Podcasts about copilot

A.M. Edition for Sept. 18. Hackers used Anthropic's AI tool, Claude, to gain access to the company's core algorithmic code. Plus, Russia seizes control of Nestle's operations in the country, as the Kremlin cracks down on more Western businesses. And the Bank of Japan follows the Fed in hiking interest rates. WSJ's Jason Douglas says central banks have learnt from recent inflation scares and are moving quickly to stave off persistent price rises. Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Alderon Games [Warp](warp.dev) [The Mad Botter Automation Promo](themadbotter.com/start) Discord Coder Conduit

Mike & Tommy are joined again by Kurt Buhler and tackle the intersection of data literacy and AI literacy, exploring how self-awareness — or the lack of it — shapes the way teams build and consume Power BI and Microsoft Fabric solutions.They dig into whether AI raises the floor while lowering the ceiling, why poor data literacy can become more dangerous in an AI-assisted world, and how to keep critical, creative, and curious thinking alive when Copilot is in every seat.News: Moving Off ODBC — A Self-Serve Scanner for Your ADBC Migration | Power BI Q&A Retirement Reminder: February 2027 Timeline UpdateGet in touch:Send in your questions or topics you want us to discuss by tweeting to @PowerBITips with the hashtag #empMailbag or submit on the PowerBI.tips Podcast Page.Visit PowerBI.tips: https://powerbi.tips/Watch the episodes live every Tuesday and Thursday morning at 730am CST on YouTube: https://www.youtube.com/powerbitipsSubscribe on Spotify: https://open.spotify.com/show/230fp78XmHHRXTiYICRLVvSubscribe on Apple: https://podcasts.apple.com/us/podcast/explicit-measures-podcast/id1568944083‎Check Out Community Jam: https://jam.powerbi.tipsFollow Mike: https://www.linkedin.com/in/michaelcarlo/Follow Tommy: https://www.linkedin.com/in/tommypuglia/

Scaling Copilot: Hidden Risks & Data ExposureMost organizations are adopting Microsoft Copilot as a natural extension of their existing Microsoft environment, but scaling it is not as simple as assigning licenses.In this discussion, Punit Bhatia and Ben Wilcox explore what really happens when Copilot is introduced into environments with scattered data, unstructured information, and complex access permissions.They highlight how AI doesn't create new risks but makes existing ones visible and usable. Copilot can surface data based on access rights even when those permissions were never properly reviewed.A real-world example shows how an employee unintentionally accessed a coworker's personal improvement plan due to misconfigured permissions, turning it into a potential privacy breach.The conversation emphasizes that successful Copilot adoption requires proper data governance, clear access controls, and mechanisms such as data classification, sensitivity labeling, and encryption.This is not a plug-and-play exercise organization that must prepare their data before scaling AI.

Interview Highlights: A Layered Toolset: LSU runs dual EDR platforms (Microsoft Defender and CrowdStrike), Splunk as its SIEM for log management, and Splunk SOAR (via partner TekStream) to coordinate incident response across the statewide SOC program. Phishing Remains Enemy #1: Across higher ed generally, phishing is the top entry point for attackers; Jain recalled a pre-MFA, pre-COVID incident where compromised accounts cascaded across multiple universities, forcing account suspensions every five minutes. AI on the Email Front Line: LSU uses AI specifically to catch executive impersonation—fake emails posing as the chancellor or vendors like Dell requesting changed payment routing numbers—flagging them for review before delivery. On the SOC side, AI builds context around alerts (device mismatches, unusual IP patterns) that a human analyst then validates before escalating—keeping a human in the loop rather than fully automating decisions. Fighting AI With AI—Proactively: LSU deploys honeypots, like a dummy Moodle instance, to lure AI-driven attackers, capture their IPs and techniques, then feed that threat intelligence into production systems to auto-block similar attacks before they happen. The Real AI Risk Is Data, Not the Tool: Jain's biggest concern with staff using ChatGPT, Copilot, or Claude isn't the AI itself—it's uploading sensitive student, HR, or research data to platforms that may train on it. Louisiana state law bars public institutions from using Chinese-linked LLMs; Jain flagged that individual (non-Enterprise) Cursor licenses can violate this because some underlying models have Chinese ties. Building Homegrown AI Tools: LSU faculty built "MikeGPT," an internal GPT-based tool on Azure, letting departments create custom agents—like one that lets students query a syllabus directly or an in-progress agent that answers questions from an 80-90 page data governance policy. Both hosts agreed that narrow, tailored AI tools solving specific institutional pain points—rather than general chatbot use—represent the most valuable and lowest-risk way to bring AI into daily operations.

Avsnitt 581 spelades in den 15 september och därför så handlar dagens avsnitt om: Alla shownotes finns på https://www.enlitenpoddomit.se , skulle det se konstigt ut i din poddspelare så titta gärna där efter alla länkar kring det vi pratar om     INTRO David har pratat på en konferens och fixat en AI-agent från 11 000 meters höjd. Johan har  BONUSLÖNK: https://www.imdb.com/title/tt30459041/episodes/?season=3  FEEDBACK AND BACKLOG Allt kan köra doom och alla kan spela doom https://games.slashdot.org/story/26/09/11/2022239/male-fruit-fly-brain-trained-to-play-doom  https://fly-brain-doom.awormuth.chatgpt.site/  Drama (igen) hos Automattic https://techcrunch.com/2026/09/12/automattic-confirms-mullenweg-has-returned-as-ceo-after-attempted-ouster-by-board/  LG nekar till avlyssning https://www.engadget.com/2256728/lg-denies-accusations-of-smart-tvs-continuously-recording-its-users/  Ratio på iPhone Duo-skärmen - 1,40:1 vilket är ungefär mittemellan 16:9 och 4:3. - IMAX har ett förhållande på 1.43:1. - Duo har exakt samma förhållande på båda skärmarna. BONUSLÖNK: https://www.imdb.com/title/tt33764258/  ALLMÄNT NYTT I Have Been Clawed (by Ringazin) https://ihavebeenclawed.com/  OpenAI hade agenter som rymde och Anthropic vill inte vara sämre https://www.anthropic.com/research/alignment-assessment-cybersecurity-incidents  ... och OpenAI hade dessutom en rymning innan https://www.engadget.com/2256741/openai-agents-hacked-rubygems/  OpenAI, Anthropic och X AI vill sakta ned https://www.bbc.com/news/articles/c14dpgm0rg4o  Men Trump tro han har lösningen () https://www.bloomberg.com/news/articles/2026-09-14/trump-rejects-calls-for-ai-guardrails-blasts-anthropic-s-amodei  https://edition.cnn.com/2026/09/14/politics/trump-vance-ai-alarms  OpenAI GPT 5.6 Sol räknar på kvant-matte https://openai.com/index/codex-quantum-computing-experiments/    MICROSOFT Schweiz yxar bort Microsoft https://www.zdnet.com/business/switzerland-replaces-microsoft-365-open-source-opendesk/  Äntligen kommer du kunna mappa om Copilot-knappen https://www.windowscentral.com/microsoft/windows-11/microsoft-adds-bigfoot-and-copilot-key-remapping-in-latest-windows-11-preview-build  APPLE iPhone 18 Pro och Duo https://www.apple.com/se/iphone-18-pro/  https://www.apple.com/se/iphone-duo/  Watch 12 och Ultra 4 som testar avlyssningsgränser och lagar https://www.latimes.com/world-nation/story/2026-09-12/apples-always-listening-watch-features-test-eavesdropping-laws  Tillägg HandOver https://www.androidauthority.com/iphone-handoff-t-mobile-3710866/ Apple gate-keep:ar ny komplikation. Rant av David. GOOGLE Google "lättar lite" på säkerhetsfunktioner https://www.androidauthority.com/google-advanced-protection-expert-features-3710762/  Google Wallet kommer till iphones https://www.androidauthority.com/google-wallet-iphone-apk-teardown-3710795/  Googles Android Auto får Gemini https://www.engadget.com/2252861/android-auto-feature-easier-long-drive/  PRYLLISTA - David: iPhone 18 Pro, bourgogne. - Johan: Shokz Openrun, hörlurar. BONUSLÖNK: https://www.amazon.se/Spigen-iPhone-Ultra-Hybrid-MagFit/dp/B0FD2LF1SC/  EGNA LÄNKAR - En Liten Podd Om IT på webben,      http://enlitenpoddomit.se/  - En Liten Podd Om IT på Facebook,      https://www.facebook.com/EnLitenPoddOmIt/  - En Liten Podd Om IT på Youtube,      https://www.youtube.com/enlitenpoddomit  - Ge oss gärna en recension    - https://podcasts.apple.com/se/podcast/en-liten-podd-om-it/id946204577?mt=2#see-all/reviews      - https://www.podchaser.com/podcasts/en-liten-podd-om-it-158069  LÄNKAR TILL VART MAN HITTAR PODDEN FÖR ATT LYSSNA - Apple Podcaster (iTunes), https://itunes.apple.com/se/podcast/en-liten-podd-om-it/id946204577  - Overcast, https://overcast.fm/itunes946204577/en-liten-podd-om-it  - Acast, https://www.acast.com/enlitenpoddomit  - Spotify, https://open.spotify.com/show/2e8wX1O4FbD6M2ocJdXBW7?si=HFFErR8YRlKrELsUD--Ujg%20  - Stitcher, https://www.stitcher.com/podcast/the-nerd-herd/en-liten-podd-om-it  - YouTube, https://www.youtube.com/enlitenpoddomit  LÄNK TILL DISCORD DÄR MAN HITTAR LIVE STREAM + CHATT - http://discord.enlitenpoddomit.se  KONTAKTUPPGIFTER johan@enlitenpoddomit.se.  david@enlitenpoddomit.se.  bjorn@enlitenpoddomit.se,  om du vill ha klistermärken.

On the latest LGM Podcast Cheryl, Dan and myself talk through the varied and extensive implications of 9/11 for the evolution of the national security state. We discuss a number of counter-factuals, and track the changes (and not so changes) in the Republican Party after 9/11. Transcript is here, laundered through Co Pilot. Please check the audio file for the accuracy of any quotes and speakers. Amazon Music Apple Podcasts Android Youtube Podchaser Podcast Index Subscribe by E-mail Audible Spotify The post LGM Podcast: The National Security Legacies of 9/11 appeared first on Lawyers, Guns & Money.

Will Oremus, a staff writer at The Atlantic, discusses the uproar over the warnings of potential threats to humanity from unregulated AI, then Andrew Prokop, senior politics correspondent at Vox, talks about possible government actions to prevent disaster from the purposeful or inadvertent consequences of AI harm.Photo: A person holds an iPhone displaying icons for Copilot, Gemini, Meta AI, Claude, ChatGPT, Grok, DeepSeek, Perplexity and Doubao in a folder titled "Artificial Intelligence" on September 15, 2026, in Shenzhen, Guangdong Province, China.(Cheng Xin/Getty Images) Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

AI demos are easy. Building AI that actually works inside a business is much harder.Andrew Brooks, founder and CEO of Contextual.io joins Ian Bergman to unpack the AI hype tax, the hidden cost of confusing ChatGPT, Copilot, and impressive prototypes with real business transformation.They explore what makes an AI system truly production ready, why even 99% accuracy can create serious problems at scale, and where human judgment, guardrails, governance, and accountability still matter.You'll also learn how to decide when to build AI capabilities versus rent existing tools, why differentiated data and workflows should guide that decision, and how AI can amplify expertise instead of simply replacing people.For founders, operators, and innovation leaders trying to turn AI experimentation into measurable business value, this conversation offers a practical framework for separating hype from what actually works.Key Topics⏱️ 04:14 What is the AI hype tax?

Den här veckan pratar Amanda och Ola om den "nya" Copilot appen men också om hur sommaren varit och vad dom ser fram emot under hösten. Länkar till vad vi pratade om:Choose a model for Copilot Cowork | Microsoft LearnMobi Fold Pouch | LogitechAvailable today: Anthropic Claude Fable 5.1 in Microsoft Copilot | Microsoft Community HubGPT-6 Astra: Frontier intelligence for work, now available in Microsoft Foundry | Microsoft Azure BlogNew meeting controls experience in Microsoft Teams – Microsoft AdoptionAI Model & API Providers Analysis | Artificial Analysis---------------------------------------------Följ ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Vi jobbar med data på LinkedIn⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Amanda | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠| ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BlueSky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Simon | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠| ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BlueSky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Ola | ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠| ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠BlueSky⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠

What It Takes to Build AI That Is Accurate Enough, Traceable Enough, and Trustworthy Enough for High-Stakes Financial WorkGuest: Nikita Komarov, CEO and Founder at Dobs.AIHost: Seth Earley, CEO at Earley Information SciencePublished on: September 9, 2026In this episode, Seth Earley speaks with Nikita Komarov, CEO and Founder of Dobs.AI, who spent seven years at McKinsey advising Fortune 1000 executives before founding a company that is rebuilding financial due diligence, internal audit, and vendor overpayment recovery from the ground up as agentic AI systems. They explore why financial professionals are the most resistant to AI adoption and why that resistance is rational, how orchestrating teams of AI agents with financial controls built in produces outputs that are deterministic enough for audit, why the difference between an efficiency tool and a production-ready AI system is enormous, and how the trusted advisor status accountants have built over decades becomes a platform for entirely new services in the AI era.Key Takeaways:Financial professionals are among the most resistant to AI adoption for a rational reason - LLMs are non-deterministic by nature, and accounting requires numbers that are 100% accurate and traceable.Building production-grade financial AI requires three levers working together: orchestrating teams of agents with defined roles, building financial controls and guardrails into the pipeline, and solving for data extraction accuracy before any analysis begins.The difference between an efficiency tool like Claude or ChatGPT and a production-ready AI system is not the model - it is the architecture, the controls, and the product thinking required to get from unstructured input to a final output a human can take to a client.DOBS AI compresses financial due diligence from a six-week engagement to 72 hours for the management meeting - cutting the cycle from week and a half to three days on that critical milestone alone.Accounting firms have more trust with clients than management consultants or lawyers, and that trust combined with recurring access creates a platform for expanding into advisory services that AI now makes possible.The pricing model reckoning is real - time and materials no longer makes sense when AI does the work in hours, and firms need to shift to value-based pricing anchored to the outcome delivered, not the hours spent.The long-term trajectory is positive, but the mid-term transition is the risk - AI is compressing decades of technological change into five to ten years, and organizations and individuals who are not adapting will be left behind.Insightful Quotes:"Large language models, they predict the next word. That's why these systems are non-deterministic. You can't say what the output will be next. That's the problem in financial services - you need 100% accuracy, but you don't know what the system is going to tell you." - Nikita Komarov"That's exactly the difference between an efficiency tool and a production-ready solution. When people say we use AI, they most likely mean Copilot or ChatGPT - and that's 5 to 10% of what's actually possible." - Nikita Komarov"You can't automate what you don't understand. The first thing you have to do is say, what is the expected output and the outcome, and then how do I verify that I actually get there?" - Seth EarleyTune in to discover why financial AI is one of the most demanding and highest-stakes applications in the enterprise - and what it actually takes to build systems that are accurate and auditable enough to trust.LinksLinkedIn: https://www.linkedin.com/in/nikita-komarov/Website: https://dobs.aiWays to Tune In:Earley AI Podcast: https://www.earley.com/earley-ai-podcast-home Apple Podcast: https://podcasts.apple.com/podcast/id1586654770 Spotify: https://open.spotify.com/show/5nkcZvVYjHHj6wtBABqLbEiHeart Radio: https://www.iheart.com/podcast/269-earley-ai-podcast-87108370/ Stitcher: https://www.stitcher.com/show/earley-ai-podcastAmazon Music: https://music.amazon.com/podcasts/18524b67-09cf-433f-82db-07b6213ad3ba/earley-ai-podcast Buzzsprout: https://earleyai.buzzsprout.com/Thanks to our sponsors:VKTREarley Information ScienceAI Powered Enterprise Book

Suze Orman is one of the most famous financial advisers in America, and she wakes up at 5 a.m. every single day. While Your Money Briefing is on a break, we're bringing you the first episode of My Monday Morning from our colleagues at The Journal podcast, where Lane Florsheim talks with Orman about when to retire, why she avoids eating out and why she doesn't trust AI. Follow The Journal here. Sign up for the WSJ's free Markets A.M. newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Partner with Jay: https://www.jayschwedelson.com/contactㅤCheck out Jay Schwedelson's new book: Stupider People Have Done It All net proceeds are donated to The V Foundation for Cancer Research, let's kick cancer's butt: https://www.amazon.com/Stupider-People-Have-Done-Marketing/dp/1637635206ㅤSubscribe to Jay's newsletter for weekly marketing tips and tactics: https://www.jayschwedelson.com/newsletterㅤRegister for GuruConference (FREE + VIRTUAL!) https://www.guruconference.comㅤCheck out Eventastic (FREE + VIRTUAL!) https://www.eventastic.comㅤConnect with Jay on LinkedIn: https://www.linkedin.com/in/schwedelson/Check out Jay's YouTube channel: https://www.youtube.com/@schwedelsonCheck out Jay's Instagram: https://www.instagram.com/jayschwedelson/Ask Jay anything: https://www.jayschwedelson.com/askㅤLeave a comment and follow the show, it really helps us out!ㅤFollow Daniel on LinkedIn and check out The Marketing Millennials podcast for sharp, no-fluff marketing insights. Subscribe to Ari Murray's newsletter at gotomillions.co for sharp, actionable marketing insights.ㅤTurns out the emails winning in an AI-summarized inbox are the ugly ones. Jay Schwedelson and Daniel Murray pick up the live text conversation from last week with fresh data on what Gmail, Apple Mail, and Copilot are actually pulling out of your sends, and the fix involves bullet points and a formula that fits in two hundred characters. There is also a genuine moral crisis about cheating on your barber, which somehow sets the tone for everything after it.ㅤBest Moments:(00:38) The haircut dilemma, and whether you can book with another stylist without earning the death stare(03:45) AI summaries pull 29 words on average, so the first 150 to 200 characters carry the whole email(04:15) Action plus product plus deadline, the formula lifting click-through rates around 19 percent(06:13) Bullets are ugly and they work, with pull rates of 77 percent in Gmail, 84 in Apple Mail, 92 in Copilot(07:29) Why vague, spammy headlines fall apart once a summary has to describe themㅤ(08:19) Over 70% of people say they are fine with AI reading their inbox to surface deals

What could your team achieve if a document that previously took two hours could be created in eight minutes? In this episode of Tech Talks Daily, I speak with Oskar Konstantyner, Chief Product Officer at Templafy, about the rapid adoption of AI agents across enterprise document workflows and what those productivity gains mean for knowledge workers. According to Templafy's proprietary usage data, AI agent adoption among its enterprise users grew from virtually zero in October 2025 to 53% by June 2026. Its analysis found that documents created without agents had a median completion time of two hours and an average of 5.6 hours across 16,000 sessions. With AI agents, the median fell to eight minutes and the average to 27 minutes across 14,000 sessions. The most common documents included pitch decks, company communications, sales materials, and product roadmaps. However, Oskar cautions against treating speed as the final measure of AI productivity. We discuss an accounting firm that could not respond to thousands of tenders because it lacked the capacity to create enough proposals. Faster document production could allow that business to participate in additional opportunities while applying its knowledge about what makes a winning submission. The benefit comes from increased commercial capacity and stronger results, rather than counting recovered hours alone. Oskar also explains what happens during those eight minutes. AI can locate relevant information, find approved content, recommend a presentation structure, apply previous lessons, and complete much of the production work. Humans remain responsible for original thinking, client judgment, factual accuracy, and final approval. In many cases, the agent may produce 60% to 90% of the document, but the beginning and end of the process remain human-led. The conversation also considers the growing volume of generic AI documents. A business already has approved slides, company descriptions, brand assets, legal statements, and sales messages. Regenerating all that material wastes tokens and risks inconsistency. Oskar argues that agents should determine when existing content should be reused, when rules should be applied, and when something genuinely new needs to be created. We also discuss why AI adoption improves when agents appear inside PowerPoint, Claude, OpenAI, and Copilot. Most employees are unlikely to abandon familiar workflows every time another AI application arrives. Is your company measuring AI success through minutes saved, or through the additional business those minutes make possible? Listen to the conversation and share your thoughts with me.

Andrew welcomes back Constantin Hager, a senior systems engineer, PowerShell User Group organizer, and brand new Microsoft MVP. They open by revisiting Constantin's infamous "size of a finger" intro from his first appearance, then dig into his talks at PSConfEU this year on dev containers with GitHub Codespaces and on Maester, the testing framework for M365 and on-prem AD. A quick detour into the GitHub vs. GitLab vs. Codeberg debate leads into the main event: a deep breakdown of Microsoft365DSC, what it actually does, how it treats M365 tenant settings like Intune, Entra, and Exchange as idempotent, drift-monitored code, and how the M365DSC Workshop wraps the notoriously painful setup process into a lab folder anyone can run. They cover the workshop's multi-tenant design, its DSC Community roots, and the honest limitations, like settings that still aren't exposed through public APIs. Constantin also shares his organic path to becoming an MVP, updates on his user group's shift to English-language talks, and a heads up on an upcoming blog series diving deeper into M365DSC. They close with news on the free PSConfEU MiniCon, happening October 13th with the CFP open until September 25th. Key Takeaways: Microsoft365DSC turns M365 tenant configuration into idempotent, drift-monitored code, and the M365DSC Workshop exists specifically to make the notoriously painful setup process approachable through a ready-to-run lab folder instead of a hundred-page whitepaper. Not everything in M365 is automatable yet. Settings without a public API (some Copilot controls, for example) still require manual portal fixes or an interactive token, so full automation has real gaps today. Constantin's MVP award grew out of years of showing up, organizing his user group, speaking at conferences, and opening GitHub issues, not out of being the loudest voice in the room. Guest Bio: Constantin Hager is a senior systems engineer based in Germany, organizer of the PowerShell User Group Inn-Salzach, and a newly awarded Microsoft MVP. A returning guest of the podcast, he's known for his enthusiasm around dev containers, PSFramework, and now Microsoft365DSC.   Resource Links: Constantin Hager on LinkedIn https://www.linkedin.com/in/constantin-hager/ Constantin's blog, The IT Guide https://the-itguide.de Andrew's blog: https://andrewpla.tech PowerShell User Group Inn-Salzach (Meetup) https://www.meetup.com/de-DE/powershell-usergroup-inn-salzach/ Maester (M365 and Entra security testing framework) https://maester.dev/ Microsoft365DSC official site https://microsoft365dsc.com Microsoft365DSC on GitHub https://github.com/microsoft/Microsoft365DSC M365DSC whitepaper and CI/CD pipeline scripts https://github.com/ykuijs/M365DSC_CICD DSC Community https://dsccommunity.org PSConfEU https://psconf.eu PSConfEU MiniCon Call for Papers (Sessionize) https://sessionize.com/psconfeu-minicon/ PDQ Discord https://discord.gg/PDQ The PowerShell Podcast on YouTube: https://youtu.be/Vy4kaayGT2M  

Build AI That WorksFull Show Noteshttps://www.theintelligenceagepodcast.com/840Mark Smth talks with James Diekman about how Accelerate Tech has scaled, where AI is actually creating value in government workflows, and why most successful projects are targeted workflow changes rather than broad rollout tools. They also dig into Australia's growing caution around AI governance, data residency, and the practical push toward local models and sovereign infrastructure.We discuss how AI is being used inside engineering and marketing, why many Copilot-style deployments lose momentum, and how project management itself may evolve as agents take on governance, reporting, and coordination tasks. James shares what's working in production, what keeps failing before launch, and where the next 6 to 12 months of AI adoption is heading.Key topicsJames Diekman shares that Accelerate Tech has grown to about 32 staff in the last six months, driven by demand in government projects and AI-enabled solutions.The conversation contrasts AI use in engineering and marketing versus more passive consumption in functions like HR and finance.James explains that the strongest results come from embedding AI into specific workflows, rather than treating it as a standalone chatbot or a broad deployment.They discuss why many Copilot rollouts see mixed adoption and usage drop-off when the tool sits outside the daily workflow.James describes the most successful approach as identifying a business process, pulling apart a sub-workflow, and then applying AI, automation, or judgment-based reasoning to that narrow area first.He notes that many AI projects never reach production, and says that out of 30-plus AI projects delivered, only five or six have made it fully into production.The discussion turns to local government systems, with James outlining how his team often acts as the integration layer, or “the plumbers,” between older council platforms and newer software.They cover Australia's increasing AI governance maturity, including state frameworks, federal requirements, and the rise of dedicated AI roles inside agencies.Mark and James debate public concerns around data centers, water use, and energy, with James emphasizing the need for better policy and the practical constraints of local compute.They explore the move toward local models, onshore hosting, and reserving compute capacity as organizations seek more control, lower risk, and better throughput.James makes the case that teams do not always need frontier models like Opus for every task and that model choice should match the job, cost, and risk.The episode closes on project management, where James outlines a “project brain” concept using agents, shared knowledge, registers, ticketing, and workflow automation to support or partially replace manual PM effort.If you want to get in touch with me, you can message me here on Linkedin.Thanks for listening

Målstyrning med Planner Planner har fått en Mål-flik, och den dyker upp i dina planer just nu. Frågan är om det är riktig målstyrning eller en att-göra-lista med en finare rubrik ovanför. För att kunna svara på det behöver vi först få ordning på begreppen: Vad som skiljer en vision från ett mål, en KPI från ett nyckelresultat, och varför OKR kräver ett lager som Planner helt saknar. Vi tar också upp historien om Microsofts föregående verktyg för målstyrning: Viva Goals lades ner vid årsskiftet, och några månader senare kommer något betydligt enklare i Planner. Slutsatsen är ärlig, och den landar i en enda fråga du kan ställa nästa gång någon säger "vi kör målstyrning i Planner". AI-skolan del 8 – Microsoft Graph och WorkIQ Varje gång Copilot svarar på något om din organisation har den först frågat Microsoft Graph, och det gör den med funktionen WorkIQ. Ändå är Graph något de flesta bara hört nämnas i förbifarten,  ett namn på en arkitekturbild och inget man kan peka på. I den här delen av AI-skolan reder vi ut vad Graph och WorkIQ faktiskt är, varför Copilot inte skulle fungera utan dem, och varför den förklarar både det Copilot hittar och det Copilot inte får se. Förstår du Graph och WorkIQ så förstår du varför behörigheter är den kanske viktigaste AI-frågan. Ordlistan bjuder på ett ord du säkert använt hundra gånger utan att riktigt kunna förklara det: API. Nyheter Word, Excel och PowerPoint får ett skrivfält direkt i Copilot-knappen, så du slipper öppna panelen alls. Brand Kits får två inställningar som gör att Copilot äntligen håller sig till mallen, och lyder instruktioner du skrivit in i mallens anteckningsfält. Teams låter dig sätta en påminnelse direkt på ett meddelande, och Purview får en retentionsregel som raderar filer ingen öppnat på länge, med den uttalade motiveringen att Copilot då svarar bättre. 

Nella scorsa puntata ho parlato di come scegliere tra modelli AI diversi all'interno delle nostre applicazioni.Ora GitHub porta un concetto simile dentro Copilot con Project HydraFusion: invece di scegliere un singolo modello, può orchestrare più modelli durante lo stesso task, cercando il compromesso migliore tra qualità, costo e velocità.In questa puntata parlo di come funziona questo approccio, delle differenze rispetto al model routing che possiamo implementare noi e, soprattutto, di quanto controllo siamo disposti a lasciare al provider.https://github.blog/ai-and-ml/github-copilot/project-hydrafusion-frontier-quality-via-multi-model-orchestration/https://github.com/orgs/community/discussions/206492https://docs.github.com/en/copilot/concepts/agents/copilot-cli/rubber-duckhttps://docs.github.com/en/copilot/concepts/models/auto-model-selection#GitHubCopilot #HydraFusion #ProjectHydraFusion #AI #ArtificialIntelligence #GenerativeAI #LLM #MultiModel #ModelRouting #AIOrchestration #CodingAgents #GitHub #Copilot #DotNet #CSharp #SoftwareDevelopment #Developers #SoftwareArchitecture #DeveloperTools #DotNetInPillole #Podcast

In this episode I ask the question about whether privacy is becoming and optional and take a deep dive into creating better data structures in places like SharePoint and Teams that help users and AI generate better results. There is also the usual look at recent cloud news from Microsoft and thoughts on how even all this is become about AI. Love your feedback. Resources CIAOPS Need to Know podcast - CIAOPS - Need to Know podcasts | CIAOPS X - https://www.twitter.com/directorcia director@ciaops.com CIAOPS Blog Join my Teams Shared Channel – CIAOPS CIAOPS Merch store - CIAOPS Become a CIAOPS Patron CIAOPS AI Dojo  CIAOPS weekly news update - CIA Brief – CIAOPS CIAOPS Labs – The Special Activities Division of the CIAOPS Support CIAOPS Get your M365 questions answered via email Join my email list A special thanks to the CIAOPS Patron community for making this podcast possible. You can find the benefits of a subscription to the community and become a member at https://www.ciaopspatron.com   Updates Take control of your EWSAllowedAppIDs list before EWS access changes Microsoft is changing how Exchange Web Services (EWS) access is managed. Organizations should review and manage their EWSAllowedAppIDs list now to ensure approved applications retain access and to avoid disruptions when the new controls take effect. Link: https://techcommunity.microsoft.com/blog/exchange/take-control-of-your-ewsallowedappids-list-before-ews-access-changes/4553534 Understanding the new 100 GB mailbox entitlement for Microsoft 365 Business suites Microsoft explains the introduction of 100 GB mailbox storage for eligible Microsoft 365 Business plans, outlining entitlement requirements and how organizations can benefit from increased mailbox capacity. Link: https://techcommunity.microsoft.com/blog/exchange/understanding-the-new-100-gb-mailbox-entitlement-for-microsoft-365-business-suit/4548243 AI Available today: OpenAI GPT-6 Astra in Microsoft Copilot Microsoft has added OpenAI's GPT-6 Astra model to Copilot, giving users access to an advanced foundation model designed to improve reasoning, content generation, and productivity experiences across Microsoft 365. Link: https://techcommunity.microsoft.com/blog/microsoft-copilot-blog/available-today-openai-gpt-6-astra-in-microsoft-copilot/4552808 Available today: Anthropic Claude Fable 5.1 in Microsoft Copilot Anthropic's Claude Fable 5.1 model is now available in Microsoft Copilot, expanding the range of AI models users can leverage for research, analysis, content creation, and complex business tasks. Link: https://techcommunity.microsoft.com/blog/microsoft-copilot-blog/available-today-anthropic-claude-fable-5-1-in-microsoft-copilot/4551974 Expanding model choice in Copilot with Grok Access to Grok models is rolling out through the Microsoft Frontier Program in Microsoft Word, Excel, and PowerPoint. We are starting with a focused release to gather customer feedback and learn how customers use the model across common productivity scenarios. https://techcommunity.microsoft.com/blog/microsoft-copilot-blog/expanding-model-choice-in-copilot-with-grok/4555749 What's New in Microsoft Copilot | August 2026 A monthly roundup of new Copilot capabilities, including model updates, workflow enhancements, productivity improvements, and new AI-powered features designed to help users work more effectively. Link: https://techcommunity.microsoft.com/blog/microsoft-copilot-blog/what%25E2%2580%2599s-new-in-microsoft-copilot--august-2026/4551960 Security What's new in Microsoft Security: August 2026 Microsoft's monthly security update covering new capabilities across Defender, Sentinel, Entra, Security Copilot, and other security products, along with recent security announcements and enhancements. Link: https://www.microsoft.com/en-us/security/blog/2026/08/27/whats-new-in-microsoft-security-august-2026/ Passkey-themed social engineering leads to identity and cloud compromise Microsoft details a campaign where attackers used passkey-related social engineering techniques to trick users into compromising credentials and cloud identities, while also providing mitigation guidance. Link: https://www.microsoft.com/en-us/security/blog/2026/09/09/passkey-themed-social-engineering-leads-identity-cloud-compromise/ Protecting organizations from AI-assisted executive impersonation and invoice fraud This security guidance examines how attackers are using AI to impersonate executives and conduct invoice fraud, alongside recommendations to help organizations detect and prevent these attacks. Link: https://www.microsoft.com/en-us/security/blog/2026/09/10/protecting-organizations-ai-assisted-executive-impersonation-invoice-fraud/ Impersonating IT support: how threat actors turn a remote session into enterprise-wide access Microsoft analyzes how attackers impersonate IT support staff to gain remote access and expand their foothold within organizations, highlighting attack techniques and defensive measures. Link: https://www.microsoft.com/en-us/security/blog/2026/09/02/impersonating-it-support-threat-actors-turn-remote-session-into-enterprise-wide-access/ From CIAOPS Image Model Comparison A practical comparison of image generation models, reviewing differences in output quality, style, and effectiveness to help users evaluate which AI image generation model best suits their needs. Link: https://directorcia.github.io/Office365/image-comparison.html   September webinar - local AI https://blog.ciaops.com/2026/09/10/ciaops-need-to-know-microsoft-365-webinar-september-5/ 

Sandrine on LinkedIn The Art of Code Alderon Games [Warp](warp.dev) [The Mad Botter Automation Promo](themadbotter.com/start) Coder Conduit

Introduction What does an insurer actually own after a few years of buying AI one use case at a time? Feathery co-founder Zack Khan argues that the answer decides whether AI ever produces more than incremental gains, because a workflow you cannot change without filing a vendor ticket is a workflow you are renting. Fresh off a $30 million round led by Portage, with Allstate and Erie both in as strategic investors, Khan walks host Joshua R. Hollander through what it takes to put workflow control in the hands of the operating teams themselves. Guest Bio Zack Khan is Co-Founder of Feathery, the AI operating and decisioning system for financial services. He started as a software engineer building complex forms at Robinhood and Nextdoor alongside co-founder Peter Dun, then became the fifth employee at Hightouch, where he led marketing as the company grew toward a $3 billion valuation. Khan and Dun founded Feathery in 2021 as a developer-focused form builder and grew it into a platform that now serves more than 300 firms, including Tokio Marine, Hiscox, Baldwin Group, and Hylant, orchestrating submission intake, virtual inspections, quoting, and benefits workflows across carriers, MGAs, and brokers. Feathery ran profitably before raising its $30 million round in July 2026. Key Topics -Vendor-led vs. team-owned workflows - Why the last generation of insurance software put every change behind a vendor ticket, and what changes when underwriting and operations teams define workflows in natural language instead. -Copilot licenses vs. step changes - Giving everyone a copilot produces small gains; redefining one workflow end to end took a carrier from an eight-hour time to quote down to fifteen minutes. -Orchestrate rather than replace - Feathery's agent, Robin, works across existing rating engines, AMSs, and even legacy desktop applications, automating the data entry instead of ripping out the system. -Rules where you want them, judgment where you need it - Deterministic guardrails handle rating inputs, while objective-driven agents handle tasks like checking a virtual inspection for a pool or a tree touching the roof. -The Baldwin Group benefits example - Custom employee benefit guides that took 20 to 30 or more hours to build now generate in minutes, across 10 to 15 assets and multiple languages, and the team's hours moved to consultative work. -What a platform actually is - Khan's test is control: if you cannot apply your own business logic to your own workflow without a vendor ticket, you bought a point solution. -Start in specialty lines - Fast-growing lines with less tech debt and less red tape are where AI transformation finds its first champions inside a large carrier. Notable Quotes "An actual platform gives you control. If you don't have fundamental control over the actual experience, and you're applying your own firm-specific business logic to your workflow, that is a point solution." "If your rating engine is working great, it's just the annoying part is typing data into it. You don't rip out the whole rating engine; you just automate the data entry part of it." "Some of our customers have gone from an eight-hour time to quote to less than fifteen minutes responding to producers. That's the stuff that actually will impact your bind ratio." "For AI to truly have the impact you want, you need to redefine the workflow, think about the end-to-end ideal process that you want your best underwriter, your best claims adjuster, your best producer to be following." Resources Guest: Feathery: https://www.feathery.io/ Zack Khan on LinkedIn: https://www.linkedin.com/in/zackkhan101/ Host & Organization: Joshua R. Hollander on LinkedIn: https://www.linkedin.com/in/joshuarhollander/ Horton International (USA): https://www.horton-usa.com/ Insurtech Leadership Podcast (LinkedIn Showcase): https://www.linkedin.com/showcase/insurtech-leadership-show Subscribe & Review If you enjoyed this episode, subscribe on your favorite platform and leave a review. The Insurtech Leadership Podcast is available on YouTube, Podbean, Apple Podcasts, and Spotify.

Across the seas of social media, small, local businesses are employing free (or really cheap) AI services like ChatGPT, Gemini and, uh, maybe Copilot?... to generate marketing materials. They're simultaneously pretty and ugly but most importantly: they get the job done. I talk with a pair of local business women and talk about their stances on this new AI flyer trend.BNLO is an independent show/opinion piece/podcast about Colorado Springs. Support it on Patreon at https://www.patreon.com/cw/TheBNLOShowYou can Buy BNLO a Coffee (make a one time donation) right here: buymeacoffee.com/BadlyneededpodcastAnd you can follow BNLO basically everywhere:Facebook: https://facebook.com/badlyneededshowInstagram: https://instagram.com/@badlyneededpodcastSpotify: https://open.spotify.com/show/4tHBZPCkzU2UKBYeLcNBiw?si=eea298914dbb4963Apple Music:  https://podcasts.apple.com/us/podcast/badly-needed-long-overdue-bnlo-a-show-about/id1632917180YouTube: https://youtube.com/@badlyneededpodcastTikTok: https://www.tiktok.com/@badlyneededpodcast Reddit:  https://www.reddit.com/user/BNLO_COS/LinkedIn: https://www.linkedin.com/company/badly-needed-long-overdue-bnlo

Dale and Amy are back in the Dirty Mo Media studio for another round of Bless Your ‘Hardt!This week Dale and Amy dive into a real debate on AI, house-cleaning robots, and self-driving cars — Amy's not buying any of it.Dale recaps his second-place finish in the CARS Tour pro late model race at Florence, his business plan for a new hardware store, and why jorts do not need to make a comeback. Amy has some beef with bathrobes, and questions whether Dale is legally allowed to have 160 unmatched socks in one drawer.Plus, Amy and Dale plan their 10th wedding anniversary trip (Germany? Switzerland? Paris?), and then Amy fills out Dale's Hinge dating profile live.For more head over to our YouTube page: https://www.youtube.com/@BlessYourHardt/videos Check out Dirty Mo Media on YouTube: https://www.youtube.com/@DirtyMoMedia Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Last night Paul and I sat down and talked through some thoughts on the cultural legacy of the 9/11 attacks. Commemoration of the 25th anniversary of the attacks feels surprisingly lowkey and seems to lack any political urgency. We work through the comparison of 9/11 as social event with other similar events, such as the sinking of the Titanic or the JFK assassination, spending quite a bit of time on the latter. We discuss the extent to which Trump as Catastrophe may have eclipsed 9/11 as Catastrophe, along with the memory-destroying aspects of the pandemic. Finally, we talk a bit about the Miracle in Ann Arbor, of which Paul has promised to write in greater detail. Transcript is here, laundered through Co Pilot. Please check the audio file for the accuracy of any quotes and speakers. Amazon Music Apple Podcasts Android Youtube Podchaser Podcast Index Subscribe by E-mail Audible Spotify Photo Credit: By Michael Foran, CC BY 2.0, https://commons.wikimedia.org/w/index.php?curid=11785530 The post LGM Podcast: 9/11 and Cultural Memory appeared first on Lawyers, Guns & Money.

In Episode 23 of Season 7 of Data Debrief, Catherine Dowden-King and Kyle Winterbottom unpack Tuesday's conversation with Greg Freeman, CEO and Founder of Data & AI Literacy Academy, and the gap between organisations that have given everyone a Copilot licence and those that are actually using AI to change how the business operates. As Catherine puts it, a Sunday league player and a Premier League player are both "playing football" – but nobody's confusing the two.They also get into the week's headline that AI has a "greater than 10% chance" of wiping out humanity, why a growing anti-AI mood outside the data bubble matters for leaders trying to drive adoption, and why a landscaper turned content creator might be the best human-in-the-loop example going.They also discuss:Why September is the real new year for data leaders, with budget season and event chaos hitting at once.Why the "AI will kill us all" headlines are irresponsible without the evidence to back them up.How to tell the difference between a credible warning and a researcher looking for a headline on the way out the door.Why 70% of Facebook comments on an AI-generated event poster are people refusing to attend, and what that tells leaders about the mood outside the bubble.What a year five "meet the teacher" evening on WhatsApp groups has in common with the AI conversations happening in boardrooms.Why the toilet-door graffiti of the 1970s and today's comment sections are the same human behaviour at a scale our brains can't cope with.How Catherine explains agentic AI at the dinner table with trains and tracks, and why it still doesn't land.Why "AI" is used to mean automation, machine learning, LLMs and agents interchangeably, and why that confusion matters.What "buttonology" means, and why both hosts are stealing the term.Why training and education are two different interventions, and why most organisations only do the first one.What Greg's three personas – the asker, the conversationalist and the process redesigner – reveal about where most employees really are.Why AI maturity scales measure who can drive the machine rather than who's transformed their thinking.Why organisations want competitive advantage but are investing in local productivity, and why the two aren't the same thing.Why picking four or five core use cases beats a Venn diagram of everything you could possibly do.Why leaders must ask "have you actually understood this?" before accepting AI-assisted work.Why people treat LLMs like Google when Google gave you sources and LLMs give you a decision.Why the absence of sponsored results in LLMs makes people less likely to question what they're served.Why the context layer, not the tool, is where the real value in AI sits.What a founder's blanket ban on "Claude content" reveals about the perception problem holding back adoption.Why whether AI sits with the CIO or the CDO comes down to whether it's seen as a tool or a transformation.Why culture has to allow people to rip up a process and fail before any of the redesign talk becomes real.Why cutting graduate intake could leave businesses with a succession crisis in a few years' time.How the Dodgy Gardener quit his day job by pairing ChatGPT garden designs with advice from tradespeople in the comments.Why attention is the digital currency of the future, and why B2C businesses will create roles to work out how to win it.

We start the week with a super-sized Good News segment: Great news about the midterms; the Missouri Supreme Court unanimously blocked the state's new Republican-drawn congressional map; A federal judge blocked Trump's second attempt to restrict birthright citizenship; A reclaimed West Virginia coal mine is becoming a solar-and-long-duration-storage plant; A new poll has Trump's approval at 33%; and more! Donald made up another story about himself on 9/11. Donald confessed to insider trading on Troth Senchul. Why Bartiromo was shit-canned. Donald will use his Qatari jet to fly to Ireland. Donald's war is costing Americans $100B. With Jody Hamilton, David Ferguson, music by Paul Melancon, Circe Link and Christian Nesmith, and more! Brought to you by Russ Rybicki, SharePower Responsible Investing. Support our sponsors and get free shipping at Quince.com/bob and bollandbranch.com/bob!See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

In today's podcast episode, we discuss whether banks can successfully encourage customers to use their AI-powered tools on their own websites and apps for personal finance management, how AI tools like ChatGPT, Gemini, Claude, Copilot, and others can convince people to use their LLMs for personal finance, and what banking consumers are most likely to want to use AI for when it comes to managing their money.   Join Senior Director of Podcasts and host Marcus Johnson, along with Principal Analyst Tiffani Montez and Head of EMARKETER Advisory Rob Rubin. Listen wherever you get your podcasts, or watch on YouTube or Spotify.   Subscribe to EMARKETER's newsletters. Go to https://www.emarketer.com/newsletters   Follow us on Instagram at: https://www.instagram.com/emarketer/   For sponsorship opportunities, contact us: advertising@emarketer.com   For more information, visit: https://www.emarketer.com/advertise/   Have questions or just want to say hi? Drop us a line at podcast@emarketer.com    For a transcript of this episode, click here: https://www.emarketer.com/content/podcast-ai-financial-advisor-banks-biggest-opportunity-biggest-threat-behind-numbers   © 2026 EMARKETER Verve is a global ad solution that helps brands, agencies, and publishers activate consumer intent in real-time across platforms. Learn more at https://verve.com/verve-intelligence/

Welcome back to Season 13 of the PolicyViz Podcast! In this quick solo kickoff, I walk you through everything I have planned this season, from a full episode on accessibility in data communication to a brand-new weekly series where I talk through some of my favorite data visualizations from the last 15 years. I'll also tell you about my very first live podcast recording, happening in November in Boston as part of the IEEE VIS Conference and the new Cluster Practitioner Summit I'm co-organizing. Along the way I'll be expanding my YouTube content around Flourish, Excel, and PowerPoint, including how I'm using AI tools like Claude and Copilot in my own workflow. It's shaping up to be a busy, lucky Season 13, and I'm so glad you're along for the ride.Subscribe to this podcast. Follow this show and everything dataviz on Instagram, LinkedIn, Substack, X, and YouTube. Check out the PolicyViz websiteto learn more about data and data visualization.Questions? Comments? Pitch ideas? Email me at: jon@policyviz.comHosted by Zencastr where you can record, transcribe, edit, and automatically publish your meetings, podcasts, and more.

AI tools such as ChatGPT, Claude and Copilot are spreading rapidly through the workplace, but simply giving employees access to AI isn't the same as teaching them how to use it effectively. In this episode of Today in Tech, Keith Shaw talks with Anthony Salcito, general manager of Enterprise at Coursera, about why companies are struggling to turn AI investments into meaningful productivity gains and why the missing piece may be human skills rather than more technology. They discuss why AI has created so much anxiety about job displacement, why companies can't treat AI deployment as employee training, and which skills workers actually need to succeed in an AI-driven workplace. They also explore the growing importance of critical thinking, decision-making, communication and lifelong learning, along with the role of certifications and microcredentials as jobs continue to change. Plus, Anthony explains what schools and universities are getting wrong about preparing students for AI and why banning students from using generative AI may miss a much bigger opportunity.

In Episode 23 of Season 7 of Driven by Data: The Podcast, Kyle Winterbottom is rejoined by Greg Freeman, CEO & Founder at Data & AI Literacy Academy, where they discuss why most organisations have mistaken tool training for AI literacy – and why a data-literate workforce, critical thinking and the willingness to rebuild processes from the ground up matter far more than knowing which buttons to press in Copilot.They also explore why data quality and governance are finally having their day in the sun, how leaders must role-model the discipline to challenge AI-generated work, and why the businesses winning with AI focused on four or five core processes rather than spinning up 95 pilots.They also discuss:Why the line between data literacy and AI literacy is blurry, and why Greg wants it to stay that way.Why a data-literate workforce is the enabler of an AI-ready workforce.Why data quality, governance and risk management have gone from "not that sexy" to the most important topics in the business.Why there isn't a Copilot buttonology programme in the world that can teach critical thinking.How AI slop is bleeding from LinkedIn into the work employees put in front of internal and external audiences.Why leaders must ask pointed questions of AI-enabled work to test whether the human in the loop has actually done their job.Why people treat LLMs like Google, and why that makes them less able to challenge the answers.What separates the asker, the conversationalist and the process redesigner, and why 98% of employees are still stuck at the first stage.Why leaders need the mindset to burn processes down and rebuild them with AI at the core, rather than layering it on top.Why enterprise learning conflates training with education, and why that is the root of the tool-centric problem.How hyperscalers and training partners are incentivised to teach the tool rather than transferable principles.Why a workforce using Copilot instead of Google is an expensive thing, not a useful thing.Why generative AI gives executives a hands-on "aha moment" that dashboards never did.Why nine out of ten AI conversations mean generative AI, and what that costs organisations in forecasting, decisioning and recommendation opportunities.Who should own the operating model conversation with the board, and why it depends on having the right kind of data and AI leader.Why cutting headcount and graduate intake is the wrong reason to do AI, and why AI-native graduates are the hires to make.What the US market's shift from 95 pilots to four or five core use cases teaches UK businesses.How democratising AI capability into local teams frees the central team to focus on the big wins.Why customer-facing AI use cases remain a minority, and what Lloyds Bank gets right.How to measure AI literacy by whether people see and solve business problems differently.

Partner with Jay: https://www.jayschwedelson.com/contactㅤCheck out Jay Schwedelson's new book: Stupider People Have Done It All net proceeds are donated to The V Foundation for Cancer Research, let's kick cancer's butt: https://www.amazon.com/Stupider-People-Have-Done-Marketing/dp/1637635206ㅤSubscribe to Jay's newsletter for weekly marketing tips and tactics: https://www.jayschwedelson.com/newsletterㅤRegister for GuruConference (FREE + VIRTUAL!) https://www.guruconference.comㅤCheck out Eventastic (FREE + VIRTUAL!) https://www.eventastic.comㅤConnect with Jay on LinkedIn: https://www.linkedin.com/in/schwedelson/Check out Jay's YouTube channel: https://www.youtube.com/@schwedelsonCheck out Jay's Instagram: https://www.instagram.com/jayschwedelson/Ask Jay anything: https://www.jayschwedelson.com/askㅤLeave a comment and follow the show, it really helps us out!ㅤFollow Daniel on LinkedIn and check out The Marketing Millennials podcast for sharp, no-fluff marketing insights. Subscribe to Ari Murray's newsletter at gotomillions.co for sharp, actionable marketing insights.ㅤYour prettiest email might be the thing quietly costing you opens. Jay Schwedelson and Daniel Murray dig into AI summaries, the feature already rewriting preheaders inside Gmail, Apple Mail, and Copilot, and why the first 150 characters of your send now matter more than the design around them. There's also a detour into Burning Man, five straight years of Coachella, and Daniel's firm position on tents.ㅤBest Moments:(02:02) Daniel explains what live text actually is and why AI put it everywhere(02:49) The one email thing Jay says will matter most over the next twelve months(03:22) Copilot hits thirty million paid users and why that changes B2B email(03:59) What counts as live text, and why your JPEG and your alt text do not(05:24) Half your preheaders will not be your preheaders within six months(06:48) The prettiest email stops winning and email structure rewinds fifteen years

AI is already reading your emails before your subscribers do, and if you're not writing for it, your message might not make it to the inbox at all. Jay breaks down "live text," the actual HTML text in your email that AI tools like Gmail summaries and Microsoft Copilot scan to build inbox previews and rewrite preheaders. Copilot alone just hit 30 million paid users, up 10 million in a single month, so this isn't a niche problem. Jay's rule: the first 150 characters of your email need to carry your most important, most specific information (webinar details, offer terms, whatever the ask is) because that's the window AI summaries pull from. Images and JPEGs don't count, and alt text doesn't count as live text either, no matter what you've been told. He predicts that within six months, half of all preheaders will be AI generated instead of whatever Marketers actually wrote. Daniel adds that the preheader isn't dead, it's just unreliable now. His fix: write your opening body copy so it echoes what you'd want the preheader to say, so if AI does pull from it, the message still lands the way you intended. He also flags that this isn't just an email problem. The same summarization behavior is starting to show up in text messages, and Marketers need to start thinking about how their SMS gets condensed too. The bigger idea: the prettiest, most designed email templates are losing right now. If AI can't read the words inside your design, it doesn't matter how good it looks. Email Marketing is quietly reverting to a text-first format, closer to how it worked 10 to 15 years ago. If you want your emails to survive the next wave of inbox AI, this is the episode for you. Follow Jay: LinkedIn: https://www.linkedin.com/in/schwedelson/ Podcast: Do This, Not That Follow Daniel: YouTube: https://www.youtube.com/@themarketingmillennials/featured Twitter: https://www.twitter.com/Dmurr68 LinkedIn: https://www.linkedin.com/in/daniel-murray-marketing Sign up for The Marketing Millennials newsletter:https://themarketingmillennials.com/ Daniel is a Workweek friend, working to produce amazing podcasts. To find out more, visit:https://workweek.com/

Viva Engage will soon be able to power the comments on SharePoint pages. Silence Teams notifications temporarily with a new option. Create interactive slides using a new Copilot skill. What else is arriving soon? 0:00 Welcome 2:35 Create interactive slides in PowerPoint with Copilot skill (Windows and Mac) - MC1461152 5:28 Upcoming changes to classic publishing sites, classic user-created pages and custom scripting in SharePoint Online - MC1464926 13:14 Microsoft Teams: Users can temporarily pause all notifications - MC1465768 17:07 Microsoft Teams: Collect information with List Form in Workflows MC1466757 20:18 Frontier [Skills] 'Explain this presentation' built in skill in PPT Copilot (Web) MC1466327 23:51 Cross-post SharePoint News to Engage MC1466762

In this episode of Life of a Dealmaker, Tarun explores the journey that led John Messer to Copilot Capital. From anthropology to building a software-focussed private equity platform from the ground up, John's career path has been anything but conventional. They discuss why understanding human behaviour remains at the heart of successful investing and a prediction about the World Cup which doesn't age well!

Brian, Brandon, and Aaron discuss enterprise AI adoption using the sales analogy of whether AI is a “vitamin” or a “painkiller,” arguing that successful transformation still requires a burning-platform event. Brandon suggests AI adoption resembles past digital transformations: without urgent pressure (e.g., a data center closing), organizations resist change and justify existing processes. Brian describes a compressed hype cycle from ChatGPT excitement to pilots and guardrails, followed by difficulties with data, cost-effective scaling, and making AI behave deterministically, while fear of competitors keeps efforts alive. They add a third category, “Whippets”, short-term, resume-driven initiatives led by leaders who leave others “holding the bag.” They debate examples like Sheetz' multiple VMs and argue that AI's promise is personal productivity, but note a lack of enterprise collaboration and shared-memory tools that limit organizational impact.SHOW: 1060SHOW TRANSCRIPT: The Enterprise AI Show #1060 TranscriptSHOW VIDEO: https://youtu.be/XZqomQosv1wSHOW LINKS:The Enterprise AI ShowSHOW SPONSORS:Nasuni - Activate your data for AI and request a demoNordLayer - Use ENTERPRISE10 for 10% off.The thesis: Successful digital transformation usually has a forcing function; you migrate the data center because the real estate got sold, not because someone promised abstract savings. Deadline + shared incentive = people actually change. AI adoption mostly lacks that: no one's forcing the migration, so it defaults to "give everyone Copilot licenses and hope."Core question: If your business is healthy and there's no burning platform, how do you adopt AI in a way that's more than expensive theater, without a crisis to manufacture urgency?Discussion topics:Forcing functions vs. vibes: What are the AI-era equivalents of "the real estate got sold"? A support team that's actually understaffed, a process with a real bottleneck, a cost center leadership is already scrutinizing vs. a mandate to "use more AI."The unknown-unknowns problem: Most orgs don't know which of their workflows AI would actually help vs. where it's a novelty. How do you go find that out cheaply, without a company-wide token-burning experiment as the discovery mechanism?Bottom-up signal vs. top-down mandate: Does real usage data (who's actually using tools, for what) surface better targets than an executive committee guessing at use cases?Contrast with the failed "abstract savings" migration: What does an AI initiative look like when it's tied to a concrete, already-painful problem instead of a general efficiency narrative?The FOMO trap: Distinguishing "we don't want to miss the platform shift" (legitimate) from "we need AI headlines for the board" (theater), and how leadership can tell which one they're actually doing.Measurement: If there's no forcing function, what replaces the natural deadline/incentive alignment as the way you know it's working, or that it's time to kill it?Final ThoughtIs the right move small, cheap, bounded bets against known pain points, treating AI adoption like a search problem, not a rollout, rather than a company-wide transformation initiative looking for a reason to exist?FEEDBACK?Email: show @ the enterprise ai show dot comBluesky: @TheEntAIShow.bsky.socialTwitter/X: @TheEntAIShowInstagram: @TheEntAIShow

In this episode, Ray Cochrane breaks down Hot Chips 2026, the engineering conference where IBM, NVIDIA, Intel, AMD, Arm, and Fujitsu all showed how their next processors actually work. The headline disclosure is a mainframe core that runs Arm natively. Ray also covers Apple’s odd M6 Mac mini naming, London’s first autonomous Uber rides, Amazon’s purchase of the company behind DuckDB, GitHub’s HydraFusion, the best of IFA 2026, and new USDA research on farmed salmon. – Want to start a podcast? It’s easy to get started! Sign-up at Blubrry – Thinking of buying a Starlink? Use my link to support the show. Subscribe to the Newsletter. Email Ray if you want to get in touch! Like and Follow Geek News Central’s Facebook Page. Support my Show Sponsor: Best Godaddy Promo Codes Get 1Password Full Summary Cochrane opens the show with a personal update. He apologizes for the late rollout and the missed Monday episode, having spent the week fighting a cold. He’s also heading to Michigan to spend time with family and visit his father’s gravesite. He hopes to record a couple of shows from his dad’s old studio while he is there, including a special episode planned for Tuesday. He also points listeners to a fresh site redesign that trades the old techie look for something cleaner and friendlier. The featured segment starts from a wrap-up post on Arm’s newsroom. However, Cochrane broadens it to cover the whole conference rather than a single article. Hot Chips has run every August since 1989, and this year’s event was the 38th, held August 23rd through the 25th at Stanford’s Memorial Auditorium. What Hot Chips Actually Is Cochrane draws a line between Hot Chips and the big consumer trade shows. CES and Computex exist for product announcements and marketing. Meanwhile, Hot Chips is an IEEE engineering conference where chip architects present block diagrams and die photos for thirty minutes at a stretch. The audience matters as much as the content. Roughly five hundred people who design chips for a living fill the room, and they would spot a fudged number immediately. No written paper is required, just the talk and the slides. In-person tickets sold out this year, as did Stanford’s dorm housing. Cochrane says he plans to cover the conference annually going forward. IBM Built a Mainframe Core That Speaks Arm The disclosure that stopped Cochrane cold came from IBM. Its next processor for IBM Z and LinuxONE runs two completely different instruction sets natively, on every one of its eleven cores. Those are z/Architecture, IBM’s own mainframe language, and AArch64, which is 64-bit Arm. Crucially, this is not emulation. Nor is it Arm cores glued onto the die beside the mainframe cores. IBM built 2,792 Arm instructions directly into the hardware, which it says is more than double the mainframe instruction count. Each core carries two separate decoders while sharing the caches, branch predictor and register files downstream. It switches between the two in nanoseconds. IBM even added dedicated hardware to flip byte order, because Arm and the mainframe store numbers in opposite directions. The payoff is Arm SystemReady compliance, meaning off-the-shelf Arm Linux runs on a mainframe unmodified. Patrick Kennedy of ServeTheHome, who was in the room, wrote: “I am sitting here still in awe of what IBM is doing here; this is not Z plus Arm cores, this is Z and Arm in one core.” Cochrane flags one precision point that is easy to get backward. IBM did not license Arm’s core designs and drop them in. Instead, it took its own mainframe core and taught it AArch64 under an architecture license, which is considerably harder engineering. The specifications are striking. The chip uses a 2nm process, with eleven cores running above 5.7GHz sustained and no turbo mode at all. Each core gets 36MB of L2 cache, backed by a 3.5GB virtual L4 pool. Furthermore, the reliability target is eight nines, which works out to roughly three tenths of one second of unplanned downtime per year. IBM gave it no name and no ship date, though the press expects “Telum III” around 2028. Arm, Fujitsu and NVIDIA Show Their Hands Arm itself had plenty to discuss, starting with an unfortunate name. Its first chip in thirty-five years is called the AGI CPU, which is a product name rather than any claim about artificial general intelligence. For three and a half decades, Arm designed processor blueprints and licensed them out, collecting royalties without competing. That era is now over. The AGI CPU is Arm’s own silicon, co-designed with lead customer Meta, running up to 136 cores on TSMC’s 3nm process at 300 watts. Arm’s CEO says the company has more than $2 billion in customer demand across the next two fiscal years. Fujitsu brought the detail Cochrane called the coolest of the conference. Its MONAKA chip packs 144 Arm-based cores, but the trick is the cache. Rather than sitting alongside the cores and eating die area, the entire last-level cache lives on a separate 5nm die with the 2nm compute die stacked directly on top. It ships in 2027 in 350-watt and 500-watt versions. NVIDIA had more stage time than anyone, with six sessions. Its new Vera CPU carries 88 cores of NVIDIA’s own Olympus design, which marks a change: the previous Grace CPU used Arm’s off-the-shelf cores. Consequently, Arm’s win here is the instruction set, not the blueprint. The memory disclosure drew the most attention, with a fully loaded system reaching 1.5TB at 1.2TB/s while the whole memory subsystem draws just 30 to 40 watts. The Caveat on NVIDIA’s Benchmark Slides Cochrane pushes back on how NVIDIA presented its numbers. On the standard SPEC integer benchmark, Vera scored 925 against AMD’s 128-core EPYC score of 898, about three percent ahead. However, the slide NVIDIA showed normalizes that same result per physical core, which makes a three percent gap look enormous. NVIDIA defends the choice, arguing that per-core throughput matters when thousands of AI agents run at once. Cochrane grants that it is a fair argument to make. Even so, his verdict is blunt: it is a different number from the headline one, and presenting it that way is not the best look. Sponsor: GoDaddy Economy hosting $6.99/month, WordPress hosting $12.99/month, domains $11.99. Website builder trial available. Use codes at geeknewscentral.com/godaddy to support the show. Apple’s Newest Chip Landed in Its Cheapest Mac Last week’s episode covered the Mac Studio half of Apple’s August announcement. Tonight Cochrane takes the other half, the new Mac mini, and finds the numbering genuinely strange. The $899 base Mac mini gets the M6, which is Apple’s first 2nm chip and the newest process the company has shipped. It brings twelve CPU cores, twelve GPU cores, and 170GB/s of memory bandwidth. Apple also introduced a third CPU core class called super cores. So Apple’s most advanced chip sits in its cheapest desktop, while the M5 Pro, M5 Max and M5 Ultra above it all carry a lower number. It gets stranger. The M6 mini has Thunderbolt 4 while the pricier M5 Pro mini has Thunderbolt 5, and the memory ceiling runs backward too. Apple explains none of it across three announcement pages. Reading between the lines, Cochrane figures the M6 is the entry point of a new generation that shipped ahead of its larger siblings. London’s Robotaxis Started Carrying Passengers Arm’s monthly roundup covers everything outside the data center, and Wayve stood out. Transport for London granted the British self-driving company private hire vehicle licenses on August 5th, the same category a minicab needs. Then on September 3rd the service launched with Uber, marking the first autonomous rides ever offered to UK passengers. A small fleet of Ford Mustang Mach-Es covers anywhere in London except the airports, with a TfL-licensed safety driver still aboard. Over 140,000 Londoners signed up. The compute runs on NVIDIA’s Arm-based automotive platform, which is why Arm claims the win. Cochrane notes the pattern: once you set a standard nobody can move off, these wins keep arriving. Two more items round it out, both about squeezing AI onto phones. Google’s Pixel 11 shipped in August with the Tensor G6, and Google claims on-device AI runs up to 3.5x faster using 3.5x less energy. Those are Google’s own unbenchmarked figures. Separately, Graphcore’s research team worked with Arm to run an 11-billion-parameter vision model on phone-class processors by squeezing each parameter to 2.7 bits, taking the model from roughly 22GB down to 3.7GB. Intel Is Pitching AI Infrastructure From a Long Way Back Intel’s newsroom post previews the AI Infra Summit, running September 15th through 17th in Santa Clara. CEO Lip-Bu Tan takes a fireside chat on Tuesday morning, with three Intel sessions across the show. Cochrane unpacks two terms first. Physical AI means AI that acts in the real world through sensors and motors rather than living on a screen, and Intel takes it seriously enough to have renamed its PC division the Client Computing and Physical AI Group in May. Disaggregated inference splits the two phases of running a model: reading your prompt is compute-hungry, while writing the answer back is memory-hungry. The context is where this gets interesting. Intel’s revenue rose 25 percent last quarter, its fastest growth since 2011. Nevertheless, in AI accelerators the company barely registers next to NVIDIA. Gaudi has effectively been abandoned, to the point that Intel stopped maintaining its open-source driver, and AMD passed Intel in data center revenue last quarter. Tellingly, when Intel demoed disaggregated inference at Computex, NVIDIA GPUs handled one phase and SambaNova chips the other while Intel supplied the coordinating CPU. Crescent Island, Intel’s actual inference chip, does not sample until later this year, and Intel declined to publish its memory bandwidth. Amazon Bought the Company Behind DuckDB On August 26th, Amazon signed a deal to acquire DuckLabs, the Amsterdam company behind DuckDB. Cochrane spends time explaining what DuckDB is, since listeners outside the data world may never have encountered it. The problem it solves is familiar. Querying a large pile of data files traditionally meant either running a database server or spinning up a data warehouse with a cluster, a bill, and a loading pipeline. Both are heavy machinery for a question you wanted answered in ten seconds. DuckDB instead ships as a library rather than a server. You add it to your program like any other package, point it at your files, and write ordinary SQL directly against them. The data never moves. The usual comparison is SQLite, which is embedded in nearly every phone and browser on earth. Where SQLite excels at looking up one record, DuckDB rebuilds that embedded idea for chewing through millions of rows. It now sees roughly 62 million monthly downloads on Python’s package index alone, up from about 25 million last October. AWS says it is buying the company, not the project. DuckDB stays free and open source under the MIT license, held by a Dutch nonprofit foundation, and the founders join AWS while continuing to run technical direction from Amsterdam. Andy Warfield, a VP and distinguished engineer at AWS, described DuckDB as “the glibc of structured data: a lean, unglamorous, ubiquitous dependency that a great deal of software links against and almost nobody has to think about.” Cochrane sits with what that ownership means. He reaches for an analogy: imagine Daniel Stenberg selling curl. He doubts it would ever happen, but the concept alone is startling given how much infrastructure depends on it. His read is that AWS is betting DuckDB becomes as foundational as curl and SQLite already are. GitHub Has One AI Model Grade Another One’s Homework GitHub shipped Project HydraFusion into Copilot as a research preview. Instead of routing your request to a single model, it picks one of three approaches per request. Sometimes one model simply answers. Alternatively, a cheaper model drafts, and a quality gate decides whether to escalate. The interesting one is Critique. One model writes the code, a separate model from a different family reviews it read-only, and the original gets one pass to revise. Despite the name, nothing is fused here. There is no voting and no merging, just one model at a time with a gate deciding whether to spend more. GitHub explained the reasoning in an earlier post: “a model reviewing its own work is still bounded by its own training biases: the same training data and techniques, the same blind spots.” Research supports it. A team at NeurIPS in 2024 showed that models recognize their own writing and score it higher than human graders do. GitHub’s earlier number had a Claude Sonnet and GPT critic pairing closing about three-quarters of the gap between Sonnet and the larger Opus model. This mirrors a workflow Cochrane uses constantly and has described on a previous episode. He runs a cross-check review with a second model from a different company, and it routinely surfaces issues the first model missed. He explains that different training data, different engineers, and different reinforcement approaches build different internal biases about what counts as correct. Looking ahead, he expects more of these “Frankenstein patterns” where models from different training families work together. The Best of IFA 2026, and What You Can Actually Buy IFA opened to the public in Berlin for its 102nd year, with about 1,900 brands. Cochrane splits The Verge’s roundup in two, since much of what generates headlines at these shows never ships. Starting with real products, iRobot’s flagship Roomba Max 875 Combo runs $1,199 and ships in about two weeks. Its SealForce feature drops a hidden skirt from the chassis when it detects carpet, sealing against the fibers so suction concentrates instead of leaking out the sides. That reaches 35,000 pascals, iRobot’s strongest yet. A step-down model at $899 carries the same trick, though Cochrane balks at both prices. Anker’s Soundcore Sleep 4 Pro earbuds arrive in November at $349.99. The charging case carries its own round touchscreen, so you pick soundscapes, set alarms, and read sleep stats without your phone. Optical sensors read heart rate and variability from the ear canal, which beats the wrist for accuracy, and the case masks a snoring partner. Cochrane remains unconvinced about sleeping with earbuds in. Philips also has smart rope lights, the Hue Liane 360, on sale now. They glow evenly around the tube rather than showing individual LEDs. They also run $400 for three meters, which works out to about $130 per meter of rope light. As for concepts nobody can buy, iRobot showed a robot vacuum that carries a smaller robot vacuum on its back in a garage and lowers it to deploy. Lenovo brought a 14-inch laptop whose screen rolls out to 17 inches at the press of a button, which reviewers call the first rollable that feels close to shippable. Tecno showed a phone with essentially no border around the screen, and Acer had a Windows gaming handheld that swivels its screen up over a keyboard. Two themes ran through the show. Humanoid robots were the loudest thing on the floor, and IFA’s own CEO framed the event as being about robots that work rather than robots that demo. Meanwhile, AI stopped being its own product category and became an ingredient, showing up in refrigerators, treadmills, dishwashers, and motorized TV mounts. Farmed Salmon Isn’t the Omega-3 Machine It Used to Be USDA scientists measured farmed salmon and found considerably less of the good fat than the government’s own database claims. EPA and DHA are two fatty acids you get almost entirely from fish. Your body can build them from the plant version, but only in tiny amounts, so the NIH’s position is that eating them is the only practical way to raise your levels. Those fatty acids are structural pieces of every cell, with DHA concentrating in the brain and retina. That is why the federal dietary guidelines, issued jointly by USDA and Health and Human Services, recommend at least eight ounces of fish a week and steer you toward salmon. That amount is calibrated to deliver about 250 milligrams a day. Published in Frontiers in Nutrition last month, the study found EPA and DHA in farmed Atlantic salmon came in 54.7 percent lower than USDA’s own reference values, last updated in 2018. A three-ounce serving fell from roughly 1,670 milligrams to about 756. Consequently, two servings a week now fall about 14 percent short of the target. Plant-derived fats meanwhile rose two to three times over. The likely cause is feed. Salmon are carnivores, and farms once fed them oily little fish. There was never going to be enough of those as the industry scaled, so crops filled the gap: soy, canola, sunflower and linseed. Importantly, the study does not claim to have proven this and calls the feed shift a plausible explanation. Independent corroboration lends it credibility. Researchers at Stirling measured a similar halving in Scottish salmon between 2006 and 2015, and Norway’s marine institute saw it across thousands of samples. There is a land dimension too. Roughly half the world’s soy grows in South America, where rainforest gets cleared for feed. Matthew Hayek, who studies the environmental cost of protein at NYU, told Inside Climate News that “soy is a major, important protein and oil ingredient in fish farming.” Adding up two decades of soy across all fish farming, he puts the extra forest clearing at around the area of Nicaragua or Bangladesh. That figure covers all fish farming rather than salmon alone, and Hayek notes it is hard to attribute soy use to any single species. Cochrane closes with two caveats. First, the study measured only eight fish, bought around Maryland, DC and Virginia over six weeks in 2023, and nearly all sourced from Chile. That is not a national survey, and the authors say plainly the sample was not large enough to change government advice. Rather, it flags that a federal database value needs rechecking, which is what the paper set out to do. Second, on whether you should care, farmed salmon still beats beef, chicken and eggs by a mile, since those carry essentially zero EPA and DHA. What it loses is its crown among fatty fish, dropping to mid-pack behind herring, sardines and mackerel and roughly level with trout. The broader health case is also softer than the 2000s suggested. A review of 86 trials covering 162,000 people found supplements barely moved heart attacks or deaths, so eating fish and swallowing fish oil are not the same claim. No producer has responded to the findings, and USDA, whose own scientists ran the study, declined an interview and did not answer emailed questions. Cochrane wraps up with housekeeping and a note that he will be back on Labor Day. The post The Mainframe Learned to Speak Arm #1875 appeared first on Geek News Central.

Welcome to the second half of the 2026 NFL Season Preview podcast! This pod covers the NFC. Transcript is here, laundered through Co Pilot. Please check the audio file for the accuracy of any quotes and speakers. Apple Podcasts Android Youtube Podchaser Podcast Index Subscribe by E-mail Audible Spotify Amazon Music The post LGM Podcast: 2026 NFC Preview appeared first on Lawyers, Guns & Money.

BONUS: When the Team Becomes the Operating System for AI — Marko Taipale on the Twin Project at Solita Most teams trying to "use AI" end up with fast individuals and a slower system. Marko Taipale ran a two-year experiment at Solita that suggests the real bottleneck isn't the tool — it's the team's operating model. In this conversation, Vasco and Marko walk through the Twin Project with ISS Finland — two teams, same ERP pricing tool, one classical agile, one with generative AI in the room — and the lessons that became the CollabAI framework. The Twin Project — Two Teams, Same Product, One With AI in the Room "We had a luxury of: do whatever you want with AI, please get at least the same results, towards the same goal."   In 2024, Marko's team at Solita was set up as the counterpart to an existing Scrum team building an ERP pricing tool for ISS Finland. Same product mission, two different operating models. The first days were chaotic and exploratory — the team tried over 120 AI tools, built a custom GPT to act as a stand-in product owner, and even sent a virtual assistant to sit silently in the other team's meetings so nobody from Marko's team had to attend. The framework grew out of what kept working, not from a plan written upfront. "Fast Individuals, Slow System" — Why Buying Licenses Doesn't Fix the Bottleneck "If you don't change your structures, AI won't do anything faster. The only thing that gets faster is the queues between your decision-making gates."   The line from Marko's book lands hard once you have seen it inside a team. A Copilot license speeds up the individual — and then the individual sits and waits for the rest of the system: reviews, handoffs, refinement, stakeholder meetings. Those queues are exactly what AI accelerates, and the team feels even more frustrated than before. The real intervention is upstream, in how the team shares context and makes decisions together. Without that, AI just makes the existing inefficiency more obvious. Drifting in the Solution Space — Why Sense-Making Has to Happen Together "None of the real problems are so simple that a single person can solve them. If it's that simple, you should automate it."   The early Twin Project team kept seeing what Marko calls drifting — small interpretive mistakes at the start of a task that twisted the solution into something unrecognisable later. Each person was reading the same docs and the same proxy-PO conversations, and each was leaving with a slightly different picture. Individual interpretation was not enough. They moved from individuals to pairs, then to whole-team sense-making sessions. The shared context only became useful when the team processed it together — and that processing turned out to be where the learning compounded. Never Leave the Daily — When Mob Programming Becomes the Operating System "This is happening so fast, we shouldn't actually leave the daily."   The team started with vanilla Scrum, extended dailies, then ran multiple per day, then realised that the meeting was the work. They drifted into mob programming without naming it — a shared virtual machine where one person controlled the screen at a time, switching every few minutes. The agile labels came later, when someone read Mob Programming by Woody Zuill (see his earlier episodes on the Scrum Master Toolbox Podcast) and saw the team's own behaviour reflected back. The takeaway: when the pace of decisions exceeds the cadence of meetings, the team has to live inside the conversation, not visit it once a day. The 40-Prototypes Moment — When the Customer Joined the Mob "You waited 37 hours to get to this point where we get feedback."   This was the turning point. Marko had built 40 different prototypes of the pricing tool in one hour, then walked into a weekly review where the customer pointed out the obvious: if the prototypes took one hour to make, the team had been waiting 37 hours to get the feedback that actually mattered. From that moment the client became part of the mob. New product directions started landing every five to seven minutes. The backlog quietly disappeared — issue management stayed, but for the AI's context, not for humans. When the product owner is in the room all day, the storage-and-handover layer stops earning its keep. The Regulation Layer — Why Sustainable Pace Gets Sharper, Not Softer, With AI "AI is a machine. It won't stop. That's why we need a regulation layer — and we have to regulate together, not individually."   What broke first in the Twin Project was not the technology — it was the people. Cognitive load and the brain's hunger for clarity become the new constraint once decisions are flying every few minutes. The old Scrum idea of sustainable pace gets a second life here, but it has to be a shared pace, set by the team, not an individual one. Engagement is the early warning signal — when people start disengaging, the system is already over its capacity. For Scrum Masters and coaches, this is where the work moves: watching the team's energy curve, not just its throughput. The Agentic Horizon — From Teammates to Agents Acting on the Team's Behalf "AI is a new player in this field. The next conversation is governance — and it has to start now."   CollabAI was about humans and AI on one screen. The next move — what Marko is now building at Agion — is agents acting on the team's behalf. The governance shape is not optional, and it is the conversation Scrum Masters and coaches need to start having before agentic systems are everywhere on the team. Marko's article series on Agion (the brain fry posts) is the public record of what they are learning as they build the governance layer for autonomous agents. About Marko Taipale Marko Taipale is the author of CollabAI: AI Teamwork In Practice (foreword by Joe Justice) and currently works at Agion Inc. At Solita, he co-led the Twin Project with ISS — two teams building the same ERP pricing tool, one with classical agile, one with generative AI embedded end-to-end — and turned what they learned into a framework for teams that want AI as a synchronous teammate, not a faster individual tool. CollabAI book · Leanpub · LinkedIn   You can link with Marko Taipale on LinkedIn.

Can AI make tax prep cheaper without replacing the experts behind it? Hector Garcia joins Blake and David to unpack Intuit's TurboTax pricing pressure, QuickBooks' push into the mid-market, and whether AI-powered services are a bridge to automation. They also examine Thomson Reuters' proprietary tax-and-legal AI model, Relate's $100 million raise, and a student's unusual path onto the New Mexico Board of Accountancy.SponsorsCloud Accountant Staffing - http://accountingpodcast.promo/casThomson Reuters - http://accountingpodcast.promo/taxautomationOnPay - http://accountingpodcast.promo/onpaySavant Labs - http://accountingpodcast.promo/savantChapters(00:00) - TAP 503 (02:47) - Intuit earnings overview (03:52) - Meet Hector Garcia (04:40) - Intuit stock and AI fears (07:57) - TurboTax pricing pressure (11:34) - TurboTax Live strategy shift (16:12) - TurboTax Live vs firms (19:47) - AI plus human bridge (22:13) - QuickBooks Free and Light (25:54) - Thomson Reuters builds LLM (30:29) - Trust security and ownership (34:16) - Vertical models disrupt giants (35:18) - Copilot partnership pitch (36:51) - Bulk AI Adoption (38:59) - Rillet Unicorn News (39:21) - Workday Buyout Math (40:50) - Switching Costs Reality (43:12) - Intuit Versus Rillet (45:26) - How Rillet Wins (48:18) - AI Spend Line Item (51:15) - Stripe Buys OpenRouter (53:22) - Reframe Conference Pitch (56:01) - Pablo Pledgebar Cautionary (57:08) - Britton Joins Board (01:01:37) - Student View On PE (01:04:57) - Education And AI Concerns (01:09:33) - Board Dynamics Explained (01:12:12) - Regulation Priorities (01:15:13) - Wrap Up And CPE  Show NotesIntuit Reports Fourth Quarter and Full Year Fiscal 2026 Results; Sets Fiscal 2027 Guidancehttps://investors.intuit.com/news-events/press-releases/detail/1320/intuit-reports-fourth-quarter-and-full-year-fiscal-2026-results-sets-fiscal-2027-guidanceThomson Reuters Leverages its World-Class Data Assets to Launch Its Own Frontier Modelhttps://www.thomsonreuters.com/en/press-releases/2026/august/thomson-reuters-leverages-its-world-class-data-assets-to-launch-its-own-frontier-modelRillet Raises $100 Million Series C at $1 Billion Valuation, Becomes Unicornhttps://techcrunch.com/2026/08/19/rillet-raises-100m-series-c-at-1b-valuation-2-years-after-emerging-from-stealth/Silver Lake Eyes $51 Billion Workday Take-Private in Mega Software Dealhttps://www.axios.com/2026/08/14/silver-lake-workday-take-privateStripe Agrees to Acquire OpenRouter to Help Businesses Optimize Token Routing and Usagehttps://stripe.com/newsroom/news/stripe-agrees-to-acquire-openrouterFrom Accounting Major and Frat Brother to Suspected of Being Penn State's Largest Drug Dealerhttps://www.nbcnews.com/news/us-news/penn-state-agostino-abbatiello-drug-scheme-rcna593041Accounting Student Appointed to the New Mexico Public Accountancy Boardhttps://news.unm.edu/news/accounting-student-appointed-to-the-new-mexico-public-accountancy-boardNeed CPE?Get CPE for listening to podcasts with Earmark: https://earmarkcpe.comSubscribe to the Earmark Podcast: https://podcast.earmarkcpe.comGet in TouchThanks for listening and the great reviews! We appreciate you! Follow and tweet @BlakeTOliver and @DavidLeary. Find us on Facebook and Instagram. If you like what you hear, please do us a favor and write a review on Apple Podcasts or Podchaser. Call us and leave a voicemail; maybe we'll play it on the show. DIAL (202) 695-1040.SponsorshipsAre you interested in sponsoring The Accounting Podcast? For details, read the prospectus.Need Accounting Conference Info? Check out our new website - accountingconferences.comLimited edition shirts, stickers, and other necessitiesTeePublic Store: http://cloudacctpod.link/merchSubscribeApple Podcasts: http://cloudacctpod.link/ApplePodcastsYouTube: https://www.youtube.com/@TheAccountingPodcastSpotify: http://cloudacctpod.link/SpotifyPodchaser: http://cloudacctpod.link/podchaserStitcher: http://cloudacctpod.link/StitcherOvercast: http://cloudacctpod.link/OvercastClassifieds Fearless Foundry - www.advisoryamplified.comExpense Bot - https://www.expensebot.ai/accountantProfitRoot - https://tryprofitroot.com/Want to get the word out about your newsletter, webinar, party, Facebook group, podcast, e-book, job posting, or that fancy Excel macro you just created? Let the listeners of The Accounting Podcast know by running a classified ad. Go here to create your classified ad: https://cloudacctpod.link/RunClassifiedAdTranscriptsThe full transcript for this episode is available by clicking on the Transcript tab at the to...

It’s just about that time! On the latest LGM podcast Erik, Scott, and myself run through our annual AFC preview. As the image above indicates, we pay the Cleveland Browns all of the attention that they deserve. Take a listen. Transcript is here, laundered through Co Pilot. Please check the audio file for the accuracy of any quotes and speakers. Apple Podcasts Android Youtube Podchaser Podcast Index Subscribe by E-mail Audible Spotify Amazon Music The post LGM Podcast: 2026 AFC Preview appeared first on Lawyers, Guns & Money.

It’s just about that time! On the latest LGM podcast Erik, Scott, and myself run through our annual AFC preview. As the image above indicates, we pay the Cleveland Browns all of the attention that they deserve. Take a listen. Transcript is here, laundered through Co Pilot. Please check the audio file for the accuracy […] The post LGM Podcast: 2026 AFC Preview appeared first on Lawyers, Guns & Money.

Most operators never learn to fight autopilot, the quiet enemy of excellence.In this episode, Lindsay Smith sits down with Mike Fedorov, COO of Applied AI Labs. With 26 years in operations and supply chain, as a former Accenture Director and Mars operations leader, Mike built his reputation staying on-site, in the detail, close to where the work breaks.He shares his philosophy that a COO's first job is to keep the lights on, and the second is to keep them on better, explaining why flat structures are replacing layers of management, how AI works best as a coordination layer, and the defining power of mentorship in his career.Sponsored by:STS Capital Partners - Your expert guides on the journey to an Extraordinary Exit™. To learn more about STS Capital Partners and how they achieve maximum value by Selling to Strategics™, complete the inquiry form here: https://stscapital.com/coo-alliance/Timestemped Highlights00:00 The Future of Flat Organizational Structures01:44 What Applied AI Labs Does02:38 The COO's Real Job: Keep the Lights On, and Better05:11 A COO Who Watches Two Operations at Once08:08 Scaling After Excelling: The Contractor Approach09:18 Why Flat Structures Are Replacing Layers of Management11:01 What Big Companies Get Right About Operations12:45 Nimbleness vs. Routine: Autopilot as the Enemy of Excellence16:06 How AI Actually Helps Operations: The Coordination Layer18:44 AI as a Copilot, Not a Replacement20:06 AI in Recruiting: What Works and What Fails27:34 Defining Moments: The Power of Mentorship30:52 How to Find and Structure Mentorship38:55 What's Next for Applied AI Labs43:55 Personal Growth: Knowing What You Don't KnowAbout the guestMike has 26 years of experience in operations and supply chain, as a former Accenture Director and, before that, leading operations planning and execution for Mars. He has run and turned around operations for some of the most complex and demanding companies in the world, and has saved major clients that were ready to walk. He leads engagements the way he has always worked: on-site, in the detail, close to the people who know where the work breaks. Operators trust Mike because he has done their job.Mentioned ResourcesMarsAccentureAbout the Co-HostLindsay Smith is an experience-driven growth strategist and former Chief Strategy Officer. She previously scaled a new region without a traditional sales team or bloated marketing budget by turning relationships, culture, and execution into revenue. She is the author of No One Needs Another Company Mug, Stop Branding, Start Experiencing, and helps leadership teams use experience as a competitive growth lever.Important LinksConnect with Cameron: Website | LinkedInExplore the COO Alliance – The World's Leading Community for Seconds in CommandGet Cameron's book: Second in Command, Unleash the Power of Your COO BookClaim your FREE copy: Second in Command: Unleash the Power of Your COO BookTake his course: Invest In Your Leaders Online Course (Use promo code PODCAST10 before the end of the month for 10% off)Chat or video call with AI Cameron via Delphi

Who will win the AI race in 2026?

BONUS: How Scrum Masters Can Use AI Without Becoming the Human API   AI is already changing the practical, everyday work of Scrum Masters and Agile Coaches. In this BONUS episode, Vasco talks with Fred Deichler about moving from curiosity to real AI-supported workflows: finding team signals faster, preparing better conversations, keeping documentation in sync, and staying focused on outcomes instead of just producing more output. From Automation to an AI Sparring Partner "I have this partner I can work with to help me ideate things."   Fred's journey into AI started before the current AI wave, with automation in Jira and the practical need to surface bottlenecks without manually watching every board. The turning point came when ChatGPT stopped feeling like a search box and started acting like a thinking partner. Faced with a team being pushed toward multiple sprint goals, Fred dumped the context into AI and asked for three to five options instead of one "right answer." That shift helped him move from a deterministic mindset into a problem-solving mindset, using AI to explore options, challenge his own assumptions, and prepare a better conversation with the team. The Monday Morning AI Context Builder "It instantly sets that context for me. It sets that tone for the whole week."   Fred describes his weekly workflow as a very practical use of AI: on Monday morning, he opens Cursor and works with his own AI harness, Atlas. Atlas is built from markdown files containing persona, skills, history, meeting transcripts, notes, and Jira data. When Fred says "good morning," the system pulls the most relevant signals forward: aging work items, backlog health, sprint goals, and where the next team conversation should focus. Instead of starting the week by hunting for data, Fred starts with questions he can bring into the first stand-up: what is stuck, what needs refinement, and what risk is already visible? Making Flow Metrics Visible Inside Jira "If you're on a page, what do you hope you could learn without asking?"   Beyond using AI as a thinking partner, Fred used AI to build a Chrome extension that surfaces useful Jira insights directly where the team already works. On the active sprint board, it shows work in progress, aging items, sprint goals, and sprint changes. In the backlog, it exposes backlog health and epic health. On the sprint report page, it adds cycle time per item so the retrospective can move from generic discussion to concrete learning. The point is not to shame the team with metrics. The point is to make the right conversation easier to start: what caused this item to take seven days, what blocked it, and what do we want to learn from that? AI as Extra Eyes and Ears for Team Conversations "It helps make sure that we don't lose sight of these things I can bring back to the team."   Fred also uses AI agents to review meeting transcripts and look for patterns that are easy to miss in the flow of daily work. The system can notice hesitation, unresolved topics, or a requirement problem that was mentioned once and never followed up. For retrospectives, Fred's Atlas setup can review sprint transcripts around day nine of the sprint and suggest themes for the next retro. It can even generate prompts for a visual retrospective board in Copilot or a Miro board from a prompt. This helps Fred avoid relying only on recency bias and creates a better starting point for the conversation the team actually needs. Keep the Human in the Loop, Especially for Outcomes "It's not about building a faster hammer. It's about identifying the outcome you're going for."   A major warning in the episode is that AI can make Scrum Masters faster at producing more of the same: more notes, more summaries, more documents, more reports. Fred argues that the useful question is not "can we automate this exact process?" but "what outcome are we trying to achieve?" In a Jira-to-Azure DevOps migration, for example, the goal was not to reproduce the current time-tracking process perfectly. The goal was to provide the information accounting needed. That outcome focus keeps the human accountable for direction, judgment, and value, while AI helps explore better ways to get there. The SPOT Framework for Finding AI Opportunities "If it meets three or four of those, this is a great opportunity for an automation to free me up to do that more human-centric work."   Fred uses a simple filter, the SPOT framework, to decide what AI should help with and what should remain human-led. A task is a good candidate when it is simple, predictable, observable, and tedious. Simple means it requires low human judgment and could be explained on an index card. Predictable means it happens repeatedly, either on a schedule or triggered by an event. Observable means the needed data is available to the agent and not locked away in someone's head or behind policy. Tedious means it consumes energy without adding much human value. When a task matches most of those criteria, Fred looks for ways AI can handle the toil while he stays focused on facilitation, coaching, and decision-making. A Small Experiment Scrum Masters Can Try Tomorrow "If a Scrum Master finds himself operating as, like, I'm moving data from point A to point B, that is a great opportunity to say, how might we do this?"   Fred's concrete advice is to start by noticing where you have become the "human API": moving data between tools, updating spreadsheets, copying information into reports, or keeping documents manually aligned with reality. Write down one tedious workflow and ask your approved work AI, "How might we automatically populate this, while keeping me in the loop?" Vasco adds a useful ideation tip: if the first five ideas are not good enough, ask for five more without repetition, then five more again. The goal is not to hand over accountability. The goal is to discover what might be possible and choose one safe, small experiment. Resources for Scrum Masters Exploring AI "It's all about building up your own idea of what's possible to start those how-might-we conversations."   For listeners who want to keep learning, Fred recommends The AI Daily Brief as a way to stay current with what is happening in AI, and Jack Roberts' YouTube channel for practical ideas around personal AI and agent-based workflows. Fred also points listeners to his own work at Triforce Agility, where he shares blog posts, resources, and updates about his talks. He will be speaking at the Kansas City Developers Conference in early September about AI and career progression, and at Scrum Day in Madison, Wisconsin in October about how AI impacts Scrum Master roles.   About Fred Deichler   Scrum Master, Agile Coach   You can link with Fred Deichler on LinkedIn and connect with Fred Deichler at Triforce Agility.

Welcome back to another episode of Data Driven! This time, hosts Andy Leonard and Frank La Vigne sit down with Jim Spignardo, Director of Cloud Strategy and AI Enablement at ProArch, to explore the transformative role of AI and data engineering in critical industries. From the challenges of bringing modern AI solutions into traditional sectors like power generation and manufacturing, to the real-world impact of tools like Copilot, this episode dives deep into automation, return on investment, and the importance of outcome-driven technology adoption.Along the way, Jim Spignardo shares hands-on stories and key lessons learned, such as how modernization happens gradually in high-stakes environments, and why ROI modeling and project management are crucial to successful innovation. Whether you're curious about Azure AI Foundry or the future of workplace automation, this is an episode packed with practical insights and tales from the cutting edge of AI implementation.LinksJim on LinkedIn -https://www.linkedin.com/in/spignardo/Watch on YouTube -https://youtu.be/XBDpsi4f0YYTime Stamps00:00 ROI Journey with Copilot04:57 Building with Azure AI Foundry08:23 Implementing AI for Power Plants12:22 Discussing 19.2K networks in power plants14:00 Ensuring data security in devices17:59 Cloud integration for IT operations23:30 Discussing RPO, RTO, and ROI modeling24:48 Switching from hobbyist to manufacturing30:15 Introducing our AI platform Vector34:34 AI Spend and ROI Dashboard36:47 Improving meeting notes integration40:48 AI in Manufacturing for Troubleshooting45:41 Managing AI Tool Adoption47:51 Tracking ROI and user intensity52:03 Consulting firms spotting product chances55:17 Increasing delivery speed with AI56:55 Automating the house routine

Mike Richards joined the ACT Annual Conference 2026 in Liverpool for the roundtable session “Treasury Talent: Skills for the Future”, held in May 2026.This session explored the future skills treasury professionals will need as AI and automation take on more routine and analytical work.Together, the panel discussed how treasury roles are changing, where human judgement still matters, whether teams may become leaner and more strategic, and what hiring managers should be looking for when building future treasury teams.Speakers included:Mike Richards, CEO & Founder at The Treasury Recruitment CompanyAlan Chitty, Group Treasurer at PepcoVictoria Underwood, Treasury Manager from Imperial BrandsThe session was facilitated by Louise Woodroffe, Head of Treasury from Hastings Direct, in association with the ACT Future Leaders in Treasury.Key topics discussed:00:00 Session Kick-off00:46 Format and Participation01:25 Meet the Panel03:45 AI Adoption Today04:57 Copilot vs Claude Debate08:25 What Humans Still Do10:17 Jobs and Team Reshaping13:44 Recruitment Market Shift15:18 Hiring for Future Skills19:22 Communication and Impact24:41 Networking and Visibility26:55 Training Juniors in AI Era30:40 Hiring for Attitude31:08 Interviewing for Curiosity34:30 Psychometrics and CV Signals37:00 Future Skills in Treasury40:54 Treasury Plus Networking43:15 Soft Skills Simplify Jargon46:04 AI Written CVs and Interviews49:44 Switching into Treasury53:12 Career Skills and Communication55:56 Roundtable and Wrap Up---

Legal is the department that can stop a business transaction cold. A contract goes into review and two weeks disappear. Procurement waits. Sales waits. And the tools that were supposed to fix that — an assistant bolted into Word, a chat window with a contract pasted into it — ask an in-house lawyer to trust a system that can give one answer today and a slightly different answer next week. In a field where the human carries the liability and the model does not, that is not a rounding error. That is the whole problem.In this episode of Talking AI, Matt Paige sits down with Emad Khazraee, co-founder and CTO of RiskVantage AI, previously VP of AI at Xometry, a data science and AI leader at Turing, an information science professor, and a fellow at Harvard's Berkman Klein Center. For years Emad told his co-founder, Mark Afshar — a practicing lawyer turned in-house counsel for big pharma — that legal AI was a bad idea: a wrapper has no moat, and Anthropic or OpenAI will do it better than you overnight. What changed his mind was an architecture, not a market: a deterministic ontology that owns the legal reasoning, and small domain-specific language models that handle the language.The conversation covers why a nine-billion-parameter model running sub-second on a commodity GPU can match a frontier model inside a single domain, how subsidized token prices are distorting the entire legal AI market, why RiskVantage AI sells to procurement and sales ops rather than to lawyers who bill by the hour, what a failed PhD project on symbolic AI taught him about where determinism belongs, and whether the billable hour survives the decade.In this episode, you'll hear about:What ChatGPT can't know about your company: its risk appetite, its baselines, and the practices it expects every single timeWhy the legal services market — north of $900 billion, by Emad's count — has every frontier lab gunning for itThe objections that made him refuse to build a legal AI company, and the one that still holdsWhy a Word plugin stopped being defensible the moment Anthropic shipped its ownHow subsidized token pricing echoes Uber and Lyft, and who gets hurt when the subsidy endsThe consistency problem: one answer today, a different answer next week, and a lawyer's confidence goneNeuro-symbolic AI in plain English — a deterministic ontology for legal risk, LLMs for document understandingThe three years Mark Afshar spent codifying legal risk before there was a productWhy a 9B domain-adapted model is “dumb enough” that it can't wander outside its sandboxKnowledge distillation, silver datasets, and self-distillation policy optimization in practiceThe sovereign-cloud niche: ITAR data, commodity GPUs, and customers whose data will never leaveOutcome-based pricing, AI-enabled law firms, and what happens to the billable hourThe access-to-justice case: pro se filings, public defenders, and what a $20 subscription changesKey Moments00:01:30 — What ChatGPT can't know: your company's risk appetite and baselines00:05:12 — $700 an hour, a tenth at a time — and Coinbase's AI mandate to outside counsel00:08:12 — Why he told his co-founder no: a wrapper has no moat00:10:22 — Subsidized tokens, Uber and Lyft, and Legora's move to consumption pricing00:14:31 — The sovereign-cloud niche: ITAR data, commodity GPUs, and data that can't leave00:16:56 — “I am on the hook for the liability, not which model I used”00:18:15 — Same question a week later, a different answer, and confidence gone00:22:13 — If a rule can govern it, you should never use an LLM00:23:00 — The PhD failure: narrative machines, Frege, and symbolic AI's rigidity00:26:53 — Mark Afshar's three years codifying legal risk into an ontology00:29:00 — Neuro-symbolic AI, explained00:31:03 — Don't use a missile to hit a fly: why smaller models are safer00:35:47 — A 9B model, sub-second on a commodity GPU, matching Fable 5 in-domain00:38:00 — Does the billable hour survive? Outcome pricing and AI-enabled firms00:42:40 — Why affordable legal access is a democratic-society problem00:44:00 — The pro se surge: people filing their own cases with ChatGPT and Claude00:48:30 — “I'm talking with Copilot.” “That's not research.”Key LinksRiskVantage AIConnect with Emad on LinkedInMentioned in this episode:AI Opportunity FinderFeeling overwhelmed by all the AI noise out there? The AI Opportunity Finder from HatchWorks cuts through the hype and gives you a clear starting point. In less than 5 minutes, you'll get tailored, high-impact AI use cases specific to your business—scored by ROI so you know exactly where to start. Whether you're looking to cut costs, automate tasks, or grow faster, this free tool gives you a personalized roadmap built for action.

In today's podcast episode, we discuss why the next few years will be so important and industry-defining for AI, why ChatGPT has driven stronger adoption than highly recognizable competitors like Meta AI and Copilot, and why Claude is best at converting users into paid subscribers.   Join Senior Director of Podcasts and host Marcus Johnson, along with Principal Analyst Nate Elliott and Analyst Grace Harmon. Listen wherever you get your podcasts, or watch on YouTube or Spotify.   Reports mentioned in this episode: https://content-na1.emarketer.com/which-ai-platforms-are-winning-in-2026?_gl=1   Get more insights like these with our free, industry-leading newsletters covering advertising, marketing, and commerce. Sign up at emarketer.com/newsletters   Purchase tickets and register for EMARKETER's The Future of Digital 2026 Summit here: https://www.emarketer.com/events/summit/2026-future-of-digital/?utm_source=events_page Follow us on Instagram at: https://www.instagram.com/emarketer/ For sponsorship opportunities contact us: advertising@emarketer.com For more information visit: https://www.emarketer.com/advertise/ Have questions or just want to say hi? Drop us a line at podcast@emarketer.com For a transcript of this episode click here: https://www.emarketer.com/content/podcast-ai-platforms-race-curious-customer-behind-numbers © 2026 EMARKETER   Awin is the all-in-one platform for smarter partner marketing. With award-winning technology, expert support and a global network of 1 million partners, Awin helps brands discover, manage and scale high-performing partnerships that drive measurable growth. Learn more.

If you’ve been spinning up AI tools — Claude Desktop, Cursor, Copilot, and so on — there’s a decent chance that API keys, credentials, and access tokens are sitting in plaintext on your laptop. With MCP, every quickstart guide tells you to paste your credentials right into a config file. That was a bad habit... Read more »

Like 99% of companies are pushing AI.