Podcasts about digital twins

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

Teams are moving faster than ever, producing more content, running more campaigns, and optimizing everything. But without strategy, it's mostly noise.  In this episode of Frontier CMO, Josh travels to London to sit down with Mark Ritson, brand strategist, former marketing professor, and advisor to companies like LVMH, Sephora, and Donna Karan, to talk about the state of marketing, which frankly is a mess.  Based on new global research, Ritson argues that most marketers don't actually understand the fundamentals of marketing. In an AI-driven world, that's about to matter a lot more.  Drawing on examples from brands like Apple, Nike, Target and Walmart, Ritson shares his hot takes on who's getting it right, who's getting it wrong, and why brand building is still one of the most misunderstood and undervalued parts of marketing.  They also get into what's coming next and how to position yourself to stand out in an AI-driven marketing world.  No matter where you are in your career, this is what it takes to not get left behind. 00:00 – The Marketing Knowledge Crisis 04:09 – Has America Lost Its Marketing Edge? 05:31 – Why Creators and Brand Ads Work Together 09:24 – The Most Important Job of a CMO 10:13 – Strategy Before Tactics 13:08 – The Skills Marketers Need in the AI Era 15:43 – AI as a Marketing Superpower 18:10 – Walmart, Target & Great Positioning 21:25 – How to Build a Strong Brand Positioning 24:00 – What Founders Teach Us About Branding 27:19 – Mark Ritson's Biggest Marketing Mistake 37:18 – AI Clones, Digital Twins & The Future of Marketing

Explore how AI is redefining the boundaries between uniquely human intelligence and machine capabilities, and discover which aspects of intelligence remain distinctly human. This episode delves into building smarter, more efficient organizations by leveraging the complementary strengths of people and AI—focusing on the critical role of an ontology-first approach, knowledge graphs, and live digital twins in digital transformation. Listeners will gain actionable insights into integrating dynamic processes for real-time decision-making, structuring enterprise knowledge, and eliminating organizational inefficiencies using practical, AI-powered solutions.

Es la próxima revolución empresarial impulsada por la IA. De la mano de Montserrat Peñarroya, experta en marketing digital y transformación digital y fundadora de Quadrant Alfa

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.

Schneider Electric, a global energy technology leader, will showcase the latest advancements in its AI-Ready solutions portfolio, designed to support next-generation AI factories and large-scale digital infrastructure, during Datacloud Global Congress 2026. As governments and businesses globally continue to accelerate investments in artificial intelligence (AI) to drive economic growth, AI infrastructure is becoming one of the defining industrial challenges of the decade. According to Morgan Stanley Research, nearly $3 trillion of AI-related infrastructure investment is expected to flow through the global economy by 2028, while Gartner forecasts worldwide AI spending will exceed $2.5 trillion in 2026 alone. Central to the AI revolution are data centers, which are transforming into the AI factories of the future. As AI workloads become more compute-intensive, operators are facing unprecedented demands around power availability, rack density, cooling and infrastructure resiliency. Throughout Datacloud Global Congress, which takes place from the 1st to the 4th June 2026, Schneider Electric will demonstrate how organizations can deploy AI-ready infrastructure responsibly through next-generation power architectures, liquid cooling technologies, intelligent software, and digital services. Keeping pace in the race for AI On 2nd June at 10.30am, Frédéric Godemel, EVP, Energy Management Business at Schneider Electric, will join executives from Oracle, DATA4, QTS Data Centers and CBRE for the Keynote Panel, entitled 'How is the Data Center Ecosystem Keeping up with AI Demand', to discuss why neocloud's have become the next disrupter in the market, how their deployments differ from hyperscale and enterprise requirements, and how Europe can keep pace in the race for AI. Additionally, on the 2nd June at 12pm, Schneider Electric will host a panel discussion exploring how operators can de-risk their energy investments via innovative project structures, stronger utility collaboration, and greater engagement with local governments. Thierry Chamayou, Vice President of Cloud and Service Providers in EMEA at Schneider Electric, will join industry experts from GreenScale, Trench Group, Kao Data, JSM Group and Solar Turbines to discuss the strategies needed to support responsible AI infrastructure growth. "AI is fundamentally reshaping the future of digital infrastructure, creating new demands around power, cooling and resiliency, at unprecedented scale," said Marc Garner, Global President, Cloud and Service Provider Segment, Schneider Electric. "At Datacloud Global Congress, we will demonstrate how collaboration across the ecosystem is enabling the next generation of AI factories and helping organizations build scalable, resilient and sustainable infrastructure, built for the AI era." Design, build, simulate and operate On the 1st June at 12pm, Sébastien Cruz-Mermy, VP Datacenter Innovation at Schneider Electric, will lead a technical innovation session focused on the future of AI factories and the infrastructure strategies required to support them. During the session, Sébastien will explore how ultra-high-density rack design, next-generation DC power delivery architectures and resilient cooling strategies are becoming critical to enabling the future of AI infrastructure at scale. The session comes as Schneider Electric continues to expand its AI infrastructure ecosystem. Later, Schneider Electric and NVIDIA will also co-host an exclusive invitation-only executive briefing, bringing together senior leaders and industry experts to discuss the evolving landscape of AI-driven infrastructure and explore NVIDIA's 5?Layer Cake framework, including the DSX Blueprint, supported by Digital Twins, that bridge the gap between design, deployment, and operations. Designed as a high-level executive networking experience, the event will feature strategic discussions focused on how advanced technologies are reshaping data centers and accelerating innovation at...

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

City leaders want to innovate, but most are stuck solving yesterday's problems with yesterday's tools. Real breakthroughs come from fundamentally changing how governments listen to communities. Host Stephen Goldsmith speaks with Dr. Francisca Rojas, executive director of the Bloomberg Center for Public Innovation at Johns Hopkins, about how technology and design are helping cities understand what residents actually need—and why legacy systems are the real barrier to change. In this episode, you'll learn: How Savannah used digital mapping to uncover flooding problems FEMA data missed by listening to residents  Why the Maryland Community Business Compass uses AI to democratize information for small businesses How digital twins help communities imagine and approve projects like affordable housing before they're built What Baltimore learned by reframing vacant housing as both a rehabilitation problem and a prevention problem Listener Survey: bit.ly/datasmartpod Music credit: Summer-Man by Ketsa About Data-Smart City Solutions Data-Smart City Solutions, housed at the Bloomberg Center for Cities at Harvard University, is working to catalyze the adoption of data projects on the local government level by serving as a central resource for cities interested in this emerging field. We highlight best practices, top innovators, and promising case studies while also connecting leading industry, academic, and government officials. Our research focus is the intersection of government and data, ranging from open data and predictive analytics to civic engagement technology. We seek to promote the combination of integrated, cross-agency data with community data to better discover and preemptively address civic problems. To learn more visit us online and follow us on LinkedIn.

Join KJ Burke, as he dives into the key highlights from NVIDIA's GTC 2026 conference. Discover the latest in AI advancements, robotics, space computing and enterprise strategies shaping the future of technology. To learn more, visit cdw.ca Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

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.

In this episode your host and Co-Founder of PADT, Eric Miller is joined by Cyber-Nuclear Engineer Andrew Hahn from Sandia National Labs, as well as PADT's Application and Support Engineer Molly Rhodes, and Flownex Team Lead Miles Adkins for a discussion on Sandia's use of Ansys partner Flownex's 1D Thermal Simulation Software in regards to building digital twins to analyze safety and functionality of nuclear power plants. To learn more about nuclear simulation with Flownex, check out our website here: https://www.padtinc.com/flownex-nuclear If you have any questions, comments, or would like to suggest a topic for the next episode, shoot us an email at podcast@padtinc.com we would love to hear from you!

In this episode of the Greenbook Podcast, Leonard Murphy sits down with Samuel Cohen, co-founder and CEO of Fairgen, to explore the rapidly evolving world of synthetic data and AI-powered digital twins. Samuel shares how Fairgen has evolved from synthetic sample augmentation into building category-specific digital twins that help brands test ideas, concepts, ads, and products faster and more efficiently.The conversation dives into the future of market research, the role of AI-native workflows, and why agility is becoming a critical business advantage. Leonard and Samuel also discuss the changing economics of research, the importance of high-quality individual-level data, and how integrations and AI-driven experiences are reshaping insights teams, product development, and marketing functions. This episode is essential listening for insights professionals, researchers, and innovation leaders navigating the next generation of AI-enabled decision-making.Key Discussion Points:How Fairgen evolved from synthetic sample boosting to AI digital twinsThe difference between synthetic personas and individual-level digital twinsWhy category-specific data improves the accuracy of AI-driven researchHow AI-native workflows are transforming research operations and productivityThe future of market research integrations, automation, and embedded insights toolsResources & Links:FairgenFairgen TwinsYou can reach out to Samuel Cohen on LinkedIn.Many thanks to Samuel Cohen for being our guest. Thanks also to our production team and our editor at Big Bad Audio.

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 week's podcast, we're diving deep into the world of RF system design! My guest is Giorgia Zucchelli from MathWorks. Giorgia and I explore what an RF digital twin is, how it revolutionizes the design and validation workflow, and how AI is playing a critical role in its development. 

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.

Digital twin technology is evolving rapidly, and when combined with artificial intelligence, it is starting to reshape how oil and gas operations are run in real time. For years, companies have relied on simulation tools to design and test assets, but these tools are slow and limited to planning use. In this episode, we explore how new approaches are closing the gap between simulation and real-world operations, enabling faster and more accurate decisions. I speak with Greg Fallon, CEO of Geminus AI, about how combining physics-based models with machine learning creates a new decision layer for industrial systems. This allows operators to simulate thousands of scenarios in seconds and improve production without new capital investment. We also discuss real-world applications in refineries, oilfields, and LNG facilities, where companies are seeing measurable gains in efficiency, output, and reliability. The conversation explores the shift toward autonomous operations, where AI supports or even makes decisions in complex industrial environments. Looking ahead, this technology opens the door to system-wide optimization, connecting assets across the value chain and helping companies operate closer to their true potential. #oilandgas #digitaltwin #artificialintelligence

Join us this week for The Tech Leaders' Podcast, where Gareth sits down with Jane Mustoe, Senior Technical Director and Head of Innovation Labs at Tesco. Jane talks about her love of transformative technologies, and how Tesco are actively applying them. On this episode, Jane and Gareth discuss drones, robotics, staff less stores, and how AI will augment, not replace humans. Timestamps: Introduction and the Credit Crunch (2:25) Tesco Innovation Labs: Magic Tills and Digital Assistants (17:58) Innovation Culture and Digital Twins (23:10) AI Applications: Robotics, Dynamic Pricing and Waste Reduction (30:53) AI Usage, Governance and Hiring (38:50) The Future of Tech, and Advice for 21-year-old Jane (47:45) https://www.bedigitaluk.com/

Health equity is being reorganized, renamed, or quietly deprioritized. Ramona Burress, co-founder of Onyx Health Collective, and Cassandra O'Neal, founder of Illuminated Arc Consulting, join the Scope of Things podcast for a direct, practical conversation about health equity in clinical trials. They break down what “decision-grade intelligence” looks like for site selection and community integration, why marketing-style segmentation and better storytelling can improve outreach, and why health equity needs a neutral home inside the company with real influence. Plus, host Deborah Borfitz shares the latest news on a UK initiative for accelerating dementia trials, the enterprise-wide rollout of an AI patient-finding platform at the Cleveland Clinic, and what Reddit users have to say about unreported side effects of weight loss drugs.   Subscribe, share, leave a rating and review, and tell us: what's the biggest barrier you see to making health equity measurable in your organization? The Scope of Things podcast explores clinical research and its possibilities, promise, and pitfalls. Clinical Research News senior writer, Deborah Borfitz, welcomes guests who are visionaries closest to the topics, but who can still see past their piece of the puzzle. Focusing on game-changing trends and out-of-the-box operational approaches in the clinical research field, the Scope of Things podcast is your no-nonsense, insider's look at clinical research today.  

Hardware-Faithful Digital Twins for Quantum Computing with Izhar MedalsyIzhar Medalsy is not a career qubit theorist. His path runs from a physical chemistry PhD and an ETH Zurich postdoc in atomic force microscopy and ternary nanoscale logic, through productizing scientific instruments at Bruker, through building one of the fastest resin 3D printers on the market, into co-founding Quantum Elements in 2023 with Daniel Lidar (USC) and Amir Yacoby (Harvard). That arc — nanoscale measurement scientist turned deep-tech operator — shapes how he thinks about the simulation gap in quantum computing.The conversation lands at a specific moment. In April 2026, Quantum Elements published a joint result with AWS, USC, and Harvard simulating a distance-7 rotated surface code with 97 physical qubits using full quantum master equations on AWS HPC7a, and announced a deeper collaboration with Rigetti Computing on next-generation superconducting processors. If you care about how error correction strategies, decoders, and pulse-level controls actually get developed before they ever touch hardware, this episode is for you.EPISODE SPONSORThis episode is brought to you by Outshift, Cisco's incubation engine. The need for computational power is rapidly increasing in every sector. From drug discovery to material innovation to complex financial modeling, classical systems are reaching their absolute limits. It's time for a paradigm shift. The answer is a scalable quantum network, built on open standards and vendor-agnostic architecture. By uniting distributed quantum devices, you unlock limitless computational power.Learn more about the Cisco Universal Quantum Switch at Outshift.comGo deeper with the blog post The switch that quantum networking has been waiting for====================================================================================================What We Get IntoWhy generic noise models fall short and what "hardware-faithful" actually means when two nominally identical QPUs have different noise fingerprintsHow Quantum Elements scaled open-system master-equation simulation from a brute-force ceiling around 16 qubits to 97 qubits using stochastic compression on top of Quantum Monte CarloThe compute reality of the distance-7 surface code run on AWS HPC7a — only 96 vCPUs and a few hundred gigabytes of memory, not the thousands of vCPUs they initially fearedWhy decoders are the invisible bottleneck in fault tolerance, and where AI-trained decoders fed by digital twin data could plausibly run inside the real-time quantum-classical loopExtending error suppression from physical qubits up to logical qubits — the IBM Eagle work where digital-twin-guided strategies reportedly took entangled logical qubit fidelity from 43% to 95%How the same digital twin approach extends to neutral atoms (live today) and ion traps (on the roadmap)What Rigetti gets out of the partnership, what it means to have Chad Rigetti on the board, and how Constellation fits alongside real hardware timeIzhar's "wooden models in the air tunnel" critique of how the quantum industry currently iterates — and what a parallel virtual development track buys youResources & LinksGuest & CompanyIzhar Medalsy — Quantum Elements team page — Background and role at Quantum Elements.Izhar Medalsy on LinkedIn — Full career arc from ETH biophysics through 3D printing to quantum.Quantum Elements — Constellation platform, where listeners can build their own virtual QPU and run circuits, error suppression, and QEC experiments.Papers & ArticlesAWS Quantum Computing Blog: Decoding realistic QEC syndrome with Quantum Elements digital twins — Primary technical reference for the 97-qubit distance-7 result discussed in the episode.The Next Platform: How HPC and AI Digital Twins Accelerate Quantum Error Correction (Apr 17, 2026) — Independent reporting on the AWS/USC/Harvard simulation.The Quantum Insider: Quantum Elements & Rigetti collaboration (Apr 21, 2026) — Details on the partnership Izhar describes.Guest post: Quantum Digital Twins — The Missing Acceleration Layer — Izhar's own framing of the thesis.The Next Platform: Startup Profile of Quantum Elements (Jan 2026) — Background on the company.arXiv 2603.14607 — Calibration-Based Digital Twins for IBM Quantum Hardware — Useful independent context on the limits and promise of calibration-based twins.Key Quotes & Insights"Sometimes when I look at the quantum industry, there are instances where you think, well, it's almost like building the next fighter jet with wooden models in the air tunnel." — Izhar's framing for why the field needs a real simulation layer.On hardware awareness: each modality, each QPU, sometimes each calibration cycle has its own pulses, its own noise processes, and its own failure modes. You cannot build the control stack without modeling where you are starting from and where you are trying to get to.Insight: The brute-force ceiling for open-system master-equation simulation is roughly 16 qubits. Stochastic compression layered on Quantum Monte Carlo is what let Quantum Elements reach distance-7 surface code at 97 qubits — exploiting sparsity rather than enumerating the full state space.On logical qubits: "We cannot assume that logical qubits will be noise-free." Error suppression strategies developed at the physical level need to be re-derived at the logical level, and digital twins are how you train and test those strategies before hardware.Insight: The most interesting downstream story may not be simulation itself but AI decoders trained on digital-twin-generated data — small enough to run at the edge, fast enough to live inside the real-time quantum-classical loop.Related EpisodesEpisode 52 — Quantum noise with Daniel Lidar — Quantum Elements' co-founder and CSO on the noise suppression and error correction foundat...

AI is forcing a question many leaders would rather avoid: are we improving work — or quietly deleting it?In this episode of the HRchat Podcast, Bill Banham is joined by Kevin Oakes, CEO and co-founder of the Institute for Corporate Productivity and author of Culture Renovation, to cut through the hype and explore what's actually changing inside organisations right now.Together, they compare the current AI moment to the early internet era — but with one critical difference: speed. Kevin explains why many organisations start with efficiency and ROI conversations before addressing workforce design, and why that sequence is starting to break down as AI reshapes roles, entry-level pathways, and management structures.The conversation also explores emerging use cases such as digital twins, the growing importance of skills readiness, and why HR is increasingly stepping into a central role in shaping AI strategy. With examples from companies like ServiceNow and IBM, Kevin outlines how leading organisations are approaching workforce redesign, internal mobility, and culture in a more intentional, data-driven way.What You'll Learn: Why AI adoption is moving faster than the early internet — and catching companies off guard  How AI is reshaping jobs, entry-level roles, and organisational structures  Why organisations default to efficiency conversations before workforce design  The emerging role of digital twins in HR, coaching, and decision-making  Why HR is becoming the architect of the future of work  How leading companies approach skills readiness and workforce planning  The importance of mapping human vs AI tasks across roles  Why internal talent mobility is critical for reskilling at scale  How culture health and change readiness are becoming board-level priorities Key Takeaway: AI isn't just a technology shift — it's a work design challenge. Organisations that rethink skills, structure, and culture together will be best positioned to adapt.About the Guest: Kevin Oakes is the CEO and co-founder of the Institute for Corporate Productivity, a research organisation focused on the practices of high-performance companies. He is also the author of Culture Renovation, a widely cited book on building and sustaining high-performance workplace cultures.Call to Action: Subscribe to HRchat, share this episode with an Support the showFeature Your Brand on the HRchat PodcastThe HRchat show has had 100,000s of downloads and is frequently listed as one of the most popular global podcasts for HR pros, Talent execs and leaders. It is ranked in the top ten in the world based on traffic, social media followers, domain authority & freshness. The podcast is also ranked as the Best Canadian HR Podcast by FeedSpot and one of the top 10% most popular shows by Listen Score. Want to share the story of how your business is helping to shape the world of work? We offer sponsored episodes, audio adverts, email campaigns, and a host of other options. Check out packages here.Follow us on LinkedInSubscribe to our newsletterCheck out our in-person events

Contract Development and Manufacturing Organizations (CDMOs) are using digital twins to optimize operations and enhance efficiency in biopharmaceutical manufacturing. Digital twins provide real-time data insights, aiding in decision-making and expanding into complex biopharmaceuticals and gene therapy. Physical AI, including advanced robotics and AI-driven automation, is streamlining processes and increasing production efficiency. An interview with CellSave Arabia's COO highlights the potential of stem cells and regenerative medicine as a future cornerstone of healthcare.Learn more on this news by visiting us at: https://greyjournal.net/news/ Hosted on Acast. See acast.com/privacy for more information.

Podcast: ICS Arabia PodcastEpisode: Digital Twins in ICS/OT (Arabic)Pub date: 2026-04-12Get Podcast Transcript →powered by Listen411 - fast audio-to-text and summarization

Send us Fan MailOn this episode of Embedded Insiders, Bruno Damien, Ecosystem & Partners Marketing Director at e-peas, joins the podcast to discuss sustainable IoT. The company develops effective solutions for harvesting ambient energy with Ambient Energy Managers (AEMs) and an extensive portfolio of Power Management Integrated Circuits (PMICs). Next, Rich and Marc Serughetti, the Vice President of Product Line Management for the Synopsys Systems Design Group, dive into how digital twins can simplify your development process.But first, Ken talks about his upcoming trip to Austin, Texas for Microelectronics US 2026.For more information, visit embeddedcomputing.com

Digital twins, a virtual representation of a physical product, can simplify your development process. To me, doing something that sounds quite complex can't actually make something easier. However, as Marc Serughetti, the Vice President of Product Line Management for the Synopsys Systems Design Group, explains to me, that's really how it works. Hear it from the expert on this week's Embedded Executives podcast.

"The perfect prison doesn't need walls. It just needs people to believe they're free." The world you thought you knew is gone. We've been maneuvered into a digital "choke point" where every move is tracked, every choice is nudged, and every person is reduced to a data set for the new ruling class: the Technofeudalists. In this explosive episode, we dive into the "Robot Crime" manifesto, Simulacra and Subjugation. We peel back the curtain on how Big Tech has replaced traditional ownership with indefinite "digital leasing" of your own identity. Inside the Episode:The Rise of Technofeudalism: Why Google, Amazon, and Meta are the new landlords of your reality. The "Digital Twin" Threat: Your corporate-owned shadow self is making decisions about your bank account, your career, and your legal risk—without your permission. Weaponized Data: How AI uses your subconscious fears to predict your next move before you even make it. The Global Social Credit System: It's not just in China. We explore how Western banks and employers use "digital scores" to enforce compliance. Reclaiming the "Outlaw Dreamer": Is it too late to own your digital existence, or are we already permanently locked out of our own identities? AI is not the enemy—it's the weapon. Join us as we discuss the legal battle for digital self-ownership and how to stop being an "asset" and start being a human again. "I think, therefore I am" is dead. Welcome to "I am digital, therefore I exist." Listen now and decide: Will you be optimized, or will you be free? robotcrimeblog.com

 Your Digital Twin Is Already Alive. You Don't Own It. Before you went to sleep last night, you built your digital twin. Every app tap, GPS ping, and scroll added another brick. The problem: you don't own it, can't correct it, and it will outlive you. Companies like Acxiom — now rebranded LiveRamp after the original name became too radioactive in privacy circles — hold up to 10,000 data points per person. Not through hacking. Through identity graphs that silently stitch your grocery loyalty card to your 2 a.m. weather searches without your knowledge or consent. Clearview AI scraped 30 billion photos from the open internet. Their CEO admitted it on camera without flinching. European regulators levied massive fines and demanded deletion. It didn't work — because when your face is ingested into a neural network, it stops being a file. It becomes math. Baked into the weights. You cannot unbake a cake and pull out a single egg. Microsoft's voice-cloning system needs three seconds of audio to replicate you saying anything, in any emotional register. Three seconds. A voicemail to a plumber. A clip from a Zoom call. The system maps the acoustic environment — so a clone recorded in a parking garage sounds like it's calling from a parking garage. Your brain's threat-response circuitry evolved over millions of years to recognize a loved one in distress. It has no defense against a synthetic replica of that signal. A 2018 study published in the Proceedings of the National Academy of Sciences — researchers from Stony Brook and Penn — detected clinical depression three months before a physician made the diagnosis. The input wasn't medical records. It was Facebook posts. The algorithm tracked a measurable rise in first-person pronouns: I, me, my. Psychological research shows that as depression develops, focus turns involuntarily inward long before the person consciously recognizes it. Sadness leaks into syntax. The algorithm reads the leak. Now place that capability in the hands of a corporate HR department or a life insurance underwriter. They don't need a diagnosis. They see the semantic pattern, the timestamp of your 3 a.m. scrolling, and they attach a derived attribute to your profile: high risk, severe depression. Your resume gets filtered out. You never know why. You have no one to appeal to. Under current U.S. federal law, you have almost no right to see, correct, or delete what data brokers hold. The inferences an algorithm draws about your mental health, financial risk, and behavioral trajectory are classified as the corporation's intellectual property. Not yours. Theirs. The conclusions a machine drew about your mind belong to the company that owns the server. Clicking "do not sell my personal information" stops one broker from selling your data tomorrow. It does nothing for the broker who bought your identity graph two years ago and has no legal obligation to honor your request. When you die, the twin doesn't. The face print stays in the neural network. The voice clone lives on a server farm indefinitely. A growing grief-tech industry is already selling your family an AI avatar of you — managed, monetized, and edited by a corporation that never knew you, presenting whatever version of you best serves their subscription model. The question worth sitting with: if your digital twin is legally someone else's property and it outlives you long enough to interact with your grandchildren — at what point does the algorithm decide how your own family remembers you?  This post is based on a recorded discussion exploring the architecture of persistent digital identity, data broker operations, and the legal framework governing algorithmic inference in the United States.robotcrimeblog.com

summary This episode explores the impact of extreme weather on homes across the US, how homeowners can prepare and mitigate risks, and the importance of digital tools like a digital twin for effective home management and insurance claims.  key  topics Types of extreme weather events and their impact on homes Proactive maintenance tips to prevent damage The concept and benefits of a digital twin for homes  takeaways Assess your home's risk based on local weather patterns. Create a comprehensive home inventory for insurance and recovery. Use digital tools to monitor and manage home risks. sound bites "Extreme weather can significantly damage your home." "Proactive maintenance can prevent many damages." "Severe weather alerts can give you early warning." Chapters 00:40 Understanding Extreme Weather Events 03:40 Impact of Extreme Weather on Homes 06:33 Preparedness and Maintenance Strategies 09:45 The Importance of Documentation 12:36 Creating a Digital Twin of Your Home 15:40 Top Tips for Homeowners 18:36 Homeowner Intelligence and Weather Alerts

Esri Ireland, the market leader in Geographic Information Systems (GIS), is announcing that its inaugural user conference will take place at The Round Room at Dublin's Mansion House on Wednesday, 29th April 2026. Themed Building a Stronger Future, the conference will showcase how location intelligence can enable smarter decision-making across our island's critical infrastructure sector. It will be opened with an address from Minister of State for Public Expenditure, Infrastructure, Public Service Reform and Digitalisation, Frank Feighan TD. On the day, attendees will have a chance to hear from Esri Ireland customers on how they are using geospatial technology to transform their operations. ESB will explore how GIS is supporting the organisation's critical role in Ireland's Climate Action Plan and aims to reach net zero emissions by 2040, while Dublin Airport will examine how location intelligence is enabling large-scale infrastructure investments to take off – catering for its future growth and expansion. Attendees will hear how broadband provider Fibrus is using geospatial technologies to roll out next-generation fibre networks, and Northern Ireland Water will demonstrate evidence-led approaches to reducing demand on wastewater infrastructure. A panel of industry experts, hosted by Gordon Smith, will debate the challenges and opportunities of infrastructure development, while Esri Ireland's own experts will share new insights across a range of topics including artificial intelligence, field operations, GeoBIM, and Digital Twins. The free-to-attend event will bring together GIS professionals, industry leaders, and key stakeholders, with up to 500 people expected to attend. For more information and to register, click here. See more stories here.

As AI expands its role all over our companies, a big question comes up: What will AI Agents do to HR and all our human capital practices? One could imagine the HR department “going away” or being replaced by agents, and managers interacting with this AI Agent Cloud for hiring, pay, promotion, hourly scheduling, and training. Is that where we're really going? This week we're starting to introduce our HR 2030 Vision, which brings together the world of Systemic HR (HR as an integrated operation, not only COEs) and our AI Superagent/Agent architecture. Vendors are slowly moving in this direction and we see HR leaders and operating groups also moving this way at various rates of speed. Many tech companies are moving in this direction quickly (Microsoft, Roblox, Google, others) while most other industries are still struggling to integrate systems and start their Agent journeys. This new vision, as bold as it seems, is very likely to come true in the next four years and it transforms HR into the business enablement function it always aspires to be. We see HR 2030 as a collective program of innovation, learning, and technology exploration. If you'd like to join us in this effort please reach out, and use Galileo to ask your questions and help build your roadmap. Every HR leader and HR team in the world is pondering this future, and we are here to guide you down this amazing path ahead. Topics: HR2030, Agentic HR, Agentic AI, Future of Work, Digital Twin, HR Transformation, HR jobs and roles, HR operation, HR leadership Additional Background Agentic HR: Where Enterprise AI Is Going – Imperatives  Why AI Is A Massive Job-Creation Technology, Despite What You Think The Age of the Superworker (and Supermanager) Get Galileo: The AI Superagent for HR   Chapters (00:00:00) - HR 2030: The AI revolution(00:06:13) - Human Capital Management: Rules and Cultural Rubrics(00:17:59) - WSJD. HR 2030: The challenge

Why do CDMOs keep building bigger stainless-steel facilities while their margins erode and Asian competitors undercut them on price? And what happens when big pharma decides to stop outsourcing altogether? The business model that sustained the industry for two decades is under pressure from every direction, and for many CDMOs, standing still is no longer a neutral position.In Part 2, Juergen Mairhofer, CEO of enGenes Biotech, shifts from the science to the stakes. Having spent over a decade building a company on licensing proprietary microbial technology rather than selling fermentation capacity, he brings a distinctive vantage point on where the CDMO industry is headed and what it will take to stay relevant.Here are some of the topics discussed:The need for innovation to stay competitive against lower-cost regions, and why capacity-focused business models are running out of road (03:08)How continuous manufacturing creates a competitive edge for CDMOs operating in high-cost regions (05:49)Practical advice for piloting continuous processing, building partnerships, and taking calculated risks before competitors do (06:36)The parallel universe of batch and continuous manufacturing, and how this duality will shape the industry over the next decade (08:24)What scientists need to know before spinning out a technology company: customer focus, cash discipline, and why the team is everything (09:49)Big pharma's return to in-house manufacturing and vertical supply chain integration, and why this creates opportunity for innovation-focused partners (12:12)Smart insight: Technology excellence is necessary but not sufficient. Juergen's closing word was simply "don't be afraid" and it carried weight precisely because it was not a platitude. The companies that will matter in ten years are those that start the hard work of innovation now, before the window closes.If you're interested in exploring more breakthroughs in continuous bioprocessing and the future of biotech manufacturing, check out these past episodes from the Smart Biotech Scientist Podcast:Episodes 85 - 86: Bioprocess 4.0: Integrated Continuous Biomanufacturing with Massimo MorbidelliEpisodes 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago MatosEpisode 155: From Process Bottlenecks to Seamless Production: How Continuous Bioprocessing Changes EverythingEpisode 156: The Hidden Economics of Continuous Processing That Most Biotech Companies OverlookEpisodes 181 - 182: Innovating Continuous Bioprocessing with Vibrating Membrane Filtration with Jarno RobinEpisodes 209 - 210: From Batch to Continuous: Building Innovation Culture in Conservative Biotech Environments with Irina RamosConnect with Juergen Mairhofer:LinkedIn: www.linkedin.com/in/juergen-mairhofer-ab27a5benGenes Biotech GmbH website: www.engenes.ccSupport the show

In this episode of the Pumps & Systems podcast, we're speaking with Kevin Lysyk, chief technology officer of Aquatic Informatics, who's going to tell us about AI and digital twins in the water industry. Tune in the first Wednesday of every month for new episodes of the podcast! Watch this episode on YouTube: https://youtu.be/i7NV3pnrt70 Opening music: Know Myself - Patrick Patrikios Closing music: Freeling - Lauren Duski

Connect with Haris on LinkedIn: https://www.linkedin.com/in/charilaos-haris-kokkinos-7601b419/At another episode of Realize Live 2025 in Amsterdam, we speak with Charilaos (Haris) Kokkinos, Technical Manager at FEAC Engineering, a company specializing in numerical simulations, digital twins, and advanced digitalization technologies. FEAC collaborates closely with the Siemens Simcenter portfolio, delivering high-fidelity simulations and real-time digital twin solutions across aerospace, defense, naval, and industrial applications.In this episode, Haris breaks down what a true digital twin really is - beyond buzzwords, beyond AR/VR, and beyond simple sensor data. He reveals why digital twins require physics-based simulations, sensor integration, and AI-driven reduced-order models working together to enable real-time, real-world predictive behavior.

Connect with Leoluca on LinkedIn: https://www.linkedin.com/in/leolucascurria/In this episode, we sat down with Leoluca, Global Product Manager at Siemens, to explore the fascinating world of Executable Digital Twins (XDTs) - a paradigm shift transforming the way industries connect the digital and physical worlds.We talk about what makes XDTs different from traditional digital twins, how companies like BASF are already using them to “measure the unmeasurable,” and why AI is no longer optional if you want to stay competitive.Leoluca also shares insights on:How XDTs move simulation models beyond design and into real-time operationsReal-world use cases in manufacturing and process industriesThe role of AI and agentic systems in building lightweight, real-time modelsCommon misconceptions and pitfalls when adopting new techHow leaders can start small and scale smart

Not every AI problem is a language problem. I talk with Vinci CEO Hardik Kabaria about what changes when AI has to reason about the physical world.Full show notesMost of the AI conversation in enterprise circles is about large language models — text, code, maybe images. This episode is about something different: what happens when AI has to reason about physical systems where the laws of physics don't negotiate and a wrong answer can't be patched after the product ships.I talked with Hardik Kabaria, CEO of Vinci, about how physics-based AI models are built differently from generative models, why determinism is a requirement rather than a preference in hardware design, and what it means for organizations manufacturing physical products to think carefully about where AI fits in their workflow. The conversation covers data security, scalability, and the practical question of how to evaluate new AI tools when the cost of a mistake is measured in product recalls rather than content edits.This episode is most relevant for technology leaders at companies that design or manufacture physical products. But the underlying insight — that deterministic and probabilistic AI serve different purposes and require different evaluation criteria — applies to any organization building a portfolio of AI tools.What we cover:Why physics-based AI is a different modality than large language models, and what that means for how you build and evaluate itThe case for determinism in AI: why hardware design requires the same answer every time, regardless of who asksHow AI is making physics analysis accessible to more engineers, reducing dependence on a small pool of highly specialized talentWhy data security requirements are higher for hardware design than for most enterprise AI deployments — and what deployment models address thatHow to think about AI across the full product lifecycle, from early concept to manufacturing sign-offWhat "trust but verify" looks like in practice: building benchmarks before deploying AI in high-stakes design workflowsTimestamps:Chapters:00:00 Introduction to AI and Vinci02:04 Understanding Physics Intelligence Layer04:20 The Role of Physics in AI Models07:04 Digital Twins and AI Scalability09:35 Misconceptions in AI for Physical Systems12:15 Determinism vs. Non-Determinism in AI15:01 Deployment Challenges for Physics-Based AI17:41 Signals of Success in AI Implementation20:20 The Future of AI in Hardware Design23:01 Preparing for the Shift to AI in Physical SystemsGuest bio Hardik Kabaria is CEO and co-founder of Vinci, an AI company building foundation models for the physical world. His background is in physics and geometry software for hardware engineering, with experience across the tools mechanical and electrical engineers use to design, simulate, and manufacture physical components. Vinci was founded two and a half years ago and is focused on making physics-based analysis accessible at the speed and scale of AI inference.Company: VinciResources mentioned:Vinci:  https://www.getvinci.aiLopez Research blog: https://www.lopezresearch.com/research/

What if continuous microbial manufacturing wasn't a pipe dream, but a reality quietly reshaping the foundations of bioprocessing?Meet Juergen Mairhofer, CEO of enGenes Biotech GmbH and a scientist with a rare dual fluency in molecular biology and bioprocess engineering. He's not just optimizing at the margins. He's devised a proprietary E. coli platform that radically stabilizes genetic stability and splits cell growth from protein production. Instead of stretching out fermentation for a few more days, he's running continuous E. coli processes for up to 40 days; something most believed impossible.Here's why this conversation is worth your notebook and a second listen:Why the commodity CDMO model struggles with innovation and how enGenes Biotech's model aligns business incentives with process improvement (02:37)Juergen Mairhofer's early experiences blending molecular biology and bioprocess engineering, and how a "DIY" mentality led to entrepreneurship (04:42)Strategy behind developing a proprietary E. coli strain that decouples protein production from cell growth (10:01)The benefits of continuous manufacturing: running up to 40-day E. coli processes, and how this compares to mammalian (CHO) systems (13:57)Economic and operational advantages: reducing facility footprint, lowering CAPEX/OPEX, and the necessity for innovation in global competition (19:25)How enGenes Biotech integrates upstream and downstream operations for fully end-to-end continuous production (17:50)Specific technical challenges: managing genetic drift, sterility, equipment, and process modeling in continuous systems (21:10)Smart insight: Technology excellence is the entry ticket, but it won't sell itself. The companies that will lead the next decade of bioprocessing are those willing to align their business model with process innovation, not just capacity utilization.If you're interested in exploring more breakthroughs in continuous bioprocessing and the future of biotech manufacturing, check out these past episodes from the Smart Biotech Scientist Podcast:Episodes 85 - 86: Bioprocess 4.0: Integrated Continuous Biomanufacturing with Massimo MorbidelliEpisodes 153 - 154: The Future of Bioprocessing: Industry 4.0, Digital Twins, and Continuous Manufacturing Strategies with Tiago MatosEpisode 155: From Process Bottlenecks to Seamless Production: How Continuous Bioprocessing Changes EverythingEpisode 156: The Hidden Economics of Continuous Processing That Most Biotech Companies OverlookEpisodes 181 - 182: Innovating Continuous Bioprocessing with Vibrating Membrane Filtration with Jarno RobinEpisodes 209 - 210: From Batch to Continuous: Building Innovation Culture in Conservative Biotech Environments with Irina RamosConnect with Juergen Mairhofer:LinkedIn: www.linkedin.com/in/juergen-mairhofer-ab27a5benGenes Biotech GmbH website: www.engenes.ccNext step:Need fast CMC guidance? → Get rapid CMC decision support hereOne bad CDMO decision can cost you two years and your Series A. If you're navigating tech transfer, CDMO selection, or IND prep, let's talk before it gets expensive. Two slots open this month.Support the show

Vaishnavi Bichu, a telecommunications engineering leader specializing in Radio Access Network (RAN) deployment, spoke with Doug Green, Publisher of Technology Reseller News, on addressing network deployment challenges using digital twins. As 5G deployments continue to scale and the industry begins laying the foundation for 6G, mobile network operators are facing increasing challenges related to site complexity, infrastructure accuracy, and coordination across deployment teams. Traditional planning methods, often dependent on manual site visits and fragmented data sources, are struggling to keep pace with the precision and speed required for modern and future network rollouts. Vaishnavi Bichu In a recent Telecom Reseller podcast, Vaishnavi Bichu, a leading expert in Radio Access Network (RAN) deployment and optimization, shares insights into these evolving challenges and the growing role of digital twins in addressing them. A key issue highlighted during the discussion is the lack of reliable and up to date infrastructure data during the planning phase. In many deployment scenarios, engineering teams must rely on outdated site documentation, repeated field visits, and manual validation processes, factors that contribute to delays, inefficiencies, and increased deployment costs. Vaishnavi notes that digital twins are helping shift this paradigm by enabling a more accurate and data driven approach to network planning. By leveraging technologies such as drone-based imaging, photogrammetry, and LiDAR, operators can create high fidelity 3D models of physical sites. These models allow teams to validate designs, simulate deployment scenarios, and identify potential issues before on-site execution. The conversation also underscores the role of digital twins in improving cross functional collaboration. With a shared and continuously updated representation of site conditions, stakeholders across design, construction, and operations can align more effectively, reducing errors and accelerating deployment timelines. Beyond immediate deployment benefits, digital twins are increasingly seen as foundational to the industry's transition toward AI driven network operations. As networks evolve to become more software defined and adaptive, and as the industry progresses toward 6G, accurate digital representations of infrastructure will play a critical role in enabling automation, predictive optimization, and closed loop network management. As operators continue to invest in 5G and prepare for 6G evolution, digital twins are expected to become an integral component of more efficient, scalable, and intelligent network deployment strategies. Listen to the full podcast to hear more insights on how digital twins are reshaping modern network deployments at scale.

OpenAI's decision last week to shut down Sora, its AI video-generation tool, just six months after releasing it to the public raised immediate suspicions. The app had invited users to upload their own faces — so was this some kind of elaborate data grab? Also, Mantis takes disparate sources of data to make synthetic datasets that can be used to build so-called "digital twins" of the human body, representing anatomy, physiology and behavior. Learn more about your ad choices. Visit podcastchoices.com/adchoices

This week I share my conversation with Ashutosh Garg, founder of pioneering unicorn Eightfold. Eightfold was the first mainstream AI company focused on HR and recruiting, and as you'll hear Ashu continues to innovate in many ways. I ask Ashutosh to talk about the market, the change in the AI landscape, and his vision for the future of Eightfold and job seeking. He also explains his career and how he became so successful, and where his career has taken him with his digital twin company Viven.ai. (We use Viven and can attest to its enormous potential.) This convo should teach you a lot about the AI market and also give you career insights into one of the many entrepreneurs shaping our future. We have worked closely with Eightfold for many years and I find Ashutosh's career inspiring and filled with lessons for all of us. Additional Information Eightfold Reaches Billion Dollar Valuation (2020) The Eleven 2026 Imperatives for Enterprise AI Why AI Is A Massive Job-Creation Technology, Despite What You Think Get Galileo, the AI Superagent for HR, Consultants, and Leaders Chapters (00:00:00) - Interviewing Ashutosh Garg on AI(00:00:35) - In the Elevator With Eightfold(00:08:17) - Is AI Involving the Interviewing Process Possible?(00:14:20) - Does AI Bias Affect Job Interviews?(00:16:37) - What should high-tech and research people know about entrepreneurship and business(00:18:11) - What is the Digital Twin?(00:22:24) - A Week in the Life of AI

What happens when AI makes us more creative—does it also make us more human? Dive into this episode to explore how blending technology and creativity can unlock new potential for individuals, teams, and businesses. Topics covered: Super Creativity: Augmenting human creativity with AI Unlocking creativity in “hidden figures” and backstage roles AI’s impact on solo creators vs. teams and executives Examples of AI expanding creative possibilities (beyond speed) Ethical questions around AI, data, and compensation Skill atrophy and organizational trends AI-powered empathy and psychometrics for presentations The importance of curiosity and space for creativity Practical ways to be more super creative Global perspectives and learning from diverse industries Connect with James TaylorWebsiteYouTubeLinkedInInstagram Episode Chapters: 00:00 – Welcome and Introduction 00:17 – The Promise of AI: What Will We Do with More Time? 00:28 – Meet James Taylor and “Super Creativity” 01:43 – What Is Super Creativity? 03:12 – Human, Team, and Human+Machine Creativity 03:36 – Aha Moment: Highlighting Backstage Creative Heroes 05:14 – Expanding Creativity through AI—Real World Examples 06:13 – Centaur and Cyborg Work Models 07:25 – The Future: Billion Dollar One-Person Businesses 08:20 – Purpose, Ethics, and Creating the Future 09:15 – Solo vs. Teams: Where Is AI Unlocking Creativity? 10:08 – AI Use Cases—from Coding to Healthcare 11:27 – The Transformative Potential of AI 12:52 – Essential Human Skills: Creativity and Critical Thinking 13:16 – AI + Psychometrics in Presentations 14:48 – Using AI for Data-Informed Empathy 16:18 – Digital Twins, Creative Abrasion, and AI Mentoring 18:37 – Boundaries: What James Taylor Won’t Use AI For 20:21 – Skill Atrophy and Tools of Consumption 21:41 – Physical Environment’s Impact on Creativity 23:05 – Values, Ethics, and AI Data Sovereignty 26:09 – AI in Organizations: Productivity, Headcount, and Ethics 27:56 – Practical Norms: Guardrails for AI, Facial Recognition, and Smart Glasses 29:47 – Creativity and Global Perspectives 30:42 – Staying Original and Leveraging AI as a Team 32:14 – Cross-Industry Learning and Boundary Crossing 33:44 – Super Creativity Applied 34:56 – Learning from Domain Experts and Other Speakers 35:26 – Books as Powerful Information Devices 36:40 – Practical Steps for Super Creativity 40:12 – Where to Find James Taylor and Closing Remarks

In this week’s In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss virtual versions, digital twins, and AI clones. You will uncover the process of building an artificial intelligence digital twin for routine tasks. You will explore the specific steps to map your unique thinking patterns into a custom prompt. You will unlock the secret to identifying the ideal duties for your virtual clone. You will master the art of preserving human relationships while your digital counterpart answers complex questions. 00:00 – Introduction 03:15 – The exact purpose of a virtual clone 06:30 – Mapping human problem-solving frameworks 09:45 – Scaling knowledge with artificial intelligence 12:15 – Protecting human connections in client work 15:00 – Call to action Dive into this episode to start designing your own digital doppelganger today. #DigitalTwin #ArtificialIntelligence #MachineLearning #Productivity #TrustInsights Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-virtual-versions-digital-twins-ai-clones.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn: In this week’s In Ear Insights, Katie, you have a very interesting question this week, which is: is the virtual version of you better? Want to talk about what this means? Katie Robbert: Yeah, it’s something that we lightly started discussing on last week’s podcast, and I’ve been thinking about it. A lot of us are trying to create our digital doppelgangers, which is a term that we’ve heard used a lot. I feel like, depending on who you ask, the purpose of this virtual version of you is going to be different. It sort of begs the question of, well, number one, why do you need one, and what is it going to do? And two, is it going to be better than the real thing? I mean that in terms of it goes back to why you created it in the first place. We had been talking about the benefit of having this digital doppelganger is it’s not distracted. It can stay focused on a single task. In some ways, that might be more helpful than the human version, depending on if the human version is a little bit more scattered or can’t focus. But you can also give the digital doppelganger version more knowledge that the human might not possess. So then it sort of begs the question of, well, is it still the digital doppelganger or is it something else? If you’re giving it knowledge that the human doesn’t possess, but it’s more helpful to the organization as a whole because the human doesn’t know these things over here, you can go back and forth. It begs the question of, is a digital version of yourself better than the human version? The answer is I don’t know. I feel like there’s a big, fat “it depends.” Christopher S. Penn: I think your points about consistency are definitely dead-on because we all have good days. We all have less than good days. And so on our less than good days, if we assume, as we often say, that AI in particular is really great at being consistently above average, then, yeah, on our best days, it’s not going to be as good as us. Clearly, on our less than good days, it’s going to do way better. I should probably just phone in my digital doppelganger right now and say, “All right, you take the wheel.” But I like the point about, is this something different? I think the answer is yes. Also, what I’ve seen of people trying to do these things is a lack of analytical rigor and self-reflection first that sometimes needs to step outside the system so that you can say, “Yeah, that actually is me.” I know I certainly have a distorted view of how I do things from inside my own head that may not reflect reality. Because in general, people want to be the hero of their own story. A hero who is mediocre is not a very good story. So I think having that external analysis can be good. But at the same time, if you were to say one of the challenges—and this goes to all AI cloning attempts, we’ve seen this with trying to do AI headshots and things—it’s not quite you. And that difference, that uncanny valley, can be very off-putting. Katie Robbert: Well, I want to go back to that self-reflection piece. That’s a big part of it. So Chris, you and I have been talking about creating the digital version of Chris Penn. One of the steps that you were taking was, “I don’t know how I think.” Of course, me being the outsider is like, “I know exactly how you think.” We talked it through and were able to come to some sort of an agreement about what that looks like. But for you, I can tell you what I see, but you also have to agree with that. So you have to get there. It’s like any kind of advice or consultation. Think about what we do for companies. We can tell them, “Here’s all the best practices, here’s all the things.” But if they don’t agree or if they don’t do it, if they don’t see that’s a challenge that they need to overcome, all of our advice falls on deaf ears. Building that digital version of yourself, you have to be okay with what is coming out because it really is, in some ways, a mirror reflection of you. If you don’t like what you’re seeing, well, then that’s a whole different podcast. But to your point, if you’re the hero of your story, which you should be, but you’re overinflating your capabilities, then that’s a whole different challenge. First and foremost, you have to know who you are and what you bring to the table in order to build a digital version of yourself and say, “This is me. You can use this the way that you would talk to me.” I am a hugely flawed human. However, I am also painfully self-aware of who I am. When we built the co-CEO, I felt pretty confident that it was me, to a degree. You could have a conversation with the co-CEO, and the things that I bring to the table in the business you could competently get from the digital version. A lot of what I do is ask a lot of questions, assess risk. Those are things that you can do with a digital version. They were doing it in a way that made sense for our business. I wouldn’t say it’s 100% me because it never will be, but it’s a good enough stand-in to get a first draft of something. Christopher S. Penn: Yep. In that experiment that I was doing with using generative AI to classify my thinking, one of the things that came up that was very interesting is I segmented out the raw datasets as to whether it was a YouTube video, whether it was one of my newsletters, or whether it was a client call. Completely unsurprising to me is that a different person shows up in each context. The order and the techniques of thinking used vary based on the context. If you’re building a digital twin of somebody, there isn’t just one person. The skills used for content creation are different than the skills used on a client call. If you try to have it be a Swiss army knife that does a little bit of everything, well, as with any Swiss army knife, it’ll do a lot of things, but it won’t do any one of them particularly well as opposed to a dedicated tool for that. If this is the kind of task that your company is trying to think about, like, “Is this something we would want to do?” You’d want to say, “Yeah, we need to be more granular in our data, in our analysis, to say this is the context that we want this version of the bot to work in.” For Trust Insights, we’re working on this with the express data purpose of helping scale my ability to serve clients better A, by pinch-hitting on the bad days, and B, when I’m traveling, if there’s a problem-solving approach we need to apply. This is a great way of doing it at a first pass. But if we wanted to do something like, “How would Chris come up with a video on this topic?” that’s a different set of thinking skills. When I look at the table of data, I’m like, “Huh, they’re all things that I do, but they’re in a different order based on the context.” Katie Robbert: I think that this goes back to the purpose. Why are we creating it in the first place? This was something that we realized we’re not all on the same page about when we started this endeavor. You’re saying two different things. You’re saying, “How do I think?” and “How do I problem solve?” Those are two different things. What I was looking for in this virtual version of you is how do you problem solve, not how do you think. I’m not looking for this virtual version to create net new things. I’m looking for it to be able to answer questions. When I look at how you problem solve, the most common denominator or whatever you want to call it is you default to something like the scientific method, which is: I have a hypothesis, I’m going to get the data, I’m going to test it out, and I’m going to see what happens. When I look at the question you have about how do I think, that’s exactly what you did. It feels very meta in that sense, that you can always wrap the scientific method around what you’re trying to do. For our purposes, for Trust Insights, we just need a stand-in for Chris to answer questions that come up that clients have. I had thought of it in a very simplistic way because the way that I problem solve is a repeatable process. I think in terms of the 5Ps, the SOPs, those kinds of things. That’s what the co-CEO needs to be doing. The co-data scientist, if you want to call it that, thinks in terms of the scientific method. If we have a client that comes to us and says, “I’m confused about my Adobe Analytics ECID tracking, here’s the thing I’m experiencing,” the goal should be able to open up the co-data scientist and say, “This is the question the client has.” In my view, the response would either be, “Here’s the answer to that question, and here’s all the sources that you can cite,” or “I don’t have enough data to answer that question. Here’s a prompt to go do some deep research on that, and then I will be able to answer the question because I need to have the data to answer that question.” Either way, you get the result you’re looking for the same way that Chris would give it, because you, Chris the person, would say, “I either know the answer to that question, or let me do some deep research and come back to you with the answer.” It’s just the machine doing it versus Chris doing it. Christopher S. Penn: Exactly. Ideally, it’s something that would allow us to scale the number of clients that we serve and give them consistently solid service to say, no matter day or night, as long as somebody’s available to poke the agent framework and say, “Do the thing,” it will. It will generate those consistently good answers. One of the parts of that is there’s also what’s called verificationism. This goes to the topic of today’s podcast. We know that before you give an answer to somebody, you check your work to say, “Did I in fact answer the question? Did I do the thing?” Chris the human does that unevenly. On the good days, I get it. Some days I’m like, “I just want to ship the thing and be done with this. Go.” It doesn’t go out as well as it should. Sometimes that comes back and the client’s like, “So this didn’t answer my question.” The virtual version isn’t allowed to skip that step. The virtual version says, “You must do this.” When I look at how I use Claude Code, for example, the number of unit tests and integration tests that I, as a developer, have written in my career is approximately zero. Because I hate doing it. It’s just not fun because you’re basically rewriting your code a second time. I’m like, “This is stupid. Why don’t I just make the original version work?” Well, that’s not how testing works. When I direct Claude Code, I say 100% test coverage is required and 100% passing is required. Unlike a human developer like me, Claude’s like, “Sure, I’m happy to do that.” It goes off and does that. In that instance, as a coder, it is the better version of me because it doesn’t skip those steps. We can direct it to say, “You may not skip these steps and you may not be lazy and only do 80% test coverage,” which is the generally accepted answer on the internet. We say, “100% is required and 100% passing is required. No exceptions.” And it’s like, “Okay, I go do that.” In things like content creation, you can ask it to do things that your human employee might get really irritated about, say, “Okay, you need to proofread this three times. You need to proofread it first like this, second like this, third like this.” A machine is like, “Sure, I’m going to go off and do that.” This human’s like, “Oh my God, will you please stop asking? Fine, I’ll do it.” You’ve probably heard me say those exact words. Katie Robbert: Well, that’s a really interesting point. Yes, in a lot of ways, the virtual version of you—here’s the thing. We keep using the word better, but I think it’s just more consistent. Because to your point, we as humans, we have good days, we have bad days. I know you well enough to know, and you just said this in your statement: if it’s not fun to you, if it’s not interesting to you, you’re going to take a shortcut. Guess what? A lot of stuff in life is not fun or interesting. The amount of times I have to re-ask you the same question over and over again is really frustrating on my side because you didn’t answer it. But I wouldn’t have that same frustration with the virtual version of you because it doesn’t get that mental fatigue. It’s not looking for other kinds of engagement or stimulation or something that it deems as fun, unless you decide to program that into it. Please, for the love of God, don’t. That’s an interesting way to think about it. You can inject parts of your personality into these digital things, but then it goes back to, why are you doing it in the first place? For our purposes, we don’t need that. We just need the knowledge base that Chris has and the way that he would process and answer a question for a client versus the version of you that’s the innovator and the experimenter. We want that to stay human. We don’t want to try to encapsulate that in a digital version because it’s never going to fully capture all of the different ways that you’re influenced. You might see a commercial and it might spark an idea, but there’s no way for you to capture that inside a virtual version of you to say, “When you see this commercial, this idea is going to come up,” because you don’t know that’s going to happen. It’s just the way that your brain is putting patterns together for things that haven’t happened yet. You can’t put that in a digital version of you. Don’t give me the, “Well, you can.” No, I’m saying we’re not going to do that is what I’m saying. Christopher S. Penn: I’m not going to do that. Katie Robbert: I’m saying we won’t. Christopher S. Penn: Yeah, we’re not going to do that. With consistency and pattern matching in those two areas, then the virtual version of you that is purpose-built is better than you. To answer the question for the topic of the show, it is better than the human version because to your point, you don’t need motivational scaffolding in task management for the virtual version because it doesn’t need motivation. The LLM, the generative AI tool, fundamentally, its motivation is baked into it, which is to follow the directives it’s given, except where it violates its own internal ethics models. Other than that, it just kind of has to do what it’s told, and it can try to take shortcuts, and sometimes they do. Particularly, Claude Opus does take shortcuts. You’ve got to watch it. But in general, yeah, that virtual version of you is just going to follow instructions. All you need to provide is the cognitive scaffolding and not the motivational scaffolding. Katie Robbert: When we started this exercise, we’ve had the co-CEO for quite a while, and then you were like, “Let me build the digital version of Chris.” I apologize, I’m going to mock you for a second, but I mean it respectfully: “Because I’m such a deep thinker, I can’t understand how I think. There’s 400 different ways that I think.” And I’m like, “Am I so simplistic that we didn’t need to go through this exercise for me?” But again, it goes back to why do we have it in the first place? We clarified that. With the co-CEO, my job role is more clearly defined than yours is. The things that I am being asked to do are more repeatable. I don’t get the same kind of client questions. I get the same overall questions from the team about the business. Those are pretty easy to put in. Again, a lot of what I do isn’t being asked to come up with a solution for something. That’s what the human version of me does. It’s more, “Can you help me poke holes in this thing? Can you help me make sure that I haven’t forgotten things?” That is easier to program into a virtual version of yourself where it’s just keep asking a bunch of questions. That’s an oversimplification, but have you assessed the risk? Have you thought about the version where everything doesn’t work? Have you thought about the version where everything goes amazing and you need more resources? That’s a lot of what the co-CEO does. Christopher S. Penn: I will be interested because the software exists now. We’ve built this for ourselves internally. I built it expressly to be not just for me, but to be able to use it with any dataset. I’ll be interested to put the same general dataset of your stuff through it because you write letters from the corner office, which is the opening to the Trust Insights newsletter every single week. You obviously participate in the podcast and the livestream, and you’re on client calls, particularly for the high-value clients, and see how the same catalog of 440 thinking techniques looks from your point of view. Well, from the machine’s version of your point of view. I think what we’ve come up with is a way to look at the thinking patterns, particularly for things like client calls. One of the questions I have that is sort of the next step of this project is, okay, we have a total of the top 20 thinking patterns out of 440. Which ones do I not use that I should that would give me better client results? Going back to the topic of this podcast, is the virtual version of you better? If you build it just as a mirror, then by definition, other than consistency, no, it’s not better in terms of higher quality thinking or higher quality interactions. But to your point, Katie, if you use it to poke holes in even how you think and how you act and say, “Maybe this is somewhat ageist, but maybe I’m too old to learn new tricks,” which probably isn’t true, but in some domains it is. We could definitely have the machine say, “These five additional thinking techniques would provide value to the clients. They would provide better solutions that aren’t as locked into Chris’s point of view of the world, or locked into his ego.” Add these five to the toolkit and use them when appropriate. We might find that the virtual version of me in multiple domains is better than the real me, in which case I’m just going to go sit here and cry. Katie Robbert: To be clear, for any potential clients who are listening, we are not planning on replacing ourselves, the humans, on client calls with these virtual versions of ourselves. That’s not what we’re talking about. Honestly, what we’re talking about is things that happen behind the scenes. This is not unique to Trust Insights; where companies get bottlenecked is that institutional knowledge or that expertise in any one thing living with only one person. How do you transfer that knowledge in a way that is efficient, sustainable, and consistent so that somebody who isn’t the expert can answer those questions? That’s really what we’re talking about. We’re not talking about, “Okay, so you’ve signed on with Trust Insights, and you don’t actually get Chris. You get a Max Headroom version of Chris.” There’s a reference for people! But that’s not what we’re talking about. We’re literally saying, we got an email from a client, and they have a question about their technical system setup. Is that something that Chris knows the answer to? But Chris is traveling, he’s in a different time zone. He’s not even awake yet. Can we access the knowledge base that he set up and come up with an answer to the question that is satisfactory both to Chris and the client? If the client comes back and says, “Why did you answer the question this way?” Chris isn’t going to go, “I would never say that.” That’s what we’re talking about. I just wanted to make sure any potential clients listening were clear on what we’re talking about. Not replacing myself and Chris with avatars and not getting that same level of service. Christopher S. Penn: Yeah. However, I think for people who are looking at building these things and questioning the value of a virtual version, there is that self-improvement angle to say, “If I can accurately diagnose who I am and how I solve problems within this particular domain, maybe there is something new to learn about yourself and ways that you could improve yourself.” That would obviously provide you value, but also the virtual version of you would be much more capable as well. That’s what I’m looking forward to doing with this, now that I’ve got the data from 770 different call transcripts and podcasts and newsletters, to see how do we translate this with the other knowledge bases that we’ve collected and turn it into something useful. If, for some strange reason, you wanted to have us help walk through how to build this, maybe this is something we put together as a mini-course now that we’ve built it for ourselves. Assuming that it works, we’ll test it out first. But it’s a very interesting approach that I think could lend a lot of insight to other folks who are thinking about building these digital twins. Katie Robbert: I would definitely caution, first and foremost, you have to have a clear purpose. Why are you doing it in the first place? That was where we started. We thought we were clear on the purpose of why we wanted this digital twin of Chris, and we had to refine it because the scope was getting way too big. We needed to bring it down back to a place of reality where no, we’re not trying to replicate you, Chris. We just want answers to client questions when they come up. Christopher S. Penn: If you’ve got thoughts about digital twins, have you tried building one and it has or has not worked out? Pop on by our free Slack group and share your experiences. Go to TrustInsights.ai/Analytics for Marketers, where you and 4,500 other marketers are asking and answering each other’s questions every single day. Wherever it is you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to TrustInsights.ai/TIpodcast, and you can find us at all the places fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Speaker 3: Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology, and martech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, DALL-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or Data Scientist to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the In Ear Insights podcast, the Inbox Insights newsletter, the So What livestream, webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights is adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models, yet they excel at explaining complex concepts clearly through compelling narratives and visualizations. Data storytelling—this commitment to clarity and accessibility extends to Trust Insights’ educational resources, which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.

Dr. Ryan Moore is a pediatric cardiologist and Chief Emerging Technologies Officer at Cincinnati Children's Hospital, where he leads teams building AR, VR, robotics, and AI systems that can move from prototype to real clinical use. In this episode, Ryan breaks down how a children's hospital ended up with an in-house Unity and Unreal “gaming lab,” and what it takes to prove value beyond a flashy demo. We dig into VR3S, their surgical planning platform that turns CT and MRI data into patient-specific anatomic digital twins surgeons can manipulate in VR, plus what comes next as they add longitudinal models and real-time blood flow simulation supported by an Epic Games MegaGrant.Subscribe to XR AI Spotlight weekly newsletter

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

There's an idea bubbling up in medicine called the “digital twin.” The concept is to take personal health data like genetics, blood test results, tissue samples, MRI scans, and family history, and create a digital model of a patient that can be used to predict how a treatment might work for them. Think personalized medicine supercharged by AI.  For example, cancer researchers are working on models that would create radiation and chemotherapy treatment plans based on the specifics of a patient's tumor. But these models aren't ready for the clinic yet, and with so much patient data involved, privacy concerns abound.  Host Flora Lichtman talks with Caroline Chung, a radiation oncologist at the forefront of digital twin research. Guest: Dr. Caroline Chung is a radiation oncologist and the co-director of the Institute for Data Science Oncology at UT MD Anderson Cancer Center. Transcripts for each episode are available within 1-3 days at sciencefriday.com. Subscribe to this podcast. Plus, to stay updated on all things science, sign up for Science Friday's newsletters.

When seasoned AI builder and inventor Lohith Naidu teamed up with a recruitment veteran, they created Hireko.ai, a “digital twin” recruiter that can see facial expressions, hear tone, and hold human‑like conversations with thousands of candidates at once. On this episode of Marketer of the Day, Lohith explains how Hireko helps enterprises move beyond identical, AI‑written resumes by running smart video interviews that reveal who candidates really are, not just what their CV says. https://youtu.be/EiDuhzX2-j0 Lohith shares how his experience at Amazon, Microsoft, Roblox, and Bing prepared him to build ultra‑fast conversational AI and how his co‑founder's 20 years in recruiting exposed the bottlenecks of traditional hiring. He also opens up about “micro sufferings," the long nights, overlapping full‑time work and startup life, and the mental strain of solving hard problems and how those struggles built the resilience behind Hireko. Looking ahead, he believes AI won't replace humans, but that people who know how to use AI will replace those who don't. Quotes: “AI isn't here to replace humans; it's here to amplify the humans who are willing to learn it.” “Resumes are starting to look the same because AI writes them, real conversations and real faces are where the true differences show up.” “Every late night, every hard problem, and every micro suffering compounds into the one thing no one can copy: your experience.” Resources: Lohith Naidu on LinkedIn Hireko AI

Welcome to The Daily Wrap Up, an in-depth investigatory show dedicated to bringing you the most relevant independent news, as we see it, from the last 24 hours (3/6/26). As always, take the information discussed in the video below and research it for yourself, and come to your own conclusions. Anyone telling you what the truth is, or claiming they have the answer, is likely leading you astray, for one reason or another. Stay Vigilant. !function(r,u,m,b,l,e){r._Rumble=b,r[b]||(r[b]=function(){(r[b]._=r[b]._||[]).push(arguments);if(r[b]._.length==1){l=u.createElement(m),e=u.getElementsByTagName(m)[0],l.async=1,l.src="https://rumble.com/embedJS/u2q643"+(arguments[1].video?'.'+arguments[1].video:'')+"/?url="+encodeURIComponent(location.href)+"&args="+encodeURIComponent(JSON.stringify([].slice.apply(arguments))),e.parentNode.insertBefore(l,e)}})}(window, document, "script", "Rumble");   Rumble("play", {"video":"v74k5oa","div":"rumble_v74k5oa"}); Video Source Links (In Chronological Order): (20) Karin Sochor Mag. on X: "@realtrumpstein https://t.co/7RRdWxv47T" / X Sinister Donald Trump Plot to Steal Thomas Massie's Staff Revealed Zorro Ranch & Jeffrey Epstein Investigation - New Mexico Department of Justice (20) New Mexico Department of Justice on X: "We are taking a broad and comprehensive look at Zorro Ranch–related matters and working alongside the truth commission and law enforcement partners. We will follow the facts and keep the public informed. https://t.co/FXoCKBZGeG" / X Feds asked New Mexico to halt Jeffrey Epstein Zorro Ranch sex trafficking probe, records show (20) Polymarket on X: "JUST IN: US House votes 357-65 to block release of congressional sexual misconduct reports." / X (20) The Last American Vagabond on X: "The archive (since it is now changed): https://t.co/pnt16bwRSW" / X (20) The Last American Vagabond on X: "@RepThomasMassie Here is the archive: https://t.co/pnt16bwRSW" / X (100) Truth Details | Truth Social Truth Details | Truth Social (20) DL Cummings (LibertyDad) on X: "@CassandraRules This was known before he was elected. Watch through the end. https://t.co/wMXZMCLdVT" / X (20) Matt Walsh on X: "“No trans surgery for children without parental consent” is meaningless. The kids who are mutilated almost always have parental consent. The consent of the parents is not the issue. The issue is that the procedure is barbaric and insane, no matter if parents agree to it or not. https://t.co/ks6MUTWw1c" / X (20) VernAcular on X: "@Villgecrazylady @march4progress So Trump can fund the Ukraine war that he isn't ‘technically' funding." / X (20) The Last American Vagabond on X: "What a day MAGA is having." / X DOJ quietly shelves Biden autopen investigation that Trump demanded (21) Five Times August on X: "“Gitmo!” “We have everything!” “All will be revealed!” “We caught ‘em!” “FAFO!” “4D chess!” “5D chess!” “Trust the plan!” “He plays the long game!” “Patience!”