The in-between tech and trust podcast

The Agentic AI Gap: When Tech is Used Before its Architecture is Ready - Anthony Alcaraz, Agentic AI Architect (EP 25)

Most enterprises have the technology to run agentic AI. They do not yet have the data architecture, identity layer, or empowered workforce to actually trust it. Anthony Alcaraz argues that the bottleneck for agentic AI has shifted from building the agents to building everything around them — and that the organisations most at risk are the ones keeping a human in the loop and calling it transformation. This conversation is for leaders sitting between AI pilots that worked and production systems that have not yet arrived. 💡Episode overview Anthony joins Eva to map what changes when AI shifts from reactive systems to agents that observe, reason, and act. The conversation moves through what enterprises miss in their own data — systems of record that capture what happened but not why — and the new attack surfaces agents introduce, including tool poisoning. Anthony names the empowerment gap inside organisations: business experts who hold the knowledge agents need, with no clear path to building anything themselves. The most provocative moment lands near the end, when Anthony argues that human-in-the-loop adoption can be a way of avoiding actual transformation rather than achieving it. 🔍 Key themes discussed * The shift from reactive to agentic systems, and what trust has to carry now * Why most enterprise data is missing the why behind decisions * Tool poisoning and the new attack surface for agents * The empowerment gap between business knowledge and technical capability * Graph architecture as the control layer for agentic reasoning * Why human-in-the-loop can be a refusal to transform 👤 About the guest Anthony Alcaraz works across three vantage points that rarely sit together: he architects agentic AI systems, invests in early-stage AI startups as an angel, and is the author of Agentic Graph RAG with O'Reilly. He spends most weeks in conversation with founders attempting to enter regulated enterprises, and most evenings building software with the same tools he writes about. His perspective on this episode comes from watching the same gap repeat itself across organisations of very different sizes — the technology is ready, and most of the systems around it are not. 📍 Chapter markers * [00:00] What changes when AI moves from reactive to agentic * [05:42] Why agents need access — and what enterprises have not built * [10:29] The three problems: data, governance, and the people in between * [23:13] Graph architecture and the missing why of enterprise data * [32:06] The empowerment gap that no one has solved yet * [45:17] In-between: where Anthony finds himself now 🔗 Links * Anthony Alcaraz LinkedIn — https://www.linkedin.com/in/anthony-alcaraz-b80763155/ * Agentic Graph RAG (O'Reilly) — https://www.oreilly.com/library/view/agentic-graphrag/9798341623163/ * Foundation Capital context graph thesis — https://foundationcapital.com/ideas/the-case-for-context-graphs * Related episode — Trust as an operating system in AI companions https://open.spotify.com/episode/5t4BtgevPOtMWUfB4jThWX?si=oGo2JPHNTeCTxbqkNXDJMw * Eva Simone Lihotzky's LinkedIn: https://www.linkedin.com/in/evalihotzky/

Why AI is making political authenticity harder to trust – Dr. Michael Cohen (EP 24)

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 overview Cohen 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 discussed * What changes when AI makes political content production fast and cheap * Eighteen years of answering every user email personally — and what that reveals about civic trust * Why he teaches the ethical line before students touch the tools * Fight Hate and the deliberate choice to stop fighting hate online * What happens when AI-generated video gets good enough to fool the generation that grew up spotting it 👤 About the guest Dr. 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 natives Timestamps approximate from transcript - adjust after final edit. 🔗 Links * Dr. Michael Cohen on LinkedIn - https://www.linkedin.com/in/michaeldavidcohen/ * Congress in Your Pocket - https://www.congressinyourpocket.com * Fight 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=1 * Eva is on LinkedIn - https://www.linkedin.com/in/evalihotzky/

Why security intelligence fails before the attack - Assaf Kipnis (EP 23)

Most security failures are organisational: This episode is about the gap between threat intelligence that exists and the human systems that never act on it, and what that costs the organisations that keep losing to attacks they already understood. Assaf Kipnis has spent over a decade inside the threat intelligence and trust and safety functions of some of the world's largest platforms. In this conversation, he maps a structural failure that runs across the industry: the team that identifies threats and the team that deploys detection operate in parallel, with no reliable mechanism to connect them. Intelligence gets produced, reports get written, and the knowledge sits unused while the same attacks return. Assaf describes what it actually took to stop a sophisticated actor group ahead of the 2020 US elections - a rare case where structure and resources aligned - and explains why that outcome is the exception rather than the rule. He also walks through the design decisions behind Catalyst Labs, the company he is now building to close the gap, and why he made provenance non-negotiable even at the cost of speed. 🎙 Key themes discussed * Why security teams are structurally rewarded for fighting fires rather than preventing them * The organisational gap between threat intelligence and detection - and why it persists even in well-resourced teams * What data provenance means in practice, and why it matters more than speed when using AI in security * How attackers learn your defences faster than you can adapt - and what the military analogy reveals * Why trust online currently feels, in Assaf's words, like a pipe dream 👤 About the guest Assaf Kipnis is the founder of Catalyst Labs, with over 12 years working across threat intelligence, information security, and trust and safety at LinkedIn, Google, Meta, and ElevenLabs. He brings the perspective of someone who has spent his career making threats legible to organisations - and watching those organisations lack the structure to act on what they could now see. 🕐 Chapter markers [00:18] Why the industry keeps fighting the same fires [08:04] What it actually took to stop an actor group - the 2020 elections case [12:36] How AI is widening an asymmetry that already existed [15:31] Catalyst Labs: the provenance problem and why speed comes second [20:35] What to build first if you're starting a threat intelligence team 🔗 Links Assaf Kipnis https://www.linkedin.com/in/assafkipnis/ KTLYST Labs https://www.ktlystlabs.com Background information on MGM / FBI reports: https://www.reuters.com/technology/cybersecurity/fbi-struggled-disrupt-dangerous-casino-hacking-gang-cyber-responders-say-2023-11-14/ Related episode: organisational trust and AI implementation with Simon Berkler https://open.spotify.com/episode/6y8PMaVUnZVAR1hOAR15DN Related episode: accountability and invisible infrastructure with Sergiu Petean https://open.spotify.com/episode/4KcsZBDgFzkSuwQVihjNR5

AI as a Mirror: How Your Organization's Trust Culture Impacts AI Implementation (EP22)

🎙️ 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 [https://www.thedive.com/en] * Simon Berkler's personal site & writing: simon-berkler.de [http://simon-berkler.de/en/about_me.html] * The Stellar Approach by Simon Berkler & Ella Lagé (2024): Amazon [https://www.amazon.com/Stellar-Approach-Regenerative-Transformation-Organisation/dp/3593519593] * Niklas Luhmann, Trust and Power — systems theory foundation for the episode's framing of trust: Amazon [https://www.amazon.com/Trust-Power-Niklas-Luhmann/dp/1509519459] * Nora Bateson & the concept of Warm Data — the distinction between warm and cold data Simon references: warmdata.life [https://www.warmdata.life/] * Peter Kruse on collective intuition and complexity — the four ways of dealing with complexity Simon draws on: artsnext.ch summary [https://artsnext.ch/en/blog-en/how-do-people-react-to-growing-complexity-with-peter-kruse/]

Navigating the AI Slop: How Editorial Judgment Is Changing (EP 21)

🎙️ Dr. Paul Elvers, Head of AI at Funke Mediengruppe 💬 Summary This week's episode of the in-between tech & trust podcast examines how AI is being used inside one of the largest media organizations in Germany, with a focus on trust, transparency, and day to day editorial practice - steered by Dr. Paul Elvers, Head of AI at Funke Medienhaus and podcast host Eva Simone Lihotzky. The conversation is for media specialists, editors, product leaders, and anyone working close to news production and consumption. The episode dives deep into the choices directly affecting credibility, audience trust, and the role journalism plays in a democratic society. 🎧 Episode overview In a detailed discussion, Dr. Paul Elvers walks through how AI actually shows up in newsroom workflows, separating real operational value from common misconceptions. Rather than debating whether AI should exist in journalism, the episode stays grounded in how it is governed, where human responsibility remains essential, and why naïve adoption is a bigger risk than cautious experimentation. The conversation also explores how audiences judge credibility in an environment flooded with synthetic content, and what media organizations can realistically do to maintain trust while adapting to new tools and distribution pressures. 🔍 Key themes discussed * Why trust in AI comes from understanding systems and accountability, not blind confidence * The difference between deliberate AI integration and careless, volume driven adoption * How “AI slop” reflects a growing difficulty in judging what is trustworthy, not just content quality * Using AI to automate necessary but unpopular newsroom tasks while keeping humans at the start and end * The role of recognizable brands and journalists in sustaining audience trust * What transparency about AI use looks like in real editorial workflows * Why AI governance in media is iterative, shared, and never fully settled