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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.    

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 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.    

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.

From a young age, Esther Colwill, a former partner at Deloitte and now country head for Australia at Korn Ferry, dreamt of climbing the highest peaks on each of the seven continents, including, of course, Mount Everest. Colwill succeeded, emerging with critical lessons that have helped her in both her private and professional life. On this week’s episode, BOSS editor Sally Patten finds out how having a goal outside work spurred on this executive’s professional career. This podcast was sponsored by Aussie Broadband. Further reading: Do what others won’t do: AFL great Chris Judd’s formula for success The former sports star and founder and portfolio manager of Cerutty Macro Fund has one rule that changed the course of his career. This exec left banking to buy a caravan park. Now he runs an empire Grant Wilckens co-founded G’day Group in 2004 and never looked back – apart from the time when he could have lost everything.See omnystudio.com/listener for privacy information.

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.    

How do you give difficult feedback without damaging trust?Katie O'Brien Ceccarini, author of Fearless Feedback: Everything Managers Have Never Been Taught About Feedback and founder of Endurance Management Coaching, brings two decades of experience leading, managing, and training teams. She shares how leaders can prepare for hard conversations, navigate emotional reactions, and say what needs to be said while preserving the relationship.Katie explains that fearless feedback starts before we ever choose the right words. It begins with our mindset, the strength of the relationship, and the intention we bring into the conversation. When we believe in the other person's ability to grow, we are more likely to communicate with conviction, clarity, and care.She also shows us how to stay present when feedback brings up defensiveness, emotion, silence, or resistance. Instead of reacting to what we see on the surface, we can get curious about what may be happening underneath. We can acknowledge someone's experience without abandoning the message, and we can refocus the conversation without turning it into a debate.When we strengthen our mindset, relationships, and delivery, we can give feedback with greater courage, build trust through difficult moments, and create conversations that help people grow.Key Takeaways:Prepare Your Mindset for Feedback: Explore how the beliefs you bring into a conversation can shape how your message is received.Strengthen the Relationship First: Consider how trust and safety can create a stronger foundation for honest feedback.Open the Conversation Well: Discover how a thoughtful headline can make difficult feedback easier to hear and engage with.Navigate Difficult Reactions: Examine how the HEAR framework can help you respond when feedback triggers defensiveness, emotion, or resistance.Make a Better Second Choice: Learn how noticing your first reaction can help you approach challenging feedback conversations with greater courage.Resources MentionedThe Inspire Your Team to Greatness assessment (the Courage Assessment) - In less than 10 minutes, find out where you're empowering and inadvertently kills productivity, and get a custom report that will tell you step by step what you need to have your team get more done. Get it here: https://courageofaleader.com/inspireyourteam/You don't need to have all the answers to lead well. Get your copy of the Clarity Kit for just $17 to learn the five practices to bring more clarity, confidence and courage into your leadership - https://courageofaleader.com/the-clarity-kit/About the Guest:Katie is a developer of people with two decades of experience leading, managing, and training teams.Her career spans early education, a decade at Yelp where she created and scaled all Customer Success training, and then as Head of Learning & Development at Opendoor she elevated employee onboarding and leadership development. She has the rare ability to bridge the gap between what HR and L&D leaders need and what actually lands with leaders in the business.In 2026, she published Fearless Feedback: Everything Managers Have Never Been Taught About Feedback, a practical guide that equips managers for the development and performance conversations that HR professionals depend on them to have.Today, Katie runs Endurance Management Coaching, partnering with organizations like eBay, Munchkin, Vital Farms, and PAN Communications to develop their managers and leaders.Over the past 20 years, she's managed, trained, and developed thousands of people, and everything she teaches comes from real teams, real conversations, and real results.Katie's leadership style is best described as human, with standards. She cares deeply and holds a high bar.When she's not working, she's getting dirty at the barn with her horse, training for triathlons, or hanging out with her son and husband in Denver.https://www.enduranceboss.com/https://www.linkedin.com/in/katie-ceccariniAbout the Host:Amy L. Riley is an internationally renowned speaker, author and consultant. She has over 2 decades of experience developing leaders at all levels. Her clients include Cisco Systems, Deloitte and Barclays.As a trusted leadership coach and consultant, Amy has worked with hundreds of leaders one-on-one, and thousands more as part of a group, to fully step into their leadership, create amazing teams and achieve extraordinary results.Amy's most popular keynote speeches are:The Courage of a Leader: The Power of a Leadership LegacyThe Courage of a Leader: Create a Competitive Advantage with Sustainable, Results-Producing Cross-System CollaborationThe Courage of a Leader: Accelerate Trust with Your Team, Customers and CommunityThe Courage of a Leader: How to Build a Happy and Successful Hybrid TeamHer new book is a #1 international best-seller and is entitled, The Courage of a Leader: How to Inspire, Engage and Get Extraordinary Results.http://www.courageofaleader.comhttps://www.linkedin.com/in/amyshooprileyThanks for listening!Thanks so much for listening to our podcast! If you enjoyed this episode and think that others could benefit from listening, please share it using the social media buttons on this page.Do you have some feedback or questions about this episode? Leave a comment in the section below!Subscribe to the podcastIf you would like to get automatic updates of new podcast episodes, you can subscribe to the, podcast on Apple Podcasts or Stitcher. You can also subscribe in your favorite podcast app.Leave us an Apple Podcasts reviewRatings and reviews from our listeners are extremely valuable to us and greatly appreciated. They help our podcast rank higher on Apple Podcasts, which exposes our show to more awesome listeners like you. If you have a minute, please leave an honest review on Apple Podcasts.Mentioned in this episode:The Inspire Your Team to Greatness Assessment (The Courage Assessment)https://courageofaleader.com/inspireyourteam/

Kylee Ingram: Decision Science, Cognitive Diversity, and the Ingredients for Better Leadership Decisions Guest: Kylee Ingram, Co Founder of Wizer Host: Julie Riga In This Episode: Kylee's journey from television producer to co founder of Wizer, a company built on decision science and organizational intelligence How an interactive kids app, a Cannes nomination, and time in a Boulder, Colorado tech accelerator led Kylee into the world of crowd science The research behind Wizer, drawn from Doctor Juliet Bourke's work at Deloitte and mathematician Scott Page at the University of Michigan The six ways people approach a decision, and why every person has a primary and secondary decision making style Why outward diversity matters, drawn from jury studies showing homogenized groups make weaker, less self aware decisions The Boeing and NASA examples, and what happens when experienced voices are sidelined for cost or convenience The three biases every decision room must account for, social bias, information bias, and capacity bias How to design a decision room with intention instead of relying on a leader's instinct or a captain's call Why buy in increases when people see their own perspective represented in the room, even indirectly The idea of defensible decisions, and how the right process helps leaders stand behind a choice with confidence The free decision profile tool at wiser.business, and how Wizer's built in communication tool guides team members on how best to reach one another Wize Snaps, a separate outreach tool originally built for the charity sector to profile a third party before reaching out Wizer's current work in the Indigenous sector in Australia, helping communities reframe their business and generate income through consultation How decision science applies well beyond the boardroom, including family conversations and small team settings Key Takeaways: Know your own bias before you walk into any decision Build decision rooms with intention, not instinct Protect experienced voices, especially when pressure pushes teams toward shortcuts Representation in the room builds real, lasting buy in Purpose driven leadership starts with genuine curiosity about how people think Small teams and families can apply decision science just as effectively as large organizations Memorable Quotes: "There's science behind how to make a good decision." "We stand here on the shoulders of some great research to build out this company." "Know that you're biased." This episode is ideal for leaders, founders, and teams who want a more self aware, purpose driven approach to leadership and decision making. Connect with Kylee Ingram on LinkedIn and at wizer.business Connect with Julie Riga: https://stacklist.app/julieriga Subscribe to Stay On Course wherever you listen to podcasts, and share this episode with a leader who needs it. #stayoncourse #decisionscience #leadership #cognitivediversity #purposedriven Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Episode Summary On this episode of the Solar Maverick Podcast, Benoy Thanjan speaks with Jacob Yang, a clean energy marketing and communications professional who has worked across the solar, energy storage and climate tech industries. Benoy and Jacob discuss how marketing in renewable energy has evolved, some of the biggest mistakes clean energy companies make when communicating with customers, and why thought leadership and personal branding are becoming increasingly important. They also explore the growing importance of LinkedIn as a business development platform, how executives can build authentic brands without constantly selling, and why sharing knowledge consistently can create opportunities long before someone becomes a customer. Jacob also shares his perspective on careers in clean energy, the skills that will become increasingly valuable, and how the industry may evolve over the next decade.   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 MW 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.   Guest Information Jacob Yang is the founder and CEO of Amp Your Story and a clean energy marketing professional with more than 10 years of marketing experience and approximately seven years of experience working in clean energy. Earlier in his career, Jacob worked at a marketing agency where his clients included utility-scale EPC companies in the Midwest and several cleantech startups. Today, he works with cleantech companies around the world as a fractional CMO. Jacob also created a free online clean energy marketing community designed to encourage collaboration, professional growth, knowledge sharing, and new voices within the industry. The community helps marketers navigate challenges ranging from clean energy policy and industry volatility to AI, emerging marketing technologies, career development, and demonstrating marketing ROI. Through his thought leadership, Jacob focuses heavily on personal branding, LinkedIn, demand generation, paid advertising, AI, and helping clean energy professionals become more effective marketers. He is also developing a clean energy LinkedIn course designed to share these strategies with a broader audience.   Stay Connected: Benoy Thanjan Email: info@reneuenergy.com  LinkedIn: Benoy Thanjan Website: https://www.reneuenergy.com Website: https://www.solarmaverickpodcast.com/   Jacob Yang LinkedIn: https://www.linkedin.com/in/jacobjuancarlosyang/   Website: https://cleanenergymarketer.com   Jacob Yang interview about Linkedin strategies https://www.youtube.com/watch?v=4dnDOdlpLZU 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.  

A Super Rugby team owner believes a damning report into the competition's viability is further proof a new financial structure is needed. A Deloitte document claims no franchise would have been profitable on a stand-alone basis since 2019, without New Zealand Rugby funding. Hurricanes co-owner Malcolm Gillies has been an advocate for privatisation, telling Mike Hosking they must adapt. He says they're outgrowing the model, and they have to look collectively at how they can get it to produce players of quality, but at the same time it needs to stand on its own. LISTEN ABOVE See omnystudio.com/listener for privacy information.

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In this episode, Eddie Wilson, Managing Director at Deloitte, and Yuchen Chen, Associate Vice President at Deloitte, discuss how healthcare organizations can identify and address patient leakage through data, analytics, and improved access strategies. They also explore how stronger network retention, care orchestration, and emerging AI capabilities can support margin growth, patient engagement, and long term strategic planning.This episode is sponsored by Deloitte.

Alma Derricks shares her extensive experience in media, branding, and innovation, discussing the importance of understanding audiences, balancing technology with human skills, and fostering innovation through dialogue. She reflects on her international experiences, the evolution of the internet, and the power of authentic engagement.Alma Derricks is a growth strategist, innovation expert, and founder of the award-winning consultancy REV, where she helps organizations navigate reinvention, accelerate growth, and build new brands and businesses. Throughout her career, she has advised global organizations on strategy and innovation, served as a Strategy Partner at Deloitte, and led commercial operations for Cirque du Soleil. Alma is also the co-creator of IGNITE, a leadership masterclass focused on communication, influence, and executive presence. A sought-after speaker at events including Adobe Summit and CES, she is passionate about helping leaders see disruption as an opportunity to create extraordinary growth.Get In Touch with Alma:Alma Derricks on LinkedInRev (her website)

Negenennegentig procent van al het stablecoinvolume en van de reserves daarachter is in dollars. Wie in Europa met stablecoins werkt, kan de Amerikaanse regels dus niet negeren. Sinds vorig jaar heeft de Verenigde Staten die regels: de GENIUS Act, de eerste federale wet voor stablecoins. Daarvoor was er niets wat voor heel Amerika gold. Gast Mauro Halve liet zich certificeren als GENIUS Act Specialist en legt de wet in deze aflevering naast onze eigen cryptowet MiCA. De opzet verschilt meteen. Europa maakte een breed kader voor de hele cryptomarkt; Amerika pakte alleen de stablecoin aan die bedoeld is om mee te betalen. En waar een Europese vergunning in heel Europa geldt, kent de GENIUS Act drie routes: een route voor bankdochters, een federale route voor de cryptobedrijven en een route via een Amerikaanse staat. Boven de tien miljard dollar aan uitgifte moet je verplicht over naar federaal toezicht. Het grootste verschil zit in de kluis. Onder MiCA moet minstens dertig procent van de dekking als bankdeposito worden aangehouden en de rest in kortlopende, risicovrije alternatieven. De GENIUS Act werkt met een gesloten lijst: contant geld, tegoeden bij de centrale bank, verzekerde deposito's, staatsobligaties met een looptijd onder 93 dagen, repo's en geldmarktfondsen. Die geldmarktfondsen mogen ook getokeniseerd zijn, en dat verklaart waarom BlackRock er zoveel van uitgeeft. Precies daarop wees Paolo Ardoino van Tether toen hij weigerde de Europese route te nemen. Hij wil zijn reserves niet bij een bank parkeren maar in staatsobligaties, en verwees daarbij naar het omvallen van Silicon Valley Bank. Tegenover die ruimte staat een strenge achterkant: wie als bestuurder zijn handtekening onder een verkeerde rapportage zet, kan in de Verenigde Staten vijf jaar de gevangenis in. Voor een Europese uitgever is de deur niet dicht, maar wel smal. De Amerikaanse minister van Financiën moet dan eerst vaststellen dat het toezicht in het thuisland vergelijkbaar is. Kim Schneider ziet in haar werk dat de grootste uitdaging voor Europese partijen ergens anders ligt: je kunt een stablecoin uitgeven, maar dan moet er ook een netwerk zijn dat hem gebruikt. Tot slot de andere grote cryptowet, CLARITY, die over de hele marktstructuur gaat. Die blijft in de Senaat hangen, terwijl de GENIUS Act er soepel doorheen ging. En Halve noemt een detail waar Europa iets van kan leren: heeft de Amerikaanse toezichthouder een vergunningsaanvraag na 120 dagen niet behandeld, dan is die automatisch goedgekeurd. Vertraging is daar het probleem van de toezichthouder. Co-host is Kim Schneider. Over de podcast Cryptocurrency are here to stay. In deze wekelijkse podcast gidst Daniël Mol je door het belangrijkste cryptonieuws, langs hypes en trends, voor- en tegenstanders en winst en verlies. In het A-deel bespreken we het laatste nieuws en in het B-deel gaan we in gesprek met een gast. Van cypherpunkpioneers tot grootbanken die aan de haal gaan met stablecoins, van Bitcoin tot Ethereum tot CBDC's. Alles passeert de revue.Reageren? Stuur dan een mail naar cryptocast@bnr.nl Gasten Mauro Halve is voorzitter van de branchevereniging voor cryptodienstverleners VBNL. Kim Schneider is web3 enterprise lead bij Deloitte. Host Daniël Mol is presentator en redacteur van de Cryptocast. Hij is sinds 2017 met Bitcoin bezig en kwam in 2021 bij het team van de Cryptocast. Redactie Daniël Mol See omnystudio.com/listener for privacy information.

Ruim een week na de hack van de Coldcard is de rust in de bitcoinwereld nog niet teruggekeerd. Het aantal slachtoffers loopt nog op en het gestolen bedrag is de grens van honderd miljoen dollar voorbij. De oorzaak lag niet bij de gebruikers maar in de software. Door een fout in de firmware werd de seed, de reeks woorden waarmee een wallet te herstellen is, aangemaakt met een softwarematige toevalsgenerator in plaats van de hardwarematige die er hoorde te zitten. Wie zijn seed op die manier heeft laten maken, kan die door een aanvaller laten narekenen. Op de blockchain is het gevolg goed te zien. In een week verhuisde ongeveer 210.000 bitcoin uit wallets van langetermijnhouders, de grootste uitstroom sinds december 2024. Dat is geen paniekverkoop maar een verhuizing, naar nieuwe wallets, naar bewaarpartijen met een vergunning en naar bitcoin ETF's. Daarmee komt de vraag op of volledig eigen beheer nog wel de veiligste route is, of dat een spreiding tussen eigen beheer en een rekening bij een bekende partij verstandiger uitpakt. Ondertussen is er een tegenbeweging opgestaan. Het Bitcoin Red Team, een groep vrijwilligers, laat AI de opensourcesoftware rondom bitcoin nalopen op dezelfde soort fouten. In de eerste 27,5 uur legden zestien onderzoekers 4.962 meldingen vast over 390 projecten, waarvan 85 als kritiek werden aangemerkt en 635 als ernstig. Niet het bitcoinprotocol zelf is het doelwit, maar de wallets, tools en toepassingen eromheen. Dat AI de fout in de Coldcard vond in code waar jaren niemand naar had gekeken, laat zien dat dezelfde techniek beide kanten dient. Coinkite, het bedrijf achter de Coldcard, was al eerder gewezen op risico's in de eigen software en heeft dat nu in een openbaar verslag vastgelegd. De volatiliteit op de cryptomarkt is extreem laag en de bitcoinkoers schommelt al weken tussen de 63 en 65 duizend dollar. De vraag is of dat aan de vakantieperiode ligt of dat er meer aan de hand is, en of we van een bodem in deze bearmarkt mogen spreken. Tot slot BIP-110. Bij blok 961.632 begon de verplichte signaleringsperiode van dit voorstel, dat niet financiële data zoals afbeeldingen tijdelijk uit bitcointransacties wil weren. De keten splitste zich, maar de afsplitsing produceerde in acht uur twee blokken terwijl de hoofdketen er 48 bij kreeg. De steun onder miners blijft rond 2,5 procent, ver onder de drempel van 55 procent. Michael Saylor riep in een essay met 110 argumenten op het voorstel te verwerpen. Voor wie bitcoin heeft, verandert er niets. Co-hosts zijn Kim Schneider en Mauro Halve. Over de podcast Cryptocurrency are here to stay. In deze wekelijkse podcast gidst Daniël Mol je door het belangrijkste cryptonieuws, langs hypes en trends, voor- en tegenstanders en winst en verlies. In het A-deel bespreken we het laatste nieuws en in het B-deel gaan we in gesprek met een gast. Van cypherpunkpioneers tot grootbanken die aan de haal gaan met stablecoins, van Bitcoin tot Ethereum tot CBDC's. Alles passeert de revue.Reageren? Stuur dan een mail naar cryptocast@bnr.nl Gasten Kim Schneider is web3 enterprise lead bij Deloitte. Mauro Halve is voorzitter bij de branchevereniging voor cryptodienstverleners VBNL. Tim Stolte is portfoliomanager bij Amdax en host van de podcast Een Nieuwe Koers. Links AI vindt kritieke lekken in de bitcoinsoftware, waarschuwt het Bitcoin Red Team Bericht van Bitcoin Red Team initiatiefnemer Calle over de audit Coinkite legt in het openbaar vast hoe de fout in de Coldcardfirmware is ontstaan Duizenden kwetsbaarheden ontdekt in bitcoinprojecten na de Coldcardhack Ongeveer 210.000 bitcoin verlaat oude wallets na het Coldcardincident Duizenden slachtoffers na een softwarefout in een bitcoinwallet Uitleg over BIP-110 en de afsplitsing van de bitcoinketen De omstreden fork BIP-110 mijnt twee blokken en valt dan stil Michael Saylor roept in 110 punten op BIP-110 te verwerpen Host Daniël Mol is presentator en redacteur van de Cryptocast. Hij is sinds 2017 met Bitcoin bezig en kwam in 2021 bij het team van de Cryptocast. Redactie Daniël Mol Donner Bakker See omnystudio.com/listener for privacy information.

Daria Rudnik is a Team Architect and Executive Leadership Coach — and the author of Clicking, a practical framework for building high-performing teams that don't depend on the leader to function. A former Chief People Officer and ex-Deloitte professional with 15 years of international experience in tech and telecom, she has helped leaders across six continents navigate financial crises, wars, and COVID-19. Today she works with executives and HR leaders who are building teams that can perform — and hold together — in an AI-driven world.In today's episode of Smashing the Plateau, you will learn how to build high-performing teams and a resilient independent career that doesn't depend on institutional support.Daria and I discuss:What drew Daria to entrepreneurship after a fast-rising corporate career [03:41]Why a corporate job is no longer the safe choice [05:15]What independents get wrong about building a team [08:45]Why shared understanding is the foundation of every strong team [09:56]The role of well-designed processes in making delegation work [10:28]The five elements of high-performing teams from her book Clicking [12:48]How Daria navigates plateaus and keeps moving forward [15:13]Why optimism and perseverance are non-negotiable for entrepreneurs [16:05]How to build a peer support network outside of corporate [18:18]What makes a community worth being part of [20:20]Why building a parallel career is the smartest first step [21:37]Learn more about Daria at https://dariarudnik.com/ and https://www.linkedin.com/in/dariarudnik/.______________________________________________________________About Smashing the PlateauSmashing the Plateau is a podcast for experienced independent leaders who have left corporate roles to build sustainable, expertise-based businesses.Each episode features a thoughtful, experience-driven conversation about what changes when you no longer have the infrastructure of an organization behind you.We explore judgment, decision-making under uncertainty, growth plateaus, identity shifts, and the role of trusted thinking partners in sustaining long-term success.______________________________________________________________Take the Next Step• Experience the power of peer perspective.Join a live guest session and connect with experienced professionals navigating similar challenges:https://smashingtheplateau.com/guest• Stay connected to the conversation.Get new episodes, reflections, and invitations delivered to your inbox:https://smashingtheplateau.com/news

Recorded live at the CLOC Global Institute (CGI) 2026, this episode of CLOC Talk features Mark Ross of Deloitte in conversation with Ari Kaplan, Founder of Ari Kaplan Advisors, for a timely discussion on the forces reshaping legal operations. Together, they unpack the latest trends in legal operations spending for 2026, explore how in-house legal teams and law firms are adapting to the rapid rise of generative AI and evolving pricing pressures, and share practical insights for navigating today's changing legal landscape. Ari also reveals the skills that will define the next generation of legal operations leaders—from relentless curiosity and strong business acumen to the ability to clearly demonstrate measurable ROI—making this a must-listen episode for anyone looking to stay ahead of what's next.

What does it take to launch a marketing campaign that resonates across dozens of countries? In this episode of The Global Marketing Show, Wendy Pease sits down with global marketing leader Tata Maytesyan, whose career spans Nike, Deloitte, British American Tobacco, Picsart, and AI consulting. Together, they explore how international brands successfully expand into global markets without losing local relevance. You'll learn: How Nike developed emotionally resonant campaigns for the FIFA World Cup Why localization is far more than translation Common international marketing mistakes companies make when entering new markets How AI is changing market research and customer insights Practical strategies for launching products across cultures The importance of local experts, translators, and cultural intelligence Why B2B localization requires a different approach than consumer marketing Whether you're a global marketer, localization professional, CMO, or business leader expanding internationally, this episode offers actionable insights on building campaigns that connect across languages, cultures, and markets. Check out the episode on The Global Marketing Show blog. 

Figures released by the central statistics office show the employment rose in July and now stands at just over 5%. For the details, Chief Economist with Deloitte, Kate English.

Join Michelle Wiltse, Founder and CEO of CompanAIn, for a grounded discussion on why the tech industry's rush toward "AI-first" branding often undermines true enterprise value. In a market flooded with companies rushing to wrap basic wrapper interfaces around large language models, Michelle took a radically different path. Having previously led agentic AI capabilities and deployed multi-agent systems for major global institutions, she chose to apply this frontier technology where it matters most: eliminating health data fragmentation for companion animals nationwide. In this episode, Michelle shares why the smartest AI strategy prioritizes deliberate restraint—using AI only where it solves genuine structural problems—and how a mission-driven mindset beats superficial "AI theater" every time.

You're spending 70% of your time on grunt work.Formatting. Resizing. Data entry. Chasing approvals.Only 30% on the work that actually matters—strategy, creativity, big ideas.AI flips that ratio.In this episode, we explore:→ How AI liberates communicators to focus on high-value work→ Why "AI fluency" is the new career literacy→ Real workflows from Tanzania, Kenya, and UgandaIf you're tired of being busy but not productive, this conversation will change how you work.Experts like Henry Mbwille, Hilda K. speak to Aggie patricia Turwomwe, and Betty Kakyo of Agile Media Africa.What's one repetitive task you wish AI could handle for you right now?

Deloitte's David Mallon on the Culture Debt AI Is Building Most organizations are treating AI like the last big tech rollout. Buy the licenses, run some training, expect results. The data says that approach is backfiring. David Mallon leads the research behind Deloitte's Global Human Capital Trends report, now in its 15th year. In this conversation, he explains why 59% of organizations taking a tech-first approach to AI are 1.6 times more likely to miss the return, and what changes when you redesign the work instead of layering tools on top of it. Brandon and David get into the human side of the shift. Work slop and what it means for developing people when entry-level roles disappear. Culture debt, and the 80% of leaders and workers who suspect their teammates are using AI to look more productive. Decision rights, and why accountability breaks down when nobody can trace a choice back to a person. And the question sitting underneath all of it: who actually owns work. There is a practical throughline here for small and mid-sized companies. You do not need a transformation budget to start. You need to think in capabilities, build friction back in where people need to practice, and say out loud what counts as good work now. About our guest David Mallon is a managing director at Deloitte Consulting LLP and head of research and chief futurist for Human Capital in the US. He has more than 25 years of experience in human capital and has been a contributor to Deloitte's Global Human Capital Trends study since its inception. He leads Insights2Action, Deloitte's Human Capital decision intelligence capability, and is a former head of research for Bersin. He co-hosts the Capital H podcast and speaks widely on organization design, culture, HR strategy, and learning. TIMESTAMPS 00:00 Welcome and what this episode covers 01:45 Why 2026 is the year leaders can no longer sit on the fence 04:15 The tech-first trap and why AI is not the last tech wave 05:40 What a human-centric approach looks like in practice 07:25 Hardwiring and softwiring when you design human and AI work 08:40 The telecom example, 5% versus 30%, and the lesson for smaller companies 10:50 How to bring an entire organization along on new tools 13:00 Fabrication, embellished profiles, and verification in hiring 15:15 Work slop, the experience gap, and building friction back in 17:30 Decision rights, machine autonomy, and where accountability lives 19:15 Culture debt and what suspicion does to a team over time 21:30 Isolating what the human actually brings to the work 22:35 A history teacher's assignment as a model for verification 24:45 The orchestration gap and what is getting in the way 27:15 Who owns work, and the trio organizations will need 29:40 Have organizational functions outlived their function 31:45 The biggest change workers felt this past year 33:25 Decisions that echo and the one message for every leader 35:20 What David wants people to take away and act on A QUICK GLIMPSE INTO OUR PODCAST Podcast: Transform Your Workplace, sponsored by Xenium HR Host: Brandon Laws In Brandon's own words: "The Transform Your Workplace podcast is your go-to source for the latest workplace trends, big ideas, and time-tested methods straight from the mouths of industry experts and respected thought-leaders." About Xenium HR Xenium HR is on a mission to transform workplaces by providing expert outsourced HR and payroll services for small and medium-sized businesses. With a people-first approach, Xenium helps organizations create thriving work environments where employees feel valued and supported. From navigating compliance to enhancing workplace culture, Xenium offers tailored solutions that empower growth and simplify HR. Whether managing employee relations, payroll processing, or implementing impactful training programs, Xenium is the trusted partner businesses rely on to elevate their workplace experience. Discover how Xenium can transform your workplace: Learn more Resource mentioned: 2026 Global Human Capital Trends report Connect with David Mallon: LinkedIn X Deloitte profile Connect with Brandon Laws: LinkedIn Instagram About Connect with Xenium HR: Website LinkedIn Facebook Twitter Instagram YouTube

In this episode of the Solar Maverick Podcast, Benoy Thanjan joins Peter Pongracz of Soliverse for a wide-ranging conversation about how the global solar market is changing. They discuss why early-stage development capital has become more difficult to secure, why investors increasingly prefer later-stage and lower-risk projects, and why solar-plus-storage is becoming the new standard in many markets. Benoy and Peter also examine the growing importance of community relationships, energy independence, repowering, solar-panel recycling, and innovation as the United States and Europe compete with China's scale and long-term energy strategy. A central theme throughout the episode is that solar technology itself is relatively simple, but developing, financing, interconnecting, and delivering projects has become increasingly complex. 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 MW 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.   Guest Information Peter Pongracz is the host of Soliverse, a European solar-industry podcast that he describes as, in his own words, "the Solar Maverick Podcast of Europe." He draws on market data from sources including SolarPower Europe and his own client work, including market segmentation research on floating PV, to bring a European and global lens to conversations about solar development, investment, and policy.   Stay Connected: Benoy Thanjan Email: info@reneuenergy.com  LinkedIn: Benoy Thanjan Website: https://www.reneuenergy.com Website: https://www.solarmaverickpodcast.com/   Peter Pongracz LinkedIn: https://www.linkedin.com/in/peterpongraczsaas/   Soliverse https://www.youtube.com/@Soliversepodcast 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.

Nick Cowell, Principal and US retail banking leader at Deloitte, joins Jim Marous to unpack the firm's new series, 2050: Banking Beyond, and the question at its center: in 25 years, will banks still own the customer relationship? They get into why that relationship is the asset most at risk as AI agents, embedded finance, and open banking move engagement outside the bank's walls, why banks know so much about customers but tell them so little, privacy becoming a premium service, and the day talking to a human costs extra. Nick also explains why the next wave of consolidation will be driven by AI readiness rather than asset size, and the three no-regrets moves every bank and credit union should make now. Hosted by Jim Marous. Subscribe to Banking Transformed for new episodes multiple times each week.

We often treat burnout as a personal failure when it is really a warning that our demands have exceeded our available time, energy, and support.Burnout prevention specialist Sophie Anderson shares five courageous habits that help us protect our energy before exhaustion takes over. She challenges us and helps us see how to practically get honest about our priorities, pause before automatically saying yes, create stronger technology boundaries, speak openly when our capacity is stretched, and make space for rest even when guilt shows up. Her perspective reminds us that capability and capacity are not the same, and that being able to do something does not always mean we have the energy or resources to take it on.By noticing the pressure we place on ourselves and making intentional changes, we can reduce overwhelm, support healthier teams, and lead with greater clarity and sustainability.Key Takeaways:Be Clear on Your Priorities:Explore how sorting what truly matters can reduce pressure and create greater focus.Say Maybe Before Saying Yes:Consider how pausing before committing can protect your time and prevent unnecessary overload.Implement Tech Boundaries:Discover how small changes to notifications and availability can reduce distraction and overwhelm.Speak Up About Overwhelm:Examine why honest conversations about capacity can strengthen support and reduce shame.Build in Rest and Recovery:Learn how making space for recovery can help you sustain your energy and leadership.Resources MentionedDownload your copy of the 5 Courageous Habits to Prevent Burnout for Leaders poster at https://www.sophieanderson.au/courageThe Inspire Your Team to Greatness assessment (the Courage Assessment) - In less than 10 minutes, find out where you're empowering and inadvertently kills productivity, and get a custom report that will tell you step by step what you need to have your team get more done. Get it here: https://courageofaleader.com/inspireyourteam/You don't need to have all the answers to lead well. Get your copy of the Clarity Kit for just $17 to learn the five practices to bring more clarity, confidence and courage into your leadership - https://courageofaleader.com/the-clarity-kit/About the Guest:Sophie Anderson is a burnout prevention specialist and the creator of the Anderson Model of Burnout Prevention (AMBP™) - a practical framework that maps how burnout accumulates across three zones: Activation, Over-functioning and Depletion, and identifies the intervention points that stop professionals from crossing the threshold into burnout.She works with professionals, leaders and organisations across Australia and worldwide through personal coaching, keynotes and workplace wellbeing programs.Connect with Sophie at : https://www.sophieanderson.au/contact or hello@sophieanderson.auhttps://www.sophieanderson.au/ambp-frameworkAbout the Host:Amy L. Riley is an internationally renowned speaker, author and consultant. She has over 2 decades of experience developing leaders at all levels. Her clients include Cisco Systems, Deloitte and Barclays.As a trusted leadership coach and consultant, Amy has worked with hundreds of leaders one-on-one, and thousands more as part of a group, to fully step into their leadership, create amazing teams and achieve extraordinary results.Amy's most popular keynote speeches are:The Courage of a Leader: The Power of a Leadership LegacyThe Courage of a Leader: Create a Competitive Advantage with Sustainable, Results-Producing Cross-System CollaborationThe Courage of a Leader: Accelerate Trust with Your Team, Customers and CommunityThe Courage of a Leader: How to Build a Happy and Successful Hybrid TeamHer new book is a #1 international best-seller and is entitled, The Courage of a Leader: How to Inspire, Engage and Get Extraordinary Results.http://www.courageofaleader.comhttps://www.linkedin.com/in/amyshooprileyThanks for listening!Thanks so much for listening to our podcast! If you enjoyed this episode and think that others could benefit from listening, please share it using the social media buttons on this page.Do you have some feedback or questions about this episode? Leave a comment in the section below!Subscribe to the podcastIf you would like to get automatic updates of new podcast episodes, you can subscribe to the, podcast on Apple Podcasts or Stitcher. You can also subscribe in your favorite podcast app.Leave us an Apple Podcasts reviewRatings and reviews from our listeners are extremely valuable to us and greatly appreciated. They help our podcast rank higher on Apple Podcasts, which exposes our show to more awesome listeners like you. If you have a minute, please leave an honest review on Apple Podcasts.Mentioned in this episode:The Inspire Your Team to Greatness Assessment (The Courage Assessment)https://courageofaleader.com/inspireyourteam/

Young adults should always be encouraged to dream big when imagining their futures. When the time comes to actualize those aspirations, though, practical details matter most.  Amy and Mike invited author Jeff Selingo to clarify how to define a dream school.    What are five things you will learn in this episode? What is the "Top 25 or bust" mentality and why do so many families still equate selectivity with quality? Do you need to attend a top-ranked school to get an exceptional college education? How do you define fit, and what specific factors should families consider when evaluating whether a college is truly the right match for a student? What is a hidden gem college and what characteristics do these institutions tend to share? What indicators should families look for when assessing a college's ability to prepare students for meaningful careers? MEET OUR GUEST Jeff Selingo is a New York Times bestselling author of four books on education and the job market, including his newest, Dream School: Finding the College That's Right for You (September 2025). Based on more than two years of research and a survey of some 3,000 parents, the book gives families permission to think more broadly about what signals a "good" college and then the tools to discover their dream school. His previous book, Who Gets In & Why: A Year Inside College Admissions, was named one of the New York Times's 100 Notable Books of the Year in 2020. For more than twenty-five years, his in-depth reporting and storytelling have provided practical insight about the future of higher education and the workforce to university leaders, corporate executives, as well as students and parents. Jeff has talked before scores of colleges, businesses, healthcare organizations, and financial services companies and helped leaders understand what's next for education and how companies can better compete for talent, especially among today's Gen Z. He has appeared at the Milken Global Institute, LinkedIn, Ohio State University, Salesforce, Spain's Bankinter Innovation Foundation, the Association of Schools Advancing Health Professions, and Deloitte, as well as before the boards of Carnegie Mellon University, the Duke Endowment, and Amherst College, among others. As both an observer of higher education and an insider with an academic appointment at one of the largest universities in the country, Jeff occupies a unique position to explain the intersection between work, life, and learning. He writes regularly for The Atlantic, the Washington Post, the New York Times, and the Wall Street Journal. His research focuses on the changing nature of work and its impact on education, paying for college, the student experience, and shifting expectations for what the public and employers want from colleges. He is co-host of the podcast, Future U., and writes a biweekly newsletter, Next. Jeff is a special advisor to the president and professor of practice at Arizona State University, where he is the founding director of the Academy for Innovative Higher Education Leadership. He has also served as a visiting scholar at Georgia Tech's Center for 21st Century Universities. In addition, Jeff regularly counsels universities and organizations on strategy. Previously, Jeff was the top editor of the Chronicle of Higher Education, where he worked for sixteen years in a variety of reporting and editing roles. His work has been honored by the Education Writers Association, Society of Professional Journalists, and the Associated Press. He received a bachelor's degree from Ithaca College and a master's degree from Johns Hopkins University. He is a member of the board of trustees at Ithaca College and the board of directors of the National Association of Independent Schools (NAIS). He lives with his wife and two daughters near Washington, D.C. Jeff previously appeared on this podcast in episode 126 to discuss Shaping an Admissions Class. Find Jeff at https://www.jeffselingo.com/. LINKS Dream School: Finding the College That's Right for You  RELATED EPISODES FINDING YOUR COLLEGE FIT FINDING YOUR AUTHENTIC SELF IN THE PATH TO COLLEGE COLLEGE ADMISSIONS INSANITY ABOUT THIS PODCAST Tests and the Rest is THE college admissions industry podcast. Explore all of our episodes on the show page. ABOUT YOUR HOSTS Mike Bergin is the president of Chariot Learning and founder of TestBright, Roots2Words, and College Eagle. Amy Seeley is the president of Seeley Test Pros and LEAP. If you're interested in working with Mike and/or Amy for test preparation, training, or consulting, get in touch through our contact page.  

A new survey from the National Association of State CIOs and Deloitte found state CISOs are facing growing demands that are not being matched by more resources.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Reclaiming Your Power: Surviving Workplace Chaos and Doing Great Work with Melissa Swift   Anika sat down with Melissa Swift, founder and CEO of Anthrome Insight, to tackle the reality of the modern workday: constant interruptions, hyper-emotional environments, and overwhelming chaos. Following the release of her new book, How to Do Great Work in a Fast Changing World, Melissa shares why systemic organizational changes are great, but individuals need actionable survival strategies now. Drawing on research from high-stakes professionals like firefighters and ER doctors, this conversation provides a practical framework for identifying your true strengths, dealing with chaotic colleagues, and recognizing when a job is fundamentally broken.   In This Episode   The COVID-era breaking point that inspired a shift from organizational strategy to individual empowerment Breaking down the "Effectiveness Architecture": Knowledge, Methods, People, and Technology The self-checkout paradox: why automating tech actually requires a massive increase in human "soft skills" The four trends making us less effective at work, with a deep dive into navigating chaos Natural chaos (the science of surprises) versus the frustration of people operating chaotically The Muppet Theory of Management: knowing when to deploy your "Order Muppets" vs. "Chaos Muppets" What corporate America can learn from firefighters about deep tech mastery and acute role clarity How organizations inadvertently gaslight employees by hiring them for jobs the company culture actively rejects Three concrete steps you can take this week to carve out space and reclaim your sanity   Timestamps   00:00 Introduction: Operating effectively in a fast-changing world 01:17 Why Melissa's audience demanded a book focused on individual survival strategies 02:52 The COVID breaking point and the metaphor of the empty Soviet streetcar 04:13 The Effectiveness Architecture: simplifying 30,000 skills into four core pillars 06:46 Why technology rollouts fail without the proper "people skills" training 09:38 Four trends destroying workplace effectiveness: intensification, emotion, transparency, and chaos 11:23 Natural chaos vs. human chaos (and the Muppet theory of team dynamics) 14:48 What white-collar workers can learn from the training habits of firefighters 19:51 The myth of the "skills gap" and the untapped power of self-directed learners 23:55 Identifying your superpower (Knowledge, Methods, People, or Tech) and overcoming stereotypes 29:45 Why role clarity is the first casualty of chaos and how to get it back 33:50 How to recognize if your job is fundamentally broken or set up to fail 39:08 Three actionable steps to reclaim your effectiveness and boundaries this week 43:35 Favorite quote: Oscar Wilde on the vital art of brevity   Key Insights & Takeaways   Insight 1: The Effectiveness Architecture Simplifies Work Instead of getting bogged down in corporate frameworks boasting 30,000 different micro-skills, individual effectiveness boils down to four clear pillars: Knowledge, Methods, People, and Technology. Identifying which pillar is your natural superpower (and which is your weakest link) allows you to partner with complementary colleagues and combat workplace stereotypes.   Insight 2: Automation Requires More "People Skills," Not Less When companies automate roles (like introducing self-checkout lanes), they often underestimate the human element. The remaining workers suddenly have to act as IT support, security, and customer de-escalation all at once. Rolling out advanced technology—including AI—without training people on the soft skills required to manage the human friction around it is a recipe for burnout.   Insight 3: Not All Chaos is Created Equal There is "natural chaos" (the inevitable surprises of a shifting economy or supply chain) and there is the unnatural chaos of colleagues operating erratically. You manage natural chaos through scenario planning, but you manage human chaos by building strict boundaries and strategically deploying the right personalities (your organized "Kermits" vs. your adaptable "Animals").   Insight 4: High-Stakes Professionals Prioritize Deep Mastery and Crisp Roles Unlike corporate workers who switch between dozens of apps a day without truly mastering any, professionals like firefighters are given the time and space to completely master a single new tool before their lives depend on it. Similarly, in an ER or on a fireground, role clarity is acute. In corporate settings, blurred roles masquerade as "collaboration," but they actually drain creativity and cause systemic failure.   Insight 5: Your Job Might Be Fundamentally Broken Sometimes your lack of effectiveness isn't your fault. If you are hired to be a "change agent" in a company that inherently rejects innovation, or if you are told to "lead through influence" without any actual resources or power, you are fighting gravity. Recognizing when a job is structurally broken is the first step in deciding whether to renegotiate your role or walk away.   Resources & Links Mentioned How to Do Great Work in a Fast Changing World by Melissa Swift Work Here Now by Melissa Swift   About Melissa Swift   Melissa Swift is the founder and CEO of Anthrome Insight, a leading organizational effectiveness consultancy. With an extensive background working with large global organizations like Mercer, Korn Ferry, Deloitte, and Capgemini, she is a recognized authority on the future of work and a regular columnist for the MIT Sloan Management Review. She is the author of Work Here Now and her latest release, How to Do Great Work in a Fast Changing World.   Connect with Melissa Swift   Website: Anthrome Insight LinkedIn: Melissa Swift Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Sean Martin catches John Sotiropoulos and Rock Lambros at the end of the OWASP GenAI Security Summit, held alongside Infosecurity Europe 2026 at ExCeL London. Both are deep in the standards work: John Sotiropoulos co-leads the OWASP Agentic Security Initiative and sits on the board of the OWASP GenAI Security Project, and Rock Lambros serves as Director of AI Standards and Governance at Zenity and co-leads the OWASP Top 10 for LLM 2026 update. The headline from the day is the launch of the Agentic Security Council, bringing Oxford University, Queen's University Belfast, CSIT, and other research institutions into the same room as practitioners and industry. John Sotiropoulos frames the reasoning bluntly: content on its own does not create change. Papers and Top 10 lists matter, but they only matter if the people building and defending systems can act on them. The number that reframes everything is twenty-two seconds. That is the average time from an initial access event to the next attacker action, down from eight hours. John Sotiropoulos puts the question directly to anyone still running a human-speed playbook: how do you respond to that? A panel on incident response with participants from AWS and Microsoft, a keynote from Microsoft's National Security Officer, and a walkthrough of the State of Agentic Security and Governance report all point at the same shift toward runtime security. Rock Lambros brings a different kind of grounding. For the first time in the four-year history of the OWASP Top 10 for LLM, the update draws on a corpus of reported incidents rather than opinion and community vote alone. It is one data point among several, but it is data, and that changes what the list can claim. A panel with the heads of AI security at Deloitte supplies the operational counterweight. As one of them puts it, security has to stop being the Ministry of No, because people will route around it and ship anyway. The report's adoption tiers and maturity levels exist for exactly that reason: security that lives only inside a PDF is not security. The invitation from both John Sotiropoulos and Rock Lambros is the same. Join the work. Research, red teamers, defenders, builders, and SecOps all looking at one view of what is actually happening is the only version of this that scales to machine speed. ⬥HOST⬥ Sean Martin, CISSP | Co-Founder, ITSPmagazine & Studio C60 | Host, Redefining CyberSecurity Podcast & Music Evolves Podcast | https://www.seanmartin.com/ ⬥GUESTS⬥ John Sotiropoulos, Deep Cyber | Co-Lead, OWASP Agentic Security Initiative; Board Director, OWASP GenAI Security Project | On LinkedIn: https://www.linkedin.com/in/jsotiropoulos/ Rock Lambros, Director of AI Standards and Governance, Zenity; Founder, RockCyber | Co-Lead, OWASP Top 10 for LLM 2026 | On LinkedIn: https://www.linkedin.com/in/rocklambros/ ⬥RESOURCES⬥ Infosecurity Europe 2026 is taking place June 2-4, 2026 | ExCeL London. Follow our coverage: https://www.itspmagazine.com/infosecurity-europe-2026-infosec-london-cybersecurity-event-coverage OWASP GenAI Security Project | https://genai.owasp.org Contribute to the OWASP GenAI Security Project | https://genai.owasp.org/contribute The Future of Cybersecurity Newsletter | https://www.linkedin.com/newsletters/7108625890296614912/ Redefining CyberSecurity Podcast | https://www.seanmartin.com/redefining-cybersecurity-podcast On Location | https://www.itspmagazine.com/on-location

The League Episode #51 – Show Notes In this episode of The League, Clean energy demand continues to accelerate as utilities, data centers, manufacturers, and large commercial customers seek more power. Solar, storage, transmission, and nuclear all have major opportunities, but the next phase of industry growth will depend less on demand and more on execution. In this episode of The League, we discuss the growing importance of permitting, interconnection, community engagement, tax-credit certainty, domestic manufacturing, and access to capital. We also examine the rising cost of grid upgrades in ISO New England, the renewed interest in nuclear development, and the continued evolution of tax equity and transferable tax-credit markets. The companies that can navigate complexity, manage risk, and move projects from development to operation will be best positioned to win.   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.  

How do digital freight broker tools actually prevent fraud while automating your day-to-day logistics operations? Today, Kary Jablonski from DAT joins us back to get straight to the facts about how recent capacity crunches and elevated fuel prices are forcing brokers to evolve or get left behind! Kary breaks down how DAT's new Carrier Management Suite and their recent tech acquisitions are helping brokers digitize tribal knowledge, drive down operational costs, and build a rock-solid, fraud-resistant carrier network. If you're serious about using data and automation in freight to make bulletproof buying decisions and scale your brokerage without adding unnecessary overhead, you can't skip this episode!   About Kary Jablonski Kary serves on the DAT Freight & Analytics Executive team as EVP and GM, Trucker Tools & Broker Growth. Kary is the former CEO of Trucker Tools, where she led the company to a successful exit. While taking on her new role at DAT, she continues to lead Trucker Tools, staying committed to the company's vision and growth. Prior to Trucker Tools, she advanced through operations, logistics, and strategic planning roles with Uber in the U.S. and internationally and worked as a consultant with Deloitte. She currently lives in Chicago and enjoys anything active, especially running, golf, and basketball.  

In today's episode, you'll discover: 1.     Why authenticity is an important part of leadership? 2.     What kind of masks do you, the leaders, wear, and what is it costing you? 3.     What Emotional Contagion is and how to Use the Emotional Contagion Effect in Positive Ways? To support these three takeaways, I chose a quote from Theodore Roosevelt: "The best executive is the one who has sense enough to pick good men to do what he wants done, and self-restraint to keep from meddling with them while they do it."  About Frans Campher: Today, my guest is Frans Campher, an executive coach, leadership strategist, and facilitator with over two decades of experience guiding high-performing leaders through the challenges of growth, authenticity, and meaningful impact. As CEO and US President of a global leadership development firm, he has worked with senior executives and teams at companies like BP, Ford Europe, GlaxoSmithKline, Deloitte, Citibank, and Johnson & Johnson. How to Get in Touch with Frans Campher: Email: frans@ildynamics.com Website: https://www.ildynamics.com/ Free Gift: https://teamtrust.ildynamics.com/ Stalk me online! Linktr.ee: https://linktr.ee/conniewhitman Communication Style Assessment (CSA)™:  https://changingthesalesgame.com/communication-style-assessment/

If your porch light seems to attract every moth and flying insect in the neighborhood, you're not imagining it. Scientists have finally figured out why artificial lights are so irresistible to many insects—and knowing what they've discovered can help you keep more bugs away this summer. https://www.nature.com/articles/s41467-024-44785-3 Some people seem to light up a room the moment they walk in. Others naturally draw people in, inspire confidence, and make lasting impressions. We call it charisma, and many assume you're either born with it or you're not. Olivia Fox Cabane says that's simply not true. In this conversation, she explains what charisma really is, why it's often misunderstood, and the surprisingly practical skills anyone can learn to become more influential and engaging. Olivia has advised leaders at Google, Deloitte, UBS, MGM, and TikTok, and is author of The Charisma Myth: How Anyone Can Master the Art and Science of Personal Magnetism (https://amzn.to/44kpuyI). For decades, we've talked about "the midlife crisis" as though it's inevitable. But what if we've gotten midlife completely wrong? Research suggests that your middle years can be one of the most productive, satisfying, and influential periods of your life. So why does this stage have such a negative reputation? And what are people in midlife uniquely positioned to do that younger and older adults often can't? Margie Lachman, Professor of Psychology at Brandeis University and director of the Lifespan Lab, shares a fresh, evidence-based perspective on why midlife deserves a lot more celebration than sympathy. She is author of Primetime: A New Vision for Midlife (https://amzn.to/4bpLbRJ). Have you ever searched everywhere for your keys or your glasses, only to discover they were sitting in plain sight the whole time? It's a surprisingly common experience, and it reveals something fascinating about how your brain decides what you do—and don't—see. https://dictionary.apa.org/inattentional-blindness PLEASE SUPPORT OUR SPONSORS WAYFAIR: Ready to upgrade your home for way less? Head to ⁠⁠⁠⁠https://Wayfair.com⁠⁠⁠⁠ right now to shop all things home and get your space ready for less.  QUINCE: Elevate your summer wardrobe. Go to ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://Quince.com/sysk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ for free shipping on your order and 365-day returns. Now available in Canada, too! SHOPIFY: It's time to turn those "what ifs" into CHA CHING with Shopify Today! Sign up for your $1 per month trail and start selling today at ⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠https://Shopify.com/sysk⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠⁠ INDEED: Get a $75 Sponsored Job credit to help get your job the premium status it deserves at ⁠⁠⁠https://Indeed.com/PODCAST⁠⁠⁠ Learn more about your ad choices. Visit megaphone.fm/adchoices

As part of The Lo Life's Level Up Series, Lo explores the skills that can help you thrive personally and professionally—and few are more misunderstood than charisma. This week, Lo sits down with internationally renowned charisma expert and New York Times bestselling author Olivia Fox Cabane, whose groundbreaking research has helped reshape the way leaders, entrepreneurs, and everyday people think about influence, connection, and presence. Drawing on decades of work at institutions including Stanford and MIT, and coaching leaders at Google, Apple, Airbnb, TikTok, Deloitte, and UBS, Olivia has become one of the world's leading voices on the science of charisma. Her own journey—from describing herself as introverted, socially awkward and autistic to becoming a globally recognized expert on human connection—makes her perspective all the more compelling.In this conversation, Lo and Olivia unpack why charisma isn't an innate gift but a learnable skill rooted in psychology and behavioral science. They explore how charisma influences leadership, careers, relationships, interviews, and everyday interactions, while examining how the rules have evolved in an era where our digital presence can be just as important as the person we are face-to-face. They also discuss why introverts can be deeply charismatic, the historical misconceptions surrounding charisma, and how these skills can be used to build trust, inspire others, and shape the next generation. Whether you're looking to become a stronger communicator, a more confident leader, or simply a more authentic version of yourself, this episode offers practical insights into a skill that can transform every area of life.See Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Peter Schiff is the host of The Peter Schiff Show podcast and a longtime economist and gold advocate. In this conversation, we break down real inflation versus the official CPI, the Fed's political motivations, and whether AI and tariffs are inflationary or deflationary. We also cover the Iran war's impact on oil, Social Security's looming collapse, and a five-year bet on bitcoin versus gold.===================Arch Public is an agentic trading platform that automates investment strategies across Stocks, Commodities, ETFs and Crypto. Whether you're rotating into AI & Gold, allocating to the S&P 500, or accumulating Bitcoin, Arch Public executes your plan 24/7 without ever taking custody of your assets or funds. Sign up today at https://www.archpublic.com, and start your FREE automated trading strategy! ===================Need liquidity without selling your crypto? Take out a Figure Crypto-Backed Loan, allowing you to borrow against your BTC, ETH, or SOL with 12-month terms, 8.91% interest rates, and no prepayment penalties. Or check out Democratized Prime (https://figuremarkets.co/pomp) and earn ~9% APY on real world assets, paid hourly. Unlock your crypto's potential today at Figure! https://figuremarkets.co/pomp Figure Lending LLC dba Figure (NMLS 1717824). Loans subject to approval. Crypto collateral may be liquidated. Terms apply - see full disclosures at figure.com/disclosures/===================Looking for a better place to trade? BloFin gives traders access to deep liquidity, advanced futures products for crypto AND TradFi assets, fast execution, and a clean, intuitive interface—all in one platform. To celebrate their partnership with us, they're giving away $100,000 in Deposit & Trade Rewards. Deposit, trade, and earn rewards based on your activity during the campaign. Check them out at ( https://partner.blofin.com/d/Pomp ).===================This episode is brought to you by mogul ( https://www.mogul.club/pomp ). Deloitte estimates that $4 trillion of real estate will move onto the blockchain over the next decade. Through tokenized residential real estate, mogul gives investors access to professionally managed properties with targeted yields, monthly rent payouts, and potential tax benefits — all without the headaches of being a landlord. Learn more and claim a special offer at https://www.mogul.club/pomp . See important disclosures at disclaimer.mogul.club.===================0:00 - Intro0:50 - How high is inflation really right now?14:36 - AI, robotics, tariffs & deportations: deflationary or not?25:24 - Will the Iran war make inflation worse?33:40 - Could inflation hit double digits? (Trump vs. Biden blame)40:29 - Social Security's looming collapse50:14 - Does Peter Schiff own bitcoin?52:43 - Bitcoin vs. gold: the real performance numbers1:00:45 - The bitcoin vs. gold bet1:05:40 - The "Crazy Uncle Portfolio"1:10:02 - What's actually in Peter Schiff's portfolio?1:18:54 - Outro

Olivia Fox Cabane reveals the behaviors and practices that help develop your personal charisma.— YOU'LL LEARN — 1) The number one myth surrounding charisma 2) How to amplify your presence in just a few seconds3) The master key to looking and feeling more powerfulSubscribe or visit AwesomeAtYourJob.com/ep1167 for clickable versions of the links below. — ABOUT IAN — Olivia Fox Cabane is a leading authority on the science of charisma and the bestselling author of The Charisma Myth and The Net And The Butterfly, translated into 36 languages. Formerly Director of Innovative Leadership for Stanford StartX, she has lectured on charisma, leadership, and innovation at Harvard, Yale, MIT, the Marine War College, and the United Nations. As cofounder of the KindEarth.Tech Foundation, she supports foodtech companies advancing environmental sustainability. Her clients include the leadership of Apple, Google, TikTok, Deloitte, UBS, and JP Morgan as well as founders of Airbnb, Brex, Paradigm, and Presight. She has been featured in The New York Times, The Economist, and The Wall Street Journal. • Book: The Charisma Myth: How Anyone Can Master the Art and Science of Personal Magnetism• Meditation: The Charisma Myth Exercises: Metta• Website: AskOlivia.com— RESOURCES MENTIONED IN THE SHOW — • Study: “Case Report: Women, Be Aware that Your Vocal Charisma can Dwindle in Remote Meetings” by Ingo Siegert and Oliver Niebuhr• Study: “Organizational Behavior and Human Decision Processes” by Jane M. Howell and Peter J. Frost• Study: “Predictors of leadership: The usual suspects and the suspect traits” by John Antonakis• Researcher: Dr. Oliver Niebuhr• Microphone: Maono HD300T• YouTube: Podcastage• Book: Influence: The Psychology of Persuasion by Robert Cialdini• Book: Radical Acceptance: Embracing Your Life With the Heart of a Buddha by Tara Brach— THANK YOU SPONSORS! — • Shopify. Sign up for your free trial at Shopify.com/awesomepod• Monarch. Get 50% off your first year with code AWESOME at Monarch.comSee Privacy Policy at https://art19.com/privacy and California Privacy Notice at https://art19.com/privacy#do-not-sell-my-info.

Geoff Woo is the co-founder and managing partner of Anti Fund and a co-founder of Ketone-IQ and Archive. In this conversation, we break down choke points and information asymmetry — how smart money hunts for bottlenecks in AI, semiconductors, tungsten, and power. We also cover defense tech, humanoid robots, biohacking, Trump's aura in the Oval Office, and where bitcoin fits into Geoff's portfolio after 13 years of holding.======================Need liquidity without selling your crypto? Take out a Figure Crypto-Backed Loan, allowing you to borrow against your BTC, ETH, or SOL with 12-month terms, 8.91% interest rates, and no prepayment penalties. Or check out Democratized Prime (https://figuremarkets.co/pomp) and earn ~9% APY on real world assets, paid hourly. Unlock your crypto's potential today at Figure! https://figuremarkets.co/pomp Figure Lending LLC dba Figure (NMLS 1717824). Loans subject to approval. Crypto collateral may be liquidated. Terms apply - see full disclosures at figure.com/disclosures/======================This episode is brought to you by mogul ( https://www.mogul.club/pomp ). Deloitte estimates that $4 trillion of real estate will move onto the blockchain over the next decade. Through tokenized residential real estate, mogul gives investors access to professionally managed properties with targeted yields, monthly rent payouts, and potential tax benefits — all without the headaches of being a landlord. Learn more and claim a special offer at https://www.mogul.club/pomp See important disclosures at disclaimer.mogul.club.======================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.======================0:00 - Intro1:00 - Why VCs are hunting for choke points in every industry4:45 - Can OpenAI & Anthropic crush every AI startup?13:40 - How Geoff uses AI daily & the tungsten choke point17:43 - Investing across public & private markets25:48 - The memory trade, CapEx & the power/fusion bet33:07 - How top investors like Peter Thiel play every trend at once35:03 - Inside Anti Fund's defense thesis39:50 - AI, robots & the ethics of force45:15 - The Waymo & teenagers story52:18 - Facial recognition & the end of privacy54:44 - Neuralink vs ultrasound brain-computer interfaces57:05 - Biohacking, sleep & the future of longevity1:06:33 - Gambling, hustle culture & internet fame1:22:20 - Geoff's honest take on bitcoin1:26:19 - Where to find Anti Fund

Jordi Visser is a veteran macro investor with 30+ years of experience and the author of the VisserLabs Substack. In this conversation, we discuss why he thinks the AI mid-cycle slowdown is over, why he's turning bullish on bitcoin, tokenization and stablecoins. We also cover the Fed's July rate decision and where he's finding value in gold, silver, and regional banks.====================Turn every conversation into a searchable business asset with PLAUD NotePro. Visit https://Plaud.ai/pomp  and use code POMP for 15% off.====================Uphold is the easiest way to buy and sell crypto unlike any other platform allowing you to trade in just one step between any supported asset. Check them out at https://www.uphold.com/pomp/ This video includes a paid sponsorship with Uphold. I'm compensated by Uphold for promoting its products and services and may receive commissions from referrals. Terms apply. Not available in all jurisdictions. Digital assets are risky and may result in the total loss of your capital.====================This episode is brought to you by mogul ( https://www.mogul.club/pomp ). Deloitte estimates that $4 trillion of real estate will move onto the blockchain over the next decade. Through tokenized residential real estate, mogul gives investors access to professionally managed properties with targeted yields, monthly rent payouts, and potential tax benefits — all without the headaches of being a landlord. Learn more and claim a special offer at https://www.mogul.club/pomp . See important disclosures at disclaimer.mogul.club.====================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.====================0:00 - Intro0:42 - Grok vs. Meta's AI price war why cheaper AI won't cut spend10:03 - Apple's Siri stumble & price hikes14:50 - Using Grok in a Tesla17:28 - Short sellers, Samsung's earnings & the mid-cycle slowdown23:34 - Why Jordi is turning bullish on bitcoin 29:55 - Tokenization, stablecoins & the AI-crypto nexus33:33 - Michael Saylor's Bitcoin sale — does it matter?38:02 - Where else Jordi is deploying capital42:04 - Does the Iran war actually matter for markets?45:00 - The new robotic hand demo that changed everything50:18 - Jordi's upcoming video

Kevin Clancy, better known as KFC, is one of the original voices of Barstool Sports and former host of KFC Radio. In this conversation, we break down how technology and AI are quietly reshaping everyday life — from self-driving cars, surveillance camera, Neuralink, humanoid robots, and AI catching medical mistakes doctors missed. We also get into the affordability crisis facing younger generations, the wild early days of Barstool, and why scarce assets like bitcoin and gold still matter in a world of abundance.=======================This episode is brought to you by TikTok for Business. If you run a company, your next wave of customers may already be on TikTok. With more than 200 million monthly active users in the U.S. and 51% unique reach, TikTok gives brands access to audiences they can't reach anywhere else. Learn how to turn that reach into growth at TikTok for Business ( https://anthonypompliano.splashthat.com/ )=======================Bitget is the world's largest Universal Exchange (UEX), serving over 125 million users with access to over 2M+ crypto tokens, and TradFi markets. Bitget's Stocks 2.0 brings 500 major equities and ETFs (like Tesla and NVIDIA) directly to your portfolio. Enjoy 1:1 mapping, deep liquidity, and USDT dividend payouts with ultra-low 0.04% fees. Upgrade your portfolio on Bitget.com today!=======================Arch Public is an agentic trading platform that automates the buying and selling of your preferred crypto strategies. Sign up today at https://www.archpublic.com and start your automated trading strategy for free. No catch. No hidden fees. Just smarter trading.=======================0:00 - Intro0:59 - Waymo "arrests" teens: the self-driving surveillance case5:03 - Flock cameras, Ring cameras & the rise of constant surveillance12:07 - Youth sports & the money behind it19:10 - Body cams, cop shootings & the truth technology reveals23:49 - The affordability crisis: housing, salaries & the American dream33:56 - Kevin's path from Deloitte to Barstool36:50 - Algorithms, authenticity & the death of discovery in podcasting45:00 - AI catching what doctors missed & the rise of humanoid robots53:08 - Neuralink, brain implants & joining the hive mind1:00:46 - Psychedelics, ignorance as bliss & wanting to unplug1:04:21 - The 2022 crash & envying the simple life1:08:16 - Barstool's origin story1:14:42 - Private equity, scarcity & the bitcoin "crazy uncle portfolio"