Podcasts about Amazon Web Services

AI executives from Archer, Demandbase and Highspot and AWS reveal how they're tackling AI's biggest challenges—from securing data, managing regulatory changes and keeping humans in the loop.Topics Include:Three AI leaders introduce their companies: Archer, Demandbase and Highspot's approaches to enterprise AIDemandbase's data strategy: Customer data stays isolated, shared data requires consent, public sources fuel trainingGeographic complexity: AI compliance varies dramatically between Germany, US, Canada, and California regulationsHighSpot tackles sales bias: Granular questions replace generic assessments for more accurate rep evaluationsSBI framework applied to AI: Specific behavioral observations create better, more actionable sales coachingAI transparency through citations: Timestamped evidence lets managers verify AI feedback and catch hallucinationsArcher handles 20-30K monthly regulations: AI helps enterprises manage overwhelming compliance requirements at scaleTwo compliance types explained: Operational (common across companies) versus business-specific regulatory requirementsEU AI Act adoption: US companies embracing European framework for responsible AI governanceHuman oversight becomes mandatory: Expert-in-the-loop reviews ensure AI decisions remain correctable and auditableThe bigger AI risk: Companies face greater danger from AI inaction than AI adoptionAgentic AI security challenges: Data layers must enforce permissions before AI access, not afterAI agents need identity management: Same access controls apply whether human clicks or AI actsHuman oversight in high stakes: Chief compliance officers demand transparency and correction capabilitiesFuture challenge identified: 80% of enterprise data behind firewalls remains invisible to AI modelsParticipants:Kayvan Alikhani - Global Head of Engineering- Emerging Solutions, Archer Integrated Risk ManagementUmberto Milletti - Chief R&D Officer, DemandbaseOliver Sharp - Co-Founder & Chief AI Officer, HighspotBrian Shadpour - General Manager, Security, Amazon Web ServicesFurther Links:Archer Integrated Risk Management: Website – LinkedIn – AWS MarketplaceDemandbase: Website – LinkedIn – AWS MarketplaceHighspot: Website – LinkedIn – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

The Prime Minister has revealed tech-giant Amazon is investing $7.5 billion into New Zealand. Speaking exclusively to Newstalk ZB, Christopher Luxon says Amazon Web Services is scheduled to be announcing the investment. He told Mike Hosking it will create up to a thousand jobs, and make an $11 billion boost to GDP. Luxon says it's probably the largest ever publicly announced technology investment in New Zealand by an international tech firm. Speaking of international investors, the Prime Minister wants foreign investors to feel comfortable here so they invest more. New rules mean Active Investor Plus visa holders can now buy or build one home in New Zealand if it's worth at least $5 million. They'll still need to invest another $5 million separately, as part of the visa's criteria. Luxon told Hosking everything will fall into place for these investors once they have a house here. He says it's not just about the first $5-10 million they're spending, it's what comes after that when they start seeing more investment opportunities. LISTEN ABOVE See omnystudio.com/listener for privacy information.

Cloud computing can feel overwhelming at first—but it doesn't have to be. In this beginner-to-pro session, InfosecTrain breaks down the fundamentals of cloud computing and explains how Amazon Web Services (AWS) delivers the backbone of today's digital world.Whether you're a student, IT professional, or career switcher, this episode will simplify complex cloud concepts and give you a clear path to understanding modern IT infrastructure.

In a fascinating discussion, Rob McGrorty, Product Leader of Agents at Amazon AGI Lab, reveals how rapidly AI agents are evolving with corporate adoption exploding as companies race to deploy production agents and the challenges and advantages they're experiencing.Topics Include:GenAI adoption outpaces all previous tech waves, growing faster than computers or internetEarly adopters tackle complex tasks while newcomers still use basic text manipulation featuresAI models double their single-call task capabilities every seven months, exponentially increasing powerAccelerating progress makes yesterday's magic mundane, unlocking mass creativity and customer demandAgents represent natural evolution: chatbots answered questions, now agents autonomously accomplish tasksAmazon's browser agent finds apartments, maps distances, ranks options using multiple transit modesCorporate adoption exploded: 33% piloting agents in 2024, 67% moving to production nowTwo main agent types today: API calling with tool use, browser automationCurrent applications mirror "RPA 2.0" - form filling, data extraction, website QA testingFuture brings multi-agent systems, self-directing loops, and agent-to-agent negotiation scenariosMajor challenges: data privacy, oversight protocols, error responsibility, and ecosystem sustainabilityTechnical hurdles include real-time accuracy measurement, latency issues, and quality assurance frameworksParticipants:Rob McGrorty – Product Leader, Agents at Amazon AGI LabSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Regresarán a clases 2 mil 114 escuelas de nivel básico para dar inicio al ciclo escolar 2025-2026 / Morena revienta informe legislativo /Querétaro se añade a programa de Amazon Web Services, para mejorar la gestión del agua.

Gagan Singh of Elastic discuses how agentic AI systems reduce analyst burnout by automatically triaging security alerts, resulting in measurable ROI for organizationsTopics Include:AI breaks security silos between teams, data, and tools in SOCsAttackers gain system access; SOC teams have only 40 minutes to detect/containAlert overload causes analyst burnout; thousands of low-value alerts overwhelm teams dailyAI inevitable for SOCs to process data, separate false positives from real threatsAgentic systems understand environment, reason through problems, take action without hand-holdingAttack discovery capability reduces hundreds of alerts to 3-4 prioritized threat discoveriesAI provides ROI metrics: processed alerts, filtered noise, hours saved for organizationsRAG (Retrieval Augmented Generation) prevents hallucination by adding enterprise context to LLMsAWS integration uses SageMaker, Bedrock, Anthropic models with Elasticsearch vector database capabilitiesEnd-to-end LLM observability tracks costs, tokens, invocations, errors, and performance bottlenecksJunior analysts detect nation-state attacks; teams shift from reactive to proactive securityFuture requires balancing costs, data richness, sovereignty, model choice, human-machine collaborationParticipants:Gagan Singh – Vice President Product Marketing, ElasticAdditional Links:Elastic – LinkedIn - Website – AWS Marketplace See how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Join Olympic silver medalist Jenna Strauch as she shares powerful insights from her remarkable journey to Olympic success. Drawing parallels between elite sport and business leadership, Strauch reveals how data-driven decision-making, constructive feedback, and a focus on process over outcomes drives high performance. She discusses building resilient teams, managing setbacks, and fostering a culture where failure leads to growth. Her experience as part of the Australian Dolphins' leadership team demonstrates that true success comes from valuing people and mentoring the next generation of leaders. This episode is essential listening for leaders looking to empower high-performance teams through times of high-stress and transformation.

Pete Rubio reveals how Rapid7 transformed to an AI-first platform that automates security investigations and accelerates results from hours to seconds.Topics Include:Pete Rubio introduces Rapid7's journey to becoming an AI-first cybersecurity platformCybersecurity teams overwhelmed by growing attack surfaces and constant alert fatigueCustomers needed faster response times, not just more alerts coming fasterLegacy tools created silos requiring manual triage that doesn't scale effectivelyAI must turn raw security data into real-time decisions humans can trustUnified data platform correlates infrastructure, applications, identity, and business context togetherAgentic AI automates investigative work, reducing analyst tasks from hours to secondsRapid7 evaluated multiple vendors, choosing AWS for performance, cost, and flexibilityNova models delivered unmatched performance for global scaling at controlled costsBedrock provided secure model deployment with governance and data privacy boundariesAWS partnership enabled co-development and rapid iteration beyond typical vendor relationshipsTransparent AI shows customers how models reach conclusions before automated actionsSOC analyst expertise continuously trains models with real-time security intelligenceGovernance frameworks and guardrails implemented from day one, not retrofitted laterFuture plans include customer AI integration and bring-your-own-model capabilitiesParticipants:Pete Rubio – Senior Vice President, Platform & Engineering, Rapid7Additional Links:Rapid 7 – LinkedIn - Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Most companies think AI can never be empathetic. But Microsoft is proving that belief completely wrong while transforming customer experiences at an unprecedented scale.Join hosts Chuck Moxley and Nick Paladino as they talk with Zehra Syeda-Sarwat, Global Head CX Strategy and Insights at Microsoft. With experience from Amazon Web Services and now leading Microsoft's ambitious mission to become the world leader in customer experience, Zehra shares how they're using AI to reimagine not just customer journeys, but employee and partner experiences in ways never done before.Key Actionable Takeaways:Use the two-by-two framework for prioritization - Plot customer pain points on effort vs. impact matrices to identify low-effort, high-impact quick wins that build momentumMeasure intention before action - Track early signals like website learning, employee training, and event attendance to predict customer loyalty before it converts to revenueConnect employee and customer friction simultaneously - Focus employee experience improvements specifically on customer-facing roles to create dual outcomes that benefit both groupsWant more tips and strategies about digital transformation and customer experience? Subscribe to our newsletter! https://www.thefrictionlessexperience.com/frictionless/ Download the Black Friday/Cyber Monday eBook: http://bluetriangle.com/ebook-Zehra Syeda-Sarwat's LinkedIn: https://www.linkedin.com/in/zehra-syedasarwat-7127a211/Zehra Syeda-Sarwat's X: https://x.com/ZehraSyedaSNick Paladino's LinkedIn: https://linkedin.com/in/npaladinoChuck Moxley's LinkedIn: https://linkedin.com/in/chuck-moxleyChapters:(00:00) Introduction(03:00) Microsoft's ambition to lead global customer experience with AI(04:00) Culture change lessons(06:00) Four foundational elements for CX transformation success(09:00) The power of internal marketing and making transformation fun(13:00) Prioritization frameworks - customer feedback and two-by-two matrices(17:00) Assessing effort vs. impact for transformation initiatives(21:00) The three Cs - connection, conviction, and commitment(25:00) Balancing quick wins with long-term strategic initiatives(27:00) Internal friction solutions that improve both employee and customer experience(31:00) Metrics that signal transformation is actually working(35:00) Measuring customer intention as a leading indicator(38:00) Why AI empathy misconceptions are holding companies back(39:00) Conclusion

Panther CEO William Lowe explains how integrating Amazon Bedrock AI into their security platform delivered 50% faster alert resolution for enterprise customers while maintaining the trust and control that security practitioners demand.Topics Include:Panther CEO explains how Amazon partnership accelerates security outcomes for customersCloud-native security platform delivers 100% visibility across enterprise environments at scaleCustomers like Dropbox and Coinbase successfully replaced Splunk with Panther's solutionPlatform processes petabytes monthly with impressive 2.3-minute average threat detection timeCritical gap identified: alert resolution still takes 8 hours despite fast detectionSecurity teams overwhelmed by growing attack surfaces and severe talent burnoutConstant context switching across tools creates inefficiency and organizational collaboration problemsAI integration with Amazon Bedrock designed to accelerate security team decision-makingFour trust principles: verifiable actions, secure design, human control, customer data ownershipResults show 50% faster alert triage; future includes Slack integration and automationParticipants:· William H Lowe – CEO, PantherSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Microsoft is facing backlash from managed service providers (MSPs) for not adequately protecting them against aggressive pricing strategies employed by larger licensing solution providers. These larger entities are reportedly undercutting smaller MSPs by as much as 20%, leading to significant margin erosion and increased competition. The Cloud Solution Provider Program, which was designed to create a more equitable environment for smaller providers, has not been effectively enforced by Microsoft, leaving many MSPs feeling abandoned. Analysts warn that this trend may result in consolidation among partners, as smaller providers struggle to compete in a landscape increasingly favoring larger firms.In the realm of cybersecurity, MSPs are grappling with severe alert fatigue, with a recent survey indicating that over 75% of providers experience this issue monthly. The report highlights that larger firms are particularly affected, with nearly half of those employing over 500 staff facing daily fatigue due to excessive tools and poor integration, which leads to a high volume of false positives. Alarmingly, one in four alerts is a false positive, and many providers are hesitant to consolidate their security tools due to concerns about migration complexity and potential feature loss. Despite the clear advantages of integrating platforms and enhancing automation, only 31% of MSPs have adopted AI or security orchestration tools to alleviate their burdens.In product news, several companies have made significant announcements. SuperOps has launched an AI marketplace for MSPs in collaboration with Amazon Web Services, aiming to streamline the adoption of AI agents for various tasks. Kaseya introduced customer responsibility matrices to help MSPs comply with Department of Defense cybersecurity requirements, while ConnectWise expanded its remote monitoring and management platform to include third-party patching for over 7,000 applications. Synchro reported impressive operational efficiency improvements for a client, and Ignite unveiled a no-code framework for creating customized AI agents.Lastly, the podcast discusses the ongoing challenges faced by Intel and the vulnerabilities in Enable's remote monitoring and management solution. Intel is receiving substantial investments from SoftBank and potential support from the U.S. government, indicating a lack of market confidence in the company's performance. Meanwhile, Enable is dealing with two critical vulnerabilities that are being actively exploited, with nearly 900 servers still unpatched. The urgency for MSPs to apply updates and validate their security measures is emphasized, as these vulnerabilities pose significant risks to their operations. Four things to know today 00:00 Microsoft Faces Backlash as MSPs Accuse CSP Program of Favoring Larger Licensing Providers04:53 From SuperOps to Egnyte, Vendors Announce AI and Security Features—Syncro Stands Out With Measurable Results07:50 Chip Market Split: Intel Relies on Bailouts, Foxconn Rides Explosive AI Demand10:24 Shadowserver: Nearly 900 N-able N-central Servers Remain Unpatched Against Critical Vulnerabilities This is the Business of Tech.    Supported by: https://www.moovila.com/ https://scalepad.com/dave/ All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech

Fintechs choose cloud technologies in the hopes that the efficiency and scalability of cloud computing will give them a competitive advantage. But cloud adoption is no silver bullet. If done incorrectly, a migration to the cloud can cause costs to balloon instead of decreasing them, leading to frustration and even lost revenue. Kinetic Skunk is an Amazon Web Services-certified partner offering cloud solutions with a specialisation in fintech start-ups. In this episode of TechCentral's TCS+, Donovan Mulder, CEO at Kinetic Skunk, explains the ins and outs of cloud adoption for fintech companies. Mulder delves into: • The importance of timing when it comes to cloud adoption and when the best time is to plan for a migration into the cloud. • Common errors fintechs that have already migrated to the cloud make that can cause costs to balloon out of control. • Why developers are often not the right people to handle cloud infrastructure architecting and provisioning (hint: it's a completely different skill set). • How gaps in cloud infrastructure architecture can lead to security holes. • The cost optimisation tools available in the AWS cloud environment. • How tools such as the AWS well-architected framework help fintech's comply with regulations such as Popia and Fica. • Advice for South African fintechs before their next cloud bill arrives. Don't miss the discussion! TechCentral

What is AWS, and why should retailers, brands, and marketers care? In this candid conversation, Amazon's Justin Honaman joins the show to demystify Amazon Web Services and its growing role in commerce. From cloud infrastructure and clean rooms to AI-powered analytics and real-world case studies, Justin explains how AWS is transforming legacy tech stacks, powering business agility, and unlocking innovation for companies of all sizes.

AWS executives reveal how generative AI is fundamentally reshaping ISV business models, from pricing strategies to go-to-market approaches, and provide actionable insights for software companies navigating this transformation.Topics Include:Alayna Broaderson and Andy Perkins introduce AWS Infrastructure Partnerships and ISV SalesGenerative AI profoundly changing how ISVs build, deliver and market software productsTwo ISV categories emerging: established SaaS companies versus pure gen AI startupsLegacy SaaS firms struggle with infrastructure modernization and potential revenue cannibalizationPure gen AI companies face scaling challenges, reliability issues and cost optimizationRevenue models shifting from subscription-based to consumption-based pricing per token/prompt/taskFuture-proofing architecture critical as technology evolves rapidly like F-35 fighter jetsData becoming key differentiator, especially domain-specific datasets in healthcare and legalBalancing cost, accuracy, latency and customer experience creates complex optimization challengesMultiple specialized models replacing single solutions, with agentic AI accelerating this trendHuman capital challenges include retraining engineering teams and finding expensive AI talentSecurity, compliance and explainability now mandatory - no more black box solutionsEnterprise customers struggle with data organization and quantifying clear gen AI ROIISV pricing models evolving with tiered structures and targeted vertical use casesTraditional SaaS playbooks failing in generative AI landscape due to ROI uncertaintyPOC-based go-to-market with free trials and case study selling proving most effectivePricing strategies include AI gates, credit systems and separate SKUs for servicesCustomer trust requires proactive security messaging and auditable, transparent AI solutionsModular architecture enables evolution as new technologies emerge in fast-changing marketAWS positioning as ultimate gen AI toolkit partner with ISV collaboration opportunitiesParticipants:Alayna Broaderson - Sr Manager, Infrastructure Technology Partnership, Amazon Web ServicesAndy Perkins - General Manager, US ISV, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

On this Live Greatly podcast episode, Kristel Bauer sits down with Corinne Low PhD, Wharton economist, mother, and author of Having It All: What Data Tells Us About Women's Lives and Getting the Most Out of Yours.  Kristel and Corinne discuss some key contributors feeding into frustrations and overwhelm in navigating work/life as well as insights into ambition, goals, fulfillment and work-life balance. Tune in now!  Key Takeaways From This Episode: Some common frustrations working mothers are facing Reframing what work is really about Tips to redesign work and life to support more fulfillment  Insights into ambition Research into what women are looking for in the workplace How women are looking for predictability and structure in the workplace ABOUT CORINNE LOW  PH.D Corinne Low is an Associate Professor of Business Economics and Public Policy at the Wharton School of the University of Pennsylvania. Her research focuses on the economics of gender and discrimination and has been published in top journals such as the American Economic Review, Quarterly Journal of Economics, and Journal of Political Economy. She was named one of Poets and Quants 40 MBA Professors under 40 in 2024. Her first book, Having It All, is forthcoming with Flatiron in September 2025. Corinne and her work have also been featured by major popular media outlets, including Forbes, Vanity Fair, The LA Times, and NPR. Corinne is the co-creator of the Incentivized Resume Rating method for measuring hiring discrimination, and regularly speaks to and works with firms looking to improve their hiring and retention practices. She has spoken to and advised firms like Google, IFM Investors, Uber, Activision Blizzard, and Amazon Web Services, in addition to teaching in Wharton's Executive Education programs. She has given talks to top academic institutions like Harvard University, Stanford University, and Oxford, as well as to organizations like the New York Federal Reserve, Brookings, and the US Department of Labor.   She received her Ph.D. in Economics from Columbia University, her B.S. in Economics and Public Policy from Duke University, and formerly worked for McKinsey and Company. Outside of work, she is the co-founder and volunteer executive director for Open Hearts Initiative, a New York City based non-profit that aims to combat the homelessness crisis through pro-housing neighborhood organizing. Connect with Corinne Order Having It All: What Data Tells Us About Women's Lives and Getting the Most Out of Yours  Website: https://www.corinnelow.com/  Instagram: https://www.instagram.com/corinnelowphd/  Linkedin: https://www.linkedin.com/in/corinne-low-64a0741b4/  About the Host of the Live Greatly podcast, Kristel Bauer: Kristel Bauer is a corporate wellness and performance expert, keynote speaker and TEDx speaker supporting organizations and individuals on their journeys for more happiness and success. She is the author of Work-Life Tango: Finding Happiness, Harmony, and Peak Performance Wherever You Work (John Murray Business November 19, 2024). With Kristel's healthcare background, she provides data driven actionable strategies to leverage happiness and high-power habits to drive growth mindsets, peak performance, profitability, well-being and a culture of excellence. Kristel's keynotes provide insights to “Live Greatly” while promoting leadership development and team building.   Kristel is the creator and host of her global top self-improvement podcast, Live Greatly. She is a contributing writer for Entrepreneur, and she is an influencer in the business and wellness space having been recognized as a Top 10 Social Media Influencer of 2021 in Forbes. As an Integrative Medicine Fellow & Physician Assistant having practiced clinically in Integrative Psychiatry, Kristel has a unique perspective into attaining a mindset for more happiness and success. Kristel has presented to groups from the American Gas Association, Bank of America, bp, Commercial Metals Company, General Mills, Northwestern University, Santander Bank and many more. Kristel has been featured in Forbes, Forest & Bluff Magazine, Authority Magazine & Podcast Magazine and she has appeared on ABC 7 Chicago, WGN Daytime Chicago, Fox 4's WDAF-TV's Great Day KC, and Ticker News. Kristel lives in the Fort Lauderdale, Florida area and she can be booked for speaking engagements worldwide. To Book Kristel as a speaker for your next event, click here. Website: www.livegreatly.co  Follow Kristel Bauer on: Instagram: @livegreatly_co  LinkedIn: Kristel Bauer Twitter: @livegreatly_co Facebook: @livegreatly.co Youtube: Live Greatly, Kristel Bauer To Watch Kristel Bauer's TEDx talk of Redefining Work/Life Balance in a COVID-19 World click here. Click HERE to check out Kristel's corporate wellness and leadership blog Click HERE to check out Kristel's Travel and Wellness Blog Disclaimer: The contents of this podcast are intended for informational and educational purposes only. Always seek the guidance of your physician for any recommendations specific to you or for any questions regarding your specific health, your sleep patterns changes to diet and exercise, or any medical conditions.  Always consult your physician before starting any supplements or new lifestyle programs. All information, views and statements shared on the Live Greatly podcast are purely the opinions of the authors, and are not medical advice or treatment recommendations.  They have not been evaluated by the food and drug administration.  Opinions of guests are their own and Kristel Bauer & this podcast does not endorse or accept responsibility for statements made by guests.  Neither Kristel Bauer nor this podcast takes responsibility for possible health consequences of a person or persons following the information in this educational content.  Always consult your physician for recommendations specific to you.

Subscribe to the video podcast: https://www.youtube.com/@DrTazMD/podcastsWhat if the dream of “having it all” is quietly destroying women's health?In this brutally honest episode of hol+, Dr. Taz MD sits down with economist and Wharton professor Dr. Corinne Low to reveal the invisible forces behind the burnout epidemic affecting ambitious, high-achieving women. From breastfeeding in Amtrak bathrooms to chasing tenure while shouldering 100% of the household load, Dr. Low shares her personal crash—and the data that proves she's not alone.While society celebrates the superwoman myth, the reality is far more dangerous: women are breaking down emotionally, physically, and hormonally under impossible expectations. Dr. Low dismantles the cultural fantasy of balance and exposes the economic and biological math that simply doesn't add up.This episode is for every woman who's ever felt exhausted, stuck, and silently wondered, “Is this it?”Dr. Low unpacks:• Why modern motherhood and careerism don't mix• The data behind stress, anxiety, and chronic fatigue in women• Why comparing yourself to men—or Instagram moms—keeps you trapped• The myth of the 50/50 marriage (and what to do instead)• The concept of “utility” and how it can reclaim your time• How egg freezing gives women economic power and freedomWhether you're navigating career, family, fertility, or all of the above, this conversation will shift how you see success, partnership, and your own worth.Topics Covered:• The Superwoman Lie and Burnout Crisis• Why Gender Equality at Home Is Still a Myth• The Economics of Time, Labor, and Emotional Load• Using Utility Theory to Redesign Your Life• Red Flags in Relationships Most Women Miss• The Case for Egg Freezing and Delaying Marriage• Redefining Success on Your Own TermsAbout Corinne LowCorinne Low is an Associate Professor of Business Economics and Public Policy at the Wharton School of the University of Pennsylvania. Her research focuses on the economics of gender and discrimination and has been published in top journals such as the American Economic Review and Journal of Political Economy. Corinne and her work have also been featured by popular media outlets, including Forbes, Vanity Fair, The LA Times, and NPR. She has spoken to and advised firms like Google, IFM Investors, Uber, and Amazon Web Services, in addition to teaching in Wharton's Executive Education programs. She has given talks to top academic institutions like Harvard, Stanford, and Oxford, as well as to organizations like the New York Federal Reserve, Brookings, and the US Department of Labor. She received her Ph.D. in Economics from Columbia University, her B.S. in Economics and Public Policy from Duke University. Her first book, Having It All, is forthcoming with Flatiron this September. Thank you to our sponsor:Timeline is offering my listeners 20% off your first order of Mitopure. Just go to timeline.com/DRTAZConnect further to Hol+ at https://holplus.co/- Don't forget to like, subscribe, and hit the notification bell to stay updated on future episodes of hol+.Stay ConnectedSubscribe to the audio podcast: https://holplus.transistor.fm/subscribeSubscribe to the video podcast: https://www.youtube.com/@DrTazMD/podcastsFollow Dr. Taz on Instagram: https://www.instagram.com/drtazmd/https://www.instagram.com/liveholplus/Join the conversation on X: https://x.com/@drtazmdTikTok: https://www.tiktok.com/@drtazmdFacebook: https://www.facebook.com/drtazmd/Follow Dr. Corinne Low on Instagram:https://www.instagram.com/corinnelowphd/Host & Production TeamHost: Dr. Taz; Produced by Rainbow Creative (Executive Producer: Matthew Jones; Lead Producer: Lauren Feighan; Editors: Jeremiah Schultz and Patrick Edwards)Don't forget to like, subscribe, and hit the notification bell to stay updated on future episodes of hol+00:00 – The Burnout Nobody Talks About05:26 – The Superwoman Illusion09:50 – The Hidden Gender Time Gap14:09 – Your Job Isn't Your Purpose18:38 – When Feminism Meets Reality23:15 – The Comparison Trap28:02 – Utility Theory for Women32:34 – Rethinking Relationships and Roles38:29 – Cultural Myths About Motherhood44.21 – The Cost of Doing It All

Join AWS Enterprise Strategists Arvind Mathur and Matthias Patzak as they explore how technology leaders can effectively engage with their CEOs about agentic AI. Drawing from a LinkedIn blog Matthias recently published, this episode reveals five essential steps for success: focusing on business impact over technology, building cross-functional transformation teams, picking the right use case, running parallel pilots at scale, and measuring real business outcomes. Learn why CTOs must proactively experiment with emerging technologies like agentic AI before C-suite conversations arise. Whether you're a technology leader looking to drive 10X value from your AI implementations or a business executive exploring AI's transformative potential, this discussion offers valuable insights for navigating the agentic AI revolution.Watch on AWS Executive Insights

In this episode of The Girl Dad Show, host Young Han sits down with Adi Prakash, Founder & CEO of Sentient Ventures and former Amazon executive. Adi shares his journey from leading strategy at Amazon Web Services to launching his own AI-native firm, designed to help companies scale smarter, faster, and more sustainably. The conversation dives deep into the realities of transitioning from corporate life to entrepreneurship, the role of family support during big career shifts, and the lessons Adi has learned as a parent. From modeling behavior for children to embracing work-life integration over the elusive “balance,” Adi offers actionable insights for both business leaders and parents. ✨ All episodes of The Girl Dad Show are proudly sponsored by Thesis, which helps founders go further together. Takeaways: Chase presence, not perfection in parenting Children reflect their parents' behaviors and attitudes Work-life balance is a myth; integration is key Compartmentalizing time can boost focus Family support is essential during transitions Entrepreneurship requires perseverance and planning Success is about not quitting, even in uncertainty

Hear how PagerDuty and Zoom built successful AI products using Amazon Q-Index to solve real customer problems like incident response and meeting intelligence, while sharing practical lessons from their early adoption journey.Topics Include:David Gordon introduces AWS Q-Business partnerships with PagerDuty and ZoomMeet Everaldo Aguiar: PagerDuty's Applied AI leader with academia and enterprise backgroundPaul Magnaghi from Zoom brings AI platform scaling experience from SeattleQ-Business launched over a year ago as managed generative AI servicePlatform enables agentic experiences: content discovery, analysis, and process automationBuilt on AWS Bedrock with enterprise guardrails and data source integrationPartners wanted backend capabilities but preferred their own UI and modelsQ-Index provides vector database functionality for ISV partner integrationsEveraldo explains PagerDuty's evolution from traditional ML to generative AI solutionsHistorical challenges: alert fatigue, noise reduction using machine learning approachesNew gen AI opportunities: incident context, relevant data surfacing, automated postmortemsEngineering teams faced learning curve with agents and high-latency user experiencesPaul discusses Zoom's existing AI: virtual backgrounds and voice isolation technologyAI Companion strategy focused on simplicity during complex generative AI adoptionProblem identified: valuable meeting conversations disappear after Zoom calls endCustomer feedback revealed need for enterprise data integration beyond basic summariesGoal: combine unstructured conversations with structured enterprise data seamlesslyPagerDuty Advanced provides agentic AI for on-call engineers during incidentsQ-Index integration accesses internal documentation: Confluence pages, runbooks, proceduresDemo shows Slack integration pulling relevant incident response documentation automaticallyAccess control lists ensure users see only data they're authorized to accessZoom's AI companion panel enables real-time meeting questions and summariesExample use cases: decision tracking, incident analysis, action item identificationAdvice for starting: standardize practices and create internal development templatesSingle data access point reduces legal and security evaluation overheadCenter of excellence approach helps teams move quickly across product divisionsCut through generative AI buzzwords to focus on real user valueFederated AWS Bedrock architecture provides model choice and flexibility meeting customersCustomer trust alignment between Zoom conversations and AWS data handlingGetting started: PagerDuty Advance available now, Zoom AI free with paid add-onsParticipants:Everaldo Aguiar – Senior Engineering Manager, Applied AI, PagerDutyPaul Magnaghi – Head of AI & ISV Go To Market, ZoomDavid Gordon - Global Business Development, Amazon Q for Business. Amazon Web ServicesFurther Links:PagerDuty Website, LinkedIn & AWS MarketplaceZoom Website, LinkedIn & AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

When a tech giant comes knocking, how does a utility say make room for them in a way that ensures the best outcomes for its existing customers? That's the question at the heart of Mississippi's leap into the global spotlight, as Amazon Web Services chose the state for one of its largest-ever data center investments. And for Entergy Mississippi, this wasn't just about plugging in servers — it was a once-in-a-generation opportunity to modernize the grid, replace retiring generation, invest in community infrastructure, and keep rates affordable for customers, including the 20% living below the poverty line. In this episode of Power Perspectives, podcast host Jason Price and producer Matt Chester are joined by Haley Fisackerly, President and CEO of Entergy Mississippi. In this conversation, Haley outlines how this deal came together after years of effort, what it means for clean energy and resiliency in the region, and how his team manages to balance the demands of hyperscale data centers with the realities of local communities. From navigating regulatory hurdles to ensuring affordability and equity, Haley shares lessons every utility leader should hear before the next big tech partnership lands on their doorstep. Key Links: Sign up for the Energy Central Daily Newsletter: https://energycentral.beehiiv.com/subscribe Energy Central Post for this episode: https://www.energycentral.com/podcasts/post/how-this-utility-ceo-helped-land-amazon-s-16-billion-power-investment-HPe8T88l2ugWwIg Video version on YouTube: https://youtu.be/RLEYYpco_ws Ask a Question to Our Future Guests: Do you have a burning question for the utility executives and energy industry thought leaders that we feature each week on Power Perspectives? Leave us a message here for your chance to be featured in an upcoming episode: www.speakpipe.com/EnergyCentralPodcast

Confused by AI jargon and unsure which tools actually move the needle for your business? We break down the real differences between traditional algorithms, large language models (LLMs), and agents — including agentic AI — and give practical guidance leaders can use now.Newsletter: Sign up for our free daily newsletterMore on this Episode: Episode PageJoin the discussion: Thoughts on this? Join the convo.Upcoming Episodes: Check out the upcoming Everyday AI Livestream lineupWebsite: YourEverydayAI.comEmail The Show: info@youreverydayai.comConnect with Jordan on LinkedInTopics Covered in This Episode:Choosing AI: Algorithms vs. AgentsUnderstanding AI Models and AgentsUsing Conditional Statements in AIImportance of Data in AI TrainingRisk Factors in Agentic AI ProjectsInnovation through AI ExperimentationEvaluating AI for Business SolutionsTimestamps:00:00 AWS AI Leader Departs Amid Talent War03:43 Meta Wins Copyright Lawsuit07:47 Choosing AI: Short or Long Term?12:58 Agentic AI: Dynamic Decision Models16:12 "Demanding Data-Driven Precision in Business"20:08 "Agentic AI: Adoption and Risks"22:05 Startup Challenges Amidst Tech Giants24:36 Balancing Innovation and Routine27:25 AGI: Future of Work and SurvivalKeywords:AI algorithms, Large Language Models, LLMs, Agents, Agentic AI, Multi agentic AI, Amazon Web Services, AWS, Vazhi Philemon, Gen AI efforts, Amazon Bedrock, talent wars in tech, OpenAI, Google, Meta, Copyright lawsuit, AI training, Sarah Silverman, Llama, Fair use in AI, Anthropic, AI deep research model, API, Webhooks, MCP, Code interpreter, Keymaker, Data labeling, Training datasets, Computer vision models, Block out time to experiment, Decision-making, If else conditional statements, Data-driven approach, AGI, Teleporting, Innovation in AI, Experiment with AI, Business leaders, Performance improvements, Sustainable business models, Corporate blade.Send Everyday AI and Jordan a text message. (We can't reply back unless you leave contact info) Ready for ROI on GenAI? Go to youreverydayai.com/partner

Teleport Co-Founder and CEO Ev Kontsevoy discusses the security vs productivity trade-off that plagues growing companies and how Teleport's trusted computing model protects against the exponential growth of cybersecurity threats.Topics Include:Teleport CEO explains how to make infrastructure "nearly unhackable" through trusted computingTraditional security vs productivity trade-off: high security kills team efficiencyCompanies buy every security solution but still get told they're at riskWhy "crown jewels" thinking fails: computers should protect everything at scaleModern infrastructure has too many access paths to enumerate and secureApple's PCC specification shows trusted computing working in real production environmentsAI revolutionizes both offensive and defensive cybersecurity capabilities for everyone80% of companies can't guarantee they've removed all ex-employee accessIdentity fragmentation across systems creates anonymous relationships and security gapsHuman error probability grows exponentially as companies scale in three dimensionsYour laptop already demonstrates trusted computing: seamless access without constant loginsApple ecosystem shows device trust at scale through secure enclavesAI agents need trusted identities just like humans and machinesAWS marketplace partnership accelerates deals and provides strategic account insightsHire someone who understands partnership dynamics before starting with AWSGenerative AI will make identity attacks cheaper and faster than everSecurity responsibility shifting from IT teams to platform engineering teamsTeleport's "steady state invariant": infrastructure locked down except during authorized workTemporary access granted through tickets, then automatically revoked after completionLegacy systems and IoT devices require extending trust models beyond cloud-nativeParticipants:Ev Kontsevoy – Co-Founder and CEO, TeleportFurther Links:Teleport WebsiteTeleport AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Raj Koo, CTO of DTEX Systems, discusses how their enterprise-grade generative AI platform detects and disarms insider threats and enables them to stay ahead of evolving risks.Topics Include:Raj Koo, CTO of DTEX Systems, joins from Adelaide to discuss insider threat detectionDTEX evolved from Adelaide startup to Bay Area headquarters, serving Fortune 500 companiesCompany specializes in understanding human behavior and intention behind insider threatsMarket shifting beyond cyber indicators to focus on behavioral analysis and detectionRecent case: US citizen sold identity to North Korean DPRK IT workersForeign entities used stolen credentials to infiltrate American companies undetectedDTEX's behavioral detection systems helped identify this sophisticated identity theft operationGenerative AI becomes double-edged sword - used by both threat actors and defendersBad actors use AI for fake resumes and deepfake interviewsDTEX uses traditional machine learning for risk modeling, GenAI for analyst interpretationGoal is empowering security analysts to work faster, not replacing human expertiseAWS GenAI Innovation Center helped develop guardrails and usage boundaries for enterpriseChallenge: enterprises must follow rules while hackers operate without ethical constraintsDTEX gains advantage through proprietary datasets unavailable to public AI modelsAWS Bedrock partnership enables private, co-located language models for data securityPrivate preview launched February 2024 with AWS Innovation Center acceleration supportSoftware leaders should prioritize privacy-by-design from day one of GenAI adoptionFuture threat: information sharing shifts from files to AI-powered data queriesMonitoring who asks what questions of AI systems becomes critical security concernDTEX contributes to OpenSearch development while building vector databases for analysisParticipants:Rajan Koo – Chief Technology Officer, DTEX SystemsFurther Links:DTEX Systems WebsiteDTEX Systems AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Amazon Web Services, Microsoft Azure, and Google Cloud dominate the infrastructure-as-a-service market, controlling 71% of the market share. This concentration is driven by the rapid growth of artificial intelligence, with global cloud spending projected to exceed $700 billion in 2025. The economic impact of AI is significant, as evidenced by Microsoft reaching a $4 trillion valuation, largely due to investments in AI infrastructure. However, this boom has created challenges for recent computer science graduates, who are struggling to find jobs in a tech industry that is increasingly adopting AI tools while simultaneously laying off employees.The job market for young tech workers is deteriorating, with unemployment rates for those aged 20 to 30 rising sharply. Economists warn of a potential "jobless recovery" for white-collar roles, as AI continues to replace routine jobs. The share of tech jobs peaked in late 2022 but has since declined, leaving graduates questioning the reliability of traditional pathways into tech careers. This shift highlights the need for companies to reassess their hiring practices and adapt to the changing landscape influenced by AI.In a concerning development, researchers have identified vulnerabilities in Google's Gemini AI Assistant that could allow attackers to hijack smart devices through manipulated calendar invites. These vulnerabilities pose significant risks as AI becomes more integrated into everyday applications. Although Google has addressed these issues, the potential for exploitation raises alarms about the security of AI systems and the importance of implementing strict controls and user training to mitigate risks.The recent launch of GPT-5 by OpenAI has sparked disappointment among users, leading to a petition for a return to the previous model. Many users feel that GPT-5 does not offer substantial improvements over its predecessor, resulting in a significant drop in OpenAI's perceived leadership in AI. This disconnect between advanced AI tools available at home and outdated technology in the workplace is causing dissatisfaction among employees, prompting organizations to evaluate their AI policies and capabilities to retain talent and enhance productivity. Four things to know today 00:00 AI Boom Fuels Cloud Giants' Growth While Squeezing Entry-Level Tech Jobs06:33 Researchers Expose Gemini AI Flaw Allowing Smart Device Hijacking via Calendar Invites08:04 GPT-5 Backlash Highlights AI Leadership Slip and Workplace Adoption Crisis11:06 Intel CEO Goes from “Conflict Risk” to “Success” in Trump's Eyes  Supported by:  https://scalepad.com/dave/https://www.moovila.com/ Tell us about a newsletter https://bit.ly/biztechnewsletter  All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech

Master the art of strategic technology investment with AWS Enterprise Finance Strategist Chris Hennesey as he reveals how to transform your CFO relationship from gatekeeper to strategic partner. As a former IT CFO drawing from decades of financial services leadership, Hennesey shares insider perspectives on how technology leaders can effectively champion initiatives while demonstrating exceptional fiscal stewardship. He emphasizes that success isn't about securing bigger budgets, but about strategic resource optimization and compelling value communication. From evaluating generative AI opportunities to presenting to the board, this episode delivers invaluable insights for technology leaders looking to strengthen their financial partnerships. Don't miss this masterclass in building the technology-finance relationships that drive digital transformation!

Amazon – vom kleinen Online-Buchladen zum wertvollsten Unternehmen der Welt.Wie schafft man es, Milliarden zu verlieren, und trotzdem ganz oben mitzuspielen? In dieser Folge schauen wir uns die verrückte Reise von Jeff Bezos und seinem Milliarden-Imperium an – und vor allem, welche Learnings du für dein eigenes Business mitnehmen kannst.Von den ersten Jahren voller Zweifel, mutigen Entscheidungen wie Amazon Prime und riskanten Innovationen bis hin zu einem der profitabelsten Segmente überhaupt: den Amazon Web Services. Wir sprechen darüber, wie Amazon Rückschläge gemeistert, neue Märkte erobert und sein Wachstum immer wieder neu strukturiert hat.Egal, ob du gerade gründest, schon selbstständig bist oder dein Unternehmen skalieren willst – die Story von Amazon zeigt, wie wichtig Geduld, Weitsicht und radikale Kundenorientierung wirklich sind.

Enterprise AI leaders from C3 AI, Resolve AI, and Scale AI reveal how Fortune 100 companies are successfully scaling agentic AI from pilots to production and share secrets for successful AI transformation.Topics Include:Panel introduces three AI leaders from Resolve AI, C3 AI, and Scale AIResolve AI builds autonomous site reliability engineers for production incident responseC3 AI provides full-stack platform for developing enterprise agentic AI workflowsScale AI helps Fortune 100 companies adopt agents with private data integrationMoving from AI pilots to production requires custom solutions, not shrink-wrap softwareSuccess demands working directly with customers to understand their specific workflowsAll enterprise AI solutions need well-curated access to internal data and resourcesSoftware engineering has permanently shifted to agentic coding with no going backAI agents rapidly improving in reasoning, tool use, and contextual understandingIndustry moving from simple co-pilots to agents solving complex multi-step problemsSpiros coins new concept: evolving from "systems of record" to "systems of knowledge"Democratized development platforms let enterprises declare their own agent workflowsSemantic business layers enable agents to understand domain-specific enterprise operationsTrust and observability remain major barriers to enterprise agent adoptionOversight layers essential for agents making longer-horizon autonomous business decisionsPerformance tracking and calibration systems needed like MLOps for reasoning chainsCEO-level top-down support required for successful AI transformation initiativesTraditional per-seat SaaS pricing models completely broken for agentic AI solutionsIndustry shifting toward outcome-based and work-completion pricing models insteadReal examples shared: agent collaboration in production engineering and sales automationParticipants:Nikhil Krishnan – SVP & Chief Technology Officer, Data Science, C3 AISpiros Xanthos – Founder and CEO, Resolve AIVijay Karunamurthy – Head of Engineering, Product and Design / Field Chief Technology Officer, Scale AIAndy Perkins – GM, US ISV Sales – Data, Analytics, GenAI, Amazon Web ServicesFurther Links:C3 – Website – AWS MarketplaceResolve AI – Website – AWS MarketplaceScale AI – Website – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Summer rewind: Greg Lindsay is an urban tech expert and a Senior Fellow at MIT. He's also a two-time Jeopardy champion and the only human to go undefeated against IBM's Watson. Greg joins thinkenergy to talk about how artificial intelligence (AI) is reshaping how we manage, consume, and produce energy—from personal devices to provincial grids, its rapid growth to the rising energy demand from AI itself. Listen in to learn how AI impacts our energy systems and what it means individually and industry-wide. Related links: ●       Greg Lindsay website: https://greglindsay.org/ ●       Greg Lindsay on LinkedIn: https://www.linkedin.com/in/greg-lindsay-8b16952/ ●       International Energy Agency (IEA): https://www.iea.org/ ●       Trevor Freeman on LinkedIn: https://www.linkedin.com/in/trevor-freeman-p-eng-cem-leed-ap-8b612114/ ●       Hydro Ottawa: https://hydroottawa.com/en    To subscribe using Apple Podcasts: https://podcasts.apple.com/us/podcast/thinkenergy/id1465129405   To subscribe using Spotify: https://open.spotify.com/show/7wFz7rdR8Gq3f2WOafjxpl   To subscribe on Libsyn: http://thinkenergy.libsyn.com/ --- Subscribe so you don't miss a video: https://www.youtube.com/user/hydroottawalimited   Follow along on Instagram: https://www.instagram.com/hydroottawa   Stay in the know on Facebook: https://www.facebook.com/HydroOttawa   Keep up with the posts on X: https://twitter.com/thinkenergypod --- Transcript: Trevor Freeman  00:00 Hi everyone. Well, summer is here, and the think energy team is stepping back a bit to recharge and plan out some content for the next season. We hope all of you get some much needed downtime as well, but we aren't planning on leaving you hanging over the next few months, we will be re releasing some of our favorite episodes from the past year that we think really highlight innovation, sustainability and community. These episodes highlight the changing nature of how we use and manage energy, and the investments needed to expand, modernize and strengthen our grid in response to that. All of this driven by people and our changing needs and relationship to energy as we move forward into a cleaner, more electrified future, the energy transition, as we talk about many times on this show. Thanks so much for listening, and we'll be back with all new content in September. Until then, happy listening.   Trevor Freeman  00:55 Welcome to think energy, a podcast that dives into the fast changing world of energy through conversations with industry leaders, innovators and people on the front lines of the energy transition. Join me, Trevor Freeman, as I explore the traditional, unconventional and up and coming facets of the energy industry. If you have any thoughts feedback or ideas for topics we should cover, please reach out to us at think energy at hydro ottawa.com, Hi everyone. Welcome back. Artificial intelligence, or AI, is a term that you're likely seeing and hearing everywhere today, and with good reason, the effectiveness and efficiency of today's AI, along with the ever increasing applications and use cases mean that in just the past few years, AI went from being a little bit fringe, maybe a little bit theoretical to very real and likely touching everyone's day to day lives in ways that we don't even notice, and we're just at the beginning of what looks to be a wave of many different ways that AI will shape and influence our society and our lives in the years to come. And the world of energy is no different. AI has the potential to change how we manage energy at all levels, from our individual devices and homes and businesses all the way up to our grids at the local, provincial and even national and international levels. At the same time, AI is also a massive consumer of energy, and the proliferation of AI data centers is putting pressure on utilities for more and more power at an unprecedented pace. But before we dive into all that, I also think it will be helpful to define what AI is. After all, the term isn't new. Like me, many of our listeners may have grown up hearing about Skynet from Terminator, or how from 2001 A Space Odyssey, but those malignant, almost sentient versions of AI aren't really what we're talking about here today. And to help shed some light on both what AI is as well as what it can do and how it might influence the world of energy, my guest today is Greg Lindsay, to put it in technical jargon, Greg's bio is super neat, so I do want to take time to run through it properly. Greg is a non resident Senior Fellow of MIT's future urban collectives lab Arizona State University's threat casting lab and the Atlantic Council's Scowcroft center for strategy and security. Most recently, he was a 2022-2023 urban tech Fellow at Cornell Tech's Jacobs Institute, where he explored the implications of AI and augmented reality at an urban scale. Previously, he was an urbanist in resident, which is a pretty cool title, at BMW minis urban tech accelerator, urban X, as well as the director of Applied Research at Montreal's new cities and Founding Director of Strategy at its mobility focused offshoot, co motion. He's advised such firms as Intel, Samsung, Audi, Hyundai, IKEA and Starbucks, along with numerous government entities such as 10 Downing Street, us, Department of Energy and NATO. And finally, and maybe coolest of all, Greg is also a two time Jeopardy champion and the only human to go undefeated against IBM's Watson. So on that note, Greg Lindsey, welcome to the show.   Greg Lindsay  04:14 Great to be here. Thanks for having me. Trevor,   Trevor Freeman  04:16 So Greg, we're here to talk about AI and the impacts that AI is going to have on energy, but AI is a bit of one of those buzzwords that we hear out there in a number of different spheres today. So let's start by setting the stage of what exactly we're talking about. So what do we mean when we say AI or artificial intelligence?   Speaker 1  04:37 Well, I'd say the first thing to keep in mind is that it is neither artificial nor intelligence. It's actually composites of many human hands making it. And of course, it's not truly intelligent either. I think there's at least two definitions for the layman's purposes. One is statistical machine learning. You know that is the previous generation of AI, we could say, doing deep, deep statistical analysis, looking for patterns fitting to. Patterns doing prediction. There's a great book, actually, by some ut professors at monk called prediction machines, which that was a great way of thinking about machine learning and sense of being able to do large scale prediction at scale. And that's how I imagine hydro, Ottawa and others are using this to model out network efficiencies and predictive maintenance and all these great uses. And then the newer, trendier version, of course, is large language models, your quads, your chat gpts, your others, which are based on transformer models, which is a whole series of work that many Canadians worked on, including Geoffrey Hinton and others. And this is what has produced the seemingly magical abilities to produce text and images on demand and large scale analysis. And that is the real power hungry beast that we think of as AI today.   Trevor Freeman  05:42 Right! So different types of AI. I just want to pick those apart a little bit. When you say machine learning, it's kind of being able to repetitively look at something or a set of data over and over and over again. And because it's a computer, it can do it, you know, 1000s or millions of times a second, and learn what, learn how to make decisions based on that. Is that fair to say?   Greg Lindsay  06:06 That's fair to say. And the thing about that is, is like you can train it on an output that you already know, large language models are just vomiting up large parts of pattern recognition, which, again, can feel like magic because of our own human brains doing it. But yeah, machine learning, you can, you know, you can train it to achieve outcomes. You can overfit the models where it like it's trained too much in the past, but, yeah, it's a large scale probabilistic prediction of things, which makes it so powerful for certain uses.   Trevor Freeman  06:26 Yeah, one of the neatest explanations or examples I've seen is, you know, you've got these language models where it seems like this AI, whether it's chat, DBT or whatever, is writing really well, like, you know, it's improving our writing. It's making things sound better. And it seems like it's got a brain behind it, but really, what it's doing is it's going out there saying, What have millions or billions of other people written like this? And how can I take the best things of that? And it can just do that really quickly, and it's learned that that model, so that's super helpful to understand what we're talking about here. So obviously, in your work, you look at the impact of AI on a number of different aspects of our world, our society. What we're talking about here today is particularly the impact of AI when it comes to energy. And I'd like to kind of bucketize our conversation a little bit today, and the first area I want to look at is, what will ai do when it comes to energy for the average Canadian? Let's say so in my home, in my business, how I move around? So I'll start with that. It's kind of a high level conversation. Let's start talking about the different ways that AI will impact you know that our average listener here?   Speaker 1  07:41 Um, yeah, I mean, we can get into a discussion about what it means for the average Canadian, and then also, of course, what it means for Canada in the world as well, because I just got back from South by Southwest in Austin, and, you know, for the second, third year in row, AI was on everyone's lips. But really it's the energy. Is the is the bottleneck. It's the forcing factor. Everyone talked about it, the fact that all the data centers we can get into that are going to be built in the direction of energy. So, so, yeah, energy holds the key to the puzzle there. But, um, you know, from the average gain standpoint, I mean, it's a question of, like, how will these tools actually play out, you know, inside of the companies that are using this, right? And that was a whole other discussion too. It's like, okay, we've been playing around with these tools for two, three years now, what do they actually use to deliver value of your large language model? So I've been saying this for 10 years. If you look at the older stuff you could start with, like smart thermostats, even look at the potential savings of this, of basically using machine learning to optimize, you know, grid optimize patterns of usage, understanding, you know, the ebbs and flows of the grid, and being able to, you know, basically send instructions back and forth. So you know there's stats. You know that, basically you know that you know you could save 10 to 25% of electricity bills. You know, based on this, you could reduce your heating bills by 10 to 15% again, it's basically using this at very large scales of the scale of hydro Ottawa, bigger, to understand this sort of pattern usage. But even then, like understanding like how weather forecasts change, and pulling that data back in to basically make fine tuning adjustments to the thermostats and things like that. So that's one stands out. And then, you know, we can think about longer term. I mean, yeah, lots have been lots has been done on imagining, like electric mobility, of course, huge in Canada, and what that's done to sort of change the overall energy mix virtual power plants. This is something that I've studied, and we've been writing about at Fast Company. At Fast Company beyond for 20 years, imagining not just, you know, the ability to basically, you know, feed renewable electricity back into the grid from people's solar or from whatever sources they have there, but the ability of utilities to basically go in and fine tune, to have that sort of demand shaping as well. And then I think the most interesting stuff, at least in demos, and also blockchain, which has had many theoretical uses, and I've got to see a real one. But one of the best theoretical ones was being able to create neighborhood scale utilities. Basically my cul de sac could have one, and we could trade clean electrons off of our solar panels through our batteries and home scale batteries, using Blockchain to basically balance this out. Yeah, so there's lots of potential, but yeah, it comes back to the notion of people want cheaper utility bills. I did this piece 10 years ago for the Atlantic Council on this we looked at a multi country survey, and the only reason anybody wanted a smart home, which they just were completely skeptical about, was to get those cheaper utility bills. So people pay for that.   Trevor Freeman  10:19 I think it's an important thing to remember, obviously, especially for like the nerds like me, who part of my driver is, I like that cool new tech. I like that thing that I can play with and see my data. But for most people, no matter what we're talking about here, when it comes to that next technology, the goal is make my life a little bit easier, give me more time or whatever, and make things cheaper. And I think especially in the energy space, people aren't putting solar panels on their roof because it looks great. And, yeah, maybe people do think it looks great, but they're putting it up there because they want cheaper electricity. And it's going to be the same when it comes to batteries. You know, there's that add on of resiliency and reliability, but at the end of the day, yeah, I want my bill to be cheaper. And what I'm hearing from you is some of the things we've already seen, like smart thermostats get better as AI gets better. Is that fair to say?   Greg Lindsay  11:12 Well, yeah, on the machine learning side, that you know, you get ever larger data points. This is why data is the coin of the realm. This is why there's a race to collect data on everything. Is why every business model is data collection and everything. Because, yes, not only can they get better, but of course, you know, you compile enough and eventually start finding statistical inferences you never meant to look for. And this is why I've been involved. Just as a side note, for example, of cities that have tried to implement their own data collection of electric scooters and eventually electric vehicles so they could understand these kinds of patterns, it's really the key to anything. And so it's that efficiency throughput which raises some really interesting philosophical questions, particularly about AI like, this is the whole discussion on deep seek. Like, if you make the models more efficient, do you have a Jevons paradox, which is the paradox of, like, the more energy you save through efficiency, the more you consume because you've made it cheaper. So what does this mean that you know that Canadian energy consumption is likely to go up the cleaner and cheaper the electrons get. It's one of those bedeviling sort of functions.   Trevor Freeman  12:06 Yeah interesting. That's definitely an interesting way of looking at it. And you referenced this earlier, and I will talk about this. But at the macro level, the amount of energy needed for these, you know, AI data centers in order to do all this stuff is, you know, we're seeing that explode.   Greg Lindsay  12:22 Yeah, I don't know that. Canadian statistics my fingertips, but I brought this up at Fast Company, like, you know, the IEA, I think International Energy Agency, you know, reported a 4.3% growth in the global electricity grid last year, and it's gonna be 4% this year. That does not sound like much. That is the equivalent of Japan. We're adding in Japan every year to the grid for at least the next two to three years. Wow. And that, you know, that's global South, air conditioning and other needs here too, but that the data centers on top is like the tip of the spear. It's changed all this consumption behavior, where now we're seeing mothballed coal plants and new plants and Three Mile Island come back online, as this race for locking up electrons, for, you know, the race to build God basically, the number of people in AI who think they're literally going to build weekly godlike intelligences, they'll, they won't stop at any expense. And so they will buy as much energy as they can get.   Trevor Freeman  13:09 Yeah, well, we'll get to that kind of grid side of things in a minute. Let's stay at the home first. So when I look at my house, we talked about smart thermostats. We're seeing more and more automation when it comes to our homes. You know, we can program our lights and our door locks and all this kind of stuff. What does ai do in order to make sure that stuff is contributing to efficiency? So I want to do all those fun things, but use the least amount of energy possible.   Greg Lindsay  13:38 Well, you know, I mean, there's, again, there's various metrics there to basically, sort of, you know, program your lights. And, you know, Nest is, you know, Google. Nest is an example of this one, too, in terms of basically learning your ebb and flow and then figuring out how to optimize it over the course of the day. So you can do that, you know, we've seen, again, like the home level. We've seen not only the growth in solar panels, but also in those sort of home battery integration. I was looking up that Tesla Powerwall was doing just great in Canada, until the last couple of months. I assume so, but I it's been, it's been heartening to see that, yeah, this sort of embrace of home energy integration, and so being able to level out, like, peak flow off the grid, so Right? Like being able to basically, at moments of peak demand, to basically draw on your own local resources and reduce that overall strain. So there's been interesting stuff there. But I want to focus for a moment on, like, terms of thinking about new uses. Because, you know, again, going back to how AI will influence the home and automation. You know, Jensen Wong of Nvidia has talked about how this will be the year of robotics. Google, Gemini just applied their models to robotics. There's startups like figure there's, again, Tesla with their optimists, and, yeah, there's a whole strain of thought that we're about to see, like home robotics, perhaps a dream from like, the 50s. I think this is a very Disney World esque Epcot Center, yeah, with this idea of jetsy, yeah, of having home robots doing work. You can see concept videos a figure like doing the actual vacuuming. I mean, we invented Roombas to this, but, but it also, I, you know, I've done a lot of work. Our own thinking around electric delivery vehicles. We could talk a lot about drones. We could talk a lot about the little robots that deliver meals on the sidewalk. There's a lot of money in business models about increasing access and people needing to maybe move less, to drive and do all these trips to bring it to them. And that's a form of home automation, and that's all batteries. That is all stuff off the grid too. So AI is that enable those things, these things that can think and move and fly and do stuff and do services on your behalf, and so people might find this huge new source of demand from that as well.   Trevor Freeman  15:29 Yeah, that's I hadn't really thought about the idea that all the all these sort of conveniences and being able to summon them to our homes cause us to move around less, which also impacts transportation, which is another area I kind of want to get to. And I know you've, you've talked a little bit about E mobility, so where do you see that going? And then, how does AI accelerate that transition, or accelerate things happening in that space?   Greg Lindsay  15:56 Yeah, I mean, I again, obviously the EV revolutions here Canada like, one of the epicenters Canada, Norway there, you know, that still has the vehicle rebates and things. So, yeah. I mean, we've seen, I'm here in Montreal, I think we've got, like, you know, 30 to 13% of sales is there, and we've got our 2035, mandate. So, yeah. I mean, you see this push, obviously, to harness all of Canada's clean, mostly hydro electricity, to do this, and, you know, reduce its dependence on fossil fuels for either, you know, Climate Change Politics reasons, but also just, you know, variable energy prices. So all of that matters. But, you know, I think the key to, like the electric mobility revolution, again, is, is how it's going to merge with AI and it's, you know, it's not going to just be the autonomous, self driving car, which is sort of like the horseless carriage of autonomy. It's gonna be all this other stuff, you know. My friend Dan Hill was in China, and he was thinking about like, electric scooters, you know. And I mentioned this to hydro Ottawa, like, the electric scooter is one of the leading causes of how we've taken internal combustion engine vehicles offline across the world, mostly in China, and put people on clean electric motors. What happens when you take those and you make those autonomous, and you do it with, like, deep seek and some cameras, and you sort of weld it all together so you could have a world of a lot more stuff in motion, and not just this world where we have to drive as much. And that, to me, is really exciting, because that changes, like urban patterns, development patterns, changes how you move around life, those kinds of things as well. That's that might be a little farther out, but, but, yeah, this sort of like this big push to build out domestic battery industries, to build charging points and the sort of infrastructure there, I think it's going to go in direction, but it doesn't look anything like, you know, a sedan or an SUV that just happens to be electric.   Trevor Freeman  17:33 I think that's a the step change is change the drive train of the existing vehicles we have, you know, an internal combustion to a battery. The exponential change is exactly what you're saying. It's rethinking this.   Greg Lindsay  17:47 Yeah, Ramesam and others have pointed out, I mean, again, like this, you know, it's, it's really funny to see this pushback on EVs, you know. I mean, I love a good, good roar of an internal combustion engine myself, but, but like, you know, Ramesam was an energy analyst, has pointed out that, like, you know, EVS were more cost competitive with ice cars in 2018 that's like, nearly a decade ago. And yeah, the efficiency of electric motors, particularly regenerative braking and everything, it just blows the cost curves away of ice though they will become the equivalent of keeping a thorough brat around your house kind of thing. Yeah, so, so yeah, it's just, it's that overall efficiency of the drive train. And that's the to me, the interesting thing about both electric motors, again, of autonomy is like, those are general purpose technologies. They get cheaper and smaller as they evolve under Moore's Law and other various laws, and so they get to apply to more and more stuff.   Trevor Freeman  18:32 Yeah. And then when you think about once, we kind of figure that out, and we're kind of already there, or close to it, if not already there, then it's opening the door to those other things you're talking about. Of, well, do we, does everybody need to have that car in their driveway? Are we rethinking how we're actually just doing transportation in general? And do we need a delivery truck? Or can it be delivery scooter? Or what does that look like?   Greg Lindsay  18:54 Well, we had a lot of those discussions for a long time, particularly in the mobility space, right? Like, and like ride hailing, you know, like, oh, you know, that was always the big pitch of an Uber is, you know, your car's parked in your driveway, like 94% of the time. You know, what happens if you're able to have no mobility? Well, we've had 15 years of Uber and these kinds of services, and we still have as many cars. But people are also taking this for mobility. It's additive. And I raised this question, this notion of like, it's just sort of more and more, more options, more availability, more access. Because the same thing seems to be going on with energy now too. You know, listeners been following along, like the conversation in Houston, you know, a week or two ago at Sarah week, like it's the whole notion of energy realism. And, you know, there's the new book out, more is more is more, which is all about the fact that we've never had an energy transition. We just kept piling up. Like the world burned more biomass last year than it did in 1900 it burned more coal last year than it did at the peak of coal. Like these ages don't really end. They just become this sort of strata as we keep piling energy up on top of it. And you know, I'm trying to sound the alarm that we won't have an energy transition. What that means for climate change? But similar thing, it's. This rebound effect, the Jevons paradox, named after Robert Stanley Jevons in his book The question of coal, where he noted the fact that, like, England was going to need more and more coal. So it's a sobering thought. But, like, I mean, you know, it's a glass half full, half empty in many ways, because the half full is like increasing technological options, increasing changes in lifestyle. You can live various ways you want, but, but, yeah, it's like, I don't know if any of it ever really goes away. We just get more and more stuff,   Trevor Freeman  20:22 Exactly, well. And, you know, to hear you talk about the robotics side of things, you know, looking at the home, yeah, more, definitely more. Okay, so we talked about kind of home automation. We've talked about transportation, how we get around. What about energy management? And I think about this at the we'll talk about the utility side again in a little bit. But, you know, at my house, or for my own personal use in my life, what is the role of, like, sort of machine learning and AI, when it comes to just helping me manage my own energy better and make better decisions when it comes to energy? ,   Greg Lindsay  20:57 Yeah, I mean, this is where it like comes in again. And you know, I'm less and less of an expert here, but I've been following this sort of discourse evolve. And right? It's the idea of, you know, yeah, create, create. This the set of tools in your home, whether it's solar panels or batteries or, you know, or Two Way Direct, bi directional to the grid, however it works. And, yeah, and people, you know, given this option of savings, and perhaps, you know, other marketing messages there to curtail behavior. You know? I mean, I think the short answer the question is, like, it's an app people want, an app that tell them basically how to increase the efficiency of their house or how to do this. And I should note that like, this has like been the this is the long term insight when it comes to like energy and the clean tech revolution. Like my Emery Levin says this great line, which I've always loved, which is, people don't want energy. They want hot showers and cold beer. And, you know, how do you, how do you deliver those things through any combination of sticks and carrots, basically like that. So, So, hence, why? Like, again, like, you know, you know, power walls, you know, and, and, and, you know, other sort of AI controlled batteries here that basically just sort of smooth out to create the sort of optimal flow of electrons into your house, whether that's coming drive directly off the grid or whether it's coming out of your backup and then recharging that the time, you know, I mean, the surveys show, like, more than half of Canadians are interested in this stuff, you know, they don't really know. I've got one set here, like, yeah, 61% are interested in home energy tech, but only 27 understand, 27% understand how to optimize them. So, yeah. So people need, I think, perhaps, more help in handing that over. And obviously, what's exciting for the, you know, the utility level is, like, you know, again, aggregate all that individual behavior together and you get more models that, hope you sort of model this out, you know, at both greater scale and ever more fine grained granularity there. So, yeah, exactly. So I think it's really interesting, you know, I don't know, like, you know, people have gamified it. What was it? I think I saw, like, what is it? The affordability fund trust tried to basically gamify AI energy apps, and it created various savings there. But a lot of this is gonna be like, as a combination like UX design and incentives design and offering this to people too, about, like, why you should want this and money's one reason, but maybe there's others.   Trevor Freeman  22:56 Yeah, and we talk about in kind of the utility sphere, we talk about how customers, they don't want all the data, and then have to go make their own decisions. They want those decisions to be made for them, and they want to say, look, I want to have you tell me the best rate plan to be on. I want to have you automatically switch me to the best rate plan when my consumption patterns change and my behavior chat patterns change. That doesn't exist today, but sort of that fast decision making that AI brings will let that become a reality sometime in the future,   Greg Lindsay  23:29 And also in theory, this is where LLMs come into play. Is like, you know, to me, what excites me the most about that is the first time, like having a true natural language interface, like having being able to converse with an, you know, an AI, let's hopefully not chat bot. I think we're moving out on chat bots, but some sort of sort of instantiation of an AI to be like, what plan should I be on? Can you tell me what my behavior is here and actually having some sort of real language conversation with it? Not decision trees, not event statements, not chat bots.   Trevor Freeman  23:54 Yeah, absolutely. Okay, so we've kind of teased around this idea of looking at the utility levels, obviously, at hydro Ottawa, you referenced this just a minute ago. We look at all these individual cases, every home that has home automation or solar storage, and we want to aggregate that and understand what, what can we do to help manage the grid, help manage all these new energy needs, shift things around. So let's talk a little bit about the role that AI can play at the utility scale in helping us manage the grid.   Greg Lindsay  24:28 All right? Well, yeah, there's couple ways to approach it. So one, of course, is like, let's go back to, like, smart meters, right? Like, and this is where I don't know how many hydro Ottawa has, but I think, like, BC Hydro has like, 2 million of them, sometimes they get politicized, because, again, this gets back to this question of, like, just, just how much nanny state you want. But, you know, you know, when you reach the millions, like, yeah, you're able to get that sort of, you know, obviously real time, real time usage, real time understanding. And again, if you can do that sort of grid management piece where you can then push back, it's visual game changer. But, but yeah. I mean, you know, yeah, be. See hydro is pulling in. I think I read like, like, basically 200 million data points a day. So that's a lot to train various models on. And, you know, I don't know exactly the kind of savings they have, but you can imagine there, whether it's, you know, them, or Toronto Hydro, or hydro Ottawa and others creating all these monitoring points. And again, this is the thing that bedells me, by the way, just philosophically about modern life, the notion of like, but I don't want you to be collecting data off me at all times, but look at what you can do if you do It's that constant push pull of some sort of combination of privacy and agency, and then just the notion of like statistics, but, but there you are, but, but, yeah, but at the grid level, then I mean, like, yeah. I mean, you can sort of do the same thing where, like, you know, I mean, predictive maintenance is the obvious one, right? I have been writing about this for large enterprise software companies for 20 years, about building these data points, modeling out the lifetime of various important pieces equipment, making sure you replace them before you have downtime and terrible things happen. I mean, as we're as we're discussing this, look at poor Heathrow Airport. I am so glad I'm not flying today, electrical substation blowing out two days of the world's most important hub offline. So that's where predictive maintenance comes in from there. And, yeah, I mean, I, you know, I again, you know, modeling out, you know, energy flow to prevent grid outages, whether that's, you know, the ice storm here in Quebec a couple years ago. What was that? April 23 I think it was, yeah, coming up in two years. Or our last ice storm, we're not the big one, but that one, you know, where we had big downtime across the grid, like basically monitoring that and then I think the other big one for AI is like, Yeah, is this, this notion of having some sort of decision support as well, too, and sense of, you know, providing scenarios and modeling out at scale the potential of it? And I don't think, I don't know about this in a grid case, but the most interesting piece I wrote for Fast Company 20 years ago was an example, ago was an example of this, which was a fledgling air taxi startup, but they were combining an agent based model, so using primitive AI to create simple rules for individual agents and build a model of how they would behave, which you can create much more complex models. Now we could talk about agents and then marrying that to this kind of predictive maintenance and operations piece, and marrying the two together. And at that point, you could have a company that didn't exist, but that could basically model itself in real time every day in the life of what it is. You can create millions and millions and millions of Monte Carlo operations. And I think that's where perhaps both sides of AI come together truly like the large language models and agents, and then the predictive machine learning. And you could basically hydro or others, could build this sort of deep time machine where you can model out all of these scenarios, millions and millions of years worth, to understand how it flows and contingencies as well. And that's where it sort of comes up. So basically something happens. And like, not only do you have a set of plans, you have an AI that has done a million sets of these plans, and can imagine potential next steps of this, or where to deploy resources. And I think in general, that's like the most powerful use of this, going back to prediction machines and just being able to really model time in a way that we've never had that capability before. And so you probably imagine the use is better than I.   Trevor Freeman  27:58 Oh man, it's super fascinating, and it's timely. We've gone through the last little while at hydro Ottawa, an exercise of updating our playbook for emergencies. So when there are outages, what kind of outage? What's the sort of, what are the trigger points to go from, you know, what we call a level one to a level two to level three. But all of this is sort of like people hours that are going into that, and we're thinking through these scenarios, and we've got a handful of them, and you're just kind of making me think, well, yeah, what if we were able to model that out? And you bring up this concept of agents, let's tease into that a little bit explain what you mean when you're talking about agents.   Greg Lindsay  28:36 Yeah, so agentic systems, as the term of art is, AI instantiations that have some level of autonomy. And the archetypal example of this is the Stanford Smallville experiment, where they took basically a dozen large language models and they gave it an architecture where they could give it a little bit of backstory, ruminate on it, basically reflect, think, decide, and then act. And in this case, they used it to plan a Valentine's Day party. So they played out real time, and the LLM agents, like, even played matchmaker. They organized the party, they sent out invitations, they did these sorts of things. Was very cute. They put it out open source, and like, three weeks later, another team of researchers basically put them to work writing software programs. So you can see they organized their own workflow. They made their own decisions. There was a CTO. They fact check their own work. And this is evolving into this grand vision of, like, 1000s, millions of agents, just like, just like you spin up today an instance of Amazon Web Services to, like, host something in the cloud. You're going to spin up an agent Nvidia has talked about doing with healthcare and others. So again, coming back to like, the energy implications of that, because it changes the whole pattern. Instead of huge training runs requiring giant data centers. You know, it's these agents who are making all these calls and doing more stuff at the edge, but, um, but yeah, in this case, it's the notion of, you know, what can you put the agents to work doing? And I bring this up again, back to, like, predictive maintenance, or for hydro Ottawa, there's another amazing paper called virtual in real life. And I chatted with one of the principal authors. It created. A half dozen agents who could play tour guide, who could direct you to a coffee shop, who do these sorts of things, but they weren't doing it in a virtual world. They were doing it in the real one. And to do it in the real world, you took the agent, you gave them a machine vision capability, so added that model so they could recognize objects, and then you set them loose inside a digital twin of the world, in this case, something very simple, Google Street View. And so in the paper, they could go into like New York Central Park, and they could count every park bench and every waste bin and do it in seconds and be 99% accurate. And so agents were monitoring the landscape. Everything's up, because you can imagine this in the real world too, that we're going to have all the time. AIS roaming the world, roaming these virtual maps, these digital twins that we build for them and constantly refresh from them, from camera data, from sensor data, from other stuff, and tell us what this is. And again, to me, it's really exciting, because that's finally like an operating system for the internet of things that makes sense, that's not so hardwired that you can ask agents, can you go out and look for this for me? Can you report back on this vital system for me? And they will be able to hook into all of these kinds of representations of real time data where they're emerging from, and give you aggregated reports on this one. And so, you know, I think we have more visibility in real time into the real world than we've ever had before.   Trevor Freeman  31:13 Yeah, I want to, I want to connect a few dots here for our listeners. So bear with me for a second. Greg. So for our listeners, there was a podcast episode we did about a year ago on our grid modernization roadmap, and we talked about one of the things we're doing with grid modernization at hydro Ottawa and utilities everywhere doing this is increasing the sensor data from our grid. So we're, you know, right now, we've got visibility sort of to our station level, sometimes one level down to some switches. But in the future, we'll have sensors everywhere on our grid, every switch, every device on our grid, will have a sensor gathering data. Obviously, you know, like you said earlier, millions and hundreds of millions of data points every second coming in. No human can kind of make decisions on that, and what you're describing is, so now we've got all this data points, we've got a network of information out there, and you could create this agent to say, Okay, you are. You're my transformer agent. Go out there and have a look at the run temperature of every transformer on the network, and tell me where the anomalies are, which ones are running a half a degree or two degrees warmer than they should be, and report back. And now I know hydro Ottawa, that the controller, the person sitting in the room, knows, Hey, we should probably go roll a truck and check on that transformer, because maybe it's getting end of life. Maybe it's about to go and you can do that across the entire grid. That's really fascinating,   Greg Lindsay  32:41 And it's really powerful, because, I mean, again, these conversations 20 years ago at IoT, you know you're going to have statistical triggers, and you would aggregate these data coming off this, and there was a lot of discussion there, but it was still very, like hardwired, and still very Yeah, I mean, I mean very probabilistic, I guess, for a word that went with agents like, yeah, you've now created an actual thing that can watch those numbers and they can aggregate from other systems. I mean, lots, lots of potential there hasn't quite been realized, but it's really exciting stuff. And this is, of course, where that whole direction of the industry is flowing. It's on everyone's lips, agents.   Trevor Freeman  33:12 Yeah. Another term you mentioned just a little bit ago that I want you to explain is a digital twin. So tell us what a digital twin is.   Greg Lindsay  33:20 So a digital twin is, well, the matrix. Perhaps you could say something like this for listeners of a certain age, but the digital twin is the idea of creating a model of a piece of equipment, of a city, of the world, of a system. And it is, importantly, it's physics based. It's ideally meant to represent and capture the real time performance of the physical object it's based on, and in this digital representation, when something happens in the physical incarnation of it, it triggers a corresponding change in state in the digital twin, and then vice versa. In theory, you know, you could have feedback loops, again, a lot of IoT stuff here, if you make changes virtually, you know, perhaps it would cause a change in behavior of the system or equipment, and the scales can change from, you know, factory equipment. Siemens, for example, does a lot of digital twin work on this. You know, SAP, big, big software companies have thought about this. But the really crazy stuff is, like, what Nvidia is proposing. So first they started with a digital twin. They very modestly called earth two, where they were going to model all the weather and climate systems of the planet down to like the block level. There's a great demo of like Jensen Wong walking you through a hurricane, typhoons striking the Taipei, 101, and how, how the wind currents are affecting the various buildings there, and how they would change that more recently, what Nvidia is doing now is, but they just at their big tech investor day, they just partner with General Motors and others to basically do autonomous cars. And what's crucial about it, they're going to train all those autonomous vehicles in an NVIDIA built digital twin in a matrix that will act, that will be populated by agents that will act like people, people ish, and they will be able to run millions of years of autonomous vehicle training in this and this is how they plan to catch up to. Waymo or, you know, if Tesla's robotaxis are ever real kind of thing, you know, Waymo built hardwired like trained on real world streets, and that's why they can only operate in certain operating domain environments. Nvidia is gambling that with large language models and transformer models combined with digital twins, you can do these huge leapfrog effects where you can basically train all sorts of synthetic agents in real world behavior that you have modeled inside the machine. So again, that's the kind, that's exactly the kind of, you know, environment that you're going to train, you know, your your grid of the future on for modeling out all your contingency scenarios.   Trevor Freeman  35:31 Yeah, again, you know, for to bring this to the to our context, a couple of years ago, we had our the direcco. It's a big, massive windstorm that was one of the most damaging storms that we've had in Ottawa's history, and we've made some improvements since then, and we've actually had some great performance since then. Imagine if we could model that derecho hitting our grid from a couple different directions and figure out, well, which lines are more vulnerable to wind speeds, which lines are more vulnerable to flying debris and trees, and then go address that and do something with that, without having to wait for that storm to hit. You know, once in a decade or longer, the other use case that we've talked about on this one is just modeling what's happening underground. So, you know, in an urban environments like Ottawa, like Montreal, where you are, there's tons of infrastructure under the ground, sewer pipes, water pipes, gas lines, electrical lines, and every time the city wants to go and dig up a road and replace that road, replace that sewer, they have to know what's underground. We want to know what's underground there, because our infrastructure is under there. As the electric utility. Imagine if you had a model where you can it's not just a map. You can actually see what's happening underground and determine what makes sense to go where, and model out these different scenarios of if we underground this line or that line there. So lots of interesting things when it comes to a digital twin. The digital twin and Agent combination is really interesting as well, and setting those agents loose on a model that they can play with and understand and learn from. So talk a little bit about.   Greg Lindsay  37:11 that. Yeah. Well, there's a couple interesting implications just the underground, you know, equipment there. One is interesting because in addition to, like, you know, you know, having captured that data through mapping and other stuff there, and having agents that could talk about it. So, you know, next you can imagine, you know, I've done some work with augmented reality XR. This is sort of what we're seeing again, you know, meta Orion has shown off their concept. Google's brought back Android XR. Meta Ray Bans are kind of an example of this. But that's where this data will come from, right? It's gonna be people wearing these wearables in the world, capturing all this camera data and others that's gonna be fed into these digital twins to refresh them. Meta has a particularly scary demo where you know where you the user, the wearer leaves their keys on their coffee table and asks metas, AI, where their coffee where their keys are, and it knows where they are. It tells them and goes back and shows them some data about it. I'm like, well, to do that, meta has to have a complete have a complete real time map of your entire house. What could go wrong. And that's what all these companies aspire to of reality. So, but yeah, you can imagine, you know, you can imagine a worker. And I've worked with a startup out of urban X, a Canada startup, Canadian startup called context steer. And you know, is the idea of having real time instructions and knowledge manuals available to workers, particularly predictive maintenance workers and line workers. So you can imagine a technician dispatched to deal with this cut in the pavement and being able to see with XR and overlay of like, what's actually under there from the digital twin, having an AI basically interface with what's sort of the work order, and basically be your assistant that can help you walk you through it, in case, you know, you run into some sort of complication there, hopefully that won't be, you know, become like, turn, turn by turn, directions for life that gets into, like, some of the questions about what we wanted out of our workforce. But there's some really interesting combinations of those things, of like, you know, yeah, mapping a world for AIS, ais that can understand it, that could ask questions in it, that can go probe it, that can give you advice on what to do in it. All those things are very close for good and for bad.   Trevor Freeman  39:03 You kind of touched on my next question here is, how do we make sure this is all in the for good or mostly in the for good category, and not the for bad category you talk in one of the papers that you wrote about, you know, AI and augmented reality in particular, really expanding the attack surface for malicious actors. So we're creating more opportunities for whatever the case may be, if it's hacking or if it's malware, or if it's just, you know, people that are up to nefarious things. How do we protect against that? How do we make sure that our systems are safe that the users of our system. So in our case, our customers, their data is safe, their the grid is safe. How do we make sure that?   Greg Lindsay  39:49 Well, the very short version is, whatever we're spending on cybersecurity, we're not spending enough. And honestly, like everybody who is no longer learning to code, because we can be a quad or ChatGPT to do it, I. Is probably there should be a whole campaign to repurpose a big chunk of tech workers into cybersecurity, into locking down these systems, into training ethical systems. There's a lot of work to be done there. But yeah, that's been the theme for you know that I've seen for 10 years. So that paper I mentioned about sort of smart homes, the Internet of Things, and why people would want a smart home? Well, yeah, the reason people were skeptical is because they saw it as basically a giant attack vector. My favorite saying about this is, is, there's a famous Arthur C Clarke quote that you know, any sufficiently advanced technology is magic Tobias Ravel, who works at Arup now does their head of foresight has this great line, any sufficiently advanced hacking will feel like a haunting meaning. If you're in a smart home that's been hacked, it will feel like you're living in a haunted house. Lights will flicker on and off, and systems will turn and go haywire. It'll be like you're living with a possessed house. And that's true of cities or any other systems. So we need to do a lot of work on just sort of like locking that down and securing that data, and that is, you know, we identified, then it has to go all the way up and down the supply chain, like you have to make sure that there is, you know, a chain of custody going back to when components are made, because a lot of the attacks on nest, for example. I mean, you want to take over a Google nest, take it off the wall and screw the back out of it, which is a good thing. It's not that many people are prying open our thermostats, but yeah, if you can get your hands on it, you can do a lot of these systems, and you can do it earlier in the supply chain and sorts of infected pieces and things. So there's a lot to be done there. And then, yeah, and then, yeah, and then there's just a question of, you know, making sure that the AIs are ethically trained and reinforced. And, you know, a few people want to listeners, want to scare themselves. You can go out and read some of the stuff leaking out of anthropic and others and make clot of, you know, models that are trying to hide their own alignments and trying to, like, basically copy themselves. Again, I don't believe that anything things are alive or intelligent, but they exhibit these behaviors as part of the probabilistic that's kind of scary. So there's a lot to be done there. But yeah, we worked on this, the group that I do foresight with Arizona State University threat casting lab. We've done some work for the Secret Service and for NATO and, yeah, there'll be, you know, large scale hackings on infrastructure. Basically the equivalent can be the equivalent can be the equivalent to a weapons of mass destruction attack. We saw how Russia targeted in 2014 the Ukrainian grid and hacked their nuclear plans. This is essential infrastructure more important than ever, giving global geopolitics say the least, so that needs to be under consideration. And I don't know, did I scare you enough yet? What are the things we've talked through here that, say the least about, you know, people being, you know, tricked and incepted by their AI girlfriends, boyfriends. You know people who are trying to AI companions. I can't possibly imagine what could go wrong there.   Trevor Freeman  42:29 I mean, it's just like, you know, I don't know if this is 15 or 20, or maybe even 25 years ago now, like, it requires a whole new level of understanding when we went from a completely analog world to a digital world and living online, and people, I would hope, to some degree, learned to be skeptical of things on the internet and learned that this is that next level. We now need to learn the right way of interacting with this stuff. And as you mentioned, building the sort of ethical code and ethical guidelines into these language models into the AI. Learning is pretty critical for our listeners. We do have a podcast episode on cybersecurity. I encourage you to go listen to it and reassure yourself that, yes, we are thinking about this stuff. And thanks, Greg, you've given us lots more to think about in that area as well. When it comes to again, looking back at utilities and managing the grid, one thing we're going to see, and we've talked a lot about this on the show, is a lot more distributed generation. So we're, you know, the days of just the central, large scale generation, long transmission lines that being the only generation on the grid. Those days are ending. We're going to see more distributed generations, solar panels on roofs, batteries. How does AI help a utility manage those better, interact with those better get more value out of those things?   Greg Lindsay  43:51 I guess that's sort of like an extension of some of the trends I was talking about earlier, which is the notion of, like, being able to model complex systems. I mean, that's effectively it, right, like you've got an increasingly complex grid with complex interplays between it, you know, figuring out how to basically based on real world performance, based on what you're able to determine about where there are correlations and codependencies in the grid, where point where choke points could emerge, where overloading could happen, and then, yeah, basically, sort of building that predictive system to Basically, sort of look for what kind of complex emergent behavior comes out of as you keep adding to it and and, you know, not just, you know, based on, you know, real world behavior, but being able to dial that up to 11, so to speak, and sort of imagine sort of these scenarios, or imagine, you know, what, what sort of long term scenarios look like in terms of, like, what the mix, how the mix changes, how the geography changes, all those sorts of things. So, yeah, I don't know how that plays out in the short term there, but it's this combination, like I'm imagining, you know, all these different components playing SimCity for real, if one will.   Trevor Freeman  44:50 And being able to do it millions and millions and millions of times in a row, to learn every possible iteration and every possible thing that might happen. Very cool. Okay. So last kind of area I want to touch on you did mention this at the beginning is the the overall power implications of of AI, of these massive data centers, obviously, at the utility, that's something we are all too keenly aware of. You know, the stat that that I find really interesting is a normal Google Search compared to, let's call it a chat GPT search. That chat GPT search, or decision making, requires 10 times the amount of energy as that just normal, you know, Google Search looking out from a database. Do you see this trend? I don't know if it's a trend. Do you see this continuing like AI is just going to use more power to do its decision making, or will we start to see more efficiencies there? And the data centers will get better at doing what they do with less energy. What is the what does the future look like in that sector?   Greg Lindsay  45:55 All the above. It's more, is more, is more! Is the trend, as far as I can see, and every decision maker who's involved in it. And again, Jensen Wong brought this up at the big Nvidia Conference. That basically he sees the only constraint on this continuing is availability of energy supplies keep it going and South by Southwest. And in some other conversations I've had with bandwidth companies, telcos, like laying 20 lumen technologies, United States is laying 20,000 new miles of fiber optic cables. They've bought 10% of Corning's total fiber optic output for the next couple of years. And their customers are the hyperscalers. They're, they're and they're rewiring the grid. That's why, I think it's interesting. This has something, of course, for thinking about utilities, is, you know, the point to point Internet of packet switching and like laying down these big fiber routes, which is why all the big data centers United States, the majority of them, are in north of them are in Northern Virginia, is because it goes back to the network hub there. Well, lumen is now wiring this like basically this giant fabric, this patchwork, which can connect data center to data center, and AI to AI and cloud to cloud, and creating this entirely new environment of how they are all directly connected to each other through some of this dedicated fiber. And so you can see how this whole pattern is changing. And you know, the same people are telling me that, like, yeah, the where they're going to build this fiber, which they wouldn't tell me exactly where, because it's very tradable, proprietary information, but, um, but it's following the energy supplies. It's following the energy corridors to the American Southwest, where there's solar and wind in Texas, where you can get natural gas, where you can get all these things. It will follow there. And I of course, assume the same is true in Canada as we build out our own sovereign data center capacity for this. So even, like deep seek, for example, you know, which is, of course, the hyper efficient Chinese model that spooked the markets back in January. Like, what do you mean? We don't need a trillion dollars in capex? Well, everyone's quite confident, including again, Jensen Wong and everybody else that, yeah, the more efficient models will increase this usage. That Jevons paradox will play out once again, and we'll see ever more of it. To me, the question is, is like as how it changes? And of course, you know, you know, this is a bubble. Let's, let's, let's be clear, data centers are a bubble, just like railroads in 1840 were a bubble. And there will be a bust, like not everyone's investments will pencil out that infrastructure will remain maybe it'll get cheaper. We find new uses for it, but it will, it will eventually bust at some point and that's what, to me, is interesting about like deep seeking, more efficient models. Is who's going to make the wrong investments in the wrong places at the wrong time? But you know, we will see as it gathers force and agents, as I mentioned. You know, they don't require, as much, you know, these monstrous training runs at City sized data centers. You know, meta wanted to spend $200 billion on a single complex, the open AI, Microsoft, Stargate, $500 billion Oracle's. Larry Ellison said that $100 billion is table stakes, which is just crazy to think about. And, you know, he's permitting three nukes on site. So there you go. I mean, it'll be fascinating to see if we have a new generation of private, private generation, right, like, which is like harkening all the way back to, you know, the early electrical grid and companies creating their own power plants on site, kind of stuff. Nicholas Carr wrote a good book about that one, about how we could see from the early electrical grid how the cloud played out. They played out very similarly. The AI cloud seems to be playing out a bit differently. So, so, yeah, I imagine that as well, but, but, yeah, well, inference happen at the edge. We need to have more distributed generation, because you're gonna have AI agents that are going to be spending more time at the point of request, whether that's a laptop or your phone or a light post or your autonomous vehicle, and it's going to need more of that generation and charging at the edge. That, to me, is the really interesting question. Like, you know, when these current generation models hit their limits, and just like with Moore's law, like, you know, you have to figure out other efficiencies in designing chips or designing AIS, how will that change the relationship to the grid? And I don't think anyone knows quite for sure yet, which is why they're just racing to lock up as many long term contracts as they possibly can just get it all, core to the market.   Trevor Freeman  49:39 Yeah, it's just another example, something that comes up in a lot of different topics that we cover on this show. Everything, obviously, is always related to the energy transition. But the idea that the energy transition is really it's not just changing fuel sources, like we talked about earlier. It's not just going from internal combustion to a battery. It's rethinking the. Relationship with energy, and it's rethinking how we do things. And, yeah, you bring up, like, more private, massive generation to deal with these things. So really, that whole relationship with energy is on scale to change. Greg, this has been a really interesting conversation. I really appreciate it. Lots to pack into this short bit of time here. We always kind of wrap up our conversations with a series of questions to our guests. So I'm going to fire those at you here. And this first one, I'm sure you've got lots of different examples here, so feel free to give more than one. What is a book that you've read that you think everybody should read?   Greg Lindsay  50:35 The first one that comes to mind is actually William Gibson's Neuromancer, which is which gave the world the notion of cyberspace and so many concepts. But I think about it a lot today. William Gibson, Vancouver based author, about how much in that book is something really think about. There is a digital twin in it, an agent called the Dixie flatline. It's like a former program where they cloned a digital twin of him. I've actually met an engineering company, Thornton Thomas Eddie that built a digital twin of one of their former top experts. So like that became real. Of course, the matrix is becoming real the Turing police. Yeah, there's a whole thing in there where there's cops to make sure that AIS don't get smarter. I've been thinking a lot about, do we need Turing police? The EU will probably create them. And so that's something where you know the proof, again, of like science fiction, its ability in world building to really make you think about these implications and help for contingency planning. A lot of foresight experts I work with think about sci fi, and we use sci fi for exactly that reason. So go read some classic cyberpunk, everybody.   Trevor Freeman  51:32 Awesome. So same question. But what's a movie or a show that you think everybody should take a look at?   Greg Lindsay  51:38 I recently watched the watch the matrix with ideas, which is fun to think about, where the villains are, agents that villains are agents. That's funny how that terms come back around. But the other one was thinking about the New Yorker recently read a piece on global demographics and the fact that, you know, globally, less and less children. And it made several references to Alfonso Quons, Children of Men from 2006 which is, sadly, probably the most prescient film of the 21st Century. Again, a classic to watch, about imagining in a world where we don't where you where you lose faith in the future, what happens, and a world that is not having children as a world that's losing faith in its own future. So that's always haunted me.   Trevor Freeman  52:12 It's funny both of those movies. So I've got kids as they get, you know, a little bit older, a little bit older, we start introducing more and more movies. And I've got this list of movies that are just, you know, impactful for my own adolescent years and growing up. And both matrix and Children of Men are on that list of really good movies that I just need my kids to get a little bit older, and then I'm excited to watch with them. If someone offered you a free round trip flight anywhere in the world, where would you go?   Greg Lindsay  52:40 I would go to Venice, Italy for the Architecture Biennale, which I will be on a plane in May, going to anyway. And the theme this year is intelligence, artificial, natural and collective. So it should be interesting to see the world's brightest architects. Let's see what we got. But yeah, Venice, every time, my favorite city in the world.   Trevor Freeman  52:58 Yeah, it's pretty wonderful. Who is someone that you admire?   Greg Lindsay  53:01 Great question.

A version of this essay has been published by firstpost.com at https://www.firstpost.com/opinion/shadow-warrior-from-crisis-to-advantage-how-india-can-outplay-the-trump-tariff-gambit-13923031.htmlA simple summary of the recent brouhaha about President Trump's imposition of 25% tariffs on India as well as his comment on India's ‘dead economy' is the following from Shakespeare's Macbeth: “full of sound and fury, signifying nothing”. Trump further imposed punitive tariffs totalling 50% on August 6th allegedly for India funding Russia's war machine via buying oil.As any negotiator knows, a good opening gambit is intended to set the stage for further parleys, so that you could arrive at a negotiated settlement that is acceptable to both parties. The opening gambit could well be a maximalist statement, or one's ‘dream outcome', the opposite of which is ‘the walkway point' beyond which you are simply not willing to make concessions. The usual outcome is somewhere in between these two positions or postures.Trump is both a tough negotiator, and prone to making broad statements from which he has no problem retreating later. It's down-and-dirty boardroom tactics that he's bringing to international trade. Therefore I think Indians don't need to get rattled. It's not the end of the world, and there will be climbdowns and adjustments. Think hard about the long term.I was on a panel discussion on this topic on TV just hours after Trump made his initial 25% announcement, and I mentioned an interplay between geo-politics and geo-economics. Trump is annoyed that his Ukraine-Russia play is not making much headway, and also that BRICS is making progress towards de-dollarization. India is caught in this crossfire (‘collateral damage') but the geo-economic facts on the ground are not favorable to Trump.I am in general agreement with Trump on his objectives of bringing manufacturing and investment back to the US, but I am not sure that he will succeed, and anyway his strong-arm tactics may backfire. I consider below what India should be prepared to do to turn adversity into opportunity.The anti-Thucydides Trap and the baleful influence of Whitehall on Deep StateWhat is remarkable, though, is that Trump 2.0 seems to be indistinguishable from the Deep State: I wondered last month if the Deep State had ‘turned' Trump. The main reason many people supported Trump in the first place was the damage the Deep State was wreaking on the US under the Obama-Biden regime. But it appears that the resourceful Deep State has now co-opted Trump for its agenda, and I can only speculate how.The net result is that there is the anti-Thucydides Trap: here is the incumbent power, the US, actively supporting the insurgent power, China, instead of suppressing it, as Graham Allison suggested as the historical pattern. It, in all fairness, did not start with Trump, but with Nixon in China in 1971. In 1985, the US trade deficit with China was $6 million. In 1986, $1.78 billion. In 1995, $35 billion.But it ballooned after China entered the WTO in 2001. $202 billion in 2005; $386 billion in 2022.In 2025, after threatening China with 150% tariffs, Trump retreated by postponing them; besides he has caved in to Chinese demands for Nvidia chips and for exemptions from Iran oil sanctions if I am not mistaken.All this can be explained by one word: leverage. China lured the US with the siren-song of the cost-leader ‘China price', tempting CEOs and Wall Street, who sleepwalked into surrender to the heft of the Chinese supply chain.Now China has cornered Trump via its monopoly over various things, the most obvious of which is rare earths. Trump really has no option but to give in to Chinese blackmail. That must make him furious: in addition to his inability to get Putin to listen to him, Xi is also ignoring him. Therefore, he will take out his frustrations on others, such as India, the EU, Japan, etc. Never mind that he's burning bridges with them.There's a Malayalam proverb that's relevant here: “angadiyil thottathinu ammayodu”. Meaning, you were humiliated in the marketplace, so you come home and take it out on your mother. This is quite likely what Trump is doing, because he believes India et al will not retaliate. In fact Japan and the EU did not retaliate, but gave in, also promising to invest large sums in the US. India could consider a different path: not active conflict, but not giving in either, because its equations with the US are different from those of the EU or Japan.Even the normally docile Japanese are beginning to notice.Beyond that, I suggested a couple of years ago that Deep State has a plan to enter into a condominium agreement with China, so that China gets Asia, and the US gets the Americas and the Pacific/Atlantic. This is exactly like the Vatican-brokered medieval division of the world between Spain and Portugal, and it probably will be equally bad for everyone else. And incidentally it makes the Quad infructuous, and deepens distrust of American motives.The Chinese are sure that they have achieved the condominium, or rather forced the Americans into it. Here is a headline from the Financial Express about their reaction to the tariffs: they are delighted that the principal obstacle in their quest for hegemony, a US-India military and economic alliance, is being blown up by Trump, and they lose no opportunity to deride India as not quite up to the mark, whereas they and the US have achieved a G2 detente.Two birds with one stone: gloat about the breakdown in the US-India relationship, and exhibit their racist disdain for India yet again.They laugh, but I bet India can do an end-run around them. As noted above, the G2 is a lot like the division of the world into Spanish and Portuguese spheres of influence in 1494. Well, that didn't end too well for either of them. They had their empires, which they looted for gold and slaves, but it made them fat, dumb and happy. The Dutch, English, and French capitalized on more dynamic economies, flexible colonial systems, and aggressive competition, overtaking the Iberian powers in global influence by the 17th century. This is a salutary historical parallel.I have long suspected that the US Deep State is being led by the nose by the malign Whitehall (the British Deep State): I call it the ‘master-blaster' syndrome. On August 6th, there was indirect confirmation of this in ex-British PM Boris Johnson's tweet about India. Let us remember he single-handedly ruined the chances of a peaceful resolution of the Ukraine War in 2022. Whitehall's mischief and meddling all over, if you read between the lines.Did I mention the British Special Force's views? Ah, Whitehall is getting a bit sloppy in its propaganda.Wait, so is India important (according to Whitehall) or unimportant (according to Trump)?Since I am very pro-American, I have a word of warning to Trump: you trust perfidious Albion at your peril. Their country is ruined, and they will not rest until they ruin yours too.I also wonder if there are British paw-prints in a recent and sudden spate of racist attacks on Indians in Ireland. A 6-year old girl was assaulted and kicked in the private parts. A nurse was gang-raped by a bunch of teenagers. Ireland has never been so racist against Indians (yes, I do remember the sad case of Savita Halappanavar, but that was religious bigotry more than racism). And I remember sudden spikes in anti-Indian attacks in Australia and Canada, both British vassals.There is no point in Indians whining about how the EU and America itself are buying more oil, palladium, rare earths, uranium etc. from Russia than India is. I am sorry to say this, but Western nations are known for hypocrisy. For example, exactly 80 years ago they dropped atomic bombs on Hiroshima and Nagasaki in Japan, but not on Germany or Italy. Why? The answer is uncomfortable. Lovely post-facto rationalization, isn't it?Remember the late lamented British East India Company that raped and pillaged India?Applying the three winning strategies to geo-economicsAs a professor of business strategy and innovation, I emphasize to my students that there are three broad ways of gaining an advantage over others: 1. Be the cost leader, 2. Be the most customer-intimate player, 3. Innovate. The US as a nation is patently not playing the cost leader; it does have some customer intimacy, but it is shrinking; its strength is in innovation.If you look at comparative advantage, the US at one time had strengths in all three of the above. Because it had the scale of a large market (and its most obvious competitors in Europe were decimated by world wars) America did enjoy an ability to be cost-competitive, especially as the dollar is the global default reserve currency. It demonstrated this by pushing through the Plaza Accords, forcing the Japanese yen to appreciate, destroying their cost advantage.In terms of customer intimacy, the US is losing its edge. Take cars for example: Americans practically invented them, and dominated the business, but they are in headlong retreat now because they simply don't make cars that people want outside the US: Japanese, Koreans, Germans and now Chinese do. Why were Ford and GM forced to leave the India market? Their “world cars” are no good in value-conscious India and other emerging markets.Innovation, yes, has been an American strength. Iconic Americans like Thomas Edison, Henry Ford, and Steve Jobs led the way in product and process innovation. US universities have produced idea after idea, and startups have ignited Silicon Valley. In fact Big Tech and aerospace/armaments are the biggest areas where the US leads these days.The armaments and aerospace tradeThat is pertinent because of two reasons: one is Trump's peevishness at India's purchase of weapons from Russia (even though that has come down from 70+% of imports to 36% according to SIPRI); two is the fact that there are significant services and intangible imports by India from the US, of for instance Big Tech services, even some routed through third countries like Ireland.Armaments and aerospace purchases from the US by India have gone up a lot: for example the Apache helicopters that arrived recently, the GE 404 engines ordered for India's indigenous fighter aircraft, Predator drones and P8-i Poseidon maritime surveillance aircraft. I suspect Trump is intent on pushing India to buy F-35s, the $110-million dollar 5th generation fighters.Unfortunately, the F-35 has a spotty track record. There were two crashes recently, one in Albuquerque in May, and the other on July 31 in Fresno, and that's $220 million dollars gone. Besides, the spectacle of a hapless British-owned F-35B sitting, forlorn, in the rain, in Trivandrum airport for weeks, lent itself to trolls, who made it the butt of jokes. I suspect India has firmly rebuffed Trump on this front, which has led to his focus on Russian arms.There might be other pushbacks too. Personally, I think India does need more P-8i submarine hunter-killer aircraft to patrol the Bay of Bengal, but India is exerting its buyer power. There are rumors of pauses in orders for Javelin and Stryker missiles as well.On the civilian aerospace front, I am astonished that all the media stories about Air India 171 and the suspicion that Boeing and/or General Electric are at fault have disappeared without a trace. Why? There had been the big narrative push to blame the poor pilots, and now that there is more than reasonable doubt that these US MNCs are to blame, there is a media blackout?Allegations about poor manufacturing practices by Boeing in North Charleston, South Carolina by whistleblowers have been damaging for the company's brand: this is where the 787 Dreamliners are put together. It would not be surprising if there is a slew of cancellations of orders for Boeing aircraft, with customers moving to Airbus. Let us note Air India and Indigo have placed some very large, multi-billion dollar orders with Boeing that may be in jeopardy.India as a consuming economy, and the services trade is hugely in the US' favorMany observers have pointed out the obvious fact that India is not an export-oriented economy, unlike, say, Japan or China. It is more of a consuming economy with a large, growing and increasingly less frugal population, and therefore it is a target for exporters rather than a competitor for exporting countries. As such, the impact of these US tariffs on India will be somewhat muted, and there are alternative destinations for India's exports, if need be.While Trump has focused on merchandise trade and India's modest surplus there, it is likely that there is a massive services trade, which is in the US' favor. All those Big Tech firms, such as Microsoft, Meta, Google and so on run a surplus in the US' favor, which may not be immediately evident because they route their sales through third countries, e.g. Ireland.These are the figures from the US Trade Representative, and quite frankly I don't believe them: there are a lot of invisible services being sold to India, and the value of Indian data is ignored.In addition to the financial implications, there are national security concerns. Take the case of Microsoft's cloud offering, Azure, which arbitrarily turned off services to Indian oil retailer Nayara on the flimsy grounds that the latter had substantial investment from Russia's Rosneft. This is an example of jurisdictional over-reach by US companies, which has dire consequences. India has been lax about controlling Big Tech, and this has to change.India is Meta's largest customer base. Whatsapp is used for practically everything. Which means that Meta has access to enormous amounts of Indian customer data, for which India is not even enforcing local storage. This is true of all other Big Tech (see OpenAI's Sam Altman below): they are playing fast and loose with Indian data, which is not in India's interest at all.Data is the new oil, says The Economist magazine. So how much should Meta, OpenAI et al be paying for Indian data? Meta is worth trillions of dollars, OpenAI half a trillion. How much of that can be attributed to Indian data?There is at least one example of how India too can play the digital game: UPI. Despite ham-handed efforts to now handicap UPI with a fee (thank you, brilliant government bureaucrats, yes, go ahead and kill the goose that lays the golden eggs), it has become a contender in a field that has long been dominated by the American duopoly of Visa and Mastercard. In other words, India can scale up and compete.It is unfortunate that India has not built up its own Big Tech behind a firewall as has been done behind the Great Firewall of China. But it is not too late. Is it possible for India-based cloud service providers to replace US Big Tech like Amazon Web Services and Microsoft Azure? Yes, there is at least one player in that market: Zoho.Second, what are the tariffs on Big Tech exports to India these days? What if India were to decide to impose a 50% tax on revenue generated in India through advertisement or through sales of services, mirroring the US's punitive taxes on Indian goods exports? Let me hasten to add that I am not suggesting this, it is merely a hypothetical argument.There could also be non-tariff barriers as China has implemented, but not India: data locality laws, forced use of local partners, data privacy laws like the EU's GDPR, anti-monopoly laws like the EU's Digital Markets Act, strict application of IPR laws like 3(k) that absolutely prohibits the patenting of software, and so on. India too can play legalistic games. This is a reason US agri-products do not pass muster: genetically modified seeds, and milk from cows fed with cattle feed from blood, offal and ground-up body parts.Similarly, in the ‘information' industry, India is likely to become the largest English-reading country in the world. I keep getting come-hither emails from the New York Times offering me $1 a month deals on their product: they want Indian customers. There are all these American media companies present in India, untrammelled by content controls or taxes. What if India were to give a choice to Bloomberg, Reuters, NYTimes, WaPo, NPR et al: 50% tax, or exit?This attack on peddlers of fake information and manufacturing consent I do suggest, and I have been suggesting for years. It would make no difference whatsoever to India if these media outlets were ejected, and they surely could cover India (well, basically what they do is to demean India) just as well from abroad. Out with them: good riddance to bad rubbish.What India needs to doI believe India needs to play the long game. It has to use its shatrubodha to realize that the US is not its enemy: in Chanakyan terms, the US is the Far Emperor. The enemy is China, or more precisely the Chinese Empire. Han China is just a rump on their south-eastern coast, but it is their conquered (and restive) colonies such as Tibet, Xinjiang, Manchuria and Inner Mongolia, that give them their current heft.But the historical trends are against China. It has in the past had stable governments for long periods, based on strong (and brutal) imperial power. Then comes the inevitable collapse, when the center falls apart, and there is absolute chaos. It is quite possible, given various trends, including demographic changes, that this may happen to China by 2050.On the other hand, (mostly thanks, I acknowledge, to China's manufacturing growth), the center of gravity of the world economy has been steadily shifting towards Asia. The momentum might swing towards India if China stumbles, but in any case the era of Atlantic dominance is probably gone for good. That was, of course, only a historical anomaly. Asia has always dominated: see Angus Maddison's magisterial history of the world economy, referred to below as well.I am reminded of the old story of the king berating his court poet for calling him “the new moon” and the emperor “the full moon”. The poet escaped being punished by pointing out that the new moon is waxing and the full moon is waning.This is the long game India has to keep in mind. Things are coming together for India to a great extent: in particular the demographic dividend, improved infrastructure, fiscal prudence, and the increasing centrality of the Indian Ocean as the locus of trade and commerce.India can attempt to gain competitive advantage in all three ways outlined above:* Cost-leadership. With a large market (assuming companies are willing to invest at scale), a low-cost labor force, and with a proven track-record of frugal innovation, India could well aim to be a cost-leader in selected areas of manufacturing. But this requires government intervention in loosening monetary policy and in reducing barriers to ease of doing business* Customer-intimacy. What works in highly value-conscious India could well work in other developing countries. For instance, the economic environment in ASEAN is largely similar to India's, and so Indian products should appeal to their residents; similarly with East Africa. Thus the Indian Ocean Rim with its huge (and in Africa's case, rapidly growing) population should be a natural fit for Indian products* Innovation. This is the hardest part, and it requires a new mindset in education and industry, to take risks and work at the bleeding edge of technology. In general, Indians have been content to replicate others' innovations at lower cost or do jugaad (which cannot scale up). To do real, disruptive innovation, first of all the services mindset should transition to a product mindset (sorry, Raghuram Rajan). Second, the quality of human capital must be improved. Third, there should be patient risk capital. Fourth, there should be entrepreneurs willing to try risky things. All of these are difficult, but doable.And what is the end point of this game? Leverage. The ability to compel others to buy from you.China has demonstrated this through its skill at being a cost-leader in industry after industry, often hollowing out entire nations through means both fair and foul. These means include far-sighted industrial policy including the acquisition of skills, technology, and raw materials, as well as hidden subsidies that support massive scaling, which ends up driving competing firms elsewhere out of business. India can learn a few lessons from them. One possible lesson is building capabilities, as David Teece of UC Berkeley suggested in 1997, that can span multiple products, sectors and even industries: the classic example is that of Nikon, whose optics strength helps it span industries such as photography, printing, and photolithography for chip manufacturing. Here is an interesting snapshot of China's capabilities today.2025 is, in a sense, a point of inflection for India just as the crisis in 1991 was. India had been content to plod along at the Nehruvian Rate of Growth of 2-3%, believing this was all it could achieve, as a ‘wounded civilization'. From that to a 6-7% growth rate is a leap, but it is not enough, nor is it testing the boundaries of what India can accomplish.1991 was the crisis that turned into an opportunity by accident. 2025 is a crisis that can be carefully and thoughtfully turned into an opportunity.The Idi Amin syndrome and the 1000 Talents program with AIThere is a key area where an American error may well be a windfall for India. This is based on the currently fashionable H1-B bashing which is really a race-bashing of Indians, and which has been taken up with gusto by certain MAGA folks. Once again, I suspect the baleful influence of Whitehall behind it, but whatever the reason, it looks like Indians are going to have a hard time settling down in the US.There are over a million Indians on H1-Bs, a large number of them software engineers, let us assume for convenience there are 250,000 of them. Given country caps of exactly 9800 a year, they have no realistic chance of getting a Green Card in the near future, and given the increasingly fraught nature of life there for brown people, they may leave the US, and possibly return to India..I call this the Idi Amin syndrome. In 1972, the dictator of Uganda went on a rampage against Indian-origin people in his country, and forcibly expelled 80,000 of them, because they were dominating the economy. There were unintended consequences: those who were ejected mostly went to the US and UK, and they have in many cases done well. But Uganda's economy virtually collapsed.That's a salutary experience. I am by no means saying that the US economy would collapse, but am pointing to the resilience of the Indians who were expelled. If, similarly, Trump forces a large number of Indians to return to India, that might well be a case of short-term pain and long-term gain: urvashi-shapam upakaram, as in the Malayalam phrase.Their return would be akin to what happened in China and Taiwan with their successful effort to attract their diaspora back. The Chinese program was called 1000 Talents, and they scoured the globe for academics and researchers of Chinese origin, and brought them back with attractive incentives and large budgets. They had a major role in energizing the Chinese economy.Similarly, Taiwan with Hsinchu University attracted high-quality talent, among which was the founder of TSMC, the globally dominant chip giant.And here is Trump offering to India on a platter at least 100,000 software engineers, especially at a time when generativeAI is decimating low-end jobs everywhere. They can work on some very compelling projects that could revolutionize Indian education, up-skilling and so on, and I am not at liberty to discuss them. Suffice to say that these could turbo-charge the Indian software industry and get it away from mundane, routine body-shopping type jobs.ConclusionThe Trump tariff tantrum is definitely a short-term problem for India, but it can be turned around, and turned into an opportunity, if only the country plays its cards right and focuses on building long-term comparative advantages and accepting the gift of a mis-step by Trump in geo-economics.In geo-politics, India and the US need each other to contain China, and so that part, being so obvious, will be taken care of more or less by default.Thus, overall, the old SWOT analysis: strengths, weaknesses, opportunities and threats. On balance, I am of the opinion that the threats contain in them the germs of opportunities. It is up to Indians to figure out how to take advantage of them. This is your game to win or lose, India!4150 words, 9 Aug 2025 This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit rajeevsrinivasan.substack.com/subscribe

Episode Summary: In this chapter of the landmark 100th episode of the "90 Miles from Needles" podcast, journalist David Morales, known for his insightful "Three Sonorans" newsletter, joins the discussion to unravel the complexities behind this development and how a community united to challenge a potentially devastating project. The episode highlights how Project Blue, backed by Amazon Web Services, planned to establish a massive data center in Tucson, Arizona. This project raised alarm due to its anticipated consumption of scarce desert resources, including water and energy. Community activists scrutinized the implications of this center, revealing its environmental impact and the economic motivations linked to enticing tax exemptions. Morales passionately articulates the broader significance of this victory and how it exemplifies a stand against exploitative initiatives pushing the limits of desert environments. The episode educates listeners on the historical connections of resource extraction in Arizona, the racial aspects of environmental degradation, and the importance of thoughtful modern policies that respect both indigenous heritage and future sustainability. With phrases like "manifest destiny" still ringing true in new forms today, this episode serves as an inspiring example of local advocacy effecting meaningful change. Key Takeaways: Project Blue's proposed data center in Tucson faced significant opposition due to excessive water and energy demands in a desert region. The initiative exemplifies environmental racism and reflects historical patterns of extraction and exploitation in Arizona. Community activism was pivotal in stopping the project, showing the power of collective action in confronting large corporations like Amazon. Kevin Dahl, a Tucson City Council member, took a hard oppositional stance that contributed to the council's unanimous decision to halt the project. The "Three Sonorans" newsletter provides valuable insights into indigenous and progressive perspectives on environmental issues in Tucson. Notable Quotes: "Now's your chance today. Stopping Project Blue is your way of stopping manifest destiny today.""It's all connected because you have energy, you have coal, you have water.""You have to know the history. You have to know all of it together.""They were trying to build this out here because our last governor passed this bill in 2013 to give huge tax incentives to data centers." Resources: David Morales’ "Three Sonorans" Newsletter: https://threesonorans.substack.com Arizona Luminaria: Coverage on the public records request that revealed Amazon's involvement: https://azluminaria.org/2025/07/21/amazon-web-services-is-company-behind-tucsons-project-blue-according-to-2023-county-memo/Become a desert defender!: https://90milesfromneedles.com/donateSee omnystudio.com/listener for privacy information.

It's Friday, August 8th, A.D. 2025. This is The Worldview in 5 Minutes heard on 140 radio stations and at www.TheWorldview.com.  I'm Adam McManus. (Adam@TheWorldview.com) By Adam McManus ISIS soldiers behead Christians in Mozambique, burning churches International observers are reporting that ISIS-aligned soldiers are beheading Christians and burning churches and homes in central and southern Africa – with some of the most brutal attacks happening in the nation of Mozambique, reports Fox News. The Middle East Media Research Institute – a counter-terrorism nonprofit based in Washington, D.C. – is sounding the alarm about what it describes as a "silent genocide" taking place by Muslim terrorists against Christians.   Alberto Fernandez, their Vice President, spoke to Fox News. FERNANDEZ: “What we see in Africa today is a kind of silent genocide or silent brutal, savage war that is occurring in the shadows and all too often ignored by the international community. We see rampaging jihadist groups from West Africa and even in the south in Mozambique. “The fact, for example, is that jihadist groups are in a position to take over, not one, not two, but several countries in Africa. It is very dangerous for the national security of the United States, let alone the security of the poor people who are there.” Fernandez spoke bluntly about the goal of these Muslim terrorist groups in Africa. FERNANDEZ: “The goal is eliminating Christian communities completely. These jihadist groups want to eliminate all the Christians in that area, take that area over, and keep pushing.” And he's grateful for President Trump's willingness to become involved. FERNANDEZ:  “The President's initiative in stopping the growing war between Rwanda and the Democratic Republic of Congo, its neighbor, is very significant, because this could have become a terrible war. We know that jihadists like to take advantage of vacuums, security vacuums, ungoverned spaces.” The migration agency said Monday that attacks by Muslim insurgents in Mozambique's northern Cabo Delgado province displaced more than 46,000 people in the span of eight days just last month. Sixty percent of those forced from their homes were children. The Muslim jihadists of Africa would do well to follow the advice of Gamaliel, the Pharisee from the time of Christ. In Acts 5:38-39, he said, “Leave these men alone! Let them go! For if their purpose or activity is of human origin, it will fail. But if it is from God, you will not be able to stop these men; you will only find yourselves fighting against God.” Amazon Web Services gives the Trump admin $1 billion coupon In the United States, Amazon Web Services is giving the Trump administration a $1 billion coupon to use their services for the federal government's digital transformation and artificial intelligence capacity, reports Politico.com. On Thursday, the General Services Administration announced a sweeping “OneGov” agreement with Amazon Web Services that would yield up to $1 billion in cost savings for federal agencies shifting to cloud services. But the Amazon deal is not exclusive. Similar OneGov agreements are in the works with other major cloud providers, including Microsoft and Google. Oracle also recently signed a deal giving government agencies a 75% discount on Oracle technology. Trump cancels half billion dollars of federal funding for UCLA over anti-Semitism The Trump administration has canceled $584 million in grants for the University of California in Los Angeles, claiming they did not take a strong enough stance against on-campus anti-Semitism, reports One America News. UCLA recently reached a $6 million settlement with three Jewish students and a Jewish professor who sued the school in a civil rights dispute, claiming pro-Palestinian protesters were permitted to block them from accessing certain areas on campus in 2024. President Donald Trump's office announced that the U.S. Department of Justice's Civil Rights Division found UCLA in violation of the Equal Rights Act of 1964 “by acting with deliberate indifference in creating a hostile educational environment for Jewish and Israeli students.” Catholic priest met homosexual prostitute in church parking lot Clemente Guerrero-Olvera, a Catholic parochial vicar at St. Ann Church in Clayton, North Carolina, was arrested and charged with soliciting prostitution with a 20-year-old man he allegedly met on the homosexual app named Grindr in the church's parking lot, reports LifeSiteNews.com. During an unrelated search for a missing person around 1:00 a.m. on August 4th, a police deputy spotted the young man, identified as Ja'Quavis Brinson, inside a vehicle in St. Ann's parking lot and another man, later identified as Guerrero-Olvera, who ran away, according to the Johnston County Report. The 47-year-old Catholic priest was promptly arrested and charged with felony solicitation of prostitution after an investigation revealed that he had arranged to meet the 20-year-old via Grindr, allegedly for the purpose of engaging in sexual activity. Guerrero-Olvera was booked at the Johnston County Detention Center and later released on a $2,500 secured bond. Brinson of Benson, North Carolina was charged with misdemeanor prostitution.  1 Corinthians 6:9-11 says, “Do not be deceived: neither the sexually immoral, nor adulterers, nor men who practice homosexuality, nor thieves, nor the greedy, nor drunkards, nor revilers, nor swindlers will inherit the kingdom of God.” Two weeks after cancellation, Colbert doubles down on liberal jokes And finally, it's been over two weeks since CBS announced on July 17th that it was cancelling The Late Show with Stephen Colbert as of May 2026. In the first show after the cancellation was announced, the leftist comedian addressed the news. COLBERT: “On Friday, Donald Trump posted, ‘I absolutely love that Colbert got fired. His talent was even less than his ratings.” (audience boos) “Over the weekend, it sunk in that they're killing off our show, but they made one mistake. They left me alive!” (audience laughs) However, Colbert has responded by doubling down on the same liberal jokes and liberal guests that made viewers (and advertising dollars) turn away in the first place, reports Newsbusters.org. According to a new Media Research Center study, Colbert's political jokes targeted conservatives and Republicans 95% of the time, and 100% of his political guests, in the two weeks since his cancellation, were liberals. In the eight episodes from July 21 through July 31, Colbert told 129 jokes about right-leaning individuals or groups compared to only seven about left-leaning people or groups. That 95% disparity is considerably higher than his 2023 number of 86% or 2024 number of 82%. The Late Show has been losing a whopping $40-50 million a year because Colbert has used his network platform to belittle half the country, reports the New York Post. COLBERT: “They pulled the plug on our show because of losses pegged between $40 million and $50 million a year. $40 million is a big number. I could see us losing $24 million, but where would Paramount have possibly spent the other 16 million? Oh, yeah.” (audience laughs) That was a dig, referencing the $16 million settlement CBS' parent company reached with President Trump just weeks ago regarding the deceptive editing of a 60 Minutes interview with Democratic presidential candidate Kamala Harris to aid her candidacy. Here's the edited version which aired on 60 Minutes in a segment with CBS reporter Bill Whitaker. WHITAKER: “But it seems that Prime Minister [Benjamin] Netanyahu is not listening.” HARRIS: “We are not going to stop pursuing what is necessary for the United States to be clear about where we stand on the need for this war to end.” And here is the unedited version, featuring Kamala's signature “word salad” which did not air on 60 Minutes. WHITAKER: “But it seems that Prime Minister Netanyahu is not listening. The Wall Street Journal said that he, that your administration has repeatedly been blindsided by Netanyahu. And in fact, he has rebuffed just about all of your administration's entreaties.” HARRIS: “Well, Bill, [long pause] the work that we have done has resulted in a number of movements in that region by Israel that were very much prompted by, or a result of, many things, including our advocacy for what needs to happen in the region. And we're not going to stop doing that. We're not going to stop pursuing what is necessary for the United States to be clear about where we stand on the need for this war to end.” Exodus 20:16 records the ninth commandment that God gave Moses on Mt. Sinai.  “You shall not bear false witness against your neighbor.” Close And that's The Worldview on this Friday, August 8th, in the year of our Lord 2025. Follow us on X or subscribe for free by Spotify, Amazon Music, or by iTunes or email to our unique Christian newscast at www.TheWorldview.com.  Plus, you can get the Generations app through Google Play or The App Store. I'm Adam McManus (Adam@TheWorldview.com). Seize the day for Jesus Christ.

Industry leaders from Automation Anywhere and AWS discuss how modern customer data collection has evolved, and practical strategies for implementing enterprise automation at scale.Topics Include:Automation Anywhere and AWS experts discuss modern enterprise automation strategiesTraditional profiting strategies may not work with today's changing business modelsCustomer data collection methods have evolved across multiple platforms significantlyModern verification processes include automated validation systems and streamlined timelinesBackground check automation is increasingly handled by AI-powered models and systemsStanford's "Wonder Bread" research paper introduced revolutionary enterprise process observation technologyWonder Bread demonstrated AI systems watching and automatically learning hospital workflowsThe technology can author workflows by observing real enterprise processesEnterprise Process Management built around observed behaviors shows promising resultsVerification challenges exist since Wonder Bread research isn't widely publicized yetProcess observation technology could transform how enterprises handle workflow creationSalesforce Wizard Interface dominates many current automation implementations in enterprisesSalesforce Agent Codes offer alternative approaches to traditional automation methodsAWS platform selection involves careful consideration of enterprise integration needsDemo implementations showcase real-world timeline expectations and deployment maturity levelsCurrent automation solutions have reached significant scale across various industriesWorkflow automation differs fundamentally from true agentic intelligence systems capabilitiesAgentic AI demonstrates autonomous decision-making beyond simple rule-based automation processesUnderstanding this distinction helps organizations choose appropriate technology approaches effectivelySession concludes with clarity on modern automation landscape and implementation strategiesParticipants:Pratyush Garikapati – Director of Products, Automation AnywhereSreenath Gotur – Snr Generative AI Specialist, Amazon Web ServicesFurther Links:Automation Anywhere websiteAutomation Anywhere – AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Cloud computing giant Amazon Web Services (AWS) has added support for the ML-KEM post-quantum key encapsulation mechanism to secure TLS connections from potential quantum threats. You can listen to all of the Quantum Minute episodes at https://QuantumMinute.com. The Quantum Minute is brought to you by Applied Quantum, a leading consultancy and solutions provider specializing in quantum computing, quantum cryptography, quantum communication, and quantum AI. Learn more at https://AppliedQuantum.com.

The General Services Administration has been on a roll lately, negotiating what it calls OneGov agreements with some of the federal government's biggest IT vendors. On Thursday, GSA announced it has negotiated a governmentwide purchasing agreement with Amazon Web Services that could save agencies up to $1 billion through credits for AWS services. The deal is the latest in a flurry of OneGov agreements GSA has initiated under the Trump administration to consolidate and centralize IT purchasing at scale and unlock greater, consistent savings for civilian agencies, rather than agencies negotiating one-off contracts with vendors themselves. As part of the governmentwide package, AWS has come to the table offering direct incentive credits that could total up to $1 billion in value for cloud services, modernization support and training. The deal will run through Dec. 31, 2028. In addition to streamlining federal IT procurement by working as a single, unified federal entity, GSA's OneGov initiative also aims to work directly with technology developers themselves, rather than intermediaries such as value-added resellers. As such, GSA touts the potential for additional savings by contracting directly with the cloud giant for its services. That deal comes just a day after GSA announced a similar one with OpenAI that will offer its ChatGPT tool to federal agencies for just $1. It marks the artificial intelligence firm's latest effort to expand use of its generative AI chatbot across the federal government. Like the AWS deal, GSA said the agreement with OpenAI supports the White House's AI Action Plan, which encourages widespread adoption of AI in the federal government. Through the partnership, OpenAI's ChatGPT Enterprise product can be purchased by federal agencies for $1 per agency for one year. GSA called this a “deeply discounted rate.” Commenting on the deal, OpenAI CEO Sam Altman said in a statement: “One of the best ways to make sure AI works for everyone is to put it in the hands of the people serving the country.” The Daily Scoop Podcast is available every Monday-Friday afternoon. If you want to hear more of the latest from Washington, subscribe to The Daily Scoop Podcast  on Apple Podcasts, Soundcloud, Spotify and YouTube.

AWS's Mark Relph draws fascinating parallels between today's AI revolution and the 1900s agricultural mechanization that delivered 2,000% productivity gains, while exploring how agentic AI will fundamentally reshape every aspect of software business models.Topics Include:Mark Relph directs AWS's data and AI partner go-to-market strategy teamHis role focuses on making ISV partners a force multiplier for customer successPreviously ran go-to-market for Amazon Bedrock, AWS's fastest growing service everCurrent AI adoption pace exceeds even the early cloud computing boom yearsHistorical parallel: 1900s agricultural mechanization delivered 2,000% productivity gains and 95% resource reductionFirst commercial self-propelled farming equipment revolutionized entire economies and never looked back500 machines formed the "Harvest Brigade" during WWII, harvesting from Texas to CanadaMark has spoken to 600+ AWS customers about GenAI over two yearsOrganizations range from AI pioneers to those still "fending off pirates" internallyGenAI has become a phenomenal assistant within organizations for content and automationAWS's AI stack has three layers: infrastructure, Bedrock, and applicationsBottom layer provides complete control over training, inference, and custom applicationsMiddle layer Bedrock serves as the "operating system" for generative AI applicationsTop layer offers ready-to-use AI through Q assistants and productivity toolsAI systems are rapidly becoming more complex with multiple model chainsMany current "agents" are just really, really long prompts (Mark's hot take)Task-specific models are emerging as one size won't fit all use casesEvolution moves from human-driven AI to agent-assisted to fully autonomous agentsAgent readiness requires APIs that allow software to interact autonomouslyTraditional UIs become unnecessary when agents interface directly with systemsCore competencies shift when AI handles the actual "doing" of tasksSales and marketing must adapt to agents delivering outcomes autonomouslyGo-to-market strategies need complete rethinking for an agentic worldThe agentic age is upon us and AWS partners should shape the futureParticipants:Mark Relph – Director – Data & AI Partner Go-To-Market, Amazon Web ServicesSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

In this episode of #Trending, host Jim Love covers various significant topics in tech news. He discusses Donald Trump's impending semiconductor tariffs and their potential impact on the US tech industry; a Washington Post investigation revealing the inefficacy of Meta's Community Notes system in combating misinformation; an Amazon Web Services (AWS) developer whose account and data were unexpectedly deleted, sparking concerns about cloud dependency; and the UK's Online Safety Act, which, while intended to protect children, may instead be heightening risks and leading to increased platform censorship and data collection. Jim also invites listeners to share their summer reading lists and promotes his novel, 'Elisa, A Tale of Quantum Kisses'. 00:00 Introduction and Summer Reading Request 01:03 Trump's Semiconductor Tariffs 03:18 Meta's Community Notes System Fails 04:55 AWS Deletes Developer's Account 06:47 UK's Online Safety Act Controversy 09:52 Conclusion and Call to Action

Explore the future of enterprise security with Abnormal AI's CIO Mike Britton, as he reveals how next-generation security operations are evolving to combat machine-speed threats. As both a security leader and AI innovator, Britton shares his advice for implementing effective agentic AI governance while maintaining operational agility. He emphasizes that success in the AI era isn't about replacing humans, but about empowering security teams to work alongside AI systems effectively. From managing agentic AI risks to building AI-ready security operations, this episode offers essential guidance for security leaders navigating the intersection of AI innovation and enterprise protection. Don't miss this opportunity to learn from a leader at the forefront of AI-powered security!Watch on AWS Executive Insights

Industry leaders from Celonis and AWS explain why 2025 marks the inflection point for agentic AI and how early adopters are gaining significant competitive advantages in efficiency and innovation.Topics Include:AWS's Cristen Hughes and Celonis's Jeff Naughton discuss AI agent transformationAndy Jassy declares AI agents will fundamentally change how we workThree key trends make AI agents practical: smarter models, longer tasks, cheaper costsAI now beats humans on complex benchmarks for the first time everClaude 3.7 cracked graduate-level reasoning where humans previously dominated completelyAI evolved from brief interactions to managing sustained multi-step complex workflowsProcessing costs plummeted 99.7% making enterprise-grade AI economically viable at scaleWe're transitioning from 2023's adaptation era to 2025's human-AI collaboration eraBy 2028, AI will suggest actions to humans rather than vice versaAgents are autonomous software that plan, act, and reason independently with minimal interventionAgent workflow: receive human request, create plan, execute actions, review, adjust, deliverFour agent components: brain (LLM), memory (context), actions (tools), persona (role definition)AWS offers three building approaches: ready-made solutions, managed platform, DIY developmentKey enterprise applications: software development acceleration, customer care automation, knowledge work optimizationManual processes like accounts payable offer huge transformation opportunities through intelligent automationDeep process analysis is critical before deploying agents for maximum effectivenessCelonis pioneered process mining to help enterprises understand their actual workflow realitiesCompanies are collections of interacting processes that agents need proper context to navigateProcess intelligence provides agents with placement guidance, data feeds, monitoring, and workflow directionCelonis-AWS partnership demonstrates order management agents that automatically handle at-risk situationsParticipants:Jeff Naughton – SVP and Fellow, CelonisCristen Hughes – Solutions Architecture Leader, ISV, North America, Amazon Web ServicesFurther Links:Celonis WebsiteCelonis on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

This week on the GeekWire Podcast: Microsoft soars past Wall Street expectations, briefly hitting a $4 trillion valuation, while Amazon faces sharper scrutiny over its AI strategy. Todd Bishop and John Cook break down the contrasting earnings results, analyst reactions, and what it all means for the future of AI — and Seattle's place in it. Plus: insights from Microsoft's Mustafa Suleyman on the future of Copilot, a throwback lesson from the Zune era, and a guestbook entry that shows just how mainstream ChatGPT has become. Related stories and links Microsoft plans record $30B in quarterly capital spending Microsoft cut product R&D jobs, added operations roles over the past year Microsoft beats expectations, says Azure revenue tops $75B annually Internal memo: Nadella urges long-term thinking as Azure marks 15 years Microsoft reaches $4 trillion valuation after big earnings report Amazon Web Services profits squeezed amid AI spending surge Amazon tops Q2 estimates with $167.7B in revenue, $18.2B in profits Can Seattle own the AI era? 20 investors and founders weigh the potential From Startup to Exit: Microsoft@50: Birth of Xbox, with Chief Xbox Officer, Robbie Bach Colin & Samir Podcast with Microsoft AI CEO Mustafa Suleyman Tim Ferriss Podcast with Expedia and Zillow co-founder Rich BartonSee omnystudio.com/listener for privacy information.

Justin DiPietro, Co-Founder & Chief Strategy Officer of Glia, shares how they are leveraging AI to enhance the customer experience in the highly regulated world of financial institutions.Topics Include:Glia provides voice, digital, and AI services for customer-facing and internal operationsBuilt on "channel-less architecture" unlike traditional contact centers that added channels sequentiallyOne interaction can move seamlessly between channels (voice, chat, SMS, social)AI applies across all channels simultaneously rather than per individual channel700 customers, primarily banks and credit unions, 370 employees, headquartered in New YorkTargets 3,500 banks and credit unions across the United States marketFocuses exclusively on financial services and other regulated industriesAI for regulated industries requires different approach than non-regulated businessesTraditional contact centers had trade-off between cost and quality of serviceAI enables higher quality while simultaneously decreasing costs for contact centersNumber one reason people call banks: "What's my balance?" (20% of calls)Financial services require 100% accuracy, not 99.999% due to trust requirementsUses AWS exclusively for security, reliability, and future-oriented technology accessReal-time system requires triple-hot redundancy; seconds matter for live callsWorks with Bedrock team; customers certify Bedrock rather than individual featuresShowed examples of competitors' AI giving illegal million-dollar loans at 0%"Responsible AI" separates probabilistic understanding from deterministic responses to customersUses three model types: client models, network models, and protective modelsTraditional NLP had 50% accuracy; their LLM approach achieves 100% understandingPolicy is "use Nova unless" they can't, primarily for speed benefitsParticipants:Justin DiPietro – Co-Founder & Chief Strategy Officer, GliaFurther Links:Glia WebsiteGlia AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

For episode 560 of the BlockHash Podcast, host Brandon Zemp is joined by Andrew Vranjes, CRO of Blockdaemon while at Permissionless 4.Previously, Andrew was VP and GM at APAC, where he has successfully led regional growth, forged strategic partnerships, and expanded market presence in the Asia Pacific Region.  Prior to joining Blockdaemon, Andrew has held leadership roles in Amazon Web Services (AWS), Cisco Systems and Singtel/Optus. In AWS, Andrew built out the teams focussed on Startups, Digital Natives, Crypto/Fintech and AWS's partnerships with the Venture Capital and Private Equity community.  ⏳ Timestamps: 0:00 | Introduction1:00 | Who is Andrew Vranjes?3:34 | Blockdaemon explained5:30 | RWAs & Tokenization9:00 | Blockdaemon roadmap10:37 | Blockdaemon at Permissionless13:07 | RAPID FIRE SESSION

Hey CX Nation,In this week's episode of The CXChronicles Podcast #262, we welcomed Pasquale DeMaio, Vice President at Amazon Web Services (AWS) based in Seattle, WA. Launched in 2006, Amazon Web Services (AWS) began exposing key infrastructure services to businesses in the form of web services -- now widely known as cloud computing.Today, Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Singapore, and Japan, customers across all industries are taking advantage of our low cost, elastic, open and flexible, secure platform.In this episode, Pasquale and Adrian chat through the Four CX Pillars: Team, Tools, Process & Feedback. Plus share some of the ideas that his team at AWS think through on a daily basis to build world class customer experiences.**Episode #262 Highlight Reel:**1. How AWS thinks about team-building strategies  2. Leveraging Amazon Connect's Unified AI Engine  3. Streamlining customer service & success to fuel growth 4. Investing & supporting employee-driven innovation & cross-team collaboration 5. Building customer-centricity into the DNA of your engineering team Click here to learn more about Pasquale DeMaioClick here to learn more about Amazon Web Services (AWS)Huge thanks to Pasquale for coming on The CXChronicles Podcast and featuring his work and efforts in pushing the customer experience & customer success space into the future.If you enjoy The CXChronicles Podcast, stop by your favorite podcast player hit the follow button and leave us a review today.For our Spotify friends, make sure you are following CXC & please leave a 5 star review so we can find new listeners & members of our community.For our Apple friends, same deal -- follow CXCP and leave us a review letting folks know why you love our customer focused content.You know what would be even better?Go tell one of your friends or teammates about CXC's content,  strategic partners (Hubspot, Intercom, & Zendesk) & On-Demand services & invite them to join the CX Nation!Want to see how your customer experience stacks up to others, ask us about the CXC Healthzone, an intelligence platform that shares benchmarks & insights from companies across the world. Huge thanks for being apart of the "CX Nation" and helping customer focused business leaders across the world make happiness a habit!Reach Out To CXC Today!Support the showContact CXChronicles Today Tweet us @cxchronicles Check out our Instagram @cxchronicles Click here to checkout the CXC website Email us at info@cxchronicles.com Remember To Make Happiness A Habit!!

Ed Bailey, Field CISO at Cribl, shares how Cribl and AWS are helping customers rethink their data strategy by making it easier to modernize, reduce complexity, and unlock long-term flexibility.Topics Include:Ed Bailey introduces topic: bridging gap between security data requirements and budgetCompanies face mismatch: 10TB data needs vs 5TB licensing budget constraintsData volumes growing exponentially while budgets remain relatively flat year-over-yearIT security data differs from BI: enormous volume, variety, complexityMany companies discover 600+ data sources during SIEM migration projects50% of SIEM data remains un-accessed within 90 days of ingestionComplex data collection architectures break frequently and require excessive maintenanceTeams spend 80% time collecting data, only 20% analyzing for valueData collection and storage are costs; analytics and insights provide business valuePoor data quality creates operational chaos requiring dozens of browser tabsSOC analysts struggle with context switching across multiple disconnected systemsTraditional vendor approach: "give us all data, we'll solve problems" is outdatedData modernization requires sharing information widely across organizational business unitsData maturity model progression: patchwork → efficiency → optimization → innovationData tiering strategy: route expensive SIEM data vs cheaper data lake storageSIEM costs ~$1/GB while data lakes cost ~$0.15-0.20/GB for storageCompliance retention data should go to object storage at penny fractionsDecouple data retention from vendor tools to enable migration flexibilityCribl platform offers integrated solutions: Stream, Search, Lake, Edge componentsCustomer success: Siemens reduced 5TB to 500GB while maintaining security effectivenessParticipants:Edward Bailey – Field CISO, CriblFurther Links:Cribl WebsiteCribl on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Discover how a tech executive with zero banking experience revitalized and transformed digital operations at Union Bank of the Philippines. In this episode of Executive Insights, Chief Transformation Officer Dennis Omila shares how his authentic approach to leadership helped him drive organizational change and advocate for banking innovation. Through people-centric strategies, Omila spearheaded a remarkable cultural transformation at Union Bank, from creating “wow experiences” for customers, to harmonizing teams and insisting on better work-life balance for employees. His journey offers a masterclass in leadership development, showing how purpose-driven, authentic leadership can propel both individual growth and organizational success. This episode is essential viewing for leaders seeking to navigate complex changes while fostering a culture of innovation and employee empowerment in any industry.The views of the individual do not necessarily reflect the views of Union Bank.

Joe Marchese, Co-Founder of Human Ventures, talks with Jessica Lessin, our founder, CEO, and editor-in-chief, about AI's effect on ad businesses. Shaown Nandi, Director of Technology at Amazon Web Services, discusses the Chief AI Officer role. Kashish Gupta and Harsha Kapre join to discuss Snowflake's investment in Hightouch, and our reporter Aaron Holmes breaks down AI's impact on lowering software switching costs.Articles discussed on this episode: https://www.theinformation.com/articles/cursors-global-success-lifted-chinahttps://www.theinformation.com/articles/ai-cloud-startup-fireworks-discusses-4-billion-valuation-deal-lightspeed-indexhttps://www.theinformation.com/articles/microsofts-rivals-lean-ai-pry-away-longtime-customershttps://www.theinformation.com/articles/spotify-is-booming-except-for-its-ad-business TITV airs on YouTube, X and LinkedIn at 10AM PT / 1PM ET. Or check us out wherever you get your podcasts.

Sam Johnson, Chief Customer Officer of Jamf, discusses the implementation of AI built on Amazon Bedrock that is a gamechanger in helping Jamf's 76,000+ customers scale their device management operations.Topics Include:Sam Johnson introduces himself as Chief Customer Officer from Jamf companyJamf's 23-year mission: help organizations succeed with Apple device managementCompany manages 33+ million devices for 76,000+ customers worldwide from MinneapolisJamf has used AI since 2018 for security threat detectionReleased first customer-facing generative AI Assistant just last year in 2024Presentation covers why, how they built it, use cases, and future plansJamf serves horizontal market from small business to Fortune 500 companiesChallenge: balance powerful platform capabilities with ease of use and adoptionAI could help get best of both worlds - power and simplicityAI also increases security posture and scales user capabilities significantlyCustomers already using ChatGPT/Claude but wanted AI embedded in productBuilt into product to reduce "doorway effect" of switching digital environmentsCreated small cross-functional team to survey land and build initial trailRest of engineering organization came behind to build the production highwayTeam needed governance layer with input from security, legal, other departmentsEvaluated multiple providers but ultimately chose Amazon Bedrock for three reasonsAWS team support, large community, and integration with existing infrastructureUses Lambda, DynamoDB, CloudWatch to support the Bedrock AI implementationAI development required longer training/validation phase than typical product featuresReleased "AI Assistant" with three skills: Reference, Explain, and Search capabilitiesParticipants:Sam Johnson – Chief Customer Officer, JamfFurther Links:Jamf.comJamf on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Mark Stevens, SVP, Channels and Alliances, discusses how SecurityScorecard's strategic partnership with AWS enables them to scale their security solutions through cloud infrastructure, marketplace integration, and co-sell programsTopics Include:SecurityScorecard founded 10 years ago to understand third-party vendor security postureCompany has grown to 3,000 enterprise customers and 200+ partners globallyEvolved from ratings to "supply chain detection and response" over last yearSupply chain threats have doubled, creating extended attack surfaces for companiesMany organizations don't know their vendor count or vulnerabilities within supply chainsSecurityScorecard provides visibility into attack surfaces and management tools for controlGenerative AI is central to their ecosystem, leveraging AWS Bedrock extensivelyThey scan the entire internet every two days at massive scaleHave scored 12 million companies with security scorecards to dateAll workloads run on AWS cloud infrastructure as their primary platformAWS partnership provides necessary scale for managing hundreds of thousands of vendorsCase study: Identified vendor misconfigurations that could shut down 1,000 locationsOwn massive 10-year data lake with tens of millions of companiesNew managed service combines AI automation with human analysts for supportLarge organizations cannot fully automate supply chain security management yetQuality threat intelligence data now valuable to SOC teams, not just riskThird-party risk management and SOC teams are slowly converging for better securityAWS marketplace integration provides frictionless customer experience and larger dealsCo-sell programs with AWS enterprise sales teams create effective flywheel motionFuture expansion includes identity management, response actions, and internal signal managementParticipants:Mark Stevens – SVP, Channels and Alliances, SecurityScorecardFurther Links:SecurityScorecard.ioSecurityScorecard AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Welcome to The Zenergy Podcast! Today, Karan sits down with Brandon Oyer, Head of America's Power & Water at Amazon Web Services. Brandon shares his role at Amazon and how he became involved at the company. They then discuss how Amazon is using AI to get more power onto the grid, how much demand comes from traditional cloud computing vs AI, and Amazon's plan to be net-zero carbon by 2040. Brandon shares several sustainability projects Amazon is currently involved in, and we get a behind-the-scenes look into what it takes to supply a region with more power. If you enjoy today's episode, be sure to like and subscribe to the podcast so you don't miss other episodes. Credits:Editing/Graphics: Desta Wondirad, Wondir Studios

Chief Product Development Officer Mitchell Johnson discusses how Sonatype protects enterprise developers from malicious open source components while keeping them productive through AI.Topics Include:Sonatype provides software supply chain solutions for enterprises using open source componentsThey serve large enterprises, government agencies, and critical infrastructure providers globallyMain challenge: keeping developers productive while maintaining secure software supply chainsCybercrime and supply chain attacks are massive, growing industries threatening developersAI adoption is happening faster than expected, profoundly changing development workflowsBad actors evolved from waiting for vulnerabilities to creating malicious componentsMalicious open source components specifically target developer and DevOps toolchainsSonatype's security research team uses AI/ML to analyze every open source componentThey can predict and block malicious components before entering customer environmentsAWS partnership helps Sonatype meet customers where they want to do businessPartnership focuses on go-to-market alignment, not just technical integrationAWS sales teams should be treated as extensions of your own sales organizationUnderstanding AWS sales structure and incentives is crucial for successful partnershipsAI development is following same pattern as open source adoption twenty years ago"Shadow AI" parallels the earlier "shadow IT" trend with open source softwareAI speeds up code generation but security review processes haven't kept paceDevelopers need a "Hippocratic Oath" - taking responsibility for AI-generated code outputWithin 24 months, professionals not skilled in AI will struggle to stay relevantSonatype's culture encourages curiosity, experimentation, and accepts failure as part of innovationTheir core mission: help developers focus on innovation, not security choresParticipants:Mitchell Johnson – Chief Product Development Officer, SonatypeFurther Links:Sonatype WebsiteSonatype on AWS MarketplaceSee how Amazon Web Services gives you the freedom to migrate, innovate, and scale your software company at https://aws.amazon.com/isv/

Microsoft is undergoing a significant restructuring, placing artificial intelligence (AI) at the forefront of its strategy. The company has announced the layoff of approximately 9,000 employees, primarily targeting generalist sales roles, as it shifts towards a model that prioritizes technical expertise over traditional relationship-building in sales. This move is part of a broader initiative to enhance its AI offerings, particularly through its Copilot program, which has seen a 50% increase in funding and a 70% rise in partner incentives. Microsoft aims to eliminate product silos and align its go-to-market strategy with customer priorities, emphasizing the importance of AI integration in sales and service delivery.OpenAI is also making waves by diversifying its cloud infrastructure, now utilizing Google Cloud alongside Microsoft, CoreWeave, and Oracle. This strategic shift comes as OpenAI prepares to introduce new features in its ChatGPT platform, including a checkout function for e-commerce, which will allow users to make purchases directly through the chatbot. The company is positioning itself to compete more directly with Microsoft's Office suite by enhancing productivity tools and integrating e-commerce capabilities, signaling a move from being a model provider to an end-user platform.Amazon Web Services (AWS) has launched a new platform called Amazon Bedrock Agent Core, designed to facilitate collaboration among AI agents across organizations. This platform aims to address concerns about job security in the face of AI advancements, as it allows for the construction of interconnected AI agents capable of performing various tasks. Unlike competitors, AWS's offering is designed to be flexible and support multiple AI frameworks, positioning it as a neutral infrastructure provider in the AI landscape.In a rapid-fire segment, several companies have announced new partnerships and product updates. iRACA has teamed up with TD Cynics to extend its secure access services, while cgen.ai has launched a platform to streamline AI workloads. Nutrien has improved its Document AI software, and Cohesity has integrated its data management platform with Microsoft 365 Copilot, enabling users to leverage backup data for informed decision-making. These developments highlight a trend towards enabling service providers to evolve from mere technical support to delivering measurable business outcomes. Four things to know today 00:00 Microsoft Shakes Up Partner Strategy with AI Funding Boost and Workforce Realignment05:42 OpenAI's Cloud Diversification and Agent Ambitions Could Upend SMB Workflows08:35 AWS Launches AgentCore to Build Networks of Interconnected AI Agents11:15 Aryaka, C-Gen.AI, Nutrient, Cohesity Roll Out Innovations Targeting Business Value This is the Business of Tech.     Supported by:  https://timezest.com/mspradio/  All our Sponsors: https://businessof.tech/sponsors/ Do you want the show on your podcast app or the written versions of the stories? Subscribe to the Business of Tech: https://www.businessof.tech/subscribe/Looking for a link from the stories? The entire script of the show, with links to articles, are posted in each story on https://www.businessof.tech/ Support the show on Patreon: https://patreon.com/mspradio/ Want to be a guest on Business of Tech: Daily 10-Minute IT Services Insights? Send Dave Sobel a message on PodMatch, here: https://www.podmatch.com/hostdetailpreview/businessoftech Want our stuff? Cool Merch? Wear “Why Do We Care?” - Visit https://mspradio.myspreadshop.com Follow us on:LinkedIn: https://www.linkedin.com/company/28908079/YouTube: https://youtube.com/mspradio/Facebook: https://www.facebook.com/mspradionews/Instagram: https://www.instagram.com/mspradio/TikTok: https://www.tiktok.com/@businessoftechBluesky: https://bsky.app/profile/businessof.tech