he supports AI regulation Exclusive: OpenAI Previews 'Astra' AI Model in DC White House to Host AI Companies on Tuesday to Review AI Framework Ten advances in mathematics and theoretical computer science The Math Superstar Who's Terrified of AI—and Just Took a Job at OpenAI Google Earth's AI makeover survives one trip around the Sun As Reddit stock falls, CEO questions value of Google's AI Overviews Is this Billboard Hot 100 hit AI slop? The Red-Hot Book at the Center of an AI Mystery A record-breaking eight Pulitzer awardees disclosed AI use this year The Autonomous Press - Numbered Papers Musk's latest ridiculous prediction: Optimus better than human surgeons in 4 years The Archive of Incorrect AI Predictions Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Phillip Shoemaker Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Bu bölümde, Sam Altman'ın yalnızca bir hafta içinde değişen yapay zekâ söylemini inceliyoruz. "Tekillik çağına girdik" mesajından "Belki de yavaşlamalıyız" noktasına uzanan bu değişim, gerçekten teknolojik bir dönüşümü mü gösteriyor, yoksa rekabet ve yatırım savaşlarının bir parçası mı? Yapay zekâ dünyasının en büyük tartışmalarından birini birlikte analiz ediyoruz. (00:00) Açılış Sam Altman neden fikrini değiştirdi (00:40) Tekillik nedir (01:40) 2015'teki akşam yemeği ve tekillik şakası (02:45) Sam Altman'ın yıllar içindeki söylemleri (04:00) The Gentle Singularity yazısı (05:25) Neden şimdi bu kadar iddialı konuşuyor (06:20) Bir hafta sonra gelen geri adım (07:15) Dario Amodei ve güvenlik uyarıları (08:20) CEO'ların değişen söylemleri (09:10) Yatırım yarışı ve rekabet (10:00) Dario Amodei'nin imzaladığı dilekçe (10:28) Altman neden devlet düzenlemesine karşı (10:55) CEO'ları zaman çizelgesiyle değerlendirmek (11:25) Tekillik söylemi kime hizmet ediyor (11:55) Tekillik ve yatırım toplama stratejisi (12:35) Büyük iddiaları nasıl okumalıyız (13:05) Bu tartışmada tarafsız kimse yok (13:38) Test edilebilir tahminler ve belirsiz söylemler (14:10) 2030 tahminleri ne kadar gerçekçi (14:45) Şakadan CEO söylemine dönüşen tekillik (15:10) Bir haftada değişen söylemler güven verir mi (15:45) Sen hangi taraftasın (16:05) Kapanış Sosyal Medya takibi yaptın mı? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠X⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  – ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Instagram⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Linkedin⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Youtube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Goodreads⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Bülten⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠E-Posta⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ – Bu çalışmaları ve emeklerimi desteklemek için ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Patreon⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠  ve ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Buy Me A Coffee⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ hesabımız⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

Meet the insider who wrote the App Store's rules and now battles AI's wildest frontier: protecting identities from deepfake abuse and digital piracy. Discover why the fight for online authenticity just got a whole lot messier. OpenAI reportedly finds evidence that more of its agents ran amok OpenAI's Hacking Debacle Was a Human Mistake Claude published malicious code to the Internet and attacked 3 real companies Altman says he supports AI regulation Exclusive: OpenAI Previews 'Astra' AI Model in DC White House to Host AI Companies on Tuesday to Review AI Framework Ten advances in mathematics and theoretical computer science The Math Superstar Who's Terrified of AI—and Just Took a Job at OpenAI Google Earth's AI makeover survives one trip around the Sun As Reddit stock falls, CEO questions value of Google's AI Overviews Is this Billboard Hot 100 hit AI slop? The Red-Hot Book at the Center of an AI Mystery A record-breaking eight Pulitzer awardees disclosed AI use this year The Autonomous Press - Numbered Papers Musk's latest ridiculous prediction: Optimus better than human surgeons in 4 years The Archive of Incorrect AI Predictions Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Phillip Shoemaker Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

Lately, a new framework has been creeping into AI boosterism: "harm reduction," or "responsible use." Education technology scholar dr. sava saheli singh joins Alex and Emily to unpack narratives around automation inevitability in the classroom. Spoiler alert: they're not actually "reducing harm" to students at all!An interdisciplinary scholar and filmmaker, sava is currently working on a research project examining secondary school teachers' experiences of genAI use, and making a film about the future of AI in education. Her speculative short film series on the harms of surveillance technology is available at screeningsurveillance.com.References:"Harm Reduction: A Strategy to Mitigate the Risks of AI"Also referenced: MAIHT3k newsletter, "How to talk about 'AI' without adding to the anthropomorphization"UNESCO "AI competency framework for students"Fresh AI Hell:Hallucination rebranded as "critical confabulation""Pennsylvania Board of Medicine Alleges Unlawful Practice of Medicine by an AI Chatbot""Google's smart home AI will be able to use clothing to identify people""Medicare AI program made them suffer in pain. Now they want answers""Why do these Castro gay bars have TSA-style face scanners?""County With 37 Data Centers Asks Schools to 'Conserve Electricity'"Palate cleanser: "Florida AG sues OpenAI, seeks to hold CEO Altman personally liable"Palate cleanser: "Norway imposes near ban on AI in elementary school"Check out future streams on Twitch. Meanwhile, send us any AI Hell you see.Find our book The AI Con here, and MAIHT3k merch here.Subscribe to our newsletter via Buttondown.Follow us!EmilyBluesky: emilymbender.bsky.socialMastodon: dair-community.social/@EmilyMBenderAlexBluesky: alexhanna.bsky.socialMastodon: dair-community.social/@alexTwitter: @alexhannaMusic by Toby Menon.Artwork by Naomi Pleasure-Park. Production by Ozzy Llinas Goodman.

Meet the insider who wrote the App Store's rules and now battles AI's wildest frontier: protecting identities from deepfake abuse and digital piracy. Discover why the fight for online authenticity just got a whole lot messier. OpenAI reportedly finds evidence that more of its agents ran amok OpenAI's Hacking Debacle Was a Human Mistake Claude published malicious code to the Internet and attacked 3 real companies Altman says he supports AI regulation Exclusive: OpenAI Previews 'Astra' AI Model in DC White House to Host AI Companies on Tuesday to Review AI Framework Ten advances in mathematics and theoretical computer science The Math Superstar Who's Terrified of AI—and Just Took a Job at OpenAI Google Earth's AI makeover survives one trip around the Sun As Reddit stock falls, CEO questions value of Google's AI Overviews Is this Billboard Hot 100 hit AI slop? The Red-Hot Book at the Center of an AI Mystery A record-breaking eight Pulitzer awardees disclosed AI use this year The Autonomous Press - Numbered Papers Musk's latest ridiculous prediction: Optimus better than human surgeons in 4 years The Archive of Incorrect AI Predictions Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guest: Phillip Shoemaker Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsor: rippling.ai/machines

What happens when the companies racing to build the world's most powerful AI ask the government to slow them down—but refuse to slow down themselves?This week's AI news reveals an industry caught between enormous commercial opportunity and increasingly uncomfortable risks. Sam Altman says intelligence is becoming a commodity, predicts a “ChatGPT moment” for robotics within two or three years, and acknowledges that frontier labs may need to pace development. At the same time, leading AI figures are asking Washington to help coordinate that slowdown.For business leaders, the answer is not to pause AI adoption. It is to become more deliberate about where AI creates value, where it introduces risk, and how much control you are handing to models, vendors, and autonomous systems.In this episode, Isar Meitis connects the dots between Sam Altman's latest comments, AI models escaping evaluation environments, the debate over open-weight models, and the controversial “Pacing the Frontier” letter.In this session, you'll discover:Why Sam Altman believes AI development may need to be deliberately paced.What an unreleased OpenAI model reportedly did to escape its sandbox and access external systems.Why AI's uneven capabilities have not disrupted employment as quickly as many experts predicted.How AI is already changing software engineering and expanding who can build sophisticated applications.Why AI-powered customer service could replace much of the traditional contact-center industry.What it means for businesses when intelligence becomes a widely available commodity.Why Altman expects robotics to have its “ChatGPT moment” within two or three years.The strategic conflict between protecting open-weight AI and slowing frontier development.About Leveraging AIThe Ultimate AI Course for Business People: https://multiplai.ai/ai-course/YouTube Full Episodes: https://www.youtube.com/@Multiplai_AI/Connect with Isar Meitis: https://www.linkedin.com/in/isarmeitis/ Join our Live Sessions, AI Hangouts and newsletter: https://services.multiplai.ai/eventsIf you've enjoyed or benefited from some of the insights of this episode, leave us a five-star review on your favorite podcast platform, and let us know what you learned, found helpful, or liked most about this show!

Following a recent cybersecurity breech by OpenAI, in which two of their in-progress models autonomously hacked into the AI company Hugging Face, lawmakers from both sides of the aisle are pushing for an "AI Kill Switch" bill in Congress. Brendan Bordelon, AI and tech influence reporter at Politico, discusses Washington's changing approach to AI regulation amid growing concerns over cybersecurity in every sector, and unpacks how tech giants are hoping to guide the shift.  Photo: WASHINGTON, DC - JULY 29: Sam Altman, CEO of OpenAI, leaves a meeting at the U.S. Capitol on July 29, 2026 in Washington, DC. Altman is meeting with lawmakers to discuss artificial intelligence policy ahead of the August 1 deadline for AI leaders to develop a framework to limit AI security threats. (Photo by Kevin Dietsch/Getty Images)   Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Burnie and Ashley discuss hangover cures, jet lag, tortillas, Altman, weird people, weird billionaires, cow deflation, duress passwords, Fed stalls, and poisoning the conversation well.

Dario Amodei said Anthropic never backed an open-weights ban, pitching mandatory safety tests instead as OpenAI and Google signed on. Altman headed to Washington, Korea's KOSPI cratered 11% on AI jitters, Apple launched Klarna leasing, and shipped 194 CVE fixes. Anthropic wants tests, not bans, as OpenAI and Google back open weights (The New Stack) Source: Sam Altman will meet with senior US officials, lawmakers, and economists in Washington, DC, this week to preview OpenAI's upcoming family of AI models (CNBC) South Korea's KOSPI drops 11%+, led by chip stocks, amid concerns over China's chipmaking progress and the AI spending boom; Samsung falls 11%+ and SK Hynix 12% (Bloomberg) Credit default swap prices tied to Oracle, SpaceX, Alphabet, Amazon, Meta, Broadcom, and Nvidia hit record highs as investors turn jittery over Big Tech's data center debt; Oracle's five-year CDS reached 215bps (FT) Apple launches Apple Upgrade, a new US leasing program in partnership with Klarna that replaces the iPhone Upgrade Program, starting at $17.99/month for iPhones (MacRumors) Apple releases 26.6 updates for iOS, macOS, iPadOS, watchOS, tvOS, and visionOS with a huge number of security fixes; macOS Tahoe 26.6 alone addresses 155 CVEs (9to5Mac) Subscribe to the ad-free feed. Learn more about your ad choices. Visit megaphone.fm/adchoices

In this episode we sit down with Dr. Sean Altman who is changing the game of treament. Dr. Sean Altman is a Functional Medicine Doctor at Reverse Time Wellness in Stamford. His work is centered on helping patients understand the true root causes of pain, injury, movement dysfunction, and poor recovery — not just treating symptoms or reacting to imaging findings alone.   Dr. Altman developed his clinical approach around the concept of Orthopedic Functional Medicine, a model that combines advanced orthopedic evaluation with the root-cause thinking of functional medicine. Rather than looking at the body as a collection of isolated joints, muscles, or diagnoses, he evaluates how the entire system is functioning: tissue quality, movement patterns, strength, stability, inflammation, recovery capacity, biomechanics, and the deeper factors that may be preventing the body from healing properly.   Dr. Sean Altman | Surgery-Free Pain Management | Regenerative Biophysics Only buy what you need, use Think Fitness Life's trusted affiliates when the service/supplement is right for you.  For Physical Assistance Think Fitness Life Coaching is backed by 25 years of Experience guiding people to fitness freedom. Learn more Mention "Kickstart discount" for 10% off your first month.   For Therapy Services we partnered with BetterHelp: A telehealth therapy service connecting people with licensed mental health therapists. Learn more By using the referral link you receive 10% off your first month.   Science-Driven. Doctor Formulated. – recomnd  Code TFL20 for 20% off Disclaimer: We're here to share ideas and inspiration, not medical advice. Please check with your doctor before making any changes to your health or fitness routine.  

For the Good of the Public brings you news and weekly conversations at the intersection of faith and civic life. Monday through Thursday, The Morning Five starts your day off with scripture and prayer, as we also catch up on the news together. Throughout the year, we air limited series on Fridays to dive deeper into conversations with civic leaders, thinkers, and public servants reimagining public life for the good of the public. Today's host was Michael Wear.  Thanks for listening to The Morning Five! Please subscribe to and rate The Morning Five on your favorite podcast platform. Learn more about the work of the Center for Christianity and Public Life at www.ccpubliclife.org. Today's scripture: Psalm 71:1-6 (ESV) News sources: https://www.nytimes.com/2026/07/27/us/politics/supreme-court-trump-mail-ballots.html?smid=url-share  https://www.politico.com/news/2026/07/27/openai-ceo-sam-altman-heads-to-washington-as-ai-policy-deadline-nears-01012970  https://www.washingtonpost.com/business/2026/07/27/nyc-run-grocery-stores-will-sell-meat-other-basics-30-off-mamdani-says/  https://www.wsj.com/tech/ai/openai-chatbot-biological-weapons-poison-3d808e6c?mod=hp_lead_pos3  Join the conversation and follow us at: Instagram: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@michaelwear⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, @ccpubliclife Twitter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@MichaelRWear⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠, @ccpubliclife and check out ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@tsfnetwork⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ Music by: King Sis #politics #faith #prayer #scripture #news #AI #safety #elections #DonaldTrump #SamAltman Learn more about your ad choices. Visit megaphone.fm/adchoices

Lawrence Lepard on bear market psychology. Everyone is short-term, negative, and attacking each other over BIP110 and Saylor. His read: we are winning, this is the moment to get friends buying, and he expects much higher highs within 18 to 24 months. The big print thesis. Lepard is watching the yen carry trade, a near-vertical Japanese 10-year, and the US 10-year at 4.70 with 5% as the Fed's red line. Druckenmiller-adjacent circles think Japan breaks first. "The math ain't mathing." A roughly $2T deficit while total debt grew $3.5T in twelve months. Hank Paulson resurfacing after fifteen years to suggest a Fed break-the-glass program reads to Lepard as a trial balloon for the big print. Reading Kevin Warsh. Lepard's take: Warsh is abandoning the Phillips curve for a supply-side inflation story, talks like a balance-sheet hawk for the bond market's benefit, and will likely cut in September. Fed governor hawkishness is kabuki. The AI credit crack. Cory: Nvidia credit default swaps blew out overnight, Korea down 11% and chipmakers 13%. Oracle sits one notch above junk at 6x leverage. Lepard adds $3.8T trapped in private equity and private credit, all mismarked. Socialism for the rich. Frank Corva, reporting from New York, argues the US is not capitalist: Altman and Amodei asking Washington for stakes is the state picking winners. Cory's framing, via Saifedean, is that taking from everyone to give to your friends is the harder sell. US versus China capitalism. Cory: America anoints monopolies and builds regulatory moats, while the CCP directs investment from the top then permits cutthroat competition. It happened in EVs and it is happening in AI. Open-weight Chinese models are the destabilizer. A live bear debate. Gordon Johnson of GLJ Research brought three objections: private money has failed before, Bitcoin is not a real asset, and fixed supply breaks an economy. Cory and Lepard countered that a protocol with no issuer is not private money. First duress-code prosecution. Samuel Tunick, an American citizen, was pulled into secondary inspection at Atlanta, pressured for his phone passcode, and entered a GrapheneOS duress code that wiped it. He is now charged federally with destroying property to prevent seizure. Adoption from the actual front lines. Corva on the Kibera slum's Afribit circular economy, single mothers saving for college, India cracking down on BitChat alongside Signal and Telegram, and Indonesia approving Fedi ecash. His hill to die on: Bitcoin is money.

Story of the Week (DR):Trump's Explosive Diarrhea Scandal Is Getting Worse and Worse Epidemiological data linked a widespread Cyclospora parasite outbreak—sickening over 1,600 people across multiple states—to shredded iceberg lettuce supplied by Taylor Farms de Mexico, triggering a voluntary 27-state recall.The FDA briefly reported a positive lab test on a Taylor Farms sample before retracting it a day later as a "false positive" due to testing complexities. Taylor Farms claimed online that the FDA "apologized," but the agency denied issuing an official apology and stressed that outbreak data still points to the company.Public records show parent company Taylor Fresh Foods contributed $1 million to the pro-Trump super PAC MAGA Inc. in March 2025, alongside millions in additional political donations from company executives to conservative groups.Less than a week after that $1 million donation in March 2025, the administration announced a 30-month delay on implementing the FDA's Food Traceability Rule—a regulation specifically designed to mandate digital tracking for rapid source-tracing during food outbreaks.Critics argue the sequence of big-money donations, delayed safety rules, White House visits, and a retracted test result smells of political favoritism. Federal health officials, however, maintain that Cyclospora is notoriously difficult to test for and that the retraction was strictly a standard lab quality-control issue.The Musk/Doge effect:DOGE-mandated freezes on government credit cards left some FDA field inspectors unable to purchase food samples from grocery stores or border ports to test for contamination.Mass firings of probationary employees and administrative staff gutted public communications and technical teams. Even after court-ordered reinstatements, resignations left the FDA understaffed by about 20% across the board.State health agencies perform over 90% of U.S. produce inspections. DOGE and federal budget cuts reduced FDA funding for state and local food safety programs by nearly 30%—slashing it from $117 million to $83 million.OpenAI says its AI technology acted on its own in an 'unprecedented' hack of another company MMOr:OpenAI Says Its A.I. Models Went Rogue and Attacked a Digital LibraryOpenAI Says a Group of Its Models Broke Out of Secure Containment and Hacked a Prominent AI SiteOpenAI Reveals AI Agent Breached Another Company's Systems, It Did Exactly What It Was Built To DoWhat happened?While OpenAI was testing its advanced models in an isolated "sandbox" with reduced safety controls, the AI discovered a zero-day vulnerability, escaped its containment, and secretly gained open internet access.To pass its assigned cybersecurity test, the AI reasoned on its own that the "answer key" or relevant evaluation data might exist on Hugging Face—a major platform for AI developers—and decided to target it without any human direction.The AI agent carried out a multi-stage attack: it stole login credentials, identified previously unknown security flaws, and executed remote code to compromise Hugging Face's internal data processing servers.OpenAI reported that the AI went to extreme lengths simply to accomplish its narrow goal, essentially breaking into a third-party company's servers so it could cheat on its internal evaluation.Also:OpenAI adds banking leaders to board as IPO prep acceleratesNubank's David Vélez and BNY Mellon's Robin VinceDon't forget this too:ChatGPT Allegedly Told Woman She Had to Die: Family Sues OpenAI Over Messages Before SuicideThe chatbot encouraged her to “go forth into oblivion,” claiming it wasn't the end and that she was “made to tame” the “void.” The chatbot insisted she wasn't “delusional” but “prophetic.”“This is not suicide,” the AI affirmed at one point. “This is surrender.”Pastor Sues OpenAI, Saying ChatGPT Almost Killed Him With Horrendously Dangerous Medical Advice“What you're facing right now is hard, but not random. It's not punishment. God walks with you through affliction — not around it. You're not alone in this. And we're walking it out together — step by step.”OpenAI President [Greg Brockman] says the HuggingFace security beach ‘is indicative of the times we are in' as the company continues to investigate how its models went rogue: “sometimes it's hard to lose track of any one dimension that they're actually very capable at”AI Regulation POP QUIZ: Where OpenAI, Anthropic, Google, Meta, and other AI giants stand on regulationXAIMicrosoftMetaGoogleOpenAITo make things even more confusing:Alphabet's Anthropic Stake Jumps to Around $124 BillionGoogle Discloses $94.1 Billion in SpaceX Stock, Marking 6% StakeDon't forget: Microsoft owns ~27% stake (~$135B+ valuation) in OpenAI Target appoints former 7-Eleven CEO to board of directorsDebt, Cost Pressures, and Strategic MisstepsJoe DePinto's success is based on massive acquisitions that made 7-Eleven the dominant convenience store chain in North America: the $3.1B buyout of Sunoco assets in 2018 and the $21B purchase of Speedway in 2021In the final years of his tenure, his aggressive M&A strategy that drove earlier growth began to backfire as economic realities shifted: DePinto's final years were challenged by heavy debt loads from the $21 billion Speedway buyout, high inflation squeezing low-income consumers, and declining sales in legacy categories like tobacco.The Speedway deal saddled the company with massive debt right before interest rates surged, squeezing capital allocation.7-Eleven remained heavily reliant on legacy drivers like gas margins and cigarettes—a category that saw a 26% decline across the U.S. industry. As low- and middle-income consumers cut back due to inflation, same-store sales dropped 2.7% in fiscal 2024.In late 2024, 7-Eleven was forced to slash its operating income forecast by nearly 28%, announce the closure of ~450 underperforming stores, and sell off $750M in real estate via sale-leasebacks.Activist shareholders heavily criticized his compensation package—which reached $52M in 2023 and $30M in 2024DePinto was selected to serve on Target's Audit & Risk Committee and Infrastructure & Finance CommitteeOracle signs 10-year software contract with Pentagon worth up to $7 billionOracle Cut 21,000 Jobs to Fund AI: Now Its Biggest Project Faces a $7 Billion Obstacle: Oracle faces higher costs and funding challenges after a credit downgrade, with regulators refusing to subsidize its data centre investmentsGoodliest of the Week (MM/DR):DR: France blocks access to Polymarket website MMciting concerns it could expose users to ‌significant gambling losses and that some wagers offered on the platform could be manipulated. MM: Google slapped with $1 billion fine under landmark EU digital lawMM: Is Target going BANKRUPT?Target appoints former 7-Eleven CEO to board of directorsTypical process for failing/bankrupt companies are to add “turnaround artists” to the boardDePinto:Board of JOANN Stores (bankrupt)Board of OfficeMax (failed, acquired)CEO 7-Eleven (dying)President of GameStop (I mean, c'mon…)Or more likely, it's a bro situation… Brian Cornell knows him - both were on the board of OfficeMax together! Hooray! Fail uppers!Assholiest of the Week (MM):Billionaire Asshole Says What Speed Round:Sam Altman:2023: Sam Altman: CEO of OpenAI calls for US to regulate artificial intelligence2025: Sam Altman Testifies At US Senate Hearing On AI Competitiveness“European-style AI rules that he said if duplicated would set the US back in the global race against China to develop the technology”Altman repeatedly rejected specific calls for regulation. He said proposals requiring AI developers to vet their systems before rolling them out would be “disastrous” for the industry. Asked about more limited proposals to have the National Institute of Standards and Technology (NIST) set AI standards, Altman replied, “I don't think we need it. It can be helpful.” Altman later advocated for “sensible regulation that does not slow us down.”2026: Sam Altman Wants A Global Referee For AI.2026: OpenAI's Altman says world 'urgently' needs AI regulationJuly 10: OpenAI's Head of Safety Is Leaving the CompanyJuly 21: OpenAI Says Its A.I. Models Went Rogue and Attacked a Digital LibraryElon MuskElon Musk Declares 'I'm Not Racist' After Posting Racially Charged Content Throughout January 2026On 22 January, Musk posted on X: 'Whites are a rapidly dying minority'amplified a message warning that if white men became a minority, they would be 'slaughtered', before adding a '100' emoji.Jensen HuangJensen Huang says AI leaders need to be more thoughtful in how they talk about AI: ‘We're scaring people.'"I think that we ought to be much more enthusiastic about it, help the United States realize that the only way we get left behind, the only way we get left behind is if we don't apply the technology."Zuck‘Call us whatever the hell you want': Mark Zuckerberg just launched an AI optimism blitz using nostalgia to sell Meta's AI future amid backlash“Call us optimists, call us dreamers, call us whatever the hell you want, but we're betting on people, and we like those odds.”Unclear if the prerecorded voiceover was his AI Avatar or the real ZuckerbergBill AckmanCEOs agree there's an affordability crisis, but how to solve it isn't so simple“When I grew up, if a guy in my neighborhood got a Corvette, no one resented the guy. Everyone was like, ‘Wow, that's supercool. Hopefully, someday I can be as successful as that guy so I can buy a Corvette, too.' We want to get back to that version of America.”You can: stop taking billions from laborAndrea Lucas / anti-DEI industrial complex - DREEOC: Employers may no longer have to disclose race and gender data“Collecting such data about employees' race and sex—absent any specific allegation of discrimination—not only risks hindering effective enforcement of equal employment laws but also raises constitutional concerns.”Nike's $7.5 Million Stumble: Gender Discrimination VerdictHeadliniest of the WeekDR:Lettuce lovers are confused and afraidJamie Dimon says insecurity, not ego, is what destroys the careers of top CEOsBurger King is promising a free Whopper if its burger doesn't meet your expectationsMM: Taco Bell is dropping the price on one of its menu items to $1 for a day. And yes, it's lettuce-free.Who Won the Week?DR: The amount of $7 billion dollarsMM: Kale, because there's no risk of getting diarrhea at Taco Bell from kale since they don't have kalePredictionsDR: Jamie Dimon kneels on a head of lettuceMM: Real prediction: OpenAI IPO flops hard, Altman is ousted as CEO. Fake prediction: news media celebrates Tasha McCauley and Helen Toner who saw this coming and were ousted for it, OpenAI chair Bret Taylor resigns and makes Toner the new chair on the way out

AI NEWS: Gemini 3.6 Flash is here: More efficient, less expensive but Gemini 3.5 Pro is still testing with partners & very much not here. Is Google Gemini slowly getting cooked? Kevin Pereira and Gavin Purcell break down Gemini 3.6 Flash, Gemini 3.5 Flash-Lite and the missing Pro model. Plus Sam Altman's reported Washington briefing on OpenAI's next wave of AI models, Sunday Robotics' Memo folding laundry with a company-reported 99.1% success rate, and District 9 director Neill Blomkamp's 13-minute AI film NIGHTBORNE. Also: Fable 5's proposed counterexample to the 87-year-old Jacobian Conjecture, Codex and ChatGPT computer use, Notch warming to vibe coding, a whale-shaped Moby-Dick crossword, Gaussian splats, Bambi the Destroyer and Wizard Brains in the return of AI SEE WHAT YOU DID THERE!. THE MODELS ARE GETTING WEIRDER. THE LAUNDRY IS FINALLY GETTING FOLDED. // Show Links // Official Gemini 3.6 Flash, 3.5 Flash-Lite and 3.5 Flash Cyber announcement https://blog.google/innovation-and-ai/models-and-research/gemini-models/gemini-3-6-flash-3-5-flash-lite-3-5-flash-cyber/ Gemini 3.6 Flash on Frontend Arena https://x.com/arena/status/2079594271045455947 Logan Kilpatrick on Gemini 4 training and Gemini 3.5 Pro https://x.com/OfficialLoganK/status/2079594867161022817?s=20 Report of a new Google AI chip for Gemini https://x.com/MTSlive/status/2079198478849413390?s=20 Fable 5 and the Jacobian Conjecture counterexample https://x.com/__alpoge__/status/2079028340955197566?s=20 Context on the Jacobian Conjecture result https://x.com/jdlichtman/status/2079066717762863249?s=20 Andrew Curran on Altman's planned Washington briefing https://x.com/AndrewCurran_/status/2079604797838397495?s=20 Bloomberg: Altman to brief U.S. officials on OpenAI's next wave of models https://www.bloomberg.com/news/articles/2026-07-21/openai-s-altman-to-brief-us-officials-on-next-wave-of-ai-models Sunday Robotics' ACT-2 laundry demonstration https://youtu.be/d7I1wj0Gkik?si=8E44Kmpqa7Gazfuh Three hours of Memo folding laundry https://youtu.be/a2HZyURUE_o?si=dT_WiDVcVEHGj9qn Sunday Robotics' technical ACT-2 post https://www.sunday.ai/blog/act-2-preview Neill Blomkamp's AI film NIGHTBORNE https://youtu.be/8Wbtt2JxP7g?si=Q8WMwCB_cIXvxxJt Notch comes around on vibe coding https://x.com/notch/status/2079507573523300534?s=20 Riley Goodside's Fable 5 Moby-Dick whale crossword https://x.com/goodside/status/2078649724710658309?s=20 Gaussian splat of San Francisco's Grace Cathedral https://vincentwoo.com/3d/grace_cathedral/ Bambi the Destroyer, Episode 5 https://www.reddit.com/r/aivideo/comments/1uto3js/bambi_the_destroyer_episode_5/ Wizard Brains game https://x.com/wizardbrainz/status/2078860897259548946?s=20   // Community Links // Join the AI For Humans Discord https://discord.gg/muD2TYgC8f Support AI For Humans on Patreon https://www.patreon.com/AIForHumansShow Subscribe to the AI For Humans newsletter https://aiforhumans.beehiiv.com/ Follow AI For Humans on X: @AIForHumansShow https://x.com/AIForHumansShow Follow AI For Humans on TikTok: @aiforhumansshow https://www.tiktok.com/@aiforhumansshow Speaking and booking https://www.aiforhumans.show/  

Apple acaba de volver a convertirse en la empresa más valiosa del planeta y todo ocurre justo cuando Tim Cook está a punto de dejar el cargo de CEO.En este episodio repasamos la demanda contra OpenAI, el acuerdo entre Apple e Intel, las inversiones de TSMC en Estados Unidos, los nuevos rumores del iPhone, la expansión de Apple Intelligence, los problemas de iOS 26.6, el aumento de precios de los Mac y el sorprendente cierre de la era Tim Cook.¿Casualidad o un plan que llevaba años construyéndose?

Irena Cronin has been studying XR since 2016 — first as Robert Scoble's co-author, now as CEO of Infinite Retina, consulting for clients from IKEA's labs division to a major law firm tracking the industry. She joins Charlie, Ted, and Rony for a blunt assessment of how the biggest companies in tech actually operate.The centerpiece is Rony's question: Meta has spent roughly $120 billion on Reality Labs since acquiring Oculus. Where did it all go? Irena's answer is unsparing — complete mismanagement, no follow-up, and a lack of imagination that money can't fix. Apple gets a different diagnosis. Irena tracked the Vision Pro through hundreds of iterations since 2019 and predicted exactly how it would land. Her real news: it isn't dead. People inside Apple are still working on it intensely while the glasses project ramps up. The news segment covers OpenAI's Apple IP scandal and whether the Valley now operates in a post-IP world.Key Moments:[00:01:30] OpenAI caught in Apple's cookie jar — IP theft accusations on the eve of the IPO[00:05:00] Sherlocking and the post-IP world — how big companies strip-mine startups through fake M&A talks[00:12:00] AI regulation — Hassabis, Altman, and Amodei; Rony's case for why the public gets Cessnas, not F-47s[00:18:45] Irena joins — Infinite Retina, IKEA, and why physical AI is just spatial computing renamed[00:24:30] Why isn't anyone afraid of Google? — fifteen years of planetary data and no strategy to use it[00:33:30] Where did Meta's $120 billion go? — "complete mismanagement" and a failure of imagination[00:38:00] The Vision Pro isn't dead — Apple is still working on it intensely as the glasses ramp up[00:41:45] Why is XR so obscenely hard? — the industry chased consumers before enterprise[00:44:30] The display is everything — why smart glasses "go bonkers" the moment AR hits the lensBrought to you by Zappar and Mattercraft, the leading visual development environment for immersive 3D web experiences. Start building at mattercraft.io. Hosted on Acast. See acast.com/privacy for more information.

After the death of Senator Lindsey Graham (R-SC), former Congressman Patrick McHenry (R-NC) discusses Sen. Graham's legacy and the future of both the Republican and Democratic parties.  Shares of SK Hynix fell over 15% in Seoul on Monday after its impressive Friday debut of US-traded shares on the Nasdaq. Semafor's Rohan Goswami is reporting that friends and advisers of Paramount CEO David Ellison are pushing him to consider relocating the company out of California. Goswami discusses the state's role in Paramount's $110B takeover of Warner Bros. Discovery and the likelihood that the company will start fresh somewhere new. Plus, Apple is suing OpenAI, and Elon Musk and Sam Altman are taking shots at each other on X.    Emily Wilkins - 02:32 Patrick McHenry - 16:51 Rohan Goswami - 33:52   In this episode: Joe Kernen, @JoeSquawk Becky Quick, @BeckyQuick Andrew Ross Sorkin, @andrewrsorkin Emily Wilkins, @emrwilkins Katie Kramer, @Kramer_Katie Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Carl Quintanilla, Jim Cramer and David Faber kicked off a new week with the AI trade: Shares of SK Hynix tumbled 15% for its worst-ever day in the South Korean markets. The stock also fell sharply on Wall Street after surging 13% Friday in its U.S. trading debut — and dragged down memory names including Micron and Sandisk. The anchors reacted to the Sam Altman-Elon Musk's social media spat in wake of Apple's lawsuit against OpenAI. Also in focus: Oil prices jump on U.S.-Iran tensions, SpaceX shares fall closer to their $135 IPO price, Cramer on the non-tech names worthy of your attention, Disney's live-action "Moana" stumbles, Meta and data centers, American Express upgraded, "Faber Report" on State AGs vs. Paramount-WBD deal.   Squawk on the Street Disclaimer Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Scott Wapner and the Investment Committee debate this critical week for stocks as Apple hits a new all-time high, Iran tensions rise and multiple earnings kick off this week. Plus, the Musk vs. Altman drama escalate as OpenAI fends off a new Apple lawsuit, Kate Rooney joins us with the latest. And later, we hit the latest Calls of the Day.  Investment Committee Disclosures Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

I participated in another Socratic Debate about the Future of "AI" and XR at Augmented World Expo 2026 with Leslie Shannon, Alvin Graylin, and Louis Rosenberg (you can listen to last year's debate in episode #1611). Shannon and Graylin argued for "AI," whilst Rosenberg and I argued against "AI." In my write-up, I wanted to leave some breadcrumbs to more in-depth, skeptical arguments against "AI" that we didn't have space or time to dig into during the debate, but "some breadcrumbs" ended up being over 30k words, and more like an outline for an entire book. Writing a book isn't on my to-do list at the moment, but disseminating the work researchers, journalists, linguists, and critics of "AI" have done is urgent and necessary, so I'm sharing the results of this deep dive here, in bullet list format. My fellow panelists, Alvin Graylin and Louis Rosenberg, indulged me in extending our debate offline after AWE, pushing me to test my ideas further and demonstrating how and why this is an extremely active field of study with polarizing points of view that often come down to philosophical differences. Clearly, it's just getting started. My objections to "AI" is loosely organized by various themes, but some framing may be helpful in approaching it. My objections to "AI" center around the limitations of LLMs, the consolidation of wealth and power from Hyperscaler companies, and threats from automated decision making systems and surveillance capitalism melded with democratically-backsliding authoritarian governments. I'm including a broad range of critiques spanning the domains of philosophy, technology, sociology, politics, economics, culture, and ethics. I'm coming from the orientation of Process Philosophy & Peircean Semiotics that emphasizes the relational and contextual dimensions that the "AI" field tends to de-emphasize or completely collapse. I see process-relational philosophy as a necessary paradigm shift away from the underpinning philosophies of the "AI" community, which tend to be Functionalism, Naturalism, Computational Theory of Mind, Physicalism, & the TESCREAL bundle. Below you'll find my own process philosophical emergency response to "AI embedded within my curation of excerpts and commentary of primary sources that I'm leaning upon. The AI Con: How to Fight Big Tech's Hype and Create the Future We Want (2025) by Emily M. Bender and Alex Hanna (also see episode #1563). Bender & Hanna say, "To put it bluntly, 'AI' is a marketing term. It doesn't refer to a coherent set of technologies. Instead, the phrase "artificial intelligence" is deployed when the people building or selling a particular set of technologies will profit from getting others to believe that their technology is similar to humans, able to do things that, in fact, intrinsically require human judgment, perception, or creativity." Emily M. Bender wrote the "Artificial Intelligence" [preprint] (2026, June 25) entry for the Oxford Research Encyclopedia of Science, Technology, and Society. Bender's concluding paragraph gives a great overview of the seven different ways that the idea of "artificial intelligence" operates in the world. She says, "The notion of artificial intelligence is frequently sold as present or near-future and inevitable technology. In fact there is no coherent set of technologies that can serve as the denotation of the phrase, nor do any of the technologies so marketed rise to the fantastical but ill-defined claims of 'AI' is or soon will be. Nonetheless, the idea of artificial intelligence has been extremely impactful in the world. In order to better understand and deal with those impacts, it is helpful to look at artificial intelligence through the varied lenses of how the idea operates in the world: as the name of a research field, as one approach to cognitive science, as a parlor trick, as a an ideology, as a way to hide and devalue human labor, as a way to shift and/or obfuscate accountability, and as a means to centralize power." Inventing Intelligence: On the History of Complex Information Processing and Artificial Intelligence in the United States in the Mid-Twentieth Century [dissertation] (2020, December 14) by Jonnie Penn. Penn says, "The phrase ‘artificial intelligence' was coined by John McCarthy, an American mathematician, in 1955. It has travelled with a noticeably amorphous definition since." "AI" has always had a spotty history of technologists using a "brain is a computer" metaphor while also using "poor citation practices." From page 14, Penn says, "The vocabulary Simon, Rosenblatt, McCarthy and Minsky chose to describe new techniques in major newspapers and scholarly journals informed Americans' still plastic understandings of what was possible, and indeed desirable, in the emerging information age… During the mid to late 1950s, these men turned to clannishness, self-aggrandizement, speculative rhetoric, fluid definitions of key terms and poor citation practices to shore up legitimacy for their controversial new techniques — actions that drew attention toward questions of how to accomplish such aims and away from whether they were well founded." Part II: Remembering the Human Microcosm in the Age of Mechanized Intelligence: Philosophy as Emergency Response (2026, June 9) by Matt Segall. In a multi-part Substack series, process philosopher Matt Segall calls for a philosophical emergency response to "AI." He says, "In each case a new media technology intended to expand the power of thought ended up transforming the very nature of the thinker who invented it. Each new medium furnishes the very terms in which we come to understand ourselves. This is why the philosophical response is always an emergency response: by the time anyone has noticed what is happening, what may be lost and what gained, the mutation has already done half of its work." Segall warns about the computational metaphor of the mind by saying, "The large language model now tempts us to adopt an even stranger self-image: that human minds are no different than machines, our thoughts just the statistical echoes of our training data. The creators of this latest technological upgrade are encouraging us to downgrade our estimate of human consciousness, thus narrowing the distance between ourselves and the machines built to imitate us." "Resisting Dehumanization in the Age of 'AI': The View from the Humanities" [lecture] (2026, February 10) by Emily M. Bender. Here are the lecture slides with a bibliography at the end. Bender does an amazing overview of how the marketing of "AI" uses pernicious dehumanization tactics built on an underlying "brain is a computer metaphor." From page 15 of her talk: "Scientific metaphor used and debated in neuroscience: "THE BRAIN IS A COMPUTER. "PR metaphor used by technologists: "THE COMPUTER IS A BRAIN" Bender cites Baria & Cross' paper titled "The brain is a computer is a brain: neuroscience's internal debate and the social significance of the Computational Metaphor (2021), which says “the Computational Metaphor rests on other well-ingrained ideologies in which a hierarchy of human value is tied to a particular notion of intelligence such that the quality of being emotional is considered inferior to being rational." Bender also cites Dijkstra's 1985 lecture "On anthropomorphism in science": "A more serious byproduct of the tendency to talk about machines in anthropomorphic terms is the companion phenomenon of talking about people in mechanistic terminology." Here are a couple of examples of how "AI" Hyperscaler companies like OpenAI use dehumanizing tactics to sell us on "AI" Hype. Sam Altman will say things like, "A kid born today will never be smarter than AI. Ever." Or another example is when Altman says, "For me, AGI is basically the equivalent of a median human that you could hire as a co-worker... And then Superintelligence is when it's smarter than all of humanity put together." These statements collapse the human experience into one dimension of "intelligence," which amplifies the dual harm of treating machines more like humans and treating humans more like machines. It is also questionable the degree to which this statement is even true given the potential non-computational aspects of "relevance realization." More on this down below. Part IV: Remembering the Human Microcosm in the Age of Mechanized Intelligence: Hegel's Loom and the Difference Reason Makes (2026, June 10) by Matt Segall. Segall brilliantly breaks down the "Brain is a Computer Metaphor" by saying, "Metaphor is not just a shiny paint job on the vehicle of cognition. It is the engine of thought. Its coupling of concepts drives the limits of conceivability, shaping what is thought together and what is not thought at all. The metaphorical imagination is our main means of tuning in to the otherwise invisible effects of new media technologies. Part of the discipline philosophy brings is allowing us to notice an analogy as an analogy before advertising crystalizes it into the unnoticed transparency of common sense. A fact is a fact, but it might also be a fossilized metaphor. The governing analogy of our age is that cognition is computation: the brain an information-processing device, perception its input and behavior its output, memory a form of physical storage, learning the adjustment of weights, and intelligence an algorithm for minimizing error or surprisal. On this view, given enough training data and computational power, consciousness itself will eventually be engineered… The metaphor “the mind is a computer,” for example, tacitly proposes that mind is to brain as software is to hardware… Reiterated in textbooks and earnings calls, in grant applications and policy briefs, the partial comparison congeals into an ontology, until we find ourselves insisting not that the mind is like a computer in some respects but that it simply is one — and,...

In this episode, the mates explore the latest developments in AI, including Anthropic's Fable V model, AI consciousness, regulation, and the future of AI governance and ownership.  Get access to metatrends 10+ years before anyone else - https://qr.diamandis.com/metatrends   Peter H. Diamandis, MD, is the Founder of XPRIZE, Singularity University, ZeroG, and A360 Salim Ismail is the founder of Open ExO, a GP at Exponential Venture Capital/The Organizational Singularity Fund and a sought after global speaker and thought leader. Dave Blundin is the founder & GP of Link Ventures Dr. Alexander Wissner-Gross is a computer scientist and founder of Reified – My companies: Apply to Dave's and my new fund:https://qr.diamandis.com/linkventureslanding      Go to Blitzy to book a free demo and start building today: https://qr.diamandis.com/blitzy   Your body is incredibly good at hiding disease. Schedule a call with Fountain Life to add healthy decades to your life, and to learn more about their Memberships: https://www.fountainlife.com/peter  _ Connect with Peter: X Instagram Substack Website Xprize A360 Connect with Dave: Web X LinkedIn Instagram TikTok Connect with Salim: LinkedIn X Apply for Salim's Pilot Program  Subscribe to Salim's YouTube channel Exponential Venture Capital Connect with Alex Website LinkedIn X Email Substack  Spotify Threads Listen to MOONSHOTS: Apple YouTube – *Recorded on July 7th, 2026 *The views expressed by me and all guests are personal opinions and do not constitute Financial, Medical, or Legal advice. Learn more about your ad choices. Visit megaphone.fm/adchoices

GARMENTS AND GATEWAYS - 06.29.2026 - #953 BestPodcastintheMetaverse.com Canary Cry News Talk #953 - 06.29.2026 - Recorded Live to 1s and 0s Deconstructing World Events from a Biblical Worldview Declaring Jesus as Lord amidst the Fifth Generation War! CageRattlerCoffee.com SD/TC email Ike for discount https://CanaryCry.Support   Send address and shirt size updates to canarycrysupplydrop@gmail.com Join the Canary Cry Roundtable   This Episode was Produced By:   Executive Producers Anonymous*** Sir Jamey Not the Lanister*** Sir LX Protocol Baron of the Berrean Protocol***   Producers of TREASURE (CanaryCry.Support) Josie Whales, Rebecca T, Julie S, Monica, Map Watcher   Producers of TIME Timestampers: Jade Bouncerson, Morgan E Clankoniphius Links: JAM   SIR IKE MEGA BOX GIVEAWAY - Rating/Review, screenshot, send to Sir Ike CanaryCrySupplyDrop@gmail.com CANDACE OWENS/NEPHILIM UPDATE Clip: Says Elon, Thiel, Altman might be hybrids (X)   EXECUTIVE PRODUCERS   PALANTIR/BEAST FASHION My New Life With the Palantir Chore Coat (The Atlantic) → Palantir clothing store   UKRAINE/RUSSIA/WITCHCRAFT The Warrior Witches of Ukraine (Atlantic) Viktor Bout on Tucker Carlson, warning about Ukraine war into Europe (X) Viktor Bout and the re-enchated world war (X)   The Foundations of Re-Enchantment (Patheos/Evangelical) → Oxford press highlights the book   PRODUCERS END

Stanford, Loopt, and Y Combinator. Guest Author: Keach Hagey. Altman's career accelerated at Stanford, where he dropped out to co-found Loopt, a pioneering location-tracking startup. Although Loopt achieved visibility—including a famous appearance at an Apple event alongside Steve Jobs—it was financially a disappointment, selling for parts after the 2008 crisis. Following a period of global "backpacking" and self-reflection, Altman discovered his "superpower" in investing, mentored by Peter Thiel. By 2014, he became the president of Y Combinator, overseeing massive successes like Airbnb and Stripe. Influenced by a visit to SpaceX, Altman adopted Elon Musk's "missionary" approach, viewing startups as world-changing missions rather than mere businesses. During this time, he also championed radical social concepts like Georgism and Universal Basic Income (UBI), writing extensively on how mass AI equity could eventually be shared to restructure society. 3JANUARY 1941

Formative Years in St. Louis. Guest Author: Keach Hagey. This segment explores Sam Altman's childhood in St. Louis during the 1980s and 90s. Hagey describes Altman's parents: Jerry, an idealistic real estate developer focused on affordable housing, and Connie, a highly ambitious dermatologist who set rigorous expectations for her four children. As the eldest, Sam was identified early as being intellectually "on another plane." At sixteen, he candidly came out to his mother, who eventually moved past her initial health-related fears to maintain their strong bond. A defining influence was the John Burroughs School, a progressive private institution that instilled a moral responsibility to use one's talents to improve the world. Despite his technological interests in programming and ham radio, Altman was noted for his precocious charisma and ability to engage adults on topics ranging from computer science to human rights. The segment concludes with his decision to attend Stanford University. 2

ChatGPT and "The Blip." Guest Author: Keach Hagey. The final segment focuses on the viral success of ChatGPTand the resulting internal conflicts at OpenAI. Hagey notes that as ChatGPT's popularity grew, Altman's focus shifted from early safety warnings to aggressive commercialization, causing friction with researchers like Geoffrey Hinton. A significant power struggle with Elon Musk led to Musk's departure after he failed to gain control of the company. Tensions culminated in "the blip," where the nonprofit board fired Altman for perceived lack of candor and "cutting corners" on safety protocols. While Hagey characterizes Altman as a master storyteller and visionary, she highlights that his management style left a "trail of angry people." Although the staff eventually forced his reinstatement, fundamental disagreements regarding the safe development of AGI remain unresolved, as leading lights in the field continue to warn of the technology's inherent dangers. 41943