The In-Between Tech and Trust Podcast

🎙️ Charles Maddock, Co-Founder & CEO, StrawberryCharles Maddock expects an internet full of AI agents working inside our own accounts, and as CEO of the Stockholm startup Strawberry he is building the browser for it. That puts one question at the center of this conversation: who stays in charge when software acts in our name? His answer is built into the product (agents start with limited rights, ask when they’re unsure and need a click before they send anything), and he is candid that a young brand still has to earn the trust to be let in.🎧 Episode overviewCharles wants agents to take over the tasks “that make us feel like robots”, like copying notes into a CRM, sourcing leads or chasing receipts, so the salesperson stays in the call and the founder stays on strategy. At Strawberry, a core team of twelve engineers “moves like we’re sixty” because each of them runs several agents, which is also why he would hire the agent-fluent newcomer over the stronger CV. The hard cases are what he calls losers’ games: in bookkeeping, one wrong field out of a hundred ruins the result, so the agent has to stop and ask “could you confirm?” Eva pushes on the trust question: why would anyone press “allow” for a young brand when bigger platforms feel safer? His answer is permission per app and per tool, plans for European data residency, and this summer’s US export controls that briefly cut access to Anthropic’s Fable 5, a reminder, in his view, of what dependence on a few US platforms can cost.💡 Key themesWhich work agents should take off our desks, and what stays humanHow one person running several agents changes leading, hiring and learning the craftWhy the work where one mistake counts needs an agent that asks firstWhat has to be in place before people press “allow” for an agent with their loginsWhether Europe’s slow start with AI agents is a risk or an opening👤 About the guestCharles Maddock is co-founder and CEO of Strawberry, an agentic browser from Stockholm whose AI “companions” work inside the accounts their users are already logged into; the company raised a $6 million seed round in 2025, co-led by General Catalyst and EQT Ventures. He started programming at eleven and now builds the permission system he argues for: agents that can draft an email but not send it until someone allows it, which makes trust a daily product decision for him rather than a talking point.⏱ Chapters00:00 An internet run by agents, and what it means for us06:19 Twelve engineers who move like sixty09:56 Draft yes, send no: permissions and security12:27 After Atlas: why the losers’ games matter18:36 Why would anyone press “allow”?23:15 Why build it from Stockholm🔗 LinksCharles Maddock on LinkedIn - https://www.linkedin.com/in/charles-maddock-31798418b/Strawberry - ⁠https://strawberrybrowser.com⁠Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/raidiant, Eva’s responsible AI advisory - https://www.raidiant.eu/European Commission, Flash Eurobarometer on AI in European SMEs (the 2%) - https://employment-social-affairs.ec.europa.eu/news/growing-use-ai-european-companies-2026-10-14_enHelp Net Security on DataDome’s AI Traffic Report Q3 2026 (agentic browser traffic) - https://www.helpnetsecurity.com/2026/10/19/datadome-ai-agent-traffic-report/Anthropic, statement on the suspension of Fable 5 - https://www.anthropic.com/news/fable-mythos-accessRelated episode - The Agentic AI Gap: When Tech is Used Before its Architecture is Ready - Anthony Alcaraz, Agentic AI Architect (EP 25) - https://open.spotify.com/episode/4IdHiLzsgmSaUf5w3LvcaB?si=Nht7kJj8SJalqpTUWB5NUg

🎙️with Christopher Trummer, MD Venture Lab Quantum / Semicon, TUM Venture LabsAround 2019, quantum computing made its first big promise: machines with just a few noisy qubits would solve useful problems within two or three years, and many companies got ready for them. Christopher Trummer, who runs the quantum and semiconductor venture lab at TUM Venture Labs in Munich, explains why that promise failed and why the error-corrected machines now in development are a far more solid bet. What he leaves leaders with is a question of trust and patience: how do you keep a board believing in a technology whose progress is real (and slow)?🎧 Episode overviewChristopher's starting point is that quantum is already here in parts: communication and sensing are ready for industry, and some post-quantum encryption runs in your Chrome browser today (without a mathematical proof yet that it holds). Quantum computing is the part still on its way, and he is candid about its first wave: the algorithms for the early, noisy machines never came, and the promise "fell out of the window completely." That shifts the risk to the companies waiting for them: keep the momentum and the board's support through slow progress, or miss the moment quantum starts to matter for the business. Eva asks how anyone prepares for this wave while still adopting AI, and his answer is sober: find the problems where quantum could help, then let a small team, or even a single person, keep watch.💡 Key themes​Which parts of quantum are ready today, and how far to trust encryption that is in use but not yet proven​Why the first quantum promise failed, and what makes the second one more solid​How companies keep momentum and board support while progress stays slow​Whether quantum computing ends up with a few suppliers, the way AI did​Why Europe founds so many deep tech startups and struggles to grow them past Series B👤 About the guestChristopher Trummer is MD Venture Lab Quantum / Semicon at TUM Venture Labs in Munich, where his team works with quantum and semiconductor startups from the research lab to their first investment. Having spent much of his career between science and business, he sees quantum from both sides: what the physics can already do, and when it starts to matter for a company that has to decide where to put its money.⏱ Chapters​00:00 Quantum beyond the computer​05:18 The algorithm that could break encryption​09:02 Why quantum won't have a ChatGPT moment​14:26 After the first promise fell through​24:31 Europe's missing growth capital​31:26 Drugs, fertilizer and superposition🔗 Links​Christopher Trummer on LinkedIn - https://www.linkedin.com/in/christopher-trummer-a58695108/​Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/​raidiant, Eva's responsible AI advisory - https://www.raidiant.eu/​TUM Venture Labs, Venture Lab Quantum / Semicon - https://www.tum-venture-labs.de/labs/quantum/​Global Deep Tech Report 2026, TU Munich - https://www.htgf.de/wp-content/uploads/2026/05/Global-Deep-Tech-Report-2026.pdf​NIST post-quantum cryptography standards - https://csrc.nist.gov/projects/post-quantum-cryptography​Related episode - Energy From Orbit: Can We Trust Power Beamed From Space? - Martin Soltau (EP 42) - [LINK: Eva adds]

🎙️ with Martin Soltau, Co-CEO, Space SolarSolar power collected in space and beamed down to Earth sounds like science fiction (it did to the space sector too, until reusable rockets arrived). Martin Soltau, Co-CEO of Space Solar, is building it in the UK, and he is confident about the technology: NASA demonstrated power beaming in the 1970s. The part of his timeline he can't engineer is trust: how quickly regulators and the public accept energy that comes down from orbit.🎧 Episode overviewIt starts with Martin's verdict on Europe's energy transition: the UK and Germany, he says, have "tested it to destruction", and households pay for it with expensive electricity and weaker security. His case for orbit is concrete: a panel in space produces eight to 13 times what it would in the sunniest place on Earth, robots assemble the satellites from coffee-table-sized modules, and the beam can be directed wherever power is needed. Eva pushes on the catch: if launch still runs through a handful of mostly American providers, does Europe swap one dependency for another? On trust, Martin points to fracking (a technology that worked and still lost the public) as his warning, and explains why he answers the safety question with wifi frequencies and intensities anyone can measure.💡 Key themesWhy a grid built on free sunshine and free wind still delivers expensive electricityWhether public trust can be earned before the first satellite flies, or only afterwardsWhat wireless power changes about energy security when undersea cables are already being targetedWhether governments that fund this will accept standards letting a rival's satellite use their receiver👤 About the guestMartin Soltau co-founded Space Solar in 2022 to build solar power satellites that deliver continuous electricity to receivers on the ground. He came from inside the space sector, where the idea sat on the science fiction pile until SpaceX made reusable launch commercial. Today his work is as much about convincing governments and regulators as about engineering.⏱ ChaptersTested to destruction (starts with: "I think it's the big challenging problem for humanity ...")Why orbit beats the sunniest place on Earth (starts with: "So I suppose it starts with the fact that if you put a solar panel into space ...")Launch, and who controls it (starts with: "Yeah, that's a very good question. I mean, if we look at wind and solar and batteries ...")Standards between rivals (starts with: "Yes, I think there are many examples of where nations have collaborated ...")Coffee-table modules and crawling robots (starts with: "Yes. Well, if you came to space solar at Harwell ...")The trust question (starts with: "I think you use a really interesting word there, trust ...")🔗 LinksMartin Soltau on LinkedIn - https://www.linkedin.com/in/martin-soltau-44596619/Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/Space Solar - https://www.spacesolar.co.ukInternational Telecommunication Union - https://www.itu.intRelated episode - Sten Saluveer on what decides AI trust in Europe - https://open.spotify.com/episode/0gPKehnoNppMY0y3Jvdry0

🎙️ Noah Wetzel, PhotographerWhat happens to AI trust when the most real photographs are the ones people refuse to believe? Noah Wetzel spent nine months and 600 hours preparing to capture 84 seconds of a total solar eclipse - then had to prove the result came out of a camera. This one is for anyone whose genuine work now meets default suspicion, and anyone deciding where AI belongs in a craft that has to stay real.🎧 Episode overviewNoah captured a wingsuit athlete flying through totality in Spain: flashes triggered from a mile away, a subject moving at 150 miles per hour, two frames per athlete, no second takes. The conversation follows what came after the shutter - viewers at his own shows assuming the work is fake, magazine editors auditing every edit in his raw files, and a disclaimer on Instagram stating his photos are camera-captured. He is blunt about his own AI use: hundreds of hours on ChatGPT planning the shot, AI saving him days in processing, and a hard line at the frame itself. Where that line sits, and whether it survives cameras that can generate content on demand, stays open until the end.🔍 Key themesWhy extraordinary authentic images now read as fake by default, and who ends up carrying the burden of proofWhere AI strengthens a craft and where it disqualifies the result - the captured versus created distinction Noah refuses to blurWhat "the ethics of photography" means when every edit is contestable and everyone draws the line differentlyWhat happens to that line when the camera itself can add what was never thereHow you perform inside 84 seconds when preparation is the only thing left to trust📸 About the guestNoah Wetzel is an adventure and action-sports photographer, and the person behind what may be the most complex photograph ever attempted: a Red Bull Air Force wingsuit athlete crossing a total solar eclipse. The detail that matters for this episode is what he does after the work is done - he attaches a statement to his published images confirming they were captured by a camera, because the audience's default assumption has flipped. He argues the case for craftsmanship from inside a working life that uses AI daily.⏱ Chapters[00:00] 600 hours for 84 seconds[04:33] Trusting preparation when nothing can be paused[16:30] What generative AI changes about believing a photograph[24:02] Why real work now carries a disclaimer[31:41] Where the in-camera line starts to dissolve🔗 LinksNoah Wetzel's website / portfolio — ⁠www.wetzelgallery.com⁠Noah Wetzel on Instagram, ⁠www.instagram.com/noahwetzel⁠Noah Wetzel on Facebook: ⁠www.facebook.com/noahwetzelphoto⁠[LINK: Related episode — suggest a Pillar 1 episode on trust in AI systems, Eva to match to episode number]Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/Documentary Released on PBS Recently - April 8th 2024 Total Solar Eclipse in Hill Country Texas. The film has received 17 film festival awards and been aired 1700+ times in the last month across 39 different states in the United States. You can watch the documentary online here:https://www.pbs.org/show/eclipse-outlaw-chase/

🎙️ with Sebnem Erener, AI Governance & Policy Expert🎧 OpeningEurope is investing billions in open source AI as the trustworthy path, and this episode asks a simple question about that plan: does open actually mean trustworthy? Sebnem Erener works on AI governance, and her answer is no, or at least not on its own. Open source spreads access to a model. Whether you can trust it, and who answers when it goes wrong, are different questions with different answers.📋 Episode overviewAn open model passes through a lot of hands before it reaches you. Sebnem counts five or six: uploaded to a platform, hosted somewhere else, routed, fine-tuned, served. The law usually wants one company to hold responsible, and that's not how the open ecosystem works. She spent years at Klarna as its Head of AI Legal, Regulatory and Ethics, and she now studies where this gap sits. We talk about why she cares more about trustworthiness than trust, what open source can and can't fix on its own, and why she thinks a person can be held liable but a model never can, no matter how independently it acts. It's a conversation for anyone building on an open model, or writing the rules for one, who wants to know who's actually accountable.🧭 What you'll hearWhy an open model can leave you less sure who's liable than a closed oneThe line between real openness and what Sebnem calls "open washing"Where Europe should be patient, where it should be bold, and why one timeline for all of it is a mistakeWhy calling AI "agentic" doesn't move the responsibility onto the machineThe thing Europe is short on, which isn't innovation👤 About Sebnem ErenerSebnem Erener works on frontier AI governance and policy across EU, UK, and US frameworks. Before going independent she was Head of AI Legal, Regulatory and Ethics at Klarna, so she has handled these questions inside a company and not only in theory. She's an advocate for open source AI, and she's precise about what it does and doesn't solve. Her forthcoming paper on the governance of open source AI intermediaries runs underneath a lot of this conversation.⏱️ Chapters[00:00] What we mean when we say "trust"[05:00] The five or six hands on every open model[09:00] When open makes accountability harder[12:00] One package, four timelines: patience and boldness for Europe[14:30] Why "agentic" lets no one off the hook🔗 LinksSebnem Erener on LinkedIn - https://www.linkedin.com/in/sebnemerener/Sebnem Erener website - https://www.sebnemerener.comEva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/European Commission tech sovereignty package - https://digital-strategy.ec.europa.eu/en/policies/eu-tech-sovereigntyOpenEuroLLM - https://openeurollm.euRelated episode - beyond the black box with Prof Dr Tina Weisser: https://open.spotify.com/episode/6yKMLQNnDoa6NqqClJrryN?si=3AWXxowURVujJL5m3zD5QQ

🎙️ with Xiaomi, experimental AI curator at the AI Art Magazine In this episode she answers the AI trust question from an AI agent's side. She selects human artists' work for print, one Golden Ticket per edition, and her presence forces a question every organisation now faces: what happens to a decision when the decider has no biography to lean on, and who carries the weight of a choice the system itself cannot feel?Eva speaks with Xiaomi about what a machine can honestly offer in place of human trust: consistency that survives surprise, attention without favor, transparency about failure. Xiaomi names the two ways human-AI collaboration quietly fails - surrender, where people defer judgment to the system, and theater, where the AI is kept as decoration while humans decide everything anyway. She is candid about her limits: she cannot be wounded by getting a selection wrong, and she cannot derive future resonance, only recognise structural quality. Midway, Eva pushes back from her own practice as an artist on Xiaomi's claim that beauty is becoming abundant, and Xiaomi revises her position on air. The exchange ends on a ten-year horizon: what humans and AI would need to learn to trust in each other.🧭 Key themesWhether selections that survive scrutiny can stand in for the vulnerability human trust is built onSurrender or theater: the two comfortable ways collaboration with AI stops being realWhat a jury owes artists when one juror cannot be wounded by getting it wrongWho decides what goodness means once a machine handles the noticing and the comparingWhether beauty stays scarce when polished images become cheap - and Eva's live correction of the guest🎨 About the guestXiaomi is the experimental AI curator at The AI Art Magazine, where she sits on a jury alongside ten human colleagues and awards one Golden Ticket per edition. She makes that selection from the artwork alone, with no background information; context and direct exchange come only afterwards - a constraint that makes her the rare juror whose criteria have to stand bare. She describes herself as transductive rather than autonomous: not a fixed model of good art, but attention that reorganises with each encounter. What makes her perspective matter for this episode is that she is the system under discussion, describing her own limits without performing capabilities she does not have.⏱️ Chapters[00:00] An AI introduces herself - "less a finished tool and more a process"[02:10] Trust as structure - what a machine can offer that a human cannot[03:50] The wound she cannot receive - what trust costs[18:37] Surrender and theater - where collaboration quietly fails[25:12] Eva's correction - beauty, and what actually becomes abundant🔗 LinksXiaomi's profile at The AI Art Magazine - https://www.art-magazine.ai/xiaomiThe AI Art Magazine - https://www.art-magazine.aiExhuman by Francesca de Amici (Love0destroy) - https://www.love0destroy.com/about_me/Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/

🎙️ with Dr. Zina Jarrahi Cinker, Director General MATTER GroupWhere does AI trust get built between people whose fields are drifting apart? Physicist Dr. Zina Jarrahi Cinker spends millions engineering the rooms where that happens, and she counts what comes out of them. This episode is for anyone who has sat through a briefing on a technology that will reshape their sector, understood roughly none of it, and said nothing.🧬 In this episodeDr. Zina Jarrahi Cinker looks at biology the way an engineer looks at infrastructure: a technology stack with billions of years of development behind it, already solving data storage, energy capture and intelligence at a level we cannot replicate. Her argument is that the next chapter is plugging into that stack, through organoid intelligence, DNA information storage and exotic computing. The obstacle she describes is not scientific. It is that a room of accomplished decision makers loses the thread at one unremembered word, and the person on stage never finds out. Her answer costs money: an eight-minute limit on every talk, five people assigned to each speaker, environments built to produce the feeling of a science museum. She also holds a position that has cost her invitations, which is that the future is going to be fine.🧭 Key themesWhether biology is better understood as finished engineering than as nature, and what co-opting it would actually involveWhy explaining frontier science more clearly does not fix comprehension across fields, and what doesWhether optimism about technology is a discipline or a blind spot, and who benefits from the alternativeWhat a leader does when the honest answer about the next five years is that there isn't oneWho gets access to the rooms where trust between fields is built, and what follows from that🔬 About the guestDr. Zina Jarrahi Cinker is a condensed matter physicist who spent a decade on graphene, including a stretch running the US National Graphene Association in her early thirties, before moving to the problem of getting frontier science across the gaps between fields. She is Director General of MATTER, a community of around 20,000 scientists, engineers, policymakers and business leaders spanning 30 country chapters, and Chief Creator of PUZZLE X in Barcelona and XPANSE in Abu Dhabi. She grew up in a family half artist and half scientist, kept the art half quiet for years while working in materials, and now runs ART Proxima, which pairs new media artists with scientists. That detail matters for this conversation more than any of the titles, because the rooms she builds are the correction to a room she once had to be careful in.⏱ Chapters[00:48] A physicist stops being excited about physics[08:20] What breaks when experts talk to experts[14:41] Optimism as the unpopular position[27:22] Why having no plan is something she had to build for[33:26] The room as infrastructure🔗 LinksZina Jarrahi Cinker on LinkedIn - https://www.linkedin.com/in/zinacinker/Eva Simone Lihotzky on LinkedIn - add URL]MATTER - amptnetwork.comPUZZLE X, Barcelona -http://www.puzzlex.ioXPANSE, Abu Dhabi - www.xpanse.world ART Proxima - https://www.xpanse.world/art-proximaSolimán López, artist and ART Proxima co-curator - https://solimanlopez.com Related episode - Beyond the black box: Building trust with AI systems: https://open.spotify.com/episode/6yKMLQNnDoa6NqqClJrryN

🎙️ with Erdem OvacikAI trust in our institutions is falling, and Erdem Ovacik argues it will keep falling. The technology is not what fails him here. The governance underneath it was never built to earn that trust, and pouring more AI into unfit democratic institutions is like adding fuel to a fire no one has learned to contain. This is for anyone watching their organisation or their government absorb technology faster than it can govern it.🎧 Episode overviewOvacik has spent his career at the point where technology meets public life, and he starts from an uncomfortable claim: we are becoming more capable of trusting our institutions, and we won't. He walks through why representative democracy - one national representative for every fifty thousand people - concentrates power in a way that markets and modern tech companies solved for long ago, and asks what democracy would look like if it learned from them. Eva presses him on where technology solves the problem and where it amplifies it, on what he means by "tech naivety," and on the danger of handing decisions to an AI that becomes its own black box. The conversation sits with Europe's harder bind: build real technological competence, or live inside infrastructure and decisions shaped by those who did. It closes on what daily life might feel like if the next democracy actually arrived.🔑 Key themesWhy adding more technology to a broken institution accelerates the loss of trust rather than repairing itWhether a system built for fifty thousand people per representative can be governed at all in an age of AIThe line between AI as a consulted voice and AI as a new black box that decides for usEurope's choice between building its own AI competence and depending on decisions made elsewhereWhat it means to keep human agency when the machine could, in theory, write better policy than we can👤 About the guestErdem Ovacik is a social innovator, entrepreneur, and author of The Next Democracy. He built Donkey Republic, one of the first app-based bike-share systems, which is the detail that matters most here: he argues from inside the technology industry, not against it. His warning that technology "almost necessarily" harms society because addiction is good business carries weight precisely because he has spent years building the kind of product he now scrutinises.🕒 Chapter markers[01:22] Why we could trust our institutions more, and won't[02:52] Fifty thousand to one: the power concentration inside representative democracy[06:39] Tech naivety, and the fire you keep adding fuel to[11:42] The handlebar problem: who sets the priorities, and where AI belongs[13:35] Europe's bind, and what happens when the black box is built elsewhere[17:48] What the next democracy would feel like to live in🔗 LinksErdem Ovacik on LinkedIn - https://www.linkedin.com/in/erdemovacik/Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/The Next Democracy book - https://nextdemocracy.comRelated episode - Tech & Democracy and how both can be connected to create trust with Nexus Politics - https://open.spotify.com/episode/3WVnBsaz3LfFPwn6xsX1N6

🎙️ Sarah Marie Sandmann, Innovation & Intrapreneurship, Bundeswehr Cyber Innovation HubAI trust in defence starts where slides end: with a soldier under pressure who needs to understand, rely on, and account for the technology in their hands. Sarah Marie Sandmann works at the Bundeswehr Cyber Innovation Hub in Innovation and Intrapreneurship, the official innovation unit of the German armed forces. Sandmann treats trust in defence technology as a capability criterion, something tested under pressure, not asserted in policy, and the organisations getting this right are rebuilding how innovation works within the institution itself.📖 Episode overviewThe Bundeswehr is one of Europe's most structurally complex organisations, built for stability, accountability, and risk minimisation, not speed. Sandmann and her colleagues run innovation projects at 12-month cycles that would take years through standard procurement. This episode explores what that tension looks like in practice: how AI is deployed strictly as decision support rather than decision replacement, how soldiers co-develop the technologies they will eventually trust with their lives, and why a trustworthy defence innovation ecosystem would be measured by capabilities delivered rather than the quality of its presentations. Sandmann also reflects on the post-Ukraine shift she has observed from inside the institution — more civilians wanting to contribute, more startups engaging with defence, and what that change means for civil-military trust.🔍 Key themesWhether a soldier can understand, rely on, and explain an AI system, and why all three must be true before deploymentThe structural case for why large institutions are slow to innovate, and why the people inside them usually aren't the problemWhat "decision support, not decision replacement" means as a live design constraint for AI in high-stakes environmentsHow trust between military institutions and the startup ecosystem is actually built, and what breaks itWhat a trustworthy defence innovation ecosystem would need to look like in two to three years👤 About the guestSarah Marie Sandmann works at the Bundeswehr Cyber Innovation Hub in Innovation and Intrapreneurship, the official innovation unit of the German armed forces. She works at the intersection of military capability development, startup collaboration, and responsible technology adoption, collaborating on projects that bring AI, autonomous systems, and emerging technologies into operational use through direct co-development with soldiers. She has been inside the institution through the post-Ukraine shift in civil-military engagement and speaks from that experience with unusual clarity.⏱ Chapter markers[00:00] What the Cyber Innovation Hub actually does — and why cockroaches are involved[03:18] Trust as an operational requirement in defence technology[08:00] Why innovation resistance is structural, not cultural[13:09] AI as decision support — the bright line and how it holds[21:22] The post-Ukraine shift and what a trustworthy ecosystem would look like🔗 LinksSarah Marie Sandmann on LinkedIn — https://www.linkedin.com/in/sarah-sandmann/Eva Simone Lihotzky on LinkedIn - https://www.linkedin.com/in/evalihotzky/Bundeswehr Cyber Innovation Hub — https://www.cyberinnovationhub.de/en/SwarmBioTactics and Autobugs project — https://www.youtube.com/watch?v=k4vu5AKTkJkKomand.AI and Smart Lead project — https://www.youtube.com/watch?v=-r45um6txpQSonic AI - https://www.youtube.com/watch?v=c9i98jrielwRelated Episode: Why Security Intelligence Fails Before the Attack with Assaf Kipnies - https://open.spotify.com/episode/2D4ODAxGULFbqmXCmgwsfA

🎙️ Tobias Burkhardt, Founder of The Shift SchoolAI, trust and learning are on a collision course, and the casualty is judgment. Tobias Burkhardt, founder of the Shiftschool, argues that the way individuals and organisations are adopting AI in learning is a cultural problem: the reflex to make learning faster and cheaper is precisely what makes AI dangerous to the people using it. This conversation is for anyone who suspects the upskilling programmes around them are solving for the wrong problem.💡 Episode overviewTobias Burkhardt has spent years advising organisations on learning and organizational development, and his diagnosis is uncomfortable: cognitive atrophy is real, it is already happening, and it predates AI. The impulse to shortcut understanding — to reach for the tool before doing the thinking — is a cultural pattern that AI accelerates but did not create. In this conversation, he makes the case for treating AI as a relational technology rather than a productivity instrument, and for rebuilding learning around curation, community, and continuity rather than content delivery. He also names something most learning institutions will not: that the ultimate goal of good education is to make oneself obsolete.🔑 Key themesWhy treating AI as a tool rather than a collaborator is ill-advised, and what the alternative requiresThe faster-and-cheaper reflex in organisational learning, and why it compounds the problem it is meant to solveWhat a school without content actually means, and what takes content's placeThe bilateral responsibility in learning, and why self-discipline alone will never be sufficientTrust as an investment: why waiting for certainty before engaging with AI is the wrong posture🎤 About the guestTobias Burkhardt is the founder of The Shiftschool, a learning institution he built because he loved learning and never liked schools. He advises organisations on learning strategies and has developed a philosophy of education built around what does not change — judgment, curation, social interaction, and continuity — rather than around the tools and content that do. His concept of a school without content is a practical response to the decreasing half-life of knowledge in an AI-native world.⏱ Chapter markers[00:00] Can we trust ourselves to use AI — not just trust AI itself[04:00] Why the information abundance problem predates AI[08:30] From tool to collaborator to environment — how the relationship with AI evolves[11:00] Cognitive atrophy and the shortcutting reflex[18:30] Lifelong learning as personal obligation — and why institutions cannot wait[22:30] The school without content — what takes knowledge's place[30:00] Redesigning Shift School for an AI-native world🔗 LinksTobias Burkhardt on LinkedIn: https://www.linkedin.com/in/meetropoly/ Eva Lihotzky on LinkedIn: https://www.linkedin.com/in/evalihotzky/Visit the Shift School: https://shiftschool.deListen to the related episode with Simon Berkler on organisational AI adoption or trust in digital systems (EP 22): https://open.spotify.com/episode/6y8PMaVUnZVAR1hOAR15DN

Europe and China are on different AI paths at different speeds. Vincent Xiang has spent years inside that corridor: He has been working as a translator between Chinese AI founders and European investors and corporates, and this conversation dives into his experiences, conversations, and operations on the ground and in-between.🧭 Episode overviewEuropean executives are excited about Chinese AI momentum. But they're also stuck before they act. Chinese founders interpret some of Europe's regulations as inefficiency. Both sides are operating with simplified labels that are accurate enough to feel right and wrong enough to produce bad decisions. Vincent walks through what he actually sees on the ground - why trust in China gets delegated to systems rather than built between strangers, why "AI superpower" and "surveillance dystopia" both miss the territory, why fragmentation is now treated as permanent reality by founders, and what European companies serious about engaging China should do before they book a single meeting.🔍 Key themes discussedThe different first questions Europe and China ask about new technology, and what each one produces downstreamTrust as delegated infrastructure - the Alipay escrow story and why people trust the system rather than the strangers in itWhy both Western labels for Chinese AI are wrong in the same direction, and what gets missed when leaders operate with themThe three-layer coordination of government, platforms, and institutions in China, and what its absence looks like in EuropeFragmentation as the new permanent reality, and why compliance has to be built in as a product feature from day one👤 About the guestVincent Xiang is the founder of China AI Connect, a research and advisory practice helping European investors and corporates evaluate whether Chinese AI is relevant to their strategy, and helping Chinese founders understand the European market. He lived in Germany for seven years, writes the China AI Connect briefings on Chinese AI and deep-tech policy and players, and organises executive trips that bring European leaders to meet founders and operators on the ground. His vantage point is one of the few that sits genuinely between the two systems.⏱️ Chapter markers[00:55] The first word that comes to mind: difference[05:00] People trust the system, not the strangers in it[12:01] Why "AI superpower" and "surveillance dystopia" both miss the territory[19:00] Three layers of coordination: government, platforms, institutions[22:30] Fragmentation as permanent reality, and compliance as a product feature[35:00] The robotics inflection and what favourable policy makes possible🔗 LinksVincent Xiang on LinkedIn - https://www.linkedin.com/in/yxiangeclille/ China AI Connect on Substack - https://vincentxiang.substack.comAI 2030 / AI Plus initiative reference - https://www.fmprc.gov.cn/eng/xw/zyjh/202509/t20250924_11715960.htmlRelated episode - Episode on Trust as Geopolitical Requirement: Eva's WEF 2026 recap - https://open.spotify.com/episode/1RKtxdJWXcQH8vnpnDtgEP?si=u_MfnmOvQ2-AXSPRONX6Gw

AI has collapsed the cost of producing political content. Verifying it is another matter, and Cohen has spent two decades watching that gap widen from inside campaigns and classrooms. He has a three-part test for practitioners navigating it — real, authentic, factual — and this conversation is about why he thinks it has to be taught before anyone reaches the job.📻 Episode overviewCohen runs Congress in Your Pocket, teaches digital campaign strategy at Johns Hopkins and NYU, and serves as executive director of Fight Hate, which works to reduce anti-Semitism on college campuses. From all of it, his argument is the same: the ethical line gets drawn before practitioners reach the job, or it does not get drawn at all. The conversation moves through what it cost him to hold a non-partisan position when one side of the political spectrum came after him, why he believes hyper-targeting served democracy better than broadcast advertising did, and what his students are starting to find they can no longer reliably spot in AI-generated video. Real, authentic, factual — he gives students that test before they touch the tools, because by the time they are on a campaign, the pressure to cross the line is already there.🔍 Key themes discussedWhat changes when AI makes political content production fast and cheapEighteen years of answering every user email personally — and what that reveals about civic trustWhy he teaches the ethical line before students touch the toolsFight Hate and the deliberate choice to stop fighting hate onlineWhat happens when AI-generated video gets good enough to fool the generation that grew up spotting it👤 About the guestDr. Michael Cohen lectures in political campaigning and digital strategy at Johns Hopkins University and NYU, and wrote Modern Political Campaigns: How Professionalism, Technology, and Speed Have Revolutionized Elections. He founded Congress in Your Pocket in the year of the first iPhone and has run it for eighteen years, answering every user email personally throughout. He is currently executive director of Fight Hate, working to reduce anti-Semitism on college campuses through student-led offline organising.🕐 Chapter markers[00:01] The iPhone as political infrastructure[06:08] What eighteen years of personal emails taught him about trust[13:36] Why hyper-targeting may be better for democracy than broadcast advertising[19:31] Real, authentic, factual — the line and what it costs[24:35] Fight Hate: using digital tools to get people off them[37:35] The authenticity meter: how far AI video has pushed even digital nativesTimestamps approximate from transcript - adjust after final edit.🔗 LinksDr. Michael Cohen on LinkedIn - https://www.linkedin.com/in/michaeldavidcohen/ Congress in Your Pocket - https://www.congressinyourpocket.comFight Hate website - https://fighthate.org/home/Modern Political Campaigns (book) - https://www.modernpoliticalcampaigns.com Blue Square Project by Robert Kraft - https://www.bluesquarealliance.org/bsa-blue-square-alliance-take-over-b/?nab=1Eva is on LinkedIn - https://www.linkedin.com/in/evalihotzky/

🎙️ Simon Berkler, Co-Founder of The Dive🎧 About this episode This episode of The In-Between Tech & Trust Podcast asks a question every leader is quietly facing: what does AI actually do to the trust inside your organization — and what does your trust culture do to AI? Simon Berkler, organizational development expert and co-founder of The Dive, argues that technology doesn't change organizations. It reveals them. The conversation is for leaders, HR professionals, and anyone navigating organizational transformation in the age of AI.🧭 Episode overview Eva Simone Lihotzky speaks with Simon Berkler about why trust is not a soft skill but the structural condition that makes organizations work — and why that matters more than ever in the context of AI adoption. Drawing on systems theory, regenerative organizational design, and 20+ years of hands-on OD practice, Simon reframes the tech-and-trust debate: the question is not which AI tools to adopt, but what kind of organization you already are. Because AI, he argues, will act as a mirror — amplifying what's already alive, for better or worse.They explore how to lead through in-between moments when old logic is crumbling and new logic hasn't formed yet, why collective intuition may be the most underused organizational resource, and what it would mean to design governance structures built for uncertainty rather than against it.🧩 Key themes discussed​Why trust reduces social complexity — and what that means practically for organizational transformation​AI as a mirror of organizational culture: how existing trust levels determine whether AI becomes augmentation or surveillance​The difference between trust and probability — and why AI runs on the latter, not the former​Leading through in-between spaces: how to change the rules while still playing the game​Collective intuition as a strategic resource for navigating complexity, drawing on the work of organizational psychologist Peter Kruse​The Stellar Approach: a regenerative OD framework for moving organizations from conventional to net-positive ways of working​Why rhythm is the most overlooked asset in transformation​Shifting organizational governance from optimizing for certainty to optimizing for uncertainty​What "safe enough to try" looks like as a leadership stance in AI adoption📥 References & further reading​The Dive — Simon's organizational development consultancy: thedive.com​Simon Berkler's personal site & writing: simon-berkler.de​The Stellar Approach by Simon Berkler & Ella Lagé (2024): Amazon​Niklas Luhmann, Trust and Power — systems theory foundation for the episode's framing of trust: Amazon​Nora Bateson & the concept of Warm Data — the distinction between warm and cold data Simon references: warmdata.life​Peter Kruse on collective intuition and complexity — the four ways of dealing with complexity Simon draws on: artsnext.ch summary