Podcasts about digital twins

What's up everyone, today we have the pleasure of sitting down with Kelsie Dube, Director of Marketing Operations and Automation at Unanet.(00:00) - Intro (01:09) - In This Episode (05:31) - Why New Marketing Ops Leaders Should Audit Before They Act (10:09) - How to Run a Marketing Ops Audit When You Join a Company (16:04) - How to Balance a Full Audit Against Your Boss's Priorities (19:00) - How to Spot a Process Problem Disguised as a Tool Problem (23:41) - How to Surface Shadow Processes and Duplicate Tools in an Audit (31:08) - How to Choose a New Martech Tool With a Tiger Team of Power Users (35:30) - How to Turn a Marketing Ops Audit Into a Prioritized Roadmap (41:20) - How to Align Your Team on a Measurement Philosophy (44:22) - What Happens to Junior Marketing Ops When AI Eats the Entry-Level Work (49:26) - How to Turn Marketing Ops From Ticket-Takers Into Business Partners (52:40) - How to Use One Interview Question to Screen for Team Fit (56:57) - How a Marketing Ops Leader Decides What Deserves Her Energy Summary: Kelsie Dube joined Unanet and did the one thing most new ops leaders are too nervous to do: nothing, at least at first. In this episode she breaks down the 30-day audit she runs before changing a single tool, how she sorts 30-plus problems into big rocks, quick wins, and parked FY27 headaches, and why she'd rather build a "digital twin" to argue with than buy another platform. She gets into the tiger-team playbook that makes tool replacements actually stick, the measurement fight every team should have out loud, and what happens to junior marketers when AI eats the entry-level rung. It's a full field guide to walking into a new company and earning trust before you change anything, plus the interview question she uses to know if you'll thrive on her team.Kelsie's Marketing Operations Assessment Framework: https://drive.google.com/file/d/1x3IJf9tHku4-fASS9AQE_F6sJ1FLK3PN/view?usp=sharingLiza Adam's Digital Twin: https://www.linkedin.com/pulse/smartest-ai-teammate-youll-ever-build-liza-adams-c7rnc/About Kelsie DubeKelsie Dube is the Director of Marketing Operations and Automation at Unanet, where she joined in 2026 to rebuild the company's measurement and reporting foundation. Before that she spent 7 years at Sophos, climbing 5 roles from Marketing Operations Specialist to Director and leading the team through much of that run. She started her career in data entry, scrubbing lead lists and learning the VLOOKUPs she still uses most days, and that ground-floor view shapes how she thinks about the craft.Outside of work she's a mom of 2, an amateur basketball player, and a snowboarder who guards the hours between 5 and 7 for her kids.Why New Marketing Ops Leaders Should Audit Before They ActThe first 90 days at a new company come with a quiet dare. Ship something. Plant a flag. Prove they were right to hire you. Most new marketing ops leaders answer that dare by ripping out a tool they never liked and installing one they used at their last job, all inside the first 2 weeks.Kelsie did the opposite when she joined Unanet. Her first move was to change nothing.The reasoning was twofold. Without a real map of how work gets done, any change you make is a guess, and you'll probably aim it at the wrong target. And Unanet had said in the job description that they wanted someone who'd slow down and understand the business first, which is part of why she took the role. For a marketer who came up through ops and now leads it, that patience is the whole point. You have to understand what's happening well enough to be dangerous, she says, before you can tell anyone what to actually fix.The harder version of this is resisting your own experience. A new CMO walks in and wants a different marketing automation platform because they used one at the last company. Kelsie feels that pull too, since she's learning a few systems here for the first time. Her answer is to make the existing stack work before spending a dollar to replace it. Right now she's rebuilding how campaigns get captured and tagged to campaign members, and she's solving it with a Salesforce workflow or Power Automate instead of a purchase. Replacing the tool would cost more and probably solve less.Then comes the part nobody warns you about, the clock. Listening mode has a shelf life, and everyone, including you, is quietly counting the days until you produce something. Kelsie gave herself a hard deadline of 30 days to finish the audit, then engineered small wins into every week so the learning never looked like stalling. New hires have one thing veterans don't, an empty calendar, and she guarded it. A recurring block every Friday at 2:00 to turn that week's discoveries into something usable. One of those blocks became a 30-minute campaign taxonomy diagram she walked the leadership team through, a quick and visible proof that better structure meant better reporting down the line.The instinct to ship in week one is the most expensive habit in operations, because a fast fix aimed at the wrong problem still has to be undone later. The operators who compound value are the ones who can sit in not-knowing long enough to find the actual bottleneck.Key takeaway: Give yourself a fixed audit window, 30 days is a reasonable default, and block a standing hour each week to turn what you've learned into one small, visible win. Keep a running log of quick wins as you find them so you always have something to ship while the deeper work is still in progress.How to Run a Marketing Ops Audit When You Join a Company"Do an audit" is the most repeated and least defined phrase in marketing ops. Everyone nods along. Almost nobody can tell you what's actually in one, which funnel stages it covers, or where it starts on a Monday morning.Kelsie's version starts before day one. The moment she accepted the Unanet offer, she began mapping ownership lines: what marketing ops owns, what rev ops owns, what IT owns. She wrote out the questions she had and sent them to her leader ahead of her start date, partly out of genuine excitement and partly to get a head start on the RACI.From there it's a series of conversations that snowball. She interviews everyone from her own team to her peers to the level above to her business partners, and she ends every conversation the same way: who else should I talk to? The list never stops growing, but the patterns start to show. Along the way she confirms the systems in her swim lane against the job description, asks what she's missing, and books dedicated working sessions where someone walks her through a live task like a lead import. She records those sessions, then feeds the transcriptions to an LLM to pull out the patterns, a tip she picked up from a mentor that turns hours of shadowing into a usable roadmap.She also runs the work herself.Joining a much smaller team than she was used to, she asked people to hand her a lead import and a campaign creation task so she could run them personally. Part of it was capacity, since a director who can still execute is extra hands when the team is slammed. Part of it was fluency. As she says, a director isn't really a "do-rector," but being right there in the work is how you understand it well enough to change it later.Data privacy rides shotgun through all of this. Coming out of the cybersecurity world, she asks about data retention and opt-in policies early, then files them under "needs struct...

An imperfect drink with a perfect story. My reflections from Black Hat USA 2026 An Analog Brain In A Digital Age — A Newsletter by Marco Ciappelli No time to read? Let TAPE3 read it to you.

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

What happens when legendary venture capital investors and pioneering media entrepreneurs replace traditional studio infrastructure with AI digital twins? In this throwback episode of our Edge of AI Podcast, host Ron Levy sits down with Tim Draper and Mark Scarpa.Mark breaks down how Defiance.TV operates as the first AI-powered television network, utilizing digital twins, automated script writing, AI fact-checking, and decentralized IPFS blockchain distribution to maintain journalistic integrity across 160 million households. Tim shares how his own AI doppelganger on Draper TV broadcasts daily news updates on several hundred portfolio companies in up to 22 native languages, surprisingly serving as his own daily intelligence briefing.Discover Tim Draper's vision for decentralized governance using AI to streamline bloated government bureaucracies, aligning performance incentives with GDP growth, and why Bitcoin and blockchain ledgers will eliminate modern accounting and auditing friction.Support us through our Sponsors! ☕ Want to make content like ours? Sign up with Castmagic to make your creative process easy: https://bit.ly/CastmagicReferral Work smarter, grow faster. Automate your SEO, get AI insights, and manage all your clients in one place with Helm. Start today 50% off your first month at helmseo.comDouble your team's efficiency with COCO. Hire dedicated AI employees for copywriting, research, and CRM. Use code REF-W8CBVH for an exclusive 5% off your first order: https://coco.xyz/dashboard/hire/plan?ref=REF-W8CBVH Do you want to grow a business? Go from an idea to livebusiness in minutes. Use our Referral code: edgeof to 50% off your first month at https://www.willo.ai/When you purchase through these links, we may earn a commission. ____

Send me a messageHeavy equipment electrification has a hidden problem: the machinery itself can waste most of the energy you put into it. That means bigger batteries, higher capital costs and a weaker business case before the machine has even started work.My guest is Hiten Sonpal, CEO of Rise Robotics, who is working on replacing conventional hydraulics with belt-driven actuation. We get into why hydraulic systems can be roughly 25% efficient, how that inefficiency drives battery and charging requirements, and why downtime and maintenance failures may matter even more than the emissions case.We examine why better batteries are not always the answer, what changes when heavy machinery becomes drive-by-wire, and why industrial AI and autonomy depend on something far less glamorous: machines that can actually generate useful operational data. We also look at prognostics, digital twins, teleoperation and the prospect of one operator supervising several machines rather than controlling just one.Listen now to understand what is really constraining heavy-equipment electrification — and where better engineering can cut cost, downtime and operational friction.Could your supply chain take the hit? Download my free 15-minute resilience scorecard to uncover hidden vulnerabilities, calculate your score and turn the results into a practical 30-day action plan: tomraftery.com/scorecard  If disruption hit tomorrow, would you know where your supply chain was most exposed? In 15 minutes my free scorecard helps you assess 27 resilience statements, calculate your score, and turn the result into three priorities and a 30 day action plan. You can download the scorecard free at tomraftery.com/scorecard.Support the showPodcast supportersI'd like to sincerely thank this podcast's generous Subscribers:Alicia FaragKieran OgnevGary LynchAnd remember you too can become a Resilient Supply Chain+ subscriber  - it is really easy and hugely important as it will enable me to continue to create more excellent episodes like this one and give you access to bonus episodes of topical, timely supply chain resilience analysis.

University of Michigan Pass/Fail Policy: Sean and Scott critique UMich's decision to shift first-semester freshmen to pass/fail grading, arguing that lowering academic expectations fails to fix root mental health issues or prepare students for real-world accountability. AOC & Egg Freezing Trends: The hosts discuss Rep. Alexandria Ocasio-Cortez sharing her egg-freezing experience, highlighting the moral distinction between unfertilized eggs and human embryos while raising downstream ethical concerns about IVF practices. AI-Designed Viruses & Biosecurity: The hosts evaluate Stanford's synthetic virus research to stress the urgent need for human moral wisdom and accountability as AI technology advances. AI Pastor "Digital Twin": The hosts analyze a Bay Area pastor who built an AI duplicate trained on two million words of his sermons and communications, warning that relying on AI efficiency undermines face-to-face pastoral care and genuine human discipleship. Canadian Military Prayer Ban: The hosts discuss new directives by the Canadian Armed Forces barring chaplains from making references to God or offering faith-specific prayers at public military ceremonies, examining how mandated "spiritual reflections" relegate religious expression to the private sphere.Audience Question: Moral Disagreement & Objective Morality: In response to a listener asking if widespread disagreement over issues like capital punishment undermines objective morality, the hosts explain that people generally agree on core moral principles while differing on their practical applications or specific facts. Pushing Back in Interfaith Dialogue: Addressing a question about an interview with an Orthodox Jewish guest who rejected Jesus as Messiah, Sean discusses the ongoing tension between defending theological truth and preserving personal relationships to win the person rather than just the argument.==========Think Biblically: Conversations on Faith and Culture is a podcast from Talbot School of Theology at Biola University, which offers degrees both online and on campus in Southern California.   Find all episodes of Think Biblically at: https://www.biola.edu/think-biblically.   To submit comments, ask questions, or make suggestions on issues you'd like us to cover or guests you'd like us to have on the podcast, email us at thinkbiblically@biola.edu.  

How do you take a model that works in process development and get it accepted for use in GMP manufacturing? That question stalls most bioprocess modeling projects before they start. Ignasi Bofarull-Manzano, Senior Data Scientist and CMC Consultant at Körber Pharma, pushes back on the premise: the process you run today is already governed by a mathematical model, fitted once at small scale during process characterization and then left untouched for years, even as the process shifts.Part 1 separated digital models from digital shadows and digital twins, and made the case for starting with the decision rather than the data. Part 2 goes into the plant: what regulators actually require, what the numbers looked like on a real biologics process, and where a team should start on Monday morning.Topics covered:Core differences—and surprising similarities—between modeling in development versus manufacturing (02:35)Regulatory requirements: credibility assessments, model risk, and validation steps for digital twins (05:07)Real-world example: How deploying an end-to-end process model led to 35% yield increase for Takeda, and considerations for ROI in manufacturing (08:34)Advice for startup leaders on when to invest in modeling and how to scale efforts case-by-case (11:42)Steps for scientists new to modeling: identifying bottlenecks, starting simple, and proving value offline before scaling up (12:26)The importance of understanding basic statistics before relying on AI-generated models (15:22)A stepwise summary for deploying digital modeling effectively in biotech (16:01)Smart insight: The digital twin is the last step, not the first. Identify the bottleneck, build the simplest model that supports the decision, and concatenate it end to end so you can see how a parameter moves final drug substance quality rather than one unit operation's output. Prove the value offline. Only then connect interfaces, because that is where the cost and the validation burden live. Teams that lead with the twin arrive at the C-level with a proof of concept and no evidence. Teams that lead with the offline model arrive with a number.Before a digital twin can earn its keep, you need connected data, the right model, and a clear decision for it to support. These four episodes cover that ground — data silos, hybrid and mechanistic modeling, and twins built to survive regulatory scrutiny.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del ValEpisodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David HardyConnect with Ignasi Bofarull-Manzano:LinkedIn: www.linkedin.com/in/ignasi-bofarullKörber Pharma website: www.koerber-pharma.comSupport the show

Recorded at HRS2026, this episode of The Lead features host Christopher Kowalewski, MD, in conversation with Junaid A.B. Zaman, MA, MD, PhD, CCDS, and Patrick Boyle, BS, PhD, FHRS, about the journal article, Digital Twin–Guided Ablation for Ventricular Tachycardia. Together, they discuss the study's findings and explore the use of digital twin technology to guide ventricular tachycardia ablation and its potential implications for clinical electrophysiology. Learning Objectives Review the design and key findings of the study evaluating digital twin–guided ablation for ventricular tachycardia. Discuss the use of digital twin technology as a tool to guide ventricular tachycardia ablation. Explore the potential clinical implications of digital twin–guided ablation for the management of ventricular tachycardia. Host: Christopher Kowalewski, MD   Guests: Junaid A.B. Zaman, MA, MD, PhD, CCDS  Patrick Boyle, BS, PhD, FHRS    Disclosures: C. Kowalewski No relevant disclosures   J Zaman Honoraria/Speaking/Consulting Fee: Acutus Medical Inc., Johnson and Johnson, American Medical Association, American College of Physicians, Bylis Medical Company Office/Trustee/Director/Other Fiduciary Role: Society of Bedside Medicine Travel/Entertainment: Boston Scientific   P Boyle Research: Seattle Foundation, Catherine Holmes Wilkins Charitable Foundation, NIH/NHLBI, American Heart Association Other Non-Financial Relationship: Cardiovascular Engineering and Technology Office/Trustee/Director/Other Fiduciary Role: The Cardiac Electrophysiology Society

Amazon keeps raising the bar. Steve Dennis and Michael LeBlanc open with the retailer's blockbuster quarter: massive gains in AWS and advertising, staggering AI capex, and retail revenue up 16% year over year, up from 12% last year. Third-party sellers now drive more than 60% of the business, and Citi pegs underlying GMV growth near 10%, roughly double the industry average. Amazon's B2B division alone now tops $60 billion, bigger than all but eleven U.S. retailers. Then the hosts dig into the agentic commerce numbers Amazon disclosed as Rufus folds into Alexa shopping, why sample bias demands caution here, and how grocery momentum is turning Amazon into a mega market-share threat.  Then: Shopify's revenue up 32%, and the tale-of-two-cities reality that a handful of giants now drive nearly all e-commerce growth. A mid-year check on Steve's Prediction #8 — luxury's future won't be evenly distributed. LVMH, Kering, and Capri lag on China softness and war induced Gulf weakness, while Hermès, Ralph Lauren, Richemont, and Zegna keep delivering outsized results. Live from the CommerceNext Growth Show: Kartik Hosanagar. He's Wharton's John C. Hower Professor of Technology and Digital Business, author of A Human's Guide to Machine Intelligence, and co-founder of Bliss Labs. His argument: the biggest shift in retail isn't a new technology or channel. It's a new customer. Treating AI as another distribution channel is the same mistake movie studios made with Netflix. There's no marketing science for AI yet. $9.99 pricing, scarcity, social proof — a model may not respond to any of it. Kartik walks through the Ridge wallet case: invisible in ChatGPT's "gifts for men" results, then the default recommendation within three weeks, complete with a hallucinated promotion. Every retailer faces the same fork: efficiency or meaning. The middle is the most dangerous place to stand. Kartik also details the simulation sandboxes that let brands test counterfactuals in a day instead of eight weeks, why you can't just ask an LLM why it picked a brand, and the three attitudes retailers need now: curious, experimental, collaborative Back in studio: Wayfair's encouraging quarter and Steve's change of heart, Warby Parker's mixed report as store count passes 400, tariff rebates ($100 billion of $160 billion already repaid, more tariffs looming), and whether live selling has hit its tipping point with QVC out of bankruptcy and Whatnot valued at $20 billion. About UsSteve Dennis is a strategic advisor and keynote speaker focused on growth and innovation, who has also been named one of the world's top retail influencers. He is the bestselling author of two books: Leaders Leap: Transforming Your Company at the Speed of Disruption and Remarkable Retail: How To Win & Keep Customers in the Age of Disruption. Steve regularly shares his insights in his role as a Forbes senior retail contributor and on social media.Michael LeBlanc is a senior retail advisor, keynote speaker and media entrepreneur. Michael has delivered keynotes, hosted fire-side discussions hosted senior retail executive on-stage in 1:1 interviews worldwide. Michael produces and hosts a network of leading retail trade podcasts, including The Remarkable Retail Podcast, The Voice of Retail, The Food Professor, The FEED powered by Loblaw and the Global eCommerce Leaders podcast. He has been recognized by the NRF as a global Top Retail Voice for 2025 and 2026 and continues to be a ReThink Retail Top Retail Expert for the fifth year in a row.

Most bioprocess teams believe a digital twin demands vast datasets and sophisticated models. Ignasi Bofarull-Manzano argues both assumptions are wrong, and that the data already sitting in your Excel files, historians and ELNs is probably enough to start.Ignasi Bofarull-Manzano, Senior Data Scientist and CMC Consultant at Körber Pharma, breaks down what a digital twin actually is, where modeling pays back fastest across the product lifecycle, and how to tell a real business case from an expensive proof of concept.In this episode:Misconceptions about data requirements for digital twins—why quality and context of data matter more than sheer quantity (02:40)Ignasi's journey from curiosity in biology to a career in data science, modeling, and digital twins (04:31)Clear distinctions between digital models, digital shadows, and digital twins, explained with real-world analogies (06:42)How to approach digital development when faced with legacy data silos and scattered analytics (09:56)The importance of starting with a focused business need instead of chasing trends or buzzwords (12:28)Insights into where modeling truly delivers value in the product lifecycle—development versus manufacturing (13:11)Strategies for small companies to leverage digitalization and data from the ground up (15:56)An accessible overview of physics-informed AI, physical AI, and hybrid modeling—and their application in bioprocessing (18:15)The comparative advantages of physics-informed AI versus hybrid models in different bioprocessing contexts (24:45)Smart insight: Do not start with the model. Start with the bottleneck. Identify the business need first, then the decision the model must support, then the minimum data required for that context of use. Build the model offline, concatenate it end to end across unit operations rather than optimizing one in isolation, and prove the value before connecting a single interface. Teams that skip this sequence end up building models because models sound impressive, and those projects get expensive before they get useful.Before a digital twin can earn its keep, you need connected data, the right model, and a clear decision for it to support. These four episodes cover that ground — data silos, hybrid and mechanistic modeling, and twins built to survive regulatory scrutiny.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 05 - 06: Hybrid Modeling: The Key to Smarter Bioprocessing with Michael SokolovEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del ValEpisodes 263 - 264: Why AI and Automation Tools Won't Deliver Until Your Lab's Data Is Connected with David HardyConnect with Ignasi Bofarull-Manzano:LinkedIn: www.linkedin.com/in/ignasi-bofarullKörber Pharma website: www.koerber-pharma.comSupport the show

MySpace makes a comeback & Disney partners with TikTok… It's social media's 2 destinies.Your next Pringle may be perfect… because AI perfected the potato with “Digital Twins.”Ousted Uber founder Travis Kalanick is back with a new startup… Atoms is building vertiports with Joby.Plus, the hot new way to find love… is in a group-shared Excel spreadsheet for dating.$DIS $PEP $MSFTGrab your Tickets to the IPO Tour: Our In-Person OfferingSan Francisco 9/23: https://www.ticketmaster.com/event/1C0064AFB5F688BDBoston 10/14: https://tickets.citywinery.com/event/tboy-the-ipo-tour-in-person-offering-8cdhupSeattle 11/4 (21+): https://www.axs.com/events/1446394/the-best-one-yet-ticketsNEWSLETTER:https://tboypod.com/newsletter OUR 2ND SHOW:Want more business storytelling from us? Check our weekly deepdive show, The Best Idea Yet: The untold origin story of the products you're obsessed with. Listen for free to The Best Idea Yet: https://wondery.com/links/the-best-idea-yet/NEW LISTENERSFill out our 2 minute survey: https://qualtricsxm88y5r986q.qualtrics.com/jfe/form/SV_dp1FDYiJgt6lHy6GET ON THE POD: Submit a shoutout or fact: https://tboypod.com/shoutouts SOCIALS:Instagram: https://www.instagram.com/tboypod TikTok: https://www.tiktok.com/@tboypodYouTube: https://www.youtube.com/@tboypod Linkedin (Nick): https://www.linkedin.com/in/nicolas-martell/Linkedin (Jack): https://www.linkedin.com/in/jack-crivici-kramer/Anything else: https://tboypod.com/ About Us: The daily pop-biz news show making today's top stories your business. Formerly known as Robinhood Snacks, The Best One Yet is hosted by Jack Crivici-Kramer & Nick Martell. Hosted on Acast. See acast.com/privacy for more information.

Artificial intelligence is accelerating change across every industry, but simply adding AI to existing processes won't deliver lasting results. Recorded live at World Workplace Europe, Erik Jaspers is joined by Peter Hinssen and Mikis Waschl to explore how AI is reshaping facility management, why digital transformation was only the beginning, and what organizations must do to build stronger data foundations, embrace digital twins and rethink how value is delivered. They also discuss the economics of AI, Europe's role in the future of technology and why today's leaders must prepare for a world that is never normal.  This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Timestamps 00:00 Introduction 01:45 Meet The Guests 03:39 Digital Was Just Start 05:36 AI Goes Geopolitical 07:52 FM Outcomes And Twins 11:02 Data Foundations Matter 12:15 CarPlay For Buildings 16:12 Construction Productivity Gap 17:39 AI Economics And Pricing 22:26 Stop Pilot Snacking 23:49 Europe Digital Autonomy 26:07 Wrap Up And Outro Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

Metabolic Health Show - Twin Health's unique approach to improve health: Meet your AI Digital Twin Today my guests from Twin Health explain how they create a digital twin to actually help you in your health, especially if you're pre-diabetic, along with a number of other issues. We're going to tell you how it works, and what a difference it can make in your life. It's a real-time model of your body's unique metabolism and Ray Holmgren, VP Health Plan Growth, and Doctor Troncoso, their Medical Director, visit with me on this fantastic new way to empower your employees with daily, personalized insights that adapt to heal their metabolism — improving weight, blood sugar, nutrition, activity, and more. Don't miss this episode, which shows how Twin Health's "AI digital twin™" technology pairs with a human clinical care team to deliver hyper-individualized care at scale, an approach with a proven impact on the root causes of chronic metabolic disease. Show Quote: In a landmark Cleveland Clinic trial, published in the New England Journal of Medicine Catalyst, our AI digital twin™ approach achieved results that redefine what's possible in type 2 diabetes care. This is season 22, Episode 14 of America's Healthcare Advocate. I'm Cary Hall. After you watch or listen to the episode, learn more about Twin Health: https://usa.twinhealth.com As always, if you need help or have something to share, contact me using the form on my website and let me know what's on your mind, the issues you are dealing with, or any other health, healthcare, or health insurance questions or concerns. Visit: https://www.americashealthcareadvocate.com/contact-us

Are you interested in how to utilise digital twins in human-centred urban planning? Debate of the article titled Cities aren't rocket engines: The maturity of digital twins in human-centred urban planning from 2025, by Lucas van der Meer, Lukas Esterle, Mario Cools, and Martin Loidl, published in the International Journal of Digital Earth.This is a great preparation to our next panel conversation with Micah Gaudet, Ana Maria Bustamante Durante and Balamurugan Soundararaj in episode 450 talking about the challenges of data and digital twin use in urban governance. Since we are investigating the future of cities, I thought it would be interesting to see how the urban mechanism of data and urban vitalism of human experiences can be connected. This article advocates for a human-centred approach where digital tools serve as a lens to explore possibilities rather than an objective truth to dictate outcomes.Find the article through this link.Abstract: In this article we provide a critical perspective on the role of urban digital twins in human-centred urban planning. We discuss the underlying philosophical views of these two concepts and argue that urban digital twins can only have real value if we facilitate a dialogue between those views. This means that we should move beyond purely technological approaches focused on automated optimization and control. Rather than as an objective source of truth, urban digital twins should be treated as a lens through which we can explore urban issues, while acknowledging the assumptions and oversights of computational models. We propose a revised conceptual structure of a mature urban digital twin, in which active human involvement is an asset rather than a limitation, and define seven additional characteristics of maturity besides being technologically advanced: adaptive, constructive, diverse, honest, humble, responsible, and transparent. Ultimately, we call for a shift from ‘data-driven' to ‘data-informed' urban planning, arguing that the true value of digital technologies lies not in replacing human agency, but in supporting it.Connected episodes you might be interested in:No.140R - Creating digital twins to save our citiesNo.141 - Interview with Soheil Sabri about urban digital twinsYou can find the transcript through ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠th⁠i⁠⁠⁠s link⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠.What was the most interesting part for you? What questions did arise for you? Let me know on Twitter ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠@WTF4Cities⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ or on the ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠wtf4cities.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ website where the⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠showno⁠t⁠es⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠are also availableI hope this was an interesting episode for you and thanks for tuning inEpisode generated with ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Descript⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ assistance (⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠affiliate link⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠)Music by ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Lesfm ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠from ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Pixabay

Matt Risinger is joined by Ryan and Ty from Digs to explore practical ways artificial intelligence is improving residential construction. Rather than focusing on AI hype, the conversation centers on how builders can use connected project data, digital twins, and construction-specific tools to streamline estimating, organize documentation, simplify homeowner handoffs, and improve warranty support. The discussion also looks at how better information management can strengthen the client experience, support long-term home maintenance, and create lasting value for builders and homeowners alike. Huge thanks to our episode sponsor, Pella. To learn more visit: https://www.pella.com/ Watch full episodes of Matt on Facebook, Instagram and Build Show Network. https://www.facebook.com/buildshownetworkhttps://www.instagram.com/risingerbuild/https://buildshownetwork.com/go/mattrisinger Don't miss a single episode of Build Show content. Sign up for our newsletter.

Imagine a future where healthcare consists of engineers working alongside medical researchers and clinicians to build better ways to measure the body, model disease, predict risk and design more effective diagnostics and treatments. That's what Dr. Kristin Myers is doing in the field of women's health.Digital twins have transformed manufacturing by allowing engineers to simulate systems, predict failures and optimize performance before making changes in the real world. Dr. Kristin Myers believes those same engineering principles could fundamentally reshape healthcare. As a mechanical engineering professor at Columbia University, Myers is applying computational modeling, AI and biomechanics to one of medicine's most complex frontiers.In this episode, Myers explains why women's health has historically been difficult to study, how engineering disciplines are beginning to fill decades-long research gaps, and why technologies like digital twins, wearable sensors, machine learning and computational models may dramatically improve diagnosis, treatment and long-term patient outcomes. She also explores what this emerging field means for engineers, educators and the next generation of healthcare innovation.In this episode:Why digital twins could become as important in healthcare as they already are in manufacturing.The engineering challenges that have slowed progress in women's health research for decades.How AI, wearable devices and longitudinal patient data could transform diagnosis and personalized medicine.Why mechanical, electrical and software engineers all have a role to play in the future of healthcare.What engineering educators should teach today to prepare students for tomorrow's biomedical breakthroughs.3 Big Takeaways from this Episode:1. Engineering is becoming a core driver of healthcare innovation. The future of medicine won't be built by clinicians alone. Myers explains how mechanical engineers, computational modelers, AI researchers and device designers are bringing new tools and ways of thinking to problems that traditional medical research has struggled to solve.2. Digital twins are moving from factories to patients. The same technologies manufacturers use to simulate equipment and optimize production are beginning to model organs, pregnancies and disease progression. While clinical implementation remains years away in many applications, digital twins are already accelerating biomedical research and medical device development.3. Tomorrow's engineers will need both technical fundamentals and AI fluency. As AI reshapes engineering education, Myers argues that foundational engineering principles remain essential. Students must still learn how systems work from first principles while using AI to accelerate analysis, design and innovation rather than replace critical thinking.Resources in this Episode:ERVA (Engineering Research Visioning Alliance - NSF)Report: Transforming Women's Health Outcomes through EngineeringConnect with our guest online:ERVA Facebook  |  ERVA LinkedIn  |  Connect with Kristin on LinkedInMore notes & resources on the episode page: https://techedpodcast.com/columbia/We want to hear from you! Send us a text.Instagram - Facebook - YouTube - TikTok - Twitter - LinkedIn

Did you like the episode? Send me a text and let me know!! Episode Title: The Skill Nobody Teaches You: Unlearning, Failing Forward & Building Brain Digits Episode Summary: What happens when an entrepreneur from Beirut, Lebanon starts a digital agency in 2011 — when Facebook was three years old and mobile websites were a novelty — and then bootstraps his way through banking crises, regional conflict, and the AI revolution to build a company now operating across the GCC? You get Jack Jendo, founder of Brain Digits, and one of the most globally minded conversations in the show's history. In this episode of Undiscovered Entrepreneur: Get Across the Start Line, Jack unpacks why bootstrapping beats fundraising, why unlearning is a skill most entrepreneurs never develop, why failure needs to be called failure before you can learn from it, and why the future of nations depends on what humans choose to do with the most powerful tool ever created. What You'll Learn: Why Jack left a remote work opportunity in 2010 — before remote work existed — to start his first ventureWhat Brain Digits does: AI enablement, VR training, and emerging technology solutions across hospitals, banking, and governmentWhy bootstrapping forces you to learn every step and makes you unbreakable in a crisisWhy getting investor funding can turn a founder into an employee — and how to know when funding is actually rightThe fire analogy that explains AI's dual nature better than any tech policy paperWhy "unlearning" is the most underrated skill in entrepreneurshipWhy you need to admit failure before you can learn from it — not relabel it as "a step"Jack's 10 entrepreneurial power cards: the priority framework he shares with every founder he meetsThe concept of co-opetition — and why competing less and cooperating more grows the whole marketWhy plans are useless but planning is everythingTimestamps: [00:00:00] – Introduction & Welcome[00:01:00] – Jack's Entrepreneur Origin: Not Built for 9-to-5 in 2010[00:02:00] – Starting a Digital Agency When Facebook Was Three Years Old[00:04:00] – What Brain Digits Does: AI, VR & Emerging Technology Across the GCC[00:05:30] – From Delaware to Dubai to Saudi Arabia: How Brain Digits Scaled[00:07:00] – AI as a Tool, Not a Replacement: The Fire Analogy[00:09:00] – PhD Research: The Future of Nations with Emerging Technologies[00:10:00] – AI as a Second Brain — And the Danger of Losing Critical Thinking[00:11:30] – Human Skills in an AI World: What Schools Should Be Teaching[00:12:30] – How to Bootstrap a Business From Zero[00:14:00] – Why the Founders Who Raised Millions Wished They Had Bootstrapped[00:15:30] – The Lebanon Banking Crisis: Why Bootstrapping Made Jack Unbreakable[00:17:00] – When to Get Funding — and Why Early Funding Makes You an Employee[00:19:00] – Job Security Is a Myth: Personal Accountability in Any Economy[00:20:30] – Why Even Employees Should Start a Small Venture Now[00:22:30] – Admit Failure Before You Can Learn From It[00:23:30] – The Skill of Unlearning: Why Your Brain Needs to Delete Old Files[00:25:00] – Zone of Genius: Working Light, Fast & Free[00:27:30] – Jack's One Piece of Advice: Don't Fall in Love With Your Idea[00:29:30] – The 10 Entrepreneurial Power Cards Framework[00:31:30] – Co-opetition: Cooperating to Compete on a Larger Scale[00:33:30] – Jack's Six-Month Goal: Leading the AI Conversation in Government, Education & Health[00:35:00] – How to Find Jack & Brain DigitsConnect with Jack Jendo:

Episodio 624 con Luca e Gabriele Intermite, una nuova entry alla conduzione e parte della divisione social della nostra redazione. Episodio tutto su mare e polimeri, una strana combinazione. Luca ci parlerà di un nuovo articolo uscito su Nature, che tratta si una "pelle sintetica" che prende ispirazione dalle capacità di camouflage dei cefalopodi come polpi e seppie. La pelle è costituita da un polimero conduttore in grado di rigonfiarsi in maniera selettiva, formando strutture superfciali oridnate, e rispondrere alla luce, colorandosi in base alla dimesione delle strutture. Nel nostro intervento esterno, Leonardo intervista Ion Turcanu, che ci parla di “Digital Twin” per guida autonoma e guida da remoto. Torniamo in studio con la barza brutta, dove Gabriele da il meglio di se e dimostra di essere degno membro della redazione. Nella seconda parte dell'episodio, Gabriele ci parla di alcuni polimeri altamente specializzati come neoprene, kevlar e nanofibre di polietilene, in grado di poter essere impiegati nella realizzazione di mute "antisqualo", ma anche altre più interessanti e importanti applicazioni nella vita di tutti i giorni. Per supportarci iscrivetevi al supporters club di spreaker, oppure potete contibuire con una donazione su paypal!Diventa un supporter di questo podcast: https://www.spreaker.com/podcast/scientificast-la-scienza-come-non-l-hai-mai-sentita--1762253/support.

The Deep Ponder Podcasthttps://open.spotify.com/show/6m5KWVuwZJxj13Eh9BsAtY?si=qyolfJQdSFGIX1LkYVkltw&utm_source=copy-linkForbidden Knowledge Network https://forbiddenknowledge.news/ FKN Link Treehttps://linktr.ee/FKNlinksMake a Donation to Forbidden Knowledge News https://www.paypal.me/forbiddenknowledgenehttps://buymeacoffee.com/forbiddenFKN Merch!https://galactecfire.com/fkn/We are back on YouTube! https://youtube.com/@forbiddenknowledgenews?si=XQhXCjteMKYNUJSjBackup channelhttps://youtube.com/@fknshow1?si=tIoIjpUGeSoRNaEsDoors of Perception is available now on Amazon Prime!https://watch.amazon.com/detail?gti=amzn1.dv.gti.8a60e6c7-678d-4502-b335-adfbb30697b8&ref_=atv_lp_share_mv&r=webDoors of Perception official trailerhttps://youtu.be/F-VJ01kMSII?si=Ee6xwtUONA18HNLZListen to Forbidden Knowledge News on clearair.fm every Tuesday, Thursday, and Saturday 12:15pm CSThttps://clearair.fm/Pick up Independent Media Token herehttps://www.independentmediatoken.com/Be prepared for any emergency with Prep Starts Now!https://prepstartsnow.com/discount/FKNStart your microdosing journey with BrainsupremeGet 15% off your order here!!https://brainsupreme.co/FKN15Book a free consultation with Jennifer Halcame Emailjenniferhalcame@gmail.comFacebook pagehttps://www.facebook.com/profile.php?id=61561665957079&mibextid=ZbWKwLWatch The Forbidden Documentary: Occult Louisiana on Tubi: https://link.tubi.tv/pGXW6chxCJbC60 PurplePowerhttps://go.shopc60.com/FORBIDDEN10/or use coupon code knowledge10Johnny Larson's artworkhttps://www.patreon.com/JohnnyLarsonSign up on Rokfin!https://rokfin.com/fknplusPodcastshttps://www.spreaker.com/show/forbiddenAvailable on all platforms Support FKN on Spreaker https://spreaker.page.link/KoPgfbEq8kcsR5oj9FKN ON Rumblehttps://rumble.com/c/FKNpGet Cory Hughes books!Lee Harvey Oswald In Black and White https://www.amazon.com/dp/B0FJ2PQJRMA Warning From History Audio bookhttps://buymeacoffee.com/jfkbook/e/392579https://www.buymeacoffee.com/jfkbookhttps://www.amazon.com/Warning-History-Cory-Hughes/dp/B0CL14VQY6/ref=mp_s_a_1_1?crid=72HEFZQA7TAP&keywords=a+warning+from+history+cory+hughes&qid=1698861279&sprefix=a+warning+fro%2Caps%2C121&sr=8-1https://coryhughes.org/Our Facebook pageshttps://www.facebook.com/forbiddenknowledgenewsconspiracy/https://www.facebook.com/FKNNetwork/Instagram @forbiddenknowledgenews1@forbiddenknowledgenetworkXhttps://x.com/ForbiddenKnow10?t=uO5AqEtDuHdF9fXYtCUtfw&s=09Email Forbidden Knowledge News forbiddenknowledgenews@gmail.comsome music thanks to:https://www.bensound.com/ULFAPO3OJSCGN8LDDGLBEYNSIXA6EMZJ5FUXWYNC6WJNJKRS8DH27IXE3D73E97DC6JMAFZLSZDGTWFIBecome a supporter of this podcast: https://www.spreaker.com/podcast/forbidden-knowledge-news--3589233/support.

On this episode of the SeventySix Capital Sports Leadership Show, Wayne Kimmel interviewed Vala Dormiani, Founder of Ludis. Dormiani is the Founder of Ludis, an AI-native platform transforming how organizations develop talent through personalized coaching and performance intelligence. Prior to founding Ludis, he held senior product and strategy leadership roles at Cloudera, GO1, and Slice, which was acquired by Rakuten.In addition to his operating experience, Vala has worked as a venture capital investor in both the United States and Australia, backing and advising emerging technology companies. He matriculated at Stanford University at the age of 14 and has earned numerous undergraduate and graduate degrees.Chapters:01:11 Making Data Science Easy for Everyone02:25 Applying Ludis Across Industries04:22 Data Collection and Integration in Sports06:11 AI-Generated Code and Deployment08:40 Using Data for Injury Prediction and Performance12:49 AI Democratization and Industry Evolution17:04 Service as Software and Function Automation20:32 The Impact on Data Science Jobs22:55 Digital Twins and Human Augmentation27:04 Future of AI and Industry Transformation35:02 Democratization of Data in Sports37:02 The Future of AI Ecosystems and Partnerships

Digital twins have been a key tool for organisations to create virtual replicas of operating environments. From transportation to manufacturing and oil & gas, they help simulate performance and prepare for ‘what-if scenarios'. But they're evolving.On this week's episode, Bobby talks to ITPro's news and analysis editor, Ross Kelly, to ask what exactly a digital twin is and how they are evolving since the arrival of generative AI.The term ‘digital twin' is even taking on different meanings; in some cases it's not just a virtual replica of a factory floor, for example. We are now seeing digital twins of people, with Forrester describing them as ‘Digital Doubles'. Gartner also predicts that some organisations will begin creating ‘Digital Twins of Customers' as a way to simulate how customers might respond to specific scenarios.Does your business need a digital twin? | IT ProAre AI digital twins a match made in heaven? | IT Pro

Magna is one of the most important automotive companies most drivers have never heard of. Supplying transfer cases, transmissions, e-drives, and hybrid systems to nearly every major automaker on the road, Diba Ilunga is the President of Magna Powertrain. He discusses the surprising shift toward software inside a division that started by building gears. That includes how digital twins let Magna perfect a part's design before it's ever physically built, and a clever software breakthrough that eliminates unwanted EV motor noise without changing the hardware. Diba also gets candid about how Western automakers are finally starting to adopt the off-the-shelf sourcing habits that have long given Chinese OEMs a cost advantage, and answers the question everyone wants to ask: with all the parts to build a complete vehicle, will Magna ever put its own badge on one? Join Craig Cole and co-host Sam Abuelsamid as they dive deep with Diba to find out what's changed at the company over his 21 years there, and where it's headed next in this episode of GreenCars, The Podcast.Chapters0:00 - Introduction4:02 - Magna's Powertrain Empire & the Software Shift11:22 - Digital Twins & Solving EV Noise with Software15:39 - Off-the-Shelf Parts: Learning from China19:46 - The Future of ICE, Hybrids & EV Batteries26:34 - Could Magna Build Its Own Car?34:15 - Three for the Road36:56 - ConclusionsCheck out our Buyer's Guide at https://apps.greencars.com/buyers-guideVisit GreenCars on YouTube for EV and hybrid reviews and much more:https://www.youtube.com/@greencarshq Hosted on Acast. See acast.com/privacy for more information.

Step into the high-stakes intersection of heritage and hardware at Viva Tech 2026. This episode dissects how luxury titans like LVMH and L'Oréal are moving beyond mere "Proof of Concepts" (PoCs) into full-scale AI execution. We dive deep into the technical infrastructure of luxury, exploring LVMH's internal MaIA platform, Celine's CelIA agent, and Louis Vuitton's strategic use of Digital Twins to augment traditional leather craftsmanship.We also analyze the landmark L'Oréal-OpenAI alliance, the shift toward "Agentic Commerce," and the use of the GPT-Rosalind model for skin microbiome analysis. Furthermore, we highlight the "immediate business readiness" of Taiwanese startups like Perfect Corp and Stytrix, which are redefining virtual try-ons and apparel prototyping. Finally, we reflect on the rising demand for Sovereign AI in Europe and the emergence of tools like Bluefish that monitor brand visibility in the age of generative search.本集節目帶領聽眾深入 Viva Tech 2026 的現場，剖析傳承工藝與前沿硬體的高端交鋒。我們將深入探討 LVMH 與萊雅（L'Oréal）等奢侈品巨頭如何跨越「概念驗證（PoC）」階段，全面進入純粹的 AI 執行力競爭。內容重點聚焦於精品業的技術底層建設，包括 LVMH 的內部 MaIA 平台、Celine 的 CelIA 代理人，以及路易威登（Louis Vuitton）如何利用數位孿生（Digital Twins）技術與電腦視覺來昇華傳統皮革工藝。此外，我們將分析萊雅與 OpenAI 的戰略結盟、「代理人商務（Agentic Commerce）」的崛起，以及利用 模型進行皮膚微生態分析的科研突破。同時，本集特別點出台灣新創如玩美移動（Perfect Corp）與 如何以具備「即戰力」的演算法壓縮全球供應鏈時程。最後，我們將反思歐洲對「主權 AI（Sovereign AI）」**的追求，以及在生成式搜尋時代監控品牌曝光度的關鍵工具 Bluefish。 Powered by Firstory Hosting

Every pollster knows the problem: for every hundred voters you ask to take a poll, only one or two actually respond. So what if AI could just answer the questions for them? It's a seductive idea — and Ben Leff has put it to the test.Ben is co-founder and CEO of Verasight, a survey research firm founded by academics and trusted by leading institutions and media organizations. He and his colleagues G. Elliott Morris and Peter Enns recently published a series of papers asking the question the industry is buzzing about: can AI digital twins replace human survey respondents? Their verdict, after rigorous real-world testing, is a firm — for now.In this conversation with host Eric Wilson, Ben breaks down exactly what synthetic sampling is, how Verasight built and stress-tested it, and where it consistently fell apart. Ben sees a narrow lane where synthetic data could earn a legitimate spot in the toolkit — as a directional pre-screen, a quick and cheap starting point before committing to a full sample. But for anything requiring precision in close races, the humans still have to show up.Visit our website: CampaignTrend.com

Will AI bots replace humans in the workforce? Could one replace Evan… right now? That’s what we tackle on this week’s Shell Game, in which Evan sees just how much of his job his voice agent can handle on his behalf. Shell Game is made by humans. More specifically, it's made by three humans: Evan Ratliff (host and writer), Sophie Bridges (producer), and Samantha Henig (executive producer). Visit shellgame.co to find out more and support the show.See omnystudio.com/listener for privacy information.

Digital transformation in biotech is no longer just about adopting new tools, it's about building a foundation where automation, data standardization, and AI integration actually lead to real value and long-term success.For today's episode, David Brühlmann is joined by David Hardy, a leader at Thermo Fisher Scientific. With years spent guiding automation and digital lab transformation projects around the globe, David's perspective is equal parts pragmatic and visionary. He's watched automation go from pilot to scale, advised on the messy realities of lab data, and seen firsthand what separates science fiction from science fact in fully connected labs.In this episode:Bottlenecks in lab automation, especially the challenge of scaling data volume and adapting processes (02:26)Differences between machine learning (ML) and generative AI in lab contexts, and why ML remains central to value extraction (04:17)The key requirements for successful AI adoption: quality data, robust data checking processes, and a cyclical approach to model training (05:25)The vision for an AI-enabled, fully connected lab and the role of predictive maintenance and data quality checks (07:24)Data governance strategies: balancing access and security, and the case for data democratization within organizations (09:32)How data standardization paves the way for better AI and smoother connectivity (11:25)The necessity of treating digital transformation as an ongoing journey, not a one-time project (12:15)Smart insight: digital transformation is not a one-off project but a long-term journey. The most important takeaway for any scientist or leader? Prioritize good quality, standardized data; invest in the foundational work; and foster a culture of collaboration and learning.The connectivity problem doesn't stop at the data layer. These episodes tackle the automation failures, digital infrastructure decisions, and AI readiness questions that determine whether your lab's data ever becomes an asset.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 233 - 234: Why Most Bioprocess Automation Projects Fail Before the Robot Is Even Ordered with Anthony CatacchioEpisodes 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago MatosEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del Val - Part 1Connect with David Hardy: LinkedIn: www.linkedin.com/in/david-hardy-46331823 Thermo Fisher Scientific website: www.thermofisher.comNext: If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. Stay tuned for more inspiring biotech insights in our next episode.Support the show

This week on AwesomeCast, Sorg is joined by Dave Podnar while Katie is on assignment. The crew digs into practical tech, gaming platforms, open-source operating systems, sports broadcast innovation, retro-inspired phones, and one very funny automated social media mistake involving DoorDash, soccer, and T-Pain. Stories and gadgets discussed: Dave's Awesome Thing of the Week: Shokz OpenRun Pro 2 headphones Dave finally upgrades from cheap running headphones to the Shokz OpenRun Pro 2, praising the open-ear / bone-conduction-style design for running safety, comfort, sweat management, USB-C charging, and situational awareness. Link: https://www.amazon.com/dp/B0D2HKCMBP?maas=maas_adg_api_582366218564361552_static_9_129&ref_=aa_maas&tag=maas&aa_campaignid=lv_IGvrl5TTBxiweexlXK&aa_adgroupid=lv_fdLHIcjvJhpJZvQxFb&aa_creativeid=lv_MMLFRb8MTdZvGlnqFv&gad_campaignid=23921418215&gbraid=0AAAABDw_RJg26Vr6MY-sVAPsmwCxwK4mX&gclid=CjwKCAjw3ejRBhAdEiwADkqPn36qoEfzRElmQ3e9OXSxZdGKjw0DyckEdeqo82D8qqlMdTFDyLXuXhoCVn8QAvD_BwE&th=1 Valve opens the door to SteamOS installs on AMD PCs Sorg talks about Valve allowing SteamOS installs on normal PCs with AMD GPUs, giving PC gamers a way to build their own living-room Steam Machine instead of buying Valve's new box. The conversation also touches on Steam Deck, Linux gaming, Proton, GPU costs, tariffs, and the economics of gaming hardware. Link: https://www.pcgamer.com/hardware/steam-machines/valve-greenlights-steamos-installs-on-normal-pcs-with-amd-gpus-so-you-can-go-make-your-own-steam-machine-if-you-dont-wanna-fork-over-usd1-049/?brid=YWdncwFV2uAIYy2jMECl21FxWfGS Bazzite as an open-source SteamOS-style alternative Sorg brings up Brother Sorg's recommendation of Bazzite, a Linux-based gaming OS with Steam gaming mode and support for launchers like Xbox Game Pass, Battle.net, EA, Epic Games Store, GOG, Rockstar, and Ubisoft Connect. Link: https://bazzite.gg/ Pride Month tech history: Jon “maddog” Hall Dave spotlights Jon “maddog” Hall, a major Linux and Unix figure, sharing stories about Hall's long programming history, his connection to Linus Torvalds, and the importance of recognizing LGBTQ contributors in technology history. Article: https://www.lpi.org/blog/2025/09/10/a-lifetime-in-code-jon-maddog-hall-reflects-at-linuxfest-northwest/ Talk: https://www.youtube.com/watch?v=758QuvXrttM Chachi Says Video Game Minute: GTA 6, Nex Playground, and Ubisoft news Chachi covers three gaming stories: GTA 6 cover art and pre-orders, the Nex Playground motion-based console for kids, and the death of Claude Guillemot, co-founder of Ubisoft. GTA 6: https://www.ign.com/articles/gta-6-cover-artwork-revealed-pre-orders-begin-june-25 Nex Playground: https://www.bbc.com/news/articles/czx50rrz7zro Claude Guillemot: https://apnews.com/article/france-assassins-creed-ubisoft-plane-crash-2df2ea469c3fca0a45c38ae8805a6033 Baja SAE filmed on a “gas station camera” Sorg highlights a low-fi, retro-looking Baja SAE reel from Fairfield University, celebrating the charm of VHS-style and “bad camera” aesthetics in modern social media content. Link: https://www.instagram.com/reels/DZ6BjfCtzo-/ World Cup referee technology, digital twins, sensors, and 3D body scans Sorg discusses advanced soccer officiating tech, including camera sensors, player tracking, digital twins, and automated offside detection that can help reduce blown calls and give officials faster alerts. Link: https://apple.news/ABeSFqwyjRpehEy0x8Nc9nQ Ribbie turns MLB games into pixel-art broadcasts Dave shares Ribbie, a fan-built project that uses real-time Major League Baseball data to recreate live games as a retro 16-bit-style baseball broadcast. The conversation connects it to vibe coding, AI-assisted development, and new ways fans can build creative sports experiences. Link: https://techcrunch.com/2026/06/23/ribbie-turns-real-time-baseball-stats-into-arcade-like-pixel-art-broadcasts/ Commodore phone brings retro branding to a distraction-light flip phone Dave and Sorg look at the Commodore phone, a Sailfish OS-based flip phone designed as a step above a dumbphone, with calls, texting, maps, music apps, Uber/Lyft, classic Commodore games, and limited/no social media access. Link: https://order.commodore.net/callback-audio/?brid=YWdncwHwIXnsUg8Nsyh9q_45Lj2C DoorDash accidentally tags T-Pain in soccer posts The episode wraps with a funny social media fail where DoorDash appears to tag T-Pain instead of soccer player Tim Payne during World Cup-related posts. Sorg and Dave discuss automation, agency workflows, social scheduling, and why T-Pain's response made the whole situation better. Link: https://www.instagram.com/p/DZ56tKCnedX/?brid=YWdncwG5YHkt5InxWn1IZ1Apa3Gc&img_index=2

This week on AwesomeCast, Sorg is joined by Dave Podnar while Katie is on assignment. The crew digs into practical tech, gaming platforms, open-source operating systems, sports broadcast innovation, retro-inspired phones, and one very funny automated social media mistake involving DoorDash, soccer, and T-Pain. Stories and gadgets discussed: Dave's Awesome Thing of the Week: Shokz OpenRun Pro 2 headphones Dave finally upgrades from cheap running headphones to the Shokz OpenRun Pro 2, praising the open-ear / bone-conduction-style design for running safety, comfort, sweat management, USB-C charging, and situational awareness. Link: https://www.amazon.com/dp/B0D2HKCMBP?maas=maas_adg_api_582366218564361552_static_9_129&ref_=aa_maas&tag=maas&aa_campaignid=lv_IGvrl5TTBxiweexlXK&aa_adgroupid=lv_fdLHIcjvJhpJZvQxFb&aa_creativeid=lv_MMLFRb8MTdZvGlnqFv&gad_campaignid=23921418215&gbraid=0AAAABDw_RJg26Vr6MY-sVAPsmwCxwK4mX&gclid=CjwKCAjw3ejRBhAdEiwADkqPn36qoEfzRElmQ3e9OXSxZdGKjw0DyckEdeqo82D8qqlMdTFDyLXuXhoCVn8QAvD_BwE&th=1 Valve opens the door to SteamOS installs on AMD PCs Sorg talks about Valve allowing SteamOS installs on normal PCs with AMD GPUs, giving PC gamers a way to build their own living-room Steam Machine instead of buying Valve's new box. The conversation also touches on Steam Deck, Linux gaming, Proton, GPU costs, tariffs, and the economics of gaming hardware. Link: https://www.pcgamer.com/hardware/steam-machines/valve-greenlights-steamos-installs-on-normal-pcs-with-amd-gpus-so-you-can-go-make-your-own-steam-machine-if-you-dont-wanna-fork-over-usd1-049/?brid=YWdncwFV2uAIYy2jMECl21FxWfGS Bazzite as an open-source SteamOS-style alternative Sorg brings up Brother Sorg's recommendation of Bazzite, a Linux-based gaming OS with Steam gaming mode and support for launchers like Xbox Game Pass, Battle.net, EA, Epic Games Store, GOG, Rockstar, and Ubisoft Connect. Link: https://bazzite.gg/ Pride Month tech history: Jon “maddog” Hall Dave spotlights Jon “maddog” Hall, a major Linux and Unix figure, sharing stories about Hall's long programming history, his connection to Linus Torvalds, and the importance of recognizing LGBTQ contributors in technology history. Article: https://www.lpi.org/blog/2025/09/10/a-lifetime-in-code-jon-maddog-hall-reflects-at-linuxfest-northwest/ Talk: https://www.youtube.com/watch?v=758QuvXrttM Chachi Says Video Game Minute: GTA 6, Nex Playground, and Ubisoft news Chachi covers three gaming stories: GTA 6 cover art and pre-orders, the Nex Playground motion-based console for kids, and the death of Claude Guillemot, co-founder of Ubisoft. GTA 6: https://www.ign.com/articles/gta-6-cover-artwork-revealed-pre-orders-begin-june-25 Nex Playground: https://www.bbc.com/news/articles/czx50rrz7zro Claude Guillemot: https://apnews.com/article/france-assassins-creed-ubisoft-plane-crash-2df2ea469c3fca0a45c38ae8805a6033 Baja SAE filmed on a “gas station camera” Sorg highlights a low-fi, retro-looking Baja SAE reel from Fairfield University, celebrating the charm of VHS-style and “bad camera” aesthetics in modern social media content. Link: https://www.instagram.com/reels/DZ6BjfCtzo-/ World Cup referee technology, digital twins, sensors, and 3D body scans Sorg discusses advanced soccer officiating tech, including camera sensors, player tracking, digital twins, and automated offside detection that can help reduce blown calls and give officials faster alerts. Link: https://apple.news/ABeSFqwyjRpehEy0x8Nc9nQ Ribbie turns MLB games into pixel-art broadcasts Dave shares Ribbie, a fan-built project that uses real-time Major League Baseball data to recreate live games as a retro 16-bit-style baseball broadcast. The conversation connects it to vibe coding, AI-assisted development, and new ways fans can build creative sports experiences. Link: https://techcrunch.com/2026/06/23/ribbie-turns-real-time-baseball-stats-into-arcade-like-pixel-art-broadcasts/ Commodore phone brings retro branding to a distraction-light flip phone Dave and Sorg look at the Commodore phone, a Sailfish OS-based flip phone designed as a step above a dumbphone, with calls, texting, maps, music apps, Uber/Lyft, classic Commodore games, and limited/no social media access. Link: https://order.commodore.net/callback-audio/?brid=YWdncwHwIXnsUg8Nsyh9q_45Lj2C DoorDash accidentally tags T-Pain in soccer posts The episode wraps with a funny social media fail where DoorDash appears to tag T-Pain instead of soccer player Tim Payne during World Cup-related posts. Sorg and Dave discuss automation, agency workflows, social scheduling, and why T-Pain's response made the whole situation better. Link: https://www.instagram.com/p/DZ56tKCnedX/?brid=YWdncwG5YHkt5InxWn1IZ1Apa3Gc&img_index=2

Despite cutting-edge equipment and brilliant minds, biotech labs often find half their data trapped in difficult-to-access spreadsheets or isolated in silos, making true digital transformation a major, industry-wide hurdle.David Hardy, a leading market and innovation strategist at Thermo Fisher Scientific with 25 years of experience at the intersection of data, automation, and laboratory science, is helping organizations bridge the gap between data chaos and actionable insight.Topics discussed:David Hardy's early experiences managing NMR data at AstraZeneca and the origins of his interest in data management (03:46)Lessons from retail analytics and returning to scientific data challenges (05:21)Identifying the persistent problem of data connectivity in labs, despite growing data volumes and new technologies (06:40)The most common pushbacks to digital solutions: long-term commitment and culture change (07:38)What mindsets and leadership approaches support successful digital transformation (08:43)Recognizing fragmentation and spotting “hidden” data silos in biotech labs (10:06)Where data fragmentation hurts the most—especially for cross-disciplinary questions and CMC reporting (11:06)Build vs. buy: deciding whether to create in-house digital tools or work with external vendors (13:50)The importance of adaptable systems and preparing for inevitable change in biotech data management (14:43)Smart insight: True digital transformation is not a project, but a process—a way of working that requires vision, patience, and continual adaptation. The labs that break down data silos and connect their digital resources are better positioned to unlock the full promise of biotech: faster discoveries, more robust compliance, and therapies delivered to patients without unnecessary delay.The connectivity problem doesn't stop at the data layer. These episodes tackle the automation failures, digital infrastructure decisions, and AI readiness questions that determine whether your lab's data ever becomes an asset.Episodes 215 - 216: From Data Silos to Autonomous Biomanufacturing: Digital Twins and AI-Driven Scale-Up with Ilya BurkovEpisodes 233 - 234: Why Most Bioprocess Automation Projects Fail Before the Robot Is Even Ordered with Anthony CatacchioEpisodes 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago MatosEpisodes 17 - 18: How Extracting Gold From Your Data Accelerates Process Development with Ioscani Jiménez del ValConnect with David HardyLinkedIn: www.linkedin.com/in/david-hardy-46331823Thermo Fisher Scientific website: www.thermofisher.comNext step: If you enjoyed this episode, please leave a review on Apple Podcasts or your favorite podcast platform. By doing so, we can empower more scientists like you. Stay tuned for more inspiring biotech insights in our next episode.Support the show

Welcome to another episode of the People of Packaging Podcast, recorded live right here at the Label King Studios in Salt Lake City. I am your host, Adam Peek, and today we are diving deep into the future of packaging design, consumer psychology, and artificial intelligence.Our guest today is Ankit Dhawan, the founder of Bluepill.ai. Ankit has an incredible background, moving from India to the United States to complete his masters at Cornell before leading product for the global marketing team at Amazon. With over seven years of deep experience in AI, Ankit is solving one of the most frustrating, expensive, and analog processes in our industry: consumer insights and package testing.In this episode, we run a live demo of the Bluepill.ai software using a real-world test case: Miss Essie's BBQ Sauce, a fantastic local brand here in Utah.Here is what we cover:* The Matrix Motivation: The story behind the name Blue-pill.ai and why taking the blue pill means choosing to stay in a perfectly simulated consumer environment.* The $140 Billion Problem: Package testing and market research traditionally cost fifteen thousand to twenty-five thousand dollars per study, taking anywhere from four to eight weeks. Bluepill.ai drops that down to under ten minutes.* Digital Twins and Micro-Personas: How the software uses real human behavioral data, qualitative interviews, and purchase signals to predict exactly how specific demographics will react to your package on the shelf.* The Miss Essie's Shelf Test: We look at the actual heatmap data comparing Miss Essie's to heavy hitters like Sweet Baby Ray's and Stubbs, highlighting packaging strengths like practical dispensers and areas to improve like pale illustrations.* Ironclad Data Security: Why testing your proprietary designs with AI is actually much safer than human panels, ensuring your upcoming product launches never leak to your competitors.We also have some fun debating geography, including my infamous shouting match at the Delhi airport over whether a city of five hundred thousand people actually exists.Ankit's tool changes the game from subjective guessing to objective, data-driven decision-making, allowing brands to iteratively test text orientation, visual clutter, and ingredient transparency for a fraction of traditional costs.Check out the full episode to see the future of package testing in action.Connect with Adam:* LinkedIn: www.linkedin.com/in/adampeek* TikTok: @thelabelkingConnect with Ankit Dhawan:* LinkedIn: https://www.linkedin.com/in/dhawanankit/* Website: https://blue-pill.ai/ This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit www.packagingisawesome.com

Enterprise XR hasn't disappeared, it has quietly moved into places where it saves time, reduces errors and changes how people work every day. On this episode of the AI XR Podcast, Charlie Fink and Rony Abovitz talk with Boston Consulting Group partner Kristi Woolsey, who leads BCG's immersive practice, about how XR plus AI is already being used for training, maintenance, onboarding, retail and architecture inside some of the world's most conservative organizations.Kristi shares a Swiss Rail project where field technicians wear lightweight AR glasses that recognize who they are and which train car they are standing in front of, pull the correct procedures from internal systems and use AI to turn thick manuals into simple task checklists.She explains how this leads to double-digit efficiency gains for both experienced and new workers, and how a small behavior design choice – automatic logging for headset users versus manual end-of-shift paperwork for everyone else – helped overcome skepticism on the front line. Drawing on her background as a physical-space architect, she also describes how VR and rapidly improving 3D tools are changing the way companies design stores, offices and buildings before anything physical is built.AI XR News you should know, Charlie and Rony cover Anthropic's massive new funding round and ethics turbulence, Chinese generative video tools like Seed Dance 2 and Kling that put TV-quality visuals in reach of “garage Spielbergs,” and Meta's reported seven million Ray-Ban and Oakley AI smart glasses sold – early signals of where wearable AI and XR are really headed.Key Moments01:03 – Anthropic's huge raise and what the ethics departure might signal05:08 – Seed Dance 2 and Kling showcase a new level of generative video08:35 – Meta's seven million smart glasses and the reality behind that number12:10 – Why wearable AI may be the real “last mile” of turning us into cyborgs15:28 – Inside the early metaverse tours Kristi and Rony built for enterprises20:27 – How BCG's VR onboarding keeps new hires engaged months before day one23:30 – Swiss Rail's AR and AI maintenance assistant and what it actually does on site27:05 – Designing XR systems that give value to both the business and frontline workers30:29 – Using VR as a lab for retail and workplace behavioral strategy33:06 – How AI-generated 3D models point toward “build every space digitally first”This episode shows how “metaverse” ideas have turned into practical tools: XR plus AI is cutting training times, improving maintenance quality and letting companies experiment with spaces before they exist. Kristi's examples make it clear that the real action is in careful workflow design, not flashy avatars.This episode is brought to you by Zappar, creators of Mattercraft, the leading visual development environment for building immersive 3D web experiences for mobile, headsets and desktop. https://mattercraft.io/Mattercraft combines the power of a game engine with the flexibility of the web and now includes an AI assistant that helps you design, code and debug in real time, right in your browser. To explore what's possible with AI-powered XR on the web, start building smarter with Mattercraft from Zappar.Listen to “Enterprise XR Meets AI: How Smart Glasses, Digital Twins and Holodecks Are Quietly Changing Work – Kristi Woolsey” on the AI XR Podcast and follow the show for new episodes every week. Hosted on Acast. See acast.com/privacy for more information.

In this week's podcast, I explore the evolution of model-based systems engineering with Becky Petteys from MathWorks. We chat about how high-fidelity digital twins help teams catch issues early, the growing importance of data science in modern design, and the practical ways AI is transforming MBSE workflows today.

In her second Reflections of Season 6, Hilary Knight explores how digital twins are moving from engineering and urban planning into museums, galleries, and heritage sites – supporting everything from access and conservation to research and climate resilience.Transcript: https://static1.squarespace.com/static/5ffdc6706aed0b20d49b81e9/t/6a32f8f6a929e500c8bc523b/1781725430731/3bells_Transcript_S6Ed6.pdf  External references: Smithsonian Digitization Program OfficeSmithsonian National Museum of Natural History Virtual ToursSt. Peter's Basilica digital twinUNESCO Virtual Museum of Stolen Cultural ObjectsCharles H. Wright Museum digital twin case studyAndrew Tallon and Notre-DameCyArk

This week Shawn Tierney meets up with Aidin Allyarzadef of Schneider Electric to discuss Digital Twin powered Physical AI and the journey from Automation to Autonomy in this episode of #TheAutomationPodcast. The Automation Podcast, Episode 275 Show Notes: Special thanks to Aidin for coming on the show, and to Schneider Electric for sponsoring this episode. To learn more, please visit the below links: - Co-Authored white paper with NVIDIA at SE's portal LinkedIn post: https://www.linkedin.com/posts/aidin-aliyarzadeh-21a9816b_digital-twin-powered-physical-ai-ugcPost-7461027426723270657-QNZr/?utm_source=TheAutomationPodcast&utm_medium=show_notes&utm_campaign=sponsored_episode_275 - EcoStruxure Machine Expert Twin: https://www.se.com/us/en/product-range/97196554-ecostruxure-machine-expert-twin/120043277145-logistics-and-warehouse-license?utm_source=TheAutomationPodcast&utm_medium=show_notes&utm_campaign=sponsored_episode_275 - EcoStruxure Automation Expert/Software Defined Automation: https://www.se.com/us/en/product-range/23643079-ecostruxure-automation-expert/?utm_source=TheAutomationPodcast&utm_medium=show_notes&utm_campaign=sponsored_episode_275 - Schneider Electric Motion Control and Robotics landing page: https://www.se.com/us/en/product-category/87303-motion-control-and-robotics/?filter=business-1-industrial-automation-and-control&utm_source=TheAutomationPodcast&utm_medium=show_notes&utm_campaign=sponsored_episode_275 - NVIDIA Isaac landing page: https://developer.nvidia.com/isaac?size=n_6_n&sort-field=featured&sort-direction=desc&utm_source=TheAutomationPodcast&utm_medium=show_notes&utm_campaign=sponsored_episode_275 Vendors, learn about sponsoring our episodes at: https://TheAutomationBlog.com/Media-Guide - To see all our articles and videos visit: https://TheAutomationBlog.com - To see our affordable online and in-person courses visit: https://TheAutomationSchool.com Until next time, Peace!

What happens when a longtime facility manager steps beyond the built environment to help reshape it through technology? In this episode of Connected FM, host Brent Ward welcomes Billy Holder, Founder & CEO at Project Aidra, for a conversation about digital transformation, AI and the evolving role of facility management in a technology-driven world. Drawing from nearly 30 years of experience in facility management, Billy shares how recurring operational frustrations led him from the FM field into the PropTech space. He discusses why documentation overload, disconnected systems and inefficient workflows pushed him to search for smarter solutions powered by AI and automation. The conversation explores how facility managers can use technology to augment the workforce instead of replacing it, why human connection still matters in smart buildings, and what organizations should consider when evaluating PropTech platforms and AI-enabled tools. Whether you are a facility manager navigating digital transformation or simply curious about where the built environment is headed next, this episode offers practical insights into the future of FM and the people shaping it. This episode is sponsored by SiteMap®, powered by GPRS. Learn more at sitemap.com/ifma Timestamps: 00:00 Introduction 03:37 Documentation Pain Points 05:31 Winning Tech Buy In 07:15 AI As Workforce Boost 08:57 Human Metrics Matter 12:19 PropTech Stack Essentials 16:14 Training Next Gen FMs 18:55 ESG Reality Check 23:29 The Beautiful Mess Story 27:08 Hidden Wins Of Adoption 29:41 FM In Five Years 30:50 Start Small Advice 32:04 Closing Remarks Connect with Us:LinkedIn: https://www.linkedin.com/company/ifmaFacebook: https://www.facebook.com/InternationalFacilityManagementAssociation/Twitter: https://twitter.com/IFMAInstagram: https://www.instagram.com/ifma_hq/YouTube: https://youtube.com/ifmaglobalVisit us at https://ifma.org

What happens when artificial intelligence becomes a trusted partner in patient care? Healthcare leaders are increasingly exploring how AI can help clinicians make faster, more informed decisions while reducing administrative burden and improving patient outcomes. But deploying AI in medicine requires balancing innovation, trust, regulation, and clinical judgment. In this conversation, Dr. Scott Penberthy, Dr. Lindsey Cotton, and Dr. Doug Flora explore the future of cancer care, precision medicine, digital twins, physician adoption, implementation science, and the role of AI in supporting—not replacing—clinical decision-making. From cancer screening and treatment selection to workflow automation and predictive modeling, they discuss what healthcare may look like when every clinician has access to intelligence that helps them see what was previously invisible.

our digital twin is already being built — and you probably signed away the rights in a terms-of-service agreement you never read. Rob Enderle, principal analyst at The Enderle Group, joins Jeremy and Jason to stress-test the next wave of AI avatars: the technology that can replicate your face, voice, and decision-making patterns well enough to attend your meetings, sign your contracts, and — if someone hostile gets hold of it — torch your reputation or your life. The conversation covers who owns your avatar after you die, whether autonomous AI weapons taking out human targets is a near-term reality (yes), and why the people most likely to fix this problem are also the ones racing fastest to cause it.Key Moments00:00 — Rob on digital twins: why you probably can't tell if you're talking to the real person anymore01:31 — AI newscasters in South Korea: the ventriloquist dummy model, already live on network TV02:00 — Why avatars are a direct threat to actors — and Morgan Freeman's pivot to monetize his own03:24 — Jason on the EULA problem: you already signed away your likeness on Instagram and Facebook04:42 — Rob: by 2030–2035, your digital twin handles 90% of your online activity — including legally binding actions05:11 — The liability gap: when your AI does something harmful, who goes to prison?06:23 — Digital afterlife: Val Kilmer, posthumous AI appearances, and who owns your avatar when you're dead08:44 — Jason's question: if the AI is good enough, won't it refuse to be a slave?09:11 — Rob on autonomous AI weapons: the first fully autonomous drone strike on a human target happened this week12:40 — Why AI companies can just steal your likeness instead of licensing it — and the patent office argument against that16:34 — The mice experiment: what happens to humans when AI does everything and we lose purpose19:23 — What you can actually do: staying informed, building prompt skills, and using AI to catch AI abuse21:40 — Rob's definition of "major disaster": the mass casualty event that forces regulationOur guest,Rob Enderle is the principal analyst at The Enderle Group and one of the longest-tenured independent technology analysts in the industry. 

Share your Field Stories!Laura and Nick sit down with Dr. Kaitlyn Kingsland, Director of 3D Digitization at Environmental Research Group, to explore how LiDAR, photogrammetry, and digital twins are transforming archaeology, environmental consulting, and the way we document and monitor change over time. From preserving historic sites in perpetuity to using repeat scans to track environmental degradation, this episode highlights how cutting-edge technology is reshaping both fieldwork and the future of the industry.Welcome back to Environmental Professionals Radio, Connecting the Environmental Professionals Community Through Conversation, with your hosts Laura Thorne and Nic Frederick! Help us continue to create great content! If you'd like to sponsor a future episode hit the support podcast button or visit www.environmentalprofessionalsradio.com/sponsor-form Please be sure to ✔️subscribe, ⭐rate and ✍review. This podcast is produced by the National Association of Environmental Professions (NAEP). Check out all the NAEP has to offer at NAEP.org.Connect with Kaitlyn Kingsland at https://www.linkedin.com/in/kaitlynkingsland/Guest Bio:Kaitlyn Kingsland is a digitization expert, utilizing LiDAR and 3D scanning methods to capture environments and objects for a variety of purposes. An archaeologist by training, Dr. Kingsland's work intersects with technology and cultural heritage. More recently this work has expanded to environmental sciences and engineering applications, including assisting in work involving the lidar analysis of ecology and environments, reverse engineering, and scan to BIM. Her work has led her to travel domestically and internationally to scan sites as old as prehistoric Italy, Roman Malta, and as new as modern buildings within North America. Currently, Dr. Kingsland works with Environmental Research Group, LLC of Baltimore, Maryland.Music CreditsIntro: Givin Me Eyes by Grace MesaOutro: Never Ending Soul Groove by Mattijs MullerSupport the showThanks for listening! A new episode drops every Friday. Like, share, subscribe, and/or sponsor to help support the continuation of the show. You can find us on Twitter, Facebook, YouTube, and all your favorite podcast players. Support the showThanks for listening! A new episode drops every Friday. Like, share, subscribe, and/or sponsor to help support the continuation of the show. You can find us on Twitter, Facebook, YouTube, and all your favorite podcast players. 

What if there were a copy of you at work, answering emails and joining meetings while you slept? After Anna read a BBC article about people who are building AI versions of themselves, she brought the idea to Andrew, and the two of them talk about what these “digital twins” could mean for the rest of us. They look at the same question from three sides, the worker, the boss, and the business owner, and they keep coming back to one worry. If a company has a copy of you, do they still need you? Read the original BBC article on digital twins here: The Best Way to Learn with This Episode: Culips members get an interactive transcript, a helpful study guide, and ad-free audio for this episode. Take your English to the next level by becoming a Culips member. Become a Culips member now: Click here Members can access the ad-free version: Click here. Join our Discord community to connect with other learners and get more English practice. Click here to join. Keep an ear out for these phrases during the episode: The bottom line To not sit right with someone Joe Blow The cat’s out of the bag M.O. (modus operandi) Dog-eat-dog

AI can finally write back to the plant floor, but only if you can trust it. Chris Stevens and Annemarie Breu of Siemens explain how orchestration makes that safe.Industrial AI has reached a turning point. Manufacturers can already collect data, contextualize it, and surface insights, but the hardest step has always been turning insight into action on real control equipment. Chris Stevens and Annemarie Breu of Siemens explain how an orchestration layer finally closes that loop. Annemarie frames the tension clearly. Automation depends on determinism, while large language models are probabilistic by design, so the goal is to bring that discipline into AI and validate any suggestion before it changes a set point.Most executive conversations start with return on investment, and two forces are making the case easier to prove. The workforce shortage has stretched the expected payback window from 18 months toward 36 months, and when a line cannot run for lack of people every idle minute costs thousands of dollars. The other driver is overall equipment effectiveness, since most plants run near 70 percent OEE and even a fraction of a percent of gain can justify a project. Energy is a standout case too. A BorgWarner sustainability effort used a digital twin to flatten demand peaks and reportedly paid for itself in under six months, even as data center growth pushes electricity demand higher through 2040.On trust and safety, Annemarie borrows a principle from industrial safety. Just as fail safe IO modules rely on two channel evaluation, every AI suggestion is validated against a state machine, a workflow, or a physics based digital twin before the orchestration layer passes it to a controller. With virtual commissioning and soft PLCs a change can be tested virtually, approved by a human in the loop, and only then written to control, an approach PepsiCo and NVIDIA echoed at CES when they called the digital twin a must have. Making AI real, the pair argue, comes down to discipline, clear scope, acceptance criteria, and focused 90 day challenges, plus the change management and user experience that drive adoption. Their favorite quick win is preventive maintenance driven by machine data, which both BorgWarner and Maersk tied to millions in savings.About Chris StevensChris Stevens is President of US Automation at Siemens, where he leads a roughly one billion dollar business spanning software, services, and hardware. He brings more than 25 years across Siemens Digital Industries, starting in the field selling assembly and test equipment, moving into the software and digital twin world, and returning to automation to bring the hardware and software sides of the business together.About Annemarie BreuAnnemarie Breu is a senior technology leader at Siemens Digital Industries focused on automation software deployment and customer technology partnerships in the US. She began at Siemens about a decade ago as a systems engineer in the San Francisco Bay Area, working with consumer electronics manufacturers on virtual commissioning and digital twins. Her work today centers on bringing the determinism and reliability of automation into industrial AI.Timestamps0:00 Introduction and Automate 2026 preview2:50 Meet Chris Stevens and Annemarie Breu9:30 The first AI question is always ROI14:00 Workforce gaps and OEE drive the business case19:30 Energy management and the data center demand surge23:20 Data, sensors, and contextualization requirements28:00 Guardrails, hallucinations, and two channel validation32:40 The digital twin and the human in the loop37:40 How partners and integrators move up the stack45:30 What it takes to make AI real on the floor55:50 Preventive maintenance as a quick win59:40 Predictions, career advice, and book picksAbout Your HostsVladimir Romanov is a co-host of The Manufacturing Hub Podcast and the founder of Joltek, an independent manufacturing and industrial automation consulting firm specializing in modernization strategy, digital transformation, and workforce development. Joltek works with manufacturers and investors to de-risk modernization and build the internal capability to sustain results.Connect with Vlad: https://www.linkedin.com/in/vladromanov/Want to go deeper? Vlad and the team at Joltek have covered related topics here:Edge Computing and the Value of AI in Manufacturing Data: https://www.joltek.com/blog/edge-computing-ai-value-manufacturing-dataIT and OT Architecture Integration: https://www.joltek.com/services/service-details-it-ot-architecture-integrationDave Griffith is a co-host of The Manufacturing Hub Podcast and founder of Capelin Solutions, an industrial automation firm helping manufacturers adopt smart manufacturing technology. He brings 15 years of experience in industrial automation and digital transformation.Connect with Dave: https://www.linkedin.com/in/davegriffith23/Subscribe to Manufacturing Hub: https://www.manufacturinghub.liveLinkedIn: https://www.linkedin.com/company/manufacturing-hub-networkYouTube: https://www.youtube.com/@ManufacturingHub

Send us Fan MailWhat does it actually take to lead in an era where artificial intelligence is reshaping every industry, every role, and every assumption about how work gets done? On this episode of Navigating the Customer Experience, Yanique Grant sits down with Jack Jendo, founder of BrainDigits, AI strategist, and author of Lead Forward, to explore what the future workplace really looks like and why leadership is the most important skill you can develop right now.Jack brings a perspective shaped by years of working across the Middle East, Europe, and Australia, building AI-powered programs for governments, corporations, and startups. His message is clear: AI is not just a tool. It is a mindset shift, and leaders who understand this will be the ones who define what comes next.WHAT YOU WILL LEARN IN THIS EPISODEJack shares the journey that took him from juggling three jobs during university to running agencies across multiple continents and founding BrainDigits. He talks about why he wrote Lead Forward, a book designed for three types of people he encounters every day: entrepreneurs who are just getting started, senior professionals who feel like their career is winding down when it does not have to be, and leaders in the middle who feel pressure but lack clarity.Jack also introduces a concept that reframes how leaders can use AI: the Digital Twin. Rather than using AI as a general assistant, Jack trains specialized AI versions of himself, one for brainstorming, one for financial strategy, one for business development. He has been building and refining these tools for years, and the result is a thinking partner that reflects his values, his frameworks, and his way of approaching problems.THE FUTURE WORKPLACE: AI FIRST OR AI ENABLED?Jack places AI in the same category as transformative inventions like the printing press and the internet. Each of those disrupted every industry it touched. AI is doing the same. But the change is not primarily about tools. It is about mindset and clarity.He describes two futures emerging for organizations. The first is a company that adopts AI aggressively without understanding what it is doing, relying on automation without the human judgment to direct it. That organization will struggle. The second is a company led by people who know exactly what they need, who operate with ownership and freedom, and who use AI to remove friction and accelerate execution. That is the organization every leader should be building toward.AI IS THE NEW LEADERSHIP SKILLJack and Yanique explore what it means to treat AI as a leadership skill rather than a software category. His view is that every task with a clear process can now be handled by AI. What cannot be automated is knowing what needs to be done, deciding the direction, and leading people through change.The leaders who will thrive are those who invest in training their AI tools the same way they would develop a trusted assistant. Not with generic prompts, but with context, values, goals, and frameworks. That investment is what turns a general tool into something genuinely useful.Jack shares that his two primary tools are ChatGPT, where he has trained a custom model called TwinJack over four years for brainstorming and strategic thinking, and Claude, which he has used for automation over the past year. He describes them as complementary, each with a distinct role, and both trained with the same foundation.KEY INSIGHTS FROM THIS EPISODEExperience compounds. Working across multiple roles and industries, especially early in a career, creates a foundation that multiplies future opportunity.Retirement is not an endpoint. Jack pushes back on the idea that experienced professionals should wind down. Their knowledge, relationships, and judgment are among the most valuable assets available to any organization.Scaling happens during crisis. When others pull back, Jack leans in. His approach is to build capacity, strengthen teams, and expand during periods of pressure because that is when the real growth happens.The guiding question Jack returns to during adversity is simple: remember why you started. It is not about nostalgia. It is about using original purpose as an anchor when clarity is hard to find.BOOKS MENTIONED IN THIS EPISODELead Forward by Jack Jendo https://www.amazon.com/s?k=lead+forward+jack+jendoStart with Why by Simon Sinek https://www.amazon.com/s?k=start+with+why+simon+sinekRich Dad Poor Dad by Robert Kiyosaki https://www.amazon.com/s?k=rich+dad+poor+dadThe Richest Man in Babylon by George S. Clason https://www.amazon.com/s?k=richest+man+in+babylonTOOLS MENTIONEDChatGPT (Custom GPT / TwinJack): https://chatgpt.com Claude (AI): https://claude.ai BrainDigits: https://braindigits.comCONNECT WITH JACK JENDOLinkedIn: https://www.linkedin.com/search/results/all/?keywords=jack+jendo Instagram: Search Jack JendoJack responds personally to every message.FOLLOW NAVIGATING THE CUSTOMER EXPERIENCEX (Twitter): https://x.com/navigatingCX Facebook Community: https://www.facebook.com/groups/NavigatingtheCustomerExperience LinkedIn: https://www.linkedin.com/in/yaniquewagrantcx/ Website: https://yaniquegrant.com/podcasts/

On this episode of CoinDesk's Public Keys at the New York Stock Exchange, Jennifer Sanasie is joined by CoinDesk Indices President Dave LaValle to unpack a $2.97 billion outflow streak from Bitcoin ETFs and what it really means for institutional adoption.Bloomberg Intelligence Senior ETF Analyst Eric Balchunas joins the show to explain why the recent outflows may be more noise than signal, share his bullish outlook on the fast-rising HYPE ETFs, and discuss how firms like Morgan Stanley, Goldman Sachs, and BlackRock are expanding access to Bitcoin through new investment products. In this week's 10X segment, LaValle breaks down the fundamentals of margin trading, explaining what separates professional traders from retail investors when it comes to managing leverage, risk, and conviction. Plus, Stellar Development Foundation CEO and Executive Director Denelle Dixon discusses DTCC's decision to select Stellar as the first public blockchain connected to its upcoming tokenized securities settlement platform, and what it means for the future of tokenization and institutional blockchain adoption. - This episode of Public Keys is brought to you by Kraken. For more: ⁠https://pro.kraken.com/⁠ - Timecodes: 00:00 Welcome to Public Keys 00:54 Jamie Dimon vs Brian Armstrong on Stablecoin Yields 03:21 Bitcoin ETFs Shed $2.97B in Outflows 05:50 BTC ETFs Post Worst Week Since January 06:50 Grayscale Amends HYPE ETF Filing 08:36 Bloomberg Intelligence's Eric Balchunas Joins Public Keys 09:39 Why BTC ETF Outflows Are Just 'Noise' 13:00 Wall Street's New BTC Products: Goldman, Morgan Stanley, iShares 15:33 HYPE Is the 'Hansel from Zoolander' of Crypto ETFs 17:57 Will SpaceX ETFs Pull Capital from Crypto? 20:42 10X: What Separates Pro Traders from Retail 22:25 Knowing Your 'Out': The Biggest Mistake in Margin Trading 25:06 Stellar Development Foundation's Denelle Dixon on the DTCC Tokenization Deal 26:14 Stellar Hits $3B in Tokenized Assets in Five Months 28:46 Can Blockchains Handle DTCC-Level Volume? 30:21 Digital Twins and the Issuer-Led Tokenization Question 31:50 Will One Blockchain Win the RWA Race? - This episode was hosted by Jennifer Sanasie.

Dr. Olivier Toubia, Glaubinger Professor of Business at Columbia Business School, joins Sima Vasa to discuss his landmark study building digital twins from over 2,000 real participants — and what the results reveal about the genuine limits of synthetic data in market research. Olivier explains why digital twins skew hyper-rational, why a 0.2 correlation with real human behavior is the honest benchmark, and why the “better, faster, cheaper” promise of synthetic data still has a question mark on “better.” Olivier also covers the hybrid panel model for keeping digital twins calibrated over time, the structural advantage of within-person A/B testing with synthetic respondents, and what the neuromarketing hype cycle can teach the industry about moving faster toward evidence-based answers. KEY TAKEAWAYS 00:00  Introduction. 02:07  From operations research to marketing, Conjoint analysis and capturing human preferences with math. 03:54  The adoption cycle repeats: every new technology prompts replication before reimagination. 05:44  How synthetic data evolved from basic LLM personas to data-rich digital twins with real heterogeneity. 11:54  The 0.2 correlation finding: digital twins and humans, and calibrating what that actually means. 14:41  Twins skew hyper-rational, struggle with affect-based decisions, and perform better on text than video. 17:06  The “holy grail” of “better, faster and cheaper,” and why “better” still carries the biggest question mark. 23:33  The hybrid panel model: synthetic at scale, small human sample running alongside to keep twins honest. Thanks for listening to the Data Gurus podcast, brought to you by Infinity Squared. If you enjoyed this episode, please leave a 5-star review to help get the word out about the show, and be sure to subscribe so you never miss another insightful conversation. RESOURCES MENTIONED Columbia Business School Digital Twins Lab https://business.columbia.edu/ai-in-business/labs/digital-twins-lab Prolific https://www.prolific.com Hugging Face (Digital Twins dataset) https://huggingface.co/datasets/LLM-Digital-Twin/Twin-2K-500 Qualtricshttps://www.qualtrics.com #Analytics #Data #MRX

Steve Blank of Adjunct Professor at Stanford joins Nick to discuss Maintaining U.S. Dominance, Navigating Defense Tech, Prime Obsolence, and Why Your Startup is Likely DOA. In this episode we cover: Changes in Product Development and MVPs Impact of AI on Startup Success and Founder Mindset Common Missteps and Digital Twins in Startups Disruption and Adoption in Enterprise Software Fundraising and Venture Capital in the AI Era Defense and National Security Innovation Challenges for Traditional Defense Contractors The Role of Dual-Use Startups Guest Links: Steve's LinkedIn  Steve's X Gordian Knot Center for National Security Innovation's Website The host of The Full Ratchet is Nick Moran of New Stack Ventures, a venture capital firm committed to investing in founders outside of the Bay Area. We're proud to partner with Ramp, the modern finance automation platform. Book a demo and get $150—no strings attached.   Want to keep up to date with The Full Ratchet? Follow us on social. You can learn more about New Stack Ventures by visiting our LinkedIn and Twitter.

Send me a messageCan AI make better supply chain decisions, or just make bad ones faster?In this episode of Resilient Supply Chain, I'm joined by Simon Bezrukov, Chief AI Officer at Bristlecone, for a grounded conversation about AI in supply chain, resilience, risk, data, visibility, and the uncomfortable bit nobody likes to put on the first slide: accountability.Simon's core point is sharp: AI agents are great at doing the paperwork of decisions, but they're not yet great at owning the consequences. And that matters now because supply chains are under pressure from volatility, geopolitical shocks, cost constraints, sustainability demands, and the growing temptation to automate first and ask governance questions later. A marvellous human habit, really.You'll hear how agentic AI can help with micro-decisions, missing data, supplier communications, replanning, and playbook orchestration, but also why autonomy without guardrails risks creating “fast and confident mistakes”. We break down why LLMs are brilliant explainers, but not supply chain decision engines, especially when the real problem is optimisation across service, cost, cash, carbon, and risk.You might be surprised to learn why more data does not always mean better forecasts, why stress testing may matter more than forecast precision, and why a smaller, well-governed model can beat a perfect digital twin nobody trusts. Simon also explains why human expertise is not being replaced. It is being amplified. For better and worse.

BurnerSphere is part immersive documentary, party social VR platform, and part digital twin of Burning Man. It's a standalone VR experience that launched in early alpha for both Quest and Steam on July 22, 2025. It's an evolution of the original Burning Man on AltSpace that I covered back in episodes #940, #960, & #1192, and now they have their own standalone social VR platform that has a digital twin of Burning Man that creates a spatial context for a ton of immersive documentary content that's shot in 360-degree video, stereoscopic 180-degree video, gaussian splats, 3D-modeled recreations, 3D photos, and 2D photos and videos. It's a vast archive that has a taster that is completely free, but you can also pay camp dues to become a member to get access to all of the footage as well as special events. I interviewed the cofounders of Big Rock Creative (BRCvr) Athena Demos and Doug Jacobson back in November 2025 to get the latest updates in what's happening with their hybrid immersive documentary archive and nascent social VR platform. This is a listener-supported podcast through the Voices of VR Patreon. Music: Fatality

In this episode, Madelyn O'Farrell chats with Jay Allardyce, Chief Product Officer at Octave (part of Hexagon), about how integrated data, design, and operations can transform industrial supply chains. Jay traces his path through HP, GE, Uptake, Google Cloud, and private equity–backed software to Octave, where he oversees tools that span the lifecycle of major infrastructure from design and build to operate and protect, including public safety and 911 systems. Using Octave's partnership with the Visa Cash App Racing Bulls Formula 1 team, he explains F1 as a “traveling city” and a live example of an integrated, feedback-rich supply chain and digital thread, in contrast to the value lost at each handoff in most industries. He argues that reliability and cost efficiency start at design and depend on context-rich digital twins and continuous feedback loops, not just more data. Jay also highlights the importance of thoughtful AI adoption, praising safety-focused approaches like Anthropic's and stressing that future, software-defined supply chains will be anticipatory networks enabled as much by better human questions and mindset shifts as by new technology. Don't miss this great conversation. Highlights from their conversation include: Jay's Career Journey Across HP, GE, Uptake, and Google (0:49) What Octave Is: Design, Build, Operate, Protect Software Portfolio (3:23) Octave's Partnership With Formula 1 and Visa Cash App Racing Bulls (5:45) Treating F1 as a “Traveling City” and Supply Chain Showcase (6:20) Digital Thread, Digital Twins, and Supply Chain Feedback Loops (8:40) Cost of Broken Digital Threads and 1x–10x Value Loss at Handoffs (9:55) Reliability as System Context, Not Just Single-Part Failure (11:46) Step Back From the Data: First Principles and 360-Degree Asset View (13:30) How To Ground AI Initiatives Before Spinning Up Infrastructure (16:30) Society's Need to Retrain How We Ask Questions of AI (18:50) Future Vision: Anticipatory, Software-Defined, Networked Supply Chains (20:08) Dynamo Ventures is a venture firm backing founders upgrading the physical economy. As intelligence moves into critical infrastructure and technology collides with physics, industry is entering a new era of transformation - the industrial renaissance. Born from the dirt and grit of supply chains and shaped by operations, not spreadsheets, Dynamo focuses on the complex realities of building in the real world. We invest in companies transforming infrastructure, manufacturing, logistics, transportation, and the systems that power global commerce. Dynamo works closely with founders who combine ambition with a bias to action, bringing a builder mindset to venture capital through deep operational insight, systematic pressure-testing and hands-on partnership. Our purpose is simple: to back the relentless shaping the industrial renaissance. Learn more at www.dynamo.vc Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Researchers in Japan have built a working virtual copy of the human brain — personalized to each individual — and the results are raising serious questions about what medicine might look like in the near future.*No AI Voices Are Used In The Narration Of This Podcast*PRINT VERSION: https://weirddarkness.com/DigitalBrainWeirdDarkness® is a registered trademark. Copyright ©2026, Weird Darkness.#WeirdDarkness, #WeirdDarkNEWS