Podcasts about Deloitte

Episode Summary In this milestone 300th episode of the Solar Maverick Podcast, Benoy Thanjan is joined by longtime friend and co-host Nate Jovanelly, Founder and CEO of SunRaise Capital. Benoy and Nate reflect on how much the solar industry has changed since the podcast began and explore where the market is headed next. They discuss the evolution of residential solar, the growing importance of battery storage, changing financing models, rising electricity costs, policy uncertainty, and the continuing challenge of customer acquisition. They also examine the advantages regional solar companies may have over national providers, how shifting customer demographics could affect the market, and why solar plus storage represents one of the industry's greatest opportunities over the next decade. The episode concludes with lessons from entrepreneurship, reflections on navigating constant change, and a special Monday Motivation.   Biographies Benoy Thanjan Benoy Thanjan is the Founder and CEO of Reneu Energy, a solar development and consulting firm, and the host of the Solar Maverick Podcast. He also serves as a strategic advisor to multiple cleantech startups. Over his career, Benoy has developed more than 100 MWs of solar projects across the United States, advised on more than 1 GW of energy projects worldwide, helped launch some of the first residential solar tax equity funds at Tesla, and brokered approximately $50 million in renewable energy credit transactions. Before founding Reneu Energy, Benoy worked in Tesla's Project Finance Group as an environmental commodities trader, where he managed one of the company's largest environmental commodities portfolios. He originated renewable energy credit transactions and worked with senior leadership to develop monetization and hedging strategies supporting the company's expansion into East Coast markets. Benoy also served as Vice President at Vanguard Energy Partners, a solar and energy storage construction company, where he developed project finance solutions for commercial-scale solar portfolios. At Ridgewood Renewable Power, a private equity fund with approximately 125 MW of U.S. renewable energy assets, he evaluated investment opportunities, supported portfolio strategy, and played a key role in the sale of the firm's renewable energy portfolio. Earlier in his career, Benoy worked in Energy Structured Finance at Deloitte & Touche and in Financial Advisory Services at Ernst & Young. He also completed an internship on the trading floor at D. E. Shaw & Co., a global investment and technology development firm. Benoy holds an MBA in Finance from Rutgers University and a Bachelor of Science in Finance and Economics from the NYU Stern School of Business, where he was an Alumni Scholar. Nathan “Nate” Jovanelly Nathan Jovanelly is the Founder and CEO of SunRaise Capital, a technology and financial infrastructure platform supporting residential solar deployment. Before founding SunRaise Capital, Nate helped build IGS Solar and later launched its residential division. Under his leadership, the division grew to 150 MW and approximately $600 million in managed assets. Across his career, Nate has been involved in deploying more than $3 billion into residential, commercial, and utility-scale solar infrastructure. Nate has worked on solar projects for organizations including Amazon, FedEx, Unilever, and numerous nonprofit groups. He is also a licensed Professional Engineer with a background in chemical engineering and serves on the Board of SunVena Solar. Stay Connected: Benoy Thanjan Email: info@reneuenergy.com  LinkedIn: Benoy Thanjan Website: https://www.reneuenergy.com Website: https://www.solarmaverickpodcast.com/ Nathan Jovanelly LinkedIn: https://www.linkedin.com/in/natejov/ SunRaise Capital: https://www.sunraisecapital.com/ Sponsor: DCE This episode of the Solar Maverick Podcast is brought to you by DCE. Since 2009, DCE has built a unique model in the solar industry by bringing design, racking, and construction together under one roof. DCE Design provides full-scope electrical design across multiple project types. DCE Solar manufactures fixed-tilt, tracker, carport, and rooftop racking systems for commercial and utility-scale projects. DCE Services handles installation. DCE Services was named the number-one commercial ground-mount installer by Solar Power World in 2025 and the number-one commercial rooftop installer in 2026. This gives customers one company and one point of accountability, from utility-scale projects with IPPs and developers to turnkey commercial and industrial projects. Learn more about DCE and its company story in SMP 288: Why Solar Racking Can Make or Break a Project, featuring DCE Founder and CEO Bill Taylor: https://podcasts.apple.com/nz/podcast/smp-288-why-solar-racking-can-make-or-break-a-project/id1441876259?i=1000774004477 You can also meet the DCE team at RE+ Las Vegas or visit: https://dcesolar.com/ Thank you to DCE for supporting the Solar Maverick Podcast. Reneu Energy Reneu Energy provides expert consulting across solar and storage project development, financing, energy strategy, and environmental commodities. The team helps clients originate, structure, and execute opportunities across community solar, commercial and industrial, utility-scale, and Renewable Energy Credit markets. To learn more, visit: https://www.reneuenergy.com/ You can also email the team at info@reneuenergy.com. Listen and Subscribe Subscribe to the Solar Maverick Podcast on Apple Podcasts, Spotify, YouTube, or your favorite podcast platform. If you enjoyed this episode, please leave a rating and review. It helps more people discover conversations with the leaders shaping the future of solar, storage, and the energy transition.  

Kevin demonstrates ways you can adopt AI in the next 100 daysSummary of the PodcastOverviewEpisode 2 of a mini-series within The Next 100 Days Podcast where Kevin Appleby and Graham Arrowsmith discuss how AI is influencing their workPre-recording conversation touched on football transfers (Nick Pope, James Trafford) before the formal recording beganKevin identified four or five distinct areas of AI usage he wanted to coverClaude as a Personal AI Assistant (Pre-Call Preparation)Kevin's primary personal AI tool is Claude Cowork, which he connects to email, Notion, HubSpot, calendar, Dropbox, and Google DriveUse case: 10 minutes before a client call, ask Claude to summarise all recent interactions across those sources — it surfaces outstanding promises, discussion topics, and what to raiseClaude can also draft emails into Gmail drafts (not sent), giving a ~90% ready version the user can refineGraham noted concern about integrating a Synology server; Kevin noted most personal data lives in Dropbox or Google Drive and Claude can connect to bothAI Agents Running AutomaticallyKevin runs two scheduled agents without manual triggeringAgent 1 (daily): Scans calendar 30 days out and checks whether each diary entry has a corresponding task in Notion; creates missing tasks automatically, including a Notion page for notesAgent 2 (weekly): Scans upcoming 30 days for GrowCFO Show or Next 100 Days podcast recordings, researches the guest online, and appends notes (bio, website links, suggested questions) to the relevant Notion taskKevin deliberately ignores AI-suggested questions to preserve natural curiosity in conversation, but acknowledges suggested questions are useful for solo-host formatsThe same agent concept applies to client/partner meetings: an agent could automatically pull last interaction notes before any key meetingAdopt AI in Finance Operations (GrowCFO Context)GrowCFO's current quarterly theme is Intelligent Finance Operations — covering the end-to-end finance cycle from purchase orders to final accountsThree-way matching: AI can match purchase orders, goods receipt notes, and invoices automatically; if all match, the invoice is paid without human interventionFraud detection: AI spots anomalies far more effectively than manual reviewReporting: AI can generate board reports and dashboards from accounting systems; demonstrated in a GrowCFO webinar with tech partner Round Treasury using ClaudeKey value: AI removes grunt work so finance professionals can focus on what the numbers mean, not just crunching themAI's Impact on Finance JobsSignificant job displacement expected in transactional/lower-level finance roles; less so for senior finance business partners whose work is relationship-basedChallenge: AI often saves portions of multiple people's jobs rather than eliminating whole roles, making headcount reduction difficult — a pattern observed in shared services projects long before AISupply of finance professionals is falling, particularly FP&A specialists in the US; AI may initially ease recruitment pressure rather than cut headcountMost finance teams are still at the "chatting with ChatGPT" stage, not yet using agentic or Cowork-style toolsA live example of a fully agentic debt-chasing system: an AI agent that checks the debtors ledger, identifies overdue accounts, calls customers, and holds an intelligent conversation about outstanding invoicesAI Connectivity LimitationsClaude's current integration with Xero is poor — only capable of producing basic debtor reports rather than actionable overdue listsCopilot and Gemini do not support Dropbox connectivity, limiting their use in Kevin's personal setupHallucination risk is reduced when AI operates within a well-defined, high-quality data "cocoon" — the importance of a single source of truthFaster Close and Rolling Forecasts"Faster close" — producing monthly accounts quickly rather than 10–15 days after month-end — has been a finance aspiration for 20+ years and AI now makes it achievableRolling forecasts with multiple scenarios (e.g., 0%, 25%, 50% tariff scenarios) can be modelled and run instantly by AI rather than taking weeks to buildAI Governance in FinanceSegregation of duties must be replicated in AI workflows: different agents or human approvers for setting up suppliers, authorising accounts, and making paymentsA single "super AI agent" handling everything end-to-end is not yet appropriate or auditableUsing AI for Research & White PapersKevin used ChatGPT Deep Research to read ~30 published documents from major consultancies (PwC, Deloitte, EY, Accenture, BCG, Forrester, etc.) on AI in finance ops in 15–20 minutes — work that previously would have taken two graduate trainees a fortnightHe gave both ChatGPT and Claude the same prompt and found ChatGPT produced a better overall result, though Claude surfaced some content ChatGPT missed; he combined bothAI is excellent at generating and researching text but structuring the final document, choosing diagrams, and making it consumable remains a human taskVirtual Board / AI AvatarsA GrowCFO partner CFO used AI avatars of their board members to anticipate board reactions to finance reports before presentingKevin tested a "virtual advisory board" in AI, including public figures like Michael Heppell, and received useful diverse perspectivesThe deeper goal: frame board materials to open up discussion rather than trigger defensive reactionsKAIOS — Kevin's AI Operating System (Personal Project)Started from a ChatGPT conversation: "What if everything you've been taught about time management is a lie?" — leading to the insight that most productivity systems just create more tasks rather than freedomEvolved into KAIOS (Kevin's AI Operating System): a Dropbox-based knowledge structure containing Kevin's values, StrengthsFinder profile, career history, stories, and frameworks — accessible by any AI model via instructions stored within the structureA book outline, introduction, and chapter frameworks have been drafted with ChatGPT's help; the project paused in favour of building KAIOS first so the book has genuine personal stories and IP embeddedGraham suggested this could be Kevin's biggest career opportunity — analogous to how MeclabsAI has built a multi-million pound business around systematised marketing knowledgeKevin noted he prefers positioning himself as an expert using AI in finance rather than as an AI expert per se, given how fast the field movesCareer Reflection & Future DirectionBoth hosts reflected on the retirement vs. continued work question; Kevin expressed enthusiasm for staying engaged with AI developments and not wanting to reach the point where technology no longer makes senseKevin acknowledged competitors in the AI-for-finance space (e.g., Nicholas Boucher's AI Finance Club) but sees his differentiation in applying deep finance and personal IP rather than competing directlyGrowCFO Show is approaching episodeThe Next 100 Days Podcast Co-HostsGraham ArrowsmithGraham founded Finely Fettled in 2014 to provide data from The UK High Net Worth Database to marketers targeting affluent and high-net-worth customers. He's the founder of MicroYES, a Partner for MeclabsAI, creating lead generation AI Agents & Workflows and introducing the MeclabsAI Platform. Graham is an inCruises Independent Partner, and is building up interest from people around the world in the World's Largest Travel Membership - inCruises. You can sign up and access 21,000+ cruises, hotels and tours by clicking HEREKevin ApplebyKevin specialises in finance transformation and implementing business change. He's the COO of GrowCFO, which provides both community and CPD-accredited training designed to grow the next generation of finance leaders. You can find Kevin on LinkedIn and at kevinappleby.com

More than 1 in 5 U.S. companies now use artificial intelligence in their daily operations, according to a recent analysis of federal data from Goldman Sachs. In theory AI offers the promise of greater productivity and profits, but even with advances in capabilities, these systems are still fundamentally unpredictable, creating new liabilities for firms that use them.That's where insurance typically comes in. But AI is disrupting that business, too. Deloitte projects insurance for AI will grow into a nearly $5 billion global business by 2032.

More than 1 in 5 U.S. companies now use artificial intelligence in their daily operations, according to a recent analysis of federal data from Goldman Sachs. In theory AI offers the promise of greater productivity and profits, but even with advances in capabilities, these systems are still fundamentally unpredictable, creating new liabilities for firms that use them.That's where insurance typically comes in. But AI is disrupting that business, too. Deloitte projects insurance for AI will grow into a nearly $5 billion global business by 2032.

Welcome to this extra episode of the Leaders in Finance Podcast, recorded live after the third Leaders in Finance Compliance Event at the H'ART Museum in Amsterdam, where over 100 participants from more than 50 organisations came together to discuss the future of compliance in financial services, with a particular focus on AI, technology and crypto. Host Kees de Wit reflects on the day's key themes together with Leanne Joseph (moderator of the event), Tom van de Laar (Head of Financial Crime Compliance & Deputy Chief Compliance Officer, Rabobank), and Pieter van Doorn (Partner, Deloitte). Together, they discuss how rapidly developing technologies are changing the compliance profession, whether AI could eventually replace parts of the compliance function, and why human judgement will remain essential. They also reflect on the rise of crypto and neobanks, the impact of regulation such as MiCAR, and the continuing tension between innovation and effective oversight. And looking ahead: how can financial institutions, regulators and other public and private parties work together more effectively, build trust and respond with greater urgency to a rapidly changing financial landscape? A big thank you to all speakers, participants and our event partners for making this another inspiring edition of the Leaders in Finance Compliance Event. Leaders in Finance is made possible by the support of EY, Mogelijk Vastgoedfinancieringen, Duna and Lepaya. More information about our partners is available on our partner page. Want to stay up to date with Leaders in Finance? Subscribe to the newsletter. Questions, suggestions or feedback? We'd love to hear from you! You can reach us via email at info@leadersinfinance.nl and visit our website. Previous guests on the Leaders in Finance Podcast include Klaas Knot (former President of DNB), Frank Elderson (Member of the Executive Board of the ECB), Gerrit Zalm (former Minister of Finance and former CEO of ABN AMRO), Ingrid de Swart (Member of the Executive Board of a.s.r.), Pinar Abay (Member of the Management Board of ING and Head of Retail Banking), Robert Swaak (former CEO of ABN AMRO), Saul van Beurden (CEO of Consumer, Small & Business Banking at Wells Fargo), David Knibbe (CEO of NN Group), Janine Vos (Member of the Managing Board of Rabobank), Nadine Klokke (CEO of Knab), Maarten Edixhoven (CEO of Van Lanschot Kempen), Jeroen Rijpkema (CEO of Triodos Bank), Nout Wellink (former President of DNB), Onno Ruding (former Minister of Finance), Laura van Geest (Member of the Executive Board of the AFM), Ali Niknam (CEO of bunq), Joanne Kellermann (Chair of PFZW), Steven Maijoor (former Chair of ESMA), Jos Baeten (former CEO of a.s.r.), Karin van Baardwijk (CEO of Robeco), and Annette Mosman (CEO of APG).

How do leaders stay effective when work is changing faster than ever? Melissa Swift, author of Effective: How to Do Great Work in a Fast-Changing World, joins Charles Good on The Good Leadership Podcast to explore what effectiveness really looks like in an era of AI, constant disruption, workplace overload, and organizational chaos. Melissa shares her Effectiveness Architecture (knowledge, methods, people, and technology) and explains why working harder is often not the same as working better. The conversation dives into some of the toughest challenges facing leaders today: managing rumors and gossip without becoming part of them, leading with transparency during uncertainty, distinguishing unavoidable chaos from self-inflicted organizational dysfunction, and recognizing when structural problems make effectiveness nearly impossible. Melissa also explains how leaders can redesign work, clarify roles, reduce unnecessary complexity, and create the conditions for people to perform at their best. Charles and Melissa also explore the future of leadership in an AI-driven workplace, including how great leaders can use AI to extend their thinking rather than outsource it, why emotionally intelligent managers remain essential, and why hope may become one of the most important human leadership capabilities of the future. If you want practical strategies to become more effective, reduce workplace chaos, improve communication, and lead through rapid change, this episode offers a powerful playbook.Melissa Swift is a founder, CEO, speaker, and longtime people consultant who helps organizations, teams, and individuals succeed in increasingly complex and chaotic workplaces. She has held leadership roles at Mercer, Korn Ferry, and Deloitte and is the author of Effective: How to Do Great Work in a Fast-Changing World. Her work focuses on helping people and organizations improve effectiveness by strengthening how they use knowledge, methods, relationships, and technology.Chapters00:00 Introduction to effectiveness in leadership01:16 Melissa introduces the effectiveness architecture02:12 Managing rumors and gossip effectively03:25 Adopting an open but discerning stance04:23 Using peer leadership to clarify signals05:18 Responding to rumors and maintaining boundaries06:30 The power of transparency during ambiguity09:21 Understanding different types of chaos12:05 Recognizing self-inflicted chaos and zombie cabin environments14:38 Creating structural changes to reduce chaos15:22 Signs of structural barriers to effectiveness17:27 Planning a thoughtful exit from intractable roles19:44 The impact of emotionally intelligent management22:55 Technology's role in leadership and management24:39 Partnering with AI to extend leadership capabilities26:34 Cultivating hope as a leadership skill29:15 One small action for leaders to improve effectiveness30:03 Connecting with Melissa Swift and her workSubscribe to The Good Leadership Podcast: [⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Apple Podcasts⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠] | [⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Spotify⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠] | [⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠YouTube⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠]LinkedIn: ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠linkedin.com/in/charlesagood⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Substack Channel (Outlearn to Outperform): ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠charlesgood.substack.com⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠LinkedIn Newsletter (The Outlearn Advantage): [⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠Subscribe⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠]⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠The Institute for Management Studies

This week on Conflict Managed we welcome Amie Fox. Together we explore: The business case for disability inclusion Work systems: design with human nature and user experience in mind What do I need people to know, feel, and do after this interaction? Moving beyond compliance to see, value & recognize others Progress, not perfection Resistance as fear Feedback culture Conflict Managed is available wherever you get your podcasts and on YouTube @ 3pconflictrestoration. Amie Fox is a managing director, trainer and keynote speaker who helps founders, HR and operations leaders in growing organizations build practical people systems that improve employee engagement, manager confidence and business performance. She moved into this work after a portfolio career across communications, training, fundraising, corporate responsibility and operations exposed her to how differently people experience workplaces. A key turning point was struggling to find early employment as a visually impaired woman, which shaped her understanding of hidden barriers, workplace design and the importance of systems that help people perform at their best. Her credibility is grounded in recent and current roles: she set up A Fox Consulting, serves as a non-executive director at Vision Ireland, and has held senior CSR and operations roles at organizations including SCOPE Eyecare & Healthcare. Through her consulting, communications, fundraising and inclusion work, she has contributed to projects with organizations such as McDonald's, Telefonica, Deloitte, Novartis, Abbott, Newstalk and John Sisk & Sons. Conflict Managed is produced by Third Party Workplace Conflict Restoration Services and hosted by Merry Brown.   #ConflictManagement #WorkplaceCulture #Communication #Podcast

AI Search Visibility: Why Brands Now Compete to Be Recalled, Not Ranked How AI Search Is Rewriting the Rules of Brand Discovery For thirty years, brand visibility meant ranking on a results page. That's over. Today, the AI search a customer runs on ChatGPT, Perplexity, or Gemini decides whether your brand gets mentioned at all — and there's no page two, no scroll, no second chance inside that answer. In this episode, Joanne Z. Tan breaks down why AI search behaves nothing like traditional search, why SEO alone can't win an AI search citation, and what actually earns a brand a place inside an AI search result: third-party validation, consistency across the web, and being recognized as an authority rather than just indexed as a page. Drawing on Harvard Business Review, MIT Sloan Management Review, and Deloitte research, Joanne lays out why AI search visibility is now a strategic asset — and why the brands that treat it that way now will own the next decade of brand discovery. To read the full article To watch as an 8-minute video About Joanne Z. Tan Joanne Z. Tan is the Founder & CEO of 10 Plus Brand, Inc. and the creator of AIXD.world (AI Experience Design). A brand strategist, thought leadership coach, brand building and marketing expert in business brands and leadership personal brands, she works with C-suite executives, founders, and senior business leaders to build brands to be recognized by humans and by AI, through her proprietary AI Native Brand Architecture™ and AIXD™ frameworks. Her guiding principle: "User experience is brand experience, and brand experience determines enterprise destiny." Subscribe to our Newsletter  Visit our websites: 10PlusBrand.com AIXD.world 10PlusProfile.com © Copyright 2026, Joanne Z. Tan, 10 Plus Brand, Inc. All rights reserved.

Change is the one constant in today's work landscape.  This episode sparks fresh ideas on leading people through change without losing their trust or buy-in.  Expert guest Richard Gerver – who has worked with Google and Deloitte and will speak on Leading Humans Through Continuous Change at CPA Congress 2026 – looks at why restructures so often fail on the human side, not the technical one.  He explains why:  Asking questions beats presenting answers  Change works better when people have ownership of it  Organisations need to move from "managing change" to becoming comfortable with continuous change  Leaders should unlock the ideas and potential already inside their organisation  Psychological safety and leader vulnerability are essential to innovation  Resilience isn't about asking people to cope with more, it's something leaders help build  If you're leading a team through a restructure, a system migration or AI adoption, this conversation reframes resilience as something you build deliberately, not something people simply have or don't.  Give it a listen this week.  Host Tahn Sharpe, INTHEBLACK Editor, CPA Australia  Guest: Richard Gerver, award-winning change and leadership speaker with a background in education, innovation and human potential  For more information on Richard Gerver, head online to his international speakers bureau page.  Richard will speak at CPA Congress 2026.  Loving this episode?  Listen to more INTHEBLACK episodes and other CPA Australia podcasts on YouTube. https://www.youtube.com/@CPAaustralia/podcasts  And don't forget to click subscribe to the channel for a wide range of content that will help your career.  CPA Australia publishes four podcasts, providing commentary and thought leadership across business, finance and accounting:   With Interest https://www.cpaaustralia.com.au/tools-and-resources/podcasts/with-interest  INTHEBLACK https://www.cpaaustralia.com.au/tools-and-resources/podcasts/intheblack  INTHEBLACK Out Loud https://www.cpaaustralia.com.au/tools-and-resources/podcasts/intheblack-outloud  Excel Tips https://www.cpaaustralia.com.au/tools-and-resources/podcasts/excel-tips   Search for them in your podcast platform. Email the podcast team at podcasts@cpaaustralia.com.au  Chapters: 00:00 Richard Gerver on leadership vulnerability and empowering teams 00:29 Introduction to Richard Gerver, change leadership expert and educator 01:27 How Richard Gerver transformed a failing school through empowerment 04:54 Employee empowerment vs top-down change management 06:15 Why continuous change is the new normal for leaders 10:51 How to get employee buy-in for organisational change 13:00 Leadership, psychological safety and encouraging innovation 16:58 Resilience, failure and learning in a changing workplace 20:29 Leading humans through change, AI adoption and ERP transformation 22:25 Richard Gerver's advice for finance leaders managing major change projects 23:41 Key takeaways and CPA Australia Congress 2026 preview

Charisma can seem like something you either have or you don't. But according to Olivia Fox Cabane, charisma is a set of behaviors that can be learned, practiced, and strengthened. Olivia is one of the world's leading authorities on the science of charisma and the bestselling author of The Charisma Myth, which has been translated into 27 languages. She has lectured at Harvard, Yale, MIT, the Marine War College, and the United Nations, and has worked with leaders from organizations including Google, Deloitte, UBS, MGM, and TikTok. In this episode, Olivia joins Dr. Cindra Kamphoff to break down the three core components of charisma: presence, power, and warmth. She explains how each one influences the way others perceive us and shares practical strategies for becoming more confident, influential, and connected. You'll learn why presence may be the most important charisma skill in today's distracted world, how your mindset shapes your body language, and why trying to control every expression can actually make you appear less authentic. Olivia also shares strategies for handling pressure, quieting your inner critic, improving first impressions, and using visualization to shift how you show up before an important conversation, presentation, or performance. In this episode, you'll learn: The three building blocks of charisma: presence, power, and warmth. Why charisma is a skill you can learn and strengthen. Simple strategies to become more present in conversations and high-pressure moments. How visualization and mindset can influence your confidence and body language. Why self-acceptance and warmth are essential for authentic charisma. How to handle mistakes, pressure, and your inner critic with greater composure.   Olivia's website: https://www.oliviafoxcabane.com/ Mentally Strong Institute: https://mentallystronginstitute.com/ Request a Free Mental Breakthrough Call: https://freementalbreakthroughcall.com/ Download the National Confidence Research Study: https://confidencestudy.com/ Order Cindra's new Book: https://www.confidencetools.com/

Wellington council is calling for some changes to help with amalgamation, while Tom also has the latest on a fiasco involving a Deloitte report.

Brenden is the founder of MasterTalk, a coaching business he started to help ambitious executives & business owners become TOP 1% communicators in their industries so that they can accelerate their success in the workplace & companies.He also hosts a successful YouTube channel by the same name with over 11,000 subscribers.He has coached many executives from companies like Salesforce, Amazon, IBM, Morgan Stanley, Blue Cross, J. Walter Thompson, Deloitte, Verizon and the list goes on."HOST'S DETAILS:___________________________________________►Debra Chantry-Taylor is a Professional EOS Implementer | Entrepreneurial Leadership & Business Coach | Business Owner►See how she can help you: https://www.businessaction.com.au/____________________________________________GUESTS DETAILS:____________________________________________►Website: https://www.rockstarcommunicator.com/

Twenty-five years ago, on September 11, 2001, America changed forever. Nearly 3,000 people were murdered, the World Trade Center disappeared from the Manhattan skyline, the Pentagon burned, and Americans watched in horror as the nation they thought they understood suddenly became something very different. We were told that 19 Islamic terrorists armed with box cutters had surprised the most powerful intelligence and military establishment on earth, penetrated American airspace, struck three of the most important targets in the United States, and changed the course of world history in less than two hours. I believed what I was told—until I didn't.“For there is nothing covered, that shall not be revealed; neither hid, that shall not be known.” Luke 12:2 (KJB)For me, 9/11 has always been personal. I worked at Deloitte & Touche in the World Financial Center directly across the street from the Twin Towers. I had a friend who worked for Empire Blue Cross and spent time working in Tower 1. I was there. I visited him. I personally saw construction activity taking place around those floors that we understood at the time to involve elevator upgrade work. Twenty-five years later, there are far too many documented facts surrounding September 11 that deserve to be discussed openly instead of mocked, censored or dismissed with the magic words “conspiracy theory.”On this episode of the Prophecy News Podcast, we show you 10 things that no one ever told you about 9/11 and the events that ushered America into a dystopian surveillance society.

Can AI prepare a tax return—and catch errors the software misses? Blake shares how Claude Cowork reconciled books, built workpapers, and completed two business returns, while David examines EY's bonuses for human judgment, Deloitte's $21 million DEI settlement, and PwC's Evergrande exposure. Plus, Cloud Accountant Staffing's Jordan Sublette explains why onshoring is joining offshore hiring strategies for accounting firms.SponsorsOnPay - http://accountingpodcast.promo/onpayThomson Reuters - http://accountingpodcast.promo/taxautomationSavant Labs - http://accountingpodcast.promo/savantCloud Accountant Staffing - http://accountingpodcast.promo/casChapters(00:00) - Ethics And Job Security (00:18) - Show Intro And Headlines (02:34) - Livestream Shoutouts (03:03) - Claude CoWork Tax Prep (06:54) - AI Catches TaxAct Errors (11:26) - Accrual Buys Puzzle (18:19) - EY Human Skills Bonuses (20:33) - Deloitte DEI Settlement (27:43) - Salaries And Big Four Fallout (31:22) - PwC Evergrande Liability (35:03) - Grant Thornton Buys CBIZ (36:25) - CBIZ Returns Reality Check (37:23) - Marcum Deal And Partner Payouts (38:34) - IRS Revenue Hits Record (40:50) - Chat Reactions And AI Ledgers (44:03) - AI Cost Seg Pitfalls (53:00) - Jordan Joins And Origin Story (55:06) - Jason Staats Partnership Explained (59:37) - Onshoring Portal And Pricing (01:04:58) - Candidate Vetting And Training (01:09:37) - Meetups And CPE Wrap Up  Show NotesAccrual to Acquire Puzzle to Expand Into Client Accounting Serviceshttps://www.cpapracticeadvisor.com/2026/09/02/accrual-takes-the-leap-into-cas-by-acquiring-puzzle/189565/EY Rewards Employees' Human Skills With $100Mhttps://www.accountingtoday.com/news/ey-rewards-employees-human-skills-with-100mDeloitte to Pay $21.5M to Settle Claims Its DEI Programs Violated Federal Civil Rights Lawhttps://www.cfo.com/news/deloitte-to-pay-215m-to-settle-claims-its-dei-programs-violated-federal-c/828948/Accounting Firm Salaries Rose Slightly This Yearhttps://www.cfobrew.com/stories/accounting-firm-salaries-rose-slightly-this-yearKPMG Australia to Axe 5% of Workforcehttps://www.cfo.com/news/kpmg-australia-to-axe-5-of-workforce-John-Sams/828636/PwC International Can't Exit Evergrande Case, HK Court Ruleshttps://www.bloomberg.com/news/articles/2026-08-26/pwc-international-can-t-exit-evergrande-case-hk-court-rulesCBIZ Told Grant Thornton No, No, No, Before Saying Yeshttps://cpatrendlines.com/2026/09/01/cbiz-told-grant-thornton-no-no-no-before-saying-yes/IRS Enforcement Activity Fell Despite Record Tax Collections, TIGTA Sayshttps://www.journalofaccountancy.com/news/2026/aug/irs-enforcement-activity-fell-despite-record-tax-collections-tigta-says/Cost Segregation in the Age of AI: What the IRS Audit Technique Guidelines Revealhttps://www.accountingtoday.com/opinion/cost-segregation-in-the-age-of-ai-what-the-irs-audit-technique-guidelines-revealNeed CPE?Get CPE for listening to podcasts with Earmark: https://earmarkcpe.comSubscribe to the Earmark Podcast: https://podcast.earmarkcpe.comGet in TouchThanks for listening and the great reviews! We appreciate you! Follow and tweet @BlakeTOliver and @DavidLeary. Find us on Facebook and Instagram. If you like what you hear, please do us a favor and write a review on Apple Podcasts or Podchaser. Call us and leave a voicemail; maybe we'll play it on the show. DIAL (202) 695-1040.SponsorshipsAre you interested in sponsoring The Accounting Podcast? For details, read the prospectus.Need Accounting Conference Info? Check out our new website - accountingconferences.comLimited edition shirts, stickers, and other necessitiesTeePublic Store: http://cloudacctpod.link/merchSubscribeApple Podcasts: http://cloudacctpod.link/ApplePodcastsYouTube: https://www.youtube.com/@TheAccountingPodcastSpotify: http://cloudacctpod.link/SpotifyPodchaser: http://cloudacctpod.link/podchaserStitcher: http://cloudacctpod.link/StitcherOvercast: http://cloudacctpod.link/OvercastClassifieds Fearless Foundry - www.advisoryamplified.comExpense Bot - https://www.expensebot.ai/accountantProfitRoot - https://tryprofitroot.com/Want to get the word out about your newsletter, webinar, party, Facebook group, podcast, e-book, job posting, or that fancy Excel macro you just created? Let the listeners of The Accounting Podcast know by running a classified ad. Go here to create your classified ad: https://cloudacctpod.link/RunClassifiedAdTranscriptsTh...

Episode Summary Benoy Thanjan, host of the Solar Maverick Podcast, sits down with Li Wang, Head of Marketing for Reneu Energy and the Solar Maverick Podcast, for a deeply personal conversation about surviving the attacks on the World Trade Center and how that day changed the course of his life. Benoy shares what he experienced inside One World Trade Center when the first plane struck, the chaos of the evacuation, and the moments that have remained with him for the past 25 years. The conversation goes beyond the events of that morning and explores the lessons Benoy carried forward: appreciating how fragile life can be, living more intentionally, helping others when it matters most, and remembering the courage and sacrifice displayed by so many people that day. Benoy also reflects on how September 11 shaped his approach to entrepreneurship, leadership, relationships, gratitude, and making the most of the time we are given. The episode also features an AI-generated visual intro and AI-generated imagery of Benoy, created to support the storytelling and reflective tone of the episode. This episode is a remembrance of those who were lost, those who responded, and everyone whose life was forever changed by September 11. Never forget. Biographies Benoy Thanjan Benoy Thanjan is the Founder and CEO of Reneu Energy, a solar development and consulting firm, and the host of the Solar Maverick Podcast. He also serves as a strategic advisor to multiple cleantech startups. Over his career, Benoy has developed more than 100 MWs of solar projects across the United States, advised on more than 1 GW of energy projects worldwide, helped launch some of the first residential solar tax equity funds at Tesla, and brokered approximately $50 million in renewable energy credit transactions. Before founding Reneu Energy, Benoy worked in Tesla's Project Finance Group as an environmental commodities trader, where he managed one of the company's largest environmental commodities portfolios. He originated renewable energy credit transactions and worked with senior leadership to develop monetization and hedging strategies supporting the company's expansion into East Coast markets. Benoy also served as Vice President at Vanguard Energy Partners, a solar and energy storage construction company, where he developed project finance solutions for commercial-scale solar portfolios. At Ridgewood Renewable Power, a private equity fund with approximately 125 MW of U.S. renewable energy assets, he evaluated investment opportunities, supported portfolio strategy, and played a key role in the sale of the firm's renewable energy portfolio. Earlier in his career, Benoy worked in Energy Structured Finance at Deloitte & Touche and in Financial Advisory Services at Ernst & Young. He also completed an internship on the trading floor at D. E. Shaw & Co., a global investment and technology development firm. Benoy holds an MBA in Finance from Rutgers University and a Bachelor of Science in Finance and Economics from the NYU Stern School of Business, where he was an Alumni Scholar.   Li Wang Li Wang is the Marketing Director of Reneu Energy and the Solar Maverick Podcast. He was there when the podcast began in 2018, encouraged Benoy to launch the show, and helped create the Solar Maverick name. Li is also a former journalist and the founder of Little Ox Workshop, a Squarespace website design studio. His journalism career includes working as a business reporter for the Times of Trenton, arts editor for the Honolulu Weekly, and film critic for the Harrisburg Patriot-News. He later earned a professional certificate in digital marketing from New York University and moved into website design, brand messaging, and content marketing. Stay Connected: Benoy Thanjan Email: info@reneuenergy.com  LinkedIn: Benoy Thanjan Website: https://www.reneuenergy.com Website: https://www.solarmaverickpodcast.com/   Li Wang LinkedIn: https://www.linkedin.com/in/liwang22/ Little Ox Workshop: https://www.littleoxworkshop.com/   Related Episodes SMP 234: A 9/11 Survivor's Journey: Resilience, Gratitude, and Purpose in Solar https://solarmaverick.podbean.com/e/smp-234-a-911-survivor-s-journey-resilience-gratitude-and-purpose-in-solar/   SMP 01: Introduction to the Solar Maverick Podcast with Benoy and Li https://solarmaverick.podbean.com/e/smp-01-introduction-to-the-solar-maverick-podcast-with-benoy-and-li/ If you have any questions or comments, email us at info@reneuenergy.com.   Sponsor This episode of the Solar Maverick Podcast is brought to you by Reneu Energy. Reneu Energy works with companies and organizations on renewable energy strategy, project development, owner's representation, project finance, renewable energy credits, and market advisory services. To learn more, visit: https://www.reneuenergy.com Listen and Subscribe Subscribe to the Solar Maverick Podcast on Apple Podcasts, Spotify, YouTube, or your favorite podcast platform. If you enjoyed this episode, please leave a rating and review. It helps more people discover conversations with the leaders shaping the future of solar, storage, and the energy transition.  

What does it actually take to buy and grow a roofing company?Nicholas Riley left Deloitte to buy Driftwood Builders Roofing, a 20-year-old roofing company in Austin, Texas. After reviewing roughly 2,000 businesses, he finally made his first home service acquisition.In this episode, John Wilson and Nicholas break down the economics of the roofing business, including $20,000 average roof replacements, cash vs. insurance margins, roofing marketing, direct mail, referral partnerships, and competing in a market with more than 1,000 roofing companies.They also cover Nicholas' biggest mistakes during his first year as an operator, what buyers should look for when acquiring a home service business, and why roofing ultimately comes down to two things: marketing and sales.━━━━━━━━━━━━━━In This Episode━━━━━━━━━━━━━━• Buying your first home service business• The economics of running a roofing company• $20K average roof replacements• Roofing marketing and direct mail• Building referral partnerships that generate leads• Why seller integrity matters when buying a business• Competing against private equity and local roofers• Why roofing is a marketing and sales business━━━━━━━━━━━━━━Sponsors ━━━━━━━━━━━━━━The Military Veteran (TMV)Hiring a VP, GM, or C-suite leader? The Military Veteran (TMV) specializes in executive search for home service businesses, connecting you with proven veteran leaders who know how to execute, build teams, and drive growth. Select Owned and Operated as your referral source when you schedule a consultation: https://themilvet.typeform.com/to/BDwkmCU0?typeform-source=www.themilvet.orgBig ReputationGet more from your Google Business Profile with Big Reputation. Automate reviews, improve local visibility, and turn more Google searches into inbound calls. Learn more: https://www.bigreputation.ai/oao?utm_source=oao&utm_medium=paid━━━━━━━━━━━━━━Connect━━━━━━━━━━━━━━John Wilsonhttps://www.linkedin.com/in/johnbwilson1/Nicholas Rileyhttps://www.instagram.com/nickriley2/https://driftwoodbuildersroofing.com/Owned and Operatedhttps://www.ownedandoperated.com/Send Us Mail!More Ways To Connect with OAOStart HereOwned and Operated Newsletter Bonus Videos From JohnLeave a ReviewJohn Wilson, CEO of Wilson CompaniesJack Carr, CEO of Rapid HVAC

Subscribe to LMSU's Patreon for some much needed Good News Stories on climate!This week our Bonus episode includes Frankie, Luke and Tennant look to the light to focus on some of things that have us excited and optimistic about climate action - because goodness knows, we could all use an upper! Run, don't walk, over to www.letmesumup.net and subscribe to our Patreon to get this sweet, sweet BoCo!—The Great Data Centre Debate rages on and your intrepid hosts are here trying to inuit just what has been going on at National Cabinet lately. The official text out of the recent meeting isn't much to go on, but according to the Bluesky community that collectively lost its mind, its carveouts galore for coal and gas powered data centres! Not so says Minister Bowen, who hosed the outrage down, but where does this leave us? State owned generation will be able to seek a Commonwealth-decided exemption from the fully-offset-with-renewables piece - IF - it will not raise costs for other users. This goes beyond just NT and QLD state owned energy (hi there TAS), but where is all the actual investment going anyway? Mostly NSW and VIC. So much more to play out here, and you know the LMSU crew is along for the rideOur main courseThe Albanese Government's long-anticipated-and-finally-arrived ‘Securing Australia's Cleaner Fuels Industry: Consultation paper on a demand mechanism' provided the feedstock for this episode's main course! Low carbon liquid fuels would help bolster Australia's fuel security and resilience, and provide a lower emissions alternative to fossil fuels, but Australia has been exporting feedstocks like canola for other countries to make LCLFs. Can Australia grow a domestic LCLF industry? What will the costs of LCLF turn out to be with more demand and supply - are learning rates a thing here? And should electrification feature more centrally in this policy response? Your intrepid hosts muse on these - and more! - in this thoughtful consultation paper.We also refer back to Episode 77, ‘Hey Big Spender, Blend A Little SAF For Me (Low Carbon, So Refined)' where we covered the CEFC and Deloitte paper on LCLFs.One more thingsTennant's One More Thing is: Irrelevance, redux: Spooky Funny Island Edition! Tennant reckons Widow's Bay is worth a watch (but don't watch it alone, OR all at once!)Frankie's One More Thing is: the just published report from UNEP ‘Limiting Overshoot: Navigating exceedance of 1.5C and pathways towards return'Luke's One More Thing is: an article in Renew Economy from energy eminence Alan Pears on battery integrated appliances like induction cooktops and their role in the electrification nation!And that's it for now, Summerupperers. There is now a one-stop-shop for all your LMSU needs: head toletmesumup.net to support us on Patreon, procure merch, find back episodes, and leave us a voicemail!

Send us Fan MailBCG is racing to hire forward deployed engineers – and the application window closes September 17.This year's pay range for campus hires is $110,000 to $190,000, up from $110,000 to $160,000 last year. Same job, new title, $30,000 more at the top of the range.Big Tech has cut entry-level hiring by more than half since 2019, opening a window for BCG to hire AI talent it usually couldn't afford. That window won't stay open forever.But BCG isn't the only firm racing through it. McKinsey and Deloitte are already building similar teams.Applications for both the AI engineer and AI scientist roles close September 17. If BCG's on your list, get moving.Resources:BCG's actual postings, for the requirements: Forward Deployed AI Engineer and Forward Deployed AI Scientist – both close September 17The pay-band math and what it signals about BCG's business: read it on our siteEvery consulting firm's application deadlinesBCG still filters on soft skills and executive presence as much as Python – Black Belt builds the interview skills the posting doesn't testConnect With Management ConsultedCreate a free MC account or download the MC app (Apple, Android) to start your prep todaySchedule a free 15min consultation with the MC TeamWatch the video version of the podcast on YouTubeFollow us on LinkedIn, Instagram, and TikTokJoin an upcoming live event – case interviews demos, expert panels, and more

El caso Plus Ultra ha vuelto a primera plana tras el paréntesis de agosto. Julio Martínez Sola, presidente de la aerolínea cuando el Consejo de Ministros aprobó el rescate, y Roberto Roselli, entonces consejero delegado, han ratificado ante el juez y ante la fiscal de Anticorrupción el pago pactado del 1% de la ayuda, unos 530.000 euros, a sociedades vinculadas a Julio Martínez Martínez, amigo y representante de José Luis Rodríguez Zapatero. En los mensajes intervenidos los propios investigados llamaban “mordida” a ese pago. Era una comisión a éxito y se abonó ante la duda de que el expediente descarrilase. Ninguno de los dos señala una voluntad concreta que tuvieran que comprar, pagaron porque creían que había que pagar. La secuencia de los hechos importa aquí más que el porcentaje pactado. En abril de 2020 Martínez Sola recibió una llamada desde un número oculto, al otro lado estaba Zapatero, que se ofreció a hacer gestiones para su empresa. En septiembre la aerolínea pidió la ayuda del fondo de la SEPI para empresas estratégicas, en enero de 2021 se firmó el contrato de consultoría que daba cobertura al 1% y el 9 de marzo el Consejo de Ministros autorizó 53 millones de euros de rescate. Una semana antes los directivos de Plus Ultra ya sabían que la operación estaba hecha. Fue el tercer expediente más rápido del fondo y uno de los menos vigilados, la SEPI no colocó a nadie en el consejo de una empresa con 350 empleados y una cuota de mercado del 0,03%. Lo que ha convertido el asunto en algo mayor que un caso de comisiones es una frase de Martínez Sola. Asegura que quien mandaba de verdad era el venezolano Rodolfo Reyes con el 64% del capital y la última palabra en el consejo, también fue él quien tomó la decisión de acudir a Zapatero. Roselli lo ha confirmado. La expresión que ambos emplean, control efectivo, es la que utiliza la normativa europea para exigir que una aerolínea comunitaria esté en manos de nacionales de la Unión Europea. Si Reyes mandaba, Plus Ultra no era una compañía española y no reunía por lo tanto los requisitos para solicitar rescates. La empresa alega que las acciones estaban a nombre de su esposa, española, pero la titularidad nominal no basta, lo decisivo es quién controla la empresa de facto. La documentación entregada a la SEPI presentaba las empresas de Reyes como accionistas distintos, y sobre esos datos trabajó Deloitte al concluir que no existían pactos entre socios. AESA avaló el rescate y hoy se remite a la información que le facilitaron los operadores. El juez Calama ha pedido a la policía judicial un informe sobre el accionariado real. El contraste con el resto de los rescates a aerolíneas es determinante, Air Europa aceptó que le impusiesen un consejero delegado junto a unas condiciones duras y devolvió sus 475 millones el año pasado. Plus Ultra ha pagado intereses, casi nada del principal y no atendió el vencimiento de marzo. La causa estuvo archivada desde 2023 por un plazo mal calculado y resucitó cuando Francia y Suiza pidieron cooperación por una red de blanqueo con ramificaciones venezolanas. Zapatero declaró en junio como investigado y negó haber hablado con la SEPI o con el Gobierno. El caso Plus Ultra tiene aún muchas preguntas sin responder y salpica de forma directa no solo a Zapatero, también al actual Gobierno que fue quien, de forma colegiada, aprobó aquel rescate. En La ContraRéplica: 0:00 Introducción 4:00 La mordida de Plus Ultra 34:16 La huelga de Airbus 46:39 Islandia y la UE 55:45 Sánchez y Ceuta · Canal de Telegram: https://t.me/lacontracronica · “Contra el pesimismo”… https://amzn.to/4m1RX2R · “Hispanos. Breve historia de los pueblos de habla hispana”… https://amzn.to/428js1G · “La ContraHistoria del comunismo”… https://amzn.to/39QP2KE · “La ContraHistoria de España. Auge, caída y vuelta a empezar de un país en 28 episodios”… https://amzn.to/3kXcZ6i · “Contra la Revolución Francesa”… https://amzn.to/4aF0LpZ · “Lutero, Calvino y Trento, la Reforma que no fue”… https://amzn.to/3shKOlK Apoya La Contra en: · Patreon... https://www.patreon.com/diazvillanueva · iVoox... https://www.ivoox.com/podcast-contracronica_sq_f1267769_1.html · Paypal... https://www.paypal.me/diazvillanueva Sígueme en: · Web... https://diazvillanueva.com · Twitter... https://twitter.com/diazvillanueva · Facebook... https://www.facebook.com/fernandodiazvillanueva1/ · Instagram... https://www.instagram.com/diazvillanueva · Linkedin… https://www.linkedin.com/in/fernando-d%C3%ADaz-villanueva-7303865/ · Flickr... https://www.flickr.com/photos/147276463@N05/?/ · Pinterest... https://www.pinterest.com/fernandodiazvillanueva Encuentra mis libros en: · Amazon... https://www.amazon.es/Fernando-Diaz-Villanueva/e/B00J2ASBXM #FernandoDiazVillanueva #plusultra #zapatero Escucha el episodio completo en la app de iVoox, o descubre todo el catálogo de iVoox Originals

Samantha Fowlds is an authority on change and knowledge management. We discuss the numerous disruptions in which we are living and working. Listen to her advice about how to support employees in this environment by helping them feel psychologically safe in order to perform.Samantha is a Principal Consultant, Executive Coach, and Change Leadership Advisor who partners with senior leaders in Financial Services and Technology to lead complex human capital development and enterprise transformation initiatives.Her work connects psychology, leadership, and change to help organizations build real capability in their people. Samantha holds a Master of Science in Knowledge Management and Consultancy with graduate studies in Applied Positive Psychology and Coaching Psychology, and has worked inside organizations like Oracle, DXC, KPMG, Deloitte, RBC, TD Bank, Scotiabank, Toronto Stock Exchange, Sunnybrook Hospital, and Cadillac Fairview.

What do you do when the funding that anchored your HR transformation disappears unexpectedly? For Seneca Polytechnic, a public college with 44,000 students in the greater Toronto area, the answer was redesigning the transformation itself. In this episode, we speak with Diana Mohan, Executive Director of Transformation and Change at Seneca Polytechnic — a 2026 HR Pacesetter Award winner — about how her team chose to continue forward rather than press pause when regulatory changes created a structural deficit across Canada's higher education sector. Diana shares how Seneca's HR team embraced AI to totally change how HR operates and scale HR services. Their approach began with listening, building trust and capability across the team, and empowering people at every level to create new AI use cases. The result has not only been more efficient service delivery and a better employee experience—it's been a strategic shift to an insight-driven, advisory HR function. Diana Mohan is a transformation specialist and trusted advisor specializing in over a decade of organizational strategy, AI adoption, and the future of work. With a career spanning Deloitte, Kearney, and executive leadership roles, she has helped many organizations translate bold ideas into practical transformation programs, enabling leaders to navigate uncertainty, accelerate change, and prepare for what's next. Additional Information New Research: How AI Transforms $400 Billion Of Corporate Learning US Workforce In 2035: A Few More Workers, A Lot More Output The Great Decoupling: How Workers Became Disconnected From Companies And AI Will Accelerate This Trend Get Galileo: How Seneca Transformed HR Chapters (00:00:00) - What Works: The Future of Work(00:00:41) - HR Pacesetter Award Winners: Diana Mouhand(00:01:52) - In the Know: The HR Team at Seneca Polytechnic(00:02:47) - How HR is on a transformation journey(00:05:13) - How Cognizant Went From Transformation to a Pivot(00:06:59) - How To Build Trust in AI at Seneca(00:12:19) - The HR org chart: AI Agents(00:16:10) - What Makes an HR Team Flexible in the Age of AI(00:18:10) - WSJD Live: The AI Transformation

It's not always the grandest idea or loudest voice in the room that shapes the future.   Breakthroughs often begin with someone willing to see possibilities others overlook. Someone who questions the status quo and is curious about why things happen. But do businesses create the conditions to support those pioneers and allow their ideas to become progress? In this episode, Anne-Marie Imafidon, co-founder and CEO of Stemettes, joins Scott Campbell, AI Advantage partner at Deloitte, to challenge the myth of lone visionaries and explore what truly makes a pioneer.   Listen to find out:  Why curiosity needs discipline   Why pioneers aren't always lone mavericks How to keep going when an idea feels impossible How to get your idea heard   Enjoyed this episode? Visit our website to learn more: deloitte.co.uk/greenroompodcasts  Find out more about social impact partner, Teach First: teachfirst.org.uk   Guests: Anne-Marie Imafidon and Scott Campbell  Hosts: Steph Dobbs and Gerry Popova   Original music: Ali Barrett  Recording date and location: London, 19.08.26 

Au sommaire : Les augmentations de salaires ont baissé en 2026, passant de 2,3-2,5% à 1,8-2,1%, selon l'étude exclusive du cabinet Deloitte.Une loi intégrale sur les violences sexistes et sexuelles arrive en commission à l'Assemblée nationale avec 7 articles concernant les entreprises.Les réassureurs vont baisser leurs tarifs de 12 à 15% cette année suite à une forte hausse des prix depuis 2023 et une relance de la concurrence.Le prix des voitures électriques a baissé de 18% en 5 ans tandis que celui des voitures thermiques a augmenté de 2%, notamment grâce à la réduction du coût des batteries.13,6% des communes ont augmenté leur taxe foncière pour 2026, avec des hausses particulièrement fortes en région parisienne et en Corse.Hébergé par Audiomeans. Visitez audiomeans.fr/politique-de-confidentialite pour plus d'informations.

AI may own the headlines, but it's far from the only technology reshaping work. Drawing on emerging tech insights from Gartner, Stanford, and Deloitte, this episode explores the technologies flying under the radar, from robotics and digital twins to quantum, wearables, and biotechnology, and what leaders need to know about their impact on people, teams, and the future of work. In this episode: Emi Baressi, Tom Bradshaw, Nic Krueger, Lee Crowson, Kate Morales, PhD, LindaAnn Rogers    I/O Career Accelerator Course: https://www.seboc.com/job Visit us https://www.seboc.com/ Follow us on LinkedIn: https://bit.ly/sebocLI Join an open-mic event: https://www.seboc.com/events   References:   Aziz, F., Qazi, A. and Li, H. (2026), “Digital twin technology and operations management efficiency in construction: the roles of information transparency, collaboration and digital readiness”, Journal of Enterprise Information Management, Vol. ahead-of-print No. ahead-of-print.  https://doi.org/10.1108/JEIM-04-2026-0719  Crawford, K., Posada, J., Okueso, Y.E., Higgins, E., Lachin, L. and Hamidi, F. (2026), “Inquiry into accessibility service possibilities for blind and low-vision university students through co-designing a 3D-printed tactile campus map”, Journal of Enabling Technologies, Vol. 20 No. 2, pp. 73–87. https://doi.org/10.1108/JET-11-2024-0076  Eriksson, K.M., Olsson, A.K. and Carlsson, L. (2024), “Beyond lean production practices and Industry 4.0 technologies toward the human-centric Industry 5.0”, Technological Sustainability, Vol. 3 No. 3, pp. 286–308. https://doi.org/10.1108/TECHS-11-2023-0049  JLL (n.d.), “JLL taps into the power of neuroscience to shape the future of work”, JLL. https://www.jll.com/en-us/newsroom/jll-taps-into-the-power-of-neuroscience-to-shape-the-future-of-work  Kawala-Sterniuk, A., Browarska, N., Al-Bakri, A., Pelc, M., Zygarlicki, J., Sidikova, M., Martinek, R. and Gorzelanczyk, E.J. (2021), “Summary of over fifty years with brain-computer interfaces—A review”, Brain Sciences, Vol. 11 No. 1, Article 43. https://doi.org/10.3390/brainsci11010043  Kies, A., De Keyser, A., Jaramillo, S., Li, J., Tang, Y. and Ud Din, I. (2025), “Wired for work: brain-computer interfaces' impact on frontline employees' well-being”, Journal of Service Management, Vol. 36 No. 1, pp. 1–26. https://doi.org/10.1108/JOSM-03-2024-0098  Li, L., Xu, Y., Zhan, Y., Pan, M., Lo, C.K.Y. and Zhou, H. (2026), “The impact of digital twin innovation on firm operational performance: a knowledge-based perspective”, International Journal of Operations & Production Management, Vol. 46 No. 9, pp. 1454–1476. https://doi.org/10.1108/IJOPM-07-2025-0681  Tathavadekar, V.P. and Mahankale, N.R. (2026), “Brain–computer interface integration in corporate learning: bidirectional enhancement frameworks for neurologically-augmented knowledge workers”, Strategic HR Review, Vol. 25 No. 3, pp. 89–92. https://doi.org/10.1108/SHR-06-2026-218  Valencia-Arias A, Velasquez Salas S, Cardona-Acevedo S, Rua Hernandez JC, Ramírez-Ramírez DM, Benjumea-Arias ML (2026), "Technology-driven workplace transformation: changes, challenges, and opportunities". Management & Sustainability: An Arab Review, Vol. ahead-of-print No. ahead-of-print. https://doi.org/10.1108/MSAR-08-2025-0295  Wei, X., Tan, W. and Tian, R. (2026), “The double-edged sword effects of STARA use on employee performance and well-being: a meta-analysis based on job demands-resources (JD-R) model”, Information Technology & People, Vol. 39 No. 5, pp. 2312–2340.  https://doi.org/10.1108/ITP-01-2025-0020 Reports  Deloitte  Deloitte. (2025, December 10). Tech trends 2026: As technology innovation and adoption accelerate, five trends reveal how successful organizations are moving from experimentation to impact. Deloitte Insights. https://www.deloitte.com/us/en/insights/topics/technology-management/tech-trends.html  Gartner  Stephan, C., Buytendijk, F., Searle, S., Alvarez, G., Li, B., & Chen, O. (2026, July 23). Hype cycle for emerging technologies, 2026. Gartner. https://www.gartner.com/en/documents/8163629  Stanford  Stanford Emerging Technology Review. (2026). The Stanford Emerging Technology Review 2026. Hoover Institution & Stanford School of Engineering, Stanford University. Stanford Emerging Technology Review 2026

Send us Fan MailDeloitte, PwC, IBM, and 4,500 other companies use Adobe Experience Cloud products.And they can't find enough people who know how to implement them.Nick Hilton, Tyler Tu, and Brian Au from Adobe's Customer Experience Certifications team named the Adobe jobs they're struggling to fill: MarTech analysts, implementation consultants, solution architects. None of them require a technical degree – what they require is proof you can use the tools.That's what Adobe is putting in front of graduate students this fall in the Case Competition World Cup Graduate Track.The 50 teams selected get a sandbox Customer Journey Analytics environment and a real business problem to solve inside it, not documentation to read about. Plus a free Adobe certification voucher.Free to enter. Applications close September 13.Resources:Grad students, apply to the Case Competition World Cup – Graduate Track presented by Adobe by Sunday, September 13Start free with Adobe CX courses and certificationsStudents: Get 50% off your certification exam applied automatically at checkout (use your university email)Connect With Management ConsultedCreate a free MC account or download the MC app (Apple, Android) to start your prep todaySchedule a free 15min consultation with the MC TeamWatch the video version of the podcast on YouTubeFollow us on LinkedIn, Instagram, and TikTokJoin an upcoming live event – case interviews demos, expert panels, and more

Vláda chce příští rok přidat peníze na investice, zdravotnictví nebo obranu. To ale znamená, že bude hospodařit s druhým největším schodkem v historii Česka. Návrh rozpočtu s deficitem 389 miliard korun kritizuje nejen opozice, ale i koaliční partneři hnutí ANO a prezident. „Pro běžného smrtelníka, dokonce ani pro profesionálního ekonoma ty stovky miliard ani procenta HDP představitelné nejsou,“ upozorňuje v Osobnosti Plus David Marek, hlavní ekonom společnosti Deloitte.Všechny díly podcastu Osobnost Plus můžete pohodlně poslouchat v mobilní aplikaci mujRozhlas pro Android a iOS nebo na webu mujRozhlas.cz.

Join host Kyle Forrest, Deloitte's Benoit Hardy-Vallée, and Cognota's Ryan Austin as they discuss the emergence of learning operations as a distinct and critical operating discipline.

This CLOC Talk episode brings together three seasoned legal operations leaders, Jenn McCarron, Co-Founder & CEO of Contracts.ai, Mary Shen O'Carroll, CEO, LegalEng Consulting Group and host Mark Ross, Principal and Growth and Markets Leader at Deloitte for a candid conversation recorded on the floor of CGI 2026. They reflect on the unexpected “right place, right time” opportunities, bold career decisions, and pivotal moments that shaped their journeys in a profession that was still finding its identity. Through personal stories, they share the lessons, risks, and opportunities that shaped their careers in legal operations. Tune in for a must-listen conversation for anyone navigating the evolving world of legal operations.

Send us Fan MailPhil Weiss, Founder of Apprise Wealth joined Tony on this episode of Get Ready Before Life Happens to talk about how understanding your financial situation, aligning your resources with your values, and having regular money conversations can help you navigate major life changes with more confidence and intention. Life transitions require clarity and confidence.Key TakeawaysYour money story shapes how you make financial decisions.Knowing your financial situation helps you see what's possible.Moving from your accumulation (saving) phase to your spending (decumulation) phase requires a mindset shift.Align time, energy, attention, and money with what matters most.Regular “money dates” strengthen communication and clarity.During major transitions, pause and avoid rushed decisions.Asking questions builds confidence and better outcomes.

In Episode 54 of The League, Benoy Thanjan and David Magid examine how clean energy is becoming increasingly connected to national security, grid resilience and government policy. They discuss Michigan's new statewide permitting process, which could overcome local zoning barriers and accelerate utility-scale solar development. They also explore Westinghouse's proposed nuclear microreactor at Fort Drum and its potential to strengthen military energy resilience. The conversation then turns to the national-security risks associated with foreign-made solar and storage equipment, including inverters, batteries and control software. Finally, Benoy and David examine California's record renewable-energy curtailment and why additional storage, transmission and grid modernization are essential to avoid wasting clean electricity.   Host Bio: Benoy Thanjan Benoy Thanjan is the Founder and CEO of Reneu Energy, solar developer and consulting firm, and a strategic advisor to multiple cleantech startups. Over his career, Benoy has developed over 100 MWs of solar projects across the U.S., helped launch the first residential solar tax equity funds at Tesla, and brokered $45 million in Renewable Energy Credits (“REC”) transactions. Prior to founding Reneu Energy, Benoy was the Environmental Commodities Trader in Tesla's Project Finance Group, where he managed one of the largest environmental commodities portfolios. He originated REC trades and co-developed a monetization and hedging strategy with senior leadership to enter the East Coast market.  As Vice President at Vanguard Energy Partners, Benoy crafted project finance solutions for commercial-scale solar portfolios. His role at Ridgewood Renewable Power, a private equity fund with 125 MWs of U.S. renewable assets, involved evaluating investment opportunities and maximizing returns. He also played a key role in the sale of the firm's renewable portfolio. Earlier in his career, Benoy worked in Energy Structured Finance at Deloitte & Touche and Financial Advisory Services at Ernst & Young, following an internship on the trading floor at D.E. Shaw & Co., a multi billion dollar hedge fund. Benoy holds an MBA in Finance from Rutgers University and a BS in Finance and Economics from NYU Stern, where he was an Alumni Scholar. Connect with Benoy on LinkedIn: https://www.linkedin.com/in/benoythanjan/ Learn more:  https://reneuenergy.com https://www.solarmaverickpodcast.com   Host Bio: David Magid David Magid is a seasoned renewable energy executive with deep expertise in solar development, financing, and operations. He has worked across the clean energy value chain, leading teams that deliver distributed generation and community solar projects. David is widely recognized for his strategic insights on interconnection, market economics, and policy trends shaping the U.S. solar industry. Connect with David on LinkedIn: https://www.linkedin.com/in/davidmagid/ If you have any questions or comments, you can email us at info@reneuenergy.com.   Please provide 5 star reviews      If you enjoyed this episode, please rate, review and share the Solar Maverick Podcast so more people can learn how to accelerate the clean energy transition.    Reneu Energy Reneu Energy provides expert consulting across solar and storage project development, financing, energy strategy, and environmental commodities. Our team helps clients originate, structure, and execute opportunities in community solar, C&I, utility-scale, and renewable energy credit markets. Email us at info@reneuenergy.com to learn more.    

In the reality of human work and collaboration today, it is inevitable to face at work intense moments of emotion (stress, anger, fear, frustration, grief…). When those emotions are suppressed in teams and leadership, they slowly cause stress, resentment and when unwatched, mental or physical health problems and burnout.According to Gallup and Deloitte research (2025):* Around 48% of employees report experiencing burnout at work.* 82% of executives report experiencing symptoms of exhaustion or burnout* Only 20% of employees worldwide are engaged at workWe can't discard the trend. The future of work and good leadership is in the ability of managing stress and emotional health at work. But most executives and employees were never trained for it, neither in personal or professional life.And when leaders are constantly operating from stress, that energy doesn't stay with them. It trickles down into teams, relationships, decision-making and ultimately into what the organization creates.When we are stressed, our nervous system moves into survival mode: fight, flight or freeze. Our thinking becomes narrower. We become more focused on protecting ourselves than creating, innovating or taking thoughtful risks.This is why self-regulation is not just a wellness practice. It is a leadership skill.Let me share this week two very simple practices, especially when you have a high-demand job.1. Heart-focused breathingThe first is heart-focused breathing, inspired by the work of the HeartMath Institute. It is a simple way to interrupt the momentum of stress and give yourself a moment before reacting.Bring your attention to the centre of your chest and imagine your breath flowing in and out through your heart area. Then breathe slowly: five seconds in, five seconds out, for around five minutes. You can use it in the morning, before a difficult conversation or presentation, when you feel activated, between meetings, or at the end of your workday to create a transition into your personal life.A good routine is to pause and practice the heart breathing three times a day.The goal isn't to make the stress disappear. It is to create enough space to choose your response rather than acting from your automatic reaction.2. The one-minute self-awareness check-inAfter the breathing, take one more minute to simply notice yourself.What am I feeling right now? Where do I feel it in my body?Are you tense? Angry? Anxious? Frustrated? Sad? Calm?You don't need to fix anything. Just notice.Our emotional state changes the way we perceive situations. The same difficult message can feel like an attack when we're exhausted and completely manageable when we're grounded.This is particularly important for leaders. Before asking “What is the right decision?”, we can also ask:“From what state am I making this decision?”Fear, anger and exhaustion can all influence the way we lead without us realizing it.Come back to yourselfI believe one of the biggest challenges for leaders today is not access to information or intelligence. AI can already process more information than we ever could.The challenge is increasingly ourselves: how we regulate, how we respond under pressure, how we hold uncertainty, and how we protect our energy and vitality.Your team doesn't only experience your words. They experience your state.So for the next 28 days, try this simple practice: five minutes of heart-focused breathing, followed by one minute of self-awareness.Do it every day. And observe what changes.Perhaps you react differently and notice your emotions earlier. Can you feel more space before reacting? Do you feel more grounded?The practice is simple.The transformation comes through repetition and new habits.You don't have to be at the mercy of your stress.Let me know how it goes after 28 days

Aman Advani is Founder and CEO of Ministry of Supply, a clothing brand using science to make the world's most comfortable clothing. The company was founded in 2012 with the mission to incorporate fundamental engineering and performance principles into clothing staples, ultimately building a wardrobe that both looks good, and feels comfortable.Prior to co-founding Ministry of Supply, Advani spent 4 years in management and non-profit consulting with Deloitte and TechnoServe. He holds a BSIE from Georgia Tech, and half an MBA from MIT, was a member of Forbes 30 under 30 list and BBJ's 40 Under 40 list, and has been featured as the cover of both ASB and Boston Magazine. Most recently, Aman was named to the NRF's 2023 "List of People Shaping Retail."In This Conversation We Discuss:[00:00] Introduction[01:58] First reactions to AI tools and Lovable[05:04] Co-founder's non-technical background[07:39] Sponsor: Klaviyo[09:45] Using Claude for CRO and analytics[13:07] Sponsor: IntelliGems[15:16] Build vs buy for AI customer service[19:12] How much human oversight remains[20:17] Sponsor: eFulfillment Service[22:04] More efficiencies from AI tools[24:22] Building an internal production system[27:05] Why AI makes agencies more valuable[29:42] Callouts[29:52] Getting teams comfortable taking the demo[32:08] Testing fast without overthinkingResources:Subscribe to Honest Ecommerce on YoutubeMachine Washable Everything ministryofsupply.com/pages/home-2  Follow Aman Advani linkedin.com/in/amanadvani Book a demo today a intelligems.io/ Get your free demo klaviyo.com/honest Lower scale costs today eFulfillmentService.com/honest If you're enjoying the show, we'd love it if you left Honest Ecommerce a review on Apple Podcasts. It makes a huge impact on the success of the podcast, and we love reading every one of your reviews!

74% of S&P 500 companies revised their GHG emissions data at least once over a decade of voluntary reporting. That's the assurance gap in practice.Deloitte's Sarah Digirolamo and Kristen Sullivan join The Pre-Read to break down what it takes to get assurance-ready before regulators require it, and why having the data isn't enough if it isn't in scattered silos.In this episode:• Why assurance readiness is a precondition for any assurance engagement, not a step within it• Why companies consistently believe their data is sufficient when it is scattered across systems, geographies, and methodologies with no central governance• How the cultural difference between sustainability teams and audit teams creates friction, and how successful organizations work through it• Why the linkage between sustainability and financial reporting is where investors are paying close attention

The Space Show Presents Leonard David, Sunday, 8-30-26 4583This was a space show discussion hosted by David Livingston with veteran space journalist Leonard David as the main guest, along with several space community members including Marshall Martin, Manuel Cuba, Bill Gowen, John Hunt, Dr. Charles Lurio, Phil Swan, Dr. A.J. Kothari, and Dr. Doug Plata. The conversation centered on the recent launch of NASA's Roman telescope, with Leonard describing it as a significant moment in astronomy that will complement existing observatories like Hubble and James Webb. The discussion also covered Leonard's skepticism about NASA's Artemis program, particularly regarding the transition from Artemis III's lunar orbit mission to a human landing on the moon, with Leonard expressing concerns about technical readiness and the need for proper testing before committing human missions. The conversation touched on China's lunar program delays, SpaceX's Starship development challenges, and broader space policy issues, with participants discussing the gap between scientific discoveries and their incorporation into educational materials for students.David hosted this program with Leonard David as the main guest, discussing recent space developments including the Roman telescope launch. Barbara reported watching the launch early in the morning and praised SpaceX's success, comparing it to the James Webb telescope deployment. The discussion touched on how new astronomical data from instruments like the Roman telescope and James Webb might be more quickly accessible to the public and citizen science groups compared to previous missions, potentially addressing the delay in updating science textbooks with current findings.Leonard expressed skepticism about the current status of NASA's Artemis program, citing concerns about the technical readiness to transition from Earth orbit operations to human lunar landings. She noted that while Artemis II was inspiring, there are missing technical links similar to those proven during Apollo missions, and she emphasized the need to avoid early human fatalities in the program. Leonard also discussed the growing space competition with China and the importance of communicating space exploration to the public in accessible ways beyond technical jargon.The group discussed China's postponement of the Chang'e 7 mission to 2027, with Barbara explaining that timing issues around lunar illumination at the South Pole were the main factor, as the mission required specific lighting conditions for the landing area. Charles clarified that the 18.3-year lunar cycle affects illumination at the South Pole more significantly than at equatorial regions, requiring precise timing rather than simply waiting for the next identical opportunity. The discussion also touched on technical challenges with the relay satellite and landing site conditions, with comparisons made to similar challenges faced by NASA's Artemis program.Leonard also expressed skepticism about current space programs, particularly regarding SpaceX's promises and Elon Musk's leadership style, comparing it to past experiences with the space shuttle program. He shared her concerns about the challenges facing private space companies like SpaceX and Blue Origin, noting that the current space program is significantly different from the Apollo program. Our guest reflected on the past involvement in promoting student payloads for the space shuttle program, where she and colleagues testified before Congress about getting students involved in NASA missions.The group discussed challenges related to lunar exploration, including the complex illumination conditions at the South Pole due to the 18.6-year lunar nodal cycle and the need for continuous computer simulations to map polar sunlight. Leonard highlighted concerns about moonquakes and the importance of gathering more data through additional seismometers before committing to a moon base. The conversation shifted to broader political and financial topics, with David expressing skepticism about government fiscal responsibility and noting that both major political parties contribute to growing national debt, which he believes will have significant impacts on future generations.The group discussed the Artemis program, particularly focusing on the need for unmanned lunar landings before human missions as part of the Artemis IV planning. Leonard expressed skepticism about claims of a cislunar economy, noting that while there are studies and interest from multiple countries including India and China, the sustainability and economic viability of lunar exploration remain uncertain. The discussion also touched on historical space missions, including challenges during Apollo 10 and 11, before being interrupted by David who mentioned a recent Deloitte report on space and the moon.David shared information about a new Deloitte report on the lunar economy, which estimates a potential economic value of $343 billion to $566 billion through 2050. He plans to review the report's assumptions and potentially invite the authors to discuss it on his space show. The group discussed the importance of reviewing economic projections and assumptions in space exploration, with Barbara emphasizing the need for sustained human presence on the moon before considering Mars missions.The group discussed the development status of Starship, particularly focusing on the differences between flights 13 and 14. Charles explained that flight 14 would attempt to catch both the booster and Starship, with the upper stage catch feature being removed recently. The discussion highlighted ongoing challenges with propellant transfer testing and concerns about space debris, with Leonard expressing worry about potential explosions during refueling operations in orbit.Our guest expressed support for the idea of a Space Academy similar to Annapolis or West Point, noting her work on rewriting a Space Career book and conducting interviews with space startups. He highlighted challenges in the space startup industry, including frequent changes in leadership and funding issues, while acknowledging the presence of successful companies in Colorado like Advanced Space and Blue Canyon. The discussion touched on SpaceX's dominance in the industry and its role in launching numerous payloads, including CubeSats, though its future plans with Falcon 9 remain uncertain.Leonard and David discussed public skepticism about UAPs (Unidentified Anomalous Phenomena), noting that recent Pentagon releases and media coverage have increased public interest. Leonard expressed concerns about missing data in Pentagon files and highlighted credible reports from military pilots, while also considering alternative explanations including military testing programs. The conversation touched on historical perspectives on UFOs, including experiences from the 1950s, and mentioned ongoing congressional efforts to protect whistleblowers who come forward with UAP information.The group discussed various topics related to space exploration and UAPs. Barbara shared insights about Mike Gold's work on NASA's UAP study and Bigelow Aerospace's involvement. The conversation then shifted to potential new propulsion technologies, with Leonard mentioning a company called Exodus that is developing innovative space travel solutions. David referred to a podcast interview with Eric Weinstein, who discussed how conventional scientific constraints may limit our understanding of space travel, propulsion, and extraterrestrial life. The conversation ended with a discussion about a new book by Steve Benner that critiques how science handled the Viking data on Mars.The group discussed Eric Weinstein, with Bill describing him as intelligent but having a persecution complex as an outsider to physics, while John Jossy mentioned Weinstein's technical paper on unified field theory. Leonard shared her experience filming with Buzz Aldrin for a new documentary and mentioned an upcoming story about a potentially significant finding on Venus. The conversation concluded with Leonard describing her childhood experience witnessing a black triangle-shaped object, which she believes was a classified U.S. aircraft based on information from Bill Scott, though John Hunt expressed skepticism about the alien origin theory.Special thanks to our sponsors:American Institute of Aeronautics and Astronautics, Helix Space in Luxembourg, Celestis Memorial Spaceflights, Astrox Corporation, Dr. Haym Benaroya of Rutgers University, The Space Settlement Progress Blog by John Jossy, The Atlantis Project, and Artless EntertainmentWe use Zoom phone numbers for program participation.For real time program participation, email Dr. Space at: drspace@thespaceshow.com for instructions and access.The Space Show is a non-profit 501C3 through its parent, One Giant Leap Foundation, Inc. To donate via Pay Pal, use:To donate with Zelle, use the email address: david@onegiantleapfoundation.org.If you prefer donating with a check, please make the check payable to One Giant Leap Foundation and mail to:One Giant Leap Foundation, 11035 Lavender Hill Drive Ste. 160-306 Las Vegas, NV 89135Upcoming Programs:Broadcast 4584: Zoom: Jules Ross with co-host John Jossy | Tuesday 01 Sep 2026 700PM PTGuests: Jules RossZoom: Jules Ross is the CEO & lead designer of Joules Space Technology which focuses on artificial gravity and space station. John Jossy is our Co-host for this program.Broadcast 4586: Hotel Mars with Dr. Craig DeForest | Wednesday 02 Sep 2026 930AM PTGuests: John Batchelor, Dr. David Livingston, Dr. Craig DeForestHotel Mars with Dr. Craig DeFores re space weather forecasts and the PUNCH ProgramFriday, Sept. 4, 2026: No Show. Labor Day Holiday Weekend | Friday 04 Sep 2026 930AM PTGuests: Dr. David LivingstonNo program due to Labor Day Holiday WeekendSunday, Sept. 6, 2026: No program due to Labor Day Holiday Weekend | Sunday 06 Sep 2026 1200PM PTGuests: Dr. David LivingstonNo program due to Labor Day Holiday Weekend Get full access to The Space Show-One Giant Leap Foundation at doctorspace.substack.com/subscribe

The episode centers on a structural shift driven by the falling cost of AI-assisted insight extraction and its impact on how buyers assess technology providers. Referencing companies such as OpenAI and Google, as well as research from the AI Revenue Institute and Gartner, Dave Sobel highlights how lowered model prices enable automated systems to rapidly analyze vendor documentation and shape procurement decisions, fundamentally changing the basis of competition from persuasion to transparent, retrievable data. A recent AI Revenue Institute study, as cited by Dave Sobel, found that over half of surveyed decision-makers had removed a vendor from consideration after an AI assistant highlighted a documented shortcoming. Simultaneously, OpenAI and Google have reduced their top-tier AI model pricing, with OpenAI dropping costs by more than 20% and Google offering a temporary 50% cut before reverting. Analysis from TD Cowen and Business Insider shows that such price cuts have driven up both usage and revenue, with OpenAI's low-cost models experiencing a 14-fold usage increase post-reduction. These developments are reinforced by Gartner's identification of the “inference paradox,” where greater AI capabilities and lower per-query costs actually raise overall spend due to increased volume and complexity of tasks. Supporting data includes Google's reported 50x annual increase in tokens processed and a Deloitte case of a healthcare provider with unplanned AI costs rising as much as 3x in a year. Alongside this, Pew Research identifies that a third of new web content on commercial sites is machine-generated, leading platforms like LinkedIn to introduce AI-detection and downranking measures. For MSPs and IT leaders, the implications are direct. Automated buyer research now prioritizes concrete, extractable data over marketing language; any absence or non-disclosure—especially around pricing—can result in removal from consideration without notice. Publishing specific, measurable facts (service boundaries, pricing logic, response times with dates) increasingly determines whether a provider is surfaced or omitted by AI agents assembling comparative analyses. Failure to clearly define offerings and exclusions results in unfavorable inferences or comparisons, increasing operational risk and transfer of accountability away from the provider. 00:00 The Buyers Brought a Machine 03:45 Cheaper Made It Bigger 06:49 Your Website Is a Deposition 10:06 Why Do We Care? Supported by: Proofpoint HaloPSA

A.M. Edition for Aug. 26. President Trump sends his landmark nuclear accord with Saudi Arabia to Congress for review, kicking off what's likely to be months of debate among lawmakers. Oxford Analytica's Rawan Maayeh breaks down whether the kingdom is likely to normalize relations with Israel as a part of the deal, as Trump has insisted. Plus, Bill Gates issues a stark warning on AI's impact on jobs and humanity, saying big tech has “no plan”. And we look ahead to Nvidia's earnings, with sky-high investor expectations for the world's most valuable company. And Luke Vargas hosts. Sign up for the WSJ's free What's News newsletter. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

In Episode 53 of The League, Benoy Thanjan and David Magid discuss how the solar industry is becoming more complex as policy uncertainty, storage, cybersecurity, and grid constraints reshape project development. They look at growing concerns around inverter supply chains and national security, as well as the impact of changing rules on financing and project timelines. The conversation also covers Massachusetts' latest energy storage solicitation and the rising costs of permitting, interconnection, and network upgrades. They also discuss ERCOT's massive queue of large-load applications driven by data centers and the increasing scrutiny around grid and resource impacts. The episode highlights why success in clean energy now requires understanding far more than just solar panels. Host Bio: Benoy Thanjan Benoy Thanjan is the Founder and CEO of Reneu Energy, solar developer and consulting firm, and a strategic advisor to multiple cleantech startups. Over his career, Benoy has developed over 100 MWs of solar projects across the U.S., helped launch the first residential solar tax equity funds at Tesla, and brokered $45 million in Renewable Energy Credits (“REC”) transactions. Prior to founding Reneu Energy, Benoy was the Environmental Commodities Trader in Tesla's Project Finance Group, where he managed one of the largest environmental commodities portfolios. He originated REC trades and co-developed a monetization and hedging strategy with senior leadership to enter the East Coast market.  As Vice President at Vanguard Energy Partners, Benoy crafted project finance solutions for commercial-scale solar portfolios. His role at Ridgewood Renewable Power, a private equity fund with 125 MWs of U.S. renewable assets, involved evaluating investment opportunities and maximizing returns. He also played a key role in the sale of the firm's renewable portfolio. Earlier in his career, Benoy worked in Energy Structured Finance at Deloitte & Touche and Financial Advisory Services at Ernst & Young, following an internship on the trading floor at D.E. Shaw & Co., a multi billion dollar hedge fund. Benoy holds an MBA in Finance from Rutgers University and a BS in Finance and Economics from NYU Stern, where he was an Alumni Scholar. Connect with Benoy on LinkedIn: https://www.linkedin.com/in/benoythanjan/ Learn more:  https://reneuenergy.com https://www.solarmaverickpodcast.com   Host Bio: David Magid David Magid is a seasoned renewable energy executive with deep expertise in solar development, financing, and operations. He has worked across the clean energy value chain, leading teams that deliver distributed generation and community solar projects. David is widely recognized for his strategic insights on interconnection, market economics, and policy trends shaping the U.S. solar industry. Connect with David on LinkedIn: https://www.linkedin.com/in/davidmagid/ If you have any questions or comments, you can email us at info@reneuenergy.com.   Please provide 5 star reviews      If you enjoyed this episode, please rate, review and share the Solar Maverick Podcast so more people can learn how to accelerate the clean energy transition.    Reneu Energy Reneu Energy provides expert consulting across solar and storage project development, financing, energy strategy, and environmental commodities. Our team helps clients originate, structure, and execute opportunities in community solar, C&I, utility-scale, and renewable energy credit markets. Email us at info@reneuenergy.com to learn more.    

We've launched Minimum Competence CLE, and our first course is now available completely free. Researching Federal Tax Issues After Loper Brightlooks at how the Supreme Court's decision ending Chevron deference changes the way lawyers should research and evaluate Treasury regulations, IRS guidance, and other federal tax authorities.Take the course and earn CLE credit at cle.minimumcomp.com.This Day in Legal History: The Declaration of the Rights of ManOn August 26, 1789, France's National Assembly adopted the Declaration of the Rights of Man and of the Citizen, one of the foundational documents of modern constitutional government. The Marquis de Lafayette played a major role in drafting it, with input from his friend Thomas Jefferson, who was then serving as the American minister in Paris. In just seventeen articles, the Declaration tried to turn Enlightenment ideas about natural rights and legitimate government into law.A lot of it will sound familiar to American ears, in part because the American and French revolutions were very much in conversation with each other. Article I declares that “men are born and remain free and equal in rights”—today's opening quote. The Declaration identifies liberty, property, security, and resistance to oppression as natural rights. It says the law must apply equally, punishment must be authorized by law, defendants are presumed innocent, and the free communication of ideas is “one of the most precious of the rights of man.” It also makes separation of powers part of the definition of constitutional government: a society where rights are not secured and powers are not separated “has no constitution at all.”The Declaration mattered well beyond France. Its ideas influenced constitutions throughout Europe and Latin America and eventually found echoes in the Universal Declaration of Human Rights in 1948. There was also an enormous gap between the Declaration's promises and what followed. Within a few years, the French Revolution had descended into the Terror, and the supposedly universal rights announced in 1789 were plainly not being extended to everyone.That makes August 26 an especially fitting date for another reason. In the United States, it is Women's Equality Day, commemorating the 1920 certification of the Nineteenth Amendment. The coincidence is a useful reminder that declaring people “equal in rights” is considerably easier than actually making them so—and that many of the people supposedly covered by universal declarations of equality had to spend generations fighting to make those words apply to them.A federal judge previously issued an injunction barring the administration from renaming the Kennedy Center for the Performing Arts after President Trump. Then, earlier this month, the Kennedy Center's board—now dominated by Trump appointees—voted 20 to 3 to change the building's signage to read “The John F. Kennedy Center for the Performing Arts, Restored and Renovated by President Donald J. Trump,” and to name the surrounding grounds “President Donald J. Trump Plaza.” The administration's argument, in a new filing, is essentially semantic: it says this doesn't violate the injunction because the building is still named the John F. Kennedy Center, and the added inscription is merely a donor acknowledgment—the kind, it says, that's “ubiquitous in similar facilities.” Democratic Congresswoman Joyce Beatty, who's part of the suit, says the board “openly defied” the court's ruling and has asked the judge to block the signage. Here's the legal question, and it's a real one: when does creative compliance with a court order become defiance of it? Courts don't just police the literal words of an injunction—they police attempts to accomplish the forbidden thing through a technical workaround. If the injunction's purpose was to stop the center from being turned into a monument to the sitting president, a judge may well look past the “we didn't technically rename it” framing to the practical reality. The judge, Christopher Cooper, has set a fast briefing schedule with deadlines today. The significance is that this small, almost absurd dispute over building signage is really a test of something fundamental: whether the executive branch will comply with a court order in substance, or look for the narrowest possible reading to get what it wanted anyway. Trump administration says new Kennedy Center renaming does not violate court order | ReutersBloomberg Law · TimeDeloitte has agreed to pay $21.5 million to settle Justice Department allegations that its diversity, equity, and inclusion programs amounted to illegal discrimination—a landmark in the administration's campaign against corporate DEI. And note the legal vehicle, because it's clever and aggressive: the DOJ brought this under the False Claims Act, the federal government's primary anti-fraud statute. The theory is that Deloitte, as a federal contractor, certified compliance with anti-discrimination requirements while allegedly running DEI programs that themselves discriminated—making its certifications false. The specific allegations: Deloitte's business units received monthly summaries tracking progress against “demographic goals”; roughly 150 senior partners and managing directors had part of their compensation tied to hitting those targets, some risking tens of thousands of dollars; and race and sex were allegedly factored into promotion decisions and access to certain training and mentoring programs. Of the $21.5 million, about $10 million is designated as restitution. Crucially, Deloitte denies the allegations and the settlement includes no admission of liability. The significance is that this reframes DEI from a corporate HR initiative into potential fraud against the United States. We've tracked the administration's use of Title VI against universities—Harvard, Columbia, William & Mary—and this is the corporate front of the same campaign, deploying the False Claims Act against a major government contractor. That's a powerful deterrent, because the False Claims Act carries treble damages and invites whistleblower suits. Whatever you think of DEI programs on the merits, the legal move here is significant: it puts every federal contractor on notice that diversity targets tied to pay and promotion could be recast as discriminatory, and therefore as a false certification the government can prosecute. Deloitte to pay $21.5 million to settle US government probe over DEI | ReutersJustice Department · Fox BusinessAnd finally, a federal appeals court has ruled that the administration cannot attach ideological conditions to federal grants for homelessness and transportation—another entry in the running saga over the limits of the executive's power over the money. The Ninth Circuit, in a decision backing Santa Clara County and other local governments, affirmed a lower court and found that the administration abused its authority by imposing new strings on grants like the Continuum of Care program, which has funded homelessness services since 1987. Those grants have long been built around a “housing-first” philosophy—the approach of getting people into permanent housing without preconditions like sobriety or employment—and the administration sought to attach conditions cutting against that model and advancing its own policy priorities. The court found the cities would suffer irreparable harm if the funds were withheld. Here's the legal principle, and longtime listeners will recognize it: back in July, we covered the anniversary of South Dakota v. Dole, the case that lets the federal government attach strings to the money it gives states—but only within limits. The conditions have to be clearly stated, related to the purpose of the funding, and not coercive. When an administration tries to bolt novel, ideological conditions onto grants Congress created for a specific purpose, courts have repeatedly said that exceeds those limits. This fits a pattern we've followed all summer—from the OMB grant clawbacks to the EPA's frozen climate funds—of courts telling the executive that money Congress appropriated for a purpose can't be turned into a lever for unrelated policy goals. The significance is that the spending power, real as it is, keeps running into the same wall: you can fund homelessness programs, or not, but you can't quietly rewrite what they're for. Trump cannot impose conditions on transportation, homelessness grants, US appeals court rules | ReutersPalo Alto Online · Mountain View Voice This is a public episode. If you'd like to discuss this with other subscribers or get access to bonus episodes, visit www.minimumcomp.com/subscribe

IRONMAN Master Coach Matt Dixon interviews Panos Kakoullis, a long-time Purple Patch athlete, at the Hawaii IRONMAN training camp. From Boardroom to Finish Line Panos Kakoullis on Balancing High-Stakes Leadership with Elite-Level Endurance Performance. Panos shares his Greek Cypriot background, growing up above a fish and chip shop, and his journey from becoming an accountant, later Senior Partner at Deloitte to CFO at Rolls Royce. He discusses his transition from powerlifting to endurance sports, influenced by his wife, and his 16 IRONMAN completions. Panos emphasizes the importance of consistency, recovery, and integrating sport into life. He highlights the role of curiosity, growth mindset, and releasing mental weight in his success. Looking ahead, he plans more marathons and a cycling trip across Italy. Panos reflects on the challenges and successes in his professional life, including the importance of integrating sport into his busy schedule. Panos recounts how he and his wife decided to do a marathon and then a triathlon, despite initially swearing never to do an IRONMAN. He emphasizes the importance of consistency over heroism and the need to prioritize recovery and nutrition. Matt and Panos wrap up the conversation, with Matt expressing his admiration for Panos' journey and achievements

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The core structural shift highlighted is the disconnect between service reliability gains from AI automation and readiness for strategic change among IT service providers and their clients. Reports from SolarWinds, Corsica Technologies, and Deloitte reveal that AI is delivering measurable productivity benefits, but those time savings are consumed by ongoing reliability work rather than being directed toward governance, process redesign, or workforce adaptation. This leaves most organizations with improved operations but unprepared to leverage AI for broader business transformation, creating a gap between what clients say they want and what providers are set up to deliver. SolarWinds' 2026 State of ITSM report found that 84% of IT teams report AI meeting or exceeding their return on investment expectations, with teams recovering roughly three hours per week in several core areas, such as issue detection and ticket triage. However, almost the same amount of capacity is then redirected to keeping those new AI systems running—83% of teams spend three or more hours weekly maintaining AI reliability. Simultaneously, Corsica Technologies' Censuswide research among 600 IT and security leaders at U.S. mid-sized businesses found that 96% claim to trust their MSP, yet two-thirds are considering switching within 12 months, citing limited AI or automation support as one of the top reasons. Additional research contextualizes the readiness gap. According to a PwC survey, only 5% of organizations report their business processes as highly prepared for AI agents, and a Cloudera study found that 95% of large companies delayed or canceled at least one AI project in the past year due to governance, compliance, or regulatory concerns. The episode also notes a public sentiment shift, citing a Pew Research poll in which over half of American adults express more concern than excitement about AI—a trend particularly strong among people under 30. Vendor product launches from companies like Kaseya and Syncro are described as offering only superficial differentiation in this environment. For MSPs and IT leaders, this dynamic presents operational risks. The default allocation of AI-driven productivity gains toward reliability tasks undermines investment in strategic readiness, reinforcing dependence on vendor offerings without improving meaningful differentiation. Most clients lack a specific benchmark for “AI readiness,” creating an open but temporary competitive opportunity for providers willing to define and document it for them. However, unless time and resources are explicitly earmarked for readiness activities—in governance, process adaptation, and client education—MSPs risk being evaluated on ill-defined criteria or commoditized platforms, increasing contract risk and exposing gaps in internal accountability. 00:00 The Two Numbers Don't Fit  04:52 Only One Half Can Take the Hours  08:02 Everyone Buys the Same Platform 11:20 Why Do We Care?  Supported by:  Pax8 TimeZest 

Melissa Swift shares strategies for navigating the chaos of the modern workplace more effectively. — YOU'LL LEARN — 1) The four trends making work feel more intense and chaotic 2) How to hone in on what makes you effective 3) Two critical questions to ask in the face of chaos Subscribe or visit AwesomeAtYourJob.com/ep1176 for clickable versions of the links below. — ABOUT MELISSA — Melissa Swift is a leading voice on how organizations, teams, and individuals can succeed in an ever-more challenging world of work. As founder and CEO of Anthrome Insight, she is a practicing consultant and keynote speaker. She has held consulting leadership roles at Capgemini, Mercer, Korn Ferry, and Deloitte. She is also the author of Work Here Now: Think Like a Human and Build a Powerhouse Workplace (Wiley, 2023).Her quarterly columns in MIT Sloan Management Review often rank among their most-read articles. Swift speaks regularly at events, including the MIT CIO Symposium, and has been quoted on the subject in The New York Times, The Wall Street Journal, NPR, Newsweek, The Economist, The Washington Post, Axios, and more.Throughout her career, Swift has pioneered techniques to reshape organizations for digital and workforce transformation, leading breakthrough projects across industries ranging from manufacturing to professional services to biotech to consumer goods. She earned her B.A. from Harvard University and her MBA from Columbia Business School.• Book: Effective: How to Do Great Work in a Fast-Changing World• LinkedIn: Melissa Swift• Website: AnthromeInsight.com— RESOURCES MENTIONED IN THE SHOW — • Database: O*ONET• Study: “Work intensification: Towards mapping the study field and defining a research agenda” by Ana Heloísa da Costa Lemos, Waleska Yone Yamakawa Zavatti Campos, and Marcelo Quintão• Book: The Warmth of Other Suns: The Epic Story of America's Great Migration by Isabel Wilkerson• Past episode: 314: How to Feel Less Busy With Laura Vanderkam• Past episode: 366: Mastering Conversations through Compassionate Curiosity with Kwame Christian• Past episode: 417: Managing Infinite Expectations with Laura Vanderkam• Past episode: 798: How to Have Difficult Conversations about Race with Kwame Christian• Past episode: 981: Using AI to Enhance Your Reading, Notes, Memory, and Decisions with Kwame Christian• Past episode: 1150: How to Reclaim Your Schedule and Own Your Time with Laura Vanderkam— THANK YOU SPONSORS! — • Shopify. Sign up for your free trial at Shopify.com/awesomepod• Vinted. Download the Vinted app for free to start selling with no seller fees!• Fitnexa. Get $10 off the SomniPods3 with the link and code AWESOMESee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Most people hear pivot and think something went wrong. Andrea Wade thinks that's backwards.Today's word is PIVOT. Not as a crisis, not as a retreat. As a design.Andrea Wade is the founder of The Pivot Lab, where she helps experienced professionals navigate career transitions with confidence. Deloitte. Arthur Andersen. Mattel. Bank of America. Years as an agent at Creative Artists Agency before she built her own. Her path has never been straight, and that is the point.We get into why a layoff feels like rejection when it usually has nothing to do with you. Why the outside world has no idea what your title means. What Andrea calls career insurance, and why you build it before you need it. Her four-part framework: reflect, research, reframe, relaunch. And the line that stopped the whole conversation, which is that you cannot be creative and dream if you are exhausted.Andrea leaves you with one thing. Zero in on what you are curious about, and what you are nervous about. Then get closer to both.00:00 Coming Up00:24 Podcast Intro00:56 Today's Word: PIVOT03:34 What a Pivot Actually Means04:32 The Layoff08:15 Nervous Is Not the Same as Fearful12:31 Your Job Title Is Not Your Skillset17:24 Career Insurance21:19 The Brilliant Women Who Want to Tap Out29:44 Reflect, Research, Reframe, Relaunch32:28 You Cannot Have It All At Once35:09 Sunk Cost45:14 You Cannot Dream If You Are Exhausted46:42 One Thing Before You Pivot48:13 Close Out48:30 On the Next ShowNew episode every Sunday.Listen on Spotify: https://open.spotify.com/show/3AZl8GfkoguBI2D4YjVlvkApple Podcasts: https://podcasts.apple.com/podcast/id1551800337howdoyoudivine.comInstagram: @howdoyoudivine#PIVOT #HowDoYouDivine #DesignedNotDrifted #careerpivot #layoffSend us Fan MailThank you for listening and for adding new dimensions to your definitions. Keep growing, keep exploring, and keep defining life on your terms.Explore the website: howdoyoudivine.comFollow the showInstagram: instagram.com/howdoyoudivineYouTube: youtube.com/@howdoyoudivineFacebook: facebook.com/howdoyoudivineMeet the founder and hostSanika is a founder, inventor, and strategist building at the intersection of culture, media, and technology. How Do You Divine?® is the anchor of HDV Media, a company built on one idea. The words we inherit quietly design the lives we lead.Every episode takes one word and examines it through culture, intellect, emotion, and lived experience. Definitions that were handed to you. Definitions you adopted. Definitions you assumed were just normal.New episode every Sunday.

When we first dicsussed the Summer of Simulative AI in 2024 we knew it would be a brief summer, but it has recently come back with a vengeance with SimGym in April and now Simile AI's $2B Series B, backed by GreenOaks and Index Ventures with prominent backers like Fei-Fei Li and Andrej Karpathy, running tens of millions of simulations for Fortune 100 clients like CVS and 85–99% accuracy vs human focus groups. Time to catch up on why this Second Summer of simulation is working!From creating Smallville, the landmark 2023 paper on Generative Agents that showed AI characters could remember, plan, socialize, and develop emergent behaviors, to now building foundation models of human behavior, Joon Sung Park is trying to answer a much bigger question: what if we could simulate the world before making decisions in it? In this episode, the Simile co-founder and CEO joins us to unpack the path from generative agents to digital twins, why today's frontier models still fail to capture how humans actually behave, and what it would take to eventually simulate all 8 billion people on Earth.We go deep on Simile's approach to modeling human behavior: long-form interviews, observational and transaction data, randomized controlled trials, population-level and individual-level models, and post-training on the causal mechanisms behind why people make decisions. Joon explains how his research created digital twins that reproduced human behavior and attitudes 85% as accurately as people reproduced their own responses, why models optimized to be rational can be bad simulations of irrational humans, and why understanding “social physics” may require changing model weights rather than simply prompting frontier LLMs.We also explore the much larger ambition behind simulation: testing products and policies before deploying them, finding counterintuitive paths toward desired outcomes, modeling emergent behavior across entire societies, and potentially tackling problems like climate change, democratic instability, and UBI. Joon reflects on scaling laws for simulation, the economics of data-center-scale simulated worlds, the connection to Thomas Schelling and psychohistory, why simulation is surprisingly similar to painting, and whether we might already be living in one.We discuss:* How Smallville and Generative Agents led to Simile* Why Joon's team asked: “What if we can just recreate the world that we live in?”* Why useful personal agents require deep models of their users* Memory architectures, Markdown files, and the limits of prompting* “Social physics” and behavioral foundation models* Why web data captures what people say more than what they actually do* Interviews, transactions, observational data, and randomized controlled trials* Why predicting the future matters less than understanding how to shape it* How Simile creates representative simulated populations* Simulation versus prediction and the connection to Foundation's psychohistory* How to evaluate simulations instead of simply stacking LLM hallucinations* Creating digital twins of 1,000 real people and reaching 85% behavioral accuracy* Why frontier models can struggle to reproduce real human behavior* Why good simulations need to reproduce human biases and mistakes* Post-training models on randomized controlled trials* Population-level versus individual-level simulation* Scaling laws for human simulation* The long-term ambition to simulate all 8 billion people on Earth* Whether simulations could help solve climate change or detect collapsing democracy* Thomas Schelling and the history of agent-based modeling* Why future simulations could require an entire data center* Multi-agent simulations and what happens when simulated people interact* Replacing expensive human panels with synthetic populations* Why market research is only the starting point for simulation* Why Joon sees simulation as surprisingly similar to painting* Using simulation to study questions like UBI* Whether we are already living in a simulation* Why AGI and simulation may be the twin technologies of advanced civilizationsJoon Sung Park* LinkedIn: https://www.linkedin.com/in/joonspark* X: https://x.com/joon_s_pk* Website: https://www.joonsungpark.com* Simile: https://www.simile.comTimestamps00:00:00 Introduction and Joon's Path from Art to AI00:01:46 Smallville, Generative Agents, and the Origins of Simulation00:05:03 “Let's Just Create a World” and the Future of Personal Agents00:09:53 Social Physics and Behavioral Foundation Models00:14:08 Prediction vs. Simulation: How Do You Shape the Future?00:16:59 How Simile Models Real People and Populations00:25:35 Evaluating Simulations, Digital Twins, and 85% Accuracy00:30:23 Post-Training Models to Reproduce Human Behavior00:40:04 Scaling Laws and Simulating 8 Billion People00:43:10 From Schelling to Society-Scale Agent Simulations00:46:13 The Cost and Economics of Simulating the World00:52:05 Real-World Use Cases, Synthetic Populations, and the Market00:57:27 The Future of Simulation, Painting, and UBI01:04:23 Are We Already Living in a Simulation?01:06:08 Building Simile and HiringTranscriptIntroduction: Joon Sung Park, Simile, and the Story So FarVibhu [00:00:00]: Today, we have Joon in the podcast. Excited to kick this one off. Very exciting company. I wanna kick off and ask you the question, talk us through the story of your life. How have you gotten here?Joon [00:00:13]: Yeah, for sure. I'm really excited to be here. A story of my life. So I was born in Korea, and I lived there for a good 11 years or so of my life, and then my family moved to Boston. So we moved when I was 11, and my parents were doctors, so they were going through their postdoctoral studies. My dad was a surgeon, so he was doing his sabbatical years at the Boston Children's Hospital. So I grew up there, not too close to tech. I was very much a music and artsy, painting kind of guy.Vibhu [00:00:49]: Painting.Joon [00:00:49]: Exactly. I got into painting a little bit later, in high school, but that's what I used to do. And then I grew up mostly in the East Coast after Korea. So I lived a good number of years in New Hampshire, and then I went to college in Pennsylvania. And I got into more of this tech scene, in college. So I was originally trained to be an artist. I thought that would be my professional career. So it wasn't a hobby. It was like, “Hey, let's make a living out of this.” And then gradually, I got really interested in this idea of, hey, the greatest artist often creates their own medium, and the best medium that we had available today was in computation. So I decided to go deeper into that, and one thing led to another, and we can go deeper into this, but I decided that research was something that I gradually got interested in, and here I am.Smallville, Generative Agents, and the 2023 Breakout PaperSwyx [00:01:46]: So there's a lot that you packed into the research components. You had one of the best papers of 2023, which was the generative agents paper, commonly known as the Smallville paper.Swyx [00:01:58]: Feel free to call back to anything else that you mentioned, but most people would have heard of you from this. Do you have any statistics on how many people have, like, read it? arXiv gives you something, right? Some stats.Joon [00:02:10]: Yeah, it's a good question. How many people have read it, I'm not sure.Joon [00:02:14]: I know we do keep track of citations, and they are going up quite fast.Swyx [00:02:23]: Yeah, Google Scholar has 7,200 citations.Vibhu [00:02:25]: I feel like it made a bigger hit than that, and it was a pretty instrumental paper. It got cited so many times.Swyx [00:02:34]: It is frequently the answer when people ask, “What is the best paper you've read recently?” It's this one.Vibhu [00:02:39]: I thought the memory component was pretty underrated. It was a very good early memory system, and one of the biggest papers.Foundation Models and the Search for Killer ApplicationsJoon [00:02:47]: Yeah, so maybe I can talk a little bit about how this particular paper came together. So when I got into research, it was back in 2020 when I started my PhD program at Stanford, and that was the year, when we were about to get GPT-3 to be available. So we already had GPT-2, and you could sense that there was this new class of models that was just becoming available in the market, and the team got very intrigued. And the general consensus was, “Well, is this model going to be useful for anything?” “It's really strange that these models are not trained to do any particular task.” But we decided to take a bet. So a large group of scholars at Stanford, and it was led by one of my co-founders, Percy Liang, and we came togetherSwyx [00:03:35]: Who coined foundation models.Joon [00:03:36]: Who coined the term foundation models. We wrote this paper, where that term came from called Opportunities and Risks of Foundation Models. And during that process, really the thing that I started to think deeply about was, here is a model that is fundamentally new in our ecosystem. The reason why this was new was it wasn't, again, trained to do anything in particular, but its premise was it could do anything and everything. It was like a stem cell, if you were to take a biology analogy. And I got really interested in this idea that, well, if we were to really think about what are the killer applications that this particular technology would enable, what would that be? Many of my colleagues were using this for simple classification, simple generations. Interesting that these models can do that, but from an interaction perspective, not that interesting. We've known how to do that for many decades. And what we came down to was these models are trained on this very broad data from the web, right? So these are human behavioral data. It's social media, Wikipedia, all these data. So if you poke at the right angle, then you could see human behavior that would just pop out that's quite realistic, and we've never seen that before.The Time Machine Game and Recreating the WorldJoon [00:04:45]: So that got us really interested. The exercise that we decided to do, with this particular group of colleagues, Michael Bernstein, Percy Liang, and myself, who ended up becoming my co-founder at Simile, we sat down and we played this game that we call the time machine game.Joon [00:05:03]: Imagine we were to get on a time machine and fast-forward 10 years and look back. What would have been the single application that will have mattered that would be the most interesting and inspiring? And when we thought, “Well, what if we can just recreate the world that we live in?” it's really hard to get more ambitious than that. Like, let's just create a world.Joon [00:05:24]: And that's where we started. And initially, we had this paper that was a precursor to the generative agents paper called Social Simulacra.Swyx [00:05:32]: Before you go further, were there other candidates for the most ambitious thing in the time machine exercise? What was number two or number three?Personal Agents, User Models, and Why Simulation Came FirstJoon [00:05:44]: There is a close second that we were considering, which ended up becoming more of these automation tools, especially the vision around really personalized agents that would do things for you.Swyx [00:05:59]: That's also happening.Joon [00:06:00]: It's also happening. But it was interesting for us, right, in that the reason why, we decided to go with the idea of simulation, one, I was a huge science fiction nerd, and this idea of creating simulation, I was personally really just fascinated. I loved the idea. It's really cool to see, like, a game town like this and just see these agents live in it. But at the same time, my bet was if you were to create a really amazing personal assistant out of this technology, what you need first is an amazing model of your users. So I told a model, “Hey, can you go buy late dinner for me?” And it orders Hawaiian pizza, and I do not like pineapples on my pizza. Then it totally failed. The way for it to not make that mistake is only by having a deep understanding of who I am. And I gave a very simple and dumb example here, but you can imagine how this core understanding of people is instrumental. This is how, if we have our family and closest friends, they have a good mental model of who we are. That's the basis of our social connection. So our bet also was this technology around simulation, creating accurate representation of people ought to precede the more complex agents that would automate the world that we live in. So that was the bet. But that was a very close second, and I'm still very much fascinated by it. I think there's a lot of interesting work that's going around. My hot take here, though, is I don't think we've seen a true personal assistant that's useful, in ways that meet the ambition of that particular line of work. I think there are early applications that are interesting, and if you talk to even ChatGPT nowadays or Claude, they know a lot about us. So a lot of the generation it's doing, I do think it's much more tailored, but I think the ambition is quite large in that field, and I don't think we quite have all the right ingredients just yet.Swyx [00:08:01]: So OpenClaw and these personal agents, what do you want to see from them that they don't currently have?Memory, Markdown, and the Limits of PromptingJoon [00:08:09]: I do think it's slowly getting there, but I do generally want them to have much deeper understanding of the person. Right now, you look at the models. OpenClaw, what it's leveraging is a Markdown file, and I think it's quite clever, right? So if you look at the generative agents paper, this was the same intuition that we had, where initially when we were creating the memory architecture for the generative agents, and, like, this is, like, back in 2022, so we didn't really quite have the idea of even agentive architecture or the term agent. But the intuition that we shared with some of the work that's coming out today was we initially thought, “Well, do we want to make the memory into, let's say, knowledge graph? Do we want to train a bespoke model?” All of these things. And what we decided to do was, “No. Just forget about all this.” These language models are quite good at modeling text and understanding and reasoning about text. So just put everything in a Markdown file or a text file. You're done. I thought that was quite interesting that we could do that, and there's a lot of strength in doing that. But also, there are limitations. It's the way you retrieve and make sense of data that's extremely large, it takes a lot of work. So I think that technology is getting better. I also do, however, think, there are certain things you just cannot shape just by prompting the model. So to some degree, you do need to touch the parameters of the model itself. So there is this work that I do think does need to happen, and it is happening. The question is, how far can we take it? How do we source data, and how do you also create an ecosystem where people are continuously feeding data to this model so it's learning about you?Vibhu [00:09:50]: What's the intuition between why you need to do it in the model?Social Physics and Behavior Foundation ModelsJoon [00:09:53]: My intuition behind the actual when do you train or even post-train a model versus just prompt a model is if the model has to learn the underlying physics of the world that it's operating in. So it has to learn new social physics. The places where it doesn't have to train are the places where it already has the physics. We trust the physics. It already has the base statistics, but it's just trying to react to an environment. Then I think you can just prompt your way into getting the actions out of it. I don't think the models that are out in the open have yet learned the complete mapping of social physics of humanity. This is one of the core theses of Simile, right? And one of the core reasons why that is the case is if you look at the data that the model was trained on, these models were trained on the web data, like, whatever was available on the web. And these are really interesting data sets, but they are fundamentally the self-exposed attitudinal data with some behavior data that's sprinkled around here and there. And it has yet to learn the really deep behavioral nature of people, not just what people say they do online, but what they do in real life. And this is one of what I would consider to be the dark knowledge of humanity that we haven't quite captured. And it's these data that would also need to get factored into the model creation.Vibhu [00:11:21]: You call it behavior foundation model.Vibhu [00:11:23]: There's a good one-liner here, but outside of that, what type of data do you need? What are you changing on the model level? How do you go about modeling, doing a behavior foundation model?The Three Data Buckets: Interviews, Behavior, and CausalityJoon [00:11:35]: We think about data in three buckets. So one bucket is interview data. It's quite interesting. Rich qualitative data is interesting. It's not behavioral, but we would literally ask people, “Hey, tell me the story of your life.”Vibhu [00:11:53]: It's just what we're doing here exactly.Joon [00:11:54]: The question that you all asked at the beginning of this interview literally is the question we also ask. And we ask our participants to go a little bit deeper, than how far I went. Maybe I can give more of my life story in lieu of this. But the reason why that data is interesting is by learning about this very long-tail information about people, you get a lot of texture around this model, like, this person as a model. So even understanding their childhood memory or even their trauma, their first love, these things, quite informative in ways that's really hard to predict. So that's one. Then there are two tranches of what I would consider to be the behavioral data. One kind of behavioral data is observational. So these might be like transaction data, or these might be data that you can get by scraping the web, right? So you can imagine why these data sets would be interesting, right, because they give you the base statistics of people's behavior.Joon [00:12:55]: But then there is the last category of data, that I personally think is perhaps the most important, which is the data that describes the causal mechanism, the whys of people. Some of this is covered by the interview data, the qualitative, because people talk about why they made certain decisions. But really, where you get to see the most behavioral aspect of this is in randomized controlled trials, like RCTs. Imagine you have the same setup, but you have a few different variables that you are trying to tweak. Can you get realistic human behavior out of it in ways where, imagine you had this particular option. Imagine you're even trying to choose whether you're going to drink coffee or not. The day you drink coffee versus the day you didn't drink coffee, does your behavior change? That's a data set that describes a causal mechanism. This is quite important in modeling people. The reason why this is important is oftentimes when people come to us, or not just to us, but the reason why people are interested in simulation isn't because they want to predict the future. If you're trying to win against the stock market, predicting the future is interesting.Prediction vs. Simulation: Shaping the FutureJoon [00:14:08]: But most people, most decision-makers, what they want to know is, how can we shape the future? It doesn't really help you to hear that your sales are going to tank in two quarters. They're just gonna say, “Wow, that sucks.” What they want to know is, well, what do we need to do now to avoid that future? That's the causal mechanism. And this is also very hard data to come by, right, because the world is our ground truth, but it happens once. So in a very controlled setup where everything is equal except for one variable, this kind of data set rarely happens. So this is a reason why this data set is both hard to come by and quite important if you're trying to model human behavior.Swyx [00:14:50]: So behavior, I think, is the hardest data set to acquire. What is out there? What is even possible? You're not going to know a lot of details about my life. I don't even have data for myself on my own health or habits, and I just don't log everything. So how can you have that data?Joon [00:15:14]: So we run a lot of randomized controlled trials.Swyx [00:15:17]: But you put people in the lab, they watch them sleep, or what?Joon [00:15:20]: We do care a lot about the consent process. People know that we invite them to be a member of this community to both share data and have themselves represented in different forms. But we bring a lot of people to the lab, or virtual lab, where we design experiments that would pose them real behavioral decisions. And often in these experimental setups, what makes the difference between what is attitudinal versus behavioral is whether the stake in your decision is real. That's ultimately what makes it behavioral. So in these setups, we are inspired by our colleagues in social sciences, psychology, and so forth. So when they run studies, the techniques they utilize is imagine there's an online store that you're inviting people to come by. Then whatever they purchase in this experiment, they actually get that item delivered. Like, these are the things that make the stakes real. So we run a lot of these experiments, and we also do partner with firms. Right now, we also have customers who are quite excited to at least give us a glimpse of the behaviors that their users exhibit so that we can get a little bit deeper understanding of how people behave in these different platforms.How Customers Use Simile: Populations, Queries, and ExperimentsVibhu [00:16:39]: I think on the customer side, they have a lot of data about their users, who has bought. They have the action data.Vibhu [00:16:47]: Can you walk us through an example of what someone comes to you for? What questions would they want solved? Do you customize a model for them? Do you have something off the shelf? What does that look like?Joon [00:16:59]: Today, when people leverage our models, it's often to better understand the population of their interest. So usually, the start of the relationship, we come together and hear about what population they want us to model, right? So it might be that if you're a CPG company that's selling to all of the US, then maybe it's fairly straightforward. You want to model the gen pop of the US. But at the same time, if there is a vertical or if there's a market that they're trying to go into, imagine, they want to better understand, let's say, people in their 20s and 30s living in California. That's a much more specific population. So we hear about this population, and we go recruit these people, with consent, and with incentives, and we collect some of their data and create a model of these people. Then what our product allows you to do is query them. So it can take as input a filter that is a description of the population that you want to talk to, just like the one I just mentioned, and an environment. The environment can literally be survey questions, behavioral experiments, It can be A/B testing. Oftentimes, the core use cases are things like concept testing, to start with. But also, people sometimes want to do focus groups or one of the fun use cases that we also serve is even modeling things like earnings calls for public companies.Joon [00:18:21]: So these are the use cases that we often start with.Swyx [00:18:23]: Concept testing, is that an established term? I've never heard of concept testing.Concept Testing, Gallup, and PoliticsJoon [00:18:27]: Yeah. So it has to do with they have, let's say, different messaging, different products, different ideas.Swyx [00:18:32]: It's like a marketing exercise.Swyx [00:18:33]: Okay, got it. Got it. Politics?Joon [00:18:36]: We do, have a strategic partnership with Gallup, and of course, Gallup is deep into policy space and so forth. Right now, we have not worked deeply with politics, like that area just yet, however.Swyx [00:18:49]: I'm curious if there is demand or if they really would have different needs that somehow fundamentally don't mix with your existing, users or people.Joon [00:19:00]: I think there's certainly demand.Joon [00:19:02]: But we are very much mindful of how this technology gets adopted and the societal impact that we'll end up having with this technology. And I do see politics as an area where a company has to be particularly thoughtful about the way they operate and make impact. So this is where we also want to make sure that we form enough of guardrail and perspective on how to leverage this technology before we go on to serve markets like the politics.Swyx [00:19:29]: I'll give people an example. one of my favorite shows is The West Wing. I don't know if people have watched.Swyx [00:19:34]: One of the key storylines is, like, the president has, multiple sclerosis, but they haven't. they need to figure out how to disclose it. So they run a poll with a fake governor and ask people to respond on the poll,Counterfactuals, Polling, and When Simulation Is UsefulSwyx [00:19:47]: They try to make decisions based on the results of that poll on, like, how well they'll be received, like where, how should we play this?Swyx [00:19:54]: And I'm like, well, I think those counterfactual things, I would use a simulation for this if I could trust it.Joon [00:20:01]: For sure.Joon [00:20:02]: In that show, how'd it go?Swyx [00:20:04]: In that show, it was, like a foregone conclusion. They were like, “We know it's bad. We just don't know how bad.” And then the poll came back. It was like, “It's really bad.” And then they just did it anyway.Joon [00:20:14]: Part of it is to show, right? So you're, you're looking at the ideaSwyx [00:20:17]: Maximizing drama.Joon [00:20:18]: How bad could it be? Oh, it's horrible.Swyx [00:20:20]: And to some extent, I think that is part of the trick of the, or the challenge or with being a customer of yours, which is that if I know it's. if I roughly know and can intuitSwyx [00:20:35]: What the effect is going to be, do I need you? What sensitivity of it, of effect do I need in order to make a decision, right? So for example, if I, my approval rating is 50%Swyx [00:20:48]: And I, they have this negative piece, news item comes out, and it drops to 30.Swyx [00:20:52]: If it drops to 20, if it drops to 40, do I care? No. It, I know it drops. It's negative. So when do I care about simulations?Joon [00:21:01]: You do something that's clearly bad, that's not popular, and people don't like you, like, yeah, it's likeSwyx [00:21:05]: You don't need a simulation.Joon [00:21:07]: Yeah. Well, so there are a couple of things. one is, there are use cases where, like every day, developers, designers, policymakers, marketers, every single day, they create assets. They create new products. And turns out, it's many of the decisions in hindsight is obvious. Yes, of course this is bad, but we still run those studies because understanding the magnitude and understanding how acute something is quite difficult, even if, we feel like, of course, like this makes sense. this is the reason why we make so many mistakes. Like, every time somebody goes online and say something that has huge backlash, you look at that and like, “What an idiot.” However, it's tough. That's one. There's also another aspect here, which is, again, this is the reason why simulation is different from prediction. In simulation, in the ideal case scenario. So what simulation is trying to show is it's trying to show each step of the way or each step that we need to take to get to a certain outcome, right? So in the most advanced simulations, sometimes the next step that we're suggesting might be quite counterintuitive. The analogy that I sometimes give, and I ground it in a more realistic example, but, I, as I mentioned, I'm a huge fan of science fiction, and I don't know how, many of the audience members have read, like, things like the Foundation series by Asimov.Simulation as a Path, Not Just a PredictionSwyx [00:22:37]: Oh, yeah. We've mentioned psychohistory a number of times.Joon [00:22:39]: Okay, fantastic. So I might be, talking to the right crew. If you read Foundation series, literally the first act is there's a group of scientists who have found out that, “Oh, our galactic empire is going to collapse, and we're going to have 30,000 years of unrest.” And they run psychohistory, the simulator that tries to teach them, “Okay, how can we keep this unrest to a 1,000 years?” And they plan this out, and the first step of that plan is to get the scientists who say, “Okay, this is coming,” exiled into this random place in this, galax- galaxy.Swyx [00:23:18]: Terminus.Joon [00:23:19]: Exactly. And that's so counterintuitive. Like, what a strange move that you literally sent the group of scientists who was raising voice around this potential collapse of galactic empire into nowhere. How is that the right first move? Well, it turns out in this particular simulation, that was the move.Joon [00:23:40]: It's these things, right? And the reason why these reasoning is possible is because you're showing the step function or each step that results in a particular outcome. So really what simulation allows you to do in its highest form is you give it not a problem or question, like what would people answer to the survey? That's not what we do. What we tell it is, “Here is a goal that we have. In the context of foundation, we want to keep the unrest to a 1,000 years. What is the path that we need to take now to get to that particular future?” And that's what simulation allows you to do. Now, translating that into real market, imagine you're a automobile company and you're about to release a, EV, and you're trying to understand, well, how do we market EV, to make sure that our stock price goes up? But what if the answer comes down that, well, you can market your EV in XYZ way, but that might change people's perception around the cars that's not EV and make your overall sales to go down. Not very intuitive, especially all you're trying to optimize is EV salesss, and that's the only thing that you're tracking, then that might result in a completely wrong solution, or at least different solution than what you would have expected, whether it's right or wrong.Joon [00:24:57]: That's the power of simulation.Swyx [00:24:58]: For listeners, we covered a similar topic with Mikhail Parakhin from Shopify, where they are working on SimGym. I don't know if he ever talked to you about it. it's very similar.Joon [00:25:07]: ISwyx [00:25:07]: The goal is increased conversion, but then the journey is very unusual.Joon [00:25:12]: Journey is unusual.Swyx [00:25:12]: Yeah. The-- He's trying to look for interventions on a shopping trajectory, which is similar to what you're saying. Like, it's not about the attitudinal, is your word for it.Swyx [00:25:24]: It's about behavior.Joon [00:25:25]: It's about behavior.Swyx [00:25:25]: And that's exactly the difference, right? It's, like, not about the near-term direction about-- but it's more about, like, how do you affect multiple turns of interactions.Vibhu [00:25:35]: You had a good quote at the start about this as well. It's not about people wanting to know the outcome. It's about how they can change it, change the way to get there, something like that. But I wanna take it back to how do we know this is grounded? LikeGrounding and Evaluating Digital TwinsVibhu [00:25:47]: How do you run evals? How do you test that simulations come through? if I was to do the same thing that you described with, say, your favorite LLM, Opus, GPT-5.6, have some agent to map out these thingsVibhu [00:26:02]: How different are the answers we would get if I give it the same goal, the same objective, make a decent system? You're saying that you need to change the model weight. You have your own solution to this. But how far off are we, and how do you check if it's grounded? you have some interesting stuff on your site that points to how you run real evals, but if you could take us through that side. I think that's one of the big concerns that people have. They're like, “LLMs hallucinate.”Vibhu [00:26:27]: “You're just hallucinating layer after layer,” right?Joon [00:26:30]: The way we do this, and this is the paper that we worked on after the generative agents paper that really became the, at least for Simile and also the field of simulation and synthetic panels, really became the foundation. Yeah, this is the paper. the paper is called Generative Agent Simulations of 1000 People. Here's what we've done. For this paper, we brought 1,000 people that's representatively sampled from the US to a virtual lab. And what we have done was we spent two hours collecting fairly wide-ranging data. In this particular study, we focused a lot on this interview data, that was, whose script was taken from this project called American Voices Project. And then we would also pair that with a lot of behavior data and so forth, whatever we can collect within two hours. And then we would send these people away for a couple of weeks. And during that time, I would use this data to create their digital twins. And I would bring the humans, participants back after 2 weeks and have them complete a battery of surveys, experiments, behavior studies. So we have the list here, which included things like behavioral economics games. We would run literally, like, Big Five personality test, General Social Survey. We would also go ahead and run the randomized controlled trials that were published on PNAS. And we would have their digital twins predict how the source individuals would have acted in these studies and surveys. And this is where we could replicate people's behaviors and attitudes 85 percent as accurately as people would replicate their own. So that was the first really paper that gave this validated results that we can model individuals in an accurate way. And what we ended up finding now, of course, in AI space, so this paper came out at the end of 2024. AI space, a year and a half, 2 years, that's a lifetime.85% Accuracy and Why Frontier Models Miss Human BehaviorSwyx [00:28:24]: Yeah. Just, for listeners who are not seeing the YouTube, I just wanna say, like, the headline figure is 85 percent accuracy, like, which is a big improvement over all the otherSwyx [00:28:34]: Methods that you showed.Joon [00:28:36]: But the part that was particularly striking to us, especially as we improved this technology even further, was the generative AI models like ChatGPT, Claude that's coming out, it does give you the right foundation. However, what they do not consider is the true attitudinal and behavioral aspect of people, especially in the population that you care about. So what these models are really good at today is they're trying to become the super rational, objective machines, right? So you go get their data from places like Mercor, Scale. You talk to professional programmers, scientists to create model that's amazing at reasoning. That's what they do. Simile doesn't care about any of this. The models that we're talking about here, what we're trying to create are models that are as dumb as I am, right? So if I make some mistakes, the model has to make the same mistake.Swyx [00:29:34]: Oh, that's very hard.Joon [00:29:35]: That's very hard.Swyx [00:29:36]: You're solving Murphy's paradox.Joon [00:29:37]: That's exactly. And this is a completely different data and training objective. This is also where we see quite a bit of discrepancy in the performance in human behavior prediction between the frontier models, Simile's model, and the models being created in this space, where in some cases, the model performance of frontier models go all the way down to 20, 30 percent, especially if you go into that more niche population on topics that our customers would care about. On more gen pop, it might be around 50 to 60 percent. So it's not very robust. Like, you wouldn't want to make your decision off of these and these findings. If you can bring that up to 85 percent, that is ultimately what people end up getting very excited about.Swyx [00:30:20]: Yeah. Do we wanna keep going on the paper, routes?Joon [00:30:23]: Yeah, for sure. So the last one, was an interesting one. So this, paper was the follow-up paper that we had, to the 1000 agents paper, where the idea was now can we augment the models even further and post-train a model based on a lot of randomized controlled trials? So this was an interesting one. The data is always the most interesting part of modeling in many ways. The data that we got here was there's this, there's this platform called Open Science Framework. So some, the audience might be familiar with this. And there has been, especially in the social sciences over the past 5 years or so, there has been this concern around replicability of studies. And so it was a bit of a crisis, the scientists acknowledged, where we rerun the study and we don't see the same finding.Post-Training on RCTs and Replication StudiesVibhu [00:31:12]: Oof.Joon [00:31:12]: It's tough. And the reason why it's there-- that was often the case was there's this survival bias where the papers that get published often need to maintain what we call the value of less than 0.05 in the experiments that we ran. That suggests that only-- there's only 5% chance that the results that we saw is false positive. But the tricky part was all the papers that were not published, and there's still a 5% chance that whatever we publish is totally just randomly generated. Like, there's a 5% chance that, hey, this effect is not real, but it just happened to be real because of the sampling bias. So because of that, what scientists started to do was they started to register their studies. So before running an experiment, they would go to this platform and say, “Here is the data. Here is the population that we're collecting, and here's the hypotheses.” And they would just say, “Here is our hypothesis.” Like, “This is what we believe.” And you cannot retroactively change those hypotheses. This is what gives us more scientific statistical confidence that whatever effect that you ended up seeing is true. So that ended up creating this really interesting platform where there's one platform that has now contains tens of thousands of real-world experiments and hypotheses. And a lot of these are really high-quality, like, professionally designed behavior studies and random- randomized controlled trials. So we got the data and the studies from this platform and used that to make a point. And this particular, model is not, something that we're serving commercially because this was a part of the open science. But this particular data set, helped us make a point that by collecting a lot of these randomized controlled trials, that are really well-designed, we can make significant improvement in model's capability to predict human behaviors. So that's what this paper was about.Vibhu [00:33:10]: Is this stuff done on a individual level? Like, do I need to tune the model per individual, per company? Is there foundation model changes and then some slight post-training? Anything you can share there?Population-Level vs. Individual-Level ModelsJoon [00:33:21]: So this particular model was trained. the data we had at the level of individuals, but this particular model was trained. We experimented with both. And this is what we end up doing at Simile too. We always train 2, distinct model. One is what we call the population-level model. The other is what we call the individual-level model. And both take very similar input, which is the description of a subpopulation or individual and a stimuli. In this particular work, we've done the same. Here, the results that we are reporting are much more geared towards individuals because we do think that is a harder task in many ways, but that's what we have done.Vibhu [00:34:02]: You seen anything on the questions that humans can solve that models can't solve? So likeHuman Biases, Mundane Choices, and What Models MissVibhu [00:34:09]: Currently, it's, I live 5 minutes walk away from a car wash. It's a 10-minute drive. Should I walk or drive?Joon [00:34:16]: Huh.Vibhu [00:34:16]: The model will say, “Oh, walk to the car wash.” And, you don't have your car.Vibhu [00:34:20]: Is anything like this a problem in simulation? You would assume, like, very simple for human to think about, but if the model is saying you should walk to the car wash, anything here?Joon [00:34:32]: It's less, what can we solve, but I think it's more about what biases or mistakes do people make that models miss. Like, imagine that you are, like the. When I was still at Stanford, I lived in Palo Alto. So it's about, I would say, 40-minute walk from the campus. You ask the model, “Okay, let's go home. What can I, what can I do?” It would likely call an Uber or, give me, the bus time. But for the longest time, I really liked walking back. And the reason why I wanted to do that was not for efficiency. It really helped me think. And I like to walk for, half an hour or 40 minutes or so a day, where I just get to, just think about ideas, research, just get lost in my thoughts. That's very human activity. Unless the model has seen that and understands the importance of that activity, it would miss these kinds of features. So that I think, is fundamentally what we're trying to model. Like, what is fundamentally human might not be the most efficient thing to do, might not be the right thing to do, but things that make us who we are.Swyx [00:35:43]: I'm curious if, there are some data sets that you really want that would materially help you. One version of this may be interesting, which is more valuable to you to acquire as a data set, all of LinkedIn, all of Twitter, all of Facebook?What Data Matters: Social Media, Transactions, and FacebookJoon [00:35:57]: It's a little bit hard to rank, in part because, there's, there's this product saying where no feedback is wrong because it teaches you something about your users. Doesn't matter what feedback.Joon [00:36:11]: I think it's a little bit like that.Swyx [00:36:12]: So just whatever is bigger.Vibhu [00:36:13]: What about a different domain? Say it was. What about all of Amazon data?Joon [00:36:17]: Oh, yeah.Vibhu [00:36:18]: Shopping data, right?Joon [00:36:18]: Shopping data. So Amazon data is interesting in that it's very much behavioral, although, like, what people do on social media, you could squint and say that is also behavioral. But the transaction data is always interesting. It is also most commonly available, however.Joon [00:36:33]: If we were to look at purely social media, like if you really, if I were, if I had to really pick, Facebook likely is interesting because I do think it is most a default version of people. Because you go to LinkedIn, it's very much professional environment. So people put up their, they have their guards up, right? And that still is interesting because that is true human attitude and behavior, but it is not your base state. you go to Twitter- Twitter, people have their own crazy personas, or depending on who you are. Like, my Twitter profile and, persona is very much, initially was I was very much an academic. “Hey, I'm here to share my studies.” Now, I share, things that's related to Simile. But Facebook is one of those more private space where people just connect with their friends. In that way, I do think it shows you a little bit more about who that person is. So if I had to pick, I'd likely pick, Facebook.Swyx [00:37:30]: Yeah. And you're interested in, like, the whole person and their background and philosophy. I, is it too clinical or too machine learning-oriented to just say this is just ways to inject variance and biases? The broad question, is, like, is this any better than a randomized, like, combinatorial explosion version? So we have a link to the TencentBillion Personas, Synthetic Demographics, and Bespoke DataSwyx [00:37:54]: Billion persona paper, where they did not do any of the groundwork that you are doing.Swyx [00:37:59]: They just did like a cross matrix of here's all the professions in the world, here's all the people, possible backgrounds in the world, do a dot product across all of them, and that's it. That's your prompt for a billion people.Swyx [00:38:12]: This will do something. I don't know if it'll do what you do, but it gets you some way, some percent of the way there.Joon [00:38:18]: So this was an interesting paper. Like, what I admired about this paper when it came out was the scale. And you do gradually want to be able to simulate really large societies and interactions. So the scale is definitely admirable. it is relying heavily on the known statistics that went into training the model. So to the extent that you believe that statistics is correct, this is not a bad way to go about this. But the thesis here, and this is something that we also have seen in the market, like if this works, then we have solved simulation.Joon [00:38:54]: It,Swyx [00:38:55]: Because I survey, like, okay, 5% of the US population is in construction.Swyx [00:39:01]: The other 5% is in medicine, whatever, right? And then you just keep going down the list, and then you do the other side. 5% has, like, the big 5 personalitySwyx [00:39:08]: Of, like, neurotic or whatever. That's it.Joon [00:39:11]: That's it. So if you believe that the underlying data set and the platform that we're leveraging has all the right statistics, then this will have solved it. you're at that point merely retrieving the knowledge that is already embedded in the model, in the model parameters. That's not, unfortunately, what we see, where there is such detailed and also niche knowledge about people that if you just take one example, it might feel very mundane, but it's quite rich when you put together, that you do need to do a lot of bespoke data collection to better understand people. And this is also, I think what makes this particular, job fun, which you want to deeply understand people, and the process of deeply understanding them requires a lot of attention to the details. And you do need to pay attention to and pay respect to the daily lives that people lead.Scaling Simulation: From Thousands to SocietiesVibhu [00:40:04]: I wanna talk about scaling simulation.Vibhu [00:40:07]: So what can't we simulate, what can we simulate, and how does scaling affect this? So how big are the models? What if we go from, 8B, like, couple 100 billionVibhu [00:40:18]: Like billion000 parameters, billion000? Do we get scaling? Any interesting emergence? Like, at a certain scale, at a certain amount of training, you uncover anything unusual and any learnings from that?Joon [00:40:31]: What we are seeing is at Simile, so we do post-train our own model. The thing that we're seeing is the early glimpse of scaling law in simulations. The more data about humans and more compute you ingest, you start to get predictive and predictable gains of the model performance in simulating it, simulating people.Vibhu [00:40:51]: Ooh. We need a scaling law curve.Joon [00:40:52]: It's scaling law. Whenever you find it's a beautiful thing. And we're starting to see the glimpse of it, which is quite exciting. But if you talk about the ambition of simulation as a whole, it's not merely about building a model. It's about building a model, then creating the agents that become the individuals in a much larger ecosystem. So they're creating this multi-agent simulation. Down the line, you want these multi-agent simulation to also live in a very rich environment, right? What we are really trying to get to at that point is, hey, can we create. All right, let's do a time machine game again, and 5 years, 10 years into the future, can we create a simulation of 8 billion people living on Earth? I think that's quite interesting. And that really is the vision. And once you get to that state, the questions that you can help answer for the society also start to change from my perspective. The answers are fundamentally about emergence of the emergent behavior of society and large groups of people.Joon [00:41:53]: So the questions that I get excited by, and maybe this is a stodgy- a bit. I have my, academic side of me.Joon [00:42:01]: And for me, it's questions like, can we help solve climate change? If you look at climate change as a problem space, this is what we, like social scientists would often call it the wicked problems, problem where you have many actors with competing incentives for trying to make a very complex decision and coordinating that coordination decision. Very difficult to really solve in real life, which is also the reason why we couldn't solve it. Can simulation help us solve that? Another one is, can we understand the signals for collapsing democracy, or can we understand or can we uncover the origin story of the monetary system? These are societal questions that we never really had a good way of answering. If we can create simulations of our society, you have to believe that these are the problems that we can solve. So that's really the ambition of this field. And, I also think, yes, I think there's a Nobel Prize to be won there, which wouldn't be surprising. And I think there's some amazing societal impact that we can have to help people make better decisions.Climate Change, Democracy, and Societal SimulationSwyx [00:43:04]: Nobel Prize in economics?Joon [00:43:06]: In economics.Swyx [00:43:06]: Oh, I see. I see. Rooting for you to write that paper.Joon [00:43:10]: One of these days. But, one of the scholars that I was deeply inspired by, When I was coming into the space of simulation, is this scholar, named Thomas Schelling.Schelling, Agent-Based Models, and the Nobel PrizeSwyx [00:43:23]: Schelling point?Joon [00:43:24]: So the canonical example of the work that he's done was he was one of the creators of agent-based modeling. So this was, like, in the 1970s and 80s. It's very early days, but this was truly one of the first exemplars of simulations. And one of the canonical model from that time, and of course many of these simulations are trying to tackle the societal problems that's most relevant for their era, it was called the model of segregation. So racial segregation was a big topic, that, we cared about. And what they've done was they created this grid world where they had red dots and blue dots. And these dots were, back in the day, like, they were the agents, and they had a simple rule that governed their behavior. If certain percentage of your neighbors are of different color and if that goes above certain threshold, then you move to a new location at random.Joon [00:44:21]: One of the striking finding of this paper or this agent-based model was for the longest time, people thought the segregation within society was caused by explicit and overt racism.Joon [00:44:34]: But if you look at this model, people's preference towards living with people of the same color, that preference can be very minute.Joon [00:44:42]: But the very small difference causes the society to segregate completely over time. This was very counterintuitive for a lot of people. And this particular work ended up informing housing policies. Mixed income housing, got really inspired by this work. And Thomas Schelling ends up winning the Nobel Prize for having laid the groundwork for very early versions of simulations. The opportunity that I do see here in the more scientific terms, is agent-based models for the longest, had impact in the 1980s, 90s, to some extent, early 2000s, but it has now gotten forgotten by the community a little bit. Because as you can imagine, red dots and blue dots is not really a rich description of people.Joon [00:45:31]: But with the emergence of things like generative AI and, in particular, generative agents, we do have an opportunity to create these agent-based models that are high fidelity enough to help us make really complex decisions. And that's the opportunity that I see. If that truly works, then yes, that is the work that will result in a Nobel Prize.Swyx [00:45:53]: Yeah. For what it's worth, and I grew up in Singapore. 80% of Singapore is in public housing, and public housing has, enforced racial quotas for exactly that reason, which is very interesting. okay, so we talk about scaling, we talk about all these, the agent possible applications.Cost, Reuse, and the Economics of SimulationSwyx [00:46:13]: I'm scared about the cost. if you even-- let's just keep it to the US, about 8 billion people.Swyx [00:46:21]: But, how much does it cost to model so many hundreds of millions of people?Joon [00:46:26]: Oftentimes today, we don't start at that scale, this stage of the, of industry and simulation as technology. But we can get our users extremely rich and meaningful insights even by modeling thousands, tens of thousands of people. And today what we do is every week we are collecting data on the scale of tens of thousands people's data, and we have panel partnerships that gets us to tens of millions of people globally. So that's what we do today.Swyx [00:46:55]: And just as a side note once you've collected one person for one studySwyx [00:46:59]: Can you reuse that same person for all the subsequent studies?Joon [00:47:03]: That's exactly right.Swyx [00:47:03]: Okay.Joon [00:47:04]: The beauty of this model and these agents is the fact that they are domain-agnostic.Joon [00:47:08]: That what you're really trying to understand is what is the fundamental nature of these people? What's their social physics? And there are a lot of, a lot of, people that does change over time. Like, even, like, even things like, how many times have you gone have you been to, like, CVS the past week? that will change. But there's so many traits about people that are also known to never change. Like, your risk tolerance doesn't really change over time. It's very consistent. So it's these things that we're trying to learn. But the scale we are operating is right now hundreds or, tens of thousands to hundreds of thousands. And in many of the core use cases that we are deployed in, and this is more than enough population, to cover those. Really, at that point, what you care about is less the number of people, but more do you have the right subpopulation of interest covered? And this is also the reason why people want a larger sample. It's not because they want, stronger statistical guarantees. It's more that can they filter down to any population of their interest. However, you can also imagine in 10 years, if we truly believe that the compute is going to scale, that we'll have much more availability for compute, and our ambition for simulation is also going to scale accordingly, there's definitely a reason for us to create an entire data center worth of simulations.Joon [00:48:35]: Or in my hunch here is I do think in the next some number of years, we will start creating simulations that will cost as much as training a foundation model. But perhaps it's going to be so valuable to the society that it would be a no-brainer. Right now, even today, like, we are training bunch of new foundation model just so we can say we trained one and we spent tens of millions. But if we can create a simulation at the level of society that would solve climate change, I would run that today. I would raise the money right now just to run that.Multi-Agent Simulation and Social InfluenceSwyx [00:49:10]: Amazing. the follow-up question is, does it also compound if you let the simulations talk to each other?Swyx [00:49:18]: Or do they already do that today? They don't, right, as far as I understand?Joon [00:49:22]: It depends on what simulation you're trying to run.Joon [00:49:24]: In the multi-agent simulation setup, the agents do talk to each other.Swyx [00:49:28]: Right, which is exactly Smallville, right?Joon [00:49:29]: That's right.Swyx [00:49:30]: But a lot of times, for example, in commerce, you're just by yourself, so there's no point talking. which is way cheaper.Vibhu [00:49:37]: But they use all these levels, right? Like, you decide what you will buy based on what other people around you buy and talk about, right?Swyx [00:49:43]: It depends.Vibhu [00:49:44]: It depends.Swyx [00:49:45]: Again, I'm, I'm coming at this from a cost point of view. I'm like, “Oh my God.” LikeVibhu [00:49:48]: I thinkSwyx [00:49:49]: If there is, like, some combinatorial thing of, like, thousands of people talking to thousands of people, then that one million X's might cost.Vibhu [00:49:56]: I have a very different view as the cost point aside. Like, running these studies in reality is a lot more expensive, right? Running any study like this is you gotta have people do it, you gotta sign people up. It's very expensive and sometimes, like, not feasible to run the study.Vibhu [00:50:14]: But the outcome or the decisions you make are very expensive on them, right? So spend X million on something that, the overall process costs 100 million might as well, right? There's, there's a lot of value to be had there. It's a small cost, but I'm excited on the cost side.Joon [00:50:33]: To some extent, and when you deploy technology, you often want to deploy in a way where you can replace existing budget or you can make things more efficient, and that is the best way to deploy. However, the way you capture the long-term value of the technology is making the argument that, no, it's the upside, that by making this better decision using simulation, you have saved yourself or made yourself hundreds of millions or even billions of dollars, and that's a case to be made.Vibhu [00:51:06]: Random tangent question. So if you're doing a lot of inference, a lot of model multi-agent stuff, are you at the point where it makes sense to, train a model that' very sparse? You're expecting to do multi-million dollar runs. Are you thinking about this in model architecture standpoint or inference efficiency, or, you're still at the research phase of it works, we're not super there yet?Joon [00:51:34]: Efficiency, we do think quite a bit about. this is technology that is deployed now in some of the largest enterprise companies in the world, and we do process significant number of queries, that are trying to, simulate the populations in the world. So efficiency is a consistent thing. we don't want to over-optimize too early, so I wouldn't say, like, this is the higher bid Right now, but this is definitely something that we think pretty carefully about.Swyx [00:52:05]: Yeah. Are there other case studies? So we, you talked about CVS, talked about Gallup, Deloitte, Wealthfront.Efficiency, Enterprise Use, and Real-World Case StudiesJoon [00:52:12]: Wealthfront is an interesting one, because one of the things they were trying to do, they were one of the first customers that wanted to do product testing that goes beyond just asking people what they think about, let's say, behavior experiments and so forth. So there, really what we had to do was reason about multimodal input, so images, but also you can also imagine, like, these agents traversing through Figma mockups or websites. So some of the things that our agents can also do is it can be given a domain, like, or, like, a website URL and go use it for a while. It's these things. And Wealthfront was one of the first, customers, that was very excited about this possibility.Vibhu [00:52:53]: What have people been asking? Like, is there any demand that we have not covered? Like, UI testing, right?Vibhu [00:52:59]: I wanna try a new. I wanna ship a new feature, test the UI, simulate how people will do it. Any interesting things that you're seeing demand for?Product Testing, Websites, and Synthetic PanelsJoon [00:53:08]: Today, a lot of the demand does come from like, the places where people have historically used human panels, we can now replace with agents, and these synthetic populations. And this is not replacing human panel. in many ways, the simulation that Simile is building is grounded. So the way that I think about this is we are trying to represent humanity at scale. And in that way, the use cases are what we would expect, but it's the scale of deployment that surprises me.Joon [00:53:44]: Turns out there are so many decisions that people make every day in these organizations, groups, and we want to be able to say, “We listen to people. We have consulted our users.” But in reality, that is rarely the case because getting to people and asking them many questions, it's difficult. It's both costly, time-consuming, but most importantly, people are just not available. If I had to answer 1000 survey questions for this one particular, vendor, even if I wanted to do that, like, I would never do it. And that's very much the case. What simulation can do is ensure that the voices of people are always represented in rooms where the decisions for them is made, right? So all the stakeholders of this particular product launch, ideally they're consulted. That's what this technology really is trying to enable.Market Size, TAM, and Human Decision-MakingSwyx [00:54:39]: In my mind, that means it skews towards more consumer focus, right? Like, anything with a wide enough customer base where you do benefit from the diversity that you represent. What are some rough statistics, just for people who are not familiar with this market in general, what's the market size that. I'm sure you have some, like, rough numbers. market size is, like, a vague questionSwyx [00:55:01]: But, like, how much do people spend?Joon [00:55:03]: So market research is a $100 billion industry.Joon [00:55:06]: But the thing about simulation is not a tool for market research. Simulation is a tool for human decision-making. So the question around what is a TAM here is quite tricky, right? Because it's easy to say, “Well, market research TAM is roughly 100 million or 100 billion.” so is it a TAM? And not really, right? Because in many ways, you're trying to inform all human decision-making. You're trying to inform every decision that are made about humans for humans. What is a TAM for that? It's really unclear. And I'll be honest. Like, I have a scientific background, I have a research background, so I didn't come into the field calculating, oh, what is the TAM for human decision-making? But I just had to assume, well, if we can inform every decision that is made about human for human, that has to be big.Swyx [00:55:58]: Some- something valuable.Joon [00:55:59]: Exactly.Swyx [00:55:59]: To some extent, you are a unicorn founder now, and you have to care as a CEO. But, like, I do think, like, yeah, when you go into these boardrooms with people that you're quoting millions of dollars of contracts for, like, you have to say, “Well, here's what you spend on humans-”Swyx [00:56:15]: “. And here's what we save you, and it's 85% similar.”Joon [00:56:19]: And certainly, the value case, is something that we care deeply about. Like, what is the value that we provide to the users and the decision-makers? But this is also where, like, as a founder, I think valuation only tells one very superficial aspect of the story, and I try not to think too much about valuation, in general, because that's not what also motivates a team or certainly doesn't. I'm, I-- Again, the interesting thing about researchers is we are happy living in academia, getting paid next to. we get paid okay. we don't get paid that much, as a researcher here in academia, but it's the impact and it's the, it's the value that we can provide to the individuals and the society that really drives us. And in that way, ultimately what drives us is the impact. Does the simulation we provide have a real impact in people's decision-making in ways that progresses our society forward? If the answer is yes, then yes. that has to be great business, and we see that in numbers, and we do care deeply about that upside story, but that's the heart of it.Where Simulation Goes NextVibhu [00:57:27]: Do you have any timeline predictions? So we talked about scaling laws of simulations.Vibhu [00:57:33]: You brought up, okay, maybe one day we can simulate how to solve climate change.Vibhu [00:57:38]: Where are we now?Vibhu [00:57:40]: If that's not the end state, what is an end state, and what does progress look like?Joon [00:57:45]: So what I sometimes tell people is simulation as industry, it feels a lot like where GPT-3.5, GPT-4 was, for the AGI saga, which is we have now technology that is powerful enough to do real damage on the verticals that we are tackling. At the same time, there's a lot of progress that is yet to come. And that's, I think, where this is. So the way I see it, I do think there will continue to be breakthroughs both in data, in algorithms, and there will be much more aggressive scaling that will also happen over the next few years. But I think that's roughly where we are.Swyx [00:58:27]: I think that was about the ro

Today, I welcome two guests to the show, Alma Derricks and Eliza VanCort, the co-creators of Ignite, a leadership experience built around a simple but incredibly timely idea. As technology and AI continue to reshape the workplace, the human skills that drive great leadership have never been more important. These two came to this work from very different directions. Alma has spent more than three decades building businesses, launching brands, and leading commercial strategy for organizations including Deloitte, HBO, Paramount, and Cirque du Soleil. Today she's the founder and managing partner of REV, where she helps organizations navigate moments of growth, reinvention, and transformation. On the other hand, Eliza is a best-selling author, communication strategist, Cornell University Fellow, TED speaker and former acting instructor whose work has helped executives and leadership teams become more confident, influential communicators in the moments that matter most. What started as a conversation became a collaboration, and that collaboration became Ignite. We spend less time talking about the program itself and spend more time talking about why it exists. What are they seeing inside executive teams? Why do communication and influence seem harder than ever? And most importantly, what human skills are becoming more valuable because of AI, not despite it?

In this episode of Better Call Daddy, host Reena Friedman Watts sits down with Kiko Zang, business executive, tech founder, and opinion leader building human-centric consumer products. Kiko is the Founder & CEO of Chomp, a social game that rewards honesty instead of punishing it and she's on a mission to fix what she calls the "performative internet." Kiko opens up about her path from boarding school in China to New Zealand and the United States, the anti-authoritarian streak she carried as a kid, and the long road to understanding her own identity across cultures. The conversation gets candid as she discusses her experience with open relationships and how it reshaped her views on love, trust, kindness, and loyalty then turns to the bigger picture: why social media rewards performance over authenticity, why women in particular self-censor online, and why honest human belief may be the scarcest, most valuable data in the age of AI. Kiko also shares her founder journey from COO at Orca (one of Solana's largest decentralized exchanges, where she helped raise $19M and grow the platform to $1B+ in 24-hour trading volume) to building Chomp, which drew 50,000 beta users sharing millions of honest answers and raised $3.6M from backers including BlueYard, JSquare, and Accomplice placing her among the 2% of female-led startups to raise venture capital. Chomp launches on iOS in 2026. Equal parts personal memoir and founder story, this episode covers identity, relationships, resilience, the loneliness epidemic, the funding gap for female founders, and what it takes to build something real in a "dead internet" full of bots and AI slop. Keywords: Kiko Zang, boarding school identity, open relationships podcast, cultural identity, female tech founder, women in venture capital, social media and authenticity, dead internet theory, Chomp app, Solana Orca DEX, honest opinions app, read the room, loneliness epidemic, Better Call Daddy podcast, Reena Friedman Watts

Steel Patriot Partners lists its priorities in an order much of the cybersecurity industry reverses. Business owners first, engineers second, security and compliance people third. Michael Parisi, Chief Growth Officer, says the sequence is deliberate and shapes how the firm opens a client conversation. The order tracks the path the founders took. Jason Ford, Co-Founder and CEO, started in the late 1990s as a government contractor at the FBI, met FISMA and SAS 70 early, and built a platform for the Treasury that sold savings bonds online before PCI was a standard. He started his first company in 2004, took it through FedRAMP in 2013, and was acquired in 2017 holding 35 to 36 authorizations to operate across multiple agencies. What changes when an advisor is free to answer directly? Parisi spent about 15 years in the Big Four across PwC and Deloitte before joining Steel Patriot Partners. Auditors hold independence, which means watching a decision head the wrong way without steering it. In an advisory seat, he says, telling an organization that its preferred direction falls apart as a business decision becomes part of the work. How does a company find out where it actually stands? Ford says compliance is one outcome among many, sitting alongside operational maturity, better visibility, and integrating AI into DevSecOps. The common gap is not knowing where you sit on your own maturity journey. That finding cuts both ways, and some organizations learn they are further along than they assumed. Buying more tools rarely closes the gap. Ford argues the work is holistic and that each organization is unique, so what fits one may fit another poorly. AI does not settle it either, since a model fed your own assumptions will hand them back. The name follows the same logic. Steel is Pittsburgh, Patriot is Boston, and Partners is the operating model, since Steel Patriot Partners advises, deploys and operates environments alongside the client. Parisi closes by asking anyone weighing a path to verify it as a business decision rather than as an information security purchase or a price comparison. This is a Brand Spotlight. A Brand Spotlight is a ~15 minute conversation designed to explore the guest, their company, and what makes their approach unique. Learn more: https://www.studioc60.com/creation#spotlight GUESTS Jason Ford, Co-Founder and CEO, Steel Patriot Partners LinkedIn: https://www.linkedin.com/in/jason-ford-5ab206/ Michael Parisi, Chief Growth Officer, Steel Patriot Partners LinkedIn: https://www.linkedin.com/in/michael-parisi-4009b2261/ RESOURCES Steel Patriot Partners: https://www.steelpatriotpartners.com/ Find Your Path, the qualifier that helps you locate your starting point: https://www.steelpatriotpartners.com/find-your-path ROI Workshop: https://www.steelpatriotpartners.com/roi-workshop ITSPmagazine event coverage: https://www.itspmagazine.com/black-hat-usa-2026-cybersecurity-event-coverage-in-las-vegas Are you interested in telling your story? ▶︎ Full Length Brand Story: https://www.studioc60.com/content-creation#full ▶︎ Brand Spotlight Story: https://www.studioc60.com/content-creation#spotlight ▶︎ Brand Highlight Story: https://www.studioc60.com/content-creation#highlight KEYWORDS jason ford, michael parisi, steel patriot partners, marco ciappelli, brand story, brand marketing, marketing podcast, brand spotlight, cybersecurity compliance, grc, fedramp, fisma, cmmc, maturity assessment, cybersecurity advisory, business risk, compliance strategy, security consulting, ai in devsecops, trusted advisor

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In Episode 52 of The League, Benoy Thanjan and David Magid discuss several major developments shaping the future of power markets. The conversation begins with a significant New York court ruling that classified battery energy storage as a public utility, potentially giving developers a stronger path through local zoning and permitting challenges. They discuss why this legal precedent could accelerate the development of standalone battery projects across the state over the next several years. They also examine New Jersey's plans to procure 1.1 GW of nuclear power while seeking to protect ratepayers from construction and cost-overrun risk. Nuclear offers the promise of reliable, carbon-free generation, but long development timelines, financing challenges, and execution risk remain major questions. The episode then turns to the rapidly growing electricity demand created by AI and data centers. Benoy shares insights from a recent Young Professionals in Energy panel focused on how the industry can deliver reliable 24/7 power quickly enough to support data center growth. The conclusion: there is no single solution. Solar, battery storage, natural gas, fuel cells, microgrids, nuclear, transmission, and demand management will all likely play a role. The real competitive advantage may belong to companies that can navigate permitting, interconnection, financing, and infrastructure deployment faster than their competitors.   Host Bio: Benoy Thanjan Benoy Thanjan is the Founder and CEO of Rene Down down down down downu Energy, solar developer and consulting firm, and a strategic advisor to multiple cleantech startups. Over his career, Benoy has developed over 100 MWs of solar projects across the U.S., helped launch the first residential solar tax equity funds at Tesla, and brokered $45 million in Renewable Energy Credits (“REC”) transactions. Prior to founding Reneu Energy, Benoy was the Environmental Commodities Trader in Tesla's Project Finance Group, where he managed one of the largest environmental commodities portfolios. He originated REC trades and co-developed a monetization and hedging strategy with senior leadership to enter the East Coast market.  As Vice President at Vanguard Energy Partners, Benoy crafted project finance solutions for commercial-scale solar portfolios. His role at Ridgewood Renewable Power, a private equity fund with 125 MWs of U.S. renewable assets, involved evaluating investment opportunities and maximizing returns. He also played a key role in the sale of the firm's renewable portfolio. Earlier in his career, Benoy worked in Energy Structured Finance at Deloitte & Touche and Financial Advisory Services at Ernst & Young, following an internship on the trading floor at D.E. Shaw & Co., a multi billion dollar hedge fund. Benoy holds an MBA in Finance from Rutgers University and a BS in Finance and Economics from NYU Stern, where he was an Alumni Scholar. Connect with Benoy on LinkedIn: https://www.linkedin.com/in/benoythanjan/ Learn more:  https://reneuenergy.com https://www.solarmaverickpodcast.com   Host Bio: David Magid David Magid is a seasoned renewable energy executive with deep expertise in solar development, financing, and operations. He has worked across the clean energy value chain, leading teams that deliver distributed generation and community solar projects. David is widely recognized for his strategic insights on interconnection, market economics, and policy trends shaping the U.S. solar industry. Connect with David on LinkedIn: https://www.linkedin.com/in/davidmagid/ If you have any questions or comments, you can email us at info@reneuenergy.com.   Please provide 5 star reviews      If you enjoyed this episode, please rate, review and share the Solar Maverick Podcast so more people can learn how to accelerate the clean energy transition.    Reneu Energy Reneu Energy provides expert consulting across solar and storage project development, financing, energy strategy, and environmental commodities. Our team helps clients originate, structure, and execute opportunities in community solar, C&I, utility-scale, and renewable energy credit markets. Email us at info@reneuenergy.com to learn more.    

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