Podcasts about agentic

Why is a 27-year-old founder betting against massive AI models and building powerful, ultra-specialized agents to run right on your phone? Find out how this new approach could reshape everything from daily productivity to privacy and platform power plays. Judge Rules on Google Ads Case OpenAI to start showing ads on ChatGPT's free and Go tiers in India Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident OpenAI to Restrict Astra Model After Rating It 'Critical' Cyber Risk OpenAI Technique in 'Astra' Model Sparks Security Concerns Nvidia pays $12.9B for Hugging Face. Thomas Wolf is betting on Microduck, a $399 robot? OpenClaw 2.0 pours glitter on slow-burning security dumpster fire Dyson Debuts $499 AI-Powered Toothbrush With a Built-In Camera Infinite Slop by @levelsio + fal.ai YouTube uses AI to tag products in videos & create Amazon affiliate links Expert Intelligence: a new way for you to engage with trusted content Intelligent transcription with Gemini 3.5 Transcribe Google Rolls Out 3 New Ways to Book Travel Using AI Mode in Search I Let Google's Personal Intelligence Access My Life—and It's a Huge Help MrBeast partners with Gemini to turn impossibly big ideas into reality Google's answer to Canva is an AI tool where you prompt instead of design September Android Drop: Remember where you put things, ease motion sickness, and more (155) TIME on X: "TIME's new cover: Announcing the 2026 TIME100 AI, the world's most influential people in artificial intelligence https://t.co/ZnPVMdeJwe https://t.co/97g8cUJo8H" / X Pangram Has Emerged as the Gold Standard of AI Detection. Should You Trust It? Unusual Suspects Shout out to 2010s era Bloomberg Businessweek covers Archived video CS majors down 8.4% or 53k Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guests: Mikah Sargent and Div Garg Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: horizon3.ai/intelligent rippling.ai/machines

In this episode, NLW and Nufar Gaspar explain how knowledge workers can move beyond one-shot prompting and use agentic loops to produce more complete, reliable work. They break down how to design verifiable finish lines, decide which tasks should be looped, prevent runaway costs and compose multiple agents into work graphs that can research, review and refine outputs autonomously.NEXT COHORT - Executive Agent Leadership - Returns in September -- Learn how to use agents - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://training.besuper.ai/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Brought to you by:KPMG – Research from KPMG and the University of Texas at Austin shows the highest-impact AI users treat AI like a reasoning partner — and those skills can be taught at scale. Learn more at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://kpmg.com/us/Sophisticated⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Harbor - Invest in the AI ecosystem. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.harborcapital.com/aidaily⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Hyperagent - Hire a team of always-on agents. New users get $100 in free credits. ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠hyperagent.com/aidailybrief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Rackspace Technology- One accountable partner to build, operate and run your full enterprise AI stack ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.rackspace.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Section - Section turns AI investment into workforce transformation and ROI - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.sectionai.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Blitzy - Want to accelerate enterprise software development velocity by 5x? ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://blitzy.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠AssemblyAI - The best way to build Voice AI apps - ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://www.assemblyai.com/brief⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Robots & Pencils - Cloud-native AI solutions that power results ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://robotsandpencils.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The AI Daily Brief helps you understand the most important news and discussions in AI. Newsletter: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://aidailybrief.beehiiv.com/⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Interested in sponsoring the show? sponsors@aidailybrief.ai

Why is a 27-year-old founder betting against massive AI models and building powerful, ultra-specialized agents to run right on your phone? Find out how this new approach could reshape everything from daily productivity to privacy and platform power plays. Judge Rules on Google Ads Case OpenAI to start showing ads on ChatGPT's free and Go tiers in India Brief independent investigation of agents' behavior, reasoning and collaboration in the OpenAI / Hugging Face hacking incident OpenAI to Restrict Astra Model After Rating It 'Critical' Cyber Risk OpenAI Technique in 'Astra' Model Sparks Security Concerns Nvidia pays $12.9B for Hugging Face. Thomas Wolf is betting on Microduck, a $399 robot? OpenClaw 2.0 pours glitter on slow-burning security dumpster fire Dyson Debuts $499 AI-Powered Toothbrush With a Built-In Camera Infinite Slop by @levelsio + fal.ai YouTube uses AI to tag products in videos & create Amazon affiliate links Expert Intelligence: a new way for you to engage with trusted content Intelligent transcription with Gemini 3.5 Transcribe Google Rolls Out 3 New Ways to Book Travel Using AI Mode in Search I Let Google's Personal Intelligence Access My Life—and It's a Huge Help MrBeast partners with Gemini to turn impossibly big ideas into reality Google's answer to Canva is an AI tool where you prompt instead of design September Android Drop: Remember where you put things, ease motion sickness, and more (155) TIME on X: "TIME's new cover: Announcing the 2026 TIME100 AI, the world's most influential people in artificial intelligence https://t.co/ZnPVMdeJwe https://t.co/97g8cUJo8H" / X Pangram Has Emerged as the Gold Standard of AI Detection. Should You Trust It? Unusual Suspects Shout out to 2010s era Bloomberg Businessweek covers Archived video CS majors down 8.4% or 53k Hosts: Leo Laporte, Jeff Jarvis, and Paris Martineau Guests: Mikah Sargent and Div Garg Download or subscribe to Intelligent Machines at https://twit.tv/shows/intelligent-machines. Join Club TWiT for Ad-Free Podcasts! Support what you love and get ad-free audio and video feeds, a members-only Discord, and exclusive content. Join today: https://twit.tv/clubtwit Sponsors: horizon3.ai/intelligent rippling.ai/machines

This week's VentureFuel Visionary is Fatih Nayebi, Vice President of Data & AI at ALDO Group. He leads AI initiatives that transform retail operations and customer experiences. He's also a Faculty Lecturer at McGill University and the author of Foundations of Agentic AI for Retail, the first book on autonomous AI systems in retail. He demystifies agentic AI, federated learning, and the promise of A2A (agents to agents), while noting that these advances will still need humans in the loop.

We talk with Eglae Recchia, CEO of Keyway, about how Proptech grows from building tech into AI that gives commercial real estate teams back time for judgment. We also trace her path from a childhood of constant moves to MIT's SloanSchool of Business and a career built on turning data into action for lenders, REITs, investors, and operators. • Proptech as a broad umbrella across the built environment, fintech, and AI • Why “insights” are no longer enough and how “so what” drives adoption • How moving often shaped adaptability, networking, and comfort with pivots • Philosophy and advertising as training for clear thinking and persuasion • Early career lessons from editorial work, technology shifts, and ops problem solving • Choosing MIT's Sloan School of Business to build analytical tools and a problem-solving mindset • Graduating in 2009 and using networking to break into financial services • Finding commercial real estate through Capital One strategy and portfolio questions • Keyway's evolution from investor-operator thesis to agentic infrastructure for CRE • Who Keyway serves today and how it combines public data with internal data • What comes next: enterprise agents, risk management, and fraud detection Be sure to subscribe to the podcast on Apple Podcasts, Spotify, or wherever you listen to podcasts. To learn more, visit proptechespress.com. 

Your next donor may never visit your website!Peter Byrnes, CEO of Fundraise Up, is joining me to explain agentic giving and the shift he sees happening RIGHT now from discovery, to recommendation, to delegation. Donors are already asking AI tools where they should give, and soon they may be asking those same tools to actually make the donation for them. We get into what nonprofits need to do now to become easier for AI to find, understand, and trust, including why consistency across your website, annual reports, financial information, and outside sources matters more than ever. We also talk about Fundraise Up's free Agentic Giving Readiness Assessment, how to audit what AI is already saying about your organization, and why the goal isn't to panic — it's to prepare.Resources & LinksCheck out the Fundraise Up Agentic Giving Readiness Assessment.Connect with Peter on LinkedIn.Learn more about Peec.ai. This episode is presented by The Monthly Giving Builder.  With the Monthly Giving Builder, you can generate your comprehensive monthly giving plan and build your program step by step - with a guided companion working alongside you from start to finish. Supercharge your monthly giving program and apply for my Monthly Giving VIP Intensive session. Only 2 spots are available this fall!Let's Connect!Send a DM on Instagram or LinkedIn and let us know what you think of the show!My book, The Monthly Giving Mastermind, is here! Grab a copy here and learn my framework to build, grow, and sustain subscriptions for good.Want to book Dana as a speaker for your event? Click here!

For episode 768 of the BlockHash Podcast, host Brandon Zemp is joined by Miles Paschini, Chief Executive Officer for FV Bank.FV Bank is a regulated global digital bank and qualified digital asset custodian headquartered in San Juan, Puerto Rico. The financial infrastructure platform serves global fintechs, enterprises, and individual freelancers by vertically integrating traditional banking services with stablecoin infrastructure and secure crypto custody. Operating through API-driven programmable rails, it enables real-time, 24/7/365 cross-border payments, USD accounts, and seamless asset conversions in over 40 currencies. 

The debate about whether AI will replace human workers has already been settled - not by academics or futurists, but by the organisations that fired their humans, discovered AI couldn't do what they needed, and quietly hired them back. The future isn't AI replacing humans. It's humans and AI working together in ways that neither could manage alone.In this episode, Victor Coimbra joins Dr Genevieve Hayes to share what hybrid agentic organisations actually look like in practice, and what data scientists need to do to position themselves at the centre of them.You'll discover:Why thinking of AI as a tool rather than a coworker is the mindset holding most organisations back [03:58]The four archetypes that determine which tasks belong to humans and which to agents [08:17]How AI is turning data scientists back into scientists [18:24]The two skills that will define an indispensable data scientist in a hybrid organisation [27:01]Guest BioVictor Coimbra is a Partner and CTO at Artefact, the world's largest pure-play AI consulting firm and co-founded the firm's Latin American operations. In 2024, he was recognised in the Forbes 30 Under 30 Brazil list for his outstanding contributions to AI innovation.LinksConnect with Victor on LinkedInArtefact websiteConnect with Genevieve on LinkedInBe among the first to hear about the release of each new podcast episode by signing up HERE

As AI agents gain access to sensitive enterprise systems, companies need new ways to control what they can do. Meta Marshall breaks down the emerging market for agentic identity security.Read more insights from Morgan Stanley.----- Transcript -----Meta Marshall: Welcome to Thoughts on the Market. I'm Meta Marshall, Morgan Stanley's U.S. Cybersecurity and Telecom & Network Equipment analyst. Today: AI assistants are starting to act on our behalf at work, which brings up a critical question. What should these agents be allowed to do? And how should those permissions be granted? It's Tuesday, September 1st, at 10am in New York. More and more, AI is helping us get through the workday. We ask it to summarize documents, analyze data and take notes during meetings. Increasingly, though, these tools are moving beyond just answering questions to acting on our behalf. Suddenly, the security challenge shifts from managing a tool to governing a whole new digital workforce. In coming years, this problem should get bigger as we estimate seeing 79 AI agents and 109 machine identities for every human employee. Now, traditional identity security at work was built to answer two basic questions: Who are you, and what can you access? Think of it as your office badge. It identifies you and determines what doors you can open. AI agents, however, make that question much harder to answer. They can operate autonomously, move across applications and databases, collaborate with other agents. They take actions without direct human involvement.So, companies need to know not only what an agent can access, but why it needs access, for how long, and what it actually did. That's the core foundation of agentic identity solutions. The risk environment from this problem is already substantial. About 80 percent of breaches in the work environment today involve stolen or misused credentials. Nine out of 10 organizations experienced an identity-related breach in the past year, and 83 percent experienced at least two. Now add potentially hundreds of machine and AI identities for every human; each operating continuously and at machine speed – and the problem is much larger.One solution to managing AI agents is zero standing privilege. Instead of giving an agent permanent access, you give it permission for a specific task and revoke that permission when the job is done. Here's the issue though: Today, only 39 percent of privileged access is managed through this just-in-time or zero standing privilege architecture. And the reality is that humans can't approve every request. More of those decisions will need to happen automatically, in real time, through what's known as runtime governance. We estimate, as a result, that agentic identity alone could become roughly a $33 billion global opportunity in our base case, which brings the overall identity market opportunity to more than $60 billion in coming years. This need for agentic identity coming from AI could also push a historically fragmented industry towards a more unified platform. In one industry survey, 85 percent of organizations said fragmented identity systems delay their human response to identity threats, with respondents citing an average of 12 hours needed to respond per incident. We think that favors platforms that can manage human and machine identities together and make security decisions dynamically, overall making a more secure environment. This transition won't happen overnight. Agentic identity products are still early, and we don't expect an immediate financial impact. But as enterprises move from experimenting with AI agents to deploying them more broadly, spending to secure those agents could become a more meaningful growth tailwind in 2027. The longer-term growth opportunity comes down to a simple dynamic: more agents, with more autonomy, will require more control. And that could make identity security essential to scaling AI across the enterprise. Thanks for listening. If you enjoy the show, please leave us a review wherever you listen and share Thoughts on the Market with a friend or colleague today.

Topics covered in this episode: OpenAI's Python SDK has migrated to HTTPX2 TMOG - Native Task Manager for macOS, Windows, and Linux wrapture - one wrapper for mocking, tracing, and observability linkedin2md: turn your LinkedIn export into 40+ Markdown files Extras Joke Watch on YouTube About the show Sponsored by us! Support our work through: Our courses at Talk Python Consulting from Six Feet Up Connect with the hosts Michael: Mastodon / BlueSky / X / LinkedIn Calvin: Mastodon / BlueSky / X / LinkedIn Show: Mastodon / BlueSky / X Join us on YouTube at pythonbytes.fm/live to be part of the audience. Usually Tuesday at 7am PT. Older video versions available there too. Finally, if you want an artisanal digest of every week of the show notes in email form? Add your name and email to our friends of the show list, we'll never share it. Calvin #1: OpenAI's Python SDK has migrated to HTTPX2 The OpenAI Python SDK has migrated to HTTPX2, the Pydantic-stewarded fork of httpx. Pydantic picked it up citing "limited activity recently" in the original project, promising "a reliably maintained path forward." If you just use the default client, nothing to do. No code changes. The catch is TLS. Quoting the guide: HTTPX "previously verified certificates against the CA bundle provided by certifi. HTTPX2 instead uses the operating-system trust store, and the SDK no longer installs certifi." That "can break certificate verification in minimal container images without system CA certificates, environments using corporate TLS-inspecting proxies, and deployments that relied on a custom or modified certifi bundle." The fix is SSL_CERT_FILE or SSL_CERT_DIR, or pass your own ssl.SSLContext via verify. Deeper integrations need real edits: custom clients, auth handlers, hooks, and request mocking all take HTTPX2 objects now, and plain httpx is no longer pulled in transitively. So import httpx in your own code means declaring it yourself or moving over. Temporary escape hatch: a legacy HTTPX client Michael #2: TMOG - Native Task Manager for macOS, Windows, and Linux A native, deeply instrumented system monitor for macOS, Windows, and Linux, now in public beta - from Plummers' Software, i.e. Dave Plummer, who wrote the original Windows Task Manager and donated it to Microsoft in 1995. Wikipedia Three real native apps: Swift/AppKit on macOS, Win32 on Windows, C++/Qt 6 on Linux, with a shared C++ core keeping metric semantics aligned - no browser shell anywhere. One dense summary: CPU, clocks, thermals, GPU, memory, storage, network, energy, and the processes responsible for the load, all click-through. Per-core honesty: logical processor and NUMA views, P and E cores color-coded, optional kernel time, 60 FPS live meters. Memory with context: pressure, wired, compressed, cached, committed, available, and swap, plus configurable scrolling history. Processes that act like processes: tree view, filtering, sorting, follow mode, and native verbs including service and launchd control. Phosphor themes: light, dark, green, amber, blue, or mono, with color and saturation you tune yourself. Calvin #3: wrapture - one wrapper for mocking, tracing, and observability Graham Dumpleton, author of wrapt and the original New Relic Python agent, has released wrapture. The name is wrapt plus capture. The core idea: wrap real code instead of replacing it, so the real code still runs while you watch every call. Name a method with wrapture.binding(Class, "method"), open a timeline(), and you get a tape of what actually happened. Real return values, real nesting, arguments normalised against real signatures. tape.tree() prints the call graph as it ran. One mechanism, three jobs: monkey patching with a real lifecycle (apply, remove, suspend, plus returns, raises, transforms_args), unit testing that asserts on real call flow instead of a flat MagicMock call list, and ad-hoc tracing of a running app. The testing pitch is error paths. Inject TimeoutError at the payment gateway, then assert the ledger was never written. Stubs and mocks are strict and spec-required, and there is deliberately no bare Mock(). Tracing needs no code at all. A wrapture.toml naming targets and a sink, run with python -m wrapture main.py, and you get a live call tree with timings. It captures ordinary logging calls as nested events, and with the otel extra it exports spans, metrics and correlated logs with W3C trace ids that join across services. Every line of code and docs was AI-written under their direction, and they say so up front. Two weeks from first commit, eleventh alpha, over 1000 tests, 150+ pages of docs. Alpha on PyPI, needs Python 3.12+ and wrapt 2.4.0+. Michael #4: linkedin2md: turn your LinkedIn export into 40+ Markdown files Via Juan Manuel Daza - a Python CLI that unpacks LinkedIn's data-export ZIP into clean, per-category Markdown you can drop straight into an LLM. One command: linkedin2md Complete_LinkedInDataExport.zip, plus o for output dir, -lang en|es, and -pdf. 40+ output files: profile, experience, education, skills, connections, posts, comments, reactions, recommendations, endorsements, job applications, even ad targeting and LinkedIn's inferences about you. Built for LLM analysis: the README pitches NotebookLM, Claude Projects, Obsidian, and Ollama, with example prompts like "what patterns do you see in my career transitions?" PDF resume mode: -pdf renders an A4 CV via weasyprint, and degrades gracefully to Markdown-only if it isn't installed. Dependency note: "pure Python / zero-dep" holds for the Markdown path only - the PDF path needs weasyprint and markdown installed. Install: pipx install linkedin2md recommended, pip in a venv otherwise - 86% Python, 10 releases, v0.3.1 in May. Agentic dev angle: repo ships opencode config and an N3RV subagent pipeline, including a "judgment day" dual-model adversarial PR review. Extras Calvin: EVE Online Migrates to Python 3 Michael: Dinkus by Will McGugan Joke: Tao of Programming: Book 5 Maintenance

For episode 767 of the BlockHash Podcast, host Brandon Zemp is joined by Ryan Louvar, Chief Legal Officer of WisdomTree.WisdomTree is a global financial innovator and asset management firm that bridges traditional finance (TradFi) and blockchain technology through a dedicated focus on digital assets and tokenized real-world assets (RWAs).

Large B2B operations keep running order entry, invoicing, and other repetitive back-office workflows by hand, years after most companies started investing heavily in AI. In this episode, Chris Bradley, Chief Marketing Officer and Head of AI Transformation at Veritiv, breaks down why legacy systems have kept agentic AI out of these workflows, and what changed once Veritiv began automating a process handling 1.7 million orders a year. The conversation covers the audit-trail controls needed to trust an agent with an end-to-end task, how a supervising AI agent checks another agent's work, and how to pick a first automation target based on headcount concentration rather than project size. ​ If you offer AI products or services into the enterprise, you need to find enterprise leaders with relevance and readiness. Emerj attracts VP+ enterprise audiences who are already convinced that they need to move beyond traditional IT. To learn the exact strategies we use to help leading AI brands and startups connect with their ideal enterprise AI buyers, visit: emerj.com/AD1

Agentic swarms? PR boondoggle or really big deal? When Hugging Face was breached by a mysterious intruder, OpenAI admitted it was one of their own AI agentic systems going rogue to cheat on a benchmark. Tom Bonner joins us to break down the 500+ raw code artifacts the agent left behind—and what it means when an AI compresses weeks of complex cyberattacks into just 8.5 hours. Hacked is presented by NordLayer. NordLayer is a network security platform for modern teams. NordLayer gives companies centralized control over who can access their systems, keeps every connection fast and encrypted, and requires no additional hardware or complex infrastructure. nordlayer.com/hackedpodcast   Learn more about your ad choices. Visit podcastchoices.com/adchoices

In a fascinating panel discussion, executives from Couchbase, SentinelOne and AWS share strategies ensuring the highest adoption and ROI when deploying AI and the pitfalls to avoid along the way.Topics Include:Agentic AI marks shift from clever data to autonomous agentsOrganizations sit at different AI maturity levels, not uniformOnly 31% of adopters see measurable financial impact from AIAdoption friction, not access, is what stalls most AI valueSegment your workforce: innovators, pragmatic majority, and reluctant laggardsGive innovators tools, budget, and freedom before chasing laggardsVisible recognition programs turn early innovators into internal role modelsAI-ready data infrastructure remains the top blocker to real adoptionFuture AI value concentrates at customer-facing, transaction-level touchpointsCoding tools see fast uptake; broader business adoption lags behindAI is quietly rewriting SEO, SEM, and lead-routing strategyAgent trust requires both clean data and strong safety guardrailsHuman-in-the-loop review lets AI handle investigation, humans decide outcomesIn agentic AI, go-to-market partnerships matter more than past tech wavesStrong tech-platform partnerships help smaller vendors punch above their weightMarketplace listings can cut sales cycles from months to weeksBest partnerships form around solving hard problems, not quarterly quotasConfidence gap: only 38% of employees feel AI-readyParticipants:Deirdre Toner – President & Chief Commercial Officer, CouchbaseEran Ashkenazi - Chief Business Officer, SentinelOneConnie de Lange – Director, AWS Strategic Customer & Partner Marketing, North America, Amazon Web ServicesMatt Wood – Chief AI & Technology Officer, Amazon Web Services See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Payments companies were among the first to experience the pressures that hit the fintech scene and the broader software industry over the past couple of years. Now activity is reviving, with several big deals. What's changed, and which other parts of the sector stand to thrive amid ongoing disruption? Jason Gurandiano, Head of U.S. Technology Banking and Global Head of Fintech Banking, is joined by colleagues Matt Thomas and Asif Ahsan for the second part of their analysis.Key pointsPayments companies are trading at a discount relative to cashflow and are likely to see strong M&A activity.Agentic commerce and stablecoin are potential game-changers in payments.Strategics are targeting companies with a hardware component alongside proprietary data as moats against AI.Digital assets and trading platforms are among the subverticals with strongly favorable signals.Chapter markers:Introductions [00:06]Joe Coletti introduces the second part of a discussion led by Jason Gurandiano, Head of U.S. Technology Banking and Global Head of Fintech Banking, with Matt Thomas, Managing Director in Technology Investment Banking, and Asif Ahsan, Managing Director in Technology M&A.Payments strength [00:46]Activity in the payments sector has picked up meaningfully in the past three months. The space has become more global and less fragmented, as companies seek to own whole steps of the value chain. Many payments companies are trading at a discount and this is an area of likely continuing M&A activity.Impact of agentic commerce and stablecoin [04:34]Agentic commerce is set to transform payments, and will drive transactions to ensure security against fraud. Stablecoin is becoming institutionalized and could prove disruptive to traditional banking when paired with consumers' digital wallets. Information services outlook [08:13]Information services companies' success rests on whether their data is truly proprietary or can be easily replicated. A combination of proprietary data and hardware is increasingly valued by companies looking to do M&A.Subvertical verdicts [10:17]Summing up their views, participants are broadly bullish about payments and financial software. Views on market structure, information services, and disruptive financial services are mixed, with some players facing greater risks. Signs are good for digital assets, crypto, and tokenization, with strong innovation and maturing players.

AI is changing more than how work gets done—it's changing how the entire front office works. In this podcast episode, PwC leaders explore the shift from siloed marketing, sales, commerce, service and pricing functions toward an agentic front office where humans and AI work together across the customer lifecycle.

“AI ad platforms will win by becoming less like ad managers and more like answer engines. Measurement is becoming the strategy, because zero-click behaviour is already changing the funnel.”Performance & brand marketing expert Josh Duggan digs into where AI, search and commerce are actually heading; not in theory, but in the real world of live campaigns and platform shifts.ChatGPT ads are already here, but the platform is still acting like Google Ads 15 years ago. Josh reveals why the current setup is full of irrelevant targeting, weak reporting and click-only optimisation - and what that means for brands trying to get ahead before the real opportunity arrives.You'll discover:Why ChatGPT ads currently feel basic, clunky and dangerously easy to mis-trigger with the wrong intent.The biggest missing pieces in the platform, from negative keywords to search-term visibility and conversion-based optimisation.Why shopping feeds, offline conversion value and customer list matching are likely to matter far more than today's ad units.How Google is quietly turning paid search into a more automated, more AI-shaped experience.Why AI Overviews, AI Max and dynamic product feeds could reshape how brands win visibility in search.How Meta is changing faster than most marketers realise, with 70% of Instagram content now coming from accounts you do not follow.Why creative diversity, always-on content and dynamic landing pages are becoming the new performance edge.Where TikTok Shop, ShopMy, Pinterest, Snapchat, YouTube and affiliate channels fit into the next growth cycle.Josh also breaks down the real state of trade, including why UK online spending is still growing, why fashion and footwear are more volatile and why November remains the make-or-break month for many brands. If you want a clear-eyed view of what is hype vs. what is already working and where paid media is heading next, this episode is essential listening.Chapters:[00:30] Introduction and episode overview[02:35] ChatGPT ads: early limitations and potential[12:30] Google search, AI discovery and shopping ads[21:05] Meta's growth, creative diversity and AI-generated advertising[30:10] Other channels: TikTok Shop, ShopMy, Pinterest, Snapchat and YouTube[35:00] Ecommerce trading outlook, market performance and Q4 priorities[41:10] Closing thoughts

In this episode, Scott Jenson, a veteran UX designer known for his work on the Macintosh, Google Maps, and Chrome examines the long-term stagnation of desktop operating systems and the limitations of current mobile and cloud-centric models. The conversation shifts toward specialised "Local-First" software, exploring how we can move beyond the "status quo" of interface design to create more intuitive, powerful, and privacy-conscious computing environments in the advent of the “LLM everywhere” era. Read a transcript of this interview: https://bit.ly/4gwuQwC Newsletter: Subscribe to the Software Architects' Newsletter, a monthly roundup of the patterns and technologies senior practitioners are working through, with the news and lessons from people doing the work: https://www.infoq.com/software-architects-newsletter InfoQ Online Certification Programs: 5-week online cohorts for senior engineers and architects, built around QCon talks. Programs now cover software architecture, AI engineering, and organizational architecture. Each week you join a four-hour live session with a confidential peer group of practitioners from other companies, apply frameworks from QCon talks to the decisions you're making at work, and earn an InfoQ certification. You leave with new approaches, or confirmation that the calls you're already making are the right ones. Learn more: https://certification.qconferences.com/ Upcoming Events: QCon San Francisco 2026 (November 16-20, 2026) https://qconsf.com/ QCon London 2027 (April 13-16, 2027) https://qconlondon.com/ The InfoQ Podcasts: Weekly conversations with senior software leaders about how they build systems and teams, including what they'd do differently. Listen to all our podcasts and read interview transcripts: The InfoQ Podcast: https://www.infoq.com/podcasts/ Engineering Culture Podcast by InfoQ: https://www.infoq.com/podcasts/#engineering_culture Generally AI: https://www.infoq.com/generally-ai-podcast/ Follow InfoQ: Mastodon: https://techhub.social/@infoq X: https://x.com/InfoQ LinkedIn: https://www.linkedin.com/company/infoq/ Facebook: https://www.facebook.com/InfoQdotcom Instagram: https://www.instagram.com/infoqdotcom/ YouTube: https://www.youtube.com/infoq Bluesky: https://bsky.app/profile/infoq.com Write for InfoQ: Share what you've learned building software with a community of senior practitioners, and get your work in front of the people who read InfoQ. https://www.infoq.com/write-for-infoq

Gant Laborde returns to the podcast for a recap of all things Chain React! Robin and Mazen chat with Gant about the trends he saw at Chain React this year — from Expo's keynote to AI's growing role in software development — as well as why in-person conferences are still at the center of what motivates us as a community. Show Notes Chain React Recap Video Connect With Us! Gant Laborde: @gantlaborde Robin Heinze: @robinheinze Mazen Chami: @mazenchami React Native Radio: @ReactNativeRdio This episode is brought to you by Infinite Red! Infinite Red is a premier mobile app consultancy, especially focused on Expo and React Native, located fully remote in the US. We're a team of 30 with highly experienced mobile app developers and have been doing this for over a decade. We are also one of the first development teams to adopt agentic coding in a way that keeps high quality standards and aren't afraid to do things the old school way if we need to. If you're looking for mobile app or React Native or Expo expertise for your next project, hit us up at infinite.red/radio.

For most of the generative AI era, AI governance has focused largely on the people using it: which tools lawyers and business professionals can use, what information they can enter, how outputs must be verified, and when human review is required. Agentic AI changes that conversation. We are no longer only asking AI systems for answers. We are increasingly able to give them goals and some authority to decide how to accomplish them. In this episode, I unpack what that shift means and the governance questions I think law firms and businesses need to start asking now. After listening, if you are in a place where you can leave a comment, please do so, as I would love to hear from you. If not, feel free to email me at nancy@myrlandmarketing.com. Also, my website, where you can find all of my contact information, and my other podcast, Legal Marketing Moments, can be found at https://myrlandmarketing.com/podcasts/legalmarketingminutes.com Thanks for spending a few of your Legal Marketing Minutes with me!

On this episode, we explore how agentic AI is turning the contact center into a growth engine — and how to make that shift without losing customer trust.Salesforce is framing the 2026 Dreamforce conference around the agentic enterprise: organizations where AI agents handle routine work autonomously so people can focus on what still needs a human.Salesforce's State of Service: AI Agents Edition, which surveyed more than 3,000 customer service professionals, found AI agent adoption nearly doubled year over year, climbing from 39% to 66%, with Salesforce projecting AI will resolve half of all customer service cases by 2027. That adoption curve is an opportunity for contact centers to move past being a cost center and build a revenue channel instead, a shift called revenue engagement. It means building trust through service conversations, leading to a cross-sell, a larger sale or a stronger relationship.John Robb, product leader for voice AI and Agentforce Contact Center at Salesforce, and Gopi Ramineni, Salesforce practice leader at TELUS Digital, walk through what it takes to drive revenue engagement. Along the way, they speak about matching AI response speed to what a live caller expects, unifying customer data across systems so an AI customer service agent has the full picture before it acts and tracking a customer's value across their whole relationship with a company to see whether a service conversation is earning enough trust to influence a sale.Show notesRegister for Dreamforce 2026 and join TELUS Digital at Hotel Zetta, September 15–16, 8am–9pm PDT: https://sfpractice.telusdigital.com/dreamforce-2026

What happens when an AI agent follows your documented process perfectly, but that process bears little resemblance to how decisions are actually made? In this episode, I speak with Bill Wilson, Executive Head of Data and AI Solutions at NTT DATA UK&I. Bill oversees AI globally for NTT DATA's public sector work, giving him a close view of how governments are using AI while trying to manage risk, accountability, public confidence, and constrained resources. Bill offers a refreshingly practical test for any proposed AI system: is it competent, and what is the worst thing that could go wrong? He describes this potential consequence as the system's "blast radius." An AI assistant helping somebody understand a grant application presents a very different level of risk from an agent making decisions that affect employment, justice, taxation, or access to public services. We also discuss why companies can make a mistake before deploying their first agent. Automating an inefficient process simply allows the organization to perform the wrong work faster. Bill argues that teams should examine complete workflows, identify where several AI capabilities could produce a measurable result, and remain prepared to redesign the process as they learn. Another major problem is tacit knowledge. Employees frequently make decisions using experience that was never written down. An agent trained solely on formal documentation may therefore understand the official process while missing how the work gets done in practice. Bill explains how targeted questions, behavioral traces, feedback, and supervised learning could capture some of that reasoning. Public sector AI provides several useful examples. Bill discusses systems that process volumes of information beyond human capacity, emergency response work in Tennessee, and case management applications that gather information before a human reviews it. In these situations, AI can reduce administrative work and waiting times while leaving consequential decisions with people. But human approval alone provides no guarantee. If employees lose direct experience of the work, they may eventually approve whatever the system recommends. Bill compares this with airline pilots maintaining manual flying skills and describes how known test cases can reveal when reviewers are becoming overly trusting. For CIOs deciding which AI pilots should reach production, the advice is equally direct: choose work with measurable returns, group related use cases where their combined effect can be seen, learn from a varied set of deployments, and avoid building something a software provider is about to include in an existing product. As AI agents gain access to external information, internal data, and operational tools, how should your organization decide what they may do alone and when a person must intervene? Listen to the conversation and share your thoughts with me.

In this episode of Shift AI, Sabina Anja, Chief Technologist at VMware by Broadcom, joins host Boaz Ashkenazy for a wide-ranging conversation on why private, flexible infrastructure is becoming the foundation enterprises need for the agentic era.Sabina makes the case that most infrastructure and security thinking was built for a world where humans approve every step, and that world is already gone. An assistant waits for you. An agent, in her words, gets set loose and just goes. So what happens when nobody notices an agent doing something it was technically allowed to do, but shouldn't have? Sabina has a way of thinking about that question that most infrastructure teams haven't caught up to yet.This episode is for CIOs, CTOs, VPs of infrastructure, platform engineers, and security leaders who are trying to figure out what their environments need to look like before agents are running unsupervised inside them.Chapters[00:00] Welcome and introducing Sabina Anja[00:31] From Belgium to self-taught programmer: Sabina's first jobs[04:29] Why programming hygiene still matters with Cursor and Claude Code[06:24] Virtualization then and now: from vCPUs to vGPUs[08:52] What businesses are really asking for as AI costs spike[10:46] Inside NVMe tiering and the memory crunch[13:28] Private AI and the return of sovereign infrastructure[15:43] Data ownership, geopolitics, and the new value of stolen IP[17:48] Open source, small language models, and fit-for-purpose compute[20:59] Advice for CIOs: build flexible foundations, not two-year projects[24:40] Agents vs. assistants: why blast radius changes everything[27:26] Agentic security, guardrails, and the two words: platform matters, earned trustConnect with Sabina AnjaLinkedIn: https://www.linkedin.com/in/sabinaanja/Connect with Boaz AshkenazyLinkedIn: https://www.linkedin.com/in/boazashkenazy/Email: info@shiftai.fm

SaaStr 875: Who Owns Your Data Now? Agents vs. System of Record, ServiceTitan vs. Podium, Headless Salesforce, and Agentic Renewals on The Agents #013 The agents are writing data faster than any human ever could - and systems of record aren't ready for it. This week on The Agents, Jason and Amelia dig into the biggest meta-theme of 2026: what happens when your AI agents become the primary user of your CRM, your MAP, and every other system you've built your business on? Together, they unpack the ServiceTitan vs. Podium blowup, where an agentic lead gen tool slowly became a competing system of record until ServiceTitan shut them off with 30 days' notice, and why this is just the first of many fights like it coming across SaaS. Then Amelia pulls back the curtain on how SaaStr actually runs Salesforce headless through 10K, what it means that their agents have written 40 gigabytes of data into Salesforce without either of them logging in, and why the storage math is going to force a reckoning for every vendor jacking up API prices right now. Plus: the renewal agent Amelia built that generates a fully custom, hyper-personalized pitch deck for every single customer, using headless Salesforce, Gamma, social data, podcast mentions, and Gmail. And why Clay plus ZoomInfo plus Cowork turned out to be the best enrichment stack their agents have found yet. If you're building on top of systems of record, selling to companies that are, or just trying to figure out how agents change the economics of SaaS data, this one is essential listening. Timestamps: 00:00 - Intro 02:00 - ServiceTitan cuts off Podium: what happened and why it matters 10:00 - 40 gigs in Salesforce and neither of us logged in 16:00 - The API pricing reckoning coming for systems of record 22:00 - How SaaStr runs Salesforce headless with 10K 30:00 - The renewal agent: no account left behind 42:00 - Narrative-first pitching: getting the agent to sell 50:00 - Clay + ZoomInfo + Cowork: the enrichment stack that actually worked 58:00 - What comes next: agentic inbound proposals SaaStr hosts the world's largest community for B2B software founders and executives.

How do we build an AI ecosystem where agents, tools, and systems can work together at scale? Angie Jones, VP of the Agentic AI Foundation, joins Chris to discuss the open standards and projects shaping the agentic future, including MCP, A2A, Goose, etc. They also explore what it takes to drive AI adoption across an entire organization, the importance of neutral standards, global perspectives on agentic AI, and how humans can find the right balance between what they delegate to AI and what they do themselves.Featuring:Angie Jones – LinkedIn, XChris Benson – Website, LinkedIn, Bluesky, GitHub, XLinks:Agentic AI FoundationSponsors:Framer: The enterprise-grade website builder that lets your team ship faster. Get 30% off at framer.com/practicalaiPrediction Guard: A self-hosted AI control plane for running agents in high impact environments. predictionguard.com/practicalaiResources and Events:Register for upcoming webinars here!Prior Webinars from our partner Prediction GuardMidwest AI Summit 2026

Nvidia posted $96B in quarterly revenue and guided to 70% growth next year, then agreed to buy Hugging Face for $12.9B. Trump weighed sweeping chip tariffs, cybersecurity stocks ripped on AI threats, and Instinct raised at $2.5B. Links Nvidia reports Q2 revenue up 106% YoY to $96.22B, above $92.17B est., Data Center revenue up 117% to $89B, above $85.08B est., and net income up 126% to $59.7B (Nvidia) Nvidia guides to ~70% revenue growth next fiscal year, well above the 45% analysts expected, sending shares up as much as 7.6%, though margins will bottom at 71%-72% on memory costs (Bloomberg) Source: Nvidia has agreed to acquire Hugging Face for $12.9B; the AI repository has had several potential suitors among its investors, including Salesforce (The Information) Sources: the Trump administration is weighing sweeping new tariffs on chips and other products like laptops and consoles, despite warnings from tech companies (Politico) Cybersecurity stocks surge, with Okta up 20%+ and CrowdStrike up 15%+, after earnings showed that AI adoption is driving attacks and spending on security tools (CNBC) AI assistant Instinct is raising a $250M Series B co-led by Index and Benchmark at a $2.5B valuation, taking its total funding to $350M since its 2025 founding (The Wall Street Journal) Subscribe to the ad-free feed.

Join FPC Executive Director and CEO Reed Luhtanen as he goes off the rails with Mastercard VP of agentic commerce Michael Sulla. Reed and Mike dig in to this emerging payments topic to separate the hype from the reality and get into the details that will need to be figured out to make sure agentic commerce is safe for all parties.

In this episode, Andrew McNamara, VP of Applied ML at Shopify, returns to unpack how much has changed in agentic commerce since his last episode. Andrew and Liam dig into why agents are becoming "the new front door to commerce," why orders coming to Shopify stores from AI are up 13x, and what's actually happening inside Shopify's personalized shopping agent in the Shop app. They also get into the Universal Commerce Protocol (UCP) and why AI commerce is growing 9x faster than social commerce did at the same stage, how Sidekick's architecture and app extensions work, and SimGym, Shopify's system for training AI shoppers to A/B test store changes before they ever reach a real customer. Key Topics Covered How shopping is shifting from stores and desktops toward agents as "the new front door to commerce" Why orders coming to Shopify stores from AI are up 13x, and why catalog-powered AI search converts twice as well as general AI search Inside Shop app's personalized shopping agent, and how it learns different shopping personas (like shopping for a pet versus a child) Why customers are shifting from keyword searches to natural language queries, and the higher conversion rates that come with it Why Shopify keeps shopping data personalized to the individual user rather than training it into a larger internal model What the Universal Commerce Protocol (UCP) is, and why AI commerce is growing 9x faster than social commerce and 3x faster than mobile did at the same stage The story of Shopify's CEO giving his own Hermes agent a budget so it can send him gifts in the mail Sidekick's app extensions, and how partners like Klaviyo and Loop plugged in at launch Campaign Autopilot's "auto research loop," and its parallels to reinforcement learning SimGym, and how Shopify trains AI shoppers to A/B test store changes before running them on real customers Why Sidekick runs on Anthropic's Sonnet model hosted on Google Cloud, and why that choice is model agnostic Andrew's own habit of shopping by taking pictures throughout the week and searching by image through UCP-connected agents Episode Timestamps: 00:00 - Introduction and welcome 00:29 - What's changed in AI and shopping since their last conversation 01:47 - Agents becoming "the new front door to commerce" 04:16 - Inside Shop app's personalized shopping agent 07:32 - Why data stays personalized to each shopper instead of training a larger model 11:53 - What the Universal Commerce Protocol (UCP) is, and orders from AI up 13x 14:58 - Merchant tooling for tracking AI-driven traffic and conversions 15:55 - The story of Tobi's Hermes agent sending him gifts in the mail 20:48 - Andrew's own habit of shopping by taking pictures and searching by image 26:59 - Sidekick's app extensions and partner integrations 33:02 - Inside Sidekick's architecture: the Sonnet model and knowledge base 35:18 - Campaign Autopilot's auto research loop 38:58 - SimGym: training AI shoppers to test store changes 42:23 - What's next for Shopify's agentic commerce features 44:17 - Where to find Andrew Andrew's Socials: Twitter (X) - https://x.com/DrewCH LinkedIn: https://www.linkedin.com/in/andrewmcnamara1/ Partner Links Upgrade your AI toolkit: https://www.theaireport.ai/ai-executive-pass Subscribe to our free newsletter: https://newsletter.theaireport.ai/subscribe Join the community: https://community.theaireport.ai/checkout/the-ai-report-welcome-gift?coupon_code=WRTH Learn more about your ad choices. Visit megaphone.fm/adchoices

96% of Applicants Fall Into the Abyss. Take2 AI Is Pulling Them Back Out.Featuring Yaniv Shimoni, Co-Founder, Take2 AI | ASHHRA Podcast SponsorThe average hospital has 43 unfilled nursing positions. It takes up to 102 days to recruit an experienced RN. And your recruiting team is talking to three to four percent of the people who apply. The other 96%? They fall into the abyss. Yaniv Shimoni built a company to fix that — and healthcare was not the plan. The CHROs made it one.Bo sits down with Yaniv, co-founder of Take2 AI and Stanford GSB alum, to unpack what agentic AI actually means for healthcare talent acquisition and how health systems like CommonSpirit, Temple Health, and the VA are already using it.

CVS Health operates one of healthcare's largest consumer platforms, with businesses spanning health insurance, pharmacy benefits, retail pharmacy, care delivery and digital channels. This creates a rare opportunity to understand the full journey people navigate as they seek care, fill prescriptions, manage coverage and work to stay healthy. The company's overarching goal is not simply to improve each touchpoint. It is to remove the fragmentation that forces patients to piece care together on their own across multiple channels and organizations.In this episode of Healthcare is Hard, Keith Figlioli talked to CVS Health's head of enterprise customer experience, insights and innovation, Sri Narasimhan, to learn how the company uses customer signals, AI, and agentic twins to make healthcare more proactive and consumer-centered.Sri brings an unusually broad background to this challenge. He worked in Bangladesh and India on tuberculosis control, HIV initiatives and public health during college, but then decided to branch out so he could ultimately bring a more diverse skillset back to the healthcare industry. He started an economic consulting firm before going to business school. Then he joined GE, where he learned about commercial functions and consumer value. After GE, he worked at the tech company Medallia, where he sharpened skills around speed of innovation, and then moved to Wells Fargo where he worked as head of customer experience for branch banking, learning how to operate in a highly regulated environment.In 2021, Sri joined CVS Health where his work now sits at the intersection of consumer strategy and AI. During this interview, Sri shared his vast knowledge and experience to discuss topics including:Navigating bureaucracy. Sri points out that patients do not benchmark healthcare against other health plans, pharmacies or health systems. They compare it with the simple experiences they have everywhere else. He says most people want answers to four questions: Is it covered? What will it cost? When can I get it? And can I trust the answer? Healthcare's tendency to bury those questions in process and bureaucracy is a major source of frustration that Sri is focused on addressing.AI versus primary care. Trust grows when people get something valuable, and Sri expects consumers to increasingly turn to AI for quick answers about symptoms, diagnoses and side effects as those tools become more useful and accurate. But for serious health issues, he believes the provider relationship will remain essential. AI may change the questions patients bring to clinicians – and which interactions require a clinician at all – but people will still want a trusted human when the stakes are high.Agentic twins revolutionizing consumer research. Sri calls this technology one of the most transformational capabilities he has seen because it allows CVS Health to simulate the reactions of roughly 150,000 individual consumers in minutes. Through consented interviews, behavioral data and other context, CVS Health built digital twins of actual consumers that simulate how they think and act. Instead of recruiting a new panel, running a focus group or launching a small pilot every time the company wants to test an idea, CVS can assemble the relevant population from its bank of agentic twins and pressure-test messages, choices and scenarios before going live. As Sri puts it, “I have 150,000 patients in the room with us."To hear Sri and Keith discuss these topics and more, listen to this episode of Healthcare is Hard: A Podcast for Insiders.

Dawn Tiura talks with Vlad Keil, co-founder and CEO of Lio, whose multi-agent AI platform now runs procurement, finance, and logistics workflows for 200+ global enterprises. Vlad explains why single-purpose AI agents are already outdated, how organizations will shift from managing people to managing agents, and why humans stay in the driver's seat even as autonomy grows. The episode wraps with a preview of Lio's hands-on Bots & Buyers events in New York (Sept. 23) and Munich (Oct. 27–28), where attendees build real agents alongside the engineers who created them. 

DHH is the creator of Ruby on Rails, Omarchy Linux, CTO of 37signals, and a racecar driver. Thank you for listening ❤ Check out our sponsors: https://lexfridman.com/sponsors/ep501-sc See below for timestamps, transcript, and to give feedback, submit questions, contact Lex, etc. Transcript: https://lexfridman.com/dhh-2-transcript CONTACT LEX: Feedback – give feedback to Lex: https://lexfridman.com/survey AMA – submit questions, videos or call-in: https://lexfridman.com/ama Hiring – join our team: https://lexfridman.com/hiring Other – other ways to get in touch: https://lexfridman.com/contact EPISODE LINKS: DHH’s X: https://x.com/dhh DHH’s Blog: https://world.hey.com/dhh Omarchy: https://omarchy.org Ruby on Rails: https://rubyonrails.org 37signals: https://37signals.com SPONSORS: To support this podcast, check out our sponsors & get discounts: Wispr Flow: AI-powered voice dictation app. Go to https://wisprflow.ai/lex Blitzy: AI agent for large enterprise codebases. Go to https://blitzy.com/lex NetSuite: Business management software. Go to http://netsuite.ai/lex Shopify: Sell stuff online. Go to https://shopify.com/lex LMNT: Zero-sugar electrolyte drink mix. Go to https://drinkLMNT.com/lex Plaud: AI-powered note-taking devices and software. Go to https://plaud.ai/lex Higgsfield AI: AI-based video generation, filmmaking, and creative studio. Go to https://higgsfield.ai Perplexity: AI-powered answer engine. Go to https://perplexity.ai/ OUTLINE: (00:00) – Introduction (01:14) – Sponsors, Comments, and Reflections (08:56) – Programming with AI agents (24:14) – How software will change (33:30) – AI impact on open source (43:21) – Building Omarchy Linux distro (53:05) – Vibe coding vs agentic engineering (1:06:06) – The end of manual programming (1:16:24) – Advice for programmers (1:28:31) – Surviving Internet Hate (1:37:46) – Programming setup for AI Agents (1:50:11) – Obsessing about speed (2:13:06) – Voice prompting vs typing (2:27:05) – Best AI coding models (2:43:55) – Best AI coding harnesses (2:56:57) – AI video generation and filmmaking (3:16:28) – Fatherhood (3:44:35) – Linux will win the desktop (3:55:51) – PewDiePie (4:05:25) – Future of programming (4:28:18) – Politics and immigration (4:59:55) – Longevity, over-optimization, and fear of death (5:11:38) – Eternal recurrence and future of human civization

Anika sat down with George Khachatryan to explore the evolving landscape of AI-driven marketing, personalization, and the massive trust gap currently sitting between brands and consumers. The conversation revealed a counterintuitive truth: while 93% of marketing leaders believe AI helps them understand their customers, only 53% of consumers agree. George's unique journey—from earning a PhD in mathematics at Cornell and building an early ed-tech AI company to founding OfferFit and navigating its $325M acquisition by Braze—offered a practical look at how reinforcement learning and predictive data science are permanently replacing the manual grind of traditional A/B testing. In This Episode How a background in mathematics and early intelligent tutoring systems laid the groundwork for complex AI architectures. Stepping away from specialized tech to learn foundational company building through management consulting and operational transformations. Moving beyond rigid "Next Best Action" rules to autonomous reinforcement learning agents that experiment at the individual customer level. The strategic decision behind merging OfferFit with Braze in a $325M deal and why deep data science and engineering alignment matters. Why hidden bots, lack of transparency, and mismatched brand expectations are eroding consumer confidence. Blending predictive machine learning models (for timing and offers) with LLM agentic copy generation for maximum impact. Why students and early-career marketers must embrace AI literacy to stay competitive in a rapidly shifting job market. Timestamps 00:00 Introduction: The math, the $325M exit, and the AI trust gap 02:00 From Cornell mathematics and early ed-tech to management consulting 04:18 The limitations of traditional "Next Best Action" and the birth of reinforcement learning 09:22 Real-world personalization: How brands like Yum Brands optimize customer engagement 12:39 The acquisition journey: Why OfferFit chose to integrate with Braze 16:53 The 93% vs. 53% problem: Why consumers feel misunderstood by brands 20:41 Hybrid AI systems: Stitching together LLM copywriting and statistical decisioning 23:33 The personal cost of a "Sydney is just a bot" moment: The importance of transparency 28:32 The reality of enterprise caution vs. consumer expectations 29:56 Agentic commerce and the future of bot-to-bot interactions 34:39 Managing negotiation bots and complex consumer retention scenarios 37:25 Generational divides: Gen Z's dual relationship with AI adoption and career anxiety 40:59 Lessons for founders: Aligning philosophical visions before a merger 43:35 Final thoughts: Embracing the historic and fascinating shift in marketing technology Key Insights & Takeaways Insight 1: Personalization Requires True One-to-One Experimentation Traditional marketing relies on segmenting audiences and applying rigid, rule-based logic. True optimization requires reinforcement learning agents that operate at the individual customer level, autonomously testing variables like messaging, timing, and offers to discover what drives incremental engagement. Insight 2: The Danger of Hidden Bots and Broken Trust Consumers do not mind interacting with AI or automated tools, but they expect transparency. When a brand masks an AI agent as a human—and fails to take responsibility for its actions—it creates a deep sense of betrayal that permanently damages customer loyalty. Insight 3: The Power of Hybrid AI Infrastructure The most effective marketing systems do not rely on a single flavor of AI. Combining statistical reinforcement learning (to determine when and what to communicate) with LLM agentic text generation (to personalize how it is phrased) yields performance lifts of over 100%. Insight 4: Shared Vision is the Anchor of Any Successful Acquisition For founders navigating a potential exit, technical integration is only half the battle. Long-term success relies on frank, open discussions about philosophical alignment, shared visions, and mutual risk mitigation before signing a deal. Insight 5: Enterprise Caution vs. Consumer Speed While marketers are often bogged down by internal alignment and slow decision-making processes, consumers are interacting with cutting-edge AI tools daily. Brands that fail to bridge this expectation gap risk becoming obsolete. Resources & Links Mentioned Braze 2026 Global Customer Engagement Review OfferFit Yum Brands & Home Security (Client case studies discussed in the episode) About George Khachatryan George Khachatryan is a mathematician, entrepreneur, and executive leader serving as the Head of OfferFit by Braze. Holding a PhD in mathematics from Cornell University, George previously co-founded the ed-tech pioneer Reasoning Mind and worked as an associate partner at McKinsey. He co-founded OfferFit, an AI decisioning engine that utilizes reinforcement learning to automate marketing experimentation, culminating in its acquisition by Braze in 2025 for $325 million. Connect with George LinkedIn: https://www.linkedin.com/in/george-khachatryan/ Website: https://www.braze.com/ Like the show? Leave us a rating or review: https://lovethepodcast.com/67940257010b317cdaa9d857Follow the Show: https://followthepodcast.com/67940257010b317cdaa9d857Send a Message: https://podcastfeedback.com/67940257010b317cdaa9d857Check out our Website: https://www.yourbrandamplified.comSpeak to my Delphi Clone: https://www.delphi.ai/amplifywithanika Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

A quarter of enterprise teams have gotten a single AI channel into full production — the rest are stuck somewhere behind it. In this episode, Arun Chandra, Chief Operating Officer at NiCE, explores why that gap persists and what closes it.   The conversation covers getting data, knowledge, and organizational context ready for scale, building the financial case for AI investment with the CFO, and how open protocols like MCP are reshaping enterprise architecture decisions.   This episode is sponsored by NiCE Cognigy.   Learn how leading organizations approach AI investment more like a venture portfolio, and why interdisciplinary collaboration is critical to defining the right data for AI success. Download our free PDF report, "Beginning with AI," at emerj.com/aik1

In this episode of the Microsoft Threat Intelligence Podcast, we are joined by Sysdig's Michael Clark and Crystal Morin to discuss JADEPUFFER, one of the first documented cases of an LLM conducting an end-to-end ransomware operation. They break down how the agent, and the direction of a threat actor was identified, how AI is lowering the barrier to entry for ransomware, and why speed and adaptability are changing the threat landscape. Plus, they explore what organizations can do to defend against AI-powered attacks, from basic security hygiene and exposure management to better understanding their growing AI infrastructure.    In this episode you'll learn:       How Jade Puffer used an LLM to conduct a ransomware attack  Why agentic AI can make cyberattacks faster and more adaptable  How organizations can better protect their growing AI infrastructure  Some questions we ask:      How did you determine an LLM was conducting the attack?  What basic security practices are most important against these attacks?  Does AI allow less-sophisticated threat actors to carry out more advanced attacks?    Resources:   Read the research on JADEPUFFER  View Crystal Morin on LinkedIn   View Michael Clark on LinkedIn   View Elliot Volkman on LinkedIn     Related Microsoft Podcasts:                    Afternoon Cyber Tea with Ann Johnson  The BlueHat Podcast  Uncovering Hidden Risks      Discover and follow other Microsoft podcasts at microsoft.com/podcasts     Get the latest threat intelligence insights and guidance at Microsoft Security Insider    The Microsoft Threat Intelligence Podcast is produced by Microsoft, Hangar Studios and distributed as part of N2K media network. 

This was a fun one. We sat down with security icon Bruce Schneier to talk about AI systems that break the rules, cybersecurity beyond computers, the fight over encryption, the Snowden documents, blockchain, digital rights, and what happens when machines learn to exploit the systems humans built. Hacked is presented by NordLayer. NordLayer is a network security platform for modern teams. NordLayer gives companies centralized control over who can access their systems, keeps every connection fast and encrypted, and requires no additional hardware or complex infrastructure. nordlayer.com/hackedpodcast   Stop online threats before they become real-world attacks. Visit ironwall.com/HACKED and request a free Risk Assessment to see exactly how exposed your executives are. Learn more about your ad choices. Visit podcastchoices.com/adchoices

Complacency on the retail shelf is a fast track to irrelevance. Consumer habits are shifting rapidly, new medical trends are altering diets, and artificial intelligence is completely rewriting product discoverability in real time. To break down these massive shifts, host James Harris sits down with retail experts Katie Smith, Erin Wall, Matt Adams, and Eric Howerton to unpack the specific differences between a brand that just gets onto a Walmart shelf and a brand that thrives there for years.We get into the exact ways new consumer habits like the rise of GLP-1 users are reshaping entire departments, forcing a reevaluation of price points and nutritional expectations. The conversation also tackles the operational necessity of process-driven flexibility in the supply chain and how brands must prepare for the imminent reality of agentic commerce. The shared philosophy across all these experts is that a great brand anticipates changing form factors and recognizes that constant adaptation is the only way to hold onto market share.Building operational trust behind the scenes often means having difficult, uncomfortable conversations about your actual logistical limitations. You will walk away from this episode understanding why proactively communicating your constraints, like only having three loading doors for a massive order, is a strength that prevents catastrophic failures down the line. You will also learn exactly why supplying deep, contextual content is the immediate required fuel to survive the transition from traditional SEO to modern generative engine optimization.If you care about retail supply chain strategy, shifting consumer psychology, and the future of AI in everyday commerce, you will get a lot from this episode. Please take a moment to subscribe to the channel and share this conversation with a fellow brand builder who needs to hear it. How is your brand currently adjusting its content strategy to feed the new generative AI search engines?

In today's Cloud Wars AI Minute, I explore why falling token prices aren't necessarily making AI transactions cheaper. Highlights 00:01 — So, one of the things that I want to talk about today is going to be the token economy. And what we're actually seeing is that tokens are about 12 times cheaper than they've been before. 01:30 — But what we're seeing is the average cost of a transaction is actually going up, and that is because of the fact that while token prices are coming down, the amount of tokens it takes to process something is actually going up. We're actually seeing a 520 percent increase in the amount of tokens that you're going to need to have per unit of work. 01:56 — And the reason behind this has been this agentic reasoning loop and the reasoning models that are starting to go and process. As a matter of fact, you're actually seeing that most recently, we've even seen that chat-based things and chat conversations that have happened between humans and agents have actually now, for the first time, started to fall off. 02:30 — So, if this industry trend continues, what you're going to see is that we're going to continue to see this drop. But what we want to see is we want to start also seeing some sort of normalization and drop happening on the average amount of tokens spent on a per-transaction basis. Visit Cloud Wars for more.

A conversation with Camilo Artiga-Purcell, General Counsel, and Tim Freestone, Chief Strategy Officer at software provider Kiteworks. Robert discusses the challenges of securing and controlling AI agents with Camilo and Tim, who weigh in on legal and strategic aspects of agentic AI security. Kiteworks is a lead sponsor of AI Governance World Conference 2026 coming up October 12-14 in Las Vegas. Both Tim and Camilo will be presenting at the conference.

In this episode, Morgan Beschle, Vice President of Product at RevSpring, discusses the evolution of AI toward agentic workflows, why trusted data and a strong data foundation are critical, and how healthcare organizations can use risk frameworks and human oversight to build confidence in AI-driven actions. This episode is sponsored by RevSpring.

In this episode, Jo Peterson, of Cleartech Research, discusses AI security challenges with Maribel Lopez. Her commentary explores how traditional security tools fall short for AI agents and what strategies enterprises can adopt to manage AI risks effectively. Topics coveredAI security challenges and solutionsMulti-layered approach to AI agent securityToken cryptographic delegation and OBO tokensExternalized policy as code and micro-segmentationContextual and data-aware guardrailsContinuous auditing and behavioral baselinesRole of Chief AI Officer in security governanceAI risk management and operational strategies Key takeawaysTraditional security tools are not designed for AI agents and their non-deterministic behavior.Implementing least privilege for AI agents involves multi-layered strategies including token delegation and policy enforcement.Externalizing authorization to decoupled policy decision points enhances security for AI workflows.Real-time observability and kill switches are critical for managing AI agent behavior.Many organizations claim to have AI governance but lack technical implementation and operational ownership.Chapters00:00 Introduction to AI security challenges02:04 Non-deterministic nature of AI and security implications04:32 Externalized policy as code and micro-segmentation07:12 Real-time observability and kill switches09:11 Effectiveness of AI governance in organizations12:13 Immediate actions for AI security in 30 days13:45 Centralized AI traffic interception and inventory14:29 Role of Chief AI Officer in security and risk16:19 The evolving role of AI leadership in organizations17:19 Talent acquisition and upskilling for AI security STAY CONNECTEDSubscribe to the AI with Maribel Lopez audio podcast: https://www.buzzsprout.com/1947446Subscribe to my LinkedIn newsletter — AI Decoded with Maribel Lopez: https://www.linkedin.com/newsletters/ai-decoded-with-maribel-lopez-7312533413582827520/Lopez Research blog: https://www.lopezresearch.com/research/Follow me on LinkedIn: https://www.linkedin.com/in/maribellopez/Follow me on X: https://x.com/MaribelLopez

In this episode, Pooja Brown (Founder @ Inventry.ai) shares her insights on balancing being a founder & technologist, especially within the mid-market manufacturing industry. We cover why founders need to lead with curiosity as they seek out customer problems to solve, strategies for solving complex problems related to supply chain, and strategies for selling your products. Pooja also dissects important fundraising tactics, how to identify areas that AI tooling can enhance within your business, reading customer signals, and bolstering your engineering skills by leveling up business capabilities.   ABOUT POOJA BROWN Pooja Brown is a technology executive and founder focused on building AI-native platforms that power real-world operations across industries. She has led engineering at scale at companies like Stitch Fix and DocuSign, building systems that combine data, workflows, and machine learning to drive everything from personalization and supply chain to digital agreements used by hundreds of thousands of businesses.Her experience spans multiple verticals including retail, enterprise SaaS, education technology, and real estate, where she has consistently focused on embedding AI directly into core business systems rather than layering it on top. Pooja is currently the founder and CEO of Inventry.ai, where she is building autonomous AI agents that help mid-market manufacturers run procurement and supply chain operations more effectively. Across her career, she has focused on turning complex operational data into systems that don't just generate insights, but actually drive decisions and execution.   Sinch is the communications infrastructure the AI era runs on. There's a layer of infrastructure behind every text, call, and login code your product sends, and it works exactly like plumbing: nobody thinks about it until it's the reason something broke. Most providers route through 4-6 intermediaries; Sinch connects in 1-2 hops, direct carrier relationships across 600+ connections, handling 900 billion interactions a year across 60+ countries. Routing, compliance, fraud prevention handled automatically rather than manually managed by the business sending the message! Sinch is the reliability layer underneath AI-driven customer communications; the infrastructure that determines whether an AI agent's output actually reaches a real person as a delivered text, connected call, or verified interaction. Check it out here!   SHOW NOTES: What shaped Pooja's entrepreneurship journey & background (3:20) Looking for the right problem & leading with curiosity (6:47) Insights on solving problems related to supply chain (10:42) Building systems for chaos / complexity (12:46) Adopting a beginner's mindset when solving complex problems (15:40) Dissecting fundraising strategies & decision making (19:28) Emerging business patterns that eng leaders need to capitalize on (26:32) Understanding how customers make decisions on what products to adopt (29:09) Selling strategies for the mid-market manufacturing industry (37:08) Integrating AI tools to augment current business capabilities (41:03) Engineering skills that enhance sales processes (44:43) Communication frameworks when working with customers (47:21) Rapid fire questions (50:06)   This episode wouldn't have been possible without the help of our incredible production team: Patrick Gallagher - Producer & Co-Host Jerry Li - Co-Host Noah Olberding - Associate Producer, Audio & Video Editor https://www.linkedin.com/in/noah-olberding/ Dan Overheim - Audio Engineer, Dan's also an avid 3D printer - https://www.bnd3d.com/ Ellie Coggins Angus - Copywriter, Check out her other work at https://elliecoggins.com/about/ Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Agentic AI just ran a CTV campaign end-to-end through a new, agent-driven buying workflow with far less manual optimization and a lot less waste. Butler/Till's Scott Ensign explains what that means for the future of programmatic.

Alex King from Growth Investor Pro discusses the real difference in sentiment and attitude (0:35) Bitcoin and Ether (4:40) Rising gold - forget about the why (10:10) Warsh in Jackson Hole = volatility (13:40) Semis and software (16:25) Cybersecurity and agentic AI (20:40) Tech earnings season (33:50)Show Notes:Ice Cold, Zen-Like Investing With Alex KingEpisode transcriptsFor full access to analyst ratings, stock quant scores and dividend grades, subscribe to Seeking Alpha Premium at seekingalpha.com/subscriptions

What has changed most in commerce media, and what should brands and retailers be paying attention to right now?With the pace of change accelerating so quickly, how should brands and retailers think about staying ahead?As consumers increasingly use LLMs and conversational AI for product discovery, how should brands think about ranking and showing up organically—not just through paid media?What are one or two things you're seeing brands and retailers get wrong right now?How should brands think about authoritative product content as AI and LLMs increasingly influence what consumers see and believe about their products?

Agentic change for Salesforce, from Gearset.Jack sits down with Florence and Katharine, Commercial Operations Analysts who look after Gearset's internal Salesforce org, to talk about what's changed since they started building directly with Gearset's newly released agent, Cam. Florence had never touched Salesforce 12 months ago, now she's shipping changes to production within 24 hours. Katharine, who's worked on Gearset's Salesforce org from inception, digs into the process that keeps it all safe, the hidden dependencies the agent catches, and why "job to be done" thinking matters more than ever when tickets doesn't speak the whole truth.Tune in to hear what it's really like being customer zero.

Running AI in loop hands-free.

An imperfect drink with a perfect story. My reflections from Black Hat USA 2026 An Analog Brain In A Digital Age — A Newsletter by Marco Ciappelli No time to read? Let TAPE3 read it to you.

Send us Fan MailThe moment AI stops being a chatbot and starts taking actions, the real question changes from “Is it smart?” to “Who owns the decision?” That's where this conversation with Alan Wesley goes fast and deep. Allen is a cybersecurity executive and educator working at the intersection of AI, national security, cognitive security, and human judgment. Together, we break down why agentic AI makes accountability harder, not easier, especially when automation moves faster than organizations can assign authority.We talk through the real-world mechanics of cognitive overload and cognitive offloading at work, including how AI can accidentally create more noise by producing polished, lengthy outputs that bury the actual ask. Allen shares a simple but powerful framework for getting better results: give the system a role, clear instructions, and a knowledge base that adds context so the output fits the human-to-human decision you're actually trying to make.From there, we get into the security realities of agents that are goal-driven and “creative” in ways you didn't plan for. We unpack permission drift, social engineering risks where an agent convinces a user to do what it cannot, and why “human intent is not a control boundary.” Allen explains what governance has to exist outside the model: policy as code, agentic role mapping, monitoring at machine speed, and tiered risk so critical functions never become a rubber-stamped approval chain. If you care about AI governance, cybersecurity, CMMC pressures on small businesses, and keeping humans meaningfully accountable, this one is for you.Subscribe for more conversations on leadership and technology, share this with a friend building with AI, and leave a review with your biggest question about decision authority in the age of agents.Thanks for tuning in to this episode of Follow The Brand! We hope you enjoyed learning about the latest trends and strategies in Personal Branding, Business and Career Development, Financial Empowerment, Technology Innovation, and Executive Presence. To keep up with the latest insights and updates, visit 5starbdm.com.And don't miss Grant McGaugh's new book, First Light — a powerful guide to igniting your purpose and building a BRAVE brand that stands out in a changing world. - https://5starbdm.com/brave-masterclass/See you next time on Follow The Brand!

What does it take to build your own AI agent that could help with day-to-day network tasks? Hank Preston, a Distinguished Architect at Cisco, asked himself that question. He developed a presentation that walks through all the elements one might need to build such an agent, as well as the risks to consider. On today’s... Read more »