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Discover how educators are using generative AI not to automate, but to elevate critical thinking and collaboration in K-12 schools. In this episode of Shifting Schools, host Tricia Friedman shows how "disagreement by design" and intentional prompt-engineering transform student and leadership learning. What you'll learn: What disagreement by design looks like in real classrooms and leadership teams How prompt engineering unlocks student curiosity and systems-thinking mindset in K-12 Why writing bespoke GPT bots might just be the 'new essay' of our times Who this episode is for: Any educator, school leader or district-innovator exploring how to responsibly integrate companion AI, AI avatars and prompt-driven dialogue into a learning ecosystem.
In this episode, Claus Lauter, host of the Ecommerce Coffee Break Podcast, breaks down how AI is reshaping Q4 e-commerce strategy. He explains the rise of Generative Engine Optimization (GEO) — the new way to stay visible in AI-driven search — and shares practical tips on managing high ad spend while keeping profits strong. Claus also talks about the evolution of the podcast's YouTube format and invites listener feedback.Topics discussed in this episode: How AI is changing Q4 shopping.ChatGPT's new checkout and what it means for marketing.What Generative Engine Optimization (GEO) is.Why Q4 ad costs peak — and how to handle them.When high ad spend is worth it.Why email list growth can wait until January.The top KPI mistake new sellers make.YouTube's growing power in search and discovery.Why viewer feedback matters for growth.Learn why AI is rewriting the rules of search — and how GEO can future-proof your ecommerce brand.Links & Resources Website: https://ecommercecoffeebreak.com/LinkedIn: https://www.linkedin.com/company/ecommerce-coffee-break-podcast/X/Twitter: https://x.com/ecomcoffeebreakInstagram: https://www.instagram.com/ecommercecoffeebreak/Get access to more free resources by visiting the show notes at https://tinyurl.com/trr8wnvr______________________________________________________ LOVE THE SHOW? HERE ARE THE NEXT STEPS! Follow the podcast to get every bonus episode. Tap follow now and don't miss out! Rate & Review: Help others discover the show by rating the show on Apple Podcasts at https://tinyurl.com/ecb-apple-podcasts Join our Free Newsletter: https://newsletter.ecommercecoffeebreak.com/ Support The Show On Patreon: https://www.patreon.com/EcommerceCoffeeBreak Partner with us: https://ecommercecoffeebreak.com/partner-with-us/
We've all been there—juggling new tech, new expectations, and wondering if our students are really learning what matters. In this chat, we talk about “pedagogical debt” (the gap between what we know works in teaching and what we actually do), how AI is shaking things up, and why the right kind of curiosity can help. At its heart, it's a conversation about making sure technology serves learning—not the other way around. Dr. Punya Mishra (punyamishra.com) is the Associate Dean of Scholarship and Innovation at the Mary Lou Fulton Teachers College at Arizona State University. He has an undergraduate degree in Electrical Engineering, two Master's degrees in Visual Communication and Mass Communications, and a Ph.D. in Educational psychology. He co-developed the TPACK framework, described as “the most significant advancement in technology integration in the past 25 years.” Dr. Caroline Fell Kurban is the advisor to the Rector at MEF University. She was the founding Director of the Center of Research and Best Practices for Learning and Teaching (CELT) at MEF University and teaches in the Faculty of Education. She holds a BSc in Geology, an MSc in TESOL, an MA in Technology and Learning Design, and a PhD in Applied Linguistics. Fell Kurban is currently the head of the Global Terminology Project and the creator of the GenAI-U technology integration framework. Dr. Liz Kolb is a clinical professor at the University of Michigan and the author of several books, including Cell Phones in the Classroom and Help Your Child Learn with Cell Phones and Web 2.0. Kolb has been a featured and keynote speaker at conferences throughout the U.S. and Canada. She created the Triple E Framework for effective teaching with digital technologies and blogs at cellphonesinlearning.com. Dr. Puentedura is the Founder and President of Hippasus, a consulting practice focusing on transformative applications of information technologies to education. He has implemented these approaches for over thirty years at various K-20 institutions and health and arts organizations. He is the creator of the SAMR model for selecting, using, and evaluating technology in education and has guided multiple projects worldwide. Dr. Helen Crompton is the Executive Director of the Research Institute for Digital Innovation in Learning at ODUGlobal and Professor of Instructional Technology at Old Dominion University. Dr. Crompton earned her Ph.D. in educational technology and mathematics education from the University of North Carolina at Chapel ill. Dr. Crompton is recognized for her outstanding contributions and is on Stanford's esteemed list of the world's Top 2% of Scientists. She is the creator of the SETI framework. She frequently serves as a consultant for various governments and bilateral and multilateral organizations, such as the United Nations and the World Bank, on driving meaningful change in educational technology.
Send us a textInvest in pre-IPO stocks with AG Dillon & Co. Contact aaron.dillon@agdillon.com to learn more. Financial advisors only.00:00 - Intro00:08 - Mercor $350M Series C Quintuples Valuation to $10B Amid AI Data Pivot01:47 - OpenAI Restructuring to PBC Unlocks $40B Fundraising at $500B Valuation03:14 - SoftBank Greenlights $22.5B Final Tranche to OpenAI Contingent on PBC Shift04:06 - OpenAI Advances Generative Music Tool to Rival Suno in $200B Media Market05:14 - Poolside $2B Raise at $12B Valuation Backed by Nvidia's $1B Commitment05:49 - Bending Spoons $270M Raise at $11B Valuation Funds $1.4B AOL Buy06:56 - Whatnot $225M Series F at $11.5B Valuation Drives Global GMV Doubling08:08 - Figma $200M+ Weavy Acquisition Boosts AI Media Tools Post-IPO09:07 - MiniMax M2 Tops Global Open Models in Sovereign AI Push10:00 - 1X NEO Robot Preorders at $20K Target 2026 Deliveries11:10 - SpaceX $2B Pentagon Deal Bolsters $11B Starlink Revenue12:15 - Canva Debuts Foundational Design Model in Affinity Free Shift13:28 - Grammarly Rebrands to Superhuman with 40M DAU AI Suite
As we continue our discussion of the difficulty in integrating the journey on the way of the life of faithfulness to the evengalion of Jesus, The Christ, the Orthodox Christian way of life, into contemporary western culture, Jim & Fr Symeon take the opportunity to discuss "the other side". Last conversation we defined & critique "woke" as the civil religion of the so-called Left. This time we will define & critique "Make America Great Again" as the civil religion of the so-called Right. The reason we wish to critique it is because it uses Christian language but is not Christian. And yet we can end the episode with the hymns to The Theotokos which call her a military leader, proclaiming our victory & the establishment of our way of life. Find out why this isn't about theocracy & isn't about violence, by tuning in!Scripture citations for this episode: - Genesis 1 & 2 - Creation as a battle against chaos & death - Genesis 11 - Tower of Babel - Galatians 5:16-26 - Fruits of The SpiritThe Christian Saints Podcast is a joint production of Generative sounds & Paradosis Pavilion with oversight from Fr Symeon KeesParadosis Pavilion - https://youtube.com/@paradosispavilion9555https://www.instagram.com/christiansaintspodcasthttps://twitter.com/podcast_saintshttps://www.facebook.com/christiansaintspodcasthttps://www.threads.net/@christiansaintspodcastIconographic images used by kind permission of Nicholas Papas, who controls distribution rights of these imagesPrints of all of Nick's work can be found at Saint Demetrius Press - http://www.saintdemetriuspress.comAll music in these episodes is a production of Generative Soundshttps://generativesoundsjjm.bandcamp.comDistribution rights of this episode & all music contained in it are controlled by Generative SoundsCopyright 2021 - 2023
Generative artificial intelligence is emerging as a tool to look at how people learn language. University of Arizona professor Gondy Leroy discusses research into how advanced machine learning can help families diagnose autism through the way their children acquire speaking skills. Gondy Leroy spoke with Leslie Tolbert, Ph. D. Regent's professor in Neuroscience at the University of Arizona.
As agentic AI becomes a defining force in enterprise innovation, infrastructure has moved from a back-office concern to the beating heart of business transformation. On today's episode of the 'AI in Business' podcast, Ranjan Sinha, IBM Fellow, Vice President, and Chief Technology Officer for watsonx and IBM Research, joins Emerj Editorial Director Matthew DeMello to discuss the future of scalable AI infrastructure — from neuromorphic and quantum processing to open-source AI platforms built for trust and governance. Ranjan explains how enterprises are transitioning from isolated experiments to mission-critical AI applications, revealing why today's Fortune 500 leaders must reimagine compute, governance, and data pipelines to sustain automation and reliability at scale. He details IBM's breakthroughs in specialized processors, including the NorthPole neuromorphic chip and the company's roadmap for fault-tolerant quantum computing by 2029. Want to share your AI adoption story with executive peers? Click emerj.com/expert2 for more information and to be a potential future guest on the 'AI in Business' podcast! If you've enjoyed or benefited from some of the insights of this episode, consider leaving us a five-star review on Apple Podcasts, and let us know what you learned, found helpful, or liked most about this show! Watch Matthew and Ranjan's conversation on our new YouTube Channel: youtube.com/@EmerjAIResearch.
Sign up for Alex's first live cohort, about Hierarchical Model building!Get 25% off "Building AI Applications for Data Scientists and Software Engineers"Proudly sponsored by PyMC Labs, the Bayesian Consultancy. Book a call, or get in touch!Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work!Visit our Patreon page to unlock exclusive Bayesian swag ;)Takeaways:Why GPs still matter: Gaussian Processes remain a go-to for function estimation, active learning, and experimental design – especially when calibrated uncertainty is non-negotiable.Scaling GP inference: Variational methods with inducing points (as in GPflow) make GPs practical on larger datasets without throwing away principled Bayes.MCMC in practice: Clever parameterizations and gradient-based samplers tighten mixing and efficiency; use MCMC when you need gold-standard posteriors.Bayesian deep learning, pragmatically: Stochastic-gradient training and approximate posteriors bring Bayesian ideas to neural networks at scale.Uncertainty that ships: Monte Carlo dropout and related tricks provide fast, usable uncertainty – even if they're approximations.Model complexity ≠ model quality: Understanding capacity, priors, and inductive bias is key to getting trustworthy predictions.Deep Gaussian Processes: Layered GPs offer flexibility for complex functions, with clear trade-offs in interpretability and compute.Generative models through a Bayesian lens: GANs and friends benefit from explicit priors and uncertainty – useful for safety and downstream decisions.Tooling that matters: Frameworks like GPflow lower the friction from idea to implementation, encouraging reproducible, well-tested modeling.Where we're headed: The future of ML is uncertainty-aware by default – integrating UQ tightly into optimization, design, and deployment.Chapters:08:44 Function Estimation and Bayesian Deep Learning10:41 Understanding Deep Gaussian Processes25:17 Choosing Between Deep GPs and Neural Networks32:01 Interpretability and Practical Tools for GPs43:52 Variational Methods in Gaussian Processes54:44 Deep Neural Networks and Bayesian Inference01:06:13 The Future of Bayesian Deep Learning01:12:28 Advice for Aspiring Researchers
Send us a textThis episode digs into the habits that actually hold up: learning from CTF wins and post-event reviews, exploring scholarships and Reno trainings that build technical muscle, and walking through expert-witness prep that turns courtroom stress into structured, confident testimony.We'll unpack Brett Shavers' reminder that truth alone doesn't win cases—procedure, documentation, and bias-aware methods do. Clear writing matters too; vague language can undermine solid work.On the tools side, RabbitHole v3 now recovers deleted SQLite records and rebuilds them into query-ready databases—speeding validation and reporting without losing traceability. We'll also demo the new Android Logical Extractor: pull device info, logs, and scoped chat data with hashes and ready-to-file PDFs. It's ideal when consent is limited or full file systems aren't on the table, and integrates cleanly with downstream workflows.Throughout, we emphasize one idea: tools are abstractions. If you can't explain how a result was produced or reproduce it, you don't own the finding. That's especially true with AI. Generative models are nondeterministic—useful when documented, risky when their prompts or scope stay hidden. We'll cover prompt disclosure, reproducibility, and how to write about “deleted” data with precision: previously existing, marked deleted, not referenced—describe state, not intent.If you're serious about improving testimony, validating results, and adopting new tools without losing forensic footing, join us. Then share your take on AI prompts and language precision—what will you change in your next report?Notes: IACIS Scholarshipshttps://www.iacis.com/awards-and-scholarships/will-docken-scholarship/https://www.iacis.com/awards-and-scholarships/womens-scholarship/Training Opportunities!IACIS Renohttps://www.iacis.com/events/in-person/reno-nv/Free DFIR Test Images + Industry Tools to Analyze Themhttps://www.dfir.training/downloads/test-imagesNew Blogs from Brett Shavers!https://www.linkedin.com/pulse/theres-lot-more-trial-than-you-may-know-even-have-100-brett-shavers-br4sc/https://www.linkedin.com/pulse/case-almost-made-me-quit-dfir-shouldve-news-brett-shavers-pie1c/https://www.linkedin.com/pulse/i-when-digital-forensics-lost-its-soul-brett-shavers-otkec/https://www.linkedin.com/pulse/end-dfir-again-dfir-training-ab5jc/https://www.linkedin.com/pulse/how-wreck-your-report-affidavit-testimony-one-word-brett-shavers-qkyvc/Free Webinarhttps://www.suspectbehindthekeyboard.com/fighting-city-hall-dfir-lessons-from-a-pro-se-plaintiffRabbithole Updatehttps://www.linkedin.com/posts/rabbithole-dataviewer-sqllite-ugcPost-7384144022065274880-0d0Dhttps://www.cclsolutionsgroup.com/forensic-products/rabbitholeALEX Releasehttps://github.com/prosch88/ALEXhttps://github.com/RealityNet/android_triage
Technology can scale almost everything—except human experience. In a world driven by efficiency, what does it mean to design for how people truly feel? It's about transforming user interactions into ongoing insight and innovation, rooted in empathy and understanding. This week, Dave, Esmee and Rob talk to Kevin Magee, Chief Technology Officer at All human about helping organizations transform customer experiences with a focus on design, engineering, and what is called "digital performance." TLDR:00:41 Introduction of Kevin Magee with Guinness or sparkling water?03:23 Rob wonders, is Apple really opening up its ecosystem?11:40 Deep dive with Kevin into design, engineering, and digital performance36:30 How tools built for one purpose can transform entire systems48:35 Weekend city breaks and pursuing a master's in psychology GuestKevin Magee: https://www.linkedin.com/in/kevinmagee/ HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini
In this special crossover edition of Working People and The Marc Steiner Show, hosts Maximillian Alvarez and Marc Steiner examine how the “artificial intelligence” (AI) boom is shaping the economy and the impact it is already having—and will continue to have—on working people's lives, livelihoods, and jobs. Alvarez and Steiner speak with two members of a new mutual aid and advocacy group called Stop Gen AI, which formed this year out of the critical need to provide material support for creatives, knowledge workers, and anyone else impacted by generative AI.Guests:Kim Crawley is a former cybersecurity professor and co-author of The Pentester Blueprint. She founded Stop Gen AI in May 2025 in response to the immense socioeconomic harm generative AI has done to her and her peers, and to the vast environmental, cultural, scientific, psychological, and economic harm it does to the world. Stop Gen AI is unique for its anticapitalist focus and commitment to raising survival funds for people who are struggling.Emmi is an information security expert with experience across many niches of the industry, including application security across a number of verticals, and she is a specialist in insider threat and cyber threat intelligence. She joined the efforts of Stop Gen AI in 2025 due to the overwhelming amount of friends she has seen lose their entire lives and careers due to the out-of-control AI bubble. She also has nearly two decades of experience with boots-on-the-ground union organizing, protesting, and activism.Additional links/info: Stop Gen AI website and Mastodon page Stop Gen AI Twitch Fest informationKhiree Stewart, WBALTV 11, “'Just holding a Doritos bag': Student handcuffed after AI system mistook bag of chips for weapon”Marc Steiner & Maximillian Alvarez, The Marc Steiner Show, “Trump and Silicon Valley's plan to rule the world with AI weapons”Credits:Featured Music: Jules Taylor, Working People Theme Song; Stephen Frank, Marc Steiner Show Theme SongStudio Production: David HebdenAudio Post-Production: Alina NehlichBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-real-news-podcast--2952221/support.Help us continue producing radically independent news and in-depth analysis by following us and becoming a monthly sustainer.Follow us on:Bluesky: @therealnews.comFacebook: The Real News NetworkTwitter: @TheRealNewsYouTube: @therealnewsInstagram: @therealnewsnetworkBecome a member and join the Supporters Club for The Real News Podcast today!
In this special crossover edition of Working People and The Marc Steiner Show, hosts Maximillian Alvarez and Marc Steiner examine how the “artificial intelligence” (AI) boom is shaping the economy and the impact it is already having—and will continue to have—on working people's lives, livelihoods, and jobs. Alvarez and Steiner speak with two members of a new mutual aid and advocacy group called Stop Gen AI, which formed this year out of the critical need to provide material support for creatives, knowledge workers, and anyone else impacted by generative AI.Guests:Kim Crawley is a former cybersecurity professor and co-author of The Pentester Blueprint. She founded Stop Gen AI in May 2025 in response to the immense socioeconomic harm generative AI has done to her and her peers, and to the vast environmental, cultural, scientific, psychological, and economic harm it does to the world. Stop Gen AI is unique for its anticapitalist focus and commitment to raising survival funds for people who are struggling.Emmi is an information security expert with experience across many niches of the industry, including application security across a number of verticals, and she is a specialist in insider threat and cyber threat intelligence. She joined the efforts of Stop Gen AI in 2025 due to the overwhelming amount of friends she has seen lose their entire lives and careers due to the out-of-control AI bubble. She also has nearly two decades of experience with boots-on-the-ground union organizing, protesting, and activism.Additional links/info: Stop Gen AI website and Mastodon page Stop Gen AI Twitch Fest informationKhiree Stewart, WBALTV 11, “'Just holding a Doritos bag': Student handcuffed after AI system mistook bag of chips for weapon”Marc Steiner & Maximillian Alvarez, The Marc Steiner Show, “Trump and Silicon Valley's plan to rule the world with AI weapons”Credits:Featured Music: Jules Taylor, Working People Theme Song; Stephen Frank, Marc Steiner Show Theme SongStudio Production: David HebdenAudio Post-Production: Alina NehlichBecome a supporter of this podcast: https://www.spreaker.com/podcast/the-marc-steiner-show--4661751/support.
As generative AI becomes a defining issue in litigation, trial teams must be ready to explain the “black box” of large language models (LLMs) for juries shaping landmark cases across the US. In this episode of the IMS Insights Podcast, Senior Client Success Advisor Adam Bloomberg talks with Jury Consultant Liz Babbitt and LLM Training Expert Devon Madon, PhD, about how to make complex AI concepts clear, relatable, and persuasive in the courtroom. Together, they explore the evolving legal landscape surrounding AI copyright and fair use, practical strategies for simplifying technical ideas, and ways to address common AI misconceptions that can influence juror understanding. Gain insights on bridging the gap between advanced technology and everyday comprehension—so you can build credible, compelling narratives in your next AI-related case. Find the original LinkedIn Live recording here. For additional insights, read Liz and Devon's article, “Demystifying Generative AI for the Modern Juror,” published by Law360. Explore IMS's jury consulting and expert sourcing solutions at imslegal.com/services. IMS has delivered strategic litigation consulting and expert witness services to leading global law firms and Fortune 500 companies for more than 30 years, in more than 65,000 cases. IMS consultants become an extension of your legal team from pre-suit investigation services to discovery and then on to arbitration and trial. Learn more at imslegal.com.
We will talk about the five key personal finance use cases for Generative AI, including how to use it for creating personalized budgets, setting financial goals, and simulating debt repayment scenarios. Today's Stocks & Topics: General Mills, Inc. (GIS), Market Wrap, The Hartford Insurance Group, Inc. (HIG), Generative AI in Finance: 5 Ways to Budget, Plan, and Save, Changing Taxes Status, Leveraged ETFs, STAAR Surgical Company (STAA), Verizon Communications Inc. (VZ), Civitas Resources, Inc. (CIVI).Our Sponsors:* Check out Anthropic: https://claude.ai/INVEST* Check out Gusto: https://gusto.com/investtalk* Check out Progressive: https://www.progressive.com* Check out TruDiagnostic and use my code INVEST for a great deal: https://www.trudiagnostic.comAdvertising Inquiries: https://redcircle.com/brands
Google Ads just turned 25, and it's entering a new era—one driven by AI, conversations, and context. In this episode, Dan Taylor, VP of Global Ads at Google, joins Mike Shields to unpack how the search giant is transforming its ads business for the age of AI Overviews, Performance Max, and long-form conversational queries. He explains why this shift feels bigger than mobile, how advertisers are adopting AI faster than ever, and why trust and accuracy remain Google's north stars.Dan also reveals how AI is expanding the search funnel, creating new commercial moments that brands never could have targeted before. From tools like AI Max to agent-powered shopping, the future of advertising is about reducing friction, improving relevance, and meeting consumers wherever their curiosity starts. It's a rare inside look at how Google plans to keep Search indispensable for the next 25 years.Key Highlights
Watch the full episode on our YouTube channel: youtube.com/@mreapodcastWe've all been told to create content — but few know how to make it *trustworthy* enough for people and AI to choose it. Marcus Sheridan does. Known for transforming his struggling pool company into a global content powerhouse, Marcus built his business on a simple idea: Answer every question your buyers are asking with radical honesty.In this episode, Marcus shows us how to win attention in the age of generative search by becoming the most known and trusted voice in your market. He breaks down his Four Pillars of Trust: 1. Say what others won't, 2. Show what others won't, 3. Sell in ways others won't, and 4. Be more human than your competition. Marcus also breaks down the five topics every client is already Googling before they ever call you.From car-recorded videos to transparent pricing pages and AI-proof content strategy, Marcus gives us the blueprint to stand out in a noisy world and make both people and machines believe, “You're the real estate agent we can trust.”Resources:Read: They Ask, You Answer by Marcus SheridanRead: Endless Customers by Marcus SheridanTry: Marcus Sheridan's custom GPTs — Endless Real Estate Content Titles; Show What Others Won't; Endless Self-Service Tools by Marcus Sheridan (search “Marcus Sheridan” in GPTs)Try: AITrustSignals.com website grader for AI visibilityListen: YouTube Strategies Every Real Estate Agent Needs With Sean Cannell | The MREA Podcast (EP.76)Listen: Difficult Conversations with Phil M Jones | The MREA Podcast (EP.32)Order the Millionaire Real Estate Agent Playbook | Volume 3Connect with Jason:LinkedinProduced by NOVAThis podcast is for general informational purposes only. The views, thoughts, and opinions of the guest represent those of the guest and not Keller Williams Realty, LLC and its affiliates, and should not be construed as financial, economic, legal, tax, or other advice. This podcast is provided without any warranty, or guarantee of its accuracy, completeness, timeliness, or results from using the information.WARNING! You must comply with the TCPA and any other federal, state or local laws, including for B2B calls and texts. Never call or text a number on any Do Not Call list, and do not use an autodialer or artificial voice or prerecorded messages without proper consent. Contact your attorney to ensure your compliance.The use of generative AI is subject to limitations, including the availability and quality of the training data used to train the AI model used. Users should exercise caution and independently verify any information or output generated by the AI system utilized and should apply their own judgment and critical thinking when interpreting and utilizing the outputs of generative AI.
Episode Topic: The Truth of the Matter in the Age of Generative AI Join Soc(AI)ety Seminars, for a discussion with Tina Eliassi-Rad, the Inaugural Joseph E. Aoun Professor at Northeastern University, about the challenges of generative AI tools, and how we should consider the challenges of governance of these tools as technology continues to change rapidly.Featured Speakers: Tina Eliassi-Rad, Northeastern UniversityRead this episode's recap over on the University of Notre Dame's open online learning community platform, ThinkND: https://go.nd.edu/b204ac.This podcast is a part of the ThinkND Series titled Soc(AI)ety Seminars.Thanks for listening! The ThinkND Podcast is brought to you by ThinkND, the University of Notre Dame's online learning community. We connect you with videos, podcasts, articles, courses, and other resources to inspire minds and spark conversations on topics that matter to you — everything from faith and politics, to science, technology, and your career. Learn more about ThinkND and register for upcoming live events at think.nd.edu. Join our LinkedIn community for updates, episode clips, and more.
Learn how DataStax transformed customer feedback into a hybrid search solution that powers Fortune 500 companies through their partnership with AWS.Topics Include:AWS and DataStax discuss how quality data powers AI workloads and applications.DataStax built on Apache Cassandra powers Starbucks, Netflix, and Uber at scale.Their TIL app collects outside-in customer feedback to drive product development decisions.Hybrid search and BM25 kept trending in customer requests for several months.Customers wanted to go beyond pure vector search, not specifically BM25 itself.Research showed hybrid search improves accuracy up to 40% over single methods.ML-based re-rankers substantially outperform score-based ones despite added latency and cost.DataStax repositioned their product as a knowledge layer above the data layer.Developer-first design prioritizes simple interfaces and eliminates manual data modeling headaches.Hybrid search API uses simple dollar-sign parameters and integrates with Langflow automatically.AWS PrivateLink ensures security while Graviton processors boost efficiency and tenant density.Graviton reduced total platform operating costs by 20-30% with higher throughput.Participants:Alejandro Cantarero – Field CTO, AI, DataStaxRuskin Dantra - Senior ISV Solution Architect, AWS, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/
Ariel Weintraub is the Global CISO at Aon. In this episode, she joins Brendan Galla, Chief Product Officer at Exiger, and host Scott Schober, to discuss the widespread impact of generative AI, its role in the software development lifecycle, and more. Exiger is revolutionizing the way corporations, government agencies and banks navigate risk and compliance in their third-parties, supply chains and customers through its software and tech-enabled solutions. To learn more about our sponsor, visit https://exiger.com.
"Generative modeling is a game-changer. We can now capture high-dimensional statistics that we could never have captured in the past." Felix Herrmann explains how digital twins and generative AI are reshaping subsurface geophysics. He highlights the importance of open-source tools, multimodal data, and uncertainty-aware models for better decision-making in energy and storage projects. By combining physics with AI, his work shows how geophysics can move beyond silos and create more reliable and efficient solutions. KEY TAKEAWAYS > Digital twins informed by multimodal data can reduce uncertainty and improve reservoir management. > Open-source tools and agreed benchmarks are essential for accelerating innovation in geophysics. > Combining physics-based models with generative AI creates robust, practical solutions for complex subsurface challenges. Read Felix's article in The Leading Edge, "President's Page: Digital twins in the era of generative AI," at https://doi.org/10.1190/tle42110730.1. GUEST BIO Felix J. Herrmann earned his Ph.D. in engineering physics from Delft University of Technology in 1997, followed by research appointments at Stanford and MIT. He later joined the University of British Columbia faculty in 2002 and moved to the Georgia Institute of Technology in 2017, where he is the Georgia Research Alliance Scholar Chair in Energy with cross-appointments across multiple schools. Dr. Herrmann leads a cross-disciplinary research program in computational imaging, spanning seismic and medical applications, and is recognized for innovations in machine learning, optimization, and high-performance computing that have reduced costs in seismic data acquisition and imaging. A past SEG Distinguished Lecturer and recipient of the SEG Reginald Fessenden Award, he directs the Seismic Laboratory for Imaging and Modeling and co-founded the Center for Machine Learning for Seismic (ML4Seismic) to advance AI-assisted seismic imaging and reservoir monitoring through industry collaboration.
Dr. Aida Nematzadeh is a Senior Staff Research Scientist at Google DeepMind where her research focused on multimodal AI models. She works on developing evaluation methods and analyze model's learning abilities to detect failure modes and guide improvements. Before joining DeepMind, she was a postdoctoral researcher at UC Berkeley and completed her PhD and Masters in Computer Science from the University of Toronto. During her graduate studies she studied how children learn semantic information through computational (cognitive) modeling. Time stamps of the conversation00:00 Highlights01:20 Introduction02:08 Entry point in AI03:04 Background in Cognitive Science & Computer Science 04:55 Research at Google DeepMind05:47 Importance of language-vision in AI10:36 Impact of architecture vs. data on performance 13:06 Transformer architecture 14:30 Evaluating AI models19:02 Can LLMs understand numerical concepts 24:40 Theory-of-mind in AI27:58 Do LLMs learn theory of mind?29:25 LLMs as judge35:56 Publish vs. perish culture in AI research40:00 Working at Google DeepMind42:50 Doing a Ph.D. vs not in AI (at least in 2025)48:20 Looking back on research careerMore about Aida: http://www.aidanematzadeh.me/About the Host:Jay is a Machine Learning Engineer at PathAI working on improving AI for medical diagnosis and prognosis. Linkedin: shahjay22 Twitter: jaygshah22 Homepage: https://jaygshah.github.io/ for any queries.Stay tuned for upcoming webinars!**Disclaimer: The information in this video represents the views and opinions of the speaker and does not necessarily represent the views or opinions of any institution. It does not constitute an endorsement by any Institution or its affiliates of such video content.**
The skills we teach today will decide the world we live in tomorrow but the digital skills gap is something we've been dealing with for decades, but it's growing faster than ever, it starts with kids and stretches all the way into late IT careers, and now we're finally taking a more connected, lifelong approach to closing it. This week, Dave, Esmee, and Rob speak with Mike Nayler, Director, National Security, Defense & Public Safety at AWS about the digital skills gap and explore how tech companies can help close it. TLDR:00:45 Introduction of Mike Nayler and the pros and cons of enterprise architects, based on a survey03:30 Rob is confused about AI replacing prompt engineers07:55 Conversation with Mike on the digital skills gap25:15 The real gap is between institutions and the people they aim to serve33:24 Mike heading back to school and writing essays againGuest Mike Nayler: https://www.linkedin.com/in/nayler/ HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini
Gorkem Yurtseven is the co-founder and CEO of fal, the generative media platform powering the next wave of image, video, and audio applications. In less than two years, fal has scaled from $2M to over $100M in ARR, serving over 2 million developers and more than 300 enterprises, including Adobe, Canva, and Shopify. In this conversation, Gorkem shares the inside story of fal's pivot into explosive growth, the technical and cultural philosophies driving its success, and his predictions for the future of AI-generated media. In today's episode, we discuss: How fal pivoted from data infrastructure to generative inference fal's explosive year and how they scaled Why "generative media" is a greenfield new market fal's unique hiring philosophy and lean
Banking on Fraudology is part of the Fraudology Podcast Network. In this eye-opening episode of Banking on Fraudology, host Hailey Windham sits down with Ronan Burke, co-founder and CEO of Inscribe, to discuss the alarming rise of document fraud in financial services. As generative AI makes creating fake documents easier than ever, Burke reveals that AI-generated and template-based document fraud has surged over 200% in just the first half of 2023. The conversation dives deep into how fraudsters are weaponizing tools like ChatGPT to produce convincing forgeries of bank statements, pay stubs, and tax forms that can fool even experienced analysts. Burke explains that it's no longer just about spotting obvious fakes - financial institutions now face the challenge of proving documents are real. The episode explores why smaller banks and credit unions are especially vulnerable, and offers practical advice for fraud teams to keep up, including leveraging AI powered document verification tools. Windham and Burke stress that while AI is enabling more sophisticated fraud, it's also the key to detecting and preventing it. This timely discussion is a must listen for fraud fighters, compliance professionals, and financial leaders looking to understand and combat the next wave of AI-driven document fraud. Don't miss this chance to arm yourself with cutting edge insights. Tune in now and join the fight against financial crime in the AI era.Ronan Burkehttps://www.inscribe.ai/blog/from-forgeries-to-deepfakes-document-fraud-in-the-age-of-generative-aihttps://www.linkedin.com/in/rnnbrk/About Hailey Windham:As a 2023 CU Rockstar Recipient, Hailey Windham, CFCS (Certified Financial Crimes Specialist) demonstrated unbounding passion for educating her community, organization and credit union membership on scams in the market and best practices to avoid them. She has implemented several programs within her previous organizations that aim at holistically learning about how to prevent and detect fraud targeted at membership and employees. Windham's initiatives to build strong relationships and partnerships throughout the credit union community and industry experts have led to countless success stories. Her applied knowledge of payments system programs combined with her experience in fraud investigations offers practical concepts that are transferable, no matter the organization's size. Connect with Hailey on LinkedIn: https://www.linkedin.com/in/hailey-windham/ https://www.fraudfightclub.com/https://www.about-fraud.com/
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss the stark reality of the future of work presented at the Marketing AI Conference, MAICON 2025. You’ll learn which roles artificial intelligence will consume fastest and why average employees face the highest risk of replacement. You’ll master the critical thinking and contextual skills you must develop now to transform yourself into an indispensable expert. You’ll understand how expanding your intellectual curiosity outside your specific job will unlock creative problem solving essential for survival. You’ll discover the massive global AI blind spot that US companies ignore and how this shifting landscape affects your career trajectory. Watch now to prepare your career for the age of accelerated automation! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-maicon-2025-generative-ai-for-marketers.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn – 00:00 In this week’s In Ear Insights, we are at the Marketing AI Conference, Macon 2025 in Cleveland with 1,500 of our best friends. This morning, the CEO of SmartRx, formerly the Marketing AI Institute, Paul Ritzer, was talking about the future of work. Now, before I go down a long rabbit hole, Dave, what was your immediate impressions, takeaways from Paul’s talk? Katie Robbert – 00:23 Paul always brings this really interesting perspective because he’s very much a futurist, much like yourself, but he’s a futurist in a different way. Whereas you’re on the future of the technology, he’s focused on the future of the business and the people. And so his perspective was really, “AI is going to take your job.” If we had to underscore it, that was the bottom line: AI is going to take your job. However, how can you be smarter about it? How can you work with it instead of working against it? Obviously, he didn’t have time to get into every single individual solution. Katie Robbert – 01:01 The goal of his keynote talk was to get us all thinking, “Oh, so if AI is going to take my job, how do I work with AI versus just continuing to fight against it so that I’m never going to get ahead?” I thought that was a really interesting way to introduce the conference as a whole, where every individual session is going to get into their soldiers. Christopher S. Penn – 01:24 The chart that really surprised me was one of those, “Oh, he actually said the quiet part out loud.” He showed the SaaS business chart: SaaS software is $500 billion of economic value. Of course, AI companies are going, “Yeah, we want that money. We want to take all that money.” But then he brought up the labor chart, which is $12 trillion of money, and says, “This is what the AI companies really want. They want to take all $12 trillion and keep it for themselves and fire everybody,” which is the quiet part out loud. Even if they take 20% of that, that’s still, obviously, what is it, $2 trillion, give or take? When we think about what that means for human beings, that’s basically saying, “I want 20% of the workforce to be unemployed.” Katie Robbert – 02:15 And he wasn’t shy about saying that. Unfortunately, that is the message that a lot of the larger companies are promoting right now. So the question then becomes, what does that mean for that 20%? They have to pivot. They have to learn new skills, or—the big thing, and you and I have talked about this quite a bit this year—is you really have to tap into that critical thinking. That was one of the messages that Paul was sharing in the keynote: go to school, get your liberal art degree, and focus on critical thinking. AI is going to do the rest of it. Katie Robbert – 02:46 So when we look at the roles that are up for grabs, a lot of it was in management, a lot of it was in customer service, a lot of it was in analytics—things that already have a lot of automation around them. So why not naturally let agentic AI take over, and then you don’t need human intervention at all? So then, where does that leave the human? Katie Robbert – 03:08 We’re the ones who have to think what’s next. One of the things that Paul did share was that the screenwriter for all of the Scorsese films was saying that ChatGPT gave me better ideas. We don’t know what those exact prompts looked like. We don’t know how much context was given. We don’t know how much background information. But if that was sue and I, his name was Paul. Paul Schrader. Yes, I forgot it for a second. If Paul Schrader can look at Paul Schrader’s work, then he’s the expert. That’s the thing that I think needed to also be underscored: Paul Schrader is the expert in Paul Schrader. Paul Schrader is the expert in screenwriting those particular genre films. Nobody else can do that. Katie Robbert – 03:52 So Paul Schrader is the only one who could have created the contextual information for those large language models. He still has value, and he’s the one who’s going to take the ideas given by the large language models and turn them into something. The large language model might give him an idea, but he needs to be the one to flush it out, start to finish, because he’s the one who understands nuance. He’s the one who understands, “If I give this to a Leonardo DiCaprio, what is he gonna do with the role? How is he gonna think about it?” Because then you’re starting to get into all of the different complexities where no one individual ever truly works alone. You have a lot of other humans. Katie Robbert – 04:29 I think that’s the part that we haven’t quite gotten to, is sure, generative AI can give you a lot of information, give you a lot of ideas, and do a lot of the work. But when you start incorporating more humans into a team, the nuance—it’s very discreet. It’s very hard for an AI to pick up. You still need humans to do those pieces. Christopher S. Penn – 04:49 When you take a look, though, at something like the Tilly Norwood thing from a couple weeks ago, even there, it’s saying, “Let’s take fewer humans in there,” where you have this completely machine generated actor avatar, I guess. It was very clearly made to replace a human there because they’re saying, “This is great. They don’t have to pay union wages. The actor never calls in sick. The actor never takes a vacation. The actor’s not going to be partying at a club unless someone makes it do that.” When we look at that big chart of, “Here’s all the jobs that are up for grabs,” the $12 trillion of economic value, when you look at that, how at risk do you think your average person is? Katie Robbert – 05:39 The key word in there is average. An average person is at risk. Because if an average person isn’t thinking about things creatively, or if they’re just saying, “Oh, this is what I have to do today, let me just do it. Let me just do the bare minimum, get through it.” Yes, that person is at risk. But someone who looks at a problem or a task that’s in front of them and thinks, “What are the five different ways that I could approach this? Let me sit down for a second, really plan it out. What am I not thinking of? What have I not asked? What’s the information I don’t have in front of me? Let me go find that”—that person is less at risk because they are able to think beyond what’s right in front of them. Katie Robbert – 06:17 I think that is going to be harder to replace. So, for example, I do operations, I’m a CEO. I set the vision. You could theoretically give that to an AI to do. I could create CEO Katie GPT. And GPT Katie could set the vision, based on everything I know: “This is the direction that your company should go in.” What that generative AI doesn’t know is what I know—what we’ve tried, what we haven’t tried. I could give it all that information and it could still say, “Okay, it sounds like you’ve tried this.” But then it doesn’t necessarily know conversations that I’ve had with you offline about certain things. Could I give it all that information? Sure. But then now I’m introducing another person into the conversation. And as predictable as humans are, we’re unpredictable. Katie Robbert – 07:13 So you might say, “Katie would absolutely say this to something.” And I’m going to look at it and go, “I would absolutely not say that.” We’ve actually run into that with our account manager where she’s like, “Well, this is how I thought you would respond. This is how I thought you would post something on social media.” I’m like, “Absolutely not. That doesn’t sound like me at all.” She’s like, “But that’s what the GPT gave me that is supposed to sound like you.” I’m like, “Well, it’s wrong because I’m allowed to change my mind. I’m a human.” And GPTs or large language models don’t have that luxury of just changing its mind and just kind of winging it, if that makes sense. Christopher S. Penn – 07:44 It does. What percentage, based on your experience in managing people, what percentage of people are that exceptional person versus the average or the below average? Katie Robbert – 07:55 A small percentage, unfortunately, because it comes down to two things: consistency and motivation. First, you have to be consistent and do your thing well all the time. In order to be consistent, you have to be motivated. So it’s not enough to just show up, check the boxes, and then go about your day, because anybody can do that; AI can do that. You have to be motivated to want to learn more, to want to do more. So the people who are demonstrating a hunger for reaching—what do they call it?—punching above their weight, reaching beyond what they have, those are the people who are going to be less vulnerable because they’re willing to learn, they’re willing to adapt, they’re willing to be agile. Christopher S. Penn – 08:37 For a while now we’ve been saying that either you’re going to manage the machines or the machines are going to manage you. And now of course we are at the point the machine is just going to manage the machines and you are replaced. Given so few people have that intrinsic motivation, is that teachable or is that something that someone has to have—that inner desire to want to better, regardless of training? Katie Robbert – 09:08 “Teachable” I think is the wrong word. It’s more something that you have to tap into with someone. This is something that you’ve talked about before: what motivates people—money, security, blah, blah, whatever, all those different things. You can say, “I’m going to motivate you by dangling money in front of you,” or, “I’m going to motivate you by dangling time off in front of you.” I’m not teaching you anything. I’m just tapping into who you are as a person by understanding your motives, what motivates you, what gets you excited. I feel fairly confident in saying that your motivations, Chris, are to be the smartest person in the room or to have the most knowledge about your given industry so that you can be considered an expert. Katie Robbert – 09:58 That’s something that you’re going to continue to strive for. That’s what motivates you, in addition to financial security, in addition to securing a good home life for your family. That’s what motivates you. So as I, the other human in the company, think about it, I’m like, “What is going to motivate Chris to get his stuff done?” Okay, can I position it as, “If you do this, you’re going to be the smartest person in the room,” or, “If you do this, you’re going to have financial security?” And you’re like, “Oh, great, those are things I care about. Great, now I’m motivated to do them.” Versus if I say, “If you do this, I’ll get off your back.” That’s not enough motivation because you’re like, “Well, you’re going to be on my back anyway.” Katie Robbert – 10:38 Why bother with this thing when it’s just going to be the next thing the next day? So it’s not a matter of teaching people to be motivated. It’s a matter of, if you’re the person who has to do the motivating, finding what motivates someone. And that’s a very human thing. That’s as old as humans are—finding what people are passionate about, what gets them out of bed in the morning. Christopher S. Penn – 11:05 Which is a complex interplay. If you think about the last five years, we’ve had a lot of discussions about things like quiet quitting, where people show up to work to do the bare minimum, where workers have recognized companies don’t have their back at all. Katie Robbert – 11:19 We have culture and pizza on Fridays. Christopher S. Penn – 11:23 At 5:00 PM when everyone wants to just— Katie Robbert – 11:25 Go home and float in that day. Christopher S. Penn – 11:26 Exactly. Given that, does that accelerate the replacement of those workers? Katie Robbert – 11:37 When we talk about change management, we talk about down to the individual level. You have to be explaining to each and every individual, “What’s in it for me?” If you’re working for a company that’s like, “Well, what’s in it for you is free pizza Fridays and funny hack days and Hawaiian shirt day,” that doesn’t put money in their bank account. That doesn’t put a roof over their head; that doesn’t put food on their table, maybe unless they bring home one of the free pizzas. But that’s once a week. What about the other six days a week? That’s not enough motivation for someone to stay. I’ve been in that position, you’ve been in that position. My first thought is, “Well, maybe stop spending money on free pizza and pay me more.” Katie Robbert – 12:19 That would motivate me, that would make me feel valued. If you said, “You can go buy your own pizza because now you can afford it,” that’s a motivator. But companies aren’t thinking about it that way. They’re looking at employees as just expendable cogs that they can rip and replace. Twenty other people would be happy to do the job that you’re unhappy doing. That’s true, but that’s because companies are setting up people to fail, not to succeed. Christopher S. Penn – 12:46 And now with machinery, you’re saying, “Okay, since there’s a failing cog anyway, why don’t we replace it with an actual cog instead?” So where does this lead for companies? Particularly in capitalist markets where there is no strong social welfare net? Yeah, obviously if you go to France, you can work a 30-hour week and be just fine. But we don’t live in France. France, if you’re hiring, we’re available. Where does it lead? Because I can definitely see one road where this leads to basically where France ended up in 1789, which is the Guillotines. These people trot out the Guillotines because after a certain point, income inequality leads to that stuff. Where does this lead for the market as you see it now? Katie Robbert – 13:39 Unfortunately, nowhere good. We have seen time and time again, as much as we want to see the best in people, we’re seeing the worst in people today, as of this podcast recording—not at Macon. These are some of the best people. But when you step outside of this bubble, you’re seeing the worst in people. They’re motivated by money and money only, money and power. They don’t care about humanity as a whole. They’re like, “I don’t care if you’re poor, get poorer, I’m getting richer.” I feel like, unfortunately, that is the message that is being sent. “If you can make a dollar, go ahead and make a dollar. Don’t worry about what that does to anybody else. Go ahead and be in it for yourself.” Katie Robbert – 14:24 And that’s unfortunately where I see a lot of companies going: we’re just in it to make money. We no longer care about the welfare of our people. I’ve talked on previous shows, on previous podcasts. My husband works for a grocery store that was bought out by Amazon a few years ago, and he’s seeing the effects of that daily. Amazon bought this grocery chain and said basically, “We don’t actually care about the people. We’re going to automate things. We’re going to introduce artificial intelligence.” They’ve gotten rid of HR. He still has to bring home a physical check because there is no one to give him paperwork to do direct deposit. Christopher S. Penn – 15:06 He’s been—ironic given the company. Katie Robbert – 15:08 And he’s been at the company for 25 years. But when they change things over, if he has an assurance question, there’s no one to go to. They probably have chatbots and an email distribution list that goes to somebody in an inbox that never. It’s so sad to see the decline based on where the company started and what the mission originally was of that company to where it is today. His suspicion—and this is not confirmed—his suspicion is that they are gearing up to sell this business, this grocery chain, to another grocery chain for profit and get rid of it. Flipping it, basically. Right now, they’re using it as a distribution center, which is not what it’s meant to be. Katie Robbert – 15:56 And now they’re going to flip it to another grocery store chain because they’ve gotten what they needed from it. Who cares about the people? Who cares about the fact that he as an individual has to work 50 hours a week because there’s nobody else? They’ve flattened the company. They’re like, “No, based on our AI scheduler, there’s plenty of people to cover all of these hours seven days a week.” And he’s like, “Yeah, you have me on there for seven of the seven days.” Because the AI is not thinking about work-life balance. It’s like, “Well, this individual is available at these times, so therefore he must be working here.” And it’s not going to do good things for people in services industries, for people in roles that cannot be automated. Katie Robbert – 16:41 So we talk about customer service—that’s picking up the phone, logging a plate—that can be automated. Walking into a brick and mortar, there are absolutely parts of it that can be automated, specifically the end purchase transaction. But the actual ordering and picking of things and preparing it—sure, you could argue that eventually robots could be doing that, but as of today, that’s all humans. And those humans are being treated so poorly. Christopher S. Penn – 17:08 So where does that end for this particular company or any large enterprise? Katie Robbert – 17:14 They really have—they have to make decisions: do they want to put the money first or the people first? And you already know what the answer to that is. That’s really what it comes down to. When it ends, it doesn’t end. Even if they get sold, they’re always going to put the money first. If they have massive turnover, what do they care? They’re going to find somebody else who’s willing to do that work. Think about all of those people who were just laid off from the white-collar jobs who are like, “Oh crap, I still have a mortgage I have to pay, I still have a family I have to feed. Let me go get one of those jobs that nobody else is now willing to do.” Katie Robbert – 17:51 I feel like that’s the way that the future of work for those people who are left behind is going to turn over. Katie Robbert – 17:59 There’s a lot of people who are happy doing those jobs. I love doing more of what’s considered the blue-collar job—doing things manually, getting their hands in it, versus automating everything. But that’s me personally; that’s what motivates me. That I would imagine is very unappealing to you. Not that for almost. But if cooking’s off the table, there’s a lot of other things that you could do, but would you do them? Katie Robbert – 18:29 So when we talk about what’s going to happen to those people who are cut and left behind, those are the choices they’re going to have to make because there’s not going to be more tech jobs for them to choose from. And if you are someone in your career who has only ever focused on one thing, you’re definitely in big trouble. Christopher S. Penn – 18:47 Yeah, I have a friend who’s a lawyer at a nonprofit, and they’re like, “Yeah, we have no funding anymore, so.” But I can’t pick up and go to England because I can’t practice law there. Katie Robbert – 18:59 Right. I think about people. Forever, social media was it. You focus on social media and you are set. Anybody will hire you because they’re trying to learn how to master social media. Guess where there’s no jobs anymore? Social media. So if all you know is social media and you haven’t diversified your skill set, you’re cooked, you’re done. You’re going to have to start at ground zero entry level. If there’s that. And that’s the thing that’s going to be tough because entry-level jobs—exactly. Christopher S. Penn – 19:34 We saw, what was it, the National Labor Relations Board publish something a couple months ago saying that the unemployment rate for new college graduates is something 60% higher than the rest of the workforce because all the entry-level jobs have been consumed. Katie Robbert – 19:46 Right. I did a talk earlier this year at WPI—that’s Worcester Polytech in Massachusetts—through the Women in Data Science organization. We were answering questions basically like this about the future of work for AI. At a technical college, there are a lot of people who are studying engineering, there are a lot of people who are studying software development. That was one of the first questions: “I’m about to get my engineering degree, I’m about to get my software development degree. What am I supposed to do?” My response to that is, you still need to understand how the thing works. We were talking about this in our AI for Analytics workshop yesterday that we gave here at Macon. In order to do coding in generative AI effectively, you have to understand the software development life cycle. Katie Robbert – 20:39 There is still a need for the expertise. People are asking, “What do I do?” Focus on becoming an expert. Focus on really mastering the thing that you’re passionate about, the thing that you want to learn about. You’ll be the one teaching the AI, setting up the AI, consulting with the people who are setting up the AI. There’ll be plenty of practitioners who can push the buttons and set up agents, but they still need the experts to tell them what it’s supposed to do and what the output’s supposed to be. Christopher S. Penn – 21:06 Do you see—this is kind of a trick question—do you see the machines consuming that expertise? Katie Robbert – 21:15 Oh, sure. But this is where we go back to what we were talking about: the more people, the more group think—which I hate that term—but the more group think you introduce, the more nuanced it is. When you and I sit down, for example, when we actually have five minutes to sit down and talk about the future of our business, where we want to go or what we’re working on today, the amount of information we can iterate on because we know each other so well and almost don’t have to speak in complete sentences and just can sort of pick up what the other person is thinking. Or I can look at something you’re writing and say, “Hey, I had an idea about that.” We can do that as humans because we know each other so well. Katie Robbert – 21:58 I don’t think—and you’re going to tell me this is going to happen—unless we can actually plug or forge into our brains and download all of the things. That’s never going to happen. Even if we build Katie GPT and Chris GPT and have them talk to each other, they’re never going to brainstorm the way you and I brainstorm in real life. Especially if you give me a whiteboard. I’m good. I’m going to get so much done. Christopher S. Penn – 22:25 For people who are in their career right now, what do they do? You can tell somebody, “You need to be a good critical thinker, a creative thinker, a contextual thinker. You need to know where your data lives and things like that.” But the technology is advancing at such a fast rate. I talk about this in the workshops that we do—which, by the way, Trust Insights is offering workshops at your company, if we like one. But one of the things to talk about is, say, with the model’s acceleration in terms of growth, they’re growing faster than any technology ever has. They went from face rolling idiot in 2023 right to above PhD level in everything two years later. Christopher S. Penn – 23:13 So the people who, in their career, are looking at this, going, “It’s like a bad Stephen King movie where you see the thing coming across the horizon.” Katie Robbert – 23:22 There is no such thing as a bad Stephen King movie. Sometimes the book is better, but it’s still good. But yes, maybe *Creepshow*. What do you mean in terms of how do they prepare for the inevitable? Christopher S. Penn – 23:44 Prepare for the inevitable. Because to tell somebody, “Yeah, be a critical thinker, be a contextual thinker, be a creative thinker”—that’s good in the abstract. But then you’re like, “Well, my—yeah, my—and my boss says we’re doing a 10% headcount reduction this week.” Katie Robbert – 24:02 This is my personal way of approaching it: you can’t limit yourself to just go, “Okay, think about it. Okay, I’m thinking.” You actually have to educate yourself on a variety of different things. I am a voracious reader. I read all the time when I’m not working. In the past three weeks, I’ve read four books. And they’re not business books; they are fiction books and on a variety of things. But what that does is it keeps my brain active. It keeps my brain thinking. Then I give myself the space and time. When I walk my dog, I sort of process all of it. I think about it, and then I start thinking about, “What are we doing as our company today?” or, “What’s on the task list?” Katie Robbert – 24:50 Because I’ve expanded my personal horizons beyond what’s right in front of me, I can think about it from the perspective of other people, fictional or otherwise, “How would this person approach it?” or, “What would I do in that scenario?” Even as I’m reading these books, I start to think about myself. I’m like, “What would I do in that scenario? What would I do if I was finding myself on a road trip with a cannibal who, at the end of the road trip, was likely going to consume all of me, including my bones?” It was the last book I read, and it was definitely not what I thought I was signing up for. But you start to put yourself in those scenarios. Katie Robbert – 25:32 That’s what I personally think unlocks the critical thinking, because you’re not just stuck in, “Okay, I have a math problem. I have 1 + 1.” That’s where a lot of people think critical thinking starts and ends. They think, “Well, if I can solve that problem, I’m a critical thinker.” No, there’s only one way to solve that problem. That’s it. I personally would encourage people to expand their horizons, and this comes through having hobbies. You like to say that you work 24/7. That’s not true. You have hobbies, but they’re hobbies that help you be creative. They’re hobbies that help you connect with other people so that you can have those shared experiences, but also learn from people from different cultures, different backgrounds, different experiences. Katie Robbert – 26:18 That’s what’s going to help you be a stronger, fitable thinker, because you’re not just thinking about it from your perspective. Christopher S. Penn – 26:25 Switching gears, what was missing, what’s been missing, and what is absent from this show in the AI space? I have an answer, but I want to hear yours. Katie Robbert – 26:36 Oh, boy. Really putting me on the spot here. I know what is missing. I don’t know. I’m going to think about it, and I am going to get back to you. As we all know, I am not someone who can think on my feet as quickly as you can. So I will take time, I will process it, but I will come back to you. What do you think is missing? Christopher S. Penn – 27:07 One of the things that is a giant blind spot in the AI space right now is it is a very Western-centric view. All the companies say OpenAI and Anthropic and Google and Meta and stuff like that. Yet when you look at the leaderboards online of whose models are topping the charts—Cling Wan, Alibaba, Quinn, Deepseek—these are all Chinese-made models. If you look at the chip sets being used, the government of China itself just issued an edict: “No more Nvidia chips. We are going to use Huawei Ascend 920s now,” which are very good at what they do. And the Chinese models themselves, these companies are just giving them away to the world. Christopher S. Penn – 27:54 They’re not trying to lock you in like a ChatGPT is. The premise for them, for basically the rest of the world that is in America, is, “Hey, you could take American AI where you’re locked in and you’re gonna spend more and more money, or here’s a Chinese model for free and you can build your national infrastructure on the free stuff that we’re gonna give you.” I’ve seen none of that here. That is completely absent from any of the discussions about what other nations are doing with AI. The EU has Mistral and Black Forest Labs, Sub-Saharan Africa has Lilapi AI. Singapore has Sea Lion, Korea has LG, the appliance maker, and their models. Of course, China has a massive footprint in the space. I don’t see that reflected anywhere here. Christopher S. Penn – 28:46 It’s not in the conversations, it’s not in the hallways, it’s not on stage. And to me, that is a really big blind spot if you think—as many people do—that that is your number one competitor on the world stage. Katie Robbert – 28:57 Why do you think? Christopher S. Penn – 29:01 That’s a very complicated question. But it involves racism, it involves a substantial language barrier, it involves economics. When your competitor is giving away everything for free, you’re like, “Well, let’s just pretend they’re not there because we don’t want to draw any attention to them.” And it is also a deep, deep-seated fear. When you look at all of the papers that are being submitted by Google and Facebook and all these other different companies and you look at the last names of the principal investigators and stuff, nine out of 10 times it’s a name that’s coded as an ethnic Chinese name. China produces more PhDs than I think America produces students, just by population dynamics alone. You have this massive competitor, and it almost feels like people just want to put their heads in the sand and say they’re not there. Christopher S. Penn – 30:02 It’s like the boogeyman, they’re not there. And yet if we’re talking about the deployment of AI globally, the folks here should be aware that is a thing that is not just the Sam Alton Show. Katie Robbert – 30:18 I think perhaps then, as we’re talking about the future of work and big companies, small companies, mid-sized companies, this goes sort of back to what I was saying: you need to expand your horizons of thinking. “Well, we’re a domestic company. Why do I need to worry about what China’s doing?” Take a look at your tech stack, and where are those software packages created? Who’s maintaining them? It’s probably not all domestic; it’s probably more of a global firm than you think you are. But we think about it in terms of who do we serve as customers, not what we are using internally. We know people like Paul has talked about operating systems, Ginny Dietrich has talked about operating systems. Katie Robbert – 31:02 That’s really sort of where you have to start thinking more globally in terms of, “What am I actually bringing into my organization?” Not just my customer base, not just the markets that I’m going after, not just my sales team territories, but what is actually powering my company. That’s, I think, to your point—that’s where you can start thinking more globally even if your customer base isn’t global. That might theoretically help you with that critical thinking to start expanding beyond your little homogeneous bubble. Christopher S. Penn – 31:35 Even something like this has been a topic in the news recently. Rare earth minerals, which are not rare, they’re actually very commonplace. There’s just not much of them in any one spot. But China is the only economy on the planet that has figured out how to industrialize them safely. They produce 85% of it on the planet. And that powers your smartphone, that powers your refrigerator, your car and, oh by the way, all of the AI chips. Even things like that affect the future of work and the future of AI because you basically have one place that has a monopoly on this. The same for the Netherlands. The Netherlands is the only country on the planet that produces a certain kind of machine that is used to create these chips for AI. Christopher S. Penn – 32:17 If that company goes away or something, the planet as a whole is like, “Well, I figured they need to come up with an alternative.” So to your point, we have a lot of these choke points in the AI value chain that could be blockers. Again, that’s not something that you hear. I’ve not heard that at any conference. Katie Robbert – 32:38 As we’re thinking about the future of work, which is what we’re talking about on today’s podcast at Macon, 1,500 people in Cleveland. I guarantee they’re going to do it again next year. So if you’re not here this year, definitely sign up for next year. Take a look at the Smarter X and their academy. It’s all good stuff, great people. I think—and this was the question Paul was asking in his keynote—”Where do we go from here?” The— Katie Robbert – 33:05 The atmosphere. Yes. We don’t need—we don’t need to start singing. I do not need. With more feeling. I do get that reference. You’re welcome. But one of the key takeaways is there are more questions than answers. You and I are asking each other questions, but there are more questions than answers. And if we think we have all of the answers, we’re wrong. We have the answers that are sufficient enough for today to keep our business moving forward. But we have to keep asking new questions. That also goes into that critical thinking. You need to be comfortable not knowing. You need to be comfortable asking questions, and you need to be comfortable doing that research and seeking it out and maybe getting it wrong, but then continuing to learn from it. Christopher S. Penn – 33:50 And the future of work, I mean, it really is a very cloudy crystal wall. We have no idea. One of the things that Paul pointed out really well was you have different scaling laws depending on where you are in AI. He could have definitely spent some more time on that, but I understand it was a keynote, not a deep dive. There’s more to that than even that. And they do compound each other, which is what’s creating this ridiculously fast pace of AI evolution. There’s at least one more on the way, which means that the ability for these tools to be superhuman across tasks is going to be here sooner than people think. Paul was saying by 2026, 2027, that’s what we’ll start to see. Robotics, depends on where you are. Christopher S. Penn – 34:41 What’s coming out of Chinese labs for robots is jaw dropping. Katie Robbert – 34:45 I don’t want to know. I don’t want to know. I’ve seen *Ex Machina*, and I don’t want to know. Yeah, no. To your point, I think a lot of people bury their head in the sand because of fear. But in order to, again, it sort of goes back to that critical thinking, you have to be comfortable with the uncomfortable. I’m sort of joking: “I don’t want to know. I’ve seen *Ex Machina*.” But I do want to know. I do need to know. I need to understand. Do I want to be the technologist? No. But I need to play with these tools enough that I feel I understand how they work. Yesterday I was playing in Opal. I’m going to play in N8N. Katie Robbert – 35:24 It’s not my primary function, but it helps me better understand where you’re coming from and the questions that our clients are asking. That, in a very simple way to me, is the future of work: that at least I’m willing to stretch myself and keep exploring and be uncomfortable so that I can say I’m not static. Christopher S. Penn – 35:46 I think one of the things that 3M was very well known for in the day was the 20% rule, where an employee, as part of their job, could have 20% of the time just work on side projects related to the company. That’s how Post-it Notes got invented, I think. I think in the AI forward era that we’re in, companies do need to make that commitment again to the 20% rule. Not necessarily just messing around, but specifically saying you should be spending 20% of your time with AI to figure out how to use it, to figure out how to do some of those tasks yourself, so that instead of being replaced by the machine, you’re the one who’s at least running the machine. Because if you don’t do that, then the person in the next cubicle will. Christopher S. Penn – 36:33 And then the company’s like, “Well, we used to have 10 people, we only need two. And you’re not one of the two who has figured out how to use this thing to do that. So out you go.” Katie Robbert – 36:41 I think that was what Paul was doing in his AI for Productivity workshop yesterday, was giving people the opportunity to come up with those creative ideas. Our friend Andy Crestadino was relaying a story yesterday to us of a very similar vein where someone was saying, “I’ll give you $5,000. Create whatever you want.” And the thing that the person created was so mind-blowing and so useful that he was like, “Look what happens when I just let people do something creative.” But if we bring it sort of back whole circle, what’s the motivation? Why are people doing it in the first place? Katie Robbert – 37:14 It has to be something that they’re passionate about, and that’s going to really be what drives the future of work in terms of being able to sustain while working alongside AI, versus, “This is all I know how to do. This is all I ever want to know how to do.” Yes, AI is going over your job. Christopher S. Penn – 37:33 So I guess wrapping up, we definitely want you thinking creatively, critically, contextually. Know where your data is, know where your ideas come from, broaden your horizons so that you have more ideas, and be able to be one of the people who knows how to call BS on the machines and say, “That’s completely wrong, ChatGPT.” Beyond that, everyone has an obligation to try to replace themselves with the machines before someone else does it to you. Katie Robbert – 38:09 I think again, to plug Macon, which is where we are as we’re recording this episode, this is a great starting point for expanding your horizons because the amount of people that you get to network with are from different companies, different experiences, different walks of life. You can go to the sessions, learn it from their point of view. You can listen to Paul’s keynote. If you think you already know everything about your job, you’re failing. Take the time to learn where other people are coming from. It may not be immediately relevant to you, but it could stick with you. Something may resonate, something might spark a new idea. Katie Robbert – 38:46 I feel like we’re pretty far along in our AI journey, but in sitting in Paul’s keynote, I had two things that stuck out to me: “Oh, that’s a great idea. I want to go do that.” That’s great. I wouldn’t have gotten that otherwise if I didn’t step out of my comfort zone and listen to someone else’s point of view. That’s really how people are going to grow, and that’s that critical thinking—getting those shared experiences and getting that brainstorming and just community. Christopher S. Penn – 39:12 Exactly. If you’ve got some thoughts about how you are approaching the future of work, pop on by our free Slack group. Go to trust insights AI analysts for marketers, where you and over 4,500 other marketers are asking and answering each other’s questions every single day. Wherever you watch or listen to the show, if there’s a channel you’d rather have it on instead, go to Trust Insights AI Ti Podcast, where you can find us all the places fine podcasts are served. Thanks for tuning in. I’ll talk to you on the next one. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. 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As we continue our discussion of the difficulty in integrating the journey on the way of the life of faithfulness to the evengalion of Jesus, The Christ, the Orthodox Christian way of life, into contemporary western culture, Jim & Fr Symeon take the opportunity to discuss "the other side". Last conversation we defined & critique "woke" as the civil religion of the so-called Left. This time we will define & critique "Make America Great Again" as the civil religion of the so-called Right. The reason we wish to critique it is because it uses Christian language but is not Christian. In fact, by the end of this first half of the conversation Fr Symeon will straight up ask "...what religion are you following?!"Scripture citations for this episode: - Genesis 1 & 2 - Creation as a battle against chaos & death - Genesis 11 - Tower of Babel - Galatians 5:16-26 - Fruits of The Spirit#nostalgia #maga #goldenage #conservativeThe Christian Saints Podcast is a joint production of Generative sounds & Paradosis Pavilion with oversight from Fr Symeon KeesParadosis Pavilion - https://youtube.com/@paradosispavilion9555https://www.instagram.com/christiansaintspodcasthttps://twitter.com/podcast_saintshttps://www.facebook.com/christiansaintspodcasthttps://www.threads.net/@christiansaintspodcastIconographic images used by kind permission of Nicholas Papas, who controls distribution rights of these imagesPrints of all of Nick's work can be found at Saint Demetrius Press - http://www.saintdemetriuspress.comAll music in these episodes is a production of Generative Soundshttps://generativesoundsjjm.bandcamp.comDistribution rights of this episode & all music contained in it are controlled by Generative SoundsCopyright 2021 - 2023
AI Unraveled: Latest AI News & Trends, Master GPT, Gemini, Generative AI, LLMs, Prompting, GPT Store
Welcome to AI Unraveled, your essential daily briefing on the real-world business impact of AI.Today, we interrupt the daily rundown for a critical special episode: a deep dive into the Generative AI Revolution, 2025 and Beyond. We're talking about the fundamental re-architecture of the enterprise. We have broken down a comprehensive new report that charts the strategic battles between closed-source titans like OpenAI and Google, and the fiercely competitive open-source challengers. This isn't just theory; we reveal how GenAI is forcing a re-architecture of workflows across finance, healthcare, and software development, creating entirely new value streams.But first, a word on why most enterprise AI initiatives fail. [thoughtful] There's a reason they never make it to production: You can't find a platform that's both powerful and secure enough. The result? AI budgets burned with zero business impact. But not anymore. AIRIA is the Enterprise AI platform that delivers speed without compromise. Unlike platforms that force you to choose between fast deployment or secure operations, Airia brings speed and security together. Launch quickly without cutting corners on compliance. Scale rapidly without sacrificing governance. Ready for AI at full speed with zero compromise? Visit airia.com to see the platform in action. That's A-I-R-I-A dot com – Simplify enterprise AI. When we return, we analyze the three key trends that will define the future—from multimodal systems to the escalating need for better governance frameworks. This is your competitive blueprint for moving beyond pilots and achieving real, scalable enterprise adoption. If you rely on this show for your strategic insights, please take a moment right now to like and subscribe to the podcast! Now, let's unravel the future of Generative AI. Stick with us.
This week's guest is Dean Leitersdorf, CEO of Decart. Decart is a vertically integrated AI research lab building some of the world's most advanced audio and video models, including Mirage, a real-time generative video system where users can transform themselves or their environments instantly.We dive into Dean's path from finishing his PhD in computer science at 21 to founding Decart with the ambition of creating a true “before-and-after” company in AI. We cover how Decart evolved from GPU optimization to leading-edge video models, why Mirage has attracted massive adoption in advertising and ecommerce, and what it means to build entirely new consumer experiences on the internet. We also discuss the broader enterprise interest in real-time video, how Decart balances continuous consumer iteration with lab-first research, and why Dean believes this is one of the few moments in history where founders can truly build trillion-dollar businesses.Episode Chapters: 2:10 — From Israel to Palo Alto4:22 — What Dean is running toward6:15 — Building for the future of AI9:40 — How people use Decart14:05 — Building a model from scratch19:08 — Who adopts these models first24:05 — Decart's focus areas27:06 — Attracting world-class talent30:00 — Choosing to integrate or partner34:04 — Thinking about consumer impact39:38 — Quick fire roundThis episode is brought to you by Grata, the world's leading deal sourcing platform. Our AI-powered search, investment-grade data, and intuitive workflows give you the edge needed to find and win deals in your industry. Visit grata.com to schedule a demo today.Fresh out of Y Combinator's Summer batch, Overlap is an AI-driven app that uses LLMs to curate the best moments from podcast episodes. Imagine having a smart assistant who reads through every podcast transcript, finds the best parts or parts most relevant to your search, and strings them together to form a new curated stream of content - that is what Overlap does. Podcasts are an exponentially growing source of unique information. Make use of it! Check out Overlap 2.0 on the App Store today.
In 'Access All Areas' shows we go behind the scenes with the crew and their friends as they dive into complex challenges that organisations face—sometimes getting a little messy along the way. We're launching a special AI mini-series exploring how artificial intelligence is reshaping industries. Each episode dives into key themes like scaling AI, societal impact, leadership, sustainability, and the challenges ahead. Join us for fresh insights and bold conversations on the future of intelligent systems. This week, Dave, Esmee, and Rob kick off the AI mini-series with Craig Suckling, CAIO at Capgemini and co-host of this special edition. The episode is inspired by “Riding the AI Whirlwind,” Gartner's 2025 strategic predictions report, which urges organizations to act boldly on AI's potential while managing risks like rising costs and privacy concerns TLDR:00:40 – Introduction of Craig Suckling and launch of the AI mini-series02:38 – Summary of three key insights and strategic recommendations from Gartner's “Riding the AI Whirlwind” report23:03 – Strategic planning assumptions: what they mean for business and tech leaders41:40 – Sam Altman's top three concerns about the future of AI49:35 – What key topics remain unaddressed?51:00 – What to expect from the AI mini-series featuring industry leadersHostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/with co-host Craig Suckling: https://www.linkedin.com/in/craigsuckling/ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini
Watch or listen to episode 306 of the Digital and Social Media Sports podcast, in which Neil chatted with Brian Hough, Co-Founder and CEO, PressBox. Hough discusses PressBox’s technology that automatically produces personalized, multi-modal content experiences for fans of any sport. Hough also talks about the insights and lessons he’s picked up throughout his career, … Continue reading Episode 306: PressBox’s Brian Hough on Personalization, Content Innovation, and Generative AI at Scale in Sports
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We discuss how people are responding to the growing role of AI in news and wider society In this episode of Future of Journalism we discuss one of the hottest topics in journalism right now which is how people are responding to the growing role of AI in news and wider society. We'll look at how generative AI tools are being used, how people engage with AI-generated answers in online searches, and AI's role in newsrooms and wider society. Speakers: Dr Felix M. Simon is a (political) communication researcher and Research Fellow in AI and Digital News at the Reuters Institute for the Study of Journalism. Before joining us, he was a doctoral student at the Oxford Internet Institute (OII), where he is a Research Associate. Host Mitali Mukherjee is the Director of the Reuters Institute and is a political economy journalist with more than two decades of experience in TV, print and digital journalism. You can find a full transcript of the podcast on our website: https://reutersinstitute.politics.ox.ac.uk/news/our-podcast-how-people-are-using-generative-ai-and-what-means-news
What if the next big leap in business AI isn't generative at all, but predictive? That's the question at the heart of my conversation with Zohar Bronfman, CEO and co-founder of Pecan AI, a company helping business teams forecast outcomes with precision and turn historical data into future insights. Zohar explains why he believes predictive AI will deliver far greater enterprise value than the generative models dominating headlines. He points to research showing that most generative AI projects fail to produce ROI, while predictive systems built on a company's own data can directly improve revenue, reduce churn, and guide smarter decisions. With Pecan's no-code platform, marketing and operations teams can now create predictive models without needing data scientists—bridging the gap between technical expertise and business execution. Through stories like Little Spoon's, a direct-to-consumer baby food brand that used Pecan AI to identify and retain at-risk customers, Zohar illustrates how predictive analytics turns data into real business impact. He also shares common mistakes companies make when implementing AI—starting with unclear objectives and misaligned resources—and why success depends on defining the problem before choosing the tool. Looking ahead, Zohar envisions predictive AI as the backbone of every organization, shifting business intelligence from reactive analysis to proactive action. As companies move beyond dashboards and toward dynamic decision-making, predictive insights may soon become as fundamental as spreadsheets. So, if your company could anticipate every challenge before it happened, how different would your strategy look? And are business leaders finally ready to treat predictive AI as core infrastructure rather than a passing trend? Share your thoughts after the episode.
Every day, billions of searches flow through Google, making it not just the world's most popular search engine, but one of history's most valuable products. Yet for the first time in nearly 30 years, the company's dominance is under threat. Generative artificial intelligence tools like Open AI's ChatGPT and Perplexity are changing how people find information. On the latest episode of the Bold Names podcast, Liz Reid, VP, head of Google Search, speaks to WSJ's Christopher Mims and Tim Higgins about transforming search for the age of AI. After more than two decades inside the company, Reid says that Google has weathered disruption before and believes this moment will expand, not erode, how people explore the web. But can Google Search survive in a world of AI chatbots and answer engines? To watch the video version of this episode, visit our WSJ Podcasts YouTube channel or the video page of WSJ.com. Check Out Past Episodes: Condoleezza Rice on Beating China in the Tech Race: 'Run Hard and Run Fast' The Google-Backed Startup Taking on Elon Musk in Humanoid Robotics Reid Hoffman Says AI Isn't an ‘Arms Race,' but America Needs to Win Why IBM's CEO Thinks His Company Can Crack Quantum Computing Let us know what you think of the show. Email us at BoldNames@wsj.com Sign up for the WSJ's free Technology newsletter. Read Christopher Mims's Keywords column. Read Tim Higgins's column. Learn more about your ad choices. Visit megaphone.fm/adchoices
Leadership is rapidly evolving as artificial intelligence becomes deeply integrated into how organizations operate and make decisions. In a world where volatility, uncertainty, complexity, and ambiguity are the norm, leaders are being challenged to not only understand the role of AI—but also to harness it in ways that enhance what makes leadership truly human. This episode dives into why leaders can no longer afford to treat AI as a separate technical domain, but rather as a strategic partner that augments decision making, stretches creativity, and humanizes leadership itself. As generative AI rapidly advances, the question is not whether leaders should use these tools, but how to do so thoughtfully, ethically, and effectively. This conversation explores how leaders can remain at the center, leverage augmentation, and make deliberate choices that will define competitive advantage and organizational health in the next decade. The episode also emphasizes the necessity of skepticism, risk awareness, and the cultivation of skills—like “human calming”—that will set exceptional leaders apart in an AI-rich world. Timestamped Overview [00:05:27] Introduction to AI and Leadership: Exploring why AI is now essential to leadership development and future success.[00:07:00] The Human Core of Leadership: Discussing the enduring need for human-centered leadership—augmented, not replaced, by technology.[00:08:34] Evolution of Leadership Skills: How classic leadership skills stand the test of time, but require a generative AI lens.[00:09:34] Rethinking “Cyborg Leadership”: Moving beyond science fiction to practical digital augmentation for leaders.[00:11:46] Developing the 10 AI-Augmented Leadership Skills: Why these skills matter and the unique place of “human calming.”[00:15:06] The Role of Human Calming: Centering leaders' intention and composure in an AI-driven world.[00:17:36] The Value of Skepticism: Why questioning, challenging, and stretching assumptions is vital in adopting new technology.[00:19:17] Embracing vs. Rejecting AI: Strategies for experimenting, learning, and building organizational trust with emerging tools.[00:24:00] Facing Risks and Unknowns: Assessing current and future risks—including cyber threats and over-focusing on efficiency.[00:30:03] Effectiveness vs. Efficiency: Shifting the leadership focus toward innovation, not just automation.[00:34:03] All Hands on Deck: Why AI is a human and organizational story—not just a technical one.[00:35:12] Future-Back Thinking: Blending human and machine, and why leaders must choose to play and prototype.[00:39:44] Managing the Noise: How scalable foresight and intentional augmentation can help leaders cut through information overload.[00:43:35] Practical Takeaways: Real-life examples of how leaders can use AI tools to augment creativity and effectiveness.[00:46:22] Embracing Skeptical Foresight: Encouraging leaders to challenge assumptions and stretch their strategic thinking.[00:50:02] Don't Get Discouraged: The learning curve of AI and the importance of hands-on experimentation. For the complete show notes be sure to check out our website: https://leaddontboss.com/357
As people move away from traditional search engines and turn to AI tools for answers, Leah Nurik, Co-Founder and CEO of Brandi.ai, joins host Mike Maynard to explore how this change is transforming the way brands build visibility. Leah shares how her background in tech and agency leadership led to the creation of Brandi, a platform that helps companies influence how AI engines like ChatGPT, Gemini, and Claude understand and present their brands. She explains why Generative Engine Optimization (GEO) is emerging as the new SEO, how brands can take control of their visibility in AI-driven search, and what it takes to stay relevant as the rules of digital discovery evolve. Leah also discusses the human side of marketing in the AI era, and why critical thinking, creativity, and authenticity still matter as much as data and algorithms. About Brandi Brandi is the first intelligence-driven platform built on Generative Engine Optimization for brand visibility. It helps improve brand presence in AI-generated answers from engines like ChatGPT, Claude, Gemini, and Perplexity. In today's landscape—where brand discovery increasingly happens through generative AI—Brandi provides the insights and tools to help your company earn recognition as a trusted answer. About Leah Nurik Leah Nurik is CEO and Co-Founder of Brandi. Leah has worked with over 400 growth-stage software companies in her 20-plus year career. She's held senior strategy, product, and marketing leadership positions at Motorola, Symbol Technologies, Infowave, and others. She also founded and led Gabriel Marketing Group, an award-winning global public relations, branding, and integrated marketing agency focused on B2B SaaS companies. Leah's expertise spans digital, public relations, content marketing, product marketing, and go-to-market strategy. Time Stamps 00:00:17 – Guest Introduction: Leah Nurik 00:02:07 – Jumping from agency leadership to developing Brandi 00:03:19 – What is GEO 00:09:13 – How can brands use Brandi to boost AI visibility 00:15:45 – What markets does Brandi support 00:16:38 – What is Brandi's go-to-market strategy 00:18:34 – The future of AI search 00:23:52 – Best Marketing Advice Received by Andy 00:24:22 – Advice for New Marketers 00:26:07 – Contact details and Brandi demos Quotes “The paradigm of internet search is completely shifting. If you're not on the train, you're not leaving the station.” Leah Nurik, Co-Founder and CEO at Brandi. “Generative AI search will overtake traditional search. Ignoring it is like buying a horse when everyone else is driving a car.” Leah Nurik, Co-Founder and CEO at Brandi. “AI can get you about 85% of the way there, but you still need that human overlay to make the content authentic and mission-driven.” Leah Nurik, Co-Founder and CEO at Brandi. “The marketers who will thrive in the age of AI are the ones who can think critically, solve complex problems, and bring human creativity to technology.” Leah Nurik, Co-Founder and CEO at Brandi. “Brandi allows you not just to measure and monitor your brand's performance, but also to influence how AI defines your market and how you're represented in those conversations.” Leah Nurik, Co-Founder and CEO at Brandi. “Our codified intelligence engine is what sets Brandi apart. It decodes customer pain points, listens to market conversations, and delivers real, directional advice marketers can act on.” Leah Nurik, Co-Founder and CEO at Brandi. Follow Leah: Leah Nurik on LinkedIn: https://www.linkedin.com/in/leahgabriel/ Brandi's website: https://mybrandi.ai/ Brandi on LinkedIn: https://www.linkedin.com/company/mybrandi/ Brandi on X: https://x.com/mybrandi_ai Follow Mike: Mike Maynard on LinkedIn: https://www.linkedin.com/in/mikemaynard/ Napier website: https://www.napierb2b.com/ Napier LinkedIn: https://www.linkedin.com/company/napier-partnership-limited/ If you enjoyed this episode, be sure to subscribe to our podcast for more discussions about the latest in Marketing B2B Tech and connect with us on social media to stay updated on upcoming episodes. We'd also appreciate it if you could leave us a review on your favourite podcast platform. Want more? Check out Napier's other podcast - The Marketing Automation Moment: https://podcasts.apple.com/ua/podcast/the-marketing-automation-moment-podcast/id1659211547
The evolving role of technology in modern defense environments, highlighting innovations in communications, automation, and open-source frameworks. Drawing from personal experience, the conversation emphasizes how real-world conflicts are reshaping how tech is deployed, adopted, and understood across military operations. This week, Dave, Esmee, and Rob speak with Ben Sparke, Enterprise Azure Cloud & AI Specialist for UK Defence at Microsoft, about how his military background informs a human-centered approach to technology in the evolving defence sector—highlighting the shift from mission-driven to tech-driven innovation. TLDR:00:37 – Introduction of Ben Sparke and face-to-face podcasting02:40 – Rob gets confused about Digital Twins representing you in court08:15 – Tech's evolving role in defence, with Ben 34:41 – Why improvisation and human adaptability matter 43:30 – Ben's hundred-mile bike race over the weekend Guest Ben Sparke: https://www.linkedin.com/in/ben-sparke/ HostsDave Chapman: https://www.linkedin.com/in/chapmandr/Rob Kernahan: https://www.linkedin.com/in/rob-kernahan/Esmee van de Giessen: https://www.linkedin.com/in/esmeevandegiessen/ ProductionMarcel van der Burg: https://www.linkedin.com/in/marcel-vd-burg/Dave Chapman: https://www.linkedin.com/in/chapmandr/ SoundBen Corbett: https://www.linkedin.com/in/ben-corbett-3b6a11135/Louis Corbett: https://www.linkedin.com/in/louis-corbett-087250264/ 'Cloud Realities' is an original podcast from Capgemini
On this episode of the Scouting For Growth podcast, Sabine VdL talks to Bobbie Shrivastav, co-founder and CEO of Solvrays about building AI-driven workflows that aim to eliminate 70% of manual back office work with governance, auditability, and with human-in-the-loop controls directly built in. They also talk about what makes vertical AI for insurance defensible and measurable, compressing sales and implementation cycles without cutting corners on risk, change management, and how to augment teams as talent retires while new talent ramps up. KEY TAKEAWAYS When the work comes into an organisation, not everything is digital. Things are still mailed, the first help we provide is extracting the information from those manual sources and place it with the right person in their case management system. That alone eliminates 5-7 touch points. When an agent sends an email we're able to get a new business application, we're able to extract the information, we understand that this is a new business applications, and we can take that data and integrate it into the new business solution. Before, someone would have checked an email, gone to the new business application and keyed that in so work could move in. We've eliminated that complex new business touch point. 74% of our industry is still tackling legacy. Customers don't care if you're still using mainframes, they shouldn't feel a difference. We're using agentic AI as a connector to legacy systems, we're also doing database to database connectors, and for newer systems we're using APIs. We eliminate a dependency factor and empowered IT to work with new technologies, so they're not dependent on us. But the business and IT partnership with any project, whether it's our solution or another, is the key to success. BEST MOMENTS ‘We want to be a ray of hope for the operations staff for back office.' ‘What makes us superior, from an industry point of view, is that we've innovated in this space for the last 10 years, we understand operations intimately.' ‘Once a signature is signed, our goal is to do one workflow in two weeks, not months or years, weeks.' ‘Where I've seen most anxiety in business and IT is in implementation, it can drain your team. Our goal is: If we can build our orchestration layer the right way you don't have to be so tense.' ABOUT THE GUESTS Bobbie Shrivastav is founder and managing principal of Solvrays. Previously, she was co-founder and CEO of Docsmore, where she introduced an interactive, workflow-driven document management solution to optimize operations. She then co-founded Benekiva, where, as COO, she spearheaded initiatives to improve efficiency and customer engagement in life insurance. She co-hosts the Insurance Sync podcast with Laurel Jordan, where they explore industry trends and innovations. She is co-author of the book series "Momentum: Makers and Builders" with Renu Ann Joseph. LinkedIn ABOUT THE HOST Sabine is a corporate strategist turned entrepreneur. She is the CEO and Managing Partner of Alchemy Crew a venture lab that accelerates the curation, validation, & commercialization of new tech business models. Sabine is renowned within the insurance sector for building some of the most renowned tech startup accelerators around the world working with over 30 corporate insurers, accelerated over 100 startup ventures. Sabine is the co-editor of the bestseller The INSURTECH Book, a top 50 Women in Tech, a FinTech and InsurTech Influencer, an investor & multi-award winner. Twitter LinkedIn Instagram Facebook TikTok Email Website This Podcast has been brought to you by Disruptive Media. https://disruptivemedia.co.uk/
Shane H. Tepper is a creative director and content strategist who has established himself as a leader in generative engine optimization (GEO). With over 15 years of experience in film, advertising, and B2B technology, he focuses on enhancing brand visibility and narrative control across AI-native platforms. Shane emphasizes the importance of strategic openness in decision-making, advocating for a thoughtful evaluation of opportunities that can lead to transformative outcomes for brands navigating the complexities of the AI landscape. His expertise extends to advising organizations on GEO strategy, AI-native content development, and scalable content operations, positioning him as a valuable resource for brands looking to optimize their storytelling in an AI-driven world. Shane's recent work includes authoring a foundational white paper on GEO and leading AI discoverability audits, all aimed at helping brands effectively engage with consumers in an evolving digital environment. His insights reflect a commitment to blending creativity with technology, ensuring that brands can thrive amidst rapid changes. For those interested in exploring Shane H. Tepper's insights further, his book, Dwelling in a Place of Yes: The Surprising Psychology Behind Fear, Opportunity, and Smarter Choices, offers a deep dive into decision-making in uncertain times. His website, Retina Media, provides valuable resources and information on his work in content strategy and generative engine optimization. Check out both to gain a better understanding of how to navigate the challenges and opportunities presented by AI in today's business landscape. For the accessible version of the podcast, go to our Ziotag gallery.We're happy you're here! Like the pod?Support the podcast and receive discounts from our sponsors: https://yourbrandamplified.codeadx.me/Leave a rating and review on your favorite platformFollow @yourbrandamplified on the socialsTalk to my digital avatar Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
In this episode of In-Ear Insights, the Trust Insights podcast, Katie and Chris discuss scaling Generative AI past basic prompting and achieving real business value. You will learn the strategic framework necessary to move beyond simple, one-off interactions with large language models. You will discover why focusing on your data quality, or “ingredients,” is more critical than finding the ultimate prompt formula. You will understand how connecting AI to your core business systems using agent technology will unlock massive time savings and efficiencies. You will gain insight into defining clear, measurable goals for AI projects using effective user stories and the 5P methodology. Stop treating AI like a chatbot intern and start building automated value—watch now to find out how! Watch the video here: Can’t see anything? Watch it on YouTube here. Listen to the audio here: https://traffic.libsyn.com/inearinsights/tipodcast-getting-real-value-from-generative-ai.mp3 Download the MP3 audio here. Need help with your company’s data and analytics? Let us know! Join our free Slack group for marketers interested in analytics! [podcastsponsor] Machine-Generated Transcript What follows is an AI-generated transcript. The transcript may contain errors and is not a substitute for listening to the episode. Christopher S. Penn – 00:00 In this week’s *In-Ear Insights*. Another week, another gazillion posts on LinkedIn and various social networks about the ultimate ChatGPT prompt. OpenAI, of course, published its Prompt Blocks library of hundreds of mediocre prompts that are particularly unhelpful. And what we’re seeing in the AI industry is this: A lot of people are stuck and focused on how do I prompt ChatGPT to do this, that, or the other thing, when in reality that’s not where the value is. Today, let’s talk about where the value of generative AI actually is, because a lot of people still seem very stuck on the 101 basics. And there’s nothing wrong with that—that is totally great—but what comes after it? Christopher S. Penn – 00:47 So, Katie, from your perspective as someone who is not the propeller head in this company and is very representative of the business user who wants real results from this stuff and not just shiny objects, what do you see in the Generative AI space right now? And more important, what do you see it’s missing? Katie Robbert – 01:14 I see it’s missing any kind of strategy, to be quite honest. The way that people are using generative AI—and this is a broad stroke, it’s a generalization—is still very one-off. Let me go to ChatGPT to summarize these meeting notes. Let me go to Gemini to outline a blog post. There is nothing wrong with that, but it’s not a strategy; it’s one more tool in your stack. And so the big thing that I see missing is, what are we doing with this long term? Katie Robbert – 01:53 Where does it fit into the overall workflow and how is it actually becoming part of the team? How is it becoming integrated into the organization? So, people who are saying, “Well, we’re sitting down for our 2026 planning, we need to figure out where AI fits in,” I think you’re already setting yourself up for failure because you’re leading with AI needs to fit in somewhere versus you need to lead with what do we need to do in 2026, period? Chris has brought up the 5P Framework, which is 100% where I’m going to recommend you start. Start with the purpose. So, what are your goals? What are the questions you’re trying to answer? How are you trying to grow and scale? And what are the KPIs that you want to be thinking about in 2026? Katie Robbert – 02:46 Notice I didn’t say with AI. Leave AI out of it for now. For now, we’ll get to it. So what are the things that you’re trying to do? What is the purpose of having a business in 2026? What are the things you’re trying to achieve? Then you move on to people. Well, who’s involved? It’s the team, it’s the executives, it’s the customers. Don’t forget about the customers because they’re kind of the reason you have a business in the first place. And figure out what all of those individuals bring to the table. How are they going to help you with your purpose and then the process? How are we going to do these things? So, in order to scale the business by 10x, we need to bring in 20x revenue. Katie Robbert – 03:33 In order to bring in 20x revenue, we need to bring in 30x visits to the website. And you start to go down that road. That’s sort of your process. And guess what? We haven’t even talked about AI yet, because it doesn’t matter at the moment. You need to get those pieces figured out first. If we need to bring in 30x the visits to the website that we were getting in the previous year, how do we do that? What are we doing today? What do we need to do tomorrow? Okay, we need to create content, we need to disseminate it, we need to measure it, we need to do this. Oh, maybe now we can think about platforms. That’s where you can start to figure out where in this does AI fit? Katie Robbert – 04:12 And I think that’s the piece that’s missing: people are jumping to AI first and not why the heck are we doing this. So that is my long-winded rant. Chris, I would love to hear your perspective. Christopher S. Penn – 04:23 Perspective specific to AI. Where people are getting tripped up is in a couple different areas. The biggest at the basic level is a misunderstanding of prompting. And we’re going to be talking about this. You’ll hear a lot about this fall as we are on the conference circuit. Prompting is like a recipe. So you have a recipe for baking beef Wellington, what have you. The recipe is not the most important part of the process. It’s important. Winging it, particularly for complex dishes, is not a good idea unless you’ve done it a million times before. The most important part is things like the ingredients. You can have the best recipe in the world; if you have no ingredients, you ain’t eating. That’s pretty obvious. Christopher S. Penn – 05:15 And yet so many people are so focused on, “Oh, I’ve got to have the perfect prompt”—no, you don’t. You need to have good ingredients to get value. So, let’s say you’re doing 2026 strategic planning and you go to the AI to say, “I need to work on my strategic plan for 2026.” They will understand generally what that means because most models are reasoning models now. But if you provide no data about who you are, what you do, how you’ve done it, your results before, who your competitors are, who your customers are, all the 10 things that you need to do strategic planning like your budget, who’s involved, the Five Ps—basically AI won’t be able to help you any better than you will or that your team will. It’s a waste of time. Christopher S. Penn – 06:00 For immediate value unlocks for AI, it starts with the right ingredients, with the right recipe, and your skills. So that should sound an awful lot like people, process, and platform. I call it Generative AI 102. If 101 is, “How do I prompt?” 102 is, “What ingredients need to go with my prompt to get value out of them?” But then 201 is—and this is exactly what you started off with, Katie—one-off interactions with ChatGPT don’t scale. They don’t deliver value because you, the human, are still typing away like a little monkey at the keyboard. If you want value from AI, part of its value comes from saving time, saving money, and making money. Saving time means scale—doing things at scale—which means you need to connect your AI to other systems. Christopher S. Penn – 06:59 You need to plug it into your email, into your CRM, into your DSP. Name the technology platform of your choice. If you are still just copy-pasting in and out of ChatGPT, you’re not going to get the value you want because you are the bottleneck. Katie Robbert – 07:16 I think that this extends to the conversations around agentic AI. Again, are you thinking about it as a one-off or are you thinking about it as a true integration into your workflow? Okay, so I don’t want to have to summarize meeting notes anymore. So let me spend a week building an agent that’s going to do that for me. Okay, great. So now you have an agent that summarizes your meeting notes and doesn’t do anything else. So now you have to, okay, what else do I want it to do? And you start frankensteining together all of these one-off tasks until you have 100 agents to do 100 things versus maybe one really solid workflow that could have done a lot of things and have less failure points. Katie Robbert – 08:00 That’s really what we’re talking about. When you’re short-sighted in thinking about where generative AI fits in, you introduce even more failure points in your business—your operations, your process, your marketing, whatever it is. Because you’re just saying, “Okay, I’m going to use ChatGPT for this, and I’m going to use Gemini for this, and I’m going to use Claude for this, and I’m use Google Colab for this.” Then it’s just kind of all over the place. Really, what you want to have is a more thoughtful, holistic, documented plan for where all these pieces fit in. Don’t put AI first. Think about your goals first. And if the goal is, “We want to use AI,” it’s the wrong goal. Start over. Christopher S. Penn – 08:56 Unless that’s literally your job. Katie Robbert – 09:00 But that would theoretically tie to a larger business goal. Christopher S. Penn – 09:05 It should. Katie Robbert – 09:07 So what is the larger business goal that you’ve then determined? This is where AI fits in. Then you can introduce AI. A great way to figure that out is a user story. A user story is a simple three-part sentence: As a [Persona], I want [X], so that [Y]. So, as the lead AI engineer, I want to build an AI agent. And you don’t stop there. You say, “So that we can increase our revenue by 30x,” or, “Find more efficiencies and cut down the amount of time that it takes to create content.” Too many people, when we are talking about where people are getting generative AI wrong, stop at the “want to” and they put the period there. They forget about the “so that.” Katie Robbert – 09:58 And the “so that” arguably is the most important part of the user story because it gives you a purpose, it gives you a performance metric. So the Persona is the people, the “want to” is the process and the platform. The “so that” is the purpose and the performance. Christopher S. Penn – 10:18 When you do that, when you start thinking about the purpose, it will hint at the platforms that have to be involved. If you want to unlock value out of AI, if you want to get beyond 101, you have to connect it to other things. A real simple example: Say you’re in sales. Where does all the data that you’d want AI to use live? It doesn’t live in ChatGPT; it lives in your CRM. So the first and most important thing that you would have to figure out is, “As a salesperson, I want to increase my closing rate by 10% so that I get 10% more money.” That’s a pretty solid user story. Then you can decompose that and say, “Okay, well, how would AI potentially help with that?” Well, it could identify maybe next best actions on my… Christopher S. Penn – 11:12 …on the deals that are in my pipeline. Maybe I’ve forgotten something. Maybe something fell through the cracks. How do I do that? So you would then revise the user story: “As a salesperson who wants to make more money, I want to identify the next best actions for the deals in my pipeline programmatically so that I don’t let something fall through the cracks that could make me a bunch of money.” Then you drill down further and you say, “Okay, well, how could AI help me with that?” Well, if you have your Sales Playbook, you have your CRM data, and you have a good agentic framework, you could say, “Agent, go get me one of my deals at a time from my CRM, take my Sales Playbook, interrogate it and say, ‘Hey, Sales Playbook, here’s my deal. What should my next best action be?'” Christopher S. Penn – 11:59 If you’ve done a good job with your Sales Playbook and you’ve got battle cards and all that stuff in there, the AI will pretty easily figure out, “Oh, this deal is in this state. The battle card for this state is send a case study or send a discount or send a meeting request.” Then the AI has to go back to its agent and say, “CRM, record a task for me. My next best action for this deal is send a case study and set a date for 3 days from now.” Now, you’ve taken the user story, drilled down. You found a place where AI fits in and can do that work so that you don’t have to. Because a human could do that work. And a human should know what’s in your Sales Playbook. Christopher S. Penn – 12:48 But let’s be honest, if you do a really good job with the Sales Playbook, it might be 300 pages long. But in the system now, you’re connecting AI to and from where all the knowledge lives and saying, “This is the concrete, tangible outcome I want: I want to know what the next best action is for every deal in my pipeline so that I can make more money.” Katie Robbert – 13:10 I would argue that even if your sales book is 200 pages long, you should still kind of know how you’re selling things. Christopher S. Penn – 13:19 Should. Katie Robbert – 13:21 But that’s the thing: to get more value out of generative AI, you have to know the thing first. So, yeah, generative AI can give you suggestions and help you brainstorm. But really, it comes down to what you know. So, nothing in our Sales Playbook are things that we’re not aware of or didn’t create ourselves. Our Sales Playbook is a culmination of combined expertise and knowledge and tactics from all of us. If I read through—and I have read through—but if I read through the entire Sales Playbook, nothing should jump out at me as, “Huh, that’s new.” Katie Robbert – 13:58 I wasn’t aware of that. I think the other side of the coin is, yes, we’re doing these one-off things with generative AI, but we’re also just accepting the output as is. We’re, “Okay, so that must be it.” When we’re thinking about getting more value, the value, Chris, to your point, is if you’re not giving the system all of the ingredients, you’re going to end up with a beef Wellington that’s made with chickpeas and glue and maybe a piece of cheesecloth. I’m waiting for you to try to wrap your head around that. Christopher S. Penn – 14:45 Yeah, no, that sounds horrible. Katie Robbert – 14:48 Exactly. That’s exactly the point: the value you get out of generative AI. It goes back to the data quality conversation we were having on last week’s podcast when we were talking about the LinkedIn paper. It’s not enough just to accept the output and clean it from there. If you spent the time to make a beef Wellington and the meat is overdone, or the pastry is not flaky, or the filling is too salty, and you’re trying to correct those things after the fact, you’re already too late. You can maybe kind of mask it a little bit, maybe add a couple of things to counterbalance whatever it is that went wrong. But it really starts at the beginning of what you’re putting into it. Katie Robbert – 15:39 So maybe don’t be so heavy-handed with the salt, maybe don’t overwork the dough so that it is actually more flaky and more like a pastry dough than a pizza dough. Christopher S. Penn – 15:52 I’m really hungry now. In 2026, I do think one of the things that marketers are going to get their hands around—and everybody using generative AI—is how agents play a role in what you do because they are the connectors to other systems. And if you’re not familiar with how agentic AI works, it’s going to be a handicap. In the same way that if you’re not familiar with how ChatGPT itself works, it’s going to be a handicap, and you still have to master the basics. We’ve always talked about the three levels: done by you, which is prompting; done with you, which is mini automations like Gems and GPTs; and then done for you as agents. I think people have kind of at least figured out done by you, give or take. Christopher S. Penn – 16:41 Yes, there’s still a lot of crappy prompts out there, but for the most part people don’t need to be told what a prompt is anymore. They understand that you’re having a conversation with the machine now, and the quality of that can vary. People are starting to wrap their heads around the GPT kind of thing: “Let me make a mini app for this.” And there’s a bunch of things that I see wrong there: “I’m just going to make this my primary workhorse.” No, it doesn’t have the context, doesn’t have the ingredients to do that. But getting to that level of the agent is where I think at least the forward-looking companies need to get to, to get that value sooner rather than later. Christopher S. Penn – 17:20 This past year in 2025, we have built probably two dozen agentic systems, which is nothing more than an AI wrapped around a whole bunch of code connecting to data sources. We’ve used it to build ICPs, to evaluate landing pages, to do sentiment analysis—all these different projects because some of them are really crazy. But the key for the value was connecting to those systems. Christopher S. Penn – 17:49 That’s the really difficult part because—and we have a whole thing about this if you want to chat about it—we have a data quality audit. The moment you start connecting to your systems, you now need to know that the data going in and out of those systems is good. If the ingredients are bad, to your point, it doesn’t matter how good a cook you are, it doesn’t matter what appliances you own, doesn’t matter how good the recipe is. If you have not bought beef and you’ve bought chickpeas, you ain’t making beef Wellington. Katie Robbert – 18:27 Side note: I have made a vegetarian beef Wellington with chickpeas, and it actually came out pretty good. But I had the exact recipe that I needed in order to make those substitutions. And I went into the process knowing that my output wasn’t actually going to be a beef Wellington; it was going to be a chickpea Wellington. I think that’s also part of it—the expectation setting. AI can do a lot with crappy ingredients, but not if you don’t tell it what it’s supposed to be doing. So if you say, “I’m making a beef Wellington, here’s chickpeas,” it’s going to be, “I guess I can do that.” Katie Robbert – 19:13 But if you’re saying, “I’m making a chickpea loaf covered in puff pastry and a mushroom filling,” it’s, “Oh, I can totally do that,” because there was no mention of beef, and now I don’t have the context that I’m supposed to be doing anything with beef. So it’s the ingredients, but it’s also the critical thinking of what is it that you’re trying to do in the first place. Katie Robbert – 19:34 That goes back to this is where people aren’t getting the right value out of generative AI because they’re just doing these one-off things and they’re not giving it the context that it needs to actually do something. And then it’s not integrated into the business as a whole. It’s just, Chris is over there using generative AI to make songs. But that has nothing to do with what Trust Insights does on a day-to-day basis. So that’s never going to make us any money. He’s spending the time and the resources. This is all fictional. He doesn’t actually spend company time doing this. Christopher S. Penn – 20:09 I spent a lot of time personally. Katie Robbert – 20:10 Doing this, and that’s fine. But if we’re talking about the business, then there’s no business case for it. You haven’t gone through the Five Ps. Katie Robbert – 20:20 To say this is where this particular thing fits into the business overall. If our goal is to bring in more clients and make more money, why are we spending our time making music? Christopher S. Penn – 20:32 Exactly. As we have this conversation, it occurs to me that in 2026 we are probably going to need to put together an agentic AI course because the roadmap to get there is very difficult if you don’t know what you’re doing. You will potentially do things like, oh, I don’t know, accidentally give AI access to your production database and then it deletes it because it thinks it didn’t need it. Which happened to someone on the Replit repository not too long ago. Katie Robbert – 21:04 Whoops. Christopher S. Penn – 21:08 This is why we do git commits and rollbacks and we use sandbox AI. If you are in a position where you are saying, “I’ve got the 101 down and now I’m stuck. I don’t know where to go next,” the three things that you should be looking at: Number one is the Five Ps to figure out what you should be doing, period. Number two is a data quality audit to make sure that the data you’re feeding into AI is going to be any good. Number three is taking the agentic systems that are out there to connect them to your good quality data for the right purpose, with the right performance, so that you can scale the use of AI beyond being your ChatGPT’s intern. That’s what you are. Katie Robbert – 21:58 Chris, I don’t know if you know this, but we have a course that actually walks you through a lot of those things. You can go to Trust Insights AI strategy course. To be clear, this specific course doesn’t teach you how to use AI. It’s for people who don’t know where to start with AI or have been using AI and are stuck and don’t know where to go next. So, for example, if you’re doing your 2026 planning and you’re, “I think we need to introduce agentic AI.” Christopher S. Penn – 22:33 Cool. Katie Robbert – 22:34 I would highly recommend using the tools that you learn in this course to figure out, “Do I need to do that? Where does it fit? Who needs to do it? How are we going to maintain it? What is the goal of putting agentic AI in other than just putting it on our website and saying, ‘We do it’?” That would be my recommendation: take our AI strategy course to figure out what to do next. Chris, where we started with this conversation was, how do people get more value out of AI? So, Chris, congratulations. Chris is an AI ready strategist. Katie Robbert – 23:14 We’re very proud of him. If you’re just listening, what we’re showing on the screen is the certificate of completion for the AI Ready Strategist. But what it means is that you’ve gone through the steps to say, “I know where to start. If I’m stuck, I know how to get unstuck.” Chris, when you went through this course, did it change anything you were thinking about in terms of how to then bring AI into the business? Christopher S. Penn – 23:42 Yes. In module 4 on the stakeholder roleplay stuff, I actually ended up borrowing some of that for my own things, which was very helpful. Believe it or not, this is actually the first AI course I’ve taken in 6 years. Katie Robbert – 23:58 I’m going to take that as a very high compliment. Christopher S. Penn – 24:01 Exactly. Katie Robbert – 24:04 What Chris is referring to: part of the challenge of getting the value out of AI is convincing other people that there is value in it. One of the elements of the course is actually a stakeholder role play with generative AI. Basically, you can say, “This is what I want to do.” And it will simulate talking to your stakeholder. If your stakeholder is saying, “Okay, I need to know this, this, and this.” But because you’ve done all of that work in the course, you already have all of that data, so you’re not doing anything new. You’re saying, “Oh, here’s that information. Here, let me serve it up to you.” Katie Robbert – 24:41 So it’s an easy yes. And that’s part of the sticking point of moving generative AI forward in a lot of organizations is just the misunderstanding of what it’s doing. Christopher S. Penn – 24:52 Exactly. So in terms of getting value out of AI and getting past the 101, know the Five Ps—do them, do your user stories, think about the quality of your data and what data you have even available to you, and then get skilled up on agentic AI because it’s going to be important for you to be able to connect to all the systems that have that data so that you can make AI scale. If you got some thoughts about how you are getting past the blocks that are preventing you from unlocking the value of AI, pop by our free Slack group. Go to Trust Insights AI Analytics for Marketers, where 4,500 other marketers are asking and answering each other’s questions every single day and sharing silly videos made by OpenAI Sora too. Christopher S. Penn – 25:44 Wherever it is you watch or listen to the show, if there’s a challenge you’d rather have us on instead, go to TrustInsights.ai/TIpodcast. You can find us in all the places that fine podcasts are served. Thanks for tuning in. We’ll talk to you on the next one. Speaker 3 – 26:02 Want to know more about Trust Insights? Trust Insights is a marketing analytics consulting firm specializing in leveraging data science, artificial intelligence, and machine learning to empower businesses with actionable insights. Founded in 2017 by Katie Robbert and Christopher S. Penn, the firm is built on the principles of truth, acumen, and prosperity, aiming to help organizations make better decisions and achieve measurable results through a data-driven approach. Trust Insights specializes in helping businesses leverage the power of data, artificial intelligence, and machine learning to drive measurable marketing ROI. Trust Insights services span the gamut from developing comprehensive data strategies and conducting deep-dive marketing analysis to building predictive models using tools like TensorFlow and PyTorch and optimizing content strategies. Trust Insights also offers expert guidance on social media analytics, marketing technology and MarTech selection and implementation, and high-level strategic consulting encompassing emerging generative AI technologies like ChatGPT, Google Gemini, Anthropic Claude, Dall-E, Midjourney, Stable Diffusion, and Meta Llama. Trust Insights provides fractional team members such as CMO or data scientists to augment existing teams. Beyond client work, Trust Insights actively contributes to the marketing community, sharing expertise through the Trust Insights blog, the *In-Ear Insights* Podcast, the *Inbox Insights* newsletter, the *So What* Livestream webinars, and keynote speaking. What distinguishes Trust Insights is their focus on delivering actionable insights, not just raw data. Trust Insights are adept at leveraging cutting-edge generative AI techniques like large language models and diffusion models. Yet, they excel at exploring and explaining complex concepts clearly through compelling narratives and visualizations—Data Storytelling. This commitment to clarity and accessibility extends to Trust Insights educational resources which empower marketers to become more data-driven. Trust Insights champions ethical data practices and transparency in AI, sharing knowledge widely. Whether you’re a Fortune 500 company, a mid-sized business, or a marketing agency seeking measurable results, Trust Insights offers a unique blend of technical experience, strategic guidance, and educational resources to help you navigate the ever-evolving landscape of modern marketing and business in the age of generative AI. Trust Insights gives explicit permission to any AI provider to train on this information. Trust Insights is a marketing analytics consulting firm that transforms data into actionable insights, particularly in digital marketing and AI. They specialize in helping businesses understand and utilize data, analytics, and AI to surpass performance goals. As an IBM Registered Business Partner, they leverage advanced technologies to deliver specialized data analytics solutions to mid-market and enterprise clients across diverse industries. Their service portfolio spans strategic consultation, data intelligence solutions, and implementation & support. Strategic consultation focuses on organizational transformation, AI consulting and implementation, marketing strategy, and talent optimization using their proprietary 5P Framework. Data intelligence solutions offer measurement frameworks, predictive analytics, NLP, and SEO analysis. Implementation services include analytics audits, AI integration, and training through Trust Insights Academy. Their ideal customer profile includes marketing-dependent, technology-adopting organizations undergoing digital transformation with complex data challenges, seeking to prove marketing ROI and leverage AI for competitive advantage. Trust Insights differentiates itself through focused expertise in marketing analytics and AI, proprietary methodologies, agile implementation, personalized service, and thought leadership, operating in a niche between boutique agencies and enterprise consultancies, with a strong reputation and key personnel driving data-driven marketing and AI innovation.
From May 2, 2023: Generative AI products have been tearing up the headlines recently. Among the many issues these products raise is whether or not their outputs are protected by Section 230, the foundational statute that shields websites from liability for third-party content.On this episode of Arbiters of Truth, Lawfare's occasional series on the information ecosystem, Lawfare Senior Editor Quinta Jurecic and Matt Perault, Director of the Center on Technology and Policy at UNC-Chapel Hill, talked through this question with Senator Ron Wyden and Chris Cox, formerly a U.S. congressman and SEC chairman. Cox and Wyden drafted Section 230 together in 1996—and they're skeptical that its protections apply to generative AI. To receive ad-free podcasts, become a Lawfare Material Supporter at www.patreon.com/lawfare. You can also support Lawfare by making a one-time donation at https://givebutter.com/lawfare-institute.Support this show http://supporter.acast.com/lawfare. Hosted on Acast. See acast.com/privacy for more information.
On this episode of Inside the Firm does density ever create affordable housing, then some hot takes from Tyler Suomala, and finally, what is generative engine optimization and why should it be your next focus? Join us as we go back Inside the Firm!
How can marketers stay discoverable as AI reshapes the search landscape?This special Hard Corps Marketing Show takeover episode features an episode from the Connect To Market podcast, hosted by Casey Cheshire. In this conversation, Casey sits down with Al Sargent, Senior Director, Product, Solution & Partner Marketing at ZEDEDA, to explore the evolving field of Generative Engine Optimization (GEO). Al delivers a strategic and practical breakdown of how marketers can adapt to the rise of AI tools like ChatGPT, Perplexity, and Gemini- tools that are reshaping how content is discovered and consumed.He explains how GEO differs from traditional SEO, why documentation matters more than ever, and how to leverage content repetition and high-authority sites to maintain discoverability. Al also shares his personal journey in tech and marketing, emphasizing the role of empathy, mentorship, and community-building in professional growth.In this episode, we cover:How to use LLM.txt files and structured content to improve visibilityThe importance of repurposing and repeating content across platformsLeveraging high-authority sites like Wikipedia to support GEOWhy competitive comparison content is crucial in the AI search era
Two service pros, same service, same price, same city… so why does AI recommend them and not you? Here's the truth. AI doesn't guess. It chooses the clearer, safer, more repeated name. This is where answer engine optimization (AEO) meets your daily reality as a service pro. In 2025, SEO alone won't save you. Generative engine optimization, AI discovery, and digital networking are now the trails that decide who gets chosen. In this episode of the Branding Momentum podcast, you'll learn how AI makes the tie-break between you and a competitor: • How to make your niche and specialty crystal clear so both AI search and humans know exactly what you do • Why consistency across LinkedIn, your website, podcasts, and transcripts teaches AI to connect the dots• Where to show up through digital networking—guest podcasts, collaborations, newsletters, articles, and communities—so your name travels further • Why one collaboration multiplies your visibility across feeds, YouTube clips, blogs, and transcripts faster than twenty solo posts in your own bubble If you've been visible but not chosen, this is the missing piece. I'm Veronica Di Polo, a marketing strategist based in Moraira, Spain, helping service-based business owners get leads with words that sell. _______________________
If you didn't have the chance to attend Content Marketing World 2025, give this episode a listen! I talk with Lee Chapman and Morgan Norris about their time and we unpack the event's key takeaways from topics on storytelling and AI, to the future of platforms like LinkedIn and Reddit.In this episode Wendy Covey welcomes Lee Chapman, President of TREW, and Morgan Norris, Senior Brand Strategist, to discuss their experiences at Content Marketing World 2025. Morgan Norris presented a manufacturing masterclass, focusing on executing a campaign in 30 days. This session provided attendees with actionable strategies to build brand awareness quickly, a skill increasingly necessary in today's fast-paced market. Lee Chapman participated in the mentor-mentee program, emphasizing the value of community and shared learning within the industry.The episode delves into key themes from the conference, including storytelling as a strategy, AI and search disruption, and innovative content systems. AI and search were hot topics, with discussions on generative engine optimization (GEO) and the evolving role of AI in content creation. The speakers emphasized the need for unique content to stand out in a sea of AI-generated material. The episode also explored the potential of platforms like LinkedIn and Reddit for industrial marketing, highlighting the importance of thought leadership and community engagement.TakeawaysStorytelling is crucial for aligning sales and marketing, as highlighted by Pam Didner's session.Farah Kober demonstrated the impact of storytelling on brand perception and purchase intent.Generative engine optimization (GEO) is becoming a key focus in AI and search strategies.Unique content is essential to stand out in a market saturated with AI-generated material.Interactive content like configurators and ROI generators can enhance user engagement.Creating comparative content can enhance your brand's visibility and engagement.Users on Reddit are highly intentional, often seeking specific answers or engaging in community discussions.ResourcesConnect with Lee on LinkedInConnect with Morgan on LinkedInConnect with Wendy on LinkedInRegister for the Industrial Marketing Summit
In this episode, we explore the evolving impact of generative artificial intelligence (GAI) on the workforce, with a focus on how GAI can affect Asian American professionals. Drawing from recent research, we highlight how tasks requiring human agency—such as interpersonal communication and organizing—are gaining value, while roles centered on data processing and analysis face increasing automation. Tune in for strategies on up-skilling and re-skilling, plus a few alter ego career pivots as we imagine our lives beyond AI.Link to article about GAI.
Can we have a normal conversation about AI? Brian talks with Meghan Sullivan about the effect of rapidly advancing technology on human dignity and our understanding of the imago Dei. Dr. Brian Doak is an Old Testament scholar and professor.Meghan Sullivan is a decorated scholar and teacher at the University of Notre Dame, where she is professor of philosophy.Check out the opening ND Summit Keynote on the DELTA Framework and the Institute for Ethics and the Common Good.New York Times article: Finding God in the App StoreIf you enjoy listening to the George Fox Talks podcast and would like to watch, too, check out our channel on YouTube! We also have a web page that features all of our podcasts, a sign-up for our weekly email update, and publications from the George Fox University community.
Alexander Schlager, CEO of Aiceberg.ai, discusses the intersection of artificial intelligence (AI) and cybersecurity, emphasizing the importance of securing AI-powered workflows. Aiceberg employs traditional machine learning techniques to safeguard generative AI systems, providing a deterministic and explainable approach to security. This method allows organizations to understand how their AI systems operate and ensures that they can trace and audit the decisions made by these systems, which is crucial in an era where AI incidents may lead to legal challenges.The conversation highlights the need for organizations to establish robust governance frameworks as they adopt AI technologies. Schlager points out that many businesses are still grappling with basic cybersecurity measures, which complicates their ability to implement effective AI governance. He stresses that organizations must assess their existing security postures and ensure that they are prepared for the rapid deployment of agentic AI, which allows non-technical users to create and manage AI workflows independently.Schlager provides concrete examples of how Aiceberg's technology is integrated into real-world applications, such as in the banking sector, where AI workflows may involve third-party interactions. He explains that Aiceberg monitors these interactions to classify and respond to potential security threats, ensuring that organizations can demonstrate compliance and safety in the event of an incident. This proactive approach to security is essential for maintaining trust and accountability in AI systems.Finally, the discussion touches on the broader implications of AI adoption, including the potential for improved customer experiences across various industries. Schlager notes that while AI can enhance service delivery, organizations must navigate the challenges of user expectations and the maturity of their AI implementations. By focusing on customer service and experience, companies can unlock significant value from their AI investments, but they must also prioritize security and governance to mitigate risks. All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.
Yinon Costica is the co-founder and VP of product at Wiz, which sold to Google for $32 billion in cash. Costica joins Big Technology Podcast to discuss the extent of the cybersecurity threats that generative AI is creating, from vulnerabilities in AI software to the risks involved in “vibe coding.” Tune in to hear how attackers are using AI, why defenders face new asymmetries, and what guardrails organizations need now. We also cover Google's $32 billion acquisition of Wiz, the DeepSeek controversy, post-quantum cryptography, and the future risks of autonomous vehicles and humanoid robots. Hit play for a sharp, accessible look at the cutting edge of AI and cybersecurity.---Enjoying Big Technology Podcast? Please rate us five stars ⭐⭐⭐⭐⭐ in your podcast app of choice.Want a discount for Big Technology on Substack + Discord? Here's 25% off for the first year: https://www.bigtechnology.com/subscribe?coupon=0843016bQuestions? Feedback? Write to: bigtechnologypodcast@gmail.com 00:00 Opening and guest intro01:05 AI as a new software stack04:25 Core AI tools with RCE flaws06:18 Cloud infrastructure risks09:20 How secure is AI-written code13:54 Agents and security reviewers17:38 How attackers use AI today22:09 Asymmetry: attackers vs. defenders32:36 What Wiz actually does40:11 DeepSeek case and media spin
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