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  • 93: Can AI Be Empathetic? Human Agency, AI Ethics & Why Trust in AI Is Falling, with Andrew Phipps

    93: Can AI Be Empathetic? Human Agency, AI Ethics & Why Trust in AI Is Falling, with Andrew Phipps
    2026/09/22
    Summary:Can a machine put itself in your shoes? In this episode, Dr. Anastassia Lauterbach talks with Andrew Phipps - a PhD researcher in philosophy at the University of Sussex focusing on AI ethics and empathy, and host of the Empathy Unbound podcast, whose 120+ conversations with researchers, clinicians, founders, psychologists, and neuroscientists serve as his philosophical field work.Andrew left a corporate career - managing over a thousand people across fifty countries — to research the question he couldn't find answered anywhere: what does a positive relationship between humans and AI actually look like? Key Takeaways:Agency is the heart of AI literacy. You can't decide about something you don't truly understandYoung people are not one group. Some refuse AI on environmental grounds, some fear the graduate jobs their parents had are vanishing, some see an entrepreneurial opening, and some embrace it for everything.Educate the educators. “Don't use AI for your homework” is not AI education. Teachers are expected to guide students without being trained themselves.Licenses are not a strategy. Ten thousand AI licenses without training is a pen without writing lessons.Nobody knows who owns AI. Unlike ordinary IT, AI belongs to the whole business — which is why it often belongs to no one. The average Chief AI Officer lasts eighteen months: barely time to learn the organization, let alone change it.AI itself cannot be ethical. It answers based on how you structure the question and what it predicts comes next. Ethics and morality live with the individuals and companies that set standards for its use.Readiness before rollout. Roughly 30% of people admit to sabotaging AI programs they fear.The trust gap is about power, not answers. Around 70% of people in China express trust in AI versus roughly 30% in the US — where trust in AI now rivals trust in politicians. What people distrust is not the output but AI's position in society: enormous power concentrated in a few largely unregulated companies led by a handful of charismatic founders.Purpose is the hidden stake. If AI and universal basic income ever detach humans from the need to work, psychologists fear for the human psyche: we have always operated with purpose.AI cannot feel empathy — and pretending otherwise is dangerous. Empathy means understanding another's feelings and acting as a consequence. AI predicts; it does not feel, reason, or possess theory of mindChapters:00:00 Introduction to AI Literacy and Human Agency03:01 Andrew Phipps: Transitioning to AI Ethics Research06:13 Understanding Human Agency in the Age of AI07:47 Generational Perspectives on AI and Employment11:51 The Role of Education in AI Literacy13:56 Preparing Educational Systems for AI Integration18:30 Corporate Approaches to AI Integration22:51 Ethical Considerations in AI Development28:27 Trust in AI and Its Societal Implications31:16 Purpose and Employment in an AI-Driven World33:55 Empathy and AI: Can Machines Understand Us?Hyperlinks:Andrew Phipps on LinkedInEmpathy Unbound ChannelAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3) First Public Reading, Romy, Roby and the Secrets of Sleep (2/3) First Public Reading, Romy, Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby Book
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    41 分
  • 92: When AI Attacks: Inside the Mind of a CISO with Yaron Levi

    92: When AI Attacks: Inside the Mind of a CISO with Yaron Levi
    2026/09/15
    Summary:Anastassia sits down with Yaron Levi, CISO of Dolby Laboratories and one of the world's most respected security leaders, to unpack the reality of AI-powered cyberattacks: from AI-generated phishing at massive scale to the first documented AI-orchestrated cyber espionage campaign. Yaron explains why the fundamentals of security haven't changed — but the speed and volume of attacks have — and why decades of technical debt are now every organization's soft underbelly.Yaron has over 20 years of hands-on experience across enterprise security, startups, and the intersection of AI and education. Prior to Dolby, Yaron was CISO of Blue Cross and Blue Shield of Kansas City, Deputy CISO at Cerner Corporation, an Information Security Business Partner at Intuit, and a Security Architect and Product Manager at eBay. He is a Research Fellow of the Cloud Security Alliance (CSA), a graduate of the FBI CISO Academy, a Boardroom Certified Qualified Technology Expert (QTE), and a venture advisor to multiple VCs and security startups.Key Takeaways:AI democratizes attack capability with LLMs lowering the bar;The fundamentals haven't changed: Breach causes remain the same year after year (compromised credentials, misconfigurations, phishing). AI simply finds and exploits them dramatically faster;Technical debt is the new attack surface with forgotten accounts, stale permissions, and abandoned servers;Third-party risk needs a rethink. Questionnaires and certifications don't guarantee anything; measuring how much you can trust a partner matters more than measuring what they might be hiding;Cybersecurity is still a young industry without one common standard. Despite ISO, NIST, and many frameworks, CISO collaboration remains largely informal — community and information sharing are essential;Start with the business, then threat model and communicate risk with a "pyramid of security needs;" Breaches rarely kill big brands — but can kill small companies. Reputation matters, yet resilience and the ability to recover matter more; repeated, unlearned-from incidents are what truly erode trust;Advice to vendors: be partners, not sellers;Data quality is king. Garbage in, garbage out: AI forces organizations to audit what data they have, retire stale data, and sometimes give old data new life;Optimism for the next generation. Like the cloud wave of 2009–2010, AI will create opportunities we can't yet imagine — it has never been easier to learn, build, and start a company.Chapters:00:00 Introduction to AI and Cybersecurity Challenges03:02 The Dual Nature of AI: Opportunities and Threats05:15 Evolving Cybersecurity Landscape: The Role of AI10:23 Addressing Speed and Human Factors in Cybersecurity13:05 Third-Party Risks in the Age of AI18:38 Collaboration Among CISOs: A Collective Approach to Security22:45 Communicating Cybersecurity Risks to Leadership24:02 Understanding Cybersecurity Needs26:56 The Pyramid of Security Needs27:54 The Role of Trust in Cybersecurity30:29 Advice for Cybersecurity Vendors34:29 Surprising Trends in Cybersecurity37:30 The Impact of AI on Data Analysis40:30 Navigating the Job Market in the Age of AIHyperlinks:LinkedIn Yaron LeviX / Twitter Yaron LeviDark Reading author page (articles)https://www.darkreading.com/author/yaron-leviAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3) First Public Reading, Romy, Roby and the Secrets of Sleep (2/3) First Public Reading, Romy, Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby Book
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    46 分
  • 91: What AI Can't Learn with César Hidalgo

    91: What AI Can't Learn with César Hidalgo
    2026/09/08
    Summary:What does knowledge mean in the age of AI? Anastassia talks to César A. Hidalgo, known for pioneering work on economic complexity, data visualization, and applied artificial intelligence.Key Takeaways:AI's knowledge is jagged. Think of the economy as a game of Scrabble. AI hands you many Rs, As, and Ts but no Us or Es. Work that needs the letters AI provides accelerates dramatically; work that depends on the missing ones becomes the bottleneck. Someone with real architecture experience gets far more out of a coding agent than someone building blind.Complementarity beats substitution. An organization's AI results depend on whether its people hold skills complementary to what the model does well. If they do, expect enormous acceleration; if they don't, expect half-baked output.Judgment is the scarce human asset. AI is very smart but does not choose directions. The future belongs to organizations built around people who can see where a system is heading and form teams around that insight. Judgment is hard to observe, but it is essential in a changing, uncertain environment.Progress does not require understanding. For most of history, technology advanced through practices that worked without anyone knowing why. Even today, many discoveries come from an unexpected impurity or an exploratory detour rather than a fully prescriptive theory. Exploration remains central.Use AI as a telescope, in your own field. AI is a tool for exploring codified knowledge, but exploration is always tied to the skill of the explorer. Plausible-sounding errors are much easier to catch in a domain where you already have expertise.Knowledge loves density. Ecuador spent one percent of GDP on Yachay, a greenfield “city of knowledge” that wanted to be good at everything in ten years; construction stalled, the supercomputer is unmaintained, and water still arrives by truck. Beijing's Zhongguancun instead bet on a single street, with government-private guiding funds, and became the template for China's shift from mass manufacturing to mass innovation.Most AI pilots fail because that is how evolution works. Attention and money are flooding into AI, so many attempts fail and a few succeed and get copied, just as web companies did in 1997.Chapters:00:00 Intro / Exploring Knowledge in the Age of AI02:26 Cesar Hidalgo: A Journey from Physics to Economics04:49 Understanding Knowledge: Factual, Conceptual, and Procedural07:40 The Laws of Knowledge and Economic Growth09:02 Tacit Knowledge: The Hidden Driver of Innovation14:01 Capturing Tacit Knowledge in Organizations19:08 The Decay of Knowledge and Its Implications20:25 AI and Human Learning: A Complementary Relationship24:45 Digital Twins: The Future of Knowledge Representation28:32 Understanding vs. Knowledge: The Key to Innovation29:06 The Role of Understanding in Technological Progress30:31 Exploration and AI: Navigating Knowledge32:34 The Value of Knowledge in Corporate Acquisitions34:33 Innovation Ecosystems: Lessons from Success and Failure38:10 Advising Fortune 50 Companies on Knowledge Management44:46 Leadership and Decision-Making in Innovation49:27 Challenges of AI Implementation and the Role of ScarcityBooks by César Hidalgo:The Infinite Alphabet: And the Laws of Knowledge (Penguin / Allen Lane, 2025): Penguin UKWhy Information Grows: The Evolution of Order, from Atoms to Economies (Basic Books, 2015)How Humans Judge Machines (MIT Press, 2021)The Atlas of Economic Complexity: Mapping Paths to Prosperity (MIT Press, 2014)Hyperlinks:César Hidalgo WebsiteCenter for Collective LearningToulouse School of Economics César Hidalgo profileCompany DatawheelCésar Hidalgo X (Twitter)César Hidalgo LinkedInAnastassia Interviewing César HidalgoAnastassia Lauterbach - LinkedInAI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby Book
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    57 分
  • 90: AI in Telecom and Mobile Networks with Chetan Sharma

    90: AI in Telecom and Mobile Networks with Chetan Sharma
    2026/09/01
    Summary:Anastassia sits down with Chetan Sharma, one of the most respected strategists in global wireless. For 25 years, Chetan has been an independent strategist advising CEOs and CTOs of the world’s leading wireless companies, e.g., NTT DoCoMo, AT&T Wireless, China Mobile, Qualcomm, Samsung, and Sprint, and he has served on the advisory boards of Ericsson, Telefónica, Kymeta, NextNav, and numerous startups. He wrote Mobile Advertising: Supercharge Your Brand in the Exploding Wireless Market and Wireless Broadband: Conflict and Convergence — with titles adopted in corporate training programs and university courses at NYU, Stanford, and the University of Tokyo. He is the editor of the Mobile Future Forward book series.Key Takeaways:Every wireless wave follows the same curve. Voice, messaging, and data each peaked once penetration hit 80–90%, then revenue declined. Operators must invest in the “fourth wave” — platforms, applications, and enterprise services — before the data curve turns down.China proved the fourth-wave thesis. By transforming from pure comms players into IT players, Chinese operators generated close to $100 billion in enterprise and platform services over five years — a scale the rest of the world hasn’t matched.AI’s home in the network is the platform layer. Intelligence will live everywhere — in the RAN (Radio Access Network), at the edge, on devices — but the platform layer orchestrates it all: energy, RAN management, compute, and the “intelligence traffic” of tokens across billions of nodes.Compute and comms are finally converging, enabling more sophisticated algorithms for spectral efficiency and network manageability.AI already delivers hard savings. Intelligent on/off orchestration of cell-site carriers alone can yield 40–60% energy savings, and AI-driven operations can resolve issues before humans even see the ticket.Security is a multi-level battle. AI lets attackers automate vulnerability hunting across operator, enterprise, and national-security layers — including legacy systems.Talent is strategy. Operators outsourced R&D and product development for decades; AI is the chance to bring it back in-house.New ventures need freedom to run. AT&T’s connected-devices unit succeeded because it operated as an independent entity with full support from the top.Europe is falling behind. A steep patent decline since ~2008–09, a weak LTE and 5G cycle, and long lags between intention, budget, and execution — compounded by AI Act–driven startup exits — put the EU at risk of missing the AI cycle too.The best telco CEOs are storytellers and portfolio managers.Hold multiple truths at once. Borrowing from the Buddhist Catuṣkoṭi: AI is simultaneously a bubble, transformative, and disruptive. Don’t wed yourself to one truth — let the situation guide you.Chapters:03:13 From 1G fraud detection with neural networks to 25 years of strategy consulting06:09 The waves of wireless: voice, messaging, data and beyond12:16 Where AI lives in the network stack — and the new security battlefield17:01 AI-run networks: What does it mean?21:10 Disaggregating the RAN, exposing network intelligence, and reinventing the operator31:17 CAPEX, economics of compute, sovereign AI and why Europe is behind39:45 What makes a great telco CEOHyperlinks:Chetan Sharma ConsultingChetan Sharma LinkedInChetan Sharma X (Twitter)Mobile Future Forward (executive summit & book series)https://www.mobilefutureforward.comAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3) First Public Reading, Romy, Roby and the Secrets of Sleep (2/3) First Public Reading, Romy, Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby Book
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    50 分
  • 89: Machine Translation, Human-in-the-Loop, and the Real Cost of AI Tokens with Josh Gould

    89: Machine Translation, Human-in-the-Loop, and the Real Cost of AI Tokens with Josh Gould
    2026/08/25
    Summary:Josh Gould, Group CEO of TheBigWord – one of the world's largest language services providers, operating in over 80 countries with a workforce of 15,000 – explains why today's LLMs look more human than 2013's systems without actually being more accurate, how his company's orchestration layer routes each job to the model that is best at that language pair and domain, and why human linguists have shifted from translating to tuning, reviewing and editing while translation demand doubles every year.Joshua (Josh) Gould has spent his career at the intersection of language technology and human expertise – including TheBigWord's multi-year collaboration with Google on human feedback for neural machine translation. TheBigWord provides translation and interpreting across borders, hospitals, courts, and healthcare systems, giving people language access when they need it most. Beyond language technology, Josh is a co-founder, investor, and board member at a healthy frozen plant-based food company, and a frequent podcast guest on AI, entrepreneurship, and the future of work.Key Takeaways:Models are old; scale is new. The architectures behind today's AI existed on paper for decades – chips, electricity, and internet-scale data made them real. The baseline of every model is still human.LLMs didn't beat 2013. Current models don't score meaningfully better than the best neural Machine Translation of a decade ago – they look more human, while filling gaps with estimations that can make them slightly less accurate.Orchestration is the moat. No single model is best at everything; TheBigWord's layer routes French–Dutch orthopedic content to one engine and English–Russian general content to another, tuned continuously by client and linguist feedback – pushing accuracy from ~94% toward 98–99%.The linguists didn't disappear – their jobs changed. Translated content demand doubles every year; unit rates fell, volumes exploded, and translators became editors, reviewers and tuners. The predicted extinction never happened.Tokens are the hidden bill. Interpreting (voice-to-voice) consumes six to ten times the tokens of written translation, and today's AI prices are loss-making subsidies: expect roughly a doubling once providers must be profitable – machine interpreting at 75% of human cost today could be 150% tomorrow.Regulation helps the big and hurts the small. Compliance infrastructure is a sales advantage for a firm with 15,000 people and a burden that can put a bedroom startup in legal jeopardy; meanwhile European governments quietly race to adopt AI and will likely be forced to soften their own rules.The football-team principle. If you're allowed eleven humans on the pitch and unlimited robots, you field eleven humans plus robots. AI supersizes people rather than replacing them – and at TheBigWord, that's a stated commitment.Chapters: 00:04 The Evolution of Machine Translation02:58 Moses, Hybrid Models and the Road to Neural MT07:46 How Google Changed the Accuracy Game13:17 The Role of Human Linguists in AI17:30 Building the Orchestration Layer20:21 The Real Cost of AI Tokens and Interpreting22:36 The Economic Value of Immigration and Language Access26:43 Cultural Nuances in Translation31:05 Navigating Global AI Regulation39:42 Regulatory Challenges in AI and TranslationHyperlinks:TheBigWordJosh Gold on LinkedInAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3)First Public Reading, Romy, Roby and the Secrets of Sleep (2/3)First Public Reading, Romy, Roby and the Secrets of Sleep (3/3)AI Snacks with Romy and Roby@romyandroby“Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby BookSubstack
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    44 分
  • 88: AI Workforce, with Sam Huber

    88: AI Workforce, with Sam Huber
    2026/08/18
    Remember Napster — the file-sharing site the entire music industry tried to kill? It's back, and it isn't a streaming service anymore. In this episode, Anastassia talks with Sam Huber, the physicist who once ran Lewis Hamilton's engine strategy at Mercedes Formula One, built an in-game advertising company from zero to 120 employees, rode the metaverse wave with Landvault, and now leads Napster's enterprise business from Dubai.Today's Napster builds «streaming intelligence»: teams of specialized, avatar-fronted AI agents — more than 20,000 of them — that companies hire like employees, onboard with their own data, and supervise like a team. Sam explains why the interface, not the intelligence, is where AI will be won; why your company's first-party data holds 90% of the value; and why the future belongs to «elastic organizations» that scale their workforce up and down like cloud computing. The conversation closes with a message every student and every worried employee should hear: AI should augment you, not replace you — and the people at risk are not those whose jobs meet AI, but those who never learned to use it.Key Takeaways:The interface is the frontier, not the model. Foundation models will keep improving without Napster's help — the huge, underexploited delta is how humans engage with AI. Hire AI agents like employees, not software. Napster's 20,000+ agents are trained on domain-specific curricula, then onboarded with your company's documents, APIs, and live data feeds — like a new hire on their first day.Ninety percent of the value is your own data. A job is not a task. Jobs begin with judgment (deciding what to do) and end with judgment (checking it was done right); AI does the middle. Abundance beats headcount-cutting. Doing $1m with 10 people instead of 20 is a capped, short-sighted strategy. The interesting question: how do you do $5–10m with more agents AND more humans to manage them?The elastic organization. What cloud computing did for servers, AI agents do for the workforce.Fragmented «Frankenstein» agents fail. A marketing agent that doesn't know what the finance agent is doing optimizes one silo and may hurt the business. Agents need organizational context — teams, reporting lines, who sits where — to be labor, not software.The Gulf has deadlines; Europe has debates. Government AI strategies in the region set automation targets by year, creating urgency at chairman and sovereign-fund level to redesign operating models — not to buy point solutions. Meanwhile, Europe's demographic crunch makes automation a necessity, not a choice.Chapters:02:20 — Sam Huber's Journey: From Physics to AI08:32 — The Evolution of Napster and AI Integration14:02 — Humanizing AI: The Role of Specialized Agents19:07 — Data Sourcing and Privacy in AI Solutions21:10 — The Value of First-Party Data22:54 — Governance and Transparency in AI24:10 — AI as an Augmenter, Not a Replacer28:46 — Elastic Organizations and Creative Industries33:18 — The Future of HR in the Age of AI39:42 — Education and AI: Preparing for the Future Hyperlinks:NapsterSam Huber on LinkedInSam Huber on InstagramAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3)First Public Reading, Romy, Roby and the Secrets of Sleep (2/3)First Public Reading, Romy, Roby and the Secrets of Sleep (3/3)AI Snacks with Romy and Roby@romyandroby“Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby BookSubstack
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    43 分
  • 87: Klara and the Sun Explained: Ishiguro's AI, Love & Sacrifice (Before the Movie) — with Rae Muhlstock

    87: Klara and the Sun Explained: Ishiguro's AI, Love & Sacrifice (Before the Movie) — with Rae Muhlstock
    2026/08/11
    Summary:Anastassia is joined by Professor Rae Muhlstock for a close reading of Kazuo Ishiguro’s Klara and the Sun — a Nobel laureate’s quiet, devastating novel narrated by an AI, and soon a major film directed by Taika Waititi, starring Jenna Ortega and Amy Adams. Dr. Rae Muhlstock is a Teaching Professor in the Writing and Critical Inquiry Program at the University at Albany (SUNY). She holds a PhD from the University at Buffalo, specializing in 20th and 21st century fiction, film, and narrative theory, and teaches a beloved course on AI, science fiction, and philosophy. Her published work spans the Classical myth of the labyrinth. She chairs the annual WCI Film Festival in Albany.This episode is part of our ongoing series “AI Through the Lens of Storytellers,” in which Anastassia and Rae discuss portrayals of AI in books and movies — from Ex Machina and Her to Data in Star Trek and After Yang — because these stories shape our expectations about AI technologies. And because art is an experiment: a way for thinkers to explore future scenarios that can’t be tested in labs.Key Takeaways:Klara is three archetypes in one. The perfect, willingly self-erasing servant; the worshipper who builds a private theology around the Sun; and the doppelgänger — a double whose purpose raises an ethical horror the novel only slowly reveals.Sacrifice may be a benchmark of life. Klara’s sacrifice is arguably the purest in the book because it comes from love without societal pressure.The most human character is the AI. Ishiguro’s humans are trapped in systems and status; it is Klara who models feeling, connection, and grace.AI in the real world is drifting toward religion. With institutions of enormous power, apostles, heretics, prophecies of doom, and millions of followers, today’s AI discourse mimics the structure of a religious movement.Keep your agency. Today’s AI mimics; it does not understand, reason, or learn from counterfactuals.Fiction is how thinkers experiment. Scientists experiment in labs; philosophers and artists experiment through stories.Chapters: 00:04 — Welcome to the storytellers series: From Ex Machina, Her, Star Trek, and After Yang to Ishiguro’s quietest, most devastating AI.02:24 — Klara goes to Hollywood: The upcoming Taika Waititi film with Jenna Ortega and Amy Adams — and what a Nobel laureate’s AI meditation becoming a studio production says about our cultural moment.03:19 — The plot (and why it’s the least interesting thing): Rae on Ishiguro’s “quiet science fiction,” first-person narrators, and the reader who always knows both more and less than Klara.07:34 — Three faces of Klara: The perfect slave, the worshipper of the Sun, and the doppelgänger — archetypes, ethical horror, and a hierarchy of machines.12:44 — Sacrifice as a sign of life: What Klara, Data, and The Creator suggest about love, agency, and the line between performing and being.19:14 — Is AI becoming a religion?: Churches, apostles, heretics, and cults in today’s AI discourse — and Capaldi, the man who doesn’t believe in the human soul.29:27 — Keep your agency: Why AI mimics rather than understands, the missing math of counterfactuals and causality, and the “chosen ones” narrative of AI builders.36:42 — Fiction as an experiment — and the classroom: Checking ChatGPT’s math, cheating smart vs. cheating dumb, hidden environmental costs, and why we must always ask why. Hyperlinks:Rae Muhlstock University at Albany faculty profileLinkedInAnastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3)First Public Reading, Romy, Roby and the Secrets of Sleep (2/3)First Public Reading, Romy, Roby and the Secrets of Sleep (3/3)AI Snacks with Romy and Roby@romyandroby“Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby BookSubstack
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    42 分
  • 86: AI Changing Medical School with Dr. Marschall Runge on the Future of Doctors

    86: AI Changing Medical School with Dr. Marschall Runge on the Future of Doctors
    2026/08/04
    Dr. Marschall Runge returns for a second conversation, this time about the people who will practice medicine next. Marschall S. Runge, MD, PhD, is a practicing cardiologist who spent a decade as executive vice president for medical affairs at the University of Michigan, dean of the Medical School, and CEO of Michigan Medicine — running one of America's largest academic health systems while continuing to see patients.He earned his PhD in cardiovascular molecular biology at Vanderbilt and his MD at Johns Hopkins, where he also completed his internal medicine residency, followed by a cardiology fellowship at Massachusetts General Hospital. He now leads a Michigan cohort combining epigenetic aging clocks, mitochondrial inheritance, cardiorespiratory fitness, and chronic inflammation in a single dataset.He has authored more than 195 peer-reviewed papers and holds five healthcare patents. A third nonfiction book, on epigenetic clocks and biological aging, is in development with Forbes Books, alongside a forthcoming Substack (The Longevity Switch) and a University of Michigan MOOC on epigenetics and aging.Chapters00:00 Introduction and guest overview01:12 AI's role in healthcare transformation04:01 AI as a valuable assistant in medicine05:08 Redefining medical curriculum for AI era09:34 Openness of medical institutions to AI11:47 Changing accreditation and certification with AI16:08 Leadership in AI adoption in healthcare18:39 International perspectives on AI in medicine19:13 AI's impact on radiology and diagnostics21:46 AI in clinical workflows and operations24:49 The evolving role of nurses with AI30:37 Digital twins and clinical trials33:18 Transition period and future outlookHyperlinks:The Great Healthcare Disruption: Big Tech, Bold Policy, and the Future of American Medicine (Forbes Books, 2025) — USA TODAY Best-Seller and Global Book Awards Gold Medal winner. An insider's account of the forces reshaping American healthcare. Coded to Kill (Post Hill Press, 2023) — a techno-medical thriller.Amazon author pageDr. Runge WebsiteOpenEvidence — the physician-only clinical AI Runge calls superb University of Michigan Medical SchoolU-M Center for Academic Innovation Anastassia Lauterbach - LinkedInFirst Public Reading, Romy, Roby and the Secrets of Sleep (1/3) First Public Reading, Romy, Roby and the Secrets of Sleep (2/3) First Public Reading, Romy, Roby and the Secrets of Sleep (3/3) AI Snacks with Romy and Roby@romyandroby “Leading Through Disruption”AI EdutainmentThe AI Imperative BookRomy & Roby BookSubstack
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    35 分