Épisodes

  • 97: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 2

    97: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 2
    Oct 20 2026
    SummaryPart 2 builds on the previous episode, describing how to establish AI governance in an organization, and what AI literacy truly means for business results.Anthony Scriffignano, PhD is one of the world's best data scientists — a thinker who has spent more than 40 years at the intersection of linguistics, advanced algorithms, business intelligence, and the very human problem of making good decisions from imperfect information. Anthony is currently a Distinguished Fellow and member of the Alfred Lee Loomis Innovation Council at The Stimson Center. He also serves on advisory committees in government, the private sector, and academia, and has briefed the National Security Telecommunications Advisory Committee and contributed to reports to the President on Big Data Analytics and Emerging Technologies. He served on the OECD Network of Experts group on implementing Trustworthy AI.His most recent publication is a co-authored chapter — "Illuminating the Invisible: AI's Role in Understanding Complex, Opaque, and Dynamic Ecosystems" — in the Springer volume Artificial Intelligence Revolution (2026, edited by Max Rangeley and Nicholas Fairfax), co-written with Dr. David Bray.Key Takeaways:Compliance is what you must do; governance is what you should do. Governance costs money, time, and opportunity. The right question is not "how do we maximise governance?" but "where do we sit in the continuum between maximum safety and maximum agility, and are we there on purpose?" The human-in-the-loop section challenges the common assumption that more humans equals better decisions. Anthony identifies three limits: humans cannot always make decisions fast enough; even the same human given the same decision repeatedly will not always make the same call; and humans have bias, fatigue, and optimism. The emerging solution is cognitive AI that watches experts making decisions, converges on patterns, and eventually can substitute a synthetic human into the loop for low-stakes votes.Agentic AI is described as a system of semi-autonomous entities, each doing a small piece of a problem, orchestrated together, some inside the enterprise and some outside. Auditing every individual agent is not feasible. The answer is a clinical layer that monitors the health of the agentic ecosystem as a whole — taking the pulse, measuring blood pressure — and raises flags when the system-level behaviour deviates from its guardrails. The language to do this does not fully exist yet.On shadow AI: Anastassia's survey found 64% of respondents in SMEa are using AI tools their employer has not sanctioned. Anthony suspects the real number is higher and points to non-response bias. His prescription is not HR enforcement, but understanding the underserved need: if employees are using shadow AI, the organisation has failed to provide them with the tools they need. Create a safe sandbox, serve the need, and watch what they build.Chinese LLM approach takes a fundamentally different approach from the US— leaner, faster, cheaper, focused rather than voracious.AI literacy versus AI fluency. Fluency — knowing the buzzwords, knowing how to prompt — is the lowest form of useful knowledge. Literacy means knowing what you do not know: understanding the limits of the tool, the provenance of the output, and when you should not trust it. Hyperlinks:Anthony’s LinkedInStimson Center Fellow Profile https://www.stimson.org/ppl/anthony-scriffignano/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 Book
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    43 min
  • 96: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 1

    96: How to Build Data Strategy and AI That Actually Work with Anthony Scriffignano, Part 1
    Oct 13 2026
    SummaryThe conversation focuses on how to build a data strategy addressing the needs of a business first, not what is available on the market in terms of an AI model.Anthony Scriffignano, PhD is one of the world's best data scientists — a thinker who has spent more than 40 years at the intersection of linguistics, advanced algorithms, business intelligence, and the very human problem of making good decisions from imperfect information. Anthony is currently a Distinguished Fellow and member of the Alfred Lee Loomis Innovation Council at The Stimson Center. He also serves on advisory committees in government, the private sector, and academia, and has briefed the National Security Telecommunications Advisory Committee and contributed to reports to the President on Big Data Analytics and Emerging Technologies. He served on the OECD Network of Experts group on implementing Trustworthy AI.His most recent publication is a co-authored chapter — "Illuminating the Invisible: AI's Role in Understanding Complex, Opaque, and Dynamic Ecosystems" — in the Springer volume Artificial Intelligence Revolution (2026, edited by Max Rangeley and Nicholas Fairfax), co-written with Dr. David Bray.Key Takeaways:Data before models. Most AI failures begin in the data, not in the model. Ask the three Ps before you touch a model: Provenance, Permissible use, Perturbation.Decision elasticity. The right question is not "is this data perfect?" but "how wrong could it be and would I still make the same call?" If you cannot answer that, you are not ready to decide.The walled garden method. You can detect disruption inside a system you cannot observe directly by modelling the ecosystem immediately outside it — the haversine annulus, Z-scores, Jensen-Shannon divergence.Start with needs, not tools. Four-box needs analysis: known met, known unmet, unknown met (the danger box), unknown unmet (the blue sky boxSynthetic data compounds error. Pre-LLM synthetic data let you estimate error. Post-LLM, models are generating training data that other models consume, and error compounds multiplicatively rather than averaging out.Hyperlinks:Anthony’s LinkedInStimson Center Fellow Profile https://www.stimson.org/ppl/anthony-scriffignano/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 BookReferenced Concepts:The haversine annulus — a geodesically corrected ring around a walled garden used to model its immediately dependent ecosystem.Jensen-Shannon divergence — an information-theoretic measure used to compress multiple differently-scaled signals into a single change detector.Bayesian inference / updating: start with conditional probability, update as evidence accumulates.Canonical models of malfeasance — Anthony's term for the well-documented patterns of fraud that recur around disasters, insurance events, and public contracts.Dispositive threshold — the point at which you have enough data to make a decision — but just because you can does not mean you should.Shadow AI — unsanctioned AI tools used by employees without employer knowledge or governance; 64% prevalence in Anastassia's SME survey.
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    45 min
  • 95: Science Fiction, Mars & AI Consciousness — What Realistic Sci-Fi Teaches Us with Peter Cawdron

    95: Science Fiction, Mars & AI Consciousness — What Realistic Sci-Fi Teaches Us with Peter Cawdron
    Oct 6 2026

    In episode 95 of AI Snacks with Romy and Roby, Dr. Anastassia Lauterbach sits down with science fiction author Peter Cawdron — 37 books, NASA and SpaceX consultations, and a First Contact series 36 novels deep — to explore what realistic science actually tells us about living on Mars, what AI can and cannot do, and what it would take for a machine to develop genuine values. The conversation moves from the brutal physics of Martian habitation to the Chinese Room thought experiment, from Maslow's hierarchy of needs to the moral arc of the universe. A rare episode where science fiction, engineering, and philosophy arrive at the same question: what makes us human, and can we build that into a machine?


    Peter Cawdron is a science fiction author based on the Sunshine Coast, Queensland, Australia, originally from Auckland, New Zealand. He is the author of 37 books, known for grounding his fiction in the hardest available science — consulting with NASA flight controllers, SpaceX engineers, and space mission specialists to ensure his stories adhere as closely as possible to physical reality.


    Key Topics


    • The influence of real science on sci-fi storytelling and how experts shape the research process
    • Living on Mars — radiation, soil toxicity, underground habitats, and the engineering of survival
    • The Chinese Room thought experiment and why LLMs are not reasoning
    • The Gutenberg Press phase of AI — transformational but still to be determined
    • Jevons' Paradox and why AI may create more jobs than it eliminates
    • Artificial intelligence vs. artificial consciousness — a fundamental distinction
    • Maslow's hierarchy of needs and why AI is stuck at the base
    • The Warren Buffett problem — why digital avatars are not the person
    • A proposed test for AGI — give it all human knowledge up to 1900 and ask it to find Einstein
    • How to give AI values, morals, and culture — and why it matters for avoiding a Terminator future
    • The seesaw between selfishness and cooperation that defines human civilization


    Chapters


    00:00 Introduction to the episode and guest


    01:01 Peter Cawdron's background and journey into sci-fi


    02:17 The role of science and curiosity in human progress


    04:07 Historical perspective on science and its impact on society


    05:31 How real science influences sci-fi writing


    08:02 Researching realistic space habitats and challenges on Mars


    10:30 The plausibility of colonizing Mars and environmental challenges


    17:36 AI as an existential threat and the concept of dumb AI


    19:18 The exponential growth of AI and its societal implications


    21:33 AI as a Gutenberg Press revolution and its future impact


    25:14 The importance of values, morals, and culture in AI development


    30:28 The Chinese Room thought experiment and AI understanding


    35:42 Characters and themes of digital avatars and grief


    41:30 Consciousness, self-awareness, and the brain's complexity


    44:06 AI morality, values, and the risk of Terminator-like futures


    48:47 Human traits — loyalty, trust, and the essence of humanity


    53:45 Upcoming projects and books by Peter Cawdron


    Guest Links


    • Peter Cawdron on Amazon
    • First Contact series
    • To Live and Die on Mars (August 2026)
    • To Live and Die on Earth (September 2026)
    • The Song of the Sirens (upcoming)


    Follow AI Snacks with Romy and Roby

    • Podcast: romyandroby.ai
    • Instagram: @romyandroby
    • LinkedIn: Dr. Anastassia Lauterbach
    • Book: Romy, Roby and the Secrets of Sleep — available on Amazon
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    57 min
  • 94: Voice AI & Conversation Design Explained: Dark Patterns, Emotional Debt & Being a Good AI Operator — with Anne Cantera

    94: Voice AI & Conversation Design Explained: Dark Patterns, Emotional Debt & Being a Good AI Operator — with Anne Cantera
    Sep 29 2026
    SummaryAnastassia and Anne Cantera discuss voice interfaces and key design principles for making voice chatbots "right." Anne designs voice experiences at scale, builds orchestration tooling for multi-agent systems, and teaches what she calls omnimodal design. Anne created VoiceofAI.io - a free AI upskilling resource for designers.Key Takeaways:Getting good AI output is a skill: provide requirements, design systems, and data so the model never has to make things up.Never let AI write for you - let it interview you. That's the difference between AI-assisted and AI-generated — a thought innovator versus a slop generator.The future is omnimodal design - technology that offers what you need, in the moment you need it.Disclosure is non-negotiable. Voice AI can now listen and speak at the same time - making it easier than ever to fool people. Pretending a bot is human is a dark pattern.Trust is gained in drops and lost in buckets. Companies that ship half-baked voice agents accumulate “emotional debt.”Fast, easy, cheap: Voice projects are none of the three.Replacing humans is backfiring. From flopped plans to outsource support to agents, to companies quietly rehiring.Narrow domains win. A small, women-owned bank with one product can build a better chatbot than an enterprise with 110 products.Bring in experts early — and learn to spot them. Leadership ego (“we don't need consultants”) makes messes that cost more to clean up later. But AI literacy comes first: an influencer with 500,000 followers and no AI work history is not an expert.LLMs are a step, not the final frontier.Chapters:00:00 Intro: when the machines around us began to speak02:24 Welcome Anne: dialing in from Long Island03:26 From chef to conversation designer: an unusual path into AI05:52 Self-taught in NLP: free chatbots, LexisNexis, and skills no school teaches09:27 Omnimodal design: the perfect modality at the perfect moment11:46 Being a good AI operator: spec-driven work and the interview technique14:07 Human skills come before prompts16:34 Voice tech today: machines that listen and speak at once18:55 Ethics and dark patterns: why Anne left corporate21:16 Emotional debt: trust is gained in drops and lost in buckets24:42 Elastic organizations: core beliefs before AI design26:59 Theory of mind: why machines cannot relate to us31:39 Enterprise vs. everyone else: why small companies build better bots36:42 Domains and jobs: machine translation, where nobody was laid off39:42 Leadership ego: bring in your experts early40:37 AI literacy first: wannabes, influencers, and real experts43:49 The upskilling epidemic — and why VoiceofAI.io is free49:20 Beyond transformers: LLMs are a step, not the final frontier52:43 Robotics: industry first, our homes later54:13 The AI class divide: haves, have-nots, and AI teachers55:29 Closing: two more episodes promised — design and ethics Hyperlinks:Anne Cantera LinkedInElementyl Intelligence WebsiteThe Voice of Intelligence Design Handbook and WebsiteAnne Cantera Article on Conversational SystemsAnastassia 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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    53 min
  • 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
    Sep 22 2026
    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 min
  • 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
    Sep 15 2026
    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 min
  • 91: What AI Can't Learn with César Hidalgo

    91: What AI Can't Learn with César Hidalgo
    Sep 8 2026
    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 min
  • 90: AI in Telecom and Mobile Networks with Chetan Sharma

    90: AI in Telecom and Mobile Networks with Chetan Sharma
    Sep 1 2026
    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 min